jade.RAbD package

jade.RAbD.AnalyzeAntibodyDesigns module

class jade.RAbD.AnalyzeAntibodyDesigns.CompareAntibodyDesignStrategies(db_dir, out_dir_name, strategies=[], jsons=[])[source]

Class mainly for comparing different Antibody Design strategies using our Features Databases.

copy_all_models()[source]
copy_top()[source]
create_score_subset_database(score_name, prefix, features_type='antibody')[source]
get_csv_data(top=False, summary=False)[source]

Get data by converting everything to a pandas dataframe first. For now, one function pretty much does everything.

Return type:[pandas.Dataframe],[str]
get_db_path(strategy, features_type='antibody')[source]
get_full_features_type(type)[source]
get_pandas_dataframe()[source]

Gets a pandas Dataframe for all :rtype: pandas.DataFrame

get_strategies()[source]
get_top_dataframe_by_all_scores()[source]

Get a pandas DataFrame for top, grouped by the type of score that is on. :rtype: pandas.DataFrame

get_top_from_dataframe(score_name)[source]

Gets a pandas Dataframe for top :rtype: pandas.DataFrame

output_all_data_as_excel_file(top=True)[source]
output_csv_data(top=False, summary=False)[source]

Output a CSV file of combined or individual data.

output_len_or_clus_alignment(alignment_type, features_type='antibody')[source]
output_len_or_clus_enrichment(alignment_type, features_type='antibody')[source]
output_len_or_clus_recovery(alignment_type, features_type='antibody')[source]
output_stats()[source]

Depracated in favor of dataframe summaries.

run_clustal_omega(processors, output_format='fasta', extra_options='')[source]
run_clustal_omega_on_all_combined(processors, output_format, extra_options='')[source]
run_features(type, plot_name='')[source]
set_cdrs_from_list(cdr_list)[source]
set_strategies(strategies)[source]
set_strategies_from_databases()[source]

Set the strategies from the db_dir/databases directory :return:

set_strategies_from_db_dir_top_dir()[source]
set_strategies_from_json_infos()[source]

Uses self.json, which are AnalysisInfo classes, to populate.

Returns:
class jade.RAbD.AnalyzeAntibodyDesigns.Perc(count, total)[source]

Simple class for holding enrichment/recovery information

get_count()[source]
get_formated_perc(perc)[source]
get_perc_decimal()[source]
get_perc_whole()[source]
get_total()[source]
jade.RAbD.AnalyzeAntibodyDesigns.calculate_enrichments(all_decoy_data, cdr, decoy_list=None)[source]

Returns defaultdict of [count_type] : Perc

jade.RAbD.AnalyzeAntibodyDesigns.calculate_observed_value(value, all_decoy_data, cdr, decoy_list=None)[source]

Calculate the enrichment of some value

jade.RAbD.AnalyzeAntibodyDesigns.calculate_recovery(native_data, all_decoy_data, cdr, decoy_list=None)[source]

Calculate the recovery of some value to native Returns

jade.RAbD.AnalyzeAntibodyDesigns.count_native_matches(decoy_data, native_data, cdrs)[source]
jade.RAbD.AnalyzeAntibodyDesigns.get_star_if_native(decoy_data, native_data, cdr)[source]
jade.RAbD.AnalyzeAntibodyDesigns.get_str(value)[source]