My first ever shot of the moon!

Moon as seen on Sept 1, 2014
Moon as seen on Sept 1, 2014 at Denver, Colorado, USA

Well, finally I’ve found some time to shoot the moon. Not a great moon to shoot, but nevertheless, it was my first attempt to capture it. Total time spent from shooting to processing: 15-20 minutes.

This is my first moon photo! And I want to call it Denver Moon :)

I was really excited to see how the shot would come out. And it turned out satisfactory! 420 mm is not long enough for moon photography but cropping had solved the problem and I’ve got a reasonably clear moon shot. Well, actually on a cropped sensor camera like Canon EOS 40D, 420 mm is equivalent to 670mm on a full-frame body! So, it was not bad. Here’s the EXIF data for those who need them:

Canon EOS 40D
EF300mm f/4L IS USM +1.4x
ƒ/5.6 420.0 mm
Shutter speed 1/60
ISO 100

Taking a photo of the moon is not that difficult. You need a sturdy tripod with a good head and a reasonably long lens. I used Canon 300mm f/4 L IS USM lens with the 1.4x III extender. That gave me a reasonably good reach to have this photo.

Back in August, I missed the supermoon. It was cloudy around where I live and I did not have time to research where it would have been clear to photograph it. The next supermoon is supposed to be on Sept 9, 2014. That’s about a week from now. I hope this time the sky would be free from any cloud.

I processed it in Lightroom and then cropped. The final image is 2116 x 1411 at 300 px per inch. For exif data, please click the link to Flickr.

See it on Flickr

Books I am reading now-a-days

People will freak out if they see which books I am (literally) carrying these days–but I am listing them here anyway:

  1. Qur’an with English translation (on my phone, and I read it often)
  2. Applied Predictive Modelling by Kuhn and Johnson
  3. Applied Multivariate Analysis by Johnson and Wichern
  4. The Elements of Statistical Learning by Hastie, Tibshirani, and Friedman
  5. R Graphics Cookbook by Chang
  6. Statistical Methods for Social Sciences by Agresti and Finlay
  7. Statistical Programming with SAS/IML Software by Rick Wicklin
  8. The R Software: Fundamentals of Programming and Statistical Analysis by Pierre Lafaye de Micheaux, R´emy Drouilhet and Benoit Liquet
  9. Software for Data Analysis: Programming with R by John M. Chambers
  10. Repeated Measurements and Cross-Over Designs by Damaraju Raghavarao, Lakshmi Padgett


What’s yours?