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)
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)
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)
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)