def test_neutral_also():
for term in list_of_terms:
patient = Semiology(
term.strip(),
symptoms_side=Laterality.LEFT,
dominant_hemisphere=Laterality.LEFT,
)
###
##
# # 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,
# )
#
##
###
heatmap, _ = patient.get_num_datapoints_dict()
assert isinstance(heatmap, dict)
def run_query():
parser = ArgumentParser(description="Epilepsy SVT query")
parser.add_argument(
'semio'
) # where the data is locally for dev purposes when updating data to ensure works- make into test later
parser.add_argument('symptoms_side')
parser.add_argument(
'dominant_hemisphere') # add default later e.g. Laterality.LEFT
parser.add_argument(
'--true', '-t', action='store_true'
) # -t: future use to be able to run or omit a section of the code
arguments = parser.parse_args()
symptoms_side = arguments.semio
dominant_hemisphere = arguments.dominant_hemisphere
heatmap = Semiology(
arguments.semio,
Laterality.symptoms_side,
Laterality.dominant_hemisphere,
)
num_patients_dict, _ = heatmap.get_num_datapoints_dict()
print('Result:', num_patients_dict)
# output2 = output1.SOMEFUNCTION(arguments.true)
# -t: future use to be able to run or omit a section of the code
try:
if arguments.true:
pass
else:
pass
except (TypeError):
pass
def _test_neutral_only(self):
self.gen_term = self.list_of_terms_wrapper()
term = str(list(self.gen_term))
patient = Semiology(
term.strip(),
symptoms_side=Laterality.LEFT,
dominant_hemisphere=Laterality.LEFT,
)
heatmap, _ = patient.get_num_datapoints_dict()
assert isinstance(heatmap, dict)
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
def test_combine_semiologies_proportions_sum_to_1(self):
patient = Semiology(
'Head Version',
symptoms_side=Laterality.LEFT,
dominant_hemisphere=Laterality.NEUTRAL,
normalise_to_localising_values=True, # default is False
)
patient2 = Semiology(
'Epigastric',
symptoms_side=Laterality.LEFT,
dominant_hemisphere=Laterality.NEUTRAL,
normalise_to_localising_values=True, # 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
df = combine_semiologies([patient, patient2],
normalise_method='proportions')
assert (df.sum(axis=1)).all() == 1
import mega_analysis
from mega_analysis import Semiology, Laterality
from pathlib import Path
from mega_analysis.crosstab.file_paths import file_paths
from mega_analysis.crosstab.mega_analysis.MEGA_ANALYSIS import MEGA_ANALYSIS
file = Path(__file__).parent/'resources' / \
'semiologies_postictalsonly_neutral_also.txt'
list_of_terms = list(open(file, 'r'))
for term in list_of_terms:
patient = Semiology(
term.strip(),
symptoms_side=Laterality.LEFT,
dominant_hemisphere=Laterality.LEFT,
include_postictals=True,
)
###
##
# # 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,
import mega_analysis
from mega_analysis import Semiology, Laterality
from mega_analysis.crosstab.file_paths import file_paths
from mega_analysis.crosstab.mega_analysis.MEGA_ANALYSIS import MEGA_ANALYSIS
from mega_analysis.semiology import get_df_from_semiologies, combine_semiologies
method = 'Bayesian only'
Patient_VisualRight = Semiology(
'Visual',
symptoms_side=Laterality.RIGHT,
dominant_hemisphere=Laterality.NEUTRAL,
normalise_to_localising_values=True,
global_lateralisation=
True, # again not relevant as using BAyesian only - not using the df from this, but just the lateralising values
# include_et_topology_ez=False, # not relevant as using Bayesian only
# include_cortical_stimulation=False,
# include_spontaneous_semiology=True,
)
# df_proportions, all_combind_gif_dfs = get_df_from_semiologies([Patient_VisualRight], method=method)
# # we want <:
# assert round(all_combind_gif_dfs.loc['Visual', 32], 3) < round(all_combind_gif_dfs.loc['Visual', 33], 3)
num_datapoints_dict, all_combined_gif_df = Patient_VisualRight.get_num_datapoints_dict(
method=method)
assert round(all_combined_gif_df.loc[32, 'pt #s'], 3) < round(
all_combined_gif_df.loc[33, 'pt #s'], 3)
# new query
patient = Semiology(
# 'Figure of 4',
num_datapoints_dict = {}
for term in tqdm(['Epigastric'],
desc='Semiologies',
bar_format="{l_bar}%s{bar}%s{r_bar}" %
(Fore.RED, Fore.RESET)):
if term.strip() in [
"No Semiology - Only Stimulation Studies", "Hypomotor"
]:
continue
patient = Semiology(
term.strip(),
symptoms_side=Laterality.NEUTRAL,
dominant_hemisphere=Laterality.NEUTRAL,
granular=True, # hierarchy reversal
include_cortical_stimulation=False,
include_et_topology_ez=False,
include_spontaneous_semiology=True, # SS
normalise_to_localising_values=True, # normalise to pt numbers
include_paeds_and_adults=True, # paeds and adults
)
num_datapoints_dict[
term.strip()], all_combined_gif_df = patient.get_num_datapoints_dict()
# set the zero ones:
num_datapoints_dict['Hypomotor'] = num_datapoints_dict['Epigastric'].copy()
for k, v in num_datapoints_dict['Hypomotor'].items():
num_datapoints_dict['Hypomotor'][k] = 0
ali = pd.DataFrame.from_dict(num_datapoints_dict, orient='index')
sides = Laterality.LEFT, Laterality.RIGHT
logging.basicConfig(
filename=log_path,
level=logging.INFO,
filemode='w',
)
rows = []
for semiology_term in get_all_semiology_terms():
for symptoms_side in sides:
for dominant_hemisphere in sides:
tic = time.time()
semiology = Semiology(
semiology_term,
symptoms_side,
dominant_hemisphere,
)
try:
scores_dict, _ = semiology.get_num_datapoints_dict()
except Exception as e:
logging.error(e)
scores_dict = None
toc = time.time()
seconds = toc - tic
if scores_dict is None:
function = logging.error
elif seconds > 1:
function = logging.warning
else:
function = logging.info
from mega_analysis.crosstab.file_paths import file_paths
from mega_analysis.crosstab.mega_analysis.MEGA_ANALYSIS import MEGA_ANALYSIS
patient = Semiology(
# 'Figure of 4',
# symptoms_side=Laterality.LEFT,
# dominant_hemisphere=Laterality.LEFT,
# 'Blink',
# Laterality.NEUTRAL,
# Laterality.LEFT,
# 'All Automatisms (oral, automotor)',
# Laterality.LEFT,
# Laterality.LEFT,
# 'Grimace', Laterality.NEUTRAL, Laterality.NEUTRAL,
# 'latexceedsloc', # switch to dummy data
# symptoms_side=Laterality.LEFT,
# dominant_hemisphere=Laterality.LEFT,
'Tonic',
symptoms_side=Laterality.LEFT,
dominant_hemisphere=Laterality.LEFT,
normalise_to_localising_values=False, # default is False
)
###
##
# # if we want to use the dummy_data instead of real Semio2Brain DataFrame:
from mega_analysis.crosstab.mega_analysis.MEGA_ANALYSIS import MEGA_ANALYSIS
file = Path(__file__).parent / 'resources' / 'semiologies_neutral_only.txt'
list_of_terms = list(open(file, 'r'))
# list_of_terms = ['Mimetic Automatisms',
# 'No Semiology - Only Stimulation Studies',
# 'Non-Specific Aura', 'Olfactory-Gustatory', 'Palilalia', 'Psychic', 'Spitting', 'Vestibular', 'Vocalisation', 'Whistling']
for term in tqdm(list_of_terms,
desc='Neutral Only Semiologies',
bar_format="{l_bar}%s{bar}%s{r_bar}" %
(Fore.RED, Fore.RESET)):
patient = Semiology(
term.strip(),
symptoms_side=Laterality.NEUTRAL,
dominant_hemisphere=Laterality.NEUTRAL,
)
###
##
# # 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,