def _change_subdimensions_images_features(dimension, subdimension,
data_scores):
context = dash.callback_context.triggered
if not context or context[0]["prop_id"].split(
".")[0] == "dimension_images_features":
first_subdimension = list(TREE_IMAGES[dimension].keys())[0]
option_subdimension = get_options_from_list(
list(TREE_IMAGES[dimension].keys()))
value_subdimension = list(TREE_IMAGES[dimension].keys())[0]
option_sub_subdimension = get_options_from_list(
TREE_IMAGES[dimension][first_subdimension])
value_sub_subdimension = TREE_IMAGES[dimension][first_subdimension][0]
else:
option_subdimension = get_options_from_list(
list(TREE_IMAGES[dimension].keys()))
value_subdimension = subdimension
option_sub_subdimension = get_options_from_list(
TREE_IMAGES[dimension][subdimension])
value_sub_subdimension = TREE_IMAGES[dimension][subdimension][0]
scores_raw = (pd.DataFrame(data_scores).set_index([
"dimension", "subdimension", "sub_subdimension"
]).loc[(dimension, value_subdimension,
value_sub_subdimension)].set_index("algorithm"))
scores = (scores_raw.drop(
index=scores_raw.index[scores_raw.index == "*"]).sort_values(
"r2", ascending=False).round(3))
title = ""
for algorithm in scores.index:
title += f"The {ALGORITHMS[algorithm]} has a R² of {scores.loc[algorithm, 'r2']} +- {scores.loc[algorithm, 'r2_std']}. "
return option_subdimension, value_subdimension, option_sub_subdimension, value_sub_subdimension, title
def _change_channel(dimension, subdimension, sub_subdimension):
nb_channel = INFORMATION_TIME_SERIES[dimension][subdimension][
sub_subdimension]["nb_channel"]
return [
get_options_from_list(range(nb_channel)), 0,
get_options_from_list(range(nb_channel)), 0
]
def _change_controls_category_features_multivariate(main_category):
categories = MAIN_CATEGORIES_TO_CATEGORIES[main_category].copy()
for category_to_remove in MULTIVARIATE_CATEGORIES_TO_REMOVE:
if category_to_remove in categories:
categories.remove(category_to_remove)
return get_options_from_list(categories), categories[0]
def _change_controls_category(main_category):
if main_category == "All":
list_categories = list(
pd.Index(MAIN_CATEGORIES_TO_CATEGORIES[main_category]).drop(
["Genetics", "Phenotypic"]))
else:
list_categories = MAIN_CATEGORIES_TO_CATEGORIES[main_category]
return get_options_from_list(["All"] + list_categories), "All"
def _change_subdimensions(dimension, subdimension):
context = dash.callback_context.triggered
if not context or context[0]["prop_id"].split(".")[0] == "dimension_scalars":
first_subdimension = list(TREE_SCALARS[dimension].keys())[0]
return (
get_options_from_list(list(TREE_SCALARS[dimension].keys())),
list(TREE_SCALARS[dimension].keys())[0],
get_options_from_list(TREE_SCALARS[dimension][first_subdimension]),
TREE_SCALARS[dimension][first_subdimension][0],
)
else:
return (
get_options_from_list(list(TREE_SCALARS[dimension].keys())),
subdimension,
get_options_from_list(TREE_SCALARS[dimension][subdimension]),
TREE_SCALARS[dimension][subdimension][0],
)
def _change_controls_average(dimension_subdimension_1):
if dimension_subdimension_1 in ["MainDimensions", "SubDimensions"]:
return {
"display": "none"
}, get_options_from_list(["average"]), "average"
else:
average_dimensions_subdimension = {"average": "average"}
average_dimensions_subdimension.update(DIMENSIONS_SUBDIMENSIONS)
del average_dimensions_subdimension[dimension_subdimension_1]
return ({
"display": "block"
}, get_options_from_dict(average_dimensions_subdimension), "average")
def _change_category_category(main_category):
return get_options_from_list(["All"] + MAIN_CATEGORIES_TO_CATEGORIES[main_category]), "All"
def _change_feature(scalars_data):
features = pd.DataFrame(scalars_data).columns.drop(["id", "sex", "chronological_age"] + ETHNICITIES)
return get_options_from_list(features), features[0]