SS_Matrix.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Parameter estimation for models of cell motility"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<strong>Summary</strong>\n",
    "\n",
    "<div style=\"text-align: justify\"> \n",
    "Parameter recovery studies of active cell motion in the CPM using the FitMultiCell pipeline have not provided a satisfying aproximation of the real posterior distributions using the time-averaged mean-squared-displacement (TAMSD) and the direction-autocorrelation function (DAC) as the summary statistics (SS) functions. In order to tackle and solve this problem, new functions summarizing information about particles' trajectories in space are introduced and their applicability as SS functions is studied by using different combinations of them in the FitMultiCell pipeline and observing their performance for different percentages of microscopic time steps (1%, 10%, and 100%). It is found that the pipeline's performance increases with some of the newly introduced functions and with higher time-step percentages. \n",
    "</div>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<strong>Introduction</strong>\n",
    "\n",
    "<div style=\"text-align: justify\"> \n",
    "There is a major question on which modes cells use for migrating and how this process is controlled under different biophysical conditions. Mathematical and computational models can provide insights into these kind of mechanisms and can also help to validate experimental results and provide a broader scope on how some processes depend on specific parameters. During the last decades, quantities such as the mean-squared displacement (MSD) and the velocity autocorrelation function (VAC) have been extensively used to identify the type of motion a cell performs and how persistent it is. In <a href=\"https://www.nature.com/articles/nprot.2014.131\"> Gorelik 2014</a>, a comprehensive protocol explains in detail the advantages of both this functions. However, some studies have also pointed out the limitations of the MSD and VAC functions like in <a href=\"https://link.springer.com/article/10.1007%2Fs00397-013-0694-6\">Gal et al. 2013</a>. In short terms, this failure may be attributed to the wide spread of values on the calculation of both functions for individual random processes (more noticeable in the VAC) and due to averaging and oversimplification errors.\n",
    "\n",
    "As a response, several approaches have been suggested for singles-particle trajectory analysis, where both directionality and displacement are the main focus of the analysis. A popular list of these alternative functions is the following:\n",
    "\n",
    "<ol>\n",
    "    <li>Temporally resolved detection of active regimes</li>\n",
    "    <li>Temporally reolved detection of trapping regimes</li>\n",
    "    <li>Trajectory spread in space (radius of gyration)</li>\n",
    "    <li>Van Hove displacement distributions and deviations from Gaussian statistics</li>\n",
    "    <li>Self-similarity of a trajectory</li>\n",
    "</ol>\n",
    "\n",
    "This notebook summarizes the concepts of the most relevant methods above and concludes by showing their efficiency to calculate posterior distributions under an Approximate Bayesian Computation algorithm. \n",
    "    </div>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<strong>0. Imports</strong>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "ERROR:External:Install rpy2 to enable simple support for the R language.\n"
     ]
    }
   ],
   "source": [
    "import pyabc\n",
    "import fitmulticell as fmc\n",
    "import scipy as sp\n",
    "from scipy import stats\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import os\n",
    "import tempfile\n",
    "%matplotlib inline\n",
    "import matplotlib.pyplot as plt\n",
    "import tidynamics                     # to get sliding history stats in N*logN instead of N^2\n",
    "from pyabc import SingleCoreSampler\n",
    "\n",
    "import seaborn as sns"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<strong>Mathematical formulations</strong>\n",
    "\n",
    "Definitions for the relevant distance function methods mentioned at the introduction (already tested):\n",
    "\n",
    "<u> Directionality ratio </u>: Ratio between distance of initial to final end points $d$ and total length of the trajectory $D$ (working better when having a constant velocity):\n",
    "\n",
    "$$\n",
    "DR = \\frac{d}{D}.\n",
    "$$\n",
    "\n",
    "<u>Trajectory spread in space- radius of gyration</u>:\n",
    "The radius of gyration ($R_g$) is defined as the root mean square distance between each time-dependent step and the center of mass. With a trajectory with a total number $N$ of time steps, $R_g$ is given by\n",
    "\n",
    "$$\n",
    "R_g^2 = \\frac{1}{N}\\sum_{i=1}^N (\\vec{R}_i - \\vec{R}_{cm})^2.\n",
    "$$\n",
    "\n",
    "<u>Van Hove displacement distributions</u>:\n",
    "It has been previously established that deviations from Gaussian statistics can reveal the existence of active modes of motion. The deviation from the Gaussian behaviour can be quantified by the time-dependent non-Gaussian parameter (NGP):\n",
    "\n",
    "$$\n",
    "\\alpha_2(\\tau) = \\frac{\\langle \\Delta x^4(\\tau) \\rangle}{3 \\langle \\Delta x^2(\\tau) \\rangle^2} - 1.\n",
    "$$\n",
    "\n",
    "However, more recent studies have suggested an improved and equivalent measure known as the heterogeneity ratio $HR = M_2(\\tau) / M_1^2(\\tau)$, where $M_1$ and $M_2$ are the weighted ensemble-averaged MSD and weighted ensemble-averaged variance of the MSD, respectively."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Directionality ratio:\n",
    "def distDR(R_data):\n",
    "    D = 0\n",
    "    N_total = len(R_data)\n",
    "    Dr = np.zeros(N_total-1)\n",
    "    for i in range(N_total-1):\n",
    "        d = np.linalg.norm(R_data[i+1] - R_data[0])\n",
    "        D += np.linalg.norm(R_data[i+1] - R_data[i])\n",
    "        Dr[i] = d / D\n",
    "    return Dr\n",
    "\n",
    "# Radius of gyration:\n",
    "def distRG(R_data, time_interval=1):\n",
    "    \"\"\"\n",
    "    Calculates the radius of gyration for R_data corresponding to a single trajectory over time.\n",
    "    # Data structure:\n",
    "    -> R_data: N_steps x 2\n",
    "    \"\"\"\n",
    "    N_total = len(R_data)\n",
    "    Rg = np.zeros(N_total)\n",
    "    for i in range(N_total-1):\n",
    "        R_cm = sum(R_data[:i+2]) / (i+2)\n",
    "        #print(R_cm)\n",
    "        Rg[i+1] = (sum((R_data[:i+2] - R_cm)**2)).sum() / ((i+1)*time_interval)\n",
    "    Rg[0] = 0\n",
    "    return np.sqrt(Rg)\n",
    "\n",
    "# Heterogeneity ratio:\n",
    "def distHR(dataset_msd, time_interval=50):\n",
    "    N = len(dataset_msd)\n",
    "    M2 = np.zeros(N)\n",
    "    HR = np.zeros(N)\n",
    "    for i in range(N-1):\n",
    "        M2[i+1] = np.var(dataset_msd[:i+2])\n",
    "        #print(M2[i+1])\n",
    "    HR[1:] = M2[1:]/dataset_msd[1:]**2\n",
    "    return HR"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Define all SS functions ready to use in the FitMultiCell pipeline:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Time-averaged mean square displacement:\n",
    "def distanceMSD(val1, val2):\n",
    "    diff = tidynamics.msd(np.column_stack([val1['IdSumstat__cell.center.x'][t_init:],val1['IdSumstat__cell.center.y'][t_init:]])) - val2['IdSumstat__MSD']\n",
    "    d = np.sqrt(np.sum(diff**2))\n",
    "    return d\n",
    "\n",
    "# Direction autocorrelation function:\n",
    "def distanceVAC(val1, val2):\n",
    "    diff = tidynamics.acf(np.column_stack([val1['IdSumstat__velocity.x'][t_init:],val1['IdSumstat__velocity.y'][t_init:]])) - val2['IdSumstat__VAC']\n",
    "    d = np.sqrt(np.sum(diff**2))\n",
    "    return d\n",
    "\n",
    "# Directionality ratio:\n",
    "def distanceDR(val1, val2):\n",
    "    diff = distDR(np.column_stack([val1['IdSumstat__cell.center.x'][t_init:],val1['IdSumstat__cell.center.y'][t_init:]])) - val2['IdSumstat__DR']\n",
    "    d = np.sqrt(np.sum(diff**2))\n",
    "    return d\n",
    "\n",
    "# Radius of Gyration:\n",
    "def distanceRG(val1, val2):\n",
    "    R = distRG(np.column_stack([val1['IdSumstat__cell.center.x'][t_init:],val1['IdSumstat__cell.center.y'][t_init:]]))\n",
    "    d = np.sqrt(np.sum((R - val2['IdSumstat__RG'])**2))\n",
    "    return d\n",
    "\n",
    "# Heterogeneity ratio:\n",
    "def distanceHR(val1, val2):\n",
    "    diff = distHR(tidynamics.msd(np.column_stack([val1['IdSumstat__cell.center.x'][t_init:],val1['IdSumstat__cell.center.y'][t_init:]]))) - val2['IdSumstat__HR']\n",
    "    d = np.sqrt(np.sum(diff**2))\n",
    "    return d\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Create \"reference data\" with each SS function (averaging over 100 simulations)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "MorpheusModel {\n",
      "\tname      : Demo_CellMotility100.xml\n",
      "}\n"
     ]
    }
   ],
   "source": [
    "file_ = \"Demo_CellMotility100.xml\"\n",
    "par_map = {'motion_strength': './Global/Constant[@symbol=\"motion_strength\"]',\n",
    "           'noise_level': './Global/Constant[@symbol=\"noise_level\"]'}\n",
    "model = fmc.model.MorpheusModel(\n",
    "    file_, par_map=par_map,\n",
    "    #executable=\"/Applications/Morpheus.app/Contents/MacOS/bin/morpheus\",\n",
    "    executable=\"/Applications/Morpheus.app/Contents/MacOS/morpheus\",\n",
    "    show_stdout=False, show_stderr=True,\n",
    "    raise_on_error=False)\n",
    "\n",
    "print(model)\n",
    "\n",
    "true_pars = {'motion_strength': 1.0, 'noise_level': 0.1}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [],
   "source": [
    "N = 100\n",
    "tot_time = 401\n",
    "t_init = 100\n",
    "arrayMSD = np.zeros((N, tot_time))\n",
    "arrayVAC = np.zeros((N, tot_time))\n",
    "arrayDR = np.zeros((N, tot_time-1))\n",
    "arrayRG = np.zeros((N, tot_time))\n",
    "arrayHR = np.zeros((N, tot_time))\n",
    "\n",
    "for i in range(N):\n",
    "    simulated_data = model.sample(true_pars)\n",
    "    val1vec = np.column_stack([simulated_data['IdSumstat__cell.center.x'][t_init:],simulated_data['IdSumstat__cell.center.y'][t_init:]])\n",
    "    val1vecmsd = tidynamics.msd(val1vec)\n",
    "    arrayMSD[i] = val1vecmsd\n",
    "    arrayVAC[i] = tidynamics.acf(np.column_stack([simulated_data['IdSumstat__velocity.x'][t_init:],simulated_data['IdSumstat__velocity.y'][t_init:]]))\n",
    "    arrayDR[i] = distDR(val1vec)\n",
    "    arrayRG[i] = distRG(val1vec)\n",
    "    arrayHR[i] = distHR(val1vecmsd)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [],
   "source": [
    "MSD = np.array([arrayMSD[:,i].mean() for i in range(401)])\n",
    "VAC = np.array([arrayVAC[:,i].mean() for i in range(401)])\n",
    "DR = np.array([arrayDR[:,i].mean() for i in range(400)])\n",
    "RG = np.array([arrayRG[:,i].mean() for i in range(401)])\n",
    "#HR = np.array([arrayHR[:,i].mean() for i in range(5)])\n",
    "measured_data = {'IdSumstat__time': np.arange(0, 401, 1),\\\n",
    "                 'IdSumstat__MSD': MSD,\\\n",
    "                 'IdSumstat__VAC':VAC,\\\n",
    "                 'IdSumstat__DR': DR,\\\n",
    "                 'IdSumstat__RG': RG}#,\\\n",
    "                 #'IdSumstat__HR': HR}"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Define the pipeline as a function:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [],
   "source": [
    "def fmc_mot_pipeline(file_name, t_init, time_interval, populations, dist_functions, dist_functions_names, percentage='1'):\n",
    "    file_ = file_name#\"Demo_CellMotility.xml\"\n",
    "    par_map = {'motion_strength': './Global/Constant[@symbol=\"motion_strength\"]',\n",
    "               'noise_level': './Global/Constant[@symbol=\"noise_level\"]'}\n",
    "    model = fmc.model.MorpheusModel(\n",
    "        file_, par_map=par_map,\n",
    "        #executable=\"/Applications/Morpheus.app/Contents/MacOS/bin/morpheus\",\n",
    "        executable=\"/Applications/Morpheus.app/Contents/MacOS/morpheus\",\n",
    "        show_stdout=False, show_stderr=True,\n",
    "        raise_on_error=False)\n",
    "\n",
    "    print(model)\n",
    "    \n",
    "    limits = {key: (0.1 * val, 10 * val) for key, val in true_pars.items()}\n",
    "\n",
    "    prior = pyabc.Distribution(**{key: pyabc.RV(\"uniform\", lb, ub - lb)\n",
    "                                  for key, (lb, ub) in limits.items()})\n",
    "    #true_pars = {'motion_strength': 1.0, 'noise_level': 0.1}\n",
    "    distance = pyabc.AdaptiveAggregatedDistance(dist_functions, adaptive=False)\n",
    "    \n",
    "    abc = pyabc.ABCSMC(model, prior, distance, population_size=100)\n",
    "    # Store specific db_path:\n",
    "    \n",
    "    db_path = \"sqlite:///\" + dist_functions_names + percentage + \".db\"\n",
    "    history = abc.new(db_path, measured_data)\n",
    "    abc.run(max_nr_populations=populations)\n",
    "    h = pyabc.History(db_path)\n",
    "    return h"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Test it works properly:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "INFO:Sampler:Parallelizing the sampling on 4 cores.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "MorpheusModel {\n",
      "\tname      : Demo_CellMotility.xml\n",
      "}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "INFO:History:Start <ABCSMC(id=1, start_time=2021-01-12 11:38:33.126281, end_time=None)>\n",
      "INFO:ABC:Calibration sample before t=0.\n",
      "INFO:Epsilon:initial epsilon is 0.0\n",
      "INFO:ABC:t: 0, eps: 0.0.\n",
      "INFO:ABC:Acceptance rate: 1 / 4 = 2.5000e-01, ESS=1.0000e+00.\n",
      "INFO:pyabc.util:Stopping: maximum number of generations.\n",
      "INFO:History:Done <ABCSMC(id=1, start_time=2021-01-12 11:38:33.126281, end_time=2021-01-12 11:38:49.875089)>\n"
     ]
    }
   ],
   "source": [
    "data = fmc_mot_pipeline(\"Demo_CellMotility.xml\", t_init=1, time_interval=100, populations=1, dist_functions=[distanceMSD, distanceRG], dist_functions_names='RG_MSD')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/sebas/opt/anaconda3/lib/python3.8/site-packages/pyabc/visualization/kde.py:350: MatplotlibDeprecationWarning: shading='flat' when X and Y have the same dimensions as C is deprecated since 3.3.  Either specify the corners of the quadrilaterals with X and Y, or pass shading='auto', 'nearest' or 'gouraud', or set rcParams['pcolor.shading'].  This will become an error two minor releases later.\n",
      "  mesh = ax.pcolormesh(X, Y, PDF, **kwargs)\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 360x360 with 5 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "db_path = \"sqlite:///RG_MSD1.db\"\n",
    "h = pyabc.History(db_path)\n",
    "limits = {key: (0.1 * val, 10 * val) for key, val in true_pars.items()}\n",
    "pyabc.visualization.plot_epsilons(h)\n",
    "df, w = h.get_distribution(t=0)\n",
    "pyabc.visualization.plot_kde_matrix(df, w, limits=limits, refval=true_pars)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Now the juicy part: loop over SSs:\n",
    "After testing, the Heterogeneity Ratio was discarded because its efficienty was not nearly as good as the rest and it was consuming too much computational time. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "INFO:Sampler:Parallelizing the sampling on 4 cores.\n",
      "INFO:History:Start <ABCSMC(id=1, start_time=2021-01-12 11:45:41.562360, end_time=None)>\n",
      "INFO:ABC:Calibration sample before t=0.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Working with  <function distanceVAC at 0x7fe53fb2b3a0>\n",
      "MorpheusModel {\n",
      "\tname      : Demo_CellMotility.xml\n",
      "}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "INFO:Epsilon:initial epsilon is 0.46039643249365736\n",
      "INFO:ABC:t: 0, eps: 0.46039643249365736.\n",
      "INFO:ABC:Acceptance rate: 100 / 166 = 6.0241e-01, ESS=1.0000e+02.\n",
      "INFO:ABC:t: 1, eps: 0.2054271355693765.\n",
      "INFO:ABC:Acceptance rate: 100 / 285 = 3.5088e-01, ESS=8.3294e+01.\n",
      "INFO:ABC:t: 2, eps: 0.1209627000151214.\n",
      "INFO:ABC:Acceptance rate: 100 / 309 = 3.2362e-01, ESS=4.6208e+01.\n",
      "INFO:ABC:t: 3, eps: 0.09468883510942727.\n",
      "INFO:ABC:Acceptance rate: 100 / 798 = 1.2531e-01, ESS=8.9065e+01.\n",
      "INFO:ABC:t: 4, eps: 0.07407028973689109.\n",
      "INFO:ABC:Acceptance rate: 100 / 1428 = 7.0028e-02, ESS=9.1956e+01.\n",
      "INFO:ABC:t: 5, eps: 0.06136377203077132.\n",
      "INFO:ABC:Acceptance rate: 100 / 2189 = 4.5683e-02, ESS=1.7661e+01.\n",
      "INFO:pyabc.util:Stopping: maximum number of generations.\n",
      "INFO:History:Done <ABCSMC(id=1, start_time=2021-01-12 11:45:41.562360, end_time=2021-01-12 12:59:33.060050)>\n",
      "INFO:Sampler:Parallelizing the sampling on 4 cores.\n",
      "INFO:History:Start <ABCSMC(id=1, start_time=2021-01-12 12:59:33.139478, end_time=None)>\n",
      "INFO:ABC:Calibration sample before t=0.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Working with  RG_VAC\n",
      "MorpheusModel {\n",
      "\tname      : Demo_CellMotility.xml\n",
      "}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "INFO:Epsilon:initial epsilon is 1.055062439148108\n",
      "INFO:ABC:t: 0, eps: 1.055062439148108.\n",
      "INFO:ABC:Acceptance rate: 100 / 179 = 5.5866e-01, ESS=1.0000e+02.\n",
      "INFO:ABC:t: 1, eps: 0.8485511474362767.\n",
      "INFO:ABC:Acceptance rate: 100 / 250 = 4.0000e-01, ESS=8.7128e+01.\n",
      "INFO:ABC:t: 2, eps: 0.6855702296797526.\n",
      "INFO:ABC:Acceptance rate: 100 / 330 = 3.0303e-01, ESS=7.7301e+01.\n",
      "INFO:ABC:t: 3, eps: 0.5541568027916732.\n",
      "INFO:ABC:Acceptance rate: 100 / 457 = 2.1882e-01, ESS=8.3794e+01.\n",
      "INFO:ABC:t: 4, eps: 0.43193446897430743.\n",
      "INFO:ABC:Acceptance rate: 100 / 520 = 1.9231e-01, ESS=7.6302e+01.\n",
      "INFO:ABC:t: 5, eps: 0.35250185189027133.\n",
      "INFO:ABC:Acceptance rate: 100 / 466 = 2.1459e-01, ESS=8.6412e+01.\n",
      "INFO:pyabc.util:Stopping: maximum number of generations.\n",
      "INFO:History:Done <ABCSMC(id=1, start_time=2021-01-12 12:59:33.139478, end_time=2021-01-12 13:30:38.255628)>\n",
      "INFO:Sampler:Parallelizing the sampling on 4 cores.\n",
      "INFO:History:Start <ABCSMC(id=1, start_time=2021-01-12 13:30:38.344185, end_time=None)>\n",
      "INFO:ABC:Calibration sample before t=0.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Working with  <function distanceRG at 0x7fe53fb2b4c0>\n",
      "MorpheusModel {\n",
      "\tname      : Demo_CellMotility.xml\n",
      "}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "INFO:Epsilon:initial epsilon is 0.5754252654852396\n",
      "INFO:ABC:t: 0, eps: 0.5754252654852396.\n",
      "INFO:ABC:Acceptance rate: 100 / 216 = 4.6296e-01, ESS=1.0000e+02.\n",
      "INFO:ABC:t: 1, eps: 0.37900995911649543.\n",
      "INFO:ABC:Acceptance rate: 100 / 249 = 4.0161e-01, ESS=8.2315e+01.\n",
      "INFO:ABC:t: 2, eps: 0.2530859744731256.\n",
      "INFO:ABC:Acceptance rate: 100 / 475 = 2.1053e-01, ESS=6.9859e+01.\n",
      "INFO:ABC:t: 3, eps: 0.2018726421491916.\n",
      "INFO:ABC:Acceptance rate: 100 / 811 = 1.2330e-01, ESS=8.6726e+01.\n",
      "INFO:ABC:t: 4, eps: 0.15657418840839116.\n",
      "INFO:ABC:Acceptance rate: 100 / 1340 = 7.4627e-02, ESS=7.2610e+01.\n",
      "INFO:ABC:t: 5, eps: 0.12641034844899396.\n",
      "INFO:ABC:Acceptance rate: 100 / 2048 = 4.8828e-02, ESS=6.0511e+01.\n",
      "INFO:pyabc.util:Stopping: maximum number of generations.\n",
      "INFO:History:Done <ABCSMC(id=1, start_time=2021-01-12 13:30:38.344185, end_time=2021-01-12 14:41:50.823029)>\n",
      "INFO:Sampler:Parallelizing the sampling on 4 cores.\n",
      "INFO:History:Start <ABCSMC(id=1, start_time=2021-01-12 14:41:50.972673, end_time=None)>\n",
      "INFO:ABC:Calibration sample before t=0.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Working with  MSD_VAC\n",
      "MorpheusModel {\n",
      "\tname      : Demo_CellMotility.xml\n",
      "}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "INFO:Epsilon:initial epsilon is 1.011161081833951\n",
      "INFO:ABC:t: 0, eps: 1.011161081833951.\n",
      "INFO:ABC:Acceptance rate: 100 / 206 = 4.8544e-01, ESS=1.0000e+02.\n",
      "INFO:ABC:t: 1, eps: 0.8451839039367588.\n",
      "INFO:ABC:Acceptance rate: 100 / 305 = 3.2787e-01, ESS=7.1936e+01.\n",
      "INFO:ABC:t: 2, eps: 0.6862576712980013.\n",
      "INFO:ABC:Acceptance rate: 100 / 481 = 2.0790e-01, ESS=8.9387e+01.\n",
      "INFO:ABC:t: 3, eps: 0.5494719930107457.\n",
      "INFO:ABC:Acceptance rate: 100 / 592 = 1.6892e-01, ESS=7.3078e+01.\n",
      "INFO:ABC:t: 4, eps: 0.4420643032185497.\n",
      "INFO:ABC:Acceptance rate: 100 / 670 = 1.4925e-01, ESS=8.6096e+01.\n",
      "INFO:ABC:t: 5, eps: 0.33409610746207447.\n",
      "INFO:ABC:Acceptance rate: 100 / 759 = 1.3175e-01, ESS=4.1466e+01.\n",
      "INFO:pyabc.util:Stopping: maximum number of generations.\n",
      "INFO:History:Done <ABCSMC(id=1, start_time=2021-01-12 14:41:50.972673, end_time=2021-01-12 15:29:11.592285)>\n",
      "INFO:Sampler:Parallelizing the sampling on 4 cores.\n",
      "INFO:History:Start <ABCSMC(id=1, start_time=2021-01-12 15:29:11.645946, end_time=None)>\n",
      "INFO:ABC:Calibration sample before t=0.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Working with  MSD_RG\n",
      "MorpheusModel {\n",
      "\tname      : Demo_CellMotility.xml\n",
      "}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "INFO:Epsilon:initial epsilon is 1.3607756476535102\n",
      "INFO:ABC:t: 0, eps: 1.3607756476535102.\n",
      "INFO:ABC:Acceptance rate: 100 / 225 = 4.4444e-01, ESS=1.0000e+02.\n",
      "INFO:ABC:t: 1, eps: 1.0475724644812296.\n",
      "INFO:ABC:Acceptance rate: 100 / 271 = 3.6900e-01, ESS=8.3907e+01.\n",
      "INFO:ABC:t: 2, eps: 0.8164467731476269.\n",
      "INFO:ABC:Acceptance rate: 100 / 391 = 2.5575e-01, ESS=8.5666e+01.\n",
      "INFO:ABC:t: 3, eps: 0.5963047980911801.\n",
      "INFO:ABC:Acceptance rate: 100 / 539 = 1.8553e-01, ESS=7.9334e+01.\n",
      "INFO:ABC:t: 4, eps: 0.4544016074259722.\n",
      "INFO:ABC:Acceptance rate: 100 / 1269 = 7.8802e-02, ESS=9.1694e+01.\n",
      "INFO:ABC:t: 5, eps: 0.348250070486194.\n",
      "INFO:ABC:Acceptance rate: 100 / 1648 = 6.0680e-02, ESS=6.5892e+01.\n",
      "INFO:pyabc.util:Stopping: maximum number of generations.\n",
      "INFO:History:Done <ABCSMC(id=1, start_time=2021-01-12 15:29:11.645946, end_time=2021-01-12 16:35:01.106722)>\n",
      "INFO:Sampler:Parallelizing the sampling on 4 cores.\n",
      "INFO:History:Start <ABCSMC(id=1, start_time=2021-01-12 16:35:01.171029, end_time=None)>\n",
      "INFO:ABC:Calibration sample before t=0.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Working with  <function distanceMSD at 0x7fe53fb2b310>\n",
      "MorpheusModel {\n",
      "\tname      : Demo_CellMotility.xml\n",
      "}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "INFO:Epsilon:initial epsilon is 0.20591512209143165\n",
      "INFO:ABC:t: 0, eps: 0.20591512209143165.\n",
      "INFO:ABC:Acceptance rate: 100 / 191 = 5.2356e-01, ESS=1.0000e+02.\n",
      "INFO:ABC:t: 1, eps: 0.15351386251654137.\n",
      "INFO:ABC:Acceptance rate: 100 / 267 = 3.7453e-01, ESS=5.0835e+01.\n",
      "INFO:ABC:t: 2, eps: 0.11480885656113031.\n",
      "INFO:ABC:Acceptance rate: 100 / 361 = 2.7701e-01, ESS=8.7596e+01.\n",
      "INFO:ABC:t: 3, eps: 0.0786083182905185.\n",
      "INFO:ABC:Acceptance rate: 100 / 671 = 1.4903e-01, ESS=9.1441e+01.\n",
      "INFO:ABC:t: 4, eps: 0.05492931142121323.\n",
      "INFO:ABC:Acceptance rate: 100 / 1252 = 7.9872e-02, ESS=7.2007e+01.\n",
      "INFO:ABC:t: 5, eps: 0.03896060863651063.\n",
      "INFO:ABC:Acceptance rate: 100 / 2805 = 3.5651e-02, ESS=8.1408e+01.\n",
      "INFO:pyabc.util:Stopping: maximum number of generations.\n",
      "INFO:History:Done <ABCSMC(id=1, start_time=2021-01-12 16:35:01.171029, end_time=2021-01-12 17:55:24.780560)>\n",
      "INFO:Sampler:Parallelizing the sampling on 4 cores.\n",
      "INFO:History:Start <ABCSMC(id=1, start_time=2021-01-12 17:55:24.830924, end_time=None)>\n",
      "INFO:ABC:Calibration sample before t=0.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Working with  DR_VAC\n",
      "MorpheusModel {\n",
      "\tname      : Demo_CellMotility.xml\n",
      "}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "INFO:Epsilon:initial epsilon is 0.8004496943224138\n",
      "INFO:ABC:t: 0, eps: 0.8004496943224138.\n",
      "INFO:ABC:Acceptance rate: 100 / 183 = 5.4645e-01, ESS=1.0000e+02.\n",
      "INFO:ABC:t: 1, eps: 0.5305316699908733.\n",
      "INFO:ABC:Acceptance rate: 100 / 297 = 3.3670e-01, ESS=8.9686e+01.\n",
      "INFO:ABC:t: 2, eps: 0.423432688024408.\n",
      "INFO:ABC:Acceptance rate: 100 / 367 = 2.7248e-01, ESS=8.8773e+01.\n",
      "INFO:ABC:t: 3, eps: 0.339086626242354.\n",
      "INFO:ABC:Acceptance rate: 100 / 575 = 1.7391e-01, ESS=9.0107e+01.\n",
      "INFO:ABC:t: 4, eps: 0.259602922469403.\n",
      "INFO:ABC:Acceptance rate: 100 / 1035 = 9.6618e-02, ESS=8.3927e+01.\n",
      "INFO:ABC:t: 5, eps: 0.2287054386726482.\n",
      "INFO:ABC:Acceptance rate: 100 / 1433 = 6.9784e-02, ESS=6.3464e+01.\n",
      "INFO:pyabc.util:Stopping: maximum number of generations.\n",
      "INFO:History:Done <ABCSMC(id=1, start_time=2021-01-12 17:55:24.830924, end_time=2021-01-12 18:48:04.603221)>\n",
      "INFO:Sampler:Parallelizing the sampling on 4 cores.\n",
      "INFO:History:Start <ABCSMC(id=1, start_time=2021-01-12 18:48:04.654160, end_time=None)>\n",
      "INFO:ABC:Calibration sample before t=0.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Working with  DR_RG\n",
      "MorpheusModel {\n",
      "\tname      : Demo_CellMotility.xml\n",
      "}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "INFO:Epsilon:initial epsilon is 1.1239046278529843\n",
      "INFO:ABC:t: 0, eps: 1.1239046278529843.\n",
      "INFO:ABC:Acceptance rate: 100 / 207 = 4.8309e-01, ESS=1.0000e+02.\n",
      "INFO:ABC:t: 1, eps: 0.7955862190173035.\n",
      "INFO:ABC:Acceptance rate: 100 / 417 = 2.3981e-01, ESS=9.3252e+01.\n",
      "INFO:ABC:t: 2, eps: 0.6010725641056054.\n",
      "INFO:ABC:Acceptance rate: 100 / 686 = 1.4577e-01, ESS=7.0782e+01.\n",
      "INFO:ABC:t: 3, eps: 0.46271303517524975.\n",
      "INFO:ABC:Acceptance rate: 100 / 975 = 1.0256e-01, ESS=6.2714e+01.\n",
      "INFO:ABC:t: 4, eps: 0.39623991380078954.\n",
      "INFO:ABC:Acceptance rate: 100 / 1905 = 5.2493e-02, ESS=8.3989e+01.\n",
      "INFO:ABC:t: 5, eps: 0.3106306656917354.\n",
      "INFO:ABC:Acceptance rate: 100 / 2960 = 3.3784e-02, ESS=5.5015e+01.\n",
      "INFO:pyabc.util:Stopping: maximum number of generations.\n",
      "INFO:History:Done <ABCSMC(id=1, start_time=2021-01-12 18:48:04.654160, end_time=2021-01-12 20:27:56.405472)>\n",
      "INFO:Sampler:Parallelizing the sampling on 4 cores.\n",
      "INFO:History:Start <ABCSMC(id=1, start_time=2021-01-12 20:27:56.465645, end_time=None)>\n",
      "INFO:ABC:Calibration sample before t=0.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Working with  DR_MSD\n",
      "MorpheusModel {\n",
      "\tname      : Demo_CellMotility.xml\n",
      "}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "INFO:Epsilon:initial epsilon is 0.9701489329102755\n",
      "INFO:ABC:t: 0, eps: 0.9701489329102755.\n",
      "INFO:ABC:Acceptance rate: 100 / 197 = 5.0761e-01, ESS=1.0000e+02.\n",
      "INFO:ABC:t: 1, eps: 0.7171655502096654.\n",
      "INFO:ABC:Acceptance rate: 100 / 337 = 2.9674e-01, ESS=9.0914e+01.\n",
      "INFO:ABC:t: 2, eps: 0.5234542522629889.\n",
      "INFO:ABC:Acceptance rate: 100 / 504 = 1.9841e-01, ESS=8.5483e+01.\n",
      "INFO:ABC:t: 3, eps: 0.39245326249003487.\n",
      "INFO:ABC:Acceptance rate: 100 / 993 = 1.0070e-01, ESS=8.3420e+01.\n",
      "INFO:ABC:t: 4, eps: 0.3072575353493259.\n",
      "INFO:ABC:Acceptance rate: 100 / 1694 = 5.9032e-02, ESS=4.2791e+01.\n",
      "INFO:ABC:t: 5, eps: 0.2676750053647485.\n",
      "INFO:ABC:Acceptance rate: 100 / 3607 = 2.7724e-02, ESS=5.1007e+01.\n",
      "INFO:pyabc.util:Stopping: maximum number of generations.\n",
      "INFO:History:Done <ABCSMC(id=1, start_time=2021-01-12 20:27:56.465645, end_time=2021-01-12 22:09:27.156701)>\n",
      "INFO:Sampler:Parallelizing the sampling on 4 cores.\n",
      "INFO:History:Start <ABCSMC(id=1, start_time=2021-01-12 22:09:27.207865, end_time=None)>\n",
      "INFO:ABC:Calibration sample before t=0.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Working with  <function distanceDR at 0x7fe53fb2b430>\n",
      "MorpheusModel {\n",
      "\tname      : Demo_CellMotility.xml\n",
      "}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "INFO:Epsilon:initial epsilon is 0.3508789052019136\n",
      "INFO:ABC:t: 0, eps: 0.3508789052019136.\n",
      "INFO:ABC:Acceptance rate: 100 / 189 = 5.2910e-01, ESS=1.0000e+02.\n",
      "INFO:ABC:t: 1, eps: 0.2056775709259678.\n",
      "INFO:ABC:Acceptance rate: 100 / 438 = 2.2831e-01, ESS=8.9995e+01.\n",
      "INFO:ABC:t: 2, eps: 0.1480976237062622.\n",
      "INFO:ABC:Acceptance rate: 100 / 889 = 1.1249e-01, ESS=9.1313e+01.\n",
      "INFO:ABC:t: 3, eps: 0.11620229086550954.\n",
      "INFO:ABC:Acceptance rate: 100 / 1659 = 6.0277e-02, ESS=9.1349e+01.\n",
      "INFO:ABC:t: 4, eps: 0.0886441491141654.\n",
      "INFO:ABC:Acceptance rate: 100 / 4289 = 2.3315e-02, ESS=8.6044e+01.\n",
      "INFO:ABC:t: 5, eps: 0.0694061509795279.\n",
      "INFO:ABC:Acceptance rate: 100 / 9664 = 1.0348e-02, ESS=8.6112e+01.\n",
      "INFO:pyabc.util:Stopping: maximum number of generations.\n",
      "INFO:History:Done <ABCSMC(id=1, start_time=2021-01-12 22:09:27.207865, end_time=2021-01-13 01:53:45.191752)>\n"
     ]
    }
   ],
   "source": [
    "# Extensions of SS functions:\n",
    "#ss_list = ['MSD', 'VAC', 'DR', 'RG', 'HR']\n",
    "ss_list = ['VAC', 'RG', 'MSD', 'DR']\n",
    "\n",
    "for el1 in ss_list:\n",
    "    measured_list = []\n",
    "    for el2 in ss_list:\n",
    "        measured_list.append(el2)\n",
    "        if (el1 != el2) and (el1 not in measured_list):\n",
    "            #print(el1,el2)\n",
    "            functions_names = []\n",
    "            d = el1 + '_' + el2\n",
    "            functions_names.append('data_' + el1 + '_' + el2)\n",
    "            #data = ['data%s' % os.join.el1]\n",
    "            dist1 = globals()['distance%s' % el1]\n",
    "            dist2 = globals()['distance%s' % el2]\n",
    "            distances = [dist1,dist2]\n",
    "            print('Working with ',d)\n",
    "            data = fmc_mot_pipeline(\"Demo_CellMotility.xml\", t_init=1, time_interval=100, populations=6, dist_functions=distances, dist_functions_names=d)\n",
    "            globals()['data_' + el1 + '_'+ el2] = data\n",
    "        elif (el1 == el2):\n",
    "            d = el1 + '_' + el2\n",
    "            dist1 = globals()['distance%s' % el1]\n",
    "            dist2 = globals()['distance%s' % el2]\n",
    "            distances = [dist1,dist2]\n",
    "            print('Working with ', str(dist1))\n",
    "            data = fmc_mot_pipeline(\"Demo_CellMotility.xml\", t_init=1, time_interval=100, populations=6, dist_functions=distances[0], dist_functions_names=d)\n",
    "            globals()['data_' + el1 + '_'+ el2] = data            \n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Run with t = 10%"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "INFO:Sampler:Parallelizing the sampling on 4 cores.\n",
      "INFO:History:Start <ABCSMC(id=2, start_time=2021-01-13 10:51:39.574524, end_time=None)>\n",
      "INFO:ABC:Calibration sample before t=0.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Working with  <function distanceVAC at 0x7fe540059af0>\n",
      "MorpheusModel {\n",
      "\tname      : Demo_CellMotility10.xml\n",
      "}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "INFO:Epsilon:initial epsilon is 0.24426699184971126\n",
      "INFO:ABC:t: 0, eps: 0.24426699184971126.\n",
      "INFO:ABC:Acceptance rate: 100 / 233 = 4.2918e-01, ESS=1.0000e+02.\n",
      "INFO:ABC:t: 1, eps: 0.17499755044966778.\n",
      "INFO:ABC:Acceptance rate: 100 / 257 = 3.8911e-01, ESS=8.8825e+01.\n",
      "INFO:ABC:t: 2, eps: 0.13302503205596625.\n",
      "INFO:ABC:Acceptance rate: 100 / 260 = 3.8462e-01, ESS=6.9568e+01.\n",
      "INFO:ABC:t: 3, eps: 0.11376068204624622.\n",
      "INFO:ABC:Acceptance rate: 100 / 469 = 2.1322e-01, ESS=6.2896e+01.\n",
      "INFO:ABC:t: 4, eps: 0.10031790789847994.\n",
      "INFO:ABC:Acceptance rate: 100 / 513 = 1.9493e-01, ESS=6.6186e+01.\n",
      "INFO:ABC:t: 5, eps: 0.08562895110781506.\n",
      "INFO:ABC:Acceptance rate: 100 / 660 = 1.5152e-01, ESS=8.4995e+01.\n",
      "INFO:pyabc.util:Stopping: maximum number of generations.\n",
      "INFO:History:Done <ABCSMC(id=2, start_time=2021-01-13 10:51:39.574524, end_time=2021-01-13 11:29:46.318318)>\n",
      "INFO:Sampler:Parallelizing the sampling on 4 cores.\n",
      "INFO:History:Start <ABCSMC(id=1, start_time=2021-01-13 11:29:46.376862, end_time=None)>\n",
      "INFO:ABC:Calibration sample before t=0.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Working with  RG_VAC\n",
      "MorpheusModel {\n",
      "\tname      : Demo_CellMotility10.xml\n",
      "}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "INFO:Epsilon:initial epsilon is 0.8938781280758488\n",
      "INFO:ABC:t: 0, eps: 0.8938781280758488.\n",
      "INFO:ABC:Acceptance rate: 100 / 226 = 4.4248e-01, ESS=1.0000e+02.\n",
      "INFO:ABC:t: 1, eps: 0.7382510149406947.\n",
      "INFO:ABC:Acceptance rate: 100 / 319 = 3.1348e-01, ESS=9.4572e+01.\n",
      "INFO:ABC:t: 2, eps: 0.6183423936937901.\n",
      "INFO:ABC:Acceptance rate: 100 / 279 = 3.5842e-01, ESS=5.4717e+01.\n",
      "INFO:ABC:t: 3, eps: 0.5125675798756942.\n",
      "INFO:ABC:Acceptance rate: 100 / 425 = 2.3529e-01, ESS=8.6044e+01.\n",
      "INFO:ABC:t: 4, eps: 0.4060165987761464.\n",
      "INFO:ABC:Acceptance rate: 100 / 488 = 2.0492e-01, ESS=3.2749e+01.\n",
      "INFO:ABC:t: 5, eps: 0.3252329760792724.\n",
      "INFO:ABC:Acceptance rate: 100 / 580 = 1.7241e-01, ESS=3.2698e+01.\n",
      "INFO:pyabc.util:Stopping: maximum number of generations.\n",
      "INFO:History:Done <ABCSMC(id=1, start_time=2021-01-13 11:29:46.376862, end_time=2021-01-13 12:05:29.869429)>\n",
      "INFO:Sampler:Parallelizing the sampling on 4 cores.\n",
      "INFO:History:Start <ABCSMC(id=1, start_time=2021-01-13 12:05:29.935965, end_time=None)>\n",
      "INFO:ABC:Calibration sample before t=0.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Working with  <function distanceRG at 0x7fe540059280>\n",
      "MorpheusModel {\n",
      "\tname      : Demo_CellMotility10.xml\n",
      "}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "INFO:Epsilon:initial epsilon is 0.4559920332448719\n",
      "INFO:ABC:t: 0, eps: 0.4559920332448719.\n",
      "INFO:ABC:Acceptance rate: 100 / 260 = 3.8462e-01, ESS=1.0000e+02.\n",
      "INFO:ABC:t: 1, eps: 0.3150452319096866.\n",
      "INFO:ABC:Acceptance rate: 100 / 244 = 4.0984e-01, ESS=9.1840e+01.\n",
      "INFO:ABC:t: 2, eps: 0.24744844813554884.\n",
      "INFO:ABC:Acceptance rate: 100 / 256 = 3.9062e-01, ESS=3.5150e+01.\n",
      "INFO:ABC:t: 3, eps: 0.19800853963116333.\n",
      "INFO:ABC:Acceptance rate: 100 / 748 = 1.3369e-01, ESS=8.9614e+01.\n",
      "INFO:ABC:t: 4, eps: 0.15372327823241938.\n",
      "INFO:ABC:Acceptance rate: 100 / 948 = 1.0549e-01, ESS=6.2629e+01.\n",
      "INFO:ABC:t: 5, eps: 0.12652355472635757.\n",
      "INFO:ABC:Acceptance rate: 100 / 1586 = 6.3052e-02, ESS=7.4273e+01.\n",
      "INFO:pyabc.util:Stopping: maximum number of generations.\n",
      "INFO:History:Done <ABCSMC(id=1, start_time=2021-01-13 12:05:29.935965, end_time=2021-01-13 13:03:29.775048)>\n",
      "INFO:Sampler:Parallelizing the sampling on 4 cores.\n",
      "INFO:History:Start <ABCSMC(id=1, start_time=2021-01-13 13:03:29.852345, end_time=None)>\n",
      "INFO:ABC:Calibration sample before t=0.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Working with  MSD_VAC\n",
      "MorpheusModel {\n",
      "\tname      : Demo_CellMotility10.xml\n",
      "}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "INFO:Epsilon:initial epsilon is 0.9636768484983518\n",
      "INFO:ABC:t: 0, eps: 0.9636768484983518.\n",
      "INFO:ABC:Acceptance rate: 100 / 162 = 6.1728e-01, ESS=1.0000e+02.\n",
      "INFO:ABC:t: 1, eps: 0.8045557106433412.\n",
      "INFO:ABC:Acceptance rate: 100 / 274 = 3.6496e-01, ESS=6.5279e+01.\n",
      "INFO:ABC:t: 2, eps: 0.7021385647671893.\n",
      "INFO:ABC:Acceptance rate: 100 / 503 = 1.9881e-01, ESS=8.1715e+01.\n",
      "INFO:ABC:t: 3, eps: 0.5580078625912805.\n",
      "INFO:ABC:Acceptance rate: 100 / 574 = 1.7422e-01, ESS=8.4005e+01.\n",
      "INFO:ABC:t: 4, eps: 0.4394326792179517.\n",
      "INFO:ABC:Acceptance rate: 100 / 807 = 1.2392e-01, ESS=8.6523e+01.\n",
      "INFO:ABC:t: 5, eps: 0.34414829980703393.\n",
      "INFO:ABC:Acceptance rate: 100 / 1048 = 9.5420e-02, ESS=6.9474e+01.\n",
      "INFO:pyabc.util:Stopping: maximum number of generations.\n",
      "INFO:History:Done <ABCSMC(id=1, start_time=2021-01-13 13:03:29.852345, end_time=2021-01-13 13:51:47.529119)>\n",
      "INFO:Sampler:Parallelizing the sampling on 4 cores.\n",
      "INFO:History:Start <ABCSMC(id=1, start_time=2021-01-13 13:51:47.616452, end_time=None)>\n",
      "INFO:ABC:Calibration sample before t=0.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Working with  MSD_RG\n",
      "MorpheusModel {\n",
      "\tname      : Demo_CellMotility10.xml\n",
      "}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "INFO:Epsilon:initial epsilon is 1.039067968827793\n",
      "INFO:ABC:t: 0, eps: 1.039067968827793.\n",
      "INFO:ABC:Acceptance rate: 100 / 226 = 4.4248e-01, ESS=1.0000e+02.\n",
      "INFO:ABC:t: 1, eps: 0.7023463268817846.\n",
      "INFO:ABC:Acceptance rate: 100 / 294 = 3.4014e-01, ESS=8.2242e+01.\n",
      "INFO:ABC:t: 2, eps: 0.5386195531935217.\n",
      "INFO:ABC:Acceptance rate: 100 / 490 = 2.0408e-01, ESS=7.6470e+01.\n",
      "INFO:ABC:t: 3, eps: 0.41981176740067055.\n",
      "INFO:ABC:Acceptance rate: 100 / 640 = 1.5625e-01, ESS=8.3711e+01.\n",
      "INFO:ABC:t: 4, eps: 0.31852009119701347.\n",
      "INFO:ABC:Acceptance rate: 100 / 1114 = 8.9767e-02, ESS=8.1042e+01.\n",
      "INFO:ABC:t: 5, eps: 0.2505999721194094.\n",
      "INFO:ABC:Acceptance rate: 100 / 2297 = 4.3535e-02, ESS=8.4158e+01.\n",
      "INFO:pyabc.util:Stopping: maximum number of generations.\n",
      "INFO:History:Done <ABCSMC(id=1, start_time=2021-01-13 13:51:47.616452, end_time=2021-01-13 15:02:00.141656)>\n",
      "INFO:Sampler:Parallelizing the sampling on 4 cores.\n",
      "INFO:History:Start <ABCSMC(id=1, start_time=2021-01-13 15:02:00.195252, end_time=None)>\n",
      "INFO:ABC:Calibration sample before t=0.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Working with  <function distanceMSD at 0x7fe540370e50>\n",
      "MorpheusModel {\n",
      "\tname      : Demo_CellMotility10.xml\n",
      "}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "INFO:Epsilon:initial epsilon is 0.4900307130699406\n",
      "INFO:ABC:t: 0, eps: 0.4900307130699406.\n",
      "INFO:ABC:Acceptance rate: 100 / 224 = 4.4643e-01, ESS=1.0000e+02.\n",
      "INFO:ABC:t: 1, eps: 0.3515269948527513.\n",
      "INFO:ABC:Acceptance rate: 100 / 349 = 2.8653e-01, ESS=8.9021e+01.\n",
      "INFO:ABC:t: 2, eps: 0.2601302758754754.\n",
      "INFO:ABC:Acceptance rate: 100 / 430 = 2.3256e-01, ESS=9.0885e+01.\n",
      "INFO:ABC:t: 3, eps: 0.18158212025751072.\n",
      "INFO:ABC:Acceptance rate: 100 / 636 = 1.5723e-01, ESS=8.4098e+01.\n",
      "INFO:ABC:t: 4, eps: 0.12054571376803952.\n",
      "INFO:ABC:Acceptance rate: 100 / 1708 = 5.8548e-02, ESS=4.2569e+01.\n",
      "INFO:ABC:t: 5, eps: 0.0897641271303162.\n",
      "INFO:ABC:Acceptance rate: 100 / 3761 = 2.6589e-02, ESS=7.9753e+01.\n",
      "INFO:pyabc.util:Stopping: maximum number of generations.\n",
      "INFO:History:Done <ABCSMC(id=1, start_time=2021-01-13 15:02:00.195252, end_time=2021-01-13 16:47:02.290114)>\n",
      "INFO:Sampler:Parallelizing the sampling on 4 cores.\n",
      "INFO:History:Start <ABCSMC(id=1, start_time=2021-01-13 16:47:02.344395, end_time=None)>\n",
      "INFO:ABC:Calibration sample before t=0.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Working with  DR_VAC\n",
      "MorpheusModel {\n",
      "\tname      : Demo_CellMotility10.xml\n",
      "}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "INFO:Epsilon:initial epsilon is 1.2799563857735725\n",
      "INFO:ABC:t: 0, eps: 1.2799563857735725.\n",
      "INFO:ABC:Acceptance rate: 100 / 160 = 6.2500e-01, ESS=1.0000e+02.\n",
      "INFO:ABC:t: 1, eps: 0.9707815507362703.\n",
      "INFO:ABC:Acceptance rate: 100 / 330 = 3.0303e-01, ESS=9.0346e+01.\n",
      "INFO:ABC:t: 2, eps: 0.6804776673332297.\n",
      "INFO:ABC:Acceptance rate: 100 / 339 = 2.9499e-01, ESS=9.1441e+01.\n",
      "INFO:ABC:t: 3, eps: 0.4429532558934695.\n",
      "INFO:ABC:Acceptance rate: 100 / 352 = 2.8409e-01, ESS=8.3332e+01.\n",
      "INFO:ABC:t: 4, eps: 0.33846842245658826.\n",
      "INFO:ABC:Acceptance rate: 100 / 454 = 2.2026e-01, ESS=8.1795e+01.\n",
      "INFO:ABC:t: 5, eps: 0.28047380230503244.\n",
      "INFO:ABC:Acceptance rate: 100 / 823 = 1.2151e-01, ESS=1.7111e+01.\n",
      "INFO:pyabc.util:Stopping: maximum number of generations.\n",
      "INFO:History:Done <ABCSMC(id=1, start_time=2021-01-13 16:47:02.344395, end_time=2021-01-13 17:23:37.000317)>\n",
      "INFO:Sampler:Parallelizing the sampling on 4 cores.\n",
      "INFO:History:Start <ABCSMC(id=1, start_time=2021-01-13 17:23:37.051860, end_time=None)>\n",
      "INFO:ABC:Calibration sample before t=0.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Working with  DR_RG\n",
      "MorpheusModel {\n",
      "\tname      : Demo_CellMotility10.xml\n",
      "}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "INFO:Epsilon:initial epsilon is 1.1897690157909566\n",
      "INFO:ABC:t: 0, eps: 1.1897690157909566.\n",
      "INFO:ABC:Acceptance rate: 100 / 226 = 4.4248e-01, ESS=1.0000e+02.\n",
      "INFO:ABC:t: 1, eps: 0.8768635827889238.\n",
      "INFO:ABC:Acceptance rate: 100 / 291 = 3.4364e-01, ESS=9.5771e+01.\n",
      "INFO:ABC:t: 2, eps: 0.6575748805302708.\n",
      "INFO:ABC:Acceptance rate: 100 / 408 = 2.4510e-01, ESS=6.0732e+01.\n",
      "INFO:ABC:t: 3, eps: 0.488441326786388.\n",
      "INFO:ABC:Acceptance rate: 100 / 744 = 1.3441e-01, ESS=8.9763e+01.\n",
      "INFO:ABC:t: 4, eps: 0.41180075830064045.\n",
      "INFO:ABC:Acceptance rate: 100 / 522 = 1.9157e-01, ESS=7.7590e+01.\n",
      "INFO:ABC:t: 5, eps: 0.34042141181699037.\n",
      "INFO:ABC:Acceptance rate: 100 / 534 = 1.8727e-01, ESS=5.8195e+01.\n",
      "INFO:pyabc.util:Stopping: maximum number of generations.\n",
      "INFO:History:Done <ABCSMC(id=1, start_time=2021-01-13 17:23:37.051860, end_time=2021-01-13 18:01:26.706615)>\n",
      "INFO:Sampler:Parallelizing the sampling on 4 cores.\n",
      "INFO:History:Start <ABCSMC(id=1, start_time=2021-01-13 18:01:26.762366, end_time=None)>\n",
      "INFO:ABC:Calibration sample before t=0.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Working with  DR_MSD\n",
      "MorpheusModel {\n",
      "\tname      : Demo_CellMotility10.xml\n",
      "}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "INFO:Epsilon:initial epsilon is 0.9526693419086585\n",
      "INFO:ABC:t: 0, eps: 0.9526693419086585.\n",
      "INFO:ABC:Acceptance rate: 100 / 222 = 4.5045e-01, ESS=1.0000e+02.\n",
      "INFO:ABC:t: 1, eps: 0.7769205706593939.\n",
      "INFO:ABC:Acceptance rate: 100 / 244 = 4.0984e-01, ESS=8.5516e+01.\n",
      "INFO:ABC:t: 2, eps: 0.5528950470396092.\n",
      "INFO:ABC:Acceptance rate: 100 / 346 = 2.8902e-01, ESS=8.4312e+01.\n",
      "INFO:ABC:t: 3, eps: 0.40154944647361357.\n",
      "INFO:ABC:Acceptance rate: 100 / 387 = 2.5840e-01, ESS=5.5435e+01.\n",
      "INFO:ABC:t: 4, eps: 0.28394273476345955.\n",
      "INFO:ABC:Acceptance rate: 100 / 776 = 1.2887e-01, ESS=3.5338e+01.\n",
      "INFO:ABC:t: 5, eps: 0.24551086902336813.\n",
      "INFO:ABC:Acceptance rate: 100 / 1047 = 9.5511e-02, ESS=4.6048e+01.\n",
      "INFO:pyabc.util:Stopping: maximum number of generations.\n",
      "INFO:History:Done <ABCSMC(id=1, start_time=2021-01-13 18:01:26.762366, end_time=2021-01-13 18:43:32.339916)>\n",
      "INFO:Sampler:Parallelizing the sampling on 4 cores.\n",
      "INFO:History:Start <ABCSMC(id=1, start_time=2021-01-13 18:43:32.394647, end_time=None)>\n",
      "INFO:ABC:Calibration sample before t=0.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Working with  <function distanceDR at 0x7fe53fb2b3a0>\n",
      "MorpheusModel {\n",
      "\tname      : Demo_CellMotility10.xml\n",
      "}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "INFO:Epsilon:initial epsilon is 0.7361502262346405\n",
      "INFO:ABC:t: 0, eps: 0.7361502262346405.\n",
      "INFO:ABC:Acceptance rate: 100 / 223 = 4.4843e-01, ESS=1.0000e+02.\n",
      "INFO:ABC:t: 1, eps: 0.46347173432940086.\n",
      "INFO:ABC:Acceptance rate: 100 / 237 = 4.2194e-01, ESS=8.6445e+01.\n",
      "INFO:ABC:t: 2, eps: 0.2610954919061373.\n",
      "INFO:ABC:Acceptance rate: 100 / 301 = 3.3223e-01, ESS=9.4424e+01.\n",
      "INFO:ABC:t: 3, eps: 0.16836796076270893.\n",
      "INFO:ABC:Acceptance rate: 100 / 465 = 2.1505e-01, ESS=8.6576e+01.\n",
      "INFO:ABC:t: 4, eps: 0.1255929788795505.\n",
      "INFO:ABC:Acceptance rate: 100 / 674 = 1.4837e-01, ESS=7.1815e+01.\n",
      "INFO:ABC:t: 5, eps: 0.10186621998243871.\n",
      "INFO:ABC:Acceptance rate: 100 / 1060 = 9.4340e-02, ESS=7.9440e+01.\n",
      "INFO:pyabc.util:Stopping: maximum number of generations.\n",
      "INFO:History:Done <ABCSMC(id=1, start_time=2021-01-13 18:43:32.394647, end_time=2021-01-13 19:24:33.093249)>\n"
     ]
    }
   ],
   "source": [
    "# Test it works properly:\n",
    "#fmc_mot_pipeline(\"Demo_CellMotility10.xml\", t_init=10, time_interval=10, populations=1, dist_functions=[distanceMSD, distanceRG], dist_functions_names='RG_MSD', percentage='10')\n",
    "\n",
    "ss_list = ['VAC', 'RG', 'MSD', 'DR']\n",
    "\n",
    "for el1 in ss_list:\n",
    "    measured_list = []\n",
    "    for el2 in ss_list:\n",
    "        measured_list.append(el2)\n",
    "        if (el1 != el2) and (el1 not in measured_list):\n",
    "            #print(el1,el2)\n",
    "            functions_names = []\n",
    "            d = el1 + '_' + el2\n",
    "            functions_names.append('data_' + el1 + '_' + el2)\n",
    "            #data = ['data%s' % os.join.el1]\n",
    "            dist1 = globals()['distance%s' % el1]\n",
    "            dist2 = globals()['distance%s' % el2]\n",
    "            distances = [dist1,dist2]\n",
    "            print('Working with ',d)\n",
    "            data = fmc_mot_pipeline(\"Demo_CellMotility10.xml\", t_init=10, time_interval=10, populations=6, dist_functions=distances, dist_functions_names=d, percentage='10')\n",
    "            globals()['data_' + el1 + '_'+ el2] = data\n",
    "        elif (el1 == el2):\n",
    "            d = el1 + '_' + el2\n",
    "            dist1 = globals()['distance%s' % el1]\n",
    "            dist2 = globals()['distance%s' % el2]\n",
    "            distances = [dist1,dist2]\n",
    "            print('Working with ', str(dist1))\n",
    "            data = fmc_mot_pipeline(\"Demo_CellMotility10.xml\", t_init=10, time_interval=10, populations=6, dist_functions=distances[0], dist_functions_names=d, percentage='10')\n",
    "            globals()['data_' + el1 + '_'+ el2] = data            \n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## With 100%"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "INFO:Sampler:Parallelizing the sampling on 4 cores.\n",
      "INFO:History:Start <ABCSMC(id=1, start_time=2021-01-13 20:27:14.007134, end_time=None)>\n",
      "INFO:ABC:Calibration sample before t=0.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Working with  <function distanceVAC at 0x7fe540370e50>\n",
      "MorpheusModel {\n",
      "\tname      : Demo_CellMotility100.xml\n",
      "}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "INFO:Epsilon:initial epsilon is 0.27839191822953374\n",
      "INFO:ABC:t: 0, eps: 0.27839191822953374.\n",
      "INFO:ABC:Acceptance rate: 100 / 162 = 6.1728e-01, ESS=1.0000e+02.\n",
      "INFO:ABC:t: 1, eps: 0.19151404264579805.\n",
      "INFO:ABC:Acceptance rate: 100 / 235 = 4.2553e-01, ESS=7.1217e+01.\n",
      "INFO:ABC:t: 2, eps: 0.15644838444155473.\n",
      "INFO:ABC:Acceptance rate: 100 / 274 = 3.6496e-01, ESS=6.2909e+01.\n",
      "INFO:ABC:t: 3, eps: 0.13714280363440776.\n",
      "INFO:ABC:Acceptance rate: 100 / 453 = 2.2075e-01, ESS=4.6980e+01.\n",
      "INFO:ABC:t: 4, eps: 0.12464864082576742.\n",
      "INFO:ABC:Acceptance rate: 100 / 984 = 1.0163e-01, ESS=9.5448e+01.\n",
      "INFO:ABC:t: 5, eps: 0.10546304367405974.\n",
      "INFO:ABC:Acceptance rate: 100 / 551 = 1.8149e-01, ESS=9.0708e+01.\n",
      "INFO:pyabc.util:Stopping: maximum number of generations.\n",
      "INFO:History:Done <ABCSMC(id=1, start_time=2021-01-13 20:27:14.007134, end_time=2021-01-13 21:19:58.635624)>\n",
      "INFO:Sampler:Parallelizing the sampling on 4 cores.\n",
      "INFO:History:Start <ABCSMC(id=1, start_time=2021-01-13 21:19:58.687549, end_time=None)>\n",
      "INFO:ABC:Calibration sample before t=0.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Working with  RG_VAC\n",
      "MorpheusModel {\n",
      "\tname      : Demo_CellMotility100.xml\n",
      "}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "INFO:Epsilon:initial epsilon is 0.7785277530664145\n",
      "INFO:ABC:t: 0, eps: 0.7785277530664145.\n",
      "INFO:ABC:Acceptance rate: 100 / 202 = 4.9505e-01, ESS=1.0000e+02.\n",
      "INFO:ABC:t: 1, eps: 0.601586178664664.\n",
      "INFO:ABC:Acceptance rate: 100 / 278 = 3.5971e-01, ESS=8.0406e+01.\n",
      "INFO:ABC:t: 2, eps: 0.4893871183208841.\n",
      "INFO:ABC:Acceptance rate: 100 / 407 = 2.4570e-01, ESS=4.2766e+01.\n",
      "INFO:ABC:t: 3, eps: 0.4019266403860096.\n",
      "INFO:ABC:Acceptance rate: 100 / 656 = 1.5244e-01, ESS=6.0046e+01.\n",
      "INFO:ABC:t: 4, eps: 0.3393780798506956.\n",
      "INFO:ABC:Acceptance rate: 100 / 750 = 1.3333e-01, ESS=9.1689e+01.\n",
      "INFO:ABC:t: 5, eps: 0.27289006827865403.\n",
      "INFO:ABC:Acceptance rate: 100 / 494 = 2.0243e-01, ESS=6.3638e+01.\n",
      "INFO:pyabc.util:Stopping: maximum number of generations.\n",
      "INFO:History:Done <ABCSMC(id=1, start_time=2021-01-13 21:19:58.687549, end_time=2021-01-13 22:10:19.270858)>\n",
      "INFO:Sampler:Parallelizing the sampling on 4 cores.\n",
      "INFO:History:Start <ABCSMC(id=1, start_time=2021-01-13 22:10:19.332136, end_time=None)>\n",
      "INFO:ABC:Calibration sample before t=0.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Working with  <function distanceRG at 0x7fe540599550>\n",
      "MorpheusModel {\n",
      "\tname      : Demo_CellMotility100.xml\n",
      "}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "INFO:Epsilon:initial epsilon is 0.4424423049730778\n",
      "INFO:ABC:t: 0, eps: 0.4424423049730778.\n",
      "INFO:ABC:Acceptance rate: 100 / 261 = 3.8314e-01, ESS=1.0000e+02.\n",
      "INFO:ABC:t: 1, eps: 0.29764154589352754.\n",
      "INFO:ABC:Acceptance rate: 100 / 338 = 2.9586e-01, ESS=8.2910e+01.\n",
      "INFO:ABC:t: 2, eps: 0.21180375599541573.\n",
      "INFO:ABC:Acceptance rate: 100 / 401 = 2.4938e-01, ESS=8.1618e+01.\n",
      "INFO:ABC:t: 3, eps: 0.16196359682867506.\n",
      "INFO:ABC:Acceptance rate: 100 / 682 = 1.4663e-01, ESS=6.7939e+01.\n",
      "INFO:ABC:t: 4, eps: 0.13130245285842884.\n",
      "INFO:ABC:Acceptance rate: 100 / 1473 = 6.7889e-02, ESS=8.2104e+01.\n",
      "INFO:ABC:t: 5, eps: 0.10694051883252226.\n",
      "INFO:ABC:Acceptance rate: 100 / 1712 = 5.8411e-02, ESS=8.3131e+01.\n",
      "INFO:pyabc.util:Stopping: maximum number of generations.\n",
      "INFO:History:Done <ABCSMC(id=1, start_time=2021-01-13 22:10:19.332136, end_time=2021-01-13 23:32:53.157533)>\n",
      "INFO:Sampler:Parallelizing the sampling on 4 cores.\n",
      "INFO:History:Start <ABCSMC(id=1, start_time=2021-01-13 23:32:53.273151, end_time=None)>\n",
      "INFO:ABC:Calibration sample before t=0.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Working with  MSD_VAC\n",
      "MorpheusModel {\n",
      "\tname      : Demo_CellMotility100.xml\n",
      "}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "INFO:Epsilon:initial epsilon is 0.6964339798182028\n",
      "INFO:ABC:t: 0, eps: 0.6964339798182028.\n",
      "INFO:ABC:Acceptance rate: 100 / 230 = 4.3478e-01, ESS=1.0000e+02.\n",
      "INFO:ABC:t: 1, eps: 0.6306138799811645.\n",
      "INFO:ABC:Acceptance rate: 100 / 387 = 2.5840e-01, ESS=7.7093e+01.\n",
      "INFO:ABC:t: 2, eps: 0.5094245090370325.\n",
      "INFO:ABC:Acceptance rate: 100 / 639 = 1.5649e-01, ESS=8.6221e+01.\n",
      "INFO:ABC:t: 3, eps: 0.4045177886228727.\n",
      "INFO:ABC:Acceptance rate: 100 / 619 = 1.6155e-01, ESS=7.5577e+01.\n",
      "INFO:ABC:t: 4, eps: 0.29894672081530654.\n",
      "INFO:ABC:Acceptance rate: 100 / 942 = 1.0616e-01, ESS=8.9770e+01.\n",
      "INFO:ABC:t: 5, eps: 0.2368275878748573.\n",
      "INFO:ABC:Acceptance rate: 100 / 1210 = 8.2645e-02, ESS=8.0990e+01.\n",
      "INFO:pyabc.util:Stopping: maximum number of generations.\n",
      "INFO:History:Done <ABCSMC(id=1, start_time=2021-01-13 23:32:53.273151, end_time=2021-01-14 00:28:18.038531)>\n",
      "INFO:Sampler:Parallelizing the sampling on 4 cores.\n",
      "INFO:History:Start <ABCSMC(id=1, start_time=2021-01-14 00:28:18.092216, end_time=None)>\n",
      "INFO:ABC:Calibration sample before t=0.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Working with  MSD_RG\n",
      "MorpheusModel {\n",
      "\tname      : Demo_CellMotility100.xml\n",
      "}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "INFO:Epsilon:initial epsilon is 1.0515625415106888\n",
      "INFO:ABC:t: 0, eps: 1.0515625415106888.\n",
      "INFO:ABC:Acceptance rate: 100 / 179 = 5.5866e-01, ESS=1.0000e+02.\n",
      "INFO:ABC:t: 1, eps: 0.7316776930849198.\n",
      "INFO:ABC:Acceptance rate: 100 / 224 = 4.4643e-01, ESS=8.8168e+01.\n",
      "INFO:ABC:t: 2, eps: 0.5191046550385462.\n",
      "INFO:ABC:Acceptance rate: 100 / 424 = 2.3585e-01, ESS=8.8397e+01.\n",
      "INFO:ABC:t: 3, eps: 0.4103083663324618.\n",
      "INFO:ABC:Acceptance rate: 100 / 663 = 1.5083e-01, ESS=6.5312e+01.\n",
      "INFO:ABC:t: 4, eps: 0.34686385174742396.\n",
      "INFO:ABC:Acceptance rate: 100 / 882 = 1.1338e-01, ESS=7.7995e+01.\n",
      "INFO:ABC:t: 5, eps: 0.276070808681482.\n",
      "INFO:ABC:Acceptance rate: 100 / 1571 = 6.3654e-02, ESS=4.8043e+01.\n",
      "INFO:pyabc.util:Stopping: maximum number of generations.\n",
      "INFO:History:Done <ABCSMC(id=1, start_time=2021-01-14 00:28:18.092216, end_time=2021-01-14 01:29:18.653381)>\n",
      "INFO:Sampler:Parallelizing the sampling on 4 cores.\n",
      "INFO:History:Start <ABCSMC(id=1, start_time=2021-01-14 01:29:18.706384, end_time=None)>\n",
      "INFO:ABC:Calibration sample before t=0.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Working with  <function distanceMSD at 0x7fe540505940>\n",
      "MorpheusModel {\n",
      "\tname      : Demo_CellMotility100.xml\n",
      "}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "INFO:Epsilon:initial epsilon is 0.5858493044056188\n",
      "INFO:ABC:t: 0, eps: 0.5858493044056188.\n",
      "INFO:ABC:Acceptance rate: 100 / 190 = 5.2632e-01, ESS=1.0000e+02.\n",
      "INFO:ABC:t: 1, eps: 0.4211723173999256.\n",
      "INFO:ABC:Acceptance rate: 100 / 325 = 3.0769e-01, ESS=8.4071e+01.\n",
      "INFO:ABC:t: 2, eps: 0.28842371233855846.\n",
      "INFO:ABC:Acceptance rate: 100 / 485 = 2.0619e-01, ESS=8.9789e+01.\n",
      "INFO:ABC:t: 3, eps: 0.19910299639863227.\n",
      "INFO:ABC:Acceptance rate: 100 / 631 = 1.5848e-01, ESS=7.1824e+01.\n",
      "INFO:ABC:t: 4, eps: 0.14186252517020018.\n",
      "INFO:ABC:Acceptance rate: 100 / 1594 = 6.2735e-02, ESS=8.9347e+01.\n",
      "INFO:ABC:t: 5, eps: 0.10408732533921287.\n",
      "INFO:ABC:Acceptance rate: 100 / 2895 = 3.4542e-02, ESS=6.6541e+01.\n",
      "INFO:pyabc.util:Stopping: maximum number of generations.\n",
      "INFO:History:Done <ABCSMC(id=1, start_time=2021-01-14 01:29:18.706384, end_time=2021-01-14 02:54:21.404624)>\n",
      "INFO:Sampler:Parallelizing the sampling on 4 cores.\n",
      "INFO:History:Start <ABCSMC(id=1, start_time=2021-01-14 02:54:21.455869, end_time=None)>\n",
      "INFO:ABC:Calibration sample before t=0.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Working with  DR_VAC\n",
      "MorpheusModel {\n",
      "\tname      : Demo_CellMotility100.xml\n",
      "}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "INFO:Epsilon:initial epsilon is 1.1938729228937164\n",
      "INFO:ABC:t: 0, eps: 1.1938729228937164.\n",
      "INFO:ABC:Acceptance rate: 100 / 204 = 4.9020e-01, ESS=1.0000e+02.\n",
      "INFO:ABC:t: 1, eps: 1.051694949638314.\n",
      "INFO:ABC:Acceptance rate: 100 / 216 = 4.6296e-01, ESS=6.8206e+01.\n",
      "INFO:ABC:t: 2, eps: 0.8777480495720357.\n",
      "INFO:ABC:Acceptance rate: 100 / 378 = 2.6455e-01, ESS=8.9942e+01.\n",
      "INFO:ABC:t: 3, eps: 0.5784092770099207.\n",
      "INFO:ABC:Acceptance rate: 100 / 295 = 3.3898e-01, ESS=9.0717e+01.\n",
      "INFO:ABC:t: 4, eps: 0.35634936608409384.\n",
      "INFO:ABC:Acceptance rate: 100 / 396 = 2.5253e-01, ESS=9.4971e+01.\n",
      "INFO:ABC:t: 5, eps: 0.3021000108292845.\n",
      "INFO:ABC:Acceptance rate: 100 / 515 = 1.9417e-01, ESS=8.4330e+01.\n",
      "INFO:pyabc.util:Stopping: maximum number of generations.\n",
      "INFO:History:Done <ABCSMC(id=1, start_time=2021-01-14 02:54:21.455869, end_time=2021-01-14 03:23:20.660576)>\n",
      "INFO:Sampler:Parallelizing the sampling on 4 cores.\n",
      "INFO:History:Start <ABCSMC(id=1, start_time=2021-01-14 03:23:20.715818, end_time=None)>\n",
      "INFO:ABC:Calibration sample before t=0.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Working with  DR_RG\n",
      "MorpheusModel {\n",
      "\tname      : Demo_CellMotility100.xml\n",
      "}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "INFO:Epsilon:initial epsilon is 1.281241454695579\n",
      "INFO:ABC:t: 0, eps: 1.281241454695579.\n",
      "INFO:ABC:Acceptance rate: 100 / 196 = 5.1020e-01, ESS=1.0000e+02.\n",
      "INFO:ABC:t: 1, eps: 0.8502854023496407.\n",
      "INFO:ABC:Acceptance rate: 100 / 278 = 3.5971e-01, ESS=8.1229e+01.\n",
      "INFO:ABC:t: 2, eps: 0.7265603765057976.\n",
      "INFO:ABC:Acceptance rate: 100 / 347 = 2.8818e-01, ESS=8.3166e+01.\n",
      "INFO:ABC:t: 3, eps: 0.5953201363549154.\n",
      "INFO:ABC:Acceptance rate: 100 / 362 = 2.7624e-01, ESS=8.4709e+01.\n",
      "INFO:ABC:t: 4, eps: 0.43866169321763904.\n",
      "INFO:ABC:Acceptance rate: 100 / 496 = 2.0161e-01, ESS=6.6058e+01.\n",
      "INFO:ABC:t: 5, eps: 0.33849187632329003.\n",
      "INFO:ABC:Acceptance rate: 100 / 655 = 1.5267e-01, ESS=7.4653e+01.\n",
      "INFO:pyabc.util:Stopping: maximum number of generations.\n",
      "INFO:History:Done <ABCSMC(id=1, start_time=2021-01-14 03:23:20.715818, end_time=2021-01-14 03:59:31.285560)>\n",
      "INFO:Sampler:Parallelizing the sampling on 4 cores.\n",
      "INFO:History:Start <ABCSMC(id=1, start_time=2021-01-14 03:59:31.337474, end_time=None)>\n",
      "INFO:ABC:Calibration sample before t=0.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Working with  DR_MSD\n",
      "MorpheusModel {\n",
      "\tname      : Demo_CellMotility100.xml\n",
      "}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "INFO:Epsilon:initial epsilon is 1.0530636624847707\n",
      "INFO:ABC:t: 0, eps: 1.0530636624847707.\n",
      "INFO:ABC:Acceptance rate: 100 / 194 = 5.1546e-01, ESS=1.0000e+02.\n",
      "INFO:ABC:t: 1, eps: 0.7527189509325944.\n",
      "INFO:ABC:Acceptance rate: 100 / 282 = 3.5461e-01, ESS=7.9583e+01.\n",
      "INFO:ABC:t: 2, eps: 0.5283452212568631.\n",
      "INFO:ABC:Acceptance rate: 100 / 305 = 3.2787e-01, ESS=8.0015e+01.\n",
      "INFO:ABC:t: 3, eps: 0.3676871954408751.\n",
      "INFO:ABC:Acceptance rate: 100 / 417 = 2.3981e-01, ESS=8.9185e+01.\n",
      "INFO:ABC:t: 4, eps: 0.26952968054340926.\n",
      "INFO:ABC:Acceptance rate: 100 / 497 = 2.0121e-01, ESS=7.1317e+01.\n",
      "INFO:ABC:t: 5, eps: 0.2206133886429504.\n",
      "INFO:ABC:Acceptance rate: 100 / 854 = 1.1710e-01, ESS=8.2482e+01.\n",
      "INFO:pyabc.util:Stopping: maximum number of generations.\n",
      "INFO:History:Done <ABCSMC(id=1, start_time=2021-01-14 03:59:31.337474, end_time=2021-01-14 04:35:34.978387)>\n",
      "INFO:Sampler:Parallelizing the sampling on 4 cores.\n",
      "INFO:History:Start <ABCSMC(id=1, start_time=2021-01-14 04:35:35.035667, end_time=None)>\n",
      "INFO:ABC:Calibration sample before t=0.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Working with  <function distanceDR at 0x7fe54073aaf0>\n",
      "MorpheusModel {\n",
      "\tname      : Demo_CellMotility100.xml\n",
      "}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "INFO:Epsilon:initial epsilon is 0.7905232316807194\n",
      "INFO:ABC:t: 0, eps: 0.7905232316807194.\n",
      "INFO:ABC:Acceptance rate: 100 / 193 = 5.1813e-01, ESS=1.0000e+02.\n",
      "INFO:ABC:t: 1, eps: 0.5191457840537118.\n",
      "INFO:ABC:Acceptance rate: 100 / 208 = 4.8077e-01, ESS=9.1489e+01.\n",
      "INFO:ABC:t: 2, eps: 0.2221886417678301.\n",
      "INFO:ABC:Acceptance rate: 100 / 255 = 3.9216e-01, ESS=9.6229e+01.\n",
      "INFO:ABC:t: 3, eps: 0.14832174077701102.\n",
      "INFO:ABC:Acceptance rate: 100 / 325 = 3.0769e-01, ESS=8.0727e+01.\n",
      "INFO:ABC:t: 4, eps: 0.11388383686900262.\n",
      "INFO:ABC:Acceptance rate: 100 / 475 = 2.1053e-01, ESS=4.2199e+01.\n",
      "INFO:ABC:t: 5, eps: 0.0856103423732749.\n",
      "INFO:ABC:Acceptance rate: 100 / 1360 = 7.3529e-02, ESS=9.1155e+01.\n",
      "INFO:pyabc.util:Stopping: maximum number of generations.\n",
      "INFO:History:Done <ABCSMC(id=1, start_time=2021-01-14 04:35:35.035667, end_time=2021-01-14 05:15:04.940986)>\n"
     ]
    }
   ],
   "source": [
    "ss_list = ['VAC', 'RG', 'MSD', 'DR']\n",
    "\n",
    "for el1 in ss_list:\n",
    "    measured_list = []\n",
    "    for el2 in ss_list:\n",
    "        measured_list.append(el2)\n",
    "        if (el1 != el2) and (el1 not in measured_list):\n",
    "            #print(el1,el2)\n",
    "            functions_names = []\n",
    "            d = el1 + '_' + el2\n",
    "            functions_names.append('data_' + el1 + '_' + el2)\n",
    "            #data = ['data%s' % os.join.el1]\n",
    "            dist1 = globals()['distance%s' % el1]\n",
    "            dist2 = globals()['distance%s' % el2]\n",
    "            distances = [dist1,dist2]\n",
    "            print('Working with ',d)\n",
    "            data = fmc_mot_pipeline(\"Demo_CellMotility100.xml\", t_init=100, time_interval=1, populations=6, dist_functions=distances, dist_functions_names=d, percentage='100')\n",
    "            globals()['data_' + el1 + '_'+ el2] = data\n",
    "        elif (el1 == el2):\n",
    "            d = el1 + '_' + el2\n",
    "            dist1 = globals()['distance%s' % el1]\n",
    "            dist2 = globals()['distance%s' % el2]\n",
    "            distances = [dist1,dist2]\n",
    "            print('Working with ', str(dist1))\n",
    "            data = fmc_mot_pipeline(\"Demo_CellMotility100.xml\", t_init=100, time_interval=1, populations=6, dist_functions=distances[0], dist_functions_names=d, percentage='100')\n",
    "            globals()['data_' + el1 + '_'+ el2] = data "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Quality of fit:"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The quality of the fit ($QF$) will be assesed with the following equation:\n",
    "\n",
    "$$\n",
    "QF = \\frac{(\\mathrm{True parameter} - \\langle x \\rangle)^2}{\\sigma^2},\n",
    "$$\n",
    "\n",
    "where $\\langle x \\rangle$ is the posterior's mean and $\\sigma^2$ is the variance. Then values closer to zero refer to the best fits."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [],
   "source": [
    "SSs = ['DR_DR', 'DR_MSD', 'DR_RG', 'DR_VAC', 'MSD_MSD', 'MSD_RG', 'MSD_VAC', 'RG_RG', 'RG_VAC', 'VAC_VAC']\n",
    "time_per = ['1', '10', '100']\n",
    "\n",
    "\n",
    "def quality_matrix(SS_list = SSs, times = time_per):\n",
    "    quality_matrix_1 = np.zeros((len(SS_list), 3))\n",
    "    #quality_matrix_2 = np.zeros((len(SS_list), 3))\n",
    "    for idx, el in enumerate(SS_list):\n",
    "        for idx2, t in enumerate(times):\n",
    "            db_path = \"sqlite:///\" + el + t + \".db\"\n",
    "            h = pyabc.History(db_path)\n",
    "            df, w = h.get_distribution(t=5)\n",
    "            mean_1 = np.sum(df.motion_strength.values * w)\n",
    "            mean_2 = np.sum(df.noise_level.values * w)\n",
    "            var_1 = np.sum(w * (mean_1 - df.motion_strength.values)**2)\n",
    "            var_2 = np.sum(w * (mean_2 - df.noise_level.values)**2)\n",
    "            quality_matrix_1[idx, idx2] = (true_pars['motion_strength'] - mean_1)**2 / var_1 + (true_pars['noise_level'] - mean_2)**2 / var_2\n",
    "    return quality_matrix_1\n",
    "\n",
    "df1 = quality_matrix()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [],
   "source": [
    "def HeatMap_function(df, title, transpose = True, colors = 'Greens', dropDuplicates = True):\n",
    "\n",
    "    if transpose:\n",
    "        df = df.T\n",
    "    \n",
    "    if dropDuplicates:    \n",
    "        mask = np.zeros_like(df, dtype=np.bool)\n",
    "        mask = np.invert(mask)\n",
    "        mask[np.triu_indices_from(mask)] = False\n",
    "\n",
    "    # Set background color / chart style\n",
    "    sns.set_style(style = 'white')\n",
    "\n",
    "    # Set up  matplotlib figure\n",
    "    f, ax = plt.subplots(figsize=(15, 11))\n",
    "    ax.set_title(title)\n",
    "  \n",
    "    # Add diverging colormap from red to blue\n",
    "    # cmap = sns.diverging_palette(250, 10, as_cmap=True)\n",
    "    cmap=plt.get_cmap(colors)\n",
    "    \n",
    "    # Draw correlation plot with or without duplicates\n",
    "    if dropDuplicates:\n",
    "        sns.heatmap(df, annot=True, mask=mask, cmap=cmap, \n",
    "                square=True,\n",
    "                linewidth=.5, cbar_kws={\"shrink\": .5}, ax=ax)\n",
    "    else:\n",
    "        sns.heatmap(df, annot=True, cmap=cmap, \n",
    "                square=True,\n",
    "                linewidth=.5, cbar_kws={\"shrink\": .5}, ax=ax)\n",
    "\n",
    "    ax.xaxis.set_ticks_position('top')\n",
    "    ax.yaxis.set_ticks_position('right')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x792 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df = pd.DataFrame(df1, columns=time_per, index=SSs)\n",
    "HeatMap_function(df = df, title = 'Quality of fit', transpose = False,\n",
    "                 colors = 'Greens', dropDuplicates = False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Then for this pipeline the best fit would be the one with the DR/RG combination. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/sebas/opt/anaconda3/lib/python3.8/site-packages/pyabc/visualization/kde.py:350: MatplotlibDeprecationWarning: shading='flat' when X and Y have the same dimensions as C is deprecated since 3.3.  Either specify the corners of the quadrilaterals with X and Y, or pass shading='auto', 'nearest' or 'gouraud', or set rcParams['pcolor.shading'].  This will become an error two minor releases later.\n",
      "  mesh = ax.pcolormesh(X, Y, PDF, **kwargs)\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 360x360 with 5 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "db_path = \"sqlite:///\" + \"DR_RG100.db\"\n",
    "true_pars = {'motion_strength': 1.0, 'noise_level': 0.1}\n",
    "limits = {key: (0.1 * val, 10 * val) for key, val in true_pars.items()}\n",
    "\n",
    "h = pyabc.History(db_path)\n",
    "pyabc.visualization.plot_epsilons(h)\n",
    "df, w = h.get_distribution(t=5)\n",
    "pyabc.visualization.plot_kde_matrix(df, w, limits=limits, refval=true_pars)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The percentage of time points seems to be less critical in other combinations such as with RG/VAC. The KDE matrix for each combination and time percentage can be seen on the following figure:\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x1080 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "img = plt.imread('SS_summary.png')\n",
    "fig = plt.figure(figsize=(15,15))\n",
    "plt.imshow(img)\n",
    "plt.axis('off')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "A further analysis and discussion will follow as this notebook's goal was to demonstrate how to implement the FitMultiCell pipeline for studies on cell motility. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "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": "3.8.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}