def ptype_lat_z(fg, ax, lat, HT):
''' NOTE: HT needs to be given in meter
'''
ax.set_xlim(lat[85], 90)
ax.set_ylim(6000,0)
xl(ax,'Latitude'); yl(ax,'Depth [m]');
utils_plt.plot_topo(ax, lat, HT)
def plot_BSF(var, TorUgrid, nlevels=100, mappingtoolbox='basemap', proj='ortho', min = [], max = []):
''' uses utils_plt.emptyBasemapFig()'''
# colormap
if min == []: min = np.floor(np.nanmin(var)) # minimum value of varin
if max == []: max = np.ceil(np.nanmax(var)) # maximum value of varin
#cmap, norm = utils_plt.get_cmap(min, max, nlevels, scheme=utils_plt.get_viridis()) # viridis
cmap = utils_plt.shiftCMap(ml.cm.seismic, midpoint=1-max/(max-min), name='shifted') # shifted blue white red
# add cyclic border (to prevent gap in pcolor plot)
var = utils_conv.add_cyclic(var)
# choose U or T grid
if TorUgrid == 'U':
xvar = var.ULONG.values
yvar = var.ULAT.values
elif TorUgrid == 'T':
xvar = var.TLONG.values
yvar = var.TLAT.values
# draw plot in new figure
if mappingtoolbox == 'basemap':
fig, map = utils_plt.emptyBasemapFig(proj)
c1 = map.pcolor(xvar,yvar,var.values,latlon=True, cmap=cmap, rasterized=True)
map.colorbar()
elif mappingtoolbox == 'cartopy':
fig, map = True, True#TODO
return(fig, map)
def ptype_lat_z(fg, ax, lat, HT):
''' NOTE: HT needs to be given in meter
'''
ax.set_xlim(lat[85], 90)
ax.set_ylim(6000, 0)
xl(ax, 'Latitude')
yl(ax, 'Depth [m]')
utils_plt.plot_topo(ax, lat, HT)
def plot_BSF(var,
TorUgrid,
nlevels=100,
mappingtoolbox='basemap',
proj='ortho',
min=[],
max=[]):
''' uses utils_plt.emptyBasemapFig()'''
# colormap
if min == []: min = np.floor(np.nanmin(var)) # minimum value of varin
if max == []: max = np.ceil(np.nanmax(var)) # maximum value of varin
#cmap, norm = utils_plt.get_cmap(min, max, nlevels, scheme=utils_plt.get_viridis()) # viridis
cmap = utils_plt.shiftCMap(ml.cm.seismic,
midpoint=1 - max / (max - min),
name='shifted') # shifted blue white red
# add cyclic border (to prevent gap in pcolor plot)
var = utils_conv.add_cyclic(var)
# choose U or T grid
if TorUgrid == 'U':
xvar = var.ULONG.values
yvar = var.ULAT.values
elif TorUgrid == 'T':
xvar = var.TLONG.values
yvar = var.TLAT.values
# draw plot in new figure
if mappingtoolbox == 'basemap':
fig, map = utils_plt.emptyBasemapFig(proj)
c1 = map.pcolor(xvar,
yvar,
var.values,
latlon=True,
cmap=cmap,
rasterized=True)
map.colorbar()
elif mappingtoolbox == 'cartopy':
fig, map = True, True #TODO
return (fig, map)
'''
import matplotlib.pyplot as plt
import matplotlib as ml
import CESM_utils_plt as utils_plt
plt.ion() # enable interactive mode
path_fig = '../figures/160711/'
# =======================================================================================
# CCSM4 representations
# =======================================================================================
# -----------------------------------------------------------------------------------------
# BSF on geographical grid calculated by model
BSF_model = utils_mask.mask_ATLANTIC(ncdat.BSF.isel(time=0), ncdat.REGION_MASK)
fig, map = utils_plt.plot_BSF(BSF_model, 'T', nlevels=10)
plt.title('BSF model on T grid')
utils_plt.print2pdf(fig, path_fig + 'BSF_model_T')
# -----------------------------------------------------------------------------------------
# MOC on geographical grid calculated by model
MOC_model = ncdat.MOC.isel(time=0, transport_reg=1, moc_comp=0)
#MOC_model = MOC_model - MOC_model[:,-1] # normalisation
fig, ax = utils_plt.plot_MOC(MOC_model.lat_aux_grid,
MOC_model.moc_z,
MOC_model,
nlevels=10,
plttype='pcolor+contour')
plt.plot(lat_auxgrd, HT_auxgrd_xmax) # plot seafloor
plt.title('MOC model')
plt.xlim([-36, 90])
utils_plt.print2pdf(fig, path_fig + 'MOC_model')
# x-axes
lat_mgrd = ncdat.TLAT.isel(
nlon=0) # mean of LAT for each j #! very inappropriate
ax_long = np.arange(len(lat_mgrd)) # lat_mgrd
# y-axes
ax_vol = ax_vol_glob
ticks_vol = ticks_vol_glob
# dMOC_mgrd_V (in Sv)
# --stretch---------------------------------------
fig, ax = utils_plt.plot_MOC(ax_long,
ax_vol,
dMOCm,
min,
max,
nlevels_col,
levels_cont,
plttype='pcolor+contour',
to_subplot=[2, 2, 1])
#plt.xlim([-36,73])
plt.title('dMOC mgrd (stretch)')
plt.xlim([81, 383])
plt.yticks(ticks_vol)
plt.gca().set_yticklabels(ticks_dens_rd)
# --linear----------------------------------------
fig, ax = utils_plt.plot_MOC(ax_long,
DBc,
dMOCm,
min,
max,
'HU_mgrd_xmax') # load from file
except:
HU_mgrd_xmax = utils_mask.calc_H_mgrd_xmax(ncdat, 'U', path_mgrd)
# #######################################################################################
# PLOTTING
# #######################################################################################
plt.ion() # enable interactive mode
path_fig = 'figures_Jun16/'
# =======================================================================================
# CCSM4 representations
# =======================================================================================
# -----------------------------------------------------------------------------------------
# BSF on geographical grid calculated by model
BSF_model = utils_mask.mask_ATLANTIC(ncdat.BSF.isel(time=0), ncdat.REGION_MASK)
fig, map = utils_plt.plot_BSF(BSF_model, 'T', nlevels=10)
plt.title('BSF model on T grid')
#utils_plt.print2pdf(fig, 'testfigures/BSF_model_T')
# -----------------------------------------------------------------------------------------
# MOC on geographical grid calculated by model
MOC_model = ncdat.MOC.isel(time=0, transport_reg=1, moc_comp=0)
MOC_model = MOC_model - MOC_model[:, -1] # normalisation
fig, ax = utils_plt.plot_MOC(MOC_model.lat_aux_grid,
MOC_model.moc_z,
MOC_model,
nlevels=40,
plttype='pcolor+contour')
plt.plot(lat_auxgrd, HT_auxgrd_xmax) # plot seafloor
plt.title('MOC model')
plt.xlim([-36, 90])
#utils_plt.print2pdf(fig, 'testfigures/MOC_model')
'''
import matplotlib.pyplot as plt
import matplotlib as ml
import CESM_utils_plt as utils_plt
plt.ion() # enable interactive mode
path_fig = '../figures/160711/'
# =======================================================================================
# Calculated on model grid
# =======================================================================================
# -----------------------------------------------------------------------------------------
# MOC_mgrd_W
fig, ax = utils_plt.plot_MOC(lat_mgrd,
ncdat.z_w_top,
MOC_mgrd_W,
nlevels=10,
plttype='pcolor+contour')
plt.plot(lat_mgrd, HT_mgrd_xmax) # plot seafloor #! it's the T-grid!!!
plt.title('MOC mgrd W')
plt.xlim([-36, 90])
utils_plt.print2pdf(fig, path_fig + 'MOC_mgrd_W')
# -----------------------------------------------------------------------------------------
# dMOC_mgrid_W(in Sv)
fig, ax = utils_plt.plot_MOC(lat_mgrd,
db,
dMOC_mgrd_W,
nlevels=10,
plttype='pcolor+contour')
plt.title('dMOC mgrd W (sigma2)')
TODO: add '.values' where possible to speed up code.
'''
import matplotlib.pyplot as plt
import matplotlib as ml
import CESM_utils_plt as utils_plt
plt.ion() # enable interactive mode
path_fig = '../figures/160711/'
# =======================================================================================
# Calculated on model grid
# =======================================================================================
# -----------------------------------------------------------------------------------------
# MOC_mgrd_W
fig, ax = utils_plt.plot_MOC(lat_mgrd, ncdat.z_w_top, MOC_mgrd_W, nlevels=10, plttype='pcolor+contour')
plt.plot(lat_mgrd,HT_mgrd_xmax) # plot seafloor #! it's the T-grid!!!
plt.title('MOC mgrd W')
plt.xlim([-36,90])
utils_plt.print2pdf(fig, path_fig+'MOC_mgrd_W')
# -----------------------------------------------------------------------------------------
# dMOC_mgrid_W(in Sv)
fig, ax = utils_plt.plot_MOC(lat_mgrd, db, dMOC_mgrd_W, nlevels=10, plttype='pcolor+contour')
plt.title('dMOC mgrd W (sigma2)')
plt.suptitle('density binning from {} to {} in {} steps'.format(dbc.min(), dbc.max(), len(dbc)))
plt.xlim([-36,73])
plt.yticks(ticks_vol)
plt.gca().set_yticklabels(ticks_dens)
#utils_plt.print2pdf(fig, path_fig+'dMOC_mgrd_W_sig2')
'''
import matplotlib.pyplot as plt
import matplotlib as ml
import CESM_utils_plt as utils_plt
plt.ion() # enable interactive mode
path_fig = '../figures/160711/'
# =======================================================================================
# Calculated on model grid
# =======================================================================================
# -----------------------------------------------------------------------------------------
# MOC_mgrd_V
fig, ax = utils_plt.plot_MOC(lat_mgrd, ncdat.z_t, MOC_mgrd_V, nlevels=100, plttype='pcolor+contour')
plt.plot(lat_mgrd,HT_mgrd_xmax) # plot seafloor
plt.title('MOC mgrd V')
plt.xlim([-36,90])
#utils_plt.print2pdf(fig, path_fig+'MOC_mgrd_V')
# -----------------------------------------------------------------------------------------
# dMOC_mgrid_V (in Sv)
fig, ax = utils_plt.plot_MOC(lat_mgrd, dbc, dMOC_mgrd_V, nlevels=10, plttype='pcolor+contour')
plt.title('dMOC mgrd W (sigma2)')
plt.suptitle('density binning from {} to {} in {} steps'.format(dbc.min(), dbc.max(), len(dbc)))
plt.xlim([-36,73])
plt.yticks(ticks_vol_glob)
plt.gca().set_yticklabels(ticks_dens)
#utils_plt.print2pdf(fig, path_fig+'dMOC_mgrd_V_sig2')
# =======================================================================================
except: HU_auxgrd_xmax = utils_mask.calc_H_auxgrd_xmax(lat_auxgrd, ncdat, 'U', path_auxgrd)
try: HU_mgrd_xmax = utils_misc.loadvar(path_mgrd+'HU_mgrd_xmax') # load from file
except: HU_mgrd_xmax = utils_mask.calc_H_mgrd_xmax(ncdat, 'U', path_mgrd)
# #######################################################################################
# PLOTTING
# #######################################################################################
plt.ion() # enable interactive mode
path_fig = 'figures_Jun16/'
# =======================================================================================
# CCSM4 representations
# =======================================================================================
# -----------------------------------------------------------------------------------------
# BSF on geographical grid calculated by model
BSF_model = utils_mask.mask_ATLANTIC(ncdat.BSF.isel(time=0), ncdat.REGION_MASK)
fig, map = utils_plt.plot_BSF(BSF_model, 'T', nlevels = 10)
plt.title('BSF model on T grid')
#utils_plt.print2pdf(fig, 'testfigures/BSF_model_T')
# -----------------------------------------------------------------------------------------
# MOC on geographical grid calculated by model
MOC_model = ncdat.MOC.isel(time=0, transport_reg=1, moc_comp=0)
MOC_model = MOC_model - MOC_model[:,-1] # normalisation
fig, ax = utils_plt.plot_MOC(MOC_model.lat_aux_grid, MOC_model.moc_z, MOC_model, nlevels=40, plttype='pcolor+contour')
plt.plot(lat_auxgrd,HT_auxgrd_xmax) # plot seafloor
plt.title('MOC model')
plt.xlim([-36,90])
#utils_plt.print2pdf(fig, 'testfigures/MOC_model')
# =======================================================================================
# Calculated on model grid
# =======================================================================================
'''
import matplotlib.pyplot as plt
import matplotlib as ml
import CESM_utils_plt as utils_plt
plt.ion() # enable interactive mode
path_fig = '../figures/160711/'
# =======================================================================================
# BSF / MOC
# =======================================================================================
var = MV[0,:,:]
var[190:300,-65:-45] = np.zeros_like(var[190:300,-65:-45])
fig, map = utils_plt.plot_BSF(var, 'T', nlevels = 10)
# -----------------------------------------------------------------------------------------
# BSF on geographical grid calculated by model
BSF_model = utils_mask.mask_ATLANTIC(ncdat.BSF.isel(time=0), ncdat.REGION_MASK)
fig, map = utils_plt.plot_BSF(BSF_model, 'T', nlevels = 10)
plt.title('BSF model on T grid')
utils_plt.print2pdf(fig, path_fig+'BSF_model_T')
# -----------------------------------------------------------------------------------------
# BSF on model grid
fig, map = utils_plt.plot_BSF(BSF_mmgrd, 'U', nlevels=100)
plt.title('BSF mgrd on U grid')
utils_plt.print2pdf(fig, 'testfigures/BSF_mgrd_U')
# -----------------------------------------------------------------------------------------
# MOC on geographical grid calculated by model
MOC_model = ncdat.MOC.isel(time=0, transport_reg=1, moc_comp=0)
TODO: add '.values' where possible to speed up code.
'''
import matplotlib.pyplot as plt
import matplotlib as ml
import CESM_utils_plt as utils_plt
plt.ion() # enable interactive mode
path_fig = '../figures/160711/'
# =======================================================================================
# BSF / MOC
# =======================================================================================
var = MV[0, :, :]
var[190:300, -65:-45] = np.zeros_like(var[190:300, -65:-45])
fig, map = utils_plt.plot_BSF(var, 'T', nlevels=10)
# -----------------------------------------------------------------------------------------
# BSF on geographical grid calculated by model
BSF_model = utils_mask.mask_ATLANTIC(ncdat.BSF.isel(time=0), ncdat.REGION_MASK)
fig, map = utils_plt.plot_BSF(BSF_model, 'T', nlevels=10)
plt.title('BSF model on T grid')
utils_plt.print2pdf(fig, path_fig + 'BSF_model_T')
# -----------------------------------------------------------------------------------------
# BSF on model grid
fig, map = utils_plt.plot_BSF(BSF_mmgrd, 'U', nlevels=100)
plt.title('BSF mgrd on U grid')
utils_plt.print2pdf(fig, 'testfigures/BSF_mgrd_U')
# -----------------------------------------------------------------------------------------
# MOC on geographical grid calculated by model
MOC_model = ncdat.MOC.isel(time=0, transport_reg=1, moc_comp=0)
import CESM_utils_plt as utils_plt
plt.ion() # enable interactive mode
path_fig = '../figures/160711/'
# -----------------------------------------------------------------------------------------
# COMBINATION of MWxint_auxgrd and MOC_auxgrd
fig = plt.figure()
plt.suptitle('density binning from {} to {} in {} steps'.format(
dbc.min(), dbc.max(), len(dbc)))
plt.subplot(3, 1, 1)
plt.title('MOC on depth axis on auxgrd')
ax = utils_plt.plot_MOC(lat_auxgrd,
z_w_auxgrd,
MOC_auxgrd_W,
nlevels=10,
plttype='pcolor+contour',
to_newfigure=False)
plt.plot(lat_auxgrd, HT_auxgrd_xmax) # plot seafloor
plt.xlim([-36, 90])
plt.subplot(3, 1, 2)
plt.title('MW on depth axis longitudinally integrated on auxgrd (in Sv)')
plt.ylabel('depth')
ax = utils_plt.plot_MOC(lat_auxgrd,
z_w_auxgrd,
MWxint_auxgrd,
nlevels=10,
plttype='pcolor+contour',
to_newfigure=False)
plt.plot(lat_auxgrd, HT_auxgrd_xmax) # plot seafloor
'''
import matplotlib.pyplot as plt
import matplotlib as ml
import CESM_utils_plt as utils_plt
plt.ion() # enable interactive mode
path_fig = '../figures/160711/'
# =======================================================================================
# CCSM4 representations
# =======================================================================================
# -----------------------------------------------------------------------------------------
# BSF on geographical grid calculated by model
BSF_model = utils_mask.mask_ATLANTIC(ncdat.BSF.isel(time=0), ncdat.REGION_MASK)
fig, map = utils_plt.plot_BSF(BSF_model, 'T', nlevels = 10)
plt.title('BSF model on T grid')
utils_plt.print2pdf(fig, path_fig+'BSF_model_T')
# -----------------------------------------------------------------------------------------
# MOC on geographical grid calculated by model
MOC_model = ncdat.MOC.isel(time=0, transport_reg=1, moc_comp=0)
#MOC_model = MOC_model - MOC_model[:,-1] # normalisation
fig, ax = utils_plt.plot_MOC(MOC_model.lat_aux_grid, MOC_model.moc_z, MOC_model, nlevels=10, plttype='pcolor+contour')
plt.plot(lat_auxgrd,HT_auxgrd_xmax) # plot seafloor
plt.title('MOC model')
plt.xlim([-36,90])
utils_plt.print2pdf(fig, path_fig+'MOC_model')
# =======================================================================================
# Calculated on model grid
# =======================================================================================
try:
HU_mgrd_xmax = utils_misc.loadvar(path_grd +
'HU_mgrd_xmax') # load from file
except:
HU_mgrd_xmax = utils_mask.calc_H_mgrd_xmax(ncdat, 'U', path_grd)
# =============================================================================
# Streamfunctions
# =============================================================================
lat_mgrd = ncdat.TLAT.isel(
nlon=0) # mean of LAT for each j #! very inappropriate
# -----------------------------------------------------------------------------
# BSF on geographical grid calculated by model
fig, map = utils_plt.plot_BSF(BSF.isel(time=0), 'T', nlevels=10)
plt.title('BSF model on T grid')
#utils_plt.print2pdf(fig, 'testfigures/BSF_model_T')
# -----------------------------------------------------------------------------
# MOC on geographical grid calculated by model
MOCsel = MOC.isel(time=0)
fig, ax = utils_plt.plot_MOC(MOCsel.lat_aux_grid,
MOCsel.moc_z,
MOCsel,
nlevels=40,
plttype='pcolor+contour')
plt.plot(lat_mgrd, HT_auxgrd_xmax) # plot seafloor
plt.title('MOC model')
plt.xlim([-36, 90])
#utils_plt.print2pdf(fig, path_figs+'MOC_model')
method = '0'
dMOCm = dMOC_mgrd_V_0
dMOCaux = dMOC_auxgrd_V_0
# x-axes
lat_mgrd = ncdat.TLAT.isel(nlon=0) # mean of LAT for each j #! very inappropriate
ax_long = np.arange(len(lat_mgrd)) # lat_mgrd
# y-axes
ax_vol = ax_vol_glob
ticks_vol = ticks_vol_glob
# dMOC_mgrd_V (in Sv)
# --stretch---------------------------------------
fig, ax = utils_plt.plot_MOC(ax_long, ax_vol, dMOCm, min, max, nlevels_col, levels_cont, plttype='pcolor+contour', to_subplot=[2,2,1])
#plt.xlim([-36,73])
plt.title('dMOC mgrd (stretch)')
plt.xlim([81,383])
plt.yticks(ticks_vol)
plt.gca().set_yticklabels(ticks_dens_rd)
# --linear----------------------------------------
fig, ax = utils_plt.plot_MOC(ax_long, DBc, dMOCm, min, max, nlevels_col, levels_cont, plttype='pcolor+contour', to_newfig=False, to_subplot=[2,2,2])
plt.title('dMOC mgrd (linear)')
plt.xlim([81,383])
plt.ylim([38,30])
# dMOCaux (in Sv)
# --stretch---------------------------------------
fig, ax = utils_plt.plot_MOC(lat_auxgrd, ax_vol, dMOCaux, min, max, nlevels_col, levels_cont, plttype='pcolor+contour', to_newfig=False, to_subplot=[2,2,3])
plt.title('dMOC auxgrd (stretch)')
import CESM_utils_plt as utils_plt
plt.ion() # enable interactive mode
path_fig = '../figures/160711/'
# -----------------------------------------------------------------------------------------
# COMBINATION of MWxint_auxgrd and MOC_auxgrd
fig = plt.figure()
plt.suptitle('density binning from {} to {} in {} steps'.format(dbc.min(), dbc.max(), len(dbc)))
plt.subplot(3,1,1)
plt.title('MOC on depth axis on auxgrd')
ax = utils_plt.plot_MOC(lat_auxgrd, z_w_auxgrd, MOC_auxgrd_W, nlevels=10, plttype='pcolor+contour', to_newfigure=False)
plt.plot(lat_auxgrd,HT_auxgrd_xmax) # plot seafloor
plt.xlim([-36,90])
plt.subplot(3,1,2)
plt.title('MW on depth axis longitudinally integrated on auxgrd (in Sv)')
plt.ylabel('depth')
ax = utils_plt.plot_MOC(lat_auxgrd, z_w_auxgrd, MWxint_auxgrd, nlevels=10, plttype='pcolor+contour', to_newfigure=False)
plt.plot(lat_auxgrd,HT_auxgrd_xmax) # plot seafloor
plt.xlim([-36,90])
plt.subplot(3,1,3)
plt.plot(lat_auxgrd, np.nansum(MWxint_auxgrd,axis=0), '.-k')
plt.colorbar()
plt.xlim([-36,90])
plt.ylabel('sum over whole density-axis (in Sv)')
# =============================================================================
try: HT_mgrd_xmax = utils_misc.loadvar(path_grd+'HT_mgrd_xmax') # load from file
except: HT_mgrd_xmax = utils_mask.calc_H_mgrd_xmax(ncdat, 'T', path_grd)
try: HU_mgrd_xmax = utils_misc.loadvar(path_grd+'HU_mgrd_xmax') # load from file
except: HU_mgrd_xmax = utils_mask.calc_H_mgrd_xmax(ncdat, 'U', path_grd)
# =============================================================================
# Streamfunctions
# =============================================================================
lat_mgrd = ncdat.TLAT.isel(nlon=0) # mean of LAT for each j #! very inappropriate
# -----------------------------------------------------------------------------
# BSF on geographical grid calculated by model
fig, map = utils_plt.plot_BSF(BSF.isel(time=0), 'T', nlevels = 10)
plt.title('BSF model on T grid')
#utils_plt.print2pdf(fig, 'testfigures/BSF_model_T')
# -----------------------------------------------------------------------------
# MOC on geographical grid calculated by model
MOCsel = MOC.isel(time=0)
fig, ax = utils_plt.plot_MOC(MOCsel.lat_aux_grid, MOCsel.moc_z, MOCsel, nlevels=40, plttype='pcolor+contour')
plt.plot(lat_mgrd, HT_auxgrd_xmax) # plot seafloor
plt.title('MOC model')
plt.xlim([-36,90])
#utils_plt.print2pdf(fig, path_figs+'MOC_model')
# =============================================================================
# Streamfunction Indices
# =============================================================================