semvec/pypi-packages huggingface dataset:
symgroupy 0.5.12
symgroupy

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SymGroup
=========
Software to calculate continuous symmetry measures of 
molecular structures


Installation instructions
---------------------------------------------------------

1. Requirements
  - Lapack & Blas libraries
  - Fortran77 compiler
  - cmake 2.6
  - (optional) MKL


2a. Install as standalone binary
   ```shell
   ./configure (see --help for available options)
   cd build
   make install
   ```
2b. Compile as a python module
   ```shell
   cd python
   python setup.py install --user
   ```
2c. Obtain from PyPi repository
```shell
pip install symgroupy --user
```

Usage
-----
```shell
./symgroupy input.zdat
```
for further information check the manual at:  
https://symgroup.readthedocs.io


Python API
----------

```python
from symgroupy import Symgroupy

fen4 = Symgroupy(coordinates=[[15.5182, -1.68040, 22.81703],
                              [6.78978, -3.22298, 23.08474],
                              [6.27712, -0.12712, 21.76775],
                              [4.24692, -3.22298, 22.54931],
                              [4.75958, -0.12712, 23.86630]],
                 group='c3',
                 multi=8,
                 labels=['Fe', 'N', 'N', 'N', 'N'],
                 central_atom=1)

print('CSM: {}'.format(fen4.csm))
print('Optimum axis: {}'.format(fen4.optimum_axis))
print('Optimum permutation: {}'.format(fen4.optimum_permutation))
print('Nearest structure')
print(fen4.nearest_structure)
print('Reference axis')
print(fen4.reference_axis)
print('multi CSM')
print(fen4.csm_multi)
print('multi axis')
print(fen4.axis_multi)

```

Authors
--------------------------------------------------------

This software has been developed by David Casanova
<br>Python module by Abel Carreras

The theoretical background implemented in this software is described in:
<br>Pinsky M, Dryzun C, Casanova D, Alemany P, Avnir D, J Comput Chem. 29:2712-21 (2008)
<br>Pinsky M, Casanova D, Alemany P, Alvarez S, Avnir D, Dryzun C, Kizner Z, Sterkin A.  J Comput Chem. 29:190-7 (2008)
datagen-python 0.1.0
A Python utility for generating synthetic data for various domains
Requires Python: >=3.8

# DataGen

A Python utility for generating synthetic data for various domains including financial, healthcare, and text data. The library provides specialized generators with built-in domain knowledge and patterns.

## Features
- Multiple specialized data generators:
  - Financial data (OHLCV market data)
  - Healthcare data (patient records, medical events)
  - Text data (structured text content)
  - Generic tabular data with customizable schemas
- Built-in save functionality for all generators
- Support for various data types including numerical, categorical, datetime, and text
- Pattern-based generation from example datasets

## Installation
```bash
pip install -r requirements.txt
```

## Usage

### Financial Data Generation
```python
from datagen.financial import OHLCVGenerator

# Create a financial data generator
generator = OHLCVGenerator()

# Generate OHLCV market data
data = generator.generate(
    start_date="2023-01-01",
    end_date="2023-12-31",
    initial_price=100,
    volatility=0.2
)

# Save the generated data to a CSV file
generator.save_to_csv("market_data.csv")
```

### Healthcare Data Generation
```python
from datagen.healthcare import HealthcareDataGenerator

# Create a healthcare data generator
generator = HealthcareDataGenerator()

# Generate patient records
data = generator.generate(num_patients=100)

# Save the generated data
generator.save("patient_records.csv")
```

### Text Data Generation
```python
from datagen.text import TextGenerator

# Create a text data generator
generator = TextGenerator()

# Generate structured text data
data = generator.generate(rows=50)

# Save the generated data
generator.save("text_data.csv")
```

### Generic Data Generation
```python
from datagen import DataGenerator

# Generate data from scratch
generator = DataGenerator()
df = generator.generate(
    schema={
        'name': 'name',
        'age': 'integer[18:80]',
        'email': 'email'
    },
    rows=100
)

# Or generate based on example data
example_df = pd.read_csv('example.csv')
generator = DataGenerator(example_df)
synthetic_df = generator.generate(rows=100)

# Save generated data
generator.save("generated_data.csv")
```

## Data Types and Schemas
The generic data generator supports various data types that can be specified in the schema:
- Basic types: integer, float, string, boolean
- Ranges: integer[min:max], float[min:max]
- Special types: name, email, date, datetime
- Categories: category[value1,value2,...]

## Contributing
Contributions are welcome! Please feel free to submit a Pull Request.