logistische Gleichung + Feigenbaumdiagramm.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Logistische Gleichung"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Herleitung$^1$:"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Die logistische Gleichung, aufgestellt von Pierre Francois Verhulst (1804-1849), stellt die Entwicklung einer Population modellhaft dar. Aus der Größe $X(n)$ der Population zu einem gewissen Zeitpunkt, soll auf die Größe $X(n+1)$ nach einer Fortpflanzungsperiode geschlossen werden. Dabei werden die Faktoren Vermehrung und Verhungern berücksichtigt."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Die neue Population wächst pro Zeitperiode um den Wachstumsfaktor $w$. Daraus ergibt sich folgende Formel für die Größe der Population nach $n+1$ Perioden:\n",
    "\n",
    "$$ X(n+1) = X(n) \\cdot w = X(0) \\cdot w^{n+1} $$\n",
    "\n",
    "wobei $X(0)$ der Startwert der Population ist, $w$ ist der Wachstumsfaktor und $n$ ist die Anzahl Fortpflanzungsperioden."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "So würde eine Population unendlich wachsen. Da es aber auch beschränkende Faktoren gibt (wie z.B. Nahrungsangebot) muss es auch einen limitierenden Bremsfaktor geben. Die Überlegung ist, dass sich die Sterberate erhöht, je näher sich die Population eine theoretische Maximalgröße annähert. Mit zunehmender Sterberate beginnt nun auch die Wachstumsrate wieder zu sinken. Also muss der Wachstumsfaktor $w$ proportional zur Differenz zwischen der Maximalgröße $M$ und der aktuellen Populationsgröße $X$ sein:\n",
    "\n",
    "$$ w = q \\cdot (M-X)$$\n",
    "\n",
    "mit $q$ als Proportionalitätskonstante."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Hängt also $w$ von der aktuellen Populationsgröße ab, so erhalten wir die logistische Gleichung $L$. Wir berechnen dabei die aktuelle Größe $X(n+1)$ aus der Populationsgröße in der Vorperiode $X_n$:\n",
    "\n",
    "$$ X(n+1) = q\\cdot(M-X(n))\\cdot X(n)$$"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Die nachfolgende Funktion berechnet die aktuelle Populationsgröße $X(n)$ aus eine Anfangsgröße $X0$."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "def L(X0, M, q, n):\n",
    "    \"\"\" \n",
    "    :param X0: int, Sartwert\n",
    "    :param M: int, Maximalgröße der Population\n",
    "    :param q: float, Proportionalitätskonstante\n",
    "    :param n: Anzahl Fortpflanzungsperioden\n",
    "    :return: aktuelle Populationsgröße\n",
    "    \"\"\"\n",
    "    tmp = X0\n",
    "    for i in range(n):\n",
    "         tmp = q*(M - tmp)*tmp\n",
    "    return tmp"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Die Logistische Gleichung kann nun noch umgeformt werden. Und zwar wird die Populationsgröße $X$ als Bruchteil der Maximalgröße $M$ angegeben und die Proportionalitätskonstante mit $M$ multipliziert. \n",
    "\n",
    "\\begin{eqnarray}\n",
    "    & X(n+1) &=& q\\cdot(M-X(n))\\cdot X(n)               &|&  X \\rightarrow x \\cdot M \\\\\n",
    "    \\Leftrightarrow & x(n+1)M &=& q(M-x(n)M)x(n)M \\quad &|& :M \\\\\n",
    "    \\Leftrightarrow & x(n+1) &=& qM(1-x(n))x(n)         &|& qM \\rightarrow r \\\\\n",
    "    \\Leftrightarrow & x(n+1) &=& r(1-x(n))x(n)\n",
    "\\end{eqnarray}"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Die Implementierung  der umgeformten logistischen Gleichung lautet:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "from itertools import repeat\n",
    "\n",
    "def L(x0, r, n):\n",
    "    \"\"\" \n",
    "    :param x0: int (x0 kann auch ein numpy-Array sein), Startwert\n",
    "    :param r: float, Proportionalitätskonstante\n",
    "    :param n: Anzahl Fortpflanzungsperioden\n",
    "    :return: Populationsgröße als Bruchteil der Maximalgröße\n",
    "    \"\"\"\n",
    "    tmp = x0\n",
    "    for _ in repeat(None, n):\n",
    "        tmp = r*(1 - tmp)*tmp\n",
    "    return tmp"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Verhalten in Abhängigkeit von r$^4$$^{,5}$:"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Bei verschiedenen $r$ können die folgenden Verhaltensweisen für große $n$ beobachtet werden. Dabei hängt dieses Verhalten nicht vom Anfangswert $x_0$ ab, sondern nur von $r$.\n",
    "\n",
    "Zuerst wollen wir die Fixpunkte der logistischen Gleichung berechnen:\n",
    "\\begin{eqnarray}\n",
    "    & f(x^*) &=& x^* \\quad ^2\\\\               \n",
    "    \\Leftrightarrow & rx^*(1-x^*) &=& x^* \\\\\n",
    "    \\Leftrightarrow & -r(x^*)^2 + rx^* - x^* &=& 0 \\\\\n",
    "    \\Leftrightarrow & x^*(-rx^* + r - 1) &=& 0 \\quad \\rightarrow {x_1}^*=0 \\\\\n",
    "    \\Leftrightarrow & rx^* &=& r-1 \\rightarrow {x_2}^*=\\frac{r-1}{r}\n",
    "\\end{eqnarray}\n",
    "\n",
    "\n",
    "\n",
    "Die nachfolgende Funktion plottet die logistische Gleichung (x-Achse: Generation; y-Achse: Populationswert), sowie deren Grenzwertverhalten:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "def plotL(x0, r, n, g):\n",
    "    \"\"\" \n",
    "    :param x0: int (x0 kann auch ein numpy-Array sein), Startwert\n",
    "    :param r: float, Proportionalitätskonstante\n",
    "    :param n: Anzahl Fortpflanzungsperioden\n",
    "    :param g: Auswahl an verschiedenen Grenzwerten 0-3 für bestimmte Bereiche von r\n",
    "    :return: plottet L\n",
    "    \"\"\"\n",
    "    toBePlotted = []\n",
    "    points = []\n",
    "    tmp = n\n",
    "    \n",
    "    if g == 0:\n",
    "        toBePlotted.append(plot((g), xmax=100, xmin=0, thickness=1.0, color='blue', legend_label='Grenzwert'))\n",
    "    if g == 1:\n",
    "        toBePlotted.append(plot(((r-1)/r), xmax=100, xmin=0, thickness=0.7, legend_label='Grenzwert'))\n",
    "    if g == 2:\n",
    "        toBePlotted.append(plot((L(x0, r, tmp)), xmax=100, xmin=0, thickness=0.7, color='yellow', legend_label='Haeufungspunkt1'))\n",
    "        toBePlotted.append(plot((L(x0, r, (tmp-1))), xmax=100, xmin=0, thickness=0.7, legend_label='Haeufungspunkt2'))\n",
    "    if g == 3:\n",
    "        toBePlotted.append(plot((L(x0, r, tmp)), xmax=100, xmin=0, thickness=0.7, color='yellow', legend_label='Haeufungspunkt1'))\n",
    "        toBePlotted.append(plot((L(x0, r, (tmp-1))), xmax=100, xmin=0, thickness=0.7, color='green', legend_label='Haeufungspunkt2'))\n",
    "        toBePlotted.append(plot((L(x0, r, (tmp-2))), xmax=100, xmin=0, thickness=0.7, color='brown', legend_label='Haeufungspunkt3'))\n",
    "        toBePlotted.append(plot((L(x0, r, (tmp-3))), xmax=100, xmin=0, thickness=0.7, legend_label='Haeufungspunkt4'))\n",
    "    \n",
    "    for k in range(tmp):\n",
    "        points.append((n, L(x0, r, n)))\n",
    "        n = n - 1\n",
    "    toBePlotted.append(list_plot(points, color='red', pointsize=5, figsize=8, axes_labels=[\"Generation\",\"Population\"], fontsize=6,))\n",
    "    for i in range(len(points) - 1):\n",
    "        toBePlotted.append(line([points[i],points[i+1]],thickness=0.5,color='black'))\n",
    "    return sum(toBePlotted)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "$0 <= r <= 1$:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
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      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import ipywidgets as widgets\n",
    "from ipywidgets import interact,fixed\n",
    "\n",
    "n_widget = widgets.IntSlider(min=10, max=1000, step=1, value=100, continuous_update=False)\n",
    "x0_widget = widgets.FloatSlider(value=0.7, min=0.01, max=0.99, step=0.01, continuous_update=False, description='x0:')\n",
    "r_widget = widgets.FloatSlider(value=0.94, min=0.01, max=0.99, step=0.01, continuous_update=False, description='r:')\n",
    "i0 = interact(plotL, x0=x0_widget, r=r_widget, n=n_widget, g=fixed(0))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Fazit: Für r-Werte zwischen 0 und 1 stirbt die Population immer aus."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "$1 < r < 2$:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
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       "version_minor": 0
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      "text/plain": [
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      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "x0_widget = widgets.FloatSlider(value=0.98, min=0.01, max=0.99, step=0.01, continuous_update=False, description='x0:')\n",
    "n_widget = widgets.IntSlider(min=10, max=1000, step=1, value=100, continuous_update=False)\n",
    "r_widget = widgets.FloatSlider(value=1.18, min=1.01, max=1.99, step=0.01, continuous_update=False, description='r:')\n",
    "i1 = interact(plotL, x0=x0_widget, r=r_widget, n=n_widget, g=fixed(1))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Fazit: Für r-Werte zwischen 1 und 2 nähert sich die Population monoton dem Grenzwert $\\frac{r-1}{r}$ an."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "$2 < r < 3$:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "version_minor": 0
      },
      "text/plain": [
       "aW50ZXJhY3RpdmUoY2hpbGRyZW49KEZsb2F0U2xpZGVyKHZhbHVlPTAuNCwgY29udGludW91c191cGRhdGU9RmFsc2UsIGRlc2NyaXB0aW9uPXUneDA6JywgbWF4PTAuOTksIG1pbj0wLjAxLCDigKY=\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "x0_widget = widgets.FloatSlider(value=0.4, min=0.01, max=0.99, step=0.01, continuous_update=False, description='x0:')\n",
    "n_widget = widgets.IntSlider(min=10, max=1000, step=1, value=100, continuous_update=False)\n",
    "r_widget = widgets.FloatSlider(value=2.9, min=2.01, max=2.99, continuous_update=False, step=0.01, description='r:')\n",
    "i2 = interact(plotL, x0=x0_widget, r=r_widget, n=n_widget, g=fixed(1))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "$3 < r < (1 + \\sqrt{6})$"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Fazit: Für r-Werte zwischen 2 und 3 nähert sich die Population dem Grenzwert $\\frac{r-1}{r}$ alternierend."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "f14fdf27cb314168ba35db9ca520a337",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "aW50ZXJhY3RpdmUoY2hpbGRyZW49KEZsb2F0U2xpZGVyKHZhbHVlPTAuNjgsIGNvbnRpbnVvdXNfdXBkYXRlPUZhbHNlLCBkZXNjcmlwdGlvbj11J3gwOicsIG1heD0wLjk5LCBtaW49MC4wMSzigKY=\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "x0_widget = widgets.FloatSlider(value=0.68, min=0.01, max=0.99, step=0.01, continuous_update=False, description='x0:')\n",
    "n_widget = widgets.IntSlider(min=10, max=1000, step=1, value=100, continuous_update=False)\n",
    "r_widget = widgets.FloatSlider(value=3.2, min=3.0, max=3.449, continuous_update=False, step=0.01, description='r:')    \n",
    "    \n",
    "i3 = interact(plotL, x0=x0_widget, r=r_widget, n=n_widget, g=fixed(2))    \n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Fazit: Für r-Werte zwischen 3 und $1 + \\sqrt{6}$ wechseln die Populationswerte zwischen zwei Häufungspunktlagern, die von r abhängen."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "$(1 + \\sqrt{6}) < r < 3.54$:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "bb06ef4520424113a786513a57a2225c",
       "version_major": 2,
       "version_minor": 0
      },
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      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "x0_widget = widgets.FloatSlider(value=0.96, min=0.01, max=0.99, step=0.01, continuous_update=False, description='x0:')\n",
    "n_widget = widgets.IntSlider(min=10, max=1000, step=1, value=100, continuous_update=False)\n",
    "r_widget = widgets.FloatSlider(value=3.53, min=3.45, max=3.54, continuous_update=False, step=0.01, description='r:')    \n",
    "    \n",
    "i3 = interact(plotL, x0=x0_widget, r=r_widget, n=n_widget, g=fixed(3))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Fazit: Für r-Werte zwischen $1 + \\sqrt{6}$ und ca. $3,54$ wechseln die Populationswerte abwechselnd zwischen vier Häufungspunktlagern, die von r abhängen."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "$r=3,8$:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "f5fd97139147437a8b90526767974f90",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "aW50ZXJhY3RpdmUoY2hpbGRyZW49KEZsb2F0U2xpZGVyKHZhbHVlPTAuNDEsIGNvbnRpbnVvdXNfdXBkYXRlPUZhbHNlLCBkZXNjcmlwdGlvbj11J3gwOicsIG1heD0wLjk5LCBtaW49MC4wMSzigKY=\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "x0_widget = widgets.FloatSlider(value=0.41, min=0.01, max=0.99, step=0.01, continuous_update=False, description='x0:')\n",
    "n_widget = widgets.IntSlider(min=10, max=1000, step=1, value=100, continuous_update=False) \n",
    "    \n",
    "i4 = interact(plotL, x0=x0_widget, r=fixed(3.79), n=n_widget, g=fixed(4))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Fazit: keine Tendenzen für hohe n ersichtlich."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Beobachtungen insgesamt: Es ist zu beobachten, dass sich die Intervalle mit gleichem Grenzwertverhalten mit steigendem $r$ drastisch verkleinern und immer rascher aufeinander folgen. Ebenso wird es immer undurchsichtiger, woran sich die Populationswerte mit steigendem $n$ annähern. Tatsächlich verdoppeln sich die Häufungspunkte in den folgenden Bifurkationsintervallen immer weiter. Das Längenverhältnis zweier benachbarter Bifurkationsintervalle nähert sich dabei $δ$ an.  Man  bezeichnet $δ$  als  erste  Feigenbaumkonstante  und  erhält  sie durch$^3$: \n",
    "$$δ=\\lim\\limits_{n \\to \\infty}\\frac{r_n-r_{n-1}}{r_{n+1}-r_n}=4.669201609...$$\n",
    " Bei $r$ annähernd $3,57$ beginnt Chaos. Häufungspunkte sind dann irgendwann nicht mehr erkennbar und die kleinste Änderung eines Startwertes führt zu einer komplett anderen Populationsentwicklung."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Feigenbaumdiagramm"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Dies führt uns zum Feigenbaumdiagramm. Ein Feigenbaumdiagramm entsteht durch Aufzeichnen der Wertepaare $(r, x)$. Man waehlt fuer das Diagramm eine bestimmte Anzahl von Iterationen $n$, die dann fuer alle berechneten Punkte des Diagrammes beibehalten wird. Fuer jeden Wert $r$ werden dann eine Reihe von $x0$ Startwerten zwischen 0 und 1 ausgewaehlt."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Import für widgets\n",
    "import ipywidgets as widgets\n",
    "from ipywidgets import interact,interact_manual\n",
    "from IPython.display import display\n",
    "\n",
    "# Import für numpy-Arrays\n",
    "import numpy as np\n",
    "\n",
    "# Import für Timer\n",
    "import time\n",
    "\n",
    "# Zufallsgenerator für Zahlen zwischen 0 und 1.\n",
    "from random import random  \n",
    "\n",
    "global myText\n",
    "\n",
    "def FeigenbaumPoints(n, x0Amount, rAmount, rmin, rmax):\n",
    "\n",
    "    print(\"\\f\")\n",
    "    starttime = time.time()\n",
    "    \n",
    "    \"\"\"\n",
    "    :param n: Iterationen/Generationen n\n",
    "    :param x0Amount: Anzahl an Startwerten für jeden r-Wert\n",
    "    :param rAmount: Anzahl r-Werte\n",
    "    :param rmin: kleinster r-Wert\n",
    "    :param rmax: größter r-Wert\n",
    "    \"\"\"\n",
    "    \n",
    "    global punkte\n",
    "    \n",
    "    rs = None  # array für die r-Koordinaten\n",
    "    ls = None  # array für die Populationswerte\n",
    "    x0 = np.linspace(0, 1, x0Amount) # Liste von Startwerten\n",
    "    \n",
    "    p = 0  # Zähler für Punkte\n",
    "    for r in np.linspace(rmin, rmax, rAmount):\n",
    "        \n",
    "        p += x0Amount\n",
    "        \n",
    "        myText.value = \"r = {0:.3f}, berechnete Punkte={1:d}\".format(r, p)\n",
    "        \n",
    "        # Array von r-Koordinaten\n",
    "        if rs is None:\n",
    "            rs = np.full((x0Amount, 1), r)\n",
    "        else:\n",
    "            rs = np.vstack((rs, np.full((x0Amount, 1), r)))  # Liste anhängen\n",
    "            \n",
    "        # Berechnung für nächstes r mit allen Startwerten x0\n",
    "        x_val = np.array(L(x0, r, n)).reshape((x0Amount, 1))\n",
    "        \n",
    "        # Array von x-Werten\n",
    "        if ls is None:\n",
    "            ls = x_val\n",
    "        else:\n",
    "            ls = np.vstack((ls, x_val))  # Liste anhängen\n",
    "    \n",
    "    punkte = np.hstack((rs, ls)) # Zusammenfügen von rs und ls zu einer Liste von Punkten\n",
    "    endtime = time.time()\n",
    "    print(\"Zeit: {0:.2f}s\\nBereit zum plotten.\".format(endtime-starttime))   \n",
    "    \n",
    "\n",
    "def PlotFeigenbaum(dotsize, transparent):\n",
    "    \"\"\"\n",
    "    :param points: Array von Punkten, das geplottet werden soll.\n",
    "    :param dotsize: Wertebereich sollte zwischen 1 und 5 liegen.\n",
    "    :param transparent: Wertebereich sollte zwischen 0 und 1 liegen. - je mehr x0-Werte für ein r berechnet werden, desto kleiner sollte dieser Werte sein.\n",
    "    \"\"\"\n",
    "    plot = list_plot(punkte, pointsize=dotsize, alpha=transparent, figsize=10, axes_labels=[\"R\",\"X(n)\"])\n",
    "    plot.save(\"Feigenbaum.png\")\n",
    "    show(plot)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Bitte erst Konfiguration einstellen bzw. annehmen und erst dann plotten.\n"
     ]
    },
    {
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       "version_minor": 0
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      "text/plain": [
       "Text(value=u'')"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
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     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
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      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "print(\"Bitte erst Konfiguration einstellen bzw. annehmen und erst dann plotten.\")\n",
    "myText = widgets.Text()\n",
    "display(myText)\n",
    "\n",
    "\n",
    "n_widget = widgets.IntSlider(min=10, max=10000, step=1, value=1000, continuous_update=False)\n",
    "x0_widget = widgets.IntSlider(min=10, max=10000, step=1, value=300,continuous_update=False)\n",
    "rA_widget = widgets.IntSlider(min=100, max=3000, step=1, value=1000, continuous_update=False)\n",
    "rmin_widget = widgets.FloatSlider(value=2.8, min=1.0, max=3.95, step=0.0001, continuous_update=False, description='rMin:')\n",
    "rmax_widget = widgets.FloatSlider(value=4.0, min=1.05, max=4.0, step=0.0001, continuous_update=False, description='rMax:')\n",
    "\n",
    "interact_config=interact_manual.options(manual_name=\"Annehmen\")\n",
    "i = interact_config(FeigenbaumPoints, n=n_widget, x0Amount=x0_widget, rAmount=rA_widget, rmin=rmin_widget, rmax=rmax_widget)\n",
    "\n",
    "\n",
    "dotsize_widget = widgets.FloatSlider(value=1.0, min=1, max=5.0, step=0.01, continuous_update=False, description='Pointsize:')\n",
    "transparent_widget = widgets.FloatSlider(value=0.3, min=0.01, max=1.0, step=0.01, continuous_update=False, description='Transparenz:')\n",
    "\n",
    "interact_plot=interact_manual.options(manual_name=\"Plotten\")\n",
    "i2 = interact_plot(PlotFeigenbaum, dotsize=dotsize_widget, transparent=transparent_widget)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Beobachtungen: Im Feigenbaum erkennt man die oben beschriebenen Abschnitte wieder. Die gabelförmigen Abschnitte stellen jeweils eine Verhaltensänderung im System dar (siehe Änderung der Anzahl Häufungspunkte oben). Man erkennt auch, dass die Intervalle zur nächsten Gabelung jeweils kleiner werden und die Anzahl Gabelungen (Bifurkationen) sich mit steigendem $r$ immer wieder verdoppelt. Ab ca. $3,6$ existiert fast ein Kontinuum von Werten für ein r, woran man sieht das schon kleinste Änderungen im Startwert $x0$ komplett unterschiedliche Ergebnisse hervorbringen (im Einzelfall natürlich noch deterministisch, d.h. für einen einzeln Wert $x0$ bzw. r wird immer dasselbe Ergebnis herauskommen), wohingegen davor $x0$ überhaupt keinen Einfluss hatte (bei großen $n$ natürlich). Mit weiter steigendem r sind dann ab dem Wert 4 alle Populationswerte zwischen 0 und 1 vorhanden und eine Tendenz nicht erkennbar$^6$. Interessant ist noch, das inmitten des Chaos \"kleine Inseln der Ordnung\" zu finden sind, z.b. um den r-Wert $3,8$."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### periodische \"Fenster\":"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Vergrößert man diese Bereiche, so stößt man auf kleinere Feigenbäumchen."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 1600x960 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import matplotlib.image as mpimg\n",
    "\n",
    "img=mpimg.imread('Ordnungsinsel 3,87.png')\n",
    "plt.figure(figsize=(20,12), dpi=80)\n",
    "plt.axis(\"off\")\n",
    "imgplot = plt.imshow(img)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Dieses Auftreten derselben grundlegenden Merkmale wird als Selbstähnlichkeit bezeichnet und ist eine typische Eigenschaft der Fraktale."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Quellen:\n",
    "$^1$: Pierre-François Verhulst: Notice sur la loi que la population suit dans son accroissement. In: Correspondance Mathématique et Physique. Band 10, 1838\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "$^2$: Robert May: Simple mathematical models with very complicated dynamics Nature V. 261, S. 460, 10 Juni 1976"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "$^3$: Keith Briggs: A precise calculation of the Feigenbaum constants. Mathematics of Computation 57, Juli 1991, S. 435–439"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "$^4$: Nagashima, H., Baba, Y., Introduction to Chaos, IOP Publishing Ltd., Bristol and Philadelphia, 1999"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "$^5$: http://www.bio-physics.at/wiki/index.php?title=Logistische_Gleichung"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "$^6$: Loistl, O., Chaostheorie - zur Theorie nicht linearer dynamischer Systeme, München, S. 31, 1996"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "$^7$: http://www.fraktalwelt.de/systeme/fa_chaos.pdf"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "SageMath 8.6",
   "language": "",
   "name": "sagemath"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 2
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
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