{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 1. Basic modeling with mecabio"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "\n",
    "import matplotlib.pylab as plt\n",
    "\n",
    "from kt_simul.mecabio import Structure, Point, Link\n",
    "from kt_simul.mecabio import Model, dampedspring, viscous\n",
    "from kt_simul.mecabio import spring, dashpot, linear_fv, contraction\n",
    "\n",
    "from kt_simul.visu import StructureViewer\n",
    "from kt_simul.visu import StructureWidget\n",
    "\n",
    "import pandas as pd\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Simulate two points linked together by a linear force which push on them."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "struct = Structure('simple 2 points system')\n",
    "p0 = struct.add_point(idx=0, init_pos=[2, 0, 0], color=\"black\")\n",
    "p1 = struct.add_point(idx=1, init_pos=[-2, 0, 0], color=\"green\")\n",
    "lnk = struct.add_link(p0, p1)\n",
    "\n",
    "struct.update_geometry()\n",
    "m = Model(struct)\n",
    "\n",
    "def model_update(step):\n",
    "\n",
    "    m.Bvect *= 0\n",
    "    linear_fv(m, lnk, F_stall=1, v_max=1, gamma=1)\n",
    "    struct.register_history(step)\n",
    "\n",
    "for i in range(0, 30):\n",
    "    m.solve()\n",
    "    model_update(i)\n",
    "    \n",
    "struct.end_history()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Visualize points trajectories in 2D."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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RossTw667q66xTue8fY5e02sJ6/MH5uvIIUeqel+1Kusr9UHVB6rZ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\nYH8p8CoAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f2d27844dd8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "_ = struct.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Visualize points trajectories in 3D with vispy."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "%matplotlib qt\n",
    "\n",
    "# Pure vispy\n",
    "v = StructureViewer(structure=struct)\n",
    "v.play()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Vispy + Qt\n",
    "w = StructureWidget()\n",
    "w.add_structure(struct)\n",
    "w.show()"
   ]
  }
 ],
 "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": "3.4.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 0
}