def indice_multivar(in_files1, var1,
in_files2, var2,
out_file,
indice_name,
time_range,
slice_mode,
project,
N_lev=None,
callback=None):
'''
This function returns result NetCDF file containing a climate indice based on two variable (ETR, DTR, vDTR).
:param in_files1: input NetCDF files corresponding to the first variable
:type in_files1: list of str
:param var1: first variable to process (e.g. "tasmax")
:type var1: str
:param in_files2: input NetCDF files corresponding to the second variable
:type in_files2: list of str
:param var2: second variable to process (e.g. "tasmin")
:type var2: str
:param out_file: output file name
:type out_file: str
:param indice_name: climate indice name
:type indice_name: str
:param time_range: time range (dt1 should be the first day of year/month, dt2 - the last day of year/month). Note: to include dt2 -> add in datetime hour/minute (HH=23, mm=59).
:type time_range: list of 2 datetime objects [dt1, dt2]
:param slice_mode: "year" or "month"
:type slice_mode: str
:param project: project name ("CMIP5" or "CORDEX")
:type slice_mode: str
:rtype: output NetCDF file name (str)
.. note:: Both file lists must contain the same number steps.
.. note:: First variable is always bigger that the second variable (par ex. var1="tasmax" and var2="tasmin")
'''
inc1 = Dataset(in_files1[0], 'r')
inc2 = Dataset(in_files2[0], 'r')
onc = Dataset(out_file, 'w' ,format="NETCDF3_CLASSIC")
fill_val1 = get_att_value(inc1, var1, '_FillValue')
fill_val2 = get_att_value(inc2, var2, '_FillValue')
indice_dim = copy_var_dim(inc1, onc, var1, project) # tuple ('time', 'lat', 'lon')
nb_rows = inc1.variables[indice_dim[1]].shape[0]
nb_columns = inc1.variables[indice_dim[2]].shape[0]
calend = get_att_value(inc1, indice_dim[0], 'calendar')
units = get_att_value(inc1, indice_dim[0], 'units')
inc1.close()
inc2.close()
ind_type = 'f'
ind = onc.createVariable(indice_name, ind_type, (indice_dim[0], indice_dim[1], indice_dim[2]), fill_value = fill_val1)
dt_begin = time_range[0] # datetime object
dt_end = time_range[1] # datetime object
############################
glob_dict_timeStep_indice = {}
############################
for in_file1, in_file2 in zip(in_files1, in_files2):
nc1 = Dataset(in_file1, 'r')
nc2 = Dataset(in_file2, 'r')
time_steps_vect1 = get_list_dates_from_nc(nc1, 'dt')
time_steps_vect2 = get_list_dates_from_nc(nc2, 'dt')
if time_steps_vect1 != time_steps_vect2:
print 'Error: ...........'
else:
dict_year_chunk1 = get_dict_year_chunk(time_steps_vect1)
if N_lev==None:
values1 = nc1.variables[var1]
values2 = nc2.variables[var2]
else:
values1 = nc1.variables[var1][:,N_lev,:,:]
values2 = nc2.variables[var2][:,N_lev,:,:]
for year in sorted(dict_year_chunk1.keys()):
if year>=dt_begin.year and year<=dt_end.year:
i1 = dict_year_chunk1[year][0]
i2 = dict_year_chunk1[year][1]
values_current_chunk1 = values1[i1:i2+1,:,:] # on charge les donnees (pour 1 annee) pour faire le traitement
values_current_chunk2 = values2[i1:i2+1,:,:]
time_steps_current_chunk1 = numpy.array(time_steps_vect1[i1:i2+1])
if (slice_mode=='year'):
mydict_TimeStep_3DArray1=get_dict_year_3Darr(values_current_chunk1, time_steps_current_chunk1)
mydict_TimeStep_3DArray2=get_dict_year_3Darr(values_current_chunk2, time_steps_current_chunk1)
elif (slice_mode=='month'):
mydict_TimeStep_3DArray1=get_dict_month_3Darr(values_current_chunk1, time_steps_current_chunk1)
mydict_TimeStep_3DArray2=get_dict_month_3Darr(values_current_chunk2, time_steps_current_chunk1)
mydict_indice=get_dict_timeStep_indice_multivar(mydict_TimeStep_3DArray1, mydict_TimeStep_3DArray2, indice_name, fill_val1, fill_val2, ind, onc)
glob_dict_timeStep_indice.update(mydict_indice)
del values_current_chunk1, values_current_chunk2, time_steps_current_chunk1
print "Processed: ", year
#counter_year = counter_year + 1
#print counter_year, total_nb_years_to_process
#status = "Year processed {0}/{1} ({3})".format(counter_year, total_nb_years_to_process, year)
#print status
#else:
#print "data not processed ", year
#callback("Skipping year %d" % year,percentageComplete)
#time.sleep(0.01)
# #time.sleep(1.01)
# pbar.update(i+1)
# i+=1
#
#pbar.finish()
nc1.close()
nc2.close()
#pbar_files.finish()
#print '---'
#print sorted(glob_dict_timeStep_indice.keys())
#print '---'
glob_indice = get_globindice(glob_dict_timeStep_indice, nb_rows, nb_columns) # tuple (time_step_vect, indice_2D_arr)
ind[:,:,:] = glob_indice[0][:,:,:]
# set global attributs
#eval(indice_name + '_setglobattr(onc)')
## for all:
#setglobattr_history(onc, indice_name, slice_mode, dt_begin, dt_end)
#onc.setncattr('institution', '')
onc.setncattr('source', '') # Here soon will be source meta data
#onc.setncattr('comment', '')
#onc.setncattr('reference', '')
# set global attributs
set_globattr.title(onc, indice_name)
set_globattr.references(onc)
set_globattr.comment(onc, indice_name)
set_globattr.institution(onc, institution_str='Climate impact portal (http://climate4impact.eu)')
set_globattr.history2(onc, slice_mode, indice_name, time_range)
# set variable attributs
eval('set_longname_units.' + indice_name + '_setvarattr(ind)')
# for all:
ind.missing_value = fill_val1
ind.setncattr('standard_name', 'ECA_indice')
#print indice[1][:] # must be float or str!
#time_steps = [str(i) for i in indice[1][:]]
time_steps_indice_dt = glob_indice[1][:]
time_bnds_dt = get_glob_time_bnds(time_steps_indice_dt, slice_mode)
set_time_values(onc, time_steps_indice_dt, calend, units)
set_timebnds_values(onc, time_bnds_dt, calend, units)
onc.close()
return out_file
def indice(in_files,
out_file,
var,
indice_name,
time_range,
slice_mode,
project,
threshold=None,
N_lev=None,
callback=None):
'''
This function returns result NetCDF file containing a simple climate indice (based on one variable).
:param in_files: input NetCDF files
:type in_files: list of str
:param out_file: output file name
:type out_file: str
:param var: variable name to process
:type var: str
:param indice_name: climate indice name
:type indice_name: str
:param time_range: time range (dt1 should be the first day of year/month, dt2 - the last day of year/month). Note: to include dt2 -> add in datetime hour/minute (HH=23, mm=59).
:type time_range: list of 2 datetime objects [dt1, dt2]
:param slice_mode: "year" or "month"
:type slice_mode: str
:param project: project name ("CMIP5" or "CORDEX")
:type project: str
:param threshold: user defined threshold for certain indices
:type threshold: float
:rtype: output NetCDF file name (str)
'''
#print "DADA"
#callback("Init Opening "+in_files[0],0);
inc = Dataset(in_files[0], 'r')
#callback("Finished opening "+in_files[0],0);
onc = Dataset(out_file, 'w' ,format="NETCDF3_CLASSIC")
fill_val = get_att_value(inc, var, '_FillValue')
indice_dim = copy_var_dim(inc, onc, var, project) # tuple ('time', 'lat', 'lon')
nb_rows = inc.variables[indice_dim[1]].shape[0]
nb_columns = inc.variables[indice_dim[2]].shape[0]
calend = get_att_value(inc, indice_dim[0], 'calendar')
units = get_att_value(inc, indice_dim[0], 'units')
inc.close()
ind_type = 'f'
ind = onc.createVariable(indice_name, ind_type, (indice_dim[0], indice_dim[1], indice_dim[2]), fill_value = fill_val)
dt_begin = time_range[0] # datetime object
dt_end = time_range[1] # datetime object
############################
glob_dict_timeStep_indice = {}
############################
#j=0
#pbar_files = ProgressBar(widgets=[Percentage(),' ', Bar()], maxval=len(in_files)).start()
total_nb_years_to_process = dt_end.year -dt_begin.year + 1
for ifile in in_files:
#pbar_files.widgets[1]= ' processing file ' +str(j+1)
#time.sleep(1.01)
#pbar_files.update(j+1)
#j+=1
#callback("Opening "+ifile,0);
nc = Dataset(ifile, 'r')
time_steps_vect = get_list_dates_from_nc(nc, 'dt')
dict_year_chunk = get_dict_year_chunk(time_steps_vect)
#print dict_year_chunk
if N_lev==None:
values = nc.variables[var]
else:
values = nc.variables[var][:,N_lev,:,:]
#pbar = ProgressBar(widgets=['',Percentage(), Bar()], maxval=len(dict_year_chunk.keys())).start()
#i=0
currentStep=1
totalSteps=len(dict_year_chunk.keys())
counter_year = 0
for year in sorted(dict_year_chunk.keys()):
#pbar.widgets[0]= ' <'+str(year)+' processed> '
percentageComplete = (currentStep/totalSteps)*100
#callback("Processing year %d/%d %d" % (currentStep,totalSteps,year),percentageComplete)
if year>=dt_begin.year and year<=dt_end.year:
#callback("Processing year %d/%d %d" % (currentStep,totalSteps,year),percentageComplete)
i1 = dict_year_chunk[year][0]
i2 = dict_year_chunk[year][1]
#print i1, i2
values_current_chunk = values[i1:i2+1,:,:] # on charge les donnees (pour 1 annee) pour faire le traitement
time_steps_current_chunk = numpy.array(time_steps_vect[i1:i2+1])
if (slice_mode=='year'):
mydict_TimeStep_3DArray=get_dict_year_3Darr(values_current_chunk, time_steps_current_chunk)
elif (slice_mode=='month'):
mydict_TimeStep_3DArray=get_dict_month_3Darr(values_current_chunk, time_steps_current_chunk)
mydict_indice=get_dict_timeStep_indice(mydict_TimeStep_3DArray, indice_name, fill_val, ind, onc, threshold)
glob_dict_timeStep_indice.update(mydict_indice)
del values_current_chunk, time_steps_current_chunk
print "Processed: ", year
#counter_year = counter_year + 1
#print counter_year, total_nb_years_to_process
#status = "Year processed {0}/{1} ({3})".format(counter_year, total_nb_years_to_process, year)
#print status
#else:
#print "data not processed ", year
#callback("Skipping year %d" % year,percentageComplete)
#time.sleep(0.01)
# #time.sleep(1.01)
# pbar.update(i+1)
# i+=1
#
#pbar.finish()
nc.close()
#pbar_files.finish()
#print '---'
#print sorted(glob_dict_timeStep_indice.keys())
#print '---'
glob_indice = get_globindice(glob_dict_timeStep_indice, nb_rows, nb_columns) # tuple (time_step_vect, indice_2D_arr)
ind[:,:,:] = glob_indice[0][:,:,:]
# set global attributs
#eval(indice_name + '_setglobattr(onc)')
## for all:
#setglobattr_history(onc, indice_name, slice_mode, dt_begin, dt_end)
#onc.setncattr('institution', '')
onc.setncattr('source', '')
#onc.setncattr('comment', '')
#onc.setncattr('reference', '')
# set global attributs
# title
if threshold != None:
onc.setncattr('title', 'Indice {0} with user defined threshold'.format(indice_name))
else:
set_globattr.title(onc, indice_name)
set_globattr.references(onc)
set_globattr.comment(onc, indice_name)
set_globattr.institution(onc, institution_str='Climate impact portal (http://climate4impact.eu)')
set_globattr.history2(onc, slice_mode, indice_name, time_range)
# set variable attributs
if threshold != None:
eval('set_longname_units_custom_indices.' + indice_name + '_setvarattr(ind, threshold)')
else:
eval('set_longname_units.' + indice_name + '_setvarattr(ind)')
ind.setncattr('standard_name', 'ECA_indice')
# for all:
ind.missing_value = fill_val
#print indice[1][:] # must be float or str!
#time_steps = [str(i) for i in indice[1][:]]
time_steps_indice_dt = glob_indice[1][:]
time_bnds_dt = get_glob_time_bnds(time_steps_indice_dt, slice_mode)
set_time_values(onc, time_steps_indice_dt, calend, units)
set_timebnds_values(onc, time_bnds_dt, calend, units)
onc.close()
return out_file
