Esempio n. 1
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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)
Esempio n. 2
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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
Esempio n. 4
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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)
Esempio n. 5
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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
Esempio n. 6
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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
Esempio n. 8
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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()
Esempio n. 10
0
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)
Esempio n. 11
0
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)
Esempio n. 12
0
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)
Esempio n. 13
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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)
Esempio n. 14
0
    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()