Plotting functions

The different plotting functions are based on Matplotlib and will return a Matplotlib figure object. The dataset can hence be plotted using commands of the form:

>>> fig = dataset.PlotFunction(option1=1, option2=True)
>>> fig.show()

Each plot types can be stored into a new or existing figure object with the plot() method of the DataSet class:

>>> # Plot in a new figure
>>> fig = dataset.PlotType(option1=1, ...)
>>> fig.show()

>>> # Plot in an existing figure
>>> fig = plt.figure()
>>> dataset.PlotType(fig=fig)
>>> fig.show()

and/or saved into a file providing a file name to the plot() method:

>>> # Saving plot in a file
>>> dataset.PlotType(filename='MyPlot.png')

The different available plotting functions are listed in the sections below.

Map plotting

All map displays are done through the plot() method of the DataSet class:

>>> fig, cmap = dataset.plot(plottype='2D-SURFACE', ...)

The plot() method returns both a Figure and the associated Colorbar so that the typical display sequence is:

>>> # Classic display command
>>> fig, cmap = dataset.plot(plottype='2D-SURFACE', ...)
>>> fig.show()

>>> # One-line command
>>> dataset.plot(plottype='2D-SURFACE', ...)[0].show()

Interpolation

To be displayed as a map, most of the plot types need a gridded dataset (i.e. z_image). Use the interpolate() method of the DataSet class to grid the dataset:

>>> # Linear interpolation
>>> dataset.interpolate(interpolation='linear')

>>> # No interpolation
>>> dataset.interpolate(interpolation='none')

To get the list of the available plotting interpolations use the command:

>>> interpolation_getlist()
['none', 'nearest', 'bilinear', 'bicubic', 'spline16', 'sinc']

see Dataset operation

Color maps

The list of the available color maps can be obtained with the command:

>>> colormap_getlist()
['Blues', 'BrBG', 'BuGn', 'BuPu', 'CMRmap', 'GnBu', 'Greens', 'Greys',
 'OrRd', 'Oranges', 'PRGn', 'PiYG', 'PuBu', 'PuOr', 'PuRd', 'Purples',
 'RdBu', 'RdGy', 'RdPu', 'RdYlBu', 'RdYlGn', 'Reds', 'Spectral', 'Wistia',
 'YlGn', 'YlGnBu', 'YlOrBr', 'YlOrRd', 'afmhot', 'autumn', 'binary',
 'bone', 'bwr', 'copper', 'gist_earth', 'gist_gray', 'gist_heat',
 'gist_yarg', 'gnuplot', 'gray', 'hot', 'hsv', 'jet', 'ocean', 'pink',
 'spectral', 'terrain']

You can plot a specific color map using the following command:

>>> # Plotting a color map
>>> fig = colormap_plot('BrBG')
>>> fig.show()

>>> # Plotting the reverse color map
>>> colormap_plot('BrBG', creversed=True).show()
_images/figPlotColorMapBrBG.png

Fig. 8 Plot - BrBG color map.
_images/figPlotColorMapBrBG_r.png

Fig. 9 Plot - BrBG reverse color map.

And you can save it to a file using:

>>> # Saving the color map as a .png file
>>> cmapfilename = 'colormap.png'
>>> colormap_plot(cmname, filename=cmapfilename)

You can also manually add “_r” (instead of using creversed=True) to the color map name to obtain the reversed color map:

>>> cmapnb = 6
>>> cmaplist = colormap_getlist()
>>> for i in range (cmapnb):
>>>     colormap_plot(cmaplist[i-1], filename="CMAP_" + str(i) + ".png")

Blues

_images/figPlotColorMap_0.png

BrBG

_images/figPlotColorMap_1.png

BuGn

_images/figPlotColorMap_2.png

BuPu

_images/figPlotColorMap_3.png

CMRmap

_images/figPlotColorMap_4.png

GnBu

_images/figPlotColorMap_5.png

or you can build figure and plot objects to display them in a new window:

>>> cm_fig = None
>>> first_time = True
>>> for cmapname in cmaplist:
>>>     cm_fig = colormap_plot(cmapname, fig=cm_fig)
>>>     if (first_time == True):
>>>         fig.show()
>>>         first_time = False
>>>     fig.draw()

Map types

It is possible to plot the dataset as a map using different plot types. To display the dataset as a map simply use the plot() method of the DataSet class:

>>> dataset.plot(plottype=PlotType, option1=..., option2=...)

The list of the a available plot types can be obtained by the command:

>>> from geophpy.dataset import plottype_getlist
>>> plottype_getlist()
['2D-SCATTER', '2D-SURFACE', '2D-CONTOUR', '2D-CONTOURF', '2D-POSTMAP']

You can overlays the measured data points to the map using the comand:

>>> # Data points display
>>> dataset.plot(plottype='2D-SURFACE', pointsdisplay=True)

2-D scatter and postmap plots

Display your raw data as a scatter plot to get a quick look at it or your data point position as a postmap:

>>> dataset.info.cmapname = 'gist_ncar'
>>> fig, cmap = dataset.plot(plottype='2D-SCATTER', cmmin=-20, cmmax=20)
>>> fig.show()

>>>  fig, cmap = dataset.plot(plottype='2D-POSTMAP')
>>> fig.show()
_images/figPlotScatter.png

Fig. 10 Plot - Displaying data as scatter plot.
_images/figQuickStartPostmap.png

Fig. 11 Plot - Displaying data points position.

2-D surface plot

You can plot a dataset as a 2-D surface map using different interpolation for the display:

>>> # Dataset 2-D surface plot
>>> dataset.plot('2D-SURFACE', 'gray_r', plot.png,
interpolation='bilinear', transparent=True, dpi=400)

>>> dataset.plot('2D-SURFACE', 'gray_r', plot.png,
interpolation='bicubic', transparent=True, dpi=400)
_images/figCarto4.png

Fig. 12 Plot - Map using the ‘bilinear’ interpolation
_images/figCarto5.png

Fig. 13 Plot - Map using the ‘bilinear’ interpolation.

2-D Contour plots

You can plot a dataset as a 2-D (filled or unfilled) contour plot using:

>>> # Dataset 2-D contour plot
>>> fig, cmap = dataset.plot('2D-CONTOUR', levels=100, cmmin=-20, cmmax=20)
>>> fig.show()

>>> # Dataset 2-D Filled-contour plot
>>> fig, cmap = dataset.plot('2D-CONTOURF', levels=100, cmmin=-20, cmmax=20)
>>> fig.show()
_images/figPlotContour.png

Fig. 14 Plot - Displaying data as a contour plot.
_images/figPlotContourF.png

Fig. 15 Plot - Displaying data as a filled contour plot.

Plot options

Axis, label and color bar display

You can customize the display by enabling/disabling the axis, label and color bar display:

>>> # Custom axis, label and color map display
>>> dataset.plot(plottype='2D-SURFACE', labeldisplay=False)
>>> dataset.plot(plottype='2D-SURFACE', axisdisplay=False, cmapdisplay=False)
_images/figPlotOptions1.png

Fig. 16 Plot - Disabling the label display.
_images/figPlotOptions2.png

Fig. 17 Plot - Disabling the axis and color bar display.

Adding points and rectangles

Overlay some specific points or add rectangles:

>>> import geophpy.plotting.plot as gplt

>>> # Custom rectangles display
>>> xmin, xmax, ymin, ymax = dataset.get_gridextent()
>>> area_extents = [[15, 25, 15, 20], [35, xmax-0.5, 5, ymax-0.5]]
>>> rectangles = gplt.extents2rectangles(area_extents)
>>> dataset.plot(plottype='2D-SURFACE', rects=rectangles)

>>> # Custom points display
>>> points = [[22, 17], [45, 25]]
>>> dataset.plot(plottype='2D-SURFACE', rects=rectangles, points=points)
_images/figPlotOverlays1.png

Fig. 18 Plot - Overlaying custom rectangles.
_images/figPlotOverlays2.png

Fig. 19 Plot - Overlaying custom points.

Note

You can used the extents2rectangles() method of the plot module to convert area extent (xmin, xmax, ymin, ymax) to rectangle (x, y, width, height) to be displayed:

>>> import geophpy.plotting.plot as gplt
>>> area_extents = [ [xmin, xmax ymin, ymax], ...]
>>> rectangles = gplt.extents2rectangles(area_extents)
>>> dataset.plot(plottype='2D-SURFACE', rects=rectanges)

Histogram

To adjust the limits of color map you must view the limits of the data set:

>>> zmin, zmax = dataset.histo_getlimits()

You can plot the histogram curve in black, or respecting the dataset color map and with or without a color bar:

>>> # Black histogram
>>> valmin= -20, valmax = 20
>>> dataset.histo_plot(zmin=valmin, zmax=valmax, cmapdisplay=False, coloredhisto=False)

>>> # Colored histogram
>>> dataset.histo_plot(zmin=valmin, zmax=valmax, cmapdisplay=True, coloredhisto=True)
_images/figPlotHisto1.png

Fig. 20 Plot - Dataset Histogram.
_images/figPlotHisto2.png

Fig. 21 Plot - Dataset colored histogram.

 
>>> # Saving histogram to a file
>>> dataset.histo_plot(filename='histogram.png')

Correlation

You can plot the correlation map between a profile and mean of its surrounding profiles (see Festoon filtering):

>>> dataset.correlation_plotmap(method="Crosscorr")

or the mean correlation profile is used as correlation map (see see Festoon filtering):

>>> dataset.correlation_plotsum(method="Crosscorr")
_images/figCorrelationMap.png

Fig. 22 Plot - Dataset correlation map.
_images/figCorrelationSum.png

Fig. 23 Plot - Dataset mean correlation profile.

Mean cross-track profile

Before and after destriping mean cross-track profiles can be displayed with the following commands:

>>> dataset.meantrack_plot(Nprof=4, method='additive', Ndeg=None, plotflag='raw')

>>> dataset.meantrack_plot(Nprof=4, method='additive', Ndeg=None, plotflag='both')
_images/figDestriping3.png

Fig. 24 Plot - Dataset raw mean cross-track profile.
_images/figDestriping4.png

Fig. 25 Plot - Dataset destripped mean cross-track profile..