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 plot_global_production(**kw):
af = nd.ArrayFrame(kw['production_per_cell_uri'])
af.show(vmin=0,
vmax=1500,
use_basemap=True,
title='Meat Production',
cbar_label='Tons per cell')
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
def create_scenario_calorie_csv(input_dir, output_uri):
scenario_results = [[
'',
'carbon',
'wy',
'n_export',
'p_export',
'sed_export',
'caloric_production',
'carbon_diff_from_baseline',
'wy_diff_from_baseline',
'n_export_diff_from_baseline',
'p_export_diff_from_baseline',
'sed_export_diff_from_baseline',
'caloric_production_diff_from_baseline',
'carbon_percent_diff_from_baseline',
'wy_percent_diff_from_baseline',
'n_export_percent_diff_from_baseline',
'p_export_percent_diff_from_baseline',
'sed_export_percent_diff_from_baseline',
'caloric_production_percent_diff_from_baseline',
'carbon_diff_from_baseline',
'wy_diff_from_baseline',
'n_export_diff_from_baseline',
'p_export_diff_from_baseline',
'sed_export_diff_from_baseline',
'caloric_production_diff_from_baseline',
'carbon_percent_diff_from_bau',
'wy_percent_diff_from_bau',
'n_export_percent_diff_from_bau',
'p_export_percent_diff_from_bau',
'sed_export_percent_diff_from_bau',
'caloric_production_percent_diff_from_bau',
]]
# Calculate Sum
baseline_results = []
bau_results = []
for scenario in scenarios:
scenario_result = []
scenario_results.append(scenario_result)
scenario_result.append(scenario)
carbon_result_uri = os.path.join(runs_folder, run_name, scenario,
'carbon/tot_c_cur.tif')
carbon = nd.ArrayFrame(carbon_result_uri)
wy = nd.ArrayFrame(
os.path.join(runs_folder, run_name, scenario,
'hydropower_water_yield/output/per_pixel/wyield.tif'))
n_export = nd.ArrayFrame(
os.path.join(runs_folder, run_name, scenario, 'ndr/n_export.tif'))
p_export = nd.ArrayFrame(
os.path.join(runs_folder, run_name, scenario, 'ndr/p_export.tif'))
sed_export = nd.ArrayFrame(
os.path.join(runs_folder, run_name, scenario,
'sdr/sed_export.tif'))
# calories = nd.ArrayFrame(os.path.join(input_dir, scenario, 'caloric_production.tif' ))
calories = nd.ArrayFrame(
os.path.join(runs_folder, run_name, scenario,
'nutritional_adequacy/caloric_production.tif'))
if scenario == 'Baseline':
baseline_results.append(carbon.sum(
)) #np.sum(carbon[carbon.data!=carbon.no_data_value])
baseline_results.append(wy.sum())
baseline_results.append(n_export.sum())
baseline_results.append(p_export.sum())
baseline_results.append(sed_export.sum())
baseline_results.append(calories.sum())
# TODO Generalize BAU
elif scenario == 'Trend':
bau_results.append(carbon.sum())
bau_results.append(wy.sum())
bau_results.append(n_export.sum())
bau_results.append(p_export.sum())
bau_results.append(sed_export.sum())
bau_results.append(calories.sum())
scenario_result.append(str(float(carbon.sum())))
scenario_result.append(str(float(wy.sum())))
scenario_result.append(str(float(n_export.sum())))
scenario_result.append(str(float(p_export.sum())))
scenario_result.append(str(float(sed_export.sum())))
scenario_result.append(str(float(calories.sum())))
if scenario not in ['Baseline']:
scenario_result.append(
str(float(carbon.sum() - baseline_results[0]) * 100))
scenario_result.append(
str(float(wy.sum() - baseline_results[1]) * 100))
scenario_result.append(
str(float(n_export.sum() - baseline_results[2]) * -1.0 *
100)) # -1 means interpret as retention
scenario_result.append(
str(float(p_export.sum() - baseline_results[3]) * -1.0 * 100))
scenario_result.append(
str(
float(sed_export.sum() - baseline_results[4]) * -1.0 *
100))
scenario_result.append(
str(float(calories.sum() - baseline_results[5]) * 100))
scenario_result.append(
str(
float((carbon.sum() - baseline_results[0]) /
baseline_results[0]) * 100))
scenario_result.append(
str(
float(
(wy.sum() - baseline_results[1]) / baseline_results[1])
* 100))
scenario_result.append(
str(
float((n_export.sum() - baseline_results[2]) /
baseline_results[2]) * -1.0 * 100))
scenario_result.append(
str(
float((p_export.sum() - baseline_results[3]) /
baseline_results[3]) * -1.0 * 100))
scenario_result.append(
str(
float((sed_export.sum() - baseline_results[4]) /
baseline_results[4]) * -1.0 * 100))
scenario_result.append(
str(
float((calories.sum() - baseline_results[5]) /
baseline_results[5]) * 100))
if scenario not in ['Baseline', 'BAU', 'Trend']:
scenario_result.append(
str(float(carbon.sum() - bau_results[0]) * 100))
scenario_result.append(str(float(wy.sum() - bau_results[1]) * 100))
scenario_result.append(
str(float(n_export.sum() - bau_results[2]) * -1.0 * 100))
scenario_result.append(
str(float(p_export.sum() - bau_results[3]) * -1.0 * 100))
scenario_result.append(
str(float(sed_export.sum() - bau_results[4]) * -1.0 * 100))
scenario_result.append(
str(float(calories.sum() - bau_results[5]) * 100))
scenario_result.append(
str(
float((carbon.sum() - bau_results[0]) / bau_results[0]) *
100))
scenario_result.append(
str(float((wy.sum() - bau_results[1]) / bau_results[1]) * 100))
scenario_result.append(
str(
float((n_export.sum() - bau_results[2]) / bau_results[2]) *
-1.0 * 100))
scenario_result.append(
str(
float((p_export.sum() - bau_results[3]) / bau_results[3]) *
-1.0 * 100))
scenario_result.append(
str(
float(
(sed_export.sum() - bau_results[4]) / bau_results[4]) *
-1.0 * 100))
scenario_result.append(
str(
float((calories.sum() - bau_results[5]) / bau_results[5]) *
100))
nd.pp(scenario_results)
hazelbean.python_object_to_csv(scenario_results, os.path.join(output_uri))
]
run_name = 'r3'
scenarios = [
"Baseline",
"Trend",
"ILM",
"TAR",
]
overall_results_dir = os.path.join(runs_folder, run_name)
match_uri = os.path.join(runs_folder, run_name, 'Baseline',
'carbon/tot_c_cur.tif')
match = nd.ArrayFrame(match_uri)
#
results_csv_uri = os.path.join(runs_folder, run_name, 'calories_results.csv')
do_create_scenario_calorie_csv = 1
if do_create_scenario_calorie_csv:
create_scenario_calorie_csv(input_dir, results_csv_uri)
do_create_percent_difference_from_baseline_bar_chart = 1
if do_create_percent_difference_from_baseline_bar_chart:
difference_from_baseline_csv_uri = os.path.join(
runs_folder, run_name, 'proportion_differences_from_bau.csv')
difference_from_baseline_barchart_uri = os.path.join(
runs_folder, run_name, 'difference_from_baseline.png')
create_percent_difference_from_baseline_bar_chart(
results_csv_uri, difference_from_baseline_barchart_uri)
def write_pasture_production_to_raster(**kw):
L.info('Running write_pasture_production_to_raster')
ids = nd.ArrayFrame(kw['country_ids_raster_uri'])
ids = ids.set_data_type(7)
ids = ids.set_no_data_value(-9999.0)
ids_present = nd.get_value_count_odict_from_array(ids.data)
df = pd.read_csv(kw['pasture_csv_uri'])
rules = dict(zip(df['id'], df['Value']))
# For countries that are not in the database, we don't want to write the id value and instead want zero.
for k, v in ids_present.items():
if k not in rules:
rules[k] = 0
# Write the values in the rules (Production tons) to the locations of the countrys.
production_by_country_array = nd.reclassify_int_array_by_dict_to_floats(
ids.data.astype(np.int), rules).astype(np.float64)
production_by_country = nd.ArrayFrame(
production_by_country_array,
ids,
data_type=7,
output_uri=kw['production_by_country_uri'])
proportion_pasture = nd.ArrayFrame(kw['proportion_pasture_uri'])
global_production = np.zeros(proportion_pasture.shape)
for country_id, production_total in rules.items():
L.info(str(country_id) + ', ' + str(production_total))
if production_total > 0:
proportion_in_country = np.where(ids.data == country_id,
proportion_pasture.data, 0)
total_proportion_in_country = np.sum(proportion_in_country)
if total_proportion_in_country > 0:
L.info('production_total ' + str(production_total))
L.info('total_proportion_in_country ' +
str(total_proportion_in_country))
production_per_proportion = production_total / total_proportion_in_country
L.info('production_per_proportion ' +
str(production_per_proportion))
# production = production_per_proportion * proportion_pasture.data
production = np.where(
ids.data == country_id,
production_per_proportion * proportion_pasture.data, 0)
L.info('production ' + str(np.sum(production)))
global_production += production
L.info('global_production ' + str(np.sum(global_production)))
global_production_af = nd.ArrayFrame(
global_production,
proportion_pasture,
output_uri=kw['production_per_cell_uri'])
return kw
def calc_caloric_production_from_lulc_uri(input_lulc_uri, aoi_uri, output_uri):
# First check that the required files exist, creating them if not.
# Data from Johnson et al 2016.
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]
calories_resampled_uri = os.path.join(scenario_dir, 'calories_per_ha_2000.tif')
if not os.path.exists(calories_resampled_uri):
# Get global 5m calories map from base data
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 map to projection of lulc
calories_per_cell_projected_uri = os.path.join(baseline_dir, 'calories_per_cell_projected.tif')
output_wkt = nd.get_projection_from_uri(input_lulc_uri)
# print('projection', projection)
# output_wkt = projection.ExportToWkt()
calories_per_cell_projected = nd.reproject(calories_per_cell, calories_per_cell_projected_uri,
output_wkt=output_wkt, no_data_value=ndv)
# Clip the global data to the project aoi, but keep at 5 min for math summation reasons later
clipped_calories_per_5m_cell_uri = 'input/Baseline/clipped_calories_per_5m_cell.tif'
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)
# Load the baseline lulc for adjustment factor calculation and as a match_af
baseline_lulc_uri = os.path.join(baseline_dir, 'lulc.tif')
baseline_lulc = nd.ArrayFrame(baseline_lulc_uri)
input_lulc = nd.ArrayFrame(input_lulc_uri)
# Resample baseline lulc to the intput_lulc (a slight size change happens with the scenario generator)
baseline_resampled_lulc = baseline_lulc.resample(input_lulc, discard_at_exit=True)
# Resample calories to lulc
calories_resampled = clipped_calories_per_5m_cell.resample(input_lulc, output_uri=calories_resampled_uri, no_data_value=ndv)
calories_resampled = None
input_lulc = nd.ArrayFrame(input_lulc_uri)
ndv = input_lulc.no_data_value
calories_resampled = nd.ArrayFrame(calories_resampled_uri)
baseline_lulc_uri = os.path.join(baseline_dir, 'lulc.tif')
baseline_lulc = nd.ArrayFrame(baseline_lulc_uri)
baseline_resampled_lulc = baseline_lulc.resample(input_lulc, discard_at_exit=True)
# baseline_lulc_uri = os.path.join(baseline_dir, 'lulc.tif')
# print('baseline_lulc_uri', baseline_lulc_uri)
# baseline_lulc = nd.ArrayFrame(baseline_lulc_uri)
clipped_calories_per_5m_cell_uri = 'input/Baseline/clipped_calories_per_5m_cell.tif'
clipped_calories_per_5m_cell = nd.ArrayFrame(clipped_calories_per_5m_cell_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(baseline_resampled_lulc.data == 12, calories_resampled.data, 0)
unscaled_calories_baseline = np.where(baseline_resampled_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
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_af = nd.ArrayFrame(output_calories, input_lulc, data_type=6, no_data_value=ndv, output_uri=output_uri)
print('Sum of ' + output_calories_af.uri + ': ' + str(output_calories_af.sum()))
def create_scenario_outputs_csv(**kw):
input_dir = kw.get('input_dir')
runs_dir = kw.get('runs_dir')
run_name = kw.get('run_name')
current_run_dir = kw.get('current_run_dir')
scenario_outputs_csv_uri = kw.get('scenario_outputs_csv_uri')
scenarios = kw.get('scenario_names')
# TODO Generalize
scenario_results = [[
'',
'carbon',
'wy',
'n_export',
'p_export',
'sed_export',
'caloric_production',
'carbon_diff_from_baseline',
'wy_diff_from_baseline',
'n_export_diff_from_baseline',
'p_export_diff_from_baseline',
'sed_export_diff_from_baseline',
'caloric_production_diff_from_baseline',
'carbon_percent_diff_from_baseline',
'wy_percent_diff_from_baseline',
'n_export_percent_diff_from_baseline',
'p_export_percent_diff_from_baseline',
'sed_export_percent_diff_from_baseline',
'caloric_production_percent_diff_from_baseline',
'carbon_diff_from_baseline',
'wy_diff_from_baseline',
'n_export_diff_from_baseline',
'p_export_diff_from_baseline',
'sed_export_diff_from_baseline',
'caloric_production_diff_from_baseline',
'carbon_percent_diff_from_bau',
'wy_percent_diff_from_bau',
'n_export_percent_diff_from_bau',
'p_export_percent_diff_from_bau',
'sed_export_percent_diff_from_bau',
'caloric_production_percent_diff_from_bau',
]]
# Calculate Sum
baseline_results = []
bau_results = []
for scenario in scenarios:
print('Adding ', scenario, ' to output csv.')
scenario_result = []
scenario_results.append(scenario_result)
scenario_result.append(scenario)
carbon_result_uri = os.path.join(runs_dir, run_name, scenario,
'carbon/tot_c_cur.tif')
carbon = nd.ArrayFrame(carbon_result_uri)
wy = nd.ArrayFrame(
os.path.join(runs_dir, run_name, scenario,
'hydropower_water_yield/output/per_pixel/wyield.tif'))
n_export = nd.ArrayFrame(
os.path.join(runs_dir, run_name, scenario, 'ndr/n_export.tif'))
p_export = nd.ArrayFrame(
os.path.join(runs_dir, run_name, scenario, 'ndr/p_export.tif'))
sed_export = nd.ArrayFrame(
os.path.join(runs_dir, run_name, scenario, 'sdr/sed_export.tif'))
calories = nd.ArrayFrame(
os.path.join(input_dir, scenario, 'caloric_production.tif'))
if scenario == kw['baseline_scenario_name']:
baseline_results.append(carbon.sum(
)) # np.sum(carbon[carbon.data!=carbon.no_data_value])
baseline_results.append(wy.sum())
baseline_results.append(n_export.sum())
baseline_results.append(p_export.sum())
baseline_results.append(sed_export.sum())
baseline_results.append(calories.sum())
elif scenario == kw['bau_scenario_name']:
bau_results.append(carbon.sum())
bau_results.append(wy.sum())
bau_results.append(n_export.sum())
bau_results.append(p_export.sum())
bau_results.append(sed_export.sum())
bau_results.append(calories.sum())
scenario_result.append(str(float(carbon.sum())))
scenario_result.append(str(float(wy.sum())))
scenario_result.append(str(float(n_export.sum())))
scenario_result.append(str(float(p_export.sum())))
scenario_result.append(str(float(sed_export.sum())))
scenario_result.append(str(float(calories.sum())))
if scenario not in [kw['baseline_scenario_name']]:
scenario_result.append(
str(float(carbon.sum() - baseline_results[0]) * 100))
scenario_result.append(
str(float(wy.sum() - baseline_results[1]) * 100))
scenario_result.append(
str(float(n_export.sum() - baseline_results[2]) * -1.0 *
100)) # -1 means interpret as retention
scenario_result.append(
str(float(p_export.sum() - baseline_results[3]) * -1.0 * 100))
scenario_result.append(
str(
float(sed_export.sum() - baseline_results[4]) * -1.0 *
100))
scenario_result.append(
str(float(calories.sum() - baseline_results[5]) * 100))
scenario_result.append(
str(
float((carbon.sum() - baseline_results[0]) /
baseline_results[0]) * 100))
scenario_result.append(
str(
float(
(wy.sum() - baseline_results[1]) / baseline_results[1])
* 100))
scenario_result.append(
str(
float((n_export.sum() - baseline_results[2]) /
baseline_results[2]) * -1.0 * 100))
scenario_result.append(
str(
float((p_export.sum() - baseline_results[3]) /
baseline_results[3]) * -1.0 * 100))
scenario_result.append(
str(
float((sed_export.sum() - baseline_results[4]) /
baseline_results[4]) * -1.0 * 100))
scenario_result.append(
str(
float((calories.sum() - baseline_results[5]) /
baseline_results[5]) * 100))
if scenario not in [
kw['baseline_scenario_name'], kw['bau_scenario_name']
]:
scenario_result.append(
str(float(carbon.sum() - bau_results[0]) * 100))
scenario_result.append(str(float(wy.sum() - bau_results[1]) * 100))
scenario_result.append(
str(float(n_export.sum() - bau_results[2]) * -1.0 * 100))
scenario_result.append(
str(float(p_export.sum() - bau_results[3]) * -1.0 * 100))
scenario_result.append(
str(float(sed_export.sum() - bau_results[4]) * -1.0 * 100))
scenario_result.append(
str(float(calories.sum() - bau_results[5]) * 100))
scenario_result.append(
str(
float((carbon.sum() - bau_results[0]) / bau_results[0]) *
100))
scenario_result.append(
str(float((wy.sum() - bau_results[1]) / bau_results[1]) * 100))
scenario_result.append(
str(
float((n_export.sum() - bau_results[2]) / bau_results[2]) *
-1.0 * 100))
scenario_result.append(
str(
float((p_export.sum() - bau_results[3]) / bau_results[3]) *
-1.0 * 100))
scenario_result.append(
str(
float(
(sed_export.sum() - bau_results[4]) / bau_results[4]) *
-1.0 * 100))
scenario_result.append(
str(
float((calories.sum() - bau_results[5]) / bau_results[5]) *
100))
scenario_result.to_file
return kw
runs_folder = os.path.join(output_folder, 'runs')
results_names = ['carbon', 'wy', 'n_export', 'p_export', 'sed_export']
run_name = 'full2'
scenarios = ['Baseline', 'Trend2030', 'ILM2030']
overall_results_dir = os.path.join(runs_folder, run_name)
do_marginal_ranking = False
if do_marginal_ranking:
carbon_result_uri = os.path.join(runs_folder, run_name, 'Baseline',
'carbon/tot_c_cur.tif')
carbon = nd.ArrayFrame(carbon_result_uri)
# carbon.show(output_uri=os.path.join(runs_folder, run_name, 'Baseline', 'tot_c_cur.png'))
wy = nd.ArrayFrame(
os.path.join(runs_folder, run_name, 'Baseline',
'hydropower_water_yield/output/per_pixel/wyield.tif'))
# wy.show(output_uri=os.path.join(runs_folder, run_name, 'Baseline', 'wyield.png'))
n_export = nd.ArrayFrame(
os.path.join(runs_folder, run_name, 'Baseline', 'ndr/n_export.tif'))
# n_export.show(output_uri=os.path.join(runs_folder, run_name, 'Baseline', 'n_export.png'))
p_export = nd.ArrayFrame(
os.path.join(runs_folder, run_name, 'Baseline', 'ndr/p_export.tif'))
# p_export.show(output_uri=os.path.join(runs_folder, run_name, 'Baseline', 'p_export.png'))
