def hofm_plot_params(cfg, hofm_var, var_number, observations):
"""Prepeare configuration for Hovmoeller plot."""
model_filenames = get_clim_model_filenames(cfg, hofm_var)
model_filenames = OrderedDict(
sorted(model_filenames.items(), key=lambda t: t[0]))
# remove "model" that contain observations,
# since there will be no monthly data
model_filenames = model_filenames.copy()
if observations:
del model_filenames[observations]
# set the color map if not default
if cfg['hofm_cmap']:
cmap = get_cmap(cfg['hofm_cmap'][var_number])
else:
cmap = get_cmap('Spectral_r')
# set the number of columns in the output figure
# if defined
if cfg['hofm_ncol']:
ncols = cfg['hofm_ncol']
else:
ncols = 3
# get the levels for plots of this variable
vmin, vmax, sstep, roundlimit = cfg['hofm_limits'][var_number]
plot_params = {}
plot_params['variable'] = hofm_var
plot_params['model_filenames'] = model_filenames
plot_params['cmap'] = cmap
plot_params['ncols'] = ncols
plot_params['levels'] = np.round(np.linspace(vmin, vmax, sstep),
roundlimit)
plot_params['observations'] = observations
return plot_params
def plot2d_bias_params(cfg, plot2d_bias_var, var_number, observations):
"""Prepeare configuration for plot2d bias."""
model_filenames = get_clim_model_filenames(cfg, plot2d_bias_var)
model_filenames = OrderedDict(
sorted(model_filenames.items(), key=lambda t: t[0]))
# setup the color map
if cfg['plot2d_bias_cmap']:
cmap = get_cmap(cfg['plot2d_bias_cmap'][var_number])
else:
cmap = get_cmap('Spectral_r')
# setup the number of columns
if cfg['plot2d_bias_ncol']:
ncols = cfg['plot2d_bias_ncol']
else:
ncols = 3
# setup color limits
vmin, vmax, sstep, roundlimit = cfg['plot2d_bias_limits'][var_number]
plot_params = {}
plot_params['variable'] = plot2d_bias_var
plot_params['model_filenames'] = model_filenames
plot_params['cmap'] = cmap
plot_params['ncols'] = ncols
plot_params['levels'] = np.round(np.linspace(vmin, vmax, sstep),
roundlimit)
plot_params['dpi'] = 100
plot_params['observations'] = observations
plot_params['projection'] = ccrs.NorthPolarStereo()
plot_params['bbox'] = (-180, 180, 60, 90)
return plot_params
def run_hofm_data(cfg):
"""Extract data for Hovmoeller diagrams.
Parameters
----------
cfg: dict
configuration dictionary ESMValTool format.
areacello_fx : dict
configuration dictionary with names of the areacello_fx
files associated to dataset names.
diagworkdir: str
path to the diagnostic work directory.
"""
logger.info("The `hofm_data` is True, going \
to extract monthly values for `hofm_regions`")
logger.info("`hofm_vars` are: %s", cfg['hofm_vars'])
# doing the loop for every variable
for hofm_var in cfg['hofm_vars']:
logger.info("Processing %s", hofm_var)
# get dictionary with model names as key and path to the
# preprocessed file as a value
model_filenames = get_clim_model_filenames(cfg, hofm_var)
model_filenames = OrderedDict(
sorted(model_filenames.items(), key=lambda t: t[0]))
# loop over regions and models
for mmodel, region in itertools.product(model_filenames,
cfg['hofm_regions']):
# actual extraction of the data for specific model and region
hofm_data(cfg, model_filenames, mmodel, hofm_var, region)
def run_aw_core_2d(cfg, aw_core_parameters):
"""Plot temperature spatial distribution at AW core depth.
Parameters
----------
cfg: dict
configuration dictionary ESMValTool format.
aw_core_parameters: dict
dictionary that contain AW core parameters generated
by run_aw_core function.
"""
model_filenames = get_clim_model_filenames(cfg, 'thetao')
model_filenames = OrderedDict(
sorted(model_filenames.items(), key=lambda t: t[0]))
aw_core_parameters = aw_core(model_filenames, cfg['work_dir'], 'EB',
'thetao')
# this is now just using plot2d_original_grid with
# additional `explicit_depths` parameter
plot_params = {}
plot_params['variable'] = 'thetao'
plot_params['model_filenames'] = model_filenames
plot_params['depth'] = 0
plot_params['cmap'] = cm.Spectral_r
plot_params['ncols'] = 4
plot_params['levels'] = np.round(np.linspace(-2, 2.3, 41), 1)
plot_params['dpi'] = 100
plot_params['explicit_depths'] = aw_core_parameters
plot_params['projection'] = ccrs.NorthPolarStereo()
plot_params['bbox'] = (-180, 180, 60, 90)
plot2d_original_grid(cfg, plot_params)
def plot2d_params(cfg, plot2d_var, var_number):
"""Prepeare configuration for plot2d."""
model_filenames = get_clim_model_filenames(cfg, plot2d_var)
model_filenames = OrderedDict(
sorted(model_filenames.items(), key=lambda t: t[0]))
# set the color map
if cfg['plot2d_cmap']:
cmap = get_cmap(cfg['plot2d_cmap'][var_number])
else:
cmap = get_cmap('Spectral_r')
# set the number of columns in the plot
if cfg['plot2d_ncol']:
ncols = cfg['plot2d_ncol']
else:
ncols = 4
# create color limits for the plot
vmin, vmax, sstep, roundlimit = cfg['plot2d_limits'][var_number]
# loop over depths
plot_params = {}
plot_params['variable'] = plot2d_var
plot_params['model_filenames'] = model_filenames
plot_params['cmap'] = cmap
plot_params['ncols'] = ncols
plot_params['levels'] = np.round(np.linspace(vmin, vmax, sstep),
roundlimit)
plot_params['dpi'] = 100
plot_params['explicit_depths'] = None
plot_params['projection'] = ccrs.NorthPolarStereo()
plot_params['bbox'] = (-180, 180, 60, 90)
return plot_params
def run_mean(cfg, observations):
"""Create time mean.
Parameters
----------
cfg: dict
configuration dictionary ESMValTool format.
observations: str
name of the observation data set
"""
# loop over variables
for hofm_var in cfg['hofm_vars']:
model_filenames = get_clim_model_filenames(
cfg,
hofm_var,
)
model_filenames = OrderedDict(
sorted(model_filenames.items(), key=lambda t: t[0]))
# loop over models
for model in model_filenames:
timmean(cfg,
model_filenames,
model,
hofm_var,
observations=observations)
def run_aw_core(cfg):
"""Calculate depth and temperature of the Atlantic Water core.
Parameters
----------
cfg: dict
configuration dictionary ESMValTool format.
"""
model_filenames = get_clim_model_filenames(cfg, 'thetao')
model_filenames = OrderedDict(
sorted(model_filenames.items(), key=lambda t: t[0]))
aw_core_parameters = aw_core(model_filenames, cfg['work_dir'], 'EB',
'thetao')
plot_aw_core_stat(aw_core_parameters, cfg['plot_dir'])
return aw_core_parameters
def plot_profile_params(cfg, hofm_var, observations):
"""Prepeare configuration for profile plot."""
model_filenames = get_clim_model_filenames(cfg, hofm_var)
model_filenames = OrderedDict(
sorted(model_filenames.items(), key=lambda t: t[0]))
plot_params = {}
plot_params['variable'] = hofm_var
plot_params['model_filenames'] = model_filenames
plot_params['cmap'] = cm.Set2
plot_params['dpi'] = 100
plot_params['observations'] = observations
return plot_params
def tsdiag_plot_parameters(cfg):
"""Prepeare configuration for TS plots."""
# get the dictionary with model file names
model_filenames = get_clim_model_filenames(cfg, 'thetao')
model_filenames = OrderedDict(
sorted(model_filenames.items(), key=lambda t: t[0]))
# setting the number of columns for the plot
if cfg['tsdiag_ncol']:
ncols = cfg['tsdiag_ncol']
else:
ncols = 3
plot_params = {}
plot_params['model_filenames'] = model_filenames
plot_params['ncols'] = ncols
plot_params['cmap'] = cm.Set1
return plot_params
def transect_plot_params(cfg, trans_var, var_number):
"""Prepeare configuration for transect plot."""
model_filenames = get_clim_model_filenames(cfg, trans_var)
model_filenames = OrderedDict(
sorted(model_filenames.items(), key=lambda t: t[0]))
# loop over regions
for mmodel, region in itertools.product(model_filenames,
cfg['transects_regions']):
# ploting a transect
transect_data(cfg, mmodel, trans_var, region)
# setup a color map
if cfg['transects_cmap']:
cmap = get_cmap(cfg['transects_cmap'][var_number])
else:
cmap = get_cmap('Spectral_r')
# setup number of columns
if cfg['transects_ncol']:
ncols = cfg['transects_ncol']
else:
ncols = 3
# setup color limits
vmin, vmax, sstep, roundlimit = cfg['transects_limits'][var_number]
plot_params = {}
plot_params['variable'] = trans_var
plot_params['model_filenames'] = model_filenames
plot_params['cmap'] = cmap
plot_params['ncols'] = ncols
plot_params['levels'] = np.round(np.linspace(vmin, vmax, sstep),
roundlimit)
plot_params['dpi'] = 100
plot_params['projection'] = ccrs.NorthPolarStereo()
plot_params['bbox'] = (-180, 180, 60, 90)
return plot_params
