{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"If you are not using GitHub [click here to open the notebook using nbviewer](http://nbviewer.jupyter.org/github/biocore/emperor/blob/new-api/examples/keyboard.ipynb)."
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "slide"
}
},
"source": [
"# Emperor's Python API\n",
"\n",
"**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).**"
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"collapsed": false,
"slideshow": {
"slide_type": "slide"
}
},
"outputs": [],
"source": [
"import pandas as pd, numpy as np\n",
"from emperor import Emperor, nbinstall\n",
"from skbio import OrdinationResults\n",
"\n",
"from emperor.qiime_backports.parse import parse_mapping_file\n",
"from emperor.qiime_backports.format import format_mapping_file\n",
"\n",
"from skbio.io.util import open_file\n",
"\n",
"nbinstall()\n",
"\n",
"def load_mf(fn):\n",
" with open_file(fn) as f:\n",
" mapping_data, header, _ = parse_mapping_file(f)\n",
" _mapping_file = pd.DataFrame(mapping_data, columns=header)\n",
" _mapping_file.set_index('SampleID', inplace=True)\n",
" return _mapping_file\n",
"\n",
"def write_mf(f, _df):\n",
" with open(f, 'w') as fp:\n",
" lines = format_mapping_file(['SampleID'] + _df.columns.tolist(),\n",
" list(_df.itertuples()))\n",
" fp.write(lines+'\\n')"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"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).\n",
"\n",
"Specifically, here we will reproduce *Figure 1 A*."
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {
"collapsed": false,
"slideshow": {
"slide_type": "slide"
}
},
"outputs": [],
"source": [
"mf = load_mf('keyboard/mapping-file.txt')\n",
"res = OrdinationResults.read('keyboard/unweighted-unifrac.even1000.txt')"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"# change the remote parameter to False/True depending on what you want to do\n",
"x = Emperor(res, mf, remote=True)"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/html": [
"\n",
"\n",
"\n",
"\n",
"\n",
"\n",
""
],
"text/plain": [
""
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"text/plain": [
""
]
},
"execution_count": 4,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"x"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.5.1"
},
"widgets": {
"state": {},
"version": "1.1.2"
}
},
"nbformat": 4,
"nbformat_minor": 0
}
