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Re: Optimizing if statement check over a numpy value

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Heli Nix wrote:

> Is there any way that I can optimize this if statement.

Array processing is much faster in numpy. Maybe this is close to what you 
want

import numpy as N
# input data
vals = N.array([42, 1, 5, 3.14, 53, 1, 12, 11, 1])
# list of items to exclude
exclude = [1]
# convert to a boolean array
exclbool = N.zeros(vals.shape, dtype=bool)
exclbool[exclude] = True
# do replacement
ones = vals==1.0
# Note: ~ is numpy.logical_not
vals[ones & (~exclbool)] = 1e-20

I think you'll have to convert your HDF array into a numpy array first, 
using numpy.array().

Jeremy

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Thread

Optimizing if statement  check over a numpy value Heli Nix <hemla21@gmail.com> - 2015-07-23 02:21 -0700
  Re: Optimizing if statement check over a numpy value MRAB <python@mrabarnett.plus.com> - 2015-07-23 10:55 +0100
  Re: Optimizing if statement check over a numpy value Laura Creighton <lac@openend.se> - 2015-07-23 12:13 +0200
  Re: Optimizing if statement  check over a numpy value Jeremy Sanders <jeremy@jeremysanders.net> - 2015-07-23 13:42 +0200
    Re: Optimizing if statement  check over a numpy value Heli Nix <hemla21@gmail.com> - 2015-07-29 07:23 -0700

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