def build(self, variable, ftp):
"""Build initial dataset."""
# We need to build these three files
paths = self.get_paths(variable)
# Retrieve rasters
self.get_rasters(variable, ftp)
# Reproject for area calculations
for file in glob(os.path.join(self.tif_folder, "*tif")):
self.reproject(file)
# Convert originals to NetCDF file
files = glob(os.path.join(self.tif_folder, variable + "*tif"))
toNetCDF(tfiles=files, ncfiles=None, savepath=paths["original"],
index=variable, proj=4326, year1=1895, month1=1,
year2=TODAYS_DATE.year - 2, month2=12, wmode="w",
percentiles=False)
# Convert projected files to NetCDF file
files = glob(os.path.join(self.tif_folder, "proj_*tif"))
toNetCDFAlbers(tfiles=files, ncfiles=None, savepath=paths["albers"],
index=variable, proj=PROJ, year1=1895, month1=1,
year2=TODAYS_DATE.year - 2, month2=12, wmode="w",
percentiles=False)
# Create a percentile dataset
toNetCDFPercentile(paths["original"], paths["percentile"])
# Clear temp folder
self._clear_tif()
ncfiles=None,
savepath=nc_path,
index=index,
proj=4326,
year1=1980,
month1=1,
year2=todays_date.year,
month2=12,
wmode='w',
percentiles=False)
toNetCDFAlbers(tfiles=tfiles_proj,
ncfiles=None,
savepath=nc_path_proj,
index=index,
proj=proj,
year1=1980,
month1=1,
year2=todays_date.year,
month2=12,
wmode='w',
percentiles=False)
# Now lets get the percentile values
pc_path = os.path.join(data_path,
"data/droughtindices/netcdfs/" + "percentiles",
index + ".nc")
toNetCDFPercentile(nc_path, pc_path)
# Close connection with FTP server
ftp.quit()
percentiles=False)
# We are also including percentiles, so lets build another dataset
pc_path = os.path.join(data_path,
'data/droughtindices/netcdfs/percentiles',
index_map[index] + '.nc')
print("Calculating Percentiles...")
toNetCDFPercentile(nc_path, pc_path)
# Now create the alber's netcdf
toNetCDFAlbers(tfiles_proj,
ncfiles,
nc_path_proj,
index,
proj=proj,
year1=1895,
month1=1,
year2=todays_date.year,
month2=todays_date.month,
wmode='w',
percentiles=False)
# We use a pre-made min/max value data frame to set color scales
print("Downloads and transformations complete, establishing index ranges...")
index_paths = [
os.path.join(data_path, 'data/droughtindices/netcdfs', i + '.nc')
for i in local_indices
]
maxes = {}
mins = {}
for i in tqdm(range(len(index_paths)), position=0):
ncfiles=None,
savepath=original_path,
index=variable,
proj=4326,
year1=1895,
month1=1,
year2=todays_date.year,
month2=12,
wmode='w',
percentiles=False)
toNetCDFAlbers(tfiles=tfiles_proj,
ncfiles=None,
savepath=albers_path,
index=variable,
proj=proj,
year1=1895,
month1=1,
year2=todays_date.year,
month2=12,
wmode='w',
percentiles=False)
toNetCDFPercentile(original_path, percentile_path)
# Empty tif folder
for t in glob(os.path.join(tif_folder, '*')):
os.remove(t)
# One last thing, we only have min and max vapor pressure deficit
meanNC(minsrc='data/droughtindices/netcdfs/albers/vpdmin.nc',
maxsrc='data/droughtindices/netcdfs/albers/vpdmax.nc',
dst='data/droughtindices/netcdfs/albers/vpdmean.nc')