Multicellular-Network.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Multicellular Network Growth\n",
    "***\n",
    "\n",
    "This notebook shows growth simulations of cell processes in a tissue.\n",
    "\n",
    "***"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "First, we import all relevant libraries and the framework itself:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "from time import time as timet\n",
    "import concurrent.futures\n",
    "import os\n",
    "import itertools\n",
    "import multiprocessing\n",
    "from pathlib import Path\n",
    "\n",
    "import tifffile as tif\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "from scipy.interpolate import splprep, splev\n",
    "from mpl_toolkits.mplot3d import Axes3D"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now we need to import the main simulation classes as well as some auxiliary modules for configuration and plotting:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Import the framework's main module\n",
    "import Initialize_Network_Growing as ing\n",
    "\n",
    "# This python file loads the parameter setting for the growth simulation\n",
    "import Start_Conditions.Parameter_Importer as Parameter_Importer\n",
    "\n",
    "# This python file initializes the starting nodes and growth cones: \n",
    "import Start_Conditions.Node_Maker as Node_Maker\n",
    "\n",
    "# This loads the images containing the structure of teh environment:\n",
    "import Start_Conditions.StructField_Maker as StructField_Maker\n",
    "\n",
    "# Growth step functionality\n",
    "import Math_and_Simulation.Step_Maker as Step_Maker\n",
    "\n",
    "# Functions for plotting and making movies\n",
    "import Plots_and_Movies.Plot_Maker as Plot_Maker\n",
    "import Plots_and_Movies.Movie_Maker as Movie_Maker\n",
    "\n",
    "# Functions for generating the networkx graph\n",
    "import Network_Graph_Construction.Network_X as Network_X\n",
    "import Network_Graph_Construction.Update_Network as Update_Network"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "***\n",
    "## 2) Setup the simulation environment\n",
    "\n",
    "In our scenario, the network forms between three layers of osteocytes, representing a simplified geometry similar to lamellar bone. From each cell, N processes grow out (ref Buenzli).\n",
    "\n",
    "We start by defining the cell centers in a 3-2-3, 2-3-2, 3-2-3 arrangement inside a 256³ cube:\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "cells = np.array([[128,64,128],[128,128,128],[128,192,128],\n",
    "                  [64,96,128], [64,160,128],\n",
    "                  [192,96,128],[192,160,128],\n",
    "                  [128,96,96], [128,160,96],\n",
    "                  [64,64,96],  [64,128,96],  [64,192,96],\n",
    "                  [192,64,96], [192,128,96], [192,192,96],\n",
    "                  [128,96,160],[128,160,160],\n",
    "                  [64,64,160], [64,128,160], [64,192,160],\n",
    "                  [192,64,160],[192,128,160],[192,192,160]]);\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "To get a quick 3-cell simulation run, execute this cell instead:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "cells = np.array([[128,64,128],[128,128,128],[128,192,128]]);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Define number of cells and cell shape:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "N_cells = cells.shape[0]\n",
    "\n",
    "# offset of cell surface from cell center\n",
    "w = 7\n",
    "l = 12\n",
    "h = 4"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Next, we define the outgrowing cell processes at each cell location, pointing outwards and equally distributed in all directions.\n",
    "\n",
    "After setting the number of outgrowing processes per cell, we first generate the angles (az, pol) of the outgrowing processes for each cell:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "# number of outgrowing processes per cell\n",
    "N = 20\n",
    "\n",
    "# equally distributed angles\n",
    "idx = np.arange(0, N, dtype=float) + 0.5;\n",
    "phi = np.arccos(1 - 2*idx/N);\n",
    "theta = np.pi * idx * (1 + 5**0.5);\n",
    "\n",
    "# azimuth and polar angles of initial direction\n",
    "az = np.mod(np.rad2deg(phi),180);\n",
    "pol = np.mod(np.rad2deg(theta),360);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now we also need the starting positions on the surface of each cell, again equally distributed (for details, see other notebook):"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "# positions on cell surface relative to center\n",
    "x = w * np.cos(theta) * np.sin(phi);\n",
    "y = l * np.sin(theta) * np.sin(phi);\n",
    "z = h * np.cos(phi)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "By combining the starting positions and angles relative to the cell center with the positions of all N cell centers, we can now generate the arrays with all positions:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "# generate column vectors for positions of all cells\n",
    "xc = np.squeeze(np.repeat(cells[:,0],N) + np.tile(x,(1,N_cells)));\n",
    "yc = np.squeeze(np.repeat(cells[:,1],N) + np.tile(y,(1,N_cells)));\n",
    "zc = np.squeeze(np.repeat(cells[:,2],N) + np.tile(z,(1,N_cells)))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Same for the angles:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "azc = np.squeeze(np.tile(az,(1,N_cells)));\n",
    "polc = np.squeeze(np.tile(pol,(1,N_cells)));"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Finally, we need the cell index for the last column:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "# index column to identify processes from the same cell\n",
    "idc = np.repeat(np.arange(1, N_cells+1, dtype=float),N);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We stack the arrays together as columns and write them to the config file:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "# save starting positions to text file\n",
    "np.savetxt('Examples/Osteocytes/starting_positions_ocy.csv',np.column_stack((xc,yc,zc,azc,polc,idc)), fmt='%.2f', delimiter=',', header='Starting Positions\\nX,Y,Z,Az,Pol,CN')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "***\n",
    "\n",
    "## 3) Perform simulation\n",
    "\n",
    "Set the location of the configuration files (.csv text files):"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Path to this notebook\n",
    "home = os.getcwd() \n",
    "\n",
    "# Locations of csv configuration files\n",
    "path_multilayer = home + '/Parameter_Files/multilayer_dir_parameters.csv'\n",
    "path_startpar = home + '/Examples/Osteocytes/starting_positions_ocy.csv'\n",
    "path_growth = home + '/Examples/Osteocytes/growth_parameters.csv'\n",
    "path_internal = home + '/Examples/Osteocytes/internal_parameters.csv'\n",
    "path_structured_images = home + '/Examples/Osteocytes/structured_image_dir.csv'"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Generate the structured image - an oriented bone matrix with perpendicular lamellae where the cells are:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# generate and save empty volume \n",
    "fe = np.zeros((256,256,256));\n",
    "tif.imsave(home +'/Examples/Osteocytes/empty.tif',np.float32(fe))\n",
    "\n",
    "# create 256³ structured image procedurally\n",
    "f = np.zeros((4,4));\n",
    "\n",
    "# some collagen fibers\n",
    "f[0:2,0:2] = 300;\n",
    "\n",
    "# tile to fill volume\n",
    "f2 = np.tile(f, [256,64,64]);\n",
    "\n",
    "# introduce some lamellae with perpendicular fibers\n",
    "f2[64:256:64,:,::16] = 1;\n",
    "f2[64:256:64,::16,:] = 1;\n",
    "\n",
    "# save at the appropriate position\n",
    "tif.imsave(home +'/Examples/Osteocytes/fiberz.tif',np.float32(f2))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Extract settings and initialize the dictionaries of network objects as well as the computation grid:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The number of starting cones is : 60\n"
     ]
    }
   ],
   "source": [
    "# Generate feature maps from image data of growth environment\n",
    "features = ing.StructField_Maker.structured_field(path_structured_images, home, sigma_divd=2, sigma_divt1=2, sigma_divt2=2)\n",
    "\n",
    "# Initialise the computation grid\n",
    "field_ref = ing.StructField_Maker.interpol_external_fields(path_multilayer)\n",
    "\n",
    "# Initialize the object dictionaries\n",
    "obj_par_env = ing.init_objects_parameter_enviroment(path_startpar,path_internal,path_growth,path_multilayer)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The growth process is implemented as parallel simulation, therefore the data need to be transferred into shared memory objects, using the [**manager concept**](https://docs.python.org/3/library/multiprocessing.html#managers) from the python [**multiprocessing**](https://docs.python.org/3/library/multiprocessing.html) library:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Extract individual dictionaries\n",
    "cod, nod, edd, flavour, ed_names, no_names, co_names, field, steps, instances, dim, radius  = obj_par_env\n",
    "\n",
    "# Create shared memory proxy objects for all parameter and class objects\n",
    "mgr, eddp, nodp, flavourp, ed_namesp, \\\n",
    "no_namesp, co_namesp, fieldp, forcep = ing.init_manager_init(edd, nod, flavour,\n",
    "                                                            ed_names, no_names,\n",
    "                                                            co_names, field,\n",
    "                                                            field_ref)\n",
    "\n",
    "# Transfer proxy objects to the correct namespace (needed when running in notebook)\n",
    "ing.mgr = mgr\n",
    "ing.eddp = eddp\n",
    "ing.nodp = nodp\n",
    "ing.flavourp = flavourp\n",
    "ing.ed_namesp = ed_namesp\n",
    "ing.no_namesp = no_namesp\n",
    "ing.co_namesp = co_namesp\n",
    "ing.fieldp = fieldp\n",
    "ing.forcep = forcep\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Finally, set the number of instances (simulation steps) and substeps per instance, and run the parallel simulation: "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Instance number  0  with  5  steps  and  60 cones,  60 edges and  63 nodes.\n",
      "Instance number  1  with  5  steps  and  75 cones,  84 edges and  87 nodes.\n",
      "Instance number  2  with  5  steps  and  107 cones,  128 edges and  131 nodes.\n",
      "Instance number  3  with  5  steps  and  135 cones,  166 edges and  169 nodes.\n",
      "Instance number  4  with  5  steps  and  150 cones,  188 edges and  191 nodes.\n",
      "Instance number  5  with  5  steps  and  175 cones,  224 edges and  227 nodes.\n",
      "Instance number  6  with  5  steps  and  211 cones,  280 edges and  283 nodes.\n",
      "Instance number  7  with  5  steps  and  240 cones,  324 edges and  327 nodes.\n",
      "Instance number  8  with  5  steps  and  264 cones,  366 edges and  369 nodes.\n",
      "Instance number  9  with  5  steps  and  302 cones,  424 edges and  427 nodes.\n",
      "After updating the merge events there are 545 edges and  513 nodes in the network graph.\n",
      "Operation for 357 growth cones with 50 growth steps took 1.1714552481969198 min\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/phk57mf/pythrahyper_net/Plots_and_Movies/Plot_Maker.py:60: UserWarning: Matplotlib is currently using module://ipykernel.pylab.backend_inline, which is a non-GUI backend, so cannot show the figure.\n",
      "  fig.show()\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"
    }
   ],
   "source": [
    "# Starting the growth process simulation\n",
    "\n",
    "%matplotlib inline\n",
    "\n",
    "instances = 10\n",
    "steps = 5\n",
    "\n",
    "growing_results = ing.init_par_growing(cod, nod, edd, steps,instances, dim, radius,eddp, nodp, flavourp, fieldp, ed_namesp, no_namesp, co_namesp,forcep,\n",
    "                                  timer = True,\n",
    "                                  plot = True,\n",
    "                                  Voronoi = False,\n",
    "                                  network_x = False)\n",
    "\n",
    "cod, nod, edd, flavour, ed_names, no_names, co_names, vor, G_vor, subG_vor, G, subG = growing_results"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Write results to file:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pickle\n",
    "\n",
    "with open('Examples/Osteocytes/cod.pkl', 'wb') as f:\n",
    "    pickle.dump(cod, f)\n",
    "    \n",
    "with open('Examples/Osteocytes/edd.pkl', 'wb') as f:\n",
    "    pickle.dump(edd, f)\n",
    "    \n",
    "with open('Examples/Osteocytes/nod.pkl', 'wb') as f:\n",
    "    pickle.dump(nod, f)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Generate networkx graph, add nodes and edges, and write to file:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "import networkx as nx\n",
    "\n",
    "G = nx.Graph()\n",
    "    \n",
    "nodes = [key for key in nod.keys()]    \n",
    "\n",
    "G.add_nodes_from(nodes)\n",
    "    \n",
    "edges = [(edd[key].nodes[0],edd[key].nodes[1],len(edd[key].pos_list)) for key in edd.keys()]\n",
    "\n",
    "G.add_weighted_edges_from(edges)\n",
    "\n",
    "thr = 1;\n",
    "for key in edd.keys():\n",
    "    dx = edd[key].x_pos1 - edd[key].x_pos2;\n",
    "    dy = edd[key].y_pos1 - edd[key].y_pos2;\n",
    "    dz = edd[key].z_pos1 - edd[key].z_pos2;\n",
    "    d = np.sqrt(dx**2 + dy**2 + dz**2);\n",
    "    n1,n2 = edd[key].nodes[:]        \n",
    "    if (d<thr) and (n1 in G) and (n2 in G):\n",
    "        G = nx.contracted_nodes(G,edd[key].nodes[0],edd[key].nodes[1],);\n",
    "    \n",
    "G.remove_edges_from(nx.selfloop_edges(G))\n",
    "        \n",
    "with open('Examples/Osteocytes/G.pkl', 'wb') as f:\n",
    "    nx.write_gpickle(G, f)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Plot the graph:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure(figsize=(5,5))\n",
    "ax = plt.subplot(111)\n",
    "\n",
    "nx.draw_spring(G,with_labels = False,node_size = 10, node_color =  'r', node_shape = 'o')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Get an estimate of the average clustering coefficient:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.00899142265601893"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "nx.average_clustering(G)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "For more examples how to examine graph properties, and for regenerating the plots in the paper using Mayavi, please look at the Multiple-Parameter Analysis Notebook."
   ]
  },
  {
   "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": "3.7.9"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}