semvec/pypi-packages huggingface dataset:
htmd-pdb2pqr 0.0.2
PDB2PQR: an automated pipeline for the setup of Poisson-Boltzmann electrostatics calculations
License: BSD

pdb2pqr
============

This directory is the home for the [PDB2PQR software](http://www.poissonboltzmann.org/docs/structures-ready/). 

Some useful information:
* See the [install notes](INSTALL.md) to build pdb2pqr.
* Using pdb2pqr [binaries](BINARY_README.md)
* Latest [changelog](ChangeLog.md)
* For testing the [server](ServerNotes.md) (for developers)


Please see the [user guide](http://www.poissonboltzmann.org/docs/pdb2pqr-algorithm-description/) for documentation and the [COPYING file](COPYING) for license information.

Please see [programmer's guide](http://www.poissonboltzmann.org/docs/pdb2pqr-programmers/) for information on working with the PDB2PQR code.
npyi 0.1.3
API wrapper around the NPPES API
License: BSD
Keywords: npyi,npi,nppes

.. image:: https://badge.fury.io/py/npyi.svg
    :target: https://badge.fury.io/py/npyi

.. image:: https://travis-ci.com/andyh1203/npyi.svg?branch=master
    :target: https://travis-ci.com/andyh1203/npyi

.. image:: https://codecov.io/gh/andyh1203/npyi/branch/master/graph/badge.svg
  :target: https://codecov.io/gh/andyh1203/npyi

.. image:: https://img.shields.io/badge/License-BSD%203--Clause-blue.svg
   :target: https://opensource.org/licenses/BSD-3-Clause

.. image:: https://img.shields.io/pypi/pyversions/npyi.svg
   :target: https://pypi.org/project/npyi/

.. image:: https://pyup.io/repos/github/andyh1203/npyi/shield.svg
  :target: https://pyup.io/repos/github/andyh1203/npyi/
  :alt: Updates

NPyI
==============

.. pull-quote::
  API wrapper around the NPPES API.
  https://npiregistry.cms.hhs.gov/registry/help-api



* Documentation: https://npyi.readthedocs.io/en/latest/


Install
-------

.. code-block:: shell

   pip install npyi


Examples
--------

.. code-block::

    from npyi import npi

    # sample search by first_name / last_name
    response = npi.search(search_params={'first_name': 'Andrew', 'last_name': 'Jackson'})
    print(response.keys()) # dict_keys(['result_count', 'results'])
    first_entry = response['results'][0]
    print(first_entry['basic']['first_name']) # ANDREW
    print(first_entry['basic']['last_name']) # JACKSON

    # sample search by NPI number
    response = search(search_params={'number': '1417367343'})
    print(response['result_count']) # 1
    print(response['results'][0]['number']) # 1417367343

    # sample search by city
    response = search(search_params={'city': 'San Francisco'})
    first_entry = response['results'][0]
    print(len(first_entry['addresses'])) # 2 (different address purposes - LOCATION and MAILING)
    print(first_entry['addresses'][0]['city']) # SAN FRANCISCO

    # limit example
    response = search(search_params={'first_name': 'Andrew', 'city': 'New York'}, limit=50)
    print(response['result_count']) # 50
    response = search(search_params={'first_name': 'Andrew', 'city': 'New York'}, limit=1000)
    print(response['result_count']) # 200 (200 is the max)
    response = search(search_params={'first_name': 'Andrew', 'city': 'New York'})
    print(response['result_count']) # 10 (10 is the default)