def create_data():
"""
SHORTCUT Went manual here to speed up volta processing, but this should eventually be made programatic and break reliance on geoecon_utils
:return:
"""
project_folder = 'C:/mesh_alpha_0.2/projects/volta/'
bulk_data_folder = 'E:/bulk_data/'
input_folder = project_folder + 'input/Baseline/'
output_folder = project_folder + 'input/Baseline/'
bounding_box = (-5.35, 14.866667, 2.266667, 5.775)
lulc_uri = input_folder + 'lulc_2012.tif'
lulc = utilities.as_array(lulc_uri)
crop_extent_1km_uri = bulk_data_folder + 'iiasa-ifpri_cropland/cropland_hybrid_14052014v8/Hybrid_14052014V8.tif'
crop_extent_1km_clipped_uri = output_folder + 'cropland_extent_1km.tif'
utilities.clip_by_coords_with_gdal(crop_extent_1km_uri,
crop_extent_1km_clipped_uri,
bounding_box[0],
bounding_box[1],
bounding_box[2],
bounding_box[3],
projection='WGS84')
maize_input_uri = 'C:mesh_alpha_0.2/Base_Data/nutrition/crop_data/maize/maize_HarvestedAreaFraction.tif'
maize_5min_uri = input_folder + 'maize_HarvestedAreaFraction_5min.tif'
utilities.clip_by_coords_with_gdal(maize_input_uri,
maize_5min_uri,
bounding_box[0],
bounding_box[1],
bounding_box[2],
bounding_box[3],
projection='WGS84')
population_uri = input_folder + 'pop.tif'
lulc_uri = input_folder + 'lulc_2012.tif'
lulc_1km_uri = input_folder + 'lulc_1km_2012.tif'
bounding_box_pop = hb.get_bounding_box(lulc_uri)
hb.resize_and_resample_dataset_uri(lulc_uri, bounding_box_pop, 847.704968,
lulc_1km_uri, 'bilinear')
lulc = utilities.as_array(lulc_uri)
def calc_caloric_production_from_lulc_uri(input_lulc_uri, ui=None, **kw):
# First check that the required files exist, creating them if not.
# Data from Johnson et al 2016.
aoi_uri = kw['aoi_uri']
output_dir = kw['workspace_dir']
working_dir = os.path.split(os.path.split(input_lulc_uri)[0])[0]
baseline_dir = os.path.join(working_dir, 'Baseline')
scenario_dir = os.path.split(input_lulc_uri)[0]
# intermediate files
global_calories_per_cell_uri = os.path.join(kw['model_base_data_dir'],
'calories_per_cell.tif')
clipped_calories_per_5m_cell_uri = os.path.join(
output_dir, 'clipped_calories_per_5m_cell.tif')
calories_resampled_uri = os.path.join(output_dir,
'calories_per_ha_2000.tif')
# Get global 5m calories map from base data and project the full global map to projection of lulc, but keeping the global resolution
global_calories_per_cell_projected_uri = os.path.join(
output_dir, 'global_calories_per_cell_projected.tif')
output_wkt = hb.get_dataset_projection_wkt_uri(input_lulc_uri)
# Set cell size based on size at equator. Need to fully think this through.
# cell_size = 111319.49079327358 * hb.get_cell_size_from_uri(global_calories_per_cell_uri)
cell_size = hb.get_cell_size_from_uri(global_calories_per_cell_uri)
# Reproject the global calorie data into lulc projection
hb.reproject_dataset_uri(global_calories_per_cell_uri, cell_size,
output_wkt, 'bilinear',
global_calories_per_cell_projected_uri)
# Clip the global data to the project aoi, but keep at 5 min for math summation reasons later
hb.clip_dataset_uri(global_calories_per_cell_projected_uri,
aoi_uri,
clipped_calories_per_5m_cell_uri,
assert_projections=True,
process_pool=None)
# Now that it's clipped, it's small enough to resample to lulc resolution
hb.resize_and_resample_dataset_uri(
clipped_calories_per_5m_cell_uri, hb.get_bounding_box(input_lulc_uri),
hb.get_cell_size_from_uri(input_lulc_uri), calories_resampled_uri,
'bilinear')
# Load both baseline lulc and scenario lulc. Even if only 1 scenario is being included, still must have the baseline calculated
# to calibrate how the baseline data extrapolates to the scenario.
baseline_lulc_uri = os.path.join(
baseline_dir, 'lulc.tif') # TODO NOTE manual edit used for Tanzania
# baseline_resampled_uri = baseline_lulc_uri.replace('.tif', '_resampled.tif')
baseline_resampled_uri = os.path.join(output_dir, 'lulc_resampled.tif')
hb.resize_and_resample_dataset_uri(
baseline_lulc_uri, hb.get_bounding_box(input_lulc_uri),
hb.get_cell_size_from_uri(input_lulc_uri), baseline_resampled_uri,
'nearest')
# 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.
baseline_resampled_array = utilities.as_array(baseline_resampled_uri)
calories_resampled_array = utilities.as_array(calories_resampled_uri)
unscaled_calories_baseline = np.where(baseline_resampled_array == 12,
calories_resampled_array, 0)
unscaled_calories_baseline = np.where(baseline_resampled_array == 14,
0.5 * calories_resampled_array,
unscaled_calories_baseline)
# Do the same replacement for the scenario lulc
input_lulc_array = utilities.as_array(input_lulc_uri)
unscaled_calories_input_lulc = np.where(input_lulc_array == 12,
calories_resampled_array, 0)
unscaled_calories_input_lulc = np.where(input_lulc_array == 14,
0.5 * calories_resampled_array,
unscaled_calories_input_lulc)
note = """In ilm
for both ghana and honduras,
55% from most intensive
35% from mosaic
10% agro
honduras FOOD PRODUCTION grew by 16%
ghana 45%
IDEA Use monfreda, subset out perrenial trees, to get yield of agroforestry
"""
if 'Honduras' in ui.root_app.project_folder or 'Ghana' in ui.root_app.project_folder:
unscaled_calories_input_lulc = np.where(input_lulc_array == 12,
calories_resampled_array, 0)
unscaled_calories_input_lulc = np.where(
input_lulc_array == 14, 0.35 * calories_resampled_array,
unscaled_calories_input_lulc)
# CUSTOM calc in agroforestry value following Johan's suggested %
unscaled_calories_input_lulc = np.where(input_lulc_array == 17,
0.6 * calories_resampled_array,
unscaled_calories_input_lulc)
if 'ilm' in scenario_dir.lower():
avg_palm_cal_per_reg_cal = (8516 / 1295) * (
3000 / 1800
) # oil palm cal per kg/ avg crop calkg * (ilm ratio improvement over trend, which is assumed to be same as in earthstat.
avg_palm_cal_per_reg_cal = 0.1
unscaled_calories_input_lulc = np.where(
input_lulc_array == 18,
avg_palm_cal_per_reg_cal * calories_resampled_array,
unscaled_calories_input_lulc)
if 'Tanzania' in ui.root_app.project_folder or 'Ghana' in ui.root_app.project_folder:
unscaled_calories_input_lulc = np.where(input_lulc_array == 12,
calories_resampled_array, 0)
unscaled_calories_input_lulc = np.where(
input_lulc_array == 14, 0.35 * calories_resampled_array,
unscaled_calories_input_lulc)
# CUSTOM calc in agroforestry value following Johan's suggested %
unscaled_calories_input_lulc = np.where(input_lulc_array == 17,
0.6 * calories_resampled_array,
unscaled_calories_input_lulc)
if 'ilm' in scenario_dir.lower():
avg_palm_cal_per_reg_cal = (8516 / 1295) * (
3000 / 1800
) # oil palm cal per kg/ avg crop calkg * (ilm ratio improvement over trend, which is assumed to be same as in earthstat.
avg_palm_cal_per_reg_cal = 0.1
unscaled_calories_input_lulc = np.where(
input_lulc_array == 18,
avg_palm_cal_per_reg_cal * calories_resampled_array,
unscaled_calories_input_lulc)
# 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.
clipped_calories_per_5m_cell_array = utilities.as_array(
clipped_calories_per_5m_cell_uri)
n_calories_present = np.nansum(clipped_calories_per_5m_cell_array)
n_unscaled_calories_in_baseline = np.nansum(unscaled_calories_baseline)
adjustment_factor = n_calories_present / n_unscaled_calories_in_baseline
# Scale the scenario calories by the baseline adjustment factor
output_calories = unscaled_calories_input_lulc * adjustment_factor
output_calories_uri = os.path.join(output_dir, 'caloric_production.tif')
ui.update_run_log('Total calories: ' + str(np.nansum(output_calories)))
ui.update_run_log('Creating caloric_production.tif')
utilities.save_array_as_geotiff(output_calories,
output_calories_uri,
input_lulc_uri,
data_type_override=7)
def execute(kw, ui):
if not kw:
kw = create_default_kw(ui)
kw['output_folder'] = kw['workspace_dir']
ui.update_run_log('Calculating crop-specific production')
baseline_lulc_uri = ui.root_app.project_key_raster
if not kw.get('model_base_data_dir'):
kw['model_base_data_dir'] = os.path.join(ui.root_app.base_data_folder,
'models',
'nutritional_adequacy')
bounding_box = hb.get_datasource_bounding_box(ui.root_app.project_aoi)
ui.update_run_log('Loading input maps. Bounding box set to: ' +
str(bounding_box))
# TODO Unimplemented switch here.
run_full_nutritional_model = False # OTW just cal calories
if run_full_nutritional_model:
try:
os.remove(
os.path.join(kw['output_folder'],
'crop_proportion_baseline_500m.tif'))
os.remove(
os.path.join(kw['output_folder'],
'crop_proportion_baseline_1km.tif'))
os.remove(
os.path.join(kw['output_folder'], 'crop_proportion_500m.tif'))
os.remove(
os.path.join(kw['output_folder'], 'crop_proportion_1km.tif'))
except:
'no'
match_uri = kw['lulc_uri']
# Load the LULC array as the baseline
lulc_baseline = hb.as_array(baseline_lulc_uri)
no_data_value = hb.get_nodata_from_uri(baseline_lulc_uri)
# Extract the nan_mask for later
lulc_nan_mask = np.where(lulc_baseline == no_data_value, 1,
0).astype(np.int8)
# Calculate the proportion of the grid-cell that is in cultivation as a function of the LULC.
# For MODIS, this means 12 and 14 are 1.0 and 0.5 respectively.
crop_proportion_baseline = np.where(lulc_baseline == 12, 1.0, 0.0)
# TODO START HERE, i missed a nan mask and now the results have near infinite value. create a robust solution
# crop_proportion_baseline[lulc_nan_mask] = np.nan
crop_proportion_baseline = np.where(lulc_baseline == 14, .5,
crop_proportion_baseline)
# BUG If the files are not in the normal folder and onl linked to, it fails to find them.
try:
os.mkdir(os.path.join(kw['output_folder'], 'YieldTonsPerCell'))
except:
'Dir already exists.'
clipped_dir = os.path.join(kw['output_folder'],
'crop_production_and_harvested_area')
try:
os.mkdir(clipped_dir)
except:
'Dir already exists.'
try:
os.mkdir(os.path.join(kw['output_folder'], 'nutrient_production'))
except:
'Dir already exists.'
crop_proportion_baseline_500m_uri = os.path.join(
kw['output_folder'], 'YieldTonsPerCell',
'crop_proportion_baseline_500m.tif')
utilities.save_array_as_geotiff(crop_proportion_baseline,
crop_proportion_baseline_500m_uri,
kw['lulc_uri'])
crop_proportion_baseline_1km_uri = os.path.join(
kw['output_folder'], 'YieldTonsPerCell',
'crop_proportion_baseline_1km.tif')
population_bounding_box = utilities.get_bounding_box(
kw['population_uri'])
cell_size = utilities.get_cell_size_from_uri(kw['population_uri'])
hb.resize_and_resample_dataset_uri(crop_proportion_baseline_500m_uri,
population_bounding_box, cell_size,
crop_proportion_baseline_1km_uri,
'bilinear')
if kw['lulc_uri'] != baseline_lulc_uri:
lulc_scenario = utilities.as_array(kw['lulc_uri'])
crop_proportion = np.where(lulc_scenario == 12, 1.0, 0.0)
crop_proportion = np.where(lulc_scenario == 14, .5,
crop_proportion)
crop_proportion_500m_uri = os.path.join(
kw['output_folder'], 'YieldTonsPerCell',
'crop_proportion_500m.tif')
utilities.save_array_as_geotiff(crop_proportion,
crop_proportion_500m_uri,
kw['lulc_uri'])
crop_proportion_1km_uri = os.path.join(kw['output_folder'],
'YieldTonsPerCell',
'crop_proportion_1km.tif')
# original_dataset_uri, bounding_box, out_pixel_size, output_uri, resample_method
hb.resize_and_resample_dataset_uri(crop_proportion_500m_uri,
population_bounding_box,
cell_size,
crop_proportion_1km_uri,
'bilinear')
crop_proportion_baseline_1km = utilities.as_array(
crop_proportion_baseline_1km_uri)
crop_proportion_1km = utilities.as_array(crop_proportion_1km_uri)
change_ratio = np.where(
crop_proportion_baseline_1km > 0,
crop_proportion_1km / crop_proportion_baseline_1km, 1.0)
change_ratio_mean = np.mean(change_ratio)
else:
change_ratio_mean = 1.0
ui.update_run_log('Loading input maps')
crop_maps_folder = kw['crop_maps_folder']
nutritional_content_odict = utilities.file_to_python_object(
kw['nutritional_content_table_uri'], declare_type='2d_odict'
) # outputs as OrderedDict([('almond', OrderedDict([('fraction_refuse', '0.6'), ('Protein', '212.2'), ('Lipid', '494.2'), etc
nutritional_requirements_odict = utilities.file_to_python_object(
kw['nutritional_requirements_table_uri'],
declare_type='2d_indexed_odict')
population = utilities.as_array(kw['population_uri'])
demographic_groups_list = kw['demographic_groups_list']
demographics_folder = kw['demographics_folder']
ui.update_run_log('Calculating crop-specific production')
lulc_array = utilities.as_array(kw['lulc_uri'])
lulc_wkt = hb.get_dataset_projection_wkt_uri(kw['lulc_uri'])
harvested_area_ha_filenames = []
harvested_area_fraction_filenames = []
yield_tons_per_ha_filenames = []
yield_tons_per_cell_filenames = []
ha_array = 0
yield_per_ha_array = 0
# Calculate ha per cell
cell_size = hb.get_cell_size_from_uri(kw['lulc_uri'])
ha_per_cell = np.ones(lulc_array.shape) * (cell_size**2 / 10000)
ha_per_cell_uri = os.path.join(kw['output_folder'], 'ha_per_cell.tif')
utilities.save_array_as_geotiff(ha_per_cell, ha_per_cell_uri,
kw['lulc_uri'])
force_recalculation = False
for folder_name in os.listdir(crop_maps_folder):
current_folder = os.path.join(crop_maps_folder, folder_name)
if os.path.isdir(current_folder):
print('current_folder', current_folder)
current_crop_name = folder_name.split('_', 1)[0]
input_harvested_area_fraction_uri = os.path.join(
current_folder,
current_crop_name + '_HarvestedAreaFraction.tif')
clipped_harvested_area_fraction_uri = os.path.join(
clipped_dir,
current_crop_name + '_HarvestedAreaFraction.tif')
input_yield_tons_per_ha_uri = os.path.join(
current_folder, current_crop_name + '_YieldPerHectare.tif')
clipped_yield_tons_per_ha_uri = os.path.join(
clipped_dir, current_crop_name + '_YieldPerHectare.tif')
yield_tons_per_cell_uri = os.path.join(
clipped_dir, current_crop_name + '_YieldTonsPerCell.tif')
if not os.path.exists(
clipped_harvested_area_fraction_uri
) or not os.path.exists(
clipped_yield_tons_per_ha_uri) or not os.path.exists(
yield_tons_per_cell_uri) or force_recalculation:
# hb.clip_dataset_uri(input_harvested_area_fraction_uri, kw['aoi_uri'], clipped_harvested_area_fraction_uri)
utilities.clip_by_shape_with_buffered_intermediate_uri(
input_harvested_area_fraction_uri,
kw['aoi_uri'],
clipped_harvested_area_fraction_uri,
match_uri,
resampling_method='bilinear')
harvested_area_fraction_array = utilities.as_array(
clipped_harvested_area_fraction_uri)
# hb.clip_dataset_uri(input_yield_tons_per_ha_uri, kw['aoi_uri'], clipped_yield_tons_per_ha_uri)
utilities.clip_by_shape_with_buffered_intermediate_uri(
input_yield_tons_per_ha_uri,
kw['aoi_uri'],
clipped_yield_tons_per_ha_uri,
match_uri,
resampling_method='bilinear')
yield_tons_per_ha_array = utilities.as_array(
clipped_yield_tons_per_ha_uri)
nan1 = utilities.get_nodata_from_uri(
input_harvested_area_fraction_uri)
nan2 = utilities.get_nodata_from_uri(
input_yield_tons_per_ha_uri)
nan_mask = np.where((yield_tons_per_ha_array == nan1) & (
harvested_area_fraction_array == nan2))
yield_tons_per_cell_array = yield_tons_per_ha_array * harvested_area_fraction_array * ha_per_cell
yield_tons_per_cell_array[nan_mask] == nan1
# NOTE forcing ndv to zero for calcualtions
utilities.save_array_as_geotiff(yield_tons_per_cell_array,
yield_tons_per_cell_uri,
kw['lulc_uri'],
data_type_override=7,
no_data_value_override=0)
harvested_area_fraction_filenames.append(
clipped_harvested_area_fraction_uri)
yield_tons_per_ha_filenames.append(
clipped_yield_tons_per_ha_uri)
yield_tons_per_cell_filenames.append(yield_tons_per_cell_uri)
ui.update_run_log('Creating yield (tons) map for ' +
folder_name)
match_5min_uri = os.path.join(kw['output_folder'],
'crop_production_and_harvested_area',
'maize_HarvestedAreaFraction.tif')
# match_5min_uri = os.path.join(ui.root_app.base_data_folder, 'models/crop_production/global_dataset/observed_yield/rice_yield_map.tif')
match_array = utilities.as_array(match_5min_uri)
# TODO Figure out if nans all right
nan3 = utilities.get_nodata_from_uri(
clipped_harvested_area_fraction_uri)
array = utilities.as_array(clipped_harvested_area_fraction_uri)
nan_mask = np.where(array == nan3, True, False).astype(np.bool)
# TODO Why default to run always?
# Fat = np.zeros(match_array.shape).astype(np.float64)
#[Fatnan_mask] = nan3
Energy = np.zeros(match_array.shape).astype(np.float64)
Energy[nan_mask] = nan3
Protein = np.zeros(match_array.shape).astype(np.float64)
Protein[nan_mask] = nan3
VitA = np.zeros(match_array.shape).astype(np.float64)
VitA[nan_mask] = nan3
VitC = np.zeros(match_array.shape).astype(np.float64)
VitC[nan_mask] = nan3
VitE = np.zeros(match_array.shape).astype(np.float64)
VitE[nan_mask] = nan3
Thiamin = np.zeros(match_array.shape).astype(np.float64)
Thiamin[nan_mask] = nan3
Riboflavin = np.zeros(match_array.shape).astype(np.float64)
Riboflavin[nan_mask] = nan3
Niacin = np.zeros(match_array.shape).astype(np.float64)
Niacin[nan_mask] = nan3
VitB6 = np.zeros(match_array.shape).astype(np.float64)
VitB6[nan_mask] = nan3
Folate = np.zeros(match_array.shape).astype(np.float64)
Folate[nan_mask] = nan3
VitB12 = np.zeros(match_array.shape).astype(np.float64)
VitB12[nan_mask] = nan3
Ca = np.zeros(match_array.shape).astype(np.float64)
Ca[nan_mask] = nan3
Ph = np.zeros(match_array.shape).astype(np.float64)
Ph[nan_mask] = nan3
Mg = np.zeros(match_array.shape).astype(np.float64)
Mg[nan_mask] = nan3
K = np.zeros(match_array.shape).astype(np.float64)
K[nan_mask] = nan3
Na = np.zeros(match_array.shape).astype(np.float64)
Na[nan_mask] = nan3
Fe = np.zeros(match_array.shape).astype(np.float64)
Fe[nan_mask] = nan3
Zn = np.zeros(match_array.shape).astype(np.float64)
Zn[nan_mask] = nan3
Cu = np.zeros(match_array.shape).astype(np.float64)
Cu[nan_mask] = nan3
for i in range(len(yield_tons_per_cell_filenames)):
current_crop_name = os.path.splitext(
os.path.split(
harvested_area_fraction_filenames[i])[1])[0].split('_',
1)[0]
ui.update_run_log('Calculating nutritional contribution of ' +
current_crop_name)
if current_crop_name in nutritional_content_odict.keys():
print('adding Nutritional content of ' + current_crop_name)
# Fat += utilities.as_array(yield_tons_per_cell_filenames[i]) * 1000.0 * float(nutritional_content_odict[current_crop_name]['Fat'])
Energy += utilities.as_array(
yield_tons_per_cell_filenames[i]) * 1000.0 * float(
nutritional_content_odict[current_crop_name]['Energy'])
Protein += utilities.as_array(
yield_tons_per_cell_filenames[i]) * 1000.0 * float(
nutritional_content_odict[current_crop_name]
['Protein'])
VitA += utilities.as_array(
yield_tons_per_cell_filenames[i]) * 1000.0 * float(
nutritional_content_odict[current_crop_name]['VitA'])
VitC += utilities.as_array(
yield_tons_per_cell_filenames[i]) * 1000.0 * float(
nutritional_content_odict[current_crop_name]['VitC'])
VitE += utilities.as_array(
yield_tons_per_cell_filenames[i]) * 1000.0 * float(
nutritional_content_odict[current_crop_name]['VitE'])
Thiamin += utilities.as_array(
yield_tons_per_cell_filenames[i]) * 1000.0 * float(
nutritional_content_odict[current_crop_name]
['Thiamin'])
Riboflavin += utilities.as_array(
yield_tons_per_cell_filenames[i]) * 1000.0 * float(
nutritional_content_odict[current_crop_name]
['Riboflavin'])
Niacin += utilities.as_array(
yield_tons_per_cell_filenames[i]) * 1000.0 * float(
nutritional_content_odict[current_crop_name]['Niacin'])
VitB6 += utilities.as_array(
yield_tons_per_cell_filenames[i]) * 1000.0 * float(
nutritional_content_odict[current_crop_name]['VitB6'])
Folate += utilities.as_array(
yield_tons_per_cell_filenames[i]) * 1000.0 * float(
nutritional_content_odict[current_crop_name]['Folate'])
VitB12 += utilities.as_array(
yield_tons_per_cell_filenames[i]) * 1000.0 * float(
nutritional_content_odict[current_crop_name]['VitB12'])
Ca += utilities.as_array(
yield_tons_per_cell_filenames[i]) * 1000.0 * float(
nutritional_content_odict[current_crop_name]['Ca'])
Ph += utilities.as_array(
yield_tons_per_cell_filenames[i]) * 1000.0 * float(
nutritional_content_odict[current_crop_name]['Ph'])
Mg += utilities.as_array(
yield_tons_per_cell_filenames[i]) * 1000.0 * float(
nutritional_content_odict[current_crop_name]['Energy'])
K += utilities.as_array(
yield_tons_per_cell_filenames[i]) * 1000.0 * float(
nutritional_content_odict[current_crop_name]['Mg'])
Na += utilities.as_array(
yield_tons_per_cell_filenames[i]) * 1000.0 * float(
nutritional_content_odict[current_crop_name]['K'])
Fe += utilities.as_array(
yield_tons_per_cell_filenames[i]) * 1000.0 * float(
nutritional_content_odict[current_crop_name]['Fe'])
Zn += utilities.as_array(
yield_tons_per_cell_filenames[i]) * 1000.0 * float(
nutritional_content_odict[current_crop_name]['Energy'])
Cu += utilities.as_array(
yield_tons_per_cell_filenames[i]) * 1000.0 * float(
nutritional_content_odict[current_crop_name]['Zn'])
# TODO make this happen earlier in calcs
# Fat *= change_ratio_mean
Energy *= change_ratio_mean
Protein *= change_ratio_mean
VitA *= change_ratio_mean
VitC *= change_ratio_mean
VitE *= change_ratio_mean
Thiamin *= change_ratio_mean
Riboflavin *= change_ratio_mean
Niacin *= change_ratio_mean
VitB6 *= change_ratio_mean
Folate *= change_ratio_mean
VitB12 *= change_ratio_mean
Ca *= change_ratio_mean
Ph *= change_ratio_mean
Mg *= change_ratio_mean
K *= change_ratio_mean
Na *= change_ratio_mean
Fe *= change_ratio_mean
Zn *= change_ratio_mean
Cu *= change_ratio_mean
# Fat *= change_ratio_mean
Energy[nan_mask] = 0
Protein[nan_mask] = 0
VitA[nan_mask] = 0
VitC[nan_mask] = 0
VitE[nan_mask] = 0
Thiamin[nan_mask] = 0
Riboflavin[nan_mask] = 0
Niacin[nan_mask] = 0
VitB6[nan_mask] = 0
Folate[nan_mask] = 0
VitB12[nan_mask] = 0
Ca[nan_mask] = 0
Ph[nan_mask] = 0
Mg[nan_mask] = 0
K[nan_mask] = 0
Na[nan_mask] = 0
Fe[nan_mask] = 0
Zn[nan_mask] = 0
Cu[nan_mask] = 0
match_1km_uri = os.path.join(kw['output_folder'], 'YieldTonsPerCell',
'crop_proportion_baseline_1km.tif')
utilities.save_array_as_geotiff(
Energy,
os.path.join(kw['output_folder'], 'nutrient_production',
'Energy_per_cell_5min.tif'),
match_5min_uri,
data_type_override=7,
no_data_value_override=nan3)
utilities.resample_preserve_sum(
os.path.join(kw['output_folder'], 'nutrient_production',
'Energy_per_cell_5min.tif'),
os.path.join(kw['output_folder'], 'nutrient_production',
'Energy_per_cell_1km.tif'), match_1km_uri)
# utilities.save_array_as_geotiff(Fat, os.path.join(kw['output_folder'], 'nutrient_production', 'Fat_per_cell_5min.tif'), match_5min_uri)
# utilities.resample_preserve_sum(os.path.join(kw['output_folder'], 'nutrient_production', 'Fat_per_cell_5min.tif'), os.path.join(kw['output_folder'], 'nutrient_production', 'Fat_per_cell_1km.tif'), match_1km_uri)
utilities.save_array_as_geotiff(
Protein,
os.path.join(kw['output_folder'], 'nutrient_production',
'Protein_per_cell_5min.tif'),
match_5min_uri,
no_data_value_override=nan3)
utilities.resample_preserve_sum(
os.path.join(kw['output_folder'], 'nutrient_production',
'Protein_per_cell_5min.tif'),
os.path.join(kw['output_folder'], 'nutrient_production',
'Protein_per_cell_1km.tif'), match_1km_uri)
utilities.save_array_as_geotiff(
VitA,
os.path.join(kw['output_folder'], 'nutrient_production',
'VitA_per_cell_5min.tif'), match_5min_uri)
utilities.resample_preserve_sum(
os.path.join(kw['output_folder'], 'nutrient_production',
'VitA_per_cell_5min.tif'),
os.path.join(kw['output_folder'], 'nutrient_production',
'VitA_per_cell_1km.tif'), match_1km_uri)
utilities.save_array_as_geotiff(
VitC,
os.path.join(kw['output_folder'], 'nutrient_production',
'VitC_per_cell_5min.tif'), match_5min_uri)
utilities.resample_preserve_sum(
os.path.join(kw['output_folder'], 'nutrient_production',
'VitC_per_cell_5min.tif'),
os.path.join(kw['output_folder'], 'nutrient_production',
'VitC_per_cell_1km.tif'), match_1km_uri)
utilities.save_array_as_geotiff(
VitE,
os.path.join(kw['output_folder'], 'nutrient_production',
'VitE_per_cell_5min.tif'), match_5min_uri)
utilities.resample_preserve_sum(
os.path.join(kw['output_folder'], 'nutrient_production',
'VitE_per_cell_5min.tif'),
os.path.join(kw['output_folder'], 'nutrient_production',
'VitE_per_cell_1km.tif'), match_1km_uri)
utilities.save_array_as_geotiff(
Thiamin,
os.path.join(kw['output_folder'], 'nutrient_production',
'Thiamin_per_cell_5min.tif'), match_5min_uri)
utilities.resample_preserve_sum(
os.path.join(kw['output_folder'], 'nutrient_production',
'Thiamin_per_cell_5min.tif'),
os.path.join(kw['output_folder'], 'nutrient_production',
'Thiamin_per_cell_1km.tif'), match_1km_uri)
utilities.save_array_as_geotiff(
Riboflavin,
os.path.join(kw['output_folder'], 'nutrient_production',
'Riboflavin_per_cell_5min.tif'), match_5min_uri)
utilities.resample_preserve_sum(
os.path.join(kw['output_folder'], 'nutrient_production',
'Riboflavin_per_cell_5min.tif'),
os.path.join(kw['output_folder'], 'nutrient_production',
'Riboflavin_per_cell_1km.tif'), match_1km_uri)
utilities.save_array_as_geotiff(
Niacin,
os.path.join(kw['output_folder'], 'nutrient_production',
'Niacin_per_cell_5min.tif'), match_5min_uri)
utilities.resample_preserve_sum(
os.path.join(kw['output_folder'], 'nutrient_production',
'Niacin_per_cell_5min.tif'),
os.path.join(kw['output_folder'], 'nutrient_production',
'Niacin_per_cell_1km.tif'), match_1km_uri)
utilities.save_array_as_geotiff(
VitB6,
os.path.join(kw['output_folder'], 'nutrient_production',
'VitB6_per_cell_5min.tif'), match_5min_uri)
utilities.resample_preserve_sum(
os.path.join(kw['output_folder'], 'nutrient_production',
'VitB6_per_cell_5min.tif'),
os.path.join(kw['output_folder'], 'nutrient_production',
'VitB6_per_cell_1km.tif'), match_1km_uri)
utilities.save_array_as_geotiff(
Folate,
os.path.join(kw['output_folder'], 'nutrient_production',
'Folate_per_cell_5min.tif'), match_5min_uri)
utilities.resample_preserve_sum(
os.path.join(kw['output_folder'], 'nutrient_production',
'Folate_per_cell_5min.tif'),
os.path.join(kw['output_folder'], 'nutrient_production',
'Folate_per_cell_1km.tif'), match_1km_uri)
utilities.save_array_as_geotiff(
VitB12,
os.path.join(kw['output_folder'], 'nutrient_production',
'VitB12_per_cell_5min.tif'), match_5min_uri)
utilities.resample_preserve_sum(
os.path.join(kw['output_folder'], 'nutrient_production',
'VitB12_per_cell_5min.tif'),
os.path.join(kw['output_folder'], 'nutrient_production',
'VitB12_per_cell_1km.tif'), match_1km_uri)
utilities.save_array_as_geotiff(
Ca,
os.path.join(kw['output_folder'], 'nutrient_production',
'Ca_per_cell_5min.tif'), match_5min_uri)
utilities.resample_preserve_sum(
os.path.join(kw['output_folder'], 'nutrient_production',
'Ca_per_cell_5min.tif'),
os.path.join(kw['output_folder'], 'nutrient_production',
'Ca_per_cell_1km.tif'), match_1km_uri)
utilities.save_array_as_geotiff(
Ph,
os.path.join(kw['output_folder'], 'nutrient_production',
'Ph_per_cell_5min.tif'), match_5min_uri)
utilities.resample_preserve_sum(
os.path.join(kw['output_folder'], 'nutrient_production',
'Ph_per_cell_5min.tif'),
os.path.join(kw['output_folder'], 'nutrient_production',
'Ph_per_cell_1km.tif'), match_1km_uri)
utilities.save_array_as_geotiff(
Mg,
os.path.join(kw['output_folder'], 'nutrient_production',
'Mg_per_cell_5min.tif'), match_5min_uri)
utilities.resample_preserve_sum(
os.path.join(kw['output_folder'], 'nutrient_production',
'Mg_per_cell_5min.tif'),
os.path.join(kw['output_folder'], 'nutrient_production',
'Mg_per_cell_1km.tif'), match_1km_uri)
utilities.save_array_as_geotiff(
K,
os.path.join(kw['output_folder'], 'nutrient_production',
'K_per_cell_5min.tif'), match_5min_uri)
utilities.resample_preserve_sum(
os.path.join(kw['output_folder'], 'nutrient_production',
'K_per_cell_5min.tif'),
os.path.join(kw['output_folder'], 'nutrient_production',
'K_per_cell_1km.tif'), match_1km_uri)
utilities.save_array_as_geotiff(
Na,
os.path.join(kw['output_folder'], 'nutrient_production',
'Na_per_cell_5min.tif'), match_5min_uri)
utilities.resample_preserve_sum(
os.path.join(kw['output_folder'], 'nutrient_production',
'Na_per_cell_5min.tif'),
os.path.join(kw['output_folder'], 'nutrient_production',
'Na_per_cell_1km.tif'), match_1km_uri)
utilities.save_array_as_geotiff(
Fe,
os.path.join(kw['output_folder'], 'nutrient_production',
'Fe_per_cell_5min.tif'), match_5min_uri)
utilities.resample_preserve_sum(
os.path.join(kw['output_folder'], 'nutrient_production',
'Fe_per_cell_5min.tif'),
os.path.join(kw['output_folder'], 'nutrient_production',
'Fe_per_cell_1km.tif'), match_1km_uri)
utilities.save_array_as_geotiff(
Zn,
os.path.join(kw['output_folder'], 'nutrient_production',
'Zn_per_cell_5min.tif'), match_5min_uri)
utilities.resample_preserve_sum(
os.path.join(kw['output_folder'], 'nutrient_production',
'Zn_per_cell_5min.tif'),
os.path.join(kw['output_folder'], 'nutrient_production',
'Zn_per_cell_1km.tif'), match_1km_uri)
utilities.save_array_as_geotiff(
Cu,
os.path.join(kw['output_folder'], 'nutrient_production',
'Cu_per_cell_5min.tif'), match_5min_uri)
utilities.resample_preserve_sum(
os.path.join(kw['output_folder'], 'nutrient_production',
'Cu_per_cell_5min.tif'),
os.path.join(kw['output_folder'], 'nutrient_production',
'Cu_per_cell_1km.tif'), match_1km_uri)
# calculate demand
calculate_demand = True
if calculate_demand:
# TODO FOR HONDURAS need to switch to a non-population-map derived resolution. Incorporate decision making unit map to set the desired resolution
ui.update_run_log('Calculating total nutrient demand')
overall_nutrient_sum = 0
overall_nutrient_requirement_sum = 0
overall_ratio_array = np.zeros(population.shape)
overall_ratio = 0
population_zero_normalized = np.where(population < 0, 0,
population)
for nutrient in nutritional_requirements_odict:
nutrient_uri = os.path.join(kw['output_folder'],
'nutrient_production',
nutrient + '_per_cell_1km.tif')
nutrient_array = utilities.as_array(nutrient_uri)
nutrient_requirement_array = population_zero_normalized * float(
nutritional_requirements_odict[nutrient]
['recommended_daily_allowance']) * 365.0
# nutrient_requirement_array[nan_mask] = np.nan
# nutrient_requirement_array[nutrient_requirement_array<=0] = np.nan
nutrient_provision_ratio = np.where(
(nutrient_array / nutrient_requirement_array > 0) &
(nutrient_array / nutrient_requirement_array <
999999999999999999999999999999),
nutrient_array / nutrient_requirement_array, 0)
overall_ratio_array += nutrient_provision_ratio
nutrient_sum = np.nansum(nutrient_array)
overall_nutrient_sum += nutrient_sum
nutrient_requirement_sum = np.nansum(
nutrient_requirement_array)
overall_nutrient_requirement_sum += nutrient_requirement_sum
output_string = 'Full landscape produced ' + str(
nutrient_sum
) + ' of ' + nutrient + ' compared to a national requirement of ' + str(
nutrient_requirement_sum
) + ', yielding nutritional adequacy ratio of ' + str(
nutrient_sum / nutrient_requirement_sum) + '.'
ui.update_run_log(output_string)
overall_ratio += nutrient_provision_ratio
utilities.save_array_as_geotiff(
nutrient_provision_ratio,
nutrient_uri.replace('_per_cell_1km.tif',
'_adequacy_ratio.tif'), nutrient_uri)
overall_ratio_array *= 1.0 / 19.0
overall_ratio = (1.0 / 19.0) * (overall_nutrient_sum /
overall_nutrient_requirement_sum)
output_string = 'Overall nutrion adequacy ratio: ' + str(
overall_ratio) + '.'
ui.update_run_log(output_string)
overall_ratio_uri = os.path.join(kw['output_folder'],
'overall_adequacy_ratio.tif')
utilities.save_array_as_geotiff(overall_ratio_array,
overall_ratio_uri, nutrient_uri)
run_calories_only_model = True
if run_calories_only_model:
calc_caloric_production_from_lulc_uri(kw['lulc_uri'], ui=ui, **kw)
return