def plot_section(file_handle, record, xq, j=0, variable='salt',eta='e',clim=(33.8,34.55), plot_grid=True, rep='pcm', xlim=(320,800), ylim=(-720,0), cmap=plt.cm.jet, ax=None, show_stats=True):
"""Plots a section of by reading vertical grid and scalar variable and super-sampling
both in order to plot vertical and horizontal reconstructions.
Optional arguments have defaults for plotting salinity and overlaying the grid.
"""
e = file_handle.variables[eta][record,:,j,:] # Vertical grid positions
s = file_handle.variables[variable][record,:,j,:] # Scalar field to color
if variable == 'u': # we need u at h pts
s = 0.5 * (s[:,0:-1]+s[:,1::])
x,z,q = m6toolbox.section2quadmesh(xq, e, s, representation=rep) # This yields three areas at twice the model resolution
if ax is None:
fig, ax = plt.subplots(nrows=1, ncols=1, figsize=(8,6))
cs = ax.pcolormesh(x, z, q, cmap=cmap, vmin=clim[0], vmax=clim[1]);
cb =plt.colorbar(cs,ax=ax)
if plot_grid: ax.plot(x, z.T, 'k');
ax.set_ylim(ylim)
ax.set_xlim(xlim)
if show_stats > 0:
sMin = s.min(); sMax = s.max()
ax.annotate('max=%.5g\nmin=%.5g'%(sMax,sMin), xy=(0.0,1.01),
xycoords='axes fraction', verticalalignment='bottom', fontsize=10)
def plot_section(file_handle,
record,
variable,
y=358,
vmin=None,
vmax=None,
plot_grid=True,
rep='linear'):
"""Plots a section of by reading vertical grid and scalar variable and super-sampling
both in order to plot vertical and horizontal reconstructions.
Optional arguments have defaults for plotting salinity and overlaying the grid.
"""
e = file_handle.variables['e'][record, :, 426,
370:391] # Vertical grid positions
s = file_handle.variables[variable][record, :, y,
370:391] # Scalar field to color
x, z, q = m6toolbox.section2quadmesh(
xq[426, 369:391], e, s, representation=rep
) # This yields three areas at twice the model resolution
cs = plt.pcolormesh(x, z, q, vmin=vmin, vmax=vmax, cmap=cmap)
if plot_grid: plt.plot(x, z.T, 'k')
# if plot_grid: plt.plot(x, z.T, 'k', hold=True);
# plt.ylim(-700,1)
plt.ylim(-60, 3)
cbar = plt.colorbar(cs, orientation='horizontal')
cbar.ax.tick_params(labelsize=15)
def plot_section(file_handle,
xq,
ax,
data1,
data2,
im=250,
eta='e',
yvar='yh',
rep='pcm',
xlim=(0, 650),
ylim=(-2000, 0),
cmap=plt.cm.bwr,
hidex=False,
hidey=False,
m1=-2,
m2=2):
"""Plots a section of by reading vertical grid and scalar variable and super-sampling
both in order to plot vertical and horizontal reconstructions.
Optional arguments have defaults for plotting salinity and overlaying the grid.
"""
font = {
'family': 'serif',
'color': 'darkred',
'weight': 'normal',
'size': 18,
}
e = file_handle.variables[eta][-24::, :, :,
im].mean(axis=0) # Vertical grid positions
y = file_handle.variables[yvar][:]
x, z, q = m6toolbox.section2quadmesh(
xq, e, data1, representation=rep
) # This yields three areas at twice the model resolution
#cs = ax.pcolormesh(x, z, q,norm=LogNorm(vmin=1, vmax=110), cmap=cmap)
cs = ax.pcolormesh(x, z, q, vmin=m1, vmax=m2, cmap=cmap)
if (len(data2.shape) > 1):
z = 0.5 * (e[0:-1, :] + e[1:, :])
[Y, TMP] = np.meshgrid(y, z[:, 0])
s = ax.contour(Y, z, data2 - 1000, [37.12], colors='gray', lw=10)
ax.clabel(s, inline=1, fontsize=16, fmt='%4.2f', manual=[(500, -500)])
else:
print 'This wont be plotted'
#ax.plot(y,data2,'gray',lw=2);
ax.set_ylim(ylim)
ax.set_xlim(xlim)
if not hidex:
#ax.axes.get_xaxis().set_ticks([])
ax.set_xlabel('y [km]')
if not hidey:
#ax.axes.get_yaxis().set_ticks([])
ax.set_ylabel('Depth [m]')
return cs
def plot_section(file_handle, record, xq, i=0, variable='salt',eta='e',clim=(33.8,34.55), plot_grid=True, rep='pcm', xlim=(320,800), ylim=(-720,0), cmap=plt.cm.jet):
"""Plots a section of by reading vertical grid and scalar variable and super-sampling
both in order to plot vertical and horizontal reconstructions.
Optional arguments have defaults for plotting salinity and overlaying the grid.
"""
e = file_handle.variables[eta][record,:,:,i] # Vertical grid positions
s = file_handle.variables[variable][record,:,:,i] # Scalar field to color
x,z,q = m6toolbox.section2quadmesh(xq, e, s, representation=rep) # This yields three areas at twice the model resolution
plt.pcolormesh(x, z, q, cmap=cmap);
plt.colorbar()
plt.clim(clim)
if plot_grid: plt.plot(x, z.T, 'k', hold=True);
plt.ylim(ylim)
plt.xlim(xlim)
def plot_section(file_handle, record, xq, ax, i=250, variable='temp',eta='e',yvar='yh',clim=(0,30), plot_grid=True, rep='pcm', xlim=(0,1000), ylim=(-4000,0), cmap=plt.cm.jet, hide_ticks=False, cb=False):
"""Plots a section of by reading vertical grid and scalar variable and super-sampling
both in order to plot vertical and horizontal reconstructions.
Optional arguments have defaults for plotting salinity and overlaying the grid.
"""
font = {'family': 'serif',
'color': 'darkred',
'weight': 'normal',
'size': 16,
}
import matplotlib
matplotlib.rcParams.update({'font.size': 16})
e = file_handle.variables[eta][record,:,:,i] # Vertical grid positions
s = file_handle.variables[variable][record,:,:,i] # Scalar field to color
y = file_handle.variables[yvar][:]
x = file_handle.variables['xh'][:]
x,z,q = m6toolbox.section2quadmesh(xq, e, s, representation=rep) # This yields three areas at twice the model resolution
cs = ax.pcolormesh(x, z, q,norm=LogNorm(vmin=1, vmax=110), cmap=cmap)
if cb:
cbar=plt.colorbar(cs,orientation='horizontal')
cbar.ax.set_xlabel(r'$\Delta z$ [m]')
##cs.set_clim(clim)
[Y,TMP] = np.meshgrid(y,e[:,0])
print e.shape, Y.shape
if plot_grid:
#ax.plot(Y.T, e.T, 'k', lw=0.5, hold=True)
ax.plot(Y.T, e.T, 'k', lw=0.5)
ax.plot(y,e[0,:],'k',lw=1); plt.plot(y,e[-1,:],'k',lw=1);
if variable == 'v':
ax.text(5,-500, r'max$(|v|)$:'+str(np.max(np.abs(s))), fontdict=font)
ax.set_ylim(ylim)
ax.set_xlim(xlim)
if hide_ticks:
ax.axes.get_xaxis().set_ticks([])
ax.axes.get_yaxis().set_ticks([])
return #cs
def render(var, args, elevation=None, frame=0):
var.getData() # Actually read data from file
# Optionally mask out a specific value
if args.ignore:
var.data = np.ma.masked_array(var.data, mask=[var.data == args.ignore])
if args.ignorelt:
var.data = np.ma.masked_array(var.data,
mask=[var.data <= args.ignorelt])
if args.ignoregt:
var.data = np.ma.masked_array(var.data,
mask=[var.data >= args.ignoregt])
if args.scale:
var.data = args.scale * var.data
if args.list: print('createUI: Data =\n', var.data)
if args.stats:
dMin = np.min(var.data)
dMax = np.max(var.data)
if dMin == 0 and dMax > 0:
dMin = np.min(var.data[var.data != 0])
print('Mininum=', dMin, '(ignoring zeros) Maximum=', dMax)
elif dMax == 0 and dMin < 0:
dMax = np.max(var.data[var.data != 0])
print('Mininum=', dMin, 'Maximum=', dMax, '(ignoring zeros)')
else:
print('Mininum=', dMin, 'Maximum=', dMax)
if args.offset: var.data = var.data + args.offset
if args.log10: var.data = np.ma.log10(var.data)
# Now plot
if var.rank == 0:
for d in var.allDims:
print('%s = %g %s' % (d.name, d.values[0], d.units))
print(var.vname + ' = ', var.data, ' ' + var.units)
exit(0)
elif var.rank == 1: # Line plot
if var.dims[0].isZaxis: # Transpose 1d plot
xCoord = var.data
yData = var.dims[0].values
plt.plot(xCoord, yData)
plt.xlabel(var.label)
plt.ylabel(var.dims[0].label)
if var.dims[0].values[0] > var.dims[0].values[-1]:
plt.gca().invert_yaxis()
if var.dims[0].positiveDown: plt.gca().invert_yaxis()
else: # Normal 1d plot
xCoord = var.dims[0].values
yData = var.data
plt.plot(xCoord, yData)
plt.xlabel(var.dims[0].label)
plt.xlim(var.dims[0].limits[0], var.dims[0].limits[-1])
plt.ylabel(var.label)
elif var.rank == 2: # Pseudo color plot
# Add an extra element to coordinate to force pcolormesh to draw all cells
if var.dims[1].isZaxis: # Happens for S(t,z)
xCoord = extrapCoord(var.dims[0].values)
yCoord = extrapCoord(var.dims[1].values)
zData = np.transpose(var.data)
xLabel = var.dims[0].label
xLims = var.dims[0].limits
yLabel = var.dims[1].label
yLims = var.dims[1].limits
yDim = var.dims[1]
else:
xLabel = var.dims[1].label
xLims = var.dims[1].limits
yLabel = var.dims[0].label
yLims = var.dims[0].limits
if args.supergrid is None:
if args.oceanstatic is None:
xCoord = extrapCoord(var.dims[1].values)
yCoord = extrapCoord(var.dims[0].values)
else:
xCoord, xLims = readOSvar(args.oceanstatic, 'geolon_c',
var.dims)
yCoord, yLims = readOSvar(args.oceanstatic, 'geolat_c',
var.dims)
xLabel = 'Longitude (\u00B0E)'
yLabel = 'Latitude (\u00B0N)'
else:
xCoord, xLims = readSGvar(args.supergrid, 'x', var.dims)
yCoord, yLims = readSGvar(args.supergrid, 'y', var.dims)
xLabel = 'Longitude (\u00B0E)'
yLabel = 'Latitude (\u00B0N)'
zData = var.data
yDim = var.dims[0]
if yDim.isZaxis and not elevation is None: # Z on y axis ?
if elevation.refreshable: elevation.getData()
#yCoord = elevation.data
xCoord, yCoord, zData = m6toolbox.section2quadmesh(
xCoord, elevation.data, zData, representation='pcm')
yLims = (np.amin(yCoord[-1, :]), np.amax(yCoord[0, :]))
#yCoord = extrapElevation( yCoord )
yLabel = 'Elevation (m)'
plt.pcolormesh(xCoord, yCoord, zData)
if args.coordlines:
plt.plot(xCoord, yCoord.T, 'k')
if yDim.isZaxis and elevation is None: # Z on y axis ?
if yCoord[0] > yCoord[-1]:
plt.gca().invert_yaxis()
yLims = reversed(yLims)
if yDim.positiveDown:
plt.gca().invert_yaxis()
yLims = reversed(yLims)
if len(var.label) > 50: fontsize = 10
elif len(var.label) > 30: fontsize = 12
else: fontsize = 14
if args.scale:
plt.title(var.label + ' x%e' % (args.scale[0]), fontsize=fontsize)
else:
plt.title(var.label, fontsize=fontsize)
plt.xlim(xLims)
plt.ylim(yLims)
plt.xlabel(xLabel)
plt.ylabel(yLabel)
makeGuessAboutCmap(clim=args.clim, colormap=args.colormap)
plt.tight_layout()
plt.colorbar(fraction=.08)
axis = plt.gca()
if var.singleDims:
text = ''
for d in var.singleDims:
if len(text): text = text + ' '
text = text + d.name + ' = ' + str(d.values[0])
if d.units: text = text + ' (' + d.units + ')'
axis.annotate(text,
xy=(0.005, .995),
xycoords='figure fraction',
verticalalignment='top',
fontsize=8)
if args.output:
if args.animate:
dt = time.time() - start_time
nf = var.singleDims[0].initialLen
print('Writing file "%s" (%i/%i)'%(args.output%(frame),frame,nf), \
'Elapsed %.1fs, %.2f FPS, total %.1fs, remaining %.1fs'%(dt, frame/dt, 1.*nf/frame*dt, (1.*nf/frame-1.)*dt))
try:
plt.savefig(args.output % (frame), pad_inches=0.)
except:
raise MyError(
'output filename must contain %D.Di when animating')
else:
plt.savefig(args.output, pad_inches=0.)
elif not args.animate: # Interactive and static
def keyPress(event):
if event.key == 'q': exit(0)
if var.rank == 1:
def statusMesg(x, y):
# -1 needed because of extension for pcolormesh
i = min(list(range(len(xCoord) - 1)),
key=lambda l: abs(xCoord[l] - x))
if not i is None:
val = yData[i]
if val is np.ma.masked:
return 'x=%.3f %s(%i)=NaN' % (x, var.vname, i + 1)
else:
return 'x=%.3f %s(%i)=%g' % (x, var.vname, i + 1, val)
else:
return 'x=%.3f y=%.3f' % (x, y)
elif var.rank == 2:
def statusMesg(x, y):
if len(xCoord.shape) == 1:
# -2 needed because of coords are for vertices and need to be averaged to centers
i = min(list(range(len(xCoord) - 2)),
key=lambda l: abs(
(xCoord[l] + xCoord[l + 1]) / 2. - x))
j = min(list(range(len(yCoord) - 2)),
key=lambda l: abs(
(yCoord[l] + yCoord[l + 1]) / 2. - y))
else:
idx = np.abs(
np.fabs(xCoord[0:-1, 0:-1] + xCoord[1:, 1:] +
xCoord[0:-1, 1:] + xCoord[1:, 0:-1] - 4 * x) +
np.fabs(yCoord[0:-1, 0:-1] + yCoord[1:, 1:] +
yCoord[0:-1, 1:] + yCoord[1:, 0:-1] -
4 * y)).argmin()
j, i = np.unravel_index(idx, zData.shape)
if not i is None:
val = zData[j, i]
if val is np.ma.masked:
return 'x,y=%.3f,%.3f %s(%i,%i)=NaN' % (
x, y, var.vname, i + 1, j + 1)
else:
return 'x,y=%.3f,%.3f %s(%i,%i)=%g' % (
x, y, var.vname, i + 1, j + 1, val)
else:
return 'x,y=%.3f,%.3f' % (x, y)
xmin, xmax = axis.get_xlim()
ymin, ymax = axis.get_ylim()
def zoom(event): # Scroll wheel up/down
if event.button == 'up':
scaleFactor = 1 / 1.5 # deal with zoom in
elif event.button == 'down':
scaleFactor = 1.5 # deal with zoom out
elif event.button == 2:
scaleFactor = 1.0
else:
return
axmin, axmax = axis.get_xlim()
aymin, aymax = axis.get_ylim()
(axmin, axmax), (aymin, aymax) = newLims(
(axmin, axmax), (aymin, aymax), (event.xdata, event.ydata),
(xmin, xmax), (ymin, ymax), scaleFactor)
if axmin is None: return
axis.set_xlim(axmin, axmax)
axis.set_ylim(aymin, aymax)
plt.draw() # force re-draw
plt.gcf().canvas.mpl_connect('scroll_event', zoom)
def zoom2(event):
zoom(event)
plt.gcf().canvas.mpl_connect('button_press_event', zoom2)
plt.gca().format_coord = statusMesg
plt.gcf().canvas.mpl_connect('key_press_event', keyPress)
def plot_vertical_section(lon,lat,data_mean,difference_on,title,p_values,cscale,field,layer_interface,axes_direction,lat_lon_bounds,file_type,colorbar_on=True,return_data_map=False):
if axes_direction=='yz':
x=lat
upper_bound=lat_lon_bounds[0];
lower_bound=lat_lon_bounds[1];
if axes_direction=='xz':
x=lon
upper_bound=lat_lon_bounds[2];
lower_bound=lat_lon_bounds[3];
if file_type=='ocean_month':
representation='linear' #'pcm'
#representation='pcm'
M=layer_interface.shape
layers=range(0,M[0]-1)
q=data_mean[range(0,M[0]-1),:] ;q=q[:,range(0,len(x)-1) ]
layer_interface=layer_interface[:,range(0,len(x)-1)]
(X, Z, Q)=section2quadmesh(x, layer_interface, q, representation)
if file_type=='ocean_month_z':
X=x
Z=layer_interface
Q=data_mean
#Plotting
if difference_on==1:
#cmap = plt.set_cmap('bwr')
cmap = 'bwr'
if cscale==None:
if field=='temp':
cscale=0.5
if field=='salt':
cscale=0.1
if field=='u':
cscale=0.005
if field=='rho':
cscale=0.05
#cNorm = MidpointNormalize(vmin=-cscale, vmax=cscale,midpoint=0)
cNorm = mpl.colors.Normalize(vmin=-cscale, vmax=cscale)
if difference_on==0:
#cmap = plt.set_cmap('jet')
cmap = 'jet'
if field=='temp':
cscale_min=-3 ; cscale_max=10
if field=='rho':
cscale_min=1026 ; cscale_max=1029
if field=='salt':
cscale_min=33 ; cscale_max=35
#cmap = plt.set_cmap('jet')
if field=='u':
cscale_min=-.05 ; cscale_max=.05
#cmap = plt.set_cmap('bwr')
cmap = 'bwr'
#cNorm = MidpointNormalize(vmin=cscale_min, vmax=cscale_max,midpoint=0)
cNorm = mpl.colors.Normalize(vmin=cscale_min, vmax=cscale_max)
#cmap = plt.set_cmap('jet')
print X.shape
print Z.shape
print Q.shape
datamap=pcolormesh(X,Z,Q,cmap=cmap,norm=cNorm,facecolor='white')
#datamap=pcolormesh(X,-Z[::-1],Q[:,:],cmap=cmap,norm=cNorm,facecolor='white')
#datamap=pcolormesh(X,-Z[::-1],Q[:,:],cmap=cmap,norm=cNorm,facecolor='white')
#Overlaying significance
Z2=Z[:-1]
#if p_values!=None:
if p_values is not None:
#print 'plotting p_values'
p_values[np.where(Q.mask)]=0.001
levels1=np.array([0.05,999.])
#levels1=np.array([0.01,0.02,0.03,0.04,0.05,999.])
cNorm2=mpl.colors.Normalize(vmin=400, vmax=1000)
CS = contourf(X, Z2, p_values,levels=levels1, hatches=[' '], fill=False,cmap='Greys',norm=cNorm2 ) # hatches=['+'] also an option, hatches=['//']
#CS = contourf(X, Z2, p_values,levels=levels1, hatches=['//'], fill=False,cmap='bwr' ) # hatches=['+'] ,hatches=['///] also an option
#CS = contour(X, Z2, p_values,levels=levels1,linewidth=5.,color='black' ) # hatches=['+'] also an option
#datamap=pcolormesh(X,Z,p_values,cmap=cmap,norm=cNorm,facecolor='white')
#CS = m.contourf(x, y, p_values,levels=levels1, fill=False,cmap='bwr' ) # hatches=['+'] also an option
axis = plt.gca()
landcolor=[.5,.5,.5]
axis.set_axis_bgcolor(landcolor)
plt.xlim( (lower_bound,upper_bound))
#plt.ylim((-1500,0) )
plt.ylim((0 ,1500))
#plt.ylim((-1500 ,1500))
axis.invert_yaxis()
#plt.gca.invert_yaxis()
#plt.ylim((-5000,0) )
if title!=None:
plt.title(title)
if colorbar_on==True:
plt.colorbar()
if return_data_map==True:
return datamap
def render(var, args, elevation=None, frame=0):
var.getData() # Actually read data from file
# Optionally mask out a specific value
if args.ignore:
var.data = np.ma.masked_array(var.data, mask=[var.data==args.ignore])
if args.ignorelt:
var.data = np.ma.masked_array(var.data, mask=[var.data<=args.ignorelt])
if args.ignoregt:
var.data = np.ma.masked_array(var.data, mask=[var.data>=args.ignoregt])
if args.scale:
var.data = args.scale * var.data
if args.list: print('createUI: Data =\n',var.data)
if args.stats:
dMin = np.min(var.data); dMax = np.max(var.data)
if dMin==0 and dMax>0:
dMin = np.min(var.data[var.data!=0])
print('Mininum=',dMin,'(ignoring zeros) Maximum=',dMax)
elif dMax==0 and dMin<0:
dMax = np.max(var.data[var.data!=0])
print('Mininum=',dMin,'Maximum=',dMax,'(ignoring zeros)')
else: print('Mininum=',dMin,'Maximum=',dMax)
if args.offset: var.data = var.data + args.offset
if args.log10: var.data = np.ma.log10(var.data)
# Now plot
if var.rank==0:
for d in var.allDims:
print('%s = %g %s'%(d.name,d.values[0],d.units))
print(var.vname+' = ',var.data,' '+var.units)
exit(0)
elif var.rank==1: # Line plot
if var.dims[0].isZaxis: # Transpose 1d plot
xCoord = var.data ; yData = var.dims[0].values
plt.plot(xCoord, yData)
plt.xlabel(var.label)
plt.ylabel(var.dims[0].label);
if var.dims[0].values[0]>var.dims[0].values[-1]: plt.gca().invert_yaxis()
if var.dims[0].positiveDown: plt.gca().invert_yaxis()
else: # Normal 1d plot
xCoord = var.dims[0].values; yData = var.data
plt.plot(xCoord, yData)
plt.xlabel(var.dims[0].label); plt.xlim(var.dims[0].limits[0], var.dims[0].limits[-1])
plt.ylabel(var.label)
elif var.rank==2: # Pseudo color plot
# Add an extra element to coordinate to force pcolormesh to draw all cells
if var.dims[1].isZaxis: # Happens for S(t,z)
xCoord = extrapCoord( var.dims[0].values); yCoord = extrapCoord( var.dims[1].values)
zData = np.transpose(var.data)
xLabel = var.dims[0].label; xLims = var.dims[0].limits
yLabel = var.dims[1].label; yLims = var.dims[1].limits
yDim = var.dims[1]
else:
xLabel = var.dims[1].label; xLims = var.dims[1].limits
yLabel = var.dims[0].label; yLims = var.dims[0].limits
if args.supergrid is None:
if args.oceanstatic is None:
xCoord = extrapCoord( var.dims[1].values); yCoord = extrapCoord( var.dims[0].values)
else:
xCoord, xLims = readOSvar(args.oceanstatic, 'geolon_c', var.dims)
yCoord, yLims = readOSvar(args.oceanstatic, 'geolat_c', var.dims)
xLabel = 'Longitude (\u00B0E)' ; yLabel = 'Latitude (\u00B0N)'
else:
xCoord, xLims = readSGvar(args.supergrid, 'x', var.dims)
yCoord, yLims = readSGvar(args.supergrid, 'y', var.dims)
xLabel = 'Longitude (\u00B0E)' ; yLabel = 'Latitude (\u00B0N)'
zData = var.data
yDim = var.dims[0]
if yDim.isZaxis and not elevation is None: # Z on y axis ?
if elevation.refreshable: elevation.getData()
#yCoord = elevation.data
xCoord, yCoord, zData = m6toolbox.section2quadmesh(xCoord, elevation.data, zData, representation='pcm')
yLims = (np.amin(yCoord[-1,:]), np.amax(yCoord[0,:]))
#yCoord = extrapElevation( yCoord )
yLabel = 'Elevation (m)'
plt.pcolormesh(xCoord,yCoord,zData)
if yDim.isZaxis and elevation is None: # Z on y axis ?
if yCoord[0]>yCoord[-1]: plt.gca().invert_yaxis(); yLims = reversed(yLims)
if yDim.positiveDown: plt.gca().invert_yaxis(); yLims = reversed(yLims)
if len(var.label)>50: fontsize=10
elif len(var.label)>30: fontsize=12
else: fontsize=14;
if args.scale: plt.title(var.label+' x%e'%(args.scale[0]),fontsize=fontsize)
else: plt.title(var.label,fontsize=fontsize)
plt.xlim(xLims); plt.ylim(yLims)
plt.xlabel(xLabel) ; plt.ylabel(yLabel)
makeGuessAboutCmap(clim=args.clim, colormap=args.colormap)
plt.tight_layout()
plt.colorbar(fraction=.08)
axis=plt.gca()
if var.singleDims:
text = ''
for d in var.singleDims:
if len(text): text = text+' '
text = text + d.name + ' = ' + str(d.values[0])
if d.units: text = text + ' (' + d.units + ')'
axis.annotate(text, xy=(0.005,.995), xycoords='figure fraction', verticalalignment='top', fontsize=8)
if args.output:
if args.animate:
dt = time.time() - start_time
nf = var.singleDims[0].initialLen
print('Writing file "%s" (%i/%i)'%(args.output%(frame),frame,nf), \
'Elapsed %.1fs, %.2f FPS, total %.1fs, remaining %.1fs'%(dt, frame/dt, 1.*nf/frame*dt, (1.*nf/frame-1.)*dt))
try: plt.savefig(args.output%(frame),pad_inches=0.)
except: raise MyError('output filename must contain %D.Di when animating')
else: plt.savefig(args.output,pad_inches=0.)
elif not args.animate: # Interactive and static
def keyPress(event):
if event.key=='q': exit(0)
if var.rank==1:
def statusMesg(x,y):
# -1 needed because of extension for pcolormesh
i = min(list(range(len(xCoord)-1)), key=lambda l: abs(xCoord[l]-x))
if not i is None:
val = yData[i]
if val is np.ma.masked: return 'x=%.3f %s(%i)=NaN'%(x,var.vname,i+1)
else: return 'x=%.3f %s(%i)=%g'%(x,var.vname,i+1,val)
else: return 'x=%.3f y=%.3f'%(x,y)
elif var.rank==2:
def statusMesg(x,y):
if len(xCoord.shape)==1:
# -2 needed because of coords are for vertices and need to be averaged to centers
i = min(list(range(len(xCoord)-2)), key=lambda l: abs((xCoord[l]+xCoord[l+1])/2.-x))
j = min(list(range(len(yCoord)-2)), key=lambda l: abs((yCoord[l]+yCoord[l+1])/2.-y))
else:
idx = np.abs( np.fabs( xCoord[0:-1,0:-1]+xCoord[1:,1:]+xCoord[0:-1,1:]+xCoord[1:,0:-1]-4*x)
+np.fabs( yCoord[0:-1,0:-1]+yCoord[1:,1:]+yCoord[0:-1,1:]+yCoord[1:,0:-1]-4*y) ).argmin()
j,i = np.unravel_index(idx,zData.shape)
if not i is None:
val = zData[j,i]
if val is np.ma.masked: return 'x,y=%.3f,%.3f %s(%i,%i)=NaN'%(x,y,var.vname,i+1,j+1)
else: return 'x,y=%.3f,%.3f %s(%i,%i)=%g'%(x,y,var.vname,i+1,j+1,val)
else: return 'x,y=%.3f,%.3f'%(x,y)
xmin,xmax=axis.get_xlim(); ymin,ymax=axis.get_ylim();
def zoom(event): # Scroll wheel up/down
if event.button == 'up': scaleFactor = 1/1.5 # deal with zoom in
elif event.button == 'down': scaleFactor = 1.5 # deal with zoom out
elif event.button == 2: scaleFactor = 1.0
else: return
axmin,axmax=axis.get_xlim(); aymin,aymax=axis.get_ylim();
(axmin,axmax),(aymin,aymax) = newLims(
(axmin,axmax), (aymin,aymax), (event.xdata, event.ydata),
(xmin,xmax), (ymin,ymax), scaleFactor)
if axmin is None: return
axis.set_xlim(axmin, axmax); axis.set_ylim(aymin, aymax)
plt.draw() # force re-draw
plt.gcf().canvas.mpl_connect('scroll_event', zoom)
def zoom2(event): zoom(event)
plt.gcf().canvas.mpl_connect('button_press_event', zoom2)
plt.gca().format_coord = statusMesg
plt.gcf().canvas.mpl_connect('key_press_event', keyPress)
'''
if __name__ == "__main__":
# we could add some optional command-line argument here but so far none...
parser = OptionParser()
options, args = parser.parse_args()
if(len(args) < 2):
print "usage: plt_overtuningStreamfunction.py <ncfile.nc> 10"
exit(1)
# file name
fname = args[0]
# time indice
t = int(args[1])
x=Dataset(fname).variables['nx'][:]
nx=np.zeros(len(x)+1); nx[0:-1]=x; nx[-1]=x[-1]
e=Dataset(fname).variables['e'][t,:]
var=Dataset(fname).variables['overturningStreamfunction'][t,:]
xCoord, yCoord, zData = m6toolbox.section2quadmesh(nx, e, var, representation='linear')
plt.figure()
plt.pcolormesh(xCoord, yCoord, zData)
plt.colorbar()
plt.xlabel('x (km)')
plt.ylabel('Elevation (m)')
plt.ylim((-720,0))
#plt.savefig(diags[d]+'.png',bbox_inches='tight')
plt.show()
def yzcompare(field1, field2, y=None, z=None,
ylabel=None, yunits=None, zlabel=None, zunits=None,
splitscale=None,
title1='', title2='', title3='A - B', addplabel=True, suptitle='',
clim=None, colormap=None, extend=None, centerlabels=False,
dlim=None, dcolormap=None, dextend=None, centerdlabels=False,
nbins=None, landcolor=[.5,.5,.5],
aspect=None, resolution=None, axis=None, npanels=3,
ignore=None, save=None, debug=False, show=False, interactive=False):
"""
Renders n-panel plot of two scalar fields, field1(x,y) and field2(x,y).
Arguments:
field1 Scalar 2D array to be plotted and compared to field2.
field2 Scalar 2D array to be plotted and compared to field1.
y y coordinate (1D array). If y is the same size as field then y is treated as
the cell center coordinates.
z z coordinate (1D or 2D array). If z is the same size as field then z is treated as
the cell center coordinates.
ylabel The label for the y axis. Default 'Latitude'.
yunits The units for the y axis. Default 'degrees N'.
zlabel The label for the z axis. Default 'Elevation'.
zunits The units for the z axis. Default 'm'.
splitscale A list of depths to define equal regions of projection in the vertical, e.g. [0.,-1000,-6500]
title1 The title to place at the top of panel 1. Default ''.
title2 The title to place at the top of panel 1. Default ''.
title3 The title to place at the top of panel 1. Default 'A-B'.
addplabel Adds a 'A:' or 'B:' to the title1 and title2. Default True.
suptitle The super-title to place at the top of the figure. Default ''.
clim A tuple of (min,max) color range OR a list of contour levels for the field plots. Default None.
colormap The name of the colormap to use for the field plots. Default None.
extend Can be one of 'both', 'neither', 'max', 'min'. Default None.
centerlabels If True, will move the colorbar labels to the middle of the interval. Default False.
dlim A tuple of (min,max) color range OR a list of contour levels for the difference plot. Default None.
dcolormap The name of the colormap to use for the differece plot. Default None.
dextend For the difference colorbar. Can be one of 'both', 'neither', 'max', 'min'. Default None.
centerdlabels If True, will move the difference colorbar labels to the middle of the interval. Default False.
nbins The number of colors levels (used is clim is missing or only specifies the color range).
landcolor An rgb tuple to use for the color of land (no data). Default [.5,.5,.5].
aspect The aspect ratio of the figure, given as a tuple (W,H). Default [16,9].
resolution The vertical resolution of the figure given in pixels. Default 1280.
axis The axis handle to plot to. Default None.
npanels Number of panels to display (1, 2 or 3). Default 3.
ignore A value to use as no-data (NaN). Default None.
save Name of file to save figure in. Default None.
debug If true, report stuff for debugging. Default False.
show If true, causes the figure to appear on screen. Used for testing. Default False.
interactive If true, adds interactive features such as zoom, close and cursor. Default False.
"""
if (field1.shape)!=(field2.shape): raise Exception('field1 and field2 must be the same shape')
# Create coordinates if not provided
ylabel, yunits, zlabel, zunits = createYZlabels(y, z, ylabel, yunits, zlabel, zunits)
if debug: print('y,z label/units=',ylabel,yunits,zlabel,zunits)
if len(y)==z.shape[-1]: y= expand(y)
elif len(y)==z.shape[-1]+1: y= y
else: raise Exception('Length of y coordinate should be equal or 1 longer than horizontal length of z')
if ignore is not None: maskedField1 = numpy.ma.masked_array(field1, mask=[field1==ignore])
else: maskedField1 = field1.copy()
yCoord, zCoord, field1 = m6toolbox.section2quadmesh(y, z, maskedField1)
# Diagnose statistics
yzWeighting = yzWeight(y, z)
s1Min, s1Max, s1Mean, s1Std, s1RMS = myStats(maskedField1, yzWeighting, debug=debug)
if ignore is not None: maskedField2 = numpy.ma.masked_array(field2, mask=[field2==ignore])
else: maskedField2 = field2.copy()
yCoord, zCoord, field2 = m6toolbox.section2quadmesh(y, z, maskedField2)
s2Min, s2Max, s2Mean, s2Std, s2RMS = myStats(maskedField2, yzWeighting, debug=debug)
dMin, dMax, dMean, dStd, dRMS = myStats(maskedField1 - maskedField2, yzWeighting, debug=debug)
dRxy = corr(maskedField1 - s1Mean, maskedField2 - s2Mean, yzWeighting)
s12Min = min(s1Min, s2Min); s12Max = max(s1Max, s2Max)
xLims = numpy.amin(yCoord), numpy.amax(yCoord); yLims = boundaryStats(zCoord)
if debug:
print('s1: min, max, mean =', s1Min, s1Max, s1Mean)
print('s2: min, max, mean =', s2Min, s2Max, s2Mean)
print('s12: min, max =', s12Min, s12Max)
# Choose colormap
if nbins is None and (clim is None or len(clim)==2): cBins=35
else: cBins=nbins
if nbins is None and (dlim is None or len(dlim)==2): nbins=35
if colormap is None: colormap = chooseColorMap(s12Min, s12Max)
cmap, norm, extend = chooseColorLevels(s12Min, s12Max, colormap, clim=clim, nbins=cBins, extend=extend)
def annotateStats(axis, sMin, sMax, sMean, sStd, sRMS):
axis.annotate('max=%.5g\nmin=%.5g'%(sMax,sMin), xy=(0.0,1.025), xycoords='axes fraction', verticalalignment='bottom', fontsize=10)
if sMean is not None:
axis.annotate('mean=%.5g\nrms=%.5g'%(sMean,sRMS), xy=(1.0,1.025), xycoords='axes fraction', verticalalignment='bottom', horizontalalignment='right', fontsize=10)
axis.annotate(' sd=%.5g\n'%(sStd), xy=(1.0,1.025), xycoords='axes fraction', verticalalignment='bottom', horizontalalignment='left', fontsize=10)
if addplabel: preTitleA = 'A: '; preTitleB = 'B: '
else: preTitleA = ''; preTitleB = ''
if axis is None:
setFigureSize(aspect, resolution, npanels=npanels, debug=debug)
#plt.gcf().subplots_adjust(left=.13, right=.94, wspace=0, bottom=.05, top=.94, hspace=0.15)
if npanels in [2, 3]:
axis = plt.subplot(npanels,1,1)
plt.pcolormesh(yCoord, zCoord, field1, cmap=cmap, norm=norm)
if interactive: addStatusBar(yCoord, zCoord, field1)
cb1 = plt.colorbar(fraction=.08, pad=0.02, extend=extend)
if centerlabels and len(clim)>2: cb1.set_ticks( 0.5*(clim[:-1]+clim[1:]) )
axis.set_axis_bgcolor(landcolor)
plt.xlim( xLims ); plt.ylim( yLims )
if splitscale is not None:
for zzz in splitscale[1:-1]: plt.axhline(zzz,color='k',linestyle='--')
axis.set_yscale('splitscale', zval=splitscale)
annotateStats(axis, s1Min, s1Max, s1Mean, s1Std, s1RMS)
axis.set_xticklabels([''])
if len(zlabel+zunits)>0: plt.ylabel(label(zlabel, zunits))
if len(title1)>0: plt.title(preTitleA+title1)
axis = plt.subplot(npanels,1,2)
plt.pcolormesh(yCoord, zCoord, field2, cmap=cmap, norm=norm)
if interactive: addStatusBar(yCoord, zCoord, field2)
cb2 = plt.colorbar(fraction=.08, pad=0.02, extend=extend)
if centerlabels and len(clim)>2: cb2.set_ticks( 0.5*(clim[:-1]+clim[1:]) )
axis.set_axis_bgcolor(landcolor)
plt.xlim( xLims ); plt.ylim( yLims )
if splitscale is not None:
for zzz in splitscale[1:-1]: plt.axhline(zzz,color='k',linestyle='--')
axis.set_yscale('splitscale', zval=splitscale)
annotateStats(axis, s2Min, s2Max, s2Mean, s2Std, s2RMS)
if npanels>2: axis.set_xticklabels([''])
if len(zlabel+zunits)>0: plt.ylabel(label(zlabel, zunits))
if len(title2)>0: plt.title(preTitleB+title2)
if npanels in [1, 3]:
axis = plt.subplot(npanels,1,npanels)
if dcolormap is None: dcolormap = chooseColorMap(dMin, dMax)
if dlim is None and dStd>0:
cmap, norm, dextend = chooseColorLevels(dMean-2.*dStd, dMean+2.*dStd, dcolormap, clim=dlim, nbins=nbins, extend='both', autocenter=True)
else:
cmap, norm, dextend = chooseColorLevels(dMin, dMax, dcolormap, clim=dlim, nbins=nbins, extend=dextend, autocenter=True)
plt.pcolormesh(yCoord, zCoord, field1 - field2, cmap=cmap, norm=norm)
if interactive: addStatusBar(yCoord, zCoord, field1 - field2)
cb3 = plt.colorbar(fraction=.08, pad=0.02, extend=dextend)
if centerdlabels and len(dlim)>2: cb3.set_ticks( 0.5*(dlim[:-1]+dlim[1:]) )
axis.set_axis_bgcolor(landcolor)
plt.xlim( xLims ); plt.ylim( yLims )
if splitscale is not None:
for zzz in splitscale[1:-1]: plt.axhline(zzz,color='k',linestyle='--')
axis.set_yscale('splitscale', zval=splitscale)
annotateStats(axis, dMin, dMax, dMean, dStd, dRMS)
if len(zlabel+zunits)>0: plt.ylabel(label(zlabel, zunits))
axis.annotate(' r(A,B)=%.5g\n'%(dRxy), xy=(1.0,-1.07), xycoords='axes fraction', verticalalignment='top', horizontalalignment='center', fontsize=10)
if len(ylabel+yunits)>0: plt.xlabel(label(ylabel, yunits))
if len(title3)>0: plt.title(title3)
if len(suptitle)>0: plt.suptitle(suptitle)
if save is not None: plt.savefig(save)
if interactive: addInteractiveCallbacks()
if show: plt.show(block=False)
def yzplot(field, y=None, z=None,
ylabel=None, yunits=None, zlabel=None, zunits=None,
splitscale=None,
title='', suptitle='',
clim=None, colormap=None, extend=None, centerlabels=False,
nbins=None, landcolor=[.5,.5,.5],
aspect=[16,9], resolution=576, axis=None,
ignore=None, save=None, debug=False, show=False, interactive=False):
"""
Renders section plot of scalar field, field(x,z).
Arguments:
field Scalar 2D array to be plotted.
y y (or x) coordinate (1D array). If y is the same size as field then x is treated as
the cell center coordinates.
z z coordinate (1D or 2D array). If z is the same size as field then y is treated as
the cell center coordinates.
ylabel The label for the x axis. Default 'Latitude'.
yunits The units for the x axis. Default 'degrees N'.
zlabel The label for the z axis. Default 'Elevation'.
zunits The units for the z axis. Default 'm'.
splitscale A list of depths to define equal regions of projection in the vertical, e.g. [0.,-1000,-6500]
title The title to place at the top of the panel. Default ''.
suptitle The super-title to place at the top of the figure. Default ''.
clim A tuple of (min,max) color range OR a list of contour levels. Default None.
colormap The name of the colormap to use. Default None.
extend Can be one of 'both', 'neither', 'max', 'min'. Default None.
centerlabels If True, will move the colorbar labels to the middle of the interval. Default False.
nbins The number of colors levels (used is clim is missing or only specifies the color range).
landcolor An rgb tuple to use for the color of land (no data). Default [.5,.5,.5].
aspect The aspect ratio of the figure, given as a tuple (W,H). Default [16,9].
resolution The vertical resolution of the figure given in pixels. Default 720.
axis The axis handle to plot to. Default None.
ignore A value to use as no-data (NaN). Default None.
save Name of file to save figure in. Default None.
debug If true, report stuff for debugging. Default False.
show If true, causes the figure to appear on screen. Used for testing. Default False.
interactive If true, adds interactive features such as zoom, close and cursor. Default False.
"""
# Create coordinates if not provided
ylabel, yunits, zlabel, zunits = createYZlabels(y, z, ylabel, yunits, zlabel, zunits)
if debug: print('y,z label/units=',ylabel,yunits,zlabel,zunits)
if len(y)==z.shape[-1]: y = expand(y)
elif len(y)==z.shape[-1]+1: y = y
else: raise Exception('Length of y coordinate should be equal or 1 longer than horizontal length of z')
if ignore is not None: maskedField = numpy.ma.masked_array(field, mask=[field==ignore])
else: maskedField = field.copy()
yCoord, zCoord, field2 = m6toolbox.section2quadmesh(y, z, maskedField)
# Diagnose statistics
sMin, sMax, sMean, sStd, sRMS = myStats(maskedField, yzWeight(y, z), debug=debug)
yLims = numpy.amin(yCoord), numpy.amax(yCoord)
zLims = boundaryStats(zCoord)
# Choose colormap
if nbins is None and (clim is None or len(clim)==2): nbins=35
if colormap is None: colormap = chooseColorMap(sMin, sMax)
cmap, norm, extend = chooseColorLevels(sMin, sMax, colormap, clim=clim, nbins=nbins, extend=extend)
if axis is None:
setFigureSize(aspect, resolution, debug=debug)
#plt.gcf().subplots_adjust(left=.10, right=.99, wspace=0, bottom=.09, top=.9, hspace=0)
axis = plt.gca()
plt.pcolormesh(yCoord, zCoord, field2, cmap=cmap, norm=norm)
if interactive: addStatusBar(yCoord, zCoord, field2)
cb = plt.colorbar(fraction=.08, pad=0.02, extend=extend)
if centerlabels and len(clim)>2: cb.set_ticks( 0.5*(clim[:-1]+clim[1:]) )
axis.set_axis_bgcolor(landcolor)
if splitscale is not None:
for zzz in splitscale[1:-1]: plt.axhline(zzz,color='k',linestyle='--')
axis.set_yscale('splitscale', zval=splitscale)
plt.xlim( yLims ); plt.ylim( zLims )
axis.annotate('max=%.5g\nmin=%.5g'%(sMax,sMin), xy=(0.0,1.01), xycoords='axes fraction', verticalalignment='bottom', fontsize=10)
if sMean is not None:
axis.annotate('mean=%.5g\nrms=%.5g'%(sMean,sRMS), xy=(1.0,1.01), xycoords='axes fraction', verticalalignment='bottom', horizontalalignment='right', fontsize=10)
axis.annotate(' sd=%.5g\n'%(sStd), xy=(1.0,1.01), xycoords='axes fraction', verticalalignment='bottom', horizontalalignment='left', fontsize=10)
if len(ylabel+yunits)>0: plt.xlabel(label(ylabel, yunits))
if len(zlabel+zunits)>0: plt.ylabel(label(zlabel, zunits))
if len(title)>0: plt.title(title)
if len(suptitle)>0: plt.suptitle(suptitle)
if save is not None: plt.savefig(save)
if interactive: addInteractiveCallbacks()
if show: plt.show(block=False)
def yzcompare(field1, field2, y=None, z=None,
ylabel=None, yunits=None, zlabel=None, zunits=None,
splitscale=None,
title1='', title2='', title3='A - B', addplabel=True, suptitle='',
clim=None, colormap=None, extend=None, centerlabels=False,
dlim=None, dcolormap=None, dextend=None, centerdlabels=False,
nbins=None, landcolor=[.5,.5,.5],
aspect=None, resolution=None, axis=None, npanels=3,
ignore=None, save=None, debug=False, show=False, interactive=False):
"""
Renders n-panel plot of two scalar fields, field1(x,y) and field2(x,y).
Arguments:
field1 Scalar 2D array to be plotted and compared to field2.
field2 Scalar 2D array to be plotted and compared to field1.
y y coordinate (1D array). If y is the same size as field then y is treated as
the cell center coordinates.
z z coordinate (1D or 2D array). If z is the same size as field then z is treated as
the cell center coordinates.
ylabel The label for the y axis. Default 'Latitude'.
yunits The units for the y axis. Default 'degrees N'.
zlabel The label for the z axis. Default 'Elevation'.
zunits The units for the z axis. Default 'm'.
splitscale A list of depths to define equal regions of projection in the vertical, e.g. [0.,-1000,-6500]
title1 The title to place at the top of panel 1. Default ''.
title2 The title to place at the top of panel 1. Default ''.
title3 The title to place at the top of panel 1. Default 'A-B'.
addplabel Adds a 'A:' or 'B:' to the title1 and title2. Default True.
suptitle The super-title to place at the top of the figure. Default ''.
clim A tuple of (min,max) color range OR a list of contour levels for the field plots. Default None.
colormap The name of the colormap to use for the field plots. Default None.
extend Can be one of 'both', 'neither', 'max', 'min'. Default None.
centerlabels If True, will move the colorbar labels to the middle of the interval. Default False.
dlim A tuple of (min,max) color range OR a list of contour levels for the difference plot. Default None.
dcolormap The name of the colormap to use for the differece plot. Default None.
dextend For the difference colorbar. Can be one of 'both', 'neither', 'max', 'min'. Default None.
centerdlabels If True, will move the difference colorbar labels to the middle of the interval. Default False.
nbins The number of colors levels (used is clim is missing or only specifies the color range).
landcolor An rgb tuple to use for the color of land (no data). Default [.5,.5,.5].
aspect The aspect ratio of the figure, given as a tuple (W,H). Default [16,9].
resolution The vertical resolution of the figure given in pixels. Default 1280.
axis The axis handle to plot to. Default None.
npanels Number of panels to display (1, 2 or 3). Default 3.
ignore A value to use as no-data (NaN). Default None.
save Name of file to save figure in. Default None.
debug If true, report stuff for debugging. Default False.
show If true, causes the figure to appear on screen. Used for testing. Default False.
interactive If true, adds interactive features such as zoom, close and cursor. Default False.
"""
if (field1.shape)!=(field2.shape): raise Exception('field1 and field2 must be the same shape')
# Create coordinates if not provided
ylabel, yunits, zlabel, zunits = createYZlabels(y, z, ylabel, yunits, zlabel, zunits)
if debug: print 'y,z label/units=',ylabel,yunits,zlabel,zunits
if len(y)==z.shape[-1]: y= expand(y)
elif len(y)==z.shape[-1]+1: y= y
else: raise Exception('Length of y coordinate should be equal or 1 longer than horizontal length of z')
if ignore!=None: maskedField1 = numpy.ma.masked_array(field1, mask=[field1==ignore])
else: maskedField1 = field1.copy()
yCoord, zCoord, field1 = m6toolbox.section2quadmesh(y, z, maskedField1)
# Diagnose statistics
yzWeighting = yzWeight(y, z)
s1Min, s1Max, s1Mean, s1Std, s1RMS = myStats(maskedField1, yzWeighting, debug=debug)
if ignore!=None: maskedField2 = numpy.ma.masked_array(field2, mask=[field2==ignore])
else: maskedField2 = field2.copy()
yCoord, zCoord, field2 = m6toolbox.section2quadmesh(y, z, maskedField2)
s2Min, s2Max, s2Mean, s2Std, s2RMS = myStats(maskedField2, yzWeighting, debug=debug)
dMin, dMax, dMean, dStd, dRMS = myStats(maskedField1 - maskedField2, yzWeighting, debug=debug)
dRxy = corr(maskedField1 - s1Mean, maskedField2 - s2Mean, yzWeighting)
s12Min = min(s1Min, s2Min); s12Max = max(s1Max, s2Max)
xLims = numpy.amin(yCoord), numpy.amax(yCoord); yLims = boundaryStats(zCoord)
if debug:
print 's1: min, max, mean =', s1Min, s1Max, s1Mean
print 's2: min, max, mean =', s2Min, s2Max, s2Mean
print 's12: min, max =', s12Min, s12Max
# Choose colormap
if nbins==None and (clim==None or len(clim)==2): cBins=35
else: cBins=nbins
if nbins==None and (dlim==None or len(dlim)==2): nbins=35
if colormap==None: colormap = chooseColorMap(s12Min, s12Max)
cmap, norm, extend = chooseColorLevels(s12Min, s12Max, colormap, clim=clim, nbins=cBins, extend=extend)
def annotateStats(axis, sMin, sMax, sMean, sStd, sRMS):
axis.annotate('max=%.5g\nmin=%.5g'%(sMax,sMin), xy=(0.0,1.025), xycoords='axes fraction', verticalalignment='bottom', fontsize=10)
if sMean!=None:
axis.annotate('mean=%.5g\nrms=%.5g'%(sMean,sRMS), xy=(1.0,1.025), xycoords='axes fraction', verticalalignment='bottom', horizontalalignment='right', fontsize=10)
axis.annotate(' sd=%.5g\n'%(sStd), xy=(1.0,1.025), xycoords='axes fraction', verticalalignment='bottom', horizontalalignment='left', fontsize=10)
if addplabel: preTitleA = 'A: '; preTitleB = 'B: '
else: preTitleA = ''; preTitleB = ''
if axis==None:
setFigureSize(aspect, resolution, npanels=npanels, debug=debug)
#plt.gcf().subplots_adjust(left=.13, right=.94, wspace=0, bottom=.05, top=.94, hspace=0.15)
if npanels in [2, 3]:
axis = plt.subplot(npanels,1,1)
plt.pcolormesh(yCoord, zCoord, field1, cmap=cmap, norm=norm)
if interactive: addStatusBar(yCoord, zCoord, field1)
cb1 = plt.colorbar(fraction=.08, pad=0.02, extend=extend)
if centerlabels and len(clim)>2: cb1.set_ticks( 0.5*(clim[:-1]+clim[1:]) )
axis.set_axis_bgcolor(landcolor)
plt.xlim( xLims ); plt.ylim( yLims )
if splitscale!=None:
for zzz in splitscale[1:-1]: plt.axhline(zzz,color='k',linestyle='--')
axis.set_yscale('splitscale', zval=splitscale)
annotateStats(axis, s1Min, s1Max, s1Mean, s1Std, s1RMS)
axis.set_xticklabels([''])
if len(zlabel+zunits)>0: plt.ylabel(label(zlabel, zunits))
if len(title1)>0: plt.title(preTitleA+title1)
axis = plt.subplot(npanels,1,2)
plt.pcolormesh(yCoord, zCoord, field2, cmap=cmap, norm=norm)
if interactive: addStatusBar(yCoord, zCoord, field2)
cb2 = plt.colorbar(fraction=.08, pad=0.02, extend=extend)
if centerlabels and len(clim)>2: cb2.set_ticks( 0.5*(clim[:-1]+clim[1:]) )
axis.set_axis_bgcolor(landcolor)
plt.xlim( xLims ); plt.ylim( yLims )
if splitscale!=None:
for zzz in splitscale[1:-1]: plt.axhline(zzz,color='k',linestyle='--')
axis.set_yscale('splitscale', zval=splitscale)
annotateStats(axis, s2Min, s2Max, s2Mean, s2Std, s2RMS)
if npanels>2: axis.set_xticklabels([''])
if len(zlabel+zunits)>0: plt.ylabel(label(zlabel, zunits))
if len(title2)>0: plt.title(preTitleB+title2)
if npanels in [1, 3]:
axis = plt.subplot(npanels,1,npanels)
if dcolormap==None: dcolormap = chooseColorMap(dMin, dMax)
cmap, norm, extend = chooseColorLevels(dMin, dMax, dcolormap, clim=dlim, nbins=nbins, extend=dextend)
plt.pcolormesh(yCoord, zCoord, field1 - field2, cmap=cmap, norm=norm)
if interactive: addStatusBar(yCoord, zCoord, field1 - field2)
cb3 = plt.colorbar(fraction=.08, pad=0.02, extend=dextend)
if centerdlabels and len(dlim)>2: cb3.set_ticks( 0.5*(dlim[:-1]+dlim[1:]) )
axis.set_axis_bgcolor(landcolor)
plt.xlim( xLims ); plt.ylim( yLims )
if splitscale!=None:
for zzz in splitscale[1:-1]: plt.axhline(zzz,color='k',linestyle='--')
axis.set_yscale('splitscale', zval=splitscale)
annotateStats(axis, dMin, dMax, dMean, dStd, dRMS)
if len(zlabel+zunits)>0: plt.ylabel(label(zlabel, zunits))
axis.annotate(' r(A,B)=%.5g\n'%(dRxy), xy=(1.0,-0.20), xycoords='axes fraction', verticalalignment='bottom', horizontalalignment='center', fontsize=10)
if len(ylabel+yunits)>0: plt.xlabel(label(ylabel, yunits))
if len(title3)>0: plt.title(title3)
if len(suptitle)>0: plt.suptitle(suptitle)
if save!=None: plt.savefig(save)
if interactive: addInteractiveCallbacks()
if show: plt.show(block=False)
def yzplot(field, y=None, z=None,
ylabel=None, yunits=None, zlabel=None, zunits=None,
splitscale=None,
title='', suptitle='',
clim=None, colormap=None, extend=None, centerlabels=False,
nbins=None, landcolor=[.5,.5,.5],
aspect=[16,9], resolution=576, axis=None,
ignore=None, save=None, debug=False, show=False, interactive=False):
"""
Renders section plot of scalar field, field(x,z).
Arguments:
field Scalar 2D array to be plotted.
y y (or x) coordinate (1D array). If y is the same size as field then x is treated as
the cell center coordinates.
z z coordinate (1D or 2D array). If z is the same size as field then y is treated as
the cell center coordinates.
ylabel The label for the x axis. Default 'Latitude'.
yunits The units for the x axis. Default 'degrees N'.
zlabel The label for the z axis. Default 'Elevation'.
zunits The units for the z axis. Default 'm'.
splitscale A list of depths to define equal regions of projection in the vertical, e.g. [0.,-1000,-6500]
title The title to place at the top of the panel. Default ''.
suptitle The super-title to place at the top of the figure. Default ''.
clim A tuple of (min,max) color range OR a list of contour levels. Default None.
colormap The name of the colormap to use. Default None.
extend Can be one of 'both', 'neither', 'max', 'min'. Default None.
centerlabels If True, will move the colorbar labels to the middle of the interval. Default False.
nbins The number of colors levels (used is clim is missing or only specifies the color range).
landcolor An rgb tuple to use for the color of land (no data). Default [.5,.5,.5].
aspect The aspect ratio of the figure, given as a tuple (W,H). Default [16,9].
resolution The vertical resolution of the figure given in pixels. Default 720.
axis The axis handle to plot to. Default None.
ignore A value to use as no-data (NaN). Default None.
save Name of file to save figure in. Default None.
debug If true, report stuff for debugging. Default False.
show If true, causes the figure to appear on screen. Used for testing. Default False.
interactive If true, adds interactive features such as zoom, close and cursor. Default False.
"""
# Create coordinates if not provided
ylabel, yunits, zlabel, zunits = createYZlabels(y, z, ylabel, yunits, zlabel, zunits)
if debug: print 'y,z label/units=',ylabel,yunits,zlabel,zunits
if len(y)==z.shape[-1]: y = expand(y)
elif len(y)==z.shape[-1]+1: y = y
else: raise Exception('Length of y coordinate should be equal or 1 longer than horizontal length of z')
if ignore!=None: maskedField = numpy.ma.masked_array(field, mask=[field==ignore])
else: maskedField = field.copy()
yCoord, zCoord, field2 = m6toolbox.section2quadmesh(y, z, maskedField)
# Diagnose statistics
sMin, sMax, sMean, sStd, sRMS = myStats(maskedField, yzWeight(y, z), debug=debug)
yLims = numpy.amin(yCoord), numpy.amax(yCoord)
zLims = boundaryStats(zCoord)
# Choose colormap
if nbins==None and (clim==None or len(clim)==2): nbins=35
if colormap==None: colormap = chooseColorMap(sMin, sMax)
cmap, norm, extend = chooseColorLevels(sMin, sMax, colormap, clim=clim, nbins=nbins, extend=extend)
if axis==None:
setFigureSize(aspect, resolution, debug=debug)
#plt.gcf().subplots_adjust(left=.10, right=.99, wspace=0, bottom=.09, top=.9, hspace=0)
axis = plt.gca()
plt.pcolormesh(yCoord, zCoord, field2, cmap=cmap, norm=norm)
if interactive: addStatusBar(yCoord, zCoord, field2)
cb = plt.colorbar(fraction=.08, pad=0.02, extend=extend)
if centerlabels and len(clim)>2: cb.set_ticks( 0.5*(clim[:-1]+clim[1:]) )
axis.set_axis_bgcolor(landcolor)
if splitscale!=None:
for zzz in splitscale[1:-1]: plt.axhline(zzz,color='k',linestyle='--')
axis.set_yscale('splitscale', zval=splitscale)
plt.xlim( yLims ); plt.ylim( zLims )
axis.annotate('max=%.5g\nmin=%.5g'%(sMax,sMin), xy=(0.0,1.01), xycoords='axes fraction', verticalalignment='bottom', fontsize=10)
if sMean!=None:
axis.annotate('mean=%.5g\nrms=%.5g'%(sMean,sRMS), xy=(1.0,1.01), xycoords='axes fraction', verticalalignment='bottom', horizontalalignment='right', fontsize=10)
axis.annotate(' sd=%.5g\n'%(sStd), xy=(1.0,1.01), xycoords='axes fraction', verticalalignment='bottom', horizontalalignment='left', fontsize=10)
if len(ylabel+yunits)>0: plt.xlabel(label(ylabel, yunits))
if len(zlabel+zunits)>0: plt.ylabel(label(zlabel, zunits))
if len(title)>0: plt.title(title)
if len(suptitle)>0: plt.suptitle(suptitle)
if save!=None: plt.savefig(save)
if interactive: addInteractiveCallbacks()
if show: plt.show(block=False)
options, args = parser.parse_args()
if (len(args) < 2):
print "usage: plt_overtuningStreamfunction.py <ncfile.nc> 10"
exit(1)
# file name
fname = args[0]
# time indice
t = int(args[1])
x = Dataset(fname).variables['nx'][:]
nx = np.zeros(len(x) + 1)
nx[0:-1] = x
nx[-1] = x[-1]
e = Dataset(fname).variables['e'][t, :]
var = Dataset(fname).variables['overturningStreamfunction'][t, :]
xCoord, yCoord, zData = m6toolbox.section2quadmesh(nx,
e,
var,
representation='linear')
plt.figure()
plt.pcolormesh(xCoord, yCoord, zData)
plt.colorbar()
plt.xlabel('x (km)')
plt.ylabel('Elevation (m)')
plt.ylim((-720, 0))
#plt.savefig(diags[d]+'.png',bbox_inches='tight')
plt.show()
