semvec/pypi-packages huggingface dataset:
svviz 1.6.2
A read visualizer for structural variants
License: MIT
Keywords: bioinformatics,structural variants,sequence analysis

.. image:: https://raw.githubusercontent.com/svviz/svviz/master/docs/example.png
    :width: 80%
    :align: center

*****
svviz
*****

Author: Noah Spies

Latest version: |pypi|_

``svviz`` visualizes high-throughput sequencing data relevant to a structural variant. Only reads supporting the variant or the reference allele will be shown. svviz can operate in both an interactive web browser view to closely inspect individual variants, or in batch mode, allowing multiple variants (annotated in a VCF file) to be analyzed simultaneously.

Visit the `project site <http://svviz.github.io/svviz/>`_ for a tour of the features and example output.


.. |pypi| image:: https://img.shields.io/pypi/v/svviz.png   
.. _pypi: http://svviz.readthedocs.io/en/latest/install.html



Quickstart
==========

svviz has been tested on Mac OS X and Linux, and should work on Windows, although that has not been tested.

1. Ensure that you have a working compiler by following `these instructions <http://railsapps.github.io/xcode-command-line-tools.html>`_ (OS X only) and that `python <http://docs.python-guide.org/en/latest/>`_ and the python package `pip <https://pip.pypa.io/en/latest/installing.html>`_ are installed.
2. Install the latest version of svviz from github using the following terminal command: ``sudo pip install -U svviz``. (The sudo may not be necessary depending on your setup.)
3. Run the following command, which downloads example data and runs it through svviz: ``svviz demo``. After several processing steps, a web browser window should open, with separate, interactive views of the reference and alternate alleles.

See the `documentation <http://svviz.readthedocs.io/>`_ for more detailed help, or run ``svviz -h`` to get help on command line arguments.

Please submit bug reports or feature requests on the `github issue tracker <https://github.com/svviz/svviz/issues>`_.

Publication
-----------

svviz has been `published in Bioinformatics <http://dx.doi.org/10.1093/bioinformatics/btv478>`_. If you found svviz useful for your research, please cite svviz as follows:

Spies N, Zook JM, Salit M, Sidow A. 2015. svviz: a read viewer for validating structural variants. Bioinformatics doi:bioinformatics/btv478.
spyder-reports 0.1.1
Spyder-IDE plugin for Markdown reports using Pweave.
License: MIT
Keywords: Spyder

Spyder reports plugin 
=====================

Spyder plugin to render Markdown reports using
`Pweave <https://github.com/mpastell/Pweave>`_ as a backend.

Build status
------------
|circleci status| |coverage|

Project information
-------------------
|license| |pypi version| |gitter|

.. |circleci status| image:: https://img.shields.io/circleci/project/github/spyder-ide/spyder-reports/master.svg
  :target: https://circleci.com/gh/spyder-ide/spyder-reports/tree/master
  :alt: Circle-CI build status
.. |license| image:: https://img.shields.io/pypi/l/spyder-reports.svg
  :target: LICENSE.txt
  :alt: License
.. |pypi version| image:: https://img.shields.io/pypi/v/spyder-reports.svg
  :target: https://pypi.python.org/pypi/spyder-reports
  :alt: Latest PyPI version
.. |gitter| image:: https://badges.gitter.im/spyder-ide/spyder-reports.svg
  :target: https://gitter.im/spyder-ide/spyder-reports
  :alt: Join the chat at https://gitter.im/spyder-ide/spyder-reports
.. |coverage| image:: https://coveralls.io/repos/github/spyder-ide/spyder-reports/badge.svg
  :target: https://coveralls.io/github/spyder-ide/spyder-reports?branch=master
  :alt: Code Coverage


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

Using conda

``conda install spyder-reports -c spyder-ide``

Using pip

``pip install spyder-reports``


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

This project depends on:

* `Spyder <https://github.com/spyder-ide/spyder>`_ >= 3.2
* `Pweave <https://github.com/mpastell/Pweave>`_
* `Pandoc <https://github.com/jgm/pandoc>`_


Overview
--------

.. image:: https://github.com/spyder-ide/spyder-reports/blob/master/doc/reports_screenshot.png
   :alt: Reports Screenshot
smartbytes-monitor 0.1.6
A Python package to monitor your Airtel Broadband network stats, remaining data, days left, etc
License: MIT
Keywords: airtel,stats,monitor,data-usage,smartbytes-tracker

smartbytes-monitor
==================

Python package to monitor your Airtel broadband network stats like remaining data, days
left, etc.

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

::

    pip install smartbytes-monitor

or if you like to live on the bleeding edge:

::

    python setup.py install

Usage
-----

Just launch the command ``smartbytes`` and it will tell you about your
broadband plan information.

::

    $ smartbytes
    =============================
    Total data : 30.0 GB
    Data left  : 2.6 GB
    Days left  : 1 day(s)
    DSL number : 0172xxxxxxx:_dsl
    =============================

This tool can also used as a library:

::

    >>> import smartbytes
    >>> airtel = smartbytes.smartbytes()
    >>> airtel.data_total
    u'30.0 GB'
    >>> airtel.data_left
    u'2.6 GB'
    >>> airtel.days_left
    u'1 day(s)'
    >>> airtel.dsl_number
    u'0172xxxxxxx:_dsl'

License
-------

``The MIT License``
analyzefit 0.3.8
Performs analysis of the fit of a model.
License: MIT

[![PyPI](https://img.shields.io/pypi/v/analyzefit.svg)](https://pypi.python.org/pypi/analyzefit/)[![Build Status](https://travis-ci.org/wsmorgan/analyzefit.svg?branch=master)](https://travis-ci.org/wsmorgan/analyzefit)[![codecov](https://codecov.io/gh/wsmorgan/analyzefit/branch/master/graph/badge.svg)](https://codecov.io/gh/wsmorgan/analyzefit)[![Code Health](https://landscape.io/github/wsmorgan/analyzefit/master/landscape.svg?style=flat)](https://landscape.io/github/wsmorgan/analyzefit/master)

# analyzefit

Analyze fit is a python package that performs standard analysis on the
fit of a regression model. The analysis class validate method will
create a residuals vs fitted plot, a quantile plot, a spread location
plot, and a leverage plot for the model provided as well as print the
accuracy scores for any metric the user likes. For example:

![alt_text](../master/support/images/validation.png)

If a detailed plot is desired then the plots can also be generated
individually using the methods res_vs_fit, quantile, spread_loc, and
leverage respectively. By default when the plots are created
individually they are rendered in an interactive inverontment using
the bokeh plotting package. For example:

![alt text](../master/support/images/interactive.pdf)

This allows the user to determine which points the model is failing to
predict.

Full API Documentation available at: [github pages](https://wsmorgan.github.io/analysefit/).

## Installing the code

To install analyzefit you may either pip install:

```
pip install analyzefit
```

or clone this repository and install manually:

```
python setup.py install
```

# Validating a Model

To use analyze fit simply pass the feature matrix, target values, and
the model to the analysis class then call the validate method, (or any
other plotting method). For example:

```
import pandas as pd
import numpy as np
from sklearn.linear_model import LinearRegression
from sklearn.metrics import mean_squared_error, r2_score
from sklearn.model_selection import train_test_split
from analyzefit import Analysis

df = pd.read_csv('https://archive.ics.uci.edu/ml/machine-learning-databases/housing/housing.data', header=None,sep="\s+")
df.columns = ["CRIM","ZN","INDUS","CHAS","NOX","RM","AGE","DIS","RAD","TAX","PTRATIO","B","LSTAT","MEDV"]
X = df.iloc[:,:-1].values
y = df[["MEDV"]].values
X_train, X_test,y_train,y_test = train_test_split(X,y, test_size=0.3,random_state=0)
slr = LinearRegression()
slr.fit(X_train,y_train)

an = Analysis(X_train, y_train, slr)
an.validate()

an.validate(X=X_test, y=y_test, metric=[mean_squared_error, r2_score])

an.res_vs_fit()

an.quantile()

an.spread_loc()

an.leverage()
```

## Python Packages Used

- numpy

- matplotlib

- bokeh

- sklearn
AskQuora 0.2.7
Quora Q&A right from the command line
License: MIT
Keywords: quora,terminal,command-line,question,python

AskQuora
========

|pypi.python.org| |build Status|

Quora Q&A right from the command line!

Inspired by https://github.com/gleitz/howdoi

Installation & Usage:
---------------------

::

    sudo pip install -U askquora

or if you like to live on the bleeding edge:

::

    sudo python setup.py install

Run it using ``askquora``

::

    usage: askquora [-h] [-n] [QUERY [QUERY ...]]

    Quora Q&A right from the command-line

    positional arguments:
      QUERY           the question to find the answer of

    optional arguments:
      -h, --help      show this help message and exit
      -n, --no-color  do not colorize or style text

Contributing:
-------------

Any PR's in bug fixes, features or even this documentation are most
welcome!

Disclaimer:
-----------

This is not an official Quora product.

License:
--------

``The MIT License``

.. |pypi.python.org| image:: https://img.shields.io/pypi/v/AskQuora.svg
   :target: https://pypi.org/project/AskQuora/
.. |build Status| image:: https://travis-ci.org/ritiek/AskQuora.svg?branch=master
   :target: https://travis-ci.org/ritiek/AskQuora/