[Numpy-discussion] Matrix vs array in ma.minimum

David Cournapeau cournape@gmail....
Thu Jan 14 22:06:36 CST 2010

On Fri, Jan 15, 2010 at 11:59 AM, Pierre GM <pgmdevlist@gmail.com> wrote:
> On Jan 14, 2010, at 8:52 PM, David Cournapeau wrote:
>> Pierre GM wrote:
>>> Er, no.
>>> np.ma.minimum(a, b) returns the lowest value of a and b element-wsie, or the the lowest element of a is b is None. The behavior is inherited from the very first implementation of maskedarray in numeric. This itself is unexpected, since np.minimum requires at least 2 input arguments.
>>> As you observed, the current function breaks down w/ np.matrix objects when only one argument is given (and when the axis is None): we call umath.minimum.reduce on the ravelled matirx, which returns the ravelled matrix. One would expect a scalar, so yes, this behavior is also unexpected.
>>> Now, which way should we go ? Keep np.ma.minimum as it is (fixing the bug so that a scalar is returned if the function is called with only 1 argument and an axis  None) ? Adapt it to match np.minimum ?
>> I am not a user of Masked Array, so I don't know what is the most
>> desirable behavior.
> I'm not a regular user of np.minimum.

Damn, I thought I coul
>> The problem appears when using pylab.imshow on
>> matrices, because matplotlib (and not matlab :) ) uses masked arrays
>> when normalizing the values.
> David, you mind pointing me to the relevan part of the code and/or give me an example ?

Here is a self-contained example reproducing the matplotlib pb:

import numpy as np
from numpy import ma
import matplotlib.cbook as cbook

class Normalize:
    Normalize a given value to the 0-1 range
    def __init__(self, vmin=None, vmax=None, clip=False):
        If *vmin* or *vmax* is not given, they are taken from the input's
        minimum and maximum value respectively.  If *clip* is *True* and
        the given value falls outside the range, the returned value
        will be 0 or 1, whichever is closer. Returns 0 if::


        Works with scalars or arrays, including masked arrays.  If
        *clip* is *True*, masked values are set to 1; otherwise they
        remain masked.  Clipping silently defeats the purpose of setting
        the over, under, and masked colors in the colormap, so it is
        likely to lead to surprises; therefore the default is
        *clip* = *False*.
        self.vmin = vmin
        self.vmax = vmax
        self.clip = clip

    def __call__(self, value, clip=None):
        if clip is None:
            clip = self.clip

        if cbook.iterable(value):
            vtype = 'array'
            val = ma.asarray(value).astype(np.float)
            vtype = 'scalar'
            val = ma.array([value]).astype(np.float)

        vmin, vmax = self.vmin, self.vmax
        if vmin > vmax:
            raise ValueError("minvalue must be less than or equal to maxvalue")
        elif vmin==vmax:
            return 0.0 * val
            if clip:
                mask = ma.getmask(val)
                val = ma.array(np.clip(val.filled(vmax), vmin, vmax),
            result = (val-vmin) * (1.0/(vmax-vmin))
        if vtype == 'scalar':
            result = result[0]
        return result

    def inverse(self, value):
        if not self.scaled():
            raise ValueError("Not invertible until scaled")
        vmin, vmax = self.vmin, self.vmax

        if cbook.iterable(value):
            val = ma.asarray(value)
            return vmin + val * (vmax - vmin)
            return vmin + value * (vmax - vmin)

    def autoscale(self, A):
        Set *vmin*, *vmax* to min, max of *A*.
        self.vmin = ma.minimum(A)
        self.vmax = ma.maximum(A)

    def autoscale_None(self, A):
        ' autoscale only None-valued vmin or vmax'
        if self.vmin is None: self.vmin = ma.minimum(A)
        if self.vmax is None: self.vmax = ma.maximum(A)

    def scaled(self):
        'return true if vmin and vmax set'
        return (self.vmin is not None and self.vmax is not None)

if __name__ == "__main__":
    x = np.random.randn(10, 10)
    mx = np.matrix(x)
    print Normalize()(x)
    print Normalize()(mx)

More information about the NumPy-Discussion mailing list