📔️ 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
TextAttack 🐙 is a Python framework for adversarial attacks, data augmentation, and model training in NLP

TextAttack 🐙 Generating adversarial examples for NLP models [TextAttack Documentation on ReadTheDocs] About • Setup • Usage • Design About TextAttack

QData 2.2k Jan 03, 2023
Creating a Feed of MISP Events from ThreatFox (by abuse.ch)

ThreatFox2Misp Creating a Feed of MISP Events from ThreatFox (by abuse.ch) What will it do? This will fetch IOCs from ThreatFox by Abuse.ch, convert t

17 Nov 22, 2022
Tool to check whether a GCP bucket is public or not.

Tool to check publicly accessible GCP bucket. Blog https://justm0rph3u5.medium.com/gcp-inspector-auditing-publicly-exposed-gcp-bucket-ac6cad55618c Wha

DIVYANSHU SHUKLA 7 Nov 24, 2022
Pipelines de datos, 2021.

Este repo ilustra un proceso sencillo de automatización de transformación y modelado de datos, a través de un pipeline utilizando Luigi. Stack princip

Rodolfo Ferro 8 May 19, 2022
LSTM model - IMDB review sentiment analysis

NLP - Movie review sentiment analysis The colab notebook contains the code for building a LSTM Recurrent Neural Network that gives 87-88% accuracy on

Sundeep Bhimireddy 1 Jan 29, 2022
A python project made to generate code using either OpenAI's codex or GPT-J (Although not as good as codex)

CodeJ A python project made to generate code using either OpenAI's codex or GPT-J (Although not as good as codex) Install requirements pip install -r

TheProtagonist 1 Dec 06, 2021
Shared, streaming Python dict

UltraDict Sychronized, streaming Python dictionary that uses shared memory as a backend Warning: This is an early hack. There are only few unit tests

Ronny Rentner 192 Dec 23, 2022
OpenAI CLIP text encoders for multiple languages!

Multilingual-CLIP OpenAI CLIP text encoders for any language Colab Notebook · Pre-trained Models · Report Bug Overview OpenAI recently released the pa

Fredrik Carlsson 481 Dec 30, 2022
fastNLP: A Modularized and Extensible NLP Framework. Currently still in incubation.

fastNLP fastNLP是一款轻量级的自然语言处理(NLP)工具包,目标是快速实现NLP任务以及构建复杂模型。 fastNLP具有如下的特性: 统一的Tabular式数据容器,简化数据预处理过程; 内置多种数据集的Loader和Pipe,省去预处理代码; 各种方便的NLP工具,例如Embedd

fastNLP 2.8k Jan 01, 2023
Conversational-AI-ChatBot - Intelligent ChatBot built with Microsoft's DialoGPT transformer to make conversations with human users!

Conversational AI ChatBot Intelligent ChatBot built with Microsoft's DialoGPT transformer to make conversations with human users! In this project? Thi

Rajkumar Lakshmanamoorthy 6 Nov 30, 2022
📔️ Generate a text-based journal from a template file.

JGen 📔️ Generate a text-based journal from a template file. Contents Getting Started Example Overview Usage Details Reserved Keywords Gotchas Getting

Harrison Broadbent 21 Sep 25, 2022
Open-Source Toolkit for End-to-End Speech Recognition leveraging PyTorch-Lightning and Hydra.

OpenSpeech provides reference implementations of various ASR modeling papers and three languages recipe to perform tasks on automatic speech recogniti

Soohwan Kim 26 Dec 14, 2022
Multilingual Emotion classification using BERT (fine-tuning). Published at the WASSA workshop (ACL2022).

XLM-EMO: Multilingual Emotion Prediction in Social Media Text Abstract Detecting emotion in text allows social and computational scientists to study h

MilaNLP 35 Sep 17, 2022
Every Google, Azure & IBM text to speech voice for free

TTS-Grabber Quick thing i made about a year ago to download any text with any tts voice, over 630 voices to choose from currently. It will split the i

16 Dec 07, 2022
Huggingface Transformers + Adapters = ❤️

adapter-transformers A friendly fork of HuggingFace's Transformers, adding Adapters to PyTorch language models adapter-transformers is an extension of

AdapterHub 1.2k Jan 09, 2023
Simplified diarization pipeline using some pretrained models - audio file to diarized segments in a few lines of code

simple_diarizer Simplified diarization pipeline using some pretrained models. Made to be a simple as possible to go from an input audio file to diariz

Chau 65 Dec 30, 2022
Python package to easily retrain OpenAI's GPT-2 text-generating model on new texts

gpt-2-simple A simple Python package that wraps existing model fine-tuning and generation scripts for OpenAI's GPT-2 text generation model (specifical

Max Woolf 3.1k Jan 07, 2023
FewCLUE: 为中文NLP定制的小样本学习测评基准

FewCLUE: 为中文NLP定制的小样本学习测评基准

CLUE benchmark 387 Jan 04, 2023
Dense Passage Retriever - is a set of tools and models for open domain Q&A task.

Dense Passage Retrieval Dense Passage Retrieval (DPR) - is a set of tools and models for state-of-the-art open-domain Q&A research. It is based on the

Meta Research 1.1k Jan 07, 2023
Residual2Vec: Debiasing graph embedding using random graphs

Residual2Vec: Debiasing graph embedding using random graphs This repository contains the code for S. Kojaku, J. Yoon, I. Constantino, and Y.-Y. Ahn, R

SADAMORI KOJAKU 5 Oct 12, 2022