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

# # Examine presentation of John 1:1 (GBI versus LFT)
# 
# **Work in progress!**

# ## Table of content <a class="anchor" id="TOC"></a>
# 
# * <a href="#bullet1">1 - Introduction</a>
# * <a href="#bullet2">2 - Load Text-Fabric app and data</a>
# * <a href="#bullet3">3 - Performing the queries</a>
#     * <a href="#bullet3x1">3.1 - The GBI implementation</a>
#          * <a href="#bullet3x1x1">3.1.1 - The input XML (GBI nodes)</a>
#     * <a href="#bullet3x2">3.2 - The LFT implementation</a>
#          * <a href="#bullet3x2x1">3.2.1 - The input XML (Low Fat Tree)</a>
# * <a href="#bullet4">4 - Discussion</a>  
#     * <a href="#bullet4x1">4.1 - Handling of conjunctions</a>
#     * <a href="#bullet4x2">4.2 - TBD</a>
# * <a href="#bullet5">5 - Atribution and footnotes</a>
# * <a href="#bullet6">6 - Required libraries</a>

# # 1 - Introduction <a class="anchor" id="bullet1"></a>
# ##### [Back to TOC](#TOC)

# This Jupyter Notebook demonstrates the effects of the differences in Text-Fabric datastructure between the GBI and LFT implementation.

# # 2 - Load Text-Fabric app and data <a class="anchor" id="bullet2"></a>
# ##### [Back to TOC](#TOC)

# In[1]:


get_ipython().run_line_magic('load_ext', 'autoreload')
get_ipython().run_line_magic('autoreload', '2')


# In[2]:


# Loading the Text-Fabric code
# Note: it is assumed Text-Fabric is installed in your environment.
from tf.fabric import Fabric
from tf.app import use


# In[3]:


# load the N1904GBI app and data
# Since two distinct Text-Fabric dataset are loaed, the option hoist=globals() SHOULD NOT be used!
N1904GBI = use ("tonyjurg/Nestle1904gbi",version='0.4')


# In[4]:


# The following will push the Text-Fabric stylesheet to this notebook (to facilitate proper display with notebook viewer)
N1904GBI.dh(N1904GBI.getCss())
# Note: this only is required once, because both apps utilize the same stylesheets


# In[5]:


# Set default view in a way to limit noise as much as possible.
N1904GBI.displaySetup(condensed=True, multiFeatures=False,queryFeatures=False)


# In[6]:


# load the N1904LFT app and data
# Since two distinct Text-Fabric dataset are loaed, the option hoist=globals() SHOULD NOT be used!
N1904LFT= use ("tonyjurg/Nestle1904lft",version='0.6')


# In[7]:


# Set default view in a way to limit noise as much as possible.
N1904LFT.displaySetup(condensed=True, multiFeatures=False,queryFeatures=False)


# # 3 - Performing the queries <a class="anchor" id="bullet3"></a>
# ##### [Back to TOC](#TOC)

# First we will define a query template to select John 1:1 which will be used for both queries on both the GBI and LFT Text-Fabric dataset.

# In[6]:


VerseQuery = '''
book book=John
  chapter chapter=1
     verse verse=1
'''

GBIVerseResults = N1904GBI.search(VerseQuery)
LFTVerseResults = N1904LFT.search(VerseQuery)


# In[12]:


EthosQuery = '''
verse
  word lemma=ἔθος 
'''

EthosResults = N1904GBI.search(EthosQuery)
N1904GBI.table(EthosResults)


# ## 3.1 - The GBI implementation <a class="anchor" id="bullet3x1"></a>
# ##### [Back to TOC](#TOC)
# 
# The following is the tree presentation for the GBI implementation:

# ```
#       <Tree>
#         <Node Cat="S" ... >
#           <Node Cat="CL" ... Rule="Conj3CL" ... >
#             <Node Cat="CL" ... Rule="P-VC-S" ... >
#               <Node Cat="P" ... Rule="pp2P" ... >
#                 <Node Cat="pp" ... Rule="PrepNp" ... >
#                   <Node ... Cat="prep" ... >Ἐν</Node>
#                   <Node Cat="np" ... Rule="N2NP" ... ">
#                     <Node ... Cat="noun" ... >ἀρχῇ</Node>
#                   </Node>
#                 </Node>
#               </Node>
#               <Node Cat="VC" ... Rule="vp2VC" ... >
#                 <Node Cat="vp" ... Rule="V2VP" ... >
#                   <Node ... Cat="verb" ... >ἦν</Node>
#                 </Node>
#               </Node>
#               <Node Cat="S" ... Rule="Np2S" ... >
#                 <Node Cat="np" ... Rule="DetNP" ... >
#                   <Node ... Cat="det" ...>ὁ</Node>
#                   <Node Cat="np" ... Rule="N2NP" ... >
#                     <Node ... Cat="noun" ... >Λόγος,</Node>
#                   </Node>
#                 </Node>
#               </Node>
#             </Node>
#             <Node ... Cat="conj" ...>καὶ</Node>
#             <Node Cat="CL" ... Rule="S-VC-P" ... >
#               <Node Cat="S" ... Rule="Np2S" ... >
#                 <Node Cat="np" ... Rule="DetNP" ... >
#                   <Node ... Cat="det" ... >ὁ</Node>
#                   <Node Cat="np" ... Rule="N2NP" ...>
#                     <Node ... Cat="noun" ... >Λόγος</Node>
#                   </Node>
#                 </Node>
#               </Node>
#               <Node Cat="VC" ... Rule="vp2VC" ... >
#                 <Node Cat="vp" ... Rule="V2VP" ... >
#                   <Node ... Cat="verb" ... >ἦν</Node>
#                 </Node>
#               </Node>
#               <Node Cat="P" ... Rule="pp2P" ... >
#                 <Node Cat="pp" ... Rule="PrepNp" ... >
#                   <Node ... Cat="prep" ... >πρὸς</Node>
#                   <Node Cat="np" ... Rule="DetNP" ... >
#                     <Node ... Cat="det" ... >τὸν</Node>
#                     <Node Cat="np" ... Rule="N2NP" ... >
#                       <Node ... Cat="noun" ... >Θεόν,</Node>
#                     </Node>
#                   </Node>
#                 </Node>
#               </Node>
#             </Node>
#             <Node ... Cat="conj" ... >καὶ</Node>
#             <Node Cat="CL" ... Rule="P-VC-S" ... >
#               <Node Cat="P" ... Rule="Np2P" ... >
#                 <Node Cat="np" ... Rule="N2NP" ... >
#                   <Node ... Cat="noun" ... >Θεὸς</Node>
#                 </Node>
#               </Node>
#               <Node Cat="VC" ... Rule="vp2VC" ... >
#                 <Node Cat="vp" ... Rule="V2VP" ... >
#                   <Node ... Cat="verb" ... >ἦν</Node>
#                 </Node>
#               </Node>
#               <Node Cat="S" ... Rule="Np2S" ... >
#                 <Node Cat="np" ... Rule="DetNP" ... >
#                   <Node ... Cat="det" ... >ὁ</Node>
#                   <Node Cat="np" ... Rule="N2NP" ... >
#                     <Node ... Cat="noun" ... >Λόγος.</Node>
#                   </Node>
#                 </Node>
#               </Node>
#             </Node>
#           </Node>
#         </Node>
#       </Tree>
# ```

# ## 3.2 - The LFT implementation <a class="anchor" id="bullet3x2"></a>
# ##### [Back to TOC](#TOC)
# 
# The following is the tree presentation for the LFT implementation:

# In[ ]:


N1904LFT.show(LFTVerseResults, start=1, end=2)


# ## 3.2.1 - The input XML (Low Fat Tree)<a class="anchor" id="bullet3x2x1"></a>
# ##### [Back to TOC](#TOC) 
# 
# For comparison. This is the structure of the raw XML with most of the \<w\> attributes removed). Click [here](https://github.com/tonyjurg/Nestle1904LFT/blob/main/data/xml/20230321/04-john.xml) to view the source file.

# ```
# <sentence>
#       <p>
#          <milestone unit="verse" id="JHN 1:1">JHN 1:1</milestone> Ἐν ἀρχῇ ἦν ὁ Λόγος, καὶ ὁ Λόγος ἦν πρὸς τὸν Θεόν, καὶ Θεὸς ἦν ὁ Λόγος.</p>
#       <wg>
#          <wg rule="Conj3CL">
#             <wg class="cl" rule="P-VC-S" ... >
#                <wg class="pp" rule="PrepNp" role="p">
#                   <w ref="JHN 1:1!1" ... >Ἐν</w>
#                   <w ref="JHN 1:1!2" ... >ἀρχῇ</w>
#                </wg>
#                <w role="vc"
#                   ref="JHN 1:1!3" ... >ἦν</w>
#                <wg class="np" ... rule="DetNP" role="s">
#                   <w ref="JHN 1:1!4" ... >ὁ</w>
#                   <w ref="JHN 1:1!5" ... >Λόγος</w>
#                </wg>
#             </wg>
#             <wg type="group">
#                <w ref="JHN 1:1!6" ... >καὶ</w>
#                <wg class="cl" ... rule="S-VC-P" ... >
#                   <wg class="np" ... rule="DetNP" role="s">
#                      <w ref="JHN 1:1!7" ... >ὁ</w>
#                      <w ref="JHN 1:1!8" ... >Λόγος</w>
#                   </wg>
#                   <w role="vc" ref="JHN 1:1!9" ... >ἦν</w>
#                   <wg class="pp" ... rule="PrepNp" role="p">
#                      <w ref="JHN 1:1!10" ... >πρὸς</w>
#                      <wg class="np" ... rule="DetNP">
#                         <w ref="JHN 1:1!11" ... >τὸν</w>
#                         <w ref="JHN 1:1!12" ... >Θεόν</w>
#                      </wg>
#                   </wg>
#                </wg>
#             </wg>
#             <wg type="group">
#                <w ref="JHN 1:1!13" ... >καὶ</w>
#                <wg class="cl" rule="P-VC-S" junction="coordinate">
#                   <w role="p" ref="JHN 1:1!14" ... >Θεὸς</w>
#                   <w role="vc" ref="JHN 1:1!15" ... >ἦν</w>
#                   <wg class="np" articular="true" rule="DetNP" role="s">
#                      <w ref="JHN 1:1!16" ... >ὁ</w>
#                      <w ref="JHN 1:1!17" ... >Λόγος</w>
#                   </wg>
#                </wg>
#             </wg>
#          </wg>
#       </wg>
#    </sentence>
# ```

# This is the input XML for the low Fat Tree with the stylesheets 
# [`treedown.css`](../../data/xml/20230321/treedown.css) and [`boxwood.css`](../../data/xml/20230321/boxwood.css) applied:
#     
# <img src="images/john1v1_xml_source_css.jpg">

# # 4 - Discussion <a class="anchor" id="bullet4"></a>
# ##### [Back to TOC](#TOC)

# The following sections provide a discussion about the differences between GBI and LFT dataset.

# ## 4.1 - Handling of conjunctions <a class="anchor" id="bullet4x1"></a>
# ##### [Back to TOC](#TOC)
# 
# There is a fundamental difference in handling of conjunctions between the GBI and LFT implementation. In the GBI they are concidered part of a clause, in the LFT outside the clause. (***note: this may be reconsidered.**)
# 

# ## 4.2 - More ... <a class="anchor" id="bullet4x2"></a>
# ##### [Back to TOC](#TOC)
# 
# TBA

# # 5 - Attribution and footnotes<a class="anchor" id="bullet5"></a>
# ##### [Back to TOC](#TOC)
# 
# N.A.

# # 6 - Required libraries <a class="anchor" id="bullet6"></a>
# ##### [Back to TOC](#TOC)
# 
# The scripts in this notebook require (beside `text-fabric`) the following Python libraries to be installed in the environment:
# 
#     {none}
# 
# You can install any missing library from within Jupyter Notebook using either`pip` or `pip3`.

# In[ ]: