InVEST Model Entry Points¶

All InVEST models share a consistent python API:

The model has a function called execute that takes a single python dict ("args") as its argument.

This arguments dict contains an entry, 'workspace_dir', which points to the folder on disk where all files created by the model should be saved.

Calling a model requires importing the model’s execute function and then calling the model with the correct parameters. For example, if you were to call the Carbon Storage and Sequestration model, your script might include

import natcap.invest.carbon.carbon_combined
args = {
    'workspace_dir': 'path/to/workspace'
    # Other arguments, as needed for Carbon.
}

natcap.invest.carbon.carbon_combined.execute(args)

For examples of scripts that could be created around a model run, or multiple successive model runs, see Creating Sample Python Scripts.

Available Models and Tools:

Annual Water Yield: Reservoir Hydropower Production
Coastal Blue Carbon
Coastal Blue Carbon Preprocessor
Crop Production Percentile Model
Crop Production Regression Model
Finfish Aquaculture
Fisheries: Habitat Scenario Tool
Forest Carbon Edge Effect
GLOBIO
Habitat Quality
InVEST Carbon Model
InVEST Habitat Risk Assessment (HRA) Model
InVEST Pollination Model
Recreation
RouteDEM: Hydrological routing
Scenario Generator: Proximity-Based
Sediment Delivery Ratio
Wave Energy
Wind Energy

Annual Water Yield: Reservoir Hydropower Production ¶

natcap.invest.hydropower.hydropower_water_yield.execute(args)¶

Annual Water Yield: Reservoir Hydropower Production.

Executes the hydropower/water_yield model

Parameters:

args['workspace_dir'] (string) – a path to the directory that will write output and other temporary files during calculation. (required)
args['lulc_path'] (string) – a path to a land use/land cover raster whose LULC indexes correspond to indexes in the biophysical table input. Used for determining soil retention and other biophysical properties of the landscape. (required)
args['depth_to_root_rest_layer_path'] (string) – a path to an input raster describing the depth of “good” soil before reaching this restrictive layer (required)
args['precipitation_path'] (string) – a path to an input raster describing the average annual precipitation value for each cell (mm) (required)
args['pawc_path'] (string) – a path to an input raster describing the plant available water content value for each cell. Plant Available Water Content fraction (PAWC) is the fraction of water that can be stored in the soil profile that is available for plants’ use. PAWC is a fraction from 0 to 1 (required)
args['eto_path'] (string) – a path to an input raster describing the annual average evapotranspiration value for each cell. Potential evapotranspiration is the potential loss of water from soil by both evaporation from the soil and transpiration by healthy Alfalfa (or grass) if sufficient water is available (mm) (required)
args['watersheds_path'] (string) – a path to an input shapefile of the watersheds of interest as polygons. (required)
args['sub_watersheds_path'] (string) – a path to an input shapefile of the subwatersheds of interest that are contained in the args['watersheds_path'] shape provided as input. (optional)
args['biophysical_table_path'] (string) – a path to an input CSV table of land use/land cover classes, containing data on biophysical coefficients such as root_depth (mm) and Kc, which are required. A column with header LULC_veg is also required which should have values of 1 or 0, 1 indicating a land cover type of vegetation, a 0 indicating non vegetation or wetland, water. NOTE: these data are attributes of each LULC class rather than attributes of individual cells in the raster map (required)
args['seasonality_constant'] (float) – floating point value between 1 and 30 corresponding to the seasonal distribution of precipitation (required)
args['results_suffix'] (string) – a string that will be concatenated onto the end of file names (optional)
args['demand_table_path'] (string) – (optional) if a non-empty string, a path to an input CSV table of LULC classes, showing consumptive water use for each landuse / land-cover type (cubic meters per year) to calculate water scarcity.
args['valuation_table_path'] (string) –
(optional) if a non-empty string, a path to an input CSV table of hydropower stations with the following fields to calculate valuation:

(‘ws_id’, ‘time_span’, ‘discount’, ‘efficiency’, ‘fraction’, ‘cost’, ‘height’, ‘kw_price’)

Required if calculate_valuation is True.
args['n_workers'] (int) – (optional) The number of worker processes to use for processing this model. If omitted, computation will take place in the current process.

Returns:

None

Coastal Blue Carbon ¶

natcap.invest.coastal_blue_carbon.coastal_blue_carbon.execute(args)¶

Coastal Blue Carbon.

Parameters:

workspace_dir (str) – location into which all intermediate and output files should be placed.
results_suffix (str) – a string to append to output filenames.
lulc_lookup_uri (str) – filepath to a CSV table used to convert the lulc code to a name. Also used to determine if a given lulc type is a coastal blue carbon habitat.
lulc_transition_matrix_uri (str) – generated by the preprocessor. This file must be edited before it can be used by the main model. The left-most column represents the source lulc class, and the top row represents the destination lulc class.
carbon_pool_initial_uri (str) – the provided CSV table contains information related to the initial conditions of the carbon stock within each of the three pools of a habitat. Biomass includes carbon stored above and below ground. All non-coastal blue carbon habitat lulc classes are assumed to contain no carbon. The values for ‘biomass’, ‘soil’, and ‘litter’ should be given in terms of Megatonnes CO2 e/ ha.
carbon_pool_transient_uri (str) – the provided CSV table contains information related to the transition of carbon into and out of coastal blue carbon pools. All non-coastal blue carbon habitat lulc classes are assumed to neither sequester nor emit carbon as a result of change. The ‘yearly_accumulation’ values should be given in terms of Megatonnes of CO2 e/ha-yr. The ‘half-life’ values must be given in terms of years. The ‘disturbance’ values must be given as a decimal (e.g. 0.5 for 50%) of stock distrubed given a transition occurs away from a lulc-class.
lulc_baseline_map_uri (str) – a GDAL-supported raster representing the baseline landscape/seascape.
lulc_baseline_year (int) – The year of the baseline snapshot.
lulc_transition_maps_list (list) – a list of GDAL-supported rasters representing the landscape/seascape at particular points in time. Provided in chronological order.
lulc_transition_years_list (list) – a list of years that respectively correspond to transition years of the rasters. Provided in chronological order.
analysis_year (int) – optional. Indicates how many timesteps to run the transient analysis beyond the last transition year. Must come chronologically after the last transition year if provided. Otherwise, the final timestep of the model will be set to the last transition year.
do_economic_analysis (bool) – boolean value indicating whether model should run economic analysis.
do_price_table (bool) – boolean value indicating whether a price table is included in the arguments and to be used or a price and interest rate is provided and to be used instead.
price (float) – the price per Megatonne CO2 e at the base year.
inflation_rate (float) – the interest rate on the price per Megatonne CO2e, compounded yearly. Provided as a percentage (e.g. 3.0 for 3%).
price_table_uri (bool) – if args[‘do_price_table’] is set to True the provided CSV table is used in place of the initial price and interest rate inputs. The table contains the price per Megatonne CO2e sequestered for a given year, for all years from the original snapshot to the analysis year, if provided.
discount_rate (float) – the discount rate on future valuations of sequestered carbon, compounded yearly. Provided as a percentage (e.g. 3.0 for 3%).

Example Args:

args = {
    'workspace_dir': 'path/to/workspace/',
    'results_suffix': '',
    'lulc_lookup_uri': 'path/to/lulc_lookup_uri',
    'lulc_transition_matrix_uri': 'path/to/lulc_transition_uri',
    'carbon_pool_initial_uri': 'path/to/carbon_pool_initial_uri',
    'carbon_pool_transient_uri': 'path/to/carbon_pool_transient_uri',
    'lulc_baseline_map_uri': 'path/to/baseline_map.tif',
    'lulc_baseline_year': <int>,
    'lulc_transition_maps_list': [raster1_uri, raster2_uri, ...],
    'lulc_transition_years_list': [2000, 2005, ...],
    'analysis_year': 2100,
    'do_economic_analysis': '<boolean>',
    'do_price_table': '<boolean>',
    'price': '<float>',
    'inflation_rate': '<float>',
    'price_table_uri': 'path/to/price_table',
    'discount_rate': '<float>'
}

Coastal Blue Carbon Preprocessor ¶

natcap.invest.coastal_blue_carbon.preprocessor.execute(args)¶

Coastal Blue Carbon Preprocessor.

The preprocessor accepts a list of rasters and checks for cell-transitions across the rasters. The preprocessor outputs a CSV file representing a matrix of land cover transitions, each cell prefilled with a string indicating whether carbon accumulates or is disturbed as a result of the transition, if a transition occurs.

Parameters:	workspace_dir (string) – directory path to workspace results_suffix (string) – append to outputs directory name if provided lulc_lookup_uri (string) – filepath of lulc lookup table lulc_snapshot_list (list) – a list of filepaths to lulc rasters

Example Args:

args = {
    'workspace_dir': 'path/to/workspace_dir/',
    'results_suffix': '',
    'lulc_lookup_uri': 'path/to/lookup.csv',
    'lulc_snapshot_list': ['path/to/raster1', 'path/to/raster2', ...]
}

Crop Production Percentile Model ¶

natcap.invest.crop_production_percentile.execute(args)¶

Crop Production Percentile Model.

This model will take a landcover (crop cover?) map and produce yields, production, and observed crop yields, a nutrient table, and a clipped observed map.

Parameters:

args['workspace_dir'] (string) – output directory for intermediate, temporary, and final files
args['results_suffix'] (string) – (optional) string to append to any output file names
args['landcover_raster_path'] (string) – path to landcover raster
args['landcover_to_crop_table_path'] (string) –
path to a table that converts landcover types to crop names that has two headers: * lucode: integer value corresponding to a landcover code in

args[‘landcover_raster_path’].
- crop_name: a string that must match one of the crops in args[‘model_data_path’]/climate_bin_maps/[cropname]_* A ValueError is raised if strings don’t match.
args['aggregate_polygon_path'] (string) – path to polygon shapefile that will be used to aggregate crop yields and total nutrient value. (optional, if value is None, then skipped)
args['model_data_path'] (string) –
path to the InVEST Crop Production global data directory. This model expects that the following directories are subdirectories of this path * climate_bin_maps (contains [cropname]_climate_bin.tif files) * climate_percentile_yield (contains

[cropname]_percentile_yield_table.csv files)

Please see the InVEST user’s guide chapter on crop production for details about how to download these data.
args['n_workers'] (int) – (optional) The number of worker processes to use for processing this model. If omitted, computation will take place in the current process.

Returns:

None.

Crop Production Regression Model ¶

natcap.invest.crop_production_regression.execute(args)¶

Crop Production Regression Model.

This model will take a landcover (crop cover?), N, P, and K map and produce modeled yields, and a nutrient table.

Parameters:

args['workspace_dir'] (string) – output directory for intermediate, temporary, and final files
args['results_suffix'] (string) – (optional) string to append to any output file names
args['landcover_raster_path'] (string) – path to landcover raster
args['landcover_to_crop_table_path'] (string) –
path to a table that converts landcover types to crop names that has two headers: * lucode: integer value corresponding to a landcover code in

args[‘landcover_raster_path’].
- crop_name: a string that must match one of the crops in args[‘model_data_path’]/climate_regression_yield_tables/[cropname]_* A ValueError is raised if strings don’t match.
args['fertilization_rate_table_path'] (string) – path to CSV table that contains fertilization rates for the crops in the simulation, though it can contain additional crops not used in the simulation. The headers must be ‘crop_name’, ‘nitrogen_rate’, ‘phosphorous_rate’, and ‘potassium_rate’, where ‘crop_name’ is the name string used to identify crops in the ‘landcover_to_crop_table_path’, and rates are in units kg/Ha.
args['aggregate_polygon_path'] (string) – path to polygon shapefile that will be used to aggregate crop yields and total nutrient value. (optional, if value is None, then skipped)
args['model_data_path'] (string) –
path to the InVEST Crop Production global data directory. This model expects that the following directories are subdirectories of this path * climate_bin_maps (contains [cropname]_climate_bin.tif files) * climate_percentile_yield (contains

[cropname]_percentile_yield_table.csv files)

Please see the InVEST user’s guide chapter on crop production for details about how to download these data.

Returns:

None.

Finfish Aquaculture ¶

natcap.invest.finfish_aquaculture.finfish_aquaculture.execute(args)¶

Finfish Aquaculture.

This function will take care of preparing files passed into the finfish aquaculture model. It will handle all files/inputs associated with biophysical and valuation calculations and manipulations. It will create objects to be passed to the aquaculture_core.py module. It may write log, warning, or error messages to stdout.

Parameters:

workspace_dir (string) – The directory in which to place all result files.
ff_farm_loc (string) – URI that points to a shape file of fishery locations
farm_ID (string) – column heading used to describe individual farms. Used to link GIS location data to later inputs.
g_param_a (float) – Growth parameter alpha, used in modeling fish growth, should be an int or float.
g_param_b (float) – Growth parameter beta, used in modeling fish growth, should be an int or float.
g_param_tau (float) – Growth parameter tau, used in modeling fish growth, should be an int or float
use_uncertainty (boolean) –
g_param_a_sd (float) – (description)
g_param_b_sd (float) – (description)
num_monte_carlo_runs (int) –
water_temp_tbl (string) – URI to a CSV table where daily water temperature values are stored from one year
farm_op_tbl (string) – URI to CSV table of static variables for calculations
outplant_buffer (int) – This value will allow the outplanting start day to be flexible plus or minus the number of days specified here.
do_valuation (boolean) – Boolean that indicates whether or not valuation should be performed on the aquaculture model
p_per_kg (float) – Market price per kilogram of processed fish
frac_p (float) – Fraction of market price that accounts for costs rather than profit
discount (float) – Daily market discount rate

Example Args Dictionary:

{
    'workspace_dir': 'path/to/workspace_dir',
    'ff_farm_loc': 'path/to/shapefile',
    'farm_ID': 'FarmID'
    'g_param_a': 0.038,
    'g_param_b': 0.6667,
    'g_param_tau': 0.08,
    'use_uncertainty': True,
    'g_param_a_sd': 0.005,
    'g_param_b_sd': 0.05,
    'num_monte_carlo_runs': 1000,
    'water_temp_tbl': 'path/to/water_temp_tbl',
    'farm_op_tbl': 'path/to/farm_op_tbl',
    'outplant_buffer': 3,
    'do_valuation': True,
    'p_per_kg': 2.25,
    'frac_p': 0.3,
    'discount': 0.000192,
}

Fisheries: Habitat Scenario Tool ¶

natcap.invest.fisheries.fisheries_hst.execute(args)¶

Fisheries: Habitat Scenario Tool.

The Fisheries Habitat Scenario Tool generates a new Population Parameters CSV File with modified survival attributes across classes and regions based on habitat area changes and class-level dependencies on those habitats.

args[‘workspace_dir’] (str): location into which the resultant: modified Population Parameters CSV file should be placed.
args[‘sexsp’] (str): specifies whether or not the age and stage: classes are distinguished by sex. Options: ‘Yes’ or ‘No’
args[‘population_csv_path’] (str): location of the population: parameters csv file. This file contains all age and stage specific parameters.
args[‘habitat_chg_csv_path’] (str): location of the habitat change: parameters csv file. This file contains habitat area change information.
args[‘habitat_dep_csv_path’] (str): location of the habitat dependency: parameters csv file. This file contains habitat-class dependency information.
args[‘gamma’] (float): describes the relationship between a change: in habitat area and a change in survival of life stages dependent on that habitat

Returns:	None

Example Args:

args = {
    'workspace_dir': 'path/to/workspace_dir/',
    'sexsp': 'Yes',
    'population_csv_path': 'path/to/csv',
    'habitat_chg_csv_path': 'path/to/csv',
    'habitat_dep_csv_path': 'path/to/csv',
    'gamma': 0.5,
}

Note

Modified Population Parameters CSV File saved to ‘workspace_dir/output/’

Forest Carbon Edge Effect ¶

natcap.invest.forest_carbon_edge_effect.execute(args)¶

Forest Carbon Edge Effect.

InVEST Carbon Edge Model calculates the carbon due to edge effects in tropical forest pixels.

Parameters:

args['workspace_dir'] (string) – a path to the directory that will write output and other temporary files during calculation. (required)
args['results_suffix'] (string) – a string to append to any output file name (optional)
args['n_nearest_model_points'] (int) – number of nearest neighbor model points to search for
args['aoi_vector_path'] (string) – (optional) if present, a path to a shapefile that will be used to aggregate carbon stock results at the end of the run.
args['biophysical_table_path'] (string) –
a path to a CSV table that has at least the fields ‘lucode’ and ‘c_above’. If args['compute_forest_edge_effects'] == True, table must also contain an ‘is_tropical_forest’ field. If args['pools_to_calculate'] == 'all', this table must contain the fields ‘c_below’, ‘c_dead’, and ‘c_soil’.
- lucode: an integer that corresponds to landcover codes in the raster args['lulc_raster_path']
- is_tropical_forest: either 0 or 1 indicating whether the landcover type is forest (1) or not (0). If 1, the value in c_above is ignored and instead calculated from the edge regression model.
- c_above: floating point number indicating tons of above ground carbon per hectare for that landcover type
- {'c_below', 'c_dead', 'c_soil'}: three other optional carbon pools that will statically map landcover types to the carbon densities in the table.
Example:
lucode,is_tropical_forest,c_above,c_soil,c_dead,c_below 0,0,32.8,5,5.2,2.1 1,1,n/a,2.5,0.0,0.0 2,1,n/a,1.8,1.0,0.0 16,0,28.1,4.3,0.0,2.0
Note the “n/a” in c_above are optional since that field is ignored when is_tropical_forest==1.
args['lulc_raster_path'] (string) – path to a integer landcover code raster
args['pools_to_calculate'] (string) – if “all” then all carbon pools will be calculted. If any other value only above ground carbon pools will be calculated and expect only a ‘c_above’ header in the biophysical table. If “all” model expects ‘c_above’, ‘c_below’, ‘c_dead’, ‘c_soil’ in header of biophysical_table and will make a translated carbon map for each based off the landcover map.
args['compute_forest_edge_effects'] (boolean) – if True, requires biophysical table to have ‘is_tropical_forest’ forest field, and any landcover codes that have a 1 in this column calculate carbon stocks using the Chaplin-Kramer et. al method and ignore ‘c_above’.
args['tropical_forest_edge_carbon_model_vector_path'] (string) –
path to a shapefile that defines the regions for the local carbon edge models. Has at least the fields ‘method’, ‘theta1’, ‘theta2’, ‘theta3’. Where ‘method’ is an int between 1..3 describing the biomass regression model, and the thetas are floating point numbers that have different meanings depending on the ‘method’ parameter. Specifically,
- method 1 (asymptotic model):
  biomass = theta1 - theta2 * exp(-theta3 * edge_dist_km)
- method 2 (logarithmic model):
  # NOTE: theta3 is ignored for this method biomass = theta1 + theta2 * numpy.log(edge_dist_km)
- method 3 (linear regression):
  biomass = theta1 + theta2 * edge_dist_km
args['biomass_to_carbon_conversion_factor'] (string/float) – Number by which to multiply forest biomass to convert to carbon in the edge effect calculation.
args['n_workers'] (int) – (optional) The number of worker processes to use for processing this model. If omitted, computation will take place in the current process.

Returns:

None

GLOBIO ¶

natcap.invest.globio.execute(args)¶

GLOBIO.

The model operates in two modes. Mode (a) generates a landcover map based on a base landcover map and information about crop yields, infrastructure, and more. Mode (b) assumes the globio landcover map is generated. These modes are used below to describe input parameters.

Parameters:

args['workspace_dir'] (string) – output directory for intermediate, temporary, and final files
args['predefined_globio'] (boolean) – if True then “mode (b)” else “mode (a)”
args['results_suffix'] (string) – (optional) string to append to any output files
args['lulc_path'] (string) – used in “mode (a)” path to a base landcover map with integer codes
args['lulc_to_globio_table_path'] (string) –
used in “mode (a)” path to table that translates the land-cover args[‘lulc_path’] to intermediate GLOBIO classes, from which they will be further differentiated using the additional data in the model. Contains at least the following fields:
- ’lucode’: Land use and land cover class code of the dataset used. LULC codes match the ‘values’ column in the LULC raster of mode (b) and must be numeric and unique.
- ’globio_lucode’: The LULC code corresponding to the GLOBIO class to which it should be converted, using intermediate codes described in the example below.
args['infrastructure_dir'] (string) – used in “mode (a) and (b)” a path to a folder containing maps of either gdal compatible rasters or OGR compatible shapefiles. These data will be used in the infrastructure to calculation of MSA.
args['pasture_path'] (string) – used in “mode (a)” path to pasture raster
args['potential_vegetation_path'] (string) – used in “mode (a)” path to potential vegetation raster
args['pasture_threshold'] (float) – used in “mode (a)”
args['intensification_fraction'] (float) – used in “mode (a)”; a value between 0 and 1 denoting proportion of total agriculture that should be classified as ‘high input’
args['primary_threshold'] (float) – used in “mode (a)”
args['msa_parameters_path'] (string) – path to MSA classification parameters
args['aoi_path'] (string) – (optional) if it exists then final MSA raster is summarized by AOI
args['globio_lulc_path'] (string) – used in “mode (b)” path to predefined globio raster.
args['n_workers'] (int) – (optional) The number of worker processes to use for processing this model. If omitted, computation will take place in the current process.

Returns:

None

Habitat Quality ¶

natcap.invest.habitat_quality.execute(args)¶

Habitat Quality.

Open files necessary for the portion of the habitat_quality model.

Parameters:

workspace_dir (string) – a path to the directory that will write output and other temporary files (required)
lulc_cur_path (string) – a path to an input land use/land cover raster (required)
lulc_fut_path (string) – a path to an input land use/land cover raster (optional)
lulc_bas_path (string) – a path to an input land use/land cover raster (optional, but required for rarity calculations)
threat_folder (string) – a path to the directory that will contain all threat rasters (required)
threats_table_path (string) – a path to an input CSV containing data of all the considered threats. Each row is a degradation source and each column a different attribute of the source with the following names: ‘THREAT’,’MAX_DIST’,’WEIGHT’ (required).
access_vector_path (string) – a path to an input polygon shapefile containing data on the relative protection against threats (optional)
sensitivity_table_path (string) – a path to an input CSV file of LULC types, whether they are considered habitat, and their sensitivity to each threat (required)
half_saturation_constant (float) – a python float that determines the spread and central tendency of habitat quality scores (required)
suffix (string) – a python string that will be inserted into all raster path paths just before the file extension.

Example Args Dictionary:

{
    'workspace_dir': 'path/to/workspace_dir',
    'lulc_cur_path': 'path/to/lulc_cur_raster',
    'lulc_fut_path': 'path/to/lulc_fut_raster',
    'lulc_bas_path': 'path/to/lulc_bas_raster',
    'threat_raster_folder': 'path/to/threat_rasters/',
    'threats_table_path': 'path/to/threats_csv',
    'access_vector_path': 'path/to/access_shapefile',
    'sensitivity_table_path': 'path/to/sensitivity_csv',
    'half_saturation_constant': 0.5,
    'suffix': '_results',
}

Returns:	None

InVEST Carbon Model ¶

natcap.invest.carbon.execute(args)¶

InVEST Carbon Model.

Calculate the amount of carbon stocks given a landscape, or the difference due to a future change, and/or the tradeoffs between that and a REDD scenario, and calculate economic valuation on those scenarios.

The model can operate on a single scenario, a combined present and future scenario, as well as an additional REDD scenario.

Parameters:

args['workspace_dir'] (string) – a path to the directory that will write output and other temporary files during calculation.
args['results_suffix'] (string) – appended to any output file name.
args['lulc_cur_path'] (string) – a path to a raster representing the current carbon stocks.
args['calc_sequestration'] (bool) – if true, sequestration should be calculated and ‘lulc_fut_path’ and ‘do_redd’ should be defined.
args['lulc_fut_path'] (string) – a path to a raster representing future landcover scenario. Optional, but if present and well defined will trigger a sequestration calculation.
args['do_redd'] (bool) – if true, REDD analysis should be calculated and ‘lulc_redd_path’ should be defined
args['lulc_redd_path'] (string) – a path to a raster representing the alternative REDD scenario which is only possible if the args[‘lulc_fut_path’] is present and well defined.
args['carbon_pools_path'] (string) – path to CSV or that indexes carbon storage density to lulc codes. (required if ‘do_uncertainty’ is false)
args['lulc_cur_year'] (int/string) – an integer representing the year of args[‘lulc_cur_path’] used if args[‘calc_sequestration’] is True.
args['lulc_fut_year'] (int/string) – an integer representing the year of args[‘lulc_fut_path’] used in valuation if it exists. Required if args[‘do_valuation’] is True and args[‘lulc_fut_path’] is present and well defined.
args['do_valuation'] (bool) – if true then run the valuation model on available outputs. At a minimum will run on carbon stocks, if sequestration with a future scenario is done and/or a REDD scenario calculate NPV for either and report in final HTML document.
args['price_per_metric_ton_of_c'] (float) – Is the present value of carbon per metric ton. Used if args[‘do_valuation’] is present and True.
args['discount_rate'] (float) – Discount rate used if NPV calculations are required. Used if args[‘do_valuation’] is present and True.
args['rate_change'] (float) – Annual rate of change in price of carbon as a percentage. Used if args[‘do_valuation’] is present and True.
args['n_workers'] (int) – (optional) The number of worker processes to use for processing this model. If omitted, computation will take place in the current process.

Returns:

None.

InVEST Habitat Risk Assessment (HRA) Model ¶

natcap.invest.hra.execute(args)¶

InVEST Habitat Risk Assessment (HRA) Model.

Parameters:

args['workspace_dir'] (str) – a path to the output workspace folder. It will overwrite any files that exist if the path already exists.
args['results_suffix'] (str) – a string appended to each output file path. (optional)
args['info_table_path'] (str) – a path to the 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.
args['criteria_table_path'] (str) – a path to the CSV or Excel file that contains the set of criteria ranking of each stressor on each habitat.
args['resolution'] (int) – a number representing the desired pixel dimensions of output rasters in meters.
args['max_rating'] (str, int or float) – a number representing the highest potential value that should be represented in rating in the criteria scores table.
args['risk_eq'] (str) – a string identifying the equation that should be used in calculating risk scores for each H-S overlap cell. This will be either ‘Euclidean’ or ‘Multiplicative’.
args['decay_eq'] (str) – a string identifying the equation that should be used in calculating the decay of stressor buffer influence. This can be ‘None’, ‘Linear’, or ‘Exponential’.
args['aoi_vector_path'] (str) – a path to the shapefile containing one or more planning regions used to get the average risk value for each habitat-stressor combination over each area. Optionally, if each of the shapefile features contain a ‘name’ field, it will be used as a way of identifying each individual shape.
args['n_workers'] (int) – the number of worker processes to use for processing this model. If omitted, computation will take place in the current process. (optional)
args['visualize_outputs'] (bool) – if True, create output GeoJSONs and save them in a visualization_outputs folder, so users can visualize results on the web app. Default to True if not specified. (optional)

Returns:

None.

InVEST Pollination Model ¶

natcap.invest.pollination.execute(args)¶

InVEST Pollination Model.

Parameters:

args['workspace_dir'] (string) – a path to the output workspace folder. Will overwrite any files that exist if the path already exists.
args['results_suffix'] (string) – string appended to each output file path.
args['landcover_raster_path'] (string) – file path to a landcover raster.
args['guild_table_path'] (string) –
file path to a table indicating the bee species to analyze in this model run. Table headers must include:
- ’species’: a bee species whose column string names will
  
  be referred to in other tables and the model will output analyses per species.
- one or more columns matching _NESTING_SUITABILITY_PATTERN
  
  with values in the range [0.0, 1.0] indicating the suitability of the given species to nest in a particular substrate.
- one or more columns matching _FORAGING_ACTIVITY_RE_PATTERN
  
  with values in the range [0.0, 1.0] indicating the relative level of foraging activity for that species during a particular season.
- _ALPHA_HEADER the sigma average flight distance of that bee
  
  species in meters.
- ’relative_abundance’: a weight indicating the relative
  
  abundance of the particular species with respect to the sum of all relative abundance weights in the table.
args['landcover_biophysical_table_path'] (string) –
path to a table mapping landcover codes in args[‘landcover_path’] to indexes of nesting availability for each nesting substrate referenced in guilds table as well as indexes of abundance of floral resources on that landcover type per season in the bee activity columns of the guild table.

All indexes are in the range [0.0, 1.0].
Columns in the table must be at least
- ’lucode’: representing all the unique landcover codes in
  
  the raster ast args[‘landcover_path’]
- For every nesting matching _NESTING_SUITABILITY_PATTERN in the guild stable, a column matching the pattern in _LANDCOVER_NESTING_INDEX_HEADER.
- For every season matching _FORAGING_ACTIVITY_RE_PATTERN in the guilds table, a column matching the pattern in _LANDCOVER_FLORAL_RESOURCES_INDEX_HEADER.
args['farm_vector_path'] (string) –
(optional) path to a single layer polygon shapefile representing farms. If present will trigger the farm yield component of the model.

The layer must have at least the following fields:
- season (string): season in which the farm needs pollination
- crop_type (string): a text field to identify the crop type for
  
  summary statistics.
- half_sat (float): a real in the range [0.0, 1.0] representing
  
  the proportion of wild pollinators to achieve a 50% yield of that crop.
- p_dep (float): a number in the range [0.0, 1.0]
  
  representing the proportion of yield dependent on pollinators.
- p_managed (float): proportion of pollinators that come from
  
  non-native/managed hives.
- fr_[season] (float): one or more fields that match this pattern
  
  such that season also matches the season headers in the biophysical and guild table. Any areas that overlap the landcover map will replace seasonal floral resources with this value. Ranges from 0..1.
- n_[substrate] (float): One or more fields that match this
  
  pattern such that substrate also matches the nesting substrate headers in the biophysical and guild table. Any areas that overlap the landcover map will replace nesting substrate suitability with this value. Ranges from 0..1.
args['n_workers'] (int) – (optional) The number of worker processes to use for processing this model. If omitted, computation will take place in the current process.

Returns:

None

Recreation ¶

natcap.invest.recreation.recmodel_client.execute(args)¶

Recreation.

Execute recreation client model on remote server.

Parameters:

args['workspace_dir'] (string) – path to workspace directory
args['aoi_path'] (string) – path to AOI vector
args['hostname'] (string) – FQDN to recreation server
args['port'] (string or int) – port on hostname for recreation server
args['start_year'] (string) – start year in form YYYY. This year is the inclusive lower bound to consider points in the PUD and regression.
args['end_year'] (string) – end year in form YYYY. This year is the inclusive upper bound to consider points in the PUD and regression.
args['grid_aoi'] (boolean) – if true the polygon vector in args[‘aoi_path’] should be gridded into a new vector and the recreation model should be executed on that
args['grid_type'] (string) – optional, but must exist if args[‘grid_aoi’] is True. Is one of ‘hexagon’ or ‘square’ and indicates the style of gridding.
args['cell_size'] (string/float) – optional, but must exist if args[‘grid_aoi’] is True. Indicates the cell size of square pixels and the width of the horizontal axis for the hexagonal cells.
args['compute_regression'] (boolean) – if True, then process the predictor table and scenario table (if present).
args['predictor_table_path'] (string) –
required if args[‘compute_regression’] is True. Path to a table that describes the regression predictors, their IDs and types. Must contain the fields ‘id’, ‘path’, and ‘type’ where:
- ’id’: is a <=10 character length ID that is used to uniquely describe the predictor. It will be added to the output result shapefile attribute table which is an ESRI Shapefile, thus limited to 10 characters.
- ’path’: an absolute or relative (to this table) path to the predictor dataset, either a vector or raster type.
- ’type’: one of the following,
  ’raster_mean’: mean of values in the raster under the response polygon
  
  ’raster_sum’: sum of values in the raster under the response polygon
  
  ’point_count’: count of the points contained in the response polygon
  
  ’point_nearest_distance’: distance to the nearest point from the response polygon
  
  ’line_intersect_length’: length of lines that intersect with the response polygon in projected units of AOI
  
  ’polygon_area’: area of the polygon contained within response polygon in projected units of AOI
args['scenario_predictor_table_path'] (string) – (optional) if present runs the scenario mode of the recreation model with the datasets described in the table on this path. Field headers are identical to args[‘predictor_table_path’] and ids in the table are required to be identical to the predictor list.
args['results_suffix'] (string) – optional, if exists is appended to any output file paths.

Returns:

None

RouteDEM: Hydrological routing ¶

natcap.invest.routedem.execute(args)¶

RouteDEM: Hydrological routing.

This model exposes the pygeoprocessing D8 and Multiple Flow Direction routing functionality as an InVEST model.

This tool will always fill pits on the input DEM.

Parameters:

args['workspace_dir'] (string) – output directory for intermediate, temporary, and final files
args['results_suffix'] (string) – (optional) string to append to any output file names
args['dem_path'] (string) – path to a digital elevation raster
args['dem_band_index'] (int) – Optional. The band index to operate on. If not provided, band index 1 is assumed.
args['algorithm'] (string) – The routing algorithm to use. Must be one of ‘D8’ or ‘MFD’ (case-insensitive). Required when calculating flow direction, flow accumulation, stream threshold, and downstream distance.
args['calculate_flow_direction'] (bool) – If True, model will calculate flow direction for the filled DEM.
args['calculate_flow_accumulation'] (bool) – If True, model will calculate a flow accumulation raster. Only applies when args[‘calculate_flow_direction’] is True.
args['calculate_stream_threshold'] (bool) – if True, model will calculate a stream classification layer by thresholding flow accumulation to the provided value in args['threshold_flow_accumulation']. Only applies when args[‘calculate_flow_accumulation’] and args[‘calculate_flow_direction’] are True.
args['threshold_flow_accumulation'] (int) – The number of upstream cells that must flow into a cell before it’s classified as a stream.
args['calculate_downstream_distance'] (bool) – If True, and a stream threshold is calculated, model will calculate a downstream distance raster in units of pixels. Only applies when args[‘calculate_flow_accumulation’], args[‘calculate_flow_direction’], and args[‘calculate_stream_threshold’] are all True.
args['calculate_slope'] (bool) – If True, model will calculate a slope raster from the DEM.
args['n_workers'] (int) – The n_workers parameter to pass to the task graph. The default is -1 if not provided.

Returns:

None

Scenario Generator: Proximity-Based ¶

natcap.invest.scenario_gen_proximity.execute(args)¶

Scenario Generator: Proximity-Based.

Main entry point for proximity based scenario generator model.

Parameters:

args['workspace_dir'] (string) – output directory for intermediate, temporary, and final files
args['results_suffix'] (string) – (optional) string to append to any output files
args['base_lulc_path'] (string) – path to the base landcover map
args['replacment_lucode'] (string or int) – code to replace when converting pixels
args['area_to_convert'] (string or float) – max area (Ha) to convert
args['focal_landcover_codes'] (string) – a space separated string of landcover codes that are used to determine the proximity when refering to “towards” or “away” from the base landcover codes
args['convertible_landcover_codes'] (string) – a space separated string of landcover codes that can be converted in the generation phase found in args[‘base_lulc_path’].
args['n_fragmentation_steps'] (string) – an int as a string indicating the number of steps to take for the fragmentation conversion
args['aoi_path'] (string) – (optional) path to a shapefile that indicates area of interest. If present, the expansion scenario operates only under that AOI and the output raster is clipped to that shape.
args['convert_farthest_from_edge'] (boolean) – if True will run the conversion simulation starting from the furthest pixel from the edge and work inwards. Workspace will contain output files named ‘toward_base{suffix}.{tif,csv}.
args['convert_nearest_to_edge'] (boolean) – if True will run the conversion simulation starting from the nearest pixel on the edge and work inwards. Workspace will contain output files named ‘toward_base{suffix}.{tif,csv}.
args['n_workers'] (int) – (optional) The number of worker processes to use for processing this model. If omitted, computation will take place in the current process.

Returns:

None.

Sediment Delivery Ratio ¶

natcap.invest.sdr.execute(args)¶

Sediment Delivery Ratio.

This function calculates the sediment export and retention of a landscape using the sediment delivery ratio model described in the InVEST user’s guide.

Parameters:

args['workspace_dir'] (string) – output directory for intermediate, temporary, and final files
args['results_suffix'] (string) – (optional) string to append to any output file names
args['dem_path'] (string) – path to a digital elevation raster
args['erosivity_path'] (string) – path to rainfall erosivity index raster
args['erodibility_path'] (string) – a path to soil erodibility raster
args['lulc_path'] (string) – path to land use/land cover raster
args['watersheds_path'] (string) – path to vector of the watersheds
args['biophysical_table_path'] (string) – path to CSV file with biophysical information of each land use classes. contain the fields ‘usle_c’ and ‘usle_p’
args['threshold_flow_accumulation'] (number) – number of upstream pixels on the dem to threshold to a stream.
args['k_param'] (number) – k calibration parameter
args['sdr_max'] (number) – max value the SDR
args['ic_0_param'] (number) – ic_0 calibration parameter
args['drainage_path'] (string) – (optional) path to drainage raster that is used to add additional drainage areas to the internally calculated stream layer
args['n_workers'] (int) – if present, indicates how many worker processes should be used in parallel processing. -1 indicates single process mode, 0 is single process but non-blocking mode, and >= 1 is number of processes.

Returns:

None.

Wave Energy ¶

natcap.invest.wave_energy.execute(args)¶

Wave Energy.

Executes both the biophysical and valuation parts of the wave energy model (WEM). Files will be written on disk to the intermediate and output directories. The outputs computed for biophysical and valuation include: wave energy capacity raster, wave power raster, net present value raster, percentile rasters for the previous three, and a point shapefile of the wave points with attributes.

Parameters:

workspace_dir (str) – Where the intermediate and output folder/files will be saved. (required)
wave_base_data_path (str) – Directory location of wave base data including WAVEWATCH III (WW3) data and analysis area shapefile. (required)
analysis_area_path (str) – A string identifying the analysis area of interest. Used to determine wave data shapefile, wave data text file, and analysis area boundary shape. (required)
aoi_path (str) – A polygon OGR vector outlining a more detailed area within the analysis area. This vector should be projected with linear units being in meters. (required to run Valuation model)
machine_perf_path (str) – The path of a CSV file that holds the machine performance table. (required)
machine_param_path (str) – The path of a CSV file that holds the machine parameter table. (required)
dem_path (str) – The path of the Global Digital Elevation Model (DEM). (required)
suffix (str) – A python string of characters to append to each output filename (optional)
valuation_container (boolean) – Indicates whether the model includes valuation
land_gridPts_path (str) – A CSV file path containing the Landing and Power Grid Connection Points table. (required for Valuation)
machine_econ_path (str) – A CSV file path for the machine economic parameters table. (required for Valuation)
number_of_machines (int) – An integer specifying the number of machines for a wave farm site. (required for Valuation)
n_workers (int) – The number of worker processes to use for processing this model. If omitted, computation will take place in the current process. (optional)

Example Args Dictionary:

{
    'workspace_dir': 'path/to/workspace_dir',
    'wave_base_data_path': 'path/to/base_data_dir',
    'analysis_area_path': 'West Coast of North America and Hawaii',
    'aoi_path': 'path/to/vector',
    'machine_perf_path': 'path/to/csv',
    'machine_param_path': 'path/to/csv',
    'dem_path': 'path/to/raster',
    'suffix': '_results',
    'valuation_container': True,
    'land_gridPts_path': 'path/to/csv',
    'machine_econ_path': 'path/to/csv',
    'number_of_machines': 28,
}

Wind Energy ¶

natcap.invest.wind_energy.execute(args)¶

Wind Energy.

This module handles the execution of the wind energy model given the following dictionary:

Parameters:

workspace_dir (str) – a path to the output workspace folder (required)
wind_data_path (str) – path to a CSV file with the following header: [‘LONG’,’LATI’,’LAM’, ‘K’, ‘REF’]. Each following row is a location with at least the Longitude, Latitude, Scale (‘LAM’), Shape (‘K’), and reference height (‘REF’) at which the data was collected (required)
aoi_vector_path (str) – a path to an OGR polygon vector that is projected in linear units of meters. The polygon specifies the area of interest for the wind data points. If limiting the wind farm bins by distance, then the aoi should also cover a portion of the land polygon that is of interest (optional for biophysical and no distance masking, required for biophysical and distance masking, required for valuation)
bathymetry_path (str) – a path to a GDAL raster that has the depth values of the area of interest (required)
land_polygon_vector_path (str) – a path to an OGR polygon vector that provides a coastline for determining distances from wind farm bins. Enabled by AOI and required if wanting to mask by distances or run valuation
global_wind_parameters_path (str) – a float for the average distance in kilometers from a grid connection point to a land connection point (required for valuation if grid connection points are not provided)
suffix (str) – a str to append to the end of the output files (optional)
turbine_parameters_path (str) – a path to a CSV file that holds the turbines biophysical parameters as well as valuation parameters (required)
number_of_turbines (int) – an integer value for the number of machines for the wind farm (required for valuation)
min_depth (float) – a float value for the minimum depth for offshore wind farm installation (meters) (required)
max_depth (float) – a float value for the maximum depth for offshore wind farm installation (meters) (required)
min_distance (float) – a float value for the minimum distance from shore for offshore wind farm installation (meters) The land polygon must be selected for this input to be active (optional, required for valuation)
max_distance (float) – a float value for the maximum distance from shore for offshore wind farm installation (meters) The land polygon must be selected for this input to be active (optional, required for valuation)
valuation_container (boolean) – Indicates whether model includes valuation
foundation_cost (float) – a float representing how much the foundation will cost for the specific type of turbine (required for valuation)
discount_rate (float) – a float value for the discount rate (required for valuation)
grid_points_path (str) – a path to a CSV file that specifies the landing and grid point locations (optional)
avg_grid_distance (float) – a float for the average distance in kilometers from a grid connection point to a land connection point (required for valuation if grid connection points are not provided)
price_table (boolean) – a bool indicating whether to use the wind energy price table or not (required)
wind_schedule (str) – a path to a CSV file for the yearly prices of wind energy for the lifespan of the farm (required if ‘price_table’ is true)
wind_price (float) – a float for the wind energy price at year 0 (required if price_table is false)
rate_change (float) – a float as a percent for the annual rate of change in the price of wind energy. (required if price_table is false)
n_workers (int) – The number of worker processes to use for processing this model. If omitted, computation will take place in the current process. (optional)

Example Args Dictionary:

{
    'workspace_dir': 'path/to/workspace_dir',
    'wind_data_path': 'path/to/file',
    'aoi_vector_path': 'path/to/shapefile',
    'bathymetry_path': 'path/to/raster',
    'land_polygon_vector_path': 'path/to/shapefile',
    'global_wind_parameters_path': 'path/to/csv',
    'suffix': '_results',
    'turbine_parameters_path': 'path/to/csv',
    'number_of_turbines': 10,
    'min_depth': 3,
    'max_depth': 60,
    'min_distance': 0,
    'max_distance': 200000,
    'valuation_container': True,
    'foundation_cost': 3.4,
    'discount_rate': 7.0,
    'grid_points_path': 'path/to/csv',
    'avg_grid_distance': 4,
    'price_table': True,
    'wind_schedule': 'path/to/csv',
    'wind_price': 0.4,
    'rate_change': 0.0,
}

Returns:	None