Microsoft Word - ds2500_lab_sunspots.docx DS 2501: Intermediate Programming with Data / Lab Practicum Prof. Rachlin Northeastern University The Sunspot Cycle Overview Let’s learn a little about...

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Microsoft Word - ds2500_lab_sunspots.docx DS 2501: Intermediate Programming with Data / Lab Practicum Prof. Rachlin Northeastern University The Sunspot Cycle Overview Let’s learn a little about Astronomy this week! Sunspots appear on the surface of the sun (the photosphere) and are regions of relatively cooler temperatures and high magnetic activity. With an appropriate solar filter, sunspots are easily visible through a telescope. (Warning: never look at the sun through binoculars or a telescope without the right solar filters or you will be permanently blinded!). Some sunspots are so large that they can be seen during a sunset with the naked eye. The number of sunspots varies over approximately an 11-year cycle. Visualize the data provided using a python visualization library of your own choosing. The data provided is from the SILSO: Sunspot Index and Long-term Solar Observations website. (https://wwwbis.sidc.be/silso/datafiles). The file contains the month-by-month average daily sunspot numbers going back to 1749! Your task is to plot the number of sunspots over time and use your visualization to manually derive an estimate for the length of the sunspot cycle. Thought exercises There is some speculation that there may be other patterns embedded in the data. Perhaps the amplitude of the peak varies. Perhaps the actual period of the cycle (measured from peak to peak) fluctuates according to some pattern. What do you think? How might you measure the length of the sunspot cycle computationally? Though not required, feel free to try to do this! Time series data analysis is a rich topic and worthy of a class project. What to Submit Submit your code and any visualizations in PNG, JPG, or PDF format. You can include your derived estimate of the length of the sunspot cycle as a triple-quoted comment embedded at the bottom of your python code. 1749;01;1749.042; 96.7; -1.0; -1;1 1749;02;1749.123; 104.3; -1.0; -1;1 1749;03;1749.204; 116.7; -1.0; -1;1 1749;04;1749.288; 92.8; -1.0; -1;1 1749;05;1749.371; 141.7; -1.0; -1;1 1749;06;1749.455; 139.2; -1.0; -1;1 1749;07;1749.538; 158.0; -1.0; -1;1 1749;08;1749.623; 110.5; -1.0; -1;1 1749;09;1749.707; 126.5; -1.0; -1;1 1749;10;1749.790; 125.8; -1.0; -1;1 1749;11;1749.874; 264.3; -1.0; -1;1 1749;12;1749.958; 142.0; -1.0; -1;1 1750;01;1750.042; 122.2; -1.0; -1;1 1750;02;1750.123; 126.5; -1.0; -1;1 1750;03;1750.204; 148.7; -1.0; -1;1 1750;04;1750.288; 147.2; -1.0; -1;1 1750;05;1750.371; 150.0; -1.0; -1;1 1750;06;1750.455; 166.7; -1.0; -1;1 1750;07;1750.538; 142.3; -1.0; -1;1 1750;08;1750.623; 171.7; -1.0; -1;1 1750;09;1750.707; 152.0; -1.0; -1;1 1750;10;1750.790; 109.5; -1.0; -1;1 1750;11;1750.874; 105.5; -1.0; -1;1 1750;12;1750.958; 125.7; -1.0; -1;1 1751;01;1751.042; 116.7; -1.0; -1;1 1751;02;1751.123; 72.5; -1.0; -1;1 1751;03;1751.204; 75.5; -1.0; -1;1 1751;04;1751.288; 94.0; -1.0; -1;1 1751;05;1751.371; 101.2; -1.0; -1;1 1751;06;1751.455; 84.5; -1.0; -1;1 1751;07;1751.538; 110.5; -1.0; -1;1 1751;08;1751.623; 99.7; -1.0; -1;1 1751;09;1751.707; 39.2; -1.0; -1;1 1751;10;1751.790; 38.7; -1.0; -1;1 1751;11;1751.874; 47.5; -1.0; -1;1 1751;12;1751.958; 73.3; -1.0; -1;1 1752;01;1752.042; 58.3; -1.0; -1;1 1752;02;1752.124; 83.3; -1.0; -1;1 1752;03;1752.206; 118.3; -1.0; -1;1 1752;04;1752.290; 98.8; -1.0; -1;1 1752;05;1752.373; 99.5; -1.0; -1;1 1752;06;1752.456; 66.0; -1.0; -1;1 1752;07;1752.540; 130.7; -1.0; -1;1 1752;08;1752.624; 48.8; -1.0; -1;1 1752;09;1752.708; 45.2; -1.0; -1;1 1752;10;1752.791; 77.7; -1.0; -1;1 1752;11;1752.874; 62.7; -1.0; -1;1 1752;12;1752.958; 66.7; -1.0; -1;1 1753;01;1753.042; 73.3; -1.0; -1;1 1753;02;1753.123; 53.3; -1.0; -1;1 1753;03;1753.204; 76.2; -1.0; -1;1 1753;04;1753.288; 63.3; -1.0; -1;1 1753;05;1753.371; 60.0; -1.0; -1;1 1753;06;1753.455; 52.8; -1.0; -1;1 1753;07;1753.538; 36.7; -1.0; -1;1 1753;08;1753.623; 65.0; -1.0; -1;1 1753;09;1753.707; 46.7; -1.0; -1;1 1753;10;1753.790; 41.7; -1.0; -1;1 1753;11;1753.874; 33.3; -1.0; -1;1 1753;12;1753.958; 11.2; -1.0; -1;1 1754;01;1754.042; 0.0; -1.0; -1;1 1754;02;1754.123; 5.0; -1.0; -1;1 1754;03;1754.204; 2.8; -1.0; -1;1 1754;04;1754.288; 22.8; -1.0; -1;1 1754;05;1754.371; 34.5; -1.0; -1;1 1754;06;1754.455; 44.5; -1.0; -1;1 1754;07;1754.538; 31.3; -1.0; -1;1 1754;08;1754.623; 20.5; -1.0; -1;1 1754;09;1754.707; 13.7; -1.0; -1;1 1754;10;1754.790; 40.2; -1.0; -1;1 1754;11;1754.874; 22.0; -1.0; -1;1 1754;12;1754.958; 7.0; -1.0; -1;1 1755;01;1755.042; 17.0; -1.0; -1;1 1755;02;1755.123; 18.7; -1.0; -1;1 1755;03;1755.204; 11.3; -1.0; -1;1 1755;04;1755.288; 10.8; -1.0; -1;1 1755;05;1755.371; 0.0; -1.0; -1;1 1755;06;1755.455; 0.0; -1.0; -1;1 1755;07;1755.538; 14.3; -1.0; -1;1 1755;08;1755.623; 5.3; -1.0; -1;1 1755;09;1755.707; 29.7; -1.0; -1;1 1755;10;1755.790; 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81.7; -1.0; -1;1 1758;04;1758.288; 120.5; -1.0; -1;1 1758;05;1758.371; 77.3; -1.0; -1;1 1758;06;1758.455; 75.0; -1.0; -1;1 1758;07;1758.538; 73.3; -1.0; -1;1 1758;08;1758.623; 64.5; -1.0; -1;1 1758;09;1758.707; 104.2; -1.0; -1;1 1758;10;1758.790; 62.8; -1.0; -1;1 1758;11;1758.874; 71.7; -1.0; -1;1 1758;12;1758.958; 71.7; -1.0; -1;1 1759;01;1759.042; 80.5; -1.0; -1;1 1759;02;1759.123; 73.3; -1.0; -1;1 1759;03;1759.204; 78.0; -1.0; -1;1 1759;04;1759.288; 78.3; -1.0; -1;1 1759;05;1759.371; 81.7; -1.0; -1;1 1759;06;1759.455; 83.3; -1.0; -1;1 1759;07;1759.538; 85.0; -1.0; -1;1 1759;08;1759.623; 118.8; -1.0; -1;1 1759;09;1759.707; 128.7; -1.0; -1;1 1759;10;1759.790; 99.5; -1.0; -1;1 1759;11;1759.874; 77.2; -1.0; -1;1 1759;12;1759.958; 95.0; -1.0; -1;1 1760;01;1760.042; 112.2; -1.0; -1;1 1760;02;1760.124; 99.2; -1.0; -1;1 1760;03;1760.206; 124.5; -1.0; -1;1 1760;04;1760.290; 97.2; -1.0; -1;1 1760;05;1760.373; 120.0; -1.0; -1;1 1760;06;1760.456; 80.5; -1.0; -1;1 1760;07;1760.540; 110.0; -1.0; -1;1 1760;08;1760.624; 126.0; -1.0; -1;1 1760;09;1760.708; 102.2; -1.0; -1;1 1760;10;1760.791; 84.3; -1.0; -1;1 1760;11;1760.874; 99.5; -1.0; -1;1 1760;12;1760.958; 101.7; -1.0; -1;1 1761;01;1761.042; 116.7; -1.0; -1;1 1761;02;1761.123; 151.7; -1.0; -1;1 1761;03;1761.204; 134.5; -1.0; -1;1 1761;04;1761.288; 119.5; -1.0; -1;1 1761;05;1761.371; 178.7; -1.0; -1;1 1761;06;1761.455; 165.5; -1.0; -1;1 1761;07;1761.538; 156.8; -1.0; -1;1 1761;08;1761.623; 151.8; -1.0; -1;1 1761;09;1761.707; 167.8; -1.0; -1;1 1761;10;1761.790; 147.8; -1.0; -1;1 1761;11;1761.874; 149.5; -1.0; -1;1 1761;12;1761.958; 76.7; -1.0; -1;1 1762;01;1762.042; 73.0; -1.0; -1;1 1762;02;1762.123; 121.3; -1.0; -1;1 1762;03;1762.204; 76.2; -1.0; -1;1 1762;04;1762.288; 100.3; -1.0; -1;1 1762;05;1762.371; 66.5; -1.0; -1;1 1762;06;1762.455; 128.5; -1.0; -1;1 1762;07;1762.538; 56.3; -1.0; -1;1 1762;08;1762.623; 112.8; -1.0; -1;1 1762;09;1762.707; 114.2; -1.0; -1;1 1762;10;1762.790; 115.5; -1.0; -1;1 1762;11;1762.874; 129.7; -1.0; -1;1 1762;12;1762.958; 128.7; -1.0; -1;1 1763;01;1763.042; 94.2; -1.0;
Answered 1 days AfterSep 17, 2021

Answer To: Microsoft Word - ds2500_lab_sunspots.docx DS 2501: Intermediate Programming with Data / Lab...

Pritam Kumar answered on Sep 18 2021
178 Votes
{
"cells": [
{
"cell_type": "code",
"execution_count": 91,
"metadata": {},
"outputs": [],
"source": [
"import csv\n",
"import datetime\n",
"import warnings\n",
"import numpy as np\n",
"import pandas as pd\n",
"import statsmodels.api as sm \n",
"import matplotlib.pyplot as plt\n",
"\n",
"from datetime import datetime, timedelta\n",
"from statsmodels.tsa.seasonal import seasonal_decompose\n",
"from matplotlib import pyplot"
]
},
{
"cell_type": "code",
"execution_count": 92,
"metadata": {},
"outputs": [
{
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0123456
0174911749.04296.7-1.0-11
1174921749.123104.3-1.0-11
2174931749.204116.7-1.0-11
3174941749.28892.8-1.0-11
4174951749.371141.7-1.0-11
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" 0 1 2 3 4 5 6\n",
"0 1749 1 1749.042 96.7 -1.0 -1 1\n",
"1 1749 2 1749.123 104.3 -1.0 -1 1\n",
"2 1749 3 1749.204 116.7 -1.0 -1 1\n",
"3 1749 4 1749.288 92.8 -1.0 -1 1\n",
"4 1749 5 1749.371 141.7 -1.0 -1 1"
]
},
"execution_count": 92,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"data = pd.read_csv(\"D:\\\\New\\\\sunspotsdata.csv\",sep=';',header=None)\n",
"data.head()"
]
},
{
"cell_type": "code",
"execution_count": 93,
"metadata": {},
"outputs": [
{
"data": {
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"
YearMonthDateMonthly Mean Total Sunspot NumberMonthly mean standard deviationNumber of observations usedDefinitive marker
0174911749.04296.7-1.0-11
1174921749.123104.3-1.0-11
2174931749.204116.7-1.0-11
3174941749.28892.8-1.0-11
4174951749.371141.7-1.0-11
\n",
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"text/plain": [
" Year Month Date Monthly Mean Total Sunspot Number \\\n",
"0 1749 1 1749.042 96.7 \n",
"1 1749 2 1749.123 104.3 \n",
"2 1749 3 1749.204 116.7 \n",
"3 1749 4 1749.288 92.8 \n",
"4 1749 5 1749.371 141.7 \n",
"\n",
" Monthly mean standard deviation Number of observations used \\\n",
"0 -1.0 -1 \n",
"1 -1.0 -1 \n",
"2 -1.0 -1 \n",
"3 -1.0 -1 \n",
"4 -1.0 -1 \n",
"\n",
" Definitive marker \n",
"0 1 \n",
"1 1 \n",
"2 1 \n",
"3 1 \n",
"4 1 "
]
},
"execution_count": 93,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"data = data.rename(columns={0: \"Year\", 1: \"Month\", 2: \"Date\", 3: \"Monthly Mean Total Sunspot Number\",\n",
" 4: \"Monthly mean standard deviation\", 5: \"Number of observations used\", 6: \"Definitive marker\"})\n",
"data.head()"
]
},
{
"cell_type": "code",
"execution_count": 94,
"metadata": {},
"outputs": [],
"source": [
"#date fraction to timestamp format\n",
"FullDate = []\n",
"\n",
"for i in data['Date']:\n",
" start = i\n",
" year = int(start)\n",
" rem = start - year\n",
" base = datetime(year, 1, 1)\n",
" result = base + timedelta(seconds=(base.replace(year=base.year + 1) - base).total_seconds() * rem)\n",
" \n",
" FullDate.append(result)"
]
}
,
{
"cell_type": "code",
"execution_count": 95,
"metadata": {},
"outputs": [
{
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"
YearMonthDateMonthly Mean Total Sunspot NumberMonthly mean standard deviationNumber of observations usedDefinitive markerFullDate
0174911749.04296.7-1.0-111749-01-16 07:55:12.000005
1174921749.123104.3-1.0-111749-02-14 21:28:47.999994
2174931749.204116.7-1.0-111749-03-16 11:02:23.999998
3174941749.28892.8-1.0-111749-04-16 02:52:47.999993
4174951749.371141.7-1.0-111749-05-16 09:57:36.000003
\n",
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],
"text/plain": [
" Year Month Date Monthly Mean Total Sunspot Number \\\n",
"0 1749 1 1749.042 96.7 \n",
"1 1749 2 1749.123 104.3 \n",
"2 1749 3 1749.204 116.7 \n",
"3 1749 4 1749.288 92.8 \n",
"4 1749 5 1749.371 141.7 \n",
"\n",
" Monthly mean standard deviation Number of observations used \\\n",
"0 -1.0 -1 \n",
"1 -1.0 -1 \n",
"2 -1.0 -1 \n",
"3 -1.0 -1 \n",
"4 -1.0 -1 \n",
"\n",
" Definitive marker FullDate \n",
"0 1 1749-01-16 07:55:12.000005 \n",
"1 1 1749-02-14 21:28:47.999994 \n",
"2 1 1749-03-16 11:02:23.999998 \n",
"3 1 1749-04-16 02:52:47.999993 \n",
"4 1 1749-05-16 09:57:36.000003 "
]
},
"execution_count": 95,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"data[\"FullDate\"] = FullDate\n",
"data.head()"
]
},
{
"cell_type": "code",
"execution_count": 96,
"metadata": {},
"outputs": [
{
"data": {
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"
YearMonthDateMonthly Mean Total Sunspot NumberMonthly mean standard deviationNumber of observations usedDefinitive markerFullDate
0174911749.04296.7-1.0-111749-01-16
1174921749.123104.3-1.0-111749-02-14
2174931749.204116.7-1.0-111749-03-16
3174941749.28892.8-1.0-111749-04-16
4174951749.371141.7-1.0-111749-05-16
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" Year Month Date Monthly Mean Total Sunspot Number \\\n",
"0 1749 1 1749.042 96.7 \n",
"1 1749 2 1749.123 104.3 \n",
"2 1749 3 1749.204 116.7 \n",
"3 1749 4 1749.288 92.8 \n",
"4 1749 5 1749.371 141.7 \n",
"\n",
" Monthly mean standard deviation Number of observations used \\\n",
"0 -1.0 -1 \n",
"1 -1.0 -1 \n",
"2 -1.0 -1 \n",
"3 -1.0 -1 \n",
"4 -1.0 -1 \n",
"\n",
" Definitive marker FullDate \n",
"0 1 1749-01-16 \n",
"1 1 1749-02-14 \n",
"2 1 1749-03-16 \n",
"3 1 1749-04-16 \n",
"4 1 1749-05-16 "
]
},
"execution_count": 96,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"#need only YYYY-MM-DD\n",
"data['FullDate'] = pd.to_datetime(data['FullDate']).dt.date\n",
"data.head()"
]
},
{
"cell_type": "code",
"execution_count": 97,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"
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"\n",
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"
DateMonthly Mean Total Sunspot Number
01749-01-1696.7
11749-02-14104.3
21749-03-16116.7
31749-04-1692.8
41749-05-16141.7
\n",
"
"
],
"text/plain": [
" Date Monthly Mean Total Sunspot Number\n",
"0 1749-01-16 96.7\n",
"1 1749-02-14 104.3\n",
"2 1749-03-16 116.7\n",
"3 1749-04-16 92.8\n",
"4 1749-05-16 141.7"
]
},
"execution_count": 97,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df = pd.DataFrame(data['FullDate'])\n",
"df['Monthly Mean Total Sunspot Number'] = data['Monthly Mean Total Sunspot Number']\n",
"\n",
"df = df.rename(columns={\"FullDate\": \"Date\"})\n",
"df.head()"
]
},
{
"cell_type": "code",
"execution_count": 98,
"metadata": {},
"outputs": [],
"source": [
"# define a function to plot time series data\n",
"def plot_series(time, series, col = 'dodgerblue', lab = 'original', format=\"-\", start=0, end=None):\n",
" plt.style.use('seaborn')\n",
" plt.plot(time[start:end], series[start:end], format, color = col, label = lab)\n",
" plt.xlabel(\"Time\")\n",
" plt.ylabel(\"Series\")\n",
" # display the grid\n",
" plt.grid(True)\n",
" # got current figure, then autoformat date\n",
" plt.gcf().autofmt_xdate() \n",
" # format datetime\n",
" date_formate = mpl_dates.DateFormatter('%b/%d/%Y') \n",
" # set the format to out x-axis, gca is the get current axis\n",
" plt.gca().xaxis.set_major_formatter(date_formate)\n",
" plt.tight_layout() \n",
" plt.legend(loc = 'best')"
]
},
{
"cell_type": "code",
"execution_count": 99,
"metadata": {},
"outputs": [
{
"data": {
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\n",
"text/plain": [
"
"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"time_step = []\n",
"sunspots = []\n",
"for time, value in zip(df['Date'],df['Monthly Mean Total Sunspot Number']):\n",
" time_step.append(time)\n",
" sunspots.append(float(value))\n",
"\n",
"# plot our data\n",
"plt.figure(figsize=(10, 6))\n",
"\n",
"plot_series(time_step, sunspots)"
]
},
{
"cell_type": "code",
"execution_count": 100,
"metadata": {
"scrolled": true
},
"outputs": [
{
"data": {
"image/png":...
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