code: def total_receipts(pronac, dt):
      
      dataframe = data.planilha_comprovacao
      project = dataframe.loc[dataframe['PRONAC'] == pronac]
  
      segment_id = project.iloc[0]["idSegmento"]
      segments_cache = data.segment_projects_agg
      segments_cache = segments_cache.to_dict(orient="index")
  
      mean = segments_cache[segment_id]["mean"]
      std = segments_cache[segment_id]["<lambda>"]
      total_receipts = project.shape[0]
      is_outlier = gaussian_outlier.is_outlier(total_receipts, mean, std)
      maximum_expected = gaussian_outlier.maximum_expected_value(mean, std)
      return {
              "is_outlier": is_outlier,
              "valor": total_receipts,
              "maximo_esperado": maximum_expected,
              "minimo_esperado": 0,
          }
docs: This metric calculates the project total of receipts
      and compare it to projects in the same segment
      output:
              is_outlier: True if projects receipts is not compatible
                          to others projects in the same segment
              total_receipts: absolute number of receipts
              maximum_expected_in_segment: maximum receipts expected in segment