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