def get_controls_side_time_series_features(side):
first_dimension = list(TREE_TIME_SERIES.keys())[0]
first_subdimension = list(TREE_TIME_SERIES[first_dimension].keys())[0]
first_sub_subdimension = TREE_TIME_SERIES[first_dimension][
first_subdimension][0]
nb_channel = INFORMATION_TIME_SERIES[first_dimension][first_subdimension][
first_sub_subdimension]["nb_channel"]
if side == "left":
value_idx = 0
else: # side == "right":
value_idx = 1
return [
get_item_radio_items(f"sex_{side}_time_series_features",
SEX_LEGEND,
"Select sex :",
value_idx=value_idx),
get_item_radio_items(f"age_group_{side}_time_series_features",
AGE_GROUP_LEGEND,
"Select age group :",
value_idx=1),
get_item_radio_items(f"aging_rate_{side}_time_series_features",
AGING_RATE_LEGEND,
"Select aging rate :",
value_idx=1),
get_drop_down(f"sample_{side}_time_series_features", SAMPLE_LEGEND,
"Select sample :"),
get_drop_down(f"channel_{side}_time_series_features",
range(nb_channel),
"Select channel :",
from_dict=False),
]
def get_controls_tab_heatmap_multivariate_results():
return dbc.Card([
get_item_radio_items(
"main_category_heatmap_multivariate_results",
list(MAIN_CATEGORIES_TO_CATEGORIES.keys()),
"Select X main category: ",
from_dict=False,
),
get_item_radio_items(
"algorithm_heatmap_multivariate_results",
{
"best_algorithm": ALGORITHMS["best_algorithm"],
"elastic_net": ALGORITHMS["elastic_net"],
"light_gbm": ALGORITHMS["light_gbm"],
"neural_network": ALGORITHMS["neural_network"],
},
"Select an Algorithm :",
),
html.Div(
[
html.H5("Composition of the best algorithm"),
dcc.Loading(
dcc.Graph(id="pie_chart_heatmap_multivariate_results",
config=DOWNLOAD_CONFIG)),
],
id="div_pie_chart_heatmap_multivariate_results",
style={"display": "none"},
),
])
def get_controls_tab_category_multivariate():
categories = pd.Index(MAIN_CATEGORIES_TO_CATEGORIES["All"]).drop(
MULTIVARIATE_CATEGORIES_TO_REMOVE)
return dbc.Card([
get_item_radio_items(
"main_category_category_multivariate",
list(MAIN_CATEGORIES_TO_CATEGORIES.keys()),
"Select X main category: ",
from_dict=False,
),
get_drop_down("category_category_multivariate",
categories,
"Select X subcategory: ",
from_dict=False),
get_item_radio_items("order_type_category_multivariate", ORDER_TYPES,
"Order by:"),
get_item_radio_items(
"algorithm_category",
{
"elastic_net": ALGORITHMS["elastic_net"],
"light_gbm": ALGORITHMS["light_gbm"],
"neural_network": ALGORITHMS["neural_network"],
},
"Select an Algorithm :",
),
get_item_radio_items("correlation_type_category_multivariate",
CORRELATION_TYPES, "Select correlation type :"),
])
def get_controls_tab_bar_plot_multivariate_results():
return dbc.Card([
get_item_radio_items(
"main_category_bar_plot_multivariate_results",
list(MAIN_CATEGORIES_TO_CATEGORIES.keys()),
"Select X main category: ",
from_dict=False,
),
get_drop_down(
"dimension_bar_plot_multivariate_results",
DIMENSIONS_SUBDIMENSIONS,
"Select an aging dimension : ",
),
get_item_radio_items(
"display_mode_bar_plot_multivariate_results",
DISPLAY_MODE,
"Rank by : ",
),
get_item_radio_items(
"algorithm_bar_plot_multivariate_results",
{
"best_algorithm": ALGORITHMS["best_algorithm"],
"elastic_net": ALGORITHMS["elastic_net"],
"light_gbm": ALGORITHMS["light_gbm"],
"neural_network": ALGORITHMS["neural_network"],
},
"Select an Algorithm :",
),
])
def get_controls_images_features():
first_dimension = list(TREE_IMAGES.keys())[0]
first_subdimension = list(TREE_IMAGES[first_dimension].keys())[0]
return [
get_item_radio_items("dimension_images_features",
list(TREE_IMAGES.keys()),
"Select main aging dimesion :",
from_dict=False),
get_item_radio_items(
"subdimension_images_features",
list(TREE_IMAGES[first_dimension].keys()),
"Select subdimension :",
from_dict=False,
),
get_drop_down(
"sub_subdimension_images_features",
TREE_IMAGES[first_dimension][first_subdimension],
"Select sub-subdimension :",
from_dict=False,
),
get_check_list("display_mode_images_features",
DISPLAY_MODE,
"Select a display mode",
from_dict=False),
]
def get_controls_comparison(order, default_category):
return dbc.Card([
get_drop_down(
f"{order}_category_comparison",
MAIN_CATEGORIES_TO_CATEGORIES["All"] + [
f"All_{main_category}"
for main_category in MAIN_CATEGORIES_TO_CATEGORIES.keys()
],
f"Select {order} category to compare: ",
from_dict=False,
value=default_category,
),
html.Div(
[
get_item_radio_items(f"{order}_uni_or_multi_comparison",
UNIVARIATE_OR_MULTIVARIATE,
"Select the type of XWAS :"),
get_item_radio_items(f"{order}_method_comparison",
SUBSET_METHODS, "Select method :"),
get_item_radio_items(f"{order}_correlation_type_comparison",
CORRELATION_TYPES,
"Select correlation type :"),
],
id=f"{order}_hiden_settings",
style={"display": "none"},
),
])
def get_controls_tab_custom_dimensions():
return dbc.Card([
get_item_radio_items(
"sample_definition_custom_dimensions",
SAMPLE_DEFINITION,
"Select the way we define a sample: ",
value_idx=2,
),
get_item_radio_items("order_type_custom_dimensions", ORDER_TYPES,
"Order by:"),
])
def get_controls_tab():
return dbc.Card(
[
get_item_radio_items(
"main_category_univariate_summary",
list(MAIN_CATEGORIES_TO_CATEGORIES.keys()),
"Select X main category: ",
from_dict=False,
),
get_item_radio_items("item_univariate_summary", ITEMS_LEGEND, "Select :"),
]
)
def get_controls_tab_univariate_dimension():
return dbc.Card(
[
get_drop_down(
"dimension_subdimension_univariate_dimension", DIMENSIONS_SUBDIMENSIONS, "Select an aging dimension: "
),
get_item_radio_items("subset_method_univariate_dimension", SUBSET_METHODS, "Select subset method :"),
get_item_radio_items(
"correlation_type_univariate_dimension", CORRELATION_TYPES, "Select correlation type :"
),
]
)
def get_controls_tab_average_multivariate():
main_dimensions_subdimension = {
"MainDimensions": "MainDimensions",
"SubDimensions": "SubDimensions"
}
main_dimensions_subdimension.update(DIMENSIONS_SUBDIMENSIONS)
average_dimensions_subdimension = {"average": "average"}
average_dimensions_subdimension.update(DIMENSIONS_SUBDIMENSIONS)
return dbc.Card([
get_item_radio_items(
"main_category_average_multivariate",
list(MAIN_CATEGORIES_TO_CATEGORIES.keys()),
"Select X main category: ",
from_dict=False,
),
get_drop_down(
"dimension_subdimension_1_average_multivariate",
main_dimensions_subdimension,
"Select an aging dimension 1: ",
),
html.Div(
[
get_drop_down(
"dimension_subdimension_2_average_multivariate",
average_dimensions_subdimension,
"Select an aging dimension 2: ",
)
],
id="hiden_dimension_subdimension_2_average_multivariate",
style={"display": "none"},
),
get_item_radio_items(
"display_mode_average_multivariate",
DISPLAY_MODE,
"Rank by : ",
),
get_item_radio_items(
"algorithm_average_multivariate",
{
"elastic_net": ALGORITHMS["elastic_net"],
"light_gbm": ALGORITHMS["light_gbm"],
"neural_network": ALGORITHMS["neural_network"],
},
"Select an algorithm :",
),
get_item_radio_items("correlation_type_average_multivariate",
CORRELATION_TYPES, "Select correlation type :"),
])
def get_controls_table_scalars_features():
return [
get_item_radio_items(
"correlation_type_scalars_features",
CORRELATION_TYPES,
"Select sub-subdimension :",
),
dbc.FormGroup([
html.P("Correlation between feature importances/correlation : "),
dash_table.DataTable(
id="table_correlation_scalars_features",
columns=[{
"id": key,
"name": name
} for key, name in FEATURES_CORRELATIONS_TABLE_COLUMNS.items()
],
style_cell={
"textAlign": "left",
"fontSize": 10
},
sort_action="custom",
sort_mode="single",
),
html.Br(),
]),
]
def get_controls_tab_dimension_multivariate():
return dbc.Card([
get_drop_down("dimension_subdimension_dimension_multivariate",
DIMENSIONS_SUBDIMENSIONS, "Select an aging dimension: "),
get_item_radio_items(
"algorithm_dimension_multivariate",
{
"elastic_net": ALGORITHMS["elastic_net"],
"light_gbm": ALGORITHMS["light_gbm"],
"neural_network": ALGORITHMS["neural_network"],
},
"Select an Algorithm :",
),
get_item_radio_items("correlation_type_dimension_multivariate",
CORRELATION_TYPES, "Select correlation type :"),
])
def get_controls_tab_univariate_category():
return dbc.Card(
[
get_item_radio_items(
"main_category_univariate_category",
list(MAIN_CATEGORIES_TO_CATEGORIES.keys()),
"Select X main category: ",
from_dict=False,
),
get_drop_down("category_univariate_category", ["All"], "Select X subcategory: ", from_dict=False),
get_item_radio_items("order_type_univariate_category", ORDER_TYPES, "Order by:"),
get_item_radio_items("subset_method_univariate_category", SUBSET_METHODS, "Select subset method :"),
get_item_radio_items(
"correlation_type_univariate_category", CORRELATION_TYPES, "Select correlation type :"
),
]
)
def get_controls_side_video(side):
if side == "left":
value_idx = 0
else: # side == "right":
value_idx = 1
return [
get_item_radio_items(f"sex_{side}_video",
SEX_LEGEND,
"Select sex :",
value_idx=value_idx),
get_item_radio_items(f"age_{side}_video",
AGE_GROUP_LEGEND,
"Select age group :",
value_idx=1),
get_drop_down(f"sample_{side}_video", SAMPLE_LEGEND,
"Select sample :"),
]
def get_controls_tab_all_dimensions():
return dbc.Card(
[
get_item_radio_items(
"sample_definition_all_dimensions",
SAMPLE_DEFINITION,
"Select the way we define a sample: ",
value_idx=2,
),
get_item_radio_items("order_type_all_dimensions", ORDER_TYPES, "Order by:"),
get_drop_down(
"dimension_all_dimensions",
["All"] + CUSTOM_DIMENSIONS.get_level_values("dimension").drop_duplicates().tolist(),
"Select an aging dimension: ",
from_dict=False,
),
]
)
def get_controls_tab_univariate_average():
main_dimensions_subdimension = {
"MainDimensions": "MainDimensions",
"SubDimensions": "SubDimensions"
}
main_dimensions_subdimension.update(DIMENSIONS_SUBDIMENSIONS)
average_dimensions_subdimension = {"average": "average"}
average_dimensions_subdimension.update(DIMENSIONS_SUBDIMENSIONS)
return dbc.Card([
get_item_radio_items(
"main_category_univariate_average",
list(MAIN_CATEGORIES_TO_CATEGORIES.keys()),
"Select X main category: ",
from_dict=False,
),
get_drop_down(
"dimension_subdimension_1_univariate_average",
main_dimensions_subdimension,
"Select an aging dimension 1: ",
),
html.Div(
[
get_drop_down(
"dimension_subdimension_2_univariate_average",
average_dimensions_subdimension,
"Select an aging dimension 2: ",
)
],
id="hiden_dimension_subdimension_2_univariate_average",
style={"display": "none"},
),
get_item_radio_items(
"display_mode_univariate_average",
DISPLAY_MODE,
"Rank by : ",
),
get_item_radio_items("subset_method_univariate_average",
SUBSET_METHODS, "Select subset method :"),
get_item_radio_items("correlation_type_univariate_average",
CORRELATION_TYPES, "Select correlation type :"),
])
def get_controls_side_video_features(side):
if side == "left":
value_idx = 0
else: # side == "right":
value_idx = 1
return [
get_item_radio_items(f"sex_{side}_video_features",
SEX_LEGEND,
"Select sex :",
value_idx=value_idx),
get_item_radio_items(f"age_{side}_video_features",
AGE_GROUP_LEGEND,
"Select age group :",
value_idx=1),
get_item_radio_items(f"aging_rate_{side}_video_features",
AGING_RATE_LEGEND,
"Select aging rate :",
value_idx=1),
]
def get_controls_scalars_features():
first_dimension = list(TREE_SCALARS.keys())[0]
first_subdimension = list(TREE_SCALARS[first_dimension].keys())[0]
return [
get_item_radio_items("dimension_scalars_features",
list(TREE_SCALARS.keys()),
"Select main aging dimesion :",
from_dict=False),
get_item_radio_items(
"subdimension_scalars_features",
list(TREE_SCALARS[first_dimension].keys()),
"Select subdimension :",
from_dict=False,
),
get_item_radio_items(
"sub_subdimension_scalars_features",
TREE_SCALARS[first_dimension][first_subdimension],
"Select sub-subdimension :",
from_dict=False,
),
]
def get_controls_scalars():
first_dimension = list(TREE_SCALARS.keys())[0]
first_subdimension = list(TREE_SCALARS[first_dimension].keys())[0]
return [
get_item_radio_items(
"dimension_scalars", list(TREE_SCALARS.keys()), "Select main aging dimesion :", from_dict=False
),
get_item_radio_items(
"subdimension_scalars",
list(TREE_SCALARS[first_dimension].keys()),
"Select subdimension :",
from_dict=False,
),
get_item_radio_items(
"sub_subdimension_scalars",
TREE_SCALARS[first_dimension][first_subdimension],
"Select sub-subdimension :",
from_dict=False,
),
get_range_slider("age_range_scalars", 35, 85, "Filter with an age range : "),
]
def get_controls_time_series():
first_dimension = list(TREE_TIME_SERIES.keys())[0]
first_subdimension = list(TREE_TIME_SERIES[first_dimension].keys())[0]
return [
get_item_radio_items("dimension_time_series",
list(TREE_TIME_SERIES.keys()),
"Select main aging dimesion :",
from_dict=False),
get_item_radio_items(
"subdimension_time_series",
list(TREE_TIME_SERIES[first_dimension].keys()),
"Select subdimension :",
from_dict=False,
),
get_drop_down(
"sub_subdimension_time_series",
TREE_TIME_SERIES[first_dimension][first_subdimension],
"Select sub-subdimension :",
from_dict=False,
),
]
def get_controls_features_multivariate():
return dbc.Card([
get_item_radio_items(
"main_category_features_multivariate",
list(MAIN_CATEGORIES_TO_CATEGORIES.keys()),
"Select X main category: ",
from_dict=False,
),
get_drop_down("category_features_multivariate", ["..."],
"Select X subcategory: ",
from_dict=False),
get_drop_down("dimension_subdimension_features_multivariate",
DIMENSIONS_SUBDIMENSIONS, "Select an aging dimension: "),
])
def get_controls_tab_univariate_volcano():
return dbc.Card([
get_item_radio_items(
"main_category_univariate_volcano",
list(MAIN_CATEGORIES_TO_CATEGORIES.keys()),
"Select X main category: ",
from_dict=False,
),
get_drop_down("category_univariate_volcano", ["All"],
"Select X subcategory: ",
from_dict=False),
get_drop_down(
"dimension_univariate_volcano",
DIMENSIONS_SUBDIMENSIONS,
"Select an aging dimension: ",
),
])
def get_controls_heritability():
return dbc.Card(
get_item_radio_items("order_type_heritability",
ORDER_TYPES_HERITABILITY, "Order by:"))
def get_controls_genetics_correlations():
return dbc.Card(get_item_radio_items("order_type_genetics_correlations", ORDER_TYPES, "Order by:"))
