{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "autoscroll": false,
    "collapsed": false,
    "ein.hycell": false,
    "ein.tags": "worksheet-0",
    "jupyter": {
     "outputs_hidden": false
    },
    "slideshow": {
     "slide_type": "-"
    }
   },
   "outputs": [],
   "source": [
    "import matplotlib as mpl\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "import numpy.random as rnd\n",
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "ein.tags": "worksheet-0",
    "slideshow": {
     "slide_type": "-"
    }
   },
   "source": [
    "Creare una griglia regolare è facile con arange, dal modulo numpy\n",
    "Normalmente l'intervallo non include l'estremo superiore, come per range,\n",
    "se avete bisogno di un comportamento più flessibile provate linspace.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "autoscroll": false,
    "collapsed": false,
    "ein.hycell": false,
    "ein.tags": "worksheet-0",
    "jupyter": {
     "outputs_hidden": false
    },
    "slideshow": {
     "slide_type": "-"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([-1.00000000e+00, -8.00000000e-01, -6.00000000e-01, -4.00000000e-01,\n",
       "       -2.00000000e-01, -2.22044605e-16,  2.00000000e-01,  4.00000000e-01,\n",
       "        6.00000000e-01,  8.00000000e-01,  1.00000000e+00,  1.20000000e+00,\n",
       "        1.40000000e+00,  1.60000000e+00,  1.80000000e+00,  2.00000000e+00,\n",
       "        2.20000000e+00,  2.40000000e+00,  2.60000000e+00,  2.80000000e+00])"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x = np.arange(\n",
    "    -1,\n",
    "    3,\n",
    "    0.2,\n",
    ")\n",
    "\n",
    "x"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "ein.tags": "worksheet-0",
    "slideshow": {
     "slide_type": "-"
    }
   },
   "source": [
    "avete provato linspace?\n",
    "Gli array ammettono un'algebra flessibile con tutte le consuete operazioni matematiche."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "autoscroll": false,
    "collapsed": false,
    "ein.hycell": false,
    "ein.tags": "worksheet-0",
    "jupyter": {
     "outputs_hidden": false
    },
    "slideshow": {
     "slide_type": "-"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0.95852902, 0.76264391, 0.67535753, 0.69058166, 0.80133067,\n",
       "       1.        , 1.27866933, 1.62941834, 2.04464247, 2.51735609,\n",
       "       3.04147098, 3.61203909, 4.22544973, 4.8795736 , 5.57384763,\n",
       "       6.30929743, 7.0884964 , 7.91546318, 8.79550137, 9.73498815])"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y = x * x + 0.2 * x + 1.0 + np.sin(x)\n",
    "y"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "ein.tags": "worksheet-0",
    "slideshow": {
     "slide_type": "-"
    }
   },
   "source": [
    "Ottenere un grafico esplorativo è molto semplice, plt.plot è quanto serve.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "autoscroll": false,
    "collapsed": false,
    "ein.hycell": false,
    "ein.tags": "worksheet-0",
    "jupyter": {
     "outputs_hidden": false
    },
    "slideshow": {
     "slide_type": "-"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x7f2eeba775e0>]"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "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": [
    "plt.plot(x, y)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "ein.tags": "worksheet-0",
    "slideshow": {
     "slide_type": "-"
    }
   },
   "source": [
    "Potete anche rappresentare più dati nello stesso grafico, il modo migliore è chiamare plt.plot più volte.\n",
    "Provate a generare anche una seconda serie di dati, diciamo y2 e rappresentarla insieme alla prima.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "ein.tags": "worksheet-0",
    "slideshow": {
     "slide_type": "-"
    }
   },
   "source": [
    "anche modificare l'aspetto di un grafico è possibile in molti modi.\n",
    "il più semplice ma meno flessibile è usare una stringa di formattazione.\n",
    "la stringa codifica sinteticamente:\n",
    "linea (-, -. --, :) ,\n",
    "simboli (*, o, ^, p, h) e tanti altri che trovate nella documentazione,\n",
    "colore (r:red, g:green, b: blue, c: cyan ...)\n",
    "provate a modificare la stringa di formattazione"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "autoscroll": false,
    "collapsed": false,
    "ein.hycell": false,
    "ein.tags": "worksheet-0",
    "jupyter": {
     "outputs_hidden": false
    },
    "slideshow": {
     "slide_type": "-"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x7f2eeb959a00>]"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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pydUE337bX6wsLIQnnoAxYxTekhYtZiWSaSNH+g0Yzj0XDj4YXn/db8YgkiYFuEimVF1NsLQUPvnEb8ZQUhJeXZIz1IUikikrVvgRJkn5+X7TYa0mKAFRgItkwurV0L07rF8PZpCXB7t3+y4UrSYoAVGAiwRtxgw49VTYssX3dd96KyxYoNUEJXDqAxcJSnk5DBsGDzwA3/seTJv25cWotJqgBEwBLhKEzZvhRz/yQwT794cHH/TT40UySAEukq758/1O8Tt2wJQpcK1WlJDsUB+4SH05Bw89BN//PrRoAQsXKrwlqxTgIvXx2WfQuzcMGuTXMykq8jvoiGSRAlykrlat8hcpp0+HX/0KZs70mw2LZJkCXKQ2ybVMNm/2I0tOO83PqJwzB+66S5svSGh0EVOkNiNH+guV3brB8uV+Qapp0+Coo8KuTBo4BbhITaquZbJ8ub9fulThLZGg734iNVm7Fjp3rvy5aVOtZSKRogAXqc6uXXDvvX4ZWPCTcsrLtZaJRIq6UESqev99uOoq+Mtf4Ljj/Dret94Kkyb5C5oiEaEAF0mqqPBT4IcPh9at4bXXfHgnaS0TiRgFuAjAxo3Qp4/fq/Kyy2DyZGjVKuyqRPZLfeAiL7wAJ50E777rg3vGDIW3xIICXBquXbvg5pt9i7tDB1iyBH7yE78Bg0gMKMClYVqyBE45xbe4hw71re8TTgi7KpE6UYBLw1JRAb/5jZ9N+dln/kLlmDF+jLdIzCjAJfcl1zJ5/3244ALf4r70Uj+zcu9RJiIxo1EokvtGjvQTcjp1gkaNfLfJj3+svm6JPbXAJXfl5/uQnjjRb76wezcUF8PAgQpvSdmu0l0Mnzucw8Ycxoi5IyguK87q+/dHAS65ybl996XMz9daJjETdngWriuk/UPtGbdgHDs+38HYBWNpN7YdhesKs/L+2ijAJfds2AA//KHfXPigg3xrOy/Pt8C1lklWpROgUQjPyYsns61kG8Xlvu6S8hK2lWxj8uLJWXl/bRTgkjsqKnx3yTe/6WdUPvAAnHWWX8dkwQK45Ra/KYOkLMwAjnp4RoEuYkpuWLMG+vb1Fyu7dvULTx19NNxxR+VrtJZJnRSuK6Tn1J6UlJVQXF7M2AVjeWTxI8z80Uy6tO9S6/uTAZpUUl5CSXkJkxdPTun9Uju1wCXeyspg9Gjo2BFWrIDHH/dbnR19dNiVRUI6LeiG0IKtTd9T+tIqvxX5jfMByG+cT6v8VvQ9pW9W3l8bBbjEV1ERFBTAiBG+z3vVKrjxRo0wScj0BbRMi0J4dmnfhfWD1jPo9EEcmncod3S6g/WD1qf8DSLd99dGAS7xU1wMd97pd4b/5BO/GNUf/pCTFyfj3IJON0CjEp7NmzRnVNdRbL9rO/efez/NmzTP6vv3x5xzgX1YbQoKClxRUVHWjic5YtMm6N0bpk6FlSuhXz+/3Vm/fn4a/KGHhl1hRlTtg85vnE/zJs1T7oO+buZ1PLPimX2ev/bEa5nSc0rKxy8uK6akvKTOxwcoLitmVOEoJhRNYMCpAxjeeXigAdZQmNli51xB1efr3QI3s7Zm9oaZrTKzlWZ2e3olitQguSv8OefAeefBAQfAvHnwyCORD+84t6CDaMFmsvUp6Y1CKQcGO+eWmNlBwGIzm+Oc+zCg2qShq7or/Jo1/n7DBr+2ScSlO4ojXX1P6cvsv83epwVdlz7gZACP6joqg5VKfdW7Be6c2+ScW5J4/BmwCjgyqMJEeP55aNmy8ue8vFjNpMyFFrREWyAXMc2sA3AysLCa3/UzsyIzK9q6dWsQh5Nc9/e/w5VXQo8evgWenElZWpr1mZSZXMeiNkGMolAXRm5LO8DN7EBgBvAz59zOqr93zk1yzhU45wpat26d7uEkl+3cCcOGwde/Dv/3f3Dvvb7PO6SZlGEPw1MLWmqT1igUM2sC/An4s3Puwdper1EoUq3ycnjsMbjnHti6Fa6/3k/OOTLcHrkojOIQgcyMQjHgMWBVKuEtUq1XX4WTT/at669/HRYtgqeeCj28g6AWtGRaOl0oZwLXAeea2dLE7eKA6pJct2oVXHIJdOvmJ+ZMnw5vvulnVgYozD5sUB+0ZFY6o1DmO+fMOXeSc+47idusIIuTHJHc0mzzZj9zcuBAOPFEP7b717+GDz+EXr0CnwKfbh92ptexEElX5KfSh92CkgAkJ+L06gXHHQcTJvhZlH/7GwwZ8uVNFwKU7jA+dYFI1EV6Kn26U4klZFUn4iQ1beo3V8iwdC9CikRF4BcxsyHsiRCSht274b774MADK59r1sxPxFm3LuWP0TcwkZpFOsAlhnbtgrFj/XrcQ4f6CTjJiThlZXWaiKM+bJH9U4BLMLZvh/vvh/bt/S44xx/vN1bo3LneE3HUhy2yf5HeUi2IxXgkwz7+2Le4J0yAzz6DSy+Fn/8czjjD//688ypfG8KWZlqMSXJZpFvgakFF2Pr1fjhghw5+KODFF8PSpfDyy5XhjfqwRTIp0qNQJCL23lDh00/9JgpTpvi+7euv933dxx+/z9vSHUWkqegiXixHoUhEjBzpd3s/80z4xjf8Mq/9+8NHH8Gjj1Yb3qA+bJFMi3QfuIQsL49dFbsZ1QUmDoX+i9Yy4p/QvKICxo3LSgnqwxapWc63wNUHW0fO+db2TTdR2M7RfhCM+x7syIexnaDdsKYUvvNc2FWKCDke4GGv5xwrGzbAqFG+O6RLF5g2jck92rKtORQ39S8paQLbGpUy+R8zU/pIjcMWyaycDnDN5KzF55/7C5PdukG7dnD33X4Z16ee8uO1T++U1serD1sks9QHnuN2le5i1OxhTFw0kf6nDWDEhaNpvnwVPPEEPPss7NgBbdv68O7TB445JtDjqw9bJHPiEeB7D2PL4n6IcffFML5dn1LcZA9jF/2ORxaMZ+aze+jycR5cdhnceCOcey40arTP+zWRSiTa4tGFcs89fjnS++6r09uC6IMN+yJovY9fUcHkwd/3XUgHlANQ0riCbc32MLnA/D+Kzz4L559fbXiDukBEoi7aE3lqWo60WTMoKUlpA4DismJGFY5iQtEEBpw6gOGdh6e8K0oQy9nuKt3FqLdGMbFoIv0L+jOiy4jMHX/dOr/+yJw5MHcu13XZxjMd933Ztcf2Yso101OqQUTCF8+JPGvXwtVXVy74nwzs3bv9okk33eRbkR9/XONHpLOlVboXQdMdBVPr8XfuhJdegttugxNO8NPa+/b1wwAvucRPvKlOfn5KxxeRaIt2gLdp45cfLSurXJb06qvhf/8XTjsNXnjBry/91a9Cx44weDDMnu2XNN3b3lt6ZVHGRsGsWA5nnQUtW0L37v6C5DHHwIMPwgcfwMaN8NRT9L18NK3Km5Jf4S915Fc0plV5U/Vhi+SIaAc4+Nb1LbdULkdaUgI33+w3wd261e9iPno0HH44/O53flGlww6Dc87xy5suXAi//GW9+tBDVVLiv11UZ9ly/y1k6FB44w34979h1iwYNAi+9a0vvql0ad+F9b/YzqAuQ30f9tl3sf4X29WHLZIjot0HXlfFxT6oX3vN395/v/rXNWkCr7/uu2HatIHG1Q/G+WIxpdJdlOz5nPzGeTRv0iLlPvBat/SqqPCr+v3lL7BmTeX9mjWwfj2F7aHnj6C4MZQ0hfxSaE5jZl7wGF3Ovr5Op0ZE4qumPvDcCvCqPvigsvW+Z0/1r2nUyE9ead/eT2apcl/c5nBGjb6ICY2WMGDPdxk++q3q+9H37PFdPclbaSmF/5xPz7l9KS75jJID9pC/5wCaH9CMmWtPpcvS7fDXv375Iu1BB/m+7OTt+OMpnvE8o/79AhNOhQHvwfATfkLzCZqIJNKQNMwAB78bzKRJfiPd0lI/7nnwYD9iY/16f0s+XrfOTymvKez31rKl/7xkYFdUVPuy4iYwqjOVATwfmpeb3/ggEdJf3B9xxL4ja3r29N8S+vXz/x2bNsHM1Kayi0huaLgBXtcA3LPHv2bdOli2zL9n5UooL/et9Xbt/OiOQw7xXTFNmvh/HKp7XFwMM2bAkiU+7PMSk2cefFATkkQkZTUFeDxmYqZj77BOZUuvRo3gqKP87cwzYcUKf8vL8yF84YV++7BUffQRvPde5fsPPVThLSKBiP4olLBVHQVT16GI6b5fRKQGud+FIiISc/GciSkiIjVSgIuIxJQCXEQkphTgIiIxpQAXEYkpBbiISExldRihmW0F1tXz7YcDnwRYTtBUX3pUX3pUX/qiXGN751zrqk9mNcDTYWZF1Y2DjArVlx7Vlx7Vl7441FiVulBERGJKAS4iElNxCvBJYRdQC9WXHtWXHtWXvjjU+CWx6QMXEZEvi1MLXERE9qIAFxGJqcgGuJldYWYrzazCzGoc2mNmF5rZGjP7m5kNy2J9Lc1sjpn9NXF/WA2v+4eZrTCzpWaW8bV0azsf5j2c+P1yM/tupmuqY33nmNmnifO11Mx+kcXaHjezLWb2QQ2/D/XcpVhjmOevrZm9YWarEv/v3l7Na0I7hynWF9r5qxfnXCRvwDeAE4B5QEENr2kEfAQcDTQFlgHfzFJ9vwaGJR4PA8bU8Lp/AIdnqaZazwdwMTAbMOB0YGEW/0xTqe8c4E8h/Z3rAnwX+KCG34d27upQY5jnrw3w3cTjg4C/ROzvXyr1hXb+6nOLbAvcObfKObemlpedBvzNObfWOVcKPA90z3x1kDjOU4nHTwE9snTc/UnlfHQHnnbeAuBQM2sTofpC45wrBP69n5eEee6AlGoMjXNuk3NuSeLxZ8Aq4MgqLwvtHKZYX6xENsBTdCTwz71+3kD2/kCOcM5tAv8XA/hKDa9zwKtmttjM+mW4plTOR5jnLNVjdzKzZWY228y+lZ3SUhLmuauL0M+fmXUATgYWVvlVJM7hfuqDCJy/VIW6qbGZvQZUt8PvCOfci6l8RDXPBTYucn/11eFjznTO/cvMvgLMMbPViVZUJqRyPjJ6zmqRyrGX4Nd9+I+ZXQy8AByX6cJSFOa5S1Xo58/MDgRmAD9zzu2s+utq3pLVc1hLfaGfv7oINcCdc+el+REbgLZ7/XwU8K80P/ML+6vPzD42szbOuU2Jr4BbaviMfyXut5jZH/HdCJkK8FTOR0bPWS1qPfbe/0M552aZ2QQzO9w5F4VFhsI8dykJ+/yZWRN8OP7eOTezmpeEeg5rqy/s81dXce9CWQQcZ2ZfM7OmQG/gpSwd+yWgT+JxH2Cfbwxm1sLMDko+Bi4Aqh09EJBUzsdLwPWJ0QCnA58mu4KyoNb6zOyrZmaJx6fh/45uy1J9tQnz3KUkzPOXOO5jwCrn3IM1vCy0c5hKfRH/+7evsK+i1nQDLsP/a70b+Bj4c+L5/wJm7fW6i/FXkz/Cd71kq75WwFzgr4n7llXrw4+2WJa4rcxGfdWdD+AW4JbEYwPGJ36/ghpG+IRY322Jc7UMWACckcXangM2AWWJv3s/jtK5S7HGMM/fWfjukOXA0sTt4qicwxTrC+381eemqfQiIjEV9y4UEZEGSwEuIhJTCnARkZhSgIuIxJQCXEQkphTgIiIxpQAXEYmp/wewGAHefFPI1wAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "y2 = y * np.exp(-0.5 * x)\n",
    "plt.plot(x, y, \"r*-\")\n",
    "plt.plot(x, y2, \"gh\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "ein.tags": "worksheet-0",
    "slideshow": {
     "slide_type": "-"
    }
   },
   "source": [
    "per una formattazione più raffinata è possibile usare variabili dedicate, come sotto:\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "autoscroll": false,
    "collapsed": false,
    "ein.hycell": false,
    "ein.tags": "worksheet-0",
    "jupyter": {
     "outputs_hidden": false
    },
    "slideshow": {
     "slide_type": "-"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x7f2eeb8ca370>]"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "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": [
    "plt.plot(\n",
    "    x,\n",
    "    y,\n",
    "    color=\"blue\",\n",
    "    marker=\"o\",\n",
    "    ls=\"dashed\",\n",
    "    markerfacecolor=\"red\",\n",
    "    markersize=10,\n",
    "    markeredgecolor=\"green\",\n",
    "    markeredgewidth=2,\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "ein.tags": "worksheet-0",
    "slideshow": {
     "slide_type": "-"
    }
   },
   "source": [
    "provate a cambiare alcuni parametri, eventualmente consultando la documentazione.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "ein.tags": "worksheet-0",
    "slideshow": {
     "slide_type": "-"
    }
   },
   "source": [
    "naturalmente è possibile aggiustare tutti gli aspetti del grafico.\n",
    "gli assi: estremi, barre, etichetta\n",
    "griglie: orizzontali, verticali, entrambe\n",
    "dati: legende, eventualmente con annotazioni matematiche sofisticate"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "autoscroll": false,
    "collapsed": false,
    "ein.hycell": false,
    "ein.tags": "worksheet-0",
    "jupyter": {
     "outputs_hidden": false
    },
    "slideshow": {
     "slide_type": "-"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x7f2eeb8df940>"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "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": [
    "plt.plot(x, y, label=\"dati y\")\n",
    "y2 = 5 * np.sin(x) * np.exp(-x)\n",
    "plt.plot(x, y2, label=\"$5 sin(x) e^{-x}$\")\n",
    "plt.legend()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "ein.tags": "worksheet-0",
    "slideshow": {
     "slide_type": "-"
    }
   },
   "source": [
    "adesso provate a modificare i limiti dell'asse x, tramite plt.xlim\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "ein.tags": "worksheet-0",
    "slideshow": {
     "slide_type": "-"
    }
   },
   "source": [
    "è possibile cambiare le posizioni delle tacche:\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "autoscroll": false,
    "collapsed": false,
    "ein.hycell": false,
    "ein.tags": "worksheet-0",
    "jupyter": {
     "outputs_hidden": false
    },
    "slideshow": {
     "slide_type": "-"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "([<matplotlib.axis.XTick at 0x7f2eeb852820>,\n",
       "  <matplotlib.axis.XTick at 0x7f2eeb852610>,\n",
       "  <matplotlib.axis.XTick at 0x7f2eeb8f6b80>,\n",
       "  <matplotlib.axis.XTick at 0x7f2eeb7e0520>],\n",
       " [Text(0, 0, ''), Text(0, 0, ''), Text(0, 0, ''), Text(0, 0, '')])"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "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": [
    "plt.plot(x, y)\n",
    "plt.grid(axis=\"y\")\n",
    "plt.xticks(range(-1, 6, 2))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "ein.tags": "worksheet-0",
    "slideshow": {
     "slide_type": "-"
    }
   },
   "source": [
    "è possibile annotare il grafico con frecce, testo o altri oggetti grafici\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "autoscroll": false,
    "collapsed": false,
    "ein.hycell": false,
    "ein.tags": "worksheet-0",
    "jupyter": {
     "outputs_hidden": false
    },
    "slideshow": {
     "slide_type": "-"
    }
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "<ipython-input-9-2867bd933be6>:2: MatplotlibDeprecationWarning: The 's' parameter of annotate() has been renamed 'text' since Matplotlib 3.3; support for the old name will be dropped two minor releases later.\n",
      "  plt.annotate(\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "Text(1, 5, 'primo punto del grafico')"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "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": [
    "plt.plot(x, y)\n",
    "plt.annotate(\n",
    "    s=\"primo punto del grafico\",\n",
    "    xy=(x[0], y[0]),\n",
    "    xytext=(1, 5),\n",
    "    arrowprops=dict(arrowstyle=\"fancy\"),\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "autoscroll": false,
    "collapsed": false,
    "ein.hycell": false,
    "ein.tags": "worksheet-0",
    "jupyter": {
     "outputs_hidden": false
    },
    "slideshow": {
     "slide_type": "-"
    }
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "<ipython-input-10-86d413e302a3>:2: MatplotlibDeprecationWarning: The 's' parameter of annotate() has been renamed 'text' since Matplotlib 3.3; support for the old name will be dropped two minor releases later.\n",
      "  plt.annotate(\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "Text(1, 5, 'primo punto\\n del grafico')"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "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": [
    "plt.plot(x, y)\n",
    "plt.annotate(\n",
    "    s=\"primo punto\\n del grafico\",\n",
    "    xy=(x[0], y[0]),\n",
    "    xytext=(1, 5),\n",
    "    arrowprops=dict(arrowstyle=\"->\", connectionstyle=\"angle3, angleB=40\"),\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "ein.tags": "worksheet-0",
    "slideshow": {
     "slide_type": "-"
    }
   },
   "source": [
    "possiamo porre più grafici sulla stessa figura, provate anche a modificare la chiamata a subplots in maniera da avere l'asse y uguale."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "autoscroll": false,
    "collapsed": false,
    "ein.hycell": false,
    "ein.tags": "worksheet-0",
    "jupyter": {
     "outputs_hidden": false
    },
    "slideshow": {
     "slide_type": "-"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x7f2eeb6a4df0>]"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 360x720 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, axes = plt.subplots(ncols=2, figsize=(5, 10), sharey=True)  # due colonne, una riga\n",
    "axes[0].plot(x, y)\n",
    "axes[1].plot(x, y2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "ein.tags": "worksheet-0",
    "slideshow": {
     "slide_type": "-"
    }
   },
   "source": [
    "molti altri tipi di grafico sono possibili, considereremo:\n",
    "scatterplot\n",
    "barplot\n",
    "linee di livello\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "ein.tags": "worksheet-0",
    "slideshow": {
     "slide_type": "-"
    }
   },
   "source": [
    "generiamo qualche dato da rappresentare, useremo un generatore di numeri casuali"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "autoscroll": false,
    "collapsed": false,
    "ein.hycell": false,
    "ein.tags": "worksheet-0",
    "jupyter": {
     "outputs_hidden": false
    },
    "slideshow": {
     "slide_type": "-"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PathCollection at 0x7f2eeb60a040>"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "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": [
    "xd = rnd.randn(100)\n",
    "yd = rnd.randn(100)\n",
    "plt.scatter(xd, yd)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "ein.tags": "worksheet-0",
    "slideshow": {
     "slide_type": "-"
    }
   },
   "source": [
    "possiamo mettere in relazione ulteriori grandezze codificandole nella dimensione dei simboli e persino nel colore"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "autoscroll": false,
    "collapsed": false,
    "ein.hycell": false,
    "ein.tags": "worksheet-0",
    "jupyter": {
     "outputs_hidden": false
    },
    "slideshow": {
     "slide_type": "-"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PathCollection at 0x7f2eeb5e1580>"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "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": [
    "td = rnd.randn(100)\n",
    "ud = rnd.randn(100) ** 2  # grandezze positive per le areee dei punti\n",
    "plt.scatter(xd, yd, c=td, s=ud * 20)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "ein.tags": "worksheet-0",
    "slideshow": {
     "slide_type": "-"
    }
   },
   "source": [
    "i grafici a barre sono usati principalmente per rappresentare conteggi"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "autoscroll": false,
    "collapsed": false,
    "ein.hycell": false,
    "ein.tags": "worksheet-0",
    "jupyter": {
     "outputs_hidden": false
    },
    "slideshow": {
     "slide_type": "-"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<BarContainer object of 5 artists>"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "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": [
    "conteggi = rnd.poisson(10, 5)\n",
    "xc = [\n",
    "    \"A\",\n",
    "    \"B\",\n",
    "    \"C\",\n",
    "    \"D\",\n",
    "    \"E\",\n",
    "]  # nel caso dei barplot, le ascisse possono anche essere categoriche\n",
    "plt.bar(xc, conteggi)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "ein.tags": "worksheet-0",
    "slideshow": {
     "slide_type": "-"
    }
   },
   "source": [
    "si possono cambiare diversi parametri del grafico: larghezza, colore, bordo; provate\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "autoscroll": false,
    "collapsed": false,
    "ein.hycell": false,
    "ein.tags": "worksheet-0",
    "jupyter": {
     "outputs_hidden": false
    },
    "slideshow": {
     "slide_type": "-"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "([<matplotlib.axis.XTick at 0x7f2eeb584d90>,\n",
       "  <matplotlib.axis.XTick at 0x7f2eeb584d60>,\n",
       "  <matplotlib.axis.XTick at 0x7f2eeb57e910>,\n",
       "  <matplotlib.axis.XTick at 0x7f2eeb4c8f70>,\n",
       "  <matplotlib.axis.XTick at 0x7f2eeb4d94c0>],\n",
       " [Text(1, 0, 'A'),\n",
       "  Text(2, 0, 'B'),\n",
       "  Text(3, 0, 'C'),\n",
       "  Text(4, 0, 'D'),\n",
       "  Text(5, 0, 'E')])"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "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": [
    "conteggi2 = rnd.poisson(10, 5)\n",
    "conteggi3 = rnd.poisson(10, 5)\n",
    "larghezza = 0.2\n",
    "posizioni = np.arange(1, 6)\n",
    "plt.bar(posizioni, conteggi, width=larghezza)\n",
    "plt.bar(posizioni + larghezza, conteggi2, width=larghezza, color=\"red\")\n",
    "plt.bar(posizioni - larghezza, conteggi3, width=larghezza)\n",
    "plt.xticks(posizioni, xc)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "ein.tags": "worksheet-0",
    "slideshow": {
     "slide_type": "-"
    }
   },
   "source": [
    "è possibile rappresentare dati tridimensionali in vari modi, uno è tramite linee di livello"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "autoscroll": false,
    "collapsed": false,
    "ein.hycell": false,
    "ein.tags": "worksheet-0",
    "jupyter": {
     "outputs_hidden": false
    },
    "slideshow": {
     "slide_type": "-"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<a list of 7 text.Text objects>"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "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": [
    "xx = np.arange(-3, 3, 0.3)\n",
    "xms, yms = np.meshgrid(xx, xx)\n",
    "z = np.exp(-(xms * xms + yms * yms) * 0.3) * np.sin(xms)\n",
    "ax = plt.axes(aspect=\"equal\")\n",
    "cs = ax.contour(xms, yms, z, 10)\n",
    "plt.clabel(cs, inline=True, fontsize=10)  # etichette delle linee"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "autoscroll": false,
    "collapsed": false,
    "ein.hycell": false,
    "ein.tags": "worksheet-0",
    "jupyter": {
     "outputs_hidden": false
    },
    "slideshow": {
     "slide_type": "-"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.colorbar.Colorbar at 0x7f2eeb3c7fa0>"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "ax = plt.axes(aspect=\"equal\")\n",
    "imm = ax.contourf(xms, yms, z, 12)\n",
    "plt.colorbar(imm)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "autoscroll": false,
    "collapsed": false,
    "ein.hycell": false,
    "ein.tags": "worksheet-0",
    "jupyter": {
     "outputs_hidden": false
    },
    "slideshow": {
     "slide_type": "-"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.colorbar.Colorbar at 0x7f2eeb63af40>"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "ax = plt.axes(aspect=1.5)\n",
    "imm = ax.contourf(xms, yms, z, 12, cmap=\"coolwarm\")\n",
    "plt.colorbar(imm)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 864x288 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, axs = plt.subplots(ncols=3, figsize=(12, 4))\n",
    "for ax in axs:\n",
    "    g = rnd.normal(size=500)\n",
    "    h = ax.hist(g, bins=20)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x7f2eeb181490>]"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "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": [
    "x = np.arange(11)\n",
    "y = 2 + 3 * x + rnd.uniform(-1, 1, size=11)\n",
    "a, b = np.polyfit(x, y, deg=1)\n",
    "y_est = a * x + b\n",
    "y_err = x.std() * np.sqrt(\n",
    "    1 / len(x) + (x - x.mean()) ** 2 / np.sum((x - x.mean()) ** 2)\n",
    ")\n",
    "\n",
    "fig, ax = plt.subplots()\n",
    "ax.plot(x, y_est, \"-\")\n",
    "ax.fill_between(x, y_est - y_err, y_est + y_err, alpha=0.2)\n",
    "ax.plot(x, y, \"o\", color=\"tab:brown\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PolyCollection at 0x7f2eeb0dc9a0>"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "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": [
    "fig, ax = plt.subplots()\n",
    "x = np.arange(0, 4 * np.pi, 0.01)\n",
    "y = np.sin(x)\n",
    "ax.plot(x, y, color=\"black\")\n",
    "\n",
    "threshold = 0.75\n",
    "ax.axhline(threshold, color=\"green\", lw=2, alpha=0.7)\n",
    "ax.fill_between(\n",
    "    x,\n",
    "    0,\n",
    "    1,\n",
    "    where=y > threshold,\n",
    "    color=\"green\",\n",
    "    alpha=0.5,\n",
    "    transform=ax.get_xaxis_transform(),\n",
    ")"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "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": "3.10.4"
  },
  "name": "grafici.ipynb"
 },
 "nbformat": 4,
 "nbformat_minor": 4
}
