diff --git a/AVANCE_CODIGO_PENDULO_.ipynb b/AVANCE_CODIGO_PENDULO_.ipynb
new file mode 100644
index 0000000000000000000000000000000000000000..570b8b78760325dcda8b67e1bd6f140ebc9a622d
--- /dev/null
+++ b/AVANCE_CODIGO_PENDULO_.ipynb
@@ -0,0 +1,648 @@
+{
+  "metadata": {
+    "language_info": {
+      "codemirror_mode": {
+        "name": "python",
+        "version": 3
+      },
+      "file_extension": ".py",
+      "mimetype": "text/x-python",
+      "name": "python",
+      "nbconvert_exporter": "python",
+      "pygments_lexer": "ipython3",
+      "version": "3.8"
+    },
+    "kernelspec": {
+      "name": "python",
+      "display_name": "Python (Pyodide)",
+      "language": "python"
+    }
+  },
+  "nbformat_minor": 4,
+  "nbformat": 4,
+  "cells": [
+    {
+      "cell_type": "code",
+      "source": "import numpy as np\nimport matplotlib.pyplot as plt",
+      "metadata": {
+        "trusted": true
+      },
+      "execution_count": 1,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": "datos= np.genfromtxt('MassB.txt')",
+      "metadata": {
+        "trusted": true
+      },
+      "execution_count": 2,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": "datos",
+      "metadata": {
+        "trusted": true
+      },
+      "execution_count": 3,
+      "outputs": [
+        {
+          "execution_count": 3,
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": "array([[4.  , 4.02, 4.04, 3.95],\n       [4.16, 4.18, 4.17, 4.18],\n       [4.42, 4.41, 4.43, 4.4 ],\n       [4.87, 4.92, 4.88, 4.88],\n       [5.14, 5.13, 5.16, 5.14],\n       [5.26, 5.17, 5.15, 5.24],\n       [5.43, 5.44, 5.43, 5.46],\n       [5.7 , 5.74, 5.71, 5.73]])"
+          },
+          "metadata": {}
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": "t=datos/5\nt",
+      "metadata": {
+        "trusted": true
+      },
+      "execution_count": 4,
+      "outputs": [
+        {
+          "execution_count": 4,
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": "array([[0.8  , 0.804, 0.808, 0.79 ],\n       [0.832, 0.836, 0.834, 0.836],\n       [0.884, 0.882, 0.886, 0.88 ],\n       [0.974, 0.984, 0.976, 0.976],\n       [1.028, 1.026, 1.032, 1.028],\n       [1.052, 1.034, 1.03 , 1.048],\n       [1.086, 1.088, 1.086, 1.092],\n       [1.14 , 1.148, 1.142, 1.146]])"
+          },
+          "metadata": {}
+        }
+      ]
+    },
+    {
+      "cell_type": "raw",
+      "source": "# datos promedios del Periodo",
+      "metadata": {}
+    },
+    {
+      "cell_type": "code",
+      "source": "tp=t[0]\nT1=np.mean(tp)\nT1\n#14cm#",
+      "metadata": {
+        "trusted": true
+      },
+      "execution_count": 5,
+      "outputs": [
+        {
+          "execution_count": 5,
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": "0.8005"
+          },
+          "metadata": {}
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": "tp2=t[1]\nT2=np.mean(tp2)\nT2\n\n#16cm#",
+      "metadata": {
+        "trusted": true
+      },
+      "execution_count": 6,
+      "outputs": [
+        {
+          "execution_count": 6,
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": "0.8345"
+          },
+          "metadata": {}
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": "tp3=t[2]\nT3=np.mean(tp3)\nT3\n#18cm#",
+      "metadata": {
+        "trusted": true
+      },
+      "execution_count": 7,
+      "outputs": [
+        {
+          "execution_count": 7,
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": "0.883"
+          },
+          "metadata": {}
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": "tp4=t[3]\nT4=np.mean(tp4)\nT4\n#22cm#",
+      "metadata": {
+        "trusted": true
+      },
+      "execution_count": 8,
+      "outputs": [
+        {
+          "execution_count": 8,
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": "0.9775"
+          },
+          "metadata": {}
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": "tp5=t[4]\nT5=np.mean(tp5)\nT5\n#24cm#",
+      "metadata": {
+        "trusted": true
+      },
+      "execution_count": 9,
+      "outputs": [
+        {
+          "execution_count": 9,
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": "1.0285000000000002"
+          },
+          "metadata": {}
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": "tp6=t[5]\nT6=np.mean(tp6)\nT6\n#26cm#",
+      "metadata": {
+        "trusted": true
+      },
+      "execution_count": 10,
+      "outputs": [
+        {
+          "execution_count": 10,
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": "1.0410000000000001"
+          },
+          "metadata": {}
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": "tp7=t[6]\nT7=np.mean(tp7)\nT7\n#28cm#",
+      "metadata": {
+        "trusted": true
+      },
+      "execution_count": 11,
+      "outputs": [
+        {
+          "execution_count": 11,
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": "1.088"
+          },
+          "metadata": {}
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": "tp8=t[7]\nT8=np.mean(tp8)\nT8\n#30cm#",
+      "metadata": {
+        "trusted": true
+      },
+      "execution_count": 12,
+      "outputs": [
+        {
+          "execution_count": 12,
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": "1.1440000000000001"
+          },
+          "metadata": {}
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": "def gravedad (l,T_p):\n    g=4*np.pi**2*l/T_p**2\n    return (g)",
+      "metadata": {
+        "trusted": true
+      },
+      "execution_count": 13,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": "g1=gravedad(0.14,T1)\nprint (g1)",
+      "metadata": {
+        "trusted": true
+      },
+      "execution_count": 14,
+      "outputs": [
+        {
+          "name": "stdout",
+          "text": "8.625119082912407\n",
+          "output_type": "stream"
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": "g2=gravedad(0.16,T2)\nprint (g2)",
+      "metadata": {
+        "trusted": true
+      },
+      "execution_count": 15,
+      "outputs": [
+        {
+          "name": "stdout",
+          "text": "9.070412483082855\n",
+          "output_type": "stream"
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": "g3=gravedad(0.18,T3)\nprint (g3)",
+      "metadata": {
+        "trusted": true
+      },
+      "execution_count": 16,
+      "outputs": [
+        {
+          "name": "stdout",
+          "text": "9.11403799307716\n",
+          "output_type": "stream"
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": "g4=gravedad(0.24,T4)\nprint (g4)",
+      "metadata": {
+        "trusted": true
+      },
+      "execution_count": 17,
+      "outputs": [
+        {
+          "name": "stdout",
+          "text": "9.916021193001912\n",
+          "output_type": "stream"
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": " g5=gravedad(0.22,T5)\nprint (g5)",
+      "metadata": {
+        "trusted": true
+      },
+      "execution_count": 18,
+      "outputs": [
+        {
+          "name": "stdout",
+          "text": "8.210579781959067\n",
+          "output_type": "stream"
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": "g6=gravedad(0.24,T6)\nprint (g6)",
+      "metadata": {
+        "trusted": true
+      },
+      "execution_count": 19,
+      "outputs": [
+        {
+          "name": "stdout",
+          "text": "8.743182011169136\n",
+          "output_type": "stream"
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": "g7=gravedad(0.26,T7)\nprint (g7)",
+      "metadata": {
+        "trusted": true
+      },
+      "execution_count": 20,
+      "outputs": [
+        {
+          "name": "stdout",
+          "text": "8.671121946242542\n",
+          "output_type": "stream"
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": "g8=gravedad(0.30,T8)\nprint (g8)",
+      "metadata": {
+        "trusted": true
+      },
+      "execution_count": 21,
+      "outputs": [
+        {
+          "name": "stdout",
+          "text": "9.049590812285462\n",
+          "output_type": "stream"
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": "#G=[g1,g2,g3,g4,g5,g6,g7,g8]\n#G\n",
+      "metadata": {
+        "trusted": true
+      },
+      "execution_count": 22,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": "#g_prom=np.mean(G)\n#g_prom",
+      "metadata": {
+        "trusted": true
+      },
+      "execution_count": 23,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": "T_p=np.array([T1,T2,T3,T4,T5,T6,T7,T8])\nT_p",
+      "metadata": {
+        "trusted": true
+      },
+      "execution_count": 24,
+      "outputs": [
+        {
+          "execution_count": 24,
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": "array([0.8005, 0.8345, 0.883 , 0.9775, 1.0285, 1.041 , 1.088 , 1.144 ])"
+          },
+          "metadata": {}
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": "t_err=t.std(axis=1)\nt_err\n",
+      "metadata": {
+        "trusted": true
+      },
+      "execution_count": 25,
+      "outputs": [
+        {
+          "execution_count": 25,
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": "array([0.00668954, 0.00165831, 0.00223607, 0.00384057, 0.00217945,\n       0.00921954, 0.00244949, 0.00316228])"
+          },
+          "metadata": {}
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": "l=np.arange(14,31,2)\nL=l/100\nL_list=L.tolist()\nL_list",
+      "metadata": {
+        "trusted": true
+      },
+      "execution_count": 26,
+      "outputs": [
+        {
+          "execution_count": 26,
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": "[0.14, 0.16, 0.18, 0.2, 0.22, 0.24, 0.26, 0.28, 0.3]"
+          },
+          "metadata": {}
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": "del L_list[3]\nL_list\nL_p=np.array(L_list)",
+      "metadata": {
+        "trusted": true
+      },
+      "execution_count": 27,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": "l_err=L_p.std()\nl_err",
+      "metadata": {
+        "trusted": true
+      },
+      "execution_count": 28,
+      "outputs": [
+        {
+          "execution_count": 28,
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": "0.054256336035526764"
+          },
+          "metadata": {}
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": "plt.figure()\nplt.errorbar(T_p,L_p,l_err,t_err,fmt='o',color='k')\nplt.xlabel('T(s)')\nplt.ylabel('L(m)')\nplt.savefig('error.pdf', bbox_inches='tight')\n",
+      "metadata": {
+        "trusted": true
+      },
+      "execution_count": 29,
+      "outputs": [
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": "<Figure size 640x480 with 1 Axes>",
+            "image/png": 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"
+          },
+          "metadata": {}
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": "#Y= np.log(np.abs(T_p))\n#Y\n",
+      "metadata": {
+        "trusted": true
+      },
+      "execution_count": 30,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": "#X=np.log(2*np.pi*np.sqrt(L_p/g_prom))\n#X",
+      "metadata": {
+        "trusted": true
+      },
+      "execution_count": 31,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": "plt.figure()\nplt.plot(T_p,L_p)\nplt.xlabel(\"L(m)\")\nplt.ylabel(\"T(s)\")\nplt.savefig('periodo.pdf', bbox_inches='tight')\nplt.title(\"Grafica del Periodo (T) en funcion de la longitd (L)\")\n",
+      "metadata": {
+        "trusted": true
+      },
+      "execution_count": 32,
+      "outputs": [
+        {
+          "execution_count": 32,
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": "Text(0.5, 1.0, 'Grafica del Periodo (T) en funcion de la longitd (L)')"
+          },
+          "metadata": {}
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": "<Figure size 640x480 with 1 Axes>",
+            "image/png": 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"
+          },
+          "metadata": {}
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": "plt.figure()\nplt.plot(X1,Y2)\nplt.xlabel(\"ln(L)(m)\")\n\nplt.ylabel(\"ln(T)(s)\")\nplt.savefig('Tlineal.pdf', bbox_inches='tight')\nplt.title(\"Grafica lienal de Periodo (T) en funcion de la longitd (L)\")\n",
+      "metadata": {
+        "trusted": true
+      },
+      "execution_count": 33,
+      "outputs": [
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": "<Figure size 640x480 with 0 Axes>"
+          },
+          "metadata": {}
+        },
+        {
+          "ename": "<class 'NameError'>",
+          "evalue": "name 'X1' is not defined",
+          "traceback": [
+            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+            "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
+            "Cell \u001b[0;32mIn[33], line 2\u001b[0m\n\u001b[1;32m      1\u001b[0m plt\u001b[38;5;241m.\u001b[39mfigure()\n\u001b[0;32m----> 2\u001b[0m plt\u001b[38;5;241m.\u001b[39mplot(\u001b[43mX1\u001b[49m,Y2)\n\u001b[1;32m      3\u001b[0m plt\u001b[38;5;241m.\u001b[39mxlabel(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mln(L)(m)\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n\u001b[1;32m      5\u001b[0m plt\u001b[38;5;241m.\u001b[39mylabel(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mln(T)(s)\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n",
+            "\u001b[0;31mNameError\u001b[0m: name 'X1' is not defined"
+          ],
+          "output_type": "error"
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": "X1= np.log(L_p)\nX1",
+      "metadata": {
+        "trusted": true
+      },
+      "execution_count": 34,
+      "outputs": [
+        {
+          "execution_count": 34,
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": "array([-1.96611286, -1.83258146, -1.71479843, -1.51412773, -1.42711636,\n       -1.34707365, -1.27296568, -1.2039728 ])"
+          },
+          "metadata": {}
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": "Y2= np.log(np.abs(T_p))\nY2",
+      "metadata": {
+        "trusted": true
+      },
+      "execution_count": 35,
+      "outputs": [
+        {
+          "execution_count": 35,
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": "array([-0.22251875, -0.18092254, -0.12443008, -0.02275699,  0.02810143,\n        0.04018179,  0.08434115,  0.13453089])"
+          },
+          "metadata": {}
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": "fit= np.polyfit(X1,Y2,1)\nfit",
+      "metadata": {
+        "trusted": true
+      },
+      "execution_count": 36,
+      "outputs": [
+        {
+          "execution_count": 36,
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": "array([0.46757705, 0.68472351])"
+          },
+          "metadata": {}
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": "g_v= (2*np.pi/np.exp(fit[1]))**2\ng_v",
+      "metadata": {
+        "trusted": true
+      },
+      "execution_count": 37,
+      "outputs": [
+        {
+          "execution_count": 37,
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": "10.037289568148584"
+          },
+          "metadata": {}
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": "",
+      "metadata": {
+        "trusted": true
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": "",
+      "metadata": {},
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": "",
+      "metadata": {},
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": "",
+      "metadata": {},
+      "execution_count": null,
+      "outputs": []
+    }
+  ]
+}
\ No newline at end of file