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['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['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['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='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)