def p_GIFs(
global_lateralisation=False,
include_paeds_and_adults=True,
include_only_postictals=False,
symptom_laterality='NEUTRAL',
dominance='NEUTRAL',
):
"""
Return the normalised/unnormalised marginal probabilities for each GIF parcellation.
for ictal semiologies only (excluding postictals)
see marginal_GIF_probabilities() for sensitivity analyses
e.g. by adding include_concordance=False for data queries excluding concordance
or changing laterality or age
"""
# normalised
patient_all_semiology_norm = Semiology(
".*",
symptoms_side=Laterality.NEUTRAL,
dominant_hemisphere=Laterality.NEUTRAL,
include_postictals=False,
include_paeds_and_adults=include_paeds_and_adults,
normalise_to_localising_values=True,
global_lateralisation=global_lateralisation,
)
if symptom_laterality == 'left':
patient_all_semiology_norm.symptoms_side = Laterality.LEFT
if dominance == 'left':
patient_all_semiology_norm.dominant_hemisphere = Laterality.LEFT
patient_all_semiology_norm.include_only_postictals = include_only_postictals
all_combined_gifs_norm, _ = patient_all_semiology_norm.get_num_datapoints_dict(
)
p_GIF_norm = marginal_GIF_probabilities(all_combined_gifs_norm)
# now not normalised version
patient_all_semiology_notnorm = Semiology(
".*",
symptoms_side=Laterality.NEUTRAL,
dominant_hemisphere=Laterality.NEUTRAL,
include_postictals=False,
include_paeds_and_adults=include_paeds_and_adults,
normalise_to_localising_values=False,
global_lateralisation=global_lateralisation,
)
if symptom_laterality == 'left':
patient_all_semiology_notnorm.symptoms_side = Laterality.LEFT
if dominance == 'left':
patient_all_semiology_notnorm.dominant_hemisphere = Laterality.LEFT
patient_all_semiology_notnorm.include_only_postictals = include_only_postictals
all_combined_gifs_notnorm, _ = patient_all_semiology_notnorm.get_num_datapoints_dict(
)
p_GIF_notnorm = marginal_GIF_probabilities(all_combined_gifs_notnorm)
return p_GIF_norm, p_GIF_notnorm
def test_latnotloc_and_latandloc_2(self):
"""
Test capturing the lateralising but not localising data rather than skipping it.
integrated with lat and loc data.
"""
patient = Semiology('lat_', Laterality.LEFT, Laterality.LEFT)
patient.data_frame = self.df
lat_not_loc_all_combined_gifs = patient.query_lateralisation(
one_map_dummy)
# inspect result
lat_not_loc_result, _ = patient.query_semiology()
self.assertIs(type(lat_not_loc_all_combined_gifs), pd.DataFrame)
assert not lat_not_loc_all_combined_gifs.empty
# drop the zero entries - should be only the IL left ones which aren't MTG of TL:
lat_not_loc_all_combined_gifs = lat_not_loc_all_combined_gifs[[
'Gif Parcellations', 'pt #s'
]].astype({
'Gif Parcellations': 'int32',
'pt #s': 'int32'
})
lat_not_loc_all_combined_gifs.set_index('Gif Parcellations',
inplace=True)
lat_not_loc_gifsclean = lat_not_loc_all_combined_gifs.loc[
lat_not_loc_all_combined_gifs['pt #s'] != 0, :]
gifs_right, gifs_left = gifs_lat_factor()
lat_not_loc_gifsclean_rights = (lat_not_loc_gifsclean.drop(
index=156).index.isin(gifs_right).all())
# inspect result assertions
assert (lat_not_loc_result.Localising.sum() == 1)
assert (lat_not_loc_result['Lateralising'].sum() == 2)
# all_combined_gifs assertions
# all except GIF 156 (L MTG) are in the right GIFs:
assert ((lat_not_loc_gifsclean_rights == True))
assert ((lat_not_loc_gifsclean.index.isin(gifs_left)).any() == True)
# assert using shape as all pt #s are 1:
assert (lat_not_loc_gifsclean['pt #s'].sum() ==
lat_not_loc_gifsclean.shape[0])
# check that latnotloc gives 1 and latandloc adds zero to right MTG GIF #155
heatmap, _ = patient.get_num_datapoints_dict(method='minmax')
assert heatmap[155] == 1 # right
def test_latexceedsloc_3(self):
"""
Test capturing lateralisation value when it exceeds localising value (part1) and combining with lat_no_loc and lat_and_loc (part 2).
Note that the default in Q_L of normalise_lat_to_loc = False and using norm_ratio = lower_value / higher_value
results in capping of lateralisation influence on data visualisation.
In the specific case of latexceedsloc semiology, despite 500 lat cumulative datapoints and 2 localising points,
the GIF results are:
{155: 2.0, 156:1.0}
"""
patient = Semiology('latexceedsloc', Laterality.LEFT, Laterality.LEFT)
patient.data_frame = self.df
# (part 1) test latexceedsloc alone using norm_ratio/oddsratio method of Q_L:
heatmap, _ = patient.get_num_datapoints_dict(method='minmax')
assert heatmap[156] == 200.0
assert heatmap[155] == 300.0
# (part 2) combine above with lat_not_loc and lat_and_loc:
patient = Semiology('lat', Laterality.LEFT, Laterality.LEFT)
patient.data_frame = self.df
lat_allgifs = patient.query_lateralisation(one_map_dummy)
# drop the zero entries - should be only the IL left ones which aren't MTG of TL:
lat_allgifs = lat_allgifs[['Gif Parcellations', 'pt #s']].astype({
'Gif Parcellations':
'int32',
'pt #s':
'int32'
})
lat_allgifs.set_index('Gif Parcellations', inplace=True)
lat_allgifs = lat_allgifs.loc[lat_allgifs['pt #s'] != 0, :]
# assert using shape this not all are 1 so should not be the same:
assert not lat_allgifs['pt #s'].sum() == lat_allgifs.shape[0]
# check the MTG on the left (IL) GIF # 156 is == 3,
# which it isn't due to the norm_ratio method in Q_L - as can be seen from part 1
# so instead we see in part 1 156 was 1, but in the spreadsheet it was 2. so will give 2.
# 3 is better but lost due to norm_ratio method in Q_L
assert (lat_allgifs.loc[156, 'pt #s'] == 201)
# for the right sided GIF, 155, latexceedsloc gives 2 [/],
# lat_not_loc gives 1 (CL) (See test_lat_not_loc_1)[/], and
# lat_and_loc adds none [/]
assert (lat_allgifs.loc[155, 'pt #s'] == 301)
def test_lat_not_loc_1(self):
"""
Test capturing the lateralising but not localising data rather than skipping it.
As implemented in QUERY_LATERALISATION in branch "Lateralising but no localising value".
"""
patient = Semiology('lat_not_loc', Laterality.LEFT, Laterality.LEFT)
patient.data_frame = self.df
lat_not_loc_all_combined_gifs = patient.query_lateralisation(
one_map_dummy)
# inspect result
lat_not_loc_result, num_query_loc = patient.query_semiology()
self.assertIs(type(lat_not_loc_all_combined_gifs), pd.DataFrame)
assert not lat_not_loc_all_combined_gifs.empty
# drop the zero entries as these are from the CL/IL zeros:
lat_not_loc_all_combined_gifs = lat_not_loc_all_combined_gifs[[
'Gif Parcellations', 'pt #s'
]].astype({
'Gif Parcellations': 'int32',
'pt #s': 'int32'
})
lat_not_loc_all_combined_gifs.set_index('Gif Parcellations',
inplace=True)
lat_not_loc_gifsclean = lat_not_loc_all_combined_gifs.loc[
lat_not_loc_all_combined_gifs['pt #s'] != 0, :]
# now we know only the CL data remains in this dummy data, which is on the RIGHT.
gifs_right, gifs_left = gifs_lat_factor()
lat_not_loc_gifsclean_rights = (
lat_not_loc_gifsclean.index.isin(gifs_right).all())
# inspect result assertions
assert (lat_not_loc_result.Localising.sum() == 0)
assert (lat_not_loc_result['Lateralising'].sum() == 1)
# all_combined_gifs assertions
assert ((lat_not_loc_gifsclean_rights == True))
assert ((lat_not_loc_gifsclean.index.isin(gifs_left)).any() == False)
assert (lat_not_loc_gifsclean['pt #s'].sum() ==
lat_not_loc_gifsclean.shape[0])
# test MTG on right 155 gif # gives 1:
heatmap, _ = patient.get_num_datapoints_dict(method='minmax')
assert 156 not in heatmap # left
assert heatmap[155] == 1 # right
def test_only_postictal_cases(self):
"""
Include only the single postictal aphasia. Note Dominant in dummy data.
cf test_postictal_exclusions
"""
# Low level test
df_postictal = only_postictal_cases(self.df)
query, num_query_lat, num_query_loc = QUERY_SEMIOLOGY(
df_postictal,
semiology_term=['aphasia'],
ignore_case=True,
semiology_dict_path=None,
col1='Reported Semiology',
col2='Semiology Category',
)
assert (query['Localising'].sum() == 1)
assert (query['Lateralising'].sum() == 1)
# High level test
patient = Semiology('aphasia',
Laterality.NEUTRAL,
dominant_hemisphere=Laterality.LEFT)
patient.data_frame = self.df
patient.include_postictals = True
patient.include_only_postictals = True
patient.granular = True
lat_allgifs = patient.query_lateralisation(one_map_dummy)
lat_allgifs = lat_allgifs[['Gif Parcellations', 'pt #s']].astype({
'Gif Parcellations':
'int32',
'pt #s':
'int32'
})
lat_allgifs.set_index('Gif Parcellations', inplace=True)
# note that dominant_hemisphere == Laterality.LEFT as set above. Just to clarify results change if dominace changes. Also 1's become 2's if not granular.
assert (lat_allgifs.loc[164, 'pt #s'] == 1)
assert (lat_allgifs.loc[166, 'pt #s'] == 1)
assert (lat_allgifs.loc[163, 'pt #s'] == 0)
assert (lat_allgifs.loc[165, 'pt #s'] == 0)
lat_allgifs = lat_allgifs.loc[lat_allgifs['pt #s'] != 0, :]
SVT_output, _ = patient.get_num_datapoints_dict(method='minmax')
assert SVT_output == dict((lat_allgifs)['pt #s'])