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ashepherd/MBVL bacteria vs diatom abundance prototype.ipynb

Forked from joefutrelle/MBVL bacteria vs diatom abundance prototype.ipynb
Created October 24, 2016 13:58
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MBVL bacteria vs diatom abundance prototype.ipynb
{
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"# utilities to support loading files and converting formats\n",
"import pandas as pd\n",
"import requests\n",
"from scipy.io import loadmat\n",
"from tempfile import TemporaryFile\n",
"import datetime as dt\n",
"from contextlib import contextmanager\n",
"import re\n",
"\n",
"# download a URL to a temporary file\n",
"@contextmanager\n",
"def download(url):\n",
" with TemporaryFile() as f:\n",
" f.write(requests.get(url).content)\n",
" f.seek(0)\n",
" yield f\n",
"\n",
"# load a MATLAB file from a given URL\n",
"def loadmat_url(url):\n",
" with download(url) as f:\n",
" return loadmat(f, squeeze_me=True)\n",
"\n",
"# given a file ID or GDrive sharing link, generate a direct Google Drive link\n",
"def gdrive_link(file_id):\n",
" file_id = re.sub(r'.*=','',file_id)\n",
" return 'https://drive.google.com/uc?export=download&id=%s' % file_id\n",
"\n",
"# convert MATLAB datetum to datetime\n",
"def matlab2datetime(matlab_datenum):\n",
" day = dt.datetime.fromordinal(int(matlab_datenum))\n",
" dayfrac = dt.timedelta(days=matlab_datenum%1) - dt.timedelta(days = 366)\n",
" return day + dayfrac"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"# Part 1: Bacteria relative abundance"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>AFP_MVCO_Bv6--MVCO_254</th>\n",
" <th>AFP_MVCO_Bv6--MVCO_257</th>\n",
" <th>AFP_MVCO_Bv6--MVCO_258</th>\n",
" <th>AFP_MVCO_Bv6--MVCO_259</th>\n",
" <th>AFP_MVCO_Bv6--MVCO_260</th>\n",
" <th>AFP_MVCO_Bv6--MVCO_261</th>\n",
" <th>AFP_MVCO_Bv6--MVCO_262</th>\n",
" <th>AFP_MVCO_Bv6--MVCO_263</th>\n",
" <th>AFP_MVCO_Bv6--MVCO_265</th>\n",
" <th>AFP_MVCO_Bv6--MVCO_266</th>\n",
" <th>...</th>\n",
" <th>AFP_MVCO_Bv6--MVCO_313</th>\n",
" <th>AFP_MVCO_Bv6--MVCO_314</th>\n",
" <th>AFP_MVCO_Bv6--MVCO_315</th>\n",
" <th>AFP_MVCO_Bv6--MVCO_316</th>\n",
" <th>AFP_MVCO_Bv6--MVCO_317</th>\n",
" <th>AFP_MVCO_Bv6--MVCO_318</th>\n",
" <th>AFP_MVCO_Bv6--MVCO_319</th>\n",
" <th>AFP_MVCO_Bv6--MVCO_320</th>\n",
" <th>AFP_MVCO_Bv6--MVCO_321</th>\n",
" <th>AFP_MVCO_Bv6--MVCO_322</th>\n",
" </tr>\n",
" <tr>\n",
" <th>taxa</th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>Bacteria;Acidobacteria;Acidobacteriia;Acidobacteriales;Acidobacteriaceae;Acidobacterium</th>\n",
" <td>0.0</td>\n",
" <td>0.000000</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.000000</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>...</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.000000</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.000002</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Bacteria;Acidobacteria;Acidobacteriia;Acidobacteriales;Acidobacteriaceae;Bryocella</th>\n",
" <td>0.0</td>\n",
" <td>0.000000</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.000000</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>...</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.000000</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Bacteria;Acidobacteria;Acidobacteriia;Acidobacteriales;Acidobacteriaceae;Granulicella</th>\n",
" <td>0.0</td>\n",
" <td>0.000000</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.000000</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>...</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.000000</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Bacteria;Acidobacteria;Acidobacteriia;Acidobacteriales;Acidobacteriaceae;Telmatobacter</th>\n",
" <td>0.0</td>\n",
" <td>0.000005</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.000000</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>...</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.000000</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Bacteria;Acidobacteria;Acidobacteriia;Acidobacteriales;Acidobacteriaceae;Terriglobus</th>\n",
" <td>0.0</td>\n",
" <td>0.000000</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.000005</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>...</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.000003</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.000000</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>5 rows × 53 columns</p>\n",
"</div>"
],
"text/plain": [
" AFP_MVCO_Bv6--MVCO_254 \\\n",
"taxa \n",
"Bacteria;Acidobacteria;Acidobacteriia;Acidobact... 0.0 \n",
"Bacteria;Acidobacteria;Acidobacteriia;Acidobact... 0.0 \n",
"Bacteria;Acidobacteria;Acidobacteriia;Acidobact... 0.0 \n",
"Bacteria;Acidobacteria;Acidobacteriia;Acidobact... 0.0 \n",
"Bacteria;Acidobacteria;Acidobacteriia;Acidobact... 0.0 \n",
"\n",
" AFP_MVCO_Bv6--MVCO_257 \\\n",
"taxa \n",
"Bacteria;Acidobacteria;Acidobacteriia;Acidobact... 0.000000 \n",
"Bacteria;Acidobacteria;Acidobacteriia;Acidobact... 0.000000 \n",
"Bacteria;Acidobacteria;Acidobacteriia;Acidobact... 0.000000 \n",
"Bacteria;Acidobacteria;Acidobacteriia;Acidobact... 0.000005 \n",
"Bacteria;Acidobacteria;Acidobacteriia;Acidobact... 0.000000 \n",
"\n",
" AFP_MVCO_Bv6--MVCO_258 \\\n",
"taxa \n",
"Bacteria;Acidobacteria;Acidobacteriia;Acidobact... 0.0 \n",
"Bacteria;Acidobacteria;Acidobacteriia;Acidobact... 0.0 \n",
"Bacteria;Acidobacteria;Acidobacteriia;Acidobact... 0.0 \n",
"Bacteria;Acidobacteria;Acidobacteriia;Acidobact... 0.0 \n",
"Bacteria;Acidobacteria;Acidobacteriia;Acidobact... 0.0 \n",
"\n",
" AFP_MVCO_Bv6--MVCO_259 \\\n",
"taxa \n",
"Bacteria;Acidobacteria;Acidobacteriia;Acidobact... 0.0 \n",
"Bacteria;Acidobacteria;Acidobacteriia;Acidobact... 0.0 \n",
"Bacteria;Acidobacteria;Acidobacteriia;Acidobact... 0.0 \n",
"Bacteria;Acidobacteria;Acidobacteriia;Acidobact... 0.0 \n",
"Bacteria;Acidobacteria;Acidobacteriia;Acidobact... 0.0 \n",
"\n",
" AFP_MVCO_Bv6--MVCO_260 \\\n",
"taxa \n",
"Bacteria;Acidobacteria;Acidobacteriia;Acidobact... 0.0 \n",
"Bacteria;Acidobacteria;Acidobacteriia;Acidobact... 0.0 \n",
"Bacteria;Acidobacteria;Acidobacteriia;Acidobact... 0.0 \n",
"Bacteria;Acidobacteria;Acidobacteriia;Acidobact... 0.0 \n",
"Bacteria;Acidobacteria;Acidobacteriia;Acidobact... 0.0 \n",
"\n",
" AFP_MVCO_Bv6--MVCO_261 \\\n",
"taxa \n",
"Bacteria;Acidobacteria;Acidobacteriia;Acidobact... 0.000000 \n",
"Bacteria;Acidobacteria;Acidobacteriia;Acidobact... 0.000000 \n",
"Bacteria;Acidobacteria;Acidobacteriia;Acidobact... 0.000000 \n",
"Bacteria;Acidobacteria;Acidobacteriia;Acidobact... 0.000000 \n",
"Bacteria;Acidobacteria;Acidobacteriia;Acidobact... 0.000005 \n",
"\n",
" AFP_MVCO_Bv6--MVCO_262 \\\n",
"taxa \n",
"Bacteria;Acidobacteria;Acidobacteriia;Acidobact... 0.0 \n",
"Bacteria;Acidobacteria;Acidobacteriia;Acidobact... 0.0 \n",
"Bacteria;Acidobacteria;Acidobacteriia;Acidobact... 0.0 \n",
"Bacteria;Acidobacteria;Acidobacteriia;Acidobact... 0.0 \n",
"Bacteria;Acidobacteria;Acidobacteriia;Acidobact... 0.0 \n",
"\n",
" AFP_MVCO_Bv6--MVCO_263 \\\n",
"taxa \n",
"Bacteria;Acidobacteria;Acidobacteriia;Acidobact... 0.0 \n",
"Bacteria;Acidobacteria;Acidobacteriia;Acidobact... 0.0 \n",
"Bacteria;Acidobacteria;Acidobacteriia;Acidobact... 0.0 \n",
"Bacteria;Acidobacteria;Acidobacteriia;Acidobact... 0.0 \n",
"Bacteria;Acidobacteria;Acidobacteriia;Acidobact... 0.0 \n",
"\n",
" AFP_MVCO_Bv6--MVCO_265 \\\n",
"taxa \n",
"Bacteria;Acidobacteria;Acidobacteriia;Acidobact... 0.0 \n",
"Bacteria;Acidobacteria;Acidobacteriia;Acidobact... 0.0 \n",
"Bacteria;Acidobacteria;Acidobacteriia;Acidobact... 0.0 \n",
"Bacteria;Acidobacteria;Acidobacteriia;Acidobact... 0.0 \n",
"Bacteria;Acidobacteria;Acidobacteriia;Acidobact... 0.0 \n",
"\n",
" AFP_MVCO_Bv6--MVCO_266 \\\n",
"taxa \n",
"Bacteria;Acidobacteria;Acidobacteriia;Acidobact... 0.0 \n",
"Bacteria;Acidobacteria;Acidobacteriia;Acidobact... 0.0 \n",
"Bacteria;Acidobacteria;Acidobacteriia;Acidobact... 0.0 \n",
"Bacteria;Acidobacteria;Acidobacteriia;Acidobact... 0.0 \n",
"Bacteria;Acidobacteria;Acidobacteriia;Acidobact... 0.0 \n",
"\n",
" ... \\\n",
"taxa ... \n",
"Bacteria;Acidobacteria;Acidobacteriia;Acidobact... ... \n",
"Bacteria;Acidobacteria;Acidobacteriia;Acidobact... ... \n",
"Bacteria;Acidobacteria;Acidobacteriia;Acidobact... ... \n",
"Bacteria;Acidobacteria;Acidobacteriia;Acidobact... ... \n",
"Bacteria;Acidobacteria;Acidobacteriia;Acidobact... ... \n",
"\n",
" AFP_MVCO_Bv6--MVCO_313 \\\n",
"taxa \n",
"Bacteria;Acidobacteria;Acidobacteriia;Acidobact... 0.0 \n",
"Bacteria;Acidobacteria;Acidobacteriia;Acidobact... 0.0 \n",
"Bacteria;Acidobacteria;Acidobacteriia;Acidobact... 0.0 \n",
"Bacteria;Acidobacteria;Acidobacteriia;Acidobact... 0.0 \n",
"Bacteria;Acidobacteria;Acidobacteriia;Acidobact... 0.0 \n",
"\n",
" AFP_MVCO_Bv6--MVCO_314 \\\n",
"taxa \n",
"Bacteria;Acidobacteria;Acidobacteriia;Acidobact... 0.0 \n",
"Bacteria;Acidobacteria;Acidobacteriia;Acidobact... 0.0 \n",
"Bacteria;Acidobacteria;Acidobacteriia;Acidobact... 0.0 \n",
"Bacteria;Acidobacteria;Acidobacteriia;Acidobact... 0.0 \n",
"Bacteria;Acidobacteria;Acidobacteriia;Acidobact... 0.0 \n",
"\n",
" AFP_MVCO_Bv6--MVCO_315 \\\n",
"taxa \n",
"Bacteria;Acidobacteria;Acidobacteriia;Acidobact... 0.0 \n",
"Bacteria;Acidobacteria;Acidobacteriia;Acidobact... 0.0 \n",
"Bacteria;Acidobacteria;Acidobacteriia;Acidobact... 0.0 \n",
"Bacteria;Acidobacteria;Acidobacteriia;Acidobact... 0.0 \n",
"Bacteria;Acidobacteria;Acidobacteriia;Acidobact... 0.0 \n",
"\n",
" AFP_MVCO_Bv6--MVCO_316 \\\n",
"taxa \n",
"Bacteria;Acidobacteria;Acidobacteriia;Acidobact... 0.000000 \n",
"Bacteria;Acidobacteria;Acidobacteriia;Acidobact... 0.000000 \n",
"Bacteria;Acidobacteria;Acidobacteriia;Acidobact... 0.000000 \n",
"Bacteria;Acidobacteria;Acidobacteriia;Acidobact... 0.000000 \n",
"Bacteria;Acidobacteria;Acidobacteriia;Acidobact... 0.000003 \n",
"\n",
" AFP_MVCO_Bv6--MVCO_317 \\\n",
"taxa \n",
"Bacteria;Acidobacteria;Acidobacteriia;Acidobact... 0.0 \n",
"Bacteria;Acidobacteria;Acidobacteriia;Acidobact... 0.0 \n",
"Bacteria;Acidobacteria;Acidobacteriia;Acidobact... 0.0 \n",
"Bacteria;Acidobacteria;Acidobacteriia;Acidobact... 0.0 \n",
"Bacteria;Acidobacteria;Acidobacteriia;Acidobact... 0.0 \n",
"\n",
" AFP_MVCO_Bv6--MVCO_318 \\\n",
"taxa \n",
"Bacteria;Acidobacteria;Acidobacteriia;Acidobact... 0.0 \n",
"Bacteria;Acidobacteria;Acidobacteriia;Acidobact... 0.0 \n",
"Bacteria;Acidobacteria;Acidobacteriia;Acidobact... 0.0 \n",
"Bacteria;Acidobacteria;Acidobacteriia;Acidobact... 0.0 \n",
"Bacteria;Acidobacteria;Acidobacteriia;Acidobact... 0.0 \n",
"\n",
" AFP_MVCO_Bv6--MVCO_319 \\\n",
"taxa \n",
"Bacteria;Acidobacteria;Acidobacteriia;Acidobact... 0.0 \n",
"Bacteria;Acidobacteria;Acidobacteriia;Acidobact... 0.0 \n",
"Bacteria;Acidobacteria;Acidobacteriia;Acidobact... 0.0 \n",
"Bacteria;Acidobacteria;Acidobacteriia;Acidobact... 0.0 \n",
"Bacteria;Acidobacteria;Acidobacteriia;Acidobact... 0.0 \n",
"\n",
" AFP_MVCO_Bv6--MVCO_320 \\\n",
"taxa \n",
"Bacteria;Acidobacteria;Acidobacteriia;Acidobact... 0.0 \n",
"Bacteria;Acidobacteria;Acidobacteriia;Acidobact... 0.0 \n",
"Bacteria;Acidobacteria;Acidobacteriia;Acidobact... 0.0 \n",
"Bacteria;Acidobacteria;Acidobacteriia;Acidobact... 0.0 \n",
"Bacteria;Acidobacteria;Acidobacteriia;Acidobact... 0.0 \n",
"\n",
" AFP_MVCO_Bv6--MVCO_321 \\\n",
"taxa \n",
"Bacteria;Acidobacteria;Acidobacteriia;Acidobact... 0.0 \n",
"Bacteria;Acidobacteria;Acidobacteriia;Acidobact... 0.0 \n",
"Bacteria;Acidobacteria;Acidobacteriia;Acidobact... 0.0 \n",
"Bacteria;Acidobacteria;Acidobacteriia;Acidobact... 0.0 \n",
"Bacteria;Acidobacteria;Acidobacteriia;Acidobact... 0.0 \n",
"\n",
" AFP_MVCO_Bv6--MVCO_322 \n",
"taxa \n",
"Bacteria;Acidobacteria;Acidobacteriia;Acidobact... 0.000002 \n",
"Bacteria;Acidobacteria;Acidobacteriia;Acidobact... 0.000000 \n",
"Bacteria;Acidobacteria;Acidobacteriia;Acidobact... 0.000000 \n",
"Bacteria;Acidobacteria;Acidobacteriia;Acidobact... 0.000000 \n",
"Bacteria;Acidobacteria;Acidobacteriia;Acidobact... 0.000000 \n",
"\n",
"[5 rows x 53 columns]"
]
},
"execution_count": 3,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# URL for 'MBVL_VAMPS_output_norm_frequency.mtx'\n",
"bact_csv_url = 'https://drive.google.com/open?id=0B2pXDTrPhbz_enU0a0tNTUJ1X28'\n",
"\n",
"with download(gdrive_link(bact_csv_url)) as f:\n",
" bact_csv = pd.read_csv(f, sep='\\t')\n",
"\n",
"bact_csv.index = bact_csv.pop('taxa')\n",
"bact_csv.head()"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"'Bacteria;Deferribacteres;Deferribacteres;Deferribacterales;family_NA;genus_NA'"
]
},
"execution_count": 4,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# determine most \"abundant\" taxon\n",
"top_taxon = bact_csv.sum(axis=1).sort_values().index[-1]\n",
"top_taxon"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>Sample_name</th>\n",
" <th>MVCO_event_number</th>\n",
" <th>Temperature</th>\n",
" <th>Nitrate+nitrite</th>\n",
" <th>Ammonium</th>\n",
" <th>Phosphate</th>\n",
" <th>Silicate</th>\n",
" </tr>\n",
" <tr>\n",
" <th>Sample_date</th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>2010-08-27</th>\n",
" <td>AFP_MVCO_Bv6--MVCO_254</td>\n",
" <td>MVCO_254</td>\n",
" <td>20.128</td>\n",
" <td>0.286</td>\n",
" <td>0.000</td>\n",
" <td>0.175</td>\n",
" <td>8.107</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2010-10-19</th>\n",
" <td>AFP_MVCO_Bv6--MVCO_257</td>\n",
" <td>MVCO_257</td>\n",
" <td>14.939</td>\n",
" <td>0.000</td>\n",
" <td>1.272</td>\n",
" <td>0.116</td>\n",
" <td>5.632</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2010-10-24</th>\n",
" <td>AFP_MVCO_Bv6--MVCO_258</td>\n",
" <td>MVCO_258</td>\n",
" <td>14.001</td>\n",
" <td>0.523</td>\n",
" <td>1.669</td>\n",
" <td>0.066</td>\n",
" <td>2.677</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2010-11-02</th>\n",
" <td>AFP_MVCO_Bv6--MVCO_259</td>\n",
" <td>MVCO_259</td>\n",
" <td>13.321</td>\n",
" <td>0.077</td>\n",
" <td>2.103</td>\n",
" <td>0.075</td>\n",
" <td>3.953</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2010-11-16</th>\n",
" <td>AFP_MVCO_Bv6--MVCO_260</td>\n",
" <td>MVCO_260</td>\n",
" <td>10.899</td>\n",
" <td>0.078</td>\n",
" <td>0.868</td>\n",
" <td>0.075</td>\n",
" <td>5.959</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" Sample_name MVCO_event_number Temperature \\\n",
"Sample_date \n",
"2010-08-27 AFP_MVCO_Bv6--MVCO_254 MVCO_254 20.128 \n",
"2010-10-19 AFP_MVCO_Bv6--MVCO_257 MVCO_257 14.939 \n",
"2010-10-24 AFP_MVCO_Bv6--MVCO_258 MVCO_258 14.001 \n",
"2010-11-02 AFP_MVCO_Bv6--MVCO_259 MVCO_259 13.321 \n",
"2010-11-16 AFP_MVCO_Bv6--MVCO_260 MVCO_260 10.899 \n",
"\n",
" Nitrate+nitrite Ammonium Phosphate Silicate \n",
"Sample_date \n",
"2010-08-27 0.286 0.000 0.175 8.107 \n",
"2010-10-19 0.000 1.272 0.116 5.632 \n",
"2010-10-24 0.523 1.669 0.066 2.677 \n",
"2010-11-02 0.077 2.103 0.075 3.953 \n",
"2010-11-16 0.078 0.868 0.075 5.959 "
]
},
"execution_count": 5,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# get the dates for each event number from metadata file called \"event_number_dates.txt\"\n",
"event_number_dates_url = 'https://drive.google.com/open?id=0BzoJnj-e6BWpOVZOSjlNY3J5WEE'\n",
"\n",
"with download(gdrive_link(event_number_dates_url)) as f:\n",
" end = pd.read_csv(f, sep='\\t', parse_dates=['Sample_date'])\n",
"\n",
"end.index = end.pop('Sample_date')\n",
"end.head()"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"Sample_date\n",
"2010-08-27 0.089477\n",
"2010-10-19 0.241144\n",
"2010-10-24 0.165028\n",
"2010-11-02 0.188907\n",
"2010-11-16 0.197343\n",
"Name: Bacteria;Deferribacteres;Deferribacteres;Deferribacterales;family_NA;genus_NA, dtype: float64"
]
},
"execution_count": 6,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# get the time series for the top taxon\n",
"# here we depend on the matrices being the same length\n",
"bact_ts = bact_csv.ix[top_taxon]\n",
"bact_ts.index = end.index\n",
"bact_ts.head()"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"<matplotlib.axes._subplots.AxesSubplot at 0xb0a6748>"
]
},
"execution_count": 7,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
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I/WFWG/vua+0s0ZIBufD441ZQLolstZX1QnrppfKd891321YJKIZo8OniXwI2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\nvtkmgdkCF//KcOqp8MtfVto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"text/plain": [
"<matplotlib.figure.Figure at 0xafe4ba8>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"%matplotlib inline\n",
"\n",
"# plot relative abundance over time\n",
"bact_ts.plot()"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"# Part two: diatom abundance"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>Asterionellopsis</th>\n",
" <th>Ceratium</th>\n",
" <th>Chaetoceros</th>\n",
" <th>Corethron</th>\n",
" <th>Cylindrotheca</th>\n",
" <th>DactFragCerataul</th>\n",
" <th>Dactyliosolen</th>\n",
" <th>Dictyocha</th>\n",
" <th>Dinobryon</th>\n",
" <th>Ditylum</th>\n",
" <th>...</th>\n",
" <th>zooplankton</th>\n",
" <th>camera_spot</th>\n",
" <th>Eutintinnus</th>\n",
" <th>Favella</th>\n",
" <th>Helicostomella_subulata</th>\n",
" <th>Stenosemella_sp1</th>\n",
" <th>Stenosemella_sp2</th>\n",
" <th>Tintinnidium</th>\n",
" <th>Tintinnopsis</th>\n",
" <th>Balanion_sp</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>2006-06-07</th>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>48886.569594</td>\n",
" <td>0.000000</td>\n",
" <td>36850.456311</td>\n",
" <td>8424.933243</td>\n",
" <td>22273.431363</td>\n",
" <td>12144.836565</td>\n",
" <td>5305.235580</td>\n",
" <td>27822.10648</td>\n",
" <td>...</td>\n",
" <td>0.0</td>\n",
" <td>0.000000</td>\n",
" <td>364141.255908</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.000000</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.000000</td>\n",
" <td>0.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2006-06-14</th>\n",
" <td>0.000000</td>\n",
" <td>40727.600880</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>311076.023384</td>\n",
" <td>0.000000</td>\n",
" <td>21700.079155</td>\n",
" <td>0.00000</td>\n",
" <td>...</td>\n",
" <td>0.0</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>9275.247774</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>19269.934755</td>\n",
" <td>0.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2006-06-20</th>\n",
" <td>15334.076422</td>\n",
" <td>23529.849061</td>\n",
" <td>144168.056263</td>\n",
" <td>0.000000</td>\n",
" <td>18291.440669</td>\n",
" <td>92163.506159</td>\n",
" <td>148380.516265</td>\n",
" <td>0.000000</td>\n",
" <td>26702.680564</td>\n",
" <td>0.00000</td>\n",
" <td>...</td>\n",
" <td>0.0</td>\n",
" <td>360.028704</td>\n",
" <td>0.000000</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>11261.123753</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>39499.538954</td>\n",
" <td>0.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2006-06-21</th>\n",
" <td>1412.885129</td>\n",
" <td>18930.064370</td>\n",
" <td>87882.408820</td>\n",
" <td>0.000000</td>\n",
" <td>4681.497559</td>\n",
" <td>0.000000</td>\n",
" <td>75824.472661</td>\n",
" <td>0.000000</td>\n",
" <td>387.014745</td>\n",
" <td>0.00000</td>\n",
" <td>...</td>\n",
" <td>0.0</td>\n",
" <td>2116.811297</td>\n",
" <td>0.000000</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.000000</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.000000</td>\n",
" <td>0.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2006-07-09</th>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>12096.070852</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>427997.066825</td>\n",
" <td>0.000000</td>\n",
" <td>19832.625208</td>\n",
" <td>0.00000</td>\n",
" <td>...</td>\n",
" <td>0.0</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>33219.860466</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>6706.624962</td>\n",
" <td>0.0</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>5 rows × 122 columns</p>\n",
"</div>"
],
"text/plain": [
" Asterionellopsis Ceratium Chaetoceros Corethron \\\n",
"2006-06-07 0.000000 0.000000 48886.569594 0.000000 \n",
"2006-06-14 0.000000 40727.600880 0.000000 0.000000 \n",
"2006-06-20 15334.076422 23529.849061 144168.056263 0.000000 \n",
"2006-06-21 1412.885129 18930.064370 87882.408820 0.000000 \n",
"2006-07-09 0.000000 0.000000 0.000000 12096.070852 \n",
"\n",
" Cylindrotheca DactFragCerataul Dactyliosolen Dictyocha \\\n",
"2006-06-07 36850.456311 8424.933243 22273.431363 12144.836565 \n",
"2006-06-14 0.000000 0.000000 311076.023384 0.000000 \n",
"2006-06-20 18291.440669 92163.506159 148380.516265 0.000000 \n",
"2006-06-21 4681.497559 0.000000 75824.472661 0.000000 \n",
"2006-07-09 0.000000 0.000000 427997.066825 0.000000 \n",
"\n",
" Dinobryon Ditylum ... zooplankton camera_spot \\\n",
"2006-06-07 5305.235580 27822.10648 ... 0.0 0.000000 \n",
"2006-06-14 21700.079155 0.00000 ... 0.0 0.000000 \n",
"2006-06-20 26702.680564 0.00000 ... 0.0 360.028704 \n",
"2006-06-21 387.014745 0.00000 ... 0.0 2116.811297 \n",
"2006-07-09 19832.625208 0.00000 ... 0.0 0.000000 \n",
"\n",
" Eutintinnus Favella Helicostomella_subulata Stenosemella_sp1 \\\n",
"2006-06-07 364141.255908 0.0 0.0 0.000000 \n",
"2006-06-14 0.000000 0.0 0.0 9275.247774 \n",
"2006-06-20 0.000000 0.0 0.0 11261.123753 \n",
"2006-06-21 0.000000 0.0 0.0 0.000000 \n",
"2006-07-09 0.000000 0.0 0.0 33219.860466 \n",
"\n",
" Stenosemella_sp2 Tintinnidium Tintinnopsis Balanion_sp \n",
"2006-06-07 0.0 0.0 0.000000 0.0 \n",
"2006-06-14 0.0 0.0 19269.934755 0.0 \n",
"2006-06-20 0.0 0.0 39499.538954 0.0 \n",
"2006-06-21 0.0 0.0 0.000000 0.0 \n",
"2006-07-09 0.0 0.0 6706.624962 0.0 \n",
"\n",
"[5 rows x 122 columns]"
]
},
"execution_count": 9,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# ID for Google Drive staged \"count_biovol_manual_current_day\" as of 2016-09-28\n",
"# this is biovolume per class per day for manually-classified targets\n",
"file_url = 'https://drive.google.com/open?id=0BzoJnj-e6BWpWVA2S2ttdDdLY1k'\n",
"\n",
"# load it\n",
"mat = loadmat_url(gdrive_link(file_url))\n",
"\n",
"# convert MATLAB times to datetimes\n",
"ts = [matlab2datetime(m) for m in mat['matdate_bin']]\n",
"\n",
"# create a biovolume matrix with datetime index and classes as columns\n",
"cbv = pd.DataFrame(mat['classbiovol_bin'],columns=mat['class2use'],index=ts)\n",
"cbv.head()"
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"# these are the 1-based indices of columns identifying diatom classes\n",
"# derived from https://github.com/hsosik/ifcb-analysis/blob/master/analyze_by_class/get_diatom_ind.m\n",
"DIATOM_IND = [1,3,4,5,6,7,10,11,12,13,14,15,16,17,18,19,20,21,25,36,37,42,44,45,47,48,49,50,51,53,54,55,56,57,58,59,61,62,63,64,66,69,91,92,108]\n",
"\n",
"# remove non-diatom classes from the matrix (in-place)\n",
"def remove_non_diatoms(cbv):\n",
" all_classes = mat['class2use']\n",
" diatom_classes = [all_classes[i-1] for i in DIATOM_IND]\n",
" non_diatom_classes = set(all_classes).difference(diatom_classes)\n",
" for c in non_diatom_classes:\n",
" del cbv[c]\n",
" return cbv\n",
"\n",
"# scale biovolume by observed volume (from ml_analyzed_mat_bin field in mat file)\n",
"def scale_by_volume(cbv):\n",
" return cbv / mat['ml_analyzed_mat_bin']\n",
"\n",
"# subset the matrix into an arbitrary time region (defaults to region of interest)\n",
"def time_slice(df, start='2010-01-01',end='2013-12-31'):\n",
" return df[start : end]"
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"(Timestamp('2010-08-27 00:00:00'), Timestamp('2013-10-29 00:00:00'))"
]
},
"execution_count": 11,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# compute time region covered by bacteria series\n",
"start = bact_ts.index.min()\n",
"end = bact_ts.index.max()\n",
"start, end"
]
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>Asterionellopsis</th>\n",
" <th>Chaetoceros</th>\n",
" <th>Corethron</th>\n",
" <th>Cylindrotheca</th>\n",
" <th>DactFragCerataul</th>\n",
" <th>Dactyliosolen</th>\n",
" <th>Ditylum</th>\n",
" <th>Ephemera</th>\n",
" <th>Eucampia</th>\n",
" <th>Guinardia_delicatula</th>\n",
" <th>...</th>\n",
" <th>diatom_flagellate</th>\n",
" <th>Chaetoceros_didymus</th>\n",
" <th>Leptocylindrus_mediterraneus</th>\n",
" <th>Chaetoceros_didymus_flagellate</th>\n",
" <th>pennates_on_diatoms</th>\n",
" <th>Delphineis</th>\n",
" <th>Ditylum_parasite</th>\n",
" <th>Bacillaria</th>\n",
" <th>Bidulphia</th>\n",
" <th>pennate_morphotype1</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>2010-08-27</th>\n",
" <td>94699.102066</td>\n",
" <td>83229.151213</td>\n",
" <td>0.000000</td>\n",
" <td>3864.538393</td>\n",
" <td>0.0</td>\n",
" <td>44499.244205</td>\n",
" <td>771.221746</td>\n",
" <td>650.597659</td>\n",
" <td>0.000000</td>\n",
" <td>4005.975631</td>\n",
" <td>...</td>\n",
" <td>1134.517790</td>\n",
" <td>2276.464409</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>20453.818159</td>\n",
" <td>0.0</td>\n",
" <td>0.00000</td>\n",
" <td>0.000000</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2010-08-28</th>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>103.781898</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>28822.076737</td>\n",
" <td>4310.609644</td>\n",
" <td>1058.936621</td>\n",
" <td>37.852087</td>\n",
" <td>NaN</td>\n",
" <td>...</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2010-09-06</th>\n",
" <td>NaN</td>\n",
" <td>31192.068624</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>...</td>\n",
" <td>166.367338</td>\n",
" <td>920.990725</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>4069.642841</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2010-09-12</th>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>1294.607502</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>7105.116985</td>\n",
" <td>11247.322358</td>\n",
" <td>689.856643</td>\n",
" <td>0.000000</td>\n",
" <td>NaN</td>\n",
" <td>...</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2010-09-16</th>\n",
" <td>0.000000</td>\n",
" <td>8543.067147</td>\n",
" <td>0.000000</td>\n",
" <td>384.740842</td>\n",
" <td>0.0</td>\n",
" <td>20276.236597</td>\n",
" <td>22694.340316</td>\n",
" <td>0.000000</td>\n",
" <td>2751.970168</td>\n",
" <td>12710.308563</td>\n",
" <td>...</td>\n",
" <td>1053.508959</td>\n",
" <td>204.746823</td>\n",
" <td>656.936936</td>\n",
" <td>739.722876</td>\n",
" <td>2742.584080</td>\n",
" <td>0.0</td>\n",
" <td>806.23285</td>\n",
" <td>1736.920411</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>5 rows × 45 columns</p>\n",
"</div>"
],
"text/plain": [
" Asterionellopsis Chaetoceros Corethron Cylindrotheca \\\n",
"2010-08-27 94699.102066 83229.151213 0.000000 3864.538393 \n",
"2010-08-28 NaN NaN 103.781898 NaN \n",
"2010-09-06 NaN 31192.068624 NaN NaN \n",
"2010-09-12 NaN NaN 1294.607502 NaN \n",
"2010-09-16 0.000000 8543.067147 0.000000 384.740842 \n",
"\n",
" DactFragCerataul Dactyliosolen Ditylum Ephemera \\\n",
"2010-08-27 0.0 44499.244205 771.221746 650.597659 \n",
"2010-08-28 NaN 28822.076737 4310.609644 1058.936621 \n",
"2010-09-06 NaN NaN NaN NaN \n",
"2010-09-12 NaN 7105.116985 11247.322358 689.856643 \n",
"2010-09-16 0.0 20276.236597 22694.340316 0.000000 \n",
"\n",
" Eucampia Guinardia_delicatula ... \\\n",
"2010-08-27 0.000000 4005.975631 ... \n",
"2010-08-28 37.852087 NaN ... \n",
"2010-09-06 NaN NaN ... \n",
"2010-09-12 0.000000 NaN ... \n",
"2010-09-16 2751.970168 12710.308563 ... \n",
"\n",
" diatom_flagellate Chaetoceros_didymus \\\n",
"2010-08-27 1134.517790 2276.464409 \n",
"2010-08-28 NaN NaN \n",
"2010-09-06 166.367338 920.990725 \n",
"2010-09-12 NaN NaN \n",
"2010-09-16 1053.508959 204.746823 \n",
"\n",
" Leptocylindrus_mediterraneus Chaetoceros_didymus_flagellate \\\n",
"2010-08-27 0.000000 0.000000 \n",
"2010-08-28 NaN NaN \n",
"2010-09-06 0.000000 0.000000 \n",
"2010-09-12 NaN NaN \n",
"2010-09-16 656.936936 739.722876 \n",
"\n",
" pennates_on_diatoms Delphineis Ditylum_parasite Bacillaria \\\n",
"2010-08-27 20453.818159 0.0 0.00000 0.000000 \n",
"2010-08-28 NaN NaN NaN NaN \n",
"2010-09-06 4069.642841 NaN NaN NaN \n",
"2010-09-12 NaN NaN NaN NaN \n",
"2010-09-16 2742.584080 0.0 806.23285 1736.920411 \n",
"\n",
" Bidulphia pennate_morphotype1 \n",
"2010-08-27 0.0 0.0 \n",
"2010-08-28 NaN NaN \n",
"2010-09-06 NaN NaN \n",
"2010-09-12 NaN NaN \n",
"2010-09-16 0.0 0.0 \n",
"\n",
"[5 rows x 45 columns]"
]
},
"execution_count": 12,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# perform the operations defined above\n",
"ab = scale_by_volume(cbv)\n",
"remove_non_diatoms(ab)\n",
"ab = time_slice(ab, start, end)\n",
"ab.head()"
]
},
{
"cell_type": "code",
"execution_count": 13,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"'Guinardia_delicatula'"
]
},
"execution_count": 13,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# sum concentrations of three Guinardia classes\n",
"G_CLASSES = ['Guinardia_delicatula', 'G_delicatula_parasite', 'G_delicatula_external_parasite']\n",
"\n",
"# when summing, propagate NaNs\n",
"g_ab = ab[G_CLASSES].sum(axis=1, skipna=False)\n",
"\n",
"summed_ab = ab\n",
"\n",
"# now remove the sublcasses and add the summed class\n",
"for gc in G_CLASSES:\n",
" summed_ab = summed_ab.drop(gc, 1)\n",
"summed_ab['Guinardia_delicatula'] = g_ab\n",
"\n",
"# compute the most abundant class based on sum of concentration\n",
"def most_abundant_class(df):\n",
" return df.sum(axis=0).sort_values().index[-1]\n",
"\n",
"top_class = most_abundant_class(summed_ab)\n",
"top_class"
]
},
{
"cell_type": "code",
"execution_count": 14,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"2010-08-27 4005.975631\n",
"2010-09-16 12710.308563\n",
"2010-09-28 15505.632146\n",
"2010-10-12 7875.186867\n",
"2010-10-22 273.025632\n",
"Name: Guinardia_delicatula, dtype: float64"
]
},
"execution_count": 14,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# get its concentration\n",
"diatom_ts = summed_ab[top_class]\n",
"\n",
"# now drop NaNs from the resulting time series\n",
"diatom_ts.dropna(inplace=True)\n",
"diatom_ts.head()"
]
},
{
"cell_type": "code",
"execution_count": 15,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"<matplotlib.axes._subplots.AxesSubplot at 0xb288f98>"
]
},
"execution_count": 15,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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IwZF9o0L+xSIystjrKCdRT+MTn7Aa6pYt2fMWE5qC5jEaqWvI1+tkd0QVZs+2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\n",
"text/plain": [
"<matplotlib.figure.Figure at 0xb1747f0>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"%matplotlib inline\n",
"\n",
"# plot the time series\n",
"diatom_ts.plot(legend=False)"
]
},
{
"cell_type": "code",
"execution_count": 16,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"[<matplotlib.lines.Line2D at 0xb9795c0>]"
]
},
"execution_count": 16,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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7YF+y3r3Lt+xG0GIOlu5rbGxeyiTOUkc+n/iEpUa2bs1+LNiclblzo1eszoce\nPWzW/4QJJoQvvJD+2KlT7QaZuoJrnEaJPXvsbxNcm6tc+BVAli0r4CIx2szzsZcHySWCCv7/B+3m\nS5faQpk//WnmaziBas0YrIrtSlVtBKZjy/3uQ1Vnq6pfEW02zasuZj03V/Jx8T39tN3EfCt2FE47\nzc4rdkmbQvArSKRSzjRfagQl0vJLVYwIqnNnMxf861/Rjl+50j4L/ftnP7YQamosKvq//7MHnrCV\nenfvhptvbh09+cQlUBs2WKSdy3egmBQ8DhWW4sszgirEIAHhLr6zzjJ3aZBgig+ax6FUbZrCdddl\nHyN0AtWa1CV8g8v+hvFl4NE8z81KPhFULuk9n9697Zwf/Sj3PpaKVAefz6GHlm/ZjaDF3Mf/UjU1\nWTor7ggKzLr72GPZj4Pm9F5cBolsfPKTNkn85z83V1bQEj9tmrn20rlL4xKopKT3fAoehwpL8eUR\nQe3YYZmIj3wk/66EmSSefBLmz2/ZFkzxgT1UvfqqGWu2bIFLLsn+Xj17mg5X20Ka6YjVJCEiJwIX\nAnmNNQVXiky3rHE6gcr08JSPQIF9cO64o2WeOEmEpfggWREUNAvUrl1WSqoYteB8gYpScmbevOKN\nP6VjxAirpL9xo6WT1q618aWbboLrr09/XrUKVMFzoWKqxTdrFhxxRGFRfeoD8s6dlm5O/f1SU3z7\n7Wci9a1vWZQdJbpNzUhUO1EC/tAlfFMP8owRdwMTvEWrIp/rE2UJ41xdfCtX2tPG4YdnvXQrDj4Y\nfvELOP98exqKOzVVCNu3201u+PDW+w49NHo0UShz5sCKFc3rJG3ebDfjIP4XqhjpPZ/hwy0ySyfa\nQebOhe9+tzj9yET37vDQQ5bSO+YY+J//sf+rTE/vgwZZxFWowCTFYu4Te4ovzwiq0PEnaH3/8cd/\nU8fYwh6uzz/fPq/H5rBKkv99ilrkuJKJ8iw7FxgmIoNEpCNwDrbc7z5EZCDwMHC+qv43l3NzoanJ\nBnpzEainnzYnVb5P7WefbYPXV1+d3/nFYtEiW/Qt7KmrlBHUn/8MX/oSLF5srzNFUHGXOUplwgR4\n9NHMxzQ1WURSTINEJmpq4Hvfg9/8BqZPt7lTmYjLKJG0CGr4cPuM5r1cekwmiULHnyBcoDp1yh5B\ngdnJb2q1TGBm2lIElfW2rap7gcuAJ4DFwHRVXSoiXxMRf57zddgSvneIyMsiMifTufl2dutWKzSZ\nOtk2k0DucLu8AAAgAElEQVT9+c+W/imEX/0K/vKX6IPwpSBTpFBKgVq+3NIUvoBnEqhiRlAQbRxq\n+XIbI0t9yCk1Eyeau/T447MfG8eE3aQJ1H772eci789pDJUkdu7MvLpBVMIE6qMftQgqmHIOE6h8\ncAKVgqo+pqojVXW4qt7std2lqnd7219R1T6qepSqHqmqYzKdmy9hDj5IL1DvvGOTJj/72ULe1SoI\n//a3cNFFNpiZBNI5+AD69bPvr2/tLibLl8Pll1t/oLXNHEonUCeeaDfyTAPIxbaX50LUqL4aIygo\ncBwqhkoSs2ebyahr1zz74JHq4luzptm89O679q9qeIovH5xAJZR0f+B0AnXvvfCZz7R80MqXcePM\nKhws3FhO0jn4oHTLbuzda27Bk06yv82OHeWNoLp0sSfXTEtqF3OCbrE4+ujC14ZKokAVNA4Vg808\njvQetHbxrV1rn/lRo5rHobZts7RfPsVoU4kiUCIyVUTWi8jCQFsvEXlCRJaJyOMi0jOw7xoRWSEi\nS0XklED7USKy0Cu0cFugvaOITPfOecEb5vH3TfKOXyYiFwTaB4vIbG/fAyKS1QNRtQKl2hz1xMUt\nt9iT7B//GN8186GpycagMpkBSpHme/tt+3t0725FV19/Pdxm/r73WXpj+/biG00mTMic5ivn+FO+\nBI0S+VJVAqUai808DoMEhKf4+vdvXpwR4kvvQeSK5vdgBRKCXA08qaojgaeAawBE5DDgc8BoYCI2\nVONPwrgTuEhVRwAjRMS/5kXAZlUdDtwG3OpdqxdwPXAMcCxwQ0AIbwF+4l1rq3eNjFStQP3nP9ae\nb32tMLp0sdVUv/GN8obYb75pacdevdIfUwqBWr682bE3YoTNa9m2zeZqBOnQwaKqN98svkBNnGhG\niXR285Ur46lgXkoKNUo0Ndl4V79+8farUPKeC7V7t0VMwcHoHE0SDQ32/1no+BO0vv+sWWOOyWAE\nFVd6D6JFUKr6HJA6IHEGcK+3fS9wprd9OuYP2KOqbwErgDEi0g/orqr+RJv7AucEr/UQ4E/kGQ88\noap1qroV8x9M8PaNxcx0/vt/OtvvWnECFTa4HVaLb+pUi57inox5zDFWBPSii2JYtjpPUgvEhlFq\ngRo50iznPXqEL6Hev7/Z0YstUCNH2g3DdxUG2bvXnmSTFklEoRCB2rTJotxOneLtU6H4Y1A5f49S\n03uQcy2+F1+0CdKpl8mHsAjq4IOLF0H162fj8VHXoApwkKquB1DVdYDfo9SCCmu8tv5YcQWfYKGF\nfed4Zrg6Eemd7loi0gfYoqpNgWtlnfhQcQIVJYKqq7PlMs4/vzj9uPZa+9LfeWdxrp+NTAYJn1II\n1LJlLSOoF15oPf7k4wtUMapIBBFpjqJS2bDBos4OHYrbh2JQiEAlbQ6UT58+9rfIeQnz1PQe5BxB\nxTX+BC3vP6rN/9/BFGZcAjVz5kx++MPJdOo0me9+d3Khl4vzETtKKJBzuFCVAjV9ug3cF6swZocO\n8LvfWQWA5cuL8x6ZyGSQ8ClVBDVypG2PHGmTmbMJVCkmO6cbh1qzpvj194pFIQKVxPEnn7zSfKkO\nPsjZJFHI+k+pdOjQbJLYutVed+tm38HVqy0jGVeKr7a2lsmTJzNq1GTOPntyrqevF5G+AF76boPX\nvgY4JHCcX1AhXXuLc0SkHdBDVTeTpjiDqm4CenoFxFOvlZaKEqhMNvPgZ9NP7xWTkSNh8mSL0vII\ntQsiSrWEIUNsOY5iLruRGkG9915mgXrjjdII1Nixlm7cvr1leyUL1ODBVioqnyXlky5QORsl0qX4\nIn7Yd+0yN+fHPpbj+6Yh+IAcjFaDqz3HmeKDyFZzoWXUMgP4orc9CXgk0H6O58wbAgwD5nhpwDoR\nGeOZJi5IOcdfUvEszHQB8DgwTkR6eoaJcV4bwNPesanvn5aKEqgoEdSiRfYhGZ/qXykCl1xihoBc\nZ4IXwrZtdpMKK3EUpEsXS2cV4vzKRF2dpTmHDLHXBxxg4pRJoBobSyNQ3bpZ6ZinnmrZntRUVxQK\nMUokWaDymguVLsUXMYKaM8fet0ePHN83DcH7T+pDkL+sSGqh2ELJJlAi8gdgFua8e1tELgRuxsRj\nGXCS9xpVXQI8CCwB/glcorpvZPBSYCqwHFuZws9NTAUOEJEVwDcxhyBembsbgXnAi8AUzyyBd8yV\nIrIcK+yQdd3phBTfj0YUgXrgAYtqwgbq46amBu65x9aZOvVUu4EUG7/EUZTfz0/zFSNqePVV60dw\nsumIEa0t5j5+H0pVz9CvKnFGYHGXSo6goFmgTjstt/NWrsyeEi4Xo0aFL32ekbAUXw4RVFz2cp90\nERQ0R4ipS20USv/+lj5Mh6qel2bXyWmOvwlo9aitqi8BHwhp341Z08OuNQ2YFtL+JmY9j0zFRFD+\nTOwwF1/wA/Lmm+bOKRX9+9syCl/4gllXi02U9J5PMZfdWLQIPpDysR05MnMEBaUTKL8uX9AhVskR\nFOQ/YXfOnOTO/cprDCosxZeDSeKVV+KdrJ0qUGERVJlSfBVPxQjUjh2W5ggrSxL8gKxfD337lrZv\n555ronHNNcV/rygOPp9iGiXCBOrii61yRxilFqjDDrP7VTB9VC0RVC7s3GlmlqifmVIzaJCNLedU\nlqtAk8SOHfGl96B1ii8sgip1iq9aqBiByuSCCX5A4n5Sicodd9hSCv/+d3HfJ4qDz6fUAnXccen7\n1rOniVOpBCrMbl7pApWPUWL+fFtqJo4SO8WgXTsbT83JDRs2BpVDiq+hId7/j6CLLzVK9+dCbdwY\nb4FiJ1AJI52DD8ofQYGltqZOhQsvNKtpMWhqsrGfcguUarhAZcJfaK2Ua2qlVjev9BSfiC39kksU\n9eKLua01VA5ydvKlS/FFjKDiXtU5U4rvgANMwLp3j3f+nROohBElgtqzx6qNpxuoLzbjx8OnPlW8\ngrJvvGFCmKnEUZBiCdSaNWahzTVSHTWqtNHt2LE2eXjHDntq3rmzfJ+NuMg1zTdnTvrl5JNCzuNQ\nBZokGhqKJ1CpKT6wKCruz32PHsVZmTppVMyvmEmg/FJHGzfaDTzK0smxsXKlLbvrceutdlP405/i\nf6tc0ntgJVHq6uwGHSe5Rk8+M2bEWxsxGz16mDng6aftyfZ974u/9FWpyVWgqjKCKrCSRNwpPl+g\n/FJaqTUPR42K18HnM2BA/NdMGpEESkQmiMhrXpn0q0L2jxSRWSKyS0SuTNn3LRF51SvZ/ntvZd2c\nSefgg+YPSFnGnxYtggcf3Peya1crKHvZZfHPQcrFwQf2nS3Gshv5ClQ58MehKn38yScXgVq/3ubN\nZZszV25ynguVLoIqc4rv3Xctu5G6oGoxIiiojs9zNrIKlFea4nasSu3hwLkiMirlsE3A5cCPUs49\n2Gs/SlU/iM27OiefjkZJ8ZVl/GnHjlYhyrHHmqMt7oKyuTj4fIqR5qtUgark8SefIUMsVRnFKDFn\njtmpkx41jhhh1RYiVyoq0GZejBRfY2P6z9jZZ8MVV8T3fj5OoIwx2AzilaraCEzHSq3vQ1U3ehO6\nwor+tAO6eotTdQHW5tPRbALlh9clj6BCBArg+9+3Pv/61/G9VZQq5qm0dYF6//utBNMzz1THFzqX\nihKVkN4DM8707ZtDpF9gJYliuPj27Elvwhk0CE44Ib7386mGz3M2oghUavn0YMn1jKjqWuAnwNtY\nYcCtqpphvdP0RHHxlS2C2rmzVXOHDnDjjVbZIg62bTMBHjYst/PiFqjGRpt4ePjh8V2zmIjYpN0/\n/KF6vtDVJlCQ4zhUASYJVUvxFWMMKtXBV2yq5fOciaKaJERkfyzaGoSt/dFNRNKV4MhIlBRfWSKo\nnTvTuhAOOyy+aucLF5oo5FrCKW6BWrHCBmdLaRcvlIkT7Z5WDSk+iCZQTU1WEDXpDj6fnMahCkjx\nNTbaoXEaqfz7T6nTyG1BoKL8mULLp0e8/snAG14ZdkTkz8BHgT+EHTx58uR927W1tdQGCmZFEagt\nW8owIJwmggK7kW/dGv7Alyv5pPcgfoGqpPSez8knm7BXyxf66KPhW9/KfMzy5eZoLYZ7rBiMGmWC\nGokCTBJxjz9BywiqlA8E1fJ5zkQUgZoLDBORQcA7mMnh3AzHB4dk3waOE5H9gN1YBd20H8OgQKWS\n2Ahqxw4b5Nizp9VjWU1N8yz5QgvJ5mOQgJbLbsQxb6ISBapnT0u3JrVgaq74RolMKe1KSu+BCdR9\n90U8uACbedwOPihfim/QoNK9V7nIesvylvO9DFtbfjG2dv1SEfmaiHwVQET6isgq4FvAtV55926q\nOgdbr/5lYAEmXnfn2sndu+0L2bNn+P6gQJVlDArSRlF+schCyVeguna1/7d81hEKoxIFCqxOYtQJ\nzkknSkWJSpigG8SfrJvV9freeyZEqevXRzRJxG2QgOwuvmJRjpJupSZSJtZbA2RkSttdge31tFx5\nMXjcFGBKAX3cV8cqnV02aJIoSwTl/xtSgXLEiMIFyi9xlK8w+Gm+OL48ixZVTyRSyfjjUKeeGr7/\nxRfh858vbZ8Kwf/eZjJDAc3jT6k3g4gmiWJEUEEXX1tIu5WSiqgkke1D6z/BlC3FBxkjqEKNEv/9\nrwn0/vvnd35cy25s324PAUOHFn4tR2FkMko0NFg0cuSRpe1TIYhELHkUlt6DnCKoYqT4duywqi1x\nFoR1VIhAZRp/AvuAbN5sTzIld5cFI6gQ4kjx5Zve84nLKLF4sbmtSrEYpCMzH/5weoF6+WW72cd9\nIy42kazmYQ4+iBxBFSvFt2qVlThqC/XxSklF/HdmE6h27Sy8LktO1o+c0kRQI0ZYBFVIRYl8HXw+\ncQlUpY4/VSNDhtgz0fr1rffNmVNZBgmfSAKVzhJbZpPE22+79F4xqAqBat/eBKocy2ywY4e5ENJE\nUD17WkaikNL4SYmgnEAlh0xGiUpz8PlEmguV0BTf+vXVM88uSVSMQGXK7bZvby61skRQO3aYeqaJ\noKDwcahci8Sm4gSqOkk3DvXii5Xl4POJPAaVwBQfJEugwop0i0gvEXlCRJaJyOMi0jNw/DUiskJE\nlorIKYH2o7xrLBeR2wLtHUVkunfOCyIyMLBvknf8MhG5oJDfo2IEKlsE1dRUxgjqoIMyrmlRyDjU\nwoWWljj00Dz7hy0zsXVrRg3NSj6LFDqKS5hAvfuujceOHBl+TpIZMsRWAMj4OU03BhUxgiqWiw+S\nk+JLU6T7XOBq4ElVHQk8BVzjHX8Y8DlgNDARuENkn03yTuAiVR0BjBCR8V77RcBmVR0O3Abc6l2r\nF3A9cAxwLHBDUAhzpWoECpIbQRViNb/5ZqsaUIgxoabGlgsvZNmNdessrVSWhwBHKGEC5Vcwr8TB\n+vbtzSG6YkWGg2KIoIqR4oNkRVC0LNLdGav+cwZwr7f/XuBMb/t0bH7rHlV9C1gBjBGRfkB3VfWL\nK9wXOCd4rYeAsd72eOAJVa1T1a3Y/NkJ+f4SFfExjmIzh+qLoP77X3jiCVu6o1AKTfP50VPSl25o\nSxx6qAUUGzY0t1Vqes8n6zhUpjEol+IDQot013lFuvt6c1ZR1XWA/0ifWhB8jdfWHysO7hMsFL7v\nHK+YQ52I9M5wrbwo5dqzeZPYCKqx0b4U+++fdQwqH4H60Y9MnELm/+ZMXALlSA5Bo8TEidY2Zw5c\nckl5+1UIWcehMtnMy5Ti8+8/pUjxzZw5k5kzZ2Y8JqVIdx3wJxH5PJDqJY5xtTqK8uhaVQJVljJH\nXbpYPaEMEdSQIeYy3L27dYWWdLzzji3UG0eZJIhHoD72sXj64ogPP803caKNE86ZA9OmlbtX+TNq\nFPz97xkO2L49/GaQQwRVySm+1CLaU6aEFulJLdL9F6xI93oR6auq6730nR97r6FlJSC/IHi69uA5\na0WkHdBDVTeLyBqgNuWcp3P/TY3Ep/j27rUq5b17pz+mbBHUzp0mTlkEqkMHK+z4+uvRL/2zn8EX\nvhBfNWoXQVUnH/4wzJtn2ytWWHDRr195+1QIWedCxWAzL0aKr0uXeDIdMbGvSLdndjgJWALMAL7o\nHTMJeMTbngGc4znzhgDDgDleGrBORMZ417kg5ZxJ3vZZmOkC4HFgnIj09AwT47y2vEh8BLV5s80l\nyrR+S1kjqK5d7dOZxSLnp/miLPS3ZQtMnWoVAeKiEIHau9duGpWySGFb4uij4dvftu1KnaAbZNQo\nm5KRtvp+gZUkdu2KX8C7drUH0KSMz6rqHBHxi3Q3ev/eDXQHHhSRLwErMeceqrpERB7ERKwRuER1\nX2mBS4FpwH7AP726rABTgftFZAWwCVvlAlXdIiI3AvOwFOIUzyyRF4kXqGzpPTCBat8+/1p1eeML\nVJYICnIbh/rVr+BTn4KBA7MfG5UhQ8zFp9r6i7R2rd0M0n1xX3/d9oU9uDrKS9AoUakTdIN06wZ9\n+sDKlfaZbUWBlSSKEUGNGAHPPhvvNQslTZHuzVj6L+z4m4CbQtpfAlrlTlR1N57AheybholawSQ+\nxZe1ujEmTgcdVAZrbY4RVJTJujt3wi9/CVddFVMfPbp1s+91cNmNzZvhu9+1m9wPfpD+XJfeSy5B\no0SlO/h8gmm+PXvggQcsUvzBD0if4ivjgoXgisQWi8QLVJQI6pBD4MtfLk1/WpBDBBV1LtRvfgPH\nH29227gZOtTSfA0NcOutJpp1dfDTn8Lq1enPcwKVbI4+GmbNsmK+hS6MmQRGjYL58+H2223Bzzvv\nhI9/3JvzlWmibplq8TmKR1UIVO/eEG5mKTJBgYo4BpWJ996DH//YFtcrBoceCr/4hYnlnDnw3HNw\n11020J6pVqATqGRz9NFw7732dy15Nf8icPjhcN118O9/wx/+YOmzCy/0lozJlOIrk0nCUTwiCZSI\nTBCR17z6Sq2STyIyUkRmicguEbkyZV9PEfmTV+NpsYjklCWPIlBlI5jiyxJBHXSQfX82bkx/zB/+\nYDeZY46JuZ8exx1n7/+nP8FDDzWXw+nf38ah0uEEKtkcfbQt91AN6T2ACy6wSP8vf4GPfMTafJOP\nJrCShKN4ZDVJiEgNcDtmVVwLzBWRR1Q1aAbdhNV+OjPkEj/H3B9neWU3cnrGe/fdwurQFZXgPKgs\nEZRIcxQVlq9uaoJbbrG0RrG47DL7SaVvXxOuxsbmumI+O3ZYdDV8ePH65SiMoUPN6VrpBgmfLl2s\nNFeQ7t3tR7fVIwXYzF2Kr7KIEkGNAVao6kpVbQSmY7OU96GqGz23x55gu4j0AD6uqvd4x+1R1W25\ndDDREZQ/DypCBAXNa0OF8de/2hdw7Njw/cWkfXv7Pw5bW2jJEhPWTDZ/R3kRge98B8aPz35sJTNi\n6F7kvd3hClPGauaO4hFFoFJrKwXrMWVjCLBRRO4RkfkicreI5PT8EsXFVzZyGIOC9ONQqnDTTTb2\nVK65FP37h49DufReZXDttcmppl0sRg1qYE+HzuFfEmeSqEqK/VzcHjgKuFRV53nriVwN3BB28OTJ\nk/dt+yU9Eh1B7dhhYU/ECGrkSBtnSuXf/7bTzzij9b5SkU6gFiwobDVfhyMuRgzYye52XegQtrPM\nNnNHcYgiUGuA4JTRYD2mbKwGVqmqV4yFh4C0M3yCAuWTeIHq188+8bt2ZZj+bqSLoG66yeY9lXOJ\nhIMPDjdKzJ9fXuF0OHyGD2iggc6Ezhcv40RdR/GIckucCwwTkUEi0hEraTEjw/H74m+vtPsqERnh\nNfk1oSKhaim+xE6C81N8NTX2qW9oyHj4sGHmRAo+6M2ZY5UazjuvyH3NQlgE1dQEr7wCRx5Znj45\nHEEG991JfVMaj5UzSVQlWSMoVd0rIpdhC0/VAFNVdamIfM12690i0hervdQdaBKRK4DDVLUe+Abw\nexHpALwBXBi1c9u2QceOCX7i8QUKmseh/NchdOlijrm33jLnFVj09L//29o9V2oOPhieTqk5vGKF\nRa+9epWnTw5HkEEHNvB2Yxp1cTbzqiTSGJRXIHBkSttdge31tCzLHjxuAbb8b84kOr0HzTZzaB6H\nytJhP803dKg55GbNgt//vgR9zULYXKj5862MjsORBPbv1MAy7czmzSGrG0RI8TU12WT4qEveOMpP\noitJJF6gghFTHk6+W26Bb3wjGbP/w1J8TqAcSUIadkLXLlZRIpUIJgl/PbakVB13ZCfRApVoizm0\nTPHlMBdq2TKr1vz3v8Ollxa5jxEJM0k4gXIkioYG2nfrHC5QESIol96rPBItUImPoFLHoCJazZcv\nt5p7X/5yGZYIScP++1slifp6e61qAuUMEo7EsHMnHXp2Dl/4M4JJwjn4Ko9E1weoKIGKsOQGmEC9\n8or9LInsZyw+Is1pvpEjzcjRtWsZFoF0ONLR0EDn3hlSfFkiKOfgqzwSH0El1mIOeUVQhxxiT3Jn\nn528pbmDaT6X3nMkjoYGuh6QIcUXIYJ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sR1Dt2sH73w8LF6Y/Nh+BgvKMQzU1OVH0efNN\nt7JxpZJDBOWyFNVFogTqgAPKNH/BN0lA9jRfvgJVjnGoI46wldwaGkr7vknhyivhhhvgxRdh9Ghb\nBsVReUSNoGpqXARVZSROoErOzp321OU/oWUTqJ07KyeCWrTI/t2zp7TvmwSammDaNJg928pajx8P\n995b7l458iGiSYJ27VwEVWUkSqDKapDwQ7dsTr4dO/JbD6ScTr733ivP+5aTxYstKn7sMdu+/35b\n5djVRKw8XIqvzeIEyjdI+HzgA7BsWfqbeiWNQfm0RYH6z39szQURS+/16AGf/CQ88EC5e+bIFWeS\nyAkRGSAiT4nIYhFZJCLf8Np7icgTIrJMRB4XkZ6Bc64RkRUislRETgm0HyUiC0VkuYjcFmjvKCLT\nvXNeEJGBgX2TvOOXicgFhfwuTqD8CMqnc2c49FB76g6jksagfKpdoN54o/Wqxb5ABZk0ydJ+P/iB\niZajMoia4nMRlM8e4EpVPRz4CHCpiIwCrgaeVNWRwFPANQAichjwOWA0MBG4Q2SfG+BO4CJVHQGM\nEJHxXvtFwGZVHQ7cBtzqXasXcD1wDHAscENQCHPFCVTQIOGTKc2Xr0ANHAirVpXnCa+xsfTvWUru\nuQduvLH5tWq4QI0da3/vWbPgv/+tfuGuVB59FL7+9ebXUWtfOpMEAKq6TlVf8bbrgaXAAOAMwB+I\nvRc409s+HVuIdo+qvgWsAMaISD+gu6rO9Y67L3BO8FoPAWO97fHAE6pap6pbsYVu0y5Sm41IAiUi\nE0TkNS9suypk/0gRmSUiu0TkylzODZKIFB9kNkrkK1D77WeR2tq1uZ9bKNV+I96xw8aXfBv5G2/Y\njWro0JbHtWtnUwgefdT+Fhs2lL6vjuzMmAH/93/N30FnksgbERkMHAHMBvp6i8uiquuAg7zD+gOr\nAqet8dr6A6sD7au9thbneKuu14lI7wzXyousFU9FpAa4HTgJWAvMFZFHVPW1wGGbgMtpVtdczt1H\nIlJ8YAL117+GH79+vaXr8sEfhzokbPHhmAk69ypRoL73PejdG/73f7MfW18Pq1dbVDRsGDz8MJxx\nRvicBX+iXd++JlADBsTbb0fhzJoF558P3/8+/OMfuZkkqjyCmjlzJjNnzox0rIh0w6KbK1S1XkRS\nJwLGOTGwKBOEokRQY4AVqrpSVRuB6Vh4tw9V3aiqL2G5z5zODVK25d7DUnwLFrR+Gnv+efjXv+B/\n/ie/9yrlONTWrc3blSZQDQ1w003wl79EO37HDnvC9r+406fDOedkPuegg9wk5iSybZs9aNxxB7z6\nqolVLiaJKo+gamtrmTx58r6fdIhIe0yc7ldVfwLgehHp6+3vB/gphDVA8Kl5gNeWrr3FOSLSDuih\nqpu99oFpzsmZKAKVGrIFw7xYz01MBNW7t62K+8YbzW3bttlT3V13kXe55FI6+SpZoN55x/6NerOp\nr4eTTrI037JlVq4qdfwpFT+CciSLF1+Eo4+Gbt3g+uvh2mudSSI/fgssUdWfB9pmAF/0ticBjwTa\nz/GceUOAYcAcLw1YJyJjPNPEBSnnTPK2z8JMFwCPA+NEpKdnmBjnteVFDosaFZ9p0ybvG96pra2l\ntra2+G8aFkFB8zjUsGH2+vLLYdw4OP30/N9ryBB47rn8z8+FLVvMmPH225UnUOvXmy08KLKZ2LED\nzjoLrrvOUnhnnWVP05k46CAnUEnkhRdsYjWY6/KWW6zQcpQUn7OZAyAixwOfBxaJyMtYKu97wC3A\ngyLyJWAl5txDVZeIyIPAEqARuER1X12wS4FpwH7AP1X1Ma99KnC/iKzAhnjO8a61RURuBOZ57zvF\nM0vkRRSBKiRky+ncm2+enPW+EjthLj5odvKddRY8+KBVJJg/v7D3GjzYJoyWgi1brPjt6NGVJ1Ab\nNlgF+O3box1fXw8f/KCVNrrmGksLZcOl+JLJrFlwySW23b49TJkC553nIqgcUNXngXR30tDlHFX1\nJuCmkPaXgA+EtO/GE7iQfdMwUSuYKCm+ucAwERkkIh0xpZyR4fjgYFlO55ZcnCA8xQfNEdSqVRY9\n/f73+bn3gpR6DGr//aFjx8oTqPXrLfqLWkPQd1ZefbWlUP0n8Ey4FF/yaGpqLk3lc/bZJlh9+2Y/\nvw2YJNoaWSMoVd0rIpdhfvYaYKqqLhWRr9luvdsbeJsHdAeaROQK4DDPOdLq3KL9NvmQKcU3f76l\nGa64Aj784cLf65BDbHylsRE6dCj8epnYssUEasuWyhSoQYOip0OD1v/Bg6Od41J8yWPJEvu7BAej\na2rgV7+Kdn4bMEm0NSKNQXl5x5EpbXcFttfT0u2R8dzEsHs37Npl4x2pHHKI3dgbG+GqjNO3otOh\ngxksVq/O36oelTfftJt8fX3lCZSf4tu1K9rx9fU2qJ4LLsWXPGbNihb9psOl+KqORFWSKDn++FPY\nfBkRuPlm+N3v4s09lsrJt3SpjT9VaorvkEPsASLKDSefydMuxZc8XngBPvrR/M93JomqwwlUWHrP\n56tftSf5OBk8uDTjUEuXwqhRlSlQGzZYpNmpk4lUJpqaok/kDHLggVbZ3D1xJ4dZswoTKBdBVR1O\noEo9O7gURondu81ePnx4ZQrU+vWWguvcOXuaz5/EWZPjR7lTJ4u6olrZHcVl40b7ux92WP7XcCaJ\nqqNtC9Ts2XYTLyWlSPG9/rpFfh07VqZArVtnKbj99svu5Mtn/MnHGSWSwwsvwJgxhaXTnUmi6mi7\nAvXee/DLX8Jll5X2fQcPtomHxeTVV5ufRDt2rKxq5vX1FjX16WOTbuvqMh+fb/FecEaJJFFoeg9c\niq8KabsCNX26mQiOOKK071uKMaiXXzabPFReBLVmjRVwFTGR2rQp8/GFCFTfvuaodJSfQg0S4EwS\nVUjbFChV+MlP4NvfLv17DxhgT+3FFI1XXqlcgVq1qrnae+/esHlz5uMLSfGdcw5897tmKHEUH1X7\n+6bS2AgvvQTHHlvY9V0EVXW0TYF68klbjmJC3uto5U/79nDwwS0L0caJamVHUKtXNy+BkS2C2rgR\nfv3r/COoz37WphKcfLJNEnUUl7/9Dd7/fiu8HGTBAsss9Mx74VXDmSSqjrYpUH70FDb/qRSceir8\n6U/FufY779hTZH+vaHwlC1S2COq222wJ90LE/vzzrSDpKaeYI9BRPP78Z/t36tSW7XGMP4EzSVQh\nbU+gFi2yJ7bPf758fbjwQlumvBhfJj968sW30gQqmOLLFEHt2mXR0yc/CYceWth7fuELNhb54IOF\nXceRnsZGi6CmTbMHi+CCmnGMP4GLoKqQtidQP/2pOfc6dSpfH/z1bp55Jv5rB9N7YPX43n03/vcp\nFlEjqD/+0X7Pv/3NUraFcvHFcOedhV/HEc4zz9jSNZ/+tM0FfOih5n0ugnKkoW0J1Nq1tpT7xReX\ntx8i8KUvWRQVN6kCNWaMPaFWCqljUGHiqmpTBC6/PL73nTjR5l8VuqSKI5w//7l5Jepvfxt+9CP7\nO65ebalVf921QqhEk8R//gMnnFBZD5ElpG0J1O23W2ovU3mjUvH5z8OMGdnn+eRKqkANG2aVJVat\nsrTKa6/F+35xE0zxHXMMPPtsy3QQwAMP2P/bxInxvW+7dlba6q67sh/rCEcVHn0UHn64ZQWQpib4\ny18segI47TSbHvDMM80LFMYxHpzNZn7PPTB3buHvExerVsHnPmdltz75STcGGkLbEaj6erj7bvjW\nt8rdE+PAA22Z8j/+Mb5rbt1qT2LB6hgi8P+3d+ZhUlTnGv99A6hgFBeQXeCqyCIorqAgi7gGRIxG\nURGVoEnALWrCNYmgZnFBgxq3xAWjXjdc0LjFBHNFXFDBDVyIV0WCGwhIRhadee8f32mnpmeGGaCr\nu2c87/PU011Vp6vervecOtt3vm+//WDWLDfM6NateFuZpaVeSDMNiM6dPS5UMuzG7bd7C3zatPV3\nb1QbxozxeahsK7OIumHKFB8+nzIFBg50gx1wjy0tWngATXDdzjkHJk/O3fBe5rrZeXvhQrfUXLTI\n886RRxZHb2XVKjjiCA+yOW2a+8089tiqjbHvOL47FdStt8KAAbDDDoVmUoFcD/O9+qpHls1+ce+7\nr1dQr77q+y+/nJv7ST6nN2XKxl1n0SKvFJKLdDM48sgK66+pUz1i7owZsOuuG3fP6tC6NRx4oHuw\nr69YsMAr2trWj+Uajzziw3YzZnivd+hQH15+6aXKw3sZjBrl+fC++3JbQZWV+Ut++nTvqfXu7RXA\n5Mmex7p399GLQhpTSDB2LOy8M5x7ruf3P//Ze52nn+7nIxySat2AQ4C3gXeBX9SQ5mpgAfAqsFvi\n+NnAm8DrwJ3AJjX8Xqnhm2+kzp2l555L7x4bgq+/ltq0kebNy831rrxSGjeu6vFZs6RWrXwbOVI6\n++yNv9eaNX6dLl2kbbeV/vOfDbvOW29J220ntWzp3AYOrHx+/nypXTvpiSec/9tvbzz3deEf/5B2\n2UUqL0/3PmmgvNyf3777Sjvt5M82H5g7V2rRQnrhhcrHH3zQdd1mG+nVV6v+7qKLpMaNpdLS3PB4\n/nlp662ltm39GUyd6tf+8MOK42++KR1wgHT00dLKlbm57/riiiuk3r2r/u8VK/z4kCHS3/9epzwY\n3psb9M6uD1tdKqcS4F9AR6BJqIC6ZqU5FHg0fN8HeCF8bwv8X6ZSAu4BTqzhPrWKIUlPP/10ndJV\nwn33SX37rv/v0uCSjQkTpHPPzQ2XUaOkm26qenL1ai+gjz8uffCB1L69dPPN1V/opZe8EEvS2rXS\nKadIY8b4NTKYPVvq2VM67DBpyRJp2DDpxhsrc6kLPvpI2n5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"text/plain": [
"<matplotlib.figure.Figure at 0xb1b8748>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"# now plot both time series together\n",
"%matplotlib inline\n",
"from matplotlib import pyplot as plt\n",
"\n",
"fig, ax1 = plt.subplots()\n",
"\n",
"ax1.plot(bact_ts.index.date, bact_ts)\n",
"plt.xticks(rotation=45)\n",
"\n",
"ax2 = ax1.twinx()\n",
"ax2.plot(diatom_ts.index.date, diatom_ts, c='red')"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "Python [Root]",
"language": "python",
"name": "Python [Root]"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 2
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython2",
"version": "2.7.12"
}
},
"nbformat": 4,
"nbformat_minor": 0
}
@ashepherd
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Plotting two time series datasets in Jupyter notebook

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