Vander_Plus_4_Matplotlib_04.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "a5cff816",
   "metadata": {},
   "source": [
    "# Визуализация погрешностей\n",
    "ИМХО: ничто не сравнится с originlab в работе с погрешностями\n",
    "ПС. сейчас узнаем"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b810e3c7",
   "metadata": {},
   "source": [
    "## Простые планки погрешностей"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "56b812cc",
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "plt.style.use('seaborn-whitegrid')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "fcc63624",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "8c437a45",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 0.        ,  0.20408163,  0.40816327,  0.6122449 ,  0.81632653,\n",
       "        1.02040816,  1.2244898 ,  1.42857143,  1.63265306,  1.83673469,\n",
       "        2.04081633,  2.24489796,  2.44897959,  2.65306122,  2.85714286,\n",
       "        3.06122449,  3.26530612,  3.46938776,  3.67346939,  3.87755102,\n",
       "        4.08163265,  4.28571429,  4.48979592,  4.69387755,  4.89795918,\n",
       "        5.10204082,  5.30612245,  5.51020408,  5.71428571,  5.91836735,\n",
       "        6.12244898,  6.32653061,  6.53061224,  6.73469388,  6.93877551,\n",
       "        7.14285714,  7.34693878,  7.55102041,  7.75510204,  7.95918367,\n",
       "        8.16326531,  8.36734694,  8.57142857,  8.7755102 ,  8.97959184,\n",
       "        9.18367347,  9.3877551 ,  9.59183673,  9.79591837, 10.        ])"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x = np.linspace(0, 10, 50)\n",
    "x"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "36e7393a",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<ErrorbarContainer object of 3 artists>"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "dy = 0.8\n",
    "y = np.sin(x) + dy * np.random.rand(50)\n",
    "plt.errorbar(x, y, yerr=dy, fmt='.k')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "4e535a66",
   "metadata": {},
   "outputs": [],
   "source": [
    "# plt.errorbar?\n",
    "\n",
    "# plt.errorbar(\n",
    "#     x,\n",
    "#     y,\n",
    "#     yerr=None,\n",
    "#     xerr=None,\n",
    "#     fmt='',\n",
    "#     ecolor=None,\n",
    "#     elinewidth=None,\n",
    "#     capsize=None,\n",
    "#     barsabove=False,\n",
    "#     lolims=False,\n",
    "#     uplims=False,\n",
    "#     xlolims=False,\n",
    "#     xuplims=False,\n",
    "#     errorevery=1,\n",
    "#     capthick=None,\n",
    "#     *,\n",
    "#     data=None,\n",
    "#     **kwargs,\n",
    "# )\n",
    "\n",
    "# Parameters\n",
    "# ----------\n",
    "# x, y : float or array-like\n",
    "#     The data positions.\n",
    "\n",
    "# xerr, yerr : float or array-like, shape(N,) or shape(2, N), optional\n",
    "#     The errorbar sizes:\n",
    "\n",
    "#     - scalar: Symmetric +/- values for all data points.\n",
    "#     - shape(N,): Symmetric +/-values for each data point.\n",
    "#     - shape(2, N): Separate - and + values for each bar. First row\n",
    "#       contains the lower errors, the second row contains the upper\n",
    "#       errors.\n",
    "#     - *None*: No errorbar.\n",
    "\n",
    "#     Note that all error arrays should have *positive* values.\n",
    "\n",
    "#     See :doc:`/gallery/statistics/errorbar_features`\n",
    "#     for an example on the usage of ``xerr`` and ``yerr``.\n",
    "\n",
    "# fmt : str, default: ''\n",
    "#     The format for the data points / data lines. See `.plot` for\n",
    "#     details.\n",
    "\n",
    "#     Use 'none' (case insensitive) to plot errorbars without any data\n",
    "#     markers.\n",
    "\n",
    "# ecolor : color, default: None\n",
    "#     The color of the errorbar lines.  If None, use the color of the\n",
    "#     line connecting the markers.\n",
    "\n",
    "# elinewidth : float, default: None\n",
    "#     The linewidth of the errorbar lines. If None, the linewidth of\n",
    "#     the current style is used.\n",
    "\n",
    "# capsize : float, default: :rc:`errorbar.capsize`\n",
    "#     The length of the error bar caps in points.\n",
    "\n",
    "# capthick : float, default: None\n",
    "#     An alias to the keyword argument *markeredgewidth* (a.k.a. *mew*).\n",
    "#     This setting is a more sensible name for the property that\n",
    "#     controls the thickness of the error bar cap in points. For\n",
    "#     backwards compatibility, if *mew* or *markeredgewidth* are given,\n",
    "#     then they will over-ride *capthick*. This may change in future\n",
    "#     releases.\n",
    "\n",
    "# barsabove : bool, default: False\n",
    "#     If True, will plot the errorbars above the plot\n",
    "#     symbols. Default is below.\n",
    "\n",
    "# lolims, uplims, xlolims, xuplims : bool, default: False\n",
    "#     These arguments can be used to indicate that a value gives only\n",
    "#     upper/lower limits.  In that case a caret symbol is used to\n",
    "#     indicate this. *lims*-arguments may be scalars, or array-likes of\n",
    "#     the same length as *xerr* and *yerr*.  To use limits with inverted\n",
    "#     axes, `~.Axes.set_xlim` or `~.Axes.set_ylim` must be called before\n",
    "#     :meth:`errorbar`.  Note the tricky parameter names: setting e.g.\n",
    "#     *lolims* to True means that the y-value is a *lower* limit of the\n",
    "#     True value, so, only an *upward*-pointing arrow will be drawn!\n",
    "\n",
    "# errorevery : int or (int, int), default: 1\n",
    "#     draws error bars on a subset of the data. *errorevery* =N draws\n",
    "#     error bars on the points (x[::N], y[::N]).\n",
    "#     *errorevery* =(start, N) draws error bars on the points\n",
    "#     (x[start::N], y[start::N]). e.g. errorevery=(6, 3)\n",
    "#     adds error bars to the data at (x[6], x[9], x[12], x[15], ...).\n",
    "#     Used to avoid overlapping error bars when two series share x-axis\n",
    "#     values."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "0fbf27e8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<ErrorbarContainer object of 3 artists>"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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06dPR0dERNL+zsxNNTU3o6enB7Nmz8dprr8VeUoMl2i3bSqPNNTU1oampSXVAMHOMF44vE0yE/GpA23epx4+AnYYFSJIkSVK70ObNmzFv3jwUFhYCAGbPno0LFy5g9OhHvw8HDx6E0+lEVlYW1q5di6qqKhQVFYWsx+12h1wORmNgYED+f+hgRnpNN3vbepVXzTaKi4vR3d0dso7Jkyfjo48+Mu2z9fl8SElJUbUuLdvWUyzfk9/vR1paGlpbW7F37175R7Surg4lJSUjrj/ex6eZ245Er3Mj3PTW1la4XC74fD55XlpaGrZu3YqSkhJD9m+ovr4+FBQUhJ2nqYaelZUFr9crvw4EAnIwlyQJy5YtQ3Z2NgCgsLAQV69eDRvQAcDhcKje/tArgqHL6zXd7G3rVV4127h161bYddy6dQsOh8O0z9bj8QSdALHutxFi+Z7S0tJw+fJlNDQ0BF0tNTQ0ICcnB9XV1boeO0Yct0pXTIPT79y5E7fzUu37w013OBzIyclRvEo14twYyu12K87T1Cman5+PS5cuAQCuXLmCKVOmyPN6e3tRUlICr9cLSZLQ1tZm20u8RMLOT+uwUydcNCZNmiT/hZs+apT1k+2qq6tx/vx5fPHFFzh//rxl+yA01dDnzp2LTz/9FJWVlZAkCTt27MC5c+fQ19eHiooK1NXVoba2FqmpqZg5c6bcNEPWMbxtkZ2f1mGnTjiyFk0BfdSoUdi6dWvQtLy8PPn/srIylJWVxVQwUWjplDGiN394bUmkbAi7s1MnXKILlxlm5jnD0RZVUhtslXrmI63HrMwMUbIhjBDPTBpeLdmDFZ9DyoCukl4nbyKn04mQVhjPpxLxaskerPgcUgZ0Mhwf0TYyXi1ZnxX7OqzfvUxEZEFWzAwTJqDbZTQ0Iops4sSJ8p+VWXGcHCGaXKzYOUE0nAh9B2ZkYFmVFfs6hAjoVuycIBpOhL4Du5Y7XqzW1yFEQLdi5wSRnVm9uYPCE6IN3YqdE2Qvt2/flv9I+XZ9io5ZfXpCBHQrdk6QvfT09Mh/dmaXDkWRmfmEIyECenV1NZqamjB58mQkJSVh8uTJaGpqYvs5JRy1NWv+AOjPzMHVhGhDB8zvnOAJQUbQe+wQPZtUeA48Ymafnu0CutUGwxlkRFtjUlISHn/88bhvh6zJ6um5bG9/xMzB1WzV5GKnp2/Hw6hRo4TtqOKNYSMTbZx0M8WzqcnMPj1b1dCZby6mSDXP/Px8M4tmqZuBmJ6rn3h+l2becGSrgK73Ac02P2uI9EP9j3/8w6RSPWKlm4E4Trp9mNWnp6nJJRAIwOVyoaKiAjU1NSEH2cWLF7F48WJUVFTg1KlTuhQU0J5vrnR5xVxba2DNMzpMz1XGbJ1HNAX0CxcuoL+/HydPnsT69euxa9cueZ7f78fOnTvx5z//Gc3NzTh58qRuub1aD+hEDdx2Och5Y1h0mJ6rLFHP8eE0BXS3241Zs2YBAKZPnx70FOvr168jNzcXY8eORWpqKgoKCtDe3q5LYXlAq2OXgzyRa57DO4NbW1sjvt8uDysmcyRJkiSpXWjz5s2YN2+e/PDn2bNn48KFCxg9ejTa29tx/Phx7Nu3DwCwf/9+5OTkoLy8PGQ9brc75ESOxsDAgPx/cnLyiNNF4fP5kJaWFtdtqPlsW1tbsXfvXrnjp66uDiUlJRHXo3ZdPp8PKSkpiutSsw9allE7Xc1n0traCpfLBZ/PJ09PS0vD1q1bUVJSoutxbuVzw4jjOlpqj1s9p0err68PBQUFYedp6hTNysqC1+uVXwcCAYwePTrsPK/Xi+zsbMV1ORwO1dsfekUwdHml6aLweDxx369oP9uWlhY0NDQEZaY0NDQgJycH1dXVEb+LcPMcDgcWLlwoTx/sRNqzZ09QgIwmW0DLcaD2mAo3Xe1nsnDhwqBgDjwKbocOHUJ9fb2ux7mVzw0jjutoqT1u9ZweLbfbrThPU5NLfn4+Ll26BAC4cuUKpkyZIs/Ly8tDV1cX7t+/j/7+frS3t+O5557TshmyMCNyoltaWuByuRTvO7DagFpqPxN2BpPeNNXQ586di08//RSVlZWQJAk7duzAuXPn0NfXh4qKCmzcuBErVqyAJElYvHix5dtwST0jgtHmzZtDarBD7zvQklIYz7xytZ8J0xCtx+oJBCPRFNBHjRqFrVu3Bk3Ly8uT/58zZw7mzJkTW8nI0owIRvH40YhnXrnaz2T79u1BN1QBj9rQE6Ez2KriXfmM99Altrr1n6zDiMwUu6Uzqv1MwmVtbd26lZkrgjJi6BIGdNLEiBTS7du3h2Q/WDmdUctnMjwNcTAjhsRjRL+TrW79J2uJ9+3N1dXV+Prrr1VnuZjJ7GGcybqM6HdiQCdLKykpCZvOSGQ3RvQ7scmFiMgARvQ7sYZOlADsno4nAiOG1WVAp6jYJSBY9YlWZuO9INYQ7z4WNrlQVNQM9GXW04cS/YlWRAzopCszgyof0UbxZIfhqBnQSVdmBlWOjULxZIfhqBnQSVdmBlW73VlKpDcGdNKVmUE1kR+UQQQwoNMwsbYTmhlUjXqilVmdvkQjYdoiBYm1fXCkXNt4pxXGOy1ssNN36EMsVq1aJW9bT1bufCNrYkAn3SkFVSODYbxE6vTVex+s3PlG1sQmFzKMCGmFzKQhK2NAJ8OIEAyZSUNWpqnJxefzob6+Ht988w0yMzOxe/dujB8/Pug927Ztw+XLl5GZmQkAaGxsjPiwaBKfCI9cC/eUIWbSJDYr9XVoqqG/++67mDJlCk6cOIGysjI0NjaGvKezsxPvvPMOmpub0dzczGBOQqQVas2kYWaMuKx0w5GmGrrb7cYvf/lLAMCLL74YEtADgQC6urrgcrlw584dLFmyBEuWLIm9tGRrRow2ZwS1mTQidAaTPSRJkiRFesPp06dx7NixoGkTJkyAy+VCXl4eAoEAZs+ejUuXLsnze3t78de//hXLly/HwMAAamtrsWPHDjzzzDNB63G73SE1tmgMDAzI/ycnJ484XRQ+ny/kkWxm0/JdqFnG5/MhJSVF1TbUbru1tTXoqUh1dXXyo+D02EZxcTG6u7sx3OTJk/HRRx8Fvd/v94/4HYt2nFvxuNZDvOJUX18fCgoKws4bsYZeXl6O8vLyoGlr166F1+sFAHi9XowZMyZofnp6Ompra5Geng4AeP7553Ht2rWQgA4ADocjur0YoqOjI+zyStNF4fF4LLdfWr4LNct4PJ6gEyCabajZdktLCxoaGoJqzw0NDcjJyUF1dbUu27h16xbCuXXrFhwOR9D709LSRvyORTvOrXhc6yFeccrtdivO09SGnp+fj48//hgAcOnSpZBfi6+++gpOpxMDAwPw+/24fPkypk6dqmVTRHFlRColM2PIKJra0KuqqrBhwwZUVVUhJSUFb731FgDg6NGjyM3NRXFxMUpLS7F06VKkpKRg0aJFePrpp3UteDhW6m0mezAilZKZMWQUTQE9PT0df/zjH0OmL1++XP5/5cqVWLlypfaSaWCFXmayFyNSKUXpDCbr463/lNCMqj3He4wZIoB3ilKCi5RXztxxioUZTzhiDZ0SXrjaM3PHKVZmNAGzhk4UhggDiVHiYUAnCkOEgcQo8bDJhSgMKw8kxvRcUmLLGvrEiRORlJTEA1swZnQiKbHyQGJWGgyKrMWWNfRJkybh7t27PKAFY6Xvk7njZEe2DOhE8X42KcDccbIfWza5UGIbTCns7u6GJElySuFIeeLMKyfRMaCT7WhJKdT6I0BkJwzoZDtaUgqtnFdupc5gsje2oVNMzAhCWlIKjcwrV/uZDO0Mvnv3rt7FoQTCgE4xMSIzZXiA1DKglpF55VbK1qHEwiYXsrzheddaHtRs5bxyIr2whk62pDalkHnllAhiCugffvghPvjgA/mJRUOdOnUK7733HkaPHo3Vq1ejqKgolk0RxYx55WQFEydOxJ07d/D444/rvm7NAX3btm345JNPwj7ktKenB83NzXj//ffx7bffwul04qc//SlSU1NjKiwRkd3F8053zW3o+fn5aGhoCDvviy++wHPPPYfU1FRkZ2cjNzcX165d07opIiKKwog19NOnT+PYsWNB03bs2IGXXnoJbW1tYZfp7e1Fdna2/DozMxO9vb0xFpWIiCIZMaCXl5ejvLxc1UqzsrLg9Xrl116vNyjAD+XxeFSte5DP59O8rF3ZdZ8jlXmk/Yl2n5Xeo2Xbek2PRGkZu37HseA+6ycuWS7PPvss9u3bh2+//Rb9/f24fv06pkyZEva94drgo+HxeDQva1d22ueOjg75/+FljjRvuEj7rLQeLdvWa3ok0Sxjp+9YL9xnddxut+I8XQP60aNHkZubi+LiYtTU1MDpdEKSJNTV1eGxxx7Tc1NERDRMTAF9xowZmDFjhvx6+fLl8v9Lly7F0qVLY1k9kSYcE4USFW8sorgwM6jy1ntKVAzoFBcMqkTG41guRESCYEAnIhIEAzoRkSDYhk6kATNpyIoY0MlwIgRDdvqSFTGgk+EYDInig23oRESCYEAnIhIEAzoRkSDYhk5kEBE6g8naGNCJDMLOYIo3NrkQEQmCNXQisDmExMCATgQ2h5AY2ORCRCSImGroH374IT744AO89dZbIfO2bduGy5cvIzMzEwDQ2Nio+KBoIiKKneaAvm3bNnzyySeKDzrt7OzEO++8g/Hjx2suHBERRU9zk0t+fj4aGhrCzgsEAujq6oLL5UJlZSXOnDmjdTNEppo4caL8R2R1I9bQT58+jWPHjgVN27FjB1566SW0tbWFXaavrw8///nPsXz5cgwMDKC2thbTpk3DM888E/Jej8ejqeA+n0/zsnbFfVam5XNRu8zdu3dVb0MtfseJIV77PGJALy8vR3l5uaqVpqeno7a2Funp6QCA559/HteuXQsb0JWabEbi8Xg0L2tX3OdgHR0d8v/Rfi5aljESv+PEEMs+u91uxXlxyXL56quv4HQ6MTAwAL/fj8uXL2Pq1Knx2BQREf1/uuahHz16FLm5uSguLkZpaSmWLl2KlJQULFq0CE8//bSemyIiomFiCugzZszAjBkz5NfLly+X/1+5ciVWrlwZy+qJiEgF3lhERCQI3vpPtsVUQqJgDOhkW1rGX+GPAImMAZ0SCgfhIpGxDZ2ISBAM6EREgmBAJyISBAM6EZEgGNCJiATBgE5EJAgGdCIiQTCgExEJIkmSJMmsjUca15eIiMIrKCgIO93UgE5ERPphkwsRkSAY0ImIBGGrgB4IBOByuVBRUYGamhp0dXWZXaS48/v9qK+vh9PpxJIlS/DRRx+ZXSTDfPPNNygsLMT169fNLooh3n77bVRUVOBnP/sZTp8+bXZx4s7v92P9+vWorKyE0+kU/nv+/PPPUVNTAwDo6upCVVUVnE4nfvvb3yIQCOiyDVsF9AsXLqC/vx8nT57E+vXrsWvXLrOLFHdnz57FuHHjcOLECRw5cgS///3vzS6SIfx+P1wuF9LS0swuiiHa2trwn//8B++++y6am5tx69Yts4sUdx9//DG+++47vPfee1izZg327dtndpHi5siRI9iyZQu+/fZbAMDOnTvxxhtv4MSJE5AkSbeKmq0CutvtxqxZswAA06dPD3qCu6gWLFiAdevWya+Tk5NNLI1xdu/ejcrKSnzve98zuyiG+OSTTzBlyhSsWbMGv/rVrzB79myzixR3P/jBDzAwMIBAIIDe3l6MHi3uaN65ubk4cOCA/LqzsxM/+clPAAAvvvgiPvvsM122Y6tPsLe3F1lZWfLr5ORkfPfdd0IfCJmZmQAe7fvrr7+ON954w9wCGeBvf/sbxo8fj1mzZqGpqcns4hji3r17+Prrr3H48GHcuHEDq1evxgcffICkpCSzixY3GRkZuHnzJhYuXIh79+7h8OHDZhcpbubPn48bN27IryVJkr/bzMxM/O9//9NlO7aqoWdlZcHr9cqvA4GA0MF8UHd3N2pra7Fo0SKUlpaaXZy4e//99/HZZ5+hpqYGHo8HGzZsQE9Pj9nFiqtx48bhhRdeQGpqKp566ik89thjuHv3rtnFiqu//OUveOGFF/DPf/4Tf//737Fx40a5SUJ0o0b9X+j1er0YM2aMPuvVZS0Gyc/Px6VLlwAAV65cwZQpU0wuUfzduXMHr776Kurr67FkyRKzi2OIlpYWHD9+HM3NzXA4HNi9e7fwj44rKCjAv/71L0iShNu3b+Phw4cYN26c2cWKqzFjxiA7OxsAMHbsWHz33XcYGBgwuVTG+OEPf4i2tjYAwKVLl/DjH/9Yl/Xaqno7d+5cfPrpp6isrIQkSdixY4fZRYq7w4cP48GDB2hsbERjYyOARx0sidJZmCiKiorw73//G0uWLIEkSXC5XML3l/ziF7/Apk2b4HQ64ff7UVdXh4yMDLOLZYgNGzbgN7/5Df7whz/gqaeewvz583VZL+8UJSIShK2aXIiISBkDOhGRIBjQiYgEwYBORCQIBnQiIkEwoBMRCYIBnYhIEAzoRESC+H+LD0+Arp0tJgAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.errorbar(x, y, yerr=dy, fmt='o', color='black', ecolor='lightgrey', elinewidth=3, capsize=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "4967346c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<ErrorbarContainer object of 3 artists>"
      ]
     },
     "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": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.errorbar(x, y, yerr=dy, fmt='o', color='black', ecolor='lightgrey', elinewidth=2, capsize=10)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f3cbc6a0",
   "metadata": {},
   "source": [
    "## Непрерывные погрешности\n",
    "Для случаев, когда надо отображать планки погрешностей для непрерывных велични"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "04a67471",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.gaussian_process import GaussianProcessRegressor"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "1ab3cd18",
   "metadata": {},
   "outputs": [],
   "source": [
    "# описываем модель, и отрисовываем некоторые данные\n",
    "model = lambda x: x * np.sin(x)\n",
    "xdata = np.array([1,3,5,6,8])\n",
    "ydata = model(xdata)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "64ac4ee1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 0.84147098,  0.42336002, -4.79462137, -1.67649299,  7.91486597])"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ydata"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "7df84d0c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1, 3, 5, 6, 8])"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "xdata"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "e23eb559",
   "metadata": {},
   "outputs": [],
   "source": [
    "# выполним подгонку Гауссова процесса\n",
    "from sklearn.gaussian_process.kernels import DotProduct, WhiteKernel\n",
    "kernel = DotProduct() + WhiteKernel()\n",
    "gp = GaussianProcessRegressor(kernel=kernel, random_state=0).fit(xdata[:, np.newaxis], ydata)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "3ac853e9",
   "metadata": {},
   "outputs": [],
   "source": [
    "xfit = np.linspace(0, 10, 1000)\n",
    "yfit = gp.predict(xfit[:, np.newaxis])\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "f68b4b1a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(0.0, 10.0)"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(xdata, ydata, 'or')\n",
    "plt.plot(xfit, yfit, '-', color = 'grey')\n",
    "plt.fill_between(xfit, yfit, color = 'gray', alpha = 0.2)\n",
    "plt.xlim(0, 10)\n",
    "# хрень"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5f07182a",
   "metadata": {},
   "source": [
    "###### Короче, для закрашивания областей м/у графиками ``plt.fill_between`` и  ``plt.fill``\n",
    "Полубому непрерывные погрешности уже встоены в библиотеку\n",
    "Так же это реализовано в пакете ``Seaborn``"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "bad10ea5",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "2ad15098",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "8771c499",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "c6878cfc",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "1b26c1b0",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "8ac1e915",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "d6b20f29",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "98ce82ce",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "a96ea159",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "ddd7b95a",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "d122d517",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "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": "3.9.7"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}