#!/usr/bin/python

# coding=UTF-8

### create 3D-Plot of color patches.

### Assumes: List of Patches for measured values of Inking curve.

###

### To do the plot correctly:

### 0. set up tval: use one of CMYK_C...CMYK_K

### 1. get list of Points

### 2. convert Lab colors to sRGB

### 3. Plot small- sized Points

### 4. Plot Uncertainty-sized spheres (Rings of dE)

### 5. Generate Spline Fit (enumerate SampleID as time variable in spline fit)

### 6. Plot Spline

#### SETUP HERE

cdwpath= '/Users/tspr/Desktop/LabPlot'

h5filename = "./Farben.h5"

tvals_in = "CMYK_M"

import locale

locale.setlocale(locale.LC_ALL, 'de_DE')

from tables import *

from colormath.color_objects import LabColor

from mpl_toolkits.mplot3d import *

import matplotlib.pyplot as plt

import matplotlib.mlab as mlab

import numpy as np

from scipy import interpolate

from scipy.stats import lognorm

# import seaborn as sns

# sns.set(style="whitegrid")

def abL_2_sRGB(a_val,b_val,L_val):

intermediatecolor = LabColor(lab_l=L_val,lab_a=a_val,lab_b=b_val).convert_to('rgb')

normtuple = (float(intermediatecolor.rgb_r)/256,float(intermediatecolor.rgb_g)/256,float(intermediatecolor.rgb_b)/256)

return normtuple

class CGATScolors(IsDescription):

LAB_B = FloatCol()

h5file = openFile(h5filename, mode = "r")

table=h5file.getNode('/Tabellen/Patches')

# Convert Lab to rgb for plotting and append to data points

# make sure, your installation of matplotlib is patched according to:

# https://github.com/login?return_to=%2Fmatplotlib%2Fmatplotlib%2Fissues%2F1692

colours=[]

tvals=[]

avals=[]

bvals=[]

Lvals=[]

for row in table[:]:

colours.append(abL_2_sRGB(a_val=row['LAB_A'],b_val=row['LAB_B'],L_val=row['LAB_L']))

tvals.append(row[tvals_in]/100)

avals.append(row['LAB_A'])

bvals.append(row['LAB_B'])

Lvals.append(row['LAB_L'])

h5file.close()

# Create Interpolation functions for a,b,L

a_pchip=interpolate.pchip(tvals,avals)

b_pchip=interpolate.pchip(tvals,bvals)

L_pchip=interpolate.pchip(tvals,Lvals)

pch_Inter=[a_pchip,b_pchip,L_pchip]

# Start plotting

# Disable depth shading

plt.ion()

art3d.zalpha = lambda *args:args[0]

# Setup canvas

fig = plt.figure()

ax = fig.gca(projection='3d')

ax.set_xlabel('a')

ax.set_ylabel('b')

ax.set_zlabel('L')

ax.set_xlim(-100,100)

ax.set_ylim(-100,100)

ax.set_zlim(0,100)

# plot the grey axis

gx=np.linspace(0,0,101)

gy=np.linspace(0,0,101)

gz=np.linspace(0,100,101)

gaxis=ax.plot(gx,gy,gz,c=(0,0,0))

# plot the points

#pointsplot=ax.scatter3D(table.cols.LAB_A[:],table.cols.LAB_B[:],table.cols.LAB_L[:],s=20,c=colours,linewidth=0)#

pointsplot=ax.scatter3D(avals,bvals,Lvals,s=20,c=colours,linewidth=0)

# maybe plot some cross marks as well

#q=ax.scatter3D(table.cols.LAB_A[:],table.cols.LAB_B[:],table.cols.LAB_L[:], s=100,c=(0,0,0), norm=None, marker='+', linewidth=1,alpha=0.5)

# plot the Interpolated Path

linerange = np.arange(np.amin(tvals),np.amax(tvals),0.01)

interline=ax.plot(a_pchip(linerange),b_pchip(linerange),L_pchip(linerange),c=(0,0,0),linewidth=1)

cmpos=0.80

crossmark=ax.scatter3D(a_pchip(cmpos),b_pchip(cmpos),L_pchip(cmpos),marker='+',c=abL_2_sRGB(a_pchip(cmpos),b_pchip(cmpos),L_pchip(cmpos)),s=200, linewidth=1)

# show the thing

fig.show()

# create 100 nomrally distributed points along the line around cmpos.

# range should be 95% within +-5% of range of t-values (2 sigma)

# scale= sigma. so sigma = 5 (percent) * range / 2(sides)*2(sigma)*100(percent)

scale=(5*(linerange.max()-linerange.min())/400)

zufallspositionen = np.random.normal(loc=cmpos,scale=scale,size=1000)

rnd_a = a_pchip(zufallspositionen)

rnd_b = b_pchip(zufallspositionen)

rnd_L = L_pchip(zufallspositionen)

# add some noise (size: Lab units)

noise_size = 0.1

rnd_a += np.random.normal(0,noise_size,1000)

rnd_b += np.random.normal(0,noise_size,1000)

rnd_L += np.random.normal(0,noise_size,1000)

cntr_a = a_pchip(cmpos)

cntr_b = b_pchip(cmpos)

cntr_L = L_pchip(cmpos)

delta_a = cntr_a - rnd_a

delta_b = cntr_b - rnd_b

delta_L = cntr_L - rnd_L

delta_E = sqrt(square(delta_a) +square(delta_b) + square(delta_L))

# plot them green

rndplt=ax.scatter3D(rnd_a,rnd_b,rnd_L,marker='*',c='green',s=50,linewidth=1)

# histogramm delta E

plt.figure()

n, bins, patches = plt.hist(delta_E,bins=50,color='blue',normed=True,histtype='bar')

lnrm_shape, lnrm_loc, lnrm_scale = lognorm.fit(delta_E)

x= np.linspace(0, delta_E.max(), num=400)

y = lognorm.pdf(x,lnrm_shape,loc=lnrm_loc,scale=lnrm_scale)

pdflne=plt.plot(x,y,'r--',linewidth=2)