def plot_index_t_x_z(self,
numb=[],
figsize=[],
do_subplot=[],
do_output=True):
from set_inputarray import inputarray
fsize = 16
#_______________________________________________________________________
if isinstance(numb, int) == True: numb = [numb]
if len(numb) == 0: numb = range(0, len(self.box_define))
#_______________________________________________________________________
for ii in numb:
#fig = plt.figure(figsize=(12, 6))
#ax1 = plt.gca()
if len(figsize) == 0: figsize = [10, 5]
#___________________________________________________________________________
# plot is not part of subplot
if len(do_subplot) == 0:
fig = plt.figure(figsize=figsize)
ax1 = plt.gca()
else:
fig = do_subplot[0]
ax1 = do_subplot[1]
fig.sca(ax1)
resolution = 'c'
fsize = 14
#___________________________________________________________________
cnumb = 30
cmin = np.nanmin(self.value[ii])
cmax = np.nanmax(self.value[ii])
cref = cmin + (cmax - cmin) / 2
cref = np.around(cref,
-np.int32(np.floor(np.log10(np.abs(cref))) - 1))
#cref =0.0
#___________________________________________________________________
# if anomaly data
if self.anom == True:
cmax, cmin, cref = np.nanmax(self.value[ii]), np.nanmin(
self.value[ii]), 0.0
self.cmap = 'blue2red'
#___________________________________________________________________
# if predefined color range
if len(self.crange) != 0:
if len(self.crange) == 2:
cmin = np.float(self.crange[0])
cmax = np.float(self.crange[1])
cref = np.around(
cref, -np.int32(np.floor(np.log10(np.abs(cref))) - 1))
elif len(self.crange) == 3:
cmin = np.float(self.crange[0])
cmax = np.float(self.crange[1])
cref = np.float(self.crange[2])
else:
print(' this colorrange definition is not supported !!!')
print(
'data.crange=[cmin,cmax] or data.crange=[cmin,cmax,cref]'
)
if do_output == True:
print('[cmin,cmax,cref] = [' + str(cmin) + ', ' + str(cmax) +
', ' + str(cref) + ']')
if do_output == True: print('[cnum]=', cnumb)
cmap0, clevel = colormap_c2c(cmin, cmax, cref, cnumb, self.cmap)
if do_output == True: print('clevel = ', clevel)
do_drawedges = True
if clevel.size > 50: do_drawedges = False
# overwrite discrete colormap
#cmap0 = cmocean.cm.balance
#do_drawedges=False
#___________________________________________________________________
# make pcolor or contour plot
#depth = self.depth[:-1] + (self.depth[1:]-self.depth[:-1])/2.0
#depth = self.zlev[:-1] + (self.zlev[1:]-self.zlev[:-1])/2.0
depth = -self.zlev
depthlim = np.sum(~np.isnan(self.value[ii][0, :])).max()
if depthlim == depth.shape: depthlim = depthlim - 1
yy, xx = np.meshgrid(depth, self.time)
#___________________________________________________________________
data_plot = np.copy(self.value[ii])
data_plot[data_plot < clevel[0]] = clevel[0] + np.finfo(
np.float32).eps
data_plot[data_plot > clevel[-1]] = clevel[-1] - np.finfo(
np.float32).eps
#___________________________________________________________________
if inputarray['which_plot'] == 'pcolor':
hp = ax1.pcolormesh(
xx[:, 0:depthlim],
yy[:, 0:depthlim],
data_plot[:, 0:depthlim],
shading='flat', #flat
antialiased=False,
edgecolor='None',
cmap=cmap0),
#vmin=np.nanmin(data_plot), vmax=np.nanmax(data_plot))
else:
hp = ax1.contourf(xx[:, 0:depthlim],
yy[:, 0:depthlim],
data_plot[:, 0:depthlim],
levels=clevel,
antialiased=True,
cmap=cmap0,
vmin=clevel[0],
vmax=clevel[-1])
ax1.contour(xx[:, 0:depthlim],
yy[:, 0:depthlim],
data_plot[:, 0:depthlim],
levels=clevel,
antialiased=True,
colors='k',
linewidths=[0.25, 0.1],
linestyles=['solid'],
vmin=np.nanmin(data_plot),
vmax=np.nanmax(data_plot))
#hp.cmap.set_under([0.4,0.4,0.4])
#___________________________________________________________________
# set main axes
ax1.set_xlim(self.time.min(), self.time.max())
ax1.set_ylim(0, depth[depthlim - 1])
ax1.invert_yaxis()
#ax1.set_axis_bgcolor([0.25,0.25,0.25])
ax1.tick_params(axis='both',
which='major',
direction='out',
length=8,
labelsize=fsize)
ax1.minorticks_on()
ax1.tick_params(axis='both',
which='minor',
direction='out',
length=4,
labelsize=fsize)
ax1.set_xlabel('Time [years]', fontdict=dict(fontsize=fsize))
ax1.set_ylabel('Depth [km]', fontdict=dict(fontsize=fsize))
plt.title(self.descript + ' - ' + self.box_define[0][2],
fontdict=dict(fontsize=fsize * 2),
verticalalignment='bottom')
#___________________________________________________________________
# draw colorbar
#divider = make_axes_locatable(ax1)
#cax = divider.append_axes("right", size="2.5%", pad=0.5)
#plt.clim(clevel[0],clevel[-1])
him = ax1.get_images()
for im in ax1.get_images():
im.set_clim(clevel[0], clevel[-1])
#cbar = plt.colorbar(hp,ax=ax1,cax=cax,ticks=clevel,drawedges=do_drawedges)
cbar = plt.colorbar(hp,
ax=ax1,
ticks=clevel,
drawedges=do_drawedges)
cbar.set_label(self.lname + ' ' + self.unit + '\n' + self.str_time,
size=fsize)
cl = plt.getp(cbar.ax, 'ymajorticklabels')
plt.setp(cl, fontsize=fsize)
# kickout some colormap labels if there are to many
ncbar_l = len(cbar.ax.get_yticklabels()[:])
idx_cref = np.where(clevel == cref)[0]
idx_cref = np.asscalar(idx_cref)
nmax_cbar_l = 10
nstep = ncbar_l / nmax_cbar_l
nstep = np.int(np.floor(nstep))
#plt.setp(cbar.ax.get_yticklabels()[:], visible=False)
#plt.setp(cbar.ax.get_yticklabels()[idx_cref::nstep], visible=True)
#plt.setp(cbar.ax.get_yticklabels()[idx_cref::-nstep], visible=True)
if nstep == 0: nstep = 1
tickl = cbar.ax.get_yticklabels()
idx = np.arange(0, len(tickl), 1)
idxb = np.ones((len(tickl), ), dtype=bool)
idxb[idx_cref::nstep] = False
idxb[idx_cref::-nstep] = False
idx = idx[idxb == True]
for ii in list(idx):
tickl[ii] = ''
cbar.ax.set_yticklabels(tickl)
# reposition colorbar and axes
plt.tight_layout()
pos_ax = ax1.get_position()
ax1.set_position(
[pos_ax.x0, pos_ax.y0, 0.90 - pos_ax.y0, pos_ax.height])
pos_ax = ax1.get_position()
pos_cbar = cbar.ax.get_position()
cbar.ax.set_position([
pos_ax.x0 + pos_ax.width + 0.01, pos_cbar.y0, pos_cbar.width,
pos_cbar.height
])
fig.canvas.draw()
#___________________________________________________________________
# save figure
if inputarray['save_fig'] == True:
print(' --> save figure: png')
str_times = self.str_time.replace(' ', '').replace(':', '')
str_deps = self.str_dep.replace(' ', '').replace(',',
'').replace(
':', '')
sfname = 'boxplot_' + self.box_define[ii][
2] + '_' + self.descript + '_' + self.sname + '_' + str_times + '_' + str_deps + '.png'
sdname = inputarray['save_figpath']
if os.path.isdir(sdname) == False: os.mkdir(sdname)
plt.savefig(sdname+sfname, \
format='png', dpi=600, \
bbox_inches='tight', pad_inches=0,\
transparent=True,frameon=True)
#___________________________________________________________________
plt.show(block=False)
print('finish')
return (fig, ax1, cbar)
def plot_xmoc(lat,
depth,
moc,
bottom=[],
which_moc='gmoc',
str_descript='',
str_time='',
figsize=[],
crange=[],
cnumb=20,
do_clabel=True,
do_subplot=[]):
#___________________________________________________________________________
if len(figsize) == 0: figsize = [13, 6]
#___________________________________________________________________________
# plot is not part of subplot
if len(do_subplot) == 0:
fig = plt.figure(figsize=figsize)
ax1 = plt.gca()
else:
fig = do_subplot[0]
ax1 = do_subplot[1]
resolution = 'c'
fsize = 10
txtx, txty = lat[0] + 5, depth[-1] + 100
#+_________________________________________________________________________+
#| set minimum, maximum and reference values for the creation of the |
#| adjustable colormap |
#+_________________________________________________________________________+
#cnumb = 20; # minimum number of colors
#cmin,cmax,cref = -6,16,0 # [cmin, cmax, cref] --> MLD2
cmin, cmax, cref = moc[np.where(depth <= -500)[0][0]::, :].min(
), moc[np.where(
depth <= -500)[0][0]::, :].max(), 0.0 # [cmin, cmax, cref] --> MLD2
if len(crange) != 0: cmin, cmax, cref = crange[0], crange[1], 0.0
print(cmin, cmax, cref)
cmap0, clevel = colormap_c2c(cmin, cmax, cref, cnumb, 'blue2red')
cbot = [0.5, 0.5, 0.5]
do_drawedges = True
if clevel.size > 30: do_drawedges = False
#+_________________________________________________________________________+
#| plot AXES1 |
#+_________________________________________________________________________+
plt.sca(ax1)
data_plot = moc
data_plot[data_plot < clevel[0]] = clevel[0] + np.finfo(np.float32).eps
data_plot[data_plot > clevel[-1]] = clevel[-1] - np.finfo(np.float32).eps
hp1 = plt.contourf(lat,
depth,
data_plot,
levels=clevel,
extend='both',
cmap=cmap0)
CS = plt.contour(lat,
depth,
data_plot,
levels=clevel,
colors='k',
linewidths=[0.5, 0.25],
antialised=True)
if do_clabel:
ax1.clabel(CS,
CS.levels[np.where(CS.levels != cref)],
inline=1,
inline_spacing=1,
fontsize=6,
fmt='%1.1f Sv')
if len(bottom) > 0:
ax1.plot(lat, bottom, color='k')
ax1.fill_between(lat, bottom, depth[-1], color=cbot,
zorder=2) #,alpha=0.95)
#___________________________________________________________________________
for im in ax1.get_images():
im.set_clim(clevel[0], clevel[-1])
#___________________________________________________________________________
ax1.text(txtx,
txty,
str_descript,
fontsize=14,
fontweight='bold',
horizontalalignment='left')
ax1.grid(color='k', linestyle='-', linewidth=0.25, alpha=1.0)
ax1.set_xlabel('Latitudes [deg]', fontsize=12)
ax1.set_ylabel('Depth [m]', fontsize=12)
#+_________________________________________________________________________+
#| plot first colorbar |
#+_________________________________________________________________________+
cbar1 = plt.colorbar(hp1,
ax=ax1,
ticks=clevel,
drawedges=True,
extend='neither',
extendrect=False,
extendfrac='auto')
cbar1.set_label('Global Meridional Overturning Circulation [Sv]' + '\n' +
str_time,
size=fsize + 2)
if which_moc == 'gmoc':
cbar1.set_label('Global Meridional Overturning Circulation [Sv]' +
'\n' + str_time,
size=fsize + 2)
elif which_moc == 'amoc' or which_moc == 'amoc2':
cbar1.set_label('Atlantic Meridional Overturning Circulation [Sv]' +
'\n' + str_time,
size=fsize + 2)
elif which_moc == 'pmoc':
cbar1.set_label(
'Indo-Pacific Meridional Overturning Circulation [Sv]' + '\n' +
str_time,
size=fsize + 2)
elif which_moc == 'imoc':
cbar1.set_label('Indo Meridional Overturning Circulation [Sv]' + '\n' +
str_time,
size=fsize + 2)
ncbar_l = len(cbar1.ax.get_yticklabels()[:])
idx_cref = np.where(clevel == cref)[0]
idx_cref = np.asscalar(idx_cref)
nmax_cbar_l = 10
nstep = ncbar_l / nmax_cbar_l
nstep = np.int(np.floor(nstep))
if nstep == 0: nstep = 1
plt.setp(cbar1.ax.get_yticklabels()[:], visible=False)
plt.setp(cbar1.ax.get_yticklabels()[idx_cref::nstep], visible=True)
plt.setp(cbar1.ax.get_yticklabels()[idx_cref::-nstep], visible=True)
plt.show(block=False)
fig.canvas.draw()
return (fig, ax1)
def plot_map_clima_panel_matrix(inputarray, mesh, var_rows, var_columns, which_var, \
whatisrow='depth', whatiscolumn='descript/path',\
which_year=[1989,2009], which_mon=list(range(1,12+1)), which_dep=np.arange(0,250+1,10), \
which_woa='woa2018', do_rmse=True, fsize=10, do_enumerate=True):
nr, nc = len(var_rows), len(var_columns)
#___________________________________________________________________________
data1 = fesom_data(inputarray) # init fesom2.0 data object
data1.var, data1.year, data1.month, data1.depth = which_var, which_year, which_mon, which_dep
#___________________________________________________________________________
if data1.var=='temp' : cmin1,cmax1,cref1,cnumb1 = -4.0, 4.0, 0.0, 20 # [cmin, cmax, cref] --> salt+
if data1.var=='salt' : cmin1,cmax1,cref1,cnumb1 = -0.5, 0.5, 0.0, 20 # [cmin, cmax, cref] --> salt
cmap1,clevel1= colormap_c2c(cmin1,cmax1,cref1,cnumb1,'blue2red')
do_drawedges=True
#___________________________________________________________________________
# LOAD WOA CLIMATOLOGY
if which_woa=='woa2005': clim_descr, clim_path, clim_fname = 'WOA05', '../view/woa2005/', 'woa2005TS.nc'
if which_woa=='woa2018': clim_descr, clim_path, clim_fname = 'WOA18', '/work/ollie/pscholz/INIT_HYDRO/woa2018/', 'woa2018TS_0.25deg.nc'
clim = clim_data(clim_path,clim_fname,data1.var)
clim.descript = data1.descript+'-'+clim_descr
clim.crange,clim.cmap, clim.cnumb = data1.crange,data1.cmap, data1.cnumb
clim.str_time, clim.str_dep = data1.str_time, data1.str_dep
clim.sname, clim.lname, clim.unit = data1.sname, data1.lname, data1.unit
clim.proj, clim.proj_lon, clim.proj_lat= data1.proj, data1.proj_lon, data1.proj_lat
#___________________________________________________________________________
# setup figures and axes
if nc!=1 and nr!=1: fig, ax = plt.subplots(nrows=nr, ncols=nc, figsize=[4*nc,3*nr])
elif nc==1 : fig, ax = plt.subplots(nrows=nr, ncols=nc, figsize=[8*nc,4*nr])
elif nr==1 : fig, ax = plt.subplots(nrows=nr, ncols=nc, figsize=[4*nc,8*nr])
plt.subplots_adjust(bottom=0.05, top=0.95, left=0.05, right=0.95,wspace=0.01, hspace=0.01)
#___________________________________________________________________________
# start row/column loop
print(' --> total nr. of matrix panels: {:d} |'.format(nr*nc), end='')
ni = 0
for nri in range(0,nr):
for nci in range(0,nc):
ni = ni+1
print('{:d}|'.format(ni), end='')
#___________________________________________________________________
# if subplot should be left empty
if not var_columns[nci]:
ax[nri,nci].remove()
continue
#___________________________________________________________________
# set row/column specific info
if whatisrow =='depth':
data1.depth = var_rows[nri]
elif whatiscolumn =='depth':
data1.depth = var_columns[nri]
else:
print(' --> ERROR: this panel matrix configuration is not supported')
exit
if whatisrow =='descript/path':
data1.descript, data1.path = var_rows[nci][0],var_rows[nci][1]
elif whatiscolumn =='descript/path':
data1.descript, data1.path = var_columns[nci][0],var_columns[nci][1]
else:
print(' --> ERROR: this panel matrix configuration is not supported')
exit
#___________________________________________________________________
# load data
fesom_load_data_horiz_netcdf4(mesh,data1,do_output=False)
data11 = clim_data_anom(mesh,clim,data1,clim_descr)
#___________________________________________________________________
# compute RMSE
meanbias1 = np.nansum(np.abs(data11.anom))/np.isnan(data11.anom).sum()
#___________________________________________________________________
# do axis writtings
xl, yl, str_tl, str_yl, str_xl = [], [], [], [], []
if nri == nr-1 : xl = [0,0,0,1]
if nci == 0 : yl = [1,0,0,0]
if whatisrow =='depth':
if nci == 0: str_yl=data11.str_dep[7::]+'\n\n'
elif whatisrow =='descript/path':
if nci == 0: str_yl=data11.descript+'\n\n'
if whatiscolumn =='depth':
if nri == 0: str_tl=data11.str_dep[7::]
elif whatiscolumn =='descript/path':
if nri == 0: str_tl=data11.descript
str_err=[]
if do_rmse:
if data1.var=='temp': str_err='\n Err={:2.2f}°C'.format(meanbias1)
if data1.var=='salt': str_err='\n Err={:2.2f}psu'.format(meanbias1)
#___________________________________________________________________
# do subplot
if nc==1 and nr==1:
dumax,hp = plot_map_clima(fig, ax, mesh, data11, inputarray, clevel1, cmap1, \
xl=xl, yl=yl, str_tl=str_tl, str_yl=str_yl, str_xl=str_xl, \
str_txt=str_err, fsize=fsize )
elif nc!=1 and nr!=1:
dumax,hp = plot_map_clima(fig, ax[nri,nci], mesh, data11, inputarray, clevel1, cmap1, \
xl=xl, yl=yl, str_tl=str_tl, str_yl=str_yl, str_xl=str_xl, \
str_txt=str_err, fsize=fsize )
elif nc==1:
dumax,hp = plot_map_clima(fig, ax[nri], mesh, data11, inputarray, clevel1, cmap1, \
xl=xl, yl=yl, str_tl=str_tl, str_yl=str_yl, str_xl=str_xl, \
str_txt=str_err, fsize=fsize )
elif nr==1:
dumax,hp = plot_map_clima(fig, ax[nci], mesh, data11, inputarray, clevel1, cmap1, \
xl=xl, yl=yl, str_tl=str_tl, str_yl=str_yl, str_xl=str_xl, \
str_txt=str_err, fsize=fsize )
#___________________________________________________________________
# enumerate a, b, c, ....
fac = 2.5
if do_enumerate:
txtx, txty = inputarray['which_box'][0]+fac,inputarray['which_box'][3]-fac
if nc*nr>26 : str_enum = chr(ord('`')+nci+1)+str(nri+1)+')'
else : str_enum = chr(ord('`')+ni)+')'
if nc==1 and nr==1:
ax.text(txtx,txty,str_enum, \
fontweight='normal',horizontalalignment='left',\
verticalalignment='top',fontsize=fsize)
elif nc!=1 and nr!=1:
ax[nri,nci].text(txtx,txty,str_enum, \
fontweight='normal',horizontalalignment='left',\
verticalalignment='top',fontsize=fsize)
elif nc==1:
ax[nri].text(txtx,txty,str_enum, \
fontweight='normal',horizontalalignment='left',\
verticalalignment='top',fontsize=fsize)
elif nr==1:
ax[nci].text(txtx,txty,str_enum, \
fontweight='normal',horizontalalignment='left',\
verticalalignment='top',fontsize=fsize)
#___________________________________________________________________________
# do colorbar
str_unit = data1.unit
if data1.var=='temp': str_unit = '[°C]'
cbar1 = setup_colorbar(hp, ax, clevel1, cref1, data1.lname+' anomaly '+' '+str_unit+'\n '+data1.str_time, ncl=10, orientation='vertical')
#___________________________________________________________________________
# adapt panel and colorbar positions
fig.canvas.draw()
pos_cbar1 = cbar1.ax.get_position()
if nc==1 and nr==1: pos_ax = ax.get_position()
elif nc!=1 and nr!=1: pos_ax = ax[0,0].get_position()
else : pos_ax = ax[0].get_position()
x0,y0 = 0.1, 0.1
#xg, yg = 0.005, 0.005
xg, yg = 0.01, 0.01
w, h = pos_ax.width, pos_ax.height
fac = 0.70/(w*nc+xg*(nc-1))
w, h = w*fac, h*fac
print(w,h)
for nri in range(0,nr):
for nci in range(0,nc):
if nc==1 and nr==1: ax.set_position( [x0+(w+xg)*nci, y0+(h+yg)*(nr-nri-1), w, h])
elif nc!=1 and nr!=1: ax[nri,nci].set_position( [x0+(w+xg)*nci, y0+(h+yg)*(nr-nri-1), w, h])
elif nc==1 : ax[nri].set_position( [x0+(w+xg)*nci, y0+(h+yg)*(nr-nri-1), w, h])
elif nr==1 : ax[nci].set_position( [x0+(w+xg)*nci, y0+(h+yg)*(nr-nri-1), w, h])
cbar1.ax.set_position([x0+(w+xg)*nc, y0, pos_cbar1.width, nr*h+(nr-1)*yg])
#cbar1.ax.set_aspect(1/(pos_cbar1.width/(nr*h+(nr-1)*yg)/20*4))
cbar1.ax.set_aspect(1/(pos_cbar1.width/(nr*h+(nr-1)*yg)/20*4*(5-(nr-1))))
fig.canvas.draw()
#___________________________________________________________________________
# return value
return(data11)