def _readnc(filenc=None, argdict=myargs):
if argdict.has_key('compared_configs'):
compare = True
else:
argdict['compared_configs'] = [argdict['config']]
compare = False
# get the section names corresponding to the config
section_dict = rs.define_all_sections(argdict, compare)
#
outdict = {} # creates the dictionnary which will contain the arrays
date = rs.get_datetime(filenc)
outdict['date'] = date
list_truenames = section_dict.keys()
list_truenames.sort()
for section in list_truenames:
shortname = section_dict[section]['shortname'] # temporary
sens = section_dict[section]['sens'] # variables
try:
outdict['mass_' + shortname] = npy.array(
(sens), 'f') * rs.readfilenc(filenc, 'vtrp' + '_' + shortname)
outdict['heat_' + shortname] = npy.array(
(sens), 'f') * rs.readfilenc(filenc, 'htrp' + '_' + shortname)
outdict['salt_' + shortname] = npy.array(
(sens), 'f') * rs.readfilenc(filenc, 'strp' + '_' + shortname)
except:
outdict['mass_' + shortname] = npy.zeros((len(outdict['date'])))
outdict['heat_' + shortname] = npy.zeros((len(outdict['date'])))
outdict['salt_' + shortname] = npy.zeros((len(outdict['date'])))
return outdict # return the dictionnary of values
def _readnc(filenc=None, argdict=myargs):
if argdict.has_key('compared_configs'):
compare = True
else:
argdict['compared_configs'] = [argdict['config']]
compare = False
# get the section names corresponding to the config
section_dict = rs.define_all_sections(argdict, compare,
'drakkar_trpsig_table.txt')
#
outdict = {} # creates the dictionnary which will contain the arrays
outdict['date'] = rs.get_datetime(filenc)
outdict['sigma_class'] = rs.readfilenc(filenc, 'sigma_class')[0, :]
list_truenames = section_dict.keys()
list_truenames.sort()
for section in list_truenames:
shortname = section_dict[section]['shortname'] # temporary
try:
outdict['sigtrp_' + shortname] = rs.readfilenc(
filenc, 'sigtrp' + '_' + shortname)
except:
outdict['sigtrp_' + shortname] = npy.zeros(
(len(outdict['date']), len(outdict['sigma_class'])))
return outdict # return the dictionnary of values
def _readnc(filenc=None,argdict=myargs):
#
if argdict['config'].find('ORCA') == 0:
list_field = ['zomht_glo', 'zomht_atl', 'zomht_inp','hflx_glo' , 'hflx_atl' , 'hflx_inp']
else:
list_field = ['zomht_glo', 'hflx_glo']
#
outdict = {} # creates the dictionnary which will contain the arrays
outdict['date'] = rs.get_datetime(filenc)
outdict['lat'] = rs.readfilenc(filenc, 'nav_lat')
for field in list_field:
temp = rs.readfilenc(filenc,field)
## remove spurious areas
if field.find('atl') != -1:
ymax = npy.abs( outdict['lat'] - 70 ).argmin() # find index of 70N
ymin = npy.abs( outdict['lat'] + 30 ).argmin() # find index of 30S
temp[:,ymax:] = 0.
temp[:,:ymin] = 0.
if field.find('inp') != -1:
ymin = npy.abs( outdict['lat'] + 30 ).argmin() # find index of 30S
temp[:,:ymin] = 0.
outdict[field] = rs.remove_spval(temp, 9999, 0)
return outdict # return the dictionnary of values
def _readnc(filenc=None, filenc2=None, argdict=myargs):
#
outdict = {} # creates the dictionnary which will contain the arrays
outdict['date_model'] = rs.get_datetime(filenc)
outdict['date_model_heat'] = rs.get_datetime(filenc2)
if argdict['config'].find('ORCA') == 0 or argdict['config'].find(
'EORCA') == 0 or argdict['config'].find('eORCA') == 0:
data_list = [
'maxmoc_Glo_maxmoc', 'maxmoc_Atl_maxmoc', 'maxmoc_Aus_maxmoc',
'minmoc_Glo_minmoc', 'minmoc_Atl_minmoc', 'minmoc_Inp_minmoc',
'minmoc_Inp_minmoc2', 'minmoc_Aus_minmoc'
]
data_list_heat = ['zomht_glo', 'zomht_atl']
elif argdict['config'].find('NATL') == 0 or argdict['config'].find(
'NACHOS') == 0:
data_list = ['maxmoc_Glo_maxmoc', 'minmoc_Glo_minmoc']
data_list_heat = ['zomht_glo']
elif 'PERIANT' in argdict['config']: # this should be checked.
data_list = ['maxmoc_Glo_maxmoc', 'minmoc_Glo_minmoc']
data_list_heat = []
else:
print "config not supported"
sys.exit()
#
for k in data_list:
outdict[k] = rs.readfilenc(filenc, k)
#
lat = rs.readfilenc(filenc2, 'nav_lat')
index20N = rs.get_index(lat, 20.)
for k in data_list_heat:
outdict[k] = rs.readfilenc(filenc2, k)[:, index20N]
return outdict # return the dictionnary of values
def _readnc(filenc=None,argdict=myargs):
outdict = {} # creates the dictionnary which will contain the arrays
date = rs.get_datetime(filenc)
outdict['date'] = date
outdict['ice1kerg1plt'] = rs.readfilenc(filenc, 'mean_ileadfra_KERG4')
outdict['ice1kerg2plt'] = rs.readfilenc(filenc, 'mean_iicethic_KERG4')
return outdict # return the dictionnary of values
def _readnc(filenc=None, argdict=myargs):
outdict = {} # creates the dictionnary which will contain the arrays
date = rs.get_datetime(filenc)
outdict['date'] = date
outdict['co2kerg1plt'] = rs.readfilenc(filenc, 'mean_3DDelc_KERG1')
outdict['co2kerg2plt'] = rs.readfilenc(filenc, 'mean_3DDelc_KERG3')
outdict['co2kerg3plt'] = rs.readfilenc(filenc, 'mean_3DDelc_KERG5')
outdict['co2kerg4plt'] = rs.readfilenc(filenc, 'mean_3DDelc_KERG6')
return outdict # return the dictionnary of values
def _readnc(filenc=None,levitus=None):
#
outdict = {} # creates the dictionnary which will contain the arrays
outdict['levels'] = rs.readfilenc(filenc,'gdept')
outdict['date'] = rs.get_datetime(filenc)
outdict['tmodel'] = rs.readfilenc(filenc,'mean_votemper') #2D
outdict['smodel'] = rs.readfilenc(filenc,'mean_vosaline')
outdict['tlev'] = rs.readfilenc(levitus,'mean_votemper') #1D
outdict['slev'] = rs.readfilenc(levitus,'mean_vosaline')
return outdict # return the dictionnary of values
def _readnc(filemodel=None,fileobs=None):
"""Read the netcdf files and return a dictionnary of output.
Remark
------
All the keys of outdict will be loaded as local variables in plot()
"""
#
outdict = {} # creates the dictionnary which will contain the arrays
outdict['levels'] = rs.readfilenc(filemodel,'gdept') ## read "gdept" in netcdf file "filemodel"
outdict['year'] = rs.get_years(filemodel) ## read time_counter in "filemodel"
outdict['diag1'] = rs.readfilenc(filemodel,'diag1_name_in_file') ## ...
outdict['diag2'] = rs.readfilenc(filemodel,'diag2_name_in_file')
outdict['obs1'] = rs.readfilenc(fileobs,'obs1_name_in_file')
outdict['obs2'] = rs.readfilenc(fileobs,'obs2_name_in_file')
return outdict # return the dictionnary of values
def _readnc(filenc=None):
outdict = {} # creates the dictionnary which will contain the arrays
outdict['tprof3'] = rs.readfilenc(filenc, 'mean_votemper_KERG3')
outdict['sprof3'] = rs.readfilenc(filenc, 'mean_vosaline_KERG3')
outdict['dprof3'] = rs.readfilenc(filenc, 'mean_vosigma0_KERG3')
outdict['mld3'] = rs.readfilenc(filenc, 'mean_somxl010_KERG3')
outdict['tprof1'] = rs.readfilenc(filenc, 'mean_votemper_KERG1')
outdict['sprof1'] = rs.readfilenc(filenc, 'mean_vosaline_KERG1')
outdict['dprof1'] = rs.readfilenc(filenc, 'mean_vosigma0_KERG1')
outdict['mld1'] = rs.readfilenc(filenc, 'mean_somxl010_KERG1')
outdict['prof'] = rs.readfilenc(filenc, 'gdept')
outdict['date'] = rs.get_datetime(filenc)
return outdict # return the dictionnary of values
def _readnc(filenc=None,fileobs=None,argdict=myargs):
#
outdict = {} # creates the dictionnary which will contain the arrays
outdict['date_model'] = rs.get_datetime(filenc)
outdict['NVolume' ] = rs.readfilenc(filenc, 'NVolume' ) / 1000
outdict['NArea' ] = rs.readfilenc(filenc, 'NArea' ) / 1000
outdict['NExnsidc' ] = rs.readfilenc(filenc, 'NExnsidc' ) / 1000
outdict['SVolume' ] = rs.readfilenc(filenc, 'SVolume' ) / 1000
outdict['SArea' ] = rs.readfilenc(filenc, 'SArea' ) / 1000
outdict['SExnsidc' ] = rs.readfilenc(filenc, 'SExnsidc' ) / 1000
#
outdict['dateobs' ] = rs.get_datetime(fileobs)
outdict['NORTH_ICE_EXTENT'] = rs.readfilenc(fileobs, 'NORTH_ICE_EXTENT')
outdict['NORTH_ICE_AREA'] = rs.readfilenc(fileobs, 'NORTH_ICE_AREA')
outdict['SOUTH_ICE_EXTENT'] = rs.readfilenc(fileobs, 'SOUTH_ICE_EXTENT')
outdict['SOUTH_ICE_AREA'] = rs.readfilenc(fileobs, 'SOUTH_ICE_AREA')
return outdict # return the dictionnary of values
def _readnc(filenc=None, fileobs=None, argdict=myargs):
import matplotlib.pylab as plt
from numpy import nan
outdict = {} # creates the dictionnary which will contain the arrays
lon_obs = rs.readfilenc(fileobs, 'nav_lon')
lat_obs = rs.readfilenc(fileobs, 'nav_lat')
dep_obs = rs.readfilenc(fileobs, 'depthu')
uzo_obs = rs.readfilenc(fileobs, 'vozocrtx')
### rebuild a dictionary
## 1. define moorings name
## this is a bit heavy but it is necessary to split
## the data from 4d arrays of NC file
lon_moor = lon_obs[0, :]
moor_name = []
ind_swap_EW = npy.where(lon_moor > 180.)
lon_moor[ind_swap_EW] = 360. - lon_moor[ind_swap_EW]
for km in range(len(lon_moor)):
moor_name.append(str(int(lon_moor[km])) +
'e') # default lon is towards east
# for km in ind_swap_EW[0]: (old version)
for km in list(ind_swap_EW[0].data):
moor_name[km] = moor_name[km].replace(
'e', 'w') # correct for western moorings
outdict['coord'] = moor_name
## 2. fill with observations data
for km in range(len(moor_name)):
outdict['u_' + moor_name[km] + '_obs'] = uzo_obs[:, km]
outdict['dept_' + moor_name[km] + '_obs'] = dep_obs[:, km]
## 3. model outputs
outdict['dept_mod'] = rs.readfilenc(filenc, 'depth')
outdict['time_mod'] = rs.get_datetime(filenc)
km = 0
for moor in moor_name:
try:
temp = rs.readfilenc(filenc, 'u_' + moor)
if len(temp.shape) == 1:
outdict['u_' + moor_name[km] + '_mod'] = temp
else:
outdict['u_' + moor_name[km] + '_mod'] = temp.mean(0)
except:
print 'fail to read mooring ', moor
pass
km = km + 1
return outdict #return the dictionnary of values
def _readnc(filenc=None,fileobs=None,argdict=myargs):
# get the section names corresponding to the config
if argdict.has_key('compared_configs'):
compare = True
else:
argdict['compared_configs'] = [ argdict['config'] ]
compare = False
# get the section names corresponding to the config
section_dict = rs.define_all_sections(argdict,compare)
truenames = section_dict.keys()
truenames.sort()
# test on existence of florida bahamas section in list
found = None
for k in range(len(truenames)):
if truenames[k].find('FLORIDA_BAHAMAS') >= 0:
found = 1
if found is None:
print "Your domain do not contain the Florida Bahamas section"
sys.exit()
else:
pass
#
outdict = {} # creates the dictionnary which will contain the arrays
outdict['datemodel'] = rs.get_datetime(filenc)
try:
outdict['trpmodel'] = -1 * rs.readfilenc(filenc, 'vtrp_floba' )
except:
outdict['trpmodel'] = npy.zeros((len(outdict['datemodel'])))
outdict['dateobs'] = rs.get_datetime(fileobs)
outdict['trpobs'] = rs.readfilenc(fileobs, 'CABLE')
#
return outdict # return the dictionnary of values
def _readnc(filenc=None):
outdict = {} # creates the dictionnary which will contain the arrays
outdict['tmean'] = rs.readfilenc(filenc, 'mean_3Dvotemper')
outdict['smean'] = rs.readfilenc(filenc, 'mean_3Dvosaline')
outdict['sshmean'] = rs.readfilenc(filenc, 'mean_3Dsossheig')
outdict['levels'] = rs.readfilenc(filenc, 'gdept')
outdict['date'] = rs.get_datetime(filenc)
outdict['tmodel'] = rs.readfilenc(filenc, 'mean_votemper')
outdict['smodel'] = rs.readfilenc(filenc, 'mean_vosaline')
return outdict # return the dictionnary of values
def _readnc(filesnc=None):
filenc, levitus = filesnc
outdict = {} # creates the dictionnary which will contain the arrays
outdict['tmean'] = rs.readfilenc(filenc, 'mean_3Dvotemper')
outdict['smean'] = rs.readfilenc(filenc, 'mean_3Dvosaline')
outdict['sshmean'] = rs.readfilenc(filenc, 'mean_3Dsossheig')
outdict['levels'] = rs.readfilenc(filenc, 'gdept')
outdict['date'] = rs.get_datetime(filenc)
outdict['tmodel'] = rs.readfilenc(filenc, 'mean_votemper')
outdict['smodel'] = rs.readfilenc(filenc, 'mean_vosaline')
# if myargs['monitor_frequency'] == '1m':
# outdict['tlev'] = rs.readfilenc(levitus,'mean_votemper')[0,:]
# outdict['slev'] = rs.readfilenc(levitus,'mean_vosaline')[0,:]
# else:
outdict['tlev'] = rs.readfilenc(levitus, 'mean_votemper')
outdict['slev'] = rs.readfilenc(levitus, 'mean_vosaline')
return outdict # return the dictionnary of values
def _readnc(filenc=None, fileobs=None, fileobs2=None, argdict=myargs):
#
outdict = {} # creates the dictionnary which will contain the arrays
outdict['datemodel'] = rs.get_datetime(filenc)
outdict['NINO12_model'] = rs.readfilenc(filenc, 'mean_votemper_NINO12')
outdict['NINO34_model'] = rs.readfilenc(filenc, 'mean_votemper_NINO34')
outdict['NINO3_model'] = rs.readfilenc(filenc, 'mean_votemper_NINO3')
outdict['NINO4_model'] = rs.readfilenc(filenc, 'mean_votemper_NINO4')
#
outdict['dateobs'] = rs.get_datetime(fileobs)
outdict['NINO12_obs'] = rs.readfilenc(fileobs, 'NINO1+2')
outdict['NINO34_obs'] = rs.readfilenc(fileobs, 'NINO3.4')
outdict['NINO3_obs'] = rs.readfilenc(fileobs, 'NINO3')
outdict['NINO4_obs'] = rs.readfilenc(fileobs, 'NINO4')
#outdict['SOI' ] = rs.readfilenc(fileobs2, 'SOI')
return outdict # return the dictionnary of values
def _readnc(filenc=None, argdict=myargs):
#
outdict = {} # creates the dictionnary which will contain the arrays
outdict['datemodel'] = rs.get_datetime(filenc)
outdict['NVolume' ] = rs.readfilenc(filenc, 'NVolume' )
outdict['NArea' ] = rs.readfilenc(filenc, 'NArea' )
outdict['NExnsidc' ] = rs.readfilenc(filenc, 'NExnsidc' )
outdict['SVolume' ] = rs.readfilenc(filenc, 'SVolume' )
outdict['SArea' ] = rs.readfilenc(filenc, 'SArea' )
outdict['SExnsidc' ] = rs.readfilenc(filenc, 'SExnsidc' )
#
return outdict # return the dictionnary of values
def _readnc(filenc=None, argdict=myargs):
#
outdict = {} # creates the dictionnary which will contain the arrays
outdict['date_model'] = rs.get_datetime(filenc)
if argdict['config'].find('ORCA') == 0 or argdict['config'].find(
'EORCA') == 0 or argdict['config'].find('eORCA') == 0:
data_list = [
'maxmoc_Glo_maxmoc40N', 'maxmoc_Glo_maxmoc30S',
'maxmoc_Atl_maxmoc40N', 'maxmoc_Atl_maxmoc30S',
'minmoc_Inp_minmoc30S', 'maxmoc_Aus_maxmoc50S'
]
elif argdict['config'].find('NATL') == 0 or argdict['config'].find(
'NACHOS'):
data_list = ['maxmoc_Glo_maxmoc40N', 'maxmoc_Glo_maxmoc15S']
else:
print "config not supported"
sys.exit()
#
for k in data_list:
outdict[k] = rs.readfilenc(filenc, k)
return outdict # return the dictionnary of values
def _readnc(filenc=None, argdict=myargs):
if argdict.has_key('compared_configs'):
compare_different_configs = (len(argdict['compared_configs']) > 1)
else:
compare_different_configs = False
# get the section names corresponding to the config
outdict = {} # creates the dictionnary which will contain the arrays
date = rs.get_datetime(filenc)
mld1kerg1 = []
mld1kerg2 = []
mld1kerg3 = []
mld1kerg4 = []
mld2kerg1 = []
mld2kerg2 = []
mld2kerg3 = []
mld2kerg4 = []
mld3kerg1 = []
mld3kerg2 = []
mld3kerg3 = []
mld3kerg4 = []
mld1kerg1.append(rs.readfilenc(filenc, 'mean_somxl010_KERG1'))
mld1kerg2.append(rs.readfilenc(filenc, 'mean_somxl010_KERG3'))
mld1kerg3.append(rs.readfilenc(filenc, 'mean_somxl010_KERG5'))
mld1kerg4.append(rs.readfilenc(filenc, 'mean_somxl010_KERG6'))
mld2kerg1.append(rs.readfilenc(filenc, 'mean_somxl030_KERG1'))
mld2kerg2.append(rs.readfilenc(filenc, 'mean_somxl030_KERG3'))
mld2kerg3.append(rs.readfilenc(filenc, 'mean_somxl030_KERG5'))
mld2kerg4.append(rs.readfilenc(filenc, 'mean_somxl030_KERG6'))
mld3kerg1.append(rs.readfilenc(filenc, 'mean_somxlt02_KERG1'))
mld3kerg2.append(rs.readfilenc(filenc, 'mean_somxlt02_KERG3'))
mld3kerg3.append(rs.readfilenc(filenc, 'mean_somxlt02_KERG5'))
mld3kerg4.append(rs.readfilenc(filenc, 'mean_somxlt02_KERG6'))
mld1kerg1 = npy.reshape(mld1kerg1, len(date))
mld1kerg2 = npy.reshape(mld1kerg2, len(date))
mld1kerg3 = npy.reshape(mld1kerg3, len(date))
mld1kerg4 = npy.reshape(mld1kerg4, len(date))
mld2kerg1 = npy.reshape(mld2kerg1, len(date))
mld2kerg2 = npy.reshape(mld2kerg2, len(date))
mld2kerg3 = npy.reshape(mld2kerg3, len(date))
mld2kerg4 = npy.reshape(mld2kerg4, len(date))
mld3kerg1 = npy.reshape(mld3kerg1, len(date))
mld3kerg2 = npy.reshape(mld3kerg2, len(date))
mld3kerg3 = npy.reshape(mld3kerg3, len(date))
mld3kerg4 = npy.reshape(mld3kerg4, len(date))
big = npy.transpose(npy.ones(len(date)))
outdict['date'] = date
outdict['mld1kerg1plt'] = -1 * mld1kerg1 * big
outdict['mld1kerg2plt'] = -1 * mld1kerg2 * big
outdict['mld1kerg3plt'] = -1 * mld1kerg3 * big
outdict['mld1kerg4plt'] = -1 * mld1kerg4 * big
outdict['mld2kerg1plt'] = -1 * mld2kerg1 * big
outdict['mld2kerg2plt'] = -1 * mld2kerg2 * big
outdict['mld2kerg3plt'] = -1 * mld2kerg3 * big
outdict['mld2kerg4plt'] = -1 * mld2kerg4 * big
outdict['mld3kerg1plt'] = -1 * mld3kerg1 * big
outdict['mld3kerg2plt'] = -1 * mld3kerg2 * big
outdict['mld3kerg3plt'] = -1 * mld3kerg3 * big
outdict['mld3kerg4plt'] = -1 * mld3kerg4 * big
return outdict # return the dictionnary of values
def _readnc(filenc=None,argdict=myargs):
outdict = {} # creates the dictionnary which will contain the arrays
date = rs.get_datetime(filenc)
outdict['date'] = date
outdict['no3kerg1plt'] = rs.readfilenc(filenc, 'mean_3DNO3_KERG1')
outdict['no3kerg2plt'] = rs.readfilenc(filenc, 'mean_3DNO3_KERG3')
outdict['no3kerg3plt'] = rs.readfilenc(filenc, 'mean_3DNO3_KERG5')
outdict['no3kerg4plt'] = rs.readfilenc(filenc, 'mean_3DNO3_KERG6')
outdict['po4kerg1plt'] = rs.readfilenc(filenc, 'mean_3DPO4_KERG1')
outdict['po4kerg2plt'] = rs.readfilenc(filenc, 'mean_3DPO4_KERG3')
outdict['po4kerg3plt'] = rs.readfilenc(filenc, 'mean_3DPO4_KERG5')
outdict['po4kerg4plt'] = rs.readfilenc(filenc, 'mean_3DPO4_KERG6')
outdict['sikerg1plt'] = rs.readfilenc(filenc, 'mean_3DSi_KERG1')
outdict['sikerg2plt'] = rs.readfilenc(filenc, 'mean_3DSi_KERG3')
outdict['sikerg3plt'] = rs.readfilenc(filenc, 'mean_3DSi_KERG5')
outdict['sikerg4plt'] = rs.readfilenc(filenc, 'mean_3DSi_KERG6')
outdict['ferkerg1plt'] = rs.readfilenc(filenc, 'mean_3DFer_KERG1')
outdict['ferkerg2plt'] = rs.readfilenc(filenc, 'mean_3DFer_KERG3')
outdict['ferkerg3plt'] = rs.readfilenc(filenc, 'mean_3DFer_KERG5')
outdict['ferkerg4plt'] = rs.readfilenc(filenc, 'mean_3DFer_KERG6')
return outdict # return the dictionnary of values
def _readnc(filenc=None,
fileobs1=None,
fileobs2=None,
fileobs3=None,
fileobs4=None,
fileobs5=None,
argdict=myargs):
import matplotlib.pylab as plt
from numpy import nan
outdict = {} # creates the dictionnary which will contain the arrays
coord = ['0n156e', '0n165e', '0n170w', '0n140w', '0n110w']
##### Donnees d'observation ######
for j in range(1, 6):
#Profondeur
outdict['o_Profondeur' + str(j)] = rs.readfilenc(
vars()['fileobs' + str(j)], 'depth')
depth_obs = rs.readfilenc(vars()['fileobs' + str(j)], 'depth')
#Vitesse
vars()['Uzonale_obs' + str(j)] = rs.readfilenc(
vars()['fileobs' + str(j)], 'U_320')
vars()['tabmoy' + str(j)] = []
for i in range(0, len(vars()['Uzonale_obs' + str(j)][0]), 1):
profil = vars()['Uzonale_obs' + str(j)][:, i]
vars()['val_nul' + str(j) + '_' + str(i)] = []
for k in range(0, len(profil), 1):
if profil[k] > 1e35:
vars()['val_nul' + str(j) + '_' + str(i)].append(k)
profil[k] = 0
vars()['tabmoy' + str(j)].append(rs.mean_0(profil))
outdict['o_Vitesse' + str(j)] = vars()['tabmoy' + str(j)]
##Serie
vars()['Indice_profondeur_serie' + str(j)] = rs.getIndex(
vars()['tabmoy' + str(j)], max(vars()['tabmoy' + str(j)]))
outdict['o_serie_Profondeur' +
str(j)] = depth_obs[vars()['Indice_profondeur_serie' + str(j)]]
vars()['Variation_serie_obs' +
str(j)] = vars()['Uzonale_obs' +
str(j)][:,
vars()['Indice_profondeur_serie' +
str(j)]]
#Calendrier
time = rs.readfilenc(vars()['fileobs' + str(j)], 'time')
vars()['temps_annee_obs' + str(j)] = time / 365
Origine_temps = [1991, 1986, 2002, 1983, 1980]
vars()['Calendrier_obs' +
str(j)] = Origine_temps[j - 1] + vars()['temps_annee_obs' +
str(j)]
#Annee premiere observation
vars()['Origine_o' + str(j)] = vars()['Calendrier_obs' + str(j)][0]
#Annee derniere observation
vars()[('shape_o' + str(j))] = plt.shape(vars()['Calendrier_obs' +
str(j)])
vars()['Fin_o' + str(j)] = int(
vars()['Calendrier_obs' + str(j)][vars()['shape_o' + str(j)][0] -
1])
vars()['Duree_o' + str(j)] = vars()['Fin_o' + str(j)] - int(
vars()['Origine_o' + str(j)]) + 1
a = vars()['Origine_o' + str(j)]
#Moyennes annuelles des observations
vars()['Variation_moyenne_annuelle' + str(j)] = []
vars()['Annees' + str(j)] = []
for i in range(0, vars()['Duree_o' + str(j)]):
vars()['Variation_sur_une_annee' + str(j) + '_' + str(i)] = []
vars()['Annees' + str(j)].append(a + i)
for k in range(0, plt.shape(vars()['Calendrier_obs' + str(j)])[0]):
if a + i <= vars()['Calendrier_obs' + str(j)][k] <= a + i + 1:
vars()['Variation_sur_une_annee' + str(j) + '_' +
str(i)].append(vars()['Variation_serie_obs' +
str(j)][k])
vars()['Moyenne_annuelle_obs' + str(j) + '_' + str(i)] = rs.mean_0(
vars()['Variation_sur_une_annee' + str(j) + '_' + str(i)])
vars()['Variation_moyenne_annuelle' + str(j)].append(
vars()['Moyenne_annuelle_obs' + str(j) + '_' + str(i)])
outdict['o_serie_Annees' + str(j)] = vars()['Annees' + str(j)]
for i in range(
0,
plt.shape(vars()['Variation_moyenne_annuelle' + str(j)])[0]):
if vars()['Variation_moyenne_annuelle' + str(j)][i] == 0:
vars()['Variation_moyenne_annuelle' + str(j)][i] = nan
outdict['o_serie_Variation_moyenne_annuelle' +
str(j)] = vars()['Variation_moyenne_annuelle' + str(j)]
###### Donnees Modele ######
coordonnees = ['156e', '165e', '170w', '140w', '110w']
outdict['m_Profondeur'] = rs.readfilenc(filenc, 'depth')
outdict['m_Temps_serie'] = rs.readfilenc(filenc, 'time_counter')
for j in range(1, 6):
temp = rs.readfilenc(filenc, 'u_' + coordonnees[j - 1])
if len(temp.shape) == 1:
outdict['m_Vitesse' + str(j)] = temp
else:
outdict['m_Vitesse' + str(j)] = temp[-1, :]
outdict['m_Valeur_sous_courant' + str(j)] = rs.readfilenc(
filenc, 'u_' + coordonnees[j - 1] + '_UC')
#longitude
#lon=rs.readfilenc(filenc,'nav_lon')
#vars()['long_model'+str(j)]=''
#if lon<0:
# vars()['long_model'+str(j)] = `int(-lon)`+'W'
#else :
# vars()['long_model'+str(j)] = `int(lon)`+'E'
#outdict['m_Longitude'+str(j)] = vars()['long_model'+str(j)]
outdict['m_Longitude' + str(j)] = coordonnees[j - 1].upper()
#latitude
#lat=rs.readfilenc(filenc,'nav_lat')
lat = 0.
vars()['lati_model' + str(j)] = ` int(lat) ` + 'N'
if lat < 0:
vars()['lati_model' + str(j)] = ` int(lat) ` + 'S'
outdict['m_Latitude' + str(j)] = vars()['lati_model' + str(j)]
#
return outdict #return the dictionnary of values
