Start a notebook to study how to do it.

I added a small description of why we need the instrumental profile.
Adds a function to detect the peaks of a vector

I have obtained it in https://gist.github.com/endolith/250860

I also made a test to detect the peak of one gaussian.
Write test for two gaussians.

Test if the function `peakdet` is capable of detecting two gaussians.
Change name of test file.

With the previous name, pytest was not working. It did not detected the
python file with the tests.
Creates function to fit gaussian.

The emission lines found in the lamp spectrum can be considered as being
Gaussians. Thus, we will need to fit each line with a Gaussians in order
to obtain the FWHM.

I implemented a function that fits a Gaussians. I found inspiration in
this blog post
http://starrydude.wordpress.com/2011/12/09/multiple-gaussian-fitting-in-python/
Adds function that finds and fits emission lines

It detects the emission lines from a spectrum and fits gaussians,
returning its parameters. I have tested with two gaussians and it
worked.
Adds function to obtain FWHM

The full width at half maximum of the emission lines from the lamp
spectrum give us the instrumental profile. The function that fits the
gaussian returns the standard deviation. With this new function we can
get the FWHM of the fitted emission lines.
Adds function to obtain the instrumental profile.

This function summarizes everything. The user just enter with the
spectrum, and the `inst_profile` function returns the position of the
emission lines and its FWHM.
Adds delta threshold as argument for some function

This had to be put because the code was not working for real data. The
user needs to find the best value for the data.
Reject FWHM<0

Sometimes the algorithm finds negatives values for the standard
deviation, and thus, for the FWHM. I have no idea what it can be a
negative standard deviation but I am rejecting it.
Explain how to get the instrumental profile

I used a real lamp spectrum for this.