numpy.polynomial.polyutils.
mapdomain
Apply linear map to input points.
The linear map offset + scale*x that maps the domain old to the domain new is applied to the points x.
offset + scale*x
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.
See also
getdomain, mapparms
getdomain
mapparms
Notes
Effectively, this implements:
System Message: WARNING/2 (x\_out = new[0] + m(x - old[0]) )
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where
System Message: WARNING/2 (m = \frac{new[1]-new[0]}{old[1]-old[0]} )
Examples
>>> 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 vary
