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

# # Visualizing an interactive ordination

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

nbinstall()

def load_mf(fn, index='#SampleID'):
    _df = pd.read_csv(fn, sep='\t', dtype=str, keep_default_na=False, na_values=[])
    _df.set_index(index, inplace=True)
    return _df


# 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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viz = Emperor(res, mf, remote=False)


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viz