#!/usr/bin/env python
# coding: utf-8

# # Jupyter (iPython) Notebook to reproduce the table values in TB-2018-001
# 
# Dependencies
# ------------
# 
#     numpy (http://www.numpy.org/)
#     colour-science (http://www.colour-science.org/)
#     iPython (https://ipython.org/)
#     pandas (https://pandas.pydata.org/)
# 
# Copyright Academy of Motion Picture Arts and Sciences

# # Setup Notebook

# In[1]:


# Imports for Calculations
import colour
import numpy as np


# In[2]:


# Imports for Display of Tables
from IPython.display import HTML, display
import pandas as pd


# In[3]:


# Turn off any warnings
import warnings
warnings.simplefilter("ignore")


# ## Table 1 - CCT of canonical CIE daylight illuminants

# Specify nominal CCT values for canonical CIE Daylight Illuminants

# In[4]:


cct_pre68 = np.array([5000, 5500, 6500, 7500])


# Define the correction factor

# In[5]:


c_factor = 1.4388 / 1.4380


# Apply the correction factor to determine the actual CCT values for the daylight illuminants

# In[6]:


cct_post68 = cct_pre68 * c_factor


# In[7]:


# Print the table
df_T1 = pd.DataFrame( np.c_[cct_pre68, cct_post68, cct_post68], 
                      index = ['D50', 'D55', 'D65', 'D75'], 
                      columns = ['CCT before 1968', 
                                 'CCT Current (round to 3 decimal places)', 
                                 'CCT Current (round to 0 decimal places)'] )

df_T1.iloc[:, 0] = df_T1.iloc[:, 0].map('{:,.0f} K'.format)
df_T1.iloc[:, 1] = df_T1.iloc[:, 1].map('{:,.3f} K'.format)
df_T1.iloc[:, 2] = df_T1.iloc[:, 2].map('{:,.0f} K'.format)

display(df_T1)


# ## CCT of "D60"

# In[8]:


# Calculate the CCT of a 'D60' CIE Daylight Illuminant assuming the name follows the canonical illuminants
cct_d60 = 6000 * c_factor


# In[9]:


# Print the result
df_cctd60 = pd.DataFrame( cct_d60, 
                          index = ['D60'], 
                          columns = ['CCT'] )

display(df_cctd60)


# ## Table 2 - CIE xy chromaticity coordinates rounded to 5 decimal places

# In[10]:


# Calculate the ACES white point CIE xy values
xy_aces = colour.models.ACES_2065_1_COLOURSPACE.whitepoint

# Calculate the CIE xy values for CIE Daylight of 6000K
xy_d6000 = colour.CCT_to_xy(6000, method='CIE Illuminant D Series')

# Calculate the CIE xy values for CIE Daylight of 'D60'
xy_d60 = colour.CCT_to_xy(cct_d60 , method='CIE Illuminant D Series')

# Place in an array
xy = np.array([xy_aces,xy_d6000,xy_d60])


# In[11]:


# Print the table
df_T2 = pd.DataFrame( xy, 
                      index = ['ACES White Point', 'CIE Daylight 6000K', 'CIE Daylight 6003K'], 
                      columns = ['CIE x', 'CIE y'] )

pd.set_option('precision', 5)

display(df_T2)


# ## Table 3 - CIE u′v′ chromaticity coordinates and ∆u′v′ from the ACES white point rounded to 5 decimal places

# In[12]:


# Calculate the CIE 1976 UCS u'v' values
uvp = colour.xy_to_Luv_uv(xy)

# Calculate the ∆u′v′ from the ACES white point
delta_uvp = colour.delta_E(uvp[0,:], uvp, method='cie1976')


# In[13]:


# Print the table
df_T3 = pd.DataFrame( np.c_[uvp, delta_uvp], 
                      index = ['ACES White Point', 'CIE Daylight 6000K', 'CIE Daylight 6003K'], 
                      columns = ['CIE u\'', 'CIE v\'', '∆u\'v\''] )

pd.set_option('precision', 5)

display(df_T3)


# ## Table 4 - Estimation of the CCT of the ACES white point rounded to 2 decimal places

# In[14]:


# Calculate the CIE 1960 UCS uv values
uv = colour.xy_to_UCS_uv(xy)

# Calculate the CCT from the chromaticity coordinates using various methods
cct_robertson = colour.uv_to_CCT(uv[0,:],'Robertson 1968')
cct_hernandez = colour.xy_to_CCT(xy[0,:],'Hernandez 1999')
cct_ohno = colour.uv_to_CCT(uv[0,:], 'Ohno 2013')
cct_mccamy = colour.xy_to_CCT(xy[0,:],'McCamy 1992')


# In[15]:


# Print the table
df_T4 = pd.DataFrame( np.c_[cct_robertson[0], cct_hernandez, cct_ohno[0], cct_mccamy].T, 
                      index = ['Robertson', 'Hernandez-Andres', 'Ohno', 'McCamy'], 
                      columns = ['ACES white point CCT'] )

df_T4.index.name = 'CCT Estimation Method'

pd.set_option('precision', 2)

display(df_T4)


# ## Table 5 - Chromaticity difference from projected LAD patch

# In[16]:


# Specify the CIE xy values of a projected LAD patch
xy_lad = np.array([0.32170, 0.33568])

# Daylight range from 5500 K to 6500 K in 100 K increments over which to calculate the differences
cct_dseries = np.arange(5500,6600,100)

# Calculate the CIE xy chromaticity coordinates of CIE Daylight
xy_dseries = colour.CCT_to_xy(cct_dseries, method='CIE Illuminant D Series')

# Calculate the CIE UCS 1976 u'v'
uvp_lad = colour.xy_to_Luv_uv(xy_lad)
uvp_dseries = colour.xy_to_Luv_uv(xy_dseries)

# Calculate the ∆u′v′ between the LAD and the CIE Daylight chromaticity coordinates
delta_uvp_lad = colour.delta_E(uvp_lad, uvp_dseries, method='cie1976')


# In[17]:


# Print the table
df_T5 = pd.DataFrame( delta_uvp_lad.T, 
                      index = cct_dseries, 
                      columns =  ['∆u\'v\' from LAD chromaticity'])

df_T5.index.name = 'CCT Estimation Method'

pd.set_option('precision', 6)

display(df_T5)