Propagate_information_filter_LAI lacks inflation

[NPounder]

Propagate_information_filter_LAI isn't using the Q_matrix and I think it should be.

KaFKA-InferenceEngine/kafka/inference/kf_tools.py

Lines 198 to 217 in 659ec41

	def propagate_information_filter_LAI(x_analysis, P_analysis,
	P_analysis_inverse,
	M_matrix, Q_matrix):
	x_forecast = M_matrix.dot(x_analysis)
	x_prior, c_prior, c_inv_prior = tip_prior()
	n_pixels = len(x_analysis)/7
	x0 = np.array([x_prior for i in xrange(n_pixels)]).flatten()
	x0[6::7] = x_forecast[6::7] # Update LAI
	print "LAI:", -2*np.log(x_forecast[6::7])
	lai_post_cov = P_analysis_inverse.diagonal()
	c_inv_prior_mat = []
	for n in xrange(n_pixels):
	c_inv_prior[6,6] = lai_post_cov[n]
	c_inv_prior_mat.append(c_inv_prior)
	P_forecast_inverse=block_diag(c_inv_prior_mat, dtype=np.float32)
	return x0, None, P_forecast_inverse

[NPounder]

Branch timePropagation fixes this