def wrapper_TS_GIFs(
p_S_norm,
global_lateralisation: bool = False,
include_paeds_and_adults: bool = True,
include_only_postictals: bool = False,
symptom_laterality='NEUTRAL',
dominance='NEUTRAL',
normalise_to_localising_values=True,
hierarchy_reversal: bool = True,
):
"""
Get all Gifs for all semiologies from Toplogical (include_spontaneous_semiology=False) studies, add their dict values and use this as the denominator to return p_S_given_GIF.
As it queries all semiologies, too labour intensive to do on the fly, hence results are cached for the double NEUTRAL case.
See below to make more efficient.
"""
# initialise
pt = {}
all_combined_gifs_superdict = {}
added_all_gifs = {}
added_all_gifs = Counter(added_all_gifs)
p_S_given_GIF = pd.DataFrame()
for semiology in p_S_norm.index:
pt[semiology] = Semiology(
semiology,
include_spontaneous_semiology=False, # crucial
symptoms_side=Laterality.NEUTRAL,
dominant_hemisphere=Laterality.NEUTRAL,
include_postictals=False,
include_paeds_and_adults=include_paeds_and_adults,
normalise_to_localising_values=normalise_to_localising_values,
global_lateralisation=global_lateralisation,
)
pt[semiology].include_only_postictals = include_only_postictals
pt[semiology].granular = hierarchy_reversal
all_combined_gifs_superdict[semiology], all_combined_gif_df = pt[
semiology].get_num_datapoints_dict(
method='not proportions'
) # method=anything but default proportions
# now add the dictionaries: this seems to repeat what marginal_GIF_probabilities does - can make more efficient
for semio_dict_result, v in all_combined_gifs_superdict.items(
): # equivalent: for semiology in p_S_norm.index:
temp_dict = Counter(all_combined_gifs_superdict[semio_dict_result])
added_all_gifs = added_all_gifs + temp_dict
# turn counter back to dict
added_all_gifs = dict(added_all_gifs)
# so totals for each semiology, given a GIF, is added_all_gifs[GIF #]
# now we need to look at each individual GIF and semio
for semiology in p_S_norm.index:
for GIF_no, v in all_combined_gifs_superdict[semiology].items():
p_S_given_GIF.loc[semiology, GIF_no] = v / added_all_gifs[GIF_no]
return p_S_given_GIF
normalise_to_localising_values=False, # default is False
)
###
##
# # if we want to use the dummy_data instead of real Semio2Brain DataFrame:
# repo_dir, resources_dir, dummy_data_path, dummy_semiology_dict_path = \
# file_paths(dummy_data=True)
# patient.data_frame, _, _ = MEGA_ANALYSIS(
# excel_data=dummy_data_path,
# n_rows=100,
# usecols="A:DH",
# header=1,
# exclude_data=False,
# plot=True,
# )
#
##
###
###
##
# # if we want to set top_level_lobes to True:
patient.granular = False
patient.top_level_lobes = True
heatmap = patient.get_num_datapoints_dict()
print("\nSemiology: ", patient.term)
print('\nResult:', heatmap, '\n')