On Thu, Jul 31, 2008 at 5:36 AM, Andrew Dalke
The user base for numpy might be .. 10,000 people? 100,000 people? Let's go with the latter, and assume that with command-line scripts, CGI scripts, and the other programs that people write in order to help do research means that numpy is started on average 10 times a day.
100,000 people * 10 times / day * 0.1 seconds per startup = almost 28 people-hours spent each day waiting for numpy to start.
I'm willing to spend a few days to achieve that.
Perhaps there's fewer people than I'm estimating. OTOH, perhaps there are more imports of numpy per day. An order of magnitude less time is still a couple of hours each day as the world waits to import all of the numpy libraries.
If on average people import numpy 10 times a day and it could be made 0.1 seconds faster then that's 1 second per person per day. If it takes on average 5 minutes to learn to import the module directly and the onus is all on numpy, then after 1 year of use the efficiency has made up for it, and the benefits continue to grow.
Just think of the savings that could be achieved if all 2.1 million Walmart employees were outfitted with colostomy bags. 0.5 hours / day for bathroom breaks * 2,100,000 employees * 365 days/year * $7/hour = $2,682,750,000/year Granted, I'm probably not the first to run these numbers. -- Nathan Bell wnbell@gmail.com http://graphics.cs.uiuc.edu/~wnbell/