def find_xml_fx(name, project='', experiment='', realm=''):
list_obs = ReferenceObservations().keys()
if name in list_obs:
file_area = join_path(xmldir,
'obs_' + str(name) + '_glob_fx_O_areacell.xml')
file_land = join_path(xmldir,
'obs_' + str(name) + '_glob_fx_O_landmask.xml')
else:
file_area = join_path(
xmldir,
str(name) + '_' + str(project) + '_' + str(experiment) +
'_r0i0p0_glob_fx_' + str(realm) + '_areacell.xml')
file_land = join_path(
xmldir,
str(name) + '_' + str(project) + '_' + str(experiment) +
'_r0i0p0_glob_fx_' + str(realm) + '_landmask.xml')
try:
xml = CDMS2open(file_area)
except:
file_area = None
try:
xml = CDMS2open(file_land)
except:
file_land = None
return file_area, file_land
def find_xml(name,
frequency,
variable,
project='',
experiment='',
ensemble='',
realm=''):
list_obs = ReferenceObservations().keys()
if name in list_obs:
file_name, file_area, file_land = find_xml_obs(name, frequency,
variable)
else:
file_name, file_area, file_land = find_xml_cmip(
name, project, experiment, ensemble, frequency, realm, variable)
return file_name, file_area, file_land
def find_xml_fx(name, project='', experiment='', realm=''):
list_obs = ReferenceObservations().keys()
if name in list_obs:
file_area = OSpath__join(xmldir, 'obs_' + str(name) + '_areacell.xml')
file_land = OSpath__join(xmldir, 'obs_' + str(name) + '_landmask.xml')
else:
file_area = OSpath__join(
xmldir,
str(name) + '_' + str(project) + '_' + str(experiment) +
'_r0i0p0_glob_fx_' + str(realm) + '_areacell.xml')
file_land = OSpath__join(
xmldir,
str(name) + '_' + str(project) + '_' + str(experiment) +
'_r0i0p0_glob_fx_' + str(realm) + '_landmask.xml')
return file_area, file_land
# references
ref_colors = {'model': 'r', 'obs': 'k'}
ref_obs = {
'ssh': 'AVISO',
'pr': 'GPCPv2.3',
'sst': 'HadISST',
'lhf': 'Tropflux',
'lwr': 'Tropflux',
'shf': 'Tropflux',
'swr': 'Tropflux',
'taux': 'Tropflux',
'thf': 'Tropflux'
}
# metric collections
list_MC = sorted(defCollection().keys(), key=lambda v: v.upper())
list_obs = sorted(ReferenceObservations().keys(), key=lambda v: v.upper())
# model
model = "CNRM-CM5"
# path
path_main = '/Users/yannplanton/Documents/Yann/Fac/2016_2018_postdoc_LOCEAN/2018_06_ENSO_metrics/2019_05_report/Data/v20190515'
path_plot = '/Users/yannplanton/Documents/Yann/Fac/2016_2018_postdoc_LOCEAN/2018_06_ENSO_metrics/2019_05_report/Wiki'
# ---------------------------------------------------#
# get arguments
try:
SYSargv[1]
except:
print ' no argument given'
print ''
metric_col = 'ENSO_perf' # 'CM6_perf'
experiment = 'historical'
else:
if mc_name == 'MC1':
list_obs = ['Tropflux']
elif mc_name == 'ENSO_perf':
list_obs = ['Tropflux','GPCPv2.3']
elif mc_name == 'ENSO_tel':
list_obs = ['HadISST','GPCPv2.3']
print '\033[95m' + str(list_obs) + '\033[0m'
#
# finding file and variable name in file for each observations dataset
#
dict_obs = dict()
for obs in list_obs:
# @jiwoo: be sure to add your datasets to EnsoCollectionsLib.ReferenceObservations if needed
dict_var = ReferenceObservations(obs)['variable_name_in_file']
dict_obs[obs] = dict()
for var in list_variables:
#
# finding variable name in file
#
# @jiwoo: correct / adapt the 'varname' in
# EnsoCollectionsLib.ReferenceObservations(obs)['variable_name_in_file'][var] if it is not correct or if you
# changed a name in the xml
# I usually alias the variable names from observations and models in the xml in order to have the same name
# for sst (or any other variable) in every xml. This way I don not need to go through this function to know the
# variable name in file
try: var_in_file = dict_var[var]['var_name']
except:
print '\033[95m' + str(var) + " is not available for " + str(obs) + " or unscripted" + '\033[0m'
else: