semvec/pypi-packages huggingface dataset:
bclr 0.3.3
bclr - Bayesian changepoint detection via Logistic Regression
Keywords: changepoint, bayesian, logistic regression

![bclr logo](https://raw.githubusercontent.com/manilasoldier/bclr/refs/heads/master/bclr_logo.png)

***

A package for <b>B</b>ayesian <b>C</b>hangepoint detection via <b>L</b>ogistic <b>R</b>egression.

These methods were developed in [Thomas, Jauch, and Matteson (2025)](https://arxiv.org/abs/2401.02917).

## Installation

The simplest way to install the package, is to install from <code>PyPI</code> via the code:

```
pip install bclr
```

Alternatively, if you would like to install the source code, you can clone the repository and then install via pip (Linux/Mac):

```
git clone https://github.com/manilasoldier/bclr.git
cd bclr
pip install .
```

or run

```
pip install git+https://github.com/manilasoldier/bclr.git
```

## Running the code

```python
import bclr
import numpy as np
```

```python
np.random.seed(800037)
a1 = np.random.standard_t(10, size=(320, 6))
a2 = np.random.standard_t(1.5, size=(480, 6))

# "Gaussian" type embedding
Xs = np.exp(-np.r_[a1, a2]**2)
```

Let's first investigate the single changepoint setup, before moving the multiple one. 

First, let's specify a prior for the $\beta$ coefficients (we need to provide a mean and covariance for the single changepoint setup).

### Single changepoint setup

```python
rng1 = np.random.default_rng(800037) #for reproducibility
prior_covS = np.diag(np.repeat(3, 6))
prior_mean = np.repeat(0, 6)

bclrS = bclr.BayesCC(X = Xs, prior_mean = prior_mean, prior_cov = prior_covS, 
                     n_iter = 2000)
bclrS.fit(rng = rng1)
bclrS.transform(verbose=False)
```

```python
bclrS.plot_k()
```
 
![png](https://raw.githubusercontent.com/manilasoldier/bclr/refs/heads/master/output_9_0.png)

### Multiple changepoint setup

Now we consider the multiple changepoint setup and the whole series $X$, where specify $J = 10$ (i.e. <code>cps=10</code>). 

Then, we will go ahead and fit <code>bclr</code> to the data and estimate (predict) the changes. 

Note: there is no need to specify a prior_mean for the multiple changepoint setup.

```python
np.random.seed(199203)
b1 = np.random.randn(100, 10)
b2 = np.random.randn(80, 10) + np.broadcast_to(np.random.randn(10), (80, 10))
b3 = np.random.randn(140, 10) + np.broadcast_to(np.random.randn(10), (140, 10))
b4 = np.random.randn(80, 10) + np.broadcast_to(np.random.randn(10), (80, 10))

Xm = np.r_[b1, b2, b3, b4]
```

```python
rng2 = np.random.default_rng(199203) #for reproducibility
prior_covM = np.diag(np.repeat(3, 10))
bclrM = bclr.MultiBayesCC(X = Xm, cps = 10, prior_cov = prior_covM, 
                          rng = rng2, warnings=False)
cps = bclrM.fit_predict()
```

Now we can look at

- where the changes are estimated to be located, 
- the posterior probability of the estimates, and 
- the normalized entropy of the segment distribution.

```python
print(cps)
```

       Location  Posterior Probability  Normalized Entropy
    0      99.0                  0.946            0.047544
    1     180.0                  1.000            0.000000
    2     320.0                  0.996            0.005354
crimson-folder-watcher 0.1.0
Your package description.
Requires Python: >=3.9

# Crimson Folder Watcher

A lightweight Python package for watching folders and applying custom transformations to files.

## Installation

```bash
pip install crimson-folder-watcher
```

## Features

- 🔍 Monitor directories for file changes in real-time
- 🔄 Apply custom transformations to files based on patterns
- ⚙️ Flexible file filtering with include/exclude patterns
- 🛠️ Powerful API for custom file editing functions
- 📤 Output transformed files to a separate directory

## Usage

For complete examples and usage instructions, check the [quickstart.ipynb](example/quickstart.ipynb) notebook.

## Key Concepts

- **FolderWatcher**: Monitors folders and processes file changes
- **FileEditor**: Manages file content transformation logic
- **Filters**: Provides file pattern-based include/exclude filtering

## Documentation

For detailed API documentation and examples, refer to [quickstart.ipynb](example/quickstart.ipynb).

## License

MIT License

## Contributing

Contributions are welcome! Please feel free to submit a Pull Request.
bigbank 0.1.4
Really useless library that has no reason to exist !!!
Requires Python: >=3.6

# BigBank

Really useless library that has no reason to exist !!!

## Installation
pip install bigbank

## Usage
```python
from bigbank import BigBank, CooldownError
import time

bank = BigBank()
while True:
    print(bank.get_balance())  # Check balance
    try:
        print(bank.generate_money())  # Earn money
    except CooldownError as e:
        print(e)
    time.sleep(1)
```
compressed-rtf 1.0.7
Compressed Rich Text Format (RTF) compression and decompression package
Keywords: compressed-rtf, lzfu, mela, rtf

# compressed_rtf

[![PyPI](https://img.shields.io/pypi/v/compressed_rtf)](https://pypi.org/project/compressed-rtf/)
[![Build Status](https://travis-ci.org/delimitry/compressed_rtf.svg?branch=master)](https://travis-ci.org/delimitry/compressed_rtf)
[![Coverage Status](https://coveralls.io/repos/github/delimitry/compressed_rtf/badge.svg?branch=master)](https://coveralls.io/github/delimitry/compressed_rtf?branch=master)
[![MIT license](http://img.shields.io/badge/license-MIT-brightgreen.svg)](https://github.com/delimitry/compressed_rtf/blob/master/LICENSE)

Compressed Rich Text Format (RTF) compression worker in Python

Description:
------------

Compressed RTF also known as "LZFu" compression format

Based on Rich Text Format (RTF) Compression Algorithm:

https://msdn.microsoft.com/en-us/library/cc463890(v=exchg.80).aspx


Usage example:
--------------

```python
>>> from compressed_rtf import compress, decompress
>>>
>>> data = '{\\rtf1\\ansi\\ansicpg1252\\pard test}'
>>> comp = compress(data, compressed=True)  # compressed
>>> comp
'#\x00\x00\x00"\x00\x00\x00LZFu3\\\xe8t\x03\x00\n\x00rcpg125\x922\n\xf3 t\x07\x90t}\x0f\x10'
>>>
>>> raw = compress(data, compressed=False)  # raw/uncompressed
>>> raw
'.\x00\x00\x00"\x00\x00\x00MELA \xdf\x12\xce{\\rtf1\\ansi\\ansicpg1252\\pard test}'
>>>
>>> decompress(comp)
'{\\rtf1\\ansi\\ansicpg1252\\pard test}'
>>>
>>> decompress(raw)
'{\\rtf1\\ansi\\ansicpg1252\\pard test}'
>>>
```

License:
--------
Released under [The MIT License](https://github.com/delimitry/compressed_rtf/blob/master/LICENSE).