From 9bb2a24692ca438279fdfc6b5c6f662642df765e Mon Sep 17 00:00:00 2001
From: Daniel Brown <ddb@star.sr.bham.ac.uk>
Date: Tue, 1 Dec 2015 16:22:42 +0000
Subject: [PATCH] first go at putting in plotting functions
---
bin/test_plot.py | 13 +-
pykat/__init__.py | 4 +-
pykat/components.py | 4 +-
pykat/detectors.py | 1 +
pykat/finesse.py | 86 ++-
pykat/plotting.py | 142 +++--
pykat/profiling.py | 6 +-
pykat/tools/modematching.py | 4 +-
pykat/tools/plotting/beamtrace.py | 3 +-
pykat/tools/plotting/colormap.py | 644 ++++++++++----------
pykat/tools/plotting/tools.py | 974 +++++++++++++++---------------
11 files changed, 983 insertions(+), 898 deletions(-)
diff --git a/bin/test_plot.py b/bin/test_plot.py
index 7f14ab2..2b41826 100644
--- a/bin/test_plot.py
+++ b/bin/test_plot.py
@@ -1,3 +1,6 @@
+import pykat
+pykat.init_pykat_plotting(mode="display", dpi=100)
+
from pykat import finesse
from pykat.detectors import *
from pykat.components import *
@@ -5,7 +8,6 @@ from pykat.commands import *
from pykat.structs import *
import numpy as np
-import pylab as pl
code = """
l l1 1 0 0 n1
@@ -15,10 +17,12 @@ s s2 10 1 n3 n4
m m2 0.5 0.5 0 n4 n5
s s3 10 1 n5 n6
-yaxis abs:deg
+yaxis re:im
+ad circ 0 0 0 n4
pd pd_cav n3
+
cav c1 m1 n3 m2 n4
attr m1 Rc 1
@@ -27,7 +31,7 @@ attr m1 Rc 1
kat = finesse.kat()
kat.parseCommands(code)
-kat.add(xaxis("lin", [0, 360], kat.m2.phi, 100))
+kat.add(xaxis("lin", [0, 360], kat.m2.phi, 500))
kat.m1.Rcx = -1000.0
kat.m1.Rcy = -1000.0
@@ -37,5 +41,4 @@ kat.m2.Rcy = 1000.0
kat.maxtem = 0
out = kat.run()
-out.plot()
-
+fig = out.plot("test_plot.pdf")
diff --git a/pykat/__init__.py b/pykat/__init__.py
index 420f930..647d331 100644
--- a/pykat/__init__.py
+++ b/pykat/__init__.py
@@ -8,7 +8,9 @@ __version__ = "0.8.1"
# This flag is used to switch on the gui features in pkat at import time
USE_GUI = False
HAS_OPTIVIS = False
+
import imp
+
try:
imp.find_module('optivis')
HAS_OPTIVIS = True
@@ -26,6 +28,6 @@ import pykat.commands as commands
from pykat.optics.gaussian_beams import beam_param
-
+from pykat.plotting import init_pykat_plotting
diff --git a/pykat/components.py b/pykat/components.py
index 0bb6bbc..2f38226 100644
--- a/pykat/components.py
+++ b/pykat/components.py
@@ -1063,7 +1063,7 @@ class modulator(Component):
self._requested_node_names.append(node1)
self._requested_node_names.append(node2)
self._svgItem = None
- self.__f = Param("f", self, SIfloat(f))
+ self.__f = Param("f", self, SIfloat(f), canFsig=True, fsig_name="fre")
self.__midx = Param("midx", self, SIfloat(midx))
self.__phase = Param("phase", self, SIfloat(phase), canFsig=True, fsig_name="phase")
self.__order = int(order)
@@ -1171,7 +1171,7 @@ class laser(Component):
self._requested_node_names.append(node)
self.__power = Param("P", self, SIfloat(P), canFsig=True, fsig_name="amp")
- self.__f_offset = Param("f", self, SIfloat(f), canFsig=True, fsig_name="f")
+ self.__f_offset = Param("f", self, SIfloat(f), canFsig=True, fsig_name="freq")
self.__phase = Param("phase", self, SIfloat(phase), canFsig=True, fsig_name="phase")
self.__noise = AttrParam("noise", self, None)
self._svgItem = None
diff --git a/pykat/detectors.py b/pykat/detectors.py
index 2628531..ec28374 100644
--- a/pykat/detectors.py
+++ b/pykat/detectors.py
@@ -65,6 +65,7 @@ class BaseDetector(object) :
self._mask = {}
self.__scale = []
self.__removed = False
+ self.noplot = False
self._alternate_beam = []
self._nodes = []
diff --git a/pykat/finesse.py b/pykat/finesse.py
index 66f9567..36744be 100644
--- a/pykat/finesse.py
+++ b/pykat/finesse.py
@@ -240,13 +240,74 @@ class katRun(object):
self.katVersion = None
self.yaxis = None
- def plot(self, logy=False):
- import pylab
+ def plot(self, filename=None, show=True):
+ import matplotlib.pyplot as pyplot
+ import pykat.plotting as plt
+
+ kat = pykat.finesse.kat()
+ kat.verbose = False
+ kat.parseCommands(self.katScript)
+
+ plot_cmd = None
+
+ if "log" in kat.yaxis:
+ if kat.xaxis.scale == "log":
+ plot_cmd = pyplot.loglog
+ else:
+ plot_cmd = pyplot.semilogy
+ else:
+ if kat.xaxis.scale == "log":
+ plot_cmd = pyplot.semilogx
+ else:
+ plot_cmd = pyplot.plot
+
+ dual_plot = False
+
+ if ":" in kat.yaxis:
+ fig = plt.figure(width="full", height=1)
+ dual_plot = True
+ else:
+ fig = plt.figure(width="full")
+
+ for lbl in self.ylabels:
+ lbl = lbl.split()[0]
+ if not dual_plot:
+ plot_cmd(self.x, np.abs(self[lbl]))
+ else:
+ pyplot.subplot(2,1,1)
+ plot_cmd(self.x, np.abs(self[lbl]))
- pylab.plot(self.x, self.y)
- pylab.legend(self.ylabels,0)
- pylab.xlabel(self.xlabel)
- pylab.show()
+ pyplot.subplot(2,1,2)
+ plot_cmd(self.x, np.angle(self[lbl]))
+
+ if dual_plot:
+ pyplot.subplot(2,1,1)
+ pyplot.xlabel(self.xlabel, fontsize=pyplot.rcParams["font.size"])
+ pyplot.xlim(self.x.min(), self.x.max())
+
+ if "abs" in kat.yaxis: pyplot.ylabel("Absolute [au]", fontsize=pyplot.rcParams["font.size"])
+ if "re" in kat.yaxis: pyplot.ylabel("Real part [au]", fontsize=pyplot.rcParams["font.size"])
+
+ pyplot.subplot(2,1,2)
+ pyplot.xlabel(self.xlabel, fontsize=pyplot.rcParams["font.size"])
+ pyplot.xlim(self.x.min(), self.x.max())
+
+ if "deg" in kat.yaxis: pyplot.ylabel("Phase [deg]", fontsize=pyplot.rcParams["font.size"])
+ if "im" in kat.yaxis: pyplot.ylabel("Imaginary part [au]", fontsize=pyplot.rcParams["font.size"])
+ else:
+ pyplot.xlabel(self.xlabel, fontsize=pyplot.rcParams["font.size"])
+ pyplot.ylabel(" [au]")
+ pyplot.xlim(self.x.min(), self.x.max())
+
+ fig.tight_layout()
+
+ if filename is not None:
+ fig.savefig(filename)
+
+ if show:
+ pyplot.show(fig)
+
+ return fig
def savekatRun(self, filename):
with open(filename,'w') as outfile:
@@ -916,8 +977,7 @@ class kat(object):
elif(first == "fsig"):
after_process.append((line, self.__currentTag))
elif(first == "noplot"):
- obj = line
- #self.__blocks[self.__currentTag].contents.append(line)
+ after_process.append((line, self.__currentTag))
else:
if self.verbose:
print ("Parsing `{0}` into pykat object not implemented yet, added as extra line.".format(line))
@@ -938,7 +998,7 @@ class kat(object):
# components to exist first before they can be processed
for item in after_process:
line = item[0]
- first = line.split(" ",1)[0]
+ first, rest = line.split(" ",1)
block = item[1]
if first == "gauss" or first == "gauss*" or first == "gauss**":
@@ -955,6 +1015,11 @@ class kat(object):
elif (first == "variable"):
self.add(pykat.commands.variable.parseFinesseText(line, self), block=block)
+ elif (first == "noplot"):
+ if not hasattr(self, rest):
+ raise pkex.BasePyKatException("noplot command `{0}` refers to non-existing detector".format(line))
+
+ getattr(self, rest).noplot = True
elif (first == "scale"):
v = line.split()
@@ -1264,7 +1329,7 @@ class kat(object):
err="".join((err,str(line, 'utf-8')))
- [out,errpipe] = p.communicate()
+ [out, errpipe] = p.communicate()
if six.PY2:
_out = str(out).split("\n")
@@ -1286,6 +1351,7 @@ class kat(object):
# get the version number
ix = out.find(b'build ') + 6
ix2 = out.find(b')',ix)
+
r.katVersion = out[ix:ix2]
r.runDateTime = datetime.datetime.now()
diff --git a/pykat/plotting.py b/pykat/plotting.py
index 7abd88c..095b734 100644
--- a/pykat/plotting.py
+++ b/pykat/plotting.py
@@ -9,79 +9,87 @@ from __future__ import division
from __future__ import print_function
from __future__ import unicode_literals
-import numpy as np
-import matplotlib
+# Should be either display (showing in windows) or paper (saving for paper/report/etc.)
+__mode__ = None
+__DPI__ = None
-BACKEND = 'Qt4Agg'
-matplotlib.use(BACKEND)
-
-from matplotlib import rc
-import matplotlib.pyplot as plt
-
-mainpid = -1
-
-def plot1D(run, title=""):
+def in_ipython():
+ try:
+ cfg = get_ipython()
+ return True
+ except NameError:
+ return False
+
+
+def init_pykat_plotting(mode="display", dpi=100):
+ import matplotlib as mpl
- rc('font', **pp.font)
- rc('xtick',labelsize=pp.TICK_SIZE)
- rc('ytick',labelsize=pp.TICK_SIZE)
- rc('text', usetex=pp.USETEX)
- rc('axes', labelsize = pp.LABEL_SIZE)
+ __DPI__ = int(dpi)
- fig=plt.figure()
- fig.set_size_inches(pp.fig_size)
- fig.set_dpi(pp.FIG_DPI)
-
- ax1 = fig.add_subplot(111)
- ax1.set_xlim(np.min(run.x),np.max(run.x))
- traces = ax1.plot(run.x,run.y)
- ax1.grid(pp.GRID)
+ if in_ipython():
+ from IPython.display import set_matplotlib_formats
+ set_matplotlib_formats('pdf', 'svg')
+ ipy = get_ipython()
+ ipy.magic("matplotlib inline")
- ax1.set_xlabel(run.xlabel)
- legends = run.ylabels
- ax1.legend(traces, legends, loc=0, shadow=pp.SHADOW,prop={'size':pp.LEGEND_SIZE})
+ if mode == "display":
+ __mode__ = mode
+
+ elif mode == "paper":
+ __mode__ = mode
+
+ mpl.use("pgf")
+
+ pgf_with_pdflatex = {
+ "pgf.texsystem": "pdflatex",
+ "pgf.preamble": [
+ r"\usepackage{amsmath, amssymb}",
+ r"\usepackage{mathtools, siunitx}" ,
+ r"\usepackage{amsmath}",
+ r"\usepackage[utf8x]{inputenc}",
+ r"\usepackage[T1]{fontenc}"
+ ]
+ }
- if pp.PRINT_TITLE:
- plt.title(title)
+ mpl.rcParams.update(pgf_with_pdflatex)
+ else:
+ raise(BaseException("Plotting mode must be either 'display' or 'paper'."))
- if pp.SCREEN_TITLE:
- fig.canvas.manager.set_window_title(title)
+ mpl.rcParams.update({"figure.figsize": (6, 3.708)})
+ mpl.rcParams.update({'font.size': 11})
+ mpl.rcParams.update({'figure.dpi': __DPI__})
+ mpl.rcParams.update({'savefig.dpi': __DPI__})
+ mpl.rcParams.update({'font.family': "serif"})
+ mpl.rcParams.update({'axes.grid': True})
+ mpl.rcParams.update({'axes.axisbelow': True})
+ mpl.rcParams.update({'grid.linewidth': 0.25})
+ mpl.rcParams.update({'grid.linestyle': ":"})
+ mpl.rcParams.update({'grid.color': (0.7,0.7,0.7,1)})
+ mpl.rcParams.update({'savefig.bbox': "tight"})
+ mpl.rcParams.update({'savefig.pad_inches': 0.05})
+ mpl.rcParams.update({'xtick.labelsize': "small"})
+ mpl.rcParams.update({'ytick.labelsize': "small"})
+ mpl.rcParams.update({'axes.formatter.useoffset': False})
+
+def figure(width="full", height=0.618, textwidth=6, **kwargs):
+ """
+ Options:
+ width: 'full', 'half' (0.49*textwidth) (default: full)
+ height: relative height to width (default: 1/golden ratio = 0.618)
+ textwidth: Width of text in inches (default: 9.84252 in = 25 cm )
+ """
+ import matplotlib.pyplot as plt
+
+ if width == "full":
+ fig_size = [textwidth, textwidth*height]
+ elif width == "half":
+ fig_size = [textwidth*0.49, textwidth*height*0.49]
else:
- fig.canvas.manager.set_window_title('')
+ raise(BaseException("width must be either 'full' or 'half'."))
- #plt.ion()
- plt.show()
+ fig = plt.figure(figsize=fig_size, dpi=__DPI__)
+
+ return fig
+
-class pp():
- # set some gobal settings first
- BACKEND = 'Qt4Agg' # matplotlib backend
- FIG_DPI=90 # DPI of on sceen plot
- # Some help in calculating good figure size for Latex
- # documents. Starting with plot size in pt,
- # get this from LaTeX using \showthe\columnwidth
- fig_width_pt = 484.0
- inches_per_pt = 1.0/72.27 # Convert TeX pt to inches
- golden_mean = (np.sqrt(5)-1.0)/2.0 # Aesthetic ratio
- fig_width = fig_width_pt*inches_per_pt # width in inches
- fig_height = fig_width*golden_mean # height in inches
- fig_size = [fig_width,fig_height]
- # some plot options:
- LINEWIDTH = 1 # linewidths of traces in plot
- AA = True # antialiasing of traces
- USETEX = False # use Latex encoding in text
- SHADOW = False # shadow of legend box
- GRID = True # grid on or off
- # font sizes for normal text, tick labels and legend
- FONT_SIZE = 10 # size of normal text
- TICK_SIZE = 10 # size of tick labels
- LABEL_SIZE = 10 # size of axes labels
- LEGEND_SIZE = 10 # size of legend
- # font family and type
- font = {'family':'sans-serif','sans-serif':['Helvetica'],'size':FONT_SIZE}
- DPI=300 # DPI for saving via savefig
- # print options given to savefig command:
- print_options = {'dpi':DPI, 'transparent':True, 'bbox_inches':'tight', 'pad_inches':0.1}
- # for Palatino and other serif fonts use:
- #font = {'family':'serif','serif':['Palatino']}
- SCREEN_TITLE = True # show title on screen?
- PRINT_TITLE = False # show title in saved file?
+
\ No newline at end of file
diff --git a/pykat/profiling.py b/pykat/profiling.py
index 4d1a7b9..f0b43bf 100644
--- a/pykat/profiling.py
+++ b/pykat/profiling.py
@@ -3,10 +3,10 @@ from __future__ import division
from __future__ import print_function
from __future__ import unicode_literals
-import numpy as np
-import pylab as pl
-
def plotReducedPerformanceData(perfdata, ordered=False):
+ import numpy as np
+ import pylab as pl
+
labels = []
times = []
diff --git a/pykat/tools/modematching.py b/pykat/tools/modematching.py
index e14483a..b089b2a 100644
--- a/pykat/tools/modematching.py
+++ b/pykat/tools/modematching.py
@@ -1,4 +1,4 @@
-import pylab as pl
+#import pylab as pl # removed by DDB 1/12/2015
import scipy.optimize as opt
from pykat import finesse
from pykat.detectors import *
@@ -7,7 +7,7 @@ from pykat.commands import *
from pykat.structs import *
from numpy import *
# from modematch import modematch
-import pykat.utilities.optics.ABCD as abcd
+import pykat.optics.ABCD as abcd
import time
diff --git a/pykat/tools/plotting/beamtrace.py b/pykat/tools/plotting/beamtrace.py
index 6f9f3c9..dc99be3 100644
--- a/pykat/tools/plotting/beamtrace.py
+++ b/pykat/tools/plotting/beamtrace.py
@@ -2,9 +2,10 @@ import pykat
import pykat.exceptions as pkex
import copy
import numpy as np
-import pylab
def plot_beam_trace(_kat, from_node, to_node):
+ import pylab
+
if _kat == None:
raise pkex.BasePyKatException('kat object in None')
diff --git a/pykat/tools/plotting/colormap.py b/pykat/tools/plotting/colormap.py
index 03d4f39..bc312d6 100644
--- a/pykat/tools/plotting/colormap.py
+++ b/pykat/tools/plotting/colormap.py
@@ -1,322 +1,322 @@
-
-import numpy as np
-import matplotlib
-from matplotlib import rc
-import matplotlib.pyplot as plt
-
-# =============================================================================
-# ====================== The Class ColorMapCreator ============================
-# =============================================================================
-
-class CM:
- """
- 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
- #-
-
+#
+# import numpy as np
+# import matplotlib
+# from matplotlib import rc
+# import matplotlib.pyplot as plt
+#
+# # =============================================================================
+# # ====================== The Class ColorMapCreator ============================
+# # =============================================================================
+#
+# class CM:
+# """
+# 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
+# #-
+#
diff --git a/pykat/tools/plotting/tools.py b/pykat/tools/plotting/tools.py
index 2ddd950..74d6ee8 100644
--- a/pykat/tools/plotting/tools.py
+++ b/pykat/tools/plotting/tools.py
@@ -1,485 +1,489 @@
-# ------------------------------------------------------
-# This file is very much in flux at the moment and will
-# undergo many significant changes without notice.
-# Don't use this for anything but playing/testing yet.
-# Andreas 06.03.2014
-# ------------------------------------------------------
-
-import numpy as np
-import matplotlib
-import matplotlib.backends.backend_pdf
-from matplotlib import cm
-BACKEND = 'Qt4Agg'
-matplotlib.use(BACKEND)
-from matplotlib import rc
-import matplotlib.pyplot as plt
-
-formatter = matplotlib.ticker.EngFormatter(unit='', places=0)
-formatter.ENG_PREFIXES[-6] = 'u'
-
-
-def argmaxM(data):
- return(np.unravel_index(np.argmax(data),data.shape))
-
-
-def printPDF(fig, filename):
- if filename !='' and filename != None:
- if not filename.lower().endswith(('.pdf')):
- filename=filename+'.pdf'
- pdfp = matplotlib.backends.backend_pdf.PdfPages(filename)
- pdfp.savefig(fig, dpi=300,bbox_inches='tight')
- pdfp.close()
-
-def plot_field(field):
- ax,fig=plot_setup()
- im = ax.imshow(np.abs(field),origin='lower', aspect='auto')
- cb = fig.colorbar(im, format="%.4g")
- #cb.set_clim(-1.0*zlimit, zlimit)
- ax.autoscale(False)
- plt.show(block=0)
-
-def plot_propagation(field, grid, Lambda, axis, normalise, Lrange, nr):
- # axis = 0 for x and =1 for y crosssection
- # if normalise = 1, each slice is normalises to peak intensity
- [n,m]=field.shape
- slices = np.zeros((n,np.size(Lrange)))
- from pykat.optics.fft import FFT_propagate
- for idx, L in enumerate(Lrange):
- field2 = FFT_propagate(field,grid, Lambda, L, nr)
- if axis==0:
- slices[:,idx]=np.abs(field2[:,int(m/2)])**2
- else:
- slices[:,idx]=np.abs(field2[int(n/2),:])**2
- if normalise==1:
- peak_intensity= np.abs(field2[int(n/2),int(m/2)])**2
- slices[:,idx]=slices[:,idx]/peak_intensity
- ax,fig=plot_setup()
- im = ax.imshow(slices, aspect='auto')
- cb = fig.colorbar(im, format="%.4g")
- #cb.set_clim(-1.0*zlimit, zlimit)
- ax.autoscale(False)
- plt.show(block=0)
-
-
-def plot_power_contour(x,y,z,xlabel, ylabel, clabel, title='', filename=''):
- ax,fig=plot_setup()
- xm,ym=np.meshgrid(x,y)
- extent = [x[0], x[-1], y[0], y[-1]]
- mycm='YlOrRd_r'
- #mycm='hot'
- #im = ax.imshow(z,origin='lower',extent=extent,cmap=mycm, aspect='auto', interpolation='nearest')
- im = ax.imshow(z,origin='lower',extent=extent,cmap=mycm, aspect='auto')
- #im = ax.imshow(z,origin='lower',cmap=mycm, aspect='auto')
- cb = fig.colorbar(im, format="%.4g")
- #cb.set_clim(-1.0*zlimit, zlimit)
- ax.autoscale(False)
- #ct = ax.contour(xm,ym,z, zdir='z', levels = [0])
- #ax.clabel(ct,inline=1,fontsize=10)
- ax.set_xlabel(xlabel)
- ax.set_ylabel(ylabel)
- cb.set_label(clabel)
- ax.xaxis.set_major_formatter(formatter)
- ax.yaxis.set_major_formatter(formatter)
- fig.canvas.manager.set_window_title(title)
- plt.show(block=0)
- printPDF(fig, filename)
-
-
-
-def plot_error_contour(x,y,z,xlabel, ylabel, clabel, title='', filename=''):
- global fig, ax, cb, ct, data
- rc('font',**pp.font)
- rc('xtick',labelsize=pp.TICK_SIZE)
- rc('ytick',labelsize=pp.TICK_SIZE)
- rc('text', usetex=pp.USETEX)
- rc('axes', labelsize = pp.LABEL_SIZE)
- fig, ax =plt.subplots()
- fig.set_size_inches(pp.fig_size)
- fig.set_dpi(pp.FIG_DPI)
- xm,ym=np.meshgrid(x,y)
- RGB1 = 255*np.array([0.23, 0.299, 0.754])
- RGB2 = 255*np.array([0.706,0.016, 0.15])
- cm1 = CM(RGB1, RGB2)
- cm_values = cm1.getMap()
- #cm1.showMap()
- mycm = matplotlib.colors.ListedColormap(cm_values)
- extent = [x[0], x[-1], y[0], y[-1] ]
- data=z
- # make symmetric color display
- zlimit=np.max([abs(data.max()),abs(data.min())])
- im = ax.imshow(data,origin='lower',extent=extent,cmap=mycm, aspect='auto', interpolation='nearest')
- #im = ax.imshow(data,origin='lower',extent=extent,cmap=mycm, aspect='auto')
- cb = fig.colorbar(im, format="%.4g")
- cb.set_clim(-1.0*zlimit, zlimit)
- ax.autoscale(False)
- ct = ax.contour(xm,ym,data, zdir='z', levels = [0])
- ax.clabel(ct,inline=1,fontsize=10)
- ax.set_xlabel(xlabel)
- ax.set_ylabel(ylabel)
- cb.set_label(clabel)
- ax.xaxis.set_major_formatter(formatter)
- fig.canvas.manager.set_window_title(title)
- plt.show()
- printPDF(fig, filename)
-
-def plot_setup():
- rc('font',**pp.font)
- rc('xtick',labelsize=pp.TICK_SIZE)
- rc('ytick',labelsize=pp.TICK_SIZE)
- rc('text', usetex=pp.USETEX)
- rc('axes', labelsize = pp.LABEL_SIZE)
- fig, ax =plt.subplots()
- fig.set_size_inches(pp.fig_size)
- fig.set_dpi(pp.FIG_DPI)
- return ax,fig
-
-class pp():
- # set some gobal settings first
- BACKEND = 'Qt4Agg' # matplotlib backend
- FIG_DPI=90 # DPI of on sceen plot
- # Some help in calculating good figure size for Latex
- # documents. Starting with plot size in pt,
- # get this from LaTeX using \showthe\columnwidth
- fig_width_pt = 484.0
- inches_per_pt = 1.0/72.27 # Convert TeX pt to inches
- golden_mean = (np.sqrt(5)-1.0)/2.0 # Aesthetic ratio
- fig_width = fig_width_pt*inches_per_pt # width in inches
- fig_height = fig_width*golden_mean # height in inches
- fig_size = [fig_width,fig_height]
- # some plot options:
- LINEWIDTH = 1 # linewidths of traces in plot
- AA = True # antialiasing of traces
- USETEX = False # use Latex encoding in text
- SHADOW = False # shadow of legend box
- GRID = True # grid on or off
- # font sizes for normal text, tick labels and legend
- FONT_SIZE = 10 # size of normal text
- TICK_SIZE = 10 # size of tick labels
- LABEL_SIZE = 10 # size of axes labels
- LEGEND_SIZE = 10 # size of legend
- # font family and type
- font = {'family':'sans-serif','sans-serif':['Helvetica'],'size':FONT_SIZE}
- DPI=300 # DPI for saving via savefig
- # print options given to savefig command:
- print_options = {'dpi':DPI, 'transparent':True, 'bbox_inches':'tight', 'pad_inches':0.1}
- # for Palatino and other serif fonts use:
- #font = {'family':'serif','serif':['Palatino']}
- SCREEN_TITLE = True # show title on screen?
- PRINT_TITLE = False # show title in saved file?
-
-# =============================================================================
-# ====================== The Class ColorMapCreator ============================
-# =============================================================================
-
-class CM:
- """
- 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
- #-
-
+# This all needs to be reworked to fit in with new plotting
+# methods. Daniel 1/12/2015
+
+
+# # ------------------------------------------------------
+# # This file is very much in flux at the moment and will
+# # undergo many significant changes without notice.
+# # Don't use this for anything but playing/testing yet.
+# # Andreas 06.03.2014
+# # ------------------------------------------------------
+#
+# import numpy as np
+# import matplotlib
+# import matplotlib.backends.backend_pdf
+# from matplotlib import cm
+# BACKEND = 'Qt4Agg'
+# matplotlib.use(BACKEND)
+# from matplotlib import rc
+# import matplotlib.pyplot as plt
+#
+# formatter = matplotlib.ticker.EngFormatter(unit='', places=0)
+# formatter.ENG_PREFIXES[-6] = 'u'
+#
+#
+# def argmaxM(data):
+# return(np.unravel_index(np.argmax(data),data.shape))
+#
+#
+# def printPDF(fig, filename):
+# if filename !='' and filename != None:
+# if not filename.lower().endswith(('.pdf')):
+# filename=filename+'.pdf'
+# pdfp = matplotlib.backends.backend_pdf.PdfPages(filename)
+# pdfp.savefig(fig, dpi=300,bbox_inches='tight')
+# pdfp.close()
+#
+# def plot_field(field):
+# ax,fig=plot_setup()
+# im = ax.imshow(np.abs(field),origin='lower', aspect='auto')
+# cb = fig.colorbar(im, format="%.4g")
+# #cb.set_clim(-1.0*zlimit, zlimit)
+# ax.autoscale(False)
+# plt.show(block=0)
+#
+# def plot_propagation(field, grid, Lambda, axis, normalise, Lrange, nr):
+# # axis = 0 for x and =1 for y crosssection
+# # if normalise = 1, each slice is normalises to peak intensity
+# [n,m]=field.shape
+# slices = np.zeros((n,np.size(Lrange)))
+# from pykat.optics.fft import FFT_propagate
+# for idx, L in enumerate(Lrange):
+# field2 = FFT_propagate(field,grid, Lambda, L, nr)
+# if axis==0:
+# slices[:,idx]=np.abs(field2[:,int(m/2)])**2
+# else:
+# slices[:,idx]=np.abs(field2[int(n/2),:])**2
+# if normalise==1:
+# peak_intensity= np.abs(field2[int(n/2),int(m/2)])**2
+# slices[:,idx]=slices[:,idx]/peak_intensity
+# ax,fig=plot_setup()
+# im = ax.imshow(slices, aspect='auto')
+# cb = fig.colorbar(im, format="%.4g")
+# #cb.set_clim(-1.0*zlimit, zlimit)
+# ax.autoscale(False)
+# plt.show(block=0)
+#
+#
+# def plot_power_contour(x,y,z,xlabel, ylabel, clabel, title='', filename=''):
+# ax,fig=plot_setup()
+# xm,ym=np.meshgrid(x,y)
+# extent = [x[0], x[-1], y[0], y[-1]]
+# mycm='YlOrRd_r'
+# #mycm='hot'
+# #im = ax.imshow(z,origin='lower',extent=extent,cmap=mycm, aspect='auto', interpolation='nearest')
+# im = ax.imshow(z,origin='lower',extent=extent,cmap=mycm, aspect='auto')
+# #im = ax.imshow(z,origin='lower',cmap=mycm, aspect='auto')
+# cb = fig.colorbar(im, format="%.4g")
+# #cb.set_clim(-1.0*zlimit, zlimit)
+# ax.autoscale(False)
+# #ct = ax.contour(xm,ym,z, zdir='z', levels = [0])
+# #ax.clabel(ct,inline=1,fontsize=10)
+# ax.set_xlabel(xlabel)
+# ax.set_ylabel(ylabel)
+# cb.set_label(clabel)
+# ax.xaxis.set_major_formatter(formatter)
+# ax.yaxis.set_major_formatter(formatter)
+# fig.canvas.manager.set_window_title(title)
+# plt.show(block=0)
+# printPDF(fig, filename)
+#
+#
+#
+# def plot_error_contour(x,y,z,xlabel, ylabel, clabel, title='', filename=''):
+# global fig, ax, cb, ct, data
+# rc('font',**pp.font)
+# rc('xtick',labelsize=pp.TICK_SIZE)
+# rc('ytick',labelsize=pp.TICK_SIZE)
+# rc('text', usetex=pp.USETEX)
+# rc('axes', labelsize = pp.LABEL_SIZE)
+# fig, ax =plt.subplots()
+# fig.set_size_inches(pp.fig_size)
+# fig.set_dpi(pp.FIG_DPI)
+# xm,ym=np.meshgrid(x,y)
+# RGB1 = 255*np.array([0.23, 0.299, 0.754])
+# RGB2 = 255*np.array([0.706,0.016, 0.15])
+# cm1 = CM(RGB1, RGB2)
+# cm_values = cm1.getMap()
+# #cm1.showMap()
+# mycm = matplotlib.colors.ListedColormap(cm_values)
+# extent = [x[0], x[-1], y[0], y[-1] ]
+# data=z
+# # make symmetric color display
+# zlimit=np.max([abs(data.max()),abs(data.min())])
+# im = ax.imshow(data,origin='lower',extent=extent,cmap=mycm, aspect='auto', interpolation='nearest')
+# #im = ax.imshow(data,origin='lower',extent=extent,cmap=mycm, aspect='auto')
+# cb = fig.colorbar(im, format="%.4g")
+# cb.set_clim(-1.0*zlimit, zlimit)
+# ax.autoscale(False)
+# ct = ax.contour(xm,ym,data, zdir='z', levels = [0])
+# ax.clabel(ct,inline=1,fontsize=10)
+# ax.set_xlabel(xlabel)
+# ax.set_ylabel(ylabel)
+# cb.set_label(clabel)
+# ax.xaxis.set_major_formatter(formatter)
+# fig.canvas.manager.set_window_title(title)
+# plt.show()
+# printPDF(fig, filename)
+#
+# def plot_setup():
+# rc('font',**pp.font)
+# rc('xtick',labelsize=pp.TICK_SIZE)
+# rc('ytick',labelsize=pp.TICK_SIZE)
+# rc('text', usetex=pp.USETEX)
+# rc('axes', labelsize = pp.LABEL_SIZE)
+# fig, ax =plt.subplots()
+# fig.set_size_inches(pp.fig_size)
+# fig.set_dpi(pp.FIG_DPI)
+# return ax,fig
+#
+# class pp():
+# # set some gobal settings first
+# BACKEND = 'Qt4Agg' # matplotlib backend
+# FIG_DPI=90 # DPI of on sceen plot
+# # Some help in calculating good figure size for Latex
+# # documents. Starting with plot size in pt,
+# # get this from LaTeX using \showthe\columnwidth
+# fig_width_pt = 484.0
+# inches_per_pt = 1.0/72.27 # Convert TeX pt to inches
+# golden_mean = (np.sqrt(5)-1.0)/2.0 # Aesthetic ratio
+# fig_width = fig_width_pt*inches_per_pt # width in inches
+# fig_height = fig_width*golden_mean # height in inches
+# fig_size = [fig_width,fig_height]
+# # some plot options:
+# LINEWIDTH = 1 # linewidths of traces in plot
+# AA = True # antialiasing of traces
+# USETEX = False # use Latex encoding in text
+# SHADOW = False # shadow of legend box
+# GRID = True # grid on or off
+# # font sizes for normal text, tick labels and legend
+# FONT_SIZE = 10 # size of normal text
+# TICK_SIZE = 10 # size of tick labels
+# LABEL_SIZE = 10 # size of axes labels
+# LEGEND_SIZE = 10 # size of legend
+# # font family and type
+# font = {'family':'sans-serif','sans-serif':['Helvetica'],'size':FONT_SIZE}
+# DPI=300 # DPI for saving via savefig
+# # print options given to savefig command:
+# print_options = {'dpi':DPI, 'transparent':True, 'bbox_inches':'tight', 'pad_inches':0.1}
+# # for Palatino and other serif fonts use:
+# #font = {'family':'serif','serif':['Palatino']}
+# SCREEN_TITLE = True # show title on screen?
+# PRINT_TITLE = False # show title in saved file?
+#
+# # =============================================================================
+# # ====================== The Class ColorMapCreator ============================
+# # =============================================================================
+#
+# class CM:
+# """
+# 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
+# #-
+#
--
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