# imexam User Methods¶

These are methods particular to the imexam package which are meant to aid users in their image analysis. They are called from the main object you created with imexam.connect().

At the top library level, the follow commands are available::

imexam.connect(): connect to a viewer and return a control object
imexam.display_help(): Takes you to the help documents for your installed version of imexam
imexam.defpars: contains the default plotting function dictionaries
imexam.imexamine: this class contains the plotting functions and can be instantiated by itself
imexam.set_logging(): set the logging parameters for your session.


Each object has access to it’s own logging which can be edited using viewer.setlog() The following will also be available for those not on a Windows system, where the XPA and DS9 are installed::

imexam.display_xpa_help(): Takes you to the XPA help page for DS9
imexam.list_active_ds9(): returns a dictionary of available DS9 sessions for connection


You can always get the commands available to your local viewer by asking the control object for them directly. If you called your control object “viewer” then the following example will return the list::

viewer.show_window_commands()  # will return a list of available commands


Not all viewers have all commands implemented, commands which are available but not yet fully implemented should return an error to that affect.

alignwcs(on=True):

Align the images in the viewer using the WCS in their headers

viewer.alignwcs()

blink(blink=True, interval=None):
For viewers with multiple frames, blink the images
clear_contour():
Clear contours from the screen
close():

close the image viewing window and end the connection.

viewer.close()

cmap(color=None, load=None, invert=False, save=False,filename=’colormap.ds9’):
Set the colormap for the window
colorbar(on=True):
Turn the colorbar in the window on or off
contour(on=True, construct=True):
Show contours in the window
crosshair(x=None, y=None, coordsys=”physical”, skyframe=”wcs”, skyformat=”fk5”, match=False, lock=False):
Control the position of the crosshair in the current frame
cursor(x=None, y=None):
Move the cursor in the window to the specified pixel location
disp_header():
Display the image header
frame(n=None):
Convenience function to change or report the frame
get_data():
Return a numpy array of the data displayed in the current frame
get_filename():

Return the filename for the data in the current window

In [1]: viewer.get_data_filename()
Out[2]: '/Users/sosey/ssb/imexam/iabf01bzq_flt.fits'

get_frame_info():

Return more explicit information about the data displayed in the current frame. A dictionary of the information is returned.

In [1]: viewer.get_frame_info()

{'extname': 'SCI',
'extver': 1,
'filename': '/Users/sosey/ssb/imexam/iabf01bzq_flt.fits',
'iscube': False,
'mef': True,
'naxis': 0,
'numaxis': 2,
'user_array': None}

get_header():
Return the header of the dataset in the current frame
get_image():
Return the full image object for the data in the current frame
get_slice_info():
Return the slice tuple for the image currently displayed
get_viewer_info():

Return a dictionary which contains information about all frames which have data loaded. This could be useful to users who are scripting an analysis for polling what items are available, how many frames or displayed, what type of data is hanging around, etc ...

In [1]: viewer.get_viewer_info()

{'1': {'extname': 'SCI',
'extver': 1,
'filename': '/Users/sosey/ssb/imexam/iabf01bzq_flt.fits',
'iscube': False,
'mef': True,
'naxis': 0,
'numaxis': 2,
'user_array': None}}

grab():
Take a snapshop of the image view
grid(on=True, param=False):
Turn a grid on and off in the window
hideme():
Reduce the precedence of the window
iscube():
Boolean return if the image is multidimensional cube
load_fits(fname=””, extver=1, extname=None):
Load a fits image into the current frame
load_mef_as_cube(filename=None):
Load a Mult-Extension-Fits image into one frame as an image cube
load_mef_as_multi(filename=None):
Load a Mult-Extension-Fits image into multiple frames
load_region(filename):
Load regions from a file which uses standard formatting
load_rgb(red, green, blue, scale=False, lockwcs=False):
Load three images into an RGB colored frame
make_region(infile,doLabels=False):

Make an input reg file which contains rows with “x,y,comment” into a region file that the DS9 viewer recognizes.

infile: str
input filename
labels: bool
add labels to the regions
header: int
number of header lines in text file to skip
textoff: int
offset in pixels for labels
rtype: str
region type, one of the acceptable DS9 regions
size: int
size of the region type
Here's what the input file 'test' looks like:

100,100, 1
200,200, 2
300,300, comment 3

viewer.make_region('test',labels=True)

And the output region file:

image; circle(100,100,5)
image;text(110.0,110.0{ 1 })# font="time 12 bold"
image; circle(200,200,5)
image;text(210.0,210.0{ 2 })# font="time 12 bold"
image; circle(300,300,5)
image;text(310.0,310.0{ comment 3 })# font="time 12 bold"


Now let’s load the region file into our image:

mark_region_from_array(input_points,rtype=”circle”,ptype=”image”,textoff=10,size=5):

mark regions on the display given a list of tuples, a single tuple, or a string, where each object has x,y,comment specified

input_points: an iterable
contains: (x,y,comment) tuples
ptype: string
the reference system for the point locations, image|physical|fk5
rtype: string
the matplotlib style marker type to display
size: int
the size of the region marker
textoff: string
the offset for the comment text, if comment is empty it will not show
locations=list()
locations.append( (100,100,1) )
locations.append( (200,200,2) )
locations.append( (300,300,'comment 3') )

viewer.mark_region_from_array(locations)

match(coordsys=”wcs”, frame=True, crop=False, fslice=False,
scale=False, bin=False, colorbar=False, smooth=False, crosshair=False):

Match all other frames to the current frame

nancolor(color=’red’):
Set the not-a-number color
panto_image(x, y):
Convenience function to change to x,y physical image coordinates
panto_wcs(x, y, system=’fk5’):
Pan to the wcs location in the image
readcursor():

Returns image coordinate postion and key pressed as a tuple of the for float(x), float(y), str(key).

In [1]: viewer.readcursor()
Out[2]: (56.0, 28.333333, 'a')

or with a click of the first mouse button

In [1]: viewer.readcursor()
Out[2]: (67.333333, 80.0, '<1>')

reopen():
Reopen a closed viewing window, mostly used for ginga windows right now
rotate(value=None, to=False):
Rotate the current frame (in degrees)
save_regions(filename=None):
Save the regions currently displayed in the window to a regions file
save_rgb(filename=None):
Save an rgbimage frame as an MEF fits file
scale(scale=’zscale’):
Scale the pixel values in the window, zscale is the default
set_region(region_string):
Use this to send the DS9 viewer a formatted region string it’s expecting

For example, in DS9:

viewer.set_region("text 110.0 110.0 '1' #font=times")

See the DS9 XPA documentation for more examples.

show_xpa_commands():
Print the available XPA commands (DS9 only)
showme():
Raise the precedence of the viewing window
showpix():
Display a pixel value table
snapsave(filename=None, format=None, resolution=100):
Create a snapshot of the current window in the specified format
valid_data_in_viewer():
Return bool if valid file or array is loaded into the viewer
view(img, header=None, frame=None, asFits=False):

Load an image array into the image viewing frame, if no frame is specified, the current frame is used. If no frame exists, then a new one is created. A basic header is created and sent to DS9. You can look at this header with disp_header() but get_header() will return an error because it looks for a filename, and no file was loaded, just the array.

image_array=fits.getdata('image.fits')
viewer.view(image_array)

or

image_array=numpy.ones([100,100])*numpy.random.rand(100)
viewer.view(image_array)

zoom(par=None):
Zoom using the specified command in par
zoomtofit():
Zoom the image to fit the window
setlog(self, filename=None, on=True, level=logging.DEBUG):

Turn on and off imexam logging to the a file. You can set the filename to something specific or let the package record to the default logfile. Once you give the object a logfile name, it will continue to use that file until you change it.

In [5]: viewer.setlog()
Saving imexam commands to imexam_log.txt


This is what’s displayed in the terminal when you use imexam():

and this is what shows up in the logfile:

You can see there are some leftovers from a previous logging session to the same file. You can toggle logging during a session too:

viewer.setlog(on=False)

#and to turn off even messages to the screen:

viewer.setlog(on=False,level=logging.CRITICAL)

unlearn():
Reset all the imexam default function parameters
plotname():

change or show the default save plotname for imexamine

In [1]: viewer.plotname()
imexam_plot.pdf

In [2]: viewer.plotname('myplot.jpg')
In [3]: viewer.plotname()
myplot.jpg


The extension of the filename controls the plot type.

display_help():

Display the help documentation into a webpage from the locally installed version. This is done from the main package:

In [1]: import imexam

In [2]: imexam.display_help()