# Create a list of user_ids to exclude based on marginal distribution
exclude_id_based_on_marginal_distribution = calculate_marginal_distribution_for_each_user(project_short_name)
# Filter the include_ids removing any that should be excluded based on marginal distributions
include_ids = [id for id in include_ids if id not in exclude_id_based_on_marginal_distribution]
gold_standard_data = define_gold_standard_data(project_short_name=project_short_name)
expert_ids = define_gold_standard_ids(project_short_name=project_short_name)
#combined_dict = build_combined_dict_keyed_on_composite_key(project_short_name=project_short_name, user_ids_to_include=include_ids, expert_project_short_name=gold_standard_data, expert_user_ids_to_include=expert_ids)
### CLEAN FROM HERE ###
user_dict = create_individual_dict(project_short_name=project_short_name,user_ids_to_include=include_ids)
if project_short_name == 'tb2-r2.0':
ihc.get_gs_data_2r20()
else:
gs_dict = create_individual_dict(project_short_name=gold_standard_data, user_ids_to_include=expert_ids)
list_of_composite_keys_in_both = []
for k in gs_dict.keys():
if (k in user_dict.keys()) and (':0:0' in k): #converts data stored by keys back to data stored by task/image
list_of_composite_keys_in_both.append(k)
list_of_composite_keys_in_both.sort()
for key in list_of_composite_keys_in_both:
user_answers = pandas.DataFrame(user_dict[key]['ihc'])
gs_answers = pandas.DataFrame(gs_dict[key]['ihc'])
user_prop_mean = user_answers.sum(axis=1)['proportion']/len(user_answers.keys())
expert_prop_mean = gs_answers.sum(axis=1)['proportion']/len(gs_answers.keys())
