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=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['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='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=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=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)
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=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['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='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='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=u'Coastal Vulnerability',
target=coastal_vulnerability.execute,
validator=coastal_vulnerability.validate,
localdoc=u'coastal_vulnerability.html')
self.aoi_vector_path = inputs.File(
args_key=u'aoi_vector_path',
helptext=(
u"path to a polygon vector that is projected in "
u"a coordinate system with units of meters. "
u"The polygon should intersect the landmass and "
u"the shelf contour line"),
label=u'Area of Interest (Vector)',
validator=self.validator)
self.add_input(self.aoi_vector_path)
self.model_resolution = inputs.Text(
args_key=u'model_resolution',
helptext=(
u"distance in meters at which the coastline "
u"will be resolved. Coastline features smaller than this "
u"distance will not be represented by the shoreline points. "
u"Points will be spaced at intervals of half the model "
u"resolution."),
label=u'Model resolution (meters)',
validator=self.validator)
self.add_input(self.model_resolution)
self.landmass_vector_path = inputs.File(
args_key=u'landmass_vector_path',
helptext=(
u"path to a polygon vector representing landmasses "
u"in the region of interest."),
label=u'Landmass (Vector)',
validator=self.validator)
self.add_input(self.landmass_vector_path)
self.wwiii_vector_path = inputs.File(
args_key=u'wwiii_vector_path',
helptext=(
u"path to a point vector containing wind and wave "
u"data. This global dataset is provided with the InVEST "
u"sample data."),
label=u'WaveWatchIII (Vector)',
validator=self.validator)
self.add_input(self.wwiii_vector_path)
self.max_fetch_distance = inputs.Text(
args_key=u'max_fetch_distance',
helptext=(
u"maximum distance in meters to extend rays from shore points. "
u"Points with rays equal to this distance accumulate "
u"ocean-driven wave exposure along those rays and "
u"local-wind-driven wave exposure along the shorter rays."),
label=u'Maximum Fetch Distance (meters)',
validator=self.validator)
self.add_input(self.max_fetch_distance)
self.shelf_contour_vector_path = inputs.File(
args_key=u'shelf_contour_vector_path',
helptext=(
u"path to a polygon or polyline vector delineating the edge "
u"of the continental shelf or another bathymetry contour. "),
label=u'Continental Shelf Contour (Vector)',
validator=self.validator)
self.add_input(self.shelf_contour_vector_path)
self.dem_path = inputs.File(
args_key=u'dem_path',
helptext=(
u"path to a raster representing elevation on land. "),
label=u'Digital Elevation Model (Raster)',
validator=self.validator)
self.add_input(self.dem_path)
self.dem_averaging_radius = inputs.Text(
args_key=u'dem_averaging_radius',
helptext=(
u"a radius around each shore point within which to "
u"average the elevation values of the DEM raster. "),
label=u'Elevation averaging radius (meters)',
validator=self.validator)
self.add_input(self.dem_averaging_radius)
self.habitat_table_path = inputs.File(
args_key=u'habitat_table_path',
helptext=(
u"path to a CSV file that specifies habitat layer input data "
u"and parameters."),
label=u'Habitats Table (CSV)',
validator=self.validator)
self.add_input(self.habitat_table_path)
self.geomorphology_vector_path = inputs.File(
args_key=u'geomorphology_vector_path',
helptext=(
u"path to a polyline vector that has a field called "
u"'RANK' with values from 1 to 5. "),
label=u'Geomorphology (Vector) (optional)',
validator=self.validator)
self.add_input(self.geomorphology_vector_path)
self.geomorphology_fill_value = inputs.Dropdown(
args_key=u'geomorphology_fill_value',
helptext=(
u"a value from 1 to 5 that will be used as a geomorphology "
u"rank for any points not proximate (given the model "
u"resolution) to the geomorphology_vector_path."),
label=u'Geomorphology fill value',
interactive=False,
options=('1', '2', '3', '4', '5'))
# validator=self.validator)
self.add_input(self.geomorphology_fill_value)
self.population_raster_path = inputs.File(
args_key=u'population_raster_path',
helptext=(
u"path to a raster with values representing totals per pixel."),
label=u'Human Population (Raster) (optional)',
validator=self.validator)
self.add_input(self.population_raster_path)
self.population_radius = inputs.Text(
args_key=u'population_radius',
helptext=(
u"a radius around each shore point within which to "
u"compute the average population density."),
label=u'Population search radius (meters)',
interactive=False,
validator=self.validator)
self.add_input(self.population_radius)
self.slr_vector_path = inputs.File(
args_key=u'slr_vector_path',
helptext=(
u"path to a point vector with a field of sea-level-rise rates"
u"or amounts."),
label=u'Sea Level Rise (Vector) (optional)',
validator=self.validator)
self.add_input(self.slr_vector_path)
self.slr_field = inputs.Dropdown(
args_key=u'slr_field',
helptext=(
u"the name of a field in the SLR vector table"
u"from which to load values"),
label=u'Sea Level Rise fieldname',
interactive=False,
options=('UNKNOWN',)) # No options until valid OGR vector provided
# validator=self.validator)
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'Forest Carbon Edge Effect Model',
target=natcap.invest.forest_carbon_edge_effect.execute,
validator=natcap.invest.forest_carbon_edge_effect.validate,
localdoc=u'forest_carbon_edge_effect.html')
self.lulc_raster_path = inputs.File(
args_key=u'lulc_raster_path',
helptext=(u"A GDAL-supported raster file, with an integer LULC "
u"code for each cell."),
label=u'Land-Use/Land-Cover Map (raster)',
validator=self.validator)
self.add_input(self.lulc_raster_path)
self.biophysical_table_path = inputs.File(
args_key=u'biophysical_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', 'is_tropical_forest', 'c_above'. If the "
u"user selects 'all carbon pools' the table must also "
u"contain entries for 'c_below', 'c_soil', and "
u"'c_dead'. See the InVEST Forest Carbon User's Guide "
u"for more information about these fields."),
label=u'Biophysical Table (csv)',
validator=self.validator)
self.add_input(self.biophysical_table_path)
self.pools_to_calculate = inputs.Dropdown(
args_key=u'pools_to_calculate',
helptext=(u"If 'all carbon pools' is selected then the headers "
u"'c_above', 'c_below', 'c_dead', 'c_soil' are used in "
u"the carbon pool calculation. Otherwise only "
u"'c_above' is considered."),
label=u'Carbon Pools to Calculate',
options=[u'all carbon pools', u'above ground only'],
return_value_map={
'all carbon pools': 'all',
'above ground only': 'above_ground'
})
self.add_input(self.pools_to_calculate)
self.compute_forest_edge_effects = inputs.Checkbox(
args_key=u'compute_forest_edge_effects',
helptext=(u"If selected, will use the Chaplin-Kramer, et. al "
u"method to account for above ground carbon stocks in "
u"tropical forest types indicated by a '1' in the "
u"'is_tropical_forest' field in the biophysical table."),
label=u'Compute forest edge effects')
self.add_input(self.compute_forest_edge_effects)
self.tropical_forest_edge_carbon_model_vector_path = inputs.File(
args_key=u'tropical_forest_edge_carbon_model_vector_path',
helptext=(u"A shapefile with fields 'method', 'theta1', "
u"'theta2', 'theta3' describing the global forest "
u"carbon edge models. Provided as default data for the "
u"model."),
interactive=False,
label=u'Global forest carbon edge regression models (vector)',
validator=self.validator)
self.add_input(self.tropical_forest_edge_carbon_model_vector_path)
self.n_nearest_model_points = inputs.Text(
args_key=u'n_nearest_model_points',
helptext=(u"Used when calculating the biomass in a pixel. This "
u"number determines the number of closest regression "
u"models that are used when calculating the total "
u"biomass. Each local model is linearly weighted by "
u"distance such that the biomass in the pixel is a "
u"function of each of these points with the closest "
u"point having the highest effect."),
interactive=False,
label=u'Number of nearest model points to average',
validator=self.validator)
self.add_input(self.n_nearest_model_points)
self.biomass_to_carbon_conversion_factor = inputs.Text(
args_key=u'biomass_to_carbon_conversion_factor',
helptext=(u"Number by which to scale forest edge biomass to "
u"convert to carbon. Default value is 0.47 (according "
u"to IPCC 2006). This pertains to forest classes only; "
u"values in the biophysical table for non-forest "
u"classes should already be in terms of carbon, not "
u"biomass."),
interactive=False,
label=u'Forest Edge Biomass to Carbon Conversion Factor',
validator=self.validator)
self.add_input(self.biomass_to_carbon_conversion_factor)
self.aoi_vector_path = inputs.File(
args_key=u'aoi_vector_path',
helptext=(u"This is a set of polygons that will be used to "
u"aggregate carbon values at the end of the run if "
u"provided."),
label=u'Service areas of interest <em>(optional)</em> (vector)',
validator=self.validator)
self.add_input(self.aoi_vector_path)
# Set interactivity, requirement as input sufficiency changes
self.compute_forest_edge_effects.sufficiency_changed.connect(
self.tropical_forest_edge_carbon_model_vector_path.set_interactive)
self.compute_forest_edge_effects.sufficiency_changed.connect(
self.n_nearest_model_points.set_interactive)
self.compute_forest_edge_effects.sufficiency_changed.connect(
self.biomass_to_carbon_conversion_factor.set_interactive)
def __init__(self):
model.InVESTModel.__init__(
self,
label='Urban Cooling Model',
target=natcap.invest.urban_cooling_model.execute,
validator=natcap.invest.urban_cooling_model.validate,
localdoc='urban_cooling_model.html')
self.lulc_raster_path = inputs.File(
args_key='lulc_raster_path',
helptext=('Path to landcover raster.'),
label='Land Use / Land Cover (Raster)',
validator=self.validator)
self.add_input(self.lulc_raster_path)
self.ref_eto_raster_path = inputs.File(
args_key='ref_eto_raster_path',
helptext=('Path to evapotranspiration raster.'),
label='Reference Evapotranspiration (Raster)',
validator=self.validator)
self.add_input(self.ref_eto_raster_path)
self.aoi_vector_path = inputs.File(
args_key='aoi_vector_path',
helptext=('Path to desired AOI.'),
label='Area of Interest (Vector)',
validator=self.validator)
self.add_input(self.aoi_vector_path)
self.biophysical_table_path = inputs.File(
args_key='biophysical_table_path',
helptext=(
"Table to map landcover codes to Shade, Kc, and Albedo "
"values. Must contain the fields 'lucode', 'shade', 'kc', "
"and 'albedo'."),
label='Biophysical Table (CSV)',
validator=self.validator)
self.add_input(self.biophysical_table_path)
self.t_ref = inputs.Text(
args_key='t_ref',
helptext=('Reference air temperature (real).'),
label='Baseline air temperature (°C)',
validator=self.validator)
self.t_ref.set_value("21.5")
self.add_input(self.t_ref)
self.uhi_max = inputs.Text(
args_key='uhi_max',
label='Magnitude of the UHI effect (°C)',
helptext=(
"The magnitude of the urban heat island effect, in degrees "
"C. Example: the difference between the rural reference "
"temperature and the maximum temperature observed in the "
"city."),
validator=self.validator)
self.add_input(self.uhi_max)
self.uhi_max.set_value("3.5")
self.t_air_average_radius = inputs.Text(
args_key='t_air_average_radius',
label='Air Temperature Maximum Blending Distance (m).',
helptext=(
"Radius of the averaging filter for turning T_air_nomix "
"into T_air"),
validator=self.validator)
self.add_input(self.t_air_average_radius)
self.t_air_average_radius.set_value("2000")
self.green_area_cooling_distance = inputs.Text(
args_key='green_area_cooling_distance',
label='Green Area Maximum Cooling Distance (m).',
helptext=(
"Distance (in m) over which large green areas (> 2 ha) "
"will have a cooling effect."),
validator=self.validator)
self.add_input(self.green_area_cooling_distance)
self.green_area_cooling_distance.set_value("1000")
self.cc_method = inputs.Dropdown(
label='Cooling Capacity Calculation Method',
args_key='cc_method',
helptext=(
'The method selected here determines the predictor used for '
'air temperature. If <b>"Weighted Factors"</b> is '
'selected, the Cooling Capacity calculations will use the '
'weighted factors for shade, albedo and ETI as a predictor '
'for daytime temperatures. <br/>'
'Alternatively, if <b>"Building Intensity"</b> is selected, '
'building intensity will be used as a predictor for nighttime '
'temperature instead of shade, albedo and ETI.'
),
options=('Weighted Factors', 'Building Intensity'),
return_value_map={
'Weighted Factors': 'factors',
'Building Intensity': 'intensity',
})
self.cc_method.value_changed.connect(
self._enable_cc_options)
self.add_input(self.cc_method)
self.energy_valuation_container = inputs.Container(
args_key='do_energy_valuation',
expandable=True,
expanded=True,
interactive=True,
label='Run Energy Savings Valuation Model')
self.add_input(self.energy_valuation_container)
self.productivity_valuation_container = inputs.Container(
args_key='do_productivity_valuation',
expandable=True,
expanded=True,
interactive=True,
label='Run Work Productivity Valuation Model')
self.add_input(self.productivity_valuation_container)
self.building_vector_path = inputs.File(
args_key='building_vector_path',
helptext=(
"Path to a vector of building footprints that contains at "
"least the field 'type'."),
label='Building Footprints (Vector)',
validator=self.validator)
self.energy_valuation_container.add_input(self.building_vector_path)
self.energy_consumption_table_path = inputs.File(
args_key='energy_consumption_table_path',
helptext=(
"Path to a table that maps building types to energy "
"consumption. Must contain at least the fields 'type' "
"and 'consumption'."),
label='Energy Consumption Table (CSV)',
validator=self.validator)
self.energy_valuation_container.add_input(self.energy_consumption_table_path)
self.avg_rel_humidity = inputs.Text(
args_key='avg_rel_humidity',
label='Average relative humidity (0-100%)',
helptext=(
"The average relative humidity (0-100%)."),
validator=self.validator)
self.productivity_valuation_container.add_input(self.avg_rel_humidity)
self.avg_rel_humidity.set_value("30")
self.cooling_capacity_container = inputs.Container(
expandable=True,
expanded=True,
interactive=True,
label='Manually Adjust Cooling Capacity Index Weights'
)
self.add_input(self.cooling_capacity_container)
self.cc_weight_shade = inputs.Text(
args_key='cc_weight_shade',
helptext=("Shade weight for cooling capacity index. "
"Default: 0.6"),
label='Shade',
validator=self.validator)
self.cooling_capacity_container.add_input(self.cc_weight_shade)
self.cc_weight_shade.set_value("0.6")
self.cc_weight_albedo = inputs.Text(
args_key='cc_weight_albedo',
helptext=("Albedo weight for cooling capacity index. "
"Default: 0.2"),
label='Albedo',
validator=self.validator)
self.cooling_capacity_container.add_input(self.cc_weight_albedo)
self.cc_weight_albedo.set_value("0.2")
self.cc_weight_eti = inputs.Text(
args_key='cc_weight_eti',
helptext=("Evapotranspiration index weight for cooling capacity. "
"Default: 0.2"),
label='Evapotranspiration Index',
validator=self.validator)
self.cooling_capacity_container.add_input(self.cc_weight_eti)
self.cc_weight_eti.set_value("0.2")
def __init__(self):
model.InVESTModel.__init__(
self,
label=MODEL_METADATA['routedem'].model_title,
target=routedem.execute,
validator=routedem.validate,
localdoc=MODEL_METADATA['routedem'].userguide)
self.dem_path = inputs.File(
args_key='dem_path',
helptext=(
"A GDAL-supported raster file containing a base "
"Digital Elevation Model to execute the routing "
"functionality across."),
label='Digital Elevation Model (Raster)',
validator=self.validator)
self.add_input(self.dem_path)
self.dem_band_index = inputs.Text(
args_key='dem_band_index',
helptext=(
'The band index to use from the raster. '
'This positive integer is 1-based.'
'Default: 1'),
label='Band Index (optional)',
validator=self.validator)
self.dem_band_index.set_value(1)
self.add_input(self.dem_band_index)
self.calculate_slope = inputs.Checkbox(
args_key='calculate_slope',
helptext='If selected, calculates slope raster.',
label='Calculate Slope')
self.add_input(self.calculate_slope)
self.algorithm = inputs.Dropdown(
args_key='algorithm',
label='Routing Algorithm',
helptext=(
'The routing algorithm to use. '
'<ul><li>D8: all water flows directly into the most downhill '
'of each of the 8 neighbors of a cell.</li>'
'<li>MFD: Multiple Flow Direction. Fractional flow is '
'modelled between pixels.</li></ul>'),
options=('D8', 'MFD'))
self.add_input(self.algorithm)
self.calculate_flow_direction = inputs.Checkbox(
args_key='calculate_flow_direction',
helptext='Select to calculate flow direction',
label='Calculate Flow Direction')
self.add_input(self.calculate_flow_direction)
self.calculate_flow_accumulation = inputs.Checkbox(
args_key='calculate_flow_accumulation',
helptext='Select to calculate flow accumulation.',
label='Calculate Flow Accumulation',
interactive=False)
self.add_input(self.calculate_flow_accumulation)
self.calculate_stream_threshold = inputs.Checkbox(
args_key='calculate_stream_threshold',
helptext='Select to calculate a stream threshold to flow accumulation.',
interactive=False,
label='Calculate Stream Thresholds')
self.add_input(self.calculate_stream_threshold)
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 classified as a stream."),
interactive=False,
label='Threshold Flow Accumulation Limit',
validator=self.validator)
self.add_input(self.threshold_flow_accumulation)
self.calculate_downslope_distance = inputs.Checkbox(
args_key='calculate_downslope_distance',
helptext=(
"If selected, creates a downslope distance raster "
"based on the thresholded flow accumulation stream "
"classification."),
interactive=False,
label='Calculate Distance to stream')
self.add_input(self.calculate_downslope_distance)
# Set interactivity, requirement as input sufficiency changes
self.calculate_flow_direction.sufficiency_changed.connect(
self.calculate_flow_accumulation.set_interactive)
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_downslope_distance.set_interactive)
def __init__(self):
model.InVESTModel.__init__(self,
label=u'Marine Aquaculture: Finfish',
target=finfish_aquaculture.execute,
validator=finfish_aquaculture.validate,
localdoc=u'marine_fish.html')
self.farm_location = inputs.File(
args_key=u'ff_farm_loc',
helptext=(u"An OGR-supported vector file containing polygon or "
u"point, with a latitude and longitude value and a "
u"numerical identifier for each farm. File can be "
u"named anything, but no spaces in the "
u"name.<br><br>File type: polygon shapefile or "
u".gdb<br>Rows: each row is a specific netpen or entire "
u"aquaculture farm<br>Columns: columns contain "
u"attributes about each netpen (area, location, "
u"etc.).<br>Sample data set: "
u"\InVEST\Aquaculture\Input\Finfish_Netpens.shp"),
label=u'Finfish Farm Location (Vector)',
validator=self.validator)
self.add_input(self.farm_location)
self.farm_identifier = inputs.Dropdown(
args_key=u'farm_ID',
helptext=(u"The name of a column heading used to identify each "
u"farm and link the spatial information from the "
u"shapefile to subsequent table input data (farm "
u"operation and daily water temperature at farm "
u"tables). Additionally, the numbers underneath this "
u"farm identifier name must be unique integers for all "
u"the inputs."),
interactive=False,
options=(
'UNKNOWN', ), # No options until valid OGR vector provided
label=u'Farm Identifier Name')
self.add_input(self.farm_identifier)
self.param_a = inputs.Text(
args_key=u'g_param_a',
helptext=(u"Default a = (0.038 g/day). If the user chooses to "
u"adjust these parameters, we recommend using them in "
u"the simple growth model to determine if the time "
u"taken for a fish to reach a target harvest weight "
u"typical for the region of interest is accurate."),
label=u'Fish Growth Parameter (a)',
validator=self.validator)
self.add_input(self.param_a)
self.param_b = inputs.Text(
args_key=u'g_param_b',
helptext=(u"Default b = (0.6667 g/day). If the user chooses to "
u"adjust these parameters, we recommend using them in "
u"the simple growth model to determine if the time "
u"taken for a fish to reach a target harvest weight "
u"typical for the region of interest is accurate."),
label=u'Fish Growth Parameter (b)',
validator=self.validator)
self.add_input(self.param_b)
self.param_tau = inputs.Text(
args_key=u'g_param_tau',
helptext=(u"Default tau = (0.08 C^-1). Specifies how sensitive "
u"finfish growth is to temperature. If the user "
u"chooses to adjust these parameters, we recommend "
u"using them in the simple growth model to determine if "
u"the time taken for a fish to reach a target harvest "
u"weight typical for the region of interest is "
u"accurate."),
label=u'Fish Growth Parameter (tau)',
validator=self.validator)
self.add_input(self.param_tau)
self.uncertainty_data_container = inputs.Container(
args_key=u'use_uncertainty',
expandable=True,
label=u'Enable Uncertainty Analysis')
self.add_input(self.uncertainty_data_container)
self.param_a_sd = inputs.Text(
args_key=u'g_param_a_sd',
helptext=(u"Standard deviation for fish growth parameter a. "
u"This indicates the level of uncertainty in the "
u"estimate for parameter a."),
label=u'Standard Deviation for Parameter (a)',
validator=self.validator)
self.uncertainty_data_container.add_input(self.param_a_sd)
self.param_b_sd = inputs.Text(
args_key=u'g_param_b_sd',
helptext=(u"Standard deviation for fish growth parameter b. "
u"This indicates the level of uncertainty in the "
u"estimate for parameter b."),
label=u'Standard Deviation for Parameter (b)',
validator=self.validator)
self.uncertainty_data_container.add_input(self.param_b_sd)
self.num_monte_carlo_runs = inputs.Text(
args_key=u'num_monte_carlo_runs',
helptext=(u"Number of runs of the model to perform as part of a "
u"Monte Carlo simulation. A larger number will tend to "
u"produce more consistent and reliable output, but will "
u"also take longer to run."),
label=u'Number of Monte Carlo Simulation Runs',
validator=self.validator)
self.uncertainty_data_container.add_input(self.num_monte_carlo_runs)
self.water_temperature = inputs.File(
args_key=u'water_temp_tbl',
helptext=(u"Users must provide a time series of daily water "
u"temperature (C) for each farm in the shapefile. When "
u"daily temperatures are not available, users can "
u"interpolate seasonal or monthly temperatures to a "
u"daily resolution. Water temperatures collected at "
u"existing aquaculture facilities are preferable, but "
u"if unavailable, users can consult online sources such "
u"as NOAAs 4 km AVHRR Pathfinder Data and Canadas "
u"Department of Fisheries and Oceans Oceanographic "
u"Database. The most appropriate temperatures to use "
u"are those from the upper portion of the water column, "
u"which are the temperatures experienced by the fish in "
u"the netpens."),
label=u'Table of Daily Water Temperature at Farm (CSV)',
validator=self.validator)
self.add_input(self.water_temperature)
self.farm_operations = inputs.File(
args_key=u'farm_op_tbl',
helptext=(u"A table of general and farm-specific operations "
u"parameters. Please refer to the sample data table "
u"for reference to ensure correct incorporation of data "
u"in the model.<br><br>The values for 'farm operations' "
u"(applied to all farms) and 'add new farms' (beginning "
u"with row 32) may be modified according to the user's "
u"needs . However, the location of cells in this "
u"template must not be modified. If for example, if "
u"the model is to run for three farms only, the farms "
u"should be listed in rows 10, 11 and 12 (farms 1, 2, "
u"and 3, respectively). Several default values that are "
u"applicable to Atlantic salmon farming in British "
u"Columbia are also included in the sample data table."),
label=u'Farm Operations Table (CSV)',
validator=self.validator)
self.add_input(self.farm_operations)
self.outplant_buffer = inputs.Text(
args_key=u'outplant_buffer',
helptext=(u"This value will allow the outplant start day to "
u"start plus or minus the number of days specified "
u"here."),
label=u'Outplant Date Buffer',
validator=self.validator)
self.add_input(self.outplant_buffer)
self.valuation = inputs.Checkbox(
args_key=u'do_valuation',
helptext=(u"By checking this box, a valuation analysis will be "
u"run on the model."),
label=u'Run Valuation?')
self.add_input(self.valuation)
self.market_price = inputs.Text(
args_key=u'p_per_kg',
helptext=(u"Default value comes from Urner-Berry monthly fresh "
u"sheet reports on price of farmed Atlantic salmon."),
interactive=False,
label=u'Market Price per Kilogram of Processed Fish',
validator=self.validator)
self.add_input(self.market_price)
self.fraction_price = inputs.Text(
args_key=u'frac_p',
helptext=(u"Fraction of market price that accounts for costs "
u"rather than profit. Default value is 0.3 (30%)."),
interactive=False,
label=u'Fraction of Price that Accounts to Costs',
validator=self.validator)
self.add_input(self.fraction_price)
self.discount_rate = inputs.Text(
args_key=u'discount',
helptext=(u"We use a 7% annual discount rate, adjusted to a "
u"daily rate of 0.000192 for 0.0192% (7%/365 days)."),
interactive=False,
label=u'Daily Market Discount Rate',
validator=self.validator)
self.add_input(self.discount_rate)
# Set interactivity, requirement as input sufficiency changes
self.farm_location.sufficiency_changed.connect(
self.farm_identifier.set_interactive)
self.farm_location.sufficiency_changed.connect(self._load_colnames)
self.valuation.sufficiency_changed.connect(
self.market_price.set_interactive)
self.valuation.sufficiency_changed.connect(
self.fraction_price.set_interactive)
self.valuation.sufficiency_changed.connect(
self.discount_rate.set_interactive)
