frm_exp_data = Frame(np.exp(n),meta={'mask':True,'dx':dx,'dy':dy,'cmap':'hot'}) frm_log_data = Frame(np.log(np.abs(n)),meta={'dx':dx,'dy':dy,'cmap':'hot'}) frm_phi_data = Frame(phi,meta={'dx':dx,'dy':dy,'cmap':'hot'}) frm_u_data = Frame(u,meta={'dx':dx,'dy':dy,'cmap':'hot'}) frm_du_data = Frame(np.gradient(u)[0],meta={'dx':dx,'dy':dy,'cmap':'hot'}) #we can include as many overplot as we want - just grab the canvas and draw whatever #if you are going to make movies based on stationary include nt dw_contour = Frame(mask,meta={'stationary':True,'dx':dx,'dy':dy,'contour_only':True,'alpha':.2,'colors':'green','grid':False}) # alpha_contour = Frame(mask,meta={'stationary':True,'dx':dx,'dy':dy,'contour_only':True,'alpha':.1,'colors':'k'}) dw_contour.nt = frm_n.nt a_contour = Frame(a,meta={'stationary':True,'dx':dx,'dy':dy,'contour_only':True,'alpha':.2,'colors':'blue','grid':False,'x0':0}) # alpha_contour = Frame(mask,meta={'stationary':True,'dx':dx,'dy':dy,'contour_only':True,'alpha':.1,'colors':'k'}) a_contour.nt = frm_n.nt # for t in range(frm_data.nt): # phi[t,:,:]-np.mean(phi[t,:,:]) phi_contour = Frame(phi,meta={'stationary':False,'dx':dx,'contour_only':True,'alpha':.5,'colors':'red'}) phi_contour.nt = frm_n.nt #frm_data_SOL = Frame(n[:,nx_sol:-1,:],meta={'mask':True,'dx':dx,'x0':dx*nx_sol}) #frm_data = Frame(a,meta={'data_c':a,'mask':True,'dx':dx}) print n.shape amp = abs(n).max(1).max(1)
't_array':time,'x0':dx*250.0 }) frm_exp_data = Frame(np.exp(n),meta={'mask':True,'dx':dx,'dy':dy,'cmap':'hot'}) frm_log_data = Frame(np.log(np.abs(n)),meta={'dx':dx,'dy':dy,'cmap':'hot'}) frm_phi_data = Frame(phi,meta={'dx':dx,'dy':dy,'cmap':'hot'}) frm_u_data = Frame(u,meta={'dx':dx,'dy':dy,'cmap':'hot'}) frm_du_data = Frame(np.gradient(u)[0],meta={'dx':dx,'dy':dy,'cmap':'hot'}) phi_contour = Frame(phi,meta={'stationary':False,'dx':dx,'contour_only':True,'alpha':.5,'colors':'red'}) phi_contour.nt = frm_n.nt print n.shape amp = abs(n).max(1).max(1) frm_amp = Frame(amp) dky = 1.0/zmax allk = dky*np.arange(ny)+(1e-8*dky) mu = 1.0e-2 #alpha = 3.0e-5 # beta = 6.0e-4 # Ln = 130.0/4.0 n0 = n[0,:,:].mean(axis=1) #Ln = n0/gradient(n0)