def force_geotiff_to_match_projection_ndv_and_datatype(input_path, match_path, output_path, output_datatype=None, output_ndv=None):
"""Rather than actually projecting, just change the metadata so it matches exactly. This only will be useful
if there was a data error and something got a projection defined when the underlying data wasnt actually transofmred
into that shape.
NOTE that the output will keep the same geotransform as input, and only the projection, no data and datatype will change.
"""
if not output_datatype:
output_datatype = hb.get_datatype_from_uri(match_path)
if not output_ndv:
output_ndv = hb.get_ndv_from_path(match_path)
match_wkt = hb.get_dataset_projection_wkt_uri(match_path)
input_geotransform = hb.get_geotransform_uri(input_path)
# Load the array, but use numpy to convert it to the new datatype
input_array = hb.as_array(input_path).astype(hb.gdal_number_to_numpy_type[output_datatype])
if not output_ndv:
output_ndv = -9999
hb.save_array_as_geotiff(input_array, output_path,
data_type=output_datatype,
ndv=output_ndv,
geotransform_override=input_geotransform,
projection_override=match_wkt)
def force_global_angular_data_to_equal_area_earth_grid(input_path, output_path):
output_datatype = hb.get_datatype_from_uri(input_path)
output_ndv = hb.get_ndv_from_path(input_path)
match_wkt = hb.get_dataset_projection_wkt_uri(input_path)
match_wkt = hb.get_wkt_from_epsg_code(6933)
input_geotransform = hb.get_geotransform_uri(input_path)
output_geotransform = list(hb.common_geotransforms['wec_30s'])
output_geotransform[1] = input_geotransform[1] * hb.size_of_one_arcdegree_at_equator_in_meters
output_geotransform[5] = input_geotransform[5] * hb.size_of_one_arcdegree_at_equator_in_meters
# Load the array, but use numpy to convert it to the new datatype
input_array = hb.as_array(input_path).astype(hb.gdal_number_to_numpy_type[output_datatype])
if not output_ndv:
output_ndv = -9999
hb.save_array_as_geotiff(input_array, output_path,
data_type=output_datatype,
ndv=output_ndv,
geotransform_override=output_geotransform,
projection_override=match_wkt)
def get_linear_unit_from_other_projection(input_uri, projected_uri, also_return_size=False):
input_ds = gdal.Open(input_uri)
projected_wkt = hb.get_dataset_projection_wkt_uri(projected_uri)
# Create a virtual raster that is projected based on the output WKT. This
# vrt does not save to disk and is used to get the proper projected
# bounding box and size.
vrt = gdal.AutoCreateWarpedVRT(
input_ds, None, projected_wkt, gdal.GRA_Bilinear)
geo_t = vrt.GetGeoTransform()
x_size = vrt.RasterXSize # Raster xsize
y_size = vrt.RasterYSize # Raster ysize
resolution = geo_t[1]
if also_return_size:
return resolution, x_size, y_size
else:
return resolution
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