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OK, I produced a light curve from my data. It's not a conclusive scientific test, but it's not bad considering I had no intention of producing such results. I used the original set of images I posted, plus a similar set from pre-totality (the original set were post-totality). Recall these were fixed exposure so the relative intensities are correct. In this instance they were 1/30s, f/4, ISO 100, 17 mm lens on Canon 400D.
I used DCRAW to convert the raw images with no white balance, scaling or Bayer matrix interpolation (essentially outputting the raw data points in ADU at each pixel position). I then converted the images to FITS format, and collapsed them into the various colour channels (R, G, G, B) using Python routines (NB: there are two green channels as a result of the Bayer structure). I collapsed the colours because the response from each colour filter will be different. Then, I picked a few regions of interest and examined them.
Results are attached, I used only the green filter and units are in raw ADU. Some explanation:
- Whole image (median) is just a global median pixel value of the entire image.
- Sky (median) is a median of a 50x50 pixel region of sky, approximate 50 degrees from the Sun
- Land (median) is a median of 50x50 pixel region of the landscape.
- Around Sun (mean) is the mean value of a 60x60 pixel region centered on the Sun
As evident from the initial set of images, they are extremely underexposed near totality. From the ADU counts, you can see it flattens out just above 250 ADU which is probably an indication of the bias level of the camera. So data here is not really valid. It is also worth pointing out that the "Around Sun" curve data is probably skewed too as many of the included pixels would have been fully saturated, particularly a few minutes before/after totality.
Now to spend a bit of time examining this data, along with Joe's data, and the implications for how to tackle this project!
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