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.

M1/Numerical Methods/TP1.ipynb

@@ -12,8 +12,8 @@
    },
    "outputs": [],
    "source": [
-    "import numpy as np\n",
     "import matplotlib.pyplot as plt\n",
+    "import numpy as np\n",
     "from scipy.integrate import odeint"
    ]
   },

M1/Numerical Methods/TP2.ipynb

@@ -38,10 +38,10 @@
    ],
    "source": [
     "import numpy as np\n",
-    "from sklearn.neural_network import MLPClassifier\n",
     "from sklearn.datasets import make_classification\n",
-    "from sklearn.model_selection import train_test_split\n",
     "from sklearn.metrics import accuracy_score\n",
+    "from sklearn.model_selection import train_test_split\n",
+    "from sklearn.neural_network import MLPClassifier\n",
     "\n",
     "accuracies = []\n",
     "\n",

M1/Numerical Methods/TP2_DANJOU_Arthur.ipynb

@@ -15,8 +15,8 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "import numpy as np\n",
     "import matplotlib.pyplot as plt\n",
+    "import numpy as np\n",
     "import scipy.stats as stats"
    ]
   },

M1/Numerical Methods/TP_DANJOU_Arthur.ipynb

@@ -6,10 +6,10 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "import numpy as np\n",
     "import matplotlib.pyplot as plt\n",
-    "from scipy.optimize import newton\n",
-    "from scipy.integrate import quad, odeint"
+    "import numpy as np\n",
+    "from scipy.integrate import odeint, quad\n",
+    "from scipy.optimize import newton"
    ]
   },
   {
@@ -159,9 +159,11 @@
     "\n",
     "    for n in range(N - 1):\n",
     "        p1 = f(vt[n], yn[:, n])\n",
-    "        F1 = lambda p2: f(vt[n] + h / 3, yn[:, n] + h / 6 * (p1 + p2)) - p2\n",
+    "        def F1(p2):\n",
+    "            return f(vt[n] + h / 3, yn[:, n] + h / 6 * (p1 + p2)) - p2\n",
     "        p2 = newton(F1, yn[:, n], fprime=None, tol=tol, maxiter=itmax)\n",
-    "        F2 = lambda yn1: yn[:, n] + h / 4 * (3 * p2 + f(vt[n + 1], yn1)) - yn1\n",
+    "        def F2(yn1):\n",
+    "            return yn[:, n] + h / 4 * (3 * p2 + f(vt[n + 1], yn1)) - yn1\n",
     "        yn[:, n + 1] = newton(F2, yn[:, n], fprime=None, tol=tol, maxiter=itmax)\n",
     "    return yn"
    ]
@@ -392,7 +394,7 @@
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "base",
+   "display_name": "studies",
    "language": "python",
    "name": "python3"
   },
@@ -406,7 +408,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.12.2"
+   "version": "3.13.3"
   }
  },
  "nbformat": 4,