Renaming urban_nature_supply --> urban_nature_supply_percapita.
RE:#1344
This commit is contained in:
James Douglass
parent
e3ac8611b6
commit
d2569890b5
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@ -302,8 +302,9 @@ MODEL_SPEC = {
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'output': {
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"type": "directory",
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"contents": {
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"urban_nature_supply.tif": {
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"about": "The calculated supply of urban nature.",
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"urban_nature_supply_percapita.tif": {
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"about": (
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"The calculated supply per capita of urban nature."),
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"bands": {1: {
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"type": "number",
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"units": u.m**2,
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@ -484,7 +485,7 @@ MODEL_SPEC = {
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"created_if":
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f"search_radius_mode == '{RADIUS_OPT_URBAN_NATURE}'",
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},
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"urban_nature_supply_lucode_[LUCODE].tif": {
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"urban_nature_supply_percapita_lucode_[LUCODE].tif": {
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"about": gettext(
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"The urban nature supplied to populations due to the "
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"land use land cover code LUCODE"),
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@ -501,7 +502,7 @@ MODEL_SPEC = {
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"created_if":
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f"search_radius_mode == '{RADIUS_OPT_URBAN_NATURE}'",
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},
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"urban_nature_supply_lucode_[LUCODE].tif": {
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"urban_nature_supply_percapita_lucode_[LUCODE].tif": {
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"about": gettext(
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"The urban nature supplied to populations due to "
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"the land use land cover class LUCODE."),
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@ -547,10 +548,10 @@ MODEL_SPEC = {
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"created_if":
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f"search_radius_mode == '{RADIUS_OPT_POP_GROUP}'",
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},
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"urban_nature_supply_to_[POP_GROUP].tif": {
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"urban_nature_supply_percapita_to_[POP_GROUP].tif": {
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"about": gettext(
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"The urban nature supply to population group "
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"POP_GROUP."),
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"The urban nature supply per capita to population "
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"group POP_GROUP."),
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"bands": {1: {"type": "number", "units": u.m**2/u.person}},
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"created_if":
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f"search_radius_mode == '{RADIUS_OPT_POP_GROUP}'",
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@ -579,7 +580,7 @@ MODEL_SPEC = {
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_OUTPUT_BASE_FILES = {
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'urban_nature_supply': 'urban_nature_supply.tif',
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'urban_nature_supply_percapita': 'urban_nature_supply_percapita.tif',
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'admin_boundaries': 'admin_boundaries.gpkg',
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'urban_nature_balance_percapita': 'urban_nature_balance_percapita.tif',
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'urban_nature_balance_totalpop': 'urban_nature_balance_totalpop.tif',
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@ -1005,17 +1006,17 @@ def execute(args):
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urban_nature_reclassification_task, decayed_population_task,
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])
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urban_nature_supply_task = graph.add_task(
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urban_nature_supply_percapita_task = graph.add_task(
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_convolve_and_set_lower_bound,
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kwargs={
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'signal_path_band': (
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urban_nature_population_ratio_path, 1),
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'kernel_path_band': (kernel_path, 1),
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'target_path': file_registry['urban_nature_supply'],
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'target_path': file_registry['urban_nature_supply_percapita'],
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'working_dir': intermediate_dir,
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},
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task_name='2SFCA - urban nature supply',
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target_path_list=[file_registry['urban_nature_supply']],
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target_path_list=[file_registry['urban_nature_supply_percapita']],
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dependent_task_list=[
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kernel_tasks[search_radius_m],
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urban_nature_population_ratio_task])
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@ -1044,8 +1045,8 @@ def execute(args):
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dependent_task_list=[
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kernel_tasks[search_radius_m], population_mask_task])
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partial_urban_nature_supply_paths = []
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partial_urban_nature_supply_tasks = []
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partial_urban_nature_supply_percapita_paths = []
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partial_urban_nature_supply_percapita_tasks = []
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for lucode, search_radius_m in lucode_to_search_radii:
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urban_nature_pixels_path = os.path.join(
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intermediate_dir,
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@ -1080,35 +1081,37 @@ def execute(args):
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decayed_population_tasks[search_radius_m],
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])
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urban_nature_supply_path = os.path.join(
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urban_nature_supply_percapita_path = os.path.join(
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intermediate_dir,
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f'urban_nature_supply_lucode_{lucode}{suffix}.tif')
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partial_urban_nature_supply_paths.append(urban_nature_supply_path)
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partial_urban_nature_supply_tasks.append(graph.add_task(
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f'urban_nature_supply_percapita_lucode_{lucode}{suffix}.tif')
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partial_urban_nature_supply_percapita_paths.append(
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urban_nature_supply_percapita_path)
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partial_urban_nature_supply_percapita_tasks.append(graph.add_task(
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pygeoprocessing.convolve_2d,
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kwargs={
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'signal_path_band': (
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urban_nature_population_ratio_path, 1),
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'kernel_path_band': (kernel_paths[search_radius_m], 1),
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'target_path': urban_nature_supply_path,
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'target_path': urban_nature_supply_percapita_path,
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'working_dir': intermediate_dir,
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},
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task_name=f'2SFCA - urban_nature supply for lucode {lucode}',
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target_path_list=[urban_nature_supply_path],
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target_path_list=[urban_nature_supply_percapita_path],
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dependent_task_list=[
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kernel_tasks[search_radius_m],
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urban_nature_population_ratio_task]))
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urban_nature_supply_task = graph.add_task(
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urban_nature_supply_percapita_task = graph.add_task(
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ndr._sum_rasters,
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kwargs={
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'raster_path_list': partial_urban_nature_supply_paths,
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'raster_path_list': partial_urban_nature_supply_percapita_paths,
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'target_nodata': FLOAT32_NODATA,
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'target_result_path': file_registry['urban_nature_supply'],
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'target_result_path':
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file_registry['urban_nature_supply_percapita'],
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},
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task_name='2SFCA - urban nature supply total',
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target_path_list=[file_registry['urban_nature_supply']],
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dependent_task_list=partial_urban_nature_supply_tasks
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target_path_list=[file_registry['urban_nature_supply_percapita']],
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dependent_task_list=partial_urban_nature_supply_percapita_tasks
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)
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# Search radius mode 3: search radii are defined per population group.
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@ -1191,8 +1194,8 @@ def execute(args):
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search_radii = dict(
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group_radii_table[['pop_group', 'search_radius_m']].itertuples(
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index=False, name=None))
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urban_nature_supply_by_group_paths = {}
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urban_nature_supply_by_group_tasks = []
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urban_nature_supply_percapita_by_group_paths = {}
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urban_nature_supply_percapita_by_group_tasks = []
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urban_nature_balance_totalpop_by_group_paths = {}
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urban_nature_balance_totalpop_by_group_tasks = []
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supply_population_paths = {'over': {}, 'under': {}}
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@ -1200,27 +1203,27 @@ def execute(args):
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for pop_group, proportional_pop_path in (
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proportional_population_paths.items()):
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search_radius_m = search_radii[pop_group]
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urban_nature_supply_to_group_path = os.path.join(
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urban_nature_supply_percapita_to_group_path = os.path.join(
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intermediate_dir,
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f'urban_nature_supply_to_{pop_group}{suffix}.tif')
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urban_nature_supply_by_group_paths[
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pop_group] = urban_nature_supply_to_group_path
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urban_nature_supply_by_group_task = graph.add_task(
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f'urban_nature_supply_percapita_to_{pop_group}{suffix}.tif')
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urban_nature_supply_percapita_by_group_paths[
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pop_group] = urban_nature_supply_percapita_to_group_path
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urban_nature_supply_percapita_by_group_task = graph.add_task(
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_convolve_and_set_lower_bound,
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kwargs={
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'signal_path_band': (
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file_registry['urban_nature_population_ratio'], 1),
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'kernel_path_band': (kernel_paths[search_radius_m], 1),
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'target_path': urban_nature_supply_to_group_path,
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'target_path': urban_nature_supply_percapita_to_group_path,
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'working_dir': intermediate_dir,
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},
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task_name=f'2SFCA - urban nature supply for {pop_group}',
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target_path_list=[urban_nature_supply_to_group_path],
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target_path_list=[urban_nature_supply_percapita_to_group_path],
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dependent_task_list=[
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kernel_tasks[search_radius_m],
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urban_nature_population_ratio_task])
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urban_nature_supply_by_group_tasks.append(
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urban_nature_supply_by_group_task)
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urban_nature_supply_percapita_by_group_tasks.append(
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urban_nature_supply_percapita_by_group_task)
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# Calculate SUP_DEMi_cap for each population group.
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per_cap_urban_nature_balance_pop_group_path = os.path.join(
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@ -1230,7 +1233,7 @@ def execute(args):
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pygeoprocessing.raster_calculator,
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kwargs={
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'base_raster_path_band_const_list': [
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(urban_nature_supply_to_group_path, 1),
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(urban_nature_supply_percapita_to_group_path, 1),
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(float(args['urban_nature_demand']), 'raw')
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],
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'local_op': _urban_nature_balance_percapita_op,
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@ -1244,7 +1247,7 @@ def execute(args):
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target_path_list=[
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per_cap_urban_nature_balance_pop_group_path],
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dependent_task_list=[
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urban_nature_supply_by_group_task,
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urban_nature_supply_percapita_by_group_task,
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])
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urban_nature_balance_totalpop_by_group_path = os.path.join(
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@ -1303,21 +1306,21 @@ def execute(args):
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proportional_population_tasks[pop_group],
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])
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urban_nature_supply_task = graph.add_task(
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urban_nature_supply_percapita_task = graph.add_task(
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_weighted_sum,
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kwargs={
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'raster_path_list':
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[urban_nature_supply_by_group_paths[group] for group in
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[urban_nature_supply_percapita_by_group_paths[group] for group in
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sorted(split_population_fields)],
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'weight_raster_list':
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[pop_group_proportion_paths[group] for group in
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sorted(split_population_fields)],
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'target_path': file_registry['urban_nature_supply'],
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'target_path': file_registry['urban_nature_supply_percapita'],
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},
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task_name='2SFCA - urban nature supply total',
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target_path_list=[file_registry['urban_nature_supply']],
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target_path_list=[file_registry['urban_nature_supply_percapita']],
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dependent_task_list=[
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*urban_nature_supply_by_group_tasks,
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*urban_nature_supply_percapita_by_group_tasks,
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*pop_group_proportion_tasks.values(),
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])
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@ -1325,7 +1328,7 @@ def execute(args):
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pygeoprocessing.raster_calculator,
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kwargs={
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'base_raster_path_band_const_list': [
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(file_registry['urban_nature_supply'], 1),
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(file_registry['urban_nature_supply_percapita'], 1),
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(float(args['urban_nature_demand']), 'raw')
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],
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'local_op': _urban_nature_balance_percapita_op,
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@ -1337,7 +1340,7 @@ def execute(args):
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task_name='Calculate per-capita urban nature balance',
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target_path_list=[file_registry['urban_nature_balance_percapita']],
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dependent_task_list=[
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urban_nature_supply_task,
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urban_nature_supply_percapita_task,
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])
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urban_nature_balance_totalpop_task = graph.add_task(
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@ -1388,7 +1391,7 @@ def execute(args):
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pygeoprocessing.raster_calculator,
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kwargs={
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'base_raster_path_band_const_list': [
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(file_registry['urban_nature_supply'], 1),
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(file_registry['urban_nature_supply_percapita'], 1),
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(float(args['urban_nature_demand']), 'raw')
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],
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'local_op': _urban_nature_balance_percapita_op,
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@ -1400,7 +1403,7 @@ def execute(args):
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task_name='Calculate per-capita urban nature balance',
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target_path_list=[file_registry['urban_nature_balance_percapita']],
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dependent_task_list=[
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urban_nature_supply_task,
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urban_nature_supply_percapita_task,
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])
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# This is "SUP_DEMi" from the user's guide
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@ -342,7 +342,7 @@ class UNATests(unittest.TestCase):
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from natcap.invest import urban_nature_access
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nodata = urban_nature_access.FLOAT32_NODATA
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urban_nature_supply = numpy.array([
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urban_nature_supply_percapita = numpy.array([
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[nodata, 100.5],
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[75, 100]], dtype=numpy.float32)
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urban_nature_demand = 50
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@ -353,7 +353,7 @@ class UNATests(unittest.TestCase):
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urban_nature_budget = (
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urban_nature_access._urban_nature_balance_percapita_op(
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urban_nature_supply, urban_nature_demand))
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urban_nature_supply_percapita, urban_nature_demand))
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expected_urban_nature_budget = numpy.array([
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[nodata, 50.5],
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[25, 50]], dtype=numpy.float32)
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@ -670,7 +670,7 @@ class UNATests(unittest.TestCase):
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"""UNA: all modes have same results when consistent radii.
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Although the different modes have different ways of defining their
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search radii, the urban_nature_supply raster should be numerically
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search radii, the urban_nature_supply_percapita raster should be numerically
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equivalent if they all use the same search radii.
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This is a good gut-check of basic model behavior across modes.
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@ -772,16 +772,19 @@ class UNATests(unittest.TestCase):
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uniform_radius_supply = pygeoprocessing.raster_to_numpy_array(
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os.path.join(uniform_args['workspace_dir'], 'output',
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'urban_nature_supply_uniform.tif'))
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split_urban_nature_supply = pygeoprocessing.raster_to_numpy_array(
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os.path.join(split_urban_nature_args['workspace_dir'], 'output',
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'urban_nature_supply_urban_nature.tif'))
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'urban_nature_supply_percapita_uniform.tif'))
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split_urban_nature_supply_percapita = (
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pygeoprocessing.raster_to_numpy_array(
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os.path.join(
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split_urban_nature_args['workspace_dir'], 'output',
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'urban_nature_supply_percapita_urban_nature.tif')))
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split_pop_groups_supply = pygeoprocessing.raster_to_numpy_array(
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os.path.join(pop_group_args['workspace_dir'], 'output',
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'urban_nature_supply_popgroup.tif'))
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'urban_nature_supply_percapita_popgroup.tif'))
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numpy.testing.assert_allclose(
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uniform_radius_supply, split_urban_nature_supply, rtol=1e-6)
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uniform_radius_supply, split_urban_nature_supply_percapita,
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rtol=1e-6)
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numpy.testing.assert_allclose(
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uniform_radius_supply, split_pop_groups_supply, rtol=1e-6)
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