{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "from ipywidgets import interact, FloatSlider"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0.        , 0.10204082, 0.20408163, 0.30612245, 0.40816327,\n",
       "       0.51020408, 0.6122449 , 0.71428571, 0.81632653, 0.91836735,\n",
       "       1.02040816, 1.12244898, 1.2244898 , 1.32653061, 1.42857143,\n",
       "       1.53061224, 1.63265306, 1.73469388, 1.83673469, 1.93877551,\n",
       "       2.04081633, 2.14285714, 2.24489796, 2.34693878, 2.44897959,\n",
       "       2.55102041, 2.65306122, 2.75510204, 2.85714286, 2.95918367,\n",
       "       3.06122449, 3.16326531, 3.26530612, 3.36734694, 3.46938776,\n",
       "       3.57142857, 3.67346939, 3.7755102 , 3.87755102, 3.97959184,\n",
       "       4.08163265, 4.18367347, 4.28571429, 4.3877551 , 4.48979592,\n",
       "       4.59183673, 4.69387755, 4.79591837, 4.89795918, 5.        ])"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "t = np.linspace(0.0, 5.0)\n",
    "t"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "A = 0.5"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 0.        ,  0.05093191,  0.10133397,  0.15068182,  0.19846207,\n",
       "        0.24417767,  0.28735302,  0.32753895,  0.36431739,  0.39730573,\n",
       "        0.42616078,  0.45058236,  0.47031639,  0.48515758,  0.49495154,\n",
       "        0.49959636,  0.49904374,  0.49329942,  0.48242315,  0.4665281 ,\n",
       "        0.44577962,  0.42039355,  0.39063401,  0.35681059,  0.31927516,\n",
       "        0.27841822,  0.23466481,  0.18847011,  0.1403147 ,  0.09069956,\n",
       "        0.04014084, -0.01083548, -0.06169907, -0.11192079, -0.16097816,\n",
       "       -0.20836083, -0.25357585, -0.29615286, -0.3356489 , -0.37165308,\n",
       "       -0.40379085, -0.43172786, -0.45517347, -0.47388378, -0.48766414,\n",
       "       -0.49637119, -0.49991433, -0.49825672, -0.4914156 , -0.47946214])"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y = A*np.sin(t)\n",
    "y"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "%matplotlib widget"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "4db118598c584376851bb4fde3f2ceda",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Canvas(toolbar=Toolbar(toolitems=[('Home', 'Reset original view', 'home', 'home'), ('Back', 'Back to previous …"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "lines = ax.plot(t, y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x7f772abd5f98>]"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lines"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "list"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "type(lines)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.lines.Line2D at 0x7f772abd5f98>"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "line = lines[0]\n",
    "line"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[1, 2, 3]"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "[1, 2, 3]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "def update_line(A):\n",
    "    y = A*np.sin(t)\n",
    "    return y"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 0.        ,  0.20372766,  0.40533587,  0.60272726,  0.7938483 ,\n",
       "        0.9767107 ,  1.14941208,  1.31015579,  1.45726957,  1.58922293,\n",
       "        1.70464314,  1.80232944,  1.88126557,  1.94063034,  1.97980615,\n",
       "        1.99838546,  1.99617496,  1.97319767,  1.92969262,  1.8661124 ,\n",
       "        1.78311846,  1.68157421,  1.56253605,  1.42724236,  1.27710064,\n",
       "        1.11367287,  0.93865923,  0.75388043,  0.5612588 ,  0.36279822,\n",
       "        0.16056335, -0.04334191, -0.24679627, -0.44768314, -0.64391263,\n",
       "       -0.8334433 , -1.01430342, -1.18461144, -1.34259559, -1.48661232,\n",
       "       -1.61516338, -1.72691142, -1.82069389, -1.89553514, -1.95065657,\n",
       "       -1.98548475, -1.99965734, -1.9930269 , -1.96566241, -1.91784855])"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "update_line(2.0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "def update_line(A=0.5):\n",
    "    y = A*np.sin(t)\n",
    "    return y"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 0.        ,  0.10186383,  0.20266794,  0.30136363,  0.39692415,\n",
       "        0.48835535,  0.57470604,  0.6550779 ,  0.72863478,  0.79461147,\n",
       "        0.85232157,  0.90116472,  0.94063279,  0.97031517,  0.98990308,\n",
       "        0.99919273,  0.99808748,  0.98659884,  0.96484631,  0.9330562 ,\n",
       "        0.89155923,  0.84078711,  0.78126802,  0.71362118,  0.63855032,\n",
       "        0.55683643,  0.46932961,  0.37694022,  0.2806294 ,  0.18139911,\n",
       "        0.08028167, -0.02167096, -0.12339814, -0.22384157, -0.32195632,\n",
       "       -0.41672165, -0.50715171, -0.59230572, -0.67129779, -0.74330616,\n",
       "       -0.80758169, -0.86345571, -0.91034694, -0.94776757, -0.97532829,\n",
       "       -0.99274237, -0.99982867, -0.99651345, -0.9828312 , -0.95892427])"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "update_line(A=1.0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 0.        ,  0.05093191,  0.10133397,  0.15068182,  0.19846207,\n",
       "        0.24417767,  0.28735302,  0.32753895,  0.36431739,  0.39730573,\n",
       "        0.42616078,  0.45058236,  0.47031639,  0.48515758,  0.49495154,\n",
       "        0.49959636,  0.49904374,  0.49329942,  0.48242315,  0.4665281 ,\n",
       "        0.44577962,  0.42039355,  0.39063401,  0.35681059,  0.31927516,\n",
       "        0.27841822,  0.23466481,  0.18847011,  0.1403147 ,  0.09069956,\n",
       "        0.04014084, -0.01083548, -0.06169907, -0.11192079, -0.16097816,\n",
       "       -0.20836083, -0.25357585, -0.29615286, -0.3356489 , -0.37165308,\n",
       "       -0.40379085, -0.43172786, -0.45517347, -0.47388378, -0.48766414,\n",
       "       -0.49637119, -0.49991433, -0.49825672, -0.4914156 , -0.47946214])"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "update_line()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "def update_line(A=0.5):\n",
    "    y = A*np.sin(t)\n",
    "    line.set_ydata(y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "update_line(A=0.2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "473258a7d18349c6af2376551774fb7e",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "interactive(children=(FloatSlider(value=0.5, description='A', max=5.0), Output()), _dom_classes=('widget-inter…"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "widget = interact(update_line, A=FloatSlider(value=0.5, min=0.0, max=5.0))"
   ]
  }
 ],
 "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": "3.6.7"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}