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I use the python class LSQUnivaraiteSpline to fit a spline to the continuum of the spectrum. The knots are controlled manually so that they do not lie on the absorption features (right now they only avoid the six features described above, but I should in the future add more features to avoid such as those near 625 and 650 nm). This continuum is then divided out to give extinction vs wavelength.
Left: blue is true spectrum, red is spline fit, green highlights absorption features. Right: spectrum divided by spline fit, minus 1.
There's an arbitrary factor in the spline fit, the degree of the smoothing spline, an integer from 1-5. I chose a fit of 3. This value is a compromise between basically having a gaussian fit (value of 1), and a spline that perfectly fits the spectrum noise (value of 5). I may look into how this number impacts the measurements of equivalent width.
Measurements of equivalent width
I used the textbook definition of equivalent width:
Eq Width = ∫ 1 - ( F / F_continuum) dλ
integrated over the entire feature, where F is the true spectrum and F_continuum is the spline fit. This equation comes down to integrating over (negative) the feature in the extinction curve above.
This is a plot of the equivalent widths of the 762nm OxA and 822nm water features measured over a sample of ~60 images over a three month period. The blue is OxA and the red is water.
The many zero values of water are due to my code not exactly finding the absorption feature correctly, something I will fix. But it's a start. I can include the widths of all features, but for this plot I decided only to put these two.
To do 1/25/15
- Still need to get another external drive so that I may analyze all the data, instead of just this subset
- I still would like to look at temperature data over time as well