semvec/pypi-packages huggingface dataset:
n2yo-api 0.1.3
Python wrapper for the N2YO API.

# N2YO Python API

![Python package](https://github.com/facorazza/n2yo-api/workflows/Python%20package/badge.svg) ![PyPI](https://github.com/facorazza/n2yo-api/workflows/PyPI/badge.svg) [![Binder](https://mybinder.org/badge_logo.svg)](https://mybinder.org/v2/gh/facorazza/n2yo-api/HEAD?filepath=examples.ipynb)

This python package is a wrapper to access the N2YO REST API.

For each transaction, the caller must be identified and authorized by a license key (API key). To generate a unique license key, users are requested first to register an account at [n2yo](n2yo.com). After login, please visit the profile page and scroll down to access the button that generates the API key. The new API key will be accessible in the profile and cannot be changed. If you need a new key, please contact us and provide an explanation.

## Available functions

### get_tle
Retrieve the Two Line Elements (TLE) for a satellite identified by NORAD id.

### get_satellite_positions
Retrieve the future positions of any satellite as footprints (latitude, longitude) to display orbits on maps. Also return the satellite's azimuth and elevation with respect to the observer location. Each element in the response array is one second of calculation. First element is calculated for current UTC time.

### get_visual_passes
Get predicted visual passes for any satellite relative to a location on Earth. A "visual pass" is a pass that should be optically visible on the entire (or partial) duration of crossing the sky. For that to happen, the satellite must be above the horizon, illumintaed by Sun (not in Earth shadow), and the sky dark enough to allow visual satellite observation.

### get_radio_passes
The "radio passes" are similar to "visual passes", the only difference being the requirement for the objects to be optically visible for observers. This function is useful mainly for predicting satellite passes to be used for radio communications. The quality of the pass depends essentially on the highest elevation value during the pass, which is one of the input parameters.

### get_above
The "above" function will return all objects within a given search radius above observer's location. The radius (θ), expressed in degrees, is measured relative to the point in the sky directly above an observer (azimuth).
glue-geospatial 0.4
Experimental glue plugin for geospatial imagery
Requires Python: >=3.6

Experimental glue plugin for satellite imagery
==============================================

|Build Status|

Requirements
------------

Note that this plugin requires `glue <http://glueviz.org/>`__ to be
installed - see `this
page <http://glueviz.org/en/latest/installation.html>`__ for
instructions on installing glue.

In addition, this plugin requires the
`rasterio <https://mapbox.github.io/rasterio/>`__ and
`pyproj <https://github.com/jswhit/pyproj>`__ packages to be
installed.

Installing
----------

If you are using conda, you can easily install the
plugin and all the required dependencies with::

    conda install -c glueviz glue-geospatial

Alternatively, if you don't use conda, be sure to install the above
dependencies then install the plugin with::

    pip install glue-geospatial

This will auto-register the plugin with Glue.

Using
-----

At the moment, this plugin provides a reader based on rasterio. You can
give glue any file that can be read by rasterio. For example, if
``mydata.tif`` is a GeoTIFF file, you can do::

    glue mydata.tif

and you can also load files from inside glue.

Testing
-------

To run the tests, do::

    py.test glue_geospatial

at the root of the repository. This requires the
`pytest <http://pytest.org>`__ module to be installed.

.. |Build Status| image:: https://dev.azure.com/glue-viz/glue-geospatial/_apis/build/status/glue-viz.glue-geospatial?repoName=glue-viz%2Fglue-geospatial&branchName=master
   :target: https://dev.azure.com/glue-viz/glue-geospatial/_build/latest?definitionId=10&repoName=glue-viz%2Fglue-geospatial&branchName=master
oops-datedir2amqp 0.1.0
OOPS DateDir to AMQP republisher.

*******************************************************
python-oops-datedir2amqp: Bridge a datedir repo to amqp
*******************************************************

    Copyright (c) 2011, Canonical Ltd

    This program is free software: you can redistribute it and/or modify
    it under the terms of the GNU Affero General Public License as published by
    the Free Software Foundation, either version 3 of the License, or
    (at your option) any later version.

    This program is distributed in the hope that it will be useful,
    but WITHOUT ANY WARRANTY; without even the implied warranty of
    MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
    GNU Affero General Public License for more details.

    You should have received a copy of the GNU Affero General Public License
    along with this program.  If not, see <http://www.gnu.org/licenses/>.


The oops_datedir2amqp package provides a script that will retransmit OOPS
reports (http://pypi.python.org/pypi/oops) stored in a datedir repo over amqp,
cleaning up the repo as it goes.

Dependencies
============

* Python 2.6+

* oops_datedir_repo

* oops_amqp

Testing Dependencies
====================

* rabbitfixture (http://pypi.python.org/pypi/rabbitfixture)

* testresources (http://pypi.python.org/pypi/testresources)

* testtools (http://pypi.python.org/pypi/testtools)

Usage
=====

Install the package in a virtualenv (or similar)::

  $ virtualenv oops-datedir2amqp
  $ oops-datedir2amqp/bin/pip install oops-datedir2amqp

Run the script::

  $ oops-datedir2amqp/bin/datedir2amqp \
    --host XX --username XX --password XX --vhost XX --exchange XX --repo XX

Note that the repo should be the same path you are supplying to your
DateDirRepo in whatever process is creating OOPSes.

A common use for this setup is as a fallback: in your application report to
AMQP directly, with a DateDirRepo configured as fallback, then use datedir2amqp
to pickup and respool any OOPS reports that were generated while your AMQP
server is unavailable.

Development
===========

Upstream development takes place at
https://launchpad.net/python-oops-datedir2amqp.

To run the tests, use ``tox``.
pyvrt 1.7.1
Python library to interact with VRT public APIs
Requires Python: >=3.6

# pyvrt

🐍 Python library to interact with VRT public APIs (weather, traffic…)

## Usage

```python
import pyvrt.traffic as traffic
import pyvrt.weather as weather

weather.summary()
weather.forecast(region='BE', zone='kust')

traffic.traffic_jam(km=True)
```

## Development

```shell
$ make install
$ make build
```

To debug, create an index.py file with some code and run through Python runtime:

```py
# pyvrt/media/index.py
from . import stubru

print(stubru.current())
```

```shell
$ python -m pyvrt.media.index
```
unpacme 1.0.0
UnpacMe
Requires Python: >=3.6
Keywords: OpenAPI,OpenAPI-Generator,UnpacMe

# Introduction Welcome to the UNPACME API! All the malware unpacking and file analysis features that you are familiar with on the [unpac.me](https://www.unpac.me/) website are available through our API. You can easily integrate our unpacker into your malware analysis pipeline and begin unpacking at scale!   # Authentication The public UNPACME API is publicly available and can be accessed without authentication.  In order to use the private UNPACME API you must sign up for an account with UNPACME. Once you have a valid user account you can view your personal API key in your user profile.   &lt;SecurityDefinitions /&gt;  # Response Structure When interacting with the UNPACME API, if the request was correctly handled, a &lt;b&gt;200&lt;/b&gt; HTTP status code will be returned. The body of the response will usually be a JSON object (except for file downloads).  ## Response Status Codes  Status Code  | Description | Notes ------------- | ------------- | - 200  | OK | The request was successful 400  | Bad Request | The request was somehow incorrect. This can be caused by missing arguments or arguments with wrong values. 401 | Unauthorized | The supplied credentials, if any, are not sufficient to access the resource 403 | Forbidden | The account does not have enough privileges to make the request. 404 | Not Found | The requested resource is not found 429  | Too Many Requests | The request frequency has exceeded one of the account quotas (minute, daily or monthly). Monthly quotas are reset on the 1st of the month at 00:00 UTC. 500 | Server Error | The server could not return the representation due to an internal server error   ## Error Response  If an error has occurred while handling the request an error status code will be returend along with a JSON error message with the following properties.   Property  | Description ------------- | ------------- Error  | The error type Description  | A more informative message  # Example Clients  The following clients can be used to interact with the UNPACME API directly and are provided as examples. These clients are community projects and are not maintained or developed by UNPACME. UNPACME makes no claim as to the safety of these clients, use at your own risk.    - [UnpacMe Python Client](https://github.com/larsborn/UnpacMeClient) (Python)   - [UnpacMe GO Client](https://github.com/kryptoslogic/unpacme-go) (Golang)   - [UnpacMe Library](https://github.com/R3MRUM/unpacme) (Python)   - [AssemblyLine](https://github.com/CybercentreCanada/assemblyline-service-unpacme) (Automation Service)     &lt;br&gt;   # noqa: E501
eqsormo 0.8.5
Equilibrium sorting models in Python

# Eqsormo: Equilibrium sorting models in Python

Eqsormo implements equilibrium sorting models in Python. Equilibrium sorting models are models that simulate how households will re-sort across a metropolitan region in response to a change in policy or amenities. They allow evaluating non-marginal effects of policy changes---i.e. not only how the policies will affect the utility of people in the places they currently live, but how they will affect utility after households have re-sorted across the region in response to amenity and price changes.

It currently supports discrete-choice-based sorting models, specifically of the type implemented by Tra (2007, 2010, 2013), wherein the price enters the first stage of the model as a nonlinear budget term. This is in contrast to much of the literature where a constant price coefficient is estimated in the second stage. The advantage of the Tra approach is that by including price in the first stage, where there are also fixed effects for all housing types, there is less concern about unobserved housing attributes being correlated with price.

Eqsormo is currently alpha-stage software. Any results should be carefully evaluated, and issues reported to [the issue tracker](https://github.com/mattwigway/eqsormo/issues). API documentation [is available on readthedocs](https://eqsormo.readthedocs.io/en/latest/api.html#api); additional documentation is forthcoming.

Eqsormo can be installed with `pip`: `pip install eqsormo`

## References

Tra CI (2007) Evaluating the equilibrium welfare impacts of the 1990 Clean Air Act amendments in the Los Angeles area. Doctoral dissertation. University of Maryland, College Park, College Park, MD, USA. Available at: http://hdl.handle.net/1903/7236.

Tra CI (2010) A discrete choice equilibrium approach to valuing large environmental changes. Journal of Public Economics 94(1): 183–196. DOI: 10.1016/j.jpubeco.2009.10.006.

Tra CI (2013) Measuring the General Equilibrium Benefits of Air Quality Regulation in Small Urban Areas. Land Economics 89(2): 291–307.
dltk-ai 2.0.0
Python Client for DLTK.

# DLTK SDK (Python)
[![Python 3.5](https://img.shields.io/badge/python-3.5-blue.svg)](https://www.python.org/downloads/release/python-350/)


[![](https://github.com/dltk-ai/dltk-ai-sdk/blob/master/python/dltk.png)](https://dltk.ai/)

DLTK renders a comprehensive spectrum of solutions that can be accessed by users on-demand from our pool of transformational technologies.

### Installation

DLTK SDK requires Python 3.5 + . Go to https://dltk.ai/ and create an app. On creation of an app, you will get an API Key.

```sh
import dltk_ai
c = dltk_ai.DltkAiClient('API Key')
response = c.sentiment_analysis('I am feeling good.')
print(response)
```

For more details, visit https://dltk.ai/


## License

The content of this project itself is licensed under [GNU LGPL, Version 3 (LGPL-3)](https://github.com/dltk-ai/dltk-ai-sdk/blob/master/python/LICENSE)