<jupyter_start><jupyter_text>We will use these as our input NEMO parameters for now and get the NEMO levels from the mesh mask generated after one time step   Use the negative of the value of ppsur obtained as the output of the last cell in the NEMO namelist<jupyter_code>ks = np.arange(27)
ppa0 = 155.3
ppa1 = 146.
ppkth = 12.
ppacr = 3.
depths = ppa0*ks + NEMO_tanh_integral(ks, ppa1, ppkth, ppacr)
ppsur = depths[0]
depths = depths - ppsur
print (depths[-1])
plt.plot(ks, depths, 'o')
plt.ylim((0,500))<jupyter_output>4329.69182132<jupyter_text>Decided NEMO depths are these below and they can be found in the mesh_mask.nc in /ocean/ssahu/CANYONS/Runs/trial_run_mesh_mask_gen/<jupyter_code>nav_lev = [14.75076, 24.42878, 34.46413, 45.19286, 57.26234, 71.90678, 
    91.43279, 119.99, 164.529, 235.2601, 344.0054, 499.3054, 701.1602, 
    941.0291, 1207.09, 1489.133, 1780.207, 2076.162, 2374.693, 2674.564, 
    2975.129, 3276.051, 3577.156, 3878.357, 4179.605, 4480.879, 4782.166];
print(nav_lev)
depths
ks = np.arange(40)
ppa0 = 155.3
ppa1 = 146.
ppkth = 35.
ppacr = 10.
depths = ppa0*ks + NEMO_tanh_integral(ks, ppa1, ppkth, ppacr)
ppsur = depths[0]
depths = depths - ppsur
print (depths[-1])
plt.plot(ks, depths, 'o')
# plt.ylim((0,500))
ppsur
depths_diff = np.ediff1d(depths)

fig1, (ax1, ax2) = plt.subplots(1,2,figsize=(12,10))

ax1.set_ylim(np.min(depths),np.max(depths))
ax2.set_ylim(0,np.max(depths_diff))

q1 = ax1.plot(ks,depths,'o')
ax1.set_ylabel("Depth (in m))")
ax1.set_xlabel('z index')


q2 = ax2.plot(ks[1:],depths_diff,'o')
ax2.set_ylabel(" Vertical Grid Spacing (in m))")
ax2.set_xlabel('z index')


# plt.plot(ks[1:], depths_diff, 'o')
np.where(depths<=200)
depths[17]
depths_diff[16]
np.max(depths_diff)
depths<jupyter_output><empty_output>
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