mamacneil/Fish_biomass.ipynb Secret

## Fish_biomass.ipynb
{
	"cells": [
		{
			"cell_type": "markdown",
			"metadata": {
			},
			"source": [
				"# Fish total, herbivore, planktivore, and piscivore abundance model for Chagos Rats\n",
				"\n",
				"The models here will be run in [Python](https://www.python.org), using the [PyMC3 package](https://github.com/pymc-devs/pymc3).\n",
				"\n",
				"### Data wrangling\n",
				"\n",
				"The first step is to instantiate the various python packages needed for the analysis:"
			]
		},
		{
			"cell_type": "code",
			"execution_count": 1,
			"metadata": {
				"collapsed": true
			},
			"outputs": [

			],
			"source": [
				"# Import packages\n",
				"%matplotlib inline\n",
				"import pandas as pd\n",
				"import numpy as np\n",
				"import matplotlib.pyplot as plt\n",
				"import seaborn as sns\n",
				"from mpl_toolkits.basemap import Basemap as bm\n",
				"from scipy.stats import gaussian_kde\n",
				"import scipy as sp\n",
				"import pymc3 as pm\n",
				"import matplotlib as mp\n",
				"import sqlite3\n",
				"import os\n",
				"import theano as th\n",
				"\n",
				"# Return list of unique items and an index of their position in L\n",
				"def indexall(L):\n",
				"    poo = []\n",
				"    for p in L:\n",
				"        if not p in poo:\n",
				"            poo.append(p)\n",
				"    Ix = np.array([poo.index(p) for p in L])\n",
				"    return poo,Ix\n",
				"\n",
				"# Return list of unique items and an index of their position in long, relative to short\n",
				"def subindexall(short,long):\n",
				"    poo = []\n",
				"    out = []\n",
				"    for s,l in zip(short,long):\n",
				"        if not l in poo:\n",
				"            poo.append(l)\n",
				"            out.append(s)\n",
				"    return indexall(out)\n",
				"\n",
				"# Function to standardize covariates\n",
				"def stdize(x):\n",
				"    return (x-np.mean(x))/(2*np.std(x))\n",
				"\n",
				"# Path to plot storage\n",
				"plotdir = os.getcwd()+'/plots'"
			]
		},
		{
			"cell_type": "code",
			"execution_count": 2,
			"metadata": {
			},
			"outputs": [
				{
					"data": {
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							"<table border=\"1\" class=\"dataframe\">\n",
							"  <thead>\n",
							"    <tr style=\"text-align: right;\">\n",
							"      <th></th>\n",
							"      <th>Atoll</th>\n",
							"      <th>Island</th>\n",
							"      <th>Treatment</th>\n",
							"      <th>Transect</th>\n",
							"      <th>Area</th>\n",
							"      <th>Family</th>\n",
							"      <th>Species</th>\n",
							"      <th>Function</th>\n",
							"      <th>Structure</th>\n",
							"      <th>Coral_cover</th>\n",
							"      <th>Length</th>\n",
							"      <th>Biomass</th>\n",
							"      <th>Abundance</th>\n",
							"    </tr>\n",
							"  </thead>\n",
							"  <tbody>\n",
							"    <tr>\n",
							"      <th>0</th>\n",
							"      <td>Great_Chagos_Bank</td>\n",
							"      <td>Eagle</td>\n",
							"      <td>Rats</td>\n",
							"      <td>1</td>\n",
							"      <td>150</td>\n",
							"      <td>Acanthuridae</td>\n",
							"      <td>Ctenochaetus_striatus</td>\n",
							"      <td>Herbivore</td>\n",
							"      <td>2.0</td>\n",
							"      <td>12.67</td>\n",
							"      <td>13</td>\n",
							"      <td>4.0</td>\n",
							"      <td>66.7</td>\n",
							"    </tr>\n",
							"    <tr>\n",
							"      <th>1</th>\n",
							"      <td>Great_Chagos_Bank</td>\n",
							"      <td>Eagle</td>\n",
							"      <td>Rats</td>\n",
							"      <td>1</td>\n",
							"      <td>150</td>\n",
							"      <td>Acanthuridae</td>\n",
							"      <td>Naso_brachycentron</td>\n",
							"      <td>Herbivore</td>\n",
							"      <td>2.0</td>\n",
							"      <td>12.67</td>\n",
							"      <td>33</td>\n",
							"      <td>49.6</td>\n",
							"      <td>66.7</td>\n",
							"    </tr>\n",
							"    <tr>\n",
							"      <th>2</th>\n",
							"      <td>Great_Chagos_Bank</td>\n",
							"      <td>Eagle</td>\n",
							"      <td>Rats</td>\n",
							"      <td>1</td>\n",
							"      <td>150</td>\n",
							"      <td>Scaridae</td>\n",
							"      <td>Chlorurus_sordidus</td>\n",
							"      <td>Herbivore</td>\n",
							"      <td>2.0</td>\n",
							"      <td>12.67</td>\n",
							"      <td>13</td>\n",
							"      <td>3.3</td>\n",
							"      <td>66.7</td>\n",
							"    </tr>\n",
							"    <tr>\n",
							"      <th>3</th>\n",
							"      <td>Great_Chagos_Bank</td>\n",
							"      <td>Eagle</td>\n",
							"      <td>Rats</td>\n",
							"      <td>1</td>\n",
							"      <td>150</td>\n",
							"      <td>Acanthuridae</td>\n",
							"      <td>Acanthurus_nigrofuscus</td>\n",
							"      <td>Herbivore</td>\n",
							"      <td>2.0</td>\n",
							"      <td>12.67</td>\n",
							"      <td>13</td>\n",
							"      <td>4.1</td>\n",
							"      <td>66.7</td>\n",
							"    </tr>\n",
							"    <tr>\n",
							"      <th>4</th>\n",
							"      <td>Great_Chagos_Bank</td>\n",
							"      <td>Eagle</td>\n",
							"      <td>Rats</td>\n",
							"      <td>1</td>\n",
							"      <td>150</td>\n",
							"      <td>Acanthuridae</td>\n",
							"      <td>Acanthurus_nigrofuscus</td>\n",
							"      <td>Herbivore</td>\n",
							"      <td>2.0</td>\n",
							"      <td>12.67</td>\n",
							"      <td>12</td>\n",
							"      <td>3.2</td>\n",
							"      <td>66.7</td>\n",
							"    </tr>\n",
							"  </tbody>\n",
							"</table>\n",
							"</div>"
						],
						"text/plain": [
							"               Atoll Island Treatment  Transect  Area        Family  \\\n",
							"0  Great_Chagos_Bank  Eagle      Rats         1   150  Acanthuridae   \n",
							"1  Great_Chagos_Bank  Eagle      Rats         1   150  Acanthuridae   \n",
							"2  Great_Chagos_Bank  Eagle      Rats         1   150      Scaridae   \n",
							"3  Great_Chagos_Bank  Eagle      Rats         1   150  Acanthuridae   \n",
							"4  Great_Chagos_Bank  Eagle      Rats         1   150  Acanthuridae   \n",
							"\n",
							"                  Species   Function  Structure  Coral_cover  Length  Biomass  \\\n",
							"0   Ctenochaetus_striatus  Herbivore        2.0        12.67      13      4.0   \n",
							"1      Naso_brachycentron  Herbivore        2.0        12.67      33     49.6   \n",
							"2      Chlorurus_sordidus  Herbivore        2.0        12.67      13      3.3   \n",
							"3  Acanthurus_nigrofuscus  Herbivore        2.0        12.67      13      4.1   \n",
							"4  Acanthurus_nigrofuscus  Herbivore        2.0        12.67      12      3.2   \n",
							"\n",
							"   Abundance  \n",
							"0       66.7  \n",
							"1       66.7  \n",
							"2       66.7  \n",
							"3       66.7  \n",
							"4       66.7  "
						]
					},
					"execution_count": 2,
					"metadata": {
					},
					"output_type": "execute_result"
				}
			],
			"source": [
				"# Import age and growth data\n",
				"xdata = pd.read_csv('Chagos_rats_birds_UVC_data.csv')\n",
				"xdata.head()"
			]
		},
		{
			"cell_type": "code",
			"execution_count": 3,
			"metadata": {
			},
			"outputs": [
				{
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							"      <th></th>\n",
							"      <th></th>\n",
							"      <th></th>\n",
							"      <th>Treatment</th>\n",
							"      <th>Area</th>\n",
							"      <th>Family</th>\n",
							"      <th>Species</th>\n",
							"      <th>Function</th>\n",
							"      <th>Structure</th>\n",
							"      <th>Coral_cover</th>\n",
							"      <th>Length</th>\n",
							"      <th>Biomass</th>\n",
							"      <th>Abundance</th>\n",
							"    </tr>\n",
							"    <tr>\n",
							"      <th>Atoll</th>\n",
							"      <th>Island</th>\n",
							"      <th>Transect</th>\n",
							"      <th></th>\n",
							"      <th></th>\n",
							"      <th></th>\n",
							"      <th></th>\n",
							"      <th></th>\n",
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							"  </thead>\n",
							"  <tbody>\n",
							"    <tr>\n",
							"      <th rowspan=\"5\" valign=\"top\">Great_Chagos_Bank</th>\n",
							"      <th rowspan=\"5\" valign=\"top\">Eagle</th>\n",
							"      <th>1</th>\n",
							"      <td>Rats</td>\n",
							"      <td>150</td>\n",
							"      <td>Acanthuridae</td>\n",
							"      <td>Ctenochaetus_striatus</td>\n",
							"      <td>Herbivore</td>\n",
							"      <td>2.0</td>\n",
							"      <td>12.67</td>\n",
							"      <td>13</td>\n",
							"      <td>4.0</td>\n",
							"      <td>66.7</td>\n",
							"    </tr>\n",
							"    <tr>\n",
							"      <th>1</th>\n",
							"      <td>Rats</td>\n",
							"      <td>150</td>\n",
							"      <td>Acanthuridae</td>\n",
							"      <td>Naso_brachycentron</td>\n",
							"      <td>Herbivore</td>\n",
							"      <td>2.0</td>\n",
							"      <td>12.67</td>\n",
							"      <td>33</td>\n",
							"      <td>49.6</td>\n",
							"      <td>66.7</td>\n",
							"    </tr>\n",
							"    <tr>\n",
							"      <th>1</th>\n",
							"      <td>Rats</td>\n",
							"      <td>150</td>\n",
							"      <td>Scaridae</td>\n",
							"      <td>Chlorurus_sordidus</td>\n",
							"      <td>Herbivore</td>\n",
							"      <td>2.0</td>\n",
							"      <td>12.67</td>\n",
							"      <td>13</td>\n",
							"      <td>3.3</td>\n",
							"      <td>66.7</td>\n",
							"    </tr>\n",
							"    <tr>\n",
							"      <th>1</th>\n",
							"      <td>Rats</td>\n",
							"      <td>150</td>\n",
							"      <td>Acanthuridae</td>\n",
							"      <td>Acanthurus_nigrofuscus</td>\n",
							"      <td>Herbivore</td>\n",
							"      <td>2.0</td>\n",
							"      <td>12.67</td>\n",
							"      <td>13</td>\n",
							"      <td>4.1</td>\n",
							"      <td>66.7</td>\n",
							"    </tr>\n",
							"    <tr>\n",
							"      <th>1</th>\n",
							"      <td>Rats</td>\n",
							"      <td>150</td>\n",
							"      <td>Acanthuridae</td>\n",
							"      <td>Acanthurus_nigrofuscus</td>\n",
							"      <td>Herbivore</td>\n",
							"      <td>2.0</td>\n",
							"      <td>12.67</td>\n",
							"      <td>12</td>\n",
							"      <td>3.2</td>\n",
							"      <td>66.7</td>\n",
							"    </tr>\n",
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						"text/plain": [
							"                                  Treatment  Area        Family  \\\n",
							"Atoll             Island Transect                                 \n",
							"Great_Chagos_Bank Eagle  1             Rats   150  Acanthuridae   \n",
							"                         1             Rats   150  Acanthuridae   \n",
							"                         1             Rats   150      Scaridae   \n",
							"                         1             Rats   150  Acanthuridae   \n",
							"                         1             Rats   150  Acanthuridae   \n",
							"\n",
							"                                                  Species   Function  \\\n",
							"Atoll             Island Transect                                      \n",
							"Great_Chagos_Bank Eagle  1          Ctenochaetus_striatus  Herbivore   \n",
							"                         1             Naso_brachycentron  Herbivore   \n",
							"                         1             Chlorurus_sordidus  Herbivore   \n",
							"                         1         Acanthurus_nigrofuscus  Herbivore   \n",
							"                         1         Acanthurus_nigrofuscus  Herbivore   \n",
							"\n",
							"                                   Structure  Coral_cover  Length  Biomass  \\\n",
							"Atoll             Island Transect                                            \n",
							"Great_Chagos_Bank Eagle  1               2.0        12.67      13      4.0   \n",
							"                         1               2.0        12.67      33     49.6   \n",
							"                         1               2.0        12.67      13      3.3   \n",
							"                         1               2.0        12.67      13      4.1   \n",
							"                         1               2.0        12.67      12      3.2   \n",
							"\n",
							"                                   Abundance  \n",
							"Atoll             Island Transect             \n",
							"Great_Chagos_Bank Eagle  1              66.7  \n",
							"                         1              66.7  \n",
							"                         1              66.7  \n",
							"                         1              66.7  \n",
							"                         1              66.7  "
						]
					},
					"execution_count": 3,
					"metadata": {
					},
					"output_type": "execute_result"
				}
			],
			"source": [
				"# Generate new index\n",
				"ydata = xdata.set_index(['Atoll', 'Island', 'Transect'])\n",
				"ydata.head()"
			]
		},
		{
			"cell_type": "code",
			"execution_count": 4,
			"metadata": {
				"collapsed": true
			},
			"outputs": [

			],
			"source": [
				"# Split data frames into functional groups\n",
				"Herbs = xdata[xdata.Function=='Herbivore']\n",
				"Plank = xdata[xdata.Function=='Planktivore']\n",
				"Pisci = xdata[xdata.Function=='Piscivore']\n",
				"Mixed = xdata[xdata.Function=='Mixed-diet']\n",
				"Corali = xdata[xdata.Function=='Corallivore']\n",
				"Inverti = xdata[xdata.Function=='Invertivore']\n",
				"Total = xdata"
			]
		},
		{
			"cell_type": "code",
			"execution_count": 5,
			"metadata": {
				"collapsed": true
			},
			"outputs": [

			],
			"source": [
				"# Grab summarized data for modelling\n",
				"# Responses\n",
				"h_lbiomass = np.log(Herbs.groupby(['Atoll','Island','Transect'])['Biomass'].sum().ravel())\n",
				"pl_lbiomass = np.log(Plank.groupby(['Atoll','Island','Transect'])['Biomass'].sum().ravel()+1)\n",
				"pi_lbiomass = np.log(Pisci.groupby(['Atoll','Island','Transect'])['Biomass'].sum().ravel()+1)\n",
				"mi_lbiomass = np.log(Mixed.groupby(['Atoll','Island','Transect'])['Biomass'].sum().ravel()+1)\n",
				"c_lbiomass = np.log(Corali.groupby(['Atoll','Island','Transect'])['Biomass'].sum().ravel()+1)\n",
				"i_lbiomass = np.log(Inverti.groupby(['Atoll','Island','Transect'])['Biomass'].sum().ravel()+1)\n",
				"lbiomass = np.log(Total.groupby(['Atoll','Island','Transect'])['Biomass'].sum().ravel())\n",
				"rdata = [h_lbiomass,pl_lbiomass,pi_lbiomass,mi_lbiomass,c_lbiomass,i_lbiomass]\n",
				"Res_names = ['Herbivores','Planktivores','Piscivores','Mixed-diet','Corallivores','Invertivores']\n",
				"\n",
				"# Covariates\n",
				"structure = np.array([x[0] for x in Total.groupby(['Atoll','Island','Transect'])['Structure'].unique().ravel()])\n",
				"struct = structure-np.mean(structure)\n",
				"hc = np.array([x[0] for x in Total.groupby(['Atoll','Island','Transect'])['Coral_cover'].unique().ravel()])\n",
				"treatment = np.array([x[0] for x in Total.groupby(['Atoll','Island','Transect'])['Treatment'].unique().ravel()])\n",
				"It = (treatment=='Rats')*1\n",
				"\n",
				"# Hierarchy\n",
				"Atoll_, Island_, Transect_ = np.array([np.array(x) for x in ydata.index.unique()]).T\n",
				"Atoll,Ia = subindexall(Atoll_,Island_)\n",
				"natoll = len(Atoll)\n",
				"Island,Is = indexall(Island_)\n",
				"nisland = len(Island)"
			]
		},
		{
			"cell_type": "code",
			"execution_count": 6,
			"metadata": {
			},
			"outputs": [
				{
					"data": {
						"text/plain": [
							"array([[ 1.        ,  0.19640874],\n",
							"       [ 0.19640874,  1.        ]])"
						]
					},
					"execution_count": 6,
					"metadata": {
					},
					"output_type": "execute_result"
				}
			],
			"source": [
				"# Check correlation between structure and hard coral\n",
				"np.corrcoef(structure,hc)"
			]
		},
		{
			"cell_type": "markdown",
			"metadata": {
			},
			"source": [
				"# Total biomass model"
			]
		},
		{
			"cell_type": "markdown",
			"metadata": {
			},
			"source": [
				"Set up data loop"
			]
		},
		{
			"cell_type": "code",
			"execution_count": 7,
			"metadata": {
				"collapsed": true
			},
			"outputs": [

			],
			"source": [
				"# Shared data trick from https://github.com/pymc-devs/pymc3/issues/1825\n",
				"shared_data = th.shared(rdata[0], borrow=True)"
			]
		},
		{
			"cell_type": "code",
			"execution_count": 8,
			"metadata": {
				"collapsed": true
			},
			"outputs": [

			],
			"source": [
				"Model = pm.Model()\n",
				"\n",
				"with Model:\n",
				"    # Global estimate\n",
				"    γ0 = pm.Normal('Mean_biomass', 0, 1000)\n",
				"    # Variation among islands\n",
				"    σ0 = pm.Uniform('Island_SD', 0, 100)\n",
				"    \n",
				"    # Transect-level intercepts\n",
				"    β0 = pm.Normal('Island_d15N', γ0, σ0, shape=nisland)\n",
				"    β1 = pm.Normal('Rat_effect', 0, 1000)\n",
				"    β2 = pm.Normal('Structure', 0, 1000)\n",
				"    β3 = pm.Normal('Hard_coral', 0, 1000)\n",
				"\n",
				"    # Mean model\n",
				"    μ = β0[Is]+β1*It+β2*struct+β3*hc\n",
				"    \n",
				"    # Observation error\n",
				"    σ1 = pm.Uniform('Obs_sigma', lower=0., upper=100.)\n",
				"    \n",
				"    # Likelihood\n",
				"    Yi = pm.Normal('Yi', μ, σ1, observed=shared_data)"
			]
		},
		{
			"cell_type": "code",
			"execution_count": 9,
			"metadata": {
			},
			"outputs": [
				{
					"name": "stderr",
					"output_type": "stream",
					"text": [
						"Auto-assigning NUTS sampler...\n",
						"Initializing NUTS using jitter+adapt_diag...\n",
						" 85%|████████▌ | 8972/10500 [00:35<00:06, 250.88it/s]/Users/aaronmacneil/anaconda/lib/python3.6/site-packages/pymc3/step_methods/hmc/nuts.py:452: UserWarning: The acceptance probability in chain 1 does not match the target. It is 0.714515383942, but should be close to 0.8. Try to increase the number of tuning steps.\n",
						"  % (self._chain_id, mean_accept, target_accept))\n",
						"/Users/aaronmacneil/anaconda/lib/python3.6/site-packages/pymc3/step_methods/hmc/nuts.py:468: UserWarning: Chain 1 contains 1164 diverging samples after tuning. If increasing `target_accept` does not help try to reparameterize.\n",
						"  % (self._chain_id, n_diverging))\n",
						"100%|█████████▉| 10479/10500 [00:41<00:00, 252.15it/s]/Users/aaronmacneil/anaconda/lib/python3.6/site-packages/pymc3/step_methods/hmc/nuts.py:468: UserWarning: Chain 0 contains 519 diverging samples after tuning. If increasing `target_accept` does not help try to reparameterize.\n",
						"  % (self._chain_id, n_diverging))\n",
						"100%|██████████| 10500/10500 [00:41<00:00, 252.10it/s]\n",
						"Auto-assigning NUTS sampler...\n",
						"Initializing NUTS using jitter+adapt_diag...\n",
						" 88%|████████▊ | 9258/10500 [00:36<00:04, 252.51it/s]/Users/aaronmacneil/anaconda/lib/python3.6/site-packages/pymc3/step_methods/hmc/nuts.py:468: UserWarning: Chain 1 contains 217 diverging samples after tuning. If increasing `target_accept` does not help try to reparameterize.\n",
						"  % (self._chain_id, n_diverging))\n",
						"100%|█████████▉| 10473/10500 [00:41<00:00, 254.16it/s]/Users/aaronmacneil/anaconda/lib/python3.6/site-packages/pymc3/step_methods/hmc/nuts.py:468: UserWarning: Chain 0 contains 365 diverging samples after tuning. If increasing `target_accept` does not help try to reparameterize.\n",
						"  % (self._chain_id, n_diverging))\n",
						"100%|██████████| 10500/10500 [00:41<00:00, 254.19it/s]\n",
						"Auto-assigning NUTS sampler...\n",
						"Initializing NUTS using jitter+adapt_diag...\n",
						"100%|█████████▉| 10482/10500 [00:38<00:00, 270.99it/s]/Users/aaronmacneil/anaconda/lib/python3.6/site-packages/pymc3/step_methods/hmc/nuts.py:468: UserWarning: Chain 0 contains 464 diverging samples after tuning. If increasing `target_accept` does not help try to reparameterize.\n",
						"  % (self._chain_id, n_diverging))\n",
						"100%|██████████| 10500/10500 [00:38<00:00, 270.56it/s]\n",
						"/Users/aaronmacneil/anaconda/lib/python3.6/site-packages/pymc3/step_methods/hmc/nuts.py:468: UserWarning: Chain 1 contains 797 diverging samples after tuning. If increasing `target_accept` does not help try to reparameterize.\n",
						"  % (self._chain_id, n_diverging))\n",
						"Auto-assigning NUTS sampler...\n",
						"Initializing NUTS using jitter+adapt_diag...\n",
						"100%|██████████| 10500/10500 [00:48<00:00, 218.22it/s]\n",
						"Auto-assigning NUTS sampler...\n",
						"Initializing NUTS using jitter+adapt_diag...\n",
						" 88%|████████▊ | 9237/10500 [00:39<00:05, 235.70it/s]/Users/aaronmacneil/anaconda/lib/python3.6/site-packages/pymc3/step_methods/hmc/nuts.py:468: UserWarning: Chain 1 contains 290 diverging samples after tuning. If increasing `target_accept` does not help try to reparameterize.\n",
						"  % (self._chain_id, n_diverging))\n",
						"100%|█████████▉| 10492/10500 [00:43<00:00, 239.32it/s]/Users/aaronmacneil/anaconda/lib/python3.6/site-packages/pymc3/step_methods/hmc/nuts.py:468: UserWarning: Chain 0 contains 172 diverging samples after tuning. If increasing `target_accept` does not help try to reparameterize.\n",
						"  % (self._chain_id, n_diverging))\n",
						"100%|██████████| 10500/10500 [00:43<00:00, 239.28it/s]\n",
						"Auto-assigning NUTS sampler...\n",
						"Initializing NUTS using jitter+adapt_diag...\n",
						" 99%|█████████▉| 10416/10500 [00:30<00:00, 346.53it/s]/Users/aaronmacneil/anaconda/lib/python3.6/site-packages/pymc3/step_methods/hmc/nuts.py:452: UserWarning: The acceptance probability in chain 0 does not match the target. It is 0.502850834416, but should be close to 0.8. Try to increase the number of tuning steps.\n",
						"  % (self._chain_id, mean_accept, target_accept))\n",
						"/Users/aaronmacneil/anaconda/lib/python3.6/site-packages/pymc3/step_methods/hmc/nuts.py:468: UserWarning: Chain 0 contains 2802 diverging samples after tuning. If increasing `target_accept` does not help try to reparameterize.\n",
						"  % (self._chain_id, n_diverging))\n",
						"100%|██████████| 10500/10500 [00:30<00:00, 348.61it/s]\n",
						"/Users/aaronmacneil/anaconda/lib/python3.6/site-packages/pymc3/step_methods/hmc/nuts.py:452: UserWarning: The acceptance probability in chain 1 does not match the target. It is 0.698212058515, but should be close to 0.8. Try to increase the number of tuning steps.\n",
						"  % (self._chain_id, mean_accept, target_accept))\n",
						"/Users/aaronmacneil/anaconda/lib/python3.6/site-packages/pymc3/step_methods/hmc/nuts.py:468: UserWarning: Chain 1 contains 1323 diverging samples after tuning. If increasing `target_accept` does not help try to reparameterize.\n",
						"  % (self._chain_id, n_diverging))\n"
					]
				},
				{
					"data": {
						"image/png": 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Op0O5uVvMjgIAACyMsg1b6tfvFoWFceIqAAAwFmUbtjRs2H22POEDAAD4FydI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\nu/O0fr34fY0iKTUj2zfeeKNeeeUVTZ06VQcPHlRAQIDCwsI0duxYVa1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Gy5YtU0xMjMqX\nL6933333nNts2LBB7du3V+fOnfXHH39ozJgxl5yuERsbq9dee00HDx7U1VdfrZUrV14yi8vl0t69\nezVr1iwNGzZMZcqUkdfr1Y8//qibb775vPc5efKkxo8fr6ZNm+raa68t/AOHT3TvnlRso1Judx4v\nIwKATdxxh1StGr/zUXgltmwHBQWpVatW6tixo4KDgxUYGHjOqLYk9enTR6NGjdKKFSvk5+enOnXq\n6D//+c9Ft1uzZk0NGzZMPXv2VEhIiGJiYgqVZ/r06Zo8ebLuueceBQUFKT8/X/Hx8Ro4cGDBbV58\n8UXNnj1bTqdTXq9XsbGxeuaZZ4r+4AEAAGAJjvz/nVMB05wdYWUOoG8MHfqYgoLK6PnnJ5sdxRY4\nr32HY+07HOuLKy1ztov6PbTj953HXLjbX0iJHdn2tdTUVL3xxhvnva5t27bq27evjxPBSGvXpsnp\ndFC2AQCAoSjb/ychIUEJCQlmx4CPfPTRepUvH1bwFk4AAABGcJodADBDRESkIiMjzY4BAAAsjpFt\n2FJ2drayszn9AQCAsRjZhi01aXKbateubXYMAABgcQztwZZiY5soMLCM2TEAAIDFUbZhSzNnvmrL\ntzICAAC+xTQSAAAAwCCMbMOW3nlnmcqWDdJdd7U1OwoAALAwyjZsafz4sXI6HZRtAABgKMo2bGnc\nuEkKDw8yOwYAALA4yjZsqVWre1kgCQAADMcCSQAAAMAglG3Y0gMP3K/OnTubHQMAAFgc00hgS1u3\nfiU/P4fZMQAAgMUxsg1b8ni2ac+ePWbHAAAAFkfZBgAAAAzCNBLY0s8//6RTp0IVEBBudhQAAGBh\nlG3YUtu298jpdCgjY7vZUQAAgIVRtmFLbdokKjjY3+wYAADA4ijbsKUxY17gQ20AAIDhWCAJAAAA\nGISRbdjS3LmzFRoaqG7depsdBQAAWBhlG7b06quvyOl0ULYBAIChKNuwpVmzXldkZLDZMQAAgMVR\ntmFLDRvewQJJAABgOBZIAgAAAAahbMOW2re/V82bNzc7BgAAsDimkcCWsrJOyOXyMzsGAACwOMo2\nbGn16k+Ysw0AAAzHNBIAAADAIIxsw5a2b9+mqKgQXXttNbOjAAAAC6Nsw5Z69eoup9OhjIztZkcB\nAAAWRtmGLd1/f2+FhASYHQMAAFgcZRu2NHjwEyyQBAAAhmOBJAAAAGAQRrZhS5MmvaCQkAANGjTU\n7CgAAMDCGNmGLS1btkTz5883OwYAALC4EjGyvWTJEi1ZskRer1cOh0M333yzHn/8cV1zzTWKi4vT\n9OnTVbduXcP2P336dN1www1q166dYftAybJ06QpFRYWYHeMvPB6n0tNdio31yu3OMzsOAAC4QqaX\n7UmTJmnXrl2aM2eOKlWqpLy8PKWmpqpLly56++23fZJh8ODBPtkPSo7q1WuUmAWS3bsHKS3tzz+K\nZ94pJT7eq8WLs30fCgAAFAtTy/bBgwe1dOlSffLJJwoPD5ckOZ1OtWvXTjt27NCcOXMkSYsXL9au\nXbuUk5Oj3r17q1OnTjpx4oRGjBihffv2yel0qk6dOho7dqyczgvPjPF4PJo4caLy8s6MGPbv31/3\n3HOPnnrqKVWvXl0PPPCAPv30UyUnJ8vpdKp27dpKT0/X4sWLtXnzZq1Zs0YnT57UTz/9pEqVKqlH\njx5auHCh9u7dq969e6tPnz7KysrS6NGjtXfvXh09elQhISFKTk5W1apVjT+gKBWaNg3Wrl1+hbpt\nWppLFSuG/eXyWrVytW5dVnFHAwBchMfj1LZtUkyMk1cfUWimztneunWrqlatWlC0/1dsbKy2bNki\nSQoICNDKlSs1b948TZkyRd99950+/PBDnThxQikpKVq+fLkk6ccff7zo/mbOnKnevXtrxYoVGj9+\nvD7//PNzrj9y5IiefPJJTZ48WSkpKWrYsKF++eWXgus9Ho8mTJig1atX67ffftOqVas0f/58zZ07\nV9OmTVNeXp7WrVunsmXLatmyZVq9erWio6O1aNGiKz1UKGbNmjVSTEyMKftety5Lhw4dK/gvPt57\nwdvGx3vPue3Z/yjaAOBbHo9TCQnBeuopKSEhWB4Py95QOKZPI/F6z180cnJy5HA4JEldu3aVJF11\n1VVq0qSJNm7cqObNm2vq1KlKSkpSbGysevbsqRtuuOGi+2rVqpXGjh2rjz/+WLGxsRoyZMg513s8\nHlWrVk21atWSJLVv317jxo0ruL5u3bqqVKmSJKly5cpq0qSJnE6nrrvuOp06dUrZ2dlq2bKlrrvu\nOi1YsED79u3T5s2bdcstt1zyOERGBsvlOjPaWaHCX0cyUbyqVLleku+PdXS0tHNn4W9/oZHtOnWk\nHTuKMZgPcF77DsfadzjW9pGe7pLXe6aXeL0Opae75HbnmJwKpYGpZbt+/frat2+fMjMzVaFChXOu\n27Rpk2655RatW7funKkh+fn5crlcuu666/Thhx9q06ZN+vzzz9W7d2+NHDlSLVu2vOD+unbtqubN\nm+uzzz7T+vXr9fLLLys1NbXgej8/P+Xn559zn//dt7+//znXuVx/PXyLFy/WsmXL1KNHD7Vt21YR\nERHav3//JY/FkSNnRipLyjxiq5s//y1TjvXatRe+7uyoidfrkMuVr9TUrIu+TJmZaUBAg3Be+w7H\n2nc41hdntT9EYmO9crn8C35Hx8Ze+FVJ4H+Z+hrIVVddpaSkJA0ZMuSc6RrvvPOO1qxZo379+kmS\nVq5cKUn6+eeflZ6erkaNGmnx4sUaMWKEmjRpomHDhqlJkyb67rvvLrq/rl276ptvvlGHDh30/PPP\n648//tDRo0cLrr/11lu1d+9e7dq1S5K0evVq/fHHHwUj7IWxYcMGtW/fXp07d9aNN96ojz/+WLm5\nuYW+P+zL7c5TamqWRo48dcmiDQDwrbO/oydOFL+jUSSmTyN54okn9Pbbb+uhhx5STk6OcnJyVLdu\nXS1dulTXXnutJOnUqVNq3769Tp8+rZEjR+rGG2/UVVddpc2bN6t169YKCgrSNddco/vvv/+i+xo6\ndKjGjx+vadOmyel0atCgQapcuXLB9REREXrppZc0fPhwOZ1ORUdHy+VyKSgoqNCPp0+fPho1apRW\nrFghPz8/1alTR//5z38u7+DAMJ9+ulYREcGqV6+h2VHO4Xbn8bIkAJRQbneeWrWSMjMp2ig8R/6f\n503Y2PHjxzVr1iw98sgjCgoK0s6dO9W/f3+tX7++SKPbl+PsS5G8LOkbDRpEy+l0KCNju9lRbIHz\n2nc41r7Dsb640jKNpKjfQzt+33nMhbv9hZg+sl2c9uzZo8cff/y81914442aNm3aRe8fGhqqMmXK\nqFOnTnK5XHK5XJo2bZrhRRu+99hjQxUWFmh2DAAAYHGMbJcQjGz7HsfadzjWvsOx9h2O9cUxsm0d\nPObC3f5CeJNIAAAAwCCUbdjS8OFDNHDgQLNjAAAAi6Nsw5bS0tZo1apVZscAAAAWZ6kFkkBhrVnz\nqcqXDxUrFgAAgJEY2YYtlStXTuXLlzc7BgAAsDjKNmzp7AcoAQAAGImyDVtq1OhW1ahRw+wYAADA\n4pizDVtq2LCRAgPLmB0DAABYHGUbtjRr1lxbvkk/AADwLaaRAAAAAAZhZBu29O6776hs2SDFxbU2\nOwoAALAwyjZs6fnnn5PT6VBGBmUbAAAYh7INWxozZrzCw4PMjgEAACyOsg1batMmgQWSAADAcCyQ\nBAAAAAxC2YYtPfhgL3Xt2tXsGAAAwOKYRgJb2rLFI6fTYXYMAABgcY78/Px8s0MAAAAAVsQ0EgAA\nAMAglG0AAADAIJRtAAAAwCCUbQAAAMAglG0AAADAIJRtAAAAwCCU7RLm2LFjGjBggO677z516dJF\nX375pdmRLCcvL0+jRo1Sly5dlJSUpH379pkdybJOnz6tYcOGqXv37urUqZM++ugjsyNZ3m+//aZm\nzZpp9+7dZkextDlz5qhLly7q0KGD3n77bbPjwCBbt25VUlLSXy7/+OOP1bFjR3Xp0kXLli0zIZlx\nLvSY33zzTd17771KSkpSUlKS9uzZY0K64nWp56ji+j7zoTYlzD/+8Q/dcccd6tWrl/bs2aMnnnhC\nK1euNDuWpaSlpSknJ0dvvfWWvvrqK02cOFGzZ882O5YlpaamKiIiQpMnT9bvv/+udu3aqUWLFmbH\nsqzTp09r1KhRCgwMNDuKpW3atElffvmllixZouzsbM2bN8/sSDDA3LlzlZqaqqCgoHMuP336tCZM\nmKDly5crKChI3bp1U1xcnMqXL29S0uJzoccsSTt27NCkSZMUHR1tQjJjXOw5qji/z4xslzC9ev33\nY8Rzc3MVEBBgciLr2bJli+68805JUv369bVjxw6TE1lXy5YtNXjwYElSfn6+/Pz8TE5kbZMmTVLX\nrl1VsWJFs6NY2oYNG1SjRg0NHDhQAwYM0N/+9jezI8EA119/vWbOnPmXy3fv3q3rr79e4eHh8vf3\nV4MGDZSRkWFCwuJ3occsSTt37tRrr72mbt26ac6cOT5OZoyLPUcV5/eZkW0Tvf3225o/f/45l40f\nP14xMTHKzMzUsGHD9PTTT5uUzrqOHz+u0NDQgq/9/Pzk9XrlcvHjUNxCQkIknTnmjz76qB577DGT\nE1nXihUrFBUVpTvvvFOvvfaa2XEs7ciRI/r555/16quvav/+/XrooYf073//Ww6Hw+xoKEb33HOP\n9u/f/5fLjx8/rrCwsIKvQ0JCdPz4cV9GM8yFHrMk3XvvverevbtCQ0M1aNAgrV27Vs2bN/dxwuJ1\nseeo4vw+0y5M1LlzZ3Xu3Pkvl3/77bcaMmSInnzySd1+++0mJLO20NBQnThxouDrvLzZ3lmEAAAC\nEElEQVQ8iraBDhw4oIEDB6p79+5q27at2XEs65133pHD4dDGjRv1zTffaPjw4Zo9e7YqVKhgdjTL\niYiIUNWqVeXv76+qVasqICBAhw8fVrly5cyOBh/483PIiRMnzillVpSfn6+ePXsWPM5mzZrp66+/\nLvVlW7rwc1Rxfp+ZRlLCfP/99xo8eLCmTJmiZs2amR3Hkm699VatW7dOkvTVV1+pRo0aJieyrl9/\n/VV9+vTRsGHD1KlTJ7PjWNqiRYu0cOFCLViwQLVr19akSZMo2gZp0KCB1q9fr/z8fP3yyy/Kzs5W\nRESE2bHgI9WqVdO+ffv0+++/KycnRx6PR7fccovZsQx1/PhxtWnTRidOnFB+fr42bfr/7dshjgJB\nEAXQv4YrzFmQCE4ABBSGE+BQOMZhJ9yDMyA23AYBCaYRmBWbVdNhl33vBNUpUb87XZ9v8Xf7pxnV\nZ5895/0y+/0+9/s9u90uyfNmZXmvX+PxOKfTKYvFIqWUtG376pLe1uFwyOVySdd16bouyXMBxwIf\nf9loNMr5fM50Ok0pJdvt1j7CP3A8HnO9XjOfz7PZbLJarVJKyWQySdM0ry6viq9nXq/XWS6XGQwG\nGQ6Hb/Eg+N2Mms1mud1uvfb5o5RS+iwcAAB48o0EAAAqEbYBAKASYRsAACoRtgEAoBJhGwAAKhG2\nAQCgEmEbAAAqEbYBAKCSB3EpQHzD9gWyAAAAAElFTkSuQmCC\n",
						"text/plain": [
							"<matplotlib.figure.Figure at 0x118a49eb8>"
						]
					},
					"metadata": {
					},
					"output_type": "display_data"
				},
				{
					"data": {
						"image/png": 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n+qvRo0ef8KE2f/bHD7UBqkth4a814p1tAADwB8xs+4ia8NeiiX/Vmjgmycxx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JAAAE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\nREQW4bBNRERERGQRDttERERERBb5A+XfgZL2uMamAAAAAElFTkSuQmCC\n",
						"text/plain": [
							"<matplotlib.figure.Figure at 0x11c219390>"
						]
					},
					"metadata": {
					},
					"output_type": "display_data"
				},
				{
					"data": {
						"image/png": 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KLE/VaW18bq5diYlOud02ORwleuKJw9q71664OLdcrmLT8U6pOo0zao7cXLs2bZJiY+0+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						"text/plain": [
							"<matplotlib.figure.Figure at 0x11cb984e0>"
						]
					},
					"metadata": {
					},
					"output_type": "display_data"
				},
				{
					"data": {
						"image/png": 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4DTPbPsLXXjH666tYxmUWfxyXP45JYlymYWb7d8xsnx5jrtj5T6bCGyQBAAAA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						"text/plain": [
							"<matplotlib.figure.Figure at 0x11d4d8d30>"
						]
					},
					"metadata": {
					},
					"output_type": "display_data"
				},
				{
					"data": {
						"image/png": 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nFuj7INC3X2IfSOyDQN9+yb/3ASPbtVegbXN1jmxX+gJJAAAAANb47TQSAL/b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						"text/plain": [
							"<matplotlib.figure.Figure at 0x11bc25828>"
						]
					},
					"metadata": {
					},
					"output_type": "display_data"
				},
				{
					"data": {
						"image/png": 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LVkGBXSkpYXK5bHI4ypSdXSSns9Tt6/jss3C1aHHU7csCvlZQYFdenkMJCS72Rxjy6L3C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\ngg==\n",
						"text/plain": [
							"<matplotlib.figure.Figure at 0x11d61f780>"
						]
					},
					"metadata": {
					},
					"output_type": "display_data"
				}
			],
			"source": [
				"# Iterate over datasets\n",
				"for i in range(len(Res_names)):\n",
				"    # Update new response variable\n",
				"    shared_data.set_value(rdata[i])\n",
				"    # Sampling\n",
				"    with Model:\n",
				"        # Draw samples\n",
				"        trace = pm.sample(10000, progressbar=True, njobs=2)\n",
				"    out = pm.backends.tracetab.trace_to_dataframe(trace)\n",
				"    out.to_csv(Res_names[i]+'_results.csv')\n",
				"    #fig, ax = plt.subplots()\n",
				"    pm.traceplot(trace)\n",
				"    plt.savefig(Res_names[i]+'_traceplot.pdf')\n",
				"    pm.forestplot(trace)\n",
				"    plt.savefig(Res_names[i]+'_forestplot.pdf');"
			]
		}
	],
	"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": "3.6.1"
		}
	},
	"nbformat": 4,
	"nbformat_minor": 1
}