semvec/pypi-packages huggingface dataset:
mysqldump2csv 0.1
Since AMAZON RDS not allowed `SELECT INTO OUTFILE` this should do the trick
License: MIT

# mysqldump2csv
Since Amazon RDS not allowed to use `SELECT INTO OUTFILE` to generate CSV, this script should do the trick.

# Usage example:

```bash
mysqldump -h host -u username -ppasswrd database table
--skip-extended-insert --no-create-db --no-create-info --skip-disable-keys > table.csv.gz
&& python mysqldump2csv.py -df table.csv.gz -t table -s 500000
```


** ./mysqldump2csv.py -h for more information.
CIRpy 1.0.2
Python wrapper for the NCI Chemical Identifier Resolver (CIR).
License: MIT
Keywords: python rest api chemistry cheminformatics

CIRpy
=====

.. image:: http://img.shields.io/pypi/v/CIRpy.svg?style=flat
    :target: https://pypi.python.org/pypi/CIRpy

.. image:: http://img.shields.io/pypi/l/CIRpy.svg?style=flat
    :target: https://github.com/mcs07/CIRpy/blob/master/LICENSE

.. image:: http://img.shields.io/travis/mcs07/CIRpy/master.svg?style=flat
    :target: https://travis-ci.org/mcs07/CIRpy

.. image:: http://img.shields.io/coveralls/mcs07/CIRpy/master.svg?style=flat
    :target: https://coveralls.io/r/mcs07/CIRpy?branch=master

Introduction
------------

**CIRpy** is a Python interface for the `Chemical Identifier Resolver (CIR)`_ by the CADD Group at the NCI/NIH.

CIR is a web service that will resolve any chemical identifier to another chemical representation. For example, you can
pass it a chemical name and and request the corresponding SMILES string::

    >>> import cirpy
    >>> cirpy.resolve('Aspirin', 'smiles')
    'C1=CC=CC(=C1C(O)=O)OC(C)=O'

CIRpy makes interacting with CIR through Python easy. There's no need to construct url requests and parse XML responses
— CIRpy does all this for you.

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

Install CIRpy using::

    pip install cirpy

Alternatively, try one of the other `installation options`_.

Documentation
-------------

Full documentation is available at http://cirpy.readthedocs.org.

Contribute
----------

- Feature ideas and bug reports are welcome on the `Issue Tracker`_.
- Fork the `source code`_ on GitHub, make changes and file a pull request.

Acknowledgements
----------------

All of CIRpy's functionality relies on the fantastic `CIR web service`_ created by the CADD Group at the NCI/NIH.

License
-------

CIRpy is licensed under the `MIT license`_.

.. _`Chemical Identifier Resolver (CIR)`: http://cactus.nci.nih.gov/chemical/structure
.. _`installation options`: http://cirpy.readthedocs.org/en/latest/guide/install.html
.. _`CIR web service`: http://cactus.nci.nih.gov/chemical/structure
.. _`source code`: https://github.com/mcs07/CIRpy
.. _`Issue Tracker`: https://github.com/mcs07/CIRpy/issues
.. _`MIT license`: https://github.com/mcs07/CIRpy/blob/master/LICENSE
nphelper 0.0.5
nphelper - convenient numpy helper functions
License: MIT

############################################
nphelper - convenient numpy helper functions
############################################

|docs| |travis|

This package contains some convenient helper function for numpy.  Nothing
fancy, but quite useful.
It works with python 2.7, 3.4, and 3.5.


Install
============

Simply install via ``pip``::

    pip install nphelper

``nphelper`` only depends on ``numpy``.


Usage / Features
================

Create block arrays (in matlabs's ``[A A; B B]`` spirit):

.. code:: python

    >>> from nphelper import block
    >>> A = np.array([[1, 2, 3]])
    >>> B = np.array([[2, 3, 4]])
    >>> C = np.ones((2, 6))
    >>> block([[A, B], [C]])
    array([[1, 2, 3, 2, 3, 4],
          [[1, 1, 1, 1, 1, 1],
          [[1, 1, 1, 1, 1, 1]])

Compute the cartesian product (similar to ``itertools.product``):

.. code:: python

    >>> from nphelper import cartesian_product
    >>> cartesian_product([[1, 2], [3, 4]])
    array([[1, 3],
           [1, 4],
           [2, 3],
           [2, 4]])

Easily multiply many arrays without the cubersome ``dot`` syntax. It's also
much faster than dot because it selects the fastest evaluation order.
(This is part of numpy 1.10.0.)

.. code:: python

    >>> from nphelper import multi_dot
    >>> A = np.random.random((10000, 100))
    >>> B = np.random.random((100, 1000))
    >>> C = np.random.random((1000, 5))
    >>> D = np.random.random((5, 333))
    >>> # Sick of this?
    >>> np.dot(np.dot(np.dot(A, B), C), D)  # doctest: +SKIP
    >>> # Or this?
    >>> A.dot(B).dot(C).dot(D)  # doctest: +SKIP
    >>> # Use multi_dot
    >>> multi_dot([A, B, C, D])  # doctest: +SKIP

- TODO along, maxalong, minalong, sumalong, meanalong, stdalong, varalong


Dev
===

Run the tests
-------------

Run ``tox`` to run the tests for python 2.7, 3.4, and 3.5::

    tox

You might have to install addiotional dependencies to run the tests::

- ``py.test``,
- ``nose``,
- ``python2.7-dev``,
- ``python3.4-dev``, and
- ``python3.5-dev``.

Build the Docs
--------------

::

    cd doc
    make html

You might have to install addiotional dependencies::

    pip install sphinx sphinx_rtd_theme



.. ============================================================================
.. Links

.. |docs| image:: https://readthedocs.org/projects/nphelper/badge/?version=latest
    :target: http://nphelper.readthedocs.org/en/latest/?badge=latest
    :alt: Documentation Status

.. |travis| image:: https://travis-ci.org/sotte/nphelper.svg?branch=master
    :target: https://travis-ci.org/sotte/nphelper