semvec/pypi-packages huggingface dataset:
h5ify 0.2.3
Save Python dictionaries into HDF5 files; load HDF5 files into Python dictionaries.
Requires Python: >=3.9

# h5ify

Save Python dictionaries into HDF5 files; load HDF5 files into Python dictionaries.

The dictionary can be a nested dictionary of dictionaries, the terminal values of which are numbers, lists/tuples of numbers, arrays, etc. If the value of a key is not another dictionary, it is stored as a `Dataset` in the HDF5 file, otherwise it creates a new `Group`.

The `attrs` key can be used at each level of a nested dictionary to store metadata for the corresponding `Group` objects in `.attrs`. This currently cannot be used to store `.attrs` metadata for `Dataset` objects. The value for each `attrs` key must be a dictionary that is not nested.

## Install

`pip install h5ify`

## Examples

Make a small dictionary, then save it.
```python
import h5ify

d = {'x': 1.0, 'y': 2, 'z': [1, 2, 3], 'attrs': {'info': 'README example'}}
h5ify.save('tmp.h5', d)
```

Load the saved dictionary.
```python
dd = h5ify.load('tmp.h5')
print(dd)
```
```python
{'attrs': {'info': 'README example'}, 'x': 1.0, 'y': 2, 'z': array([1, 2, 3])}
```
Note that lists/tuple are converted to numpy arrays by h5py.

You can use the usual h5py API to open the stored HDF5 file.
```python
import h5py

with h5py.File('tmp.h5', 'r') as f:
    for key, val in f.items():
        print(key, val[()])
    for key, val in f.attrs.items():
        print(key, val)
```
```python
x 1.0
y 2
z [1 2 3]
info README example
```

`h5ify` opens HDF5 files in `a` mode, meaning "Read/write if exists, create otherwise". You cannot save a dictionary with the same file name and `Dataset` keys.
```python
h5ify.save('tmp.h5', d)
```
```python
ValueError: Unable to synchronously create dataset (name already exists)
```

You can append values that are not yet saved to the same file, however.
```python
h5ify.save('tmp.h5', {'w': 42})
print(h5ify.load('tmp.h5'))
```
```python
{'attrs': {'info': 'README example'}, 'w': 42, 'x': 1.0, 'y': 2, 'z': array([1, 2, 3])}
```

Or you can overwrite by specifying the write mode:
```python
h5ify.save('tmp.h5', {**d, 'w': 42}, mode = 'r')
print(h5ify.load('tmp.h5'))
```
```python
{'attrs': {'info': 'README example'}, 'w': 42, 'x': 1.0, 'y': 2, 'z': array([1, 2, 3])}
```

Any additional keyword arguments to `h5ify.save` are passed to the `create_dataset` function in `h5py`.
```python
h5ify.save('tmp.h5', {'comp': [100]}, compression = 'gzip', compression_opts = 9)
```

That should cover it. Let me know if you have questions!
bada 0.1.1
Package for analysis of biophysical assays, such as DSF
Requires Python: >=3.12

# Biophysical Assay Data Analysis

A Python package for analyzing and visualizing Differential Scanning Fluorimetry (DSF) data. 

## Features

- data import from commonly biophysical instrument formats. Currently supported instruments are
  QuantStudio7 and Lightcycler 480.
- quick visualization for raw data and the signals first derivative
- convenient calculation of relevant features such as the melting temperature Tm (here: max first
  derivative)
- identification of atypical shapes of signals (e.g. empty wells, failed experiment)


## Installation

### From PyPI (Recommended)

The easiest way to install is via pip:

```bash
pip install bada
```

### From Source

To install the latest development version:

1. Clone the repository:
   ```bash
   git clone https://github.com/username/biophysical-assay-data-analysis.git
   cd biophysical-assay-data-analysis
   ```

2. Create a virtual environment (optional but recommended) using e.g. uv

3. Install the package in development mode:
   ```bash
   pip install -e .
   ```

## Usage

To get started with this package, please refer to the example Jupyter notebooks in the `notebooks`
folder. These notebooks provide step-by-step guides for common workflows and demonstrate the key
functionality of the package.


## License

This project is licensed under the MIT License - see the [LICENSE](LICENSE) file for details.
LemX 0.2.1
A Banglish lemmatizer and word corrector using Levenshtein Distance Developped By Pronoy Kumar Mondal Under the supervision of Md. Sadekur Rahman & Sadman Sadik Khan.
Requires Python: >=3.6

# LemX

LemX is a Banglish lemmatizer and word corrector utilizing Levenshtein Distance. It was developed by Pronoy Kumar Mondal, Washik Wali Faieaz and Kawshik Ahmed Ornob under the supervision of Md. Sadekur Rahman & Sadman Sadik Khan.

## Installation

LemX can be installed directly from PyPI using pip:

```bash
pip install lemx
```

## Usage

### Word Correction & Lemmatization

```python
from lemx import LemX

lemmatizer = LemX()

# Correct and lemmatize a single word
incorrect_word = "amr"
corrected_word = lemmatizer.correct_word(incorrect_word)

print(f"Corrected word for '{incorrect_word}': {corrected_word}")
```

```bash
# Response
Corrected word for 'amr': amar
```

### Sentence Correction & Lemmatization

```python
from lemx import LemX

lemmatizer = LemX()

# Correct and lemmatize a sentence
sentence = "ajkei ami valo asi"
corrected_sentence = lemmatizer.correct_sentence(sentence)

print(f"Corrected sentence: {corrected_sentence}")
```

```bash
# Response
ajke ami valo achi
```

## Features

- **Banglish Word Lemmatization**: Converts inflected forms to their base forms.
- **Banglish Word Correction**: Uses Levenshtein Distance for error correction.
- **Lightweight & Easy to Use**: Simple API for seamless integration.

## Contributing

We welcome contributions! If you'd like to improve LemX, feel free to:
- Open an issue for bug reports or feature requests.
- Submit a pull request with your improvements.