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

# # The heart in the Tenakh (BHSA)

# ## Table of content <a class="anchor" id="TOC"></a> (TOC)
# 
# * <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 - Print table with frequency for lev and levev</a>
#     * <a href="#bullet3x2">3.2 - Plotting the frequency per book</a>
# * <a href="#bullet4">4 - Required libraries</a>
# * <a href="#bullet5">5 - Notebook details</a>
# 

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

# In Hebrew, the words for "heart" are written as follows:
# 
# - לֵב ("lev"): This is the shorter form commonly used to mean "heart."
# - לֵבָב ("levav"): This is a slightly longer, more poetic or emphatic form of the word, also meaning "heart."
# 
# This Jupyter NoteBook investigates occurances per book.

# # 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 BHSL app and data
BHS = use ("etcbc/BHSA",hoist=globals())


# Note: Thefeature documentation can be found at [ETCBC GitHub](https://github.com/ETCBC/bhsa/blob/master/docs/features/0_home.md) 

# In[4]:


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


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

# ## 3.1 - Print table with frequency for lev and levev <a class="anchor" id="bullet3x1"></a>

# In[5]:


levQuery = '''
book
  chapter
     verse
       word lex=LBB/|LB/ 
'''

levResults = BHS.search(levQuery)


# In[6]:


# Libraries for table formatting and data display
import pandas as pd
from IPython.display import display

# Initialize dictionary for storing results
resultDict = {}

# Process each item in the levResults
for item in levResults:
    book = F.book.v(item[0])
    lex = F.lex.v(item[3])
    
    if book in resultDict:
        # If it exists, add the count to the existing value
        resultDict[book][0] += 1
        if lex == 'LB/':
            resultDict[book][1] += 1
        else:
            resultDict[book][2] += 1
    else:
        # If it doesn't exist, initialize the count as the value
        if lex == 'LB/':
            resultDict[book] = [1, 1, 0]
        else:
            resultDict[book] = [1, 0, 1]

# Convert the dictionary into a DataFrame and sort by total frequency
tableData = pd.DataFrame(
    [[key, value[0], value[1], value[2]] for key, value in resultDict.items()],
    columns=["Book", "Total", "Lev", "Levav"]
)
tableData = tableData.sort_values(by="Total", ascending=False)

# Display the table
display(tableData)


# ## 3.2 - Plotting the frequency per book <a class="anchor" id="bullet3x2"></a>

# In[7]:


import matplotlib.pyplot as plt
import seaborn as sns
import pandas as pd

# Sample data setup (replace with your actual data)
# Initialize dictionary for storing results
resultDict = {}

# Process each item in the levResults
for item in levResults:
    book = F.book.v(item[0])
    lex = F.lex.v(item[3])
    
    if book in resultDict:
        # If it exists, add the count to the existing value
        resultDict[book][0] += 1
        if lex == 'LB/':
            resultDict[book][1] += 1
        else:
            resultDict[book][2] += 1
    else:
        # If it doesn't exist, initialize the count as the value
        if lex == 'LB/':
            resultDict[book] = [1, 1, 0]
        else:
            resultDict[book] = [1, 0, 1]

# Convert the dictionary into a DataFrame and sort by total frequency
tableData = pd.DataFrame(
    [[key, value[0], value[1], value[2]] for key, value in resultDict.items()],
    columns=["Book", "Total", "LB", "LBB"]
)

# Set up the data for plotting with LB as positive and LBB as negative values
tableData['LB_Positive'] = tableData['LB']  # LB counts as positive values
tableData['LBB_Negative'] = -tableData['LBB']  # LBB counts as negative values

# Set up the plot
plt.figure(figsize=(14, 10))

# Plot LB (positive values)
lb_bars = plt.barh(tableData["Book"], tableData["LB_Positive"], color='blue', label='לֵב')

# Plot LBB (negative values)
lbb_bars = plt.barh(tableData["Book"], tableData["LBB_Negative"], color='orange', label='לֵבָב')

# Add the counts on each bar
for bar, count in zip(lb_bars, tableData['LB']):
    plt.text(bar.get_width() + 1, bar.get_y() + bar.get_height()/2, str(count), va='center', color='blue')

for bar, count in zip(lbb_bars, tableData['LBB']):
    plt.text(bar.get_width() - 1, bar.get_y() + bar.get_height()/2, str(count), va='center', ha='right', color='orange')

# Customize x-axis to show absolute values
x_ticks = plt.xticks()[0]  # Get current tick positions
plt.xticks(x_ticks, [str(int(abs(x))) for x in x_ticks])  # Set absolute values for labels

# Add labels, title, and legend
plt.xlabel("Count")
plt.ylabel("Book")
plt.title('Distribution of לֵב and לֵבָב Counts per Book in the Tenach')
plt.axvline(0, color="black", linewidth=0.5)  # Center line at x=0
plt.legend(title="Category")

# Show the plot
plt.show()


# # 4 - Required libraries<a class="anchor" id="bullet4"></a>
# ##### [Back to TOC](#TOC)
# 
# The scripts in this notebook require (beside `text-fabric`) the following Python libraries to be installed in the environment:
# 
#     IPython
#     pandas
#     matplotlib
#     seaborn
# 
# You can install any missing library from within Jupyter Notebook using either`pip` or `pip3`.

# # 5 - Notebook details<a class="anchor" id="bullet6"></a>
# ##### [Back to TOC](#TOC)
# 
# <div style="float: left;">
#   <table>
#     <tr>
#       <td><strong>Author</strong></td>
#       <td>Tony Jurg</td>
#     </tr>
#     <tr>
#       <td><strong>Version</strong></td>
#       <td>1.0</td>
#     </tr>
#     <tr>
#       <td><strong>Date</strong></td>
#       <td>4 Novermber 2024</td>
#     </tr>
#   </table>
# </div>