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207 changes: 207 additions & 0 deletions pysatTutorials/pysatSeasons - Occurrence Probability.ipynb
Original file line number Diff line number Diff line change
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{
"cells": [
{
"cell_type": "code",
"execution_count": null,
"id": "8b83f8e1",
"metadata": {},
"outputs": [],
"source": [
"import datetime as dt\n",
"import matplotlib.pyplot as plt\n",
"import numpy as np\n",
"import pandas as pds\n",
"import warnings"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "5fcb0375",
"metadata": {},
"outputs": [],
"source": [
"import pysat\n",
"import pysatSeasons\n",
"\n",
"# Set data directory if user hasn't already set one\n",
"if len(pysat.params['data_dirs']) == 0:\n",
" # Set a directory for pysat to use for data\n",
" pysat.params['data_dirs'] = '~/pysatDemo'\n",
"else:\n",
" print('pysat directory has been set previously. Leaving unchanged.')\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "8d54ac16",
"metadata": {},
"outputs": [],
"source": [
"# Register pysatNASA ICON IVM data plug-in. Only required once per install.\n",
"import pysatNASA\n",
"pysat.utils.registry.register(['pysatNASA.instruments.icon_ivm'])"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "52fc4186",
"metadata": {},
"outputs": [],
"source": [
"# Orbit breakdown info\n",
"orbit_info = {'kind': 'lt', 'index':'Magnetic_Local_Time'}\n",
"\n",
"# We will employ a centered time-based calculation. Engage pysat's data padding.\n",
"pad = pds.DateOffset(seconds=30)\n",
"\n",
"# Instantiate IVM instrument data and \n",
"ivm = pysat.Instrument('icon', 'ivm', inst_id='a', orbit_info=orbit_info,\n",
" pad=pad)\n",
"\n",
"# Analysis date range\n",
"sdate = dt.datetime(2020, 1, 1)\n",
"edate = dt.datetime(2020, 1, 15)\n",
"\n",
"# Download data\n",
"ivm.download(sdate, edate)\n",
"\n",
"# Set the range of dates for the analysis.\n",
"ivm.bounds = (sdate, edate)\n",
"\n",
"# Improvements for loading ICON metadata are currently in \n",
"# https://github.com/pysat/pysatNASA/pull/100. \n",
"warnings.simplefilter('ignore', UserWarning)\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "cd3bded4",
"metadata": {},
"outputs": [],
"source": [
"# Modify data as it is loaded to add a log ion density measurement\n",
"def add_std_dens(inst):\n",
" \"\"\"Calculate Standard Deviation in Ion Density.\n",
" \n",
" Parameters\n",
" ----------\n",
" inst : pysat.Instrument\n",
" Instrument object to operate upon.\n",
" \n",
" \"\"\"\n",
" inst['Ion_Density_STD'] = inst['Ion_Density'].rolling('30s',\n",
" center=True).std()\n",
" \n",
" return\n",
"\n",
"# Attach to IVM\n",
"ivm.custom_attach(add_std_dens)\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "ba558515",
"metadata": {},
"outputs": [],
"source": [
"# Load some data to get a sense of values\n",
"ivm.load(2020, 1)"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "5e2012fd",
"metadata": {},
"outputs": [],
"source": [
"# Plot standard deviation\n",
"ivm['Ion_Density_STD'].plot()"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "eec99502",
"metadata": {},
"outputs": [],
"source": [
"# Run Occurrence Probability\n",
"answer = pysatSeasons.occur_prob.by_orbit2D(ivm, [0, 360, 24], 'Longitude',\n",
" [-20, 20, 10], 'Magnetic_Latitude',\n",
" ['Ion_Density_STD'], [5.E3],\n",
" return_bins=True)"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "8a8adaba",
"metadata": {},
"outputs": [],
"source": [
"# A dict indexed by data_label is returned.\n",
"ans = answer['Ion_Density_STD']\n",
"\n",
"# Plot occurrence probability\n",
"f, axarr = plt.subplots(2, 1, sharex=True, sharey=True, figsize=(8.5, 11))\n",
"\n",
"# Mask for locations not observed.\n",
"masked = np.ma.array(ans['prob'], mask=np.isnan(ans['prob']))\n",
"\n",
"# Plot occurrence probability\n",
"im = axarr[0].pcolor(ans['bin_x'], ans['bin_y'], masked)\n",
"axarr[0].set_title('Occurrence Probability Delta-N > 7.E3 N/cc')\n",
"axarr[0].set_ylabel('Latitude (Degrees)')\n",
"axarr[0].set_yticks((-20, -15, -10, -5, 0, 5, 10, 15, 20))\n",
"axarr[0].set_ylim((ans['bin_y'][0], ans['bin_y'][-1]))\n",
"plt.colorbar(im, ax=axarr[0], label='Occurrence Probability')\n",
"\n",
"# Plot number of orbits per bin.\n",
"im = axarr[1].pcolor(ans['bin_x'], ans['bin_y'], ans['count'])\n",
"axarr[1].set_title('Number of Orbits in Bin')\n",
"axarr[1].set_xlabel('Longitude (Degrees)')\n",
"axarr[1].set_xticks((0, 60, 120, 180, 240, 300, 360))\n",
"axarr[1].set_xlim((ans['bin_x'][0], ans['bin_x'][-1]))\n",
"axarr[1].set_ylabel('Latitude')\n",
"plt.colorbar(im, ax=axarr[1], label='Counts')\n",
"\n",
"f.tight_layout()"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "090642e0",
"metadata": {},
"outputs": [],
"source": []
}
],
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"kernelspec": {
"display_name": "Python 3 (ipykernel)",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
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"file_extension": ".py",
"mimetype": "text/x-python",
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