Volano/Volano.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Volano"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Set"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "# import glob\n",
    "import math\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import xarray as xr\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "graph_base_name = '../grafici/graph_'\n",
    "\n",
    "style = ['white', 'dark', 'whitegrid', 'darkgrid', 'ticks'][2]\n",
    "sns.set_style(style)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  [' 0', ' 1', ' 2', ' 3', ' 4', ' 5']   [' 6', ' 7', ' 8', ' 9', ' 10', ' 11'] ['_12', '_13', '_14', '_15', '_16', '_17']\n"
     ]
    },
    {
     "data": {
      "text/html": [
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      ],
      "text/plain": [
       "[(0.9677975592919913, 0.44127456009157356, 0.5358103155058701),\n",
       " (0.9707505829046977, 0.4626541987597626, 0.26486329418330007),\n",
       " (0.8369430560927636, 0.5495828952802333, 0.1952683223448124),\n",
       " (0.7350228985632719, 0.5952719904750953, 0.1944419133847522),\n",
       " (0.6430915736746491, 0.6271955086583126, 0.19381135329796756),\n",
       " (0.5310702086647983, 0.6569113639885688, 0.1931838276161326),\n",
       " (0.3126890019504329, 0.6928754610296064, 0.1923704830330379),\n",
       " (0.1998437102815942, 0.6927711055021983, 0.4485948481767363),\n",
       " (0.20582072623426667, 0.6842209016721069, 0.5675558225732941),\n",
       " (0.21044753832183283, 0.6773105080456748, 0.6433941168468681),\n",
       " (0.2151139535594307, 0.6700707833028816, 0.7112365203426209),\n",
       " (0.22126158707091403, 0.6600977737826563, 0.7915486637100363),\n",
       " (0.23299120924703914, 0.639586552066035, 0.9260706093977744),\n",
       " (0.5392573577708815, 0.5821140171749066, 0.9584212824085171),\n",
       " (0.731751635642941, 0.5128186367840487, 0.9581005178234921),\n",
       " (0.9082572436765556, 0.40195790729656516, 0.9576909250290225),\n",
       " (0.9614880299080136, 0.3909885385134758, 0.8298287106954371),\n",
       " (0.9648889414626248, 0.41896694790705413, 0.692031255809005)]"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "palette = sns.husl_palette(18)\n",
    "print(' ', [' '+str(x) for x in range(6)],' ', [' '+str(x+6) for x in range(6)], ['_'+str(x+12) for x in range(6)])\n",
    "palette"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Import"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.read_csv('MisureVolano.txt', sep='\\t')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "arr_acc = df.query('Fase == \"acc\"').Tempo.to_numpy().reshape((10,12))\n",
    "arr_dec = df.query('Fase == \"dec\"').Tempo.to_numpy().reshape((10,6))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Accelerazione"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Media"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "def fn_sdom(arr):\n",
    "    mean = np.mean(arr)\n",
    "    sum = np.sum((arr-mean)**2)\n",
    "    std = (sum / (arr.size - 1)) ** .5\n",
    "    return std / (arr.size) ** .5\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "acc_mean = arr_acc.mean(axis=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "acc_sdom = np.apply_along_axis(fn_sdom, 0, arr_acc)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([15.6802 , 22.4146 , 27.4983 , 31.74836, 35.4952 , 38.96553,\n",
       "       42.1331 , 45.17433, 48.01526, 50.65523, 53.21699, 55.61401])"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "acc_mean"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 576x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "def plot_0acc():    \n",
    "    plt.figure(figsize=(8,6))\n",
    "\n",
    "    plt.errorbar(range(1,13), acc_mean, acc_sdom, fmt='o', capsize=6, color=palette[8], label='Tempo impiegato')\n",
    "\n",
    "    plt.gca().grid(axis='x')\n",
    "\n",
    "    plt.xlabel('Giri del volano completati')\n",
    "    plt.ylabel('Tempo impiegato [s]')\n",
    "\n",
    "    plt.xticks(range(1,13))\n",
    "    \n",
    "    plt.legend(loc='lower right')\n",
    "\n",
    "    plt.savefig('../grafici/graph_acc_tempo', dpi=200)\n",
    "\n",
    "plot_0acc()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Differenza di tempo"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "acc_diff = np.apply_along_axis(\n",
    "    lambda arr: np.convolve(arr, [1,-1], 'same'),\n",
    "    0,\n",
    "    acc_mean\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([15.6802 ,  6.7344 ,  5.0837 ,  4.25006,  3.74684,  3.47033,\n",
       "        3.16757,  3.04123,  2.84093,  2.63997,  2.56176,  2.39702])"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "acc_diff"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Errore"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0.20478754, 0.21264575, 0.28244198, 0.24068054, 0.22015435,\n",
       "       0.2010395 , 0.21291077, 0.2230632 , 0.22839382, 0.20844398,\n",
       "       0.2206878 , 0.23064632])"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "acc_sdom"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "acc_sdom_roll = np.roll(acc_sdom, 1, axis=0)\n",
    "acc_sdom_roll[0] = 0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0.20478754, 0.29522221, 0.35354163, 0.3710803 , 0.32618255,\n",
       "       0.29813557, 0.29282739, 0.30836373, 0.31925057, 0.30921292,\n",
       "       0.30356548, 0.31921909])"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "acc_diff_sigma = np.sqrt(acc_sdom**2 + acc_sdom_roll**2)\n",
    "acc_diff_sigma"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Velocità media angolare"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0.40070824, 0.93299853, 1.2359473 , 1.47837567, 1.67692917,\n",
       "       1.81054404, 1.98359793, 2.06600136, 2.21166495, 2.38002148,\n",
       "       2.45268304, 2.62124859])"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "acc_vang = 2 * np.pi / acc_diff\n",
    "acc_vang"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0.00523336, 0.04090073, 0.08595291, 0.12907961, 0.14598569,\n",
       "       0.15554359, 0.18337458, 0.209481  , 0.24853668, 0.27876582,\n",
       "       0.29063999, 0.34908036])"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "acc_vang_sig = acc_diff_sigma * 2 * np.pi / acc_diff ** 2\n",
    "acc_vang_sig"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Fit"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "def fit(X,Y):\n",
    "    N = X.size\n",
    "    DELTA = N * (X ** 2).sum() - (X.sum()) ** 2\n",
    "    a = ((X**2).sum()*(Y.sum()) - (X.sum())*(X*Y).sum()) / DELTA # q\n",
    "    b = (N*(X*Y).sum() - (X.sum())*(Y.sum())) / DELTA            # m\n",
    "    #\n",
    "    SIGy = ((((a+b*X)-Y)**2).sum()/ (N-2)) ** .5\n",
    "    SIGa = SIGy * ((X**2).sum() / DELTA) ** .5\n",
    "    SIGb = SIGy * (N / DELTA) ** .5\n",
    "    return a, SIGa, b, SIGb"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "def fit_pes(X,Y,Ysig):\n",
    "    Ypes = 1 / Ysig ** 2\n",
    "\n",
    "    N = X.size\n",
    "    DELTA = Ypes.sum()*(Ypes*X**2).sum() - (Ypes*X).sum() ** 2\n",
    "\n",
    "    a = ((Ypes*X**2).sum()*(Ypes*Y).sum() - (Ypes*X).sum()*(Ypes*X*Y).sum()) / DELTA    # q\n",
    "    b = (Ypes.sum()*(Ypes*X*Y).sum() - (Ypes*X).sum()*(Ypes*Y).sum()) / DELTA           # m\n",
    "    #\n",
    "    SIGy = ((((a+b*X)-Y)**2).sum()/ (N-2)) ** .5\n",
    "    SIGa = SIGy * ((X**2).sum() / DELTA) ** .5\n",
    "    SIGb = SIGy * (N / DELTA) ** .5\n",
    "    return a, SIGa, b, SIGb"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "acc_fit = dict(zip(['a', 'SIGa', 'b', 'SIGb'], list(fit(acc_mean, acc_vang))))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "acc_fit_pes = dict(zip(['a', 'SIGa', 'b', 'SIGb'], list(fit_pes(acc_mean, acc_vang, acc_vang_sig))))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Plot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 576x576 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "def plot_1acc():\n",
    "    colors = iter([palette[x] for x in [12,14] ])\n",
    "    \n",
    "    plt.figure(figsize=(8,8))\n",
    "\n",
    "    plt.errorbar(acc_mean, acc_vang, acc_vang_sig, fmt='o', capsize=6, color=next(colors), label='Velocità media angolare')\n",
    "\n",
    "    X = np.linspace(10,60,10000)\n",
    "    Y = [acc_fit['b'] * x + acc_fit['a'] for x in X ]\n",
    "    plt.plot(X,Y, '-', color=next(colors), label='Fit')\n",
    "\n",
    "    plt.gca().grid(axis='x')\n",
    "\n",
    "    plt.xlabel('Tempo istantaneo intermedio [s]')\n",
    "    plt.ylabel('Velocità media angolare volano [rad/s]')\n",
    "\n",
    "    # Legend\n",
    "    order = [1,0]\n",
    "    handles, labels = plt.gca().get_legend_handles_labels()\n",
    "    plt.legend([handles[i] for i in order],[labels[i] for i in order], loc='lower right', ncol=1)\n",
    "\n",
    "    plt.savefig('../grafici/graph_acc_velocita', dpi=200)\n",
    "\n",
    "plot_1acc()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 576x576 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "def plot_1acc():\n",
    "    colors = iter([palette[x] for x in [12,16,14] ])\n",
    "    \n",
    "    plt.figure(figsize=(8,8))\n",
    "\n",
    "    plt.errorbar(acc_mean, acc_vang, acc_vang_sig, fmt='o', capsize=6, color=next(colors), label='Velocità media angolare')\n",
    "\n",
    "    X = np.linspace(10,60,10000)\n",
    "    Y = [acc_fit['b'] * x + acc_fit['a'] for x in X ]\n",
    "    plt.plot(X,Y, '--', color=next(colors), label='Fit')\n",
    "\n",
    "    X = np.linspace(10,60,10000)\n",
    "    Y = [acc_fit_pes['b'] * x + acc_fit_pes['a'] for x in X ]\n",
    "    plt.plot(X,Y, color=next(colors), label='Fit con pesi')\n",
    "\n",
    "    plt.gca().grid(axis='x')\n",
    "\n",
    "    plt.xlabel('Tempo istantaneo intermedio [s]')\n",
    "    plt.ylabel('Velocità media angolare volano [rad/s]')\n",
    "\n",
    "    # Legend\n",
    "    order = [2,0,1]\n",
    "    handles, labels = plt.gca().get_legend_handles_labels()\n",
    "    plt.legend([handles[i] for i in order],[labels[i] for i in order], loc='lower right', ncol=1)\n",
    "\n",
    "plot_1acc()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.05220514895130838"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "alpha = acc_fit['b']\n",
    "alpha_sig = acc_fit['SIGb']\n",
    "alpha"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "# alpha = acc_fit_pes['b']\n",
    "# alpha_sig = acc_fit_pes['SIGb']\n",
    "# alpha"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Decelerazione"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "dec_mean = arr_dec.mean(axis=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "dec_sdom = np.apply_along_axis(fn_sdom, 0, arr_dec)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 22.43553,  46.34538,  71.05057,  96.60485, 123.0658 , 150.54508])"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dec_mean"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0.44246689, 0.33247214, 0.30872754, 0.26404082, 0.26759854,\n",
       "       0.21121169])"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dec_sdom"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 576x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "def plot_0dec():    \n",
    "    plt.figure(figsize=(8,6))\n",
    "\n",
    "    plt.errorbar(range(1,7), dec_mean, dec_sdom, fmt='o', capsize=6, color=palette[8], label='Tempo impiegato')\n",
    "\n",
    "    plt.gca().grid(axis='x')\n",
    "\n",
    "    plt.xlabel('Giri del volano completati')\n",
    "    plt.ylabel('Tempo impiegato [s]')\n",
    "\n",
    "    plt.xticks(range(1,7),range(10,70,10))\n",
    "    \n",
    "    plt.legend(loc='lower right')\n",
    "\n",
    "    plt.savefig('../grafici/graph_dec_tempo', dpi=200)\n",
    "\n",
    "plot_0dec()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Differenza di tempo"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "dec_diff = np.apply_along_axis(\n",
    "    lambda arr: np.convolve(arr, [1,-1], 'same'),\n",
    "    0,\n",
    "    dec_mean\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([22.43553, 23.90985, 24.70519, 25.55428, 26.46095, 27.47928])"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dec_diff"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Errore"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [],
   "source": [
    "dec_sdom_roll = np.roll(dec_sdom, 1, axis=0)\n",
    "dec_sdom_roll[0] = 0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0.44246689, 0.55345702, 0.45370742, 0.40623915, 0.37593421,\n",
       "       0.3409096 ])"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dec_diff_sigma = np.sqrt(dec_sdom**2 + dec_sdom_roll**2)\n",
    "dec_diff_sigma"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Velocità angolare"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([2.80055132, 2.6278648 , 2.54326532, 2.45876045, 2.37451237,\n",
       "       2.28651744])"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dec_vang = 2 * 10 * np.pi / dec_diff\n",
    "dec_vang"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0.00552316, 0.00608289, 0.00467067, 0.00390872, 0.0033735 ,\n",
       "       0.00283667])"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dec_vang_sig = dec_diff_sigma * 2 * np.pi / dec_diff ** 2\n",
    "dec_vang_sig"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Fit"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [],
   "source": [
    "dec_fit = dict(zip(['a', 'SIGa', 'b', 'SIGb'], list(fit(dec_mean, dec_vang))))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [],
   "source": [
    "dec_fit_pes = dict(zip(['a', 'SIGa', 'b', 'SIGb'], list(fit_pes(dec_mean, dec_vang, dec_vang_sig))))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Plot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 576x576 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "def plot_1dec():\n",
    "    colors = iter([palette[x] for x in [12,14] ])\n",
    "    \n",
    "    plt.figure(figsize=(8,8))\n",
    "\n",
    "    plt.errorbar(dec_mean, dec_vang, dec_vang_sig, fmt='o', capsize=6, color=next(colors), label='Velocità media angolare')\n",
    "\n",
    "    X = np.linspace(0,160,10000)\n",
    "    Y = [dec_fit['b'] * x + dec_fit['a'] for x in X ]\n",
    "    plt.plot(X,Y, '-', color=next(colors), label='Fit')\n",
    "\n",
    "    plt.gca().grid(axis='x')\n",
    "\n",
    "    plt.xlabel('Tempo istantaneo intermedio [s]')\n",
    "    plt.ylabel('Velocità media angolare volano [rad/s]')\n",
    "\n",
    "    # Legend\n",
    "    order = [1,0]\n",
    "    handles, labels = plt.gca().get_legend_handles_labels()\n",
    "    plt.legend([handles[i] for i in order],[labels[i] for i in order], loc='upper right', ncol=1)\n",
    "\n",
    "    plt.savefig('../grafici/graph_dec_velocita', dpi=200)\n",
    "\n",
    "plot_1dec()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 576x576 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "def plot_1dec():\n",
    "    colors = iter([palette[x] for x in [12,16,14] ])\n",
    "    \n",
    "    plt.figure(figsize=(8,8))\n",
    "\n",
    "    plt.errorbar(dec_mean, dec_vang, dec_vang_sig, fmt='o', capsize=6, color=next(colors), label='Velocità media angolare')\n",
    "\n",
    "    X = np.linspace(0,160,10000)\n",
    "    Y = [dec_fit['b'] * x + dec_fit['a'] for x in X ]\n",
    "    plt.plot(X,Y, '--', color=next(colors), label='Fit')\n",
    "\n",
    "    X = np.linspace(0,160,10000)\n",
    "    Y = [dec_fit_pes['b'] * x + dec_fit_pes['a'] for x in X ]\n",
    "    plt.plot(X,Y, color=next(colors), label='Fit con pesi')\n",
    "\n",
    "    plt.gca().grid(axis='x')\n",
    "\n",
    "    plt.xlabel('Tempo istantaneo intermedio [s]')\n",
    "    plt.ylabel('Velocità media angolare volano [rad/s]')\n",
    "\n",
    "    # Legend\n",
    "    order = [2,0,1]\n",
    "    handles, labels = plt.gca().get_legend_handles_labels()\n",
    "    plt.legend([handles[i] for i in order],[labels[i] for i in order], loc='upper right', ncol=1)\n",
    "\n",
    "plot_1dec()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "-0.00379683609756498"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "beta = dec_fit['b']\n",
    "beta_sig = dec_fit['SIGb']\n",
    "beta"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [],
   "source": [
    "# beta = dec_fit_pes['b']\n",
    "# beta_sig = dec_fit_pes['SIGb']\n",
    "# beta"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Calcolo I"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [],
   "source": [
    "m = 34 / 1000\n",
    "m_sig = 0.5 / 1000\n",
    "\n",
    "r = 18.95 / 1000\n",
    "r_sig = 0.01 / 1000\n",
    "\n",
    "g = 9.806\n",
    "g_sig = 0.0001"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.1128061513623801"
      ]
     },
     "execution_count": 62,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "momento_volano = (m*r*g - alpha*m*r**2)/(alpha-beta)\n",
    "momento_volano"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.003923558209904685"
      ]
     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "partials = []\n",
    "\n",
    "partials.append((r*(g-alpha*r)*m_sig/(alpha-beta))**2)\n",
    "partials.append((m*(g-2*alpha*r)*r_sig/(alpha-beta))**2)\n",
    "partials.append((m*r*g_sig/(alpha-beta))**2)\n",
    "partials.append((m*r*(beta*r-g)*alpha_sig/(alpha-beta)**2)**2)\n",
    "partials.append((m*r*(g-alpha*r)*beta_sig/(alpha-beta)**2)**2)\n",
    "\n",
    "momento_volano_sig = sum(partials) ** .5\n",
    "momento_volano_sig"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Calcolo Ma"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.0004283064675200637"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "momento_attrito = - beta * momento_volano\n",
    "momento_attrito"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "3.88371072026586e-05"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "momento_attrito_sig = ( (beta*momento_volano_sig)**2 + (momento_volano*beta_sig)**2 ) ** .5\n",
    "momento_attrito_sig"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Confronto"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [],
   "source": [
    "momento_volano_riferimento = 0.164\n",
    "momento_volano_riferimento_sig = 0.001"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 576x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "def plot4():\n",
    "    X = [0, 1]\n",
    "    Xlab = ['$I$', '$I_{Stimato}$']\n",
    "\n",
    "    Y = [momento_volano_riferimento, momento_volano]\n",
    "    Yerr = [momento_volano_riferimento_sig, momento_volano_sig]\n",
    "\n",
    "    plt.figure(figsize=(8,5))\n",
    "\n",
    "    plt.errorbar([0], Y[0], Yerr[0], fmt='*', capsize=6, color=palette[6], label='$I$')\n",
    "    plt.errorbar([1], Y[1], Yerr[1], fmt='o', capsize=6, color=palette[12], label='$I_{Stimato}$')\n",
    "\n",
    "    plt.gca().grid(axis='x')\n",
    "\n",
    "    plt.xlim(-.6,1.6)\n",
    "    plt.ylim(0.1,0.17)\n",
    "\n",
    "    plt.xticks(X, Xlab)\n",
    "    plt.ylabel('Momento di inerzia [$kg \\\\cdot m^2$]')\n",
    "\n",
    "    plt.legend(loc='upper right')\n",
    "    \n",
    "    plt.savefig(graph_base_name+'stima_I', dpi=200)\n",
    "\n",
    "plot4()"
   ]
  }
 ],
 "metadata": {
  "interpreter": {
   "hash": "ef9d2e2c39c8bbaaac17213ec19f6441f3cdbbb893e1093155d427e0145955e7"
  },
  "kernelspec": {
   "display_name": "Python 3.10.0 ('sperimB')",
   "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": "3.10.0"
  },
  "orig_nbformat": 4
 },
 "nbformat": 4,
 "nbformat_minor": 2
}