def mean_year_gp(data):
mean_gp = np.zeros((130, np.shape(data)[1], np.shape(data)[2]), 'f')
year = np.arange(1971, 2101)
day_month = np.array([31, 28, 31, 30, 31, 30, 31, 31, 30, 31, 30, 31])
end = 0
for iyear in year:
if (schaltjahr(iyear)):
day_month[1] = 29
else:
day_month[1] = 28
start = end
end = sum(day_month) + end
index = iyear - 1971
mean_gp[index] = np.mean(data[start:end, :, :], axis=0)
mean_gp = ct.niedersachsen(mean_gp)
return (mean_gp)
def extrem(data, o, v, ee):
fm = ma.array(np.zeros((3, np.shape(data)[1], np.shape(data)[2]), 'f'))
days_year = ma.array(
np.zeros((130, np.shape(data)[1], np.shape(data)[2]), 'f'))
year = np.arange(1971, 2101)
spatial_mean = np.zeros((130), 'f')
day_month = np.array([31, 28, 31, 30, 31, 30, 31, 31, 30, 31, 30, 31])
print(len(data))
end = 0
if (v == 'tas' or v == 'tasmin' or v == 'tasmax'):
data += (-273.15)
elif (v == 'pr'):
data = data * 60 * 60 * 24
# betrachte jedes jahr einzeln und rechne for this year extremereignisse aus
for index, iyear in enumerate(year):
if (schaltjahr(iyear)):
day_month[1] = 29
else:
day_month[1] = 28
start = end
april = np.sum(day_month[0:3])
sept = np.sum(day_month[0:8])
end = np.sum(day_month) + end
if ee == 'hp':
days_year[index] = heatperiod(data[start:end, :, :])
elif ee == 'vp':
days_year[index] = vegetationsphase(data[start:end, :, :])
elif ee == 'jm':
days_year[index] = jahresmittel(data[start:end, :, :], v)
elif ee == 'tp':
days_year[index] = trockenperiode(data[start:end, :, :], april,
sept)
elif ee == 'ft':
days_year[index] = frosttage(data[start:end, :, :], april)
elif ee == 'sft':
days_year[index] = speatfrost(data[start:end, :, :], april)
print(index)
if o == 'ts':
# Ausgabe Zeitreihe mit yearly Werten
# hier region wahlen, die man betrachten will
# kann auch zeitreihe mit taeglichen werten ausgeben
if ee == 'day':
return (np.mean(np.mean(ct.niedersachsen(data), axis=1), axis=1))
else:
spatial_mean = np.mean(np.mean(ct.niedersachsen(days_year),
axis=1),
axis=1)
return (spatial_mean)
elif o == 'spat':
# klimasignale ref, 21, 71
for j in range(3):
fm[j, :, :] = np.mean(ct.niedersachsen(
days_year[(0 + j * 50):(30 + j * 50), :, :]),
axis=0)
if v == 'tas':
fm[1, :, :] = fm[1, :, :] - fm[0, :, :]
fm[2, :, :] = fm[2, :, :] - fm[0, :, :]
#fm[0,:,:] = fm[0,:,:] - 273.15
#fm = ct.niedersachsen(fm)
elif v == 'pr':
#fm[0,:,:] = fm[0,:,:]/12
fm[1, :, :] = (fm[1, :, :] / fm[0, :, :] - 1) * 100
fm[2, :, :] = (fm[2, :, :] / fm[0, :, :] - 1) * 100
#fm[0,:,:] = fm[1,:,:]/fm[0,:,:]-1)*100
return (fm)
else:
sys.exit('wrong option in extrem')
def extrem(data, o, v, ee, jz):
fm = ma.array(np.zeros((3,np.shape(data)[1],np.shape(data)[2]),'f'))
days_year = ma.array(np.zeros((130,np.shape(data)[1],np.shape(data)[2]), 'f'))
vp = ma.array(np.zeros((3,130,np.shape(data)[1],np.shape(data)[2]), 'f'))
month = ma.array(np.zeros((130*12,np.shape(data)[1],np.shape(data)[2]), 'f'))
monjz = ma.array(np.zeros((130*3,np.shape(data)[1],np.shape(data)[2]), 'f'))
spatial_mean = np.zeros((130), 'f')
if ( v == 'tas' or v == 'tasmin' or v == 'tasmax'):
data += (-273.15)
elif ( v == 'pr' ):
data = data*60*60*24
ind = index_data(data,'day')
# betrachte jedes jahr einzeln und rechne for this year extremereignisse aus
for i, iyear in enumerate(np.arange(1971,2101)):
print(iyear)
index=jz_index(jz,iyear,ind)
if ee == 'wp':
days_year[i] = heatperiod(data[index,:,:], 25)
elif ee == 'hp':
days_year[i] = heatperiod(data[index,:,:], 30)
elif ee == 'vp':
days_year[i] = vegetationsphase(data[index,:,:])
elif ee == 'vp2':
days_year[i] = vegetationsphase2(data[index,:,:])
elif ee == 'st':
days_year[i] = countbigger(data[index,:,:], 25)
elif ee == 'ht':
days_year[i] = countbigger(data[index,:,:], 30)
elif ee == 'tt':
days_year[i] = countsmaller(data[index,:,:], 1)
elif ee == 'snt':
days_year[i] = countbigger(data[index,:,:], 20)
elif ee == 'jm':
days_year[i] = jahresmittel(data[index,:,:], v)
elif ee == 'tp':
days_year[i] = trockenperiode(data[ind['{}-04'.format(iyear):'{}-08'.format(iyear)],:,:])
elif ee == 'ft':
days_year[i] = frosttage(data[ind['{}-01'.format(iyear):'{}-03'.format(iyear)],:,:],
data[ind['{}-08'.format(iyear):'{}-12'.format(iyear)],:,:])
elif ee == 'sft':
days_year[i] = countsmaller(data[ind['{}-04'.format(iyear):'{}-07'.format(iyear)],:,:],0)
elif ee == 'month':
for mon in range(12):
month[mon+(i*12)] = jahresmittel(data[ind['{}-{}'.format(iyear,mon+1)],:,:],v)
'''
elif ee == 'djf':
days_year[i] = jahresmittel(data[ind['{}-12'.format(iyear):'{}-02'.format(iyear+1)],:,:],v)
elif ee == 'jja':
days_year[i] = jahresmittel(data[ind['{}-06'.format(iyear):'{}-08'.format(iyear)],:,:],v)
elif ee == 'mam':
days_year[i] = jahresmittel(data[ind['{}-03'.format(iyear):'{}-05'.format(iyear)],:,:],v)
elif ee == 'son':
days_year[i] = jahresmittel(data[ind['{}-09'.format(iyear):'{}-11'.format(iyear)],:,:],v)
'''
if o == 'ts':
# Ausgabe Zeitreihe mit yearly Werten
# hier region wahlen, die man betrachten will
# kann auch zeitreihe mit taeglichen werten ausgeben
if ee == 'day':
return(np.mean(np.mean(ct.niedersachsen(data),axis=1),axis=1))
elif ee == 'month':
return(np.mean(np.mean(ct.niedersachsen(month),axis=1),axis=1))
#elif ee == 'vp':
# spatial_mean = np.mean(np.mean(ct.niedersachsen(vp),axis=2),axis=2)
# return(spatial_mean)
else:
spatial_mean = np.mean(np.mean(ct.niedersachsen(days_year),axis=1),axis=1)
return(spatial_mean)
elif o == 'sp' or o == 'spatex':
return ct.niedersachsen(days_year)
elif o == 'spatday':
return ct.niedersachsen(data)
elif o == 'spat':
# klimasignale ref, 21, 71
for j in range(3):
fm[j,:,:] = np.mean(ct.niedersachsen(days_year[(0+j*50):(30+j*50),:,:]),axis=0)
if v == 'tas' or ee == 'vp' or ee == 'vp2':
fm[1,:,:] = fm[1,:,:] - fm[0,:,:]
fm[2,:,:] = fm[2,:,:] - fm[0,:,:]
#fm[0,:,:] = fm[0,:,:] - 273.15
#fm = ct.niedersachsen(fm)
else:
#fm[0,:,:] = fm[0,:,:]/12
fm[1,:,:] = (fm[1,:,:]/fm[0,:,:]-1)*100
fm[2,:,:] = (fm[2,:,:]/fm[0,:,:]-1)*100
#fm[0,:,:] = fm[1,:,:]/fm[0,:,:]-1)*100
return(fm)
else:
sys.exit('wrong option in extrem')
elif opt == 'testspatex':
# brauche: ensemblewert, jährlichen wert an jedem gp und für jeden modell käs an jedem gp
#jz = sys.argv[3]
fm = 'spat_fm_pr_tp'
year = 'spatex_pr_tp_year'
em = 'spat_em_pr_tp'
year, lons, lats, rlons, rlats = readdata(year, var, False)
fm, lons, lats, rlons, rlats = readdata(fm, var, False)
em, lons, lats, rlons, rlats = readdata(em, var, False)
jm = ma.array(np.zeros((2, 31, 34), 'f'))
jm = ct.niedersachsen(jm)
ind = pd.date_range('1971', '2100', freq='As')
cols = ['cm0', 'cm1', 'cm2', 'cm3', 'cm4', 'cm5', 'cm6', 'cm7', 'cm8']
for i, period in enumerate([2021, 2071]):
print(period)
for lon in range(31):
print(lon)
for lat in range(34):
ref = year[0, 0:2, lon, lat]
if ~ref.mask[0]:
#< erstellt dataframe, in dem alle hv aller modelle sind
t = pd.DataFrame(np.transpose(year[:, :, lon, lat]),
columns=cols,
index=ind)
#----- rotierte lon und lat einlesen -------------------------
f = Dataset(data[0], 'r')
rlats = f.variables['rlat'][:]
rlons = f.variables['rlon'][:]
#----- calculate lons, lats in gographical coordinates ------
lon_lat_r = np.meshgrid(rlons, rlats)
lons, lats = ct.coord_traf(2, lon_lat_r[0], lon_lat_r[1])
#----- Niedersachsen Data jedes Modells ----------------------
#nied = [ct.niedersachsen(ca.read_data(ifile, var)) for ifile in data]
nied = ma.array(np.zeros((9, 1560, 31, 34), 'f'))
#nied = np.zeros((9,1560,31,34),'f')
for i, ifile in enumerate(data):
nied[i] = ct.niedersachsen(ca.read_data(ifile, var))
#------ Datenaufbereitung --------------------------------------
'''
if (var == 'pr' ):
day_month = np.array([31,28,31,30,31,30,31,31,30,31,30,31])
for i in range(9):
for j in range(1560):
nied[i,j,:,:] = nied[i,j,:,:] * day_month[j%12]*60*60*24
'''
#----- Gebietsmittel 30 years berechnen -------------------------------
if (opt == "ts"):
jz = sys.argv[4]
fm = ca.floating_mean_ts(nied, jz, var)
ym = ca.year_mean(nied, jz)
for i in range(len(data)):
result[i] = ca.extrem(data[i],opt,var,ee)
em = ma.array(np.zeros((3,lon_count,lat_count),'f'))
for i in range(3):
em[i] = np.mean(result[:,i,:,:], axis=0)
'''
result = ma.array(np.zeros((mod_count, 130, lon_count, lat_count),
'f'))
#result = ca.Data(data,var,ee).calc()
for i in range(len(data)):
result[i] = ca.Data(data[i], var, ee).calc()
min_gp = 0
max_gp = 15
cas.plot_basemap(ct.niedersachsen(result[1, 1, :, :]), lons, lats,
min_gp, max_gp, var)
#ca.plot_basemap(em[2], lons, lats, min_gp, max_gp, var)
#ca.writing_nc(em, "spat_{}.nc".format(var), var, rlats, rlons)
elif (opt == 'ts'):
# Zeitreihen Extremereignisse aller Modelle einlesen for every year, mean over niedersachsen
if ee == 'day':
index = pd.date_range('1971-01-01', '2100-12-31', freq='D')
else:
index = pd.date_range('1971', '2100', freq='AS')
result = pd.DataFrame()