astropy:docs

Quick reference Guide

Note

Rather than showing the generic call signature for the methods on this page, these are given in the form of example values. In addition, not all of the optional keywords are mentioned here. For more information on a specific command and all the options available, type help followed by the method name, e.g. help fig.show_grayscale. When multiple optional arguments are given, this does not mean that all of them have to be specified, but just shows all the options available.

Introduction

To import APLpy:

import aplpy

To create a figure of a FITS file:

fig = aplpy.FITSFigure('myimage.fits')

Here and in the remainder of this reference guide, we use the variable name fig for the figure object, but any name can be used.

A grayscale or colorscale representation of the FITS image can be shown and hidden using the following methods:

fig.show_grayscale()
fig.hide_grayscale()
fig.show_colorscale()
fig.hide_colorscale()

To show a three-color image, use the following method, specifying the filename of the color image:

fig.show_rgb('m17.jpeg')

The figure can be interactively explored by zooming and panning. To recenter on a specific region programmatically, use the following method, specifying either a radius:

fig.recenter(33.23, 55.33, radius=0.3)  # degrees

or a separate width and height:

fig.recenter(33.23, 55.33, width=0.3, height=0.2)  # degrees

To overlay contours, use:

fig.show_contour('co_data.fits')

To save the current figure, use:

fig.save('myplot.eps')

Other formats such as PDF, PNG, etc. can be used.

Labels

Labels can be added either in world coordinates:

fig.add_label(34.455, 54.112, 'My favorite star')

or relative to the axes:

fig.add_label(0.1, 0.9, '(a)', relative=True)

Shapes

To overlay different shapes, the following methods are available:

fig.show_markers(x_world, y_world)
fig.show_circles(x_world, y_world, radius)
fig.show_ellipses(x_world, y_world, width, height)
fig.show_rectangles(x_world, y_world, width, height)
fig.show_arrows(x_world, y_world, dx, dy)

where x_world, y_world, radius, width, height, dx, and dy should be 1D arrays specified in degrees.

It is also possible to plot lines and polygons using:

fig.show_lines(line_list)
fig.show_polygons(polygon_list)

For these methods, line_list and polygon_list should be lists of 2xN Numpy arrays describing the coordinates of the vertices in degrees.

DS9 Regions

DS9 region files can be overlaid with:

fig.show_regions('myregions.reg')

Layers

Markers, shapes, regions and text labels are stored in layers. These layers can be listed using:

fig.list_layers()

Layers can be hidden and shown with the following methods:

fig.hide_layer('regions')
fig.show_layer('regions')

Any layer can be retrieved using:

layer = fig.get_layer('circles')

Finally, layers can be removed using:

fig.remove_layer('rectangles')

Coordinates

Two methods are provided to help transform coordinates between world and pixel coordinates. These accept either scalars or arrays in degrees:

x_pix, y_pix = fig.world2pixel(45.3332, 22.1932)
x_world, y_world = fig.pixel2world(np.array([1., 2., 3]), np.array([1., 3., 5.]))

Frame

To set the look of the frame around the figure, use:

fig.frame.set_linewidth(1)  # points
fig.frame.set_color('black')

Colorbar

A grid can be added and removed using the following commands:

fig.add_colorbar()
fig.remove_colorbar()

Once add_colorbar() has been called, the fig.colorbar object is created and the following methods are then available:

  • Show and hide the colorbar:

    fig.colorbar.show()
    fig.colorbar.hide()
    
  • Set where to place the colorbar:

    fig.colorbar.set_location('right')
    

    This can be one of left, right, bottom or top.

  • Set the width of the colorbar:

    fig.colorbar.set_width(0.1)  # arbitrary units, default is 0.2
    
  • Set the amount of padding between the colorbar and the parent axes:

    fig.colorbar.set_pad(0.03)  # arbitrary units, default is 0.05
    
  • Set the font properties of the labels:

    fig.colorbar.set_font(size='medium', weight='medium', \
                          stretch='normal', family='sans-serif', \
                          style='normal', variant='normal')
    
  • Add a colorbar label:

    f.colorbar.set_axis_label_text('Flux (Jy/beam)')
    
  • Set some of the colorbar label properties:

    f.colorbar.set_axis_label_font(size=12, weight='bold')
    
  • Set the padding between the colorbar and the label in points:

    f.colorbar.set_axis_label_pad(10)
    
  • Change the rotation of the colorbar label, in degrees:

    f.colorbar.set_axis_label_rotation(270)
    

Coordinate Grid

A coordinate grid can be added and removed using the following commands:

fig.add_grid()
fig.remove_grid()

Once add_grid() has been called, the fig.grid object is created and the following methods are then available:

  • Show and hide the grid:

    fig.grid.show()
    fig.grid.hide()
    
  • Set the x and y spacing for the grid:

    fig.grid.set_xspacing(0.2)  # degrees
    fig.grid.set_yspacing(0.2)  # degrees
    
  • Set the color of the grid lines:

    fig.grid.set_color('white')
    
  • Set the transparency level of the grid lines:

    fig.grid.set_alpha(0.8)
    
  • Set the line style and width for the grid lines:

    fig.grid.set_linestyle('solid')
    fig.grid.set_linewidth(1)  # points
    

Scalebar

A scalebar can be added and removed using the following commands:

fig.add_scalebar()
fig.remove_scalebar()

Once add_scalebar() has been called, the fig.scalebar object is created and the following methods are then available:

  • Show and hide the scalebar:

    fig.scalebar.show(0.2)  # length in degrees
    fig.scalebar.hide()
    
  • Change the length of the scalebar:

    fig.scalebar.set_length(0.02)  # degrees
    
  • Specify the length of the scalebar in different units:

    from astropy import units as u
    fig.scalebar.set_length(72 * u.arcsecond)
    
  • Change the label of the scalebar:

    fig.scalebar.set_label('5 pc')
    
  • Change the corner that the beam is shown in:

    fig.scalebar.set_corner('top right')
    

    This can be one of top right, top left, bottom right, bottom left, left, right, bottom or top.

  • Set whether or not to show a frame around the beam:

    fig.scalebar.set_frame(False)
    
  • Set the transparency level of the scalebar and label:

    fig.scalebar.set_alpha(0.7)
    
  • Set the color of the scalebar and label:

    fig.scalebar.set_color('white')
    
  • Set the font properties of the label:

    fig.scalebar.set_font(size='medium', weight='medium', \
                          stretch='normal', family='sans-serif', \
                          style='normal', variant='normal')
    
  • Set the line style and width for the scalebar:

    fig.scalebar.set_linestyle('solid')
    fig.scalebar.set_linewidth(3)  # points
    
  • Set multiple properties at once:

    fig.scalebar.set(linestyle='solid', color='red', ...)
    

Beam

A beam can be added and removed using the following commands:

fig.add_beam()
fig.remove_beam()

Once add_beam() has been called, the fig.beam object is created and the following methods are then available:

  • Show and hide the beam:

    fig.beam.show()
    fig.beam.hide()
    
  • Change the major and minor axes, and the position angle:

    fig.beam.set_major(0.03)  # degrees
    fig.beam.set_minor(0.02)  # degrees
    fig.beam.set_angle(45.)  # degrees
    
  • Specify the major and minor axes, and the position angle, in explicit units:

    from astropy import units as u
    fig.beam.set_major(108 * u.arcsecond)
    fig.beam.set_minor(349 * u.microradian)
    fig.beam.set_angle(45 * u.degree)
    
  • Change the corner that the beam is shown in:

    fig.beam.set_corner('top left')
    

    This can be one of top right, top left, bottom right, bottom left, left, right, bottom or top.

  • Set whether or not to show a frame around the beam:

    fig.beam.set_frame(False)
    
  • Set the transparency level of the beam:

    fig.beam.set_alpha(0.5)
    
  • Set the color of the whole beam, or the edge and face color individually:

    fig.beam.set_color('white')
    fig.beam.set_edgecolor('white')
    fig.beam.set_facecolor('green')
    
  • Set the line style and width for the edge of the beam:

    fig.beam.set_linestyle('dashed')
    fig.beam.set_linewidth(2)  # points
    
  • Set the hatch style of the beam:

    fig.beam.set_hatch('/')
    
  • Set multiple properties at once:

    fig.beam.set(facecolor='red', linestyle='dashed', ...)
    

Coordinate types

APLpy supports three types of coordinates: longitudes (in the range 0 to 360 with wrap-around), latitudes (in the range -90 to 90), and scalars (any arbitrary value). APLpy tries to guess the correct type of coordinate for each axis, but it is possible to override this:

fig.set_xaxis_coord_type('scalar')
fig.set_yaxis_coord_type('longitude')

Valid options are longitude, latitude, and scalar.

Axis labels

The methods to control the x- and y- axis labels are the following

  • Show/hide both axis labels:

    fig.axis_labels.show()
    fig.axis_labels.hide()
    
  • Show/hide the x-axis label:

    fig.axis_labels.show_x()
    fig.axis_labels.hide_x()
    
  • Show/hide the y-axis label:

    fig.axis_labels.show_y()
    fig.axis_labels.hide_y()
    
  • Set the text for the x- and y-axis labels:

    fig.axis_labels.set_xtext('Right Ascension (J2000)')
    fig.axis_labels.set_ytext('Declination (J2000)')
    
  • Set the displacement of the x- and y-axis labels from the x- and y-axis respectively:

    fig.axis_labels.set_xpad(...)
    fig.axis_labels.set_ypad(...)
    
  • Set where to place the x-axis label:

    fig.axis_labels.set_xposition('bottom')
    
  • Set where to place the y-axis label:

    fig.axis_labels.set_yposition('right')
    
  • Set the font properties of the labels:

    fig.axis_labels.set_font(size='medium', weight='medium', \
                             stretch='normal', family='sans-serif', \
                             style='normal', variant='normal')
    

Tick labels

The methods to control the numerical labels below each tick are the following

  • Show/hide all tick labels:

    fig.tick_labels.show()
    fig.tick_labels.hide()
    
  • Show/hide the x-axis labels:

    fig.tick_labels.show_x()
    fig.tick_labels.hide_x()
    
  • Show/hide the y-axis labels:

    fig.tick_labels.show_y()
    fig.tick_labels.hide_y()
    
  • Set the format for the x-axis labels (e.g hh:mm, hh:mm:ss.s, etc.):

    fig.tick_labels.set_xformat('hh:mm:ss.ss')
    
  • Set the format for the y-axis labels (e.g dd:mm, dd:mm:ss.s, etc.):

    fig.tick_labels.set_yformat('dd:mm:ss.s')
    
  • Set where to place the x-axis tick labels:

    fig.tick_labels.set_xposition('top')
    
  • Set where to place the y-axis tick labels:

    fig.tick_labels.set_yposition('left')
    
  • Set the style of the labels (‘colons’ or ‘plain’):

    fig.tick_labels.set_style('colons')
    
  • Set the font properties of the labels:

    fig.tick_labels.set_font(size='medium', weight='medium', \
                             stretch='normal', family='sans-serif', \
                             style='normal', variant='normal')
    

Ticks

The methods to control properties relating to the tick marks are the following:

  • Show/hide all ticks:

    fig.ticks.show()
    fig.ticks.hide()
    
  • Show/hide the x-axis ticks:

    fig.ticks.show_x()
    fig.ticks.hide_x()
    
  • Show/hide the y-axis ticks:

    fig.ticks.show_y()
    fig.ticks.hide_y()
    
  • Change the tick spacing for the x- and y-axis:

    fig.ticks.set_xspacing(0.04)  # degrees
    fig.ticks.set_yspacing(0.03)  # degrees
    
  • Change the length of the ticks:

    fig.ticks.set_length(10)  # points
    
  • Change the color of the ticks:

    fig.ticks.set_color('black')
    
  • Set the line width for the ticks:

    fig.ticks.set_linewidth(2)  # points
    
  • Set the number of minor ticks per major tick:

    fig.ticks.set_minor_frequency(5)
    

    Set this to 1 to get rid of minor ticks

Advanced

To control whether to refresh the display after each command, use the following method:

fig.set_auto_refresh(False)

To force a refresh, use:

fig.refresh()

To use the system LaTeX instead of the matplotlib LaTeX, use:

fig.set_system_latex(True)

The color for NaN values can be controlled using the following method:

fig.set_nan_color('black')

Finally, to change the look of the plot using pre-set themes, use:

fig.set_theme('publication')