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)
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,
# plot=True,
# )
#
##
###
heatmap = patient.get_num_datapoints_dict()
print("\nSemiology: ", term)
print('\nResult:', heatmap, '\n')
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',
# symptoms_side=Laterality.LEFT,
# dominant_hemisphere=Laterality.LEFT,
# 'Blink',
# Laterality.NEUTRAL,
# Laterality.LEFT,
# 'All Automatisms (oral, automotor)',
# Laterality.LEFT,
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')
ali.to_csv(folder_filename.csv)
print('done')
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
function(f'Semiology term: {semiology_term}')
function(f'Symptoms side: {symptoms_side}')
function(f'Dominant hemisphere: {dominant_hemisphere}')
function(f'Time: {seconds} seconds')