{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "
\n", "\n", "# Algae Example\n", "Author(s): Paul Miles | Date Created: June 18, 2018\n", "\n", "This is a simplified lake algae dynamics model. We consider phytoplankton $A$, zooplankton $Z$ and nutrition $P$ (eg. phosphorus) available for $A$ in the water. The system is affected by the water outflow/inflow $Q$, incoming phosphorus load $P_{in}$ and temperature $T$. It is described as a simple predator - pray dynamics between $A$ and $Z$. The growth of $A$ is limited by the availability of $P$ and it depends on the water temperature $T$. The inflow/outflow $Q$ affects both $A$ and $P$, but not $Z$." ] }, { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "1.9.0\n" ] } ], "source": [ "# import required packages\n", "import numpy as np\n", "import os\n", "import scipy.io as sio\n", "from scipy.integrate import odeint\n", "from pymcmcstat.MCMC import MCMC\n", "from pymcmcstat.plotting import MCMCPlotting\n", "import matplotlib.pyplot as plt\n", "import pymcmcstat\n", "print(pymcmcstat.__version__)\n", "np.seterr(over='ignore');" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The data is saved in a `.mat` file as the original example comes from the Matlab. We extract the necessary data as follows" ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [], "source": [ "# load Algae data\n", "algaedata = sio.loadmat('data_files' + os.sep + 'algaedata.mat')\n", "# extract dictionary contents\n", "adata = algaedata['data']\n", "tx = adata['xdata'][0][0]\n", "ty = adata['ydata'][0][0]\n", "xlbls = adata['xlabels'][0][0][0]\n", "ylbls = adata['ylabels'][0][0][0]" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Define Sum-of-Squares and Model Functions\n", "The model function requires evaluation of a system of ODEs. The solution implemented below is non-optimal. It is intended to serve as an example of how to couple and ODE system type problem with [pymcmcstat](https://prmiles.wordpress.ncsu.edu/codes/python-packages/pymcmcstat/). Optimizing your ODE system solver is left to the user's discretion." ] }, { "cell_type": "code", "execution_count": 53, "metadata": {}, "outputs": [], "source": [ "def algaess(theta, data):\n", " # sum-of-squares function for algae example\n", " ndp, nbatch = data.shape[0]\n", " time = data.ydata[0][:, 0]\n", " ydata = data.ydata[0][:, 1:4]\n", " xdata = data.user_defined_object[0]\n", " # last 3 parameters are the initial states\n", " y0 = np.array(theta[-3:])\n", " # evaluate model\n", " tmodel, ymodel = algaefun(time, theta, y0, xdata)\n", " res = ymodel - ydata.reshape(ymodel.shape)\n", " ss = (res**2).sum(axis=0)\n", " return ss \n", "\n", "def algaefun(time, theta, y0, xdata):\n", " \"\"\"\n", " Evaluate Ordinary Differential Equation\n", " \"\"\"\n", " sol = odeint(algaesys, y0, time, args=(theta, xdata))\n", " return time, sol\n", " \n", "def algaesys(y, t, theta, xdata):\n", " \"\"\"\n", " Model System\n", " \"\"\"\n", " A = y[0]\n", " Z = y[1]\n", " P = y[2]\n", " # control variables are assumed to be saved at each time unit interval\n", " idx = int(np.ceil(t)) - 1 \n", " if idx >= 120:\n", " idx = 119\n", " QpV = xdata[idx, 1]\n", " T = xdata[idx, 2]\n", " Pin = xdata[idx, 3]\n", " # model parameters\n", " mumax = theta[0]\n", " rhoa = theta[1]\n", " rhoz = theta[2]\n", " k = theta[3]\n", " alpha = theta[4]\n", " th = theta[5]\n", " mu = mumax*(th**(T-20))*P*((k+P)**(-1))\n", " dotA = (mu - rhoa - QpV - alpha*Z)*A\n", " dotZ = alpha*Z*A - rhoz*Z\n", " dotP = -QpV*(P-Pin) + (rhoa-mu)*A + rhoz*Z\n", " ydot = np.array([dotA, dotZ, dotP])\n", " return ydot" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Initialize MCMC Object and Setup Simulation\n", "- Define data structure\n", "- Assign parameters and define constraints\n", "- Set simulation options and model settings" ] }, { "cell_type": "code", "execution_count": 59, "metadata": {}, "outputs": [], "source": [ "# initialize MCMC object\n", "mcstat = MCMC()\n", "# initialize data structure \n", "mcstat.data.add_data_set(x=tx[:, 0],\n", " y=ty[:, 0:4],\n", " user_defined_object=tx)\n", "# initialize parameter array\n", "#theta = [0.5, 0.03, 0.1, 10, 0.02, 1.14, 0.77, 1.3, 10]\n", "# add model parameters\n", "mcstat.parameters.add_model_parameter(name='mumax', theta0=0.5, minimum=0)\n", "mcstat.parameters.add_model_parameter(name='rhoa', theta0=0.03, minimum=0)\n", "mcstat.parameters.add_model_parameter(name='rhoz', theta0=0.1, minimum=0)\n", "mcstat.parameters.add_model_parameter(name='k', theta0=10, minimum=0)\n", "mcstat.parameters.add_model_parameter(name='alpha', theta0=0.02, minimum=0)\n", "mcstat.parameters.add_model_parameter(name='th', theta0=1.14, minimum=0,\n", " maximum=np.inf, prior_mu=0.14,\n", " prior_sigma=0.2)\n", "# initial values for the model states\n", "mcstat.parameters.add_model_parameter(name='A0', theta0=0.77, minimum=0,\n", " maximum=np.inf, prior_mu=0.77,\n", " prior_sigma=2)\n", "mcstat.parameters.add_model_parameter(name='Z0', theta0=1.3, minimum=0,\n", " maximum=np.inf, prior_mu=1.3,\n", " prior_sigma=2)\n", "mcstat.parameters.add_model_parameter(name='P0', theta0=10, minimum=0,\n", " maximum=np.inf, prior_mu=10,\n", " prior_sigma=2)\n", "\n", "# Generate options\n", "mcstat.simulation_options.define_simulation_options(\n", " nsimu=1.0e3, updatesigma=True)\n", "# Define model object:\n", "mcstat.model_settings.define_model_settings(\n", " sos_function=algaess,\n", " S20=np.array([1, 1, 2]),\n", " N0=np.array([4, 4, 4]))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The code takes some time to run, so here we simply check to make sure the data structure can be processed using our sum-of-squares function. Note, we have separate sum-of-squares for each quantity of interest and there will be a separate error variance for each as well." ] }, { "cell_type": "code", "execution_count": 60, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "ss = [ 930.44890289 521.2441601 1278.11736803]\n" ] } ], "source": [ "# check model evaluation\n", "theta = [0.5, 0.03, 0.1, 10, 0.02, 1.14, 0.77, 1.3, 10]\n", "ss = algaess(theta, mcstat.data)\n", "print('ss = {}'.format(ss))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Run simulation\n", "- We run an initialize sequence of 1000, then restart and perform another 5000" ] }, { "cell_type": "code", "execution_count": 61, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "\n", "Sampling these parameters:\n", " name start [ min, max] N( mu, sigma^2)\n", " mumax: 0.50 [ 0.00e+00, inf] N( 0.00e+00, inf)\n", " rhoa: 0.03 [ 0.00e+00, inf] N( 0.00e+00, inf)\n", " rhoz: 0.10 [ 0.00e+00, inf] N( 0.00e+00, inf)\n", " k: 10.00 [ 0.00e+00, inf] N( 0.00e+00, inf)\n", " alpha: 0.02 [ 0.00e+00, inf] N( 0.00e+00, inf)\n", " th: 1.14 [ 0.00e+00, inf] N( 0.14, 0.20^2)\n", " A0: 0.77 [ 0.00e+00, inf] N( 0.77, 2.00^2)\n", " Z0: 1.30 [ 0.00e+00, inf] N( 1.30, 2.00^2)\n", " P0: 10.00 [ 0.00e+00, inf] N( 10.00, 2.00^2)\n", " [-----------------100%-----------------] 1000 of 1000 complete in 98.0 sec\n", "Sampling these parameters:\n", " name start [ min, max] N( mu, sigma^2)\n", " mumax: 0.39 [ 0.00e+00, inf] N( 0.00e+00, inf)\n", " rhoa: 0.02 [ 0.00e+00, inf] N( 0.00e+00, inf)\n", " rhoz: 0.11 [ 0.00e+00, inf] N( 0.00e+00, inf)\n", " k: 8.89 [ 0.00e+00, inf] N( 0.00e+00, inf)\n", " alpha: 0.03 [ 0.00e+00, inf] N( 0.00e+00, inf)\n", " th: 1.01 [ 0.00e+00, inf] N( 0.14, 0.20^2)\n", " A0: 0.80 [ 0.00e+00, inf] N( 0.77, 2.00^2)\n", " Z0: 1.90 [ 0.00e+00, inf] N( 1.30, 2.00^2)\n", " P0: 9.30 [ 0.00e+00, inf] N( 10.00, 2.00^2)\n", " [-----------------100%-----------------] 5001 of 5000 complete in 445.1 sec" ] } ], "source": [ "# Run simulation\n", "mcstat.run_simulation()\n", "# Rerun starting from results of previous run\n", "mcstat.simulation_options.nsimu = int(5.0e3)\n", "mcstat.run_simulation(use_previous_results=True)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Extract results and plot chain diagnostics\n", "- chain panel\n", "- density panel\n", "- pairwise correlation panel" ] }, { "cell_type": "code", "execution_count": 72, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "\n", "\n", "------------------------------\n", "name : mean std MC_err tau geweke\n", "mumax : 0.8079 0.2894 0.0537 193.0222 0.7238\n", "rhoa : 0.0482 0.0224 0.0041 167.3812 0.9930\n", "rhoz : 0.0995 0.0052 0.0004 20.5428 0.9907\n", "k : 26.5229 14.2116 2.8011 202.6484 0.5287\n", "alpha : 0.0239 0.0013 0.0001 46.3865 0.9603\n", "th : 1.0018 0.0143 0.0018 59.1035 0.9910\n", "A0 : 0.8684 0.2815 0.0304 33.5992 0.9587\n", "Z0 : 1.8728 0.4502 0.0474 46.1744 0.9658\n", "P0 : 8.9506 0.8791 0.0984 54.7130 0.9283\n", "------------------------------\n", "==============================\n", "Acceptance rate information\n", "---------------\n", "Results dictionary:\n", "Stage 1: 21.78%\n", "Stage 2: 50.86%\n", "Net : 72.64% -> 3632/5000\n", "---------------\n", "Chain provided:\n", "Net : 72.91% -> 2551/3499\n", "---------------\n", "Note, the net acceptance rate from the results dictionary\n", "may be different if you only provided a subset of the chain,\n", "e.g., removed the first part for burnin-in.\n", "------------------------------\n" ] }, { "data": { "image/png": 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\n", 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