def __init__(self):
model.InVESTModel.__init__(
self,
label=u'Crop Production Regression Model',
target=natcap.invest.crop_production_regression.execute,
validator=natcap.invest.crop_production_regression.validate,
localdoc=u'crop_production.html')
self.model_data_path = inputs.Folder(
args_key=u'model_data_path',
helptext=(u"A path to the InVEST Crop Production Data directory. "
u"These data would have been included with the InVEST "
u"installer if selected, or can be manually downloaded "
u"from http://data.naturalcapitalproject.org/invest- "
u"data/. If downloaded with InVEST, the default value "
u"should be used.</b>"),
label=u'Directory to model data',
validator=self.validator)
self.add_input(self.model_data_path)
self.landcover_raster_path = inputs.File(
args_key=u'landcover_raster_path',
helptext=(u"A raster file, representing integer land use/land "
u"code covers for each cell. This raster should have"
u"a projected coordinate system with units of meters "
u"(e.g. UTM) because pixel areas are divided by 10000"
u"in order to report some results in hectares."),
label=u'Land-Use/Land-Cover Map (raster)',
validator=self.validator)
self.add_input(self.landcover_raster_path)
self.landcover_to_crop_table_path = inputs.File(
args_key=u'landcover_to_crop_table_path',
helptext=(u"A CSV table mapping canonical crop names to land use "
u"codes contained in the landcover/use raster. The "
u"allowed crop names are barley, maize, oilpalm, "
u"potato, rice, soybean, sugarbeet, sugarcane, "
u"sunflower, and wheat."),
label=u'Landcover to Crop Table (csv)',
validator=self.validator)
self.add_input(self.landcover_to_crop_table_path)
self.fertilization_rate_table_path = inputs.File(
args_key=u'fertilization_rate_table_path',
helptext=(u"A table that maps fertilization rates to crops in "
u"the simulation. Must include the headers "
u"'crop_name', 'nitrogen_rate', 'phosphorous_rate', "
u"and 'potassium_rate'."),
label=u'Fertilization Rate Table Path (csv)',
validator=self.validator)
self.add_input(self.fertilization_rate_table_path)
self.aggregate_polygon_path = inputs.File(
args_key=u'aggregate_polygon_path',
helptext=(u"A polygon shapefile containing features with"
u"which to aggregate/summarize final results."
u"It is fine to have overlapping polygons."),
label=u'Aggregate results polygon (vector) (optional)',
validator=self.validator)
self.add_input(self.aggregate_polygon_path)
def __init__(self):
model.InVESTModel.__init__(
self,
label=MODEL_METADATA['coastal_blue_carbon'].model_title,
target=coastal_blue_carbon.execute,
validator=coastal_blue_carbon.validate,
localdoc=MODEL_METADATA['coastal_blue_carbon'].userguide)
_ui_keys = functools.partial(_create_input_kwargs_from_args_spec,
args_spec=coastal_blue_carbon.ARGS_SPEC,
validator=self.validator)
self.snapshots_table = inputs.File(
**_ui_keys('landcover_snapshot_csv'))
self.add_input(self.snapshots_table)
self.biophysical_table_path = inputs.File(
**_ui_keys('biophysical_table_path'))
self.add_input(self.biophysical_table_path)
self.landcover_transitions_table = inputs.File(
**_ui_keys('landcover_transitions_table'))
self.add_input(self.landcover_transitions_table)
self.analysis_year = inputs.Text(**_ui_keys('analysis_year'))
self.add_input(self.analysis_year)
self.do_economic_analysis = inputs.Container(
args_key='do_economic_analysis',
expandable=True,
expanded=True,
label='Calculate Net Present Value of Sequestered Carbon')
self.add_input(self.do_economic_analysis)
self.use_price_table = inputs.Checkbox(args_key='use_price_table',
helptext='',
label='Use Price Table')
self.do_economic_analysis.add_input(self.use_price_table)
self.price = inputs.Text(**_ui_keys('price'))
self.do_economic_analysis.add_input(self.price)
self.inflation_rate = inputs.Text(**_ui_keys('inflation_rate'))
self.do_economic_analysis.add_input(self.inflation_rate)
self.price_table_path = inputs.File(**_ui_keys('price_table_path'))
self.do_economic_analysis.add_input(self.price_table_path)
self.discount_rate = inputs.Text(**_ui_keys('discount_rate'))
self.do_economic_analysis.add_input(self.discount_rate)
# Set interactivity, requirement as input sufficiency changes
self.use_price_table.sufficiency_changed.connect(
self._price_table_sufficiency_changed)
def __init__(self):
model.InVESTModel.__init__(
self,
label=('Overlap Analysis Management Zone Model: Fisheries and '
'Recreation'),
target=overlap_analysis_mz.execute,
validator=overlap_analysis_mz.validate,
localdoc='../documentation/overlap_analysis.html')
self.aoi = inputs.File(
args_key='zone_layer_loc',
helptext=(
"An OGR-supported vector file. This should be a "
"vector file containing multiple polygons since the "
"Management Zones tool is used to analyze overlap "
"data."),
label='Analysis Zones Layer (Vector)',
validator=self.validator)
self.add_input(self.aoi)
self.data_dir = inputs.Folder(
args_key='overlap_data_dir_loc',
helptext=(
"The path to a folder containing ONLY the input data "
"for the Overlap Analysis model. Input data can be "
"point, line or polygon data layers indicating where "
"in the coastal and marine environment the human use "
"activity takes place."),
label='Overlap Analysis Data Directory',
validator=self.validator)
self.add_input(self.data_dir)
def __init__(self):
model.InVESTModel.__init__(
self,
label=u'RouteDEM',
target=routedem.execute,
validator=routedem.validate,
localdoc=u'../documentation/routedem.html')
self.dem_path = inputs.File(
args_key=u'dem_path',
helptext=(
u"A GDAL-supported raster file containing a base "
u"Digital Elevation Model to execute the routing "
u"functionality across."),
label=u'Digital Elevation Model (Raster)',
validator=self.validator)
self.add_input(self.dem_path)
self.calculate_slope = inputs.Checkbox(
args_key=u'calculate_slope',
helptext=u'If selected, calculates slope raster.',
label=u'Calculate Slope')
self.add_input(self.calculate_slope)
self.calculate_flow_accumulation = inputs.Checkbox(
args_key=u'calculate_flow_accumulation',
helptext=u'Select to calculate flow accumulation.',
label=u'Calculate Flow Accumulation')
self.add_input(self.calculate_flow_accumulation)
self.calculate_stream_threshold = inputs.Checkbox(
args_key=u'calculate_stream_threshold',
helptext=u'Select to calculate a stream threshold to flow accumulation.',
interactive=False,
label=u'Calculate Stream Thresholds')
self.add_input(self.calculate_stream_threshold)
self.threshold_flow_accumulation = inputs.Text(
args_key=u'threshold_flow_accumulation',
helptext=(
u"The number of upstream cells that must flow into a "
u"cell before it's classified as a stream."),
interactive=False,
label=u'Threshold Flow Accumulation Limit',
validator=self.validator)
self.add_input(self.threshold_flow_accumulation)
self.calculate_downstream_distance = inputs.Checkbox(
args_key=u'calculate_downstream_distance',
helptext=(
u"If selected, creates a downstream distance raster "
u"based on the thresholded flow accumulation stream "
u"classification."),
interactive=False,
label=u'Calculate Distance to stream')
self.add_input(self.calculate_downstream_distance)
# Set interactivity, requirement as input sufficiency changes
self.calculate_flow_accumulation.sufficiency_changed.connect(
self.calculate_stream_threshold.set_interactive)
self.calculate_stream_threshold.sufficiency_changed.connect(
self.threshold_flow_accumulation.set_interactive)
self.calculate_stream_threshold.sufficiency_changed.connect(
self.calculate_downstream_distance.set_interactive)
def __init__(self):
model.InVESTModel.__init__(
self,
label=u'DelineateIT: Watershed Delineation',
target=delineateit.execute,
validator=delineateit.validate,
localdoc=u'../documentation/delineateit.html')
self.dem_uri = inputs.File(
args_key=u'dem_uri',
helptext=(
u"A GDAL-supported raster file with an elevation value "
u"for each cell. Make sure the DEM is corrected by "
u"filling in sinks, and if necessary burning "
u"hydrographic features into the elevation model "
u"(recommended when unusual streams are observed.) See "
u"the 'Working with the DEM' section of the InVEST "
u"User's Guide for more information."),
label=u'Digital Elevation Model (Raster)',
validator=self.validator)
self.add_input(self.dem_uri)
self.outlet_shapefile_uri = inputs.File(
args_key=u'outlet_shapefile_uri',
helptext=(
u"This is a layer of points representing outlet points "
u"that the watersheds should be built around."),
label=u'Outlet Points (Vector)',
validator=self.validator)
self.add_input(self.outlet_shapefile_uri)
self.flow_threshold = inputs.Text(
args_key=u'flow_threshold',
helptext=(
u"The number of upstream cells that must flow into a "
u"cell before it's considered part of a stream such "
u"that retention stops and the remaining export is "
u"exported to the stream. Used to define streams from "
u"the DEM."),
label=u'Threshold Flow Accumulation',
validator=self.validator)
self.add_input(self.flow_threshold)
self.snap_distance = inputs.Text(
args_key=u'snap_distance',
label=u'Pixel Distance to Snap Outlet Points',
validator=self.validator)
self.add_input(self.snap_distance)
def __init__(self):
model.InVESTModel.__init__(self,
label='Coastal Blue Carbon Preprocessor',
target=preprocessor.execute,
validator=preprocessor.validate,
localdoc='coastal_blue_carbon.html')
_ui_keys = functools.partial(_create_input_kwargs_from_args_spec,
args_spec=preprocessor.ARGS_SPEC,
validator=self.validator)
self.lulc_snapshot_csv = inputs.File(
**_ui_keys('landcover_snapshot_csv'))
self.add_input(self.lulc_snapshot_csv)
self.lulc_lookup_table_path = inputs.File(
**_ui_keys('lulc_lookup_table_path'))
self.add_input(self.lulc_lookup_table_path)
def __init__(self):
model.InVESTModel.__init__(
self,
label=MODEL_METADATA['coastal_blue_carbon_preprocessor'].
model_title,
target=preprocessor.execute,
validator=preprocessor.validate,
localdoc=MODEL_METADATA['coastal_blue_carbon_preprocessor'].
userguide)
_ui_keys = functools.partial(_create_input_kwargs_from_args_spec,
args_spec=preprocessor.ARGS_SPEC,
validator=self.validator)
self.lulc_snapshot_csv = inputs.File(
**_ui_keys('landcover_snapshot_csv'))
self.add_input(self.lulc_snapshot_csv)
self.lulc_lookup_table_path = inputs.File(
**_ui_keys('lulc_lookup_table_path'))
self.add_input(self.lulc_lookup_table_path)
def __init__(self):
model.InVESTModel.__init__(self,
label=u'Coastal Blue Carbon Preprocessor',
target=preprocessor.execute,
validator=preprocessor.validate,
localdoc=u'coastal_blue_carbon.html')
self.lulc_lookup_uri = inputs.File(
args_key=u'lulc_lookup_uri',
helptext=(u"A CSV table used to map lulc classes to their values "
u"in a raster, as well as to indicate whether or not "
u"the lulc class is a coastal blue carbon habitat."),
label=u'LULC Lookup Table (CSV)',
validator=self.validator)
self.add_input(self.lulc_lookup_uri)
self.lulc_snapshot_list = inputs.Multi(
args_key=u'lulc_snapshot_list',
callable_=functools.partial(inputs.File, label="Input"),
label=u'Land Use/Land Cover Rasters (GDAL-supported)',
link_text=u'Add Another')
self.add_input(self.lulc_snapshot_list)
def __init__(self):
model.InVESTModel.__init__(
self,
label='Seasonal Water Yield',
target=seasonal_water_yield.execute,
validator=seasonal_water_yield.validate,
localdoc='../documentation/seasonalwateryield.html')
self.threshold_flow_accumulation = inputs.Text(
args_key='threshold_flow_accumulation',
helptext=("The number of upstream cells that must flow into a "
"cell before it's considered part of a stream such "
"that retention stops and the remaining export is "
"exported to the stream. Used to define streams from "
"the DEM."),
label='Threshold Flow Accumulation',
validator=self.validator)
self.add_input(self.threshold_flow_accumulation)
self.et0_dir = inputs.Folder(
args_key='et0_dir',
helptext=("The selected folder has a list of ET0 files with a "
"specified format."),
label='ET0 Directory',
validator=self.validator)
self.add_input(self.et0_dir)
self.precip_dir = inputs.Folder(
args_key='precip_dir',
helptext=("The selected folder has a list of monthly "
"precipitation files with a specified format."),
label='Precipitation Directory',
validator=self.validator)
self.add_input(self.precip_dir)
self.dem_raster_path = inputs.File(
args_key='dem_raster_path',
helptext=("A GDAL-supported raster file with an elevation value "
"for each cell. Make sure the DEM is corrected by "
"filling in sinks, and if necessary burning "
"hydrographic features into the elevation model "
"(recommended when unusual streams are observed.) See "
"the 'Working with the DEM' section of the InVEST "
"User's Guide for more information."),
label='Digital Elevation Model (Raster)',
validator=self.validator)
self.add_input(self.dem_raster_path)
self.lulc_raster_path = inputs.File(
args_key='lulc_raster_path',
helptext=("A GDAL-supported raster file, with an integer LULC "
"code for each cell."),
label='Land-Use/Land-Cover (Raster)',
validator=self.validator)
self.add_input(self.lulc_raster_path)
self.soil_group_path = inputs.File(
args_key='soil_group_path',
helptext=("Map of SCS soil groups (A, B, C, or D) mapped to "
"integer values (1, 2, 3, or 4) used in combination of "
"the LULC map to compute the CN map."),
label='Soil Group (Raster)',
validator=self.validator)
self.add_input(self.soil_group_path)
self.aoi_path = inputs.File(args_key='aoi_path',
label='AOI/Watershed (Vector)',
validator=self.validator)
self.add_input(self.aoi_path)
self.biophysical_table_path = inputs.File(
args_key='biophysical_table_path',
helptext=("A CSV table containing model information "
"corresponding to each of the land use classes in the "
"LULC raster input. It must contain the fields "
"'lucode', and 'Kc'."),
label='Biophysical Table (CSV)',
validator=self.validator)
self.add_input(self.biophysical_table_path)
self.rain_events_table_path = inputs.File(
args_key='rain_events_table_path',
label='Rain Events Table (CSV)',
validator=self.validator)
self.add_input(self.rain_events_table_path)
self.alpha_m = inputs.Text(args_key='alpha_m',
label='alpha_m Parameter',
validator=self.validator)
self.add_input(self.alpha_m)
self.beta_i = inputs.Text(args_key='beta_i',
label='beta_i Parameter',
validator=self.validator)
self.add_input(self.beta_i)
self.gamma = inputs.Text(args_key='gamma',
label='gamma Parameter',
validator=self.validator)
self.add_input(self.gamma)
self.climate_zone_container = inputs.Container(
args_key='user_defined_climate_zones',
expandable=True,
label='Climate Zones (Advanced)')
self.add_input(self.climate_zone_container)
self.climate_zone_table_path = inputs.File(
args_key='climate_zone_table_path',
label='Climate Zone Table (CSV)',
validator=self.validator)
self.climate_zone_container.add_input(self.climate_zone_table_path)
self.climate_zone_raster_path = inputs.File(
args_key='climate_zone_raster_path',
helptext=("Map of climate zones that are found in the Climate "
"Zone Table input. Pixel values correspond to cz_id."),
label='Climate Zone (Raster)',
validator=self.validator)
self.climate_zone_container.add_input(self.climate_zone_raster_path)
self.user_defined_local_recharge_container = inputs.Container(
args_key='user_defined_local_recharge',
expandable=True,
label='User Defined Recharge Layer (Advanced)')
self.add_input(self.user_defined_local_recharge_container)
self.l_path = inputs.File(args_key='l_path',
label='Local Recharge (Raster)',
validator=self.validator)
self.user_defined_local_recharge_container.add_input(self.l_path)
self.monthly_alpha_container = inputs.Container(
args_key='monthly_alpha',
expandable=True,
label='Monthly Alpha Table (Advanced)')
self.add_input(self.monthly_alpha_container)
self.monthly_alpha_path = inputs.File(
args_key='monthly_alpha_path',
label='Monthly Alpha Table (csv)',
validator=self.validator)
self.monthly_alpha_container.add_input(self.monthly_alpha_path)
# Set interactivity, requirement as input sufficiency changes
self.user_defined_local_recharge_container.sufficiency_changed.connect(
self._toggle_user_defined_local_recharge)
self.monthly_alpha_container.sufficiency_changed.connect(
self._toggle_monthly_alpha)
def __init__(self):
model.InVESTModel.__init__(
self,
label=MODEL_METADATA['fisheries'].model_title,
target=fisheries.execute,
validator=fisheries.validate,
localdoc=MODEL_METADATA['fisheries'].userguide)
self.alpha_only = inputs.Label(
text=("This tool is in an ALPHA testing stage and should "
"not be used for decision making."))
self.aoi_vector_path = inputs.File(
args_key='aoi_vector_path',
helptext=("An OGR-supported vector file used to display outputs "
"within the region(s) of interest.<br><br>The layer "
"should contain one feature for every region of "
"interest, each feature of which should have a ‘NAME’ "
"attribute. The 'NAME' attribute can be numeric or "
"alphabetic, but must be unique within the given file."),
label='Area of Interest (Vector) (Optional)',
validator=self.validator)
self.add_input(self.aoi_vector_path)
self.total_timesteps = inputs.Text(
args_key='total_timesteps',
helptext=("The number of time steps the simulation shall "
"execute before completion.<br><br>Must be a positive "
"integer."),
label='Number of Time Steps for Model Run',
validator=self.validator)
self.add_input(self.total_timesteps)
self.popu_cont = inputs.Container(label='Population Parameters')
self.add_input(self.popu_cont)
self.population_type = inputs.Dropdown(
args_key='population_type',
helptext=("Specifies whether the lifecycle classes provided in "
"the Population Parameters CSV file represent ages "
"(uniform duration) or stages.<br><br>Age-based models "
"(e.g. Lobster, Dungeness Crab) are separated by "
"uniform, fixed-length time steps (usually "
"representing a year).<br><br>Stage-based models (e.g. "
"White Shrimp) allow lifecycle-classes to have "
"nonuniform durations based on the assumed resolution "
"of the provided time step.<br><br>If the stage-based "
"model is selected, the Population Parameters CSV file "
"must include a ‘Duration’ vector alongside the "
"survival matrix that contains the number of time "
"steps that each stage lasts."),
label='Population Model Type',
options=['Age-Based', 'Stage-Based'])
self.popu_cont.add_input(self.population_type)
self.sexsp = inputs.Dropdown(
args_key='sexsp',
helptext=("Specifies whether or not the lifecycle classes "
"provided in the Populaton Parameters CSV file are "
"distinguished by sex."),
label='Population Classes are Sex-Specific',
options=['No', 'Yes'])
self.popu_cont.add_input(self.sexsp)
self.harvest_units = inputs.Dropdown(
args_key='harvest_units',
helptext=("Specifies whether the harvest output values are "
"calculated in terms of number of individuals or in "
"terms of biomass (weight).<br><br>If ‘Weight’ is "
"selected, the Population Parameters CSV file must "
"include a 'Weight' vector alongside the survival "
"matrix that contains the weight of each lifecycle "
"class and sex if model is sex-specific."),
label='Harvest by Individuals or Weight',
options=['Individuals', 'Weight'])
self.popu_cont.add_input(self.harvest_units)
self.do_batch = inputs.Checkbox(
args_key='do_batch',
helptext=("Specifies whether program will perform a single "
"model run or a batch (set) of model runs.<br><br>For "
"single model runs, users submit a filepath pointing "
"to a single Population Parameters CSV file. For "
"batch model runs, users submit a directory path "
"pointing to a set of Population Parameters CSV files."),
label='Batch Processing')
self.popu_cont.add_input(self.do_batch)
self.population_csv_path = inputs.File(
args_key='population_csv_path',
helptext=("The provided CSV file should contain all necessary "
"attributes for the sub-populations based on lifecycle "
"class, sex, and area - excluding possible migration "
"information.<br><br>Please consult the documentation "
"to learn more about what content should be provided "
"and how the CSV file should be structured."),
label='Population Parameters File (CSV)',
validator=self.validator)
self.popu_cont.add_input(self.population_csv_path)
self.population_csv_dir = inputs.Folder(
args_key='population_csv_dir',
helptext=("The provided CSV folder should contain a set of "
"Population Parameters CSV files with all necessary "
"attributes for sub-populations based on lifecycle "
"class, sex, and area - excluding possible migration "
"information.<br><br>The name of each file will serve "
"as the prefix of the outputs created by the model "
"run.<br><br>Please consult the documentation to learn "
"more about what content should be provided and how "
"the CSV file should be structured."),
interactive=False,
label='Population Parameters CSV Folder',
validator=self.validator)
self.popu_cont.add_input(self.population_csv_dir)
self.recr_cont = inputs.Container(label='Recruitment Parameters')
self.add_input(self.recr_cont)
self.total_init_recruits = inputs.Text(
args_key='total_init_recruits',
helptext=("The initial number of recruits in the population "
"model at time equal to zero.<br><br>If the model "
"contains multiple regions of interest or is "
"distinguished by sex, this value will be evenly "
"divided and distributed into each sub-population."),
label='Total Initial Recruits',
validator=self.validator)
self.recr_cont.add_input(self.total_init_recruits)
self.recruitment_type = inputs.Dropdown(
args_key='recruitment_type',
helptext=("The selected equation is used to calculate "
"recruitment into the subregions at the beginning of "
"each time step. Corresponding parameters must be "
"specified with each function:<br><br>The Beverton- "
"Holt and Ricker functions both require arguments for "
"the ‘Alpha’ and ‘Beta’ parameters.<br><br>The "
"Fecundity function requires a 'Fecundity' vector "
"alongside the survival matrix in the Population "
"Parameters CSV file indicating the per-capita "
"offspring for each lifecycle class.<br><br>The Fixed "
"function requires an argument for the ‘Total Recruits "
"per Time Step’ parameter that represents a single "
"total recruitment value to be distributed into the "
"population model at the beginning of each time step."),
label='Recruitment Function Type',
options=['Beverton-Holt', 'Ricker', 'Fecundity', 'Fixed'])
self.recr_cont.add_input(self.recruitment_type)
self.spawn_units = inputs.Dropdown(
args_key='spawn_units',
helptext=("Specifies whether the spawner abundance used in the "
"recruitment function should be calculated in terms of "
"number of individuals or in terms of biomass "
"(weight).<br><br>If 'Weight' is selected, the user "
"must provide a 'Weight' vector alongside the survival "
"matrix in the Population Parameters CSV file. The "
"'Alpha' and 'Beta' parameters provided by the user "
"should correspond to the selected choice.<br><br>Used "
"only for the Beverton-Holt and Ricker recruitment "
"functions."),
label='Spawners by Individuals or Weight (Beverton-Holt / Ricker)',
options=['Individuals', 'Weight'])
self.recr_cont.add_input(self.spawn_units)
self.alpha = inputs.Text(
args_key='alpha',
helptext=("Specifies the shape of the stock-recruit curve. "
"Used only for the Beverton-Holt and Ricker "
"recruitment functions.<br><br>Used only for the "
"Beverton-Holt and Ricker recruitment functions."),
label='Alpha (Beverton-Holt / Ricker)',
validator=self.validator)
self.recr_cont.add_input(self.alpha)
self.beta = inputs.Text(
args_key='beta',
helptext=("Specifies the shape of the stock-recruit "
"curve.<br><br>Used only for the Beverton-Holt and "
"Ricker recruitment functions."),
label='Beta (Beverton-Holt / Ricker)',
validator=self.validator)
self.recr_cont.add_input(self.beta)
self.total_recur_recruits = inputs.Text(
args_key='total_recur_recruits',
helptext=("Specifies the total number of recruits that come "
"into the population at each time step (a fixed "
"number).<br><br>Used only for the Fixed recruitment "
"function."),
label='Total Recruits per Time Step (Fixed)',
validator=self.validator)
self.recr_cont.add_input(self.total_recur_recruits)
self.migr_cont = inputs.Container(args_key='migr_cont',
expandable=True,
expanded=False,
label='Migration Parameters')
self.add_input(self.migr_cont)
self.migration_dir = inputs.Folder(
args_key='migration_dir',
helptext=("The selected folder contain CSV migration matrices "
"to be used in the simulation. Each CSV file contains "
"a single migration matrix corresponding to an "
"lifecycle class that migrates. The folder should "
"contain one CSV file for each lifecycle class that "
"migrates.<br><br>The files may be named anything, but "
"must end with an underscore followed by the name of "
"the age or stage. The name of the age or stage must "
"correspond to an age or stage within the Population "
"Parameters CSV file. For example, a migration file "
"might be named 'migration_adult.csv'.<br><br>Each "
"matrix cell should contain a decimal fraction "
"indicating the percetage of the population that will "
"move from one area to another. Each column should "
"sum to one."),
label='Migration Matrix CSV Folder (Optional)',
validator=self.validator)
self.migr_cont.add_input(self.migration_dir)
self.val_cont = inputs.Container(args_key='val_cont',
expandable=True,
expanded=False,
label='Valuation Parameters')
self.add_input(self.val_cont)
self.frac_post_process = inputs.Text(
args_key='frac_post_process',
helptext=("Decimal fraction indicating the percentage of "
"harvested catch remaining after post-harvest "
"processing is complete."),
label='Fraction of Harvest Kept After Processing',
validator=self.validator)
self.val_cont.add_input(self.frac_post_process)
self.unit_price = inputs.Text(
args_key='unit_price',
helptext=("Specifies the price per harvest unit.<br><br>If "
"‘Harvest by Individuals or Weight’ was set to "
"‘Individuals’, this should be the price per "
"individual. If set to ‘Weight’, this should be the "
"price per unit weight."),
label='Unit Price',
validator=self.validator)
self.val_cont.add_input(self.unit_price)
# Set interactivity, requirement as input sufficiency changes
self.do_batch.sufficiency_changed.connect(self._toggle_batch_runs)
# Enable/disable parameters when the recruitment function changes.
self.recruitment_type.value_changed.connect(
self._control_recruitment_parameters)
def __init__(self):
model.InVESTModel.__init__(
self,
label=MODEL_METADATA['pollination'].model_title,
target=pollination.execute,
validator=pollination.validate,
localdoc=MODEL_METADATA['pollination'].userguide)
self.landcover_raster_path = inputs.File(
args_key='landcover_raster_path',
helptext=("This is the landcover map that's used to map "
"biophyiscal properties about habitat and floral "
"resources of landcover types to a spatial layout."),
label='Land Cover Map (Raster)',
validator=self.validator)
self.add_input(self.landcover_raster_path)
self.landcover_biophysical_table_path = inputs.File(
args_key='landcover_biophysical_table_path',
helptext=("A CSV table mapping landcover codes in the landcover "
"raster to indexes of nesting availability for each "
"nesting substrate referenced in guilds table as well "
"as indexes of abundance of floral resources on that "
"landcover type per season in the bee activity columns "
"of the guild table.<br/>All indexes are in the range "
"[0.0, 1.0].<br/>Columns in the table must be at "
"least<br/>* 'lucode': representing all the unique "
"landcover codes in the raster st "
"`args['landcover_path']`<br/>* For every nesting "
"matching _NESTING_SUITABILITY_PATTERN in the guild "
"stable, a column matching the pattern in "
"`_LANDCOVER_NESTING_INDEX_HEADER`.<br/>* For every "
"season matching _FORAGING_ACTIVITY_PATTERN in the "
"guilds table, a column matching the pattern in "
"`_LANDCOVER_FLORAL_RESOURCES_INDEX_HEADER`."),
label='Land Cover Biophysical Table (CSV)',
validator=self.validator)
self.add_input(self.landcover_biophysical_table_path)
self.guild_table_path = inputs.File(
args_key='guild_table_path',
helptext=("A table indicating the bee species to analyze in "
"this model run. Table headers must include:<br/>* "
"'species': a bee species whose column string names "
"will be referred to in other tables and the model "
"will output analyses per species.<br/> * any number "
"of columns matching _NESTING_SUITABILITY_PATTERN with "
"values in the range [0.0, 1.0] indicating the "
"suitability of the given species to nest in a "
"particular substrate.<br/>* any number of "
"_FORAGING_ACTIVITY_PATTERN columns with values in the "
"range [0.0, 1.0] indicating the relative level of "
"foraging activity for that species during a "
"particular season.<br/>* 'alpha': the sigma average "
"flight distance of that bee species in meters.<br/>* "
"'relative_abundance': a weight indicating the "
"relative abundance of the particular species with "
"respect to the sum of all relative abundance weights "
"in the table."),
label='Guild Table (CSV)',
validator=self.validator)
self.add_input(self.guild_table_path)
self.farm_vector_path = inputs.File(
args_key='farm_vector_path',
helptext=("This is a layer of polygons representing farm sites "
"to be analyzed. The shapefile must have at least the "
"following fields:<br/><br/>* season (string): season "
"in which the farm needs pollination.<br/>* half_sat "
"(float): a real in the range [0.0, 1.0] representing "
"the proportion of wild pollinators to achieve a 50% "
"yield of that crop.<br/>* p_wild_dep (float): a "
"number in the range [0.0, 1.0] representing the "
"proportion of yield dependent on pollinators.<br/>* "
"p_managed (float): proportion of pollinators that "
"come from non-native/managed hives.<br/>* f_[season] "
"(float): any number of fields that match this pattern "
"such that `season` also matches the season headers in "
"the biophysical and guild table. Any areas that "
"overlap the landcover map will replace seasonal "
"floral resources with this value. Ranges from "
"0..1.<br/>* n_[substrate] (float): any number of "
"fields that match this pattern such that `substrate` "
"also matches the nesting substrate headers in the "
"biophysical and guild table. Any areas that overlap "
"the landcover map will replace nesting substrate "
"suitability with this value. Ranges from 0..1."),
label='Farm Vector (Vector) (optional)',
validator=self.validator)
self.add_input(self.farm_vector_path)
def __init__(self):
model.InVESTModel.__init__(
self,
label='Habitat Risk Assessment',
target=hra.execute,
validator=hra.validate,
localdoc='../documentation/habitat_risk_assessment.html')
self.csv_uri = inputs.Folder(
args_key='csv_uri',
helptext=(
"A folder containing multiple CSV files. Each file "
"refers to the overlap between a habitat and a "
"stressor pulled from habitat and stressor shapefiles "
"during the run of the HRA Preprocessor."),
label='Criteria Scores CSV Folder',
validator=self.validator)
self.add_input(self.csv_uri)
self.grid_size = inputs.Text(
args_key='grid_size',
helptext=(
"The size that should be used to grid the given "
"habitat and stressor shapefiles into rasters. This "
"value will be the pixel size of the completed raster "
"files."),
label='Resolution of Analysis (meters)',
validator=self.validator)
self.add_input(self.grid_size)
self.risk_eq = inputs.Dropdown(
args_key='risk_eq',
helptext=(
"Each of these represents an option of a risk "
"calculation equation. This will determine the "
"numeric output of risk for every habitat and stressor "
"overlap area."),
label='Risk Equation',
options=['Multiplicative', 'Euclidean'])
self.add_input(self.risk_eq)
self.decay_eq = inputs.Dropdown(
args_key='decay_eq',
helptext=(
"Each of these represents an option for decay "
"equations for the buffered stressors. If stressor "
"buffering is desired, these equtions will determine "
"the rate at which stressor data is reduced."),
label='Decay Equation',
options=['None', 'Linear', 'Exponential'])
self.add_input(self.decay_eq)
self.max_rating = inputs.Text(
args_key='max_rating',
helptext=(
"This is the highest score that is used to rate a "
"criteria within this model run. These values would "
"be placed within the Rating column of the habitat, "
"species, and stressor CSVs."),
label='Maximum Criteria Score',
validator=self.validator)
self.add_input(self.max_rating)
self.max_stress = inputs.Text(
args_key='max_stress',
helptext=(
"This is the largest number of stressors that are "
"suspected to overlap. This will be used in order to "
"make determinations of low, medium, and high risk for "
"any given habitat."),
label='Maximum Overlapping Stressors',
validator=self.validator)
self.add_input(self.max_stress)
self.aoi_tables = inputs.File(
args_key='aoi_tables',
helptext=(
"An OGR-supported vector file containing feature "
"subregions. The program will create additional "
"summary outputs across each subregion."),
label='Subregions (Vector)',
validator=self.validator)
self.add_input(self.aoi_tables)
def __init__(self):
model.InVESTModel.__init__(self,
label='Wind Energy',
target=wind_energy.execute,
validator=wind_energy.validate,
localdoc='wind_energy.html',
suffix_args_key='suffix')
self.wind_data = inputs.File(
args_key='wind_data_path',
helptext=("A CSV file that represents the wind input data "
"(Weibull parameters). Please see the User's Guide for "
"a more detailed description of the parameters."),
label='Wind Data Points (CSV)',
validator=self.validator)
self.add_input(self.wind_data)
self.aoi = inputs.File(
args_key='aoi_vector_path',
helptext=("Optional. An OGR-supported vector file containing a "
"single polygon defining the area of interest. The "
"AOI must be projected with linear units equal to "
"meters. If the AOI is provided it will clip and "
"project the outputs to that of the AOI. The Distance "
"inputs are dependent on the AOI and will only be "
"accessible if the AOI is selected. If the AOI is "
"selected and the Distance parameters are selected, "
"then the AOI should also cover a portion of the land "
"polygon to calculate distances correctly. An AOI is "
"required for valuation."),
label='Area Of Interest (Vector) (Optional)',
validator=self.validator)
self.add_input(self.aoi)
self.bathymetry = inputs.File(
args_key='bathymetry_path',
helptext=("A GDAL-supported raster file containing elevation "
"values represented in meters for the area of "
"interest. The DEM should cover at least the entire "
"span of the area of interest and if no AOI is "
"provided then the default global DEM should be used."),
label='Bathymetric Digital Elevation Model (Raster)',
validator=self.validator)
self.add_input(self.bathymetry)
self.land_polygon = inputs.File(
args_key='land_polygon_vector_path',
helptext=("An OGR-supported polygon vector that represents the "
"land and coastline that is of interest. For this "
"input to be selectable the AOI must be selected. The "
"AOI should also cover a portion of this land polygon "
"to properly calculate distances. This coastal "
"polygon, and the area covered by the AOI, form the "
"basis for distance calculations for wind farm "
"electrical transmission. This input is required for "
"masking by distance values and for valuation."),
interactive=False,
label='Land Polygon for Distance Calculation (Vector)',
validator=self.validator)
self.add_input(self.land_polygon)
self.global_wind_parameters = inputs.File(
args_key='global_wind_parameters_path',
helptext=("A CSV file that holds wind energy model parameters "
"for both the biophysical and valuation modules. "
"These parameters are defaulted to values that are "
"supported and reviewed in the User's Guide. It is "
"recommended that careful consideration be taken "
"before changing these values and to make a new CSV "
"file so that the default one always remains."),
label='Global Wind Energy Parameters (CSV)',
validator=self.validator)
self.add_input(self.global_wind_parameters)
self.turbine_group = inputs.Container(label='Turbine Properties')
self.add_input(self.turbine_group)
self.turbine_parameters = inputs.File(
args_key='turbine_parameters_path',
helptext=("A CSV file that contains parameters corresponding to "
"a specific turbine type. The InVEST package comes "
"with two turbine model options, 3.6 MW and 5.0 MW. A "
"new turbine class may be created by using the "
"existing file format conventions and filling in new "
"parameters. Likewise an existing class may be "
"modified according to the user's needs. It is "
"recommended that the existing default CSV files are "
"not overwritten."),
label='Turbine Type Parameters File (CSV)',
validator=self.validator)
self.turbine_group.add_input(self.turbine_parameters)
self.number_of_machines = inputs.Text(
args_key='number_of_turbines',
helptext=("An integer value indicating the number of wind "
"turbines per wind farm."),
label='Number Of Turbines',
validator=self.validator)
self.turbine_group.add_input(self.number_of_machines)
self.min_depth = inputs.Text(
args_key='min_depth',
helptext=("A floating point value in meters for the minimum "
"depth of the offshore wind farm installation."),
label='Minimum Depth for Offshore Wind Farm Installation (meters)',
validator=self.validator)
self.turbine_group.add_input(self.min_depth)
self.max_depth = inputs.Text(
args_key='max_depth',
helptext=("A floating point value in meters for the maximum "
"depth of the offshore wind farm installation."),
label='Maximum Depth for Offshore Wind Farm Installation (meters)',
validator=self.validator)
self.turbine_group.add_input(self.max_depth)
self.min_distance = inputs.Text(
args_key='min_distance',
helptext=("A floating point value in meters that represents the "
"minimum distance from shore for offshore wind farm "
"installation. Required for valuation."),
interactive=False,
label=('Minimum Distance for Offshore Wind Farm Installation '
'(meters)'),
validator=self.validator)
self.turbine_group.add_input(self.min_distance)
self.max_distance = inputs.Text(
args_key='max_distance',
helptext=("A floating point value in meters that represents the "
"maximum distance from shore for offshore wind farm "
"installation. Required for valuation."),
interactive=False,
label=('Maximum Distance for Offshore Wind Farm Installation '
'(meters)'),
validator=self.validator)
self.turbine_group.add_input(self.max_distance)
self.valuation_container = inputs.Container(
args_key='valuation_container',
expandable=True,
expanded=False,
label='Valuation')
self.add_input(self.valuation_container)
self.foundation_cost = inputs.Text(
args_key='foundation_cost',
helptext=("A floating point number for the unit cost of the "
"foundation type (in millions of dollars). The cost of "
"a foundation will depend on the type selected, which "
"itself depends on a variety of factors including "
"depth and turbine choice. Please see the User's "
"Guide for guidance on properly selecting this value."),
label='Cost of the Foundation Type (USD, in Millions)',
validator=self.validator)
self.valuation_container.add_input(self.foundation_cost)
self.discount_rate = inputs.Text(
args_key='discount_rate',
helptext=("The discount rate reflects preferences for immediate "
"benefits over future benefits (e.g., would an "
"individual rather receive $10 today or $10 five years "
"from now?). See the User's Guide for guidance on "
"selecting this value."),
label='Discount Rate',
validator=self.validator)
self.valuation_container.add_input(self.discount_rate)
self.grid_points = inputs.File(
args_key='grid_points_path',
helptext=("An optional CSV file with grid and land points to "
"determine cable distances from. An example:<br/> "
"<table border='1'> <tr> <th>ID</th> <th>TYPE</th> "
"<th>LATI</th> <th>LONG</th> </tr> <tr> <td>1</td> "
"<td>GRID</td> <td>42.957</td> <td>-70.786</td> </tr> "
"<tr> <td>2</td> <td>LAND</td> <td>42.632</td> "
"<td>-71.143</td> </tr> <tr> <td>3</td> <td>LAND</td> "
"<td>41.839</td> <td>-70.394</td> </tr> </table> "
"<br/><br/>Each point location is represented as a "
"single row with columns being <b>ID</b>, <b>TYPE</b>, "
"<b>LATI</b>, and <b>LONG</b>. The <b>LATI</b> and "
"<b>LONG</b> columns indicate the coordinates for the "
"point. The <b>TYPE</b> column relates to whether it "
"is a land or grid point. The <b>ID</b> column is a "
"simple unique integer. The shortest distance between "
"respective points is used for calculations. See the "
"User's Guide for more information."),
label='Grid Connection Points (Optional)',
validator=self.validator)
self.valuation_container.add_input(self.grid_points)
self.avg_grid_dist = inputs.Text(
args_key='avg_grid_distance',
helptext=("<b>Always required, but NOT used in the model if "
"Grid Points provided</b><br/><br/>A number in "
"kilometres that is only used if grid points are NOT "
"used in valuation. When running valuation using the "
"land polygon to compute distances, the model uses an "
"average distance to the onshore grid from coastal "
"cable landing points instead of specific grid "
"connection points. See the User's Guide for a "
"description of the approach and the method used to "
"calculate the default value."),
label='Average Shore to Grid Distance (Kilometers)',
validator=self.validator)
self.valuation_container.add_input(self.avg_grid_dist)
self.price_table = inputs.Checkbox(
args_key='price_table',
helptext=("When checked the model will use the social cost of "
"wind energy table provided in the input below. If "
"not checked the price per year will be determined "
"using the price of energy input and the annual rate "
"of change."),
label='Use Price Table')
self.valuation_container.add_input(self.price_table)
self.wind_schedule = inputs.File(
args_key='wind_schedule',
helptext=("A CSV file that has the price of wind energy per "
"kilowatt hour for each year of the wind farms life. "
"The CSV file should have the following two "
"columns:<br/><br/><b>Year:</b> a set of integers "
"indicating each year for the lifespan of the wind "
"farm. They can be in date form such as : 2010, 2011, "
"2012... OR simple time step integers such as : 0, 1, "
"2... <br/><br/><b>Price:</b> a set of floats "
"indicating the price of wind energy per kilowatt hour "
"for a particular year or time step in the wind farms "
"life.<br/><br/>An example:<br/> <table border='1'> "
"<tr><th>Year</th> <th>Price</th></tr><tr><td>0</td><t "
"d>.244</td></tr><tr><td>1</td><td>.255</td></tr><tr>< "
"td>2</td><td>.270</td></tr><tr><td>3</td><td>.275</td "
"></tr><tr><td>4</td><td>.283</td></tr><tr><td>5</td>< "
"td>.290</td></tr></table><br/><br/><b>NOTE:</b> The "
"number of years or time steps listed must match the "
"<b>time</b> parameter in the <b>Global Wind Energy "
"Parameters</b> input file above. In the above "
"example we have 6 years for the lifetime of the farm, "
"year 0 being a construction year and year 5 being the "
"last year."),
interactive=False,
label='Wind Energy Price Table (CSV)',
validator=self.validator)
self.valuation_container.add_input(self.wind_schedule)
self.wind_price = inputs.Text(
args_key='wind_price',
helptext=("The price of energy per kilowatt hour. This is the "
"price that will be used for year or time step 0 and "
"will then be adjusted based on the rate of change "
"percentage from the input below. See the User's "
"Guide for guidance about determining this value."),
label='Price of Energy per Kilowatt Hour ($/kWh)',
validator=self.validator)
self.valuation_container.add_input(self.wind_price)
self.rate_change = inputs.Text(
args_key='rate_change',
helptext=("The annual rate of change in the price of wind "
"energy. This should be expressed as a decimal "
"percentage. For example, 0.1 for a 10% annual price "
"change."),
label='Annual Rate of Change in Price of Wind Energy',
validator=self.validator)
self.valuation_container.add_input(self.rate_change)
# Set interactivity, requirement as input sufficiency changes
self.aoi.sufficiency_changed.connect(self.land_polygon.set_interactive)
self.land_polygon.sufficiency_changed.connect(
self.min_distance.set_interactive)
self.land_polygon.sufficiency_changed.connect(
self.max_distance.set_interactive)
self.price_table.sufficiency_changed.connect(
self._toggle_price_options)
def __init__(self):
model.InVESTModel.__init__(
self,
label='Scenario Generator: Proximity Based',
target=natcap.invest.scenario_gen_proximity.execute,
validator=natcap.invest.scenario_gen_proximity.validate,
localdoc='../documentation/scenario_gen_proximity.html')
self.base_lulc_path = inputs.File(args_key='base_lulc_path',
label='Base Land Use/Cover (Raster)',
validator=self.validator)
self.add_input(self.base_lulc_path)
self.aoi_path = inputs.File(
args_key='aoi_path',
helptext=("This is a set of polygons that will be used to "
"aggregate carbon values at the end of the run if "
"provided."),
label='Area of interest (Vector) (optional)',
validator=self.validator)
self.add_input(self.aoi_path)
self.area_to_convert = inputs.Text(args_key='area_to_convert',
label='Max area to convert (Ha)',
validator=self.validator)
self.add_input(self.area_to_convert)
self.focal_landcover_codes = inputs.Text(
args_key='focal_landcover_codes',
label='Focal Landcover Codes (list)',
validator=self.validator)
self.add_input(self.focal_landcover_codes)
self.convertible_landcover_codes = inputs.Text(
args_key='convertible_landcover_codes',
label='Convertible Landcover Codes (list)',
validator=self.validator)
self.add_input(self.convertible_landcover_codes)
self.replacment_lucode = inputs.Text(
args_key='replacment_lucode',
label='Replacement Landcover Code (int)',
validator=self.validator)
self.add_input(self.replacment_lucode)
self.convert_farthest_from_edge = inputs.Checkbox(
args_key='convert_farthest_from_edge',
helptext=("This scenario converts the convertible landcover "
"codes starting at the furthest pixel from the closest "
"base landcover codes and moves inward."),
label='Farthest from edge')
self.add_input(self.convert_farthest_from_edge)
self.convert_nearest_to_edge = inputs.Checkbox(
args_key='convert_nearest_to_edge',
helptext=("This scenario converts the convertible landcover "
"codes starting at the closest pixel in the base "
"landcover codes and moves outward."),
label='Nearest to edge')
self.add_input(self.convert_nearest_to_edge)
self.n_fragmentation_steps = inputs.Text(
args_key='n_fragmentation_steps',
helptext=("This parameter is used to divide the conversion "
"simulation into equal subareas of the requested max "
"area. During each sub-step the distance transform is "
"recalculated from the base landcover codes. This can "
"affect the final result if the base types are also "
"convertible types."),
label='Number of Steps in Conversion',
validator=self.validator)
self.add_input(self.n_fragmentation_steps)
def __init__(self):
model.InVESTModel.__init__(
self,
label='Habitat Risk Assessment',
target=hra.execute,
validator=hra.validate,
localdoc='../documentation/habitat_risk_assessment.html')
self.info_table_path = inputs.File(
args_key='info_table_path',
helptext=(
"A CSV or Excel file that contains the name of the habitat "
"(H) or stressor (s) on the `NAME` column that matches the "
"names in `criteria_table_path`. Each H/S has its "
"corresponding vector or raster path on the `PATH` column. "
"The `STRESSOR BUFFER (meters)` column should have a buffer "
"value if the `TYPE` column is a stressor."),
label='Habitat Stressor Information CSV or Excel File',
validator=self.validator)
self.add_input(self.info_table_path)
self.criteria_table_path = inputs.File(
args_key='criteria_table_path',
helptext=(
"A CSV or Excel file that contains the set of criteria "
"ranking (rating, DQ and weight) of each stressor on each "
"habitat, as well as the habitat resilience attributes."),
label='Criteria Scores CSV or Excel File',
validator=self.validator)
self.add_input(self.criteria_table_path)
self.resolution = inputs.Text(
args_key='resolution',
helptext=(
"The size that should be used to grid the given habitat and "
"stressor files into rasters. This value will be the pixel "
"size of the completed raster files."),
label='Resolution of Analysis (meters)',
validator=self.validator)
self.add_input(self.resolution)
self.max_rating = inputs.Text(
args_key='max_rating',
helptext=(
"This is the highest score that is used to rate a criteria "
"within this model run. This value would be used to compare "
"with the values within Rating column of the Criteria Scores "
"table."),
label='Maximum Criteria Score',
validator=self.validator)
self.add_input(self.max_rating)
self.risk_eq = inputs.Dropdown(
args_key='risk_eq',
helptext=(
"Each of these represents an option of a risk calculation "
"equation. This will determine the numeric output of risk "
"for every habitat and stressor overlap area."),
label='Risk Equation',
options=['Multiplicative', 'Euclidean'])
self.add_input(self.risk_eq)
self.decay_eq = inputs.Dropdown(
args_key='decay_eq',
helptext=(
"Each of these represents an option of a decay equation "
"for the buffered stressors. If stressor buffering is "
"desired, this equation will determine the rate at which "
"stressor data is reduced."),
label='Decay Equation',
options=['None', 'Linear', 'Exponential'])
self.add_input(self.decay_eq)
self.aoi_vector_path = inputs.File(
args_key='aoi_vector_path',
helptext=(
"An OGR-supported vector file containing feature containing "
"one or more planning regions. subregions. An optional field "
"called `name` could be added to compute average risk values "
"within each subregion."),
label='Area of Interest (Vector)',
validator=self.validator)
self.add_input(self.aoi_vector_path)
self.visualize_outputs = inputs.Checkbox(
args_key='visualize_outputs',
helptext=(
"Check to enable the generation of GeoJSON outputs. This "
"could be used to visualize the risk scores on a map in the "
"HRA visualization web application."),
label='Generate GeoJSONs for Web Visualization')
self.add_input(self.visualize_outputs)
def __init__(self):
model.InVESTModel.__init__(
self,
label=MODEL_METADATA['habitat_quality'].model_title,
target=habitat_quality.execute,
validator=habitat_quality.validate,
localdoc=MODEL_METADATA['habitat_quality'].userguide)
self.current_landcover = inputs.File(
args_key='lulc_cur_path',
helptext=("A GDAL-supported raster file. The current LULC must "
"have its' own threat rasters, where each threat "
"raster file path is defined in the <b>Threats Data</b> "
"CSV..<br/><br/> "
"Each cell should represent a LULC code as an Integer. "
"The dataset should be in a projection where the units "
"are in meters and the projection used should be "
"defined. <b>The LULC codes must match the codes in "
"the Sensitivity table</b>."),
label='Current Land Cover (Raster)',
validator=self.validator)
self.add_input(self.current_landcover)
self.future_landcover = inputs.File(
args_key='lulc_fut_path',
helptext=(
"Optional. A GDAL-supported raster file. Inputting "
"a future LULC will generate degradation, habitat "
"quality, and habitat rarity (If baseline is input) "
"outputs. The future LULC must have it's own threat "
"rasters, where each threat raster file path is defined "
"in the <b>Threats Data</b> CSV.<br/><br/>Each cell should "
"represent a LULC code as an Integer. The dataset "
"should be in a projection where the units are in "
"meters and the projection used should be defined. "
"<b>The LULC codes must match the codes in the "
"Sensitivity table</b>."),
label='Future Land Cover (Raster) (Optional)',
validator=self.validator)
self.add_input(self.future_landcover)
self.baseline_landcover = inputs.File(
args_key='lulc_bas_path',
helptext=(
"Optional. A GDAL-supported raster file. If the "
"baseline LULC is provided, rarity outputs will be "
"created for the current and future LULC. The baseline "
"LULC can have it's own threat rasters (optional), "
"where each threat raster file path is defined in the "
"<b>Threats Data</b> CSV. If there are no threat rasters and "
"the threat paths are left blank in the CSV column, "
"degradation and habitat quality outputs will not be "
"generated for the baseline LULC.<br/><br/> Each cell "
"should represent a LULC code as an Integer. The "
"dataset should be in a projection where the units are "
"in meters and the projection used should be defined. "
"The LULC codes must match the codes in the "
"Sensitivity table. If possible the baseline map "
"should refer to a time when intensive management of "
"the landscape was relatively rare."),
label='Baseline Land Cover (Raster) (Optional)',
validator=self.validator)
self.add_input(self.baseline_landcover)
self.threats_data = inputs.File(
args_key='threats_table_path',
helptext=(
"A CSV file of all the threats for the model to consider. "
"Each row in the table is a degradation source. The columns "
"(THREAT, MAX_DIST, WEIGHT, DECAY) are different attributes "
"of each degradation source. The columns (BASE_PATH, CUR_PATH, "
"FUT_PATH) specify the filepath name for the degradation "
"source where the path is relative to the THREAT CSV. "
"Column names are case-insensitive. "
"<br/><br/><b>THREAT:</b> The name of the "
"threat source must match exactly to the name "
"of it's corresponding column in the sensitivity table. "
"<br/><br/><b>MAX_DIST:</b> A number in kilometres "
"(km) for the maximum distance a threat has an "
"affect.<br/><br/><b>WEIGHT:</b> A floating point "
"value between 0 and 1 for the the threats weight "
"relative to the other threats. Depending on the type "
"of habitat under review, certain threats may cause "
"greater degradation than other "
"threats.<br/><br/><b>DECAY:</b> A string value of "
"either <b>exponential</b> or <b>linear</b> "
"representing the type of decay over space for the "
"threat. <br/><br/><b>BASE_PATH:</b> Required if baseline "
"LULC input. The THREAT raster filepath for the base scenario "
"where the filepath is relative to the THREAT CSV input. "
"Entries can be left empty if there is no baseline scenario "
"or if using the baseline LULC for rarity calculations only. "
"<br/><br/><b>CUR_PATH:</b> "
"The THREAT raster filepath for the current scenario "
"where the filepath is relative to the THREAT CSV input. "
"<br/><br/><b>FUT_PATH:</b> Required if future LULC input. "
"The THREAT raster filepath for the future scenario where the "
"filepath is relative to the THREAT CSV input."
"<br/><br/>See the user's guide for valid values for these "
"columns."),
label='Threats Data',
validator=self.validator)
self.add_input(self.threats_data)
self.accessibility_threats = inputs.File(
args_key='access_vector_path',
helptext=("An OGR-supported vector file. The input contains "
"data on the relative protection that legal / "
"institutional / social / physical barriers provide "
"against threats. The vector file should contain "
"polygons with a field <b>ACCESS</b>. The "
"<b>ACCESS</b> values should range from 0 - 1, where 1 "
"is fully accessible. Any cells not covered by a "
"polygon will be set to 1."),
label='Accessibility to Threats (Vector) (Optional)',
validator=self.validator)
self.add_input(self.accessibility_threats)
self.sensitivity_data = inputs.File(
args_key='sensitivity_table_path',
helptext=("A CSV file of LULC types, whether or not the are "
"considered habitat, and, for LULC types that are "
"habitat, their specific sensitivity to each threat. "
"Each row is a LULC type with the following columns: "
"<b>LULC, HABITAT, THREAT1, THREAT2, "
"...</b><br/><br/>. Column names are case-insensitive. "
"<b>LULC:</b> Integer values that "
"reflect each LULC code found in current, future, and "
"baseline rasters.<br/><br/><b>HABITAT:</b> A value of "
"0 or 1 (presence / absence) or a value between 0 and "
"1 (continuum) depicting the suitability of "
"habitat.<br/><br/><b>THREATN:</b> Each THREATN "
"should match exactly with the threat names given in "
"the threat CSV file, where the THREATN is the name "
"that matches. This is an floating point value "
"between 0 and 1 that represents the sensitivity of a "
"habitat to a threat. <br/><br/>.Please see the users "
"guide for more detailed information on proper column "
"values and column names for each threat."),
label='Sensitivity of Land Cover Types to Each Threat, File (CSV)',
validator=self.validator)
self.add_input(self.sensitivity_data)
self.half_saturation_constant = inputs.Text(
args_key='half_saturation_constant',
helptext=("A positive floating point value that is defaulted at "
"0.05. This is the value of the parameter k in equation "
"(4). In general, set k to half of the highest grid "
"cell degradation value on the landscape. To perform "
"this model calibration the model must be run once in "
"order to find the highest degradation value and set k "
"for the provided landscape. Note that the choice of "
"k only determines the spread and central tendency of "
"habitat quality cores and does not affect the rank."),
label='Half-Saturation Constant',
validator=self.validator)
self.add_input(self.half_saturation_constant)
def __init__(self):
model.InVESTModel.__init__(
self,
label='UrbanFloodRiskMitigation',
target=natcap.invest.urban_flood_risk_mitigation.execute,
validator=natcap.invest.urban_flood_risk_mitigation.validate,
localdoc='../documentation/urban_flood_risk_mitigation.html')
self.aoi_watersheds_path = inputs.File(
args_key='aoi_watersheds_path',
helptext=(
"path to a shapefile of (sub)watersheds or sewersheds used "
"to indicate spatial area of interest."),
label='Watershed Vector',
validator=self.validator)
self.add_input(self.aoi_watersheds_path)
self.rainfall_depth = inputs.Text(
args_key='rainfall_depth',
label='Depth of rainfall in mm',
validator=self.validator)
self.add_input(self.rainfall_depth)
self.lulc_path = inputs.File(
args_key='lulc_path',
helptext="path to a landcover raster",
label='Landcover Raster',
validator=self.validator)
self.add_input(self.lulc_path)
self.soils_hydrological_group_raster_path = inputs.File(
args_key='soils_hydrological_group_raster_path',
helptext=(
"Raster with values equal to 1, 2, 3, 4, corresponding to "
"soil hydrologic group A, B, C, or D, respectively (used to "
"derive the CN number"),
label='Soils Hydrological Group Raster',
validator=self.validator)
self.add_input(self.soils_hydrological_group_raster_path)
self.curve_number_table_path = inputs.File(
args_key='curve_number_table_path',
helptext=(
"Path to a CSV table that to map landcover codes to curve "
"numbers and contains at least the headers 'lucode', "
"'CN_A', 'CN_B', 'CN_C', 'CN_D'"),
label='Biophysical Table',
validator=self.validator)
self.add_input(self.curve_number_table_path)
self.built_infrastructure_vector_path = inputs.File(
args_key='built_infrastructure_vector_path',
helptext=(
"Path to a vector with built infrastructure footprints. "
"Attribute table contains a column 'Type' with integers "
"(e.g. 1=residential, 2=office, etc.)."),
label='Built Infrastructure Vector (optional)',
validator=self.validator)
self.add_input(self.built_infrastructure_vector_path)
self.infrastructure_damage_loss_table_path = inputs.File(
args_key='infrastructure_damage_loss_table_path',
helptext=(
"path to a a CSV table with columns 'Type' and 'Damage' "
"with values of built infrastructure type from the 'Type' "
"field in the 'Built Infrastructure Vector' and potential "
"damage loss (in $/m^2)."),
label='Built Infrastructure Damage Loss Table (optional)',
validator=self.validator)
self.add_input(self.infrastructure_damage_loss_table_path)
def __init__(self):
model.InVESTModel.__init__(
self,
label='Coastal Blue Carbon',
target=coastal_blue_carbon.execute,
validator=coastal_blue_carbon.validate,
localdoc='coastal_blue_carbon.html')
self.lulc_lookup_uri = inputs.File(
args_key='lulc_lookup_uri',
helptext=(
"A CSV table used to map lulc classes to their values "
"in a raster and to indicate whether or not the lulc "
"class is a coastal blue carbon habitat."),
label='LULC Lookup Table (CSV)',
validator=self.validator)
self.add_input(self.lulc_lookup_uri)
self.lulc_transition_matrix_uri = inputs.File(
args_key='lulc_transition_matrix_uri',
helptext=(
"Generated by the preprocessor. This file must be "
"edited before it can be used by the main model. The "
"left-most column represents the source lulc class, "
"and the top row represents the destination lulc "
"class."),
label='LULC Transition Effect of Carbon Table (CSV)',
validator=self.validator)
self.add_input(self.lulc_transition_matrix_uri)
self.carbon_pool_initial_uri = inputs.File(
args_key='carbon_pool_initial_uri',
helptext=(
"The provided CSV table contains information related "
"to the initial conditions of the carbon stock within "
"each of the three pools of a habitat. Biomass "
"includes carbon stored above and below ground. All "
"non-coastal blue carbon habitat lulc classes are "
"assumed to contain no carbon. The values for "
"‘biomass’, ‘soil’, and ‘litter’ should be given in "
"terms of Megatonnes CO2e/ha."),
label='Carbon Pool Initial Variables Table (CSV)',
validator=self.validator)
self.add_input(self.carbon_pool_initial_uri)
self.carbon_pool_transient_uri = inputs.File(
args_key='carbon_pool_transient_uri',
helptext=(
"The provided CSV table contains information related "
"to the transition of carbon into and out of coastal "
"blue carbon pools. All non-coastal blue carbon "
"habitat lulc classes are assumed to neither sequester "
"nor emit carbon as a result of change. The "
"‘yearly_accumulation’ values should be given in terms "
"of Megatonnes of CO2e/ha-yr. The ‘half-life’ values "
"must be given in terms of years. The ‘disturbance’ "
"values must be given as a decimal percentage of stock "
"distrubed given a transition occurs away from a lulc- "
"class."),
label='Carbon Pool Transient Variables Table (CSV)',
validator=self.validator)
self.add_input(self.carbon_pool_transient_uri)
self.lulc_baseline_map_uri = inputs.File(
args_key='lulc_baseline_map_uri',
helptext=(
"A GDAL-supported raster representing the baseline "
"landscape/seascape."),
label='Baseline LULC Raster (GDAL-supported)',
validator=self.validator)
self.add_input(self.lulc_baseline_map_uri)
self.lulc_baseline_year = inputs.Text(
args_key='lulc_baseline_year',
label='Year of baseline LULC raster',
validator=self.validator)
self.add_input(self.lulc_baseline_year)
self.lulc_transition_maps_list = inputs.Multi(
args_key='lulc_transition_maps_list',
callable_=functools.partial(inputs.File, label="Input"),
label='LULC Transition ("Snapshot") Rasters (GDAL-supported)',
link_text='Add Another')
self.add_input(self.lulc_transition_maps_list)
self.lulc_transition_years_list = inputs.Multi(
args_key='lulc_transition_years_list',
callable_=functools.partial(inputs.Text, label="Input"),
label='LULC Transition ("Snapshot") Years',
link_text='Add Another')
self.add_input(self.lulc_transition_years_list)
self.analysis_year = inputs.Text(
args_key='analysis_year',
helptext=(
"An analysis year extends the transient analysis "
"beyond the transition years."),
label='Analysis Year (Optional)',
validator=self.validator)
self.add_input(self.analysis_year)
self.do_economic_analysis = inputs.Container(
args_key='do_economic_analysis',
expandable=True,
expanded=True,
label='Calculate Net Present Value of Sequestered Carbon')
self.add_input(self.do_economic_analysis)
self.do_price_table = inputs.Checkbox(
args_key='do_price_table',
helptext='',
label='Use Price Table')
self.do_economic_analysis.add_input(self.do_price_table)
self.price = inputs.Text(
args_key='price',
helptext='The price per Megatonne CO2e at the base year.',
label='Price',
validator=self.validator)
self.do_economic_analysis.add_input(self.price)
self.inflation_rate = inputs.Text(
args_key='inflation_rate',
helptext="Annual change in the price per unit of carbon",
label='Interest Rate (%)',
validator=self.validator)
self.do_economic_analysis.add_input(self.inflation_rate)
self.price_table_uri = inputs.File(
args_key='price_table_uri',
helptext=(
"Can be used in place of price and interest rate "
"inputs. The provided CSV table contains the price "
"per Megatonne CO2e sequestered for a given year, for "
"all years from the original snapshot to the analysis "
"year, if provided."),
interactive=False,
label='Price Table (CSV)',
validator=self.validator)
self.do_economic_analysis.add_input(self.price_table_uri)
self.discount_rate = inputs.Text(
args_key='discount_rate',
helptext=(
"The discount rate on future valuations of "
"sequestered carbon, compounded yearly."),
label='Discount Rate (%)',
validator=self.validator)
self.do_economic_analysis.add_input(self.discount_rate)
# Set interactivity, requirement as input sufficiency changes
self.do_price_table.sufficiency_changed.connect(
self._price_table_sufficiency_changed)
def __init__(self):
model.InVESTModel.__init__(
self,
label='Scenario Generator',
target=natcap.invest.scenario_generator.scenario_generator.execute,
validator=natcap.invest.scenario_generator.scenario_generator.
validate,
localdoc='../documentation/scenario_generator.html',
suffix_args_key='suffix',
)
self.landcover = inputs.File(
args_key='landcover',
helptext=
'A GDAL-supported raster file representing land-use/land-cover.',
label='Land Cover (Raster)',
validator=self.validator)
self.add_input(self.landcover)
self.transition = inputs.File(
args_key='transition',
helptext=("This table contains the land-cover transition "
"likelihoods, priority of transitions, area change, "
"proximity suitiblity, proximity effect distance, seed "
"size, short name, and patch size."),
label='Transition Table (CSV)',
validator=self.validator)
self.add_input(self.transition)
self.calculate_priorities = inputs.Checkbox(
args_key='calculate_priorities',
helptext=("This option enables calculation of the land-cover "
"priorities using analytical hierarchical processing. "
"A matrix table must be entered below. Optionally, "
"the priorities can manually be entered in the "
"priority column of the land attributes table."),
interactive=False,
label='Calculate Priorities')
self.add_input(self.calculate_priorities)
self.priorities_csv_uri = inputs.File(
args_key='priorities_csv_uri',
helptext=("This table contains a matrix of land-cover type "
"pairwise priorities used to calculate land-cover "
"priorities."),
interactive=False,
label='Priorities Table (CSV)',
validator=self.validator)
self.add_input(self.priorities_csv_uri)
self.calculate_proximity = inputs.Container(
args_key='calculate_proximity',
expandable=True,
expanded=True,
label='Proximity')
self.add_input(self.calculate_proximity)
self.calculate_transition = inputs.Container(
args_key='calculate_transition',
expandable=True,
expanded=True,
label='Specify Transitions')
self.add_input(self.calculate_transition)
self.calculate_factors = inputs.Container(args_key='calculate_factors',
expandable=True,
expanded=True,
label='Use Factors')
self.add_input(self.calculate_factors)
self.suitability_folder = inputs.Folder(args_key='suitability_folder',
label='Factors Folder',
validator=self.validator)
self.calculate_factors.add_input(self.suitability_folder)
self.suitability = inputs.File(
args_key='suitability',
helptext=("This table lists the factors that determine "
"suitability of the land-cover for change, and "
"includes: the factor name, layer name, distance of "
"influence, suitability value, weight of the factor, "
"distance breaks, and applicable land-cover."),
label='Factors Table',
validator=self.validator)
self.calculate_factors.add_input(self.suitability)
self.weight = inputs.Text(
args_key='weight',
helptext=("The factor weight is a value between 0 and 1 which "
"determines the weight given to the factors vs. the "
"expert opinion likelihood rasters. For example, if a "
"weight of 0.3 is entered then 30% of the final "
"suitability is contributed by the factors and the "
"likelihood matrix contributes 70%. This value is "
"entered on the tool interface."),
label='Factor Weight',
validator=self.validator)
self.calculate_factors.add_input(self.weight)
self.factor_inclusion = inputs.Dropdown(
args_key='factor_inclusion',
helptext='',
interactive=False,
label='Rasterization Method',
options=[
'All touched pixels', 'Only pixels with covered center points'
])
self.calculate_factors.add_input(self.factor_inclusion)
self.calculate_constraints = inputs.Container(
args_key='calculate_constraints',
expandable=True,
label='Constraints Layer')
self.add_input(self.calculate_constraints)
self.constraints = inputs.File(
args_key='constraints',
helptext=("An OGR-supported vector file. This is a vector "
"layer which indicates the parts of the landscape that "
"are protected of have constraints to land-cover "
"change. The layer should have one field named "
"'porosity' with a value between 0 and 1 where 0 means "
"its fully protected and 1 means its fully open to "
"change."),
label='Constraints Layer (Vector)',
validator=self.validator)
self.calculate_constraints.add_input(self.constraints)
self.constraints.sufficiency_changed.connect(
self._load_colnames_constraints)
self.constraints_field = inputs.Dropdown(
args_key='constraints_field',
helptext=("The field from the override table that contains the "
"value for the override."),
interactive=False,
options=('UNKNOWN', ),
label='Constraints Field')
self.calculate_constraints.add_input(self.constraints_field)
self.override_layer = inputs.Container(args_key='override_layer',
expandable=True,
expanded=True,
label='Override Layer')
self.add_input(self.override_layer)
self.override = inputs.File(
args_key='override',
helptext=("An OGR-supported vector file. This is a vector "
"(polygon) layer with land-cover types in the same "
"scale and projection as the input land-cover. This "
"layer is used to override all the changes and is "
"applied after the rule conversion is complete."),
label='Override Layer (Vector)',
validator=self.validator)
self.override_layer.add_input(self.override)
self.override.sufficiency_changed.connect(self._load_colnames_override)
self.override_field = inputs.Dropdown(
args_key='override_field',
helptext=("The field from the override table that contains the "
"value for the override."),
interactive=False,
options=('UNKNOWN', ),
label='Override Field')
self.override_layer.add_input(self.override_field)
self.override_inclusion = inputs.Dropdown(
args_key='override_inclusion',
helptext='',
interactive=False,
label='Rasterization Method',
options=[
'All touched pixels', 'Only pixels with covered center points'
])
self.override_layer.add_input(self.override_inclusion)
self.seed = inputs.Text(
args_key='seed',
helptext=("Seed must be an integer or blank. <br/><br/>Under "
"normal conditions, parcels with the same suitability "
"are picked in a random order. Setting the seed value "
"allows the scenario generator to randomize the order "
"in which parcels are picked, but two runs with the "
"same seed will pick parcels in the same order."),
label='Seed for random parcel selection (optional)',
validator=self.validator)
self.add_input(self.seed)
# Set interactivity, requirement as input sufficiency changes
self.transition.sufficiency_changed.connect(
self.calculate_priorities.set_interactive)
self.calculate_priorities.sufficiency_changed.connect(
self.priorities_csv_uri.set_interactive)
self.calculate_factors.sufficiency_changed.connect(
self.factor_inclusion.set_interactive)
self.constraints.sufficiency_changed.connect(
self.constraints_field.set_interactive)
self.override.sufficiency_changed.connect(
self.override_field.set_interactive)
self.override_field.sufficiency_changed.connect(
self.override_inclusion.set_interactive)
def __init__(self):
model.InVESTModel.__init__(
self,
label='Coastal Vulnerability Assessment Tool',
target=coastal_vulnerability.execute,
validator=coastal_vulnerability.validate,
localdoc='../documentation/coastal_vulnerability.html',
suffix_args_key='suffix'
)
self.general_tab = inputs.Container(
interactive=True,
label='General')
self.add_input(self.general_tab)
self.area_computed = inputs.Dropdown(
args_key='area_computed',
helptext=(
"Determine if the output data is about all the coast "
"or about sheltered segments only."),
label='Output Area: Sheltered/Exposed?',
options=['both', 'sheltered'])
self.general_tab.add_input(self.area_computed)
self.area_of_interest = inputs.File(
args_key='aoi_uri',
helptext=(
"An OGR-supported, single-feature polygon vector "
"file. All outputs will be in the AOI's projection."),
label='Area of Interest (Vector)',
validator=self.validator)
self.general_tab.add_input(self.area_of_interest)
self.landmass_uri = inputs.File(
args_key='landmass_uri',
helptext=(
"An OGR-supported vector file containing a landmass "
"polygon from where the coastline will be extracted. "
"The default is the global land polygon."),
label='Land Polygon (Vector)',
validator=self.validator)
self.general_tab.add_input(self.landmass_uri)
self.bathymetry_layer = inputs.File(
args_key='bathymetry_uri',
helptext=(
"A GDAL-supported raster of the terrain elevation in "
"the area of interest. Used to compute depths along "
"fetch rays, relief and surge potential."),
label='Bathymetry Layer (Raster)',
validator=self.validator)
self.general_tab.add_input(self.bathymetry_layer)
self.bathymetry_constant = inputs.Text(
args_key='bathymetry_constant',
helptext=(
"Integer value between 1 and 5. If layer associated "
"to this field is omitted, replace all shore points "
"for this layer with a constant rank value in the "
"computation of the coastal vulnerability index. If "
"both the file and value for the layer are omitted, "
"the layer is skipped altogether."),
label='Layer Value if Path Omitted',
validator=self.validator)
self.general_tab.add_input(self.bathymetry_constant)
self.relief = inputs.File(
args_key='relief_uri',
helptext=(
"A GDAL-supported raster file containing the land "
"elevation used to compute the average land elevation "
"within a user-defined radius (see Elevation averaging "
"radius)."),
label='Relief (Raster)',
validator=self.validator)
self.general_tab.add_input(self.relief)
self.relief_constant = inputs.Text(
args_key='relief_constant',
helptext=(
"Integer value between 1 and 5. If layer associated "
"to this field is omitted, replace all shore points "
"for this layer with a constant rank value in the "
"computation of the coastal vulnerability index. If "
"both the file and value for the layer are omitted, "
"the layer is skipped altogether."),
label='Layer Value If Path Omitted',
validator=self.validator)
self.general_tab.add_input(self.relief_constant)
self.cell_size = inputs.Text(
args_key='cell_size',
helptext=(
"Cell size in meters. The higher the value, the "
"faster the computation, but the coarser the output "
"rasters produced by the model."),
label='Model Resolution (Segment Size)',
validator=self.validator)
self.general_tab.add_input(self.cell_size)
self.depth_threshold = inputs.Text(
args_key='depth_threshold',
helptext=(
"Depth in meters (integer) cutoff to determine if "
"fetch rays project over deep areas."),
label='Depth Threshold (meters)',
validator=self.validator)
self.general_tab.add_input(self.depth_threshold)
self.exposure_proportion = inputs.Text(
args_key='exposure_proportion',
helptext=(
"Minimum proportion of rays that project over exposed "
"and/or deep areas need to classify a shore segment as "
"exposed."),
label='Exposure Proportion',
validator=self.validator)
self.general_tab.add_input(self.exposure_proportion)
self.geomorphology_uri = inputs.File(
args_key='geomorphology_uri',
helptext=(
"A OGR-supported polygon vector file that has a field "
"called 'RANK' with values between 1 and 5 in the "
"attribute table."),
label='Geomorphology (Vector)',
validator=self.validator)
self.general_tab.add_input(self.geomorphology_uri)
self.geomorphology_constant = inputs.Text(
args_key='geomorphology_constant',
helptext=(
"Integer value between 1 and 5. If layer associated "
"to this field is omitted, replace all shore points "
"for this layer with a constant rank value in the "
"computation of the coastal vulnerability index. If "
"both the file and value for the layer are omitted, "
"the layer is skipped altogether."),
label='Layer Value if Path Omitted',
validator=self.validator)
self.general_tab.add_input(self.geomorphology_constant)
self.habitats_directory_uri = inputs.Folder(
args_key='habitats_directory_uri',
helptext=(
"Directory containing OGR-supported polygon vectors "
"associated with natural habitats. The name of these "
"shapefiles should be suffixed with the ID that is "
"specified in the natural habitats CSV file provided "
"along with the habitats."),
label='Natural Habitats Directory',
validator=self.validator)
self.general_tab.add_input(self.habitats_directory_uri)
self.habitats_csv_uri = inputs.File(
args_key='habitats_csv_uri',
helptext=(
"A CSV file listing the attributes for each habitat. "
"For more information, see 'Habitat Data Layer' "
"section in the model's documentation.</a>."),
interactive=False,
label='Natural Habitats Table (CSV)',
validator=self.validator)
self.general_tab.add_input(self.habitats_csv_uri)
self.habitats_constant = inputs.Text(
args_key='habitat_constant',
helptext=(
"Integer value between 1 and 5. If layer associated "
"to this field is omitted, replace all shore points "
"for this layer with a constant rank value in the "
"computation of the coastal vulnerability index. If "
"both the file and value for the layer are omitted, "
"the layer is skipped altogether."),
label='Layer Value if Path Omitted',
validator=self.validator)
self.general_tab.add_input(self.habitats_constant)
self.climatic_forcing_uri = inputs.File(
args_key='climatic_forcing_uri',
helptext=(
"An OGR-supported vector containing both wind and "
"wave information across the region of interest."),
label='Climatic Forcing Grid (Vector)',
validator=self.validator)
self.general_tab.add_input(self.climatic_forcing_uri)
self.climatic_forcing_constant = inputs.Text(
args_key='climatic_forcing_constant',
helptext=(
"Integer value between 1 and 5. If layer associated "
"to this field is omitted, replace all shore points "
"for this layer with a constant rank value in the "
"computation of the coastal vulnerability index. If "
"both the file and value for the layer are omitted, "
"the layer is skipped altogether."),
label='Layer Value if Path Omitted',
validator=self.validator)
self.general_tab.add_input(self.climatic_forcing_constant)
self.continental_shelf_uri = inputs.File(
args_key='continental_shelf_uri',
helptext=(
"An OGR-supported polygon vector delineating the "
"edges of the continental shelf. Default is global "
"continental shelf shapefile. If omitted, the user "
"can specify depth contour. See entry below."),
label='Continental Shelf (Vector)',
validator=self.validator)
self.general_tab.add_input(self.continental_shelf_uri)
self.depth_contour = inputs.Text(
args_key='depth_contour',
helptext=(
"Used to delineate shallow and deep areas. "
"Continental shelf limit is at about 150 meters."),
label='Depth Countour Level (meters)',
validator=self.validator)
self.general_tab.add_input(self.depth_contour)
self.sea_level_rise_uri = inputs.File(
args_key='sea_level_rise_uri',
helptext=(
"An OGR-supported point or polygon vector file "
"containing features with 'Trend' fields in the "
"attributes table."),
label='Sea Level Rise (Vector)',
validator=self.validator)
self.general_tab.add_input(self.sea_level_rise_uri)
self.sea_level_rise_constant = inputs.Text(
args_key='sea_level_rise_constant',
helptext=(
"Integer value between 1 and 5. If layer associated "
"to this field is omitted, replace all shore points "
"for this layer with a constant rank value in the "
"computation of the coastal vulnerability index. If "
"both the file and value for the layer are omitted, "
"the layer is skipped altogether."),
label='Layer Value if Path Omitted',
validator=self.validator)
self.general_tab.add_input(self.sea_level_rise_constant)
self.structures_uri = inputs.File(
args_key='structures_uri',
helptext=(
"An OGR-supported vector file containing rigid "
"structures used to identify the portions of the coast "
"that is armored."),
label='Structures (Vectors)',
validator=self.validator)
self.general_tab.add_input(self.structures_uri)
self.structures_constant = inputs.Text(
args_key='structures_constant',
helptext=(
"Integer value between 1 and 5. If layer associated "
"to this field is omitted, replace all shore points "
"for this layer with a constant rank value in the "
"computation of the coastal vulnerability index. If "
"both the file and value for the layer are omitted, "
"the layer is skipped altogether."),
label='Layer Value if Path Omitted',
validator=self.validator)
self.general_tab.add_input(self.structures_constant)
self.population_uri = inputs.File(
args_key='population_uri',
helptext=(
'A GDAL-supported raster file representing the population '
'density.'),
label='Population Layer (Raster)',
validator=self.validator)
self.general_tab.add_input(self.population_uri)
self.urban_center_threshold = inputs.Text(
args_key='urban_center_threshold',
helptext=(
"Minimum population required to consider the shore "
"segment a population center."),
label='Min. Population in Urban Centers',
validator=self.validator)
self.general_tab.add_input(self.urban_center_threshold)
self.additional_layer_uri = inputs.File(
args_key='additional_layer_uri',
helptext=(
"An OGR-supported vector file representing sea level "
"rise, and will be used in the computation of coastal "
"vulnerability and coastal vulnerability without "
"habitat."),
label='Additional Layer (Vector)',
validator=self.validator)
self.general_tab.add_input(self.additional_layer_uri)
self.additional_layer_constant = inputs.Text(
args_key='additional_layer_constant',
helptext=(
"Integer value between 1 and 5. If layer associated "
"to this field is omitted, replace all shore points "
"for this layer with a constant rank value in the "
"computation of the coastal vulnerability index. If "
"both the file and value for the layer are omitted, "
"the layer is skipped altogether."),
label='Layer Value if Path Omitted',
validator=self.validator)
self.general_tab.add_input(self.additional_layer_constant)
self.advanced_tab = inputs.Container(
interactive=True,
label='Advanced')
self.add_input(self.advanced_tab)
self.elevation_averaging_radius = inputs.Text(
args_key='elevation_averaging_radius',
helptext=(
"Distance in meters (integer). Each pixel average "
"elevation will be computed within this radius."),
label='Elevation Averaging Radius (meters)',
validator=self.validator)
self.advanced_tab.add_input(self.elevation_averaging_radius)
self.mean_sea_level_datum = inputs.Text(
args_key='mean_sea_level_datum',
helptext=(
"Height in meters (integer). This input is the "
"elevation of Mean Sea Level (MSL) datum relative to "
"the datum of the bathymetry layer. The model "
"transforms all depths to MSL datum. A positive value "
"means the MSL is higher than the bathymetry's zero "
"(0) elevation, so the value is subtracted from the "
"bathymetry."),
label='Mean Sea Level Datum (meters)',
validator=self.validator)
self.advanced_tab.add_input(self.mean_sea_level_datum)
self.rays_per_sector = inputs.Text(
args_key='rays_per_sector',
helptext=(
"Number of rays used to subsample the fetch distance "
"within each of the 16 sectors."),
label='Rays per Sector',
validator=self.validator)
self.advanced_tab.add_input(self.rays_per_sector)
self.max_fetch = inputs.Text(
args_key='max_fetch',
helptext=(
'Maximum fetch distance computed by the model '
'(>=60,000m).'),
label='Maximum Fetch Distance (meters)',
validator=self.validator)
self.advanced_tab.add_input(self.max_fetch)
self.spread_radius = inputs.Text(
args_key='spread_radius',
helptext=(
"Integer multiple of 'cell size'. The coast from the "
"geomorphology layer could be of a better resolution "
"than the global landmass, so the shores do not "
"necessarily overlap. To make them coincide, the "
"shore from the geomorphology layer is widened by 1 or "
"more pixels. The value should be a multiple of 'cell "
"size' that indicates how many pixels the coast from "
"the geomorphology layer is widened. The widening "
"happens on each side of the coast (n pixels landward, "
"and n pixels seaward)."),
label='Coastal Overlap (meters)',
validator=self.validator)
self.advanced_tab.add_input(self.spread_radius)
self.population_radius = inputs.Text(
args_key='population_radius',
helptext=(
"Radius length in meters used to count the number of "
"people leaving close to the coast."),
label='Coastal Neighborhood (radius in meters)',
validator=self.validator)
self.advanced_tab.add_input(self.population_radius)
# Set interactivity, requirement as input sufficiency changes
self.habitats_directory_uri.sufficiency_changed.connect(
self.habitats_csv_uri.set_interactive)
def __init__(self):
model.InVESTModel.__init__(
self,
label='Habitat Risk Assessment Preprocessor',
target=hra_preprocessor.execute,
validator=hra_preprocessor.validate,
localdoc='../documentation/habitat_risk_assessment.html')
self.habs_dir = inputs.File(
args_key='habitats_dir',
helptext=(
"Checking this box indicates that habitats should be "
"used as a base for overlap with provided stressors. "
"If checked, the path to the habitat layers folder "
"must be provided."),
hideable=True,
label='Calculate Risk to Habitats?',
validator=self.validator)
self.add_input(self.habs_dir)
self.species_dir = inputs.File(
args_key='species_dir',
helptext=(
"Checking this box indicates that species should be "
"used as a base for overlap with provided stressors. "
"If checked, the path to the species layers folder "
"must be provided."),
hideable=True,
label='Calculate Risk to Species?',
validator=self.validator)
self.add_input(self.species_dir)
self.stressor_dir = inputs.Folder(
args_key='stressors_dir',
helptext='This is the path to the stressors layers folder.',
label='Stressors Layers Folder',
validator=self.validator)
self.add_input(self.stressor_dir)
self.cur_lulc_box = inputs.Container(
expandable=False,
label='Criteria')
self.add_input(self.cur_lulc_box)
self.help_label = inputs.Label(
text=(
"(Choose at least 1 criteria for each category below, "
"and at least 4 total.)"))
self.exp_crit = inputs.Multi(
args_key='exposure_crits',
callable_=functools.partial(inputs.Text, label="Input Criteria"),
label='Exposure',
link_text='Add Another')
self.cur_lulc_box.add_input(self.exp_crit)
self.sens_crit = inputs.Multi(
args_key='sensitivity_crits',
callable_=functools.partial(inputs.Text, label="Input Criteria"),
label='Consequence: Sensitivity',
link_text='Add Another')
self.cur_lulc_box.add_input(self.sens_crit)
self.res_crit = inputs.Multi(
args_key='resilience_crits',
callable_=functools.partial(inputs.Text, label="Input Criteria"),
label='Consequence: Resilience',
link_text='Add Another')
self.cur_lulc_box.add_input(self.res_crit)
self.crit_dir = inputs.File(
args_key='criteria_dir',
helptext=(
"Checking this box indicates that model should use "
"criteria from provided shapefiles. Each shapefile in "
"the folder directories will need to contain a "
"'Rating' attribute to be used for calculations in the "
"HRA model. Refer to the HRA User's Guide for "
"information about the MANDATORY layout of the "
"shapefile directories."),
hideable=True,
label='Use Spatially Explicit Risk Score in Shapefiles',
validator=self.validator)
self.add_input(self.crit_dir)
def __init__(self):
model.InVESTModel.__init__(self,
label=u'GLOBIO',
target=natcap.invest.globio.execute,
validator=natcap.invest.globio.validate,
localdoc=u'../documentation/globio.html')
self.lulc_to_globio_table_path = inputs.File(
args_key=u'lulc_to_globio_table_path',
helptext=(u"A CSV table containing model information "
u"corresponding to each of the land use classes in the "
u"LULC raster input. It must contain the fields "
u"'lucode', 'usle_c', and 'usle_p'. See the InVEST "
u"Sediment User's Guide for more information about "
u"these fields."),
label=u'Landcover to GLOBIO Landcover Table (CSV)',
validator=self.validator)
self.add_input(self.lulc_to_globio_table_path)
self.aoi_path = inputs.File(
args_key=u'aoi_path',
helptext=(u"This is a set of polygons that can be used to "
u"aggregate MSA sum and mean to a polygon."),
label=u'AOI (Vector) (optional)',
validator=self.validator)
self.add_input(self.aoi_path)
self.land_use = inputs.File(args_key=u'lulc_path',
label=u'Land Use/Cover (Raster)',
validator=self.validator)
self.add_input(self.land_use)
self.infrastructure_dir = inputs.Folder(
args_key=u'infrastructure_dir',
label=u'Infrastructure Directory',
validator=self.validator)
self.add_input(self.infrastructure_dir)
self.pasture_path = inputs.File(args_key=u'pasture_path',
label=u'Pasture (Raster)',
validator=self.validator)
self.add_input(self.pasture_path)
self.potential_vegetation_path = inputs.File(
args_key=u'potential_vegetation_path',
label=u'Potential Vegetation (Raster)',
validator=self.validator)
self.add_input(self.potential_vegetation_path)
self.primary_threshold = inputs.Text(args_key=u'primary_threshold',
label=u'Primary Threshold',
validator=self.validator)
self.add_input(self.primary_threshold)
self.pasture_threshold = inputs.Text(args_key=u'pasture_threshold',
label=u'Pasture Threshold',
validator=self.validator)
self.add_input(self.pasture_threshold)
self.intensification_fraction = inputs.Text(
args_key=u'intensification_fraction',
helptext=(u"A value between 0 and 1 denoting proportion of total "
u"agriculture that should be classified as 'high "
u"input'."),
label=u'Proportion of of Agriculture Intensified',
validator=self.validator)
self.add_input(self.intensification_fraction)
self.msa_parameters_path = inputs.File(
args_key=u'msa_parameters_path',
helptext=(u"A CSV table containing MSA threshold values as "
u"defined in the user's guide. Provided for advanced "
u"users that may wish to change those values."),
label=u'MSA Parameter Table (CSV)',
validator=self.validator)
self.add_input(self.msa_parameters_path)
self.predefined_globio = inputs.Container(
args_key=u'predefined_globio',
expandable=True,
expanded=False,
label=u'Predefined land use map for GLOBIO')
self.add_input(self.predefined_globio)
self.globio_land_use = inputs.File(
args_key=u'globio_lulc_path',
label=u'GLOBIO Classified Land Use (Raster)',
validator=self.validator)
self.predefined_globio.add_input(self.globio_land_use)
# Set interactivity, requirement as input sufficiency changes
self.predefined_globio.sufficiency_changed.connect(
self._predefined_globio_toggled)
def __init__(self):
model.InVESTModel.__init__(
self,
label=u'InVEST Carbon Model',
target=natcap.invest.carbon.execute,
validator=natcap.invest.carbon.validate,
localdoc=u'../documentation/carbonstorage.html')
self.cur_lulc_raster = inputs.File(
args_key=u'lulc_cur_path',
helptext=(u"A GDAL-supported raster representing the land-cover "
u"of the current scenario."),
label=u'Current Land Use/Land Cover (Raster)',
validator=self.validator)
self.add_input(self.cur_lulc_raster)
self.carbon_pools_path = inputs.File(
args_key=u'carbon_pools_path',
helptext=(u"A table that maps the land-cover IDs to carbon "
u"pools. The table must contain columns of 'LULC', "
u"'C_above', 'C_Below', 'C_Soil', 'C_Dead' as described "
u"in the User's Guide. The values in LULC must at "
u"least include the LULC IDs in the land cover maps."),
label=u'Carbon Pools',
validator=self.validator)
self.add_input(self.carbon_pools_path)
self.cur_lulc_year = inputs.Text(
args_key=u'lulc_cur_year',
helptext=u'The calendar year of the current scenario.',
interactive=False,
label=u'Current Landcover Calendar Year',
validator=self.validator)
self.add_input(self.cur_lulc_year)
self.calc_sequestration = inputs.Checkbox(
helptext=(u"Check to enable sequestration analysis. This "
u"requires inputs of Land Use/Land Cover maps for both "
u"current and future scenarios."),
args_key='calc_sequestration',
label=u'Calculate Sequestration')
self.add_input(self.calc_sequestration)
self.fut_lulc_raster = inputs.File(
args_key=u'lulc_fut_path',
helptext=(u"A GDAL-supported raster representing the land-cover "
u"of the future scenario. <br><br>If REDD scenario "
u"analysis is enabled, this should be the reference, or "
u"baseline, future scenario against which to compare "
u"the REDD policy scenario."),
interactive=False,
label=u'Future Landcover (Raster)',
validator=self.validator)
self.add_input(self.fut_lulc_raster)
self.fut_lulc_year = inputs.Text(
args_key=u'lulc_fut_year',
helptext=u'The calendar year of the future scenario.',
interactive=False,
label=u'Future Landcover Calendar Year',
validator=self.validator)
self.add_input(self.fut_lulc_year)
self.redd = inputs.Checkbox(
helptext=(u"Check to enable REDD scenario analysis. This "
u"requires three Land Use/Land Cover maps: one for the "
u"current scenario, one for the future baseline "
u"scenario, and one for the future REDD policy "
u"scenario."),
interactive=False,
args_key='do_redd',
label=u'REDD Scenario Analysis')
self.add_input(self.redd)
self.redd_lulc_raster = inputs.File(
args_key=u'lulc_redd_path',
helptext=(u"A GDAL-supported raster representing the land-cover "
u"of the REDD policy future scenario. This scenario "
u"will be compared to the baseline future scenario."),
interactive=False,
label=u'REDD Policy (Raster)',
validator=self.validator)
self.add_input(self.redd_lulc_raster)
self.valuation_container = inputs.Container(
args_key=u'do_valuation',
expandable=True,
expanded=False,
interactive=False,
label=u'Run Valuation Model')
self.add_input(self.valuation_container)
self.price_per_metric_ton_of_c = inputs.Text(
args_key=u'price_per_metric_ton_of_c',
label=u'Price/Metric ton of carbon',
validator=self.validator)
self.valuation_container.add_input(self.price_per_metric_ton_of_c)
self.discount_rate = inputs.Text(
args_key=u'discount_rate',
helptext=u'The discount rate as a floating point percent.',
label=u'Market Discount in Price of Carbon (%)',
validator=self.validator)
self.valuation_container.add_input(self.discount_rate)
self.rate_change = inputs.Text(
args_key=u'rate_change',
helptext=(u"The floating point percent increase of the price of "
u"carbon per year."),
label=u'Annual Rate of Change in Price of Carbon (%)',
validator=self.validator)
self.valuation_container.add_input(self.rate_change)
# Set interactivity, requirement as input sufficiency changes
self.calc_sequestration.sufficiency_changed.connect(
self.cur_lulc_year.set_interactive)
self.calc_sequestration.sufficiency_changed.connect(
self.fut_lulc_raster.set_interactive)
self.calc_sequestration.sufficiency_changed.connect(
self.fut_lulc_year.set_interactive)
self.calc_sequestration.sufficiency_changed.connect(
self.redd.set_interactive)
self.redd.sufficiency_changed.connect(
self.redd_lulc_raster.set_interactive)
self.calc_sequestration.sufficiency_changed.connect(
self.valuation_container.set_interactive)
def __init__(self):
model.InVESTModel.__init__(
self,
label=u'Wave Energy',
target=natcap.invest.wave_energy.execute,
validator=natcap.invest.wave_energy.validate,
localdoc=u'wave_energy.html')
self.wave_base_data = inputs.Folder(
args_key=u'wave_base_data_path',
helptext=(u'Select the folder that has the packaged Wave Energy '
u'Data.'),
label=u'Wave Base Data Folder',
validator=self.validator)
self.add_input(self.wave_base_data)
self.analysis_area = inputs.Dropdown(
args_key=u'analysis_area_path',
helptext=(u"A list of analysis areas for which the model can "
u"currently be run. All the wave energy data needed "
u"for these areas are pre-packaged in the WaveData "
u"folder."),
label=u'Analysis Area',
options=(u'West Coast of North America and Hawaii',
u'East Coast of North America and Puerto Rico',
u'North Sea 4 meter resolution',
u'North Sea 10 meter resolution', u'Australia',
u'Global'))
self.add_input(self.analysis_area)
self.aoi = inputs.File(
args_key=u'aoi_path',
helptext=(u"An OGR-supported vector file containing a single "
u"polygon representing the area of interest. This "
u"input is required for computing valuation and is "
u"recommended for biophysical runs as well. The AOI "
u"should be projected in linear units of meters."),
label=u'Area of Interest (Vector)',
validator=self.validator)
self.add_input(self.aoi)
self.machine_perf_table = inputs.File(
args_key=u'machine_perf_path',
helptext=(u"A CSV Table that has the performance of a particular "
u"wave energy machine at certain sea state conditions."),
label=u'Machine Performance Table (CSV)',
validator=self.validator)
self.add_input(self.machine_perf_table)
self.machine_param_table = inputs.File(
args_key=u'machine_param_path',
helptext=(u"A CSV Table that has parameter values for a wave "
u"energy machine. This includes information on the "
u"maximum capacity of the device and the upper limits "
u"for wave height and period."),
label=u'Machine Parameter Table (CSV)',
validator=self.validator)
self.add_input(self.machine_param_table)
self.dem = inputs.File(
args_key=u'dem_path',
helptext=(u"A GDAL-supported raster file containing a digital "
u"elevation model dataset that has elevation values in "
u"meters. Used to get the cable distance for wave "
u"energy transmission."),
label=u'Global Digital Elevation Model (Raster)',
validator=self.validator)
self.add_input(self.dem)
self.valuation_container = inputs.Container(
args_key=u'valuation_container',
expandable=True,
expanded=False,
label=u'Valuation')
self.add_input(self.valuation_container)
self.land_grid_points = inputs.File(
args_key=u'land_gridPts_path',
helptext=(u"A CSV Table that has the landing points and grid "
u"points locations for computing cable distances."),
label=u'Grid Connection Points File (CSV)',
validator=self.validator)
self.valuation_container.add_input(self.land_grid_points)
self.machine_econ_table = inputs.File(
args_key=u'machine_econ_path',
helptext=(u"A CSV Table that has the economic parameters for the "
u"wave energy machine."),
label=u'Machine Economic Table (CSV)',
validator=self.validator)
self.valuation_container.add_input(self.machine_econ_table)
self.number_of_machines = inputs.Text(
args_key=u'number_of_machines',
helptext=(u"An integer for how many wave energy machines will be "
u"in the wave farm."),
label=u'Number of Machines',
validator=self.validator)
self.valuation_container.add_input(self.number_of_machines)
def __init__(self):
model.InVESTModel.__init__(
self,
label=MODEL_METADATA['fisheries_hst'].model_title,
target=fisheries_hst.execute,
validator=fisheries_hst.validate,
localdoc=MODEL_METADATA['fisheries_hst'].userguide)
self.alpha_only = inputs.Label(
text=("This tool is in an ALPHA testing stage and should "
"not be used for decision making."))
self.pop_cont = inputs.Container(args_key='pop_cont',
expanded=True,
label='Population Parameters')
self.add_input(self.pop_cont)
self.population_csv_path = inputs.File(
args_key='population_csv_path',
helptext=("A CSV file containing all necessary attributes for "
"population classes based on age/stage, sex, and area "
"- excluding possible migration "
"information.<br><br>See the 'Running the Model >> "
"Core Model >> Population Parameters' section in the "
"model's documentation for help on how to format this "
"file."),
label='Population Parameters File (CSV)',
validator=self.validator)
self.pop_cont.add_input(self.population_csv_path)
self.sexsp = inputs.Dropdown(
args_key='sexsp',
helptext=("Specifies whether or not the population classes "
"provided in the Populaton Parameters CSV file are "
"distinguished by sex."),
label='Population Classes are Sex-Specific',
options=['No', 'Yes'])
self.pop_cont.add_input(self.sexsp)
self.hab_cont = inputs.Container(args_key='hab_cont',
expanded=True,
label='Habitat Parameters')
self.add_input(self.hab_cont)
self.habitat_csv_dep_path = inputs.File(
args_key='habitat_dep_csv_path',
helptext=("A CSV file containing the habitat dependencies (0-1) "
"for each life stage or age and for each habitat type "
"included in the Habitat Change CSV File.<br><br>See "
"the 'Running the Model >> Habitat Scenario Tool >> "
"Habitat Parameters' section in the model's "
"documentation for help on how to format this file."),
label='Habitat Dependency Parameters File (CSV)',
validator=self.validator)
self.hab_cont.add_input(self.habitat_csv_dep_path)
self.habitat_chg_csv_path = inputs.File(
args_key='habitat_chg_csv_path',
helptext=("A CSV file containing the percent changes in habitat "
"area by subregion (if applicable). The habitats "
"included should be those which the population depends "
"on at any life stage.<br><br>See the 'Running the "
"Model >> Habitat Scenario Tool >> Habitat Parameters' "
"section in the model's documentation for help on how "
"to format this file."),
label='Habitat Area Change File (CSV)',
validator=self.validator)
self.hab_cont.add_input(self.habitat_chg_csv_path)
self.gamma = inputs.Text(
args_key='gamma',
helptext=("Gamma describes the relationship between a change in "
"habitat area and a change in survival of life stages "
"dependent on that habitat. Specify a value between 0 "
"and 1.<br><br>See the documentation for advice on "
"selecting a gamma value."),
label='Gamma',
validator=self.validator)
self.hab_cont.add_input(self.gamma)
def __init__(self):
model.InVESTModel.__init__(self,
label=MODEL_METADATA['sdr'].model_title,
target=natcap.invest.sdr.sdr.execute,
validator=natcap.invest.sdr.sdr.validate,
localdoc=MODEL_METADATA['sdr'].userguide)
self.dem_path = inputs.File(
args_key='dem_path',
helptext=("A GDAL-supported raster file with an elevation value "
"for each cell. Make sure the DEM is corrected by "
"filling in sinks, and if necessary burning "
"hydrographic features into the elevation model "
"(recommended when unusual streams are observed.) See "
"the 'Working with the DEM' section of the InVEST "
"User's Guide for more information."),
label='Digital Elevation Model (Raster)',
validator=self.validator)
self.add_input(self.dem_path)
self.erosivity_path = inputs.File(
args_key='erosivity_path',
helptext=("A GDAL-supported raster file, with an erosivity "
"index value for each cell. This variable depends on "
"the intensity and duration of rainfall in the area of "
"interest. The greater the intensity and duration of "
"the rain storm, the higher the erosion potential. "
"The erosivity index is widely used, but in case of "
"its absence, there are methods and equations to help "
"generate a grid using climatic data. The units are "
"MJ*mm/(ha*h*yr)."),
label='Rainfall Erosivity Index (R) (Raster)',
validator=self.validator)
self.add_input(self.erosivity_path)
self.erodibility_path = inputs.File(
args_key='erodibility_path',
helptext=("A GDAL-supported raster file, with a soil "
"erodibility value for each cell which is a measure of "
"the susceptibility of soil particles to detachment "
"and transport by rainfall and runoff. Units are in "
"T*ha*h/(ha*MJ*mm)."),
label='Soil Erodibility (Raster)',
validator=self.validator)
self.add_input(self.erodibility_path)
self.lulc_path = inputs.File(
args_key='lulc_path',
helptext=("A GDAL-supported raster file, with an integer LULC "
"code for each cell."),
label='Land-Use/Land-Cover (Raster)',
validator=self.validator)
self.add_input(self.lulc_path)
self.watersheds_path = inputs.File(
args_key='watersheds_path',
helptext=("This is a layer of polygons representing watersheds "
"such that each watershed contributes to a point of "
"interest where water quality will be analyzed. It "
"must have the integer field 'ws_id' where the values "
"uniquely identify each watershed."),
label='Watersheds (Vector)',
validator=self.validator)
self.add_input(self.watersheds_path)
self.biophysical_table_path = inputs.File(
args_key='biophysical_table_path',
helptext=("A CSV table containing model information "
"corresponding to each of the land use classes in the "
"LULC raster input. It must contain the fields "
"'lucode', 'usle_c', and 'usle_p'. See the InVEST "
"Sediment User's Guide for more information about "
"these fields."),
label='Biophysical Table (CSV)',
validator=self.validator)
self.add_input(self.biophysical_table_path)
self.threshold_flow_accumulation = inputs.Text(
args_key='threshold_flow_accumulation',
helptext=("The number of upslope cells that must flow into a "
"cell before it's considered part of a stream such "
"that retention stops and the remaining export is "
"exported to the stream. Used to define streams from "
"the DEM."),
label='Threshold Flow Accumulation',
validator=self.validator)
self.add_input(self.threshold_flow_accumulation)
self.drainage_path = inputs.File(
args_key='drainage_path',
helptext=("An optional GDAL-supported raster file mask, that "
"indicates areas that drain to the watershed. Format "
"is that 1's indicate drainage areas and 0's or nodata "
"indicate areas with no additional drainage. This "
"model is most accurate when the drainage raster "
"aligns with the DEM."),
label='Drainages (Raster) (Optional)',
validator=self.validator)
self.add_input(self.drainage_path)
self.k_param = inputs.Text(args_key='k_param',
helptext='Borselli k parameter.',
label='Borselli k Parameter',
validator=self.validator)
self.add_input(self.k_param)
self.ic_0_param = inputs.Text(args_key='ic_0_param',
helptext='Borselli IC0 parameter.',
label='Borselli IC0 Parameter',
validator=self.validator)
self.add_input(self.ic_0_param)
self.sdr_max = inputs.Text(args_key='sdr_max',
helptext='Maximum SDR value.',
label='Max SDR Value',
validator=self.validator)
self.add_input(self.sdr_max)
self.l_max = inputs.Text(
args_key='l_max',
helptext=(
'L will not exceed this value. Ranges of 122-333 (unitless) '
'are found in relevant literature.'),
label='Max L Value',
validator=self.validator)
self.add_input(self.l_max)
def __init__(self):
model.InVESTModel.__init__(
self,
label=MODEL_METADATA['recreation'].model_title,
target=recmodel_client.execute,
validator=recmodel_client.validate,
localdoc=MODEL_METADATA['recreation'].userguide)
self.internet_warning = inputs.Label(
text=("Note, this computer must have an Internet connection "
"in order to run this model."))
self.aoi_path = inputs.File(
args_key='aoi_path',
helptext=("An OGR-supported vector file representing the area "
"of interest where the model will run the analysis."),
label='Area of Interest (Vector)',
validator=self.validator)
self.add_input(self.aoi_path)
self.start_year = inputs.Text(
args_key='start_year',
helptext='Year to start PUD calculations, date starts on Jan 1st.',
label='Start Year (inclusive, must be >= 2005)',
validator=self.validator)
self.add_input(self.start_year)
self.end_year = inputs.Text(
args_key='end_year',
helptext=('Year to end PUD calculations, date ends and includes '
'Dec 31st.'),
label='End Year (inclusive, must be <= 2017)',
validator=self.validator)
self.add_input(self.end_year)
self.regression_container = inputs.Container(
args_key='compute_regression',
expandable=True,
expanded=True,
label='Compute Regression')
self.add_input(self.regression_container)
self.predictor_table_path = inputs.File(
args_key='predictor_table_path',
helptext=("A table that maps predictor IDs to files and their "
"types with required headers of 'id', 'path', and "
"'type'. The file paths can be absolute, or relative "
"to the table."),
label='Predictor Table',
validator=self.validator)
self.regression_container.add_input(self.predictor_table_path)
self.scenario_predictor_table_path = inputs.File(
args_key='scenario_predictor_table_path',
helptext=("A table that maps predictor IDs to files and their "
"types with required headers of 'id', 'path', and "
"'type'. The file paths can be absolute, or relative "
"to the table."),
label='Scenario Predictor Table (optional)',
validator=self.validator)
self.regression_container.add_input(self.scenario_predictor_table_path)
self.grid_container = inputs.Container(args_key='grid_aoi',
expandable=True,
expanded=True,
label='Grid the AOI')
self.add_input(self.grid_container)
self.grid_type = inputs.Dropdown(args_key='grid_type',
label='Grid Type',
options=['square', 'hexagon'])
self.grid_container.add_input(self.grid_type)
self.cell_size = inputs.Text(
args_key='cell_size',
helptext=("The size of the grid units measured in the "
"projection units of the AOI. For example, UTM "
"projections use meters."),
label='Cell Size',
validator=self.validator)
self.grid_container.add_input(self.cell_size)
def __init__(self):
model.InVESTModel.__init__(
self,
label=MODEL_METADATA['coastal_vulnerability'].model_title,
target=coastal_vulnerability.execute,
validator=coastal_vulnerability.validate,
localdoc=MODEL_METADATA['coastal_vulnerability'].userguide)
self.aoi_vector_path = inputs.File(
args_key='aoi_vector_path',
helptext=("Path to a polygon vector that is projected in "
"a coordinate system with units of meters. "
"Shore points will be created along all landmasses "
"within the AOI polygon(s)."),
label='Area of Interest (Vector)',
validator=self.validator)
self.add_input(self.aoi_vector_path)
self.model_resolution = inputs.Text(
args_key='model_resolution',
helptext=("Distance in meters between each shore point, "
"as measured along the landmass polygon coastline."),
label='Model resolution (meters)',
validator=self.validator)
self.add_input(self.model_resolution)
self.landmass_vector_path = inputs.File(
args_key='landmass_vector_path',
helptext=("Path to a polygon vector representing landmasses "
"in the region of interest."),
label='Landmass (Vector)',
validator=self.validator)
self.add_input(self.landmass_vector_path)
self.wwiii_vector_path = inputs.File(
args_key='wwiii_vector_path',
helptext=("Path to a point vector containing wind and wave "
"data. This global dataset is provided with the InVEST "
"sample data."),
label='WaveWatchIII (Vector)',
validator=self.validator)
self.add_input(self.wwiii_vector_path)
self.max_fetch_distance = inputs.Text(
args_key='max_fetch_distance',
helptext=(
"Maximum distance in meters to extend rays from shore points. "
"Rays extend in 16 compass directions until they intersect "
"land or reach this maximum distance. Fetch (or ray) length "
"is a component of wind and wave exposure."),
label='Maximum Fetch Distance (meters)',
validator=self.validator)
self.add_input(self.max_fetch_distance)
self.bathymetry_raster_path = inputs.File(
args_key='bathymetry_raster_path',
helptext=(
"Path to a raster representing bathymetry. "
"Bathymetry values should be negative and units of meters. "
"Positive values will be ignored."),
label='Bathymetry (Raster)',
validator=self.validator)
self.add_input(self.bathymetry_raster_path)
self.dem_path = inputs.File(
args_key='dem_path',
helptext=("Path to a raster representing elevation on land. "
"Negative values, if present, are treated as zeros. "
"If this input is unprojected, the model will project "
"it to match the AOI and resample it to a cell size "
"equal to the model resolution."),
label='Digital Elevation Model (Raster)',
validator=self.validator)
self.add_input(self.dem_path)
self.dem_averaging_radius = inputs.Text(
args_key='dem_averaging_radius',
helptext=("A radius around each shore point within which to "
"average the elevation values of the DEM raster. "),
label='Elevation averaging radius (meters)',
validator=self.validator)
self.add_input(self.dem_averaging_radius)
self.shelf_contour_vector_path = inputs.File(
args_key='shelf_contour_vector_path',
helptext=(
"Path to a polyline vector delineating the edge "
"of the continental shelf or another bathymetry contour. "),
label='Continental Shelf Contour (Vector)',
validator=self.validator)
self.add_input(self.shelf_contour_vector_path)
self.habitat_table_path = inputs.File(
args_key='habitat_table_path',
helptext=(
"Path to a CSV file that specifies habitat layer input data "
"and parameters."),
label='Habitats Table (CSV)',
validator=self.validator)
self.add_input(self.habitat_table_path)
self.geomorphology_vector_path = inputs.File(
args_key='geomorphology_vector_path',
helptext=("Path to a polyline vector that has a field called "
"'RANK' with values from 1 to 5. "),
label='Geomorphology (Vector) (optional)',
validator=self.validator)
self.add_input(self.geomorphology_vector_path)
self.geomorphology_fill_value = inputs.Dropdown(
args_key='geomorphology_fill_value',
helptext=(
"A value from 1 to 5 that will be used as a geomorphology "
"rank for any points not proximate (given the model "
"resolution) to the geomorphology_vector_path."),
label='Geomorphology fill value',
interactive=False,
options=('1', '2', '3', '4', '5'))
self.add_input(self.geomorphology_fill_value)
self.population_raster_path = inputs.File(
args_key='population_raster_path',
helptext=(
"Path to a raster with values representing totals per pixel. "
"If this input is unprojected, the model will project "
"it to match the AOI and resample it to a cell size "
"equal to the model resolution."),
label='Human Population (Raster) (optional)',
validator=self.validator)
self.add_input(self.population_raster_path)
self.population_radius = inputs.Text(
args_key='population_radius',
helptext=("A radius around each shore point within which to "
"compute the average population density."),
label='Population search radius (meters)',
interactive=False,
validator=self.validator)
self.add_input(self.population_radius)
self.slr_vector_path = inputs.File(
args_key='slr_vector_path',
helptext=(
"Path to a point vector with a field of sea-level-rise rates"
"or amounts."),
label='Sea Level Rise (Vector) (optional)',
validator=self.validator)
self.add_input(self.slr_vector_path)
self.slr_field = inputs.Dropdown(
args_key='slr_field',
helptext=("The name of a field in the SLR vector table"
"from which to load values"),
label='Sea Level Rise fieldname',
interactive=False,
options=(
'UNKNOWN', )) # No options until valid OGR vector provided
self.add_input(self.slr_field)
# Set interactivity requirement as input sufficiency changes
self.slr_vector_path.sufficiency_changed.connect(
self.slr_field.set_interactive)
self.slr_vector_path.sufficiency_changed.connect(self._load_colnames)
self.geomorphology_vector_path.sufficiency_changed.connect(
self.geomorphology_fill_value.set_interactive)
self.population_raster_path.sufficiency_changed.connect(
self.population_radius.set_interactive)
def __init__(self):
model.InVESTModel.__init__(
self,
label=u'Scenic Quality',
target=scenic_quality.execute,
validator=scenic_quality.validate,
localdoc=u'../documentation/scenic_quality.html')
self.beta_only = inputs.Label(
text=(
u"This tool is considered UNSTABLE. Users may "
u"experience performance issues and unexpected errors."))
self.general_tab = inputs.Container(
interactive=True,
label=u'General')
self.add_input(self.general_tab)
self.aoi_uri = inputs.File(
args_key=u'aoi_uri',
helptext=(
u"An OGR-supported vector file. This AOI instructs "
u"the model where to clip the input data and the extent "
u"of analysis. Users will create a polygon feature "
u"layer that defines their area of interest. The AOI "
u"must intersect the Digital Elevation Model (DEM)."),
label=u'Area of Interest (Vector)',
validator=self.validator)
self.general_tab.add_input(self.aoi_uri)
self.cell_size = inputs.Text(
args_key=u'cell_size',
helptext=u'Length (in meters) of each side of the (square) cell.',
label=u'Cell Size (meters)',
validator=self.validator)
self.general_tab.add_input(self.cell_size)
self.structure_uri = inputs.File(
args_key=u'structure_uri',
helptext=(
u"An OGR-supported vector file. The user must specify "
u"a point feature layer that indicates locations of "
u"objects that contribute to negative scenic quality, "
u"such as aquaculture netpens or wave energy "
u"facilities. In order for the viewshed analysis to "
u"run correctly, the projection of this input must be "
u"consistent with the project of the DEM input."),
label=u'Features Impacting Scenic Quality (Vector)',
validator=self.validator)
self.general_tab.add_input(self.structure_uri)
self.dem_uri = inputs.File(
args_key=u'dem_uri',
helptext=(
u"A GDAL-supported raster file. An elevation raster "
u"layer is required to conduct viewshed analysis. "
u"Elevation data allows the model to determine areas "
u"within the AOI's land-seascape where point features "
u"contributing to negative scenic quality are visible."),
label=u'Digital Elevation Model (Raster)',
validator=self.validator)
self.general_tab.add_input(self.dem_uri)
self.refraction = inputs.Text(
args_key=u'refraction',
helptext=(
u"The earth curvature correction option corrects for "
u"the curvature of the earth and refraction of visible "
u"light in air. Changes in air density curve the light "
u"downward causing an observer to see further and the "
u"earth to appear less curved. While the magnitude of "
u"this effect varies with atmospheric conditions, a "
u"standard rule of thumb is that refraction of visible "
u"light reduces the apparent curvature of the earth by "
u"one-seventh. By default, this model corrects for the "
u"curvature of the earth and sets the refractivity "
u"coefficient to 0.13."),
label=u'Refractivity Coefficient',
validator=self.validator)
self.general_tab.add_input(self.refraction)
self.pop_uri = inputs.File(
args_key=u'pop_uri',
helptext=(
u"A GDAL-supported raster file. A population raster "
u"layer is required to determine population within the "
u"AOI's land-seascape where point features contributing "
u"to negative scenic quality are visible and not "
u"visible."),
label=u'Population (Raster)',
validator=self.validator)
self.general_tab.add_input(self.pop_uri)
self.overlap_uri = inputs.File(
args_key=u'overlap_uri',
helptext=(
u"An OGR-supported vector file. The user has the "
u"option of providing a polygon feature layer where "
u"they would like to determine the impact of objects on "
u"visual quality. This input must be a polygon and "
u"projected in meters. The model will use this layer "
u"to determine what percent of the total area of each "
u"polygon feature can see at least one of the point "
u"features impacting scenic quality."),
label=u'Overlap Analysis Features (Vector)',
validator=self.validator)
self.general_tab.add_input(self.overlap_uri)
self.valuation_tab = inputs.Container(
interactive=True,
label=u'Valuation')
self.add_input(self.valuation_tab)
self.valuation_function = inputs.Dropdown(
args_key=u'valuation_function',
helptext=(
u"This field indicates the functional form f(x) the "
u"model will use to value the visual impact for each "
u"viewpoint. For distances less than 1 km (x<1), the "
u"model uses a linear form g(x) where the line passes "
u"through f(1) (i.e. g(1) == f(1)) and extends to zero "
u"with the same slope as f(1) (i.e. g'(x) == f'(1))."),
label=u'Valuation Function',
options=[u'polynomial: a + bx + cx^2 + dx^3',
u'logarithmic: a + b ln(x)'])
self.valuation_tab.add_input(self.valuation_function)
self.a_coefficient = inputs.Text(
args_key=u'a_coefficient',
helptext=(
u"First coefficient used either by the polynomial or "
u"by the logarithmic valuation function."),
label=u"'a' Coefficient (polynomial/logarithmic)",
validator=self.validator)
self.valuation_tab.add_input(self.a_coefficient)
self.b_coefficient = inputs.Text(
args_key=u'b_coefficient',
helptext=(
u"Second coefficient used either by the polynomial or "
u"by the logarithmic valuation function."),
label=u"'b' Coefficient (polynomial/logarithmic)",
validator=self.validator)
self.valuation_tab.add_input(self.b_coefficient)
self.c_coefficient = inputs.Text(
args_key=u'c_coefficient',
helptext=u"Third coefficient for the polynomial's quadratic term.",
label=u"'c' Coefficient (polynomial only)",
validator=self.validator)
self.valuation_tab.add_input(self.c_coefficient)
self.d_coefficient = inputs.Text(
args_key=u'd_coefficient',
helptext=u"Fourth coefficient for the polynomial's cubic exponent.",
label=u"'d' Coefficient (polynomial only)",
validator=self.validator)
self.valuation_tab.add_input(self.d_coefficient)
self.max_valuation_radius = inputs.Text(
args_key=u'max_valuation_radius',
helptext=(
u"Radius beyond which the valuation is set to zero. "
u"The valuation function 'f' cannot be negative at the "
u"radius 'r' (f(r)>=0)."),
label=u'Maximum Valuation Radius (meters)',
validator=self.validator)
self.valuation_tab.add_input(self.max_valuation_radius)
def __init__(self):
model.InVESTModel.__init__(self,
label=u'Scenic Quality',
target=scenic_quality.execute,
validator=scenic_quality.validate,
localdoc=u'scenic_quality.html')
self.general_tab = inputs.Container(interactive=True, label=u'General')
self.add_input(self.general_tab)
self.aoi_path = inputs.File(
args_key=u'aoi_path',
helptext=(u"An OGR-supported vector file. This AOI instructs "
u"the model where to clip the input data and the extent "
u"of analysis. Users will create a polygon feature "
u"layer that defines their area of interest. The AOI "
u"must intersect the Digital Elevation Model (DEM)."),
label=u'Area of Interest (Vector) (Required)',
validator=self.validator)
self.general_tab.add_input(self.aoi_path)
self.structure_path = inputs.File(
args_key=u'structure_path',
helptext=(u"An OGR-supported vector file. The user must specify "
u"a point feature layer that indicates locations of "
u"objects that contribute to negative scenic quality, "
u"such as aquaculture netpens or wave energy "
u"facilities. In order for the viewshed analysis to "
u"run correctly, the projection of this input must be "
u"consistent with the project of the DEM input."),
label=u'Features Impacting Scenic Quality (Vector) (Required)',
validator=self.validator)
self.general_tab.add_input(self.structure_path)
self.dem_path = inputs.File(
args_key=u'dem_path',
helptext=(u"A GDAL-supported raster file. An elevation raster "
u"layer is required to conduct viewshed analysis. "
u"Elevation data allows the model to determine areas "
u"within the AOI's land-seascape where point features "
u"contributing to negative scenic quality are visible."),
label=u'Digital Elevation Model (Raster) (Required)',
validator=self.validator)
self.general_tab.add_input(self.dem_path)
self.refraction = inputs.Text(
args_key=u'refraction',
helptext=(u"The earth curvature correction option corrects for "
u"the curvature of the earth and refraction of visible "
u"light in air. Changes in air density curve the light "
u"downward causing an observer to see further and the "
u"earth to appear less curved. While the magnitude of "
u"this effect varies with atmospheric conditions, a "
u"standard rule of thumb is that refraction of visible "
u"light reduces the apparent curvature of the earth by "
u"one-seventh. By default, this model corrects for the "
u"curvature of the earth and sets the refractivity "
u"coefficient to 0.13."),
label=u'Refractivity Coefficient (Required)',
validator=self.validator)
self.general_tab.add_input(self.refraction)
self.valuation_container = inputs.Container(args_key=u'do_valuation',
expandable=True,
expanded=False,
interactive=True,
label=u'Valuation')
self.add_input(self.valuation_container)
self.valuation_function = inputs.Dropdown(
args_key=u'valuation_function',
helptext=(u"This field indicates the functional form f(x) the "
u"model will use to value the visual impact for each "
u"viewpoint."),
label=u'Valuation Function',
options=[
u'linear: a + bx', u'logarithmic: a + b log(x+1)',
u'exponential: a * e^(-bx)'
])
self.valuation_container.add_input(self.valuation_function)
self.a_coefficient = inputs.Text(
args_key=u'a_coef',
helptext=(u"First coefficient used by the valuation function"),
label=u"'a' Coefficient (Required)",
validator=self.validator)
self.valuation_container.add_input(self.a_coefficient)
self.b_coefficient = inputs.Text(
args_key=u'b_coef',
helptext=(u"Second coefficient used by the valuation function"),
label=u"'b' Coefficient (Required)",
validator=self.validator)
self.valuation_container.add_input(self.b_coefficient)
self.max_valuation_radius = inputs.Text(
args_key=u'max_valuation_radius',
helptext=(u"Radius beyond which the valuation is set to zero. "
u"The valuation function 'f' cannot be negative at the "
u"radius 'r' (f(r)>=0)."),
label=u'Maximum Valuation Radius (meters) (Required)',
validator=self.validator)
self.valuation_container.add_input(self.max_valuation_radius)
