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

# # Speaking to objects (Nestle1904LFT)
# 
# *Work in progress! it needs to be updated to the new datastructure*

# ## Table of content <a class="anchor" id="TOC"></a>
# * <a href="#bullet1">1 - Introduction</a>
#     * <a href="#bullet1x1">1.1 - Why is this relevant?</a>
#     * <a href="#bullet1x2">1.2 - Translating into Text-Fabric queries</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 - Find phrase combinations where someone is speaking</a>
#     * <a href="#bullet3x2">3.2 - TBD</a>
# * <a href="#bullet4">4 - Discussion</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)
# 
# TBD

# ## 1.1 - Why is this relevant? <a class="anchor" id="bullet1x1"></a>
# 
# TBD

# ## 1.2 - Translating into Text-Fabric queries <a class="anchor" id="#bullet1x2"></a>
# 
# Since we are looking for situations where someone or something is speaking to an object, we first need to look for phrases with function 'Object function' (O). 
# 
# 
# It is using the classification by [Louw-Nida](https://www.laparola.net/greco/louwnida.php):
# > ```
# 1 Geographical Objects and Features
# 2 Natural Substances
# 3 Plants
# 4 Animals
# 5 Foods and Condiments
# 6 Artifacts
# 7 Constructions
# ```
# 
# Lookup of values for feature ln in [Louw-Nida Lexicon](https://www.laparola.net/greco/louwnida.php).
# 
# In the Text-Fabric database the information is stored in feature [ln](https://github.com/tonyjurg/Nestle1904LFT/blob/main/docs/features/ln.md#readme).
# 
# A related feature is [lex_dom](https://github.com/tonyjurg/Nestle1904LFT/blob/main/docs/features/lex_dom.md#readme)

# # 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 N1904 app and data
N1904 = use ("tonyjurg/Nestle1904LFT", version="0.6", hoist=globals())


# In[4]:


# The following will push the Text-Fabric stylesheet to this notebook (to facilitate proper display with notebook viewer)
N1904.dh(N1904.getCss())


# In[5]:


# Set default view in a way to limit noise as much as possible.
N1904.displaySetup(condensed=True, multiFeatures=False,queryFeatures=False)


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

# ## 3.1 - Find phrase combinations where someone is speaking<a class="anchor" id="bullet3x1"></a>
# ##### [Back to TOC](#TOC)
# 
# For demonstration purposes the table output is limited to 3 results.

# In[12]:


# Define the query template [still to be fine-tuned!]
SearchObjectPhrases = '''
sentence
    a:wg wgrole=v|p
       word lemma=λέγω
    b:wg wgrole=o
       word ln~^[12345679]\.
'''

# The following will create a list containing ordered tuples consisting of node numbers of the items as they appear in the query
ObjectPhrasesList = N1904.search(SearchObjectPhrases)

# Just print a few of the results in a table
N1904.table(ObjectPhrasesList, condensed=False, extraFeatures={'lemma'}, end=3)


# Another method to display a limit amount of output, this time using [`plainTuple`](https://annotation.github.io/text-fabric/tf/advanced/display.html#tf.advanced.display.plainTuple) is the following:

# In[13]:


# Limit the query result to 1
TruncatedObjectPhrasesList = N1904.search(SearchObjectPhrases,limit=1)
for NodesTuple in TruncatedObjectPhrasesList: N1904.plainTuple(NodesTuple)


# ## 3.2 - second try (in progress) <a class="anchor" id="bullet3x2"></a>
# ##### [Back to TOC](#TOC)
# 
# For demonstration purposes the table output is limited to the first 10 results.
# *the query as it is now only selects the use of 'lego'*

# In[14]:


MaxNumberOfResuls=10
ThisResult=0

for node in F.lemma.s('λέγω'):
    ThisResult+=1
    gloss=F.gloss_EN.v(node)
    # Following line creates a nicely formated presentation of the verse
    VerseLocation=N1904.sectionStrFromNode(node)
    # The following is an alternative allowing free formating:
    #   VerseLocation="{} {}:{}".format(F.book.v(node),F.chapter.v(node),F.verse.v(node))
    print('\n',ThisResult,'\t',VerseLocation)   
    if ThisResult == MaxNumberOfResuls: break


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

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

# # 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[ ]: