database_dumper/jupyter-template at master · MMTObservatory/database_dumper · GitHub

1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Welcome To Jupyter \n",
    "## To begin click Cells->Run All  in the menu above\n",
    " This page will allow you to do analysis on data retrieved from the extractor. Each cell is a small python script that you can run by clicking the arrow on the left side. The first cell below will retrieve your data from the extractor and store it in a pandas DataFrame called df. You can see all the ways you can analyze data in a DataFrame with [this tutorial](https://www.tutorialspoint.com/python_pandas/python_pandas_dataframe.htm). \n",
    " \n",
    " Other cells in this notebook provide some statistics and a plot of the data. \n",
    " \n",
    " [Return to the extractor](/)",
	" [Return to the job home](/job/data.html?jobid={{ jobid }})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "df = pd.read_csv(\"http://ops2.mmto.arizona.edu:8000/data/{{ jobid }}/processed.csv\")\n",
    "df.index = pd.to_datetime(df.timestamp)\n",
    "del df['timestamp']\n",
    "%matplotlib notebook\n",
	"\n",
	"# The MPL epoch and pandas epoch have diverged\n",
	"# This puts them in line but will likely break one day soon.\n",
	"import matplotlib.dates\n",
	"matplotlib.dates.set_epoch('0000-12-31')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "df.describe()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "df.plot(grid=True)"
   ]
  },
  {
   "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": "3.8.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}