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[project]
description = A Getting Started example based on a Kaiju attack on NYC
functions = functions/kaiju-building-stomp.txt
functions = functions/kaiju-building-stomp-scorch.py

[type kaiju_attack]
type.geom = geometry
type.kaiju_name = text
type.severity = lookup('kaiju_attack_severity')

[type kaiju_attack_severity]
type = range(floating, 0, 10)

[type building]
type.geom = geometry
type.name = text
type.stories = integer
type.construction = enum('timber', 'concrete', 'steel','stone','brick')

[type building_attack_outcome]
type.damage = integer

[bookmark nyc_buildings]
description = A set of NYC landmark buildings
location = data/nyc-landmarks.csv
type = building
crs-name = EPSG:4326
map-attribute.geom = geom_from_wkt(WKT)
crs-longitude-first = true

[bookmark kaiju_route]
description = The route of our kaiju through NYC
location = data/kaiju-route.csv
type = kaiju_attack
crs-name = EPSG:4326
map-attribute.geom = geom_from_wkt(WKT)
crs-longitude-first = true

[bookmark kaiju_fire]
description = The intensity and extent of the fire blast attacks
location = data/kaiju-fire.shp
type = kaiju_attack
map-attribute.kaiju_name = name
map-attribute.severity = intensity * time
map-attribute.geom = the_geom

[type boroughs]
type.geom = geometry
type.name = text

[bookmark nyc_boroughs]
description = A map showing the outlines of the NYC boroughs and islands
location = data/new-york.csv
type = boroughs
crs-name = EPSG:4326
crs-longitude-first = true
map-attribute.geom = geom_from_wkt(WKT)

[model sample_overlapping_polygons]
framework = pipeline
description = pipeline that checks sample() will match overlapping polygons
# NB: Trump Tower fully overlaps two of the hazard polygons, The Plaza Hotel partially overlaps
pipeline = input('nyc_buildings', name: 'building') \
    -> filter(building.name = 'Trump Tower' or building.name = 'The Plaza Hotel') \
    -> select({*, map(sample(building, to_coverage(bookmark('overlapping-polygons.gpkg'))), h -> h.sampled.severity) as sampled }) \
    -> select({ building.name, length(sampled) as num, min(sampled) as min, max(sampled) as max }) \
    -> sort(name) as results

[model bookmark_params]
framework = pipeline
description = pipeline with bookmark/template parameters
pipeline = input($buildings, name: 'building') \
    -> select({*, sample_one(building, to_coverage($hazard)) as sampled }) \
    -> filter(sampled.severity > 0) \
    -> group({ count(*) as Num_buildings, round(mean(building.stories), 2) as Average_stories }) as results
param.buildings = data/nyc-landmarks.csv
param.buildings.properties = bookmark-template: nyc_buildings
param.hazard = overlapping-polygons.gpkg
param.hazard.properties = bookmark
