# 5. A quick Slitjaw Image Viewer¶

IRIS Slitjaw (SJI) files are often very large and unwieldy to load into memory. On the other hand, viewing them as movies is a very effective way of getting an oversight over what a specific observing run has seen and will highlight any special events that can and will be of use in data analysis. Python’s Matplotlib has proven to be fairly slow at rendering animated data so we have built a visualizer built around GraphicsView instead. It is fairly primitive, and misses a lot of the nice features of Matplotlib, but it is fast, which is what is often needed. This module can be run both as a standalone code or inside, e.g., an ipython session.

## 5.1. Installation¶

You will first need to install the IRIS-LMSALpy repository as described there and in the first chapter of this manual. Once that is done the code can be run as a standalone by setting the path to include the location of the IRIS-LMSALpy repository on your machine. E.g., for a tcsh shell

setenv PATH $PATH":$HOME/LMSAL_HUB/iris_hub/iris_lmsalpy/iris_lmsalpy/"


## 5.2. Running as a stand-alone command¶

Once set-up is complete, the viewer can be run from the shell command line for viewing an IRIS SJI file with

im_view.py iris_l2_20160702_092921_3680258322_SJI_1330_t000.fits


The module takes three parameters:

im_view.py  -h -v -g -c <sji_fitsinputfile>


where -h is for help, -v is for verbose, -g <value> sets the gamma used in the display, and -c <value>, sets the cutoff in the histogram used for setting the max and min values of the colorscale. E.g., the min and max values of the first image are calculated, using the module histo_opt by making a histogram of the image and removing the pixels that with lower/higher intensities than cutoff/1.-cutoff percentage of filled histogram bins, with the addition that the lowest possible value is set to be the second bin of the histogram (as many points can be set to “zero” in IRIS images). The options to the im_view module can also be given in longer version --verbose, --gamma, --cutoff, --help.

Note that for comparison purposes there is also a viewer based on Matplotlib that can be run in standalone mode:

im_view_plt.py <sji_fitsinputfile>


It takes the same options as im_view.

## 5.3. Running the viewer in a Python or ipython session¶

To run the viewer in a python session is quite simple as well:

 >>> from iris_lmsalpy import im_view as im
>>> files=im.sji_files()

[0] iris_l2_20190511_162936_3803109418_SJI_2796_t000.fits
[1] iris_l2_20160702_092921_3680258322_SJI_2832_t000.fits
[2] iris_l2_20190511_162936_3803109418_SJI_1400_t000.fits
[3] iris_l2_20181009_154453_3620028413_SJI_2796_t000.fits
...

>>> im.sji_view(files[0])


Where sji_files() finds and displays all the files in the current directory with *SjI*.fits in their name and returns these filenames as a list. The routine takes the keyword dir in case the files looked for are in the directory dir instead. The viewer itself, sji_view, takes the keywords gamma and cutoff and verbose, e.g.

>>> im.sji_view(<filename>, gamma=0.3, cutoff=1.e-3, verbose=True)


## 5.4. The histo_opt module¶

The histo_opt module used to scale images in the viewer may be interesting for other uses as well. It can be called in the same manner as sji_files and sji_view

>>> # image is some |Numpy| array image
>>> from iris_lmsalpy import im_view as im
>>> levels=im.histo_opt(image, cutoff=1.5e-3, gamma=1.0, verbose=True)


where the function returns levels=(vmin,vmax) i.e., the histogram optimized max and min of the image.