{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Creating Text-Fabric from LowFat XML trees\n", "\n", "Version: 0.1.8 (May 8, 2023 - add phrases again)\n", "\n", "## Table of content \n", "* [1. Introduction](#first-bullet)\n", "* [2. Read LowFat XML data and store in pickle](#second-bullet)\n", "* [3. Sort the nodes](#third-bullet)\n", "* [4. Production Text-Fabric from pickle input](#fourth-bullet)\n", "* [5. Basic testing of the textfabric data](#fift-bullet)" ] }, { "cell_type": "markdown", "metadata": { "tags": [] }, "source": [ "\n", "## 1. Introduction \n", "##### [Back to TOC](#TOC)\n", "\n", "The source data for the conversion are the LowFat XML trees files representing the macula-greek version of the Nestle 1904 Greek New Testment. The most recent source data can be found on github https://github.com/Clear-Bible/macula-greek/tree/main/Nestle1904/lowfat. Attribution: \"MACULA Greek Linguistic Datasets, available at https://github.com/Clear-Bible/macula-greek/\". \n", "\n", "The production of the Text-Fabric files consist of two steps. First the creation of piclke files (part 1). Secondly the actual Text-Fabric creation process (part 2). Both steps are independent allowing to start from Part 2 by using the pickle files as input. \n", "\n", "\n", "\n", "Be advised that this Text-Fabric version is a test version (proof of concept) and requires further finetuning, especialy with regards of nomenclature and presentation of (sub)phrases and clauses." ] }, { "cell_type": "markdown", "metadata": { "tags": [] }, "source": [ "## 2. Read LowFat XML data and store in pickle \n", "##### [Back to TOC](#TOC)\n", "\n", "This script harvests all information from the LowFat tree data (XML nodes), puts it into a Panda DataFrame and stores the result per book in a pickle file. Note: pickling (in Python) is serialising an object into a disk file (or buffer). \n", "\n", "In the context of this script, 'Leaf' refers to those node containing the Greek word as data, which happen to be the nodes without any child (hence the analogy with the leaves on the tree). These 'leafs' can also be refered to as 'terminal nodes'. Futher, Parent1 is the leaf's parent, Parent2 is Parent1's parent, etc.\n", "\n", "For a full description of the source data see document [MACULA Greek Treebank for the Nestle 1904 Greek New Testament.pdf](https://github.com/Clear-Bible/macula-greek/blob/main/doc/MACULA%20Greek%20Treebank%20for%20the%20Nestle%201904%20Greek%20New%20Testament.pdf)" ] }, { "cell_type": "markdown", "metadata": { "tags": [] }, "source": [ "### Step 1: import various libraries" ] }, { "cell_type": "code", "execution_count": 1, "metadata": { "ExecuteTime": { "end_time": "2022-10-28T02:58:14.739227Z", "start_time": "2022-10-28T02:57:38.766097Z" } }, "outputs": [], "source": [ "import pandas as pd\n", "import sys\n", "import os\n", "import time\n", "import pickle\n", "\n", "import re #regular expressions\n", "from os import listdir\n", "from os.path import isfile, join\n", "import xml.etree.ElementTree as ET" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Step 2: initialize global data\n", "\n", "Change BaseDir, XmlDir and PklDir to match location of the datalocation and the OS used." ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [], "source": [ "BaseDir = 'C:\\\\Users\\\\tonyj\\\\my_new_Jupyter_folder\\\\Read_from_lowfat\\\\data\\\\'\n", "XmlDir = BaseDir+'xml\\\\'\n", "PklDir = BaseDir+'pkl\\\\'\n", "XlsxDir = BaseDir+'xlsx\\\\'\n", "# note: create output directory prior running this part\n", "\n", "# key: filename, [0]=book_long, [1]=book_num, [3]=book_short\n", "bo2book = {'01-matthew': ['Matthew', '1', 'Matt'],\n", " '02-mark': ['Mark', '2', 'Mark'],\n", " '03-luke': ['Luke', '3', 'Luke'],\n", " '04-john': ['John', '4', 'John'],\n", " '05-acts': ['Acts', '5', 'Acts'],\n", " '06-romans': ['Romans', '6', 'Rom'],\n", " '07-1corinthians': ['I_Corinthians', '7', '1Cor'],\n", " '08-2corinthians': ['II_Corinthians', '8', '2Cor'],\n", " '09-galatians': ['Galatians', '9', 'Gal'],\n", " '10-ephesians': ['Ephesians', '10', 'Eph'],\n", " '11-philippians': ['Philippians', '11', 'Phil'],\n", " '12-colossians': ['Colossians', '12', 'Col'],\n", " '13-1thessalonians':['I_Thessalonians', '13', '1Thess'],\n", " '14-2thessalonians':['II_Thessalonians','14', '2Thess'],\n", " '15-1timothy': ['I_Timothy', '15', '1Tim'],\n", " '16-2timothy': ['II_Timothy', '16', '2Tim'],\n", " '17-titus': ['Titus', '17', 'Titus'],\n", " '18-philemon': ['Philemon', '18', 'Phlm'],\n", " '19-hebrews': ['Hebrews', '19', 'Heb'],\n", " '20-james': ['James', '20', 'Jas'],\n", " '21-1peter': ['I_Peter', '21', '1Pet'],\n", " '22-2peter': ['II_Peter', '22', '2Pet'],\n", " '23-1john': ['I_John', '23', '1John'],\n", " '24-2john': ['II_John', '24', '2John'],\n", " '25-3john': ['III_John', '25', '3John'], \n", " '26-jude': ['Jude', '26', 'Jude'],\n", " '27-revelation': ['Revelation', '27', 'Rev']}\n", "\n", "bo2book = {'01-matthew': ['Matthew', '1', 'Matt']}" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### step 3: define Function to add parent info to each node of the XML tree\n", "\n", "In order to traverse from the 'leafs' (terminating nodes) upto the root of the tree, it is required to add information to each node pointing to the parent of each node.\n", "\n", "(concept taken from https://stackoverflow.com/questions/2170610/access-elementtree-node-parent-node)" ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [], "source": [ "def addParentInfo(et):\n", " for child in et:\n", " child.attrib['parent'] = et\n", " addParentInfo(child)\n", "\n", "def getParent(et):\n", " if 'parent' in et.attrib:\n", " return et.attrib['parent']\n", " else:\n", " return None" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Step 4: read and process the XML data and store panda dataframe in pickle" ] }, { "cell_type": "code", "execution_count": 48, "metadata": { "scrolled": true, "tags": [] }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Processing Matthew at C:\\Users\\tonyj\\my_new_Jupyter_folder\\Read_from_lowfat\\data\\xml\\01-matthew.xml\n", "......................................................................................................................................................................................\n", "Found 18299 items in 337.3681836128235 seconds\n", "\n", "Processing Mark at C:\\Users\\tonyj\\my_new_Jupyter_folder\\Read_from_lowfat\\data\\xml\\02-mark.xml\n", "................................................................................................................\n", "Found 11277 items in 144.04719877243042 seconds\n", "\n", "Processing Luke at C:\\Users\\tonyj\\my_new_Jupyter_folder\\Read_from_lowfat\\data\\xml\\03-luke.xml\n", "..................................................................................................................................................................................................\n", "Found 19456 items in 1501.197922706604 seconds\n", "\n", "Processing John at C:\\Users\\tonyj\\my_new_Jupyter_folder\\Read_from_lowfat\\data\\xml\\04-john.xml\n", "............................................................................................................................................................\n", "Found 15643 items in 237.1071105003357 seconds\n", "\n", "Processing Acts at C:\\Users\\tonyj\\my_new_Jupyter_folder\\Read_from_lowfat\\data\\xml\\05-acts.xml\n", ".......................................................................................................................................................................................\n", "Found 18393 items in 384.3644151687622 seconds\n", "\n", "Processing Romans at C:\\Users\\tonyj\\my_new_Jupyter_folder\\Read_from_lowfat\\data\\xml\\06-romans.xml\n", ".......................................................................\n", "Found 7100 items in 71.03568935394287 seconds\n", "\n", "Processing I_Corinthians at C:\\Users\\tonyj\\my_new_Jupyter_folder\\Read_from_lowfat\\data\\xml\\07-1corinthians.xml\n", "....................................................................\n", "Found 6820 items in 58.47511959075928 seconds\n", "\n", "Processing II_Corinthians at C:\\Users\\tonyj\\my_new_Jupyter_folder\\Read_from_lowfat\\data\\xml\\08-2corinthians.xml\n", "............................................\n", "Found 4469 items in 31.848721027374268 seconds\n", "\n", "Processing Galatians at C:\\Users\\tonyj\\my_new_Jupyter_folder\\Read_from_lowfat\\data\\xml\\09-galatians.xml\n", "......................\n", "Found 2228 items in 13.850211143493652 seconds\n", "\n", "Processing Ephesians at C:\\Users\\tonyj\\my_new_Jupyter_folder\\Read_from_lowfat\\data\\xml\\10-ephesians.xml\n", "........................\n", "Found 2419 items in 17.529520511627197 seconds\n", "\n", "Processing Philippians at C:\\Users\\tonyj\\my_new_Jupyter_folder\\Read_from_lowfat\\data\\xml\\11-philippians.xml\n", "................\n", "Found 1630 items in 9.271572589874268 seconds\n", "\n", "Processing Colossians at C:\\Users\\tonyj\\my_new_Jupyter_folder\\Read_from_lowfat\\data\\xml\\12-colossians.xml\n", "...............\n", "Found 1575 items in 10.389309883117676 seconds\n", "\n", "Processing I_Thessalonians at C:\\Users\\tonyj\\my_new_Jupyter_folder\\Read_from_lowfat\\data\\xml\\13-1thessalonians.xml\n", "..............\n", "Found 1473 items in 8.413437604904175 seconds\n", "\n", "Processing II_Thessalonians at C:\\Users\\tonyj\\my_new_Jupyter_folder\\Read_from_lowfat\\data\\xml\\14-2thessalonians.xml\n", "........\n", "Found 822 items in 4.284915447235107 seconds\n", "\n", "Processing I_Timothy at C:\\Users\\tonyj\\my_new_Jupyter_folder\\Read_from_lowfat\\data\\xml\\15-1timothy.xml\n", "...............\n", "Found 1588 items in 10.419771671295166 seconds\n", "\n", "Processing II_Timothy at C:\\Users\\tonyj\\my_new_Jupyter_folder\\Read_from_lowfat\\data\\xml\\16-2timothy.xml\n", "............\n", "Found 1237 items in 7.126454591751099 seconds\n", "\n", "Processing Titus at C:\\Users\\tonyj\\my_new_Jupyter_folder\\Read_from_lowfat\\data\\xml\\17-titus.xml\n", "......\n", "Found 658 items in 3.1472580432891846 seconds\n", "\n", "Processing Philemon at C:\\Users\\tonyj\\my_new_Jupyter_folder\\Read_from_lowfat\\data\\xml\\18-philemon.xml\n", "...\n", "Found 335 items in 1.3175146579742432 seconds\n", "\n", "Processing Hebrews at C:\\Users\\tonyj\\my_new_Jupyter_folder\\Read_from_lowfat\\data\\xml\\19-hebrews.xml\n", ".................................................\n", "Found 4955 items in 44.31139326095581 seconds\n", "\n", "Processing James at C:\\Users\\tonyj\\my_new_Jupyter_folder\\Read_from_lowfat\\data\\xml\\20-james.xml\n", ".................\n", "Found 1739 items in 8.570415496826172 seconds\n", "\n", "Processing I_Peter at C:\\Users\\tonyj\\my_new_Jupyter_folder\\Read_from_lowfat\\data\\xml\\21-1peter.xml\n", "................\n", "Found 1676 items in 10.489561557769775 seconds\n", "\n", "Processing II_Peter at C:\\Users\\tonyj\\my_new_Jupyter_folder\\Read_from_lowfat\\data\\xml\\22-2peter.xml\n", "..........\n", "Found 1098 items in 6.005697250366211 seconds\n", "\n", "Processing I_John at C:\\Users\\tonyj\\my_new_Jupyter_folder\\Read_from_lowfat\\data\\xml\\23-1john.xml\n", ".....................\n", "Found 2136 items in 10.843079566955566 seconds\n", "\n", "Processing II_John at C:\\Users\\tonyj\\my_new_Jupyter_folder\\Read_from_lowfat\\data\\xml\\24-2john.xml\n", "..\n", "Found 245 items in 0.9535031318664551 seconds\n", "\n", "Processing III_John at C:\\Users\\tonyj\\my_new_Jupyter_folder\\Read_from_lowfat\\data\\xml\\25-3john.xml\n", "..\n", "Found 219 items in 1.0913233757019043 seconds\n", "\n", "Processing Jude at C:\\Users\\tonyj\\my_new_Jupyter_folder\\Read_from_lowfat\\data\\xml\\26-jude.xml\n", "....\n", "Found 457 items in 1.8929190635681152 seconds\n", "\n", "Processing Revelation at C:\\Users\\tonyj\\my_new_Jupyter_folder\\Read_from_lowfat\\data\\xml\\27-revelation.xml\n", "..................................................................................................\n", "Found 9832 items in 125.92533278465271 seconds\n", "\n" ] } ], "source": [ "# set some globals\n", "monad=1\n", "CollectedItems= 0\n", "\n", "# process books in order\n", "for bo, bookinfo in bo2book.items():\n", " CollectedItems=0\n", " SentenceNumber=0\n", " WordGroupNumber=0\n", " full_df=pd.DataFrame({})\n", " book_long=bookinfo[0]\n", " booknum=bookinfo[1]\n", " book_short=bookinfo[2]\n", " InputFile = os.path.join(XmlDir, f'{bo}.xml')\n", " OutputFile = os.path.join(PklDir, f'{bo}.pkl')\n", " print(f'Processing {book_long} at {InputFile}')\n", "\n", " # send xml document to parsing process\n", " tree = ET.parse(InputFile)\n", " # Now add all the parent info to the nodes in the xtree [important!]\n", " addParentInfo(tree.getroot())\n", " start_time = time.time()\n", " \n", " # walk over all the XML data\n", " for elem in tree.iter():\n", " if elem.tag == 'sentence':\n", " # add running number to 'sentence' tags\n", " SentenceNumber+=1\n", " elem.set('SN', SentenceNumber)\n", " if elem.tag == 'wg':\n", " # add running number to 'wg' tags\n", " WordGroupNumber+=1\n", " elem.set('WGN', WordGroupNumber)\n", " if elem.tag == 'w':\n", " # all nodes containing words are tagged with 'w'\n", " \n", " # show progress on screen\n", " CollectedItems+=1\n", " if (CollectedItems%100==0): print (\".\",end='')\n", " \n", " #Leafref will contain list with book, chapter verse and wordnumber\n", " Leafref = re.sub(r'[!: ]',\" \", elem.attrib.get('ref')).split()\n", " \n", " #push value for monad to element tree \n", " elem.set('monad', monad)\n", " monad+=1\n", " \n", " # add some important computed data to the leaf\n", " elem.set('LeafName', elem.tag)\n", " elem.set('word', elem.text)\n", " elem.set('book_long', book_long)\n", " elem.set('booknum', int(booknum))\n", " elem.set('book_short', book_short)\n", " elem.set('chapter', int(Leafref[1]))\n", " elem.set('verse', int(Leafref[2]))\n", " \n", " # folling code will trace down parents upto the tree and store found attributes\n", " parentnode=getParent(elem)\n", " index=0\n", " while (parentnode):\n", " index+=1\n", " elem.set('Parent{}Name'.format(index), parentnode.tag)\n", " elem.set('Parent{}Type'.format(index), parentnode.attrib.get('type'))\n", " elem.set('Parent{}Appos'.format(index), parentnode.attrib.get('appositioncontainer'))\n", " elem.set('Parent{}Class'.format(index), parentnode.attrib.get('class'))\n", " elem.set('Parent{}Rule'.format(index), parentnode.attrib.get('rule'))\n", " elem.set('Parent{}Role'.format(index), parentnode.attrib.get('role'))\n", " elem.set('Parent{}Cltype'.format(index), parentnode.attrib.get('cltype'))\n", " elem.set('Parent{}Unit'.format(index), parentnode.attrib.get('unit'))\n", " elem.set('Parent{}Junction'.format(index), parentnode.attrib.get('junction'))\n", " elem.set('Parent{}SN'.format(index), parentnode.attrib.get('SN'))\n", " elem.set('Parent{}WGN'.format(index), parentnode.attrib.get('WGN'))\n", " currentnode=parentnode\n", " parentnode=getParent(currentnode) \n", " elem.set('parents', int(index))\n", " \n", " #this will push all elements found in the tree into a DataFrame\n", " df=pd.DataFrame(elem.attrib, index={monad})\n", " full_df=pd.concat([full_df,df])\n", " \n", " #store the resulting DataFrame per book into a pickle file for further processing\n", " df = df.convert_dtypes(convert_string=True)\n", " \n", " # sort by s=id\n", " sortkey='{http://www.w3.org/XML/1998/namespace}id'\n", " full_df.rename(columns={sortkey: 'id'}, inplace=True)\n", " full_df.sort_values(by=['id'])\n", "\n", " output = open(r\"{}\".format(OutputFile), 'wb')\n", " pickle.dump(full_df, output)\n", " output.close()\n", " print(\"\\nFound \",CollectedItems, \" items in %s seconds\\n\" % (time.time() - start_time)) \n", " " ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "# just dump some things to test the result\n", "\n", "\n", "for bo in bo2book:\n", " '''\n", " load all data into a dataframe\n", " process books in order (bookinfo is a list!)\n", " ''' \n", " InputFile = os.path.join(PklDir, f'{bo}.pkl')\n", " \n", " print(f'\\tloading {InputFile}...')\n", " pkl_file = open(InputFile, 'rb')\n", " df = pickle.load(pkl_file)\n", " pkl_file.close()\n", " \n", " # not sure if this is needed\n", " # fill dictionary of column names for this book \n", " IndexDict = {} # init an empty dictionary\n", " ItemsInRow=1\n", " for itemname in df.columns.to_list():\n", " IndexDict.update({'i_{}'.format(itemname): ItemsInRow})\n", " print (itemname)\n", " ItemsInRow+=1\n", " " ] }, { "cell_type": "markdown", "metadata": { "toc": true }, "source": [ "## 3. Nestle1904 Text-Fabric production from pickle input \n", "##### [Back to TOC](#TOC)\n", "\n", "This script creates the Text-Fabric files by recursive calling the TF walker function.\n", "API info: https://annotation.github.io/text-fabric/tf/convert/walker.html\n", "\n", "The pickle files created by step 1 are stored on Github location T.B.D." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Step 1: Load libraries and initialize some data\n", "\n" ] }, { "cell_type": "code", "execution_count": 6, "metadata": { "ExecuteTime": { "end_time": "2022-10-28T03:01:34.810259Z", "start_time": "2022-10-28T03:01:25.745112Z" } }, "outputs": [], "source": [ "import pandas as pd\n", "import os\n", "import re\n", "import gc\n", "from tf.fabric import Fabric\n", "from tf.convert.walker import CV\n", "from tf.parameters import VERSION\n", "from datetime import date\n", "import pickle\n", "\n", "BaseDir = 'C:\\\\Users\\\\tonyj\\\\my_new_Jupyter_folder\\\\Read_from_lowfat\\\\data\\\\'\n", "XmlDir = BaseDir+'xml\\\\'\n", "PklDir = BaseDir+'pkl\\\\'\n", "XlsxDir = BaseDir+'xlsx\\\\'\n", "\n", "# key: filename, [0]=book_long, [1]=book_num, [3]=book_short\n", "bo2book = {'01-matthew': ['Matthew', '1', 'Matt'],\n", " '02-mark': ['Mark', '2', 'Mark'],\n", " '03-luke': ['Luke', '3', 'Luke'],\n", " '04-john': ['John', '4', 'John'],\n", " '05-acts': ['Acts', '5', 'Acts'],\n", " '06-romans': ['Romans', '6', 'Rom'],\n", " '07-1corinthians': ['I_Corinthians', '7', '1Cor'],\n", " '08-2corinthians': ['II_Corinthians', '8', '2Cor'],\n", " '09-galatians': ['Galatians', '9', 'Gal'],\n", " '10-ephesians': ['Ephesians', '10', 'Eph'],\n", " '11-philippians': ['Philippians', '11', 'Phil'],\n", " '12-colossians': ['Colossians', '12', 'Col'],\n", " '13-1thessalonians':['I_Thessalonians', '13', '1Thess'],\n", " '14-2thessalonians':['II_Thessalonians','14', '2Thess'],\n", " '15-1timothy': ['I_Timothy', '15', '1Tim'],\n", " '16-2timothy': ['II_Timothy', '16', '2Tim'],\n", " '17-titus': ['Titus', '17', 'Titus'],\n", " '18-philemon': ['Philemon', '18', 'Phlm'],\n", " '19-hebrews': ['Hebrews', '19', 'Heb'],\n", " '20-james': ['James', '20', 'Jas'],\n", " '21-1peter': ['I_Peter', '21', '1Pet'],\n", " '22-2peter': ['II_Peter', '22', '2Pet'],\n", " '23-1john': ['I_John', '23', '1John'],\n", " '24-2john': ['II_John', '24', '2John'],\n", " '25-3john': ['III_John', '25', '3John'], \n", " '26-jude': ['Jude', '26', 'Jude'],\n", " '27-revelation': ['Revelation', '27', 'Rev']}\n", "\n", "bo2book_ = {'01-matthew': ['Matthew', '1', 'Matt']}\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Optional: export to Excel for investigation" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "# test: sorting the data\n", "import openpyxl\n", "import pickle\n", "\n", "#if True:\n", "for bo in bo2book:\n", " '''\n", " load all data into a dataframe\n", " process books in order (bookinfo is a list!)\n", " ''' \n", " InputFile = os.path.join(PklDir, f'{bo}.pkl')\n", " #InputFile = os.path.join(PklDir, '01-matthew.pkl')\n", " \n", " print(f'\\tloading {InputFile}...')\n", " pkl_file = open(InputFile, 'rb')\n", " df = pickle.load(pkl_file)\n", " pkl_file.close()\n", " \n", " # not sure if this is needed\n", " # fill dictionary of column names for this book \n", " IndexDict = {} # init an empty dictionary\n", " ItemsInRow=1\n", " for itemname in df.columns.to_list():\n", " IndexDict.update({'i_{}'.format(itemname): ItemsInRow})\n", " ItemsInRow+=1\n", " #print(itemname)\n", " \n", " # sort by id\n", " #print(df)\n", " df_sorted=df.sort_values(by=['id'])\n", " df_sorted.to_excel(os.path.join(XlsxDir, f'{bo}.xlsx'), index=False)\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Step 2 Running the TF walker function\n", "\n", "API info: https://annotation.github.io/text-fabric/tf/convert/walker.html\n", "\n", "The logic of interpreting the data is included in the director function." ] }, { "cell_type": "code", "execution_count": 8, "metadata": { "tags": [] }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "This is Text-Fabric 11.4.10\n", "55 features found and 0 ignored\n", " 0.00s Importing data from walking through the source ...\n", " | 0.00s Preparing metadata... \n", " | SECTION TYPES: book, chapter, verse\n", " | SECTION FEATURES: book, chapter, verse\n", " | STRUCTURE TYPES: book, chapter, verse\n", " | STRUCTURE FEATURES: book, chapter, verse\n", " | TEXT FEATURES:\n", " | | text-orig-full after, word\n", " | 0.00s OK\n", " | 0.00s Following director... \n", "\tWe are loading C:\\Users\\tonyj\\my_new_Jupyter_folder\\Read_from_lowfat\\data\\pkl\\01-matthew.pkl...\n", "\tWe are loading C:\\Users\\tonyj\\my_new_Jupyter_folder\\Read_from_lowfat\\data\\pkl\\02-mark.pkl...\n", "\tWe are loading C:\\Users\\tonyj\\my_new_Jupyter_folder\\Read_from_lowfat\\data\\pkl\\03-luke.pkl...\n", "\tWe are loading C:\\Users\\tonyj\\my_new_Jupyter_folder\\Read_from_lowfat\\data\\pkl\\04-john.pkl...\n", "\tWe are loading C:\\Users\\tonyj\\my_new_Jupyter_folder\\Read_from_lowfat\\data\\pkl\\05-acts.pkl...\n", "\tWe are loading C:\\Users\\tonyj\\my_new_Jupyter_folder\\Read_from_lowfat\\data\\pkl\\06-romans.pkl...\n", "\tWe are loading C:\\Users\\tonyj\\my_new_Jupyter_folder\\Read_from_lowfat\\data\\pkl\\07-1corinthians.pkl...\n", "\tWe are loading C:\\Users\\tonyj\\my_new_Jupyter_folder\\Read_from_lowfat\\data\\pkl\\08-2corinthians.pkl...\n", "\tWe are loading C:\\Users\\tonyj\\my_new_Jupyter_folder\\Read_from_lowfat\\data\\pkl\\09-galatians.pkl...\n", "\tWe are loading C:\\Users\\tonyj\\my_new_Jupyter_folder\\Read_from_lowfat\\data\\pkl\\10-ephesians.pkl...\n", "\tWe are loading C:\\Users\\tonyj\\my_new_Jupyter_folder\\Read_from_lowfat\\data\\pkl\\11-philippians.pkl...\n", "\tWe are loading C:\\Users\\tonyj\\my_new_Jupyter_folder\\Read_from_lowfat\\data\\pkl\\12-colossians.pkl...\n", "\tWe are loading C:\\Users\\tonyj\\my_new_Jupyter_folder\\Read_from_lowfat\\data\\pkl\\13-1thessalonians.pkl...\n", "\tWe are loading C:\\Users\\tonyj\\my_new_Jupyter_folder\\Read_from_lowfat\\data\\pkl\\14-2thessalonians.pkl...\n", "\tWe are loading C:\\Users\\tonyj\\my_new_Jupyter_folder\\Read_from_lowfat\\data\\pkl\\15-1timothy.pkl...\n", "\tWe are loading C:\\Users\\tonyj\\my_new_Jupyter_folder\\Read_from_lowfat\\data\\pkl\\16-2timothy.pkl...\n", "\tWe are loading C:\\Users\\tonyj\\my_new_Jupyter_folder\\Read_from_lowfat\\data\\pkl\\17-titus.pkl...\n", "\tWe are loading C:\\Users\\tonyj\\my_new_Jupyter_folder\\Read_from_lowfat\\data\\pkl\\18-philemon.pkl...\n", "\tWe are loading C:\\Users\\tonyj\\my_new_Jupyter_folder\\Read_from_lowfat\\data\\pkl\\19-hebrews.pkl...\n", "\tWe are loading C:\\Users\\tonyj\\my_new_Jupyter_folder\\Read_from_lowfat\\data\\pkl\\20-james.pkl...\n", "\tWe are loading C:\\Users\\tonyj\\my_new_Jupyter_folder\\Read_from_lowfat\\data\\pkl\\21-1peter.pkl...\n", "\tWe are loading C:\\Users\\tonyj\\my_new_Jupyter_folder\\Read_from_lowfat\\data\\pkl\\22-2peter.pkl...\n", "\tWe are loading C:\\Users\\tonyj\\my_new_Jupyter_folder\\Read_from_lowfat\\data\\pkl\\23-1john.pkl...\n", "\tWe are loading C:\\Users\\tonyj\\my_new_Jupyter_folder\\Read_from_lowfat\\data\\pkl\\24-2john.pkl...\n", "\tWe are loading C:\\Users\\tonyj\\my_new_Jupyter_folder\\Read_from_lowfat\\data\\pkl\\25-3john.pkl...\n", "\tWe are loading C:\\Users\\tonyj\\my_new_Jupyter_folder\\Read_from_lowfat\\data\\pkl\\26-jude.pkl...\n", "\tWe are loading C:\\Users\\tonyj\\my_new_Jupyter_folder\\Read_from_lowfat\\data\\pkl\\27-revelation.pkl...\n", " | 41s \"edge\" actions: 0\n", " | 41s \"feature\" actions: 267471\n", " | 41s \"node\" actions: 129692\n", " | 41s \"resume\" actions: 9629\n", " | 41s \"slot\" actions: 137779\n", " | 41s \"terminate\" actions: 277227\n", " | 27 x \"book\" node \n", " | 260 x \"chapter\" node \n", " | 8011 x \"sentence\" node \n", " | 7943 x \"verse\" node \n", " | 113451 x \"wg\" node \n", " | 137779 x \"word\" node = slot type\n", " | 267471 nodes of all types\n", " | 41s OK\n", " | 0.00s checking for nodes and edges ... \n", " | 0.00s OK\n", " | 0.00s checking (section) features ... \n", " | 0.21s OK\n", " | 0.00s reordering nodes ...\n", " | 0.03s Sorting 27 nodes of type \"book\"\n", " | 0.05s Sorting 260 nodes of type \"chapter\"\n", " | 0.06s Sorting 8011 nodes of type \"sentence\"\n", " | 0.08s Sorting 7943 nodes of type \"verse\"\n", " | 0.11s Sorting 113451 nodes of type \"wg\"\n", " | 0.22s Max node = 267471\n", " | 0.22s OK\n", " | 0.00s reassigning feature values ...\n", " | | 0.00s node feature \"after\" with 137779 nodes\n", " | | 0.04s node feature \"appos\" with 113451 nodes\n", " | | 0.08s node feature \"book\" with 27 nodes\n", " | | 0.08s node feature \"book_long\" with 137779 nodes\n", " | | 0.13s node feature \"booknumber\" with 137806 nodes\n", " | | 0.17s node feature \"bookshort\" with 137806 nodes\n", " | | 0.21s node feature \"case\" with 137779 nodes\n", " | | 0.26s node feature \"chapter\" with 153939 nodes\n", " | | 0.32s node feature \"clausetype\" with 113451 nodes\n", " | | 0.35s node feature \"containedclause\" with 137779 nodes\n", " | | 0.39s node feature \"degree\" with 137779 nodes\n", " | | 0.44s node feature \"gloss\" with 137779 nodes\n", " | | 0.47s node feature \"gn\" with 137779 nodes\n", " | | 0.52s node feature \"id\" with 137779 nodes\n", " | | 0.56s node feature \"junction\" with 113451 nodes\n", " | | 0.60s node feature \"lemma\" with 137779 nodes\n", " | | 0.64s node feature \"lex_dom\" with 137779 nodes\n", " | | 0.67s node feature \"ln\" with 137779 nodes\n", " | | 0.71s node feature \"monad\" with 137779 nodes\n", " | | 0.74s node feature \"mood\" with 137779 nodes\n", " | | 0.79s node feature \"morph\" with 137779 nodes\n", " | | 0.83s node feature \"nodeID\" with 137779 nodes\n", " | | 0.87s node feature \"normalized\" with 137779 nodes\n", " | | 0.91s node feature \"nu\" with 137779 nodes\n", " | | 0.95s node feature \"number\" with 137779 nodes\n", " | | 0.99s node feature \"orig_order\" with 137779 nodes\n", " | | 1.03s node feature \"person\" with 137779 nodes\n", " | | 1.07s node feature \"ref\" with 137779 nodes\n", " | | 1.12s node feature \"reference\" with 137779 nodes\n", " | | 1.16s node feature \"roleclausedistance\" with 137779 nodes\n", " | | 1.20s node feature \"rule\" with 113451 nodes\n", " | | 1.24s node feature \"sentence\" with 137779 nodes\n", " | | 1.28s node feature \"sp\" with 137779 nodes\n", " | | 1.32s node feature \"sp_full\" with 137779 nodes\n", " | | 1.36s node feature \"strongs\" with 137779 nodes\n", " | | 1.40s node feature \"subj_ref\" with 137779 nodes\n", " | | 1.44s node feature \"tense\" with 137779 nodes\n", " | | 1.48s node feature \"type\" with 137779 nodes\n", " | | 1.52s node feature \"unicode\" with 137779 nodes\n", " | | 1.56s node feature \"verse\" with 153733 nodes\n", " | | 1.61s node feature \"voice\" with 137779 nodes\n", " | | 1.64s node feature \"wgclass\" with 113451 nodes\n", " | | 1.68s node feature \"wglevel\" with 113451 nodes\n", " | | 1.72s node feature \"wgnum\" with 113451 nodes\n", " | | 1.75s node feature \"wgrole\" with 113451 nodes\n", " | | 1.79s node feature \"wgrolelong\" with 113451 nodes\n", " | | 1.82s node feature \"wgtype\" with 113451 nodes\n", " | | 1.86s node feature \"word\" with 137779 nodes\n", " | | 1.90s node feature \"wordlevel\" with 137779 nodes\n", " | | 1.93s node feature \"wordrole\" with 137779 nodes\n", " | | 1.97s node feature \"wordrolelong\" with 137779 nodes\n", " | 2.09s OK\n", " 0.00s Exporting 52 node and 1 edge and 1 config features to ~/my_new_Jupyter_folder/Read_from_lowfat/data:\n", " 0.00s VALIDATING oslots feature\n", " 0.02s VALIDATING oslots feature\n", " 0.02s maxSlot= 137779\n", " 0.02s maxNode= 267471\n", " 0.03s OK: oslots is valid\n", " | 0.13s T after to ~/my_new_Jupyter_folder/Read_from_lowfat/data\n", " | 0.11s T appos to ~/my_new_Jupyter_folder/Read_from_lowfat/data\n", " | 0.00s T book to ~/my_new_Jupyter_folder/Read_from_lowfat/data\n", " | 0.13s T book_long to ~/my_new_Jupyter_folder/Read_from_lowfat/data\n", " | 0.13s T booknumber to ~/my_new_Jupyter_folder/Read_from_lowfat/data\n", " | 0.13s T bookshort to ~/my_new_Jupyter_folder/Read_from_lowfat/data\n", " | 0.13s T case to ~/my_new_Jupyter_folder/Read_from_lowfat/data\n", " | 0.14s T chapter to ~/my_new_Jupyter_folder/Read_from_lowfat/data\n", " | 0.10s T clausetype to ~/my_new_Jupyter_folder/Read_from_lowfat/data\n", " | 0.13s T containedclause to ~/my_new_Jupyter_folder/Read_from_lowfat/data\n", " | 0.14s T degree to ~/my_new_Jupyter_folder/Read_from_lowfat/data\n", " | 0.14s T gloss to ~/my_new_Jupyter_folder/Read_from_lowfat/data\n", " | 0.14s T gn to ~/my_new_Jupyter_folder/Read_from_lowfat/data\n", " | 0.13s T id to ~/my_new_Jupyter_folder/Read_from_lowfat/data\n", " | 0.11s T junction to ~/my_new_Jupyter_folder/Read_from_lowfat/data\n", " | 0.16s T lemma to ~/my_new_Jupyter_folder/Read_from_lowfat/data\n", " | 0.13s T lex_dom to ~/my_new_Jupyter_folder/Read_from_lowfat/data\n", " | 0.14s T ln to ~/my_new_Jupyter_folder/Read_from_lowfat/data\n", " | 0.13s T monad to ~/my_new_Jupyter_folder/Read_from_lowfat/data\n", " | 0.13s T mood to ~/my_new_Jupyter_folder/Read_from_lowfat/data\n", " | 0.14s T morph to ~/my_new_Jupyter_folder/Read_from_lowfat/data\n", " | 0.14s T nodeID to ~/my_new_Jupyter_folder/Read_from_lowfat/data\n", " | 0.15s T normalized to ~/my_new_Jupyter_folder/Read_from_lowfat/data\n", " | 0.14s T nu to ~/my_new_Jupyter_folder/Read_from_lowfat/data\n", " | 0.15s T number to ~/my_new_Jupyter_folder/Read_from_lowfat/data\n", " | 0.13s T orig_order to ~/my_new_Jupyter_folder/Read_from_lowfat/data\n", " | 0.05s T otype to ~/my_new_Jupyter_folder/Read_from_lowfat/data\n", " | 0.13s T person to ~/my_new_Jupyter_folder/Read_from_lowfat/data\n", " | 0.14s T ref to ~/my_new_Jupyter_folder/Read_from_lowfat/data\n", " | 0.14s T reference to ~/my_new_Jupyter_folder/Read_from_lowfat/data\n", " | 0.12s T roleclausedistance to ~/my_new_Jupyter_folder/Read_from_lowfat/data\n", " | 0.12s T rule to ~/my_new_Jupyter_folder/Read_from_lowfat/data\n", " | 0.13s T sentence to ~/my_new_Jupyter_folder/Read_from_lowfat/data\n", " | 0.13s T sp to ~/my_new_Jupyter_folder/Read_from_lowfat/data\n", " | 0.13s T sp_full to ~/my_new_Jupyter_folder/Read_from_lowfat/data\n", " | 0.13s T strongs to ~/my_new_Jupyter_folder/Read_from_lowfat/data\n", " | 0.14s T subj_ref to ~/my_new_Jupyter_folder/Read_from_lowfat/data\n", " | 0.13s T tense to ~/my_new_Jupyter_folder/Read_from_lowfat/data\n", " | 0.13s T type to ~/my_new_Jupyter_folder/Read_from_lowfat/data\n", " | 0.15s T unicode to ~/my_new_Jupyter_folder/Read_from_lowfat/data\n", " | 0.14s T verse to ~/my_new_Jupyter_folder/Read_from_lowfat/data\n", " | 0.13s T voice to ~/my_new_Jupyter_folder/Read_from_lowfat/data\n", " | 0.12s T wgclass to ~/my_new_Jupyter_folder/Read_from_lowfat/data\n", " | 0.10s T wglevel to ~/my_new_Jupyter_folder/Read_from_lowfat/data\n", " | 0.16s T wgnum to ~/my_new_Jupyter_folder/Read_from_lowfat/data\n", " | 0.11s T wgrole to ~/my_new_Jupyter_folder/Read_from_lowfat/data\n", " | 0.11s T wgrolelong to ~/my_new_Jupyter_folder/Read_from_lowfat/data\n", " | 0.11s T wgtype to ~/my_new_Jupyter_folder/Read_from_lowfat/data\n", " | 0.16s T word to ~/my_new_Jupyter_folder/Read_from_lowfat/data\n", " | 0.12s T wordlevel to ~/my_new_Jupyter_folder/Read_from_lowfat/data\n", " | 0.14s T wordrole to ~/my_new_Jupyter_folder/Read_from_lowfat/data\n", " | 0.13s T wordrolelong to ~/my_new_Jupyter_folder/Read_from_lowfat/data\n", " | 0.42s T oslots to ~/my_new_Jupyter_folder/Read_from_lowfat/data\n", " | 0.00s M otext to ~/my_new_Jupyter_folder/Read_from_lowfat/data\n", " 7.15s Exported 52 node features and 1 edge features and 1 config features to ~/my_new_Jupyter_folder/Read_from_lowfat/data\n", "done\n" ] } ], "source": [ "TF = Fabric(locations=BaseDir, silent=False)\n", "cv = CV(TF)\n", "version = \"0.1.8\"\n", "\n", "###############################################\n", "# Common helper functions #\n", "###############################################\n", "\n", "#Function to prevent errors during conversion due to missing data\n", "def sanitize(input):\n", " if isinstance(input, float): return ''\n", " if isinstance(input, type(None)): return ''\n", " else: return (input)\n", "\n", "\n", "# Function to expand the syntactic categories of words or wordgroup\n", "# See also \"MACULA Greek Treebank for the Nestle 1904 Greek New Testament.pdf\" \n", "# page 5&6 (section 2.4 Syntactic Categories at Clause Level)\n", "def ExpandRole(input):\n", " if input==\"adv\": return 'Adverbial'\n", " if input==\"io\": return 'Indirect Object'\n", " if input==\"o\": return 'Object'\n", " if input==\"o2\": return 'Second Object'\n", " if input==\"s\": return 'Subject'\n", " if input==\"p\": return 'Predicate'\n", " if input==\"v\": return 'Verbal'\n", " if input==\"vc\": return 'Verbal Copula'\n", " if input=='aux': return 'Auxiliar'\n", " return ''\n", "\n", "# Function to expantion of Part of Speech labels. See also the description in \n", "# \"MACULA Greek Treebank for the Nestle 1904 Greek New Testament.pdf\" page 6&7\n", "# (2.2. Syntactic Categories at Word Level: Part of Speech Labels)\n", "def ExpandSP(input):\n", " if input=='adj': return 'adjective'\n", " if input=='conj': return 'conjunction'\n", " if input=='det': return 'determiner' \n", " if input=='intj': return 'interjection' \n", " if input=='noun': return 'noun' \n", " if input=='num': return 'numeral' \n", " if input=='prep': return 'preposition' \n", " if input=='ptcl': return 'particle' \n", " if input=='pron': return 'pronoun' \n", " if input=='verb': return 'verb' \n", " return ''\n", "\n", "###############################################\n", "# The director routine #\n", "###############################################\n", "\n", "def director(cv):\n", " \n", " ###############################################\n", " # Innitial setup of data etc. #\n", " ###############################################\n", " NoneType = type(None) # needed as tool to validate certain data\n", " IndexDict = {} # init an empty dictionary\n", " WordGroupDict={} # init a dummy dictionary\n", " PrevWordGroupSet = WordGroupSet = []\n", " PrevWordGroupList = WordGroupList = []\n", " RootWordGroup = 0\n", " WordNumber=FoundWords=WordGroupTrack=0\n", " # The following is required to recover succesfully from an abnormal condition\n", " # in the LowFat tree data where a element is labeled as \n", " # this number is arbitrary but should be high enough not to clash with 'real' WG numbers\n", " DummyWGN=200000 \n", " \n", " for bo,bookinfo in bo2book.items(): \n", " \n", " ###############################################\n", " # start of section executed for each book #\n", " ###############################################\n", " \n", " # note: bookinfo is a list! Split the data\n", " Book = bookinfo[0] \n", " BookNumber = int(bookinfo[1])\n", " BookShort = bookinfo[2]\n", " BookLoc = os.path.join(PklDir, f'{bo}.pkl') \n", " \n", " \n", " # load data for this book into a dataframe. \n", " # make sure wordorder is correct\n", " print(f'\\tWe are loading {BookLoc}...')\n", " pkl_file = open(BookLoc, 'rb')\n", " df_unsorted = pickle.load(pkl_file)\n", " pkl_file.close()\n", " df=df_unsorted.sort_values(by=['id'])\n", " \n", " \n", " # set up nodes for new book\n", " ThisBookPointer = cv.node('book')\n", " cv.feature(ThisBookPointer, book=Book, booknumber=BookNumber, bookshort=BookShort)\n", " \n", " ThisChapterPointer = cv.node('chapter')\n", " cv.feature(ThisChapterPointer, chapter=1)\n", " PreviousChapter=1\n", " \n", " ThisVersePointer = cv.node('verse')\n", " cv.feature(ThisVersePointer, verse=1)\n", " PreviousVerse=1\n", " \n", " ThisSentencePointer = cv.node('sentence')\n", " cv.feature(ThisSentencePointer, verse=1)\n", " PreviousSentence=1 \n", " \n", "\n", " '''\n", " fill dictionary of column names for this book \n", " sort to ensure proper wordorder\n", " '''\n", " ItemsInRow=1\n", " for itemname in df.columns.to_list():\n", " IndexDict.update({'i_{}'.format(itemname): ItemsInRow})\n", " ItemsInRow+=1\n", " df.sort_values(by=['id'])\n", " \n", "\n", " ###############################################\n", " # Iterate through words and construct objects #\n", " ###############################################\n", " \n", " for row in df.itertuples():\n", " WordNumber += 1\n", " FoundWords +=1\n", " \n", " # Detect and act upon changes in sentences, verse and chapter \n", " # the order of terminating and creating the nodes is critical: \n", " # close verse - close chapter - open chapter - open verse \n", " NumberOfParents = sanitize(row[IndexDict.get(\"i_parents\")])\n", " ThisSentence=int(row[IndexDict.get(\"i_Parent{}SN\".format(NumberOfParents-1))])\n", " ThisVerse = sanitize(row[IndexDict.get(\"i_verse\")])\n", " ThisChapter = sanitize(row[IndexDict.get(\"i_chapter\")])\n", " if (ThisSentence!=PreviousSentence):\n", " #cv.feature(ThisSentencePointer, statdata?)\n", " cv.terminate(ThisSentencePointer)\n", " \n", " if (ThisVerse!=PreviousVerse):\n", " #cv.feature(ThisVersePointer, statdata?)\n", " cv.terminate(ThisVersePointer)\n", "\n", " if (ThisChapter!=PreviousChapter):\n", " #cv.feature(ThisChapterPointer, statdata?)\n", " cv.terminate(ThisChapterPointer)\n", " PreviousChapter = ThisChapter\n", " ThisChapterPointer = cv.node('chapter')\n", " cv.feature(ThisChapterPointer, chapter=ThisChapter)\n", " \n", " if (ThisVerse!=PreviousVerse):\n", " PreviousVerse = ThisVerse \n", " ThisVersePointer = cv.node('verse')\n", " cv.feature(ThisVersePointer, verse=ThisVerse, chapter=ThisChapter)\n", " \n", " if (ThisSentence!=PreviousSentence):\n", " PreviousSentence=ThisSentence\n", " ThisSentencePointer = cv.node('sentence')\n", " cv.feature(ThisSentencePointer, verse=ThisVerse, chapter=ThisChapter) \n", "\n", " \n", " ###############################################\n", " # analyze and process tags #\n", " ###############################################\n", " \n", " PrevWordGroupList=WordGroupList\n", " WordGroupList=[] # stores current active WordGroup numbers\n", "\n", " for i in range(NumberOfParents-2,0,-1): # important: reversed itteration!\n", " _WGN=row[IndexDict.get(\"i_Parent{}WGN\".format(i))]\n", " if isinstance(_WGN, type(None)): \n", " # handling conditions where XML data has e.g. Acts 26:12\n", " # to recover, we need to create a dummy WG with a sufficient high WGN so it can never match any real WGN. \n", " WGN=DummyWGN\n", " else:\n", " WGN=int(_WGN)\n", " if WGN!='':\n", " WordGroupList.append(WGN)\n", " WordGroupDict[(WGN,0)]=WGN\n", " WGclass=sanitize(row[IndexDict.get(\"i_Parent{}Class\".format(i))])\n", " WGrule=sanitize(row[IndexDict.get(\"i_Parent{}Rule\".format(i))])\n", " WGtype=sanitize(row[IndexDict.get(\"i_Parent{}Type\".format(i))])\n", " if WGclass==WGrule==WGtype=='':\n", " WGclass='to be skipped?'\n", " if WGrule[-2:]=='CL' and WGclass=='': \n", " WGclass='cl*' # to simulate the way Logos presents this condition\n", " WordGroupDict[(WGN,6)]=WGclass\n", " WordGroupDict[(WGN,1)]=WGrule\n", " WordGroupDict[(WGN,8)]=WGtype\n", " WordGroupDict[(WGN,3)]=sanitize(row[IndexDict.get(\"i_Parent{}Junction\".format(i))])\n", " WordGroupDict[(WGN,2)]=sanitize(row[IndexDict.get(\"i_Parent{}Cltype\".format(i))])\n", " WordGroupDict[(WGN,7)]=sanitize(row[IndexDict.get(\"i_Parent{}Role\".format(i))])\n", "\n", " WordGroupDict[(WGN,9)]=sanitize(row[IndexDict.get(\"i_Parent{}Appos\".format(i))]) \n", " WordGroupDict[(WGN,10)]=NumberOfParents-1-i # = number of parent wordgroups \n", " if not PrevWordGroupList==WordGroupList:\n", " if RootWordGroup != WordGroupList[0]:\n", " RootWordGroup = WordGroupList[0]\n", " SuspendableWordGoupList = []\n", " # we have a new sentence. rebuild suspendable wordgroup list\n", " # some cleaning of data may be added here to save on memmory... \n", " #for k in range(6): del WordGroupDict[item,k]\n", " for item in reversed(PrevWordGroupList):\n", " if (item not in WordGroupList):\n", " # CLOSE/SUSPEND CASE\n", " SuspendableWordGoupList.append(item)\n", " cv.terminate(WordGroupDict[item,4])\n", " for item in WordGroupList:\n", " if (item not in PrevWordGroupList):\n", " if (item in SuspendableWordGoupList):\n", " # RESUME CASE\n", " #print ('\\n resume: '+str(item),end=' ')\n", " cv.resume(WordGroupDict[(item,4)])\n", " else:\n", " # CREATE CASE\n", " #print ('\\n create: '+str(item),end=' ')\n", " WordGroupDict[(item,4)]=cv.node('wg')\n", " WordGroupDict[(item,5)]=WordGroupTrack\n", " WordGroupTrack += 1\n", " cv.feature(WordGroupDict[(item,4)], wgnum=WordGroupDict[(item,0)], junction=WordGroupDict[(item,3)], \n", " clausetype=WordGroupDict[(item,2)], rule=WordGroupDict[(item,1)], wgclass=WordGroupDict[(item,6)], \n", " wgrole=WordGroupDict[(item,7)],wgrolelong=ExpandRole(WordGroupDict[(item,7)]),\n", " wgtype=WordGroupDict[(item,8)],appos=WordGroupDict[(item,8)],wglevel=WordGroupDict[(item,10)])\n", "\n", " \n", " \n", " # These roles are performed either by a WG or just a single word.\n", " Role=row[IndexDict.get(\"i_role\")]\n", " ValidRoles=[\"adv\",\"io\",\"o\",\"o2\",\"s\",\"p\",\"v\",\"vc\",\"aux\"]\n", " DistanceToRoleClause=0\n", " if isinstance (Role,str) and Role in ValidRoles: \n", " # role is assign to this word (uniqely)\n", " WordRole=Role\n", " WordRoleLong=ExpandRole(WordRole)\n", " else:\n", " # role details needs to be taken from some uptree wordgroup \n", " WordRole=WordRoleLong=''\n", " for item in range(1,NumberOfParents-1):\n", " Role = sanitize(row[IndexDict.get(\"i_Parent{}Role\".format(item))])\n", " if isinstance (Role,str) and Role in ValidRoles: \n", " WordRole=Role \n", " WordRoleLong=ExpandRole(WordRole)\n", " DistanceToRoleClause=item\n", " break\n", " \n", " # find the number of the WG containing the clause definition\n", " for item in range(1,NumberOfParents-1):\n", " WGrule = sanitize(row[IndexDict.get(\"i_Parent{}Rule\".format(item))])\n", " if row[IndexDict.get(\"i_Parent{}Class\".format(item))]=='cl' or WGrule[-2:]=='CL': \n", " ContainedClause=sanitize(row[IndexDict.get(\"i_Parent{}WGN\".format(item))])\n", " break\n", "\n", " \n", "\n", " ###############################################\n", " # analyze and process tags #\n", " ###############################################\n", " \n", " # determine syntactic categories at word level. \n", " PartOfSpeech=sanitize(row[IndexDict.get(\"i_class\")])\n", " PartOfSpeechFull=ExpandSP(PartOfSpeech)\n", " \n", " # some attributes are not present inside some (small) books. The following is to prevent exceptions.\n", " degree='' \n", " if 'i_degree' in IndexDict: degree=sanitize(row[IndexDict.get(\"i_degree\")]) \n", " subjref=''\n", " if 'i_subjref' in IndexDict: subjref=sanitize(row[IndexDict.get(\"i_subjref\")]) \n", "\n", " \n", " # create the word slots\n", " this_word = cv.slot()\n", " cv.feature(this_word, \n", " after= sanitize(row[IndexDict.get(\"i_after\")]),\n", " id= sanitize(row[IndexDict.get(\"i_id\")]),\n", " unicode= sanitize(row[IndexDict.get(\"i_unicode\")]),\n", " word= sanitize(row[IndexDict.get(\"i_word\")]),\n", " monad= sanitize(row[IndexDict.get(\"i_monad\")]),\n", " orig_order= FoundWords,\n", " book_long= sanitize(row[IndexDict.get(\"i_book_long\")]),\n", " booknumber= BookNumber,\n", " bookshort= sanitize(row[IndexDict.get(\"i_book_short\")]),\n", " chapter= ThisChapter,\n", " ref= sanitize(row[IndexDict.get(\"i_ref\")]),\n", " sp= PartOfSpeech,\n", " sp_full= PartOfSpeechFull,\n", " verse= ThisVerse,\n", " sentence= ThisSentence,\n", " normalized= sanitize(row[IndexDict.get(\"i_normalized\")]),\n", " morph= sanitize(row[IndexDict.get(\"i_morph\")]),\n", " strongs= sanitize(row[IndexDict.get(\"i_strong\")]),\n", " lex_dom= sanitize(row[IndexDict.get(\"i_domain\")]),\n", " ln= sanitize(row[IndexDict.get(\"i_ln\")]),\n", " gloss= sanitize(row[IndexDict.get(\"i_gloss\")]),\n", " gn= sanitize(row[IndexDict.get(\"i_gender\")]),\n", " nu= sanitize(row[IndexDict.get(\"i_number\")]),\n", " case= sanitize(row[IndexDict.get(\"i_case\")]),\n", " lemma= sanitize(row[IndexDict.get(\"i_lemma\")]),\n", " person= sanitize(row[IndexDict.get(\"i_person\")]),\n", " mood= sanitize(row[IndexDict.get(\"i_mood\")]),\n", " tense= sanitize(row[IndexDict.get(\"i_tense\")]),\n", " number= sanitize(row[IndexDict.get(\"i_number\")]),\n", " voice= sanitize(row[IndexDict.get(\"i_voice\")]),\n", " degree= degree,\n", " type= sanitize(row[IndexDict.get(\"i_type\")]),\n", " reference= sanitize(row[IndexDict.get(\"i_ref\")]), \n", " subj_ref= subjref,\n", " nodeID= sanitize(row[1]), #this is a fixed position in dataframe\n", " wordrole= WordRole,\n", " wordrolelong= WordRoleLong,\n", " wordlevel= NumberOfParents-1,\n", " roleclausedistance = DistanceToRoleClause,\n", " containedclause = ContainedClause\n", " )\n", " cv.terminate(this_word)\n", "\n", " \n", " '''\n", " wrap up the book. At the end of the book we need to close all nodes in proper order.\n", " ''' \n", " # close all open WordGroup nodes\n", " for item in WordGroupList:\n", " #cv.feature(WordGroupDict[(item,4)], add some stats?)\n", " cv.terminate(WordGroupDict[item,4])\n", " #cv.feature(ThisSentencePointer, statdata?)\n", " cv.terminate(ThisSentencePointer)\n", " #cv.feature(ThisVersePointer, statdata?)\n", " cv.terminate(ThisVersePointer)\n", " #cv.feature(ThisChapterPonter, statdata?)\n", " cv.terminate(ThisChapterPointer) \n", " #cv.feature(ThisBookPointer, statdata?)\n", " cv.terminate(ThisBookPointer)\n", "\n", " # clear dataframe for this book, clear the index dictionary\n", " del df\n", " IndexDict.clear()\n", " gc.collect()\n", " \n", " ###############################################\n", " # end of section executed for each book #\n", " ###############################################\n", "\n", " ###############################################\n", " # end of director function #\n", " ###############################################\n", " \n", "###############################################\n", "# Output definitions #\n", "###############################################\n", " \n", "slotType = 'word' \n", "otext = { # dictionary of config data for sections and text formats\n", " 'fmt:text-orig-full':'{word}{after}',\n", " 'sectionTypes':'book,chapter,verse',\n", " 'sectionFeatures':'book,chapter,verse',\n", " 'structureFeatures': 'book,chapter,verse',\n", " 'structureTypes': 'book,chapter,verse',\n", " }\n", "\n", "# configure metadata\n", "generic = { # dictionary of metadata meant for all features\n", " 'Name': 'Greek New Testament (N1904 based on Low Fat Tree)',\n", " 'Version': '1904',\n", " 'Editors': 'Nestle',\n", " 'Data source': 'MACULA Greek Linguistic Datasets, available at https://github.com/Clear-Bible/macula-greek/tree/main/Nestle1904/lowfat',\n", " 'Availability': 'Creative Commons Attribution 4.0 International (CC BY 4.0)', \n", " 'Converter_author': 'Tony Jurg, Vrije Universiteit Amsterdam, Netherlands', \n", " 'Converter_execution': 'Tony Jurg, Vrije Universiteit Amsterdam, Netherlands', \n", " 'Convertor_source': 'https://github.com/tonyjurg/n1904_lft',\n", " 'Converter_version': '{}'.format(version),\n", " 'TextFabric version': '{}'.format(VERSION) #imported from tf.parameters\n", " }\n", "\n", "# set of integer valued feature names\n", "intFeatures = { \n", " 'booknumber',\n", " 'chapter',\n", " 'verse',\n", " 'sentence',\n", " 'wgnum',\n", " 'orig_order',\n", " 'monad',\n", " 'wglevel'\n", " }\n", "\n", "# per feature dicts with metadata\n", "featureMeta = { \n", " 'after': {'description': 'Characters (eg. punctuations) following the word'},\n", " 'id': {'description': 'id of the word'},\n", " 'book': {'description': 'Book'},\n", " 'book_long': {'description': 'Book name (fully spelled out)'},\n", " 'booknumber': {'description': 'NT book number (Matthew=1, Mark=2, ..., Revelation=27)'},\n", " 'bookshort': {'description': 'Book name (abbreviated)'},\n", " 'chapter': {'description': 'Chapter number inside book'},\n", " 'verse': {'description': 'Verse number inside chapter'},\n", " 'sentence': {'description': 'Sentence number (counted per chapter)'},\n", " 'type': {'description': 'Wordgroup type information (verb, verbless, elided, minor, etc.)'},\n", " 'rule': {'description': 'Wordgroup rule information '},\n", " 'orig_order': {'description': 'Word order within corpus (per book)'},\n", " 'monad': {'description': 'Monad (currently: order of words in XML tree file!)'},\n", " 'word': {'description': 'Word as it appears in the text (excl. punctuations)'},\n", " 'unicode': {'description': 'Word as it arears in the text in Unicode (incl. punctuations)'},\n", " 'ref': {'description': 'ref Id'},\n", " 'sp': {'description': 'Part of Speech (abbreviated)'},\n", " 'sp_full': {'description': 'Part of Speech (long description)'}, \n", " 'normalized': {'description': 'Surface word stripped of punctations'},\n", " 'lemma': {'description': 'Lexeme (lemma)'},\n", " 'morph': {'description': 'Morphological tag (Sandborg-Petersen morphology)'},\n", " # see also discussion on relation between lex_dom and ln \n", " # @ https://github.com/Clear-Bible/macula-greek/issues/29\n", " 'lex_dom': {'description': 'Lexical domain according to Semantic Dictionary of Biblical Greek, SDBG (not present everywhere?)'},\n", " 'ln': {'description': 'Lauw-Nida lexical classification (not present everywhere?)'},\n", " 'strongs': {'description': 'Strongs number'},\n", " 'gloss': {'description': 'English gloss'},\n", " 'gn': {'description': 'Gramatical gender (Masculine, Feminine, Neuter)'},\n", " 'nu': {'description': 'Gramatical number (Singular, Plural)'},\n", " 'case': {'description': 'Gramatical case (Nominative, Genitive, Dative, Accusative, Vocative)'},\n", " 'person': {'description': 'Gramatical person of the verb (first, second, third)'},\n", " 'mood': {'description': 'Gramatical mood of the verb (passive, etc)'},\n", " 'tense': {'description': 'Gramatical tense of the verb (e.g. Present, Aorist)'},\n", " 'number': {'description': 'Gramatical number of the verb'},\n", " 'voice': {'description': 'Gramatical voice of the verb'},\n", " 'degree': {'description': 'Degree (e.g. Comparitative, Superlative)'},\n", " 'type': {'description': 'Gramatical type of noun or pronoun (e.g. Common, Personal)'},\n", " 'reference': {'description': 'Reference (to nodeID in XML source data, not yet post-processes)'},\n", " 'subj_ref': {'description': 'Subject reference (to nodeID in XML source data, not yet post-processes)'},\n", " 'nodeID': {'description': 'Node ID (as in the XML source data, not yet post-processes)'},\n", " 'junction': {'description': 'Junction data related to a wordgroup'},\n", " 'wgnum': {'description': 'Wordgroup number (counted per book)'},\n", " 'wgclass': {'description': 'Class of the wordgroup ()'},\n", " 'wgrole': {'description': 'Role of the wordgroup (abbreviated)'},\n", " 'wgrolelong': {'description': 'Role of the wordgroup (full)'},\n", " 'wordrole': {'description': 'Role of the word (abbreviated)'},\n", " 'wordrolelong':{'description': 'Role of the word (full)'},\n", " 'wgtype': {'description': 'Wordgroup type details'},\n", " 'clausetype': {'description': 'Clause type details'},\n", " 'appos': {'description': 'Apposition details'},\n", " 'wglevel': {'description': 'number of parent wordgroups for a wordgroup'},\n", " 'wordlevel': {'description': 'number of parent wordgroups for a word'},\n", " 'roleclausedistance': {'description': 'distance to wordgroup defining the role of this word'},\n", " 'containedclause': {'description': 'Contained clause (WG number)'} \n", " }\n", "\n", "\n", "###############################################\n", "# the main function #\n", "###############################################\n", "\n", "good = cv.walk(\n", " director,\n", " slotType,\n", " otext=otext,\n", " generic=generic,\n", " intFeatures=intFeatures,\n", " featureMeta=featureMeta,\n", " warn=True,\n", " force=True\n", ")\n", "\n", "if good:\n", " print (\"done\")" ] }, { "cell_type": "markdown", "metadata": { "tags": [], "toc": true }, "source": [ "## 5: Basic testing the textfabric data \n", "##### [back to TOC](#TOC)" ] }, { "cell_type": "markdown", "metadata": { "tags": [] }, "source": [ "### Step 1 load the TF data\n", "\n", "The TF will be loaded from github repository https://github.com/tonyjurg/n1904_lft" ] }, { "cell_type": "code", "execution_count": 28, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "The autoreload extension is already loaded. To reload it, use:\n", " %reload_ext autoreload\n" ] } ], "source": [ "%load_ext autoreload\n", "%autoreload 2" ] }, { "cell_type": "code", "execution_count": 29, "metadata": { "ExecuteTime": { "end_time": "2022-10-21T02:32:54.197994Z", "start_time": "2022-10-21T02:32:53.217806Z" } }, "outputs": [], "source": [ "# First, I have to laod different modules that I use for analyzing the data and for plotting:\n", "import sys, os, collections\n", "import pandas as pd\n", "import numpy as np\n", "import re\n", "\n", "\n", "from tf.fabric import Fabric\n", "from tf.app import use\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The following cell loads the TextFabric files from github repository. " ] }, { "cell_type": "code", "execution_count": 30, "metadata": { "ExecuteTime": { "end_time": "2022-10-21T02:32:55.906200Z", "start_time": "2022-10-21T02:32:55.012231Z" } }, "outputs": [ { "data": { "text/markdown": [ "**Locating corpus resources ...**" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "The requested app is not available offline\n", "\t~/text-fabric-data/github/tonyjurg/n1904_lft/app not found\n" ] }, { "data": { "text/html": [ "Status: latest release online releases versus None locally" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "downloading app, main data and requested additions ..." ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stderr", "output_type": "stream", "text": [ "File is not a zip file\n", "\tcould not save corpus data to ~/text-fabric-data/github" ] }, { "name": "stdout", "output_type": "stream", "text": [ "rate limit is 5000 requests per hour, with 5000 left for this hour\n", "\tconnecting to online GitHub repo tonyjurg/n1904_lft ... connected\n", "\tcannot find releases\n", "\tcannot find releases\n", "\tapp/__init__.py...downloaded\n", "\tapp/app.py...downloaded\n", "\tapp/config.yaml...downloaded\n", "\tapp/static...directory\n", "\t\tapp/static/css...directory\n", "\t\t\tapp/static/css/display_custom.css...downloaded\n", "\tOK\n" ] }, { "data": { "text/html": [ "app: ~/text-fabric-data/github/tonyjurg/n1904_lft/app" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "data: ~/text-fabric-data/github/tonyjurg/n1904_lft/tf/0.1.8" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "\n", " Text-Fabric: Text-Fabric API 11.4.10, tonyjurg/n1904_lft/app v3, Search Reference
\n", " Data: tonyjurg - n1904_lft 0.1.8, Character table, Feature docs
\n", "
Node types\n", "\n", " \n", " \n", " \n", " \n", " \n", " \n", "\n", "\n", " \n", " \n", " \n", " \n", "\n", "\n", "\n", " \n", " \n", " \n", " \n", "\n", "\n", "\n", " \n", " \n", " \n", " \n", "\n", "\n", "\n", " \n", " \n", " \n", " \n", "\n", "\n", "\n", " \n", " \n", " \n", " \n", "\n", "\n", "\n", " \n", " \n", " \n", " \n", "\n", "
Name# of nodes# slots/node% coverage
book275102.93100
chapter260529.92100
verse794317.35100
sentence801117.20100
wg1134517.58624
word1377791.00100
\n", " Sets: no custom sets
\n", " Features:
\n", "
Nestle 1904\n", "
\n", "\n", "
\n", "
\n", "after\n", "
\n", "
str
\n", "\n", " Characters (eg. punctuations) following the word\n", "\n", "
\n", "\n", "
\n", "
\n", "appos\n", "
\n", "
str
\n", "\n", " Apposition details\n", "\n", "
\n", "\n", "
\n", "
\n", "book\n", "
\n", "
str
\n", "\n", " Book\n", "\n", "
\n", "\n", "
\n", "
\n", "book_long\n", "
\n", "
str
\n", "\n", " Book name (fully spelled out)\n", "\n", "
\n", "\n", "
\n", "
\n", "booknumber\n", "
\n", "
int
\n", "\n", " NT book number (Matthew=1, Mark=2, ..., Revelation=27)\n", "\n", "
\n", "\n", "
\n", "
\n", "bookshort\n", "
\n", "
str
\n", "\n", " Book name (abbreviated)\n", "\n", "
\n", "\n", "
\n", "
\n", "case\n", "
\n", "
str
\n", "\n", " Gramatical case (Nominative, Genitive, Dative, Accusative, Vocative)\n", "\n", "
\n", "\n", "
\n", "
\n", "chapter\n", "
\n", "
int
\n", "\n", " Chapter number inside book\n", "\n", "
\n", "\n", "
\n", "
\n", "clausetype\n", "
\n", "
str
\n", "\n", " Clause type details\n", "\n", "
\n", "\n", "
\n", "
\n", "containedclause\n", "
\n", "
str
\n", "\n", " Contained clause (WG number)\n", "\n", "
\n", "\n", "
\n", "
\n", "degree\n", "
\n", "
str
\n", "\n", " Degree (e.g. Comparitative, Superlative)\n", "\n", "
\n", "\n", "
\n", "
\n", "gloss\n", "
\n", "
str
\n", "\n", " English gloss\n", "\n", "
\n", "\n", "
\n", "
\n", "gn\n", "
\n", "
str
\n", "\n", " Gramatical gender (Masculine, Feminine, Neuter)\n", "\n", "
\n", "\n", "
\n", "
\n", "id\n", "
\n", "
str
\n", "\n", " id of the word\n", "\n", "
\n", "\n", "
\n", "
\n", "junction\n", "
\n", "
str
\n", "\n", " Junction data related to a wordgroup\n", "\n", "
\n", "\n", "
\n", "
\n", "lemma\n", "
\n", "
str
\n", "\n", " Lexeme (lemma)\n", "\n", "
\n", "\n", "
\n", "
\n", "lex_dom\n", "
\n", "
str
\n", "\n", " Lexical domain according to Semantic Dictionary of Biblical Greek, SDBG (not present everywhere?)\n", "\n", "
\n", "\n", "
\n", "
\n", "ln\n", "
\n", "
str
\n", "\n", " Lauw-Nida lexical classification (not present everywhere?)\n", "\n", "
\n", "\n", "
\n", "
\n", "monad\n", "
\n", "
int
\n", "\n", " Monad (currently: order of words in XML tree file!)\n", "\n", "
\n", "\n", "
\n", "
\n", "mood\n", "
\n", "
str
\n", "\n", " Gramatical mood of the verb (passive, etc)\n", "\n", "
\n", "\n", "
\n", "
\n", "morph\n", "
\n", "
str
\n", "\n", " Morphological tag (Sandborg-Petersen morphology)\n", "\n", "
\n", "\n", "
\n", "
\n", "nodeID\n", "
\n", "
str
\n", "\n", " Node ID (as in the XML source data, not yet post-processes)\n", "\n", "
\n", "\n", "
\n", "
\n", "normalized\n", "
\n", "
str
\n", "\n", " Surface word stripped of punctations\n", "\n", "
\n", "\n", "
\n", "
\n", "nu\n", "
\n", "
str
\n", "\n", " Gramatical number (Singular, Plural)\n", "\n", "
\n", "\n", "
\n", "
\n", "number\n", "
\n", "
str
\n", "\n", " Gramatical number of the verb\n", "\n", "
\n", "\n", "
\n", "
\n", "orig_order\n", "
\n", "
int
\n", "\n", " Word order within corpus (per book)\n", "\n", "
\n", "\n", "
\n", "
\n", "otype\n", "
\n", "
str
\n", "\n", " \n", "\n", "
\n", "\n", "
\n", "
\n", "person\n", "
\n", "
str
\n", "\n", " Gramatical person of the verb (first, second, third)\n", "\n", "
\n", "\n", "
\n", "
\n", "ref\n", "
\n", "
str
\n", "\n", " ref Id\n", "\n", "
\n", "\n", "
\n", "
\n", "roleclausedistance\n", "
\n", "
str
\n", "\n", " distance to wordgroup defining the role of this word\n", "\n", "
\n", "\n", "
\n", "
\n", "rule\n", "
\n", "
str
\n", "\n", " Wordgroup rule information \n", "\n", "
\n", "\n", "
\n", "
\n", "sentence\n", "
\n", "
int
\n", "\n", " Sentence number (counted per chapter)\n", "\n", "
\n", "\n", "
\n", "
\n", "sp\n", "
\n", "
str
\n", "\n", " Part of Speech (abbreviated)\n", "\n", "
\n", "\n", "
\n", "
\n", "sp_full\n", "
\n", "
str
\n", "\n", " Part of Speech (long description)\n", "\n", "
\n", "\n", "
\n", "
\n", "strongs\n", "
\n", "
str
\n", "\n", " Strongs number\n", "\n", "
\n", "\n", "
\n", "
\n", "subj_ref\n", "
\n", "
str
\n", "\n", " Subject reference (to nodeID in XML source data, not yet post-processes)\n", "\n", "
\n", "\n", "
\n", "
\n", "tense\n", "
\n", "
str
\n", "\n", " Gramatical tense of the verb (e.g. Present, Aorist)\n", "\n", "
\n", "\n", "
\n", "
\n", "type\n", "
\n", "
str
\n", "\n", " Gramatical type of noun or pronoun (e.g. Common, Personal)\n", "\n", "
\n", "\n", "
\n", "
\n", "unicode\n", "
\n", "
str
\n", "\n", " Word as it arears in the text in Unicode (incl. punctuations)\n", "\n", "
\n", "\n", "
\n", "
\n", "verse\n", "
\n", "
int
\n", "\n", " Verse number inside chapter\n", "\n", "
\n", "\n", "
\n", "
\n", "voice\n", "
\n", "
str
\n", "\n", " Gramatical voice of the verb\n", "\n", "
\n", "\n", "
\n", "
\n", "wgclass\n", "
\n", "
str
\n", "\n", " Class of the wordgroup ()\n", "\n", "
\n", "\n", "
\n", "
\n", "wglevel\n", "
\n", "
int
\n", "\n", " number of parent wordgroups for a wordgroup\n", "\n", "
\n", "\n", "
\n", "
\n", "wgnum\n", "
\n", "
int
\n", "\n", " Wordgroup number (counted per book)\n", "\n", "
\n", "\n", "
\n", "
\n", "wgrole\n", "
\n", "
str
\n", "\n", " Role of the wordgroup (abbreviated)\n", "\n", "
\n", "\n", "
\n", "
\n", "wgrolelong\n", "
\n", "
str
\n", "\n", " Role of the wordgroup (full)\n", "\n", "
\n", "\n", "
\n", "
\n", "wgtype\n", "
\n", "
str
\n", "\n", " Wordgroup type details\n", "\n", "
\n", "\n", "
\n", "
\n", "word\n", "
\n", "
str
\n", "\n", " Word as it appears in the text (excl. punctuations)\n", "\n", "
\n", "\n", "
\n", "
\n", "wordgroup\n", "
\n", "
int
\n", "\n", " Wordgroup number (counted per book)\n", "\n", "
\n", "\n", "
\n", "
\n", "wordlevel\n", "
\n", "
str
\n", "\n", " number of parent wordgroups for a word\n", "\n", "
\n", "\n", "
\n", "
\n", "wordrole\n", "
\n", "
str
\n", "\n", " Role of the word (abbreviated)\n", "\n", "
\n", "\n", "
\n", "
\n", "wordrolelong\n", "
\n", "
str
\n", "\n", " Role of the word (full)\n", "\n", "
\n", "\n", "
\n", "
\n", "oslots\n", "
\n", "
none
\n", "\n", " \n", "\n", "
\n", "\n", "
\n", "
\n", "\n" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "\n", "\n" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "
Text-Fabric API: names N F E L T S C TF directly usable
" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# Loading-the-New-Testament-Text-Fabric (add a specific version, eg. 0.1.2)\n", "NA = use (\"tonyjurg/n1904_lft\", version=\"0.1.8\", hoist=globals())" ] }, { "cell_type": "code", "execution_count": 11, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "{'word': 0, 'wg': 1, 'sentence': 2, 'verse': 3, 'chapter': 4, 'book': 5}" ] }, "execution_count": 11, "metadata": {}, "output_type": "execute_result" } ], "source": [ "N.otypeRank\n" ] }, { "cell_type": "code", "execution_count": 12, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "{'text-orig-full': 'word'}" ] }, "execution_count": 12, "metadata": {}, "output_type": "execute_result" } ], "source": [ "T.formats" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Step 2 Perform some basic display \n", "\n", "note: the implementation with regards how phrases need to be displayed (esp. with regards to conjunctions) is still to be done." ] }, { "cell_type": "code", "execution_count": 13, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ " 0.01s 25 results\n" ] }, { "data": { "text/html": [ "

verse 20" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "

Matthew 1:20
verse 20
sentence
wg
wg Conj-CL
wg
wg O-S-V
ταῦτα
containedclause=234wordrole=o
δὲ
containedclause=232
wg
wg O-S-V
αὐτοῦ
containedclause=234wordrole=s
ἐνθυμηθέντος
containedclause=234wordrole=v
ἰδοὺ
containedclause=233wordrole=aux
wg NPofNP Subject
ἄγγελος
containedclause=233wordrole=s
Κυρίου
containedclause=233wordrole=s
wg PrepNp Adverbial
κατ’
containedclause=233wordrole=adv
ὄναρ
containedclause=233wordrole=adv
ἐφάνη
containedclause=233wordrole=v
αὐτῷ
containedclause=233wordrole=io
wg Adverbial
λέγων
containedclause=237wordrole=v
wg CLaCL Object
wg
wg Np2CL Minor
wg Np-Appos Auxiliar
Ἰωσὴφ
containedclause=240wordrole=aux
wg NPofNP apposition
υἱὸς
containedclause=240wordrole=aux
Δαυείδ,
containedclause=240wordrole=aux
μὴ
containedclause=239wordrole=adv
φοβηθῇς
containedclause=239wordrole=v
wg V-O-O2 Object subordinate
παραλαβεῖν
containedclause=243wordrole=v
Μαρίαν
containedclause=243wordrole=o
wg DetNP Second Object
τὴν
containedclause=243wordrole=o2
wg NPofNP
γυναῖκά
containedclause=243wordrole=o2
σου,
containedclause=243wordrole=o2
wg sub-CL Adverbial
wg S-P-VC subordinate
wg DetCL Subject
τὸ
containedclause=248wordrole=s
γὰρ
containedclause=246wordrole=adv
wg S-P-VC subordinate
wg DetCL Subject
wg ADV-V
wg PrepNp Adverbial
ἐν
containedclause=249wordrole=adv
αὐτῇ
containedclause=249wordrole=adv
γεννηθὲν
containedclause=249wordrole=v
wg PrepNp Predicate
ἐκ
containedclause=247wordrole=p
wg NpAdjp
Πνεύματός
containedclause=247wordrole=p
ἐστιν
containedclause=247wordrole=vc
wg PrepNp Predicate
wg NpAdjp
Ἁγίου·
containedclause=247wordrole=p
" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "

verse 21" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "

Matthew 1:21
verse 21
sentence
wg
wg Conj-CL
wg
wg Adverbial
wg CLaCL Object
wg
wg CLaCL coordinate
wg V-O coordinate
τέξεται
containedclause=255wordrole=v
δὲ
containedclause=238wordrole=o
wg CLaCL coordinate
wg V-O coordinate
υἱὸν
containedclause=255wordrole=o
wg
καὶ
containedclause=254wordrole=o
wg
καλέσεις
containedclause=257wordrole=v
wg DetNP Object
τὸ
containedclause=257wordrole=o
wg NPofNP
ὄνομα
containedclause=257wordrole=o
αὐτοῦ
containedclause=257wordrole=o
Ἰησοῦν·
containedclause=257wordrole=o2
wg sub-CL Adverbial
wg S-V-O-ADV subordinate
αὐτὸς
containedclause=261wordrole=s
γὰρ
containedclause=260wordrole=adv
wg S-V-O-ADV subordinate
σώσει
containedclause=261wordrole=v
wg DetNP Object
τὸν
containedclause=261wordrole=o
wg NPofNP
λαὸν
containedclause=261wordrole=o
αὐτοῦ
containedclause=261wordrole=o
wg PrepNp Adverbial
ἀπὸ
containedclause=261wordrole=adv
wg DetNP
τῶν
containedclause=261wordrole=adv
wg NPofNP
ἁμαρτιῶν
containedclause=261wordrole=adv
αὐτῶν.
containedclause=261wordrole=adv
" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "Search0 = '''\n", "book book=Matthew\n", " chapter chapter=1\n", " \n", " \n", " verse \n", "'''\n", "Search0 = NA.search(Search0)\n", "NA.show(Search0, start=20, end=21, condensed=True, extraFeatures={'containedclause','wordrole'}, suppress={'chapter'}, withNodes=False)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Step 3 dump some structure information" ] }, { "cell_type": "code", "execution_count": 14, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "A heading is a tuple of pairs (node type, feature value)\n", "\tof node types and features that have been configured as structural elements\n", "These 3 structural elements have been configured\n", "\tnode type book with heading feature book\n", "\tnode type chapter with heading feature chapter\n", "\tnode type verse with heading feature verse\n", "You can get them as a tuple with T.headings.\n", "\n", "Structure API:\n", "\tT.structure(node=None) gives the structure below node, or everything if node is None\n", "\tT.structurePretty(node=None) prints the structure below node, or everything if node is None\n", "\tT.top() gives all top-level nodes\n", "\tT.up(node) gives the (immediate) parent node\n", "\tT.down(node) gives the (immediate) children nodes\n", "\tT.headingFromNode(node) gives the heading of a node\n", "\tT.nodeFromHeading(heading) gives the node of a heading\n", "\tT.ndFromHd complete mapping from headings to nodes\n", "\tT.hdFromNd complete mapping from nodes to headings\n", "\tT.hdMult are all headings with their nodes that occur multiple times\n", "\n", "There are 8230 structural elements in the dataset.\n", "\n" ] } ], "source": [ "T.structureInfo()" ] }, { "cell_type": "code", "execution_count": 15, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "{'Availability': 'Creative Commons Attribution 4.0 International (CC BY 4.0)',\n", " 'Converter_author': 'Tony Jurg, Vrije Universiteit Amsterdam, Netherlands',\n", " 'Converter_execution': 'Tony Jurg, Vrije Universiteit Amsterdam, Netherlands',\n", " 'Converter_version': '0.1.8',\n", " 'Convertor_source': 'https://github.com/tonyjurg/n1904_lft',\n", " 'Data source': 'MACULA Greek Linguistic Datasets, available at https://github.com/Clear-Bible/macula-greek/tree/main/Nestle1904/lowfat',\n", " 'Editors': 'Nestle',\n", " 'Name': 'Greek New Testament (NA1904)',\n", " 'TextFabric version': '11.4.10',\n", " 'Version': '1904',\n", " 'fmt:text-orig-full': '{word}{after}',\n", " 'sectionFeatures': 'book,chapter,verse',\n", " 'sectionTypes': 'book,chapter,verse',\n", " 'structureFeatures': 'book,chapter,verse',\n", " 'structureTypes': 'book,chapter,verse',\n", " 'writtenBy': 'Text-Fabric',\n", " 'dateWritten': '2023-05-09T19:42:40Z'}" ] }, "execution_count": 15, "metadata": {}, "output_type": "execute_result" } ], "source": [ "TF.features['otext'].metaData\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Running text fabric browser \n", "##### [Back to TOC](#TOC)" ] }, { "cell_type": "code", "execution_count": 14, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "This is Text-Fabric 11.4.10\n", "Connecting to running kernel via 19685\n", "Connecting to running webserver via 29685\n", "Opening app in browser\n", "Press to stop the TF browser\n" ] } ], "source": [ "!text-fabric app " ] }, { "cell_type": "code", "execution_count": 45, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "^C\n" ] } ], "source": [ "!text-fabric app\n", "\n" ] }, { "cell_type": "code", "execution_count": 20, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ " 0.42s 9 results\n" ] }, { "data": { "text/html": [ "

verse 1" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "

Matthew 1:20
verse
chapter=1verse=20
sentence
chapter=1verse=20
wg
wgclass=to be skipped?wglevel=1
wg
wgclass=cl*wglevel=2
wg
wgclass=clwglevel=3
wg
wgclass=clwglevel=4
ταῦτα
chapter=1verse=20wordrole=o
δὲ
chapter=1verse=20wordrole=
wg
wgclass=clwglevel=3
wg
wgclass=clwglevel=4
αὐτοῦ
chapter=1verse=20wordrole=s
ἐνθυμηθέντος
chapter=1verse=20wordrole=v
ἰδοὺ
chapter=1verse=20wordrole=aux
wg
wgclass=npwglevel=4
ἄγγελος
chapter=1verse=20wordrole=s
Κυρίου
chapter=1verse=20wordrole=s
wg
wgclass=ppwglevel=4
κατ’
chapter=1verse=20wordrole=adv
ὄναρ
chapter=1verse=20wordrole=adv
ἐφάνη
chapter=1verse=20wordrole=v
αὐτῷ
chapter=1verse=20wordrole=io
wg
wgclass=clwglevel=4
λέγων
chapter=1verse=20wordrole=v
wg
wgclass=cl*wglevel=5
wg
wgclass=clwglevel=6
wg
wgclass=cl*wglevel=7
wg
wgclass=npwglevel=8
Ἰωσὴφ
chapter=1verse=20wordrole=aux
wg
wgclass=npwglevel=9
υἱὸς
chapter=1verse=20wordrole=aux
Δαυείδ,
chapter=1verse=20wordrole=aux
μὴ
chapter=1verse=20wordrole=adv
φοβηθῇς
chapter=1verse=20wordrole=v
wg
wgclass=clwglevel=7
παραλαβεῖν
chapter=1verse=20wordrole=v
Μαρίαν
chapter=1verse=20wordrole=o
wg
wgclass=npwglevel=8
τὴν
chapter=1verse=20wordrole=o2
wg
wgclass=npwglevel=9
γυναῖκά
chapter=1verse=20wordrole=o2
σου,
chapter=1verse=20wordrole=o2
wg
wgclass=cl*wglevel=7
wg
wgclass=clwglevel=8
wg
wgclass=cl*wglevel=9
τὸ
chapter=1verse=20wordrole=s
γὰρ
chapter=1verse=20wordrole=adv
wg
wgclass=clwglevel=8
wg
wgclass=cl*wglevel=9
wg
wgclass=clwglevel=10
wg
wgclass=ppwglevel=11
ἐν
chapter=1verse=20wordrole=adv
αὐτῇ
chapter=1verse=20wordrole=adv
γεννηθὲν
chapter=1verse=20wordrole=v
wg
wgclass=ppwglevel=9
ἐκ
chapter=1verse=20wordrole=p
wg
wgclass=npwglevel=10
Πνεύματός
chapter=1verse=20wordrole=p
ἐστιν
chapter=1verse=20wordrole=vc
wg
wgclass=ppwglevel=9
wg
wgclass=npwglevel=10
Ἁγίου·
chapter=1verse=20wordrole=p
" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "Search0 = '''\n", "book book=Matthew\n", " chapter chapter=1\n", " verse verse=20\n", " wg1:wg wgclass=cl wglevel*\n", " word wordrole=v\n", " word wordrole=o\n", " word wordrole=aux\n", "\n", "'''\n", "Search0 = NA.search(Search0)\n", "NA.show(Search0, start=1, end=1, condensed=True, colorMap={4:'pink', 5:'turquoise', 6:'lightblue', 7:'red'}, multiFeatures=False)" ] }, { "cell_type": "code", "execution_count": 18, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "^C\n" ] } ], "source": [ "!text-fabric app" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "!tf CenterBLC/NA" ] }, { "cell_type": "code", "execution_count": 43, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "This is Text-Fabric 11.4.10\n", "Connecting to running kernel via 11185\n", "Connecting to running webserver via 21185\n", "Opening ETCBC/bhsa-min in browser\n", "Press to stop the TF browser\n" ] } ], "source": [ "!tf ETCBC/bhsa-min" ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "^C\n" ] } ], "source": [ "!tf CenterBLC/NA" ] }, { "cell_type": "code", "execution_count": 24, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "This is Text-Fabric 11.4.10\n", "Connecting to running kernel via 10074\n", "Connecting to running webserver via 20074\n", "Opening tonyjurg/n1904_lft in browser\n", "Press to stop the TF browser\n" ] } ], "source": [ "!text-fabric tonyjurg/n1904_lft" ] }, { "cell_type": "code", "execution_count": 48, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "This is Text-Fabric 11.4.10\n", "Connecting to running kernel via 10074\n", "Connecting to running webserver via 20074\n", "Opening tonyjurg/n1904_lft in browser\n", "Press to stop the TF browser\n" ] } ], "source": [ "!tf tonyjurg/n1904_lft" ] }, { "cell_type": "code", "execution_count": 47, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "This is Text-Fabric 11.4.10\n", "Connecting to running kernel via 18794\n", "Connecting to running webserver via 28794\n", "Opening app:/text-fabric-data/github/tonyjurg/n1904_lft/app in browser\n", "Press to stop the TF browser\n" ] } ], "source": [ "!tf app:\\text-fabric-data\\github\\tonyjurg\\n1904_lft\\app data:\\text-fabric-data\\github\\tonyjurg\\n1904_lft\\tf\\0.1.8" ] }, { "cell_type": "code", "execution_count": 42, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "This is Text-Fabric 11.4.10\n", "Connecting to running kernel via 10074\n", "Connecting to running webserver via 20074\n", "Opening tonyjurg/n1904_lft in browser\n", "Press to stop the TF browser\n" ] } ], "source": [ "!tf tonyjurg/n1904_lft" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "Python 3 (ipykernel)", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.9.12" }, "toc": { "base_numbering": 1, "nav_menu": {}, "number_sections": true, "sideBar": true, "skip_h1_title": false, "title_cell": "Table of Contents", "title_sidebar": "Contents", "toc_cell": true, "toc_position": { "height": "calc(100% - 180px)", "left": "10px", "top": "150px", "width": "321.391px" }, "toc_section_display": true, "toc_window_display": true } }, "nbformat": 4, "nbformat_minor": 4 }