def Download(self, Waitbar=1, cores=None):
"""
This function downloads CHIRPS daily or monthly data
Keyword arguments:
Waitbar -- 1 (Default) will print a waitbar
cores -- The number of cores used to run the routine. It can be 'False'
to avoid using parallel computing routines.
"""
# Pass variables to parallel function and run
args = [self.output_folder, self.Time, self.xID, self.yID, self.lonlim, self.latlim]
if not cores:
# Create Waitbar
if Waitbar == 1:
total_amount = len(self.Dates)
amount = 0
weirdFn.printWaitBar(amount, total_amount, prefix = 'Progress:', suffix = 'Complete', length = 50)
for Date in self.Dates:
CHIRPS.RetrieveData(Date, args)
if Waitbar == 1:
amount = amount + 1
weirdFn.printWaitBar(amount, total_amount, prefix = 'Progress:', suffix = 'Complete', length = 50)
results = True
else:
results = Parallel(n_jobs=cores)(delayed(CHIRPS.RetrieveData)(Date, args)
for Date in self.Dates)
return results
def Download(self, Waitbar=1, cores=None):
"""
============================================================
Download(self, Waitbar=1, cores=None)
============================================================
Download method downloads CHIRPS data
Parameters
----------
Waitbar : TYPE, optional
will print a waitbar. The default is 1.
cores : TYPE, optional
The number of cores used to run the routine. It can be 'False'
to avoid using parallel computing routines. The default is None.
Returns
-------
results : TYPE
DESCRIPTION.
"""
# Pass variables to parallel function and run
args = [
self.output_folder, self.Time, self.xID, self.yID, self.lonlim,
self.latlim
]
if not cores:
# Create Waitbar
if Waitbar == 1:
total_amount = len(self.Dates)
amount = 0
weirdFn.printWaitBar(amount,
total_amount,
prefix='Progress:',
suffix='Complete',
length=50)
for Date in self.Dates:
CHIRPS.RetrieveData(Date, args)
if Waitbar == 1:
amount = amount + 1
weirdFn.printWaitBar(amount,
total_amount,
prefix='Progress:',
suffix='Complete',
length=50)
results = True
else:
results = Parallel(n_jobs=cores)(
delayed(CHIRPS.RetrieveData)(Date, args)
for Date in self.Dates)
return results
def DownloadData(self, Var, Waitbar):
"""
This function downloads ECMWF six-hourly, daily or monthly data
Keyword arguments:
"""
# Load factors / unit / type of variables / accounts
VarInfo = Variables(self.Time)
Varname_dir = VarInfo.file_name[Var]
# Create Out directory
out_dir = os.path.join(self.Path, self.Time, Varname_dir)
if not os.path.exists(out_dir):
os.makedirs(out_dir)
DownloadType = VarInfo.DownloadType[Var]
if DownloadType == 1:
string1 = 'oper'
string4 = "0"
string6 = "00:00:00/06:00:00/12:00:00/18:00:00"
string2 = 'sfc'
string8 = 'an'
if DownloadType == 2:
string1 = 'oper'
string4 = "12"
string6 = "00:00:00/12:00:00"
string2 = 'sfc'
string8 = 'fc'
if DownloadType == 3:
string1 = 'oper'
string4 = "0"
string6 = "00:00:00/06:00:00/12:00:00/18:00:00"
string2 = 'pl'
string8 = 'an'
parameter_number = VarInfo.number_para[Var]
string3 = '%03d.128' %(parameter_number)
string5 = '0.125/0.125'
string9 = 'ei'
string10 = '%s/%s/%s/%s' %(self.latlim_corr[1], self.lonlim_corr[0],
self.latlim_corr[0], self.lonlim_corr[1]) #N, W, S, E
# Download data by using the ECMWF API
print('Use API ECMWF to collect the data, please wait')
RemoteSensing.API(self.Path, DownloadType, string1, string2, string3, string4,
string5, string6, self.string7, string8, string9, string10)
# Open the downloaded data
NC_filename = os.path.join(self.Path,'data_interim.nc')
fh = Dataset(NC_filename, mode='r')
# Get the NC variable parameter
parameter_var = VarInfo.var_name[Var]
Var_unit = VarInfo.units[Var]
factors_add = VarInfo.factors_add[Var]
factors_mul = VarInfo.factors_mul[Var]
# Open the NC data
Data = fh.variables[parameter_var][:]
Data_time = fh.variables['time'][:]
lons = fh.variables['longitude'][:]
lats = fh.variables['latitude'][:]
# Define the georeference information
Geo_four = np.nanmax(lats)
Geo_one = np.nanmin(lons)
Geo_out = tuple([Geo_one, 0.125, 0.0, Geo_four, 0.0, -0.125])
# Create Waitbar
if Waitbar == 1:
total_amount = len(self.Dates)
amount = 0
weirdFn.printWaitBar(amount, total_amount, prefix = 'Progress:', suffix = 'Complete', length = 50)
for date in self.Dates:
# Define the year, month and day
year = date.year
month = date.month
day = date.day
# Hours since 1900-01-01
start = dt.datetime(year=1900, month=1, day=1)
end = dt.datetime(year, month, day)
diff = end - start
hours_from_start_begin = diff.total_seconds()/60/60
Date_good = np.zeros(len(Data_time))
if self.Time == 'daily':
days_later = 1
if self.Time == 'monthly':
days_later = calendar.monthrange(year,month)[1]
Date_good[np.logical_and(Data_time>=hours_from_start_begin, Data_time<(hours_from_start_begin + 24 * days_later))] = 1
Data_one = np.zeros([int(np.sum(Date_good)),int(np.size(Data,1)),int(np.size(Data,2))])
Data_one = Data[np.int_(Date_good) == 1, :, :]
# Calculate the average temperature in celcius degrees
Data_end = factors_mul * np.nanmean(Data_one,0) + factors_add
if VarInfo.types[Var] == 'flux':
Data_end = Data_end * days_later
VarOutputname = VarInfo.file_name[Var]
# Define the out name
name_out = os.path.join(out_dir, "%s_ECMWF_ERA-Interim_%s_%s_%d.%02d.%02d.tif" %(VarOutputname, Var_unit, self.Time, year,month,day))
# Create Tiff files
# Raster.Save_as_tiff(name_out, Data_end, Geo_out, "WGS84")
Raster.CreateRaster(Path=name_out, data=Data_end, geo=Geo_out, EPSG="WGS84")
if Waitbar == 1:
amount = amount + 1
weirdFn.printWaitBar(amount, total_amount, prefix = 'Progress:', suffix = 'Complete', length = 50)
fh.close()
return()