"""Compute the mixed layer depth.

Parameters

----------

SA : array_like

Absolute Salinity [g/kg]

CT : array_like

Conservative Temperature [:math:`^\circ` C (ITS-90)]

p : array_like

sea pressure [dbar]

criterion : str, optional

MLD Criteria

Mixed layer depth criteria are:

'temperature' : Computed based on constant temperature difference

criterion, CT(0) - T[mld] = 0.5 degree C.

'density' : computed based on the constant potential density difference

criterion, pd[0] - pd[mld] = 0.125 in sigma units.

`pdvar` : computed based on variable potential density criterion

pd[0] - pd[mld] = var(T[0], S[0]), where var is a variable potential

density difference which corresponds to constant temperature difference of

0.5 degree C.

Returns

-------

MLD : array_like

Mixed layer depth

idx_mld : bool array

Boolean array in the shape of p with MLD index.

Examples

--------

>>> import os

>>> import gsw

>>> import matplotlib.pyplot as plt

>>> from oceans import mld

>>> from gsw.utilities import Bunch

>>> # Read data file with check value profiles

>>> datadir = os.path.join(os.path.dirname(gsw.utilities.__file__), 'data')

>>> cv = Bunch(np.load(os.path.join(datadir, 'gsw_cv_v3_0.npz')))

>>> SA, CT, p = (cv.SA_chck_cast[:, 0], cv.CT_chck_cast[:, 0],

... cv.p_chck_cast[:, 0])

>>> fig, (ax0, ax1, ax2) = plt.subplots(nrows=1, ncols=3, sharey=True)

>>> l0 = ax0.plot(CT, -p, 'b.-')

>>> MDL, idx = mld(SA, CT, p, criterion='temperature')

>>> l1 = ax0.plot(CT[idx], -p[idx], 'ro')

>>> l2 = ax1.plot(CT, -p, 'b.-')

>>> MDL, idx = mld(SA, CT, p, criterion='density')

>>> l3 = ax1.plot(CT[idx], -p[idx], 'ro')

>>> l4 = ax2.plot(CT, -p, 'b.-')

>>> MDL, idx = mld(SA, CT, p, criterion='pdvar')

>>> l5 = ax2.plot(CT[idx], -p[idx], 'ro')

>>> _ = ax2.set_ylim(-500, 0)

References

----------

.. [1] Monterey, G., and S. Levitus, 1997: Seasonal variability of mixed

layer depth for the World Ocean. NOAA Atlas, NESDIS 14, 100 pp.

Washington, D.C.

"""

#depth_cube.data = np.ma.masked_array(np.swapaxes(np.tile(depths,[360,180,1]),0,2))

MLD_out = S.extract(iris.Constraint(depth = np.min(depth_cube.data)))

MLD_out_data = MLD_out.data

for i in range(np.shape(MLD_out)[0]):

print'calculating mixed layer for year: ',i

thetao_tmp = thetao[i]

S_tmp = S[i]

depth_cube.data = np.abs(depth_cube.data)

depth_cube = depth_cube * (-1.0)

p = gsw.p_from_z(depth_cube.data,latitude_deg.data) # dbar

SA = S_tmp.data*1.004715

CT = gsw.CT_from_pt(SA,thetao_tmp.data - 273.15)

SA, CT, p = map(np.asanyarray, (SA, CT, p))

SA, CT, p = np.broadcast_arrays(SA, CT, p)

SA, CT, p = map(ma.masked_invalid, (SA, CT, p))

p_min, idx = p.min(axis = 0), p.argmin(axis = 0)

sigma = SA.copy()

to_mask = np.where(sigma == S.data.fill_value)

sigma = gsw.rho(SA, CT, p_min) - 1000.

sigma[to_mask] = np.NAN

sig_diff = sigma[0,:,:].copy()

sig_diff += 0.125 # Levitus (1982) density criteria

sig_diff = np.tile(sig_diff,[np.shape(sigma)[0],1,1])

idx_mld = sigma <= sig_diff

#NEED TO SORT THS PIT - COMPARE WWITH OTHER AND FIX!!!!!!!!!!

MLD = ma.masked_all_like(S_tmp.data)

MLD[idx_mld] = depth_cube.data[idx_mld] * -1

MLD_out_data[i,:,:] = np.ma.max(MLD,axis=0)

##############################

# inputs:

# S = CMIP5 3D+time salinity cube (so)

# theta = CMIP5 3D+time temperature cube (thetao)

# returns:

# 2D+time cube of mixed layer depths calculated by the density criteria where is the first density > 0.125 units from the surface (as caused by T and S criteria)

# keywords: MLD mld mixed layer depth cmip5 mixed layer height

##############################

depth_cube = S[0].copy()

depths = depth_cube.coord('depth').points

#for memorys sake, do one year at a time..

depth_cube.data = np.ma.masked_array(np.swapaxes(np.tile(depths,[360,180,1]),0,2))

thetao = iris.load_cube(directory+model+'*_thetao_past1000_*Omon*.nc')

hm = S.extract(iris.Constraint(depth = np.min(depth_cube.data)))

latitude_deg = depth_cube.copy()

latitude_deg_data = hm.coord('latitude').points

latitude_deg.data = np.swapaxes(np.tile(latitude_deg_data,[np.shape(S)[1],360,1]),1,2)

hm.data = mld(S,thetao,depth_cube,latitude_deg)