code: def infer_transition_matrix_coefficient_from_data(
          self,
          source: str,
          target: str,
          state: Optional[str] = None,
          crop: Optional[str] = None,
      ):
          
          rows = engine.execute(
              f"select * from dssat where `Crop` like '{crop}'"
              f" and `State` like '{state}'"
          )
          xs, ys = lzip(*[(r["Rainfall"], r["Production"]) for r in rows])
          xs_scaled, ys_scaled = xs / np.mean(xs), ys / np.mean(ys)
          p, V = np.polyfit(xs_scaled, ys_scaled, 1, cov=True)
          self.edges[source, target]["βs"] = np.random.normal(
              p[0], np.sqrt(V[0][0]), self.res
          )
          self.sample_from_prior()
docs: Infer the distribution of a particular transition matrix
          coefficient from data.
  
          Args:
              source: The source of the edge corresponding to the matrix element
                  to infer.
              target: The target of the edge corresponding to the matrix element
                  to infer.
              state:
                  The state in South Sudan for which the transition matrix
                  coefficient should be calculated.
              crop:
                  The crop for which the transition matrix coefficient should be
                  calculated.