diff --git a/auto/3.2.5/325.ipynb b/auto/3.2.5/325.ipynb
new file mode 100644
index 0000000..19fd895
--- /dev/null
+++ b/auto/3.2.5/325.ipynb
@@ -0,0 +1,149 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "5dabef1c",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "0.11293824972675794\n",
+      "0.01226740102904371\n",
+      "0.0938096382728547\n",
+      "0.009262073610238137\n",
+      "0.40123743304422665\n",
+      "0.041224279574892234\n",
+      "0.40493839466080334\n",
+      "0.019640130500072135\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "<matplotlib.collections.LineCollection at 0x1ff88333ac0>"
+      ]
+     },
+     "execution_count": 5,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": "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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "import matplotlib.pyplot as plt\n",
+    "import numpy as np\n",
+    "\n",
+    "nu = [1588, 1581, 1571, 1561, 1552, 1541, 1531, 1522, 1601, 1596, 1612, 1620, 1630, 1640, 1651, 1663]\n",
+    "v = [30, 26.5, 21, 17.5, 14, 11.5, 10, 9, 30, 31, 25, 21.5, 17.5, 14.5, 12.5, 10.5]\n",
+    "\n",
+    "for i in range(len(v)):\n",
+    "    nu[i] /= 1596\n",
+    "    v[i] /= 31\n",
+    "    \n",
+    "fig, ax = plt.subplots()\n",
+    "\n",
+    "plt.scatter(nu, v)\n",
+    "\n",
+    "nu_100 = [1597, 1583, 1571, 1528, 1505, 1480, 1447, 1407, 1349, 1553, 1653, 1685, 1718, 1759, 1817, 1905, 2073]\n",
+    "u_100 = [3, 2.9, 2.9, 2.4, 2.1, 1.8, 1.5, 1.2, 0.9, 2.7, 2.7, 2.4, 2.1, 1.8, 1.5, 1.2, 0.9]\n",
+    "\n",
+    "for i in range(len(u_100)):\n",
+    "    nu_100[i] /= 1597\n",
+    "    u_100[i] /= 3\n",
+    "    \n",
+    "plt.scatter(nu_100, u_100)\n",
+    "\n",
+    "n = [0] * 4\n",
+    "u = [0] * 4\n",
+    "\n",
+    "n[0] = [0, 2, 10, 19, 12, 4, 7]\n",
+    "u[0] = [1.12, 0.2, 0.72, 1, 0.8, 0.38, 0.58]\n",
+    "\n",
+    "n[1] = [0, 2, 4, 7, 12, 19, 27]\n",
+    "u[1] = [1.12, 1, 0.78, 0.6, 0.38, 0.2, 0.1]\n",
+    "\n",
+    "n[2] = [0, 1, 2, 3, 4, 5, 6]\n",
+    "u[2] = [280, 65, 135, 185, 215, 240, 250]\n",
+    "\n",
+    "n[3] = [0, 1, 2, 3, 4, 5, 6]\n",
+    "u[3] = [280, 230, 150, 100, 70, 45, 30]\n",
+    "\n",
+    "for i in range(len(u[1])-1):\n",
+    "    u[1][i+1] = u[1][0] - u[1][i+1]\n",
+    "for i in range(len(u[3])-1):\n",
+    "    u[3][i+1] = u[3][0] - u[3][i+1]\n",
+    "\n",
+    "theta = [[], [], [], []]\n",
+    "avg_theta = [0] * 4\n",
+    "sigma_theta = [0] * 4\n",
+    "\n",
+    "for k in range(4):\n",
+    "    for i in range(len(n[k]) - 1):\n",
+    "        for j in range(len(n[k]) - i - 2):\n",
+    "            theta[k].append(np.abs(np.log((u[k][0] - u[k][i+1]) / (u[k][0] - u[k][j+i+2])) / (n[k][j+i+2] - n[k][i+1])))\n",
+    "            avg_theta[k] += theta[k][-1] \n",
+    "        \n",
+    "    avg_theta[k] /= len(theta[k])\n",
+    "    print(avg_theta[k])\n",
+    "    for i in range(len(theta[k])):\n",
+    "        sigma_theta[k] += (avg_theta[k] - theta[k][i]) **2\n",
+    "    sigma_theta[k] /= (len(theta[k])-1) \n",
+    "    print(np.sqrt(sigma_theta[k]))\n",
+    "    \n",
+    "ax.grid()\n",
+    "ax.hlines(0.707, 0.8, 1.3, color = 'red', linewidth = 0.5)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "1b8c0678",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "13da0f9f",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.9.12"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/auto/3.2.5/lab325.pdf b/auto/3.2.5/lab325.pdf
new file mode 100644
index 0000000..af0de2b
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diff --git a/auto/3.3.5/335.ipynb b/auto/3.3.5/335.ipynb
new file mode 100644
index 0000000..bae1ede
--- /dev/null
+++ b/auto/3.3.5/335.ipynb
@@ -0,0 +1,132 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "51cb8cd8",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "import matplotlib.pyplot as plt\n",
+    "import numpy as np\n",
+    "\n",
+    "fig, ax = plt.subplots()\n",
+    "ax.grid()\n",
+    "\n",
+    "#I_max_Cu = 1.04 A\n",
+    "\n",
+    "#A\n",
+    "I = [0.03, 0.20, 0.41, 0.62, 0.82, 1.00, 1.10, 1.20, 1.38] #<1.10\n",
+    "#mT\n",
+    "B = [44.5, 226.6, 410.5, 605.1, 805.5, 998.4, 1092.3, 1129.9, 1181.4]\n",
+    "\n",
+    "for i in range(len(B)):\n",
+    "    B[i]/=1000\n",
+    "\n",
+    "plt.scatter(I, B)\n",
+    "plt.plot(I, I)\n",
+    "plt.xlabel('I, А')\n",
+    "plt.ylabel('B, Тл')\n",
+    "\n",
+    "#U_0 = 0.15 muV\n",
+    "\n",
+    "I_Cu = [0] * 5\n",
+    "U_Cu = [0] * 5\n",
+    "\n",
+    "#0.2 A\n",
+    "I_Cu[0] = [0.2, 0.4, 0.6, 0.8, 1]\n",
+    "U_Cu[0] = [0.03, 0.06, 0.09, 0.12, 0.15]\n",
+    "\n",
+    "#0.4 A\n",
+    "I_Cu[1] = [0.2, 0.4, 0.6, 0.8, 1]\n",
+    "U_Cu[1] = [0.09, 0.18, 0.27, 0.36, 0.45]\n",
+    "\n",
+    "#0.6 A\n",
+    "I_Cu[2] = [0.2, 0.4, 0.6, 0.8, 1]\n",
+    "U_Cu[2] = [0.15, 0.27, 0.39, 0.54, 0.69]\n",
+    "\n",
+    "#0.8 A\n",
+    "I_Cu[3] = [0.2, 0.4, 0.6, 0.8, 1]\n",
+    "U_Cu[3] = [0.18, 0.30, 0.57, 0.78, 0.84]\n",
+    "\n",
+    "#1.0 A\n",
+    "I_Cu[4] = [0.2, 0.4, 0.6, 0.8, 1]\n",
+    "U_Cu[4] = [0.24, 0.54, 0.78, 0.99, 1.23]\n",
+    "\n",
+    "I_Cu_back = [0.2, 0.4, 0.6, 0.8, 1]\n",
+    "U_Cu_back = [0.24, 0.51, 0.78, 1.02, 1.17]\n",
+    "\n",
+    "#U_Cu_34 = 495 muV l = 9 mm L34 = 10 mm a = 0.05 mm\n",
+    "\n",
+    "\n",
+    "\n",
+    "\n",
+    "\n",
+    "#Al\n",
+    "\n",
+    "#R_24 = 5.567 Ohm\n",
+    "#R_34 = 2.788 Ohm\n",
+    "\n",
+    "#mA\n",
+    "I_Al_28 = [100, 80.3, 59.8, 40.2, 20.3]\n",
+    "#mV\n",
+    "U_Al1_28 = [0.123, 0.097, 0.070, 0.046, 0.021]\n",
+    "U_Al2_28 = [0.061, 0.047, 0.034, 0.021, 0.008]\n",
+    "\n",
+    "#mA\n",
+    "I_Al_37 = [100, 80.3, 59.8, 40.2, 20.3]\n",
+    "#mV\n",
+    "U_Al1_37 = [0.020, 0.008, 0.070, 0.046, 0.021]\n",
+    "U_Al2_37 = [0.011, 0.004, 0.034, 0.021, 0.008]\n",
+    "\n",
+    "#mA\n",
+    "I_Al_46 = [100, 80.3, 59.8, 40.2, 20.3]\n",
+    "#mV\n",
+    "U_Al1_46 = [0.123, 0.097, 0.070, 0.046, 0.021]\n",
+    "U_Al2_46 = [0.061, 0.047, 0.034, 0.021, 0.008]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "e5eb3d7e",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.9.12"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/auto/3.3.5/335Cu.ipynb b/auto/3.3.5/335Cu.ipynb
new file mode 100644
index 0000000..7d4cbd2
--- /dev/null
+++ b/auto/3.3.5/335Cu.ipynb
@@ -0,0 +1,134 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "id": "75466c80",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "0.14999999999999997\n",
+      "0.45\n",
+      "0.6749999999999997\n",
+      "0.8999999999999998\n",
+      "1.2149999999999996\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "import matplotlib.pyplot as plt\n",
+    "import numpy as np\n",
+    "\n",
+    "\n",
+    "\n",
+    "#U_0 = 0.15 muV\n",
+    "\n",
+    "I_Cu = [0] * 5\n",
+    "U_Cu = [0] * 5\n",
+    "\n",
+    "#0.2 A\n",
+    "I_Cu[0] = [0, 0.2, 0.4, 0.6, 0.8, 1]\n",
+    "U_Cu[0] = [0, 0.03, 0.06, 0.09, 0.12, 0.15]\n",
+    "\n",
+    "#0.4 A\n",
+    "I_Cu[1] = [0.2, 0.4, 0.6, 0.8, 1]\n",
+    "U_Cu[1] = [0.09, 0.18, 0.27, 0.36, 0.45]\n",
+    "\n",
+    "#0.6 A\n",
+    "I_Cu[2] = [0.2, 0.4, 0.6, 0.8, 1]\n",
+    "U_Cu[2] = [0.15, 0.27, 0.39, 0.54, 0.69]\n",
+    "\n",
+    "#0.8 A\n",
+    "I_Cu[3] = [0.2, 0.4, 0.6, 0.8, 1]\n",
+    "U_Cu[3] = [0.18, 0.30, 0.57, 0.78, 0.84]\n",
+    "\n",
+    "#1.0 A\n",
+    "I_Cu[4] = [0.2, 0.4, 0.6, 0.8, 1]\n",
+    "U_Cu[4] = [0.24, 0.54, 0.78, 0.99, 1.23]\n",
+    "\n",
+    "I_Cu_back = [0.2, 0.4, 0.6, 0.8, 1]\n",
+    "U_Cu_back = [0.24, 0.51, 0.78, 1.02, 1.17]\n",
+    "\n",
+    "#U_Cu_34 = 495 muV l = 9 mm L34 = 10 mm a = 0.05 mm\n",
+    "\n",
+    "k = [0] * 5\n",
+    "b = [0] * 5\n",
+    "\n",
+    "I = np.linspace(0, 1)\n",
+    "U = [0] * 5\n",
+    "k[0], b[0] = np.polyfit(I_Cu[0], U_Cu[0], deg = 1)\n",
+    "k[1], b[1] = np.polyfit(I_Cu[1], U_Cu[1], deg = 1)\n",
+    "k[2], b[2] = np.polyfit(I_Cu[2], U_Cu[2], deg = 1)\n",
+    "k[3], b[3] = np.polyfit(I_Cu[3], U_Cu[3], deg = 1)\n",
+    "k[4], b[4] = np.polyfit(I_Cu[4], U_Cu[4], deg = 1)\n",
+    "U[0] = k[0] * I + b[0]\n",
+    "U[1] = k[1] * I + b[1]\n",
+    "U[2] = k[2] * I + b[2]\n",
+    "U[3] = k[3] * I + b[3]\n",
+    "U[4] = k[4] * I + b[4]\n",
+    "\n",
+    "plt.grid()\n",
+    "plt.plot(I, U[0])\n",
+    "plt.plot(I, U[1])\n",
+    "plt.plot(I, U[2])\n",
+    "plt.plot(I, U[3])\n",
+    "plt.plot(I, U[4])\n",
+    "plt.scatter(I_Cu[0], U_Cu[0])\n",
+    "plt.scatter(I_Cu[1], U_Cu[1])\n",
+    "plt.scatter(I_Cu[2], U_Cu[2])\n",
+    "plt.scatter(I_Cu[3], U_Cu[3])\n",
+    "plt.scatter(I_Cu[4], U_Cu[4])\n",
+    "\n",
+    "plt.xlabel('B, Тл')\n",
+    "plt.ylabel('U, мкВ')\n",
+    "\n",
+    "for i in range(5):\n",
+    "    print(k[i])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "a98a4e12",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.9.12"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/auto/3.3.5/335Zn.ipynb b/auto/3.3.5/335Zn.ipynb
new file mode 100644
index 0000000..b092ded
--- /dev/null
+++ b/auto/3.3.5/335Zn.ipynb
@@ -0,0 +1,83 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "22af19e1",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "-0.5099999999999999\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "import matplotlib.pyplot as plt\n",
+    "import numpy as np\n",
+    "\n",
+    "#I = 0.81 A\n",
+    "I_Zn = [0.2, 0.4, 0.6, 0.8, 1]\n",
+    "U_Zn = [-0.09, -0.18, -0.30, -0.42, -0.48]\n",
+    "\n",
+    "k, b = np.polyfit(I_Zn, U_Zn, deg = 1)\n",
+    "\n",
+    "U = []\n",
+    "for i in range(5):\n",
+    "    U.append(k * I_Zn[i] + b) \n",
+    "\n",
+    "#U_Zn_34 = 204 muV l = 10 mm L34 = 4 mm a = 0.08 mm\n",
+    "\n",
+    "plt.scatter(I_Zn, U_Zn)\n",
+    "plt.grid()\n",
+    "plt.plot(I_Zn, U)\n",
+    "plt.xlabel('B, Тл')\n",
+    "plt.ylabel('U, мкВ')\n",
+    "print(k)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "2ae67d3e",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.9.12"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/auto/3.3.5/335kCu.ipynb b/auto/3.3.5/335kCu.ipynb
new file mode 100644
index 0000000..33ef926
--- /dev/null
+++ b/auto/3.3.5/335kCu.ipynb
@@ -0,0 +1,80 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "869ef02c",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "1.29\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "import matplotlib.pyplot as plt\n",
+    "import numpy as np\n",
+    "\n",
+    "k = [0.15, 0.45, 0.675, 0.9, 1.215]\n",
+    "I = [0.2, 0.4, 0.6, 0.8, 1]\n",
+    "\n",
+    "a, b = np.polyfit(I, k, deg = 1)\n",
+    "\n",
+    "klin = []\n",
+    "for i in range(5):\n",
+    "    klin.append(a * I[i] + b)\n",
+    "\n",
+    "plt.grid()\n",
+    "plt.scatter(I, k)\n",
+    "plt.plot(I, klin)\n",
+    "plt.xlabel('I, А')\n",
+    "plt.ylabel('k, 10^-6 В/Тл')\n",
+    "print(a)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "3f137f1d",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.9.12"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
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