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POEfficient netCDF analysis when looping through data
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  1. POEfficient netCDF analysis when looping through data
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    copied!<p>This is a follow up question related to <a href="https://stackoverflow.com/questions/18665078/loop-through-netcdf-files-and-run-calculations-python-or-r">this question</a>.</p> <p>Thanks to previous help I have successfully imported a netCDF file (or files with MFDataset) and am able to compare the different times to one another to create another cumulative dataset. Here is a piece of the current code.</p> <pre><code>from numpy import * import netCDF4 import os f = netCDF4.MFDataset('air.2m.1979.nc') atemp = f.variables['air'] ntimes, ny, nx = atemp.shape cold_days = zeros((ntimes, ny, nx), dtype=int) for i in range(ntimes): for b in range(ny): for c in range(nx): if i == 1: if atemp[i,b,c] &lt; 0: cold_days[i,b,c] = 1 else: cold_days[i,b,c] = 0 else: if atemp[i,b,c] &lt; 0: cold_days[i,b,c] = cold_days[i-1,b,c] + 1 else: cold_days[i,b,c] = 0 </code></pre> <p>This seems like a brute force way to get the job done, and though it works it takes a very long time. I'm not sure if it takes such a long time because I'm dealing with 365 349x277 matrices (35,285,645 pixels) or if my old school brute force way is simply slow in comparison to some built in python methods.</p> <p>Below is an example of what I believe the code is doing. It looks at Time and increments cold days if temp &lt; 0. If temp >= 0 than cold days resets to 0. In the below image you will see that the cell at row 2, column 1 increments each Time that passes but the cell at row 2, column 2 increments at Time 1 but resets to zero on Time 2.</p> <p>Is there a more efficient way to rip through this netCDF dataset to perform this type of operation? <img src="https://i.stack.imgur.com/cyzxU.png" alt="Here is an example image"></p>
 

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