Term_Titrage_ELEVE.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "Term_Titrage_ELEVE.ipynb",
      "provenance": [],
      "authorship_tag": "ABX9TyPXaCDKdP2DdeDH3cQ5WnYl",
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/dimpondy/SPC/blob/master/Term_Titrage_ELEVE.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "h_Un0koyPvRy"
      },
      "source": [
        "# Titrage de solution et représentation de l'évolution de grandeurs chimiques : pH,n,.."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "VErswF9mPtiY"
      },
      "source": [
        "**Objectif:**Représenter, à l’aide d’un langage de programmation, **l’évolution du pH** d'une solution en fonction du volume de solution titrante versé."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "vERkA1FdPsud",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 333
        },
        "outputId": "ee3b8668-6a73-4fc3-fd3d-c7aad88402cd"
      },
      "source": [
        "from matplotlib.pyplot import*\n",
        "import matplotlib.pyplot as plt\n",
        "\n",
        "pH=[2.9,3.2,3.6,3.9,4.2,4.6,4.9,6.3,8,10.7,11,11.3,11.5]\n",
        "Vb=[1,2,4,6,8,10,11,11.5,12,12.5,13,14,15]\n",
        "\n",
        "# Création et Affichage des données sous forme de tableau\n",
        "print(np.array([\"Vb\",(Vb),\"pH\",(pH)]))\n",
        "# Création et Affichage du graphique\n",
        "plot(Vb,pH,'--',label=\"pH en f(Volume titrant)\")\n",
        "axis([0,20,0,12])\n",
        "xlabel(\"Vb en mL\")\n",
        "ylabel(\"pH \")\n",
        "grid()\n",
        "legend()\n",
        "title(\"Evolution du pH d'acide lactique en fonction du volume de soude versé \")\n",
        "show()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "['Vb' list([1, 2, 4, 6, 8, 10, 11, 11.5, 12, 12.5, 13, 14, 15]) 'pH'\n",
            " list([2.9, 3.2, 3.6, 3.9, 4.2, 4.6, 4.9, 6.3, 8, 10.7, 11, 11.3, 11.5])]\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "08LLg4R8pyZx"
      },
      "source": [
        "**Objectif:**Représenter, à l’aide d’un langage de programmation, l’évolution des **quantités de matière** des espèces en fonction du volume de solution titrante versé."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "6UjddSmVjNeh",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 713
        },
        "outputId": "80dcb036-bd2b-4934-fcd5-30aa67c2c2c1"
      },
      "source": [
        "from matplotlib.pyplot import*\n",
        "import matplotlib.pyplot as plt\n",
        "from scipy import misc \n",
        "\n",
        "pH=[2.9,3.2,3.6,3.9,4.2,4.6,4.9,6.3,8,10.7,11,11.3,11.5]\n",
        "Vb=[1,2,4,6,8,10,11,11.5,12,12.5,13,14,15]\n",
        "Cb=0.1\n",
        "nb = [i*Cb for i in Vb]\n",
        "# Création et Affichage des données sous forme de tableau\n",
        "print(np.array([\"Volume de base versé\",(Vb), \"pH\",(pH)]))\n",
        "# Création et Affichage du graphique\n",
        "#plot(nb,pH,'b-',label=\"nb en f(Volume titrant)\")\n",
        "#dVb = 2\n",
        "#dpH = [(pH[i+1] - pH[i])/dVb for i in range(len(pH)-1)]\n",
        "\n",
        "dpH=[]\n",
        "for i in range (0,len(Vb)-1) :\n",
        "    dpHi=(pH[i+1]-pH[i])/(Vb[i+1]-Vb[i])\n",
        "    dpH.append(dpHi)\n",
        "\n",
        "plot(Vb,dpH,'b-',label=\"dpH =f(Vb)\")\n",
        "#axis([0,1.5,0,12])\n",
        "xlabel(\"nb en moL\")\n",
        "ylabel(\"pH \")\n",
        "grid()\n",
        "legend()\n",
        "title(\"Evolution de la quantité de matière restante d'acide lactique \\n en fonction du volume de soude versé \")\n",
        "show()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "['Volume de base versé'\n",
            " list([1, 2, 4, 6, 8, 10, 11, 11.5, 12, 12.5, 13, 14, 15]) 'pH'\n",
            " list([2.9, 3.2, 3.6, 3.9, 4.2, 4.6, 4.9, 6.3, 8, 10.7, 11, 11.3, 11.5])]\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "error",
          "ename": "ValueError",
          "evalue": "ignored",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mValueError\u001b[0m                                Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-19-3cc33ef12149>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m     19\u001b[0m     \u001b[0mdpH\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mappend\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdpHi\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     20\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 21\u001b[0;31m \u001b[0mplot\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mVb\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mdpH\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m'b-'\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mlabel\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m\"dpH =f(Vb)\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     22\u001b[0m \u001b[0;31m#axis([0,1.5,0,12])\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     23\u001b[0m \u001b[0mxlabel\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"nb en moL\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/matplotlib/pyplot.py\u001b[0m in \u001b[0;36mplot\u001b[0;34m(scalex, scaley, data, *args, **kwargs)\u001b[0m\n\u001b[1;32m   2761\u001b[0m     return gca().plot(\n\u001b[1;32m   2762\u001b[0m         *args, scalex=scalex, scaley=scaley, **({\"data\": data} if data\n\u001b[0;32m-> 2763\u001b[0;31m         is not None else {}), **kwargs)\n\u001b[0m\u001b[1;32m   2764\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2765\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/matplotlib/axes/_axes.py\u001b[0m in \u001b[0;36mplot\u001b[0;34m(self, scalex, scaley, data, *args, **kwargs)\u001b[0m\n\u001b[1;32m   1644\u001b[0m         \"\"\"\n\u001b[1;32m   1645\u001b[0m         \u001b[0mkwargs\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mcbook\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mnormalize_kwargs\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmlines\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mLine2D\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1646\u001b[0;31m         \u001b[0mlines\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_get_lines\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdata\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mdata\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1647\u001b[0m         \u001b[0;32mfor\u001b[0m \u001b[0mline\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mlines\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1648\u001b[0m             \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0madd_line\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mline\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/matplotlib/axes/_base.py\u001b[0m in \u001b[0;36m__call__\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m    214\u001b[0m                 \u001b[0mthis\u001b[0m \u001b[0;34m+=\u001b[0m \u001b[0margs\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    215\u001b[0m                 \u001b[0margs\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0margs\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 216\u001b[0;31m             \u001b[0;32myield\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_plot_args\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mthis\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    217\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    218\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mget_next_color\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/matplotlib/axes/_base.py\u001b[0m in \u001b[0;36m_plot_args\u001b[0;34m(self, tup, kwargs)\u001b[0m\n\u001b[1;32m    340\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    341\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mx\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mshape\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m!=\u001b[0m \u001b[0my\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mshape\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 342\u001b[0;31m             raise ValueError(f\"x and y must have same first dimension, but \"\n\u001b[0m\u001b[1;32m    343\u001b[0m                              f\"have shapes {x.shape} and {y.shape}\")\n\u001b[1;32m    344\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mx\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mndim\u001b[0m \u001b[0;34m>\u001b[0m \u001b[0;36m2\u001b[0m \u001b[0;32mor\u001b[0m \u001b[0my\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mndim\u001b[0m \u001b[0;34m>\u001b[0m \u001b[0;36m2\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;31mValueError\u001b[0m: x and y must have same first dimension, but have shapes (13,) and (12,)"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    }
  ]
}