Can i set float128 as the standard float-array in numpy

Question

So I have a problem with my numerical program, and I'm curious about whether it is a precision problem (i.e. round-off error). Is there a quick way to change all the float arrays in my program into float128 arrays, without going through my code and typing dtype='float128' all over the place. My arrays are all float64, but i never explicitly wrote dtype='float64', so i was hoping there was a way to change this default behavior.

There is a numpy.float_ constant set to float64, but changing it to numpy.float128 and asking numpy.array([1.1]).dtype keeps returning float64. — eumiro
– eumiro, Commented Mar 18, 2011 at 10:08
The support for float128 is sketchy, irc it won't work with windows. Try the same with float96. — tillsten
– tillsten, Commented Mar 18, 2011 at 10:47
I'm working a Linux (Ubuntu) system. How is the support there? The fact that it won't work on windows doesn't really matter since it is just a check for myself. — Eskil
– Eskil, Commented Mar 18, 2011 at 10:53
Late reply, but the better solution here is to use numpy.longdouble, which goes to float128 on linux and float96 on windows. — Henry Gomersall
– Henry Gomersall, Commented Mar 29, 2013 at 9:01

Sven Marnach · Accepted Answer · 2011-04-14 07:42:14Z

21

I don't think there is a central "configuration" you could change to achieve this. Some options what you could do:

If you are creating arrays only by very few of NumPy's factory functions, substitute these functions by your own versions. If you import these functions like

from numpy import empty

you can just do

from numpy import float128, empty as _empty
def empty(*args, **kwargs):
    kwargs.update(dtype=float128)
    _empty(*args, **kwargs)

If you are doing

import numpy

you can write a module mynumpy.py

from numpy import *
_empty = empty
def empty(*args, **kwargs):
    kwargs.update(dtype=float128)
    _empty(*args, **kwargs)

and import it like

import mynumpy as numpy

Refactor your code to always use dtype=myfloat. This will make such changes easy in the future. You can combine this approach with the use of numpy.empty_like(), numpy.zeros_like() and numpy.ones_like() wherever appropriate to have the actual data type hardcoded in as few places as possible.
Sub-class numpy.ndarray and only use your custom constructors to create new arrays.

edited Apr 14, 2011 at 7:42

answered Mar 18, 2011 at 11:43

Sven Marnach

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1 Comment

Will Martin Over a year ago

Trying these in order is probably best: 1-2-3. The last option (Sub-classing numpy.ndarray) is elegant, but there are pitfalls. For example, I accidentally broke array garbage collection by storing slices of the array as attributes, i.e. self.myslice = self[0, :]. My RAM would slowly fill up as I did more and more operations.

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