| 
  | __init__(self,
        imageData,
        imageWCS,
        axes=[0.1,0.1,0.8,0.8],
        cutLevels=["smart",99.5],
        colorMapName="gray",
        title=None,
        axesLabels="sexagesimal",
        axesFontFamily="serif",
        axesFontSize=12.0,
        RATickSteps="auto",
        decTickSteps="auto",
        colorBar=False,
        interpolation="bilinear")
    (Constructor)
 | source code |  Makes an ImagePlot from the given image array and astWCS. For 
  coordinate axes to work, the image and WCS should have been rotated such 
  that East is at the left, North is at the top (see e.g. astImages.clipRotatedImageSectionWCS, or astImages.resampleToTanProjection). If imageData is given as a list in the format [r, g, b], a color RGB 
  plot will be made. However, in this case the cutLevels must be specified 
  manually for each component as a list - i.e. cutLevels = [[r min, r max],
  [g min, g max], [b min, b max]]. In this case of course, the colorMap 
  will be ignored. All r, g, b image arrays must have the same 
  dimensions. Set axesLabels = None to make a plot without coordinate axes 
  plotted. The axes can be marked in either sexagesimal or decimal celestial 
  coordinates. If RATickSteps or decTickSteps are set to "auto", 
  the appropriate axis scales will be determined automatically from the 
  size of the image array and associated WCS. The tick step sizes can be 
  overidden. If the coordinate axes are in sexagesimal format a dictionary 
  in the format {'deg', 'unit'} is needed (see RA_TICK_STEPS and DEC_TICK_STEPS for examples). If the coordinate axes are
  in decimal format, the tick step size is specified simply in RA, dec 
  decimal degrees. 
    Parameters:
        imageData(numpy array or list) - image data array or list of numpy arrays [r, g, b]imageWCS(astWCS.WCS) - astWCS.WCS objectaxes(list) - specifies where in the current figure to draw the finder chart 
          (see pylab.axes)cutLevels(list) - sets the image scaling - available options:
            
              pixel values: cutLevels=[low value, high value].
            
              histogram equalisation: cutLevels=["histEq", number
              of bins ( e.g. 1024)]
            
              relative: cutLevels=["relative", cut per cent level
              (e.g. 99.5)]
            
              smart: cutLevels=["smart", cut per cent level (e.g.
              99.5)]
             ["smart", 99.5] seems to provide good scaling over a
          range of different images. Note that for RGB images, cut levels 
          must be specified manually i.e. as a list: [[r min, rmax], [g 
          min, g max], [b min, b max]]colorMapName(string) - name of a standard matplotlib colormap, e.g. "hot", 
          "cool", "gray" etc. (do 
          "help(pylab.colormaps)" in the Python interpreter to 
          see available options)title(string) - optional title for the plotaxesLabels(string) - either "sexagesimal" (for H:M:S, D:M:S), 
          "decimal" (for decimal degrees) or None (for no 
          coordinate axes labels)axesFontFamily(string) - matplotlib fontfamily, e.g. 'serif', 'sans-serif' etc.axesFontSize(float) - font size of axes labels and titles (in points)colorBar(bool) - if True, plot a vertical color bar at the side of the image 
          indicating the intensity scale.interpolation(string) - interpolation to apply to the image plot (see the documentation 
          for the matplotlib.pylab.imshow command) |