Salary Market

function get_compensation_output(companyId,include,compare,round,general_graphcode,filters,criteria):

    # 1. Extract Data  

    panel_data = loadDataFromDatabase(compagny.id, compangy.include, filters)

	compagny_data = loadDataFromDatabase(compagny.id)

    # 2. Get datasets for comparison

	compared_data = buildComparisonDatasets(compare, panel_data, compagny_data)

	# 3. For each criteria, calcul graphs of each criteria

    criteriaResults = []

    for( criteria in response.criteria)

		{

			result = null

			result = calcul_sheet_criteria(criteria,compared_data)        

			result = result[result["number_employee_panel"] > result["number_employee_company"]]

			result = result[result["number_employee_panel"] > 1]

			result = function calcul_graphs_criteria

			if(criteria.best_matching = true)

				result = bestmatching_criteria()

			criteriaResults.append(result)

		}

    # 4. Compute global-level graphs

    globalGraphs = []

	if(response.graphs != null)

		for( graph in response.graphs)

			if(graph eq graph_gap_general)

				globalGraphs = calcul_graph_gap_general

			else if(graph eq general_internal_gap_fixed)

				globalGraphs = calcul_graph_general_internal_gap_fixed

			else ;

    # 5. Build final response

    return {

        "request": request,

        "response": {

            "criteria": criteriaResults,

            "graphs": globalGraphs

        }

    }

}

function loadDataFromDatabase( compagny.id, compangy.include, filters)

function buildComparisonDatasets(rawData,compare)

function calcul_sheet_criteria

function bestmatching_criteria

function calcul_graphs_criteria

	function calcul_graph_repartition_gap

	function calcul_graph_repartition_data_bf

	function calcul_graph_repartition_data_bt

function calcul_graph_gap_general

function calcul_graph_general_internal_gap_fixed