diff --git a/ejercicio1-clase05.ipynb b/ejercicio1-clase05.ipynb index aa6209496de9aa798d2bfc50f24d9d1b2d24ed14..6d9337d10ce96413504f0f0ba2d071fbb8824538 100644 --- a/ejercicio1-clase05.ipynb +++ b/ejercicio1-clase05.ipynb @@ -23,7 +23,7 @@ }, { "cell_type": "code", - "execution_count": 1, + "execution_count": 64, "metadata": {}, "outputs": [], "source": [ @@ -33,7 +33,9 @@ "import scipy\n", "from scipy.optimize import leastsq\n", "import statistics\n", - "import math" + "import math\n", + "from tabulate import tabulate\n", + "import pandas as pd" ] }, { @@ -1325,6 +1327,38 @@ "FHWMB=2*math.sqrt(2*math.log(2))*sigmasnpB\n", "FHWMB.mean()" ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Resumiendo los valores de FHWM en las 4 matrices, obtenemos." + ] + }, + { + "cell_type": "code", + "execution_count": 63, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "+--------------------+---------+---------+---------+\n", + "| Escala de Grises | R | G | B |\n", + "|--------------------+---------+---------+---------|\n", + "| 2.09742 | 2.31156 | 2.32033 | 2.38171 |\n", + "+--------------------+---------+---------+---------+\n" + ] + } + ], + "source": [ + "df = pd.DataFrame({'Escala de Grises' : [FHWM.mean()],\n", + " 'R' : [FHWMR.mean()],\n", + " 'G' : [FHWMG.mean()],\n", + " 'B' : [FHWMB.mean()]})\n", + "print(tabulate(df, headers='keys', tablefmt='psql',showindex=\"never\"))" + ] } ], "metadata": {