# Activity analysis
## Tile ground area
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source
### tile_ground_area_m2
``` python
def tile_ground_area_m2(
obsids:NoneType=None, # If given, restrict to this iterable of obsid strings; otherwise
return for all obsids in the catalog version.
version:str='v3.1'
)->Series:
```
*Tile ground area in m² per obsid.*
Each P4 tile is 840×648 pixels; ground area =
`840 * 648 * map_scale**2`. For v3.1 `map_scale` is one of {0.25, 0.5,
1.0} m/px, giving tile ground areas of {34 020, 136 080, 544 320} m²
respectively.
## Per-tile marking density
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source
### per_tile_marking_density
``` python
def per_tile_marking_density(
version:str='v3.1',
kind:str='all', # Which markings to count for ``density_per_m2``. ``"all"`` (default)
is fan + blotch, the recommended activity proxy. The per-kind counts
``n_fans`` / ``n_blotches`` are always present.
)->DataFrame:
```
*Per-tile counts and marking density (markings per m² of ground area).*
Returned columns:
`[obsid, tile_id, n_fans, n_blotches, n_markings, map_scale, tile_ground_area_m2, density_per_m2]`.
## Per-marking ground area
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source
### per_marking_ground_area
``` python
def per_marking_ground_area(
version:str='v3.1', kind:str='both', # Which catalog(s) to process. Default ``"both"``.
cache:bool=True, cache_dir:pathlib.Path | None=None
)->DataFrame:
```
*Per-marking polygon area in m².*
Computes Shapely polygons for every fan / blotch using
`markings.{Fan,Blotch}.to_shapely` (HiRISE pixel scope), takes
`polygon.area` in pixel², and converts to m² via per-obsid `map_scale`.
Result is cached as parquet next to the coverage cache.