Metadata-Version: 2.1
Name: mdtidy
Version: 0.2.0
Summary: A Python library to clean and format markdown content into Jupyter Notebooks.
Home-page: https://github.com/davidkjeremiah/mdtidy
Author: David Jeremiah
Author-email: flasconnect@gmail.com
License: MIT
Classifier: Programming Language :: Python :: 3
Classifier: License :: OSI Approved :: MIT License
Classifier: Operating System :: OS Independent
Requires-Python: >=3.6
Description-Content-Type: text/markdown
License-File: LICENSE

# mdtidy

mdtidy is a Python library designed to clean and format markdown content into Jupyter Notebooks (.ipynb) and count code errors within the content. It's an intuitive tool for transforming raw markdown and code blocks into well-structured Jupyter notebooks.

## Features

- **Markdown to Jupyter Notebook**: Convert markdown content with embedded Python code blocks into a Jupyter Notebook file.
- **Error Counting**: Identify and count common Python error types within the content.

## Installation

You can install mdtidy via pip:

```sh
pip install mdtidy
```

## Usage

### Convert Markdown to Jupyter Notebook

```python
from mdtidy.cleaner import process_content_to_ipynb

# Example input string
input_string = """
The columns relevant to this analysis are:
- `TotalIncome`
- `Matri_AâˆšÂ±os`

Since the column `Matri_AâˆšÂ±os` is already of type float64, it can be used as is for the numerical calculations. We will create a simple scatter plot to visualize the relationship between these two variables.
``python?code_reference&code_event_index=3
import altair as alt

# Create a simple scatter plot with `Matri_AâˆšÂ±os` on the x-axis and `TotalIncome` on the y-axis.
chart = alt.Chart(df).mark_point().encode(
    x=alt.X('Matri_AâˆšÂ±os:Q', title='Years of Marriage'),
    y=alt.Y('TotalIncome:Q', title='Total Income'),
    tooltip=['Matri_AâˆšÂ±os', 'TotalIncome']
).properties(
    title='Total Income vs. Years of Marriage'
).interactive()

# Save the plot
chart.save('total_income_vs_years_of_marriage_scatterplot.json')
``
[json-tag: vegalite_1716301077.4469488_total_income_vs_years_of_marriage_scatterplot.json]

The chart titled 'Total Income vs. Years of Marriage' displays the relationship between total income and years of marriage for all divorce records.
"""

# Convert the content to a Jupyter Notebook file
process_content_to_ipynb(input_string, "cleaned_output.ipynb")
```

### Count Code Errors

```python
from mdtidy.cleaner import count_code_errors

# Example input string with errors
input_string_with_errors = """
Traceback (most recent call last):
  File "<stdin>", line 1, in <module>
NameError: name 'variable' is not defined

Traceback (most recent call last):
  File "<stdin>", line 1, in <module>
TypeError: unsupported operand type(s) for +: 'int' and 'str'
"""

# Count the code errors
error_counts = count_code_errors(input_string_with_errors)
print(error_counts)
```

## License

This project is licensed under the MIT License - see the LICENSE file for details.
