#!/usr/bin/env python
# -*- coding: utf8 -*-
#
# 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.
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__author__ = 'Jonathan Wilkins'
__email__ = 'wilkins@actimar.fr'
__date__ = '2011-01-17'
__doc__ = 'Dataset Colocalizations'
# ==============================================================================
import os, sys
import cdms2, MV2, numpy, pylab, seawater
from matplotlib.pyplot import colorbar
from vacumm.data.misc.sigma import NcSigma
from vacumm.misc import auto_scale
from vacumm.misc.atime import add as add_time, comptime, datetime as adatetime, Intervals
from vacumm.misc.axes import create_time, create_dep, create_lat, create_lon
from vacumm.misc.bases import Object
from vacumm.misc.color import cmap_magic
from vacumm.misc.io import ncget_var, ncfind_var, list_forecast_files, ncread_best_estimate, NcIterBestEstimate
from vacumm.misc.grid.misc import meshweights, resol
from vacumm.misc.grid.regridding import resol, interp1d, regrid1dold, grid2xy
from vacumm.misc.misc import is_iterable, kwfilter
from vacumm.misc.phys.constants import g
from vacumm.misc.plot import map2, curve2, section2, hov2
[docs]class Colocator(Object):
# def __init__(self, data1, data2):
# Object.__init__(self)
# self.data1 = data1
# self.data2 = data2
[docs] def coloc_mod_on_pro(self, model, profiles, varnames, select=None, method='nearest'):
'''Colocalize model on profile data.
Load model data corresponding to the selected profiles positions and time.
Returns loaded model longitudes, latitudes, depths and requested variable(s)
:Params:
- **model**: model data :class:`~vacumm.data.misc.dataset.Dataset`
- **profiles**: profile data :class:`~vacumm.data.misc.profile.ProfilesDataset`
- **varnames**: variables to load (ex: ('temp','sal') or (('temp','temperature'),('sal','salinity'))
- **select**: selector
- **method**: coloc method (**nearest** or **interp**)
:Return:
- **lons_mod**: model longitude coordinates, shape: (profile)
- **lats_mod**: model latitude coordinates, shape: (profile)
- **deps_mod**: model depth coordinates, shape: (level,profile)
- **var1**: requested variables, shape: (level,profile)
- ...
- **varN**
.. todo::
- also load and return profile data here
- exclude coords where profile data is masked (no data for specified depth)
- return time coordinates
- return depth and vars with shape (profile,level)
'''
self.verbose('Colocalizing %s on %s\nvarnames: %s\nselect: %s\n method: %s', model.__class__.__name__, profiles.__class__.__name__, varnames, select, method)
prof_pro = profiles.get_axis('profile', select=select)
if prof_pro is None or not len(prof_pro):
raise Exception('No profiles found, aborting')
lev_pro = profiles.get_axis('level', select=select)
time_pro = profiles.get_variable('time', select=select)
lons_pro = profiles.get_variable('longitude', select=select)
lats_pro = profiles.get_variable('latitude', select=select)
dates = create_time(time_pro).asComponentTime()
self.info('Number of profiles: %s', len(dates))
self.info('Profiles time coverage: %s to %s', dates[0], dates[-1])
# Init model
td = model.get_time_res()
dtmax = (td.days*86400+td.seconds, 'seconds')
self.info('Detected model time step: %s', td)
grid_mod = model.get_grid()
xres, yres = resol(grid_mod)
time_mod = model.get_time()
ctime_mod = time_mod.asComponentTime()
self.info('Model time coverage: %s to %s', ctime_mod[0], ctime_mod[-1])
level_mod = model.get_level(select=select)
lons_mod = MV2.zeros((len(prof_pro),))+MV2.masked
lats_mod = lons_mod.clone()
deps_mod = MV2.zeros((len(level_mod), len(prof_pro)))+MV2.masked
deps_mod.setAxis(1, prof_pro)
lons_mod.id, lats_mod.id, deps_mod.id = 'longitude', 'latitude', 'depth'
# Creation des variables demandees
variables = []
for n in varnames:
v = MV2.zeros((len(level_mod), len(prof_pro)))+MV2.masked
v.setAxis(1, prof_pro)
v.id = is_iterable(n) and n[0] or n
variables.append(v)
cdms2.setAutoBounds(1) # ???
# Boucle temporelle
for ip, date in enumerate(dates):
try:
# Limites spatiales
lon = lons_pro[ip]
lat = lats_pro[ip]
lon_min = lon-2*xres
lon_max = lon+2*xres
lat_min = lat-2*yres
lat_max = lat+2*yres
date_interval = (add_time(date, - dtmax[0], dtmax[1]), add_time(date, dtmax[0], dtmax[1]), 'ccb')
self.info('Colocalizing data for date %s, lon: %s, lat: %s', date, lon, lat)
# Methode 1 : donnees les plus proches
if method == 'nearest':
sel = dict(time=(date, date, 'ccb'), longitude=(lon, lon, 'ccb'), latitude=(lat, lat, 'ccb'))
# Verifier la disponibilite des donnees
if time_mod.mapIntervalExt(sel['time']) is None:
self.warning('Time interval %s not found', sel['time'])
continue
if grid_mod.getLatitude().mapInterval(sel['latitude']) is None:
self.warning('Latitude coordinate %s not found', sel['latitude'])
continue
if grid_mod.getLongitude().mapInterval(sel['longitude']) is None:
self.warning('Longitude coordinate %s not found', sel['longitude'])
continue
# Load tmp depth to get lon & lat coordinates
#tmp = model.get_depth(select=sel, squeeze=False) # tmp squeezed !!! see sigma ?
tmp = model.get_variable(varnames[0], select=sel, squeeze=False)
lons_mod[ip] = tmp.getLongitude()[0]
lats_mod[ip] = tmp.getLatitude()[0]
deps_mod[:, ip] = model.get_depth(select=sel, squeeze=True)
for iv,vn in enumerate(varnames):
variables[iv][:,ip] = model.get_variable(vn, select=sel, squeeze=True)
# Methode 2 : interpolation
elif method == 'interp':
sel = dict(time=date_interval, longitude=(lon_min, lon_max), latitude=(lat_min, lat_max))
if time_mod.mapIntervalExt(sel['time']) is None:
self.warning('Time interval %s not found', sel['time'])
continue
if grid_mod.getLatitude().mapInterval(sel['latitude']) is None:
self.warning('Latitude coordinate %s not found', sel['latitude'])
continue
if grid_mod.getLongitude().mapInterval(sel['longitude']) is None:
self.warning('Longitude coordinate %s not found', sel['longitude'])
continue
# Lectures
order = 'tzyx'
# lon & lat du profile car interp sur cette position
lons_mod[ip], lats_mod[ip] = lon, lat
deps_mod_tzyx = model.get_depth(select=sel, order=order, squeeze=True)
tmp_tzyx = []
for iv,vn in enumerate(varnames):
tmp_tzyx.append(model.get_variable(vn, select=sel, order=order, squeeze=True))
# Interpolations temporelles
mctime = tmp_tzyx[0].getTime()
mrtime = mctime.asRelativeTime()
d0 = date.torel(mctime.units).value - mrtime[0].value
d1 = mrtime[1].value - date.torel(mctime.units).value
f0 = d0 / (d0 + d1)
f1 = d1 / (d0 + d1)
deps_mod_zyx = f0 * deps_mod_tzyx[0] + f1 * deps_mod_tzyx[1]
tmp_zyx = []
for iv,vn in enumerate(varnames):
tmp_zyx.append(f0 * tmp_tzyx[iv][0] + f1 * tmp_tzyx[iv][1])
del tmp_tzyx
# Interpolations spatiales
deps_mod[:,ip] = numpy.squeeze(grid2xy(deps_mod_zyx, numpy.array([lon]), numpy.array([lat]), method='nat'))
for iv,vn in enumerate(varnames):
variables[iv][:,ip] = numpy.squeeze(grid2xy(tmp_zyx[iv], numpy.array([lon]), numpy.array([lat]), method='nat'))
del tmp_zyx
else:
raise ValueError('Invalid colocation method: %s'%(method))
except:
self.exception('Failed to colocalize data for date %s', date)
for v in [deps_mod] + variables:
v.getAxis(0).id = 'level'
v.getAxis(0).designateLevel()
data = tuple([lons_mod, lats_mod, deps_mod] + variables)
self.verbose('Colocalized data:\n %s', '\n '.join(self.describe(o) for o in data))
return data
[docs] def coloc_strat_mod_on_pro(self, model, profiles, select, **kwargs):
'''Get colocalized stratification data of a model on profiles
See: :func:`coloc_mod_on_pro`
:Return:
- **lons_mod, lats_mod, deps_mod**:
- **temp_mod, sal_mod**: temperature and salinity
- **pres_mod, dens_mod**: pressure and density
'''
# Coloc donnees modele sur profiles
varnames = (('temp','temperature'), ('sal', 'psal','salinity'))
lons_mod, lats_mod, deps_mod, temp_mod, sal_mod = \
self.coloc_mod_on_pro(model, profiles, varnames, select, **kwargs)
# Calcul pression & densite
pres_mod = MV2.zeros(temp_mod.shape)+MV2.masked
pres_mod.setAxisList(temp_mod.getAxisList())
dens_mod = pres_mod.clone()
for ip in xrange(len(lons_mod)):
pres_mod[:, ip] = seawater.csiro.pres(deps_mod[:, ip], numpy.resize([lats_mod[ip]], deps_mod[:, ip].shape))
dens_mod[:, ip] = seawater.csiro.dens(sal_mod[:, ip], temp_mod[:, ip], pres_mod[:, ip])
pres_mod.id = 'pressure'
dens_mod.id = 'density'
return lons_mod, lats_mod, deps_mod, temp_mod, sal_mod, pres_mod, dens_mod
[docs] def plot_layer_mod_on_pro(self, model, profiles, varname, depth, select=None, **kwargs):
'''Get a layer of variable for a specified depth.
:Params:
- **varname**: variable to process
- **depth**: output depth(s)
Other params, see: :func:`coloc_mod_on_pro`
'''
self.verbose('Plotting layer of colocalized %s on %s\nvarname: %s\ndepth: %s\nselect: %s\nkwargs: %s',
model.__class__.__name__, profiles.__class__.__name__, varname, depth, select, kwargs)
lons_mod, lats_mod, deps_mod, var_mod = \
self.coloc_mod_on_pro(
model, profiles,
# If varname is a list of possible names, wrap it into a
# tuple of one element as we are requiring only one variable
len(numpy.shape(varname)) and (varname,) or varname,
select, **kwargs)
odep = create_dep(is_iterable(depth) and depth or [depth])
var_mod = var_mod.reorder('-z')
var_mod = interp1d(var_mod, axo=odep, xmap=0, xmapper=deps_mod) # Required order: ...z
var_mod = var_mod.reorder('z-')
model.verbose('layer: %s', var_mod)
lons_pro, lats_pro, var_pro = profiles.get_layer(varname, depth, select=select)
profiles.verbose('layer: %s', var_pro)
# =====
# Tracés
vmin = var_pro.min()
vmax = var_pro.max()
if var_mod.count():
vmin = min(var_mod.min(), vmin)
vmax = max(var_mod.max(), vmax)
else:
self.warning('No model data')
if 'latitude' in select:
lat_min, lat_max = select['latitude'][:2]
else:
la = model.get_latitude()
lat_min, lat_max = min(la), max(la)
if 'longitude' in select:
lon_min, lon_max = select['longitude'][:2]
else:
lo = model.get_longitude()
lon_min, lon_max = min(lo), max(lo)
levels = auto_scale((vmin, vmax))
vmin = levels[0]
vmax = levels[-1]
# Création de la palette
cmap = cmap_magic(levels)
# On trace de champ du modèle moyené sur la période choisie
m = map2(lon=(lon_min, lon_max), lat=(lat_min, lat_max), show=False)
# On trace le modèle en fond
# =====
msca = None
try: msca = m.map.scatter(lons_mod, lats_mod, s=100, c=var_mod, vmin=vmin, vmax=vmax, cmap=cmap, label='model')
except: self.exception('Failed plotting model data')
# =====
# Triangulation du modèle
# import matplotlib.tri as tri
# triang = tri.Triangulation(lons_mod, lats_mod)
# # On trace le modèle en fond
# mod = m.axes.tripcolor(triang, var_mod, vmin=vmin, vmax=vmax, cmap=cmap)
# =====
# On trace les observations avec des points plus petits
psca = None
try: psca = m.map.scatter(lons_pro, lats_pro, s=25, c=var_pro, vmin=m.vmin, vmax=m.vmax, cmap=m.cmap, label='profiles')
except: self.exception('Failed plotting profile data')
# Colorbar
if msca is not None:
colorbar(msca)
elif psca is not None:
colorbar(psca)
[docs] def plot_mld_mod_on_pro(self, model, profiles, select=None, deep=False, **kwargs):
'''Plot mixed layer depth of model data correspponding to profiles position
:Params:
- **deep**: deep water computation mode if true
Other params, see: :func:`coloc_mod_on_pro`
'''
self.verbose('Plotting MLD of colocalized %s on %s\nselect: %s\ndeep: %s\nkwargs: %s',
model.__class__.__name__, profiles.__class__.__name__, select, deep, kwargs)
# =====
# Lecture des donnees de stratification
lons_mod, lats_mod, deps_mod, temp_mod, sal_mod, pres_mod, dens_mod = \
self.coloc_strat_mod_on_pro(model, profiles, select, **kwargs)
# =====
# Calcul mld modele
# MLD en eaux peu profondes
if not deep:
ddeps = meshweights(deps_mod, axis=0)
# Densité min/max
dmin = dens_mod.min(axis=0)
dmax = dens_mod.max(axis=0)
# Densité moyenne
dmean = numpy.ma.average(dens_mod, axis=0, weights=ddeps)
# Profondeur max (max+demi épaisseur)
H = deps_mod[-1]+ddeps[-1]*.5
# MLD
mld_mod = H*(dmax-dmean)/(dmax-dmin)
# MLD en eaux profondes
else:
# Profondeur de référence
dep = -10
# Axe pour interpolation
from vacumm.misc.axes import create_dep
depaxis = create_dep([dep])
# Interpolations
dens_mod_ref = dens_mod_ref.reorder('-z')
dens_mod_ref = regrid1dold(dens_mod, axo=depaxis, xmap=0, xmapper=deps_mod) # Required order: ...z
dens_mod_ref = dens_mod_ref.reorder('z-')
# Valeur du différentiel de densité (cf. de Boyer Montégut et all, 2003)
delta_dens = 0.03
# Densité cible
dens_mod_target = dens_mod_ref+0.03
# Masques de la couche mélangée et des eaux profondes
dens_mod_target3d = MV2.resize(dens_mod_target, dens_mod.shape)
dens_mod_good = (dens_mod.asma() <= dens_mod_target3d.asma()).filled(False)
dens_mod_bad = (dens_mod.asma() > dens_mod_target3d.asma()).filled(False)
# Profondeurs juste au dessus et en dessous de la MLD
deps_mod_above = MV2.masked_where(dens_mod_bad, deps_mod).min(axis=0)
deps_mod_below = MV2.masked_where(dens_mod_good, deps_mod).max(axis=0)
# Masques associés
from vacumm.misc import closeto
mask_above = closeto(deps_mod, MV2.resize(deps_mod_above, deps_mod.shape))
mask_below = closeto(deps_mod, MV2.resize(deps_mod_below, deps_mod.shape))
# Densités juste au dessus et en dessous de la MLD
dens_mod_above = MV2.masked_where(dens_mod, mask_above).max(axis=0)
dens_mod_below = MV2.masked_where(dens_mod, mask_below).min(axis=0)
# Interpolation
dens_mod_delta = dens_mod_above-dens_mod_below
deps_mod_mld = (dens_mod_target-dens_mod_above)*deps_mod_above
deps_mod_mld += (dens_mod_below-dens_mod_target)*deps_mod_below
deps_mod_mld = MV2.where(dens_mod_delta.mask, MV2.masked, deps_mod_mld/dens_mod_delta)
mld_mod = deps_mod_mld
# Finalize
mld_mod = MV2.array(mld_mod)
mld_mod.units = 'm'
mld_mod.long_name = u'Profondeur de la couche de melange'
mld_mod.setAxisList(lons_mod.getAxisList())
model.verbose('MLD: %s', mld_mod)
# =====
# Calcul mld profiles
mld_pro, lats_pro, lons_pro = profiles.get_mld(select)
profiles.verbose('MLD: %s', mld_pro)
# =====
# Tracés
vmin = mld_pro.min()
vmax = mld_pro.max()
if mld_mod.count():
vmin = min(mld_mod.min(), vmin)
vmax = max(mld_mod.max(), vmax)
else:
self.warning('No model data')
if 'latitude' in select:
lat_min, lat_max = select['latitude'][:2]
else:
la = model.get_latitude()
lat_min, lat_max = min(la), max(la)
if 'longitude' in select:
lon_min, lon_max = select['longitude'][:2]
else:
lo = model.get_longitude()
lon_min, lon_max = min(lo), max(lo)
levels = auto_scale((vmin, vmax))
vmin = levels[0]
vmax = levels[-1]
# Création de la palette
cmap = cmap_magic(levels)
# On trace de champ du modèle moyené sur la période choisie
m = map2(lon=(lon_min, lon_max), lat=(lat_min, lat_max), show=False)
# On trace le modèle en fond
# =====
msca = None
try: msca = m.map.scatter(lons_mod, lats_mod, s=100, c=mld_mod, vmin=vmin, vmax=vmax, cmap=cmap, label='model')
except: self.exception('Failed plotting model data')
# =====
# Triangulation du modèle
# import matplotlib.tri as tri
# triang = tri.Triangulation(lons_mod, lats_mod)
# # On trace le modèle en fond
# mod = m.axes.tripcolor(triang, mld_mod, vmin=vmin, vmax=vmax, cmap=cmap)
# =====
# On trace les observations avec des points plus petits
psca = None
try: psca = m.map.scatter(lons_pro, lats_pro, s=25, c=mld_pro, vmin=m.vmin, vmax=m.vmax, cmap=m.cmap, label='profiles')
except: self.exception('Failed plotting profiles data')
# Colorbar
if msca is not None:
colorbar(msca)
elif psca is not None:
colorbar(psca)
[docs] def plot_ped_mod_on_pro(self, model, profiles, select=None, **kwargs):
'''Plot potential energy deficit of model data correspponding to profiles position
:Params: See: :func:`coloc_mod_on_pro`
'''
self.verbose('Plotting PED of colocalized %s on %s\nselect: %s\nkwargs: %s',
model.__class__.__name__, profiles.__class__.__name__, select, kwargs)
# =====
# Lecture des donnees de stratification
lons_mod, lats_mod, deps_mod, temp_mod, sal_mod, pres_mod, dens_mod = \
self.coloc_strat_mod_on_pro(model, profiles, select, **kwargs)
# =====
# Calcul ped modele
ddeps = meshweights(deps_mod, axis=0)
# Densité moyenne
dmean = MV2.average(dens_mod, axis=0, weights=ddeps)
# Anomalie de densité
danom = dens_mod-dmean
# Énergie potentielle disponible
ape = danom * g
ape *= ddeps
# Deficit
ped_mod = MV2.average(ape, axis=0, weights=ddeps)
ped_mod.units = 'J.m^{-2}'
ped_mod.long_name = u"Definit d'energie potentielle"
ped_mod.setAxisList(lons_mod.getAxisList())
model.verbose('PED: %s', ped_mod)
# =====
# Calcul ped profiles
ped_pro, lats_pro, lons_pro = profiles.get_ped(select)
profiles.verbose('PED: %s', ped_pro)
# =====
# Tracés
vmin = ped_pro.min()
vmax = ped_pro.max()
if ped_mod.count():
vmin = min(ped_mod.min(), vmin)
vmax = max(ped_mod.max(), vmax)
else:
self.warning('No model data')
if 'latitude' in select:
lat_min, lat_max = select['latitude'][:2]
else:
la = model.get_latitude()
lat_min, lat_max = min(la), max(la)
if 'longitude' in select:
lon_min, lon_max = select['longitude'][:2]
else:
lo = model.get_longitude()
lon_min, lon_max = min(lo), max(lo)
levels = auto_scale((vmin, vmax))
vmin = levels[0]
vmax = levels[-1]
# Création de la palette
cmap = cmap_magic(levels)
# On trace de champ du modèle moyené sur la période choisie
m = map2(lon=(lon_min, lon_max), lat=(lat_min, lat_max), show=False)
# On trace le modèle en fond
msca = None
try: msca = m.map.scatter(lons_mod, lats_mod, s=100, c=ped_mod, vmin=vmin, vmax=vmax, cmap=cmap, label='model')
except: self.exception('Failed plotting model data')
# On trace les observations avec des points plus petits
psca = None
try: psca = m.map.scatter(lons_pro, lats_pro, s=25, c=ped_pro, vmin=m.vmin, vmax=m.vmax, cmap=m.cmap, label='profiles')
except: self.exception('Failed plotting profiles data')
# Colorbar
if msca is not None:
colorbar(msca)
elif psca is not None:
colorbar(psca)
