Viscosimetro/Viscosimetro.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Viscosimetro"
   ]
  },
  {
   "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'] ['18', '19', '20', '21', '22', '23']\n"
     ]
    },
    {
     "data": {
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      ],
      "text/plain": [
       "[(0.9677975592919913, 0.44127456009157356, 0.5358103155058701),\n",
       " (0.9699521567340649, 0.4569882390259858, 0.36385324448493633),\n",
       " (0.903599057664843, 0.511987276335809, 0.19588350060161624),\n",
       " (0.8087954113106306, 0.5634700050056693, 0.19502642696727285),\n",
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       " (0.19783576093349015, 0.6955516966063037, 0.3995301037444499),\n",
       " (0.20312757197899856, 0.6881249249803418, 0.5177618167447304),\n",
       " (0.20703735729643508, 0.6824290013722435, 0.5885318893529169),\n",
       " (0.21044753832183283, 0.6773105080456748, 0.6433941168468681),\n",
       " (0.21387918628643265, 0.6720135434784761, 0.693961140878689),\n",
       " (0.21786710662428366, 0.6656671601322255, 0.7482809385065813),\n",
       " (0.22335772267769388, 0.6565792317435265, 0.8171355503265633),\n",
       " (0.23299120924703914, 0.639586552066035, 0.9260706093977744),\n",
       " (0.4768773964929644, 0.5974418160509446, 0.9584992622400258),\n",
       " (0.6423044349219739, 0.5497680051256467, 0.9582651433656727),\n",
       " (0.774710828527837, 0.49133823414365724, 0.9580114121137316),\n",
       " (0.9082572436765556, 0.40195790729656516, 0.9576909250290225),\n",
       " (0.9603888539940703, 0.3814317878772117, 0.8683117650835491),\n",
       " (0.9633321742064956, 0.40643825645731757, 0.7592537599568671),\n",
       " (0.9656056642634557, 0.4245907603266889, 0.6579786740552919)]"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "palette = sns.husl_palette(24)\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)], [str(x+18) for x in range(6)])\n",
    "palette"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## FN"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "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"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "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": "markdown",
   "metadata": {},
   "source": [
    "## Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0.0015    , 0.0015875 , 0.002     , 0.00238125, 0.003175  ,\n",
       "       0.00396875, 0.0047625 , 0.00555625, 0.00635   , 0.00714375])"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pc_to_mm = lambda pc: pc * 25.4\n",
    "\n",
    "diametri = [1.5]\n",
    "diametri.append(pc_to_mm(2/32))\n",
    "diametri.append(2)\n",
    "\n",
    "pcs = [3,4,5,6,7,8,9]\n",
    "mms = [pc_to_mm(x/32) for x in pcs]\n",
    "\n",
    "diametri = diametri + mms\n",
    "diametri_sig = 0.001 / 100\n",
    "\n",
    "diametri = np.array(diametri)\n",
    "diametri = diametri / 1000\n",
    "diametri"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "rho_acciaio = 7.870 * 1000\n",
    "rho_acciaio_sig = 0.005 * 1000\n",
    "\n",
    "rho_liquido = 1.032 * 1000\n",
    "rho_liquido_sig = 0.001 * 1000\n",
    "\n",
    "acc_gravita = 9.806\n",
    "acc_gravita_sig = 0.001\n",
    "\n",
    "cronometro_sig = 0.00005"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## A"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Import"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "fA = glob.glob('./misure/A*')\n",
    "\n",
    "lA = [np.genfromtxt(f).T for f in fA]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Medie"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[array([  0.     ,  25.5113 ,  51.17758,  76.79378, 102.45284, 127.99312,\n",
       "        153.9262 , 179.6028 , 205.32128, 230.89884, 256.59858]),\n",
       " array([  0.     ,  21.91378,  43.96468,  66.06248,  88.27556, 110.29392,\n",
       "        132.3635 , 154.5366 , 176.82284, 198.98046, 221.27404]),\n",
       " array([  0.     ,  14.17108,  28.36666,  42.57412,  56.88004,  71.1135 ,\n",
       "         85.41686,  99.62726, 113.87266, 128.0144 , 142.46558]),\n",
       " array([ 0.     ,  9.60452, 19.32586, 29.1184 , 38.75368, 48.55176,\n",
       "        58.30356, 68.0564 , 77.84786, 87.55404, 97.46294])]"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mA = [x.mean(axis=0) for x in lA]\n",
    "mA"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[array([0.        , 0.07599596, 0.18616438, 0.16412906, 0.117919  ,\n",
       "        0.13955102, 0.22453285, 0.21814309, 0.25980308, 0.23645421,\n",
       "        0.27378009]),\n",
       " array([0.        , 0.14323426, 0.18543818, 0.25627706, 0.27323366,\n",
       "        0.40660006, 0.47250798, 0.49008238, 0.62209734, 0.65593439,\n",
       "        0.68322039]),\n",
       " array([0.        , 0.07394351, 0.08734515, 0.1255111 , 0.14097754,\n",
       "        0.1883715 , 0.25642615, 0.2684293 , 0.28526669, 0.23471603,\n",
       "        0.36038498]),\n",
       " array([0.        , 0.04895899, 0.07867371, 0.12044228, 0.14879863,\n",
       "        0.16229348, 0.21624608, 0.22131392, 0.25969751, 0.29100492,\n",
       "        0.3015565 ])]"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mA_sig = [np.apply_along_axis(fn_sdom, 0, x) for x in lA]\n",
    "mA_sig"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Fit velocità"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[(-0.15967818181819862,\n",
       "  0.05835579494382673,\n",
       "  513.4664654545455,\n",
       "  0.19727859353936003),\n",
       " (-0.2396299999999285,\n",
       "  0.07980321801624594,\n",
       "  442.5904690909089,\n",
       "  0.2697841169898306),\n",
       " (-0.08445272727272851,\n",
       "  0.03645809509608125,\n",
       "  284.88405090909083,\n",
       "  0.12325085675899972),\n",
       " (-0.13393727272727088,\n",
       "  0.04308837365788208,\n",
       "  194.9281199999999,\n",
       "  0.14566528930516467)]"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "distsA = np.array([x*5/100 for x in range(0,11)])\n",
    "\n",
    "fitsA = [fit(distsA,x) for x in mA]\n",
    "fitsA"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[0.0019475468551091283,\n",
       " 0.002259425066368969,\n",
       " 0.003510200015792072,\n",
       " 0.005130096160574475]"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "vlA = [1/x for x in [y[2] for y in fitsA]]\n",
    "vlA"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[7.482656225422859e-07,\n",
       " 1.3772483571259135e-06,\n",
       " 1.518635943153887e-06,\n",
       " 3.8336025679280945e-06]"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "vlA_sig = []\n",
    "\n",
    "for i in range(len(vlA)):\n",
    "    x = fitsA[i][2]\n",
    "    x_sig = fitsA[i][3]\n",
    "\n",
    "    s = x_sig / x**2\n",
    "    vlA_sig.append(s)\n",
    "\n",
    "vlA_sig\n",
    "    "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Plot velocità limite"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 576x576 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "symbs = iter(['o', '^', 'x', '*'])\n",
    "clrs = iter([palette[x] for x in [1, 2, 8, 9, 16, 17, 18, 19]])\n",
    "\n",
    "def plotA(n):\n",
    "    diametro = diametri[n]\n",
    "    arr = mA[n]\n",
    "    fit = fitsA[n]\n",
    "\n",
    "    #\n",
    "\n",
    "    X = distsA\n",
    "    Y = arr\n",
    "    # Yerr = [cronometro_sig for _ in range(11)]\n",
    "    Yerr = mA_sig[n]\n",
    "\n",
    "    plt.errorbar(X, Y, Yerr, fmt=next(symbs), capsize=3, label='Tempo percorrenza ' + str(n+1), color=next(clrs))\n",
    "    \n",
    "    #\n",
    "\n",
    "    fn = lambda x: fit[2] * x + fit[0]\n",
    "    Y = [fn(x) for x in X]\n",
    "    \n",
    "    plt.plot(X, Y, '-', label='Fit ' + str(n+1), color=next(clrs))\n",
    "\n",
    "    #\n",
    "\n",
    "    plt.xlabel('Distanza percorsa [m]')\n",
    "    plt.ylabel('Tempo impiegato [s]')\n",
    "\n",
    "    plt.xlim(-.05,.55)\n",
    "    plt.ylim(-60,295)\n",
    "\n",
    "    # plt.show()\n",
    "\n",
    "plt.figure(figsize=(8,8))\n",
    "\n",
    "for i, _ in enumerate(mA):\n",
    "    plotA(i)\n",
    "\n",
    "order = [4,5,6,7,0,1,2,3]\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=2)\n",
    "\n",
    "mx = -.045\n",
    "my = -.035\n",
    "plt.text(0.08+mx, 0.98+my, '$D_1$ = 1.5mm', horizontalalignment='left', verticalalignment='center', transform=plt.gca().transAxes)\n",
    "plt.text(0.08+mx, 0.95+my, '$D_2$ = 2\"/32', horizontalalignment='left', verticalalignment='center', transform=plt.gca().transAxes)\n",
    "plt.text(0.08+mx, 0.92+my, '$D_3$ = 2.0mm', horizontalalignment='left', verticalalignment='center', transform=plt.gca().transAxes)\n",
    "plt.text(0.08+mx, 0.89+my, '$D_4$ = 3\"/32', horizontalalignment='left', verticalalignment='center', transform=plt.gca().transAxes)\n",
    "\n",
    "plt.savefig(graph_base_name+'set1', dpi=200)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0.0011553 , 0.0011154 , 0.00113954, 0.00110531])"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "(np.array(diametri[:4]) ** 2) * np.array([x[2] for x in fitsA])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## B"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Import"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "fB = glob.glob('./misure/B*')\n",
    "\n",
    "lB = [np.genfromtxt(f).T for f in fB]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Medie"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[array([ 0.     , 10.57986, 21.01662, 31.78552, 42.45284, 53.093  ]),\n",
       " array([ 0.     ,  6.6448 , 13.2958 , 19.94308, 26.53912, 33.23394])]"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mB = [x.mean(axis=0) for x in lB]\n",
    "mB"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[array([0.        , 0.01265241, 0.2028749 , 0.03280228, 0.05343034,\n",
       "        0.02362577]),\n",
       " array([0.        , 0.03624092, 0.01334099, 0.0300806 , 0.02560028,\n",
       "        0.03142276])]"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mB_sig = [np.apply_along_axis(fn_sdom, 0, x) for x in lB]\n",
    "mB_sig"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Fit velocità"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[(-0.07294380952381677,\n",
       "  0.06714309750852095,\n",
       "  106.24366857142859,\n",
       "  0.22176635230568273),\n",
       " (0.0023180952380961284,\n",
       "  0.01498176711071187,\n",
       "  66.42855428571428,\n",
       "  0.04948314817936685)]"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "distsB = np.array([x*10/100 for x in range(0,6)])\n",
    "\n",
    "fitsB = [fit(distsB,x) for x in mB]\n",
    "fitsB"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[0.009412325585573044, 0.015053767325703396]"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "vlB = [1/x for x in [y[2] for y in fitsB]]\n",
    "vlB"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[1.9646696503355846e-05, 1.1213668688792878e-05]"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "vlB_sig = []\n",
    "\n",
    "for i in range(len(vlB)):\n",
    "    x = fitsB[i][2]\n",
    "    x_sig = fitsB[i][3]\n",
    "\n",
    "    s = x_sig / x**2\n",
    "    vlB_sig.append(s)\n",
    "\n",
    "vlB_sig\n",
    "    "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Plot velocità limite"
   ]
  },
  {
   "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": [
    "symbs = iter(['o', '^', 'x', '*'])\n",
    "clrs = iter([palette[x] for x in [1, 2, 8, 9, 16, 17, 18, 19]])\n",
    "\n",
    "def plotB(n):\n",
    "    diametro = diametri[n]\n",
    "    arr = mB[n]\n",
    "    fit = fitsB[n]\n",
    "\n",
    "    #\n",
    "\n",
    "    X = distsB\n",
    "    Y = arr\n",
    "    # Yerr = [cronometro_sig for _ in range(6)]\n",
    "    Yerr = mB_sig[n]\n",
    "\n",
    "    n = n+4\n",
    "    \n",
    "    plt.errorbar(X, Y, Yerr, fmt=next(symbs), capsize=3, label='Tempo percorrenza ' + str(n+1), color=next(clrs))\n",
    "    \n",
    "    #\n",
    "\n",
    "    fn = lambda x: fit[2] * x + fit[0]\n",
    "    Y = [fn(x) for x in X]\n",
    "    \n",
    "    plt.plot(X, Y, '-', label='Fit ' + str(n+1), color=next(clrs))\n",
    "\n",
    "    #\n",
    "\n",
    "    plt.xlabel('Distanza percorsa [m]')\n",
    "    plt.ylabel('Tempo impiegato [s]')\n",
    "\n",
    "    plt.xlim(-.05,.55)\n",
    "    plt.ylim(-15,70)\n",
    "\n",
    "    # plt.show()\n",
    "\n",
    "plt.figure(figsize=(8,8))\n",
    "\n",
    "for i, _ in enumerate(mB):\n",
    "    plotB(i)\n",
    "\n",
    "order = [2,3,0,1]\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=2)\n",
    "\n",
    "mx = -.045\n",
    "my = -.035\n",
    "plt.text(0.08+mx, 0.98+my, '$D_5$ = 4\"/32', horizontalalignment='left', verticalalignment='center', transform=plt.gca().transAxes)\n",
    "plt.text(0.08+mx, 0.95+my, '$D_6$ = 5\"/32', horizontalalignment='left', verticalalignment='center', transform=plt.gca().transAxes)\n",
    "\n",
    "plt.savefig(graph_base_name+'set2', dpi=200)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0.001071  , 0.00104631])"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "(np.array(diametri[4:6]) ** 2) * np.array([x[2] for x in fitsB])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## C"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Import"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "fC = glob.glob('./misure/C*')\n",
    "\n",
    "lC = [np.genfromtxt(f).T for f in fC]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Medie"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[array([ 0.    , 21.9868]),\n",
       " array([ 0.     , 14.96592]),\n",
       " array([0.     , 9.27546]),\n",
       " array([0.     , 4.98606])]"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mC = [x.mean(axis=0) for x in lC]\n",
    "mC"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Fit velocità"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "distsC = np.array([0, 50/100])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([21.9868 , 14.96592,  9.27546,  4.98606])"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tempiC = np.array([x[1] for x in mC])\n",
    "tempiC"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[0.022740917277639314,\n",
       " 0.03340923912462448,\n",
       " 0.05390568230578322,\n",
       " 0.10027957946755554]"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "vlC = [.5 / x for x in tempiC]\n",
    "vlC"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[0.0007580305761173178,\n",
       " 0.0011136413047756599,\n",
       " 0.0017968560794701133,\n",
       " 0.0033426526657253657]"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "vlC_sig = []\n",
    "\n",
    "for i in range(4):\n",
    "    S = .5\n",
    "    T = tempiC[i]\n",
    "    sigS = 0.05/3\n",
    "    sigT = 0.00005/3\n",
    "\n",
    "    sig = ((sigS/T)**2 + ((S*sigT)/T**2)**2) ** .5\n",
    "    vlC_sig.append(sig)\n",
    "\n",
    "vlC_sig"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Plot velocità limite"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 576x576 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "symbs = iter(['o', '^', 'x', '*'])\n",
    "clrs = iter([palette[x] for x in [1, 2, 8, 9, 16, 17, 18, 19]])\n",
    "\n",
    "def plotC(n):\n",
    "    diametro = diametri[n]\n",
    "    arr = mC[n]\n",
    "    tmp = tempiC[n]\n",
    "    n = n+6\n",
    "\n",
    "    #\n",
    "\n",
    "    X = distsC\n",
    "    Y = arr\n",
    "    Yerr = [cronometro_sig for _ in range(2)]\n",
    "\n",
    "    plt.errorbar(X, Y, Yerr, fmt=next(symbs), capsize=3, label='Tempo percorrenza ' + str(n+1), color=next(clrs))\n",
    "    \n",
    "    #\n",
    "\n",
    "    plt.plot([0, .5], [0, tmp], '-', label='Fit ' + str(n+1), color=next(clrs))\n",
    "\n",
    "    #\n",
    "\n",
    "    plt.xlabel('Distanza percorsa [m]')\n",
    "    plt.ylabel('Tempo impiegato [s]')\n",
    "\n",
    "    plt.xlim(-.05,.55)\n",
    "    plt.ylim(-5,25)\n",
    "\n",
    "    # plt.show()\n",
    "\n",
    "plt.figure(figsize=(8,8))\n",
    "\n",
    "for i, _ in enumerate(mC):\n",
    "    plotC(i)\n",
    "\n",
    "order = [4,5,6,7,0,1,2,3]\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=2)\n",
    "\n",
    "mx = -.045\n",
    "my = -.035\n",
    "plt.text(0.08+mx, 0.98+my, '$D_7$ = 6\"/32', horizontalalignment='left', verticalalignment='center', transform=plt.gca().transAxes)\n",
    "plt.text(0.08+mx, 0.95+my, '$D_8$ = 7\"/32', horizontalalignment='left', verticalalignment='center', transform=plt.gca().transAxes)\n",
    "plt.text(0.08+mx, 0.92+my, '$D_9$ = 8\"/32', horizontalalignment='left', verticalalignment='center', transform=plt.gca().transAxes)\n",
    "plt.text(0.08+mx, 0.89+my, '$D_{10}$ = 9\"/32', horizontalalignment='left', verticalalignment='center', transform=plt.gca().transAxes)\n",
    "\n",
    "plt.savefig(graph_base_name+'set3', dpi=200)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0.00099738, 0.00092405, 0.00074802, 0.00050891])"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "(np.array(diametri[6:]) ** 2) / vlC"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Calcolo viscosità"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Velicità limite"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0.00194755, 0.00225943, 0.0035102 , 0.0051301 , 0.00941233,\n",
       "       0.01505377, 0.02274092, 0.03340924, 0.05390568, 0.10027958])"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "vl = np.array(vlA + vlB + vlC)\n",
    "vl"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([7.48265623e-07, 1.37724836e-06, 1.51863594e-06, 3.83360257e-06,\n",
       "       1.96466965e-05, 1.12136687e-05, 7.58030576e-04, 1.11364130e-03,\n",
       "       1.79685608e-03, 3.34265267e-03])"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "vl_sig = np.array(vlA_sig + vlB_sig + vlC_sig)\n",
    "vl_sig"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Viscosità pes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [],
   "source": [
    "viscs = np.array(((diametri ** 2) * acc_gravita * (rho_acciaio - rho_liquido)) / (18 * vl))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([4.30371083, 4.15506676, 4.24498938, 4.11749386, 3.98968858,\n",
       "       3.89772116, 3.71544194, 3.44227412, 2.78651499, 1.89578233])"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "viscs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[0.05749793847062893,\n",
       " 0.05250175181487833,\n",
       " 0.04260954992038867,\n",
       " 0.034857263739265246,\n",
       " 0.026645477083121205,\n",
       " 0.020071027250442904,\n",
       " 0.12485837331969023,\n",
       " 0.11543861804720314,\n",
       " 0.09332111438613655,\n",
       " 0.06343129080798225]"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "viscs_sig = []\n",
    "\n",
    "for i in range(10):\n",
    "    D = diametri[i]\n",
    "    v = vl[i]\n",
    "    v_sig = vl_sig[i]\n",
    "\n",
    "    d1 = (D * acc_gravita * (rho_acciaio - rho_liquido) * diametri_sig) / (9 * v)\n",
    "    d2 = (D**2 * (rho_acciaio - rho_liquido) * acc_gravita_sig) / (18 * v)\n",
    "    d3 = (D**2 * acc_gravita * rho_acciaio_sig) / (18 * v)\n",
    "    d4 = (D**2 * acc_gravita * rho_liquido_sig) / (18 * v)\n",
    "    d5 = (D**2 * acc_gravita * (rho_acciaio - rho_liquido) * v_sig) / (18 * v**2)\n",
    "\n",
    "    sig = (d1**2 + d2**2 + d3**2 + d4**2 + d5**2) ** .5\n",
    "    viscs_sig.append(sig)\n",
    "\n",
    "viscs_sig"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 302.47915591,  362.78758006,  550.79007563,  823.02573476,\n",
       "       1408.48750545, 2482.33733278,   64.14527257,   75.04085297,\n",
       "        114.82598181,  248.53805853])"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "viscs_pesi = np.array([1/x**2 for x in viscs_sig])\n",
    "viscs_pesi"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "3.905001086716213"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "visc_pes = np.sum(viscs * viscs_pesi) / np.sum(viscs_pesi)\n",
    "visc_pes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.012468423228747142"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "visc_pes_sig = 1/(np.sum(viscs_pesi))**.5\n",
    "visc_pes_sig"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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XrtUUOBERCcOUEA+4PmODGTvOokJVhVYOMsSFeiLKt5Wxo1E9tLoRbfDgwVCr1fjmm28wbdo0FBQUoGnTpvj222/x3Xff6TsjERE1oJT0AsxOzkCFqgoAUFB0D7OTM5CSXmDkZFQfrY60e/TogR49emheV1ZW4uLFi8jMzMTvv/+O/v376y0gERE1rMQDWbinVFXbdk+pQuKBLB5tm7gnWjCkcePGmrnHiYhIWAqL7um0nUyH1qfHG+I9RERkfC0dZDptJ9Oh1ZH2pUuXNI951ebOnTsNEoiIiPQrLtQTs5Mzqp0il0nEiAv1NGIq0oZWpb1///5636PLBCtERGQ8D65b8+5x4dGqtFu14n9IIiJz8ehz2gVF9zB5+2/IuXGXz2mbuCe6EY2IiIRrSogHy1mgdFowhIiIiIxH5yPt27dvIzc3F+Xl5Zpt/v7+DRqKiIiIHqdTaX/99dfYvHkzrl69Ci8vL/z+++/o3LkzNm/erK98RERE9P+0Oj1eVlaGc+fOYfPmzdixYwdatmyJLVu2YOfOnXB0dNR3RiIiIoIWpZ2RkYF33nkHUqkUVlZWsLa2BgBUVFTA3d0dOTk5eg9JREREWpweLy0txapVq2BnZwdnZ2cUFxcjODgYb7zxBuzt7dGiRQtD5CQiIrJ4IrVarX6SD546dQp37txBYGAgJBJJQ+fSkMvl8Pb21tv+iYiITE1t3afTI1+JiYmaP3ft2hV9+/bFqlWrnjocERER1U+n0k5NTX1s27FjxxosDBEREdVOq0e+tm3bhqSkJOTl5VVbOOTu3bt48cUX9RaOiIiI/qZVaUdERCAwMBArVqzAtGnTNNttbGzg4OCgr2xERET0EK1K287ODnZ2dlixYoW+8xAREVEttCrt6OhoJCUlwdfXFyKRCADw4KZzkUiEM2fO6C8hERERAdCytJOSkgAA6enpeg1DREREtdPp7vH9+/ejpKQEAPDRRx9h4sSJOH/+vF6CERERUXU6lfZHH30EW1tb/Prrrzh+/DiioqIQHx+vr2xERET0EJ1KWywWAwCOHj2K6OhoBAcHQ6lU6iUYERERVadTaTs5OWH+/PnYv38/evfujYqKClRVVekrGxERET1Ep9JetWoVAgICsHHjRtjb26OoqAgzZszQVzYiIiJ6iE6lLZPJ0KZNGxw/fhxbt27FzZs3ERAQoK9sRERE9BCdSnvTpk2YPn06FAoFFAoF4uLisGXLFn1lIyIioodo9Zz2Azt27MBXX32FJk2aAAD+8Y9/4NVXX0VMTIxewhEREdHfdDrSBv6+g/zRPxMREZF+6XSkPWTIEAwfPhwhISEAgEOHDmHo0KF6CUZERETVidQPJhHX0h9//IHTp09DrVbD398fzz//vL6yAQDkcjm8vb31OgYREZEpqa37dDrSBgAfHx/4+Pg0SCgiIiLSnk7XtGfOnIni4mLN69u3b2P27Nk6DXjlyhXExMRgwIABCAsLw6ZNm3T6PBERkaXS6Ug7KysL9vb2mtdNmzaFXC7XaUCxWIxZs2bBx8cHJSUlGDp0KHr27Il27drptB8iIiJLo9ORdlVVFW7fvq15XVRUBJVKpdOALVq00Jxet7W1hZubG65du6bTPoiIiCyRTkfab775JkaMGIHQ0FAAwHfffYcJEyY88eD5+fmQy+Xo1KlTre8pLy/X+WieiIjIHOl89/jFixeRlpYGAOjRowfc3d2faOC7d+8iJiYGEyZMQL9+/Wp9H+8eJyIiS9Mgd4/v378fvXr1wujRo7F27VqsXLkS//znP3W+m1ypVCI2NhYRERF1FjYREVmmlQezsfrwhce2T+rbHlNCPIyQyDTodE37o48+gq2tLX799VekpqYiKioKCxYs0GlAtVqNuXPnws3NDW+88YZOnyUiIsvg+owNZJLqs27KJGK4PmNjpESmQafSfjBt6dGjRzFixAgEBwdDqVTqNODp06exa9cupKWlITIyEpGRkTh69KhO+yAiIvOWeCAL95TVb3S+p1Qh8UCWkRKZBp1Ojzs5OWH+/PlITU3FP/7xD1RUVKCqqkqnAf38/JCVZdl/6UREVLfCons6bbcUOh1pr1q1CgEBAdi4cSPs7e1RVFSEGTNm6CsbERFZqJYOMp22WwqdSlsmk6Ffv35o27YtgPvPXAcEBOgjFxERWbC4UM8ar2nHhXoaKZFp0Or0eHR0NJKSkuDr6wuRSKTZrlarIRKJcObMGb0FJCIiy5Nz426N17Rzbtw1UiLToFVpe3l54cyZM0hPT9d3HiIiIkwJ8bDoR7tqo1Vpt23bFsuWLcP169cxYMAAREREwMvLS9/ZiIiI6CE6zYhWUFCAvXv3Yt++fSgvL0dYWBjCwsLg6uqqt4CcEY2IiCxNbd2n8zSmD5w/fx5z5sxBVlaWXucGZ2kTEZGlaZBpTJVKJX766Sfs3bsXaWlp8Pf3x8SJExssJBEREdVOq9I+ceIE9uzZg6NHj+KFF17AwIEDsXjxYjRp0kTf+YiIiOj/aVXa69atQ0REBGbOnAkHBwc9RyIiIqKaaFXaW7Zs0XcOIiIiqodOM6IRERGR8bC0iYiIBIKlTUREJBAsbSIiIoFgaRMREQkES5uIiEggWNpEREQCwdImIiISCJY2ERGRQLC0iYiIBIKlTUREJBAsbSIiIoFgaRMREQkES5uIiEggWNpEREQCwdImIiISCJY2ERGRQLC0iYiIBIKlTUREJBAsbSIiIoFgaRMREQkES5uIiEggWNpEREQCwdImIiISCJY2ERGRQLC0iYiIBMLgpT179mz06NED4eHhhh6aiIhI0Axe2kOGDMHGjRsNPSwREZHgGby0/f390bRpU0MPS0REJHiNjR2gPuXl5ZDL5caOQUREZHQmX9rW1tbw9vY2dgwiIiKDqe1glXePExERCYTJH2nXRKlUIj8/H2VlZcaOQkYmlUrRunVrSCQSY0chItI7g5f21KlTcerUKdy6dQuBgYF49913MXz4cJ32kZ+fDzs7O7Rt2xYikUhPScnUqdVqKBQK5Ofnw9XV1dhxiIj0zuClvWLFiqfeR1lZGQubIBKJ0Lx5c1y/ft3YUYiIDEKQp8cBaF3YKw9mY/XhC49tn9S3PaaEeDR0LDIw/uJGRJZEsKWtrSkhHpgS4oFX1/8MANj+Vg8jJyIiInoyFnH3eEp6AdIvF+Fkzk30/PcRpKQXPNX+YmJi8NNPP1Xb9vnnn2PBggV1fiYjI0OncebOnYuLFy8+ScQnNmvWLHz33XcGGb+4uBhffPGF3vZPRGRuzL60U9ILMDs5AxWqKgBAQdE9zE7OeKriDgsLw759+6pt27dvX4POp65SqbBkyRK0a9euwfYJAJWVlVq/Vx/jP6y4uBhJSUk1fk2lUultXCIioTL70+OJB7JwT1m9AO4pVUg8kIUo31ZPtM/Q0FCsWrUKFRUVsLKyQn5+Pv766y906dIF8fHxyMjIQHl5OUJDQxEbG/vY5/fs2YP169dDrVajd+/eiIuLAwD4+vpizJgxOH78OGbOnInVq1djxowZ6NixY7XPBwUFYcCAATh58iQAYPny5Xjuuedw8+ZNxMfHo7CwEAAwZ84cdOnSBWvWrMFff/2FgoICNGvWDMuXL9fsS61WY/HixUhLS0Pr1q2hVqs1X4uJidGMX9v3FRQUhPDwcJw8eRJKpRKLFy/GihUrkJubi7FjxyI6OhoAsHHjRuzfvx8VFRUICQlBbGwsli9fjsuXLyMyMhIvvfQS+vTpgw8//BAtWrSAXC7Hzp07sWDBApw7dw5isRizZs1C9+7dn+i/GRGROTD70i4suqfTdm00a9YML7zwAo4dO4bg4GDs27cPAwYMgEgkwpQpU+Dg4ACVSoUxY8YgMzMTXl5ems9eu3YN//nPf5CcnAx7e3u8+eabOHToEIKDg1FaWor27dtj0qRJ9WawtbXFjh07kJKSgoSEBKxfvx5LlizB66+/Dj8/PxQWFmLs2LHYv38/AOCPP/7Atm3bIJVKq+3n4MGDyMnJwe7du3Hjxg2EhYVh6NChj41X1/fl7OyM7du3IyEhAbNmzUJSUhIqKioQFhaG6OhoHD9+HLm5udixYwfUajX++c9/4pdffsG0adNw4cIF7Nq1CwBw8uRJZGRkYPfu3XBxccFnn30GANi9ezcuXbqEsWPH4sCBA7C2tn6y/3BERAJn9qXd0kGGghoKuqWD7Kn2++AUeXBwMPbu3YuEhAQAwP79+/HVV1+hsrIS169fx6VLl6qVdkZGBrp27QpHR0cAQEREBH755RcEBwdDLBYjNDRUq/EfnIoPCwvD0qVLAQCpqanVrkGXlJSgpKQEwP0j4kcLGwB++eUXhIWFQSwWw8nJqdYj2bq+r759+wIAPDw8UFpaCltbWwD3p6AtLi7GiRMncOLECURFRQEASktL8d///hfPPvvsY+N07NgRLi4uAIDTp09j9OjRAAB3d3e0bNkSOTk51f4+iYgsidmXdlyoJ2YnZ1Q7RS6TiBEX6vlU+w0ODsa///1v/PHHHygrK4OPjw/y8vLw2WefYceOHWjatClmzZqF8vJyrfdpbW0NsVj8xJmqqqqwffv2GstZJqv9l5T6Hpuq7/t6MBtZo0aNYGVlpdneqFEjVFZWQq1WY/z48RgxYkS1/ebn5z82VpMmTTR/fvhUPRERWcCNaFG+rbB0SEdYie9/q60cZFg6pOMTX89+wMbGBl27dsWcOXM0R713796FTCaDnZ0dbty4gWPHjj32uRdeeAG//PILbt68CZVKhb1798Lf31/n8R+c9t63bx98fX0BAAEBAdi6davmPdqsjubv7499+/ZBpVLhr7/+0lwnf5g231ddAgIC8M033+Du3bsA7l8iUCgUsLGx0WyrLdvu3bsBADk5Obhy5Qrc3Nx0GpuIyJyY/ZE2cL+4k05dBtCwz2mHh4dj4sSJmlnevLy88PzzzyMsLAwuLi548cUXH/tMixYtMHXqVLz++utQq9UIDAxEcHCwzmNXVFRg+PDhqKqq0ow/d+5cLFq0CBEREVCpVPDz88OiRYvq3E9ISAjS0tIQERGBtm3b1vgLhDbfV10CAgJw6dIlzZF2kyZNkJiYiDZt2uDFF19EeHg4evXqhT59+lT73MiRIxEfH4+IiAiIxWIsXbq02pE8EZGlEalN/BykXC5/bGnOmrbVxhxnRAsKCsKOHTs018UtnS7/HoiIhKC2n2tmf6T9YEY0IiIioTP70jZHR44cMXYEIiIyArO/EY2IiMhcsLSJiIgEgqVNREQkECxtIiIigTD7G9GU3ydDdSjlse3i4ChI+g0xfCAiIqInZPZH2pJ+QyB9fzNEbl4QuXlB+v5mSN/f3CCF7enpqVmhC7i/7GX37t3x1ltvab2PNWvW4NNPP9W8fnSqT2PietdERKbF7Ev7AXVlJdSFuVDfKWqwfTZp0gQXLlxAWVkZAODEiRNwcnJ6qn1++eWXDRGtQdS13jURERme2Z8e17h1Ayi7h8qDKZAMGdNguw0MDMSPP/6I/v37Y+/evQgLC8Pp06cBALt27cKWLVugVCrRqVMnxMfHQywW4+OPP0ZKSgqeffZZODo6wsfHR7M/X19fpKen4+2338bVq1dRXl6O1157Da+++upjY+fn52PcuHHo1KkTzp8/D1dXVyxbtgwymazGscvLyzF58mRcvXoVVVVVePvttzFw4MBax3p0vevc3Nx6MwHAhg0bkJeXB4VCgczMTIwaNQpjx45tsL9zIiJLZfalXTZnLFCp1LxWpR2BKu0I0FgCacKndXxSOwMHDsRHH32El19+GVlZWRg6dChOnz6NS5cuYf/+/UhKSoJEIsGCBQuwe/dutGvXDvv27UNKSgpUKhUGDx5crbQfSEhIgIODA8rKyjBs2DD069cPzZo1e+x9OTk5WLJkCbp06YLZs2dj27Zt6NOnT41jy2QytGjRAhs2bAAA3Llzp86xHl3vuqioSKtM2dnZEIvFWLNmDfLy8vDuu++ytImIGoDZl7b1rOVQ7klC1e9pgFoNSKzQqEMXSMKjG2T/Xl5eyM/Px549e9C7d2/N9p9//hnnzp3DsGHDAABlZWVo3rw5ioqKEBwcrFkqMygoqMb9btmyBQcPHgQAXLlyBbm5uTUW5LPPPosuXboAAAYNGoQtW7bA2tq6xrHDw8OxbNkyJCYm4uWXX4afn59OY2n7vqysLKxduxZisRhisRgODg71/0USEVG9zL60RfYOEEml9wtbJAIqlRBZyyCyc2iwMYKCgvD+++9j8+bNKCoqAnB/LejBgwdj2rRp1d77+eef17t+9cmTJ5Gamort27dDJpMhJiam1nW5H92XSCSqdWwASE5OxtGjR7F8+XL07NkT/v7+Wo2lbSalUolbt26hTZs2AO4XuIeHBxQKBcaNG4eAgADk5OTggw8+gEqlwpo1a3Dv3j0olUq89dZbWLt2Lezs7NCrVy+89NJLdf49ERFZGou4EU19pxiwcwBaPgdx9yCo79xu0P0PGzYMb7/9Njw9PTXbevTogQMHDkChUAC4f2q5oKAA/v7+OHjwIMrKylBSUoIffvjhsf3duXMHTZs2hUwmw6VLl/Dbb7/VOnZhYSHS09MBAHv37kWXLl1qHfvatWuQyWSIjIzE2LFjcf78+TrHeni9a20z5eTkwN3dXfNaLpfDy8sLGRkZCAsLw7Rp0+Do6IiioiJ89dVXKCsrg729PUpLS/Hnn39CIpEgJiaGhU1EVAOzP9IGAKvXJ6F8XQIAQDL49Qbfv7OzM15/vfp+27Vrh8mTJ+PNN99EVVUVJBIJ5s+fj86dO2PgwIGIjIxEq1atNKe2HxYYGIgvv/wSERERcHV1RefOnWsd293dHTt37sT8+fPRtm1bREdHQyaT1Tj2nTt38P7776NRo0Zo3LgxFixYAE9Pz1rHatasmWa965deegmVlZX1ZsrOzq72y4tcLkfv3r1x9OhR+Pr6AgBKSkrg6OiI8+fPIz4+vtoa2c7Ozli0aBEWLlz41HfiExGZG7NfT/uBB6VtPWFOg2Uztvz8fEyYMAF79uwxdpRahYSEYPfu3ZgzZw6aNWuGyspK9O/fHz169MCRI0ewZ88eODs7o3v37jh58iSqqqpQXl6O2bNnQyKRaDUG19MmInNjsetpPzojWtmM1wBwRjRDKC4uhkQigVQqhVgsxrx586p9PSgoqNqNeIGBgYaOSEQkKBZzpE3mi/8eiMjc1PZzzSJuRCMiIjIHLG0iIiKBYGkTEREJhGBL28QvxZOB8N8BEVkSQZa2VCqFQqHgD2wLp1aroVAoIJVKjR2FiMggBPnIV+vWrZGfn4/r168bOwoZmVQqRevWrY0dg4jIIARZ2hKJBK6ursaOQUREZFBGOT1+7NgxhIaGIiQkRLNMJBEREdXN4KWtUqmwaNEibNy4EXv37sWePXtw8eJFQ8cgIiISHIOX9tmzZ/Hcc8/BxcUFVlZWCAsLw+HDhw0dg4iISHAMfk372rVrcHZ21rx2cnLC2bNna31/eXk55HK5IaIRERGZhPLy8hq3G7y0a3pMSyQS1fr+upalJCIisiQGPz3u7OyMq1eval5fu3YNLVq0MHQMIiIiwTF4aXfs2BH//e9/kZeXh4qKCuzdu7fa8oxERERUM4OfHm/cuDHmz5+PcePGQaVSYejQoWjfvr2hYxAREQmOya+nTURERPcJcu5xIiIiS8TSJiIiEgiWNhERkUAIcsEQIiIiQ9mwYQPy8vKgUCiQmZmJUaNGYezYsUbJYval/eWXX+LDDz9E8+bNoVar0bZtW8TFxcHFxcXY0TRMPaOp5wNMP6Op5wNMP6Op5wNMP6Mp5zPlbNnZ2RCLxVizZg3y8vLw7rvvGq20oTZzCxYsUG/fvl3zetu2berIyEh1VVWVEVNVZ+oZTT2fWm36GU09n1pt+hlNPZ9abfoZTTmfKWcLDw9X5+bmqtVqtfry5cvq0aNHGy2L2V/Tzs7OrvYceHR0NG7cuIErV64YMVV1pp7R1PMBpp/R1PMBpp/R1PMBpp/RlPOZajalUolbt26hTZs2AICsrCx4eHhAoVBg8ODBWL58OSZOnIiKigqsWLECS5YswYIFCwAAV65cwXvvvYdly5YhNTW1QfKYfWlfuHDhsclbrK2tUVxcbKREjzP1jKaeDzD9jKaeDzD9jKaeDzD9jKacz1Sz5eTkwN3dXfNaLpfDy8sLGRkZCAsLw7Rp0+Do6IgNGzagrKwM9vb2KC0tBQD8+eefkEgkiImJwUsvvdQgecz6mvaVK1dgY2MDW1tbzTalUonr168DAObMmYOSkhJ88MEHxopYZ8bMzExs3boVCoUCo0aNQkBAgEnlE4lEmD9/PoqKitC9e3eMHDnS4Pnqy9i6dWuUlpZi9OjRePfdd/Hyyy+bVL7CwkIsWrQI7dq1Q1hYGLp162bwfPVlbNWqFVauXImSkhJ06NABgwcPNql8JSUlmD9/PlQqFS5duoQvv/zS4Pnqy2hnZ4cJEyagWbNmcHV1xfjx400qHwBMmjQJDg4O6NGjB/r3729S2R79WV1aWoqFCxdCIpGga9euGDRokN6yZWdnw9PTU/NaLpejd+/eOHr0KHx9fQEAJSUlUKlUiI+Ph5WVlea9PXv2hLOzMxYtWoSFCxfCycnpqfOYdWlnZWWhXbt21bYlJyeje/fu8PLyQkJCAmJjY42U7r66MkZFRSEqKgq3b9/GsmXLjFLadeXz9PTEokWLUFVVhffee8/g2R6oK6OtrS1Wr15t8B9CD6svX5MmTVBRUVFtyVpDqytjWloarl27hqZNmxotY135/Pz84Ofnh0OHDqFjx45GyQfUnTE3Nxd9+vTBiBEjMGPGDJPLl5qaipiYGPj5+WHChAkG//9F15/V33//PUJDQxEUFITJkyfrtbTDw8MRHh6ueX3hwgV4eHjg888/R1FREQ4ePIjhw4fj3r17mDVrFpydndG9e3cEBgYiMTERVVVVaNmyJRwdHRskj1mX9qPXSI4fP47169fjk08+MWKq6rTJ+PHHH2PUqFHGiFdvvsOHD+OTTz4xWj6g7oypqalo165drWvTGjufq6srunbtihs3bmDp0qVYvny5yWU8fPgwOnfujBEjRiA2NhY9evQwqXwP7N69G0uWLDF4tgfqyujo6IiPP/4Y+/btQ2RkpMnlc3BwwIcffojDhw+jqKjIpLLV5Nq1a5qjX7FYbJCMAFBcXAyJRAKpVAqxWIx58+ZV+/qji1/FxcU1eAazLu2srCycOnUKaWlpUKvVcHNzw8aNG+Hm5mbsaBp1ZVSr1fjPf/6DwMBA+Pj4mFw+AOjbty/69u2L8ePHIyIiwuQyfvvttygtLcWlS5dgbW2N3r17o1Ejw97Koc2/Q3t7eyiVSoPm0jbjuXPnIJFIAMDgf3fa5AOAwsJC2NnZVTu9akoZP/30U8TGxsLf3x+xsbEYOnSoSeUDgPj4eKhUKkycONHksj3KyckJV69ehbe3N6qqqgyW097eHvv27QMAJCYmGmzch1nsgiG3bt3CypUrkZqaiuHDh+Ott94ydqTHbN68GSkpKejQoQO8vb0RHR1t7EjVnDx5EgcPHkRFRQU8PT2NerRdn+TkZDRr1swo17Tr8v333+P48eMoLi5GdHS00a5p1+XevXtYvHgxZDIZ3NzcTPK/8wcffICAgAC8+OKLxo5So+zsbHz44YdwcHCAjY0NZs6caexI1eTn52P9+vUoLS1FdHQ0/Pz8jB1Jo6af1aWlpVi8eDGsrKzQpUsXvZ4eNzUWW9pERERCY/aPfBEREZkLljYREZFAsLSJiIgEgqVNREQkECxtIiIigWBpExERCQRLm0jgvL29ERkZibCwMAwaNAj/+7//q5lwIiMjA//617/0Ov6hQ4dw8eJFvY5BRPeZ9YxoRJZAKpVi165dAACFQoFp06bhzp07iI2NRceOHfU+H/ehQ4fQp0+fx+aOBoDKyko0bswfM0QNhZOrEAmcr68v0tPTNa/z8vIwbNgwpKWl4dSpU/jss8+wfv16nD17FgkJCSgrK4NUKkVCQgLc3NyQnJyMQ4cOoaqqCtnZ2XjzzTehVCqxa9cuWFlZYcOGDXBwcMDly5excOFC3Lp1C1KpFIsXL8bt27cxYcIE2Nraws7ODmvWrMHcuXPh6+uLM2fOICgoCN7e3li2bBlUKhU6dOiAhQsXVlsJiYi0x1+BicyMi4sLqqqqoFAoqm13c3PD1q1b0bhxY6SmpmLlypVYs2YNgPsrF+3cuRMVFRUICQnB9OnTkZKSgoSEBKSkpGDMmDGYN28eFi5ciLZt2+L333/HwoULsXnzZgQFBaFPnz7VVoYqLi7G1q1bUV5ejn79+uHzzz+Hq6srZsyYgW3btmHMmDGG/CshMhssbSIzVNMJtDt37mDmzJnIzc2FSCSqtkBJt27dNItt2NnZaVYr8vDwQFZWFu7evYv09HRMmjRJ85mKiopaxx84cCAAICcnB61bt4arqysAYPDgwfjiiy9Y2kRPiKVNZGby8vIgFovRvHlzXLp0SbN99erV6NatG9auXYv8/Hy89tprmq89fLq6UaNG1Vb1UqlUUKvVsLe311w7r49MJgNQ8y8PRPTkePc4kRm5efMm4uPjMWrUKIhEompfu3PnDpycnAAAO3fu1Gm/tra2aN26Nfbv3w/gfhlnZmYCAGxsbHD37t0aP+fm5oaCggLk5uYCAHbt2gV/f3+dxiaiv7G0iQSurKxM88jXmDFj0LNnzxrXRB43bhxWrFiBESNGQKVS6TxOYmIiduzYgUGDBiEsLAyHDh0CcP9U+KeffoqoqChcvny52mesra2xdOlSTJo0CRERERCJRCa3xCyRkPDucSIiIoHgkTYREZFAsLSJiIgEgqVNREQkECxtIiIigWBpExERCQRLm4iISCBY2kRERALxf0u9LE4ZC76sAAAAAElFTkSuQmCC",
      "text/plain": [
       "<Figure size 576x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(8,5))\n",
    "\n",
    "plt.errorbar(range(11), list(viscs)+[10], list(viscs_sig)+[0], fmt='o', capsize=3, label='Valori per diametro')\n",
    "plt.errorbar([10], [visc_pes], [visc_pes_sig], fmt='*', capsize=3, color=palette[1], label='Media pesata $\\\\eta_{pes}$')\n",
    "\n",
    "plt.xticks(range(11), ['$D_{'+str(x+1)+'}$' for x in range(10)] + ['$\\\\eta_{pes}$'])\n",
    "plt.ylim(0,6)\n",
    "\n",
    "plt.xlabel('Diametro')\n",
    "plt.ylabel('Viscosità [$kg \\\\cdot m^{-1} \\\\cdot s^{-1}$]')\n",
    "\n",
    "plt.gca().grid(axis='x')\n",
    "\n",
    "plt.legend(loc='lower left')\n",
    "\n",
    "plt.savefig(graph_base_name+'viscs_e_pes', dpi=200)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Plot diametri / velocità"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([513.46646545, 442.59046909, 284.88405091, 194.92812   ,\n",
       "       106.24366857,  66.42855429,  43.9736    ,  29.93184   ,\n",
       "        18.55092   ,   9.97212   ])"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "inv_vl = 1 / vl\n",
    "inv_vl"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0.19727859, 0.26978412, 0.12325086, 0.14566529, 0.22176635,\n",
       "       0.04948315, 1.46578667, 0.997728  , 0.618364  , 0.332404  ])"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "inv_vl_sig = vl_sig / vl**2\n",
    "inv_vl_sig"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(-9.09738271022678,\n",
       " 2.0453486562456273,\n",
       " 0.0011616707788379022,\n",
       " 9.470357260093309e-06)"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "fitsP = fit(1/diametri**2, 1/vl)\n",
    "fitsP"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "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():\n",
    "    plt.figure(figsize=(8,8))\n",
    "\n",
    "    plt.errorbar(1/diametri**2, inv_vl, inv_vl_sig, fmt='o', capsize=3, label='($D_j^{-2}$,$v_{L,j}^{-1}$)', color=palette[1])\n",
    "    \n",
    "\n",
    "    fn = lambda x: fitsP[2]*x + fitsP[0]\n",
    "    plt.plot([-10,460000], [fn(-10), fn(460000)], label='Fit', color=palette[16])\n",
    "\n",
    "    plt.xlabel('1/$D^2$ [$m^{-2}$]')\n",
    "    plt.ylabel('1/$v_L$ [$s\\\\cdot m^{-1}$]')\n",
    "\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(graph_base_name+'inv_su_inv', dpi=200)\n",
    "\n",
    "plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [],
   "source": [
    "B = fitsP[2]\n",
    "B_sig = fitsP[3]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "4.327444884917288"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "visc = B * acc_gravita * (rho_acciaio - rho_liquido) / 18\n",
    "visc"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.035428907551254954"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "d1 = acc_gravita * (rho_acciaio - rho_liquido) * B_sig / 18\n",
    "d2 = B * (rho_acciaio - rho_liquido) * acc_gravita_sig / 18\n",
    "d3 = B * acc_gravita * rho_acciaio_sig / 18\n",
    "d4 = B * acc_gravita * rho_liquido_sig / 18\n",
    "\n",
    "visc_sig = (d1**2 + d2**2 + d3**2 + d4**2) ** .5\n",
    "visc_sig"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Plot confronto"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "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 = ['$\\eta$', '$\\eta_{pes}$']\n",
    "\n",
    "    Y = [visc, visc_pes]\n",
    "    Yerr = [visc_sig, visc_pes_sig]\n",
    "\n",
    "    plt.figure(figsize=(8,5))\n",
    "\n",
    "    plt.errorbar([0], Y[0], Yerr[0], fmt='o', capsize=6, color=palette[6], label='$\\eta$')\n",
    "    plt.errorbar([1], Y[1], Yerr[1], fmt='*', capsize=6, color=palette[12], label='$\\eta_{pes}$')\n",
    "\n",
    "    plt.gca().grid(axis='x')\n",
    "\n",
    "    plt.xlim(-.6,1.6)\n",
    "    plt.ylim(3.5,4.8)\n",
    "\n",
    "    plt.xticks(X, Xlab)\n",
    "    plt.ylabel('Viscosità [$kg \\\\cdot m^{-1} \\\\cdot s^{-1}$]')\n",
    "\n",
    "    plt.legend(loc='upper right')\n",
    "    \n",
    "    plt.savefig(graph_base_name+'viscosita', dpi=200)\n",
    "\n",
    "plot4()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Export Excel"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [],
   "source": [
    "pd.DataFrame(mA).to_excel('dump.xlsx')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [],
   "source": [
    "pd.DataFrame(mA_sig).to_excel('dump.xlsx')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [],
   "source": [
    "pd.DataFrame(mB).to_excel('dump.xlsx')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [],
   "source": [
    "pd.DataFrame(mB_sig).to_excel('dump.xlsx')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [],
   "source": [
    "pd.DataFrame(mC).to_excel('dump.xlsx')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [],
   "source": [
    "pd.DataFrame(vl).to_excel('dump.xlsx')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [],
   "source": [
    "pd.DataFrame(vl_sig).to_excel('dump.xlsx')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [],
   "source": [
    "pd.DataFrame(viscs).to_excel('dump.xlsx')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [],
   "source": [
    "pd.DataFrame(viscs_sig).to_excel('dump.xlsx')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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