Mechanocellular Model Cell Property Analyzer.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## This script is meant to be run after a large set of simulations and will compile a spreadsheet listing cell shape and interactions for each simulation. (The only analzyed cells will be the central two in a row of four. Simulations with different initial setups will need to be analyzed differently.)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import matplotlib.cm as cm\n",
    "%matplotlib notebook\n",
    "import os\n",
    "import time, sys\n",
    "from IPython.display import clear_output\n",
    "import math\n",
    "import pickle\n",
    "import pandas as pd\n",
    "import matplotlib\n",
    "\n",
    "plt.rcParams.update({'figure.max_open_warning': 0})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.0886226925452758\n"
     ]
    }
   ],
   "source": [
    "number_of_sides = 40\n",
    "natural_cell_area = 1\n",
    "natural_cell_radius = np.sqrt(natural_cell_area/np.pi)\n",
    "natural_spacing = 2*np.pi*natural_cell_radius/number_of_sides"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "def make_cells(**kwargs):\n",
    "    np.random.seed(kwargs.get('seed', 2*21*1995))\n",
    "    scatter = kwargs.get('scatter', natural_cell_radius*0.1)\n",
    "    compression = kwargs.get('compression', False)\n",
    "    theta = np.linspace(0, 2*np.pi, number_of_sides+1)[:-1].copy()\n",
    "    list_of_cells = []\n",
    "    for i in range(len(centers)):\n",
    "        cell = np.zeros([number_of_sides, 2])\n",
    "        cell[:,0] = natural_cell_radius*np.cos(theta)+centers[i,0]\n",
    "        cell[:,1] = natural_cell_radius*np.sin(theta)+centers[i,1]\n",
    "        cell = cell+scatter*np.random.random([number_of_sides,2])\n",
    "        list_of_cells.append(cell)\n",
    "    if not compression:\n",
    "        left_min = np.min([-3, 2*(np.min(centers[:,0])-natural_cell_radius)])\n",
    "        right_max = np.max([3, 2*(np.max(centers[:,0])+natural_cell_radius)])\n",
    "    else:\n",
    "        left_min = compression[0]\n",
    "        right_max = compression[1]\n",
    "    floorx = np.linspace(left_min, right_max, int((right_max-left_min)/natural_spacing))\n",
    "    floorz = np.zeros_like(floorx)\n",
    "    floor = np.array([floorx,floorz]).transpose()\n",
    "    return floor, list_of_cells\n",
    "\n",
    "def plot_cells(list_of_cells, **kwargs):\n",
    "    connections = kwargs.get('connections',False)\n",
    "    CCid = kwargs.get('CCid', 0.16)\n",
    "    CSid = kwargs.get('CSid', 0.1)\n",
    "    cell_cell, cell_cell_pairs = cell_cell_interactions(list_of_cells, CCid)\n",
    "    sub_cell, sub_cell_pairs = cell_substrate_interactions(list_of_cells, CSid)\n",
    "    left_min = -4.5\n",
    "    right_max = 4.5\n",
    "    top = 1.25*(np.max(centers[:,1])+natural_cell_radius)\n",
    "    bottom = -0.2\n",
    "    save_name = kwargs.get('save_name', False)\n",
    "    fig = plt.figure(figsize = (12,12))\n",
    "    ax = fig.add_subplot(111)\n",
    "    ax.set_aspect('equal')\n",
    "    ax.set_xlim([left_min,right_max])\n",
    "    ax.set_ylim([bottom,top])\n",
    "    for i in range(len(list_of_cells)):\n",
    "        if connections == True:\n",
    "            cell_i = list_of_cells[i]\n",
    "            if len(sub_cell)>0:\n",
    "                c_pts = cell_i[sub_cell_pairs[:,1].astype('int')[sub_cell==i]]\n",
    "                s_pts = floor[sub_cell_pairs[:,0].astype('int')[sub_cell==i]]\n",
    "                for j in range(len(c_pts)):\n",
    "                    x = [c_pts[j][0], s_pts[j][0]]\n",
    "                    y = [c_pts[j][1], s_pts[j][1]]\n",
    "                    ax.plot(x,y, c = 'deepskyblue', linewidth = 2)\n",
    "            for j in range(len(list_of_cells)):\n",
    "                cell_j = list_of_cells[j]\n",
    "                if len(cell_cell)>0:\n",
    "                    con_ij = np.logical_and(cell_cell[:,0]==i, cell_cell[:,1]==j)\n",
    "                    if np.sum(con_ij) > 0:\n",
    "                        ci_pts = cell_i[cell_cell_pairs[:,0].astype('int')[con_ij]]\n",
    "                        cj_pts = cell_j[cell_cell_pairs[:,1].astype('int')[con_ij]]\n",
    "                        for k in range(len(ci_pts)):\n",
    "                            x = [ci_pts[k][0],cj_pts[k][0]]\n",
    "                            y = [ci_pts[k][1],cj_pts[k][1]]\n",
    "                            ax.plot(x,y,c='darkgreen', linewidth = 3.5)\n",
    "        ax.scatter(list_of_cells[i][:,0], list_of_cells[i][:,1], color = 'k')\n",
    "        ax.plot(list_of_cells[i][:,0], list_of_cells[i][:,1], color = 'k')\n",
    "        ax.plot([list_of_cells[i][0,0], list_of_cells[i][-1,0]], \n",
    "                [list_of_cells[i][0,1], list_of_cells[i][-1,1]], color = 'k')\n",
    "    ax.scatter(floor, np.zeros_like(floor), color = 'r', alpha = 0.5)\n",
    "    ax.plot(floor, np.zeros_like(floor), color = 'r', alpha = 0.5)\n",
    "    ax.set_xticks([])\n",
    "    ax.set_yticks([])\n",
    "    if save_name != False:\n",
    "        plt.savefig(save_name+'.pdf', bbox_inches = 'tight', pad_inches = 1)\n",
    "    return\n",
    "\n",
    "def distance_matrix(xz1, xz2):\n",
    "    mutx1 = np.outer(np.ones(len(xz2[:,0])), xz1[:,0])\n",
    "    mutz1 = np.outer(np.ones(len(xz2[:,0])), xz1[:,1])\n",
    "    mutx2 = np.outer(xz2[:,0], np.ones(len(xz1[:,0])))\n",
    "    mutz2 = np.outer(xz2[:,1], np.ones(len(xz1[:,0])))\n",
    "    return np.sqrt((mutx1-mutx2)**2+(mutz1-mutz2)**2).transpose()\n",
    "\n",
    "def cell_substrate_interactions(list_of_cells, substrate_interaction_distance):\n",
    "    all_distances = []\n",
    "    cell_matrix_distances = []\n",
    "    closest_cell_points = np.zeros([len(floor), len(list_of_cells)])\n",
    "    closest_substrate_points = []\n",
    "    i = 0\n",
    "    for cell in list_of_cells:\n",
    "        distances = distance_matrix(floor, cell)\n",
    "        all_distances.append(distances)\n",
    "        cell_matrix_distances.append(distances)\n",
    "        closest_substrate_points.append(np.min(distances, axis = 0)<=substrate_interaction_distance)\n",
    "        closest_cell_points[:,i] = np.min(distances, axis = 1)\n",
    "        i+=1\n",
    "    floor_interactions = np.min(closest_cell_points, axis = 1)<=substrate_interaction_distance\n",
    "    floor_interaction_distances = closest_cell_points[floor_interactions]\n",
    "   \n",
    "    which_cell = np.zeros(np.sum(floor_interactions))\n",
    "    which_pair = np.zeros([np.sum(floor_interactions),2])\n",
    "    for j in range(len(floor_interactions[floor_interactions])):\n",
    "        which_cell[j] = np.argwhere(floor_interaction_distances[j,:]==np.min(floor_interaction_distances[j,:]))[0,0]\n",
    "        which_pair[j] = np.argwhere(cell_matrix_distances[int(which_cell[j])] == np.min(closest_cell_points, axis = 1)[floor_interactions][j])[0]\n",
    "   \n",
    "    which_cell_2 = []\n",
    "    which_pair_2 = []\n",
    "    for k in range(len(list_of_cells)):\n",
    "        this_cell = which_cell==k\n",
    "        this_cell_pairs = which_pair[this_cell]\n",
    "        repeats = []\n",
    "        for l in range(np.sum(this_cell)):\n",
    "            if np.sum(this_cell_pairs[:,1]==this_cell_pairs[l,1])>1:\n",
    "                repeats.append(this_cell_pairs[l,1])\n",
    "            else:\n",
    "                which_cell_2.append(k)\n",
    "                which_pair_2.append(this_cell_pairs[l])\n",
    "        repeats = np.sort(list(set(repeats)))\n",
    "        for item in repeats:\n",
    "            checks = this_cell_pairs[this_cell_pairs[:,1]==item]\n",
    "            dists = []\n",
    "            for m in range(len(checks)):\n",
    "                dists.append(all_distances[k][int(checks[m][0]),int(checks[m][1])])\n",
    "            keep = np.argwhere(dists == np.min(dists))[0,0]\n",
    "            which_cell_2.append(k)\n",
    "            which_pair_2.append(checks[keep])\n",
    "               \n",
    "   \n",
    "    return np.array(which_cell_2), np.array(which_pair_2)\n",
    "\n",
    "def cell_cell_interactions(list_of_cells, cell_cell_interaction_distance):\n",
    "    center_positions = np.zeros([len(list_of_cells),2])\n",
    "    for i in range(len(list_of_cells)):\n",
    "        cell_centers = list_of_cells[i].mean(0)\n",
    "        center_positions[i,0] = cell_centers[0]\n",
    "        center_positions[i,1] = cell_centers[1]\n",
    "    dist_bt_cell_centers = distance_matrix(center_positions,center_positions)\n",
    "    pairs_of_cells_that_matter = np.argwhere(dist_bt_cell_centers>0)\n",
    "    cells_that_matter = []\n",
    "    for i in range(len(pairs_of_cells_that_matter)):\n",
    "        if pairs_of_cells_that_matter[i,0]<pairs_of_cells_that_matter[i,1]:\n",
    "            cells_that_matter.append(pairs_of_cells_that_matter[i])\n",
    "    list_of_pairs = []\n",
    "    for i in range(len(cells_that_matter)):\n",
    "        cell_1 = list_of_cells[int(cells_that_matter[i][0])]\n",
    "        cell_2 = list_of_cells[int(cells_that_matter[i][1])]\n",
    "        distances = distance_matrix(cell_1,cell_2)\n",
    "        closest_cell_cell_points = np.min(distances, axis = 1)\n",
    "        cell_interactions = closest_cell_cell_points<=cell_cell_interaction_distance\n",
    "        cell_interaction_distances = closest_cell_cell_points[cell_interactions]\n",
    "        if np.sum(cell_interactions) == 0:\n",
    "            which_pair = []\n",
    "        else:\n",
    "            which_pair = np.zeros([np.sum(cell_interactions),2])\n",
    "            for j in range(np.sum(cell_interactions)):\n",
    "                which_pair[j] = np.argwhere(distances == cell_interaction_distances[j])[0]\n",
    "        list_of_pairs.append(which_pair)\n",
    "    what_cells = []\n",
    "    what_pairs = []\n",
    "    which_cells = []\n",
    "    which_pairs = []\n",
    "    for i in range(len(cells_that_matter)):\n",
    "        for j in range(len(list_of_pairs[i])):\n",
    "            pairs = list_of_pairs[i][j]\n",
    "            what_pairs.append(pairs)\n",
    "            cells = [cells_that_matter[i]]\n",
    "            what_cells.append(cells)\n",
    "            which_pairs = np.vstack(what_pairs)\n",
    "            which_cells= np.vstack(what_cells)\n",
    "\n",
    "    if len(which_cells)>0:\n",
    "        which_cells_2 = []\n",
    "        which_pairs_2 = []\n",
    "        for k in range(len(list_of_cells)):\n",
    "            for n in range(len(list_of_cells)):\n",
    "                these_cells = np.logical_and(which_cells[:,0]==k, which_cells[:,1]==n)\n",
    "                these_cell_pairs = which_pairs[these_cells]\n",
    "                repeats = []\n",
    "                for l in range(np.sum(these_cells)):\n",
    "                    if np.sum(these_cell_pairs[:,1]==these_cell_pairs[l,1])>1:\n",
    "                        repeats.append(these_cell_pairs[l,1])\n",
    "                    else:\n",
    "                        which_cells_2.append([k,n])\n",
    "                        which_pairs_2.append(these_cell_pairs[l])\n",
    "                repeats = np.sort(list(set(repeats)))\n",
    "                for item in repeats:\n",
    "                    checks = these_cell_pairs[these_cell_pairs[:,1]==item]\n",
    "                    dists = []\n",
    "                    for m in range(len(checks)):\n",
    "                        dists.append(distance_matrix(list_of_cells[k], list_of_cells[n])[int(checks[m][0]),int(checks[m][1])])\n",
    "                    keep = np.argwhere(dists == np.min(dists))[0,0]\n",
    "                    which_cells_2.append([k,n])\n",
    "                    which_pairs_2.append(checks[keep])\n",
    "    else:\n",
    "        which_cells_2 = which_cells\n",
    "        which_pairs_2 = which_pairs\n",
    "       \n",
    "    if len(np.array(which_cells_2))>0:\n",
    "        return np.concatenate([np.array(which_cells_2), np.array(which_cells_2)[:,[1,0]]]), np.concatenate([np.array(which_pairs_2), np.array(which_pairs_2)[:,[1,0]]])\n",
    "    else:\n",
    "        return np.array(which_cells_2), np.array(which_pairs_2)\n",
    "\n",
    "def area(cell):\n",
    "    x = cell[:,0]\n",
    "    z = cell[:,1]\n",
    "    return 0.5*np.sum(x*np.roll(z,-1)-z*np.roll(x,-1))\n",
    "\n",
    "def perimeter(cell):\n",
    "    ls = np.sum(np.sqrt(np.sum((np.roll(cell, -1, axis = 0) - cell)**2, axis = 1)))\n",
    "    return ls\n",
    "\n",
    "def circularity(cell):\n",
    "    circ = (4*np.pi*area(cell))/((perimeter(cell))**2)\n",
    "    return circ\n",
    "\n",
    "def average_area_center_cells(list_of_cells):\n",
    "    list_of_areas = []\n",
    "    for cell in list_of_cells[1:-1]:\n",
    "        list_of_areas.append(area(cell))\n",
    "    return (np.mean(list_of_areas), np.std(list_of_areas))\n",
    "\n",
    "def average_perimeter_center_cells(list_of_cells):\n",
    "    list_of_perimeters = []\n",
    "    for cell in list_of_cells[1:-1]:\n",
    "        list_of_perimeters.append(perimeter(cell))\n",
    "    return (np.mean(list_of_perimeters), np.std(list_of_perimeters))\n",
    "\n",
    "def average_circularity_center_cells(list_of_cells):\n",
    "    list_of_circ = []\n",
    "    for cell in list_of_cells[1:-1]:\n",
    "        list_of_circ.append(circularity(cell))\n",
    "    return (np.mean(list_of_circ), np.std(list_of_circ))\n",
    "\n",
    "def percent_cc_adhesions_center_cells(list_of_cells, cell_interaction_distance):\n",
    "    list_of_percents = []\n",
    "    for i in range(len(list_of_cells[1:-1])):\n",
    "        cell = list_of_cells[i+1]\n",
    "        list_of_percents.append(np.sum(cell_cell_interactions(list_of_cells,cell_interaction_distance)[0]==(i+1))/(2*len(cell)))\n",
    "    return (np.mean(list_of_percents), np.std(list_of_percents))\n",
    "\n",
    "def percent_cs_adhesions_center_cells(list_of_cells, substrate_interaction_distance):\n",
    "    list_of_percents = []\n",
    "    for i in range(len(list_of_cells[1:-1])):\n",
    "        cell = list_of_cells[i+1]\n",
    "        list_of_percents.append(np.sum(cell_substrate_interactions(list_of_cells,substrate_interaction_distance)[0]==(i+1))/(len(cell)))\n",
    "    return (np.mean(list_of_percents), np.std(list_of_percents))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "num_DENSITY = 5\n",
    "num_i = 4\n",
    "num_j = 5\n",
    "num_k = 7"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "gCS = [0.001,0.002,0.005,0.01,0.02,0.05,0.1]\n",
    "compressions = [[-10,10],[-3.6,3.6],[-3.2,3.2],[-2.8,2.8],[-2.4,2.4]]\n",
    "centers_un = [[[-2.7,1.6], [-0.9,1.5], [0.9,1.5], [2.7,1.5]],\n",
    "              [[-2.7,1.5], [-0.92,1.4], [0.9,1.5], [2.7,1.5]],\n",
    "              [[-2.7,1.5], [-0.9,1.5], [0.91,1.7], [2.7,1.5]],\n",
    "              [[-2.7,1.5], [-0.9,1.5], [0.9,1.5], [2.65,1.4]]]\n",
    "centers_4 =  [[[-2.7,1.6], [-0.9,1.5], [0.9,1.5], [2.7,1.5]],\n",
    "              [[-2.7,1.5], [-0.92,1.4], [0.9,1.5], [2.7,1.5]],\n",
    "              [[-2.7,1.5], [-0.9,1.5], [0.91,1.7], [2.7,1.5]],\n",
    "              [[-2.7,1.5], [-0.9,1.5], [0.9,1.5], [2.65,1.4]]]\n",
    "centers_3 =  [[[-2.7,1.6], [-0.9,1.5], [0.9,1.5], [2.7,1.5]],\n",
    "              [[-2.7,1.5], [-0.92,1.4], [0.9,1.5], [2.7,1.5]],\n",
    "              [[-2.7,1.5], [-0.9,1.5], [0.91,1.7], [2.7,1.5]],\n",
    "              [[-2.7,1.5], [-0.9,1.5], [0.9,1.5], [2.65,1.4]]]\n",
    "centers_2 =  [[[-2.4,1.6], [-0.8,1.5], [0.8,1.5], [2.4,1.5]],\n",
    "              [[-2.4,1.5], [-0.82,1.4], [0.8,1.5], [2.4,1.5]],\n",
    "              [[-2.4,1.5], [-0.8,1.5], [0.81,1.7], [2.4,1.5]],\n",
    "              [[-2.4,1.5], [-0.8,1.5], [0.8,1.5], [2.35,1.4]]]\n",
    "centers_1 =  [[[-2.1,1.6], [-0.7,1.5], [0.7,1.5], [2.1,1.5]],\n",
    "              [[-2.1,1.5], [-0.72,1.4], [0.7,1.5], [2.1,1.5]],\n",
    "              [[-2.1,1.5], [-0.7,1.5], [0.71,1.7], [2.1,1.5]],\n",
    "              [[-2.1,1.5], [-0.7,1.5], [0.7,1.5], [2.05,1.4]]]\n",
    "configurations = [centers_un,centers_4,centers_3,centers_2,centers_1]\n",
    "seeds = [2*21*95, 11*4*96, 5*9*64, 13, 42]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
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    }
   ],
   "source": [
    "directory = 'Cell-Substrate/'\n",
    "extracted_data = np.zeros([num_i*num_j*num_k*num_DENSITY,9])\n",
    "spot = 0\n",
    "for DENSITY in range(num_DENSITY):\n",
    "    for i in range(num_i):\n",
    "        centers = np.array(configurations[DENSITY][i])\n",
    "        for j in range(num_j):\n",
    "            floor, list_of_cells = make_cells(seed = seeds[j], compression = compressions[DENSITY])\n",
    "            for k in range(num_k):\n",
    "                name = 'Run_density='+str(DENSITY)+'_i='+str(i)+'_j='+str(j)+'_k='+str(k)+'.pkl'\n",
    "                with open(directory+name, 'rb') as g:\n",
    "                    run = pickle.load(g)[-1]\n",
    "                a, da = average_area_center_cells(run)\n",
    "                p, dp = average_perimeter_center_cells(run)\n",
    "                c, dc = average_circularity_center_cells(run)\n",
    "                cc, dcc = percent_cc_adhesions_center_cells(run, 0.16)\n",
    "                cs, dcs = percent_cs_adhesions_center_cells(run, 0.1)\n",
    "                extracted_data[spot,:] = [DENSITY, i, j, k, a, p, c, cc, cs]\n",
    "                print(spot)\n",
    "                spot+=1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.DataFrame(extracted_data, columns = ['DENSITY','i','j','k','area','perimeter','circularity','percentage_cell_contacts','percentage_substrate_contacts'])\n",
    "df.to_csv('Cell-Substrate Analyzed.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [],
   "source": [
    "num_DENSITY = 5\n",
    "num_i = 4\n",
    "num_j = 5\n",
    "num_k = 5"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [],
   "source": [
    "gCC = [0.00001, 0.0001, 0.001, 0.01, 0.1]\n",
    "compressions = [[-5,5],[-3.6,3.6],[-3.2,3.2],[-2.8,2.8],[-2.4,2.4]]\n",
    "centers_un = [[[-2.7,1.6], [-0.9,1.5], [0.9,1.5], [2.7,1.5]],\n",
    "              [[-2.7,1.5], [-0.92,1.4], [0.9,1.5], [2.7,1.5]],\n",
    "              [[-2.7,1.5], [-0.9,1.5], [0.91,1.7], [2.7,1.5]],\n",
    "              [[-2.7,1.5], [-0.9,1.5], [0.9,1.5], [2.65,1.4]]]\n",
    "centers_4 =  [[[-2.7,1.6], [-0.9,1.5], [0.9,1.5], [2.7,1.5]],\n",
    "              [[-2.7,1.5], [-0.92,1.4], [0.9,1.5], [2.7,1.5]],\n",
    "              [[-2.7,1.5], [-0.9,1.5], [0.91,1.7], [2.7,1.5]],\n",
    "              [[-2.7,1.5], [-0.9,1.5], [0.9,1.5], [2.65,1.4]]]\n",
    "centers_3 =  [[[-2.7,1.6], [-0.9,1.5], [0.9,1.5], [2.7,1.5]],\n",
    "              [[-2.7,1.5], [-0.92,1.4], [0.9,1.5], [2.7,1.5]],\n",
    "              [[-2.7,1.5], [-0.9,1.5], [0.91,1.7], [2.7,1.5]],\n",
    "              [[-2.7,1.5], [-0.9,1.5], [0.9,1.5], [2.65,1.4]]]\n",
    "centers_2 =  [[[-2.4,1.6], [-0.8,1.5], [0.8,1.5], [2.4,1.5]],\n",
    "              [[-2.4,1.5], [-0.82,1.4], [0.8,1.5], [2.4,1.5]],\n",
    "              [[-2.4,1.5], [-0.8,1.5], [0.81,1.7], [2.4,1.5]],\n",
    "              [[-2.4,1.5], [-0.8,1.5], [0.8,1.5], [2.35,1.4]]]\n",
    "centers_1 =  [[[-2.1,1.6], [-0.7,1.5], [0.7,1.5], [2.1,1.5]],\n",
    "              [[-2.1,1.5], [-0.72,1.4], [0.7,1.5], [2.1,1.5]],\n",
    "              [[-2.1,1.5], [-0.7,1.5], [0.71,1.7], [2.1,1.5]],\n",
    "              [[-2.1,1.5], [-0.7,1.5], [0.7,1.5], [2.05,1.4]]]\n",
    "configurations = [centers_un,centers_4,centers_3,centers_2,centers_1]\n",
    "seeds = [2*21*95, 11*4*96, 5*9*64, 13, 42]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
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     ]
    }
   ],
   "source": [
    "directory = 'Cell-Cell/'\n",
    "extracted_data = np.zeros([num_i*num_j*num_k*num_DENSITY,9])\n",
    "spot = 0\n",
    "for DENSITY in range(num_DENSITY):\n",
    "    for i in range(num_i):\n",
    "        centers = np.array(configurations[DENSITY][i])\n",
    "        for j in range(num_j):\n",
    "            floor, list_of_cells = make_cells(seed = seeds[j], compression = compressions[DENSITY])\n",
    "            for k in range(num_k):\n",
    "                name = 'Run_density='+str(DENSITY)+'_i='+str(i)+'_j='+str(j)+'_k='+str(k)+'.pkl'\n",
    "                with open(directory+name, 'rb') as g:\n",
    "                    run = pickle.load(g)[-1]\n",
    "                a, da = average_area_center_cells(run)\n",
    "                p, dp = average_perimeter_center_cells(run)\n",
    "                c, dc = average_circularity_center_cells(run)\n",
    "                cc, dcc = percent_cc_adhesions_center_cells(run, 0.16)\n",
    "                cs, dcs = percent_cs_adhesions_center_cells(run, 0.1)\n",
    "                extracted_data[spot,:] = [DENSITY, i, j, k, a, p, c, cc, cs]\n",
    "                print(spot)\n",
    "                spot+=1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.DataFrame(extracted_data, columns = ['DENSITY','i','j','k','area','perimeter','circularity','percentage_cell_contacts','percentage_substrate_contacts'])\n",
    "df.to_csv('Cell-Cell Analyzed.csv')"
   ]
  }
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