code: def verified_funds(pronac, dt):
      
      dataframe = data.planilha_comprovacao
      project = dataframe.loc[dataframe['PRONAC'] == pronac]
      segment_id = project.iloc[0]["idSegmento"]
      pronac_funds = project[
          ["idPlanilhaAprovacao", "PRONAC", "vlComprovacao", "idSegmento"]
      ]
      funds_grp = pronac_funds.drop(columns=["idPlanilhaAprovacao"]).groupby(
          ["PRONAC"]
      )
      project_funds = funds_grp.sum().loc[pronac]["vlComprovacao"]
  
      segments_info = data.verified_funds_by_segment_agg.to_dict(orient="index")
      mean = segments_info[segment_id]["mean"]
      std = segments_info[segment_id]["std"]
      is_outlier = gaussian_outlier.is_outlier(project_funds, mean, std)
      maximum_expected_funds = gaussian_outlier.maximum_expected_value(mean, std)
      return {
              "is_outlier": is_outlier,
              "valor": project_funds,
              "maximo_esperado": maximum_expected_funds,
              "minimo_esperado": 0,
      }
docs: Responsable for detecting anomalies in projects total verified funds.