def likelihood_calc(mu_emg,sigma_emg,lamb_emg,mu_norm,cov_norm,pi,X):

	
	value = logp_emg_np(X[:,0], mu_emg, sigma_emg,lamb_emg)
	
	value += np.array([mvNormal_logp_np(X[i,1:], mu_norm, cov_norm)[0] for i in np.arange(X.shape[0])])

		


	return pi*np.exp(value)
		lamb  = tr["lamb_emg"][link,label]

		pi    = tr["p"][link,label]
		
		####
		data_label = np.zeros((50,50,50,50))
		for dim in np.arange(4):
			reshape_dim = np.ones(4)
			reshape_dim[dim] = 50

			tile_dim = np.ones(4,int)*50
			tile_dim[dim] = 1

			tile_dim = tuple(tile_dim)
			if dim ==0:
				value = logp_emg_np(all_range[dim], mu[dim], sigma[dim],lamb)
			else:
				value = logp_norm_np(all_range[dim], mu[dim], sigma[dim])


			data_label+= np.tile(value.reshape(*reshape_dim),tile_dim)

		hold_data_inner += pi*np.exp(data_label)

	hold_data += hold_data_inner
	print(str(element)+":",np.round((link+count_less)/total,3),
						np.round(time.time()-start_time))


pickle.dump( hold_data, open(data_weight+"ideogram_4d_weighted_new_"+\
	str(element) +".pkl", "wb" ) )
for dim_select in np.arange(5)+1:


	xx = np.linspace(np.min(data[:,0]),np.max(data[:,0]),50)
	yy = np.linspace(np.min(data[:,dim_select]),np.max(data[:,dim_select]),50)

	mu_emg = group_dict["mu_emg"]
	mu_norm = group_dict["mu_norm"][dim_select-1]
	sigma_emg = group_dict["sigma_emg"]
	sigma_norm = group_dict["sigma_norm"][dim_select-1]
	lamb_emg = group_dict["lamb_emg"]

	contour_data = np.zeros((xx.shape[0],yy.shape[0]))
	for i,j in itertools.product(np.arange(xx.shape[0]),
								np.arange(yy.shape[0])):
		contour_data[i,j] = 	logp_emg_np(xx[i],mu_emg,sigma_emg,lamb_emg)+\
							logp_norm_np(yy[j],mu_norm,sigma_norm)

	emg_norm_2d_smart_plot([ax[dim_select,0]], [contour_data.T],xx,yy,num_lines=5,colour = "blue")

fig.tight_layout()

plt.savefig(LKJ_images_location+"pairs_plotting_largest_class_cat_colour.png")
plt.close()



group_num = 1
fig, ax =high_d_plot(data,colours = cat_like,dimension=(),alpha = .3)
group_dict = all_info[group_num]
norm_squared_contour_2d_smart_plot(ax[1:,1:],mu = group_dict["mu_norm"],