From 0ee71b6e265ddd2e764c9a62a101781bdd5877a7 Mon Sep 17 00:00:00 2001
From: Juan David Hernandez Ramirez <hernandezj@jupyterMiLAB>
Date: Thu, 18 Feb 2021 03:52:32 -0500
Subject: [PATCH] =?UTF-8?q?se=20a=C3=B1ade=20tabla=20con=20resumen?=
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---
ejercicio1-clase05.ipynb | 38 ++++++++++++++++++++++++++++++++++++--
1 file changed, 36 insertions(+), 2 deletions(-)
diff --git a/ejercicio1-clase05.ipynb b/ejercicio1-clase05.ipynb
index aa62094..6d9337d 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": {
--
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