From 128ed6a898f35013f9caa35a5d56de0b37e9942f Mon Sep 17 00:00:00 2001
From: Gerardo Semprum <semprumg@jupyterMiLAB>
Date: Fri, 26 Feb 2021 15:04:48 -0500
Subject: [PATCH] Codigo desarrollado
---
codigo/Reporte_Codigo_SemprumG.ipynb | 591 +++++++++++++++++++++++++++
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create mode 100644 codigo/Reporte_Codigo_SemprumG.ipynb
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+{
+ "cells": [
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "#### Análisis de datos.\n",
+ "Gerardo Semprúm.\n",
+ "\n",
+ "Universidad Central de Venezuela.\n",
+ "\n",
+ "Módulo de datos 1."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Importando las librerias necesarias:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 2,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "import pandas as pd\n",
+ "import numpy as np\n",
+ "import matplotlib.pyplot as plt\n",
+ "import sys \n",
+ "sys.path.append(\"../data-used/4B-1.csv\")\n",
+ "sys.path.append(\"../data-used/4B-6.csv\")\n",
+ "sys.path.append(\"../data-used/4B-9.csv\")"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Las tablas presentadas a continuación fueron el resultado analizar distintos sistemas acuosos de polimero-surfactante, con el fin de determinar la conductividad eléctrica en un rango de frecuencias entre 1 Hz y 10 kHz, usando una fuente de corriente alterna, un amplificador “Lock-in†y un condensador de placas plano-paralelas de acero inoxidable de 40mL de volumen en donde se colocó la muestra. Realizado en el laboratorio de Paramagnetismo de la \"Facultad de Ciencias\" en la \"Universidad Central de Venezuela\".\n"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Los datos con los que cuenta cada tabla son : frecuencia(f, (Hz)), frecuencia angular(w, (rad/s)), modulo de la impedancia(Z, (ohm)) y conductividad (s, (S/m)). **Nota: f y w son iguales en todas las tablas. Siendo w un valor calculado y f producido por un generador de frecuencias.**"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 3,
+ "metadata": {
+ "scrolled": true
+ },
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ " f w Z s \n",
+ "0 1 6.283185 178.909 0.004839\n",
+ "1 10 62.831853 61.973 0.013971\n",
+ "2 30 188.495559 37.566 0.023047\n",
+ "3 70 439.822971 26.152 0.033107\n",
+ "4 100 628.318531 22.600 0.038310\n",
+ "5 150 942.477796 19.405 0.044617\n",
+ "6 200 1256.637061 17.262 0.050157\n",
+ "7 300 1884.955592 13.977 0.061945\n",
+ "8 400 2513.274123 12.866 0.067291\n",
+ "9 500 3141.592654 11.681 0.074118\n",
+ "10 1000 6283.185307 8.773 0.098692\n",
+ "11 1500 9424.777961 7.874 0.109955\n",
+ "12 2000 12566.370610 7.338 0.117992\n",
+ "13 3000 18849.555920 6.803 0.127269\n",
+ "14 4000 25132.741230 6.564 0.131894\n",
+ "15 5000 31415.926540 6.379 0.135729\n",
+ "16 6000 37699.111840 6.292 0.137607\n",
+ "17 7000 43982.297150 6.220 0.139188\n",
+ "18 8000 50265.482460 6.171 0.140302\n",
+ "19 9000 56548.667760 6.135 0.141136\n",
+ "20 10000 62831.853070 6.101 0.141921\n"
+ ]
+ }
+ ],
+ "source": [
+ "#Definimos la primera solución (4B-1):\n",
+ "sol_1 = pd.read_csv(\"../data-used/4B-1.csv\",\";\")\n",
+ "print(sol_1)\n",
+ "\n",
+ "#Asignando columnas a variables para mejorar la manipulación de los datos:\n",
+ "z1 = sol_1.loc[:,\"Z\"]\n",
+ "s1 = sol_1.loc[:,\"s \"]\n",
+ "f = sol_1.loc[:,\"f\"]"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Como primer procesamiento de datos, graficamos la conductividad de dicha solución en función de la frecuencia aplicada:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 4,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "<matplotlib.collections.PathCollection at 0x7fa6fe80f5c0>"
+ ]
+ },
+ "execution_count": 4,
+ "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": [
+ "#Grafico de la Conductividad vs la frecuencia \n",
+ "plt.title(\"Conductividad 1 vs frecuencia\")\n",
+ "plt.ylabel(\"Conductividad (S/m)\")\n",
+ "plt.xlabel(\"Frecuencia(Hz)\")\n",
+ "\n",
+ "plt.scatter(f,s1,color=\"red\")"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 5,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "<matplotlib.collections.PathCollection at 0x7fa6fe71aba8>"
+ ]
+ },
+ "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": [
+ "#Grafico de la impedancia vs frecuencia:\n",
+ "plt.title(\"Impendancia 1 vs frecuencia\")\n",
+ "plt.ylabel(\"Impedancia (Ohm)\")\n",
+ "plt.xlabel(\"Frecuencia(Hz)\")\n",
+ "plt.scatter(f,z1,color=\"blue\")"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Es normal observar dicho comportamiento en las graficas debido a la ecuación que rige todo el estudio que se realiza. La relación entre conductividad-impedancia queda descrita por:\n",
+ "$$ \\sigma = \\frac{C_{cond}}{Z} $$\n",
+ "\n",
+ "Siendo sigma la letra asignada a la conductividad y C una constante asociada al condensador de placas paralelas que se usó en el experimento. \n",
+ "\n",
+ "Como sugiere la formulación, la conductividad será inversamente proporcional a la impedancia del sistema. Se obtienen ambas graficas para demostrar el comportamiento de ambas variables, en las soluciones siguientes solo se trabajará con la impedancia "
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 6,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ " f w Z s\n",
+ "0 1 6.283185 140.774 0.006150\n",
+ "1 10 62.831853 34.563 0.025050\n",
+ "2 30 188.495559 19.715 0.043916\n",
+ "3 70 439.822971 14.192 0.061006\n",
+ "4 100 628.318531 12.613 0.068643\n",
+ "5 150 942.477796 11.084 0.078109\n",
+ "6 200 1256.637061 10.038 0.086251\n",
+ "7 300 1884.955592 8.631 0.100317\n",
+ "8 400 2513.274123 7.804 0.110945\n",
+ "9 500 3141.592654 7.243 0.119529\n",
+ "10 1000 6283.185307 6.063 0.142806\n",
+ "11 1500 9424.777961 5.673 0.152609\n",
+ "12 2000 12566.370610 5.519 0.156880\n",
+ "13 3000 18849.555920 5.361 0.161490\n",
+ "14 4000 25132.741230 5.292 0.163609\n",
+ "15 5000 31415.926540 5.251 0.164881\n",
+ "16 6000 37699.111840 5.225 0.165693\n",
+ "17 7000 43982.297150 5.212 0.166124\n",
+ "18 8000 50265.482460 5.199 0.166524\n",
+ "19 9000 56548.667760 5.188 0.166882\n",
+ "20 10000 62831.853070 5.187 0.166933\n"
+ ]
+ }
+ ],
+ "source": [
+ "#Definiendo la segunda solución (4B-6):\n",
+ "sol_2 = pd.read_csv(\"../data-used/4B-6.csv\",\";\")\n",
+ "print(sol_2)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 7,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "#Asignando columnas a variables para mejorar la manipulación de los datos:\n",
+ "z2 = sol_2.loc[:,\"Z\"]\n",
+ "s2 = sol_2.loc[:,\"s\"]\n",
+ "f = sol_2.loc[:,\"f \"]"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 8,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "<matplotlib.collections.PathCollection at 0x7fa6fe691b00>"
+ ]
+ },
+ "execution_count": 8,
+ "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": [
+ "#Graficando la impendancia de la segunda solución:\n",
+ "plt.title(\"Impendancia 2 vs frecuencia\")\n",
+ "plt.ylabel(\"Impedancia (Ohm)\")\n",
+ "plt.xlabel(\"Frecuencia(Hz)\")\n",
+ "plt.scatter(f,z2,color=\"gray\")"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 9,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ " f w Z s\n",
+ "0 1 6.283185 112.323 0.00771\n",
+ "1 10 62.831853 25.317 0.03420\n",
+ "2 30 188.495559 15.905 0.05443\n",
+ "3 70 439.822971 12.770 0.06780\n",
+ "4 100 628.318531 11.825 0.07321\n",
+ "5 150 942.477796 10.788 0.08025\n",
+ "6 200 1256.637061 10.019 0.08641\n",
+ "7 300 1884.955592 8.908 0.09720\n",
+ "8 400 2513.274123 8.196 0.10563\n",
+ "9 500 3141.592654 7.678 0.11276\n",
+ "10 1000 6283.185307 6.704 0.12915\n",
+ "11 1500 9424.777961 6.394 0.13540\n",
+ "12 2000 12566.370610 6.248 0.13857\n",
+ "13 3000 18849.555920 6.108 0.14175\n",
+ "14 4000 25132.741230 6.025 0.14370\n",
+ "15 5000 31415.926540 5.979 0.14482\n",
+ "16 6000 37699.111840 5.954 0.14541\n",
+ "17 7000 43982.297150 5.932 0.14597\n",
+ "18 8000 50265.482460 5.920 0.14626\n",
+ "19 9000 56548.667760 5.918 0.14630\n",
+ "20 10000 62831.853070 5.907 0.14657\n"
+ ]
+ }
+ ],
+ "source": [
+ "#Definiendo la tercera soplución (4B-9):\n",
+ "sol_3 = pd.read_csv(\"../data-used/4B-9.csv\",\";\")\n",
+ "print(sol_3)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 10,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "#Asignando columnas a variables para mejorar la manipulación de los datos:\n",
+ "z3 = sol_3.loc[:,\"Z\"]\n",
+ "s3 = sol_3.loc[:,\"s\"]\n",
+ "f = sol_3.loc[:,\"f\"]"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 11,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "<matplotlib.collections.PathCollection at 0x7fa6fe6d3198>"
+ ]
+ },
+ "execution_count": 11,
+ "metadata": {},
+ "output_type": "execute_result"
+ },
+ {
+ "data": {
+ "image/png": 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weURsBVye583MrIX66yl8BpgBfLePvAD2HEyBkl4J7AYcARARfwf+LulA0igngFnAVcAXBlOGmZkNTn83xJuR3/cY4jInk+64+jNJ25LOUxwFbBoRi/IyDwOb9rWypBmkYMXEiROHuGpmZt1twNFHkj6RzwHU5sdI+ng/qwxkJLA9cFJEbAc8R69DRRERpN7Iy0TEzIiYGhFTx40b10A1zMystypDUo+MiCdrM/nk75ENlLkQWBgR1+X5s0lB4hFJ4wHy++IGyjAzs0GoEhRGSFJtRtIIYJ3BFhgRDwMLJL0uJ+0F3AFcAEzLadOA8wdbhpmZDU6V6xQuAc6SdEqe/1hOa8QngdmS1gHuA6aTAtSvJX0EeAB4b4NlmJnZaqoSFL5ACgT/muf/QLrGYNAi4iZgah9ZezWyXTMza0yVi9dWAifll5mZdbAqd0ndBfgyMCkvL9IAodc0t2pmZtZqVQ4fnQYcTbqeYEVzq2NmZu1UJSg8FREXN70mZmbWdlWCwpWSvg2cC7xQS4yIG5pWKzMza4sqQeEt+b08WmjQ9z4yM7Phq8roo6G+95GZmQ1TVXoKSHoH8HpgvVpaRHy1WZUyM7P2qHJDvJOB95GuQhZwCGl4qpmZdZgq9z7aOSIOB56IiK8AOwGvbW61zMysHaoEhWX5famkzYAXgfHNq5KZmbVLlXMKF+bnKXwbuIE08qihex+ZmdnwVGX00dfy5DmSLgTWi4inmlstMzNrh7pBQdK7+8kjIs5tTpXMzKxd+usp/HN+fxWwM3BFnt8D+DPpCmczM+sgdYNCREwHkPR7YJuIWJTnxwOnt6R2ZmbWUlVGH21RCwjZI8DEJtXHzMzaqMroo8slXQr8Ks+/D7iseVUyM7N2qTL66N8kvQvYLSfNjIjfNrdaZmbWDpXufUS6PuGZiLhM0ihJG0XEM82smJmZtV6Vex8dCZwNnJKTJgDnNbFOZmbWJlVONH8C2AV4GiAi7iENUzUzsw5TJSi8EBF/r81IGkm61YWZmXWYKkHhakn/AawvaR/gN8B/N7daZmbWDlWCwjHAEuBW4GPA74AvNbNSZmbWHlWGpK6UNAu4jnTY6O6I8OEjM7MONGBQyI/iPBm4l/TktcmSPhYRFze7cmZm1lpVrlP4LrBHRMwDkLQlcBHgoGBm1mGqnFN4phYQsvuAhi9ckzRC0o35GQ1ImizpOknzJJ0laZ1GyzAzs9VTJSjMkfQ7SUdImkYaefRXSe/u75kLFRwF3FmaPx44ISKmAE8AH2lg22ZmNghVgsJ6pDujvhXYnTQSaX3S8xbeOZhCJW0OvIP8WE9JAvYkXTkNMAs4aDDbNjOzwasy+mh6E8r9PvB5YKM8vzHwZEQsz/MLSbfTMDOzFqoy+mgy8Emgp7x8RBwwmAIlvRNYHBFzJe0+iPVnADMAJk70Yx3MzIZSldFH5wGnkc4lrByCMncBDpC0P+nQ1CuAE4HRkkbm3sLmwEN9rRwRM4GZAFOnTvX1EmZmQ6hKUHg+In4wVAVGxLHAsQC5p/C5iDhU0m+Ag4EzgWnA+UNVppmZVVPlRPOJko6TtJOk7WuvJtTlC8BnJM0jnWM4rQllmJlZP6r0FN4IHEYaHVQ7fBR5viERcRVwVZ6+D9ih0W2amdngVQkKhwCvKd8+28zMOlOVw0e3AaObXA8zMxsGqvQURgN3Sfor8EItcbBDUs3MbPiqEhSOa3otzMxsWKhyRfPVraiImZm1X92gIOkZ+n4Ws4CIiFc0rVZmZtYWdYNCRGxUL8/MzDpTldFHZmbWJRwUzMys4KBgZmYFBwUzMys4KJiZWcFBwczMCg4KZmZWcFAwM7OCg4KZmRUcFMzMrOCgYGZmBQcFMzMrdGdQmD8bzuuBM9ZK7/Nnt7tGZmbDQpWH7HSW+bPh+hmwYmmaX/pAmgeYfGj76mVmNgx0X0/h5i++FBBqVixN6WZmXa77gsLSB1cv3cysi3RfUBg1cfXSzcy6SPcFhW2/DiNGrZo2YlRKNzPrct0XFCYfCjvMhFGTAKX3HWb6JLOZGd04+ghSAHAQMDN7me7rKZiZWV0OCmZmVmh5UJC0haQrJd0h6XZJR+X0sZL+IOme/D6m1XUzM+t27egpLAc+GxHbADsCn5C0DXAMcHlEbAVcnufNzKyFWh4UImJRRNyQp58B7gQmAAcCs/Jis4CDWl03M7Nu19ZzCpJ6gO2A64BNI2JRznoY2LTOOjMkzZE0Z8mSJa2pqJlZl2hbUJC0IXAO8OmIeLqcFxEBRF/rRcTMiJgaEVPHjRvXgpqamXWPtgQFSWuTAsLsiDg3Jz8iaXzOHw8sbkfdzMy6WTtGHwk4DbgzIr5XyroAmJanpwHnt7puZmbdrh09hV2Aw4A9Jd2UX/sD3wT2kXQPsHeebw4/ZMfMrE8tv81FRFwDqE72Xk2vgB+yY2ZWV/dd0eyH7JiZ1dV9QcEP2TEzq6v7goIfsmNmVlf3BQU/ZMfMrK7uCwq1h+ysvfFLaSPWb199zMyGke4LCjUrl700/ffH0ggkD001sy7XnUGh3gikOUe1pz5mZsNEdwaFeiONXnzMvQUz62rdGRT6G2l07TQHBjPrWt0ZFPobaRQr4C+HwfUfb119zMyGie4MCpMPhXU27meBgHknu8dgZl2nO4MCwJtPfPn1CqsI+MuHfMM8M+sqLb8h3rBRu/ndtdPSIaN6fMM8M+si3dtTgPQlv+Ms6t+0NVux1L0GM+sK3R0UIAWGKf/CgIEBUq/hL4fBGXKAMLOO5KAAsMOPYadfwKhJFRbOj46uHVZancDgh/uY2TDnoFAz+VA46H7Y6ZcDnIAuWZ3nMNQe7rP0ASBWP6g0ElAcjMysIgeF3mo3zKvUa6D6cxgaebhPIwGl0WDUqHYFpG4rt51lu80d1WZFxJBusJWmTp0ac+bMaV4BvR/d2ZdRk1IPYyBnrEVx6GkVgg+u7H/d83ryl/ogym5k3Ub19fmNGJWCbjNHcnVbue0s221uXblDWLakuRExta889xT687JeQ6+T0avzHIZGHu7TyNPi2vmkuXY9+rTbym1n2W5z68ptUdkOCgOpnWv4YJRORiu9r050buThPo0ElHY+aa5dAanbym1n2W5z68ptUdkOCqujCBAr0/vqdBVX6XWsZlBpJKC080lz7QpI3VZuO8t2m1tXbovKdlBopcEGlUYCSiPrNqpdAanbym1n2W5z68ptUdk+0WzNNX92Ot659MH0a2bbr7cmIHVbue0s221e49rc34lmBwUzsy7j0UdmZlaJg4KZmRUcFMzMrOCgYGZmBQcFMzMrrNGjjyQtAfq4sU8lmwCPDmF11gRuc3dwm7tDI22eFBHj+spYo4NCIyTNqTckq1O5zd3Bbe4OzWqzDx+ZmVnBQcHMzArdHBRmtrsCbeA2dwe3uTs0pc1de07BzMxerpt7CmZm1ouDgpmZFboyKEh6u6S7Jc2TdEy76zNYkraQdKWkOyTdLumonD5W0h8k3ZPfx+R0SfpBbvctkrYvbWtaXv4eSdPa1aaqJI2QdKOkC/P8ZEnX5badJWmdnL5unp+X83tK2zg2p98t6W1takolkkZLOlvSXZLulLRTp+9nSUfnv+vbJP1K0nqdtp8l/VTSYkm3ldKGbL9KerOkW/M6P5DU65nCfYiIrnoBI4B7gdcA6wA3A9u0u16DbMt4YPs8vRHwv8A2wLeAY3L6McDxeXp/4GLSw6Z3BK7L6WOB+/L7mDw9pt3tG6DtnwHOAC7M878G3p+nTwb+NU9/HDg5T78fOCtPb5P3/brA5Pw3MaLd7eqnvbOAj+bpdYDRnbyfgQnAfGD90v49otP2M7AbsD1wWyltyPYrcH1eVnnd/QasU7s/lDbshJ2AS0vzxwLHtrteQ9S284F9gLuB8TltPHB3nj4F+EBp+btz/geAU0rpqyw33F7A5sDlwJ7AhfkP/lFgZO99DFwK7JSnR+bl1Hu/l5cbbi/glfkLUr3SO3Y/56CwIH/Rjcz7+W2duJ+Bnl5BYUj2a867q5S+ynL1Xt14+Kj2x1azMKet0XJ3eTvgOmDTiFiUsx4GNs3T9dq+pn0m3wc+D6zM8xsDT0bE8jxfrn/Rtpz/VF5+TWrzZGAJ8LN8yOwnkjagg/dzRDwEfAd4EFhE2m9z6ez9XDNU+3VCnu6d3q9uDAodR9KGwDnApyPi6XJepJ8IHTPuWNI7gcURMbfddWmhkaRDDCdFxHbAc6TDCoUO3M9jgANJAXEzYAPg7W2tVBu0Y792Y1B4CNiiNL95TlsjSVqbFBBmR8S5OfkRSeNz/nhgcU6v1/Y16TPZBThA0v3AmaRDSCcCoyWNzMuU61+0Lee/EniMNavNC4GFEXFdnj+bFCQ6eT/vDcyPiCUR8SJwLmnfd/J+rhmq/fpQnu6d3q9uDAp/BbbKoxjWIZ2UuqDNdRqUPJLgNODOiPheKesCoDYCYRrpXEMt/fA8imFH4KncTb0U2FfSmPwLbd+cNuxExLERsXlE9JD23RURcShwJXBwXqx3m2ufxcF5+cjp78+jViYDW5FOyg07EfEwsEDS63LSXsAddPB+Jh022lHSqPx3Xmtzx+7nkiHZrznvaUk75s/w8NK26mv3SZY2ndjZnzRS517gi+2uTwPt2JXUtbwFuCm/9icdS70cuAe4DBiblxfwo9zuW4GppW19GJiXX9Pb3baK7d+dl0YfvYb0zz4P+A2wbk5fL8/Py/mvKa3/xfxZ3E2FURltbuubgDl5X59HGmXS0fsZ+ApwF3Ab8AvSCKKO2s/Ar0jnTF4k9Qg/MpT7FZiaP797gR/Sa7BCXy/f5sLMzArdePjIzMzqcFAwM7OCg4KZmRUcFMzMrOCgYGZmBQcF6wiSVki6qfTqaXed+iNpM0lnV1huvF66E+zutelS/umSDu57bZD0HUl7Nl5j6xYjB17EbI2wLCLe1FdGvnBHEbGyr/x2iIi/8dJFWP35DHBqA0X9v7z+FQ1sw7qIewrWkST15Pvn/5x08c4Wkv5d0l/zvei/Ulr28Jx2s6Rf5LRVfoFLerY0/bLt5PLulHSq0jMAfi9p/Zw3RdJlefs3SNoyL39bad0/5bwbJO1casp7gEsqtHdqqZd0q6QAiIgHgI0lvbqBj9O6iHsK1inWl3RTnp4PHE26pcG0iLhW0r55fgfSlaEXSNqNdH+cLwE7R8Sjksb2V0g/23kwp38gIo6U9GvSF/ovgdnANyPit5LWI/0Ye1Vps4uBfSLieUlbka5ynZpvy/BERLxQWvb/lNoJMJF0Vfcc0lXPSPo2qwaSG0j3DTqnv7aZgYOCdY5VDh/lcwoPRMS1OWnf/Loxz29I+hLfFvhNRDwKEBGPD1BOve08SLqB2005fS7QI2kjYEJE/DZv//lcv/I21wZ+KOlNwArgtTl9POmW2WV/ioh3ltp5ejlT0vtIN8vbt5S8mHSnUbMBOShYJ3uuNC3gGxFxSnkBSZ+ss+5y8uFVSWuRnnbW33Z6gPIv+hXA+hXreTTwCClArQU8n9OXke7pU4mkNwBfBnaLiBWlrPXytswG5HMK1i0uBT6s9OwJJE2Q9CrSCdhDJG2c02uHj+4H3pynDyD9mu9vO32KiGeAhZIOysuvK2lUr8VeCSzKJ8IPIz0yFtJNG3uqNE7SaNJhp8Mjonfv4rWk8ypmA3JQsK4QEb8nPdP5L5JuJT2TYKOIuB34OnC1pJuB2i3ITwXemtN2Ivc66m1ngOIPAz4l6Rbgz0Dvk74/BqblsrYulfUccK+kKRWaeCAwCTi1dsIZiudtTCHdYdVsQL5LqtkwJuldwJsj4ksNrL99RPzn0NbMOpXPKZgNY3nE0sYNbGIk8N2hqo91PvcUzMys4HMKZmZWcFAwM7OCg4KZmRUcFMzMrOCgYGZmhf8PAVEOwjRNrisAAAAASUVORK5CYII=\n",
+ "text/plain": [
+ "<Figure size 432x288 with 1 Axes>"
+ ]
+ },
+ "metadata": {
+ "needs_background": "light"
+ },
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "#Graficando la impendancia de la segunda solución:\n",
+ "plt.title(\"Impendancia 3 vs frecuencia\")\n",
+ "plt.ylabel(\"Impedancia (Ohm)\")\n",
+ "plt.xlabel(\"Frecuencia(Hz)\")\n",
+ "plt.scatter(f,z3,color=\"orange\")"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Observamos el mismo comportamiento en todas las soluciones pero con cierta singularidad: Las concentraciones de polimero-surfactante varian en cada preparado, causando asà una mayor o menor conductividad dependiendo con que solución se trabaje. "
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 12,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "[<matplotlib.lines.Line2D at 0x7fa6fe63c748>,\n",
+ " <matplotlib.lines.Line2D at 0x7fa6fe5d3a20>,\n",
+ " <matplotlib.lines.Line2D at 0x7fa6fe5d3a90>]"
+ ]
+ },
+ "execution_count": 12,
+ "metadata": {},
+ "output_type": "execute_result"
+ },
+ {
+ "data": {
+ "image/png": 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\n",
+ "text/plain": [
+ "<Figure size 720x864 with 1 Axes>"
+ ]
+ },
+ "metadata": {
+ "needs_background": "light"
+ },
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "#Ajustamos el tamaño del plot a 10x12 pulgadas para observar de mejor manera el plot.\n",
+ "plt.figure(figsize=(10,12))\n",
+ "plt.subplot(111)\n",
+ "\n",
+ "plt.title(\"Impendancia vs frecuencia\")\n",
+ "plt.ylabel(\"Impedancia (Ohm)\")\n",
+ "plt.xlabel(\"Frecuencia(Hz)\")\n",
+ "plt.grid()\n",
+ "plt.xlim(-500,10500) \n",
+ "plt.ylim(3, 185)\n",
+ "\n",
+ "#Graficando la impedancia de cada una de las soluciones:\n",
+ "plt.plot(f,z1,\"ob\", f,z2,\"Dr\", f, z3,\"sy\")\n"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Después de alcanzar cierto rango de frecuencias (~1500 Hz), se obtiene una cota diferente para cada solución a pesar de que los valores alcanzados son muy cercanos. También se observa una clara disminución en la impedancia de las soluciones a medida que la frecuencia va aumentando."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "De forma equivalente, podemos graficar la conductividad:\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 13,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "[<matplotlib.lines.Line2D at 0x7fa6fe5b92e8>,\n",
+ " <matplotlib.lines.Line2D at 0x7fa6fe553550>,\n",
+ " <matplotlib.lines.Line2D at 0x7fa6fe553668>]"
+ ]
+ },
+ "execution_count": 13,
+ "metadata": {},
+ "output_type": "execute_result"
+ },
+ {
+ "data": {
+ "image/png": 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\n",
+ "text/plain": [
+ "<Figure size 720x864 with 1 Axes>"
+ ]
+ },
+ "metadata": {
+ "needs_background": "light"
+ },
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "plt.figure(figsize=(10,12))\n",
+ "plt.subplot(111)\n",
+ "\n",
+ "plt.title(\"Conductividad vs frecuencia\")\n",
+ "plt.ylabel(\"Conductividad (S/m)\")\n",
+ "plt.xlabel(\"Frecuencia(Hz)\")\n",
+ "plt.grid()\n",
+ "plt.plot(f,s1,\"ob\", f,s2,\"Dr\", f, s3,\"sy\")\n"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Si fijamos una frecuencia (en este caso, 4 kHz) podemos hacer un histograma para mostrar la conductividad o resistividad en cada una de las soluciones:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 14,
+ "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": [
+ "#Creando listas de valores de conductividad y solución que posee dicha conductividad\n",
+ "\n",
+ "cond_1 = sol_1.loc[14,\"s \"]\n",
+ "cond_2 = sol_2.loc[14,\"s\"]\n",
+ "cond_3 = sol_3.loc[14,\"s\"]\n",
+ "\n",
+ "cond = [cond_1, cond_2, cond_3]\n",
+ "sol = [\"4B-1\",\"4B-6\",\"4B-9\"]\n",
+ "\n",
+ "plt.title(\"Conductividad a 4kHz\")\n",
+ "plt.ylabel(\"Conductividad (S/m)\")\n",
+ "plt.xlabel(\"Soluciones utilizadas\")\n",
+ "plt.bar(sol,cond,width=0.3,color=\"orange\")\n",
+ "plt.grid()\n",
+ "plt.show()"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Concluyendo asà que la solución 4B-6, resulta ser la mas conductora de las tres soluciones estudiadas."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 15,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "[<matplotlib.lines.Line2D at 0x7fa6fe4b8dd8>]"
+ ]
+ },
+ "execution_count": 15,
+ "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": [
+ "plt.ylim(0.120, 0.170)\n",
+ "\n",
+ "plt.title(\"Conductividad a 4kHz\")\n",
+ "plt.ylabel(\"Conductividad (S/m)\")\n",
+ "plt.xlabel(\"Soluciones utilizadas\")\n",
+ "plt.grid()\n",
+ "plt.plot(sol,cond,\"ob\")"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": []
+ }
+ ],
+ "metadata": {
+ "kernelspec": {
+ "display_name": "Python 3",
+ "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": "3.7.3"
+ }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}
--
GitLab