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def main(sc=True, **kwargs):
"""
Main function to run the whole pipeline. The function is called from the
streamlit app. The function is called with the preset option as an argument.
The preset option is used to determine which function should be called.
The preset option is set in the config file.
Parameters
----------
sc : bool, optional
Decide if meta analysis should be done using sequence limit, by default True
Returns
-------
str or None
depending on the preset option, the function returns either the time
when the annotation was started or None
"""
from acodet.annotate import run_annotation, filter_annots_by_thresh
from acodet.train import run_training, save_model
from acodet.tfrec import write_tfrec_dataset
from acodet.hourly_presence import compute_hourly_pres, calc_val_diff
from acodet.evaluate import create_overview_plot
from acodet.combine_annotations import generate_final_annotations
from acodet.models import init_model
import acodet.global_config as conf
if "fetch_config_again" in kwargs:
import importlib
importlib.reload(conf)
kwargs["relativ_path"] = conf.SOUND_FILES_SOURCE
if "preset" in kwargs:
preset = kwargs["preset"]
else:
preset = conf.PRESET
if conf.RUN_CONFIG == 1:
if preset == 1:
timestamp_foldername = run_annotation(**kwargs)
return timestamp_foldername
elif preset == 2:
new_thresh = filter_annots_by_thresh(**kwargs)
return new_thresh
elif preset == 3:
compute_hourly_pres(sc=sc, **kwargs)
elif preset == 4:
compute_hourly_pres(**kwargs)
elif preset == 6:
calc_val_diff(**kwargs)
elif preset == 0:
timestamp_foldername = run_annotation(**kwargs)
filter_annots_by_thresh(timestamp_foldername, **kwargs)
compute_hourly_pres(timestamp_foldername, sc=sc, **kwargs)
return timestamp_foldername
elif conf.RUN_CONFIG == 2:
if preset == 1:
generate_final_annotations(**kwargs)
write_tfrec_dataset(**kwargs)
elif preset == 2:
generate_final_annotations(active_learning=False, **kwargs)
write_tfrec_dataset(active_learning=False, **kwargs)
elif conf.RUN_CONFIG == 3:
if preset == 1:
run_training(**kwargs)
elif preset == 2:
create_overview_plot(**kwargs)
elif preset == 3:
create_overview_plot("2022-05-00_00", **kwargs)
elif preset == 4:
save_model("FlatHBNA", init_model(), **kwargs)
if __name__ == "__main__":
from acodet.create_session_file import create_session_file
create_session_file()
main()
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