Sharing is Caring

The power and utility of Python and GitHub have occupied my mind the past week.  I have learned a substantial amount about these two things that fascinates me.  The power of Python has been seen for ages now.  Being fascinated by its intuitive command syntax, convenient package calling capabilities, and utilities for scientists among a broad range of studies is not new for me.  However, GitHub is completely new to me.  I have heard about it before and I have already downloaded code from some of its web pages before, but now I have learned how to use it for myself.  I now know a lot of its functions that make collaborating with other computer programmers feasible and safe.  It is but another reminder that the Age of the Internet is still throwing the world new surprises in ways to connect with others globally and quickly.  Even a young person like me, who has been raised up being surrounded by computers and fancy gadgets, is still able to be in jaw-dropping awe about something like GitHub.  And now, the lecturers at the Banneker Institute are telling me that I can use GitHub for all of my Python program-sharing needs?!  This is such a great tool!!  I cannot believe that my university's professors and astrophysics researchers do not know how to use GitHub!  Moreover, most of them do not use Python coding for their research.  These facts do not imply that my professors are doing anything wrong nor do they imply that they are behind in their technology knowledge.  However, life is much simpler now that I am well-trained in ways of Python programming and GitHub utilities.

My GitHub page is https://github.com/MWilson1. It's a work in progress.  It is exhilarating for me to understand how to use such a powerful tool like this works!  As of now, I have been adding merely my Banneker Institute assignments involving programming to that page.  Very soon, I will put my code up there to share with my peers and colleagues at the CfA.  One of the scripts will be capable of extracting a target list of objects for a future observation and subsequently find satisfactory comparison stars to observe as well.  The data from the comparison stars are necessary for the differential photometry analysis that I will conduct later this summer.  Another script will perform the data reduction process, which is necessary to initiate after the observation.  That data reduction process can be elucidated in the following equation:
\[ science = \frac{Science_{raw} - bias - \frac{(Dark - Bias)}{ExpTime_{Dark}} \times ExpTime_{Dark}}{Flat - Bias - \frac{(Dark-Bias)}{ExpTime_{Dark}}\times ExpTime_{Flat}}  \]

In simpler terms, the equation is stating that the raw science frame must be subtracted by dark and bias calibration frames as well as divided by the flat calibration frame (which has been subtracted by the dark and bias calibration frames).

On another note...
Now that I am away from my university research and my research team is still continuing to analyze data with my IDL code, this is the perfect time for me to use GitHub if ever they find a bug in my program or if they need to change something within my script for any reason.  I just thought of that.