How to create a percentile raster from .gdb containing multiple rasters?

[Anonymous User]

Hi,

I am trying to create a 5th and 95th percentile raster from a geodatabase containing multiple rasters (ranging from 26-60 rasters per .gdb).  Essentially, I would like to perform the same function as cell stats (mean, max, median, etc) but calculating percentiles rather than simple stats.  I've seen a few threads on this matter (this one in particular seems useful: Pool of raster values to calculate percentile) but none that seem to address how to do make an output percentile raster using rasters located in a .gdb.  I haven't begun developing the code yet and wanted to solicit answers to these questions before beginning.  A few questions:

1. Is there a tool that can do this or will I need to develop Python code using numpy?

2. Is it easier to do this with rasters located inside a folder or can I perform this calculation with rasters located in a .gdb?

Any help would be greatly appreciated.  Thanks!

[DanPatterson_Retired · Accepted solution]

with numpy, there is np.nanpercentile which accounts for nodata cells

How to calculate the percentile for each cell from timeseries raster 

A raster stack (3D) can be easily created

/blogs/dan_patterson/2018/02/06/cell-statistics-made-easy-raster-data-over-time 

and any statistic can be done.

If you have a ridiculously large data set, then it would be useful to split into chunks and read from a *.npy file using numpy memory management.  

As for your questions, the tools to make the raster stack are given in your thread.

Rasters in a gdb are not really the preferred rasters, but if that is all you have, just make sure you specify an output raster as a tif in a folder.  I would also np.save(…) your array in numpy format, they are very easy to work with for future work.

[DanPatterson_Retired]

resolved? or still open?