# -*- coding: utf8 -*-
"""Diagnostics about thermodynamics"""
# Copyright or © or Copr. Actimar/IFREMER (2010-2015)
#
# This software is a computer program whose purpose is to provide
# utilities for handling oceanographic and atmospheric data,
# with the ultimate goal of validating the MARS model from IFREMER.
#
# This software is governed by the CeCILL license under French law and
# abiding by the rules of distribution of free software. You can use,
# modify and/ or redistribute the software under the terms of the CeCILL
# license as circulated by CEA, CNRS and INRIA at the following URL
# "http://www.cecill.info".
#
# As a counterpart to the access to the source code and rights to copy,
# modify and redistribute granted by the license, users are provided only
# with a limited warranty and the software's author, the holder of the
# economic rights, and the successive licensors have only limited
# liability.
#
# In this respect, the user's attention is drawn to the risks associated
# with loading, using, modifying and/or developing or reproducing the
# software by the user in light of its specific status of free software,
# that may mean that it is complicated to manipulate, and that also
# therefore means that it is reserved for developers and experienced
# professionals having in-depth computer knowledge. Users are therefore
# encouraged to load and test the software's suitability as regards their
# requirements in conditions enabling the security of their systems and/or
# data to be ensured and, more generally, to use and operate it in the
# same conditions as regards security.
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# The fact that you are presently reading this means that you have had
# knowledge of the CeCILL license and that you accept its terms.
#
from warnings import warn
import numpy as N, cdms2, MV2
from genutil.grower import grower
from vacumm import VACUMMError
from vacumm.data.cf import format_var, match_var
from vacumm.misc.grid import get_zdim, get_axis_slices, get_grid, set_grid
from vacumm.misc.axes import islat
from vacumm.misc import N_choose, grow_depth, grow_lat
from vacumm.misc.misc import grow_variables
try:
from seawater import pres as sw_pres, dens as sw_dens, dens0 as sw_dens0, \
dpth as sw_depth
except:
try:
from seawater.csiro import pres as sw_pres, dens as sw_dens, dens0 as sw_dens0, \
depth as sw_depth
except:
warn('Failed to import seawater functions')
[docs]def density(temp, sal, depth=None, lat=None, potential=False,
getdepth=False, getlat=False, format_axes=False):
"""Compute density from temperature, salinity and depth (and latitude)
:Params:
- **temp**: Insitu or potential temperature.
- **sal**: Salinity.
- **depth**, optional: Depth at temperature and salinty points.
Assumed to be 0 if not found.
- **lat**, optional: Latitude. Error when not found.
- **potential**, optional: True to get the potential density (at atmospheric
pressure).
:Algo:
>>> pressure = seawater.csiro.pres(depth, lat)
>>> density = seawater.csiro.dens(sal, temp, depth)
"""
# Compute
if not potential and depth is not False: # In-situ
# Get depth and latitude
lat = grow_lat(temp, lat, mode='raise', getvar=False)
if lat is None: raise VACUMMError('No latitude found for density')
depth = grow_depth(temp, depth, mode='raise', getvar=False)
if N.abs(depth.max())<N.abs(depth.min()): # positive
depth = -depth
if (depth.asma()<0).any():
depth = depth-depth.min() # top=0
# Get density
pres = sw_pres(depth, lat)
dens = sw_dens(sal, temp, pres) ; del pres
else: # Potential
dens = sw_dens0(sal, temp)
getdepth = getlat = False
# Format
dens.setAxisList(temp.getAxisList())
set_grid(dens, get_grid(temp))
format_var(dens, 'dens', format_axes=format_axes)
# Out
if not getdepth and not getlat: return dens
dens = dens,
if getdepth: dens += depth,
if getlat: dens += lat,
return dens
[docs]def mixed_layer_depth(data, depth=None, lat=None, zaxis=None,
mode=None, deltatemp=.2, deltadens=.03, kzmax=0.0005,
potential=True, format_axes=False):
"""Get mixed layer depth from temperature and salinity
:Params:
- **temp**: Insitu or potential temperature.
- **sal**: Salinity.
- **depth**, optional: Depth at temperature and salinty points.
- **lat**, optional: Latitude.
- **mode**, optional: ``"deltatemp"``, ``"deltadens"``, ``"kz"``
or ``"twolayers"``
:Raise: :class:`~vacumm.VACUMMError` if can't get depth (and latitude for density).
"""
# TODO: positive up
# Inspection
if isinstance(data, tuple): # data = temp,sal
temp, sal=data
# Get density
if mode!='deltatemp':
res = density(temp, sal, depth=depth, lat=lat,
format_axes=False, potential=potential, getdepth=True)
if isinstance(res, tuple):
dens, depth = res
else:
dens = res
dens = dens.asma()
if mode is None:
mode = 'deltadens'
else:
temp = data[0]
# Check mode
if mode == 'kz':
warn("Switching MLD computation mode to 'deltadens'")
mode = "deltadens"
elif match_var(data, 'temp', mode='nslu'):
if mode is not None and mode!='deltatemp':
warn("Switching MLD computation mode to 'deltatemp'")
mode = 'deltatemp'
temp = data
elif match_var(data, 'dens', mode='nslu'):
if mode in ['kz', 'deltatemp']:
warn("Switching MLD computation mode to 'deltadens'")
mode = None
if mode is None:
mode = "deltadens"
dens = data
elif match_var(data, 'kz', mode='nslu'):
if mode is None:
mode = "kz"
if mode != "kz":
warn("Switching MLD computation mode to 'kz'")
kz = data
else:
if mode in ['deltadens', 'twolayers']:
dens = data
elif mode == "deltatemp":
temp = data
elif mode == "kz":
kz = data
elif mode is not None:
raise VACUMMError("Invalid MLD computation mode : '%s'"%mode)
else:
raise VACUMMError("Can't guess MLD computation mode")
temp = delta
# Find Z dim
data0 = data[0] if isinstance(data, tuple) else data
depth = grow_depth(data0, depth, mode='raise', getvar=False)
zaxis = get_zdim(data0, axis=zaxis)
if zaxis is None:
raise VACUMMError("Can't guess zaxis")
slices = get_axis_slices(data0, zaxis)
# Init MLD
axes = data0.getAxisList()
del axes[zaxis]
mld = MV2.array(data0.asma()[slices['first']], copy=1, axes=axes, copyaxes=False)
set_grid(mld, get_grid(data0))
format_var(mld, 'mld', format_axes=format_axes)
mld[:] = MV2.masked
# Two-layers
if mode=='twolayers':
densbot = dens[slices['first']]
denstop = dens[slices['last']]
del dens
H = 1.5*depth[slices['first']] - 0.5*depth[slices['firstp1']]
H = -1.5*depth[slices['last']] + 0.5*depth[slices['lastm1']]
mld[:] = -H*(densbot-denstop)/(densbot-denstop)
del H
elif mode=='deltadens':
denscrit = dens[slices['last']]+deltadens
mld[:] = -_val2z_(dens, depth, denscrit, zaxis, -1)
del dens
elif mode=='deltatemp':
tempcrit = temp[slices['last']]-deltatemp
mld[:] = -_val2z_(temp, depth, tempcrit, zaxis, 1)
elif mode=='kz':
mld[:] = -_valmin2z_(kz, depth, kzmax, zaxis, 1)
else:
raise VACUMMError("Invalid mode for computing MLD (%s)."%mode +
"Please choose one of: deltadens, twolayers")
# Mask zeros
mld[:] = MV2.masked_values(mld, 0., copy=0)
return mld
def _val2z_(var, dep, varref, axis, monosign):
"""Compute depth for a given reference data value"""
# Inits with Z axis first
var = var.asma() if cdms2.isVariable(var) else N.ma.asarray(var)
var = var.copy()
dep = dep.asma() if cdms2.isVariable(dep) else N.ma.asarray(dep)
dep = N.ma.masked_where(var.mask, dep, copy=False)
varref = varref.asma() if cdms2.isVariable(varref) else N.ma.asarray(varref)
if var.ndim-1==0:
var = var.reshape(-1,1)
dep = dep.reshape(-1,1)
varref = varref.reshape(1)
axis = 0
rvar = N.rollaxis(var, axis) if axis else var
rdep = N.rollaxis(dep, axis) if axis else dep
rdepref = rdep[0].copy()
rvar = rvar.copy()
nz = rvar.shape[0] # nb of elements along the first axis (z in general)
maskland = N.ma.getmaskarray(rvar)[-1]
# Monotonic
rvar *= monosign
varref *= monosign
dvar = N.ma.diff(rvar, axis=0)
# Find z index
dvarref = rvar-varref
iiz0 = N.arange(nz-1, dtype='i')
iiz0 = N.ma.resize(iiz0, rvar.shape[rvar.ndim-1:0:-1]+(nz-1, )).T
iiz0 = N.ma.masked_where(N.ma.diff(N.sign(dvarref), axis=0)<2, iiz0, copy=0)
iz0 = iiz0.max(axis=0)
del iiz0
maskdz = iz0.mask
iz0 = iz0.filled(0.)
# Interpolation
dvar0 = N_choose(iz0, dvar)
dvar0[maskdz] = 1.
w1 = -N_choose(iz0, dvarref)/dvar0
w1.set_fill_value(0.)
rdep.set_fill_value(0.)
rdepref = N_choose(iz0+1, rdep)*w1
rdepref += N_choose(iz0, rdep)*(1-w1)
# Masking
rdepref[maskland] = N.ma.masked
rdepref = N.ma.where(~maskland&maskdz, rdep.min(axis=0), rdepref)
return rdepref
def _valmin2z_(var, dep, varmax, axis, monosign):
"""Compute depth at which data value at lower than a reference value"""
# Inits with Z axis first
var = var.asma() if cdms2.isVariable(var) else N.ma.asarray(var)
var = var.copy()
dep = dep.asma() if cdms2.isVariable(dep) else N.ma.asarray(dep)
if var.ndim-1==0:
var = var.reshape(-1,1)
dep = dep.reshape(-1,1)
axis = 0
rvar = N.rollaxis(var, axis) if axis else var
rdep = N.rollaxis(dep, axis) if axis else dep
rvar = rvar.copy()
nz = rvar.shape[0]
maskland = N.ma.getmaskarray(rvar)[-1]
# Monotonic (greater at surface)
rvar *= monosign
# Find z index
iiz = N.arange(nz, dtype='i')
iiz = N.ma.resize(iiz, rvar.shape[rvar.ndim-1:0:-1]+(nz, )).T
iiz = N.ma.masked_where(rvar>varmax, iiz, copy=0)
iz0 = iiz.max(axis=0)
del iiz
maskz = iz0.mask
iz0 = iz0.filled(0.)
# Values
rdepref = N_choose(iz0, rdep)
# Masking
rdepref[maskland] = N.ma.masked
rdepref = N.ma.where(~maskland&maskz, rdep.min(axis=0), rdepref)
return rdepref
if __name__=='__main__':
# print 'hoho'
nx = 4
ny = 2
nz = 6
depth = N.ma.arange(nz*nx*ny**1.).reshape(nz, ny, nx)
depth = depth-depth.max()
# partie ci-dessus inutile en fait
# var = N.round(N.sqrt(-depth)*10)
# var[:,1, 2] = N.ma.masked
#
# varref = var[-1]+44#15.7
# varref[:] = 23
# varref[:] = 67.5
#
# var = var[::-1]
# varref = var[-1]*0+67.5
#
# tableaux 1D (29 elements)
var = N.ma.array([13.1168556213, 13.120059967, 13.1211280823, 13.1211280823, 13.1211280823
, 13.1211280823, 13.1179237366, 13.1136512756, 13.1093788147, 13.1083106995
, 13.1072416306, 13.1019010544, 13.066652298, 13.0527667999, 13.0784015656
, 13.1136512756, 13.1905574799, 13.2461013794, 13.2503738403, 13.2236700058
, 13.2236700058, 13.5729541779, 14.4798116684, 15.1249732971, 15.0694293976
, 15.0683612823, 15.0672931671, 15.0651569366, 15.0608844757])
depth = N.array([-2340.67651367, -2240.55786133, -2134.18212891, -2021.54858398
, -1908.9152832, -1796.28186035, -1683.64868164, -1571.01525879, -1458.38183594
, -1345.74865723, -1233.11523438, -1120.48181152, -1007.84857178, -895.215148926
, -782.581848145, -669.948547363, -563.572570801, -463.454071045, -369.592926025
, -284.4921875, -209.403320312, -150.583679199, -106.781829834, -75.4947891235
, -50.4651565552, -27.9384918213, -12.9207115173, -5.41182279587, -1.65737783909])
varref = var[-1]-0.2
print varref, var[-1]
var = N.ma.array([13.1157875061, 13.1157875061, 13.1157875061, 13.1157875061, 13.1157875061
, 13.1168556213, 13.1168556213, 13.1168556213, 13.1168556213, 13.1179237366
, 13.1189918518, 13.1275367737, 13.1414222717, 13.1168556213, 13.1019010544
, 13.1798763275, 13.2610549927, 13.2589187622, 13.2450332642, 13.2428970337
, 13.2450332642, 13.5654773712, 14.426404953, 15.0363168716, 14.9070711136
, 14.9401836395, 14.9700918198, 14.9668874741, 14.9604787827])
depth = N.ma.array([-2478.62475586, -2372.61083984, -2259.97119141, -2140.70556641
, -2021.44006348, -1902.17443848, -1782.90893555, -1663.64331055, -1544.37768555
, -1425.11218262, -1305.84655762, -1186.58093262, -1067.31530762, -948.049682617
, -828.784118652, -709.518554688, -596.87878418, -490.864959717, -391.476959229
, -301.365203857, -221.854782104, -159.571640015, -113.190582275, -80.0612487793
, -53.5577850342, -29.7046699524, -13.8025913239, -5.85155200958, -1.87603235245])
varref = var[-1]-0.2
print varref, var[-1]
depref = _val2z_(var, depth, varref, 0, 1)
# print 'var',var[:,0]
# print 'varref', varref[0]
# print 'dep',depth[:,0]
# print 'res',depref[0]
print depref
