mapdomain(x, old, new)
The linear map offset + scale*x
that maps the domain :None:None:`old`
to the domain :None:None:`new`
is applied to the points x
.
Effectively, this implements:
$$x\_out = new[0] + m(x - old[0])$$where
$$m = \frac{new[1]-new[0]}{old[1]-old[0]}$$Points to be mapped. If x
is a subtype of ndarray the subtype will be preserved.
The two domains that determine the map. Each must (successfully) convert to 1-d arrays containing precisely two values.
Array of points of the same shape as x
, after application of the linear map between the two domains.
Apply linear map to input points.
>>> from numpy.polynomial import polyutils as pu
... old_domain = (-1,1)
... new_domain = (0,2*np.pi)
... x = np.linspace(-1,1,6); x array([-1. , -0.6, -0.2, 0.2, 0.6, 1. ])
>>> x_out = pu.mapdomain(x, old_domain, new_domain); x_out array([ 0. , 1.25663706, 2.51327412, 3.76991118, 5.02654825, # may vary 6.28318531])
>>> x - pu.mapdomain(x_out, new_domain, old_domain) array([0., 0., 0., 0., 0., 0.])
Also works for complex numbers (and thus can be used to map any line in the complex plane to any other line therein).
>>> i = complex(0,1)
... old = (-1 - i, 1 + i)
... new = (-1 + i, 1 - i)
... z = np.linspace(old[0], old[1], 6); z array([-1. -1.j , -0.6-0.6j, -0.2-0.2j, 0.2+0.2j, 0.6+0.6j, 1. +1.j ])
>>> new_z = pu.mapdomain(z, old, new); new_z array([-1.0+1.j , -0.6+0.6j, -0.2+0.2j, 0.2-0.2j, 0.6-0.6j, 1.0-1.j ]) # may varySee :
The following pages refer to to this document either explicitly or contain code examples using this.
numpy.polynomial.polyutils.mapparms
numpy.polynomial.polyutils.getdomain
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