def test_MappingsCalibration_HierarchyReversal_SemioBrainBeta_TLd(self):
"""
Test the mappings TL for mapping calibration visualisation in the systReview (SemioBrain Database - beta version)
i.e. not dummy data
"""
patient = Semiology('mappings TLd', Laterality.NEUTRAL,
Laterality.NEUTRAL)
patient.data_frame = self.use_SemioBrainBeta_notDummyData()
patient.granular = False
inspect_result, _ = patient.query_semiology()
hierarchy_df = Hierarchy(inspect_result)
hierarchy_df.all_hierarchy_reversal() # deafult max option
inspect_result_reversed = hierarchy_df.new_df
# test postcodes
assert (inspect_result['TL'].sum() == 1)
assert (inspect_result['Lateral Temporal'].sum() == 1)
assert (inspect_result[
'STG (includes Transverse Temporal Gyrus, Both Planum)'].sum() == 1
)
assert 'Planum Temporale' not in inspect_result
# test hierarchy reversal
assert (inspect_result_reversed['TL'].sum() == 0)
assert (inspect_result_reversed['Lateral Temporal'].sum() == 0)
assert (inspect_result_reversed[
'STG (includes Transverse Temporal Gyrus, Both Planum)'].sum() == 1
)
assert 'Planum Temporale' not in inspect_result
assert 'Planum Polare' not in inspect_result
def test_temporal_and_frontal_hierarchy_reversal(self):
"""
test combined temporal and frontals
"""
patient = Semiology('Aphasia', Laterality.NEUTRAL, Laterality.NEUTRAL)
patient.data_frame = self.df
patient.granular = False
inspect_result, _ = patient.query_semiology()
hierarchy_df = Hierarchy(inspect_result)
hierarchy_df.temporal_hierarchy_reversal() # deafult max option
hierarchy_df.frontal_hierarchy_reversal()
inspect_result_reversed = hierarchy_df.new_df
assert hierarchy_df.frontal_hr.equals(hierarchy_df.new_df)
assert inspect_result.all().all() == inspect_result_reversed.all().all(
)
assert not hierarchy_df.temporal_hr.equals(hierarchy_df.new_df)
# ^ because self.new_df.copy() in the Hierarchy.temporal_hierarchy_reversal() method, so order matters.
assert (inspect_result_reversed['TL'].sum() == 3)
assert (inspect_result_reversed['Anterior (temporal pole)'].sum() == 5)
assert (inspect_result_reversed['Lateral Temporal'].sum() == 0)
assert (inspect_result_reversed['ITG'].sum() == 4)
assert (inspect_result_reversed['Mesial Temporal'].sum() == 5)
assert (inspect_result_reversed['FL'].sum() == 0)
assert (inspect_result_reversed['IFG (F3)\n(BA 44,45,47)'].sum() == 1)
print('\n10 combined T & F hierarchy reversals\n')
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'])
def test_hierarchy(self):
"""
first tested postcode system which duplicates mapping
before testing its reversal.
"""
patient = Semiology('Aphasia', Laterality.NEUTRAL, Laterality.NEUTRAL)
patient.data_frame = self.df
# default behaviour of query_semiology changed to use granular hierarchy reversal:
patient.granular = False
inspect_result, _ = patient.query_semiology()
assert (inspect_result['TL'].sum() == 12)
assert (inspect_result['Anterior (temporal pole)'].sum() == 5)
assert (inspect_result['Lateral Temporal'].sum() == 4)
assert (inspect_result['ITG'].sum() == 4)
assert (inspect_result['Mesial Temporal'].sum() == 5)
# # by default postictal are excluded. Otherwise add +1 to both below
assert (inspect_result['FL'].sum() == 1)
assert (inspect_result['IFG (F3)\n(BA 44,45,47)'].sum() == 1)
print('\n7 hierarchy\n')
def test_temporal_hierarchy_reversal(self):
"""
now test reversal postcode system which duplicates mapping
for new class Hierarchy
"""
patient = Semiology('Aphasia', Laterality.NEUTRAL, Laterality.NEUTRAL)
patient.data_frame = self.df
patient.granular = False
inspect_result, _ = patient.query_semiology()
# # the three lines below were integrated into default query_semiology() using.granular = True
hierarchy_df = Hierarchy(inspect_result)
hierarchy_df.temporal_hierarchy_reversal() # deafult max option
inspect_result_reversed = hierarchy_df.temporal_hr
assert (inspect_result_reversed['TL'].sum() == 3)
assert (inspect_result_reversed['Anterior (temporal pole)'].sum() == 5)
assert (inspect_result_reversed['Lateral Temporal'].sum() == 0)
assert (inspect_result_reversed['ITG'].sum() == 4)
assert (inspect_result_reversed['Mesial Temporal'].sum() == 5)
print('\n8 temporal hierarchy reversal\n')
def p_GIFs(
global_lateralisation=False,
include_paeds_and_adults=True,
include_only_postictals=False,
symptom_laterality='NEUTRAL',
dominance='NEUTRAL',
hierarchy_reversal: bool = True,
include_spontaneous_semiology: bool = False, # TS only
):
"""
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,
include_spontaneous_semiology=include_spontaneous_semiology,
)
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
patient_all_semiology_norm.granular = hierarchy_reversal
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
patient_all_semiology_notnorm.granular = hierarchy_reversal
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
