"Can I find the minimum value of this matrix so that it does not lie in a given range in a neat way?"
If you care only about the minimum value that satisfies a certain condition, and not a location, then
>>> numpy.random.seed(1)
>>> m = numpy.random.randn(5.,5.)
>>> m
array([[ 1.62434536, -0.61175641, -0.52817175, -1.07296862, 0.86540763],
[-2.3015387 , 1.74481176, -0.7612069 , 0.3190391 , -0.24937038],
[ 1.46210794, -2.06014071, -0.3224172 , -0.38405435, 1.13376944],
[-1.09989127, -0.17242821, -0.87785842, 0.04221375, 0.58281521],
[-1.10061918, 1.14472371, 0.90159072, 0.50249434, 0.90085595]])
>>> m[~ ((m < 0.5) | (m > 0.8))].min()
0.50249433890186823
If you need a location via argmin, then this is a bit more complicated, but one way is to use masked arrays:
>>> numpy.ma.array(m,mask=((m<0.5) | (m > 0.8))).argmin()
23
>>> m.flat[23]
0.50249433890186823
Note that the condition is reversed here, since the mask is True for excluded values, not incoming ones.
: , " " , , x, y, ( , ):
>>> xx, yy = numpy.indices(m.shape)
>>> points = ((xx == 0) & (yy == 0)) | ((xx > 2) & (yy < 3))
>>> points
array([[ True, False, False, False, False],
[False, False, False, False, False],
[False, False, False, False, False],
[ True, True, True, False, False],
[ True, True, True, False, False]], dtype=bool)
>>> m[points]
array([ 1.62434536, -1.09989127, -0.17242821, -0.87785842, -1.10061918,
1.14472371, 0.90159072])
>>> m[points].min()
-1.1006191772129212
, . [ mgrid; , !]
: ^), , , , 3x3 .