onstove.MCA.get_index#

MCA.get_index(datasets: dict[str, list[str]] = 'all', buffer: bool = False, name: str | None = None)[source]#

Computes a standard index based on the datasets provided.

It calls the general index() method with the provided datasets, normalizes the results including or excluding areas defined by the buffer and returns a RasterLayer with the computed data and the base_layer RasterLayer.meta information.

Parameters:

datasets: dictionary of ``category``-``list of layer names`` pairs, default ‘all’

Specifies which dataset(s) to use to compute the index.

datasets example:#

datasets={'category_1': ['layer_1', 'layer_2'],
          'category_2': [...]}

buffer: str, default ``False``

Whether to buffer the areas outside the RasterLayer.distance_limit.

name: str, optional

Name used to create the RasterLayer

Returns:

RasterLayer: The weighted average of the RasterLayer.normalized datasets based on their defined RasterLayer.weight

Examples

Clean cooking potential index for Biomass ICS created for Nepal:

>>> nepal.layers['Electricity']['Existing infra'].inverse = False
>>> nepal.layers['OnStove']['LPG_cost_mean'].inverse = False
>>> nepal.layers['Biomass']['Traveltime'].inverse = True
>>> nepal.layers['Demographics']['Wealth'].inverse = True
>>> nepal.layers['Demographics']['Population'].inverse = False
>>> nepal.layers['OnStove']['maximum_net_benefit_per_household'].inverse = False
>>> nepal.layers['OnStove']['available_biogas_mean'].inverse = True
...
>>> nepal.layers['Electricity']['Existing infra'].distance_limit = None
>>> nepal.layers['OnStove']['LPG_cost_mean'].distance_limit = None
>>> nepal.layers['Biomass']['Traveltime'].distance_limit = None
>>> nepal.layers['Demographics']['Wealth'].distance_limit = None
>>> nepal.layers['Demographics']['Population'].distance_limit = None
>>> nepal.layers['OnStove']['maximum_net_benefit_per_household'].distance_limit = None
>>> nepal.layers['OnStove']['available_biogas_mean'].distance_limit = None
...
>>> nepal.layers['Electricity']['Existing infra'].weight = 2.7
>>> nepal.layers['OnStove']['LPG_cost_mean'].weight = 3.3
>>> nepal.layers['Biomass']['Traveltime'].weight = 4.3
>>> nepal.layers['Demographics']['Wealth'].weight = 4.6
>>> nepal.layers['Demographics']['Population'].weight = 2
>>> nepal.layers['OnStove']['maximum_net_benefit_per_household'].weight = 4.1
>>> nepal.layers['OnStove']['available_biogas_mean'].weight = 3.3
...
>>> biomass_ics_index = nepal.get_index(datasets={'Demographics': ['Population', 'Wealth'],
...                                               'Electricity': ['Existing infra'],
...                                               'Biomass': ['Traveltime'],
...                                               'OnStove': ['LPG_cost_mean',
...                                                           'maximum_net_benefit_per_household',
...                                                           'available_biogas_mean']},
...                                     buffer=True, name='Biomass ICS T3')

Plotting this index produces the following output:

Biomass ICS clean cooking potential index created with OnStove