def make_annual_loadings(modifications, years=settings.NgwRasters.keys()):
# First make the loadings for just the years we have precalculated (1945, 1960, etc)
print("Building Annual Loadings")
loadings = {}
for year in years:
print(year)
base_loading_matrix = compatibility.raster_to_numpy_array(settings.NgwRasters[year])
if year >= settings.ChangeYear: # if this year is after our reductions are supposed to be made
weight_matrix = make_weight_raster(settings.LandUseRasters[year], modifications=modifications)
loadings[year] = weight_matrix * base_loading_matrix
else: # otherwise, use the straight Ngw values - no changes have been made since they're in the past
loadings[year] = base_loading_matrix
# Now that we have the values for the base years, we want to interpolate between them to make ndarrays for each year
print("Interpolating between years")
sorted_years = sorted(years)
all_years_data = None
for i, year in enumerate(sorted_years):
print(year)
if year == sorted_years[-1]: # if it's the last year, we have special behavior
break
next_data_year = sorted_years[i+1]
interval_size = next_data_year - year
all_years_in_range = create_ranges_nd(loadings[year], loadings[next_data_year], interval_size)
if all_years_data is None:
all_years_data = all_years_in_range
else:
all_years_data = numpy.concatenate([all_years_data, all_years_in_range], -1) # stack them on the last axis now
return all_years_data
def make_weight_raster(land_use, modifications): """ Given a land use raster and a set of weights, applies the weights to each land use type then sets everything else to 1 so that the raster can be used as a multiplier later :param land_use: path to a land use raster on disk :param modifications: an iterable of npsat_manager.models.Modification objects :return: """ land_use_array = compatibility.raster_to_numpy_array(land_use) land_use_array += 10000 # offset everything by 10000 so we can identify everything that's still a default later for modification in modifications: land_use_array[land_use_array == modification.crop.caml_code] = modification.reduction land_use_array[land_use_array >= 10000] = 1 return land_use_array