semvec/pypi-packages huggingface dataset:
lana 0.1.8
Experimental Linear Algebra library written in Rust
Requires Python: >=3.7
Keywords: linear algebra

## Lana 🧶

[![stability-wip](https://img.shields.io/badge/stability-wip-lightgrey.svg)](https://github.com/mkenney/software-guides/blob/master/STABILITY-BADGES.md#work-in-progress) 

**L***inear* **A**lgebra for **n***octurnal* *and* **a**dventurous *data scientists.*

## Install 

```console
pip install lana
```

## Example 

```python
from lana import Matrix, inject

zeros = Matrix.zeros((3,3))
print(zeros)
print(f"shape: {zeros.shape}, type: {type(zeros)}")
print(zeros.to_list()[0], zeros.to_list()[0][0])

mat = Matrix.matrix([[1,2,3],[4,5,6]])
print(mat)
print(f"shape: {mat.shape}, type: {type(mat)}")
for rows in mat.to_list():
    print(rows, type(rows))

submat = Matrix.matrix(inject(mat.to_list()[0]))
print(submat)
print(submat.shape, type(submat))
```
MILWRM 1.1.1
Multiplex Image Labeling With Regional Morphology
Requires Python: >=3.6

|milwrm_logo|

Multiplex Image Labeling With Regional Morphology
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~

|Latest Version|

Consensus tissue region identification from multiplex molecular image data for digital pathology

Install from PyPI:
^^^^^^^^^^^^^^^^^^
.. code:: bash

   pip install MILWRM

Or compile from source:
^^^^^^^^^^^^^^^^^^^^^^^
.. code:: bash

   git clone https://github.com/Ken-Lau-Lab/MILWRM.git
   cd MILWRM
   python setup.py install

See the |tutorials|_ directory for Jupyter notebooks detailing usage of `MILWRM`

Full documentation is available at |ken-lau-lab.github.io/MILWRM|_

.. |milwrm_logo| image:: https://github.com/Ken-Lau-Lab/MILWRM/blob/main/docs/milwrm_logo.jpg

.. |Latest Version| image:: https://img.shields.io/pypi/v/MILWRM
   :target: https://pypi.python.org/pypi/MILWRM/

.. |tutorials| replace:: ``tutorials``
.. _tutorials: tutorials

.. |ken-lau-lab.github.io/MILWRM| replace:: ``ken-lau-lab.github.io/MILWRM``
.. _ken-lau-lab.github.io/MILWRM: https://ken-lau-lab.github.io/MILWRM/
seabird-growth-rate 0.1.0
A local Python module
Requires Python: >=3.9

<a href="https://www.islas.org.mx/"><img src="https://www.islas.org.mx/img/logo.svg" align="right" width="256" /></a>
# Dummy Transformations

Para usar este repo como plantilla debemos hacer lo siguiente:

1. Presiona el botón verde que dice _Use this template_
1. Selecciona como dueño a la organización IslasGECI
1. Agrega el nombre del nuevo módulo de python
1. Presiona el botón _Create repository from template_
1. Reemplaza `dummy_transformations` por el nombre del nuevo módulo en:
    - `Makefile`
    - `pyproject.toml`
    - `tests\test_transformations.py`
1. Renombra el archivo `dummy_transformations\transformations.py` al nombre del primer archivo del
   nuevo módulo
1. Cambia la descripción del archivo `dummy_transformations\__init__.py`
1. Renombra el directorio `dummy_transformations` al nombre del nuevo módulo
1. Cambia el `codecov_token` del archivo `Makefile`

Los archivos del nuevo módulo los agregarás en la carpeta que antes se llamaba
`dummy_transformations` y las pruebas en la carpeta `tests`.
mcap-etl 0.1.4
Transform mcap (or rosbag) files into databases or other files
Keywords: ros,ros2,rosbag,mcap,timescale,etl,timeseries,database,etl

# mcap_etl

`.mcap` (MCAP) and `.bag` (ROS Bag) files pose challenges for data engineers due to their large size, complex structure, and lack of standardization. Managing and transferring these massive files requires substantial storage capacity and bandwidth, leading to slow processing times. Extracting and interpreting the desired information becomes time-consuming and error-prone due to the mix of timestamped messages and the need for custom parsing and processing pipelines.

mcap_etl is alleviating these pain points by offering a comprehensive suite of options to transform the contents of MCAP files into a structured database or alternative file formats, streamlining the data engineering process and enabling easier analysis and visualization of the captured data.

Presently, mcap_etl supports the following conversions:
* From `.mcap` to `.bag`
* From `.mcap` to TimescaleDB

We are actively seeking to extend this list and invite community contributions. Specifically, we aim to include transformations to InfluxDB, Timestream, and Parquet.

## Installation and Usage

### Installation

mcap_etl can be installed easily via pip:
```shell
pip install mcap-etl
```

### Usage

mcap_etl requires a running Timescale database. Once set up, you can execute jobs against any file. Note that the database connection parameters are optional, enhancing flexibility.
```shell
mcap_etl timescale \
    --host localhost \
    --port 5432 \
    --user postgres \
    --password password \
    --name postgres \
    /path/to/file.mcap
```

You can now perform queries against your database.

## Future Development: Hosted Solution

We are in the process of developing a hosted solution, offering:

* Managed services for data ingestion, database, and infrastructure for integrations, including S3 and Grafana.
* A tool to convert data back from Timescale to `.mcap` and `.bag` formats.
* Vector search capabilities for unstructured data types, such as imagery and audio.
* A web interface to monitor and share data with your team.

## Key Design Considerations

mcap_etl has been designed with the following key principles:

* __Timescale Hypertables__: Due to the large number of messages, we use Timescale's hypertables and take advantage of their compression features.
* __No ROS Dependency__: mcap_etl operates without any ROS dependencies, avoiding the complexity of ROS installations for simple data extraction from `.bag` or `.mcap` files. Instead, we utilize the `rosbags` project, which allows for dynamic loading of message schemas at runtime.
* __MCAP to ROS bag Transformation__: mcap_etl first converts MCAP files into ROS bags before performing data transformations. This approach avoids the need to rewrite the ingestion flow.
SwarmML 1.0
Feature Selection with PSO
Keywords: feature selection,particle swarm optimization,PSO,machine learning,data science

# SwarmML
## A python package that utilizes Particle Swarm Optimization for Feature Selection

SwarmML is a Python package that implements Particle Swarm Optimization (PSO) for feature selection. It can be used to improve the performance of classification and regression tasks by automatically selecting the most informative features from a given dataset.

## Features
- Performs feature selection using Particle Swarm Optimization.
- Supports both classification and regression tasks.
- Provides customizable parameters for PSO algorithm.
- Simple and easy to use package.

## Installation
You can install SwarmML using pip:
```
pip install SwarmML
```

## Examples

### Example 1

```
import SwarmML
from sklearn.datasets import load_iris

X,Y = load_iris(return_X_y=True)

obj = SwarmML.FeatureSelection.Particle_Swarm_Optimization('Classification')
Best_Features, Best_Score = obj.run(X, Y)
```