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)
