semvec/pypi-packages huggingface dataset:
PyComps 0.3.2
A sample Python package

# PyComps

Easy calculation of composite properties


## Brief description


A Python toolbox for the structural analysis of orthotropic layered components. 
The program implements both the micro and meso/macro mechanics in the classes *"PlyDef"* and *"Laminate"*.
Class methods allow different operations. 

### **Laminate** - meso/macro mechanics


creates a laminate object starting from the plies properties and stack-up information. 

Features:

- calculation of the laminate **stiffness matrix**
- estimation of **equivalent** tensile and flexural **orthotropic** mechanical **properties**. 
- **ply-by-ply stress** analysis in accordance with CLT or FSDT. 
- Failure verification with **maximum stress**, **maximum strain** and **Tsai-Wu** criteria

### **PlyDef** - micro mechanics

manipulation of ply objects generated from the constituents' properties and mass or volume fractions. 

Features:

- calculation of **ply mechanical properties** starting from that of isotropic constituents.
- the former can be attained with different methods: **ROM**, **Halpin-Tsai**, or **PMM**
- **cured ply thickness** estimation


### Source Code: 
https://github.com/edo-147/PyComp

### notes: 
aligned with GitHub tag v0.3.1
lscae 0.0.2
Laplacian Score-regularized CAE
Requires Python: >=3.7

# Laplacian Score-regularized Concrete Autoencoders

## Requirements:

* torch >= 1.9
* scikit-learn >= 0.24
* omegaconf >= 2.0.6
* scipy >= 1.6.0
* matplotlib

## How to use:

Install the package from pypi:
`pip install lscae`

Prepare your dataset by applying Standard Scaler on it

```python
from sklearn.preprocessing import StandardScaler
scaler = StandardScaler()
dataset = scaler.fit_transform(dataset)
```

Then load it as `torch.utils.data.Dataset` and run feature selection
Please see an example [here](https://github.com/jsvir/lscae/blob/master/example.ipynb)

```python
import lscae
import torch
from omegaconf import OmegaConf

# define you cfg parameters
cfg = OmegaConf.create({"input_dim": 100})

# define you dataset (Torch based)

dataset = torch.utils.data.Dataset(...)
dataloader = torch.utils.data.DataLoader(dataset, batch_size=cfg.batch_size, shuffle=True, drop_last=True)
lscae.Lscae(kwargs=cfg).select_features(dataloader)
```
intelmq-api 3.2.0
Intelmq-API is a FastAPI based API to intelmq, a solution for IT security teams for collecting and processing security feeds
Requires Python: >=3.7

..
   SPDX-FileCopyrightText: 2020 Birger Schacht
   SPDX-License-Identifier: AGPL-3.0-or-later

###########
intelmq-api
###########

|Tests Status| |Package Status|

.. |Tests Status| image:: https://github.com/certtools/intelmq-api/actions/workflows/python-unittests.yml/badge.svg
   :target: https://github.com/certtools/intelmq-api/actions/workflows/python-unittests.yml

.. |Package Status| image:: https://github.com/certtools/intelmq-api/actions/workflows/debian-package.yml/badge.svg
   :target: https://github.com/certtools/intelmq-api/actions/workflows/debian-package.yml

intelmq-api is a `FastAPI <https://fastapi.tiangolo.com/>`_ based API for the `intelmq <https://github.com/certtools/intelmq/>`_ project.


Extensive documentation regarding the installation, configuration and usage of the `intelmq-api` can be found in the `intelmq documentation <https://intelmq.readthedocs.io/en/maintenance/user/intelmq-api.html>`_.

*****************
Development usage
*****************

You could create a preferred virtual environment, and then install the package using:

.. code-block:: bash

   pip install -e .

For development purposes, you can run the API using the `scripts/run_dev.sh` script. It serves the
API on the local `8000` port and watches for file changes, with the automated reloading on change.

The interactive documentation is served on the `/docs` endpoint.

To set the API configuration, please export the `INTELMQ_API_CONFIG` environment variable with path
to the JSON config file in the shell you want to use. For the config reference, please check the
`intelmq_api/config.py` and the example from `contrib/api-config.json`.

If you configured the session store, you will need to authorize every request. The `/v1/login`
returns the authorization token. You can use the following command to register the user:

.. code-block:: bash

   ./scripts/intelmq-api-adduser --user UserName

*************
Security note
*************

Please be careful when deploying the API. At the current stage, it is not designed to run on
publicly exposed endpoints without additional pre-cautions.

*************
Type checking
*************

The code can be typechecked with ``mypy``. To run the type checker, use:

.. code-block:: bash

   mypy intelmq_api/