def calc_caloric_production_on_uri_list(input_uri_list, output_csv_uri):
first_lulc = True
baseline_calories = 0
output = []
for uri in input_uri_list:
row = []
row.append(nd.explode_uri(uri)['file_root'])
af = nd.ArrayFrame(uri)
# nd.pp(nd.enumerate_array_as_odict(af.data))
sum_calories = calc_caloric_production_from_lulc(uri)
row.append(str(sum_calories))
if first_lulc:
baseline_calories = sum_calories
first_lulc = False
else:
row.append(str(sum_calories - baseline_calories))
output.append(row)
hb.python_object_to_csv(output, output_csv_uri)
def calc_caloric_production_on_uri_list(input_uri_list, output_csv_uri):
first_lulc = True
baseline_calories = 0
output = []
af_list = []
for uri in input_uri_list:
row = []
row.append(nd.explode_uri(uri)['file_root'])
af = nd.ArrayFrame(uri)
sum_calories, af = calc_caloric_production_from_lulc(uri)
row.append(str(sum_calories))
# af.show()
af_list.append(af)
if first_lulc:
baseline_calories = sum_calories
first_lulc = False
else:
row.append(str(sum_calories - baseline_calories))
output.append(row)
hb.python_object_to_csv(output, output_csv_uri)
return af_list
def calc_caloric_production_from_lulc(input_lulc_uri):
# Data from Johnson et al 2016.
calories_per_cell_uri = os.path.join(ag_dir, 'calories_per_cell.tif')
calories_per_cell = nd.ArrayFrame(calories_per_cell_uri)
ndv = calories_per_cell.no_data_value
# Project the full global to robinson (to match volta projeciton and also to allow np.clip_by_shape)
calories_per_cell_projected_uri = os.path.join(
project_dir, 'calories_per_cell_projected.tif')
if not os.path.exists(calories_per_cell_projected_uri):
calories_per_cell_projected = nd.reproject(
calories_per_cell,
calories_per_cell_projected_uri,
epsg_code=54030,
no_data_value=ndv)
else:
calories_per_cell_projected = nd.ArrayFrame(
calories_per_cell_projected_uri)
# Polygon of the Volta (also in Robinson projection)
aoi_uri = 'input/Baseline/PASOS_cuencas_robinson.shp'
# Clip the global data to the volta, but keep at 5 min for math summation reasons later
clipped_calories_per_5m_cell_uri = 'input/Baseline/clipped_calories_per_5m_cell.tif'
if not os.path.exists(clipped_calories_per_5m_cell_uri):
clipped_calories_per_5m_cell = calories_per_cell_projected.clip_by_shape(
aoi_uri,
output_uri=clipped_calories_per_5m_cell_uri,
no_data_value=ndv)
else:
clipped_calories_per_5m_cell = nd.ArrayFrame(
clipped_calories_per_5m_cell_uri)
# Load the baseline lulc for adjustment factor calculation and as a match_af
lulc_uri = 'input/Baseline/lulc.tif'
lulc = nd.ArrayFrame(lulc_uri)
lulc_ndv = lulc.no_data_value
# resample to LULC's resolution. Note that this will change the sum of calories.
calories_resampled_uri = 'input/Baseline/calories_resampled.tif'
if not os.path.exists(calories_resampled_uri):
calories_resampled = clipped_calories_per_5m_cell.resample(
lulc, output_uri=calories_resampled_uri, no_data_value=ndv)
else:
calories_resampled = nd.ArrayFrame(calories_resampled_uri)
# Base on teh assumption that full ag is twice as contianing of calroies as mosaic, allocate the
# caloric presence to these two ag locations. Note that these are still not scaled, but they are
# correct relative to each other.
# This simplification means we are doing the equivilent to the invest crop model beacause
# the cells to allocate are lower res than the target.
unscaled_calories_baseline = np.where(lulc.data == 12,
calories_resampled.data, 0)
unscaled_calories_baseline = np.where(lulc.data == 14,
0.5 * calories_resampled.data,
unscaled_calories_baseline)
# Multiply the unscaled calories by this adjustment factor, which is the ratio between the actual calories present
# calculated from the 5 min resolution data, and the unscaled.
n_calories_present = np.sum(clipped_calories_per_5m_cell)
n_unscaled_calories_in_baseline = np.sum(unscaled_calories_baseline)
adjustment_factor = n_calories_present / n_unscaled_calories_in_baseline
calc_baseline_calories = False
if calc_baseline_calories:
baseline_calories = unscaled_calories_baseline * adjustment_factor
baseline_calories_uri = 'input/Baseline/baseline_calories.tif'
# NOTE, this uses numdal in a weired way because it has THREE inputs (of which the last is jammed into kwargs).
baseline_calories_af = nd.ArrayFrame(baseline_calories,
lulc,
output_uri=baseline_calories_uri,
data_type=6,
no_data_value=ndv)
input_lulc = nd.ArrayFrame(input_lulc_uri)
unscaled_calories_input_lulc = np.where(input_lulc.data == 12,
calories_resampled.data, 0)
unscaled_calories_input_lulc = np.where(input_lulc.data == 14,
0.5 * calories_resampled.data,
unscaled_calories_input_lulc)
output_calories = unscaled_calories_input_lulc * adjustment_factor
output_calories_uri = os.path.join(
project_dir,
'calories_in_' + nd.explode_uri(input_lulc_uri)['file_root'] + '.tif')
output_calories_af = nd.ArrayFrame(output_calories,
lulc,
data_type=7,
no_data_value=ndv,
output_uri=output_calories_uri)
# output_calories_af.save(output_uri=output_calories_uri)
sum_calories = output_calories_af.sum()
print('Calories from ' + input_lulc_uri + ': ' + str(sum_calories))
return sum_calories
kw['plot_intermediate_dir'] = 0
if kw['plot_intermediate_dir']:
for i in nd.get_list_of_file_uris_recursively(kw['intermediate_dir'],
filter_extensions='.tif'):
output_uri = i.replace('.tif', '.png')
if not os.path.exists(output_uri) and 'pop_30s.tif' not in i:
# if 'pop_30s.tif' not in i:
print(i)
if 'precipitation' in i or 'temperature' in i:
use_basemap = False
else:
use_basemap = True
if 'gaez' in i:
title = nd.explode_uri(i)['file_root'].replace(
'_continuous', '').replace('_', ' ').title()
else:
title = i
nd.show(i,
output_uri=output_uri,
title=title,
resolution='c',
cbar_percentiles=[2, 50, 98],
use_basemap=use_basemap)
kw['plot_fertilizer_inputs_dir'] = 0
if kw['plot_fertilizer_inputs_dir']:
dir = os.path.join(CONFIG.BASE_DATA_DIR, 'crops', 'earthstat',
'crop_fertilizer', 'fertilizer_wheat')
print(dir)