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@hayesla
Created June 15, 2020 18:36
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{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Example to download and read new GOES 16 and 17 XRS level 2 data\n",
"\n",
"The new GOES-R series data is now available from NOAA here https://www.ngdc.noaa.gov/stp/satellite/goes-r.html\n",
"\n",
"The level 2 science quality data is provided in netcdf format. This example provides an outline of how to download the data and to read the data into a sunpy.timeseries. It will implemeneted soon in sunpy.\n",
"\n",
"The function `get_goes` will download the data for a given time and satellite if the level 2 data is available. It will save it to your current path or to a given path\n",
"\n",
"The function `make_goes_timeseries` will read in a level 2 GOES-R XRS file and return a sunpy.timeseries"
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [],
"source": [
"import xarray \n",
"from sunpy.time import parse_time\n",
"from sunpy import timeseries as ts \n",
"import pandas as pd \n",
"import matplotlib.pyplot as plt \n",
"from matplotlib import dates\n",
"import urllib\n",
"import os\n",
"from collections import OrderedDict\n",
"from astropy import units as u"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## First we have a function to download the data for a given date"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [],
"source": [
"def get_goes(date, sat=16, path=None):\n",
" \"\"\"\n",
" Function to download the new GOES 16/17 XRS level 2 science 1s data\n",
" \n",
" Parameters\n",
" ----------\n",
" date : `str`\n",
" date of observation - e.g. '2017-09-10'\n",
" \n",
" sat : `int`, optional\n",
" satellite number, can be 16 or 17. Default is 16\n",
" \n",
" path : `str` \n",
" path to save file to. Default to cwd.\n",
" \n",
" \n",
" Returns\n",
" -------\n",
" if file is downloaded or already exists it will return filename/path\n",
" \n",
"\n",
" \"\"\"\n",
"\n",
" base_url_r = \"https://data.ngdc.noaa.gov/platforms/solar-space-observing-satellites/goes/\"\n",
" \n",
" date = parse_time(date)\n",
"\n",
" url = os.path.join(base_url_r, \"goes{:d}/l2/data/xrsf-l2-flx1s_science\" \\\n",
" \"/{:s}/{:s}/sci_xrsf-l2-flx1s_g{:d}_d{:s}_v1-0-0.nc\".format(sat, \n",
" date.strftime('%Y'),\n",
" date.strftime('%m'), \n",
" sat,\n",
" date.strftime('%Y%m%d')))\n",
" if path is not None:\n",
" filename = os.path.join(path, url.split('/')[-1])\n",
" else:\n",
" filename = url.split('/')[-1]\n",
" \n",
" if os.path.exists(filename):\n",
" return filename\n",
" \n",
" else:\n",
" try:\n",
" urllib.request.urlretrieve(url, filename)\n",
"\n",
" if os.path.exists(filename):\n",
" return filename\n",
" \n",
" except:\n",
" print(\"didnt download\")\n",
" return None"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Now a function to read the downloaded file"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [],
"source": [
"def make_goes_timeseries(file):\n",
" \"\"\"\n",
" Function to read in the new GOES 16&17 level2 science data\n",
" and return a sunpy.timeseries\n",
" \n",
" Parameters\n",
" ----------\n",
" file : `str`\n",
" GOES netcdf file \n",
" \n",
" Returns\n",
" -------\n",
" `sunpy.timeseries.TimeSeries`\n",
" \n",
" \"\"\"\n",
" \n",
" data = xarray.open_dataset(file)\n",
" \n",
" units = OrderedDict([('xrsa', u.W/u.m**2),\n",
" ('xrsb', u.W/u.m**2)])\n",
" \n",
" xrsb = data['xrsb_flux']\n",
" xrsa = data['xrsa_flux']\n",
"\n",
" tt = parse_time(data['time']).datetime\n",
" \n",
" data = pd.DataFrame({'xrsa': xrsa, 'xrsb': xrsb},\n",
" index=tt)\n",
" \n",
" data.sort_index(inplace=True)\n",
" \n",
" return ts.TimeSeries(data, units)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Now lets download the data and plot\n",
"Lets search for a date. The function `get_goes` will return the filename if found to exist, or will downloaded it and return filename."
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"sci_xrsf-l2-flx1s_g16_d20170910_v1-0-0.nc\n"
]
}
],
"source": [
"files = get_goes(\"2017-09-10\")\n",
"print(files)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"if no path is given it will save to current working directory. If a path is given it will save it there - for example:"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"./goes_files/sci_xrsf-l2-flx1s_g16_d20170910_v1-0-0.nc\n"
]
}
],
"source": [
"files = get_goes(\"2017-09-10\", path=\"./goes_files\")\n",
"print(files)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Now lets read and plot\n",
"Now that we have downloaded the data we can now pass the file into the function `make_goes_timeseries` which will read it and return a sunpy timeseries"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {},
"outputs": [],
"source": [
"goes_xrs = make_goes_timeseries(files)"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {},
"outputs": [
{
"data": {
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\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"fig, ax = plt.subplots()\n",
"goes_xrs.plot()\n",
"ax.set_yscale('log')\n",
"ax.set_ylabel('Flux Wm$^{-2}$')\n",
"ax.set_xlabel('Time UT')\n",
"ax.xaxis.set_major_formatter(dates.DateFormatter('%H:%M'))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## And similarly like any sunpy.timeseries you can truncate, pull out each channel etc.\n",
"See https://docs.sunpy.org/en/stable/guide/data_types/timeseries.html for more info."
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"# lets truncate the timeseries \n",
"goes_xrs_truncate = goes_xrs.truncate('2017-09-10 15:00', '2017-09-19 18:00')\n",
"\n",
"# we can similarly pull out each channel individually if you want\n",
"goeslong = goes_xrs_truncate.to_dataframe()['xrsb']\n",
"goesshort = goes_xrs_truncate.to_dataframe()['xrsa']\n",
"\n",
"# and plot them this way either\n",
"fig, ax = plt.subplots()\n",
"ax.plot(goeslong, color='r')\n",
"ax.plot(goesshort, color='b')\n",
"ax.set_yscale('log')\n",
"ax.set_ylabel('Flux Wm$^{-2}$')\n",
"ax.set_xlabel('Time UT')\n",
"ax.xaxis.set_major_formatter(dates.DateFormatter('%H:%M'))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# NOAA also produces json files for the most recent GOES-16 XRS data\n",
"Here's an example of a function that pulls the data and returns a sunpy timeseries\n",
"\n",
"The data is available here: https://www.swpc.noaa.gov/products/goes-x-ray-flux \n",
"and provides data from past 7 days. The files provided include past 7-days, past 3 days, past 1 day, and past 6-hours.\n",
"\n",
"Here's an example of how to read the json files:"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {},
"outputs": [],
"source": [
"def read_recent_noaa(interval=\"7-day\"):\n",
" \"\"\"\n",
" Function to read the NOAA recent GOES-16 XRS data and return a \n",
" sunpy.timeseries\n",
" \n",
" Parameters\n",
" ----------\n",
" interval : `str`, optional - default past 7 days\n",
" past interval file to read.\n",
" \n",
" Returns\n",
" -------\n",
" sunpy.timeseries\n",
" \n",
" \"\"\"\n",
" dict_interval = {\"7-day\": \"xrays-7-day.json\", \n",
" \"3-day\": \"xrays-3-day.json\", \n",
" \"1-day\": \"xrays-1-day.json\",\n",
" \"6-hour\": \"xrays-6-hour.json\"}\n",
" \n",
" if interval not in dict_interval.keys():\n",
" print(\"interval must be one of\", dict_interval.keys())\n",
" \n",
" noaa_file = \"https://services.swpc.noaa.gov/json/goes/primary/{:s}\".format(dict_interval[interval])\n",
" units = OrderedDict([('xrsa', u.W/u.m**2),\n",
" ('xrsb', u.W/u.m**2)])\n",
"\n",
" data = pd.read_json(noaa_file)\n",
"\n",
" data_short = data[data['energy']=='0.05-0.4nm']\n",
" data_long = data[data['energy']=='0.1-0.8nm']\n",
" time_array = [parse_time(x).datetime for x in data_short['time_tag'].values]\n",
"\n",
" df = pd.DataFrame({'xrsa': data_short['flux'].values, 'xrsb': data_long['flux'].values}, index=time_array)\n",
"\n",
" df.sort_index(inplace=True)\n",
" \n",
" return ts.TimeSeries(df, units)"
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {},
"outputs": [],
"source": [
"goes_xrs_7days = read_recent_noaa()"
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"fig, ax = plt.subplots()\n",
"goes_xrs_7days.plot()\n",
"ax.set_ylabel('Flux Wm$^{-2}$')\n",
"ax.set_xlabel('Time (UT)')\n",
"ax.set_yscale('log')\n",
"ax.set_ylim(1e-9, 1e-2)\n",
"ax.xaxis.set_major_formatter(dates.DateFormatter('%H:%M'))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Similarly if you only wanted to read the data from last 6 hour you can pass that keyword\n",
"However this data is also in the 7-day data, you could also just truncate that I guess ..."
]
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {},
"outputs": [],
"source": [
"goes_xrs_6hour = read_recent_noaa(interval='6-hour')"
]
},
{
"cell_type": "code",
"execution_count": 13,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"(1e-09, 0.01)"
]
},
"execution_count": 13,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"fig, ax = plt.subplots()\n",
"goes_xrs_6hour.plot()\n",
"ax.set_ylabel('Flux Wm$^{-2}$')\n",
"ax.set_xlabel('Time (UT)')\n",
"ax.set_yscale('log')\n",
"ax.set_ylim(1e-9, 1e-2)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
},
{
"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": "3.7.4"
}
},
"nbformat": 4,
"nbformat_minor": 4
}
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