adding a colormap creator function. Needs some more work.

pykat/utilities/plotting/colormap.py

+
+import numpy as np
+import matplotlib
+BACKEND = 'Qt4Agg'
+matplotlib.use(BACKEND)
+from matplotlib import rc
+import matplotlib.pyplot as plt
+
+# =============================================================================
+# ====================== The Class ColorMapCreator ============================
+# =============================================================================
+
+class ColorMapCreator:
+    """
+    Class ColorMapCreator:
+    Create diverging colormaps from RGB1 to RGB2 using the method of Moreland
+    or a simple CIELAB-interpolation. numColors controls the number of color
+    values to output (odd number) and divide gives the possibility to output
+    RGB-values from 0.0-1.0 instead of 0-255. If a filename different than
+    "" is given, the colormap will be saved to this file, otherwise a simple
+    output using print will be given.
+    """
+
+    # ======================== Global Variables ===============================
+
+    # Reference white-point D65
+    Xn, Yn, Zn = [95.047, 100.0, 108.883] # from Adobe Cookbook
+
+    # Transfer-matrix for the conversion of RGB to XYZ color space
+    transM = np.array([[0.4124564, 0.2126729, 0.0193339], 
+                        [0.3575761, 0.7151522, 0.1191920],
+                        [0.1804375, 0.0721750, 0.9503041]])
+
+
+    # ============================= Functions =================================
+
+
+    def __init__(self, RGB1, RGB2, numColors = 257., divide = 255., 
+                  method = "moreland", filename = ""):
+        
+        # create a class variable for the number of colors
+        self.numColors = numColors
+        
+        # assert an odd number of points
+        assert np.mod(numColors,2) == 1, \
+            "For diverging colormaps odd numbers of colors are desireable!"
+        
+        # assert a known method was specified
+        knownMethods = ["moreland", "lab"]
+        assert method in knownMethods, "Unknown method was specified!"
+        
+        if method == knownMethods[0]:
+            #generate the Msh diverging colormap
+            self.colorMap = self.generateColorMap(RGB1, RGB2, divide)
+        elif method == knownMethods[1]:
+            # generate the Lab diverging colormap
+            self.colorMap = self.generateColorMapLab(RGB1, RGB2, divide)
+
+    def getMap(self):
+        return self.colorMap
+
+    def showMap(self):
+        #rc('text', usetex=False)
+        a=np.outer(np.arange(0,1,0.01),np.ones(10))
+        fig=plt.figure(99,figsize=(10,2))
+        plt.axis("off")
+        cm = matplotlib.colors.ListedColormap(self.colorMap)
+        pm=plt.imshow(a,aspect='auto',cmap=cm,origin="lower")
+        plt.clim(0,1)
+        fig.colorbar(pm)
+        plt.draw()
+
+        
+    def rgblinear(self, RGB):
+        """
+        Conversion from the sRGB components to RGB components with physically
+        linear properties.
+        """
+        
+        # initialize the linear RGB array
+        RGBlinear = np.zeros((3,)) 
+        
+        #  calculate the linear RGB values
+        for i,value in enumerate(RGB):
+            value = float(value) / 255.
+            if value > 0.04045 :
+                value = ( ( value + 0.055 ) / 1.055 ) ** 2.4
+            else :
+                value = value / 12.92
+            RGBlinear[i] = value * 100.
+        return RGBlinear
+    #-
+
+    def sRGB(self, RGBlinear):
+        """
+        Back conversion from linear RGB to sRGB.
+        """
+        
+        # initialize the sRGB array
+        RGB = np.zeros((3,))
+        
+        #  calculate the sRGB values
+        for i,value in enumerate(RGBlinear):
+            value = float(value) / 100.
+
+            if value > 0.00313080495356037152:
+                value = (1.055 * np.power(value,1./2.4) ) - 0.055
+            else :
+                value = value * 12.92
+
+            RGB[i] = round(value * 255.)
+        return RGB
+    #-
+
+    def rgb2xyz(self, RGB):
+        """
+        Conversion of RGB to XYZ using the transfer-matrix
+        """
+        return np.dot(self.rgblinear(RGB), self.transM)
+    #-
+
+    def xyz2rgb(self, XYZ):
+        """
+        Conversion of RGB to XYZ using the transfer-matrix
+        """
+        #return np.round(np.dot(XYZ, np.array(np.matrix(transM).I)))
+        return self.sRGB(np.dot(XYZ, np.array(np.matrix(self.transM).I)))
+    #-
+
+    def rgb2Lab(self, RGB):
+        """
+        Conversion of RGB to CIELAB
+        """
+        
+        # convert RGB to XYZ
+        X, Y, Z = (self.rgb2xyz(RGB)).tolist()
+        
+        # helper function
+        def f(x):
+            limit = 0.008856
+            if x> limit:
+                return np.power(x, 1./3.)
+            else:
+                return 7.787*x + 16./116.
+        
+        # calculation of L, a and b
+        L = 116. * ( f(Y/self.Yn) - (16./116.) )
+        a = 500. * ( f(X/self.Xn) - f(Y/self.Yn) )
+        b = 200. * ( f(Y/self.Yn) - f(Z/self.Zn) )
+        return np.array([L, a, b])
+    #-
+
+    def Lab2rgb(self, Lab):
+        """
+        Conversion of CIELAB to RGB
+        """
+        
+        # unpack the Lab-array
+        L, a, b = Lab.tolist()
+        
+        # helper function
+        def finverse(x):
+            xlim = 0.008856
+            a = 7.787
+            b = 16./116.
+            ylim = a*xlim+b
+            if x > ylim:
+                return np.power(x, 3)
+            else:
+                return ( x - b ) / a
+            
+        # calculation of X, Y and Z
+        X = self.Xn * finverse( (a/500.) + (L+16.)/116. )
+        Y = self.Yn * finverse( (L+16.)/116. )
+        Z = self.Zn * finverse( (L+16.)/116. - (b/200.) )
+        
+        # conversion of XYZ to RGB
+        return self.xyz2rgb(np.array([X,Y,Z]))
+    #-
+
+    def Lab2Msh(self, Lab):
+        """
+        Conversion of CIELAB to Msh
+        """
+
+        # unpack the Lab-array
+        L, a, b = Lab.tolist()
+
+        # calculation of M, s and h
+        M = np.sqrt(np.sum(np.power(Lab, 2)))
+        s = np.arccos(L/M)
+        h = np.arctan2(b,a)
+        return np.array([M,s,h])
+    #-
+
+    def Msh2Lab(self, Msh):
+        """
+        Conversion of Msh to CIELAB
+        """
+
+        # unpack the Msh-array
+        M, s, h = Msh.tolist()
+
+        # calculation of L, a and b
+        L = M*np.cos(s)
+        a = M*np.sin(s)*np.cos(h)
+        b = M*np.sin(s)*np.sin(h)
+        return np.array([L,a,b])
+    #-
+
+    def rgb2Msh(self, RGB):
+        """ Direct conversion of RGB to Msh. """
+        return self.Lab2Msh(self.rgb2Lab(RGB))
+    #-
+
+    def Msh2rgb(self, Msh):
+        """ Direct conversion of Msh to RGB. """
+        return self.Lab2rgb(self.Msh2Lab(Msh))
+    #-
+
+    def adjustHue(self, MshSat, Munsat):
+        """
+        Function to provide an adjusted hue when interpolating to an 
+        unsaturated color in Msh space.
+        """
+        
+        # unpack the saturated Msh-array
+        Msat, ssat, hsat = MshSat.tolist()
+        
+        if Msat >= Munsat:
+            return hsat
+        else:
+            hSpin = ssat * np.sqrt(Munsat**2 - Msat**2) / \
+                    (Msat * np.sin(ssat))
+            if hsat > -np.pi/3:
+                return hsat + hSpin
+            else:
+                return hsat - hSpin
+    #-
+
+    def interpolateColor(self, RGB1, RGB2, interp):
+        """
+        Interpolation algorithm to automatically create continuous diverging
+        color maps.
+        """
+        
+        # convert RGB to Msh and unpack
+        Msh1 = self.rgb2Msh(RGB1)
+        M1, s1, h1 = Msh1.tolist()
+        Msh2 = self.rgb2Msh(RGB2)
+        M2, s2, h2 = Msh2.tolist()
+        
+        # If points saturated and distinct, place white in middle
+        if (s1>0.05) and (s2>0.05) and ( np.abs(h1-h2) > np.pi/3. ):
+            Mmid = max([M1, M2, 88.])
+            if interp < 0.5:
+                M2 = Mmid
+                s2 = 0.
+                h2 = 0.
+                interp = 2*interp
+            else:
+                M1 = Mmid
+                s1 = 0.
+                h1 = 0.
+                interp = 2*interp-1.
+                
+        # Adjust hue of unsaturated colors
+        if (s1 < 0.05) and (s2 > 0.05):
+            h1 = self.adjustHue(np.array([M2,s2,h2]), M1)
+        elif (s2 < 0.05) and (s1 > 0.05):
+            h2 = self.adjustHue(np.array([M1,s1,h1]), M2)
+            
+        # Linear interpolation on adjusted control points
+        MshMid = (1-interp)*np.array([M1,s1,h1]) + \
+                 interp*np.array([M2,s2,h2])
+        
+        return self.Msh2rgb(MshMid)
+    #-
+
+    def generateColorMap(self, RGB1, RGB2, divide):
+        """
+        Generate the complete diverging color map using the Moreland-technique
+        from RGB1 to RGB2, placing "white" in the middle. The number of points
+        given by "numPoints" controls the resolution of the colormap. The 
+        optional parameter "divide" gives the possibility to scale the whole
+        colormap, for example to have float values from 0 to 1.
+        """
+        
+        # calculate
+        scalars = np.linspace(0., 1., self.numColors)
+        RGBs = np.zeros((self.numColors, 3))
+        for i,s in enumerate(scalars):
+            RGBs[i,:] = self.interpolateColor(RGB1, RGB2, s)
+        return RGBs/divide
+    #-
+
+    def generateColorMapLab(self, RGB1, RGB2, divide):
+        """
+        Generate the complete diverging color map using a transition from
+        Lab1 to Lab2, transitioning true RGB-white. The number of points
+        given by "numPoints" controls the resolution of the colormap. The 
+        optional parameter "divide" gives the possibility to scale the whole
+        colormap, for example to have float values from 0 to 1.
+        """
+        
+        # convert to Lab-space
+        Lab1 = self.rgb2Lab(RGB1)
+        Lab2 = self.rgb2Lab(RGB2)
+        LabWhite = np.array([100., 0., 0.])
+        
+        # initialize the resulting arrays
+        Lab = np.zeros((self.numColors ,3))
+        RGBs = np.zeros((self.numColors ,3))
+        N2 = np.floor(self.numColors/2.)
+        
+        # calculate
+        for i in range(3):
+            Lab[0:N2+1, i] = np.linspace(Lab1[i], LabWhite[i], N2+1)
+            Lab[N2:, i] = np.linspace(LabWhite[i], Lab2[i], N2+1)
+        for i,l in enumerate(Lab):
+            RGBs[i,:] = self.Lab2rgb(l)
+        return RGBs/divide
+    #-
+