semvec/pypi-packages huggingface dataset:
nepali-normalizer 0.1.1
Advanced normalization toolkit for Nepali text processing
Requires Python: >=3.10

# Nepali Text Normalizer

Advanced normalization toolkit for Nepali text processing. This package provides tools to normalize Nepali text, including vowel normalization, punctuation removal, Unicode standardization, and numeral conversion.

This tool helps evaluators evaluate WER normalizing nepali text better.

## Installation

Install the package via pip:

```bash
pip install nepali-normalizer
```

## Example 

```python
# example_script.py
from nepali_normalizer.normalizer import NepaliTextNormalizerWerCer

# Define the normalizer
normalizer = NepaliTextNormalizerWerCer()

# Test strings
test_string1 = "मेरी आमा राम्री छिन asdf ''  1234"
test_string2 = "मेरि आमा राम्रि छीन asdf ''''  1234"

# Normalize and print results
test_string1_normalized = normalizer(test_string1)
test_string2_normalized = normalizer(test_string2)
print(f"Test string 1: {test_string1_normalized}")
print(f"Test string 2: {test_string2_normalized}")

# output
# Test string 1: मेरी आमा राम्री छीन asdf १२३४
# Test string 2: मेरी आमा राम्री छीन asdf १२३४
```
Fcode-Calculator 1.0.1
A Python package for scientific and electronic calculations
Requires Python: >=3.6

# 📟 Scientific & Electronic Calculator

A Python package for performing **scientific and electronic calculations**, including Ohm's Law, metric conversions, frequency conversions, physics formulas, and more.

## 🚀 Features
- Ohm’s Law & Power Law calculations
- Frequency & metric unit conversions
- Speed, distance, and time calculations
- Newton’s Laws & gravitational force computations
- Electronic component calculations (resistors, capacitors, inductors)
- CMYK to RGB color conversion

## 📦 Installation
You can install this package using `pip`:

```
pip install Fcode_Calculator
```


## 📖 Usage
**Import the package and start using the functions:**

```
import Fcode_Calculator as ps
```

##  Ohm's Law Example
```
voltage = ps.ohms_law("Voltage", I=2, R=10)
print(voltage)  # Output: 20 volts
```

## Frequency Conversion
```
freq_in_ghz = ps.freq_converter(5, "MHz", "GHz")
print(freq_in_ghz)  # Output: 0.005 GHz
```

## Einstein’s Energy Formula
```
energy = ps.einstein_mass_energy(2)
print(energy)  # Output: 1.8e+17
```

## 📚 Available Functions
 - **🔹 Electronics**
```
ohms_law(type, V=None, I=None, R=None): Calculate Ohm’s Law values.
```
```
power_law(type, V=None, I=None, P=None): Calculate power, voltage, or current.
```
```
series_resistor(*resistances): Sum of resistors in series.
```
```
parallel_resistor(*resistances): Total resistance in parallel circuits.
```

 - **🔹 Scientific **
```
speed(distance, time): Calculate speed.
```
```
distance(speed, time): Find distance.
```
```
gravitational_force(G, m1, m2, r): Compute gravitational force.
```
```
einstein_mass_energy(mass): Compute energy from mass.
```
 - **🔹 Conversions**
```
freq_converter(value, from_freq, to_freq): Convert between Hz, kHz, MHz, and GHz.
```
```
metric_converter(value, from_unit, to_unit): Convert metric units (nano, micro, kilo, etc.).
```
```
cmyk_to_rgb(c, m, y, k): Convert CMYK color values to RGB.
```

## 🛠️ Development & Contributions
If you want to improve this package:
Fork the repository on GitHub.
Clone the repository:
```
git clone https://github.com/fcode101/PyScience.git
```
```
cd scientific_calculator
```


## 📜 License
This project is licensed under the MIT License.

- 💡 Created by F-Code-101:
- 📧 Contact: techwbro@gmail.com
- 🎥 https://www.youtube.com/@F-Code101

---
pdsim-backend 1.0.1
Using Unified Planning Library to parse PDDL and generate plans for PDSim.
Requires Python: >=3.10

# PDSim Backend

Using [Unified Planning Library](https://github.com/aiplan4eu/unified-planning) to parse PDDL and generate plans for PDSim


## Install

This project is now hosted on [pypi](https://pypi.org/project/pdsim-backend/)

### Use Conda (preferred)

##### Create a new venv
`conda create -n pdsim python=3.11`

##### Activate
`conda activate pdsim`
  
##### Install PDSim-Backend
`pip install pdsim-backend`

### Use Venv
  
##### Create a new venv
`python -m venv <directory>`

##### Activate
- Windows(Powershell): `<directory>\Scripts\activate.ps1`
- Linux/macOS: `source <directory>/bin/activate`
  
##### Install PDSim-Backend
`pip install pdsim-backend` 

## Usage

### Python Mode

```python
from pdsim_backend import run_backend

run_backend(domain='./examples/pddl/blocks/domain.pddl', problem='./examples/pddl/blocks/problem.pddl')
```


### CLI Mode 

 - Provide your domain and problem files.

`python pdsim_unity.py --domain <domain_path> --problem <problem_path>`

You can provide an optional `--planner` flag, by default it'll use fast-downward, but the user will be prompted which planner is available for a specific problem.

 - Embed pdsim server in your up problem definition.

````
from pdsim_unity import pdsim_upf

< your  problem definition >

pdsim_upf(up_problem, planner_name)

````

This will create a server to communicate with unity and serve the protobuf representation of the problem and the generated plan.

PDSim will try to find the planner that suits best the planning problem provided and let you choose which one to run.

#### Some Availbale planners:
    
- [FastDownward](https://github.com/aibasel/downward)
- [Tamer](https://github.com/aiplan4eu/up-tamer)
- [Pyperplan](https://github.com/aiplan4eu/up-pyperplan)
FUll list [here](https://unified-planning.readthedocs.io/en/latest/engines/01_available_engines.html)
e3fp 1.2.7
Molecular 3D fingerprinting
Requires Python: <3.14,>=3.9
Keywords: e3fp, 3d, molecule, fingerprint, conformer

E3FP: Extended 3-Dimensional FingerPrint
========================================

|Docs Status| |CI Status| |Codecov Status| |PyPi Version| |Conda Version| |License|

E3FP [1]_ is a 3D molecular fingerprinting method inspired by Extended
Connectivity FingerPrints (ECFP) [2]_, integrating tightly with the RDKit_.

Documentation is hosted by ReadTheDocs_, and development occurs on GitHub_.

Installation and Usage
----------------------

For installation and usage instructions, see the
`documentation <http://e3fp.readthedocs.io>`__.

See the E3FP `paper repository`_ for an application of E3FP and all code used
for the E3FP paper [1]_.

License
-------

E3FP is available under the `GNU Lesser General Public License version 3.0
<https://www.gnu.org/licenses/lgpl.html>`_ (LGPLv3). See the
`documentation <http://e3fp.readthedocs.io/en/latest/overview.html#license>`__
for more details.


References
----------

.. [1] |axen2017|
.. [2] |rogers2010|

.. substitutions

.. _RDKit: http://www.rdkit.org
.. _GitHub: https://github.com/keiserlab/e3fp
.. _paper repository: https://github.com/keiserlab/e3fp-paper
.. _ReadTheDocs: http://e3fp.readthedocs.io
.. |axen2017_doi| image:: https://img.shields.io/badge/doi-10.1021/acs.jmedchem.7b00696-blue.svg
    :target: http://dx.doi.org/10.1021/acs.jmedchem.7b00696
    :alt: Access the paper
.. |axen2017| replace:: Axen SD, Huang XP, Caceres EL, Gendelev L, Roth BL, Keiser MJ. A Simple Representation Of Three-Dimensional Molecular Structure. *J. Med. Chem.* **60** (17): 7393–7409 (2017). |axen2017_doi| |bioRxiv|
.. |rogers2010_doi| image:: https://img.shields.io/badge/doi-10.1021/ci100050t-blue.svg
    :target: http://dx.doi.org/10.1021/ci100050t
    :alt: Access the paper
.. |rogers2010| replace:: Rogers D & Hahn M. Extended-connectivity fingerprints. *J. Chem. Inf. Model.* **50**: 742-54 (2010). |rogers2010_doi|
.. |CI Status| image:: https://github.com/keiserlab/e3fp/actions/workflows/ci.yml/badge.svg
   :target: https://github.com/keiserlab/e3fp/actions?query=workflow%3ACI
   :alt: CI Status
.. |Docs Status| image:: http://readthedocs.org/projects/e3fp/badge/?version=latest
   :target: http://e3fp.readthedocs.io/en/latest/?badge=latest
   :alt: Documentation Status
.. |Codecov Status| image:: https://codecov.io/github/keiserlab/e3fp/coverage.svg?branch=master
   :target: https://codecov.io/github/keiserlab/e3fp?branch=master
   :alt: Code Coverage
.. |PyPi Version| image:: https://img.shields.io/pypi/v/e3fp.svg
   :target: https://pypi.python.org/pypi/e3fp
   :alt: Package on PyPi
.. |Conda Version| image:: https://img.shields.io/conda/v/conda-forge/e3fp.svg
   :target: https://anaconda.org/conda-forge/e3fp
   :alt: Package on Anaconda
.. |License| image:: https://img.shields.io/badge/license-LGPLv3-blue.svg
   :target: https://github.com/keiserlab/e3fp/blob/master/LICENSE.txt
.. |bioRxiv| image:: https://img.shields.io/badge/bioRxiv-136705-blue.svg
    :target: https://doi.org/10.1101/136705
    :alt: Access the preprint on bioRxiv