def main(project_info):
"""Diagnostics and plotting script for Southern Hemisphere radiation."""
# ESMValProject provides some easy methods to access information in
# project_info but you can also access it directly (as in main.py)
# First we get some general configurations (ESMValProject)
# and then we read in the model configuration file
E = ESMValProject(project_info)
config_file = E.get_configfile()
plot_dir = E.get_plot_dir()
verbosity = E.get_verbosity()
# A-laue_ax+
diag_script = E.get_diag_script_name()
res = E.write_references(
diag_script, # diag script name
["A_maek_ja"], # authors
["A_eval_ma", "A_jone_co"], # contributors
[""], # diag_references
[""], # obs_references
["P_embrace"], # proj_references
project_info,
verbosity,
False)
# A-laue_ax-
modelconfig = ConfigParser.ConfigParser()
modelconfig.read(config_file)
E.ensure_directory(plot_dir)
# Check which parts of the code to run
if (modelconfig.getboolean('general', 'plot_scatter')):
info("Starting scatter plot", verbosity, 2)
process_scatter(E, modelconfig)
def main(project_info):
"""Diagnostics and plotting script for Southern Hemisphere radiation."""
# ESMValProject provides some easy methods to access information in
# project_info but you can also access it directly (as in main.py)
# First we get some general configurations (ESMValProject)
# and then we read in the model configuration file
E = ESMValProject(project_info)
config_file = E.get_configfile()
datakeys = E.get_currVars()
plot_dir = E.get_plot_dir()
verbosity = E.get_verbosity()
# A-laue_ax+
diag_script = E.get_diag_script_name()
res = E.write_references(diag_script, # diag script name
["A_maek_ja"], # authors
["A_eval_ma", "A_jone_co"], # contributors
[""], # diag_references
[""], # obs_references
["P_embrace"], # proj_references
project_info,
verbosity,
False)
# A-laue_ax-
modelconfig = ConfigParser.ConfigParser()
modelconfig.read(config_file)
E.ensure_directory(plot_dir)
# Check at which stage of the program we are
clouds = False
fluxes = False
radiation = False
if ('clt' in datakeys or 'clivi' in datakeys or 'clwvi' in datakeys):
clouds = True
elif ('hfls' in datakeys or 'hfss' in datakeys):
fluxes = True
else:
radiation = True
# Check which parts of the code to run
if (modelconfig.getboolean('general', 'plot_clouds') and clouds is True):
info("Starting to plot clouds", verbosity, 2)
process_clouds(E, modelconfig)
if (modelconfig.getboolean('general', 'plot_fluxes') and fluxes is True):
info("Starting to plot turbulent fluxes", verbosity, 2)
process_fluxes(E, modelconfig)
if (modelconfig.getboolean('general', 'plot_radiation') and radiation is True):
info("Starting to plot radiation graphs", verbosity, 2)
process_radiation(E, modelconfig)
def main(project_info):
"""Diagnostics and plotting script for Tropical Variability.
We use ts as a proxy for Sea Surface Temperature. """
# ESMValProject provides some easy methods to access information in
# project_info but you can also access it directly (as in main.py)
# First we get some general configurations (ESMValProject)
# and then we read in the model configuration file
E = ESMValProject(project_info)
config_file = E.get_configfile()
plot_dir = E.get_plot_dir()
verbosity = E.get_verbosity()
# A-laue_ax+
diag_script = E.get_diag_script_name()
res = E.write_references(
diag_script, # diag script name
["A_maek_ja"], # authors
["A_eval_ma", "A_jone_co"], # contributors
["D_li14jclim"], # diag_references
[""], # obs_references
["P_embrace"], # proj_references
project_info,
verbosity,
False)
# A-laue_ax-
modelconfig = ConfigParser.ConfigParser()
modelconfig.read(config_file)
E.ensure_directory(plot_dir)
# Here we check and process only desired parts of the diagnostics
if (modelconfig.getboolean('general', 'plot_zonal_means')):
info("Starting to plot zonal means", verbosity, 2)
process_zonal_means(E, modelconfig)
if (modelconfig.getboolean('general', 'plot_scatter')):
info("Starting scatterplot of temperature and precipitation",
verbosity, 2)
process_scatterplot(E, modelconfig)
if (modelconfig.getboolean('general', 'plot_equatorial')):
info("Starting to gather values for equatorial means", verbosity, 2)
process_equatorial_means(E, modelconfig)
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 main(project_info):
"""
;; Description
;; Main fuction
;; Call all callable fuctions to
;; read CMIP5 data, compute and plot diagnostic
"""
E = ESMValProject(project_info)
plot_dir = E.get_plot_dir()
work_dir = E.get_work_dir()
verbosity = E.get_verbosity()
fileout = work_dir
if not os.path.exists(plot_dir):
os.makedirs(plot_dir)
for model in project_info['MODELS']:
info(model, verbosity, required_verbosity=1)
if not os.path.exists(work_dir+'/sample_events'):
os.makedirs(work_dir+'/sample_events')
if not os.path.exists(work_dir+'/event_output'):
os.makedirs(work_dir+'/event_output')
# --------------------------------------
# Get input data to compute diagnostic
# --------------------------------------
# Read cmip5 model data
pr, sm, topo, lon, lat, time, time_bnds_1 = read_pr_sm_topo(project_info, model)
# --------------------------------------
# Get sm monthly climatology
# at selected local time: 6:00 am
# --------------------------------------
smclim = get_smclim(sm, lon, time)
# -------------------------------
# Compute diagnostic per month
# -------------------------------
samplefileout = fileout + 'sample_events/'
for mn in np.arange(1, 13):
# -------------------------------------------------
# Create montly arrays required by fortran routines
# -------------------------------------------------
prbef, smbef, praft, smaft, \
monthlypr, monthlysm, days_per_year = get_monthly_input(project_info, mn, time,
lon, lat, time_bnds_1, pr, sm, fileout,
samplefileout, model,
verbosity)
# -----------------------
# Run fortran routines
# -----------------------
info('Executing global_rain_sm for month ' + str(mn), verbosity, required_verbosity=1)
grs.global_rain_sm(np.asfortranarray(monthlypr),
np.asfortranarray(prbef),
np.asfortranarray(praft),
np.asfortranarray(monthlysm),
np.asfortranarray(smbef),
np.asfortranarray(smaft),
np.asfortranarray(smclim[mn - 1, :, :]),
np.asfortranarray(topo),
np.asfortranarray(lon),
np.asfortranarray(mn),
fileout, days_per_year)
info('Executing sample_events for month ' + str(mn), verbosity, required_verbosity=1)
se.sample_events(np.asfortranarray(monthlysm),
np.asfortranarray(smbef),
np.asfortranarray(smaft),
np.asfortranarray(lon),
np.asfortranarray(lat),
np.asfortranarray(mn),
fileout, days_per_year, samplefileout)
# ---------------------------------------------------
# Compute p_values (as in Fig. 3, Taylor et al 2012)
# --------------------------------------------------
info('Computing diagnostic', verbosity, required_verbosity=1)
xs, ys, p_vals = get_p_val(samplefileout)
# --------------------------------------------------
# Save diagnostic to netCDF file and plot
# --------------------------------------------------
write_nc(fileout, xs, ys, p_vals, project_info, model)
plot_diagnostic(fileout, plot_dir, project_info, model)
# --------------------------------------------------
# Remove temporary folders
# --------------------------------------------------
shutil.rmtree(str(fileout) + 'event_output')
shutil.rmtree(str(fileout) + 'sample_events')
def main(project_info):
"""
main interface routine to ESMValTool
Parameters
----------
project_info : dict
dictionary that contains all relevant informations
it is provided by the ESMValTool launcher
"""
# extract relevant information from project_info using a generic python
# interface library
# logging configuration
verbosity = project_info['GLOBAL']['verbosity']
if verbosity == 1:
logger.setLevel(logging.WARNING)
elif verbosity == 2:
logger.setLevel(logging.INFO)
else:
logger.setLevel(logging.DEBUG)
# create instance of a wrapper that allows easy access to data
E = ESMValProject(project_info)
# get information for runoff/ET script
# input file directory, returns a dict
ifile_dict = E.get_raw_inputfile()
# A-laue_ax+
diag_script = E.get_diag_script_name()
res = E.write_references(diag_script, # diag script name
["A_somm_ph", "A_hage_st"], # authors
["A_loew_al"], # contributors
["D_hagemann13jadvmodelearthsyst"], # diag_references
["E_duemenil00mpi", "E_weedon14waterresourres"], # obs_references
["P_embrace"], # proj_references
project_info,
verbosity,
False)
# A-laue_ax-
# set up of input files dictionary, rewrite ifile_dict to match for
# catchment_analysis_tool_val
# ifiles={<<<MODEL>>>:
# {<<<VAR>>>:
# {'file':<file>[, 'unit':<unit>, 'cdo':<cdo>,
# 'vname':<vname>, 'reffile':<file>,
# 'refvname':<vname>]}}}
# dictionary containing the model names as keys and a second dictionary as
# value.
# This (second) dictionaries contain the variable names (eihter 'runoff' or
# 'ET') as key and a third dictionary as value.
# This (third) dictionaries contain the key
# - 'file' for the datafile for the climatological mean
# and optionally
# - 'unit' for the unit of the data. If 'unit' is not set, it will be
# taken from the nc-file multiplied by 'm^2'
# - 'cdo' for additional cdo-commands (e.g. multiplication for
# changing
# the units)
# - 'vname' for the name of the variable in the datafile (if not set,
# the variable name as used for the key will be used).
# - 'reffile' for the reference file (if not set, defaultreffile will be
# used and <<<VAR>>> will be replaced by the variable name
# as given in the key
# - 'refvname' for the name of the variable in the reference file (if not
# set, the variable name as used for the key will be used).
ifiles = {}
for model, vlst in ifile_dict.items():
ifiles[str(model)] = {}
for var in map(str, vlst.keys()):
if var == 'evspsbl':
myvar = 'ET'
# units are are kg m-2 s-1 and therefore must be multiplied
# by the amount of seconds in one year
ifiles[str(model)][myvar] = {'unit': 'mm/a', 'vname': var,
'cdo': '-mulc,86400 -muldpy '}
elif var == 'mrro':
myvar = 'runoff'
# units are are kg m-2 s-1 and therefore must be multiplied
# by the amount of seconds in one year
ifiles[str(model)][myvar] = {'unit': 'mm/a', 'vname': var,
'cdo': '-mulc,86400 -muldpy '}
elif var == 'pr':
myvar = 'precip'
# units are are kg m-2 s-1 and therefore must be multiplied
# by the amount of seconds in one year
ifiles[str(model)][myvar] = {'unit': 'mm/a', 'vname': var,
'cdo': '-mulc,86400 -muldpy '}
else:
# only ET, mrro and precip are supported trough reference
# values. Therefore raise error
raise ValueError(
"Only mrro (runoff), pr (precipitation) and evspsbl "
"(evapotranspration) are supported!")
# try to get reformated data and if this does not work (because no
# CMIP5 project, use raw input data
try:
ifiles[str(model)][myvar]['file'] = map(str, glob.glob(
E.get_clim_model_filenames(var)[model]))
except ValueError:
indir = str(ifile_dict[model][var]['directory'])
indir = msd["dir"]
if indir[-1] != os.sep:
indir += os.sep
infile = str(ifile_dict[model][var]['files'])
ifiles[str(model)][myvar]['file'] = indir + infile
# A-laue_ax+
for file in ifiles[str(model)][myvar]['file']:
E.add_to_filelist(file)
# A-laue_ax-
POUT = str(os.path.join(E.get_plot_dir(), E.get_diag_script_name()))
POUT = POUT + os.sep
if not os.path.exists(POUT):
os.makedirs(POUT)
# catchment_dir = data_directory + 'cat/'
catchment_dir = os.path.dirname(os.path.abspath(inspect.getfile(
inspect.currentframe()))) + '/'
# set path to nc-file containing the catchment definition
pcatchment = os.path.join(catchment_dir, "aux/catchment_analysis", "big_catchments.nc")
# A-laue_ax+
E.add_to_filelist(pcatchment)
# A-laue_ax-
# ---- File input declaration ----
# input file (needs to contain grid informations). For the
# analysis the data will be regridded to pcatchments. Data is expected to
# be in mm/s (if not, see 'cdo' in ifiles dictionary below)
# ---- switches ----
# CALCULATION: switch to calculate climatological means (True) or use
# already existing files
CALCULATION = True
# KEEPTIMMEAN: switch to keep files created during calculation. if true,
# files like 'tmp_<<<MODEL>>>_<<<var>>>_<<<CATCHMENT>>>.<<<fmt>>>' will be
# stored in the output directory (POUT) containing the timmean data for
# the catchments (note that the file format <<<fmt>>> is defined by the
# input file)
KEEPTIMMEAN = False
# PLOT: switch to set plotting. If true, one plot for each model named
# POUT+<<<MODEL>>>_bias-plot.pdf containg all variables will be generated
PLOT = True
# SEPPLOTS: Integer to control diagram style. If 2: relative and absolute
# variables will be plotted in separate files named
# POUT+<<<MODEL>>>_rel-bias-plot.pdf and
# POUT+<<<MODEL>>>_abs-bias-plot.pdf.
# If 3: they will be plotted into one # figure but separated axes in file
# POUT+<<<MODEL>>>_sep-bias-plot.pdf, if 5: # they will be plotted into one
# single axes within file POUT+<<<MODEL>>>_bias-plot.pdf. Multiplication
# (e.g. 6, 15 or 30) is also possible to make more than one option at the
# same time
SEPPLOTS = 3
# ETREFCALCULATION: If True, create reference file
# ref_ET_catchments.txt from timmean of precfile and runoff data from
# ref_runoff_catchments.txt. If None, use default reference values, save
# them to defaultreffile and delete it afterwards. If False, use existing
# file defined by defaultreffile or reffile defined in ifiles (see above).
ETREFCALCULATION = None
# formatoptions for plot (ax2 modifies diagrams showing relative values)
# (for keywords see function xyplotformatter in
# catchment_analysis_tool_val)
# be aware that the additional setting of a keyword for the plot of the
# absolute values may also influence the plot of relative value. To prevent
# from that, define the option for 'ax2' manually.
# ---
# set minimal and maximal bounds of y-axis (Syntax: [ymin, ymax]). 'minmax'
# will cause y-axis limits with minimum and maximum value and makes the
# plot symmetric around 0 in case of SEPPLOTS%5 == 0. To let the plotting
# routine (i.e. pyplot) choose the limits, set ylim to None
# ---
# Set yticks: integer or list (Default (i.e. without manipulation by the
# plotting routine of catchment_analysis_tool_val): None). Defines the
# y-ticks. If integer i, every i-th tick of the automatically
# other formatoptions as provided by function
# catchment_analysis_tool_val.xyplotformatter may be included in the
# dictionary below
fmt = {
myvar: {
'ylim': 'minmax', 'yticks': None,
'ax2': {'ylim': 'minmax', 'yticks': None}
}
for myvar in ifiles.values()[0].keys()
}
# start computation
analysecatchments(
project_info,
POUT=POUT,
pcatchment=pcatchment,
ifiles=ifiles,
CALCULATION=CALCULATION,
KEEPTIMMEAN=KEEPTIMMEAN,
PLOT=PLOT,
SEPPLOTS=SEPPLOTS,
ETREFCALCULATION=ETREFCALCULATION,
fmt=fmt,
# precfile=precfile,
# defaultreffile=defaultreffile
)