{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "4967525c-ef0e-47dc-bc09-ac76bfcc8539",
   "metadata": {},
   "source": [
    "# Projet capteur de Pression  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "3cd5a6bf-8e53-4635-804f-5520801dcda7",
   "metadata": {},
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import scipy.stats"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7ceda31b-4899-489f-983b-cf595968b7cf",
   "metadata": {},
   "source": [
    "### Définition des paramètres"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "c12bbd9f-5ab9-4453-b0e1-5e12189fa71c",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "996.71832\n"
     ]
    }
   ],
   "source": [
    "T=27\n",
    "#constantes\n",
    "rho = 1.22 # en kg/m^3 \n",
    "g = 9.8 # accéleration gravitationnelle en m/s^2 \n",
    "h = 178 # altitude de lyon par rapport à la mer \n",
    "P_mer = 101800 # pression donnée par la météo pris par rapport à la mer\n",
    "P_ref = (P_mer - rho*g*h)*1e-2 # pression de référence \n",
    "print(P_ref)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "dc1f694c-ad87-4522-bdec-1dea587ac80b",
   "metadata": {},
   "source": [
    "### **Expression de la Pression**  \n",
    "\n",
    "La pression extérieure correspond à la pression du milieu dans lequel se trouve notre capteur.  \n",
    "Une relation simple peut alors être écrite :\n",
    "\n",
    "<p style=\"text-align: center;\">\n",
    "  $\\Delta P = P_{\\text{ext}} - P_{\\text{int}}$\n",
    "</p>\n",
    "\n",
    "On définit $P_{\\text{int}}$ comme la pression à l’intérieur du bocal.\n",
    "\n",
    "---\n",
    "\n",
    "#### **Expression de la pression intérieure**\n",
    "\n",
    "La loi des gaz parfaits nous donne, pour $P_0 = P_{\\text{ref}}$ :\n",
    "\n",
    "\n",
    "<p style=\"text-align: center;\">\n",
    "  $P_0 V_0 = n R T_0$\n",
    "</p>\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "où $T_0$ est la température à laquelle le gaz a été initialement enfermé.\n",
    "\n",
    "À une température $T$ quelconque, et pour un volume  \n",
    "\n",
    "<p style=\"text-align: center;\">\n",
    "  $V = V_0 + \\Delta V$\n",
    "</p>\n",
    "\n",
    "\n",
    "\n",
    "(la paroi du bocal se déforme sous l’effet de $\\Delta P$), on a :\n",
    "\n",
    "\n",
    "<p style=\"text-align: center;\">\n",
    "  $P_{\\text{int}}\\, V = n R T$\n",
    "</p>\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "Comme $n$ est constant, on obtient :\n",
    "\n",
    "\n",
    "<p style=\"text-align: center;\">\n",
    "  $P_{\\text{int}} = P_0 \\, \\frac{V_0}{V} \\, \\frac{T}{T_0}$\n",
    "</p>\n",
    "\n",
    "\n",
    "$\\Delta V$ correspond au volume de la calotte sphérique. On supposera le volume de de cette calotte sphérique négligeable devant $V_0$ pour une certaine gamme de pression $\\Delta P$ appartenant à [-50hPa; 50 hPa]\n",
    "<p style=\"text-align: center;\">\n",
    " $V \\equiv V_0$\n",
    "</p>\n",
    "cela implique : \n",
    "<p style=\"text-align: center;\">\n",
    "  $P_{\\text{int}} = P_0  \\frac{T}{T_0}$\n",
    "</p>\n",
    "\n",
    "---\n",
    "#### **Volume de notre calotte sphérique**\n",
    "*La démonstration de cette expression est donnée en annexe de la page moodle*\n",
    "<p style=\"text-align: center;\">\n",
    " $V_C = \\frac{\\pi h^2}{3} \\left( 3R - h \\right)$\n",
    "</p>\n",
    "avec\n",
    "<p style=\"text-align: center;\">\n",
    " $R = \\frac{(a^2 + h^2)}{2h}$\n",
    "</p>\n",
    "\n",
    "où $a$ est le rayon de la partie superieure du bocal\n",
    "et $h$ est la hauteur de la calotte "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "65862a4f-de9e-4295-ac8b-f498e3f44ca1",
   "metadata": {},
   "outputs": [],
   "source": [
    "def calcul_vc (list_h):\n",
    "    a = 4.7\n",
    "    volume_list = []\n",
    "    for i in range(0, len(list_h)):\n",
    "        v_calotte = (np.pi * (list_h[i]**2) * ( 3 * ((a**2 + list_h[i]**2)/(2*list_h[i])) - list_h[i]))/3 \n",
    "        volume_list.append(v_calotte)\n",
    "    return volume_list"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "c8e4a06b-1c19-4866-b538-a1776366c258",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[-6.943966961984642,\n",
       " -10.423804424610937,\n",
       " -13.913066665198,\n",
       " -17.414895276399424,\n",
       " -22.69807237203321,\n",
       " 6.943966961984642,\n",
       " 10.423804424610937,\n",
       " 13.913066665198,\n",
       " 17.414895276399424,\n",
       " 22.69807237203321]"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "list_h = [-0.2, -0.3, -0.4, -0.5, -0.65, 0.2, 0.3, 0.4, 0.5, 0.65]\n",
    "calcul_vc(list_h)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "29f602de-9449-46e7-8c25-ad089aa3ca3e",
   "metadata": {},
   "source": [
    "##### Vérification de l'hypothèse :\n",
    "  $\\frac{V_c}{V_0} \\leq 0.05  $\n",
    "Pour vérifier cette hypothèse on utilisera les valeurs limites de $V_c$ avec $V_0 = 478$ => cas d'une aspiration => $V_c < 0$\n",
    "  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "dd46e885-e35f-4c0f-a99f-8ced172453f0",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.04602510460251046\n"
     ]
    }
   ],
   "source": [
    "print (abs(-22)/ 478)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "760a8c2a-f206-4261-bf2f-bdc7c11c2019",
   "metadata": {},
   "source": [
    "Hypothèse vérifiée, alors : \n",
    "<p style=\"text-align: center;\">\n",
    "  $P_{\\text{ext}} = P_0  \\frac{T}{T_0} + \\Delta P$\n",
    "</p>\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2189ef90-8f60-482f-926a-22c2f807f43e",
   "metadata": {},
   "source": [
    "#### Calibration , Détermination de $\\Delta P$ \n",
    "\n",
    "Pour un même système, on définit :\n",
    "\n",
    "<p style=\"text-align: center;\">\n",
    " $\\Delta P = P_1 - P_0$\n",
    "</p>\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "À une température quelconque \\(T\\), on a :\n",
    "\n",
    "\n",
    "<p style=\"text-align: center;\">\n",
    " $P_1 = \\frac{nRT}{V}$\n",
    "</p>\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "où $V$ inclut le volume du bocal et le volume de la seringue (donc $V = V_{\\text{bocal}} + V_{\\text{seringue}}$).\n",
    "\n",
    "La pression de référence est :\n",
    "\n",
    "\n",
    "<p style=\"text-align: center;\">\n",
    " $P_0 = \\frac{nRT_0}{V_0}$\n",
    "</p>\n",
    "\n",
    "\n",
    "\n",
    "On en déduit :\n",
    "\n",
    "\n",
    "<p style=\"text-align: center;\">\n",
    " $P_1 = P_0 \\, \\frac{V_0}{V} \\, \\frac{T}{T_0}$\n",
    "</p>\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "---\n",
    "\n",
    "### **Cas de la calibration**\n",
    "\n",
    "Lors de la calibration, on impose $T = T_0$.  \n",
    "Ainsi :\n",
    "\n",
    "\n",
    "\n",
    "<p style=\"text-align: center;\">\n",
    "$\\Delta P_{\\text{calib}} = P_1 - P_0\n",
    "= P_0 \\left( \\frac{V_0}{V} - 1 \\right)$\n",
    "</p>\n",
    "\n",
    "\n",
    "\n",
    "avec :\n",
    "\n",
    "\n",
    "\n",
    "<p style=\"text-align: center;\">\n",
    "$V_0 = V_{\\text{bocal}} + V_{\\text{seringue}}$\n",
    "</p>\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "24485d85-2dfa-453e-83eb-3f515c4d2c1a",
   "metadata": {},
   "source": [
    "#### calcul de $\\Delta P_{\\text{calib}}$ "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "51bdc647-0f73-410d-a95c-0a471dd9e9f3",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "996.71832\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "[14.599917735736994,\n",
       " 21.938596008787044,\n",
       " 29.312600825460322,\n",
       " 36.729353061224174,\n",
       " 48.104384083638706,\n",
       " -13.927483870660012,\n",
       " -20.88681068676408,\n",
       " -27.851063490785464,\n",
       " -34.82589570932248,\n",
       " -45.18408560015666]"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Vc_list = calcul_vc(list_h)\n",
    "V_b = 478\n",
    "V_s1 = 15 \n",
    "V_s2 = 0 \n",
    "deltaP_list = []\n",
    "P0 = P_ref\n",
    "print(P0)\n",
    "for i in range (0, len(Vc_list)):\n",
    "    if Vc_list[i] > 0:\n",
    "        V_s1 -= 3\n",
    "        \n",
    "        V0 = V_b + V_s1\n",
    "        P1 = P0 * (V0 / (V0 + Vc_list[i]))\n",
    "       \n",
    "    else:\n",
    "        \n",
    "        V_s2 += 3\n",
    "        \n",
    "        V0 = V_b + V_s2\n",
    "        P1 = P0 * (V0 / (V0 + Vc_list[i]))\n",
    "        \n",
    "    \n",
    "    deltaP = P1-P0\n",
    "    \n",
    "    deltaP_list.append(deltaP)\n",
    "    \n",
    "deltaP_list"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b612c171-58df-4c9d-b2f5-c9914581eeaa",
   "metadata": {},
   "source": [
    "#### courbe de $\\Delta P_{\\text{calib}}$ en fonction de $z$"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "c108812f-611c-4a8a-bf49-00f1f7cd7d71",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "z_list = [0.8,1.7,2.5,3.4,4, -0.9,-1.8,-2.5,-3.4,-4]\n",
    "fig1, ax1 = plt.subplots()\n",
    "ax1.scatter(z_list,deltaP_list,marker='+',s=50, label='\\u0394P = f(z)')\n",
    "ax1.set(title='Courbe de \\u0394P = f(z)',\n",
    "       xlabel='Position [cm]', ylabel = \"\\u0394P [hPa]\")\n",
    "ax1.legend()\n",
    "fig1.savefig(\"courbe_deltaP_f(z).png\")\n",
    "fig1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "e64af29a-3a6f-4710-8289-1e9e8a719722",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Éq. de la droite d'étalonnage: ΔP = 11.418z +1.029 (r² = 0.993)\n"
     ]
    }
   ],
   "source": [
    "result = scipy.stats.linregress(z_list,deltaP_list)\n",
    "a0 = result.slope\n",
    "b0 = result.intercept\n",
    "r0 = result.rvalue\n",
    "print(\"Éq. de la droite d'étalonnage: \\u0394P = {:.3f}z {:+.3f} (r² = {:.3f})\".format(a0, b0, r0**2))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "33eb86d1-e9ff-458b-951a-5831bd11896a",
   "metadata": {},
   "source": [
    "#### Verification à température constante"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "cf221f3e-0fa9-45ca-8c07-826cb8a9ec47",
   "metadata": {},
   "outputs": [],
   "source": [
    "def calcul_p_calibration():\n",
    "        P_ext = P0 + a0*0 +b0\n",
    "        return P_ext"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "1074dbd3-b499-4f6c-a10a-874c0dd81e2f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "997.7476391323369"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# P_ext = P_int + delta_P \n",
    "# P_int = P_0 \n",
    "P_ext = calcul_p_calibration()\n",
    "P_ext"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a32a2dd9-b683-42e4-8974-4b53c3332344",
   "metadata": {},
   "source": [
    "#### Courbe de $P = f(Z)$"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "63a57a11-3a87-4453-b43f-6add7eb0f715",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# étalonnage de la pression \n",
    "P_ext_list= []\n",
    "for i in range(0, len(z_list)): \n",
    "    P_ext = P0*(T/27) + a0*z_list[i] +b0*(T/27)\n",
    "    P_ext_list.append(P_ext)\n",
    "fig2, ax2 = plt.subplots()\n",
    "ax2.scatter(z_list,P_ext_list,marker='+',s=50, label='P = f(z)')\n",
    "ax2.set(title='courbe de P = f(z)',\n",
    "       xlabel='Position [cm]', ylabel = \"Pression [hPa]\")\n",
    "ax2.legend()\n",
    "fig2.savefig(\"courbe_pression_f(z).png\")\n",
    "fig2"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "63106a27-97ce-48d9-b858-39e570ee39f9",
   "metadata": {},
   "source": [
    "### **Etalonnage de la pression**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "af68f837-12d6-499f-8cd5-c6981ef3ab6e",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Éq. de la droite d'étalonnage: P_ext = 11.418z +997.748 (r² = 1.000)\n"
     ]
    }
   ],
   "source": [
    "result = scipy.stats.linregress(z_list,P_ext_list)\n",
    "a = result.slope\n",
    "b = result.intercept\n",
    "r = result.rvalue\n",
    "print(\"Éq. de la droite d'étalonnage: P_ext = {:.3f}z {:+.3f} (r² = {:.3f})\".format(a, b, r**2))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "7b2b45d6-7f6f-4ee8-8cfe-446d52043aec",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[ 0.8  1.7  2.5  3.4  4.  -0.9 -1.8 -2.5 -3.4 -4. ]\n"
     ]
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "z_list = np.array(z_list)\n",
    "print(z_list)\n",
    "txt_label = r\"$ P_e ={:.3f}z {:+.3f}$ ($r^2 = {:.3f}$)\".format(a, b, r**2)\n",
    "ax2.plot(z_list, a * z_list + b, color='r', label=txt_label)\n",
    "ax2.legend();\n",
    "fig2.savefig(\"courbe_etalonnage_pression.png\")\n",
    "fig2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "37f728a5-03ea-4bf1-8562-f1882dfa6fef",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "11.418394831056101\n",
      "La sensibilité du capteur S : 0.09 cm/hPa\n"
     ]
    }
   ],
   "source": [
    "# Sensibilité\n",
    "print(a)\n",
    "S = 1 / a \n",
    "print (\"La sensibilité du capteur S : {:.2f} cm/hPa\".format(S))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "d223b428-c9aa-408d-8de4-523d89a5e1e5",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Calcul numérique de P : \n",
    "def calcul_P_ext(z):\n",
    "    P_ext = a*z + b \n",
    "    return P_ext"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "65dfa994-e2c9-42bf-b3e4-89f01473b8a0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "997.7476391323368"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Test \n",
    "P_ext = calcul_P_ext(0)\n",
    "P_ext"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "43e4437b-01e4-4fb3-877a-774907ca1cab",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "993.1802811999144"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#30/04/2026 dans la salle même condition\n",
    "P_ext = calcul_P_ext(-0.4)\n",
    "P_ext"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "e31a7fc9-ea8e-4852-b5fd-ae397f412443",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "989.7547627505975"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 30/04/2026 Poste différent Température difféerente de T0\n",
    "P_ext = calcul_P_ext(-0.7)\n",
    "P_ext"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "429f80cd-a555-4b7f-9621-10105c2e05b4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "992.0384417168087"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#30/04/2026 dans la salle de reunion au 5e etage  même condition \n",
    "P_ext = calcul_P_ext(-0.5)\n",
    "P_ext"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python [conda env:base] *",
   "language": "python",
   "name": "conda-base-py"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.12.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}