semvec/pypi-packages huggingface dataset:
econpapers 0.1.8
A package to collect data about economic papers in journals.
Requires Python: >=3.11

# Econpapers

A simple package to retrieve published papers in economics from [RePEC's EconPapers platform](https://econpapers.repec.org/).

## Installing & using

To install, copy and paste this script:

```bash
pip install econpapers
```

Then import the package:

```python
import econpapers as econ
```

You can then use the package to retrieve published papers' metadata (title, authors, year, abstract if provided, JEL codes if provided and link to the page within the site), by inputting a list like this:

```python
econ.papers_dataframe(["Econometrica"])
```
or 

```python
econ.papers_dataframe(["Econometrica", "Quarterly Journal of Economics"])
```

and the output will be saved as a [Pandas](https://github.com/pandas-dev/pandas) DataFrame.

(So far, only lists are accepted, and journal names are case-sensitive. Apologies!)
os-client-config 2.3.0
OpenStack Client Configuation Library
Requires Python: >=3.10

================
os-client-config
================

.. image:: https://governance.openstack.org/tc/badges/os-client-config.svg
    :target: https://governance.openstack.org/tc/reference/tags/index.html

.. warning::
  `os-client-config` has been superceded by `openstacksdk`_. While
  `os-client-config` will continue to exist, it is highly recommended that
  users transition to using `openstacksdk`_ directly.

`os-client-config` is a library for collecting client configuration for
using an OpenStack cloud in a consistent and comprehensive manner. It
will find cloud config for as few as 1 cloud and as many as you want to
put in a config file. It will read environment variables and config files,
and it also contains some vendor specific default values so that you don't
have to know extra info to use OpenStack

* If you have a config file, you will get the clouds listed in it
* If you have environment variables, you will get a cloud named `envvars`
* If you have neither, you will get a cloud named `defaults` with base defaults

Source
------

* Free software: Apache license
* Documentation: http://docs.openstack.org/os-client-config/latest
* Source: http://opendev.org/openstack/os-client-config
* Bugs: https://storyboard.openstack.org/#!/project/openstack/os-client-config
* Release Notes https://docs.openstack.org/releasenotes/os-client-config

.. _openstacksdk: http://docs.openstack.org/openstacksdk/latest
CNFgen 0.9.5
CNF formula generator
Requires Python: >=3.7

# CNFgen formula generator and tools

This repository provides the command

- `cnfgen` formula generator;

and the following python scripts, designed to operate from/to dimacs
CNFs:

- `cnfshuffle` random shuffler;

You  can get  a  brief help  on  how  to use  these  tools by  running
`<program_name> --help` from the command line.

Run `cnfgen --help` to get a list of the implemented formula families.
To get  more specific information  on one  class of formula  use the
command

    cnfgen <formula_type> --help

## Resources

- Webpage of the project at <http://massimolauria.net/cnfgen/>
- Technical documentation <https://cnfgen.readthedocs.io/en/latest/>
- Python Package at <https://pypi.org/project/CNFgen/>
- Github repository <https://github.com/MassimoLauria/cnfgen>
- Zenodo link (DOI) <https://zenodo.org/record/3548843>


Copyright 2012-2025  © Massimo
Lauria ([massimo.lauria@uniroma1.it](mailto:massimo.lauria@uniroma1.it))
pmk-probes 1.0.16
A Python package to control PMK's active oscilloscope probes.
Requires Python: >=3.10

# PMK Probes [![Documentation Status](https://readthedocs.org/projects/pmk-probes/badge/?version=latest)](https://pmk-probes.readthedocs.io/en/latest/?badge=latest) ![PyPI - Version](https://img.shields.io/pypi/v/pmk-probes)


pmk-probes is the official Python package to control active oscilloscope probes by [PMK](http://www.pmk.de/).

## Installation

Use the package manager [pip](https://pip.pypa.io/en/stable/) to install pmk-probes.

```bash
pip install pmk-probes
```

## Requirements

- Python 3.10 or higher
- pyserial

## Usage

```python
from pmk_probes.probes import BumbleBee2kV
from pmk_probes.power_supplies import PS03, Channel

ps = PS03('<YOUR_COM_PORT>')  # replace with the COM port of your power supply or specify ip_address parameter 
bumblebee1 = BumbleBee2kV(ps, Channel.CH1)  # use the BumbleBee probe on channel 1
print(bumblebee1.metadata.serial_number)  # print the serial number of the probe
bumblebee1.attenuation = 500  # set the attenuation to 500x
```

## Documentation

The documentation is available at [https://pmk-probes.readthedocs.io](https://pmk-probes.readthedocs.io).

## License

[BSD-3-Clause](https://opensource.org/licenses/BSD-3-Clause)
instaseis 1.5.0
Instaseis - Instant global seismological waveforms
Requires Python: >=3.11

# Instaseis - Instant high-frequency seismograms

[![Tests](https://github.com/krischer/instaseis/workflows/Tests/badge.svg)](https://github.com/krischer/instaseis/actions/workflows/test.yml)
[![Documentation](https://github.com/krischer/instaseis/workflows/Documentation/badge.svg)](https://github.com/krischer/instaseis/actions/workflows/docs.yml)

[Documentation](http://instaseis.net)

[![Instaseis](http://i.imgur.com/6LNoJD6.png)](instaseis.net)

Instaseis: Instant Global Broadband Seismograms Based on a Waveform Database

Instaseis calculates broadband seismograms from Green’s function databases
generated with AxiSEM and allows for near instantaneous (on the order of
milliseconds) extraction of seismograms. Using the 2.5D axisymmetric spectral
element method, the generation of these databases, based on reciprocity of the
Green’s functions, is very efficient and is approximately half as expensive as a
single AxiSEM forward run. Thus this enables the computation of full databases
at half the cost of the computation of seismograms for a single source in the
previous scheme and hence allows to compute databases at the highest frequencies
globally observed. By storing the basis coefficients of the numerical scheme
(Lagrange polynomials), the Green’s functions are 4th order accurate in space
and the spatial discretization respects discontinuities in the velocity model
exactly. On top, AxiSEM allows to include 2D structure in the source receiver
plane and readily includes other planets such as Mars.