Why else might we prefer this data structure over a simple NumPy array? Here are some reasons:Įxtracting amplitude (or whatever) on a horizon. Plotting the same thing from the NumPy array would require us to figure out which index corresponds to 400 ms, and then write at least 8 lines of matplotlib code. Then we can construct the DataArray like so: All we need is an array of numbers representing inlines, another for the xlines, and another for the time samples. Imagine we have a seismic volume called seismic with shape (inlines, xlines, time) and the inlines and crosslines are both numbered like 1000, 1001, 1002, etc. The DataArray object is analogous to a NumPy array, but with the Pandas-like indexing added to each dimension via a property called coords. In essence, xarray brings you Pandas-like indexing for n-dimensional arrays. But it would certainly be convenient to have real-world coordinates for the data as well as NumPy indices - maybe inline and crossline numbers, or (UTMx, UTMy) position, or the actual time position of the timeslices in milliseconds. Seismic data, on the other hand, does not fit comfortably in a table. Much easier than trying to figure out which index goes with 13.1400 metres! For example, we can use depth for the index and have row 13.1400 and row 13.2924, etc. Instead of having row 0, row 1, etc, we can use an index that makes sense for the data. But, the really powerful thing about a DataFrame is its index. Its DataFrames are implicitly two-dimensional tables, and shine when the columns represent all sorts of different quantities, e.g. If you use Python on a regular basis, you might have noticed that Pandas is eating everything, not just things made out of bamboo.īut one thing Pandas is not useful for is 2-D or 3-D (or more-D) seismic data. Maybe I’ll even keep blogging.įor now though, I’m going on holiday.
Timeslice seismic consultants software#
For sure I’ll still be around the Software Underground on a daily basis. I’m looking forward to learning tons and finding new ways to apply myself and, I hope, contributing to Equinor’s inspiring open source software program. Although I’ll always be a geoscientist at heart, I’m switching careers and joining Equinor as a software developer. I’m excited to share that my family and I are moving back to Norway. It’s not an exaggeration to say that all of it would have been impossible without your cheers, loud or soft. If you’re reading this, you’ve almost certainly been part of it. I’m so grateful to the global community of earth scientists and engineers that cheered us on, read the blog, bought the books, and hired us for work. I think we had an impact well beyond our small size. We built a solid business providing services to governments, startups, and global corporations, as well as training, community events, and open source software to students and seasoned professionals alike. But with Evan - and later Ben, Justin, Kara, Tracy, Diego, Martin and Rob - we did more than just survive. I knew there was a chance I’d be looking for work within a year, possibly even having to move back to Calgary, or on to somewhere else to find it. When I quit my job and moved to Nova Scotia in 2010, I had no idea if Agile* was going to work at all. We’ll be shutting down at the end of September. After almost 12 years of consulting, teaching, writing, and hacking, it’s time for Agile* to close its laptops for the last time.
0 kommentar(er)
