condensed multiple import statements into general one at module start.
This commit is contained in:
Philip Eisenlohr
parent
a5b3e9d64c
commit
83547ce29f
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@ -1,5 +1,7 @@
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# -*- coding: UTF-8 no BOM -*-
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import math,numpy as np
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### --- COLOR CLASS --------------------------------------------------
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class Color():
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@ -8,7 +10,6 @@ class Color():
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Color('model',[vector]).To convert and copy color from one space to other, use the methods
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convertTo('model') and expressAs('model')spectively
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'''
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import numpy
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__slots__ = [
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'model',
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@ -19,8 +20,7 @@ class Color():
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# ------------------------------------------------------------------
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def __init__(self,
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model = 'RGB',
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color = numpy.zeros(3,'d')):
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import numpy
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color = np.zeros(3,'d')):
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self.__transforms__ = \
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{'HSL': {'index': 0, 'next': self._HSL2RGB},
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@ -42,7 +42,7 @@ class Color():
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while color[0] < 0.0: color[0] += 1.0 # rewind to proper range
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self.model = model
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self.color = numpy.array(color,'d')
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self.color = np.array(color,'d')
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# ------------------------------------------------------------------
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@ -83,7 +83,7 @@ class Color():
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# with S,L,H,R,G,B running from 0 to 1
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# from http://en.wikipedia.org/wiki/HSL_and_HSV
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def _HSL2RGB(self):
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import numpy
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if self.model != 'HSL': return
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sextant = self.color[0]*6.0
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@ -91,7 +91,7 @@ class Color():
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x = c*(1.0 - abs(sextant%2 - 1.0))
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m = self.color[2] - 0.5*c
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converted = Color('RGB',numpy.array([
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converted = Color('RGB',np.array([
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[c+m, x+m, m],
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[x+m, c+m, m],
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[m, c+m, x+m],
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@ -108,10 +108,10 @@ class Color():
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# with S,L,H,R,G,B running from 0 to 1
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# from http://130.113.54.154/~monger/hsl-rgb.html
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def _RGB2HSL(self):
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import numpy
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if self.model != 'RGB': return
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HSL = numpy.zeros(3,'d')
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HSL = np.zeros(3,'d')
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maxcolor = self.color.max()
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mincolor = self.color.min()
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HSL[2] = (maxcolor + mincolor)/2.0
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@ -146,19 +146,19 @@ class Color():
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# with all values in the range of 0 to 1
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# from http://www.cs.rit.edu/~ncs/color/t_convert.html
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def _RGB2XYZ(self):
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import numpy
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if self.model != 'RGB': return
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XYZ = numpy.zeros(3,'d')
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RGB_lin = numpy.zeros(3,'d')
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convert = numpy.array([[0.412453,0.357580,0.180423],
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XYZ = np.zeros(3,'d')
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RGB_lin = np.zeros(3,'d')
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convert = np.array([[0.412453,0.357580,0.180423],
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[0.212671,0.715160,0.072169],
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[0.019334,0.119193,0.950227]])
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for i in xrange(3):
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if (self.color[i] > 0.04045): RGB_lin[i] = ((self.color[i]+0.0555)/1.0555)**2.4
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else: RGB_lin[i] = self.color[i] /12.92
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XYZ = numpy.dot(convert,RGB_lin)
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XYZ = np.dot(convert,RGB_lin)
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for i in xrange(3):
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XYZ[i] = max(XYZ[i],0.0)
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@ -173,14 +173,14 @@ class Color():
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# with all values in the range of 0 to 1
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# from http://www.cs.rit.edu/~ncs/color/t_convert.html
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def _XYZ2RGB(self):
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import numpy
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if self.model != 'XYZ': return
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convert = numpy.array([[ 3.240479,-1.537150,-0.498535],
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convert = np.array([[ 3.240479,-1.537150,-0.498535],
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[-0.969256, 1.875992, 0.041556],
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[ 0.055648,-0.204043, 1.057311]])
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RGB_lin = numpy.dot(convert,self.color)
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RGB = numpy.zeros(3,'d')
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RGB_lin = np.dot(convert,self.color)
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RGB = np.zeros(3,'d')
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for i in xrange(3):
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if (RGB_lin[i] > 0.0031308): RGB[i] = ((RGB_lin[i])**(1.0/2.4))*1.0555-0.0555
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@ -202,11 +202,11 @@ class Color():
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# with XYZ in the range of 0 to 1
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# from http://www.easyrgb.com/index.php?X=MATH&H=07#text7
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def _CIELAB2XYZ(self):
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import numpy
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if self.model != 'CIELAB': return
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ref_white = numpy.array([.95047, 1.00000, 1.08883],'d') # Observer = 2, Illuminant = D65
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XYZ = numpy.zeros(3,'d')
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ref_white = np.array([.95047, 1.00000, 1.08883],'d') # Observer = 2, Illuminant = D65
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XYZ = np.zeros(3,'d')
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XYZ[1] = (self.color[0] + 16.0 ) / 116.0
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XYZ[0] = XYZ[1] + self.color[1]/ 500.0
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@ -226,17 +226,17 @@ class Color():
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# with XYZ in the range of 0 to 1
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# from http://en.wikipedia.org/wiki/Lab_color_space, http://www.cs.rit.edu/~ncs/color/t_convert.html
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def _XYZ2CIELAB(self):
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import numpy
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if self.model != 'XYZ': return
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ref_white = numpy.array([.95047, 1.00000, 1.08883],'d') # Observer = 2, Illuminant = D65
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ref_white = np.array([.95047, 1.00000, 1.08883],'d') # Observer = 2, Illuminant = D65
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XYZ = self.color/ref_white
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for i in xrange(len(XYZ)):
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if (XYZ[i] > 216./24389 ): XYZ[i] = XYZ[i]**(1.0/3.0)
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else: XYZ[i] = (841./108. * XYZ[i]) + 16.0/116.0
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converted = Color('CIELAB', numpy.array([ 116.0 * XYZ[1] - 16.0,
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converted = Color('CIELAB', np.array([ 116.0 * XYZ[1] - 16.0,
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500.0 * (XYZ[0] - XYZ[1]),
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200.0 * (XYZ[1] - XYZ[2]) ]))
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self.model = converted.model
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@ -247,11 +247,11 @@ class Color():
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# convert CIE Lab to Msh colorspace
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# from http://www.cs.unm.edu/~kmorel/documents/ColorMaps/DivergingColorMapWorkshop.xls
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def _CIELAB2MSH(self):
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import numpy, math
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if self.model != 'CIELAB': return
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Msh = numpy.zeros(3,'d')
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Msh[0] = math.sqrt(numpy.dot(self.color,self.color))
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Msh = np.zeros(3,'d')
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Msh[0] = math.sqrt(np.dot(self.color,self.color))
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if (Msh[0] > 0.001):
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Msh[1] = math.acos(self.color[0]/Msh[0])
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if (self.color[1] != 0.0):
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@ -267,10 +267,10 @@ class Color():
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# s,h in radians
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# from http://www.cs.unm.edu/~kmorel/documents/ColorMaps/DivergingColorMapWorkshop.xls
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def _MSH2CIELAB(self):
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import numpy, math
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if self.model != 'MSH': return
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Lab = numpy.zeros(3,'d')
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Lab = np.zeros(3,'d')
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Lab[0] = self.color[0] * math.cos(self.color[1])
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Lab[1] = self.color[0] * math.sin(self.color[1]) * math.cos(self.color[2])
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Lab[2] = self.color[0] * math.sin(self.color[1]) * math.sin(self.color[2])
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@ -346,8 +346,6 @@ class Colormap():
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def interpolate_Msh(lo, hi, frac):
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import math,numpy as np
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def rad_diff(a,b):
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return abs(a[2]-b[2])
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@ -381,8 +379,8 @@ class Colormap():
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linearly interpolate color at given fraction between lower and higher color in model of lower color
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'''
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interpolation = (1.0 - frac) * numpy.array(lo.color[:]) \
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+ frac * numpy.array(hi.expressAs(lo.model).color[:])
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interpolation = (1.0 - frac) * np.array(lo.color[:]) \
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+ frac * np.array(hi.expressAs(lo.model).color[:])
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return Color(lo.model,interpolation)
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@ -411,7 +409,7 @@ class Colormap():
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'''
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format = format.lower() # consistent comparison basis
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frac = 0.5*(numpy.array(crop) + 1.0) # rescale crop range to fractions
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frac = 0.5*(np.array(crop) + 1.0) # rescale crop range to fractions
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colors = [self.color(float(i)/(steps-1)*(frac[1]-frac[0])+frac[0]).expressAs(model).color for i in xrange(steps)]
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if format == 'paraview':
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