Closed joeweiss closed 7 months ago
Other options for the property name.
on_location_species_list
, on_location_species_list_freq
expected_location_species
, expected_location_species_freq
predicted_location_species
, predicted_location_species_freq
in_predicted_list
, in_predicted_list_frequency
predicted_list_inclusion
, predicted_list_inclusion_freq
,
predicted_list_species
, predicted_list_species_freq
predicted_location_species
, predicted_location_species_freq
predicted_species_for_location
, predicted_species_for_location_freq
predicted_species
, predicted_species_freq
On second thought, the occurrence frequency is not needed for this issue.
Current working example, is_predicted_for_location_and_date
, which is very verbose, but perhaps necessary.
{'common_name': 'Engine',
'confidence': 0.516838550567627,
'end_time': 114.0,
'is_predicted_for_location_and_date': False,
'label': 'Engine_Engine',
'scientific_name': 'Engine',
'start_time': 111.0},
{'common_name': 'American Goldfinch',
'confidence': 0.39239490032196045,
'end_time': 120.0,
'is_predicted_for_location_and_date': True,
'label': 'Spinus tristis_American Goldfinch',
'scientific_name': 'Spinus tristis',
'start_time': 117.0}]
Implemented in #107, see api docs for more info.
When providing the lat/long and datetime, Analyzer filters results by an BirdNET determined expected species list. This filters out all non-bird sounds like engine, coyote, frogs, etc.
It would be helpful to have a detection return with a "in_species_list" boolean or something similar, rather then filter out non-avian sounds entirely.
Perhaps it should also include the occurrence frequency value that's output by BirdNET's species.py function.