semvec/pypi-packages huggingface dataset:
mdfrag 0.1.0
Library for splitting markdown documents into fragments.
License: MIT
Keywords: NTS,UO,Markdown,Fragmentation,Split

Have you ever wanted to break a Markdown document into chunks?
You are in luck, because that is what you can do with this package!

## Install

Install this package like any other PyPI package.

    pip install mdfrag

## Examples

This package can be invoked as a CLI tool, or used as a library in your own project.

### CLI Example

To split this document into chunks of 500 bytes, use the following CLI command:

    python -m mdfrag docs/user-guide.md 500 

> For more information, run `python -m mdfrag --help`. Additionally, see `__main__.py`.

### Code Example

Use the `mdfrag.split_markdown()` function to split your markdown document.

> See the docstring for more detailed information.
> Additionally, see `__main__.py` for an implementation of the below example.

    import mdfrag

    frags = None
    with open('docs/user-guide.md', 'r') as f:
        frags = mdfrag.split_markdown(f.read(), 512)

    # Walk through the list of fragments and write them to their appropriate files. 
    for count, fragment in enumerate(frags):
        with open(f'output_{count}.md', 'w') as dest:
            dest.write(fragment)

## Performance Considerations

If you select a small `fragment-size`, and your document contains large blocks of Markdown, the split_markdown function will struggle!
Cls-Evaluation 0.0.1
Basic metrics for evaluating classification results
License: MIT
Keywords: summary,F1 score,accuracy,confusion matrix,FDR

A confusion matrix is a summary of classification problem prediction results. 
The number of correct and incorrect predictions is summarized using count values and divided by class.

The function takes two arrays of same length and returns a list of four metrics i.e., TN, FP, FN, and TP
By using confusion matrix we can calculate precision, recall, f1 score, FDR and accuracy.
-Calculate True positive, true negative, false positive and false negative
-Accuracy calculates the number of times classifier predicts correctly.
-F1 score: Harmonic mean of precision and recall.
-Precision: What % of predicted Positive aspects are actually Positive?
-Recall: How many actual Positives are correctly classified?
-False dicovery rate (FDR)

CHANGE LOG

==============

0.0.1 (16/01/2022)
------------------
- First Release
spacedreppy 0.1.2
A spaced repetition Python library.
License: MIT
Requires Python: >=3.9,<4.0
Keywords: Spaced-repetition


# SpacedRepPy

<div style="text-align: center">

[![Build status](https://github.com/lschlessinger1/spacedreppy/workflows/build/badge.svg?branch=main&event=push)](https://github.com/lschlessinger1/spacedreppy/actions?query=workflow%3Abuild)
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[![Code style: black](https://img.shields.io/badge/code%20style-black-000000.svg)](https://github.com/psf/black)
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![Coverage Report](assets/images/coverage.svg)

A spaced repetition Python library.

</div>

## Installation

Use the package manager [pip](https://pip.pypa.io/en/stable/) to install `spacedreppy`.

```bash
pip install spacedreppy
```

## Usage

```python
from datetime import datetime
from spacedreppy.schedulers.sm2 import SM2Scheduler

# returns next due timestamp and next interval.
scheduler = SM2Scheduler()
due_timestamp, interval = scheduler.compute_next_due_interval(attempted_at=datetime.utcnow(), result=3)
```

## License
[MIT](https://choosealicense.com/licenses/mit/)