def _create_prov_record(self, filepath, caption, ancestors):
record = {
'caption': caption,
'domains': ['nhpolar'],
'ancestors': ancestors
}
with ProvenanceLogger(self.cfg) as provenance_logger:
provenance_logger.log(filepath, record)
def _create_prov_record(self, filepath, caption, ancestors):
record = {
'caption': caption,
'domains': ['nhpolar'],
'autors': ['docquier_david'],
'references': ['docquier2017cryo'],
'ancestors': ancestors
}
with ProvenanceLogger(self.cfg) as provenance_logger:
provenance_logger.log(filepath, record)
def plot_diagnostic(cube, basename, provenance_record, cfg):
"""Create diagnostic data and plot it."""
diagnostic_file = get_diagnostic_filename(basename, cfg)
logger.info("Saving analysis results to %s", diagnostic_file)
iris.save(cube, target=diagnostic_file)
if cfg['write_plots'] and cfg.get('quickplot'):
plot_file = get_plot_filename(basename, cfg)
logger.info("Plotting analysis results to %s", plot_file)
provenance_record['plot_file'] = plot_file
quickplot(cube, filename=plot_file, **cfg['quickplot'])
logger.info("Recording provenance of %s:\n%s", diagnostic_file,
pformat(provenance_record))
with ProvenanceLogger(cfg) as provenance_logger:
provenance_logger.log(diagnostic_file, provenance_record)
def _get_provenance_record(cfg, plot_file, caption, loc):
"""Create a provenance record describing the diagnostic data and plot."""
ancestor_files = [k for k in cfg["input_data"].keys() if k.endswith(".nc")]
record = {
'caption': caption,
'statistics': ['mean'],
'domains': ['global'],
'plot_types': ['map', 'metrics'],
'authors': [
'predoi_valeriu',
],
'references': [],
'plot_file': plot_file,
'ancestors': ancestor_files,
}
p_cfg = {}
p_cfg['run_dir'] = loc
with ProvenanceLogger(cfg) as provenance_logger:
provenance_logger.log(plot_file, record)
def transect_plot(cfg, plot_params):
"""Plot transects.
Parameters
----------
model_filenames:OrderedDict
OrderedDict with model names as keys and input files as values.
cmor_var: str
name of the variable
region: str
name of the region predefined in `transect_points` function.
levels: tuple
contours - (minimum, maximum, number of levels)
ncols: int
number of columns in the plot
cmap: matplotlib colormap instance
Returns
-------
None
"""
figure, axis = create_plot(plot_params['model_filenames'],
ncols=plot_params['ncols'])
# get transect positions and calculate distances between points
lon_s4new, lat_s4new = transect_points(plot_params['region'], mult=2)
dist = point_distance(lon_s4new, lat_s4new)
# loop over models
index = None
for index, mmodel in enumerate(plot_params['model_filenames']):
logger.info("Plot %s data for %s, region %s", plot_params['variable'],
mmodel, plot_params['region'])
# construct file names and get the data
ifilename = genfilename(cfg['work_dir'], plot_params['variable'],
mmodel, plot_params['region'], 'transect',
'.npy')
ifilename_depth = genfilename(cfg['work_dir'], 'depth', mmodel,
plot_params['region'],
'transect_' + plot_params['variable'],
'.npy')
ifilename_dist = genfilename(cfg['work_dir'], 'distance', mmodel,
plot_params['region'],
'transect_' + plot_params['variable'],
'.npy')
data = np.load(ifilename, allow_pickle=True)
data = np.ma.masked_equal(data.T, 0)
lev = np.load(ifilename_depth, allow_pickle=True)
dist = np.load(ifilename_dist, allow_pickle=True)
# get labeles and convert the data
cb_label, data = label_and_conversion(plot_params['variable'], data)
# index of the maximum depth
lev_limit = lev[lev <= cfg['transects_depth']].shape[0] + 1
image = axis[index].contourf(dist,
lev[:lev_limit],
data[:lev_limit, :],
levels=plot_params['levels'],
extend='both',
cmap=plot_params['cmap'])
# plot settings
axis[index].set_ylabel('Depth, m', size=15, rotation='vertical')
axis[index].set_xlabel('Along-track distance, km',
size=15,
rotation='horizontal')
axis[index].set_title(mmodel, size=20)
axis[index].set_ylim(cfg['transects_depth'], 0)
# ax[ind].invert_yaxis()
axis[index].tick_params(axis='both', labelsize=15)
# color bar settings
colorbar = figure.colorbar(image, ax=axis[index], pad=0.01)
colorbar.set_label(cb_label, rotation='vertical', size=15)
colorbar.ax.tick_params(labelsize=15)
# fig.set_constrained_layout_pads(w_pad=2./30., h_pad=2./30.,
# hspace=10, wspace=10)
for delind in range(index + 1, len(axis)):
figure.delaxes(axis[delind])
pltoutname = genfilename(cfg['plot_dir'],
plot_params['variable'],
region=plot_params['region'],
data_type='transect')
plt.savefig(pltoutname, dpi=100)
plot_params['basedir'] = cfg['plot_dir']
plot_params['ori_file'] = ifilename
plot_params['areacello'] = None
plot_params['mmodel'] = None
provenance_record = get_provenance_record(plot_params, 'transect', 'png')
with ProvenanceLogger(cfg) as provenance_logger:
provenance_logger.log(pltoutname + '.png', provenance_record)
def transect_map(cfg,
region,
projection=ccrs.NorthPolarStereo(),
bbox=(-180, 180, 60, 90),
mult=2):
"""Plot the map with points of the transect overlayed.
Parameters
----------
region: str
name of the region predefined in `transect_points` function.
diagplotdir: str
path to the plot directory
projection: instance of cartopy ccrs
cartopy progection
bbox: list
four values - [left, right, bottom, top]
mult: int
miltiplicator for the number of points.
E.g. mult=2 increase the number of points 2 times.
Returns
-------
None
"""
logger.info("Create transect map for region %s", region)
lon_s4new, lat_s4new = transect_points(region, mult=mult)
dist = point_distance(lon_s4new, lat_s4new)
figure, axis = plt.subplots(1,
1,
subplot_kw=dict(projection=projection),
constrained_layout=True)
axis.set_extent(bbox, crs=ccrs.PlateCarree())
image = axis.scatter(lon_s4new,
lat_s4new,
s=10,
c=dist,
transform=ccrs.PlateCarree(),
cmap=cm.Spectral,
edgecolors='none')
axis.coastlines(resolution="50m")
colorbar = figure.colorbar(image, ax=axis)
colorbar.set_label('Along-track distance, km',
rotation='vertical',
size=15)
pltoutname = genfilename(cfg['work_dir'],
'allvars',
region=region,
data_type='transect_map')
plt.savefig(pltoutname, dpi=100)
plot_params = {}
plot_params['basedir'] = cfg['plot_dir']
plot_params['ori_file'] = None
plot_params['areacello'] = None
plot_params['mmodel'] = None
plot_params['region'] = region
provenance_record = get_provenance_record(plot_params, 'transect_map',
'png')
with ProvenanceLogger(cfg) as provenance_logger:
provenance_logger.log(pltoutname + '.png', provenance_record)
def plot2d_bias(cfg, plot_params):
"""Plot 2d maps of the bias relative to climatology.
Parameters
----------
model_filenames:OrderedDict
OrderedDict with model names as keys and input files as values.
cmor_var: str
name of the variable
depth: int
we will plot the data on the model level
that is closest to the `depth`.
diagworkdir: str
path to the working directory
diagplotdir: str
path to the plot directory
levels: tuple
values to be used for vmin and vmax in the form of (vmin, vmax)
dpi: int
the dpi values to save the figure
observations: str
name of the observations
projection: instance of cartopy projection (ccrs)
bbox: list
bounding box. It will be the input for cartopy `set_extent`.
ncols: int
number of columns.
Retuns
------
None
"""
# setupa a base figure
figure, axis = create_plot(plot_params['model_filenames'],
ncols=plot_params['ncols'],
projection=plot_params['projection'])
# get the filename of observations
ifilename_obs = genfilename(cfg['work_dir'],
plot_params['variable'],
plot_params['observations'],
data_type='timmean',
extension='.nc')
# get the metadata for observations (we just need a size)
metadata = load_meta(
datapath=plot_params['model_filenames'][plot_params['observations']],
fxpath=None)
lon2d = metadata['lon2d']
# Create an empty array to store the mean.
# One point larger along long to acount for cyclic point
model_mean = np.zeros((lon2d.shape[0], lon2d.shape[1] + 1))
print("MODEL MEAN SHAPE {}".format(model_mean.shape))
# delete observations from the model list
model_filenames = plot_params['model_filenames'].copy()
del model_filenames[plot_params['observations']]
# loop over models
index = None
for index, mmodel in enumerate(model_filenames):
logger.info("Plot plot2d_bias %s for %s", plot_params['variable'],
mmodel)
# get the filename with the mean generated by the `timemean`
ifilename = genfilename(cfg['work_dir'],
plot_params['variable'],
mmodel,
data_type='timmean',
extension='.nc')
# do the interpolation to the observation grid
# the output is
lonc, latc, target_depth, data_obs, interpolated = interpolate_esmf(
ifilename_obs, ifilename, plot_params['depth'],
plot_params['variable'])
# get the label and convert data if needed
cb_label, data_obs = label_and_conversion(plot_params['variable'],
data_obs)
cb_label, interpolated = label_and_conversion(plot_params['variable'],
interpolated)
# add to the mean model
model_mean = model_mean + interpolated
# set the map extent
left, right, down, upper = plot_params['bbox']
axis[index].set_extent([left, right, down, upper],
crs=ccrs.PlateCarree())
# Only pcolormesh is working for now with cartopy,
# contourf is failing to plot curvilinear meshes,
# let along the unstructures ones.
image = axis[index].contourf(
lonc,
latc,
interpolated - data_obs,
levels=plot_params['levels'],
extend='both',
# vmin=contours[0],
# vmax=contours[-1],
transform=ccrs.PlateCarree(),
cmap=plot_params['cmap'],
)
# fill continents
axis[index].add_feature(
cfeature.GSHHSFeature(levels=[1],
scale="low",
facecolor="lightgray"))
axis[index].set_title("{}, {} m".format(mmodel, int(target_depth)),
size=18)
axis[index].set_rasterization_zorder(-1)
# calculate the model mean and plot it
if index:
index = index
else:
index = 0
model_mean = model_mean / len(model_filenames)
axis[index + 1].set_extent([left, right, down, upper],
crs=ccrs.PlateCarree())
image = axis[index + 1].contourf(
lonc,
latc,
model_mean - data_obs,
levels=plot_params['levels'],
extend='both',
# vmin=contours[0],
# vmax=contours[-1],
transform=ccrs.PlateCarree(),
cmap=cmo.balance,
)
axis[index + 1].add_feature(
cfeature.GSHHSFeature(levels=[1], scale="low", facecolor="lightgray"))
axis[index + 1].set_title("Model mean bias, {} m".format(
int(target_depth)),
size=18)
axis[index + 1].set_rasterization_zorder(-1)
# delete the axis that are not needed
for delind in range(index + 2, len(axis)):
figure.delaxes(axis[delind])
# set common colorbar
colorbar = figure.colorbar(image,
orientation='horizontal',
ax=axis,
pad=0.01,
shrink=0.9)
colorbar.set_label(cb_label, rotation='horizontal', size=18)
colorbar.ax.tick_params(labelsize=18)
# save the picture
pltoutname = genfilename(cfg['plot_dir'],
plot_params['variable'],
"MULTIMODEL",
data_type='plot2d_bias_{}_level'.format(
str(int(target_depth))))
plot_params['basedir'] = cfg['plot_dir']
plot_params['ori_file'] = [ifilename]
plot_params['areacello'] = None
plot_params['mmodel'] = None
plot_params['region'] = "Global"
plt.savefig(pltoutname, dpi=plot_params['dpi'])
provenance_record = get_provenance_record(plot_params, 'plot2d_bias',
'png')
with ProvenanceLogger(cfg) as provenance_logger:
provenance_logger.log(pltoutname + '.png', provenance_record)
def plot2d_original_grid(cfg, plot_params):
"""Plot 2d maps on original grid using cartopy.
Parameters
----------
model_filenames:OrderedDict
OrderedDict with model names as keys and input files as values.
cmor_var: str
name of the variable
depth: int
we will plot the data on the model
level that is closest to the `depth`.
Ignored if explicit_depths is provided.
levels: tuple
values to be used for vmin and vmax in the form of (vmin, vmax)
diagworkdir: str
path to the working directory
diagplotdir: str
path to the plot directory
cmap: matplotlib colormap
colormap
dpi: int
the dpi values to save the figure
explicit_depths: dict
Output of the `aw_core` function.
It's a dictionary where for each model there is a maximum temperature,
depth level in the model, index of the depth level in the model.
If provided the `depth` parameter is excluded.
projection: instance of cartopy projection (ccrs)
bbox: list
bounding box. It will be the input for cartopy `set_extent`.
ncols: int
number of columns.
Retuns
------
None
"""
figure, axis = create_plot(plot_params['model_filenames'],
ncols=plot_params['ncols'],
projection=plot_params['projection'])
index = None
for index, mmodel in enumerate(plot_params['model_filenames']):
logger.info("Plot plot2d_original_grid %s for %s",
plot_params['variable'], mmodel)
ifilename = genfilename(cfg['work_dir'],
plot_params['variable'],
mmodel,
data_type='timmean',
extension='.nc')
metadata = load_meta(ifilename, fxpath=None)
datafile = metadata['datafile']
lon2d = metadata['lon2d']
lat2d = metadata['lat2d']
lev = metadata['lev']
if not plot_params['explicit_depths']:
depth_target, level_target = closest_depth(lev,
plot_params['depth'])
else:
level_target = plot_params['explicit_depths'][mmodel][
'maxvalue_index']
depth_target = lev[level_target]
if datafile.variables[plot_params['variable']].ndim < 4:
data = datafile.variables[plot_params['variable']][
level_target, :, :]
else:
data = datafile.variables[plot_params['variable']][
0, level_target, :, :]
cb_label, data = label_and_conversion(plot_params['variable'], data)
left, right, down, upper = plot_params['bbox']
axis[index].set_extent([left, right, down, upper],
crs=ccrs.PlateCarree())
# Only pcolormesh is working for now with cartopy,
# contourf is failing to plot curvilinear meshes,
# let along the unstructures ones.
image = axis[index].pcolormesh(
lon2d,
lat2d,
data,
vmin=plot_params['levels'][0],
vmax=plot_params['levels'][-1],
transform=ccrs.PlateCarree(),
cmap=plot_params['cmap'],
)
axis[index].add_feature(
cfeature.GSHHSFeature(levels=[1],
scale="low",
facecolor="lightgray"))
axis[index].set_title("{}, {} m".format(mmodel,
np.round(depth_target, 1)),
size=18)
axis[index].set_rasterization_zorder(-1)
# delete unused axis
for delind in range(index + 1, len(axis)):
figure.delaxes(axis[delind])
# set common colorbar
colorbar = figure.colorbar(image,
orientation='horizontal',
ax=axis,
pad=0.01,
shrink=0.9)
colorbar.set_label(cb_label, rotation='horizontal', size=18)
colorbar.ax.tick_params(labelsize=18)
if not plot_params['explicit_depths']:
plot_type = 'plot2d_{}_depth'.format(str(plot_params['depth']))
else:
plot_type = "plot2d_different_levels"
# save the figure
pltoutname = genfilename(cfg['plot_dir'],
plot_params['variable'],
"MULTIMODEL",
data_type=plot_type)
plot_params['basedir'] = cfg['plot_dir']
plot_params['ori_file'] = [ifilename]
plot_params['areacello'] = None
plot_params['mmodel'] = None
plot_params['region'] = "Global"
plt.savefig(pltoutname, dpi=plot_params['dpi'])
provenance_record = get_provenance_record(plot_params, 'plot2d', 'png')
with ProvenanceLogger(cfg) as provenance_logger:
provenance_logger.log(pltoutname + '.png', provenance_record)
def plot_profile(cfg, plot_params):
"""Plot profiles.
From previously calculated data for Hovmoeller diagrams.
Parameters
----------
model_filenames: OrderedDict
OrderedDict with model names as keys and input files as values.
cmor_var: str
name of the CMOR variable
region: str
name of the region predefined in `hofm_regions` function.
diagworkdir: str
path to work directory.
diagplotdir: str
path to plotting directory.
cmap: matplotlib.cmap object
color map
dpi: int
dpi fro the output figure
observations: str
name of the dataset with observations
Returns
-------
None
"""
level_clim = Dataset(plot_params['model_filenames'][
plot_params['observations']]).variables['lev'][:]
plt.figure(figsize=(5, 6))
axis = plt.subplot(111)
color = iter(plot_params['cmap'](np.linspace(
0, 1, len(plot_params['model_filenames']))))
lev_limit_clim = level_clim[level_clim <= cfg['hofm_depth']].shape[0] + 1
mean_profile = np.zeros((level_clim[:lev_limit_clim].shape[0],
len(plot_params['model_filenames']) - 1))
mean_profile_counter = 0
for mmodel in plot_params['model_filenames']:
logger.info("Plot profile %s data for %s, region %s",
plot_params['variable'], mmodel, plot_params['region'])
# construct input filenames
ifilename = genfilename(cfg['work_dir'], plot_params['variable'],
mmodel, plot_params['region'], 'hofm', '.npy')
ifilename_levels = genfilename(cfg['work_dir'],
plot_params['variable'], mmodel,
plot_params['region'], 'levels', '.npy')
# load data
hofdata = np.load(ifilename, allow_pickle=True)
lev = np.load(ifilename_levels, allow_pickle=True)
# convert data if needed and set labeles
cb_label, hofdata = label_and_conversion(plot_params['variable'],
hofdata)
# set index for maximum level (max_level+1)
lev_limit = lev[lev <= cfg['hofm_depth']].shape[0] + 1
# calculate mean profile
profile = (hofdata)[:, :].mean(axis=1)
if mmodel != plot_params['observations']:
next_color = next(color)
else:
next_color = 'k'
plt.plot(profile, lev[0:lev_limit], label=mmodel, c=next_color)
# interpolate to standard levels and add to mean profile
profile_interpolated = np.interp(level_clim[:lev_limit_clim],
lev[0:lev_limit], profile)
if mmodel != plot_params['observations']:
print('include {} in to the mean'.format(mmodel))
mean_profile[:, mean_profile_counter] = profile_interpolated
mean_profile_counter += 1
# Here we are ploting the mean profile separately
mean_profile_mean = np.nanmean(mean_profile, axis=1)
plt.plot(mean_profile_mean,
level_clim[:lev_limit_clim],
label='MODEL-MEAN',
linestyle='--',
color='k',
lw=3)
plt.xticks(size=12)
plt.yticks(size=12)
plt.xlabel(cb_label, size=12, rotation='horizontal')
plt.ylabel('m', size=12, rotation='horizontal')
plt.ylim(0, cfg['hofm_depth'])
# we shift the legend and plot it
box = axis.get_position()
axis.set_position([box.x0, box.y0, box.width * 0.8, box.height])
axis.legend(loc='center left', bbox_to_anchor=(1, 0.5), fontsize=10)
plt.gca().invert_yaxis()
plot_params['basedir'] = cfg['plot_dir']
plot_params['ori_file'] = [ifilename]
plot_params['areacello'] = None
plot_params['mmodel'] = None
pltoutname = genfilename(cfg['plot_dir'], plot_params['variable'],
'MULTIMODEL', plot_params['region'], 'profile')
plt.savefig(pltoutname, dpi=plot_params['dpi'], bbox_inches='tight')
provenance_record = get_provenance_record(plot_params, 'profile', 'png')
with ProvenanceLogger(cfg) as provenance_logger:
provenance_logger.log(pltoutname + '.png', provenance_record)
def tsplot_plot(cfg, plot_params):
"""Plot a TS diagram.
Parameters
----------
model_filenames: OrderedDict
OrderedDict with model names as keys and input files as values.
max_level: float
maximum depth level the TS plot shoud go.
region: str
name of the region predefined in `hofm_regions` function.
diagworkdir: str
path to work directory.
diagplotdir: str
path to plotting directory.
ncols: str
number of columns in the resulting plot
(raws will be calculated from total number of plots)
cmap: matplotlib.cmap object
color map
observations: str
name of the dataset with observations
Returns
-------
None
"""
# Setup a figure
nplots = len(plot_params['model_filenames'])
ncols = float(plot_params['ncols'])
nrows = math.ceil(nplots / ncols)
ncols = int(ncols)
nrows = int(nrows)
nplot = 1
plt.figure(figsize=(8 * ncols, 2 * nrows * ncols))
# loop over models
for mmodel in plot_params['model_filenames']:
logger.info("Plot tsplot data for %s, region %s", mmodel,
plot_params['region'])
# load mean data created by `tsplot_data`
ifilename_t = genfilename(cfg['work_dir'], 'thetao', mmodel,
plot_params['region'], 'tsplot', '.npy')
ifilename_s = genfilename(cfg['work_dir'], 'so', mmodel,
plot_params['region'], 'tsplot', '.npy')
ifilename_depth = genfilename(cfg['work_dir'], 'depth', mmodel,
plot_params['region'], 'tsplot', '.npy')
temp = np.load(ifilename_t, allow_pickle=True)
salt = np.load(ifilename_s, allow_pickle=True)
depth = np.load(ifilename_depth, allow_pickle=True)
# Still old fashioned way to setup a plot, works best for now.
plt.subplot(nrows, ncols, nplot)
# calculate background with density isolines
si2, ti2, dens = dens_back(33, 36., -2, 6)
# convert form Kelvin if needed
if temp.min() > 100:
temp = temp - 273.15
# plot the background
contour_plot = plt.contour(si2,
ti2,
dens,
colors='k',
levels=np.linspace(dens.min(), dens.max(),
15),
alpha=0.3)
# plot the scatter plot
plt.scatter(salt[::],
temp[::],
c=depth,
s=3.0,
cmap=plot_params['cmap'],
edgecolors='none',
vmax=cfg['tsdiag_depth'])
# adjust the plot
plt.clabel(contour_plot, fontsize=12, inline=1, fmt='%1.1f')
plt.xlim(33, 36.)
plt.ylim(-2.1, 6)
plt.xlabel('Salinity', size=20)
plt.ylabel(r'Temperature, $^{\circ}$C', size=20)
plt.xticks(size=15)
plt.yticks(size=15)
# setup the colorbar
colorbar = plt.colorbar(pad=0.03)
colorbar.ax.get_yaxis().labelpad = 15
colorbar.set_label('depth, m', rotation=270, size=20)
colorbar.ax.tick_params(labelsize=15)
plt.title(mmodel, size=20)
nplot = nplot + 1
plt.tight_layout()
# save the plot
pltoutname = genfilename(cfg['plot_dir'],
'tsplot',
region=plot_params['region'],
data_type='tsplot')
plt.savefig(pltoutname, dpi=100)
plot_params['basedir'] = cfg['plot_dir']
plot_params['ori_file'] = [ifilename_t]
plot_params['areacello'] = None
plot_params['mmodel'] = None
provenance_record = get_provenance_record(plot_params, 'tsplot', 'png')
with ProvenanceLogger(cfg) as provenance_logger:
provenance_logger.log(pltoutname + '.png', provenance_record)
def hofm_plot(cfg, plot_params):
"""Plot Hovmoeller diagram from data at diagworkdir.
Parameters
----------
model_filenames: OrderedDict
OrderedDict with model names as keys and input files as values.
cmor_var: str
name of the CMOR variable
max_level: float
maximum depth level the Hovmoeller diagrams should go to.
region: str
name of the region predefined in `hofm_regions` function.
diagworkdir: str
path to work directory.
diagplotdir: str
path to plotting directory.
levels: list
levels for the contour plot.
ncols: str
number of columns in the resulting plot
(raws will be calculated from total number of plots)
cmap: matplotlib.cmap object
color map
observations: str
name of the dataset with observations
Returns
-------
None
"""
# create a basis for the muli panel figure
figure, axis = create_plot(plot_params['model_filenames'],
ncols=plot_params['ncols'])
# plot data on the figure, axis by axis
index = None
for index, mmodel in enumerate(plot_params['model_filenames']):
logger.info("Plot %s data for %s, region %s", plot_params['variable'],
mmodel, plot_params['region'])
# generate input filenames that
# the data prepared by `hofm_data` function
ifilename = genfilename(cfg['work_dir'], plot_params['variable'],
mmodel, plot_params['region'], 'hofm', '.npy')
ifilename_levels = genfilename(cfg['work_dir'],
plot_params['variable'], mmodel,
plot_params['region'], 'levels', '.npy')
ifilename_time = genfilename(cfg['work_dir'], plot_params['variable'],
mmodel, plot_params['region'], 'time',
'.npy')
# load the data
hofdata = np.load(ifilename, allow_pickle=True)
lev = np.load(ifilename_levels, allow_pickle=True)
time = np.load(ifilename_time, allow_pickle=True)
# convert data if needed and get labeles for colorbars
cb_label, hofdata = label_and_conversion(plot_params['variable'],
hofdata)
# find index of the model level closes to the `max_level`
# and add 1, to make a plot look better
lev_limit = lev[lev <= cfg['hofm_depth']].shape[0] + 1
# get the length of the time series
series_lenght = time.shape[0]
# create 2d arrays with coordinates of time and depths
months, depth = np.meshgrid(range(series_lenght), lev[0:lev_limit])
# plot an image for the model on the axis (ax[ind])
image = axis[index].contourf(months,
depth,
hofdata,
cmap=plot_params['cmap'],
levels=plot_params['levels'],
extend='both')
# Generate tick marks with years that looks ok
year_indexes, year_value = year_ticks(series_lenght, time)
# set properties of the axis
axis[index].set_xticks(year_indexes)
axis[index].set_xticklabels(year_value, size=15)
axis[index].set_title(mmodel, size=20)
axis[index].set_ylabel('m', size=15, rotation='horizontal')
axis[index].invert_yaxis()
axis[index].tick_params(axis='y', labelsize=15)
# Add a colorbar
colorbar = figure.colorbar(image, ax=axis[index], pad=0.01)
colorbar.set_label(cb_label, rotation='vertical', size=15)
colorbar.ax.tick_params(labelsize=15)
# delete unused axis
for delind in range(index + 1, len(axis)):
figure.delaxes(axis[delind])
# tighten the layout
plt.tight_layout()
# generate the path to the output file
plot_params['basedir'] = cfg['plot_dir']
plot_params['ori_file'] = [ifilename]
plot_params['areacello'] = None
plot_params['mmodel'] = None
pltoutname = genfilename(**plot_params, data_type='hofm')
plt.savefig(pltoutname, dpi=100)
provenance_record = get_provenance_record(plot_params, 'hofm', 'png')
with ProvenanceLogger(cfg) as provenance_logger:
provenance_logger.log(pltoutname + '.png', provenance_record)
def make_multiple_plots(cfg, metadata, obsname):
"""
Produce multiple panel comparison maps of model(s) and data (if provided).
If observations are not provided, plots of each model data are drawn.
Put on top row observational data (if available) and in following subplots
model difference (or data) organized in rows/cols using row/col layout.
Parameters
----------
cfg: dict
the opened global config dictionary, passed by ESMValTool.
metadata: dict
the input files dictionary
obsname: str
the preprocessed observations file.
"""
logger.debug('make_multiple_plots')
# ####
filenames = list(metadata.keys())
varname = metadata[filenames[0]]['short_name']
# plot setting
layout = metadata[filenames[0]]['layout_rowcol']
# load input data
[cubes, layers, obsname] = load_cubes(filenames, obsname, metadata)
if obsname != '':
layout[0] = layout[0] + 1
else:
logger.info('Observations not provided. Plot each model data.')
if len(filenames) > (layout[0] * layout[1]):
raise ValueError(
'Number of inputfiles is larger than layout scheme (rows x cols). '
'Revise layout_rowcol size in recipe.')
# Make a plot for each layer
for layer in layers:
fig = plt.figure()
fig.set_size_inches(layout[1] * 4., layout[0] * 2. + 2.)
# select cubes to plot
plot_cubes = select_cubes(cubes, layer, obsname,
metadata[filenames[0]])
# create individual subplot
make_subplots(plot_cubes, layout, obsname, fig)
# Determine image filename:
if obsname != '':
plot_file = ['multimodel_vs', obsname, varname, str(layer), 'maps']
else:
plot_file = ['multimodel', varname, str(layer), 'maps']
plot_file = '_'.join(plot_file)
path = diagtools.folder(cfg['plot_dir']) + plot_file
# Saving file:
if cfg['write_plots']:
logger.info('Saving plots to %s', path)
plt.savefig(path, dpi=200)
# Provenance
provenance_record = get_provenance_record(plot_file,
metadata[filenames[-1]],
obsname, filenames)
logger.info("Recording provenance of %s:\n%s", plot_file,
pformat(provenance_record))
with ProvenanceLogger(cfg) as provenance_logger:
provenance_logger.log(plot_file, provenance_record)
plt.close()