📔️ Generate a text-based journal from a template file.

Overview

JGen 📔️

Generate a text-based journal from a template file.

Contents

Getting Started

  1. Clone this repository -
  • git clone https://github.com/harrison-broadbent/JGen.git
  1. Edit "template.txt", copy and paste an example from /templates, or use the placeholder template -
  • vim template.txt
  1. Run JGen and follow the prompts -
  • python3 JGen.py
  1. Inspect "journal.txt" -
  • vim journal.txt

Example

Given the following template (available as templates/template_weekly.txt) -

_____________________________
Week: WEEKNUM, Year: YY
DD_NAME, DD MM_NAME - +++++++
DD_NAME, DD MM_NAME

Todos: - - -

Plans: - - -

and running JGen for two entries gives us -

_____________________________
Week: 10, Year: 2021
Saturday, 13 March -
Saturday, 20 March

Todos:
	-
	-
	-

Plans:
	-
	-
	-


_____________________________
Week: 11, Year: 2021
Saturday, 20 March -
Saturday, 27 March

Todos:
	-
	-
	-

Plans:
	-
	-
	-

Lets break down what happened -

  1. JGen sets it's internal date - "today's" date, from your perspective.
  2. JGen runs through line 1 and line 2 of template.txt, replacing keywords with their corresponding information and then writing the output to journal.txt.
  3. At the end of line 2 there are seven + (plus) symbols
    • JGen removes these from the output, and increments the internal date counter by 7 days.
  4. JGen fills out line 3 with the new date information, then fills out the rest of the information for the first entry.
  5. It then repeats this for the second entry, carrying over the date from the end of the first entry.
  6. JGen halts, with journal.txt containing our final output.

Overview

JGen parses a given template file to generate a journal file.

JGen runs through the template file and replaces keywords with their actual values (dates - day/month/year etc.), for a specified number of entries.

Usage

The JGen Python script contains all the code for the parser. To get started:

  • Download the JGen script.

  • Create a template.txt file (or download and rename one of the examples in /templates), and place it in the same directory as the JGen Python script.

    • See Details below for more information on creating a template.

    • See an Example to walk through a specific example of a template file.

  • Run the JGen Python script, and input the number of times the template should be reproduced.

    • Ex: 365 entries for a daily journal spanning a year, 52 entries for a weekly journal
  • journal.txt will be populated with text based on the template and the number of entries specified.

Details

See the Example section below if you want to jump straight into seeing how JGen works, by walking though an example.

JGen parses the template file, replacing any of the reserved keywords, shown below, with their corresponding date values.

Part of the templating process is to indicate using a (+) symbol when to increment the internal date counter, which JGen picks up as it parses the file. It also strips all (+) symbols from the file.

Reserved Keywords

  • DD

    • The date number.
    • 01, 05, 10, 21 etc.
  • MM

    • The month number.
    • 01, 10, 12 etc.
  • YY

    • The year.
    • 2020, 2021 etc.
  • DD_NAME

    • The name of the day.
    • Monday, Tuesday, Wednesday, Thursday, Friday, Saturday, Sunday
  • MM_NAME

    • January, February etc.
  • DAYNUM

    • Day number of the year.
    • 123, 340 etc.
  • WEEKNUM

    • Week number of the year.
    • 13, 51 etc.
  • +

    • used to increment the internal date counter

    • will only increment after the entire line has been parsed

      • for example, parsing
      DD/MM/YY+ - DD/MM/YY
      

      would give

      21/02/2050 - 21/02/2050
      

      and not

      21/02/2050 - 28/02/2050
      

Gotchas

  • + can only be used to increment the date.

    • All + symbols are removed from the output.
    • ie. journal.txt file will never contain a + character
  • As mentioned in the "reserved keywords" section of this readme, the + characters are only interpreted at the end of a line.

    • Currently, to work around this, just place the second date on a new line (like in templates/template_weekly.txt)

    • For example, parsing

      DD/MM/YY+ - DD/MM/YY
      

      would give

      21/02/2050 - 21/02/2050
      

      and not

      21/02/2050 - 28/02/2050
      
You might also like...
Count the frequency of letters or words in a text file and show a graph.

Word Counter By EBUS Coding Club Count the frequency of letters or words in a text file and show a graph. Requirements Python 3.9 or higher matplotlib

Creating an Audiobook (mp3 file) using a Ebook (epub) using BeautifulSoup and Google Text to Speech

epub2audiobook Creating an Audiobook (mp3 file) using a Ebook (epub) using BeautifulSoup and Google Text to Speech Input examples qual a pasta do seu

ADCS cert template modification and ACL enumeration

Purpose This tool is designed to aid an operator in modifying ADCS certificate templates so that a created vulnerable state can be leveraged for privi

Easily train your own text-generating neural network of any size and complexity on any text dataset with a few lines of code.
Easily train your own text-generating neural network of any size and complexity on any text dataset with a few lines of code.

textgenrnn Easily train your own text-generating neural network of any size and complexity on any text dataset with a few lines of code, or quickly tr

Code for the paper "Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer"

T5: Text-To-Text Transfer Transformer The t5 library serves primarily as code for reproducing the experiments in Exploring the Limits of Transfer Lear

Kashgari is a production-level NLP Transfer learning framework built on top of tf.keras for text-labeling and text-classification, includes Word2Vec, BERT, and GPT2 Language Embedding.

Kashgari Overview | Performance | Installation | Documentation | Contributing 🎉 🎉 🎉 We released the 2.0.0 version with TF2 Support. 🎉 🎉 🎉 If you

Easily train your own text-generating neural network of any size and complexity on any text dataset with a few lines of code.
Easily train your own text-generating neural network of any size and complexity on any text dataset with a few lines of code.

textgenrnn Easily train your own text-generating neural network of any size and complexity on any text dataset with a few lines of code, or quickly tr

Code for the paper "Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer"

T5: Text-To-Text Transfer Transformer The t5 library serves primarily as code for reproducing the experiments in Exploring the Limits of Transfer Lear

Kashgari is a production-level NLP Transfer learning framework built on top of tf.keras for text-labeling and text-classification, includes Word2Vec, BERT, and GPT2 Language Embedding.

Kashgari Overview | Performance | Installation | Documentation | Contributing 🎉 🎉 🎉 We released the 2.0.0 version with TF2 Support. 🎉 🎉 🎉 If you

Comments
  • Please update docs with example for running JGen.py

    Please update docs with example for running JGen.py

    Hello, this looks interesting and I want to test things out.

    I couldn't run the script in under 1 minute so I'm showing what I did. Possibly a simple copy paste example in the docs will help.

    image

    opened by anrei0000 3
Releases(v0.1)
Owner
Harrison Broadbent
√67
Harrison Broadbent
Toy example of an applied ML pipeline for me to experiment with MLOps tools.

Toy Machine Learning Pipeline Table of Contents About Getting Started ML task description and evaluation procedure Dataset description Repository stru

Shreya Shankar 190 Dec 21, 2022
The ability of computer software to identify words and phrases in spoken language and convert them to human-readable text

speech-recognition-py Speech recognition is the ability of computer software to identify words and phrases in spoken language and convert them to huma

Deepangshi 1 Apr 03, 2022
IndoBERTweet is the first large-scale pretrained model for Indonesian Twitter. Published at EMNLP 2021 (main conference)

IndoBERTweet 🐦 🇮🇩 1. Paper Fajri Koto, Jey Han Lau, and Timothy Baldwin. IndoBERTweet: A Pretrained Language Model for Indonesian Twitter with Effe

IndoLEM 40 Nov 30, 2022
Rhyme with AI

Local development Create a conda virtual environment and activate it: conda env create --file environment.yml conda activate rhyme-with-ai Install the

GoDataDriven 28 Nov 21, 2022
Repo for Enhanced Seq2Seq Autoencoder via Contrastive Learning for Abstractive Text Summarization

ESACL: Enhanced Seq2Seq Autoencoder via Contrastive Learning for AbstractiveText Summarization This repo is for our paper "Enhanced Seq2Seq Autoencode

Rachel Zheng 14 Nov 01, 2022
Pattern Matching in Python

Pattern Matching finalmente chega no Python 3.10. E daí? "Pattern matching", ou "correspondência de padrões" como é conhecido no Brasil. Algumas pesso

Fabricio Werneck 6 Feb 16, 2022
تولید اسم های رندوم فینگیلیش

karafs کرفس تولید اسم های رندوم فینگیلیش installation ➜ pip install karafs usage دو زبانه ➜ karafs -n 10 توت فرنگی بی ناموس toot farangi-ye bi_namoos

Vaheed NÆINI (9E) 36 Nov 24, 2022
DLO8012: Natural Language Processing & CSL804: Computational Lab - II

NATURAL-LANGUAGE-PROCESSING-AND-COMPUTATIONAL-LAB-II DLO8012: NLP & CSL804: CL-II [SEMESTER VIII] Syllabus NLP - Reference Books THE WALL MEGA SATISH

AMEY THAKUR 7 Apr 28, 2022
MMDA - multimodal document analysis

MMDA - multimodal document analysis

AI2 75 Jan 04, 2023
spaCy-wrap: For Wrapping fine-tuned transformers in spaCy pipelines

spaCy-wrap: For Wrapping fine-tuned transformers in spaCy pipelines spaCy-wrap is minimal library intended for wrapping fine-tuned transformers from t

Kenneth Enevoldsen 32 Dec 29, 2022
This library is testing the ethics of language models by using natural adversarial texts.

prompt2slip This library is testing the ethics of language models by using natural adversarial texts. This tool allows for short and simple code and v

9 Dec 28, 2021
Linear programming solver for paper-reviewer matching and mind-matching

Paper-Reviewer Matcher A python package for paper-reviewer matching algorithm based on topic modeling and linear programming. The algorithm is impleme

Titipat Achakulvisut 66 Jul 05, 2022
The official implementation of "BERT is to NLP what AlexNet is to CV: Can Pre-Trained Language Models Identify Analogies?, ACL 2021 main conference"

BERT is to NLP what AlexNet is to CV This is the official implementation of BERT is to NLP what AlexNet is to CV: Can Pre-Trained Language Models Iden

Asahi Ushio 20 Nov 03, 2022
A 30000+ Chinese MRC dataset - Delta Reading Comprehension Dataset

Delta Reading Comprehension Dataset 台達閱讀理解資料集 Delta Reading Comprehension Dataset (DRCD) 屬於通用領域繁體中文機器閱讀理解資料集。 本資料集期望成為適用於遷移學習之標準中文閱讀理解資料集。 本資料集從2,108篇

272 Dec 15, 2022
Coreference resolution for English, German and Polish, optimised for limited training data and easily extensible for further languages

Coreferee Author: Richard Paul Hudson, msg systems ag 1. Introduction 1.1 The basic idea 1.2 Getting started 1.2.1 English 1.2.2 German 1.2.3 Polish 1

msg systems ag 169 Dec 21, 2022
This program do translate english words to portuguese

Python-Dictionary This program is used to translate english words to portuguese. Web-Scraping This program use BeautifulSoap to make web scraping, so

João Assalim 1 Oct 10, 2022
Fine-tune GPT-3 with a Google Chat conversation history

Google Chat GPT-3 This repo will help you fine-tune GPT-3 with a Google Chat conversation history. The trained model will be able to converse as one o

Nate Baer 7 Dec 10, 2022
:mag: Transformers at scale for question answering & neural search. Using NLP via a modular Retriever-Reader-Pipeline. Supporting DPR, Elasticsearch, HuggingFace's Modelhub...

Haystack is an end-to-end framework that enables you to build powerful and production-ready pipelines for different search use cases. Whether you want

deepset 6.4k Jan 09, 2023
SentAugment is a data augmentation technique for semi-supervised learning in NLP.

SentAugment SentAugment is a data augmentation technique for semi-supervised learning in NLP. It uses state-of-the-art sentence embeddings to structur

Meta Research 363 Dec 30, 2022
BERTopic is a topic modeling technique that leverages 🤗 transformers and c-TF-IDF to create dense clusters allowing for easily interpretable topics whilst keeping important words in the topic descriptions

BERTopic BERTopic is a topic modeling technique that leverages 🤗 transformers and c-TF-IDF to create dense clusters allowing for easily interpretable

Maarten Grootendorst 3.6k Jan 07, 2023