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

# # Unicode normalization
# 
# Many complex Unicode characters can be expressed in more than one way. For example, an uppercase A with a superscript ring can be expressed as a single Unicode character U+212b or as a combination of a base uppercase A (U+0041) followed by a combining superscript ring (U+030a). The documents you need to collate may contain alternative representations that you would like to treat as identical for collation purposes. To do that, you can create a shadow "n" property in your pretokenized JSON (see Unit 5 of this workshop) and normalize the strings. Here’s how to do that.

# To show that the two representations are different at the underlying byte level but look alike to a human, we create two variables, which we call `a` and `b`, and assign one representation to each, which we then print so that we can examine how they look.

# In[13]:


a = "\u212b"


# In[14]:


a


# In[15]:


b = "\u0041\u030a"


# In[16]:


b


# If we check these two values for equality, Python tells us that they are not equal:

# In[17]:


a == b


# We can use the unicodedata.normalize() function to normalize both variables and then compare them. When we do that, the normalized versions are equal:

# In[18]:


import unicodedata
unicodedata.normalize('NFC',a) == unicodedata.normalize('NFC',b)


# We’ve performed this normalization by itself so that we can examine the results. In a CollateX context, you would incorporate Unicode normalization as part of the process of generating an "n" property for your pretokenized JSON tokens. See <http://unicode.org/reports/tr15/> for more information about Unicode normalization forms.