{ "cells": [ { "cell_type": "code", "execution_count": 52, "metadata": { "ExecuteTime": { "end_time": "2019-05-09T09:28:17.473624Z", "start_time": "2019-05-09T09:28:17.464625Z" }, "init_cell": true }, "outputs": [], "source": [ "from scipy.optimize import curve_fit\n", "import pandas as pd\n", "import numpy as np\n", "from uncertainties import ufloat\n", "from bokeh.plotting import figure, show, output_file, save\n", "import matplotlib.pyplot as plt" ] }, { "cell_type": "markdown", "metadata": { "ExecuteTime": { "end_time": "2019-05-08T10:57:05.228358Z", "start_time": "2019-05-08T10:57:05.225884Z" } }, "source": [ "### Data from a file into a Padans dataframe" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.html" ] }, { "cell_type": "code", "execution_count": 40, "metadata": { "ExecuteTime": { "end_time": "2019-05-08T15:37:48.147782Z", "start_time": "2019-05-08T15:37:47.917808Z" } }, "outputs": [], "source": [ "data = pd.read_csv('./data_b.txt', sep=\" \", header=None)\n", "data.columns = ['Eneergia in eV','Conteggi']" ] }, { "cell_type": "code", "execution_count": 41, "metadata": { "ExecuteTime": { "end_time": "2019-05-08T15:38:00.823382Z", "start_time": "2019-05-08T15:38:00.801638Z" } }, "outputs": [], "source": [ "data['weights']=data['Conteggi'].apply(lambda elem: 0.05*elem)" ] }, { "cell_type": "code", "execution_count": 42, "metadata": { "ExecuteTime": { "end_time": "2019-05-08T15:38:04.060394Z", "start_time": "2019-05-08T15:38:04.041340Z" } }, "outputs": [ { "data": { "text/plain": [ "Eneergia in eV int64\n", "Conteggi float64\n", "weights float64\n", "dtype: object" ] }, "execution_count": 42, "metadata": {}, "output_type": "execute_result" } ], "source": [ "data.dtypes" ] }, { "cell_type": "code", "execution_count": 43, "metadata": { "ExecuteTime": { "end_time": "2019-05-08T15:38:12.418437Z", "start_time": "2019-05-08T15:38:12.386226Z" } }, "outputs": [ { "data": { "text/html": [ "
\n", " | Eneergia in eV | \n", "Conteggi | \n", "weights | \n", "
---|---|---|---|
0 | \n", "0 | \n", "0.366037 | \n", "0.018302 | \n", "
1 | \n", "5 | \n", "4.758470 | \n", "0.237924 | \n", "
2 | \n", "10 | \n", "11.042100 | \n", "0.552105 | \n", "
3 | \n", "15 | \n", "17.813800 | \n", "0.890690 | \n", "
4 | \n", "20 | \n", "24.646600 | \n", "1.232330 | \n", "
5 | \n", "25 | \n", "32.333400 | \n", "1.616670 | \n", "
6 | \n", "30 | \n", "41.667100 | \n", "2.083355 | \n", "
7 | \n", "35 | \n", "50.817800 | \n", "2.540890 | \n", "
8 | \n", "40 | \n", "65.885900 | \n", "3.294295 | \n", "
9 | \n", "45 | \n", "77.842900 | \n", "3.892145 | \n", "
10 | \n", "50 | \n", "85.956500 | \n", "4.297825 | \n", "
11 | \n", "55 | \n", "88.945700 | \n", "4.447285 | \n", "
12 | \n", "60 | \n", "86.017500 | \n", "4.300875 | \n", "
13 | \n", "65 | \n", "84.248300 | \n", "4.212415 | \n", "
14 | \n", "70 | \n", "84.736400 | \n", "4.236820 | \n", "
15 | \n", "75 | \n", "83.455300 | \n", "4.172765 | \n", "
16 | \n", "80 | \n", "79.246000 | \n", "3.962300 | \n", "
17 | \n", "85 | \n", "69.729200 | \n", "3.486460 | \n", "
18 | \n", "90 | \n", "66.313000 | \n", "3.315650 | \n", "
19 | \n", "95 | \n", "55.759200 | \n", "2.787960 | \n", "
20 | \n", "100 | \n", "45.205400 | \n", "2.260270 | \n", "
21 | \n", "105 | \n", "29.771100 | \n", "1.488555 | \n", "
22 | \n", "110 | \n", "20.437100 | \n", "1.021855 | \n", "
23 | \n", "115 | \n", "15.556600 | \n", "0.777830 | \n", "