semvec/pypi-packages huggingface dataset:
mark-python-client 2.1.0
A client library for MARK server

# mark-python-client

[![pipeline status](https://gitlab.cylab.be/cylab/mark-python-client/badges/master/pipeline.svg)](https://gitlab.cylab.be/cylab/mark-python-client/commits/master)
[![coverage report](https://gitlab.cylab.be/cylab/mark-python-client/badges/master/coverage.svg)](https://gitlab.cylab.be/cylab/mark-python-client/commits/master)
[![PyPI version](https://badge.fury.io/py/mark-python-client.svg)](https://badge.fury.io/py/mark-python-client)
[![security: bandit](https://img.shields.io/badge/security-bandit-yellow.svg)](https://github.com/PyCQA/bandit)

A Python client for the MARK framework:

https://cylab.be/research/mark

## Installation

```bash
$ pip install mark-python-client
```

## Usage

To use the mark_python_client.py script just add it to your python code and you can invoke the different
methods defined in the script

```python
from mark_client import mark_python_client as mark_client

#initialize the MARK Client
client = mark_client.MarkClient()

#set the url of the MARK server
client.set_server_url("http://server.ip.address:ip.port")

#add evidence to the database via the MARK server
client.add_evidence(evidence_to_be_added)

#fetch the ranked list of the k-most suspicious entries
ranked_list = client.get_evidence()

#fetch evidences with a specific label and subject
evidences = client.find_evidence(label, subject)
```

## Contributing

### Installation

```bash
$ git clone https://gitlab.cylab.be/cylab/mark-python-client.git
$ cd mark-python-client
$ pip install -r requirements.txt
```

### Project overview

1. mark_client - source folder of the project

    1.1. mark_python_client.py : main code

    1.2. conftest.py : empty script used by pytest unit test library to point the global PATH variable to the modules and scripts used in the unit tests

2. tests - test folder of the project

   2.1. mark_python_client_test.py : integration test (requires a fully installed mark server)

### Testing

Currently the only implemented test is a complete integration test, which requires a fully running mark server. The recommended way to test the project is thus using gitlab-runner.

1. Install Docker
https://docs.docker.com/install/linux/docker-ce/ubuntu/

2. Install gitlab-runner
https://docs.gitlab.com/runner/

3. Run the tests
```bash
$ gitlab-runner exec docker test:integration
```
pocal 2.236
POCAL (Python Optical Coating Analysis Library)

# POCAL

POCAL (Python Optical Coating Analysis Library) allows to easily design optical coatings both as single layer and as multilayer stack, monitoring a wide variety of optical properties and, moreover, giving the possibility to automatically refine the multilayer design to achieve the desired optical goals. The library is completely open source, and it can be easily coupled to several Python-based ray tracers or libraries to work on more complex simulations. The results have been extensively tested and are comparable with the ones obtained using commercial software.

## **Downloading POCAL**

Downloading the library is very easy using pip :

```
pip install pocal
```

Supporting files can be downloaded from: https://github.com/tommasofontanot/pocal/tree/main/resources

The folder with the resources needs to be made the current working directory.

```
import os
os.chdir(resources folder)
```

## **Cite POCAL**

If you use the library, please cite the paper published in Optics Continuum: [Fontanot, T., Bhaumik, U., Kishore, R. and Meuret, Y., 2023. POCAL: a Python-based library to perform optical coating analysis and design. Optics Continuum, 2(4), pp.810-824. DOI: https://doi.org/10.1364/OPTCON.484972](https://opg.optica.org/optcon/fulltext.cfm?uri=optcon-2-4-810&id=528721)

## **Using the library**

The library can be called as follows:

```
import pocal
import os
os.chdir('resources folder')
#upload the prescription file(here 3layers.txt), angle of incidence, minimum wavelength, maximum wavelength, wavelength resolution, 
#reference wavelength, optical thickness and type of refinement
test = pocal.pocal(r'3layers.txt',40,200,1500,1,600,False,None)
```


## **Defining the Prescription file**

The prescription file represents the stacking of the materials. An example is shown below:

+----Immersing Medium----+-Glass----------+

+----Layer3----------------+-TiO2-------------50 nm+

+----Layer2----------------+-SiO2-------------60 nm+

+----Layer1----------------+-TiO2-------------70 nm+

+----Substrate-------------+-Glass------------+


To simulate the proposed design, the prescription text file must be written as follows:

![image](https://user-images.githubusercontent.com/18205576/229857312-8f7d8ce8-ad8e-46c9-bd0b-ce8e2bbe67c4.png)


## **Python tutorial**

An in-depth tutorial to use the library can be found here: [POCAL Tutorial](https://github.com/tommasofontanot/pocal/blob/main/Pocal.ipynb)

## **License**

[![License: MIT](https://img.shields.io/badge/License-MIT-yellow.svg)](https://github.com/tommasofontanot/pocal/blob/main/LICENSE.txt)
autogen-init 2.0.4
A Python package that updates init.py files across a package for direct imports.
Requires Python: >=3.6
Keywords: Python,Python package init,package init,autoinit,package initialization,init.py automation

# Autogen Init: Simplify Package Setup

Autogen-Init is a Python tool designed to make `__init__.py` file updates seamless across your package, ensuring imports are clean and easy to access. It scans your package's directories to automatically update `__init__.py` files with all necessary class and function imports. Plus, it simplifies adding version and author details to your top-level `__init__.py`, boosting package metadata.

## Features

- **Auto Updates**: Instantly refreshes `__init__.py` files with internal imports.
- **Metadata Enhancements**: Adds version and author info effortlessly.
- **Flexible Overwrites**: Choose to update or append `__init__.py` files as needed.

## 📦 Installation

Get Autogen-Init with pip for a smooth integration:

```
pip install autogen-init
```

## Usage

Activate Autogen-Init with the `run` command, setting your package's root as the start path. Opt to overwrite `__init__.py` files and add version and author data.

```python
from autoinit import run

# Quick start
run(
    start_path='C:/path/to/my_package',
    overwrite=True,
    version="1.0.0",
    author="Minwoo(Daniel) Park <parkminwoo1991@gmail.com>"
)
```
Argument of run function:
- *start_path (str): Your package's root directory.*
- *overwrite (bool): Flag to overwrite `__init__.py` files, default is `True`.*
- *version (str): Package version for the top-level `__init__.py`.*
- *author (str): Package author(s) for the top-level `__init__.py`.*

## Contributing

Your contributions are welcome! For bug reports, feature suggestions, or submissions, don't hesitate to get in touch or open a pull request.

## ©️ License

Autogen-Init is MIT licensed — see the LICENSE file for details.
IslandOfMisfitToys 0.5.0
A suite of misfit functions to test FWI
Requires Python: >=3.9

# Island of Misfit Toys

![Island of Misfit Toys Banner](IslandOfMisfitToys.jpg)

Welcome to the **Island of Misfit Toys**! See [Rudolph the Red-nosed Reindeer](https://en.wikipedia.org/wiki/Rudolph_the_Red-Nosed_Reindeer_(TV_special)) if you are confused by the logo.

[PyPI](https://pypi.org/project/IslandOfMisfitToys/) and [Documentation](https://islandofmisfittoys.readthedocs.io/en/latest/index.html). 

This repository contains implementations of various misfit functions found in seismic literature. 
Our goal is to create a comprehensive collection of these functions to aid researchers and practitioners of seismic inverse problems.

If you have interest in adding features, please add a pull request or contact me directly at tyler@oden.utexas.edu.

If you have any trouble with our code or find bugs, please let us know by filling out an issue!

## Table of Contents

- [Getting Started](#getting-started)
- [Misfit Functions (currently supported)](#misfit-functions)
- [Misfit Functions (roadmap for future)](#roadmap)

## Getting Started

To begin using the misfit functions in this repository, clone it to your local machine:

```bash
git clone https://github.com/tmasthay/IslandOfMisfitToys.git
cd IslandOfMisfitToys
```

## Misfit Functions (roadmap for future)

(1) One-dimensional $W_1$ and $W_2$ (trace-by-trace) 
  - [Yang et al. 2018](https://scholar.google.com/scholar?hl=en&as_sdt=0%2C44&q=Application+of+optimal+transport+and+the+quadratic+Wasserstein+metric+to+full-waveform+inversion&btnG=)

(2) Huber
  - [Guitton and Symes 2003](https://scholar.google.com/scholar?hl=en&as_sdt=0%2C44&q=Guitton%2C+A.%2C+and+W.+W.+Symes%2C+2003%2C+Robust+inversion+of+seismic+data+using+the+Huber+norm%3A+Geophysics&btnG=)

(3) $L^1$-$L^2$ hybrid
  - [Bube and Langan](https://scholar.google.com/scholar?hl=en&as_sdt=0%2C44&q=Hybrid+l1%E2%88%95l2+minimization+with+applications+to+tomography&btnG=)

(4) Normalized and unnormalized Sobolev norms

  - [Zhu et al. 2021](https://scholar.google.com/scholar?hl=en&as_sdt=0%2C44&q=Implicit+regularization+effects+of+the+Sobolev+norms+in+image+processing&btnG=)

(5) Fisher-Rao metric

  - [Zhou et al. 2018](https://scholar.google.com/scholar?hl=en&as_sdt=0%2C44&q=The+Wasserstein-Fisher-Rao+metric+for+waveform+based+earthquake+location&btnG=)

(6) Graph-space OT

  - [Metivier et al. 2018](https://scholar.google.com/scholar?hl=en&as_sdt=0%2C44&q=Optimal+transport+for+mitigating+cycle+skipping+in+full-waveform+inversion%3A+A+graph-space+transform+approach&btnG=)

(7) Entropic regularization OT

  - [Cuturi 2013](https://scholar.google.com/scholar?hl=en&as_sdt=0%2C44&q=Sinkhorn+distances%3A+Lightspeed+computation+of+optimal+transport&btnG=)

(8) Misfits based on reduced-order models

  - [Borcea et al. 2023](https://scholar.google.com/scholar?hl=en&as_sdt=0%2C44&q=Waveform+inversion+via+reduced+order+modeling+borcea&btnG=)
pyenet 1.3.17
A python wrapper for the ENet library

pyenet
======

pyenet is a python wrapper for the ENet library by Lee Salzman,
http://enet.bespin.org

It was originally written by Scott Robinson scott@tranzoa.com and is
currently maintained by Andrew Resch andrewresch@gmail.com

This fork is being maintained by the piqueserver team for purposes of
including patches for bugs found while developing piqueserver, and to
provide a package on pypi.

License
-------

pyenet is licensed under the BSD license, see LICENSE for details. enet
is licensed under the MIT license, see
http://enet.bespin.org/License.html

Dependencies
------------

Building pyenet requires all the same dependencies as enet plus Cython
and, obviously, Python.

Installation
------------

From pypi
~~~~~~~~~

::

    pip install pyenet

Manually from git
~~~~~~~~~~~~~~~~~

Run the setup.py build:

::

    $ python setup.py build

Once that is complete, install the new pyenet module:

::

    # python setup.py install

Packaging notes
---------------

-  update package version in ``setup.py``
-  create a virtualenv
   (``python3 -m venv venv && source venv/bin/activate``)
-  install the requirements: ``pip install -r dev-requirements.txt``
-  build the source dist: ``python setup.py sdist``
-  make sure docker is installed and running and you re on a 64bit linux
   machine
-  build the binary dists: ``./scripts/build_packages.sh``
-  upload to pypi: ``twine upload dist/* wheelhouse/pyenet*``
-  commit, tag, push to github

Usage
-----

Once you have installed pyenet, you only need to import the enet module
to start using enet in your project.

Example server:

::

    >>> import enet
    >>> host = enet.Host(enet.Address("localhost", 33333), 1, 0, 0)
    >>> event = host.service(0)

Example client:

::

    >>> import enet
    >>> host = enet.Host(None, 1, 0, 0)
    >>> peer = host.connect(enet.Address("localhost", 33333), 1)

More information on usage can be obtained from:
http://enet.bespin.org/Tutorial.html
jasperClient 0.0.3
jasper Client for connecting to Jasper Server and using rest API

# Jasper Client Python Library

## Installation

```sh
pip3 install jasperClient
```

## Utilisation

### Create a client instance

```python
from jasperClient.client import JasperClient

client = JasperClient("https://jasper.host/jasperserver/", "username", "password")
```

### Get a list of reports

```python
client.listReports()
```

### Get a dict with the required params of a report

```python
client.getParameters(path_of_the_report)
```

### Generate a report

```python
client.getReport(
    path=path_of_the_report,
    data=dict_with_report_params,
    filename="test",
    format="pdf",
)
```