diff --git a/caidalibre.ipynb b/caidalibre.ipynb
new file mode 100644
index 0000000000000000000000000000000000000000..27a563097d5b64a3252f16e32d533621d0a0c5f7
--- /dev/null
+++ b/caidalibre.ipynb
@@ -0,0 +1,239 @@
+{
+  "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": 42,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": "dt = np.genfromtxt ('datos2.1.txt')\ndt",
+      "metadata": {
+        "trusted": true
+      },
+      "execution_count": 43,
+      "outputs": [
+        {
+          "execution_count": 43,
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": "array([[ 0.      , -0.001378],\n       [ 0.033   , -0.02619 ],\n       [ 0.067   , -0.04962 ],\n       [ 0.1     , -0.09097 ],\n       [ 0.133   , -0.14    ],\n       [ 0.167   , -0.196   ],\n       [ 0.2     , -0.27    ],\n       [ 0.233   , -0.34    ],\n       [ 0.267   , -0.421   ],\n       [ 0.3     , -0.512   ],\n       [ 0.333   , -0.606   ],\n       [ 0.367   , -0.712   ],\n       [ 0.4     , -0.76    ]])"
+          },
+          "metadata": {}
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": "t= dt[:,0]\nt",
+      "metadata": {
+        "trusted": true
+      },
+      "execution_count": 44,
+      "outputs": [
+        {
+          "execution_count": 44,
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": "array([0.   , 0.033, 0.067, 0.1  , 0.133, 0.167, 0.2  , 0.233, 0.267,\n       0.3  , 0.333, 0.367, 0.4  ])"
+          },
+          "metadata": {}
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": "y=dt[:,1]\ny",
+      "metadata": {
+        "trusted": true
+      },
+      "execution_count": 45,
+      "outputs": [
+        {
+          "execution_count": 45,
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": "array([-0.001378, -0.02619 , -0.04962 , -0.09097 , -0.14    , -0.196   ,\n       -0.27    , -0.34    , -0.421   , -0.512   , -0.606   , -0.712   ,\n       -0.76    ])"
+          },
+          "metadata": {}
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": "plt.figure()\nplt.plot(t,y,'.')",
+      "metadata": {
+        "trusted": true
+      },
+      "execution_count": 46,
+      "outputs": [
+        {
+          "execution_count": 46,
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": "[<matplotlib.lines.Line2D at 0x4ebbe30>]"
+          },
+          "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": "Y= np.log(np.abs(y[1:]))\nX= np.log(t[1:])",
+      "metadata": {
+        "trusted": true
+      },
+      "execution_count": 47,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": "X\n",
+      "metadata": {
+        "trusted": true
+      },
+      "execution_count": 48,
+      "outputs": [
+        {
+          "execution_count": 48,
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": "array([-3.41124772, -2.70306266, -2.30258509, -2.01740615, -1.78976147,\n       -1.60943791, -1.45671683, -1.32050662, -1.2039728 , -1.09961279,\n       -1.00239343, -0.91629073])"
+          },
+          "metadata": {}
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": "Y",
+      "metadata": {
+        "trusted": true
+      },
+      "execution_count": 49,
+      "outputs": [
+        {
+          "execution_count": 49,
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": "array([-3.64237762, -3.0033613 , -2.3972255 , -1.96611286, -1.62964062,\n       -1.30933332, -1.07880966, -0.86512245, -0.66943065, -0.50087529,\n       -0.33967737, -0.27443685])"
+          },
+          "metadata": {}
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": "plt.figure()\nplt.plot(X,Y,'.')",
+      "metadata": {
+        "trusted": true
+      },
+      "execution_count": 50,
+      "outputs": [
+        {
+          "execution_count": 50,
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": "[<matplotlib.lines.Line2D at 0x59e23d8>]"
+          },
+          "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": "fit=np.polyfit(X,Y,1)\nfit",
+      "metadata": {
+        "trusted": true
+      },
+      "execution_count": 51,
+      "outputs": [
+        {
+          "execution_count": 51,
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": "array([1.43091898, 1.01116028])"
+          },
+          "metadata": {}
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": "fit[1]",
+      "metadata": {
+        "trusted": true
+      },
+      "execution_count": 52,
+      "outputs": [
+        {
+          "execution_count": 52,
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": "1.011160277048506"
+          },
+          "metadata": {}
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": "g=2*np.exp(fit[1])\ng",
+      "metadata": {
+        "trusted": true
+      },
+      "execution_count": 53,
+      "outputs": [
+        {
+          "execution_count": 53,
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": "5.49757704339364"
+          },
+          "metadata": {}
+        }
+      ]
+    }
+  ]
+}
\ No newline at end of file