{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Analysis of Ficus RAD-seq data"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Table of contents\n",
"[Software installation (conda)](#Required-software) \n",
"[The assembled RAD data](#The-assembled-data-sets) \n",
"[Phylogenetic analysis (raxml)](#Analysis-BPP) \n",
"[Plot results](#Plots)\n",
"\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Required software\n",
"All required software can be installed locally using *conda*. I assume here that you already have `ipyrad` installed using conda. "
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"## conda install toytree -c eaton-lab\n",
"## conda install ipyrad -c ipyrad \n",
"## conda install bpp -c ipyrad "
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"ipyrad v.0.6.20\n"
]
}
],
"source": [
"## import packages\n",
"import ipyrad as ip\n",
"import ipyrad.analysis as ipa\n",
"import numpy as np\n",
"import toyplot\n",
"import toytree\n",
"import glob\n",
"\n",
"## print ipyrad info\n",
"print \"ipyrad v.{}\".format(ip.__version__)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Cluster setup\n",
"see more information on ipyparallel setup here. "
]
},
{
"cell_type": "code",
"execution_count": 181,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"host compute node: [40 cores] on tinus\n"
]
}
],
"source": [
"## print ipyparallel cluster information\n",
"import ipyparallel as ipp\n",
"ipyclient = ipp.Client()\n",
"print ip.cluster_info(ipyclient)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Assembled data sets"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
" loading Assembly: pharma_dhi_s4\n",
" from saved path: ~/Documents/Ficus/analysis-ipyrad/pharma_dhi_s4.json\n",
" loading Assembly: america_dhi_s4\n",
" from saved path: ~/Documents/Ficus/analysis-ipyrad/america_dhi_s4.json\n"
]
}
],
"source": [
"## create subsampled pharma-clade branch\n",
"pharma = ip.load_json(\"analysis-ipyrad/pharma_dhi_s4.json\")\n",
"america = ip.load_json(\"analysis-ipyrad/america_dhi_s4.json\")"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Analysis BPP"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#### Pharmacosyceae clade"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"## a tree hypothesis (guidetree) (here based on tetrad results)\n",
"newick = \"((((glabrata, insipida), yoponensis), maxima), tonduzii);\"\n",
"\n",
"## a dictionary mapping sample names to 'species' names\n",
"imap = {\n",
" \"glabrata\": [\"B133_glabrata\", \"A97_glabrata\", \"B134_glabrata\", \"B131_glabrataXmaxima\"],\n",
" \"insipida\": [\"A95_insipida\", \"B127_insipida\", \"C15_insipida\", \n",
" \"B128_insipida\", \"B127_insipida\", \"A95_insipida\"],\n",
" \"yoponensis\": [\"C45_yoponensis\", \"C47_yoponensis\", \"C46_yoponensis\"],\n",
" \"maxima\": [\"A94_maxima\", \"C17_maxima\", \"B119_maxima\", \"B120_maxima\"],\n",
" \"tonduzii\": [\"C48_tonduzii\"],\n",
" }\n",
"\n",
"## loci must have data for at least N samples in each species.\n",
"minmap = {\n",
" \"glabrata\": 4,\n",
" \"insipida\": 4,\n",
" \"yoponensis\": 3,\n",
" \"maxima\": 4, \n",
" \"tonduzii\": 1,\n",
" }"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {},
"outputs": [
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],
"source": [
"## check your (starting) tree hypothesis\n",
"toytree.tree(newick).draw();"
]
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"## create a bpp object to run algorithm 00\n",
"boo = ipa.bpp(\n",
" locifile=pharma.outfiles.loci,\n",
" guidetree=newick, \n",
" imap=imap, \n",
" minmap=minmap, \n",
" workdir=\"analysis-bpp/\",\n",
" )"
]
},
{
"cell_type": "code",
"execution_count": 13,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"burnin 10000 \n",
"cleandata 0 \n",
"copied False \n",
"delimit_alg (0, 5) \n",
"finetune (0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01)\n",
"infer_delimit 0 \n",
"infer_sptree 0 \n",
"nsample 50000 \n",
"sampfreq 25 \n",
"seed 12345 \n",
"tauprior (2, 2000, 1) \n",
"thetaprior (2, 2000) \n",
"usedata 1 "
]
},
"execution_count": 13,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"## set some optional params, leaving others at their defaults\n",
"boo.params.burnin = 10000\n",
"boo.params.nsample = 50000\n",
"boo.params.sampfreq = 25\n",
"boo.params\n"
]
},
{
"cell_type": "code",
"execution_count": 14,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"maxloci 500 \n",
"minmap {'insipida': 4, 'tonduzii': 1, 'maxima': 4, 'glabrata': 4, 'yoponensis': 3}\n",
"minsnps 4 "
]
},
"execution_count": 14,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"## set some optional filters leaving others at their defaults\n",
"boo.filters.maxloci=500\n",
"boo.filters.minsnps=4\n",
"\n",
"## print filters\n",
"boo.filters"
]
},
{
"cell_type": "code",
"execution_count": 15,
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"input files created for job pharma-ltest (500 loci)\n"
]
}
],
"source": [
"## write files \n",
"boo.write_bpp_files(prefix=\"pharma-ltest\")"
]
},
{
"cell_type": "code",
"execution_count": 21,
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"submitted 3 bpp jobs [pharma-loci500] (500 loci)\n"
]
}
],
"source": [
"boo.submit_bpp_jobs(\n",
" prefix=\"pharma-loci500\", \n",
" nreps=3, \n",
" ipyclient=ipyclient, \n",
" seed=12345, \n",
" randomize_order=True,\n",
" )"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Tree inference"
]
},
{
"cell_type": "code",
"execution_count": 31,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"burnin 10000 \n",
"cleandata 0 \n",
"copied False \n",
"delimit_alg (0, 5) \n",
"finetune (0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01)\n",
"infer_delimit 0 \n",
"infer_sptree 1 \n",
"nsample 50000 \n",
"sampfreq 25 \n",
"seed 285080861 \n",
"tauprior (2, 2000, 1) \n",
"thetaprior (2, 2000) \n",
"usedata 1 "
]
},
"execution_count": 31,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"b10 = boo.copy()\n",
"b10.params.infer_sptree = 1\n",
"b10.params"
]
},
{
"cell_type": "code",
"execution_count": 133,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"maxloci 500 \n",
"minmap {'insipida': 4, 'tonduzii': 1, 'maxima': 4, 'glabrata': 4, 'yoponensis': 3}\n",
"minsnps 4 "
]
},
"execution_count": 133,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"b10.filters.maxloci = 500\n",
"b10.filters"
]
},
{
"cell_type": "code",
"execution_count": 47,
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"submitted 3 bpp jobs [pharma-loci500-b10] (500 loci)\n"
]
}
],
"source": [
"b10.submit_bpp_jobs(\n",
" prefix=\"pharma-loci500-b10\", \n",
" nreps=3, \n",
" ipyclient=ipyclient, \n",
" seed=12345, \n",
" randomize_order=True,\n",
" )"
]
},
{
"cell_type": "code",
"execution_count": 182,
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"submitted 10 bpp jobs [pharma-loci50-bb10] (50 loci)\n"
]
}
],
"source": [
"bb10 = b10.copy()\n",
"bb10.filters.maxloci = 50\n",
"bb10.filters\n",
"\n",
"bb10.submit_bpp_jobs(\n",
" prefix=\"pharma-loci50-bb10\", \n",
" nreps=10, \n",
" ipyclient=ipyclient, \n",
" seed=12345, \n",
" randomize_order=True,\n",
" )"
]
},
{
"cell_type": "markdown",
"metadata": {
"collapsed": true
},
"source": [
"## Plot results"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#### fixed tree"
]
},
{
"cell_type": "code",
"execution_count": 50,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>count</th>\n",
" <th>mean</th>\n",
" <th>std</th>\n",
" <th>min</th>\n",
" <th>25%</th>\n",
" <th>50%</th>\n",
" <th>75%</th>\n",
" <th>max</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>theta_1glabrata</th>\n",
" <td>760.0000</td>\n",
" <td>0.0011</td>\n",
" <td>0.0001</td>\n",
" <td>0.0008</td>\n",
" <td>0.0010</td>\n",
" <td>0.0010</td>\n",
" <td>0.0012</td>\n",
" <td>0.0016</td>\n",
" </tr>\n",
" <tr>\n",
" <th>theta_2insipida</th>\n",
" <td>760.0000</td>\n",
" <td>0.0011</td>\n",
" <td>0.0001</td>\n",
" <td>0.0008</td>\n",
" <td>0.0010</td>\n",
" <td>0.0010</td>\n",
" <td>0.0011</td>\n",
" <td>0.0017</td>\n",
" </tr>\n",
" <tr>\n",
" <th>theta_3maxima</th>\n",
" <td>760.0000</td>\n",
" <td>0.0038</td>\n",
" <td>0.0003</td>\n",
" <td>0.0030</td>\n",
" <td>0.0036</td>\n",
" <td>0.0038</td>\n",
" <td>0.0040</td>\n",
" <td>0.0046</td>\n",
" </tr>\n",
" <tr>\n",
" <th>theta_5yoponensis</th>\n",
" <td>760.0000</td>\n",
" <td>0.0020</td>\n",
" <td>0.0002</td>\n",
" <td>0.0016</td>\n",
" <td>0.0019</td>\n",
" <td>0.0020</td>\n",
" <td>0.0021</td>\n",
" <td>0.0025</td>\n",
" </tr>\n",
" <tr>\n",
" <th>theta_6glabratainsipidayoponensismaximatonduzii</th>\n",
" <td>760.0000</td>\n",
" <td>0.0157</td>\n",
" <td>0.0008</td>\n",
" <td>0.0135</td>\n",
" <td>0.0152</td>\n",
" <td>0.0157</td>\n",
" <td>0.0162</td>\n",
" <td>0.0181</td>\n",
" </tr>\n",
" <tr>\n",
" <th>theta_7glabratainsipidayoponensismaxima</th>\n",
" <td>760.0000</td>\n",
" <td>0.0015</td>\n",
" <td>0.0009</td>\n",
" <td>0.0001</td>\n",
" <td>0.0008</td>\n",
" <td>0.0013</td>\n",
" <td>0.0020</td>\n",
" <td>0.0051</td>\n",
" </tr>\n",
" <tr>\n",
" <th>theta_8glabratainsipidayoponensis</th>\n",
" <td>760.0000</td>\n",
" <td>0.0019</td>\n",
" <td>0.0012</td>\n",
" <td>0.0001</td>\n",
" <td>0.0010</td>\n",
" <td>0.0016</td>\n",
" <td>0.0024</td>\n",
" <td>0.0070</td>\n",
" </tr>\n",
" <tr>\n",
" <th>theta_9glabratainsipida</th>\n",
" <td>760.0000</td>\n",
" <td>0.0132</td>\n",
" <td>0.0019</td>\n",
" <td>0.0079</td>\n",
" <td>0.0120</td>\n",
" <td>0.0132</td>\n",
" <td>0.0143</td>\n",
" <td>0.0191</td>\n",
" </tr>\n",
" <tr>\n",
" <th>tau_6glabratainsipidayoponensismaximatonduzii</th>\n",
" <td>760.0000</td>\n",
" <td>0.0030</td>\n",
" <td>0.0002</td>\n",
" <td>0.0024</td>\n",
" <td>0.0029</td>\n",
" <td>0.0030</td>\n",
" <td>0.0031</td>\n",
" <td>0.0036</td>\n",
" </tr>\n",
" <tr>\n",
" <th>tau_7glabratainsipidayoponensismaxima</th>\n",
" <td>760.0000</td>\n",
" <td>0.0030</td>\n",
" <td>0.0002</td>\n",
" <td>0.0024</td>\n",
" <td>0.0029</td>\n",
" <td>0.0030</td>\n",
" <td>0.0031</td>\n",
" <td>0.0036</td>\n",
" </tr>\n",
" <tr>\n",
" <th>tau_8glabratainsipidayoponensis</th>\n",
" <td>760.0000</td>\n",
" <td>0.0029</td>\n",
" <td>0.0002</td>\n",
" <td>0.0024</td>\n",
" <td>0.0028</td>\n",
" <td>0.0030</td>\n",
" <td>0.0031</td>\n",
" <td>0.0036</td>\n",
" </tr>\n",
" <tr>\n",
" <th>tau_9glabratainsipida</th>\n",
" <td>760.0000</td>\n",
" <td>0.0011</td>\n",
" <td>0.0002</td>\n",
" <td>0.0008</td>\n",
" <td>0.0010</td>\n",
" <td>0.0010</td>\n",
" <td>0.0011</td>\n",
" <td>0.0016</td>\n",
" </tr>\n",
" <tr>\n",
" <th>lnL</th>\n",
" <td>760.0000</td>\n",
" <td>-69160.9028</td>\n",
" <td>43.5274</td>\n",
" <td>-69280.3200</td>\n",
" <td>-69189.8015</td>\n",
" <td>-69157.8950</td>\n",
" <td>-69129.9325</td>\n",
" <td>-68997.7510</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" count mean std \\\n",
"theta_1glabrata 760.0000 0.0011 0.0001 \n",
"theta_2insipida 760.0000 0.0011 0.0001 \n",
"theta_3maxima 760.0000 0.0038 0.0003 \n",
"theta_5yoponensis 760.0000 0.0020 0.0002 \n",
"theta_6glabratainsipidayoponensismaximatonduzii 760.0000 0.0157 0.0008 \n",
"theta_7glabratainsipidayoponensismaxima 760.0000 0.0015 0.0009 \n",
"theta_8glabratainsipidayoponensis 760.0000 0.0019 0.0012 \n",
"theta_9glabratainsipida 760.0000 0.0132 0.0019 \n",
"tau_6glabratainsipidayoponensismaximatonduzii 760.0000 0.0030 0.0002 \n",
"tau_7glabratainsipidayoponensismaxima 760.0000 0.0030 0.0002 \n",
"tau_8glabratainsipidayoponensis 760.0000 0.0029 0.0002 \n",
"tau_9glabratainsipida 760.0000 0.0011 0.0002 \n",
"lnL 760.0000 -69160.9028 43.5274 \n",
"\n",
" min 25% \\\n",
"theta_1glabrata 0.0008 0.0010 \n",
"theta_2insipida 0.0008 0.0010 \n",
"theta_3maxima 0.0030 0.0036 \n",
"theta_5yoponensis 0.0016 0.0019 \n",
"theta_6glabratainsipidayoponensismaximatonduzii 0.0135 0.0152 \n",
"theta_7glabratainsipidayoponensismaxima 0.0001 0.0008 \n",
"theta_8glabratainsipidayoponensis 0.0001 0.0010 \n",
"theta_9glabratainsipida 0.0079 0.0120 \n",
"tau_6glabratainsipidayoponensismaximatonduzii 0.0024 0.0029 \n",
"tau_7glabratainsipidayoponensismaxima 0.0024 0.0029 \n",
"tau_8glabratainsipidayoponensis 0.0024 0.0028 \n",
"tau_9glabratainsipida 0.0008 0.0010 \n",
"lnL -69280.3200 -69189.8015 \n",
"\n",
" 50% 75% \\\n",
"theta_1glabrata 0.0010 0.0012 \n",
"theta_2insipida 0.0010 0.0011 \n",
"theta_3maxima 0.0038 0.0040 \n",
"theta_5yoponensis 0.0020 0.0021 \n",
"theta_6glabratainsipidayoponensismaximatonduzii 0.0157 0.0162 \n",
"theta_7glabratainsipidayoponensismaxima 0.0013 0.0020 \n",
"theta_8glabratainsipidayoponensis 0.0016 0.0024 \n",
"theta_9glabratainsipida 0.0132 0.0143 \n",
"tau_6glabratainsipidayoponensismaximatonduzii 0.0030 0.0031 \n",
"tau_7glabratainsipidayoponensismaxima 0.0030 0.0031 \n",
"tau_8glabratainsipidayoponensis 0.0030 0.0031 \n",
"tau_9glabratainsipida 0.0010 0.0011 \n",
"lnL -69157.8950 -69129.9325 \n",
"\n",
" max \n",
"theta_1glabrata 0.0016 \n",
"theta_2insipida 0.0017 \n",
"theta_3maxima 0.0046 \n",
"theta_5yoponensis 0.0025 \n",
"theta_6glabratainsipidayoponensismaximatonduzii 0.0181 \n",
"theta_7glabratainsipidayoponensismaxima 0.0051 \n",
"theta_8glabratainsipidayoponensis 0.0070 \n",
"theta_9glabratainsipida 0.0191 \n",
"tau_6glabratainsipidayoponensismaximatonduzii 0.0036 \n",
"tau_7glabratainsipidayoponensismaxima 0.0036 \n",
"tau_8glabratainsipidayoponensis 0.0036 \n",
"tau_9glabratainsipida 0.0016 \n",
"lnL -68997.7510 "
]
},
"execution_count": 50,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"import pandas as pd\n",
"tab = pd.read_csv(\"analysis-bpp/pharma-loci500-r0.mcmc.txt\", sep=\"\\t\", index_col=0)\n",
"pd.set_option('display.float_format', lambda x: '%.4f' % x)\n",
"tab.describe().T"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#### inferred tree"
]
},
{
"cell_type": "code",
"execution_count": 190,
"metadata": {},
"outputs": [],
"source": [
"def plotit(mtre):\n",
" ## set up axes\n",
" canvas = toyplot.Canvas(width=450, height=350)\n",
" axes = canvas.cartesian()\n",
"\n",
" ## plot the tree\n",
" mtre.draw_cloudtree(\n",
" axes=axes,\n",
" edge_style={\"opacity\": 0.01},\n",
" use_edge_lengths=True,\n",
" orient='right',\n",
" );\n",
"\n",
" ## style axes\n",
" axes.y.show = False\n",
" axes.x.show = True\n",
" axes.x.ticks.show = True\n",
" axes.x.ticks.locator = toyplot.locator.Explicit(\n",
" locations=np.linspace(0, -3, 3) / 1000.,\n",
" labels=np.linspace(0, 3, 3),\n",
" )\n",
" axes.x.label.text = \"Divergence time (substitutions/site x 10<sup>-3</sup>)\"\n",
" return canvas, axes"
]
},
{
"cell_type": "code",
"execution_count": 191,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"(<toyplot.canvas.Canvas at 0x7f8a39bddd50>,\n",
" <toyplot.coordinates.Cartesian at 0x7f8a39bdd690>)"
]
},
"execution_count": 191,
"metadata": {},
"output_type": "execute_result"
},
{
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class=\"toyplot-Datum\" style=\"fill:rgb(16.1%,15.3%,14.1%);fill-opacity:1.0;font-weight:normal;opacity:1.0;stroke:none;text-anchor:start\" transform=\"translate(304.14937759336101,294.27480916030532)translate(12.0,3.375)\"><tspan style=\"font-size:12.0px\">glabrata</tspan></text><text class=\"toyplot-Datum\" style=\"fill:rgb(16.1%,15.3%,14.1%);fill-opacity:1.0;font-weight:normal;opacity:1.0;stroke:none;text-anchor:start\" transform=\"translate(304.14937759336101,234.63740458015266)translate(12.0,3.375)\"><tspan style=\"font-size:12.0px\">insipida</tspan></text><text class=\"toyplot-Datum\" style=\"fill:rgb(16.1%,15.3%,14.1%);fill-opacity:1.0;font-weight:normal;opacity:1.0;stroke:none;text-anchor:start\" transform=\"translate(304.14937759336101,175.0)translate(12.0,3.375)\"><tspan style=\"font-size:12.0px\">yoponensis</tspan></text><text class=\"toyplot-Datum\" style=\"fill:rgb(16.1%,15.3%,14.1%);fill-opacity:1.0;font-weight:normal;opacity:1.0;stroke:none;text-anchor:start\" transform=\"translate(304.14937759336095,115.36259541984731)translate(12.0,3.375)\"><tspan style=\"font-size:12.0px\">maxima</tspan></text><text class=\"toyplot-Datum\" style=\"fill:rgb(16.1%,15.3%,14.1%);fill-opacity:1.0;font-weight:normal;opacity:1.0;stroke:none;text-anchor:start\" transform=\"translate(304.14937759336095,55.725190839694662)translate(12.0,3.375)\"><tspan style=\"font-size:12.0px\">tonduzii</tspan></text></g></g></g><g class=\"toyplot-coordinates-Axis\" id=\"tb04d12560fa340589c02ca4923d19e8b\" transform=\"translate(50.0,300.0)translate(0,10.0)\"><line style=\"\" x1=\"0\" x2=\"254.14937759336098\" y1=\"0\" y2=\"0\"></line><g><line style=\"\" x1=\"254.14937759336098\" x2=\"254.14937759336098\" y1=\"0\" y2=\"-5\"></line><line style=\"\" x1=\"145.25932721060133\" x2=\"145.25932721060133\" y1=\"0\" y2=\"-5\"></line><line style=\"\" x1=\"36.3692768278417\" x2=\"36.3692768278417\" y1=\"0\" y2=\"-5\"></line></g><g><text 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"\n",
" function save_csv(data_table)\n",
" {\n",
" var uri = \"data:text/csv;charset=utf-8,\";\n",
" uri += data_table.names.join(\",\") + \"\\n\";\n",
" for(var i = 0; i != data_table.columns[0].length; ++i)\n",
" {\n",
" for(var j = 0; j != data_table.columns.length; ++j)\n",
" {\n",
" if(j)\n",
" uri += \",\";\n",
" uri += data_table.columns[j][i];\n",
" }\n",
" uri += \"\\n\";\n",
" }\n",
" uri = encodeURI(uri);\n",
"\n",
" var link = document.createElement(\"a\");\n",
" if(typeof link.download != \"undefined\")\n",
" {\n",
" link.href = uri;\n",
" link.style = \"visibility:hidden\";\n",
" link.download = data_table.filename + \".csv\";\n",
"\n",
" document.body.appendChild(link);\n",
" link.click();\n",
" document.body.removeChild(link);\n",
" }\n",
" else\n",
" {\n",
" window.open(uri);\n",
" }\n",
" }\n",
"\n",
" function open_popup(data_table)\n",
" {\n",
" return function(e)\n",
" {\n",
" var popup = document.querySelector(\"#t5f433d27d20f4d998b110f67995ed118 .toyplot-mark-popup\");\n",
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" popup.querySelector(\".toyplot-mark-popup-save-csv\").onclick = function() { popup.style.visibility = \"hidden\"; save_csv(data_table); }\n",
" popup.style.left = (e.clientX - 50) + \"px\";\n",
" popup.style.top = (e.clientY - 20) + \"px\";\n",
" popup.style.visibility = \"visible\";\n",
" e.stopPropagation();\n",
" e.preventDefault();\n",
" }\n",
"\n",
" }\n",
"\n",
" for(var i = 0; i != data_tables.length; ++i)\n",
" {\n",
" var data_table = data_tables[i];\n",
" var event_target = document.querySelector(\"#\" + data_table.id);\n",
" event_target.oncontextmenu = open_popup(data_table);\n",
" }\n",
" })();\n",
" </script><script>\n",
" (function()\n",
" {\n",
" function _sign(x)\n",
" {\n",
" return x < 0 ? -1 : x > 0 ? 1 : 0;\n",
" }\n",
"\n",
" function _mix(a, b, amount)\n",
" {\n",
" return ((1.0 - amount) * a) + (amount * b);\n",
" }\n",
"\n",
" function _log(x, base)\n",
" {\n",
" return Math.log(Math.abs(x)) / Math.log(base);\n",
" }\n",
"\n",
" function _in_range(a, x, b)\n",
" {\n",
" var left = Math.min(a, b);\n",
" var right = Math.max(a, b);\n",
" return left <= x && x <= right;\n",
" }\n",
"\n",
" function inside(range, projection)\n",
" {\n",
" for(var i = 0; i != projection.length; ++i)\n",
" {\n",
" var segment = projection[i];\n",
" if(_in_range(segment.range.min, range, segment.range.max))\n",
" return true;\n",
" }\n",
" return false;\n",
" }\n",
"\n",
" function to_domain(range, projection)\n",
" {\n",
" for(var i = 0; i != projection.length; ++i)\n",
" {\n",
" var segment = projection[i];\n",
" if(_in_range(segment.range.bounds.min, range, segment.range.bounds.max))\n",
" {\n",
" if(segment.scale == \"linear\")\n",
" {\n",
" var amount = (range - segment.range.min) / (segment.range.max - segment.range.min);\n",
" return _mix(segment.domain.min, segment.domain.max, amount)\n",
" }\n",
" else if(segment.scale[0] == \"log\")\n",
" {\n",
" var amount = (range - segment.range.min) / (segment.range.max - segment.range.min);\n",
" var base = segment.scale[1];\n",
" return _sign(segment.domain.min) * Math.pow(base, _mix(_log(segment.domain.min, base), _log(segment.domain.max, base), amount));\n",
" }\n",
" }\n",
" }\n",
" }\n",
"\n",
" function display_coordinates(e)\n",
" {\n",
" var current = svg.createSVGPoint();\n",
" current.x = e.clientX;\n",
" current.y = e.clientY;\n",
"\n",
" for(var axis_id in axes)\n",
" {\n",
" var axis = document.querySelector(\"#\" + axis_id);\n",
" var coordinates = axis.querySelector(\".toyplot-coordinates-Axis-coordinates\");\n",
" if(coordinates)\n",
" {\n",
" var projection = axes[axis_id];\n",
" var local = current.matrixTransform(axis.getScreenCTM().inverse());\n",
" if(inside(local.x, projection))\n",
" {\n",
" var domain = to_domain(local.x, projection);\n",
" coordinates.style.visibility = \"visible\";\n",
" coordinates.setAttribute(\"transform\", \"translate(\" + local.x + \")\");\n",
" var text = coordinates.querySelector(\"text\");\n",
" text.textContent = domain.toFixed(2);\n",
" }\n",
" else\n",
" {\n",
" coordinates.style.visibility= \"hidden\";\n",
" }\n",
" }\n",
" }\n",
" }\n",
"\n",
" var root_id = \"t5f433d27d20f4d998b110f67995ed118\";\n",
" var axes = {\"tb04d12560fa340589c02ca4923d19e8b\": [{\"domain\": {\"bounds\": {\"max\": Infinity, \"min\": -Infinity}, \"max\": 0.001320377142857143, \"min\": -0.0035009999999999998}, \"range\": {\"bounds\": {\"max\": Infinity, \"min\": -Infinity}, \"max\": 350.0, \"min\": 0.0}, \"scale\": \"linear\"}]};\n",
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" var svg = document.querySelector(\"#\" + root_id + \" svg\");\n",
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]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"mtre = toytree.multitree(\n",
" \"analysis-bpp/pharma-loci500-b10-r0.mcmc.txt\",\n",
" fixed_order=boo.tree.get_leaf_names(),\n",
" treeslice=(150, 1500, 5),\n",
" )\n",
"\n",
"plotit(mtre)"
]
},
{
"cell_type": "code",
"execution_count": 179,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"(<toyplot.canvas.Canvas at 0x7f8a3a84dfd0>,\n",
" <toyplot.coordinates.Cartesian at 0x7f8a444d5750>)"
]
},
"execution_count": 179,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
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"output_type": "display_data"
}
],
"source": [
"mtre = toytree.multitree(\n",
" \"analysis-bpp/pharma-loci500-b10-r2.mcmc.txt\",\n",
" fixed_order=boo.tree.get_leaf_names(),\n",
" treeslice=(150, 1500, 5),\n",
" )\n",
"\n",
"\n",
"\n",
"plotit(mtre)"
]
},
{
"cell_type": "code",
"execution_count": 209,
"metadata": {},
"outputs": [
{
"data": {
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},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"ctre = mtre.get_consensus_tree()\n",
"#ctre.root(['tonduzii', 'maxima'])\n",
"ctre.draw(width=300, height=300, node_labels=True);"
]
},
{
"cell_type": "code",
"execution_count": 201,
"metadata": {},
"outputs": [
{
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r=\"0.0\"></circle></g></g></g><g class=\"toyplot-mark-Text\" id=\"t89471ce93e674651a296dec3d5fa1997\" style=\"-toyplot-anchor-shift:12px;alignment-baseline:middle;fill:rgb(16.1%,15.3%,14.1%);fill-opacity:1.0;font-size:12px;font-weight:normal;stroke:none;text-anchor:start\"><g class=\"toyplot-Series\"><text class=\"toyplot-Datum\" style=\"fill:rgb(16.1%,15.3%,14.1%);fill-opacity:1.0;font-weight:normal;opacity:1.0;stroke:none;text-anchor:start\" transform=\"translate(304.1313661695068,294.27480916030532)translate(12.0,3.375)\"><tspan style=\"font-size:12.0px\">glabrata</tspan></text><text class=\"toyplot-Datum\" style=\"fill:rgb(16.1%,15.3%,14.1%);fill-opacity:1.0;font-weight:normal;opacity:1.0;stroke:none;text-anchor:start\" transform=\"translate(304.1313661695068,234.63740458015266)translate(12.0,3.375)\"><tspan style=\"font-size:12.0px\">insipida</tspan></text><text class=\"toyplot-Datum\" 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"\n",
" function save_csv(data_table)\n",
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" {\n",
" for(var j = 0; j != data_table.columns.length; ++j)\n",
" {\n",
" if(j)\n",
" uri += \",\";\n",
" uri += data_table.columns[j][i];\n",
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" uri += \"\\n\";\n",
" }\n",
" uri = encodeURI(uri);\n",
"\n",
" var link = document.createElement(\"a\");\n",
" if(typeof link.download != \"undefined\")\n",
" {\n",
" link.href = uri;\n",
" link.style = \"visibility:hidden\";\n",
" link.download = data_table.filename + \".csv\";\n",
"\n",
" document.body.appendChild(link);\n",
" link.click();\n",
" document.body.removeChild(link);\n",
" }\n",
" else\n",
" {\n",
" window.open(uri);\n",
" }\n",
" }\n",
"\n",
" function open_popup(data_table)\n",
" {\n",
" return function(e)\n",
" {\n",
" var popup = document.querySelector(\"#tbe1bfd22306c45229dbac9f4d59af00c .toyplot-mark-popup\");\n",
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" popup.style.left = (e.clientX - 50) + \"px\";\n",
" popup.style.top = (e.clientY - 20) + \"px\";\n",
" popup.style.visibility = \"visible\";\n",
" e.stopPropagation();\n",
" e.preventDefault();\n",
" }\n",
"\n",
" }\n",
"\n",
" for(var i = 0; i != data_tables.length; ++i)\n",
" {\n",
" var data_table = data_tables[i];\n",
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" event_target.oncontextmenu = open_popup(data_table);\n",
" }\n",
" })();\n",
" </script><script>\n",
" (function()\n",
" {\n",
" function _sign(x)\n",
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" return x < 0 ? -1 : x > 0 ? 1 : 0;\n",
" }\n",
"\n",
" function _mix(a, b, amount)\n",
" {\n",
" return ((1.0 - amount) * a) + (amount * b);\n",
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"\n",
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"\n",
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" }\n",
"\n",
" function inside(range, projection)\n",
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" for(var i = 0; i != projection.length; ++i)\n",
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" }\n",
" return false;\n",
" }\n",
"\n",
" function to_domain(range, projection)\n",
" {\n",
" for(var i = 0; i != projection.length; ++i)\n",
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" if(segment.scale == \"linear\")\n",
" {\n",
" var amount = (range - segment.range.min) / (segment.range.max - segment.range.min);\n",
" return _mix(segment.domain.min, segment.domain.max, amount)\n",
" }\n",
" else if(segment.scale[0] == \"log\")\n",
" {\n",
" var amount = (range - segment.range.min) / (segment.range.max - segment.range.min);\n",
" var base = segment.scale[1];\n",
" return _sign(segment.domain.min) * Math.pow(base, _mix(_log(segment.domain.min, base), _log(segment.domain.max, base), amount));\n",
" }\n",
" }\n",
" }\n",
" }\n",
"\n",
" function display_coordinates(e)\n",
" {\n",
" var current = svg.createSVGPoint();\n",
" current.x = e.clientX;\n",
" current.y = e.clientY;\n",
"\n",
" for(var axis_id in axes)\n",
" {\n",
" var axis = document.querySelector(\"#\" + axis_id);\n",
" var coordinates = axis.querySelector(\".toyplot-coordinates-Axis-coordinates\");\n",
" if(coordinates)\n",
" {\n",
" var projection = axes[axis_id];\n",
" var local = current.matrixTransform(axis.getScreenCTM().inverse());\n",
" if(inside(local.x, projection))\n",
" {\n",
" var domain = to_domain(local.x, projection);\n",
" coordinates.style.visibility = \"visible\";\n",
" coordinates.setAttribute(\"transform\", \"translate(\" + local.x + \")\");\n",
" var text = coordinates.querySelector(\"text\");\n",
" text.textContent = domain.toFixed(2);\n",
" }\n",
" else\n",
" {\n",
" coordinates.style.visibility= \"hidden\";\n",
" }\n",
" }\n",
" }\n",
" }\n",
"\n",
" var root_id = \"tbe1bfd22306c45229dbac9f4d59af00c\";\n",
" var axes = {\"tacb0b49e50b7454a991173a6828c526b\": [{\"domain\": {\"bounds\": {\"max\": Infinity, \"min\": -Infinity}, \"max\": 0.0014566571428571427, \"min\": -0.003865}, \"range\": {\"bounds\": {\"max\": Infinity, \"min\": -Infinity}, \"max\": 350.0, \"min\": 0.0}, \"scale\": \"linear\"}]};\n",
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]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"mtre = toytree.multitree(\n",
" \"analysis-bpp/pharma-loci50-bb10-r0.mcmc.txt\",\n",
" fixed_order=boo.tree.get_leaf_names(),\n",
" treeslice=(500, 5000, 10),\n",
" )\n",
"\n",
"plotit(mtre);"
]
},
{
"cell_type": "code",
"execution_count": 210,
"metadata": {},
"outputs": [
{
"data": {
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transform=\"translate(304.14937759336101,294.27480916030532)translate(12.0,3.375)\"><tspan style=\"font-size:12.0px\">glabrata</tspan></text><text class=\"toyplot-Datum\" style=\"fill:rgb(16.1%,15.3%,14.1%);fill-opacity:1.0;font-weight:normal;opacity:1.0;stroke:none;text-anchor:start\" transform=\"translate(304.14937759336101,234.63740458015266)translate(12.0,3.375)\"><tspan style=\"font-size:12.0px\">insipida</tspan></text><text class=\"toyplot-Datum\" style=\"fill:rgb(16.1%,15.3%,14.1%);fill-opacity:1.0;font-weight:normal;opacity:1.0;stroke:none;text-anchor:start\" transform=\"translate(304.14937759336101,175.0)translate(12.0,3.375)\"><tspan style=\"font-size:12.0px\">yoponensis</tspan></text><text class=\"toyplot-Datum\" style=\"fill:rgb(16.1%,15.3%,14.1%);fill-opacity:1.0;font-weight:normal;opacity:1.0;stroke:none;text-anchor:start\" transform=\"translate(304.14937759336101,115.36259541984731)translate(12.0,3.375)\"><tspan style=\"font-size:12.0px\">maxima</tspan></text><text 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" function _mix(a, b, amount)\n",
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" return ((1.0 - amount) * a) + (amount * b);\n",
" }\n",
"\n",
" function _log(x, base)\n",
" {\n",
" return Math.log(Math.abs(x)) / Math.log(base);\n",
" }\n",
"\n",
" function _in_range(a, x, b)\n",
" {\n",
" var left = Math.min(a, b);\n",
" var right = Math.max(a, b);\n",
" return left <= x && x <= right;\n",
" }\n",
"\n",
" function inside(range, projection)\n",
" {\n",
" for(var i = 0; i != projection.length; ++i)\n",
" {\n",
" var segment = projection[i];\n",
" if(_in_range(segment.range.min, range, segment.range.max))\n",
" return true;\n",
" }\n",
" return false;\n",
" }\n",
"\n",
" function to_domain(range, projection)\n",
" {\n",
" for(var i = 0; i != projection.length; ++i)\n",
" {\n",
" var segment = projection[i];\n",
" if(_in_range(segment.range.bounds.min, range, segment.range.bounds.max))\n",
" {\n",
" if(segment.scale == \"linear\")\n",
" {\n",
" var amount = (range - segment.range.min) / (segment.range.max - segment.range.min);\n",
" return _mix(segment.domain.min, segment.domain.max, amount)\n",
" }\n",
" else if(segment.scale[0] == \"log\")\n",
" {\n",
" var amount = (range - segment.range.min) / (segment.range.max - segment.range.min);\n",
" var base = segment.scale[1];\n",
" return _sign(segment.domain.min) * Math.pow(base, _mix(_log(segment.domain.min, base), _log(segment.domain.max, base), amount));\n",
" }\n",
" }\n",
" }\n",
" }\n",
"\n",
" function display_coordinates(e)\n",
" {\n",
" var current = svg.createSVGPoint();\n",
" current.x = e.clientX;\n",
" current.y = e.clientY;\n",
"\n",
" for(var axis_id in axes)\n",
" {\n",
" var axis = document.querySelector(\"#\" + axis_id);\n",
" var coordinates = axis.querySelector(\".toyplot-coordinates-Axis-coordinates\");\n",
" if(coordinates)\n",
" {\n",
" var projection = axes[axis_id];\n",
" var local = current.matrixTransform(axis.getScreenCTM().inverse());\n",
" if(inside(local.x, projection))\n",
" {\n",
" var domain = to_domain(local.x, projection);\n",
" coordinates.style.visibility = \"visible\";\n",
" coordinates.setAttribute(\"transform\", \"translate(\" + local.x + \")\");\n",
" var text = coordinates.querySelector(\"text\");\n",
" text.textContent = domain.toFixed(2);\n",
" }\n",
" else\n",
" {\n",
" coordinates.style.visibility= \"hidden\";\n",
" }\n",
" }\n",
" }\n",
" }\n",
"\n",
" var root_id = \"t15e45dc1a0e747a8b3bf2d73487b654f\";\n",
" var axes = {\"t1945f97dba564a0ab6498ac0f9997a9f\": [{\"domain\": {\"bounds\": {\"max\": Infinity, \"min\": -Infinity}, \"max\": 0.0014776457142857144, \"min\": -0.003918}, \"range\": {\"bounds\": {\"max\": Infinity, \"min\": -Infinity}, \"max\": 350.0, \"min\": 0.0}, \"scale\": \"linear\"}]};\n",
"\n",
" var svg = document.querySelector(\"#\" + root_id + \" svg\");\n",
" svg.addEventListener(\"click\", display_coordinates);\n",
" })();\n",
" </script></div></div>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"mtre = toytree.multitree(\n",
" \"analysis-bpp/pharma-loci50-bb10-r9.mcmc.txt\",\n",
" fixed_order=boo.tree.get_leaf_names(),\n",
" treeslice=(1000, 5000, 10),\n",
" )\n",
"\n",
"plotit(mtre);"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 2",
"language": "python",
"name": "python2"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 2
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython2",
"version": "2.7.13"
}
},
"nbformat": 4,
"nbformat_minor": 1
}