semvec/pypi-packages huggingface dataset:
quantalyze 0.1.7
Utility functions for analyzing scientific data.
Requires Python: >=3.9

<figure markdown="span">
  ![Quantalyze](docs/logo.png){ width="400" }
</figure>


<p align="center">
<a href=""><img src="https://img.shields.io/pypi/pyversions/quantalyze" alt="Python versions"></a>
<a href="https://quantalyze.readthedocs.io/en/latest/?badge=latest"><img src="https://readthedocs.org/projects/quantalyze/badge/?version=latest" alt="Documentation Status"></a>
<a href="https://pypi.org/project/quantalyze/"><img src="https://shields.io/pypi/v/quantalyze" alt="PyPI"></a>
</p>

If your analysis and visualization workflow is some combination of `pandas`, `numpy`, `scipy` and `matplotlib`, it is likely that `quantalyze` can save you a lot of time. `Quantalyze` is a set of utility functions that facilitate the analysis of scientific data in the fields of quantum matter and correlated electron systems. Most of the operations are designed to work on `pandas.DataFrame` objects and functionality is pulled from a combination of `numpy`, `scipy`.

Non-domain-specific functionality is contained within the `core` module. Domain-specific utilities are contained within their respective modules and import from the `core` module. For example, the `transport` module contains a function that calculates the magnetoresistance from resistivity data taken in magnetic fields that pulls its functionality from `core.smoothing` (binning onto equally spaced x values) and `core.symmetrization` (the symmetrization in field).
pydal-psycopg3 0.1.1
Experimental psycopg3 driver for pydal
Requires Python: >=3.10

# pydal-psycopg3

`pydal-psycopg3` is a proof-of-concept driver for [pyDAL](https://github.com/web2py/pydal), demonstrating integration
with [psycopg3](https://www.psycopg.org/psycopg3/) as a drop-in replacement for the psycopg2 driver. The goal is to
ensure databases relying on `psycopg2` functionality can seamlessly transition to `psycopg3` while preserving the same
driver schemes.

## Key Features

- **Drop-In Replacement**: The module is designed to work with existing `psycopg2` configurations.
- **Custom Scheme Support**: Supports both the original "postgres" scheme and a custom "postgres:psycopg3" scheme for
  explicit usage.
- **Compatibility with pyDAL**: Built as a registerable driver, compatible with the DAL object.

## Installation

To use this driver, install the package and its dependencies:

```bash
(uv) pip install pydal-psycopg3
# or to include the prebuilt driver:
(uv) pip install pydal-psycopg3 psycopg-binary
```

## Getting Started

To integrate `pydal-psycopg3`, use the following setup:

```python
from pydal import DAL

# Required to register the driver
import pydal_psycopg3

# Using the original scheme
db = DAL("postgres://someuser:somepass@localhost:5432/postgres")

# Or using the explicit psycopg3 scheme
db = DAL("postgres:psycopg3://someuser:somepass@localhost:5432/postgres")
```

Both approaches ensure the driver works transparently.

### Example Usage

- Define tables and perform queries just like with `psycopg2`. Here's a minimal example to get started:

```python
# Defining a table
db.define_table("example_table", db.Field("example_field"))

# Inserting data
db.example_table.insert(example_field="test data")
db.commit()

# Querying the table
rows = db(db.example_table).select()
print(rows)
```

### Driver Registration

The driver registers itself automatically when `imported` and works with either of the following schemes:

1. `postgres://` – The default scheme connects using `psycopg3` as the driver.
2. `postgres:psycopg3://` – An explicitly declared scheme to use `psycopg3`.

## Limitations

This module is in its **proof-of-concept** phase and should be used with caution in production environments.
While most functionalities are expected to work as intended, thorough testing is recommended.

## License

This project is licensed under the MIT License.
cytonormpy 1.0.2
CytoNorm for python
Requires Python: >=3.9
Keywords: batch, cytometry, cytonorm

# CytoNormPy

[![Tests][badge-tests]][link-tests]
[![Documentation][badge-docs]][link-docs]
[![License: GPL v3](https://img.shields.io/badge/License-GPLv3-blue.svg)](https://www.gnu.org/licenses/gpl-3.0)

[badge-tests]: https://img.shields.io/github/actions/workflow/status/TarikExner/CytoNormPy/pytest.yml?branch=main
[link-tests]: https://github.com/TarikExner/CytoNormPy/actions/workflows/pytest.yml
[badge-docs]: https://img.shields.io/readthedocs/cytonormpy

A python port for the CytoNorm (2.0) R library.

# Installation

It is recommended to choose conda as your package manager. Conda can be obtained, e.g., by installing the Miniconda distribution. For detailed instructions, please refer to the respective documentation.

With conda installed, open your terminal and create a new environment by executing the following commands::

    conda create -n cytonormpy python=3.10
    conda activate cytonormpy

## PyPI

Currently, cytonormpy is in beta-phase. There will be a PyPI release once the beta phase is finished.

    pip install cytonormpy


## Development Version

In order to get the latest version, install from [GitHub](https://github.com/TarikExner/CytoNormPy) using
    
    pip install git+https://github.com/TarikExner/CytoNormPy@main

Alternatively, clone the repository to your local hard drive via

    git clone https://github.com/TarikExner/CytoNormPy.git && cd CytoNormpy
    git checkout --track origin/main
    pip install .[docs, test]

## Jupyter notebooks

Jupyter notebooks are highly recommended due to their extensive visualization capabilities. Install jupyter via

    conda install jupyter

and run the notebook by entering `jupyter-notebook` into the terminal.

## Getting Started

Please refer to the [documentation][link-docs]. Examples are found under "vignettes" and currently include:
- [CytoNormPy from FCS files][link-docs-fcs-vignette]
- [CytoNormPy from AnnData objects][link-docs-anndata-vignette]
- [CytoNormPy plotting][link-docs-plotting-vignette]

## Contributing

Your contributions are welcome!

Please submit an issue or pull request via Github! Pull requests with updated documentation and accompanying unit tests are preferred but not obligate!


[link-docs]: https://cytonormpy.readthedocs.io
[link-docs-fcs-vignette]: https://cytonormpy.readthedocs.io/en/latest/vignettes/cnp_fcs_file.html
[link-docs-anndata-vignette]: https://cytonormpy.readthedocs.io/en/latest/vignettes/cnp_anndata.html
[link-docs-plotting-vignette]: https://cytonormpy.readthedocs.io/en/latest/vignettes/cnp_plotting.html