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DAMASK_EICMD
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DAMASK_EICMD/lib/damask/colormaps.py

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#!/usr/bin/env python
### --- 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 ='<ColorMap name="'+str(name)+'" space="Diverging">\n'
for i in range(len(RGB_vector)):
colormap+='<Point x="'+str(i)+'" o="1" r="'+str(RGB_vector[i][0])+'" g="'+str(RGB_vector[i][1])+'" b="'+str(RGB_vector[i][2])+'"/>\n'
colormap+='</ColorMap>'
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}'
def write_raw(RGB_vector):
return '\n'.join(['%s'%('\t'.join(map(lambda x:str(x),v))) for v in RGB_vector])
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))
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