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March 19, 2024 03:56
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| import re | |
| import json | |
| import shutil | |
| import os | |
| def md_to_json(md_file, json_file): | |
| with open(md_file, "r") as file: | |
| lines = file.readlines() | |
| json_structure = {"MajorCategories": {}} | |
| current_major = current_minor = current_sub = None | |
| for i, line in enumerate(lines): | |
| line = line.strip() # Remove leading and trailing whitespaces | |
| if not line or line.startswith("---"): | |
| continue # Skip empty lines and metadata lines | |
| print(f"Processing line {i}: {line}") # Debug print | |
| try: | |
| # Match major, minor, subcategories, and book entries | |
| major_match = re.match(r"- \[\[(\d0\d)\]\]\s*(.*)", line) | |
| minor_match = re.match(r"- \[\[(\d[1-9]\d)\]\]\s*(.*)", line) | |
| sub_match = re.match(r"- \[\[(\d{3}\.\d{2})\]\]\s*(.*)", line) | |
| book_match = re.match(r"- \[\[(\d{3}\.\d{2} [a-zA-Z])\]\]\s*(.*)", line) | |
| if major_match: | |
| current_major, title = major_match.groups() | |
| json_structure["MajorCategories"][current_major] = { | |
| "value": current_major, | |
| "title": title, | |
| "MinorCategories": {}, | |
| } | |
| print(f"Major Category: {current_major}, Title: {title}") # Debug print | |
| elif minor_match: | |
| current_minor, title = minor_match.groups() | |
| json_structure["MajorCategories"][current_major]["MinorCategories"][ | |
| current_minor | |
| ] = {"title": title, "Subcategories": {}} | |
| print(f"Minor Category: {current_minor}, Title: {title}") # Debug print | |
| elif sub_match: | |
| current_sub, title = sub_match.groups() | |
| json_structure["MajorCategories"][current_major]["MinorCategories"][ | |
| current_minor | |
| ]["Subcategories"][current_sub] = {"title": title, "Books": {}} | |
| print(f"Subcategory: {current_sub}, Title: {title}") # Debug print | |
| elif book_match: | |
| book_code, book_title = book_match.groups() | |
| json_structure["MajorCategories"][current_major]["MinorCategories"][ | |
| current_minor | |
| ]["Subcategories"][current_sub]["Books"][book_code] = book_title | |
| print(f"Book: {book_code}, Title: {book_title}") # Debug print | |
| except Exception as e: | |
| print(f"Error processing line {i}: '{line}'") | |
| print(str(e)) | |
| # Save JSON structure to file | |
| temp_json_path = os.path.join(os.getcwd(), json_file) | |
| with open(temp_json_path, "w") as outfile: | |
| json.dump(json_structure, outfile, indent=4) | |
| # Move the JSON file to the current directory (Manage) | |
| current_dir_path = os.getcwd() | |
| destination_path = os.path.join(current_dir_path, json_file) | |
| shutil.move(temp_json_path, destination_path) | |
| print(f"Moved {json_file} to {destination_path}") | |
| # Usage example | |
| md_file = "../Entrance/Call Number Index.md" | |
| json_file = "structure.json" | |
| print(f"Read from {md_file}") | |
| md_to_json(md_file, json_file) | |
| print(f"Output json file is {json_file}") |
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| { | |
| "MajorCategories": { | |
| "000": { | |
| "value": "000", | |
| "title": "IT Knowledge", | |
| "MinorCategories": { | |
| "010": { | |
| "title": "Develop Knowledge", | |
| "Subcategories": { | |
| "010.00": { | |
| "title": "Develop Computer Science Knowledge", | |
| "Books": { | |
| "010.00 a": "Essential Developer Insights", | |
| "010.00 b": "Industry Domain Knowledge" | |
| } | |
| }, | |
| "010.10": { | |
| "title": "Develop Programming Language", | |
| "Books": { | |
| "010.10 a": "Bash shell" | |
| } | |
| } | |
| } | |
| }, | |
| "020": { | |
| "title": "Operation Knowledge", | |
| "Subcategories": { | |
| "020.00": { | |
| "title": "Versioning", | |
| "Books": { | |
| "020.00 a": "Sementic Verisioning", | |
| "020.00 b": "headver", | |
| "020.00 c": "Versioning Strategy" | |
| } | |
| }, | |
| "020.10": { | |
| "title": "Git", | |
| "Books": { | |
| "020.10 a": "git init", | |
| "020.10 b": "git commands", | |
| "020.10 c": "git structure", | |
| "020.10 d": "git submodule", | |
| "020.10 e": "git gist" | |
| } | |
| } | |
| } | |
| }, | |
| "030": { | |
| "title": "Package", | |
| "Subcategories": { | |
| "030.00": { | |
| "title": "PIP", | |
| "Books": { | |
| "030.00 a": "pip name and module name", | |
| "030.00 b": "pip packages pre option in requirements" | |
| } | |
| } | |
| } | |
| }, | |
| "080": { | |
| "title": "English for IT Workers", | |
| "Subcategories": { | |
| "080.00": { | |
| "title": "Developer English", | |
| "Books": {} | |
| } | |
| } | |
| }, | |
| "090": { | |
| "title": "External Insights an Trends", | |
| "Subcategories": { | |
| "090.00": { | |
| "title": "Industry Trends", | |
| "Books": {} | |
| }, | |
| "090.10": { | |
| "title": "Live Session", | |
| "Books": { | |
| "090.10 a": "Job Definetion" | |
| } | |
| } | |
| } | |
| } | |
| } | |
| }, | |
| "100": { | |
| "value": "100", | |
| "title": "Infra", | |
| "MinorCategories": { | |
| "110": { | |
| "title": "DevOps Engineer Infra", | |
| "Subcategories": {} | |
| }, | |
| "120": { | |
| "title": "ML Engineer Infra", | |
| "Subcategories": {} | |
| } | |
| } | |
| }, | |
| "200": { | |
| "value": "200", | |
| "title": "DevOps", | |
| "MinorCategories": { | |
| "210": { | |
| "title": "CICD", | |
| "Subcategories": { | |
| "210.00": { | |
| "title": "DevOps Fundemental", | |
| "Books": { | |
| "210.00 a": "DevOps Backgrond" | |
| } | |
| }, | |
| "210.10": { | |
| "title": "DevOps Culture", | |
| "Books": {} | |
| }, | |
| "210.20": { | |
| "title": "DevOps Solutions", | |
| "Books": { | |
| "210.20 a": "GitLab" | |
| } | |
| } | |
| } | |
| } | |
| } | |
| }, | |
| "300": { | |
| "value": "300", | |
| "title": "Applications", | |
| "MinorCategories": { | |
| "310": { | |
| "title": "Development Environment", | |
| "Subcategories": { | |
| "310.00": { | |
| "title": "IDE", | |
| "Books": { | |
| "310.00 a": "Terminal", | |
| "310.00 b": "Colab" | |
| } | |
| }, | |
| "310.10": { | |
| "title": "Conda", | |
| "Books": { | |
| "310.10 a": "Conda base", | |
| "310.10 b": "Conda License" | |
| } | |
| } | |
| } | |
| }, | |
| "320": { | |
| "title": "Frontend", | |
| "Subcategories": { | |
| "320.00": { | |
| "title": "HTML", | |
| "Books": {} | |
| }, | |
| "320.10": { | |
| "title": "CSS", | |
| "Books": { | |
| "320.10 b": "Border Properties" | |
| } | |
| }, | |
| "320.30": { | |
| "title": "Java Script", | |
| "Books": {} | |
| } | |
| } | |
| }, | |
| "330": { | |
| "title": "Backend", | |
| "Subcategories": {} | |
| } | |
| } | |
| }, | |
| "400": { | |
| "value": "400", | |
| "title": "ML Engineer Basic", | |
| "MinorCategories": { | |
| "410": { | |
| "title": "Mathematics", | |
| "Subcategories": { | |
| "410.00": { | |
| "title": "Linear Algebra", | |
| "Books": { | |
| "410.00 a": "Fundamental Function", | |
| "410.00 b": "Vector", | |
| "410.00 c": "Vectors Properties", | |
| "410.00 d": "Vector Operation" | |
| } | |
| }, | |
| "410.10": { | |
| "title": "Probability", | |
| "Books": { | |
| "410.10 a": "Fundamental Function" | |
| } | |
| }, | |
| "410.20": { | |
| "title": "Statistics", | |
| "Books": { | |
| "410.20 a": "Probability Distribution and Estimation", | |
| "410.20 b": "Maximum Likelihood Estimation(MLE) and Deep Learning" | |
| } | |
| }, | |
| "410.30": { | |
| "title": "Calculus", | |
| "Books": { | |
| "410.30 a": "Fundamental" | |
| } | |
| } | |
| } | |
| }, | |
| "420": { | |
| "title": "Data", | |
| "Subcategories": { | |
| "420.00": { | |
| "title": "Structured Data", | |
| "Books": { | |
| "420.00 a": "Numerical Data (Continuous and Discrete)", | |
| "420.00 b": "Categorical Data (Ordinal and Nominal)" | |
| } | |
| }, | |
| "420.10": { | |
| "title": "Unstructured Data", | |
| "Books": {} | |
| }, | |
| "420.20": { | |
| "title": "Semi-structured Data", | |
| "Books": { | |
| "420.20 a": "JSON", | |
| "420.20 b": "YAML" | |
| } | |
| } | |
| } | |
| }, | |
| "430": { | |
| "title": "ML Development Tools", | |
| "Subcategories": { | |
| "430.00": { | |
| "title": "LangChain", | |
| "Books": { | |
| "430.00 a": "Langchain Components" | |
| } | |
| }, | |
| "430.10": { | |
| "title": "Streamlit", | |
| "Books": { | |
| "430.10 a": "LLM RAG Application Using Langchain and FAISS" | |
| } | |
| } | |
| } | |
| }, | |
| "440": { | |
| "title": "ML Methodology", | |
| "Subcategories": { | |
| "440.00": { | |
| "title": "EDA(Exploratory Data Analysis)", | |
| "Books": { | |
| "440.00 a": "Initiating Exploratory Data Analysis" | |
| } | |
| } | |
| } | |
| } | |
| } | |
| }, | |
| "500": { | |
| "value": "500", | |
| "title": "ML and DL Modeling", | |
| "MinorCategories": { | |
| "510": { | |
| "title": "Natural Language Processing (NLP)", | |
| "Subcategories": {} | |
| }, | |
| "520": { | |
| "title": "Computer Vision", | |
| "Subcategories": { | |
| "520.00": { | |
| "title": "Introduction to CV", | |
| "Books": {} | |
| }, | |
| "520.30": { | |
| "title": "Generative Models in CV", | |
| "Books": { | |
| "520.30 a": "GANs", | |
| "520.30 b": "CLIP", | |
| "520.30 c": "Stable Diffusion" | |
| } | |
| } | |
| } | |
| } | |
| } | |
| }, | |
| "600": { | |
| "value": "600", | |
| "title": "ML Libraries and Implementation", | |
| "MinorCategories": { | |
| "610": { | |
| "title": "Data Handling", | |
| "Subcategories": { | |
| "610.00": { | |
| "title": "Pandas", | |
| "Books": { | |
| "610.00 a": "Pandas-basic" | |
| } | |
| }, | |
| "610.10": { | |
| "title": "NumPy", | |
| "Books": { | |
| "610.10 a": "Numpy Fundamental functions", | |
| "610.10 b": "Numpy Appllied function" | |
| } | |
| } | |
| } | |
| }, | |
| "620": { | |
| "title": "Data visualization", | |
| "Subcategories": { | |
| "620.00": { | |
| "title": "Matplotlib", | |
| "Books": { | |
| "620.00 a": "Matplotlib Fundamental" | |
| } | |
| }, | |
| "620.10": { | |
| "title": "Seabornn", | |
| "Books": { | |
| "620.10 a": "Seaborn Fundamental" | |
| } | |
| } | |
| } | |
| }, | |
| "630": { | |
| "title": "Machine Learning Frameworks", | |
| "Subcategories": { | |
| "630.00": { | |
| "title": "scikit-learn", | |
| "Books": { | |
| "630.00 a": "scikit-learn functions" | |
| } | |
| }, | |
| "630.10": { | |
| "title": "PyTorch", | |
| "Books": {} | |
| }, | |
| "630.20": { | |
| "title": "TensorFlow", | |
| "Books": {} | |
| }, | |
| "630.30": { | |
| "title": "Langchain", | |
| "Books": { | |
| "630.30 a": "Amazon Bedrock with RAG", | |
| "630.30 b": "RAG with FAISS" | |
| } | |
| } | |
| } | |
| }, | |
| "640": { | |
| "title": "Vector Database", | |
| "Subcategories": { | |
| "640.00": { | |
| "title": "", | |
| "Books": { | |
| "640.00 a": "Milvus" | |
| } | |
| } | |
| } | |
| } | |
| } | |
| }, | |
| "700": { | |
| "value": "700", | |
| "title": "Research Paper", | |
| "MinorCategories": { | |
| "710": { | |
| "title": "methodologysh", | |
| "Subcategories": { | |
| "710.00": { | |
| "title": "Multi-agent Reinforcement Learning", | |
| "Books": { | |
| "710.00 a": "Base Domains", | |
| "710.00 b": "Paper Review" | |
| } | |
| } | |
| } | |
| } | |
| } | |
| }, | |
| "800": { | |
| "value": "800", | |
| "title": "test_major", | |
| "MinorCategories": { | |
| "810": { | |
| "title": "test_minor", | |
| "Subcategories": { | |
| "810.00": { | |
| "title": "test_sub", | |
| "Books": { | |
| "810.00 a": "test_book" | |
| } | |
| } | |
| } | |
| } | |
| } | |
| } | |
| } | |
| } |
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