# -*- coding: UTF-8 no BOM -*- ### --- COLOR CLASS -------------------------------------------------- class Color(): ''' Conversion of colors between different color-spaces. Colors should be given in the form Color('model',[vector]).To convert and copy color from one space to other, use the methods convertTo('model') and expressAs('model')spectively ''' import numpy __slots__ = [ 'model', 'color', ] # ------------------------------------------------------------------ def __init__(self, model = 'RGB', color = numpy.zeros(3,'d')): import numpy self.__transforms__ = \ {'HSL': {'index': 0, 'next': self._HSL2RGB}, 'RGB': {'index': 1, 'next': self._RGB2XYZ, 'prev': self._RGB2HSL}, 'XYZ': {'index': 2, 'next': self._XYZ2CIELAB, 'prev': self._XYZ2RGB}, 'CIELAB': {'index': 3, 'next': self._CIELAB2MSH, 'prev': self._CIELAB2XYZ}, 'MSH': {'index': 4, 'prev': self._MSH2CIELAB}, } model = model.upper() if model not in self.__transforms__.keys(): model = 'RGB' if model == 'RGB' and max(color) > 1.0: # are we RGB255 ? for i in range(3): color[i] /= 255.0 # rescale to RGB if model == 'HSL': # are we HSL ? if abs(color[0]) > 1.0: color[0] /= 360.0 # with angular hue? while color[0] >= 1.0: color[0] -= 1.0 # rewind to proper range while color[0] < 0.0: color[0] += 1.0 # rewind to proper range self.model = model self.color = numpy.array(color,'d') # ------------------------------------------------------------------ def __repr__(self): return 'Model: %s Color: %s'%(self.model,str(self.color)) # ------------------------------------------------------------------ def __str__(self): return self.__repr__() # ------------------------------------------------------------------ def convertTo(self,toModel = 'RGB'): toModel = toModel.upper() if toModel not in self.__transforms__.keys(): return sourcePos = self.__transforms__[self.model]['index'] targetPos = self.__transforms__[toModel]['index'] while sourcePos < targetPos: self.__transforms__[self.model]['next']() sourcePos += 1 while sourcePos > targetPos: self.__transforms__[self.model]['prev']() sourcePos -= 1 return self # ------------------------------------------------------------------ def expressAs(self,asModel = 'RGB'): return self.__class__(self.model,self.color).convertTo(asModel) # ------------------------------------------------------------------ # convert H(ue) S(aturation) L(uminance) to R(red) G(reen) B(lue) # with S,L,H,R,G,B running from 0 to 1 # from http://en.wikipedia.org/wiki/HSL_and_HSV def _HSL2RGB(self): import numpy if self.model != 'HSL': return sextant = self.color[0]*6.0 c = (1.0 - abs(2.0 * self.color[2] - 1.0))*self.color[1] x = c*(1.0 - abs(sextant%2 - 1.0)) m = self.color[2] - 0.5*c converted = Color('RGB',numpy.array([ [c+m, x+m, m], [x+m, c+m, m], [m, c+m, x+m], [m, x+m, c+m], [x+m, m, c+m], [c+m, m, x+m], ][int(sextant)],'d')) self.model = converted.model self.color = converted.color # ------------------------------------------------------------------ # convert R(ed) G(reen) B(lue) to H(ue) S(aturation) L(uminance) # with S,L,H,R,G,B running from 0 to 1 # from http://130.113.54.154/~monger/hsl-rgb.html def _RGB2HSL(self): import numpy if self.model != 'RGB': return HSL = numpy.zeros(3,'d') maxcolor = self.color.max() mincolor = self.color.min() HSL[2] = (maxcolor + mincolor)/2.0 if(mincolor == maxcolor): HSL[0] = 0.0 HSL[1] = 0.0 else: if (HSL[2]<0.5): HSL[1] = (maxcolor - mincolor)/(maxcolor + mincolor) else: HSL[1] = (maxcolor - mincolor)/(2.0 - maxcolor - mincolor) if (maxcolor == self.color[0]): HSL[0] = 0.0 + (self.color[1] - self.color[2])/(maxcolor - mincolor) elif (maxcolor == self.color[1]): HSL[0] = 2.0 + (self.color[2] - self.color[0])/(maxcolor - mincolor) elif (maxcolor == self.color[2]): HSL[0] = 4.0 + (self.color[0] - self.color[1])/(maxcolor - mincolor) HSL[0] = HSL[0]*60.0 # is it necessary to scale to 360 hue values? might be dangerous for small values <1..! if (HSL[0] < 0.0): HSL[0] = HSL[0] + 360.0 for i in xrange(2): HSL[i+1] = min(HSL[i+1],1.0) HSL[i+1] = max(HSL[i+1],0.0) converted = Color('HSL', HSL) self.model = converted.model self.color = converted.color # ------------------------------------------------------------------ # convert R(ed) G(reen) B(lue) to CIE XYZ # with all values in the range of 0 to 1 # from http://www.cs.rit.edu/~ncs/color/t_convert.html def _RGB2XYZ(self): import numpy if self.model != 'RGB': return XYZ = numpy.zeros(3,'d') RGB_lin = numpy.zeros(3,'d') convert = numpy.array([[0.412453,0.357580,0.180423], [0.212671,0.715160,0.072169], [0.019334,0.119193,0.950227]]) for i in xrange(3): if (self.color[i] > 0.04045): RGB_lin[i] = ((self.color[i]+0.0555)/1.0555)**2.4 else: RGB_lin[i] = self.color[i] /12.92 XYZ = numpy.dot(convert,RGB_lin) for i in xrange(3): XYZ[i] = max(XYZ[i],0.0) converted = Color('XYZ', XYZ) self.model = converted.model self.color = converted.color # ------------------------------------------------------------------ # convert CIE XYZ to R(ed) G(reen) B(lue) # with all values in the range of 0 to 1 # from http://www.cs.rit.edu/~ncs/color/t_convert.html def _XYZ2RGB(self): import numpy if self.model != 'XYZ': return convert = numpy.array([[ 3.240479,-1.537150,-0.498535], [-0.969256, 1.875992, 0.041556], [ 0.055648,-0.204043, 1.057311]]) RGB_lin = numpy.dot(convert,self.color) RGB = numpy.zeros(3,'d') for i in xrange(3): if (RGB_lin[i] > 0.0031308): RGB[i] = ((RGB_lin[i])**(1.0/2.4))*1.0555-0.0555 else: RGB[i] = RGB_lin[i] *12.92 for i in xrange(3): RGB[i] = min(RGB[i],1.0) RGB[i] = max(RGB[i],0.0) maxVal = max(RGB) # clipping colors according to the display gamut if (maxVal > 1.0): RGB /= maxVal converted = Color('RGB', RGB) self.model = converted.model self.color = converted.color # ------------------------------------------------------------------ # convert CIE Lab to CIE XYZ # with XYZ in the range of 0 to 1 # from http://www.easyrgb.com/index.php?X=MATH&H=07#text7 def _CIELAB2XYZ(self): import numpy if self.model != 'CIELAB': return ref_white = numpy.array([.95047, 1.00000, 1.08883],'d') # Observer = 2, Illuminant = D65 XYZ = numpy.zeros(3,'d') XYZ[1] = (self.color[0] + 16.0 ) / 116.0 XYZ[0] = XYZ[1] + self.color[1]/ 500.0 XYZ[2] = XYZ[1] - self.color[2]/ 200.0 for i in xrange(len(XYZ)): if (XYZ[i] > 6./29. ): XYZ[i] = XYZ[i]**3. else: XYZ[i] = 108./841. * (XYZ[i] - 4./29.) converted = Color('XYZ', XYZ*ref_white) self.model = converted.model self.color = converted.color # ------------------------------------------------------------------ # convert CIE XYZ to CIE Lab # with XYZ in the range of 0 to 1 # from http://en.wikipedia.org/wiki/Lab_color_space, http://www.cs.rit.edu/~ncs/color/t_convert.html def _XYZ2CIELAB(self): import numpy if self.model != 'XYZ': return ref_white = numpy.array([.95047, 1.00000, 1.08883],'d') # Observer = 2, Illuminant = D65 XYZ = self.color/ref_white for i in xrange(len(XYZ)): if (XYZ[i] > 216./24389 ): XYZ[i] = XYZ[i]**(1.0/3.0) else: XYZ[i] = (841./108. * XYZ[i]) + 16.0/116.0 converted = Color('CIELAB', numpy.array([ 116.0 * XYZ[1] - 16.0, 500.0 * (XYZ[0] - XYZ[1]), 200.0 * (XYZ[1] - XYZ[2]) ])) self.model = converted.model self.color = converted.color # ------------------------------------------------------------------ # convert CIE Lab to Msh colorspace # from http://www.cs.unm.edu/~kmorel/documents/ColorMaps/DivergingColorMapWorkshop.xls def _CIELAB2MSH(self): import numpy, math if self.model != 'CIELAB': return Msh = numpy.zeros(3,'d') Msh[0] = math.sqrt(numpy.dot(self.color,self.color)) if (Msh[0] > 0.001): Msh[1] = math.acos(self.color[0]/Msh[0]) if (self.color[1] != 0.0): Msh[2] = math.atan2(self.color[2],self.color[1]) converted = Color('MSH', Msh) self.model = converted.model self.color = converted.color # ------------------------------------------------------------------ # convert Msh colorspace to CIE Lab # s,h in radians # from http://www.cs.unm.edu/~kmorel/documents/ColorMaps/DivergingColorMapWorkshop.xls def _MSH2CIELAB(self): import numpy, math if self.model != 'MSH': return Lab = numpy.zeros(3,'d') Lab[0] = self.color[0] * math.cos(self.color[1]) Lab[1] = self.color[0] * math.sin(self.color[1]) * math.cos(self.color[2]) Lab[2] = self.color[0] * math.sin(self.color[1]) * math.sin(self.color[2]) converted = Color('CIELAB', Lab) self.model = converted.model self.color = converted.color ### --- COLORMAP CLASS ----------------------------------------------- class Colormap(): ''' perceptually uniform diverging or sequential colormaps. ''' __slots__ = [ 'left', 'right', 'interpolate', ] __predefined__ = { 'gray': {'left': Color('HSL',[0,1,1]), 'right': Color('HSL',[0,0,0.15]), 'interpolate': 'perceptualuniform'}, 'grey': {'left': Color('HSL',[0,1,1]), 'right': Color('HSL',[0,0,0.15]), 'interpolate': 'perceptualuniform'}, 'red': {'left': Color('HSL',[0,1,0.14]), 'right': Color('HSL',[0,0.35,0.91]), 'interpolate': 'perceptualuniform'}, 'green': {'left': Color('HSL',[0.33333,1,0.14]), 'right': Color('HSL',[0.33333,0.35,0.91]), 'interpolate': 'perceptualuniform'}, 'blue': {'left': Color('HSL',[0.66,1,0.14]), 'right': Color('HSL',[0.66,0.35,0.91]), 'interpolate': 'perceptualuniform'}, 'seaweed': {'left': Color('HSL',[0.78,1.0,0.1]), 'right': Color('HSL',[0.40000,0.1,0.9]), 'interpolate': 'perceptualuniform'}, 'bluebrown': {'left': Color('HSL',[0.65,0.53,0.49]), 'right': Color('HSL',[0.11,0.75,0.38]), 'interpolate': 'perceptualuniform'}, 'redgreen': {'left': Color('HSL',[0.97,0.96,0.36]), 'right': Color('HSL',[0.33333,1.0,0.14]), 'interpolate': 'perceptualuniform'}, 'bluered': {'left': Color('HSL',[0.65,0.53,0.49]), 'right': Color('HSL',[0.97,0.96,0.36]), 'interpolate': 'perceptualuniform'}, 'blueredrainbow':{'left': Color('HSL',[2.0/3.0,1,0.5]), 'right': Color('HSL',[0,1,0.5]), 'interpolate': 'linear' }, } # ------------------------------------------------------------------ def __init__(self, left = Color('RGB',[1,1,1]), right = Color('RGB',[0,0,0]), interpolate = 'perceptualuniform', predefined = None ): if str(predefined).lower() in self.__predefined__: left = self.__predefined__[predefined.lower()]['left'] right= self.__predefined__[predefined.lower()]['right'] interpolate = self.__predefined__[predefined.lower()]['interpolate'] if left.__class__.__name__ != 'Color': left = Color() if right.__class__.__name__ != 'Color': right = Color() self.left = left self.right = right self.interpolate = interpolate # ------------------------------------------------------------------ def __repr__(self): return 'Left: %s Right: %s'%(self.left,self.right) # ------------------------------------------------------------------ def invert(self): (self.left, self.right) = (self.right, self.left) return self # ------------------------------------------------------------------ def export(self,name = 'uniformPerceptualColorMap',\ format = 'paraview',\ steps = 2,\ crop = [-1.0,1.0], model = 'RGB'): ''' [RGB] colormap for use in paraview or gmsh, or as raw string, or array. arguments: name, format, steps, crop. format is one of (paraview, gmsh, raw, list). crop selects a (sub)range in [-1.0,1.0]. generates sequential map if one limiting color is either white or black, diverging map otherwise. ''' import copy,numpy,math def interpolate_Msh(lo, hi, frac): def rad_diff(a,b): return abs(a[2]-b[2]) def adjust_hue(Msh_sat, Msh_unsat): # if saturation of one of the two colors is too less than the other, hue of the less if Msh_sat[0] >= Msh_unsat[0]: return Msh_sat[2] else: hSpin = Msh_sat[1]/math.sin(Msh_sat[1])*math.sqrt(Msh_unsat[0]**2.0-Msh_sat[0]**2)/Msh_sat[0] if Msh_sat[2] < - math.pi/3.0: hSpin *= -1.0 return Msh_sat[2] + hSpin Msh1 = numpy.array(lo[:]) Msh2 = numpy.array(hi[:]) if (Msh1[1] > 0.05 and Msh2[1] > 0.05 and rad_diff(Msh1,Msh2) > math.pi/3.0): M_mid = max(Msh1[0],Msh2[0],88.0) if frac < 0.5: Msh2 = numpy.array([M_mid,0.0,0.0],'d') frac *= 2.0 else: Msh1 = numpy.array([M_mid,0.0,0.0],'d') frac = 2.0*frac - 1.0 if Msh1[1] < 0.05 and Msh2[1] > 0.05: Msh1[2] = adjust_hue(Msh2,Msh1) elif Msh1[1] > 0.05 and Msh2[1] < 0.05: Msh2[2] = adjust_hue(Msh1,Msh2) Msh = (1.0 - frac) * Msh1 + frac * Msh2 return Color('MSH',Msh) def interpolate_linear(lo, hi, frac): ''' linearly interpolate color at given fraction between lower and higher color in model of lower color ''' interpolation = (1.0 - frac) * numpy.array(lo.color[:]) \ + frac * numpy.array(hi.expressAs(lo.model).color[:]) return Color(lo.model,interpolation) def write_paraview(RGB_vector): colormap = '\n' for i in range(len(RGB_vector)): colormap += '\n'%(RGB_vector[i][0],RGB_vector[i][1],RGB_vector[i][2]) colormap += '\n' return colormap def write_gmsh(RGB_vector): return 'View.ColorTable = {\n' \ + ',\n'.join(['{%s}'%(','.join(map(lambda x:str(x*255.0),v))) for v in RGB_vector]) \ + '\n}\n' def write_raw(RGB_vector): return '\n'.join(['%s'%('\t'.join(map(lambda x:str(x),v))) for v in RGB_vector]) \ + '\n' def write_GOM(RGB_vector): return '1 1 ' + str(name) + ' 9 ' + str(name) + ' 0 1 0 3 0 0 -1 9 \ 0 0 0 255 255 255 0 0 255 ' \ + '30 NO_UNIT 1 1 64 64 64 255 1 0 0 0 0 0 0 3 0 ' + str(len(RGB_vector)) \ + ' '.join([' 0 %s 255 1'%(' '.join(map(lambda x:str(int(x*255.0)),v))) for v in reversed(RGB_vector)])+' ' colors = [] frac = (numpy.array(crop) + 1.0)/2.0 if self.interpolate == 'perceptualuniform': for i in range(steps): colors.append(interpolate_Msh(self.left.expressAs('MSH').color, self.right.expressAs('MSH').color, float(i)/(steps-1)*(frac[1]-frac[0])+frac[0])) elif self.interpolate == 'linear': for i in range(steps): colors.append(interpolate_linear(self.left, self.right, float(i)/(steps-1)*(frac[1]-frac[0])+frac[0])) else: raise NameError('unknown interpolation method') return {\ 'paraview': write_paraview, 'gmsh': write_gmsh, 'gom': write_GOM, 'raw': write_raw, 'list': lambda x: x, }[format.lower()](map(lambda x:x.expressAs(model).color,colors))