Refactor code in numerical methods notebooks

- Updated import order in Point_Fixe.ipynb for consistency. - Changed lambda functions to regular function definitions for clarity in Point_Fixe.ipynb. - Added numpy import in TP1_EDO_EulerExp.ipynb, TP2_Lokta_Volterra.ipynb, and TP3_Convergence.ipynb for better readability. - Modified for loops in TP1_EDO_EulerExp.ipynb and TP2_Lokta_Volterra.ipynb to include strict=False for compatibility with future Python versions.
2026-01-14 15:54:13 +01:00 · 2025-09-01 16:14:53 +02:00
parent dfee405ea0
commit 8cf328e18a
31 changed files with 177 additions and 156 deletions
--- a/Numériques/DM1.ipynb
+++ b/Numériques/DM1.ipynb
@@ -9,8 +9,8 @@
    "%matplotlib inline\n",
    "%config InlineBackend.figure_format = 'retina'\n",
    "\n",
-    "import numpy as np  # pour les numpy array\n",
    "import matplotlib.pyplot as plt  # librairie graphique\n",
+    "import numpy as np  # pour les numpy array\n",
    "from scipy.integrate import odeint  # seulement odeint"
   ]
  },
--- a/Numériques/DM2.ipynb
+++ b/Numériques/DM2.ipynb
@@ -19,8 +19,8 @@
   },
   "outputs": [],
   "source": [
-    "import numpy as np\n",
-    "import matplotlib.pyplot as plt"
+    "import matplotlib.pyplot as plt\n",
+    "import numpy as np"
   ]
  },
  {
--- a/Numériques/DM3.ipynb
+++ b/Numériques/DM3.ipynb
@@ -19,10 +19,10 @@
   },
   "outputs": [],
   "source": [
+    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
-    "from scipy.special import roots_legendre\n",
    "from scipy.integrate import quad\n",
-    "import matplotlib.pyplot as plt"
+    "from scipy.special import roots_legendre"
   ]
  },
  {
@@ -710,14 +710,14 @@
    "\n",
    "for N in range(1, 11):\n",
    "    approx_errors = []\n",
-    "    for k in [x for x in range(0, 11, 2)]:\n",
+    "    for k in list(range(0, 11, 2)):\n",
    "        I_approx = gauss(lambda x: f(x, k), N)\n",
    "        I_exact = 2 / (k + 1) if k % 2 == 0 else 0\n",
    "        approx_error = np.abs(I_approx - I_exact)\n",
    "        approx_errors.append(approx_error)\n",
    "    print(\n",
-    "        \"{:5d}     |    \".format(N)\n",
-    "        + \" \".join(\"{:.3f}    \".format(e) for e in approx_errors)\n",
+    "        f\"{N:5d}     |    \"\n",
+    "        + \" \".join(f\"{e:.3f}    \" for e in approx_errors)\n",
    "    )"
   ]
  },
@@ -768,14 +768,14 @@
    "\n",
    "for N in range(1, 11):\n",
    "    approx_errors = []\n",
-    "    for k in [x for x in range(0, 11, 2)]:\n",
+    "    for k in list(range(0, 11, 2)):\n",
    "        I_approx = fejer(lambda x: f(x, k), N)\n",
    "        I_exact = 2 / (k + 1) if k % 2 == 0 else 0\n",
    "        approx_error = np.abs(I_approx - I_exact)\n",
    "        approx_errors.append(approx_error)\n",
    "    print(\n",
-    "        \"{:5d}     |    \".format(N)\n",
-    "        + \" \".join(\"{:.3f}    \".format(e) for e in approx_errors)\n",
+    "        f\"{N:5d}     |    \"\n",
+    "        + \" \".join(f\"{e:.3f}    \" for e in approx_errors)\n",
    "    )"
   ]
  },
--- a/Numériques/Interpolation_2.ipynb
+++ b/Numériques/Interpolation_2.ipynb
@@ -22,8 +22,8 @@
    "%matplotlib inline\n",
    "%config InlineBackend.figure_format = 'retina'\n",
    "\n",
-    "import numpy as np  # pour les numpy array\n",
-    "import matplotlib.pyplot as plt  # librairie graphique"
+    "import matplotlib.pyplot as plt  # librairie graphique\n",
+    "import numpy as np  # pour les numpy array"
   ]
  },
  {
@@ -331,7 +331,8 @@
    }
   ],
   "source": [
-    "f = lambda x: 1 / (1 + x**2)\n",
+    "def f(x):\n",
+    "    return 1 / (1 + x**2)\n",
    "a, b = -5, 5\n",
    "xx = np.linspace(a, b, 200)\n",
    "\n",
@@ -372,7 +373,8 @@
    }
   ],
   "source": [
-    "f = lambda x: 1 / (1 + x**2)\n",
+    "def f(x):\n",
+    "    return 1 / (1 + x**2)\n",
    "a, b = -5, 5\n",
    "xx = np.linspace(a, b, 200)\n",
    "\n",
--- a/Numériques/Intégration.ipynb
+++ b/Numériques/Intégration.ipynb
--- a/Numériques/Point_Fixe.ipynb
+++ b/Numériques/Point_Fixe.ipynb
@@ -20,8 +20,8 @@
    "%matplotlib inline\n",
    "%config InlineBackend.figure_format = 'retina'\n",
    "\n",
-    "import numpy as np\n",
-    "import matplotlib.pyplot as plt"
+    "import matplotlib.pyplot as plt\n",
+    "import numpy as np"
   ]
  },
  {
@@ -63,11 +63,16 @@
   },
   "outputs": [],
   "source": [
-    "f1 = lambda x: np.exp(x) - 1 - x\n",
-    "f2 = lambda x: x - np.sin(x)\n",
-    "f3 = lambda x: x + np.sin(x)\n",
-    "f4 = lambda x: x + np.cos(x) - 1\n",
-    "f5 = lambda x: x - np.cos(x) + 1"
+    "def f1(x):\n",
+    "    return np.exp(x) - 1 - x\n",
+    "def f2(x):\n",
+    "    return x - np.sin(x)\n",
+    "def f3(x):\n",
+    "    return x + np.sin(x)\n",
+    "def f4(x):\n",
+    "    return x + np.cos(x) - 1\n",
+    "def f5(x):\n",
+    "    return x - np.cos(x) + 1"
   ]
  },
  {
--- a/Différentielles/TP1_EDO_EulerExp.ipynb
+++ b/Différentielles/TP1_EDO_EulerExp.ipynb
@@ -100,8 +100,8 @@
   ],
   "source": [
    "%matplotlib inline\n",
-    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
+    "import numpy as np\n",
    "\n",
    "\n",
    "# Fonction f définissant l'EDO\n",
@@ -189,8 +189,8 @@
   ],
   "source": [
    "%matplotlib inline\n",
-    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
+    "import numpy as np\n",
    "\n",
    "\n",
    "# Fonction f définissant l'EDO\n",
@@ -367,7 +367,7 @@
    "T = 1\n",
    "y0 = 1\n",
    "\n",
-    "for f, uex in zip([f1, f2], [uex1, uex2]):\n",
+    "for f, uex in zip([f1, f2], [uex1, uex2], strict=False):\n",
    "    plt.figure()\n",
    "    t = np.arange(0, 1, 1e-3)\n",
    "    y = uex(t, y0)\n",
--- a/Différentielles/TP2_Lokta_Volterra.ipynb
+++ b/Différentielles/TP2_Lokta_Volterra.ipynb
@@ -92,14 +92,14 @@
   ],
   "source": [
    "%matplotlib inline\n",
-    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
+    "import numpy as np\n",
    "\n",
    "\n",
    "# Fonction F définissant l'EDO\n",
    "def F(Y):\n",
-    "    x = Y[0]\n",
-    "    y = Y[1]\n",
+    "    Y[0]\n",
+    "    Y[1]\n",
    "    A = np.array([[0, 1], [-2, -3]])\n",
    "    return np.dot(A, Y)\n",
    "\n",
@@ -132,7 +132,7 @@
    "## Représentation des solutions pour chaque valeur de la donnée initiale\n",
    "tt = np.linspace(-10, 10, 100)\n",
    "t0 = tt[0]\n",
-    "for x, y in zip([1, -2, 0, 1, 3], [2, -2, -4, -2, 4]):\n",
+    "for x, y in zip([1, -2, 0, 1, 3], [2, -2, -4, -2, 4], strict=False):\n",
    "    sol = uex(tt, t0, [x, y])\n",
    "    plt.plot(sol[0], sol[1], label=f\"$((x0, y0) = ({x}, {y})$\")\n",
    "    plt.scatter(x, y)\n",
--- a/Différentielles/TP3_Convergence.ipynb
+++ b/Différentielles/TP3_Convergence.ipynb
@@ -73,8 +73,8 @@
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
-    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
+    "import numpy as np\n",
    "\n",
    "\n",
    "## Question 1\n",
@@ -746,8 +746,9 @@
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
-    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
+    "import numpy as np\n",
+    "\n",
    "# Question 3\n",
    "\n",
    "\n",
--- a/Numériques/TP1_Equation_de_Poisson.ipynb
+++ b/Numériques/TP1_Equation_de_Poisson.ipynb
@@ -358,7 +358,7 @@
    "H, E = np.array(H), np.array(E)\n",
    "\n",
    "plt.xlabel(\"$h$\")\n",
-    "plt.ylabel(\"$\\max_{j=0,\\dots,M+1}|u(x_j)-u_j|$\")\n",
+    "plt.ylabel(r\"$\\max_{j=0,\\dots,M+1}|u(x_j)-u_j|$\")\n",
    "plt.legend(fontsize=7)\n",
    "plt.title(\n",
    "    \"Différence en valeur absolue entre la solution exacte et la solution approchée\"\n",
@@ -589,7 +589,7 @@
    "    H.append(h)\n",
    "\n",
    "plt.xlabel(\"$h$\")\n",
-    "plt.ylabel(\"$\\max_{j=0,\\dots,M+1}|u(x_j)-u_j|$\")\n",
+    "plt.ylabel(r\"$\\max_{j=0,\\dots,M+1}|u(x_j)-u_j|$\")\n",
    "plt.legend(fontsize=7)\n",
    "plt.title(\n",
    "    \"Différence en valeur absolue entre la solution exacte et la solution approchée\"\n",
@@ -833,7 +833,7 @@
    "    H.append(h)\n",
    "\n",
    "plt.xlabel(\"$h$\")\n",
-    "plt.ylabel(\"$\\max_{j=0,\\dots,M+1}|u(x_j)-u_j|$\")\n",
+    "plt.ylabel(r\"$\\max_{j=0,\\dots,M+1}|u(x_j)-u_j|$\")\n",
    "plt.legend(fontsize=7)\n",
    "plt.title(\n",
    "    \"Différence en valeur absolue entre la solution exacte et la solution approchée\"\n",
--- a/Numérique/Segregation.ipynb
+++ b/Numérique/Segregation.ipynb
@@ -21,9 +21,10 @@
    }
   ],
   "source": [
-    "import numpy as np\n",
+    "import itertools\n",
+    "\n",
    "import matplotlib.pyplot as plt\n",
-    "import itertools"
+    "import numpy as np"
   ]
  },
  {
@@ -139,7 +140,7 @@
    "        return S * 2 / (self.Ntot**2)\n",
    "\n",
    "    def simuler(self, T=400, move_satisfaits=True):\n",
-    "        for t in range(1, int((1 - self.p) * self.M**2 * T)):\n",
+    "        for _t in range(1, int((1 - self.p) * self.M**2 * T)):\n",
    "            agents = [\n",
    "                (i, j)\n",
    "                for i, row in enumerate(self.grille)\n",
--- a/L3/Statistiques/TP1.ipynb
+++ b/L3/Statistiques/TP1.ipynb
@@ -9,8 +9,8 @@
   },
   "outputs": [],
   "source": [
-    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
+    "import numpy as np\n",
    "import scipy.stats as stats"
   ]
  },
--- a/L3/Statistiques/TP2.ipynb
+++ b/L3/Statistiques/TP2.ipynb
@@ -9,11 +9,11 @@
   },
   "outputs": [],
   "source": [
-    "import numpy as np\n",
-    "import scipy.stats as stats\n",
-    "import scipy.special as sp\n",
    "import matplotlib.pyplot as plt\n",
-    "import scipy.optimize as opt"
+    "import numpy as np\n",
+    "import scipy.optimize as opt\n",
+    "import scipy.special as sp\n",
+    "import scipy.stats as stats"
   ]
  },
  {