\n",
"### PhD seminar series at Chair for Computer Aided Architectural Design (CAAD), ETH Zurich\n",
"\n",
"\n",
"[Vahid Moosavi](https://vahidmoosavi.com/)\n",
""
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"\n",
"\n",
"# First Session: Introduction \n",
"\n",
"\n",
"\n",
" 20th Semptember 2016"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### \n",
"\n",
"## The general set up:\n",
"### What is the purpose of these seminars? \n",
"### What are the expectations from designers and architects?\n",
"### How to doemsticate these new computational capacities into the realm of design or how to re-define design?"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### \n",
"\n",
"### What do we mean by modeling? and Why we need to model things? \n",
"\n",
"* **generalization**\n",
"* **formalization**\n",
"* **control**\n",
"* **ultimately to communicate**\n",
"* **what else?**\n",
"### \n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## A formalism for the modeling process\n",
"\n",
"![](Images/RosenModel.png)\n",
"### \n",
"### Main Tasks in Modeling\n",
"#### Prediction/Classification\n",
"#### Identification of relationships\n",
"#### finding important aspects in relation to the target\n",
"#### pattern recoginition\n",
"#### etc."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### \n",
"### There have been diffrent approaches for the notion of modeling\n",
"### But different approaches have limits in different levels of complexity\n",
"![](Images/Model_Coplexity.jpg)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## But it has been always like that\n",
"### Life and death of computational (Urban) modeling concepts\n",
"![](Images/ngrams3.png)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### And if we follow the trends ---> Nowadays Big Data! \n",
"\n",
"####
Google Trends
\n",
"![](Images/GoogleTrends.png)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"\n",
"\n",
"## And if we look at the undelrying mechansims\n",
"#### A historical view to computational modeling and advent of Big Data\n",
"![](Images/Computational Capacities.png)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Some examples\n",
"* Analytical\n",
"* Many of classical physics equations\n",
"* Newton's Law of Motion\n",
" * F=MA \n",
"\n",
"![](Images/NewtonSLaw.svg)\n",
"### \n",
"\n",
"* Centralized simulation models \n",
" * Chaotic systems (Lorenz systems example)\n",
" * N-body problems in systems biology\n",
"* Decentralized simulation models\n",
" * Agent Based models in transportations\n",
" * Cellular automata"
]
},
{
"cell_type": "markdown",
"metadata": {
"collapsed": true
},
"source": [
"# An Example of unpredictable determinism: Lorenz Systems"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"#Code from: https://jakevdp.github.io/blog/2013/02/16/animating-the-lorentz-system-in-3d/\n",
"\n",
"\n",
"# import numpy as np\n",
"# from scipy import integrate\n",
"\n",
"# # Note: t0 is required for the odeint function, though it's not used here.\n",
"# def lorentz_deriv((x, y, z), t0, sigma=10., beta=8./3, rho=28.0):\n",
"# \"\"\"Compute the time-derivative of a Lorenz system.\"\"\"\n",
"# return [sigma * (y - x), x * (rho - z) - y, x * y - beta * z]\n",
"\n",
"# x0 = [1, 1, 1] # starting vector\n",
"# t = np.linspace(0, 3, 1000) # one thousand time steps\n",
"# x_t = integrate.odeint(lorentz_deriv, x0, t)\n",
"\n",
"\n",
"\n",
"\n",
"\n",
"import numpy as np\n",
"from scipy import integrate\n",
"\n",
"from matplotlib import pyplot as plt\n",
"from mpl_toolkits.mplot3d import Axes3D\n",
"from matplotlib.colors import cnames\n",
"from matplotlib import animation\n",
"%matplotlib inline\n",
"N_trajectories = 10\n",
"\n",
"\n",
"#dx/dt = sigma(y-x)\n",
"#dy/dt = x(rho-z)-y\n",
"#dz/dt = xy-beta*z\n",
"\n",
"def lorentz_deriv((x, y, z), t0, sigma=10., beta=8./3, rho=28.0):\n",
" \"\"\"Compute the time-derivative of a Lorentz system.\"\"\"\n",
" return [sigma * (y - x), x * (rho - z) - y, x * y - beta * z]\n",
"\n",
"\n",
"# Choose random starting points, uniformly distributed from -15 to 15\n",
"np.random.seed(1)\n",
"x0 = -15 + 30 * np.random.random((N_trajectories, 3))\n",
"\n",
"# Solve for the trajectories\n",
"t = np.linspace(0, 7, 1000)\n",
"x_t = np.asarray([integrate.odeint(lorentz_deriv, x0i, t)\n",
" for x0i in x0])\n",
"\n",
"# Set up figure & 3D axis for animation\n",
"fig = plt.figure()\n",
"ax = fig.add_axes([0, 0, 1, 1], projection='3d')\n",
"ax.axis('off')\n",
"plt.set_cmap(plt.cm.YlOrRd_r)\n",
"plt.set_cmap(plt.cm.hot)\n",
"# choose a different color for each trajectory\n",
"colors = plt.cm.jet(np.linspace(0, 1, N_trajectories))\n",
"\n",
"# set up lines and points\n",
"lines = sum([ax.plot([], [], [], '-', c=c)\n",
" for c in colors], [])\n",
"pts = sum([ax.plot([], [], [], 'o', c=c)\n",
" for c in colors], [])\n",
"\n",
"# prepare the axes limits\n",
"ax.set_xlim((-25, 25))\n",
"ax.set_ylim((-35, 35))\n",
"ax.set_zlim((5, 55))\n",
"\n",
"# set point-of-view: specified by (altitude degrees, azimuth degrees)\n",
"ax.view_init(30, 0)\n",
"\n",
"# initialization function: plot the background of each frame\n",
"def init():\n",
" for line, pt in zip(lines, pts):\n",
" line.set_data([], [])\n",
" line.set_3d_properties([])\n",
"\n",
" pt.set_data([], [])\n",
" pt.set_3d_properties([])\n",
" return lines + pts\n",
"\n",
"# animation function. This will be called sequentially with the frame number\n",
"def animate(i):\n",
" # we'll step two time-steps per frame. This leads to nice results.\n",
" i = (2 * i) % x_t.shape[1]\n",
"\n",
" for line, pt, xi in zip(lines, pts, x_t):\n",
" x, y, z = xi[:i].T\n",
" line.set_data(x, y)\n",
" line.set_3d_properties(z)\n",
"\n",
" pt.set_data(x[-1:], y[-1:])\n",
" pt.set_3d_properties(z[-1:])\n",
"\n",
" ax.view_init(30, 0.3 * i)\n",
" fig.canvas.draw()\n",
" return lines + pts\n",
"\n",
"# instantiate the animator.\n",
"anim = animation.FuncAnimation(fig, animate, init_func=init,\n",
" frames=500, interval=10, blit=True)\n",
"\n",
"# Save as mp4. This requires mplayer or ffmpeg to be installed\n",
"anim.save('./Images/lorentz_attractor.mp4', fps=15, extra_args=['-vcodec', 'libx264'],dpi=200)\n",
"\n",
"plt.close()"
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/html": [
"\n",
"\n"
],
"text/plain": [
""
]
},
"execution_count": 10,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"from IPython.display import HTML\n",
"HTML(\"\"\"\n",
"\n",
"\"\"\")"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {
"collapsed": false
},
"outputs": [
{
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mWtYS9rYGmxlNidw2qyuXUzvbHLUwgOke4QQX3k2v8epUlnlxLU1hXI2A8N0jYD9noz1P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\nCIiAIiIAiIgCIiAIiIAiIgCIiAIiIAiIgCIiA//Z\n",
"text/html": [
"\n",
" \n",
" "
],
"text/plain": [
""
]
},
"execution_count": 3,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"from IPython.display import YouTubeVideo\n",
"YouTubeVideo('JZoGO0MrZPA',width=700, height=600)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"\n",
"# A major shift: \n",
"# Knowing Vs. Learning (Theory Driven Vs. Data Driven) Beyond domain expertism\n",
"\n",
"### \n",
"##