#!/usr/bin/env python
# coding: utf-8

# # Emperor's Python API
# 
# **This notebook demonstrate Emperor's new Python API, which can and will change as we continue to exercise this interface, for more information, have a look at the [pull request here](https://github.com/biocore/emperor/pull/405).**

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import pandas as pd, numpy as np
from emperor import Emperor, nbinstall
from skbio import OrdinationResults

from emperor.qiime_backports.parse import parse_mapping_file
from emperor.qiime_backports.format import format_mapping_file

from skbio.io.util import open_file

nbinstall()

def load_mf(fn):
    with open_file(fn) as f:
        mapping_data, header, _ = parse_mapping_file(f)
        _mapping_file = pd.DataFrame(mapping_data, columns=header)
        _mapping_file.set_index('SampleID', inplace=True)
    return _mapping_file

def write_mf(f, _df):
    with open(f, 'w') as fp:
        lines = format_mapping_file(['SampleID'] + _df.columns.tolist(),
                                    list(_df.itertuples()))
        fp.write(lines+'\n')


# We are going to load data from [Fierer et al. 2010](http://www.pnas.org/content/107/14/6477.full) (the data was retrieved from study [232](https://qiita.ucsd.edu/study/description/232) in [Qiita](https://qiita.ucsd.edu), remember you need to be logged in to access the study).
# 
# Specifically, here we will reproduce *Figure 1 A*.

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mf = load_mf('keyboard/mapping-file.txt')
res = OrdinationResults.read('keyboard/unweighted-unifrac.even1000.txt')


# If you want to share your notebook via GitHub use `remote=True` and make sure you share your notebook using nbviewer.

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x = Emperor(res, mf, remote=True)


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x