Merge remote-tracking branch 'origin/master'

pyspedas_examples/examples/ex_mpause_2.py

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+"""
+This example generates a plot of the magnetopause locations using the mpause_2 function from the pyspedas.utilities module.
+The plot includes grid, labels, and title. The x-axis is inverted to match the IDL behavior.
+
+This is similar to the IDL example crib_magnetopause.pro (first part).
+"""
+
+import matplotlib.pyplot as plt
+from pyspedas.utilities.mpause_2 import mpause_2
+
+
+def ex_mpause_2():
+
+    # Call the function without parameters to use default values
+    xmp, ymp = mpause_2()
+
+    # Set the range for the axes
+    x_limits = (-300, 100)
+    y_limits = (-100, 100)
+
+    # Create the plot
+    plt.figure()
+    plt.plot(xmp, ymp)
+
+    # Set the limits of x and y axes
+    plt.xlim(x_limits)
+    plt.ylim(y_limits)
+
+    # Invert the x-axis to match the IDL behavior
+    plt.gca().invert_xaxis()
+
+    # Add grid, labels, and title
+    plt.grid(True)
+    plt.xlabel("XMP")
+    plt.ylabel("YMP")
+    plt.title("Magnetopause Locations")
+
+    # Display the plot
+    plt.show()
+
+
+# To run this function, uncomment the following line:
+# ex_mpause_2()

pyspedas_examples/examples/ex_mpause_t96.py

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+"""
+This example loads position data for THEMIS thd probe for a specified time range,
+converts the position data from GEI to GSM coordinates,
+calls the mpause_t96 function with the position data, and plots the magnetopause.
+
+This is similar to the IDL example crib_magnetopause.pro (second part).
+"""
+
+from pyspedas.themis import state
+from pyspedas.cotrans.cotrans import cotrans
+from pyspedas.utilities.mpause_t96 import mpause_t96
+import matplotlib.pyplot as plt
+from pytplot import get_data
+
+
+def ex_mpause_t96():
+
+    # Set the date and load one day of data
+    date_start = '2019-01-05/00:00:00'
+    date_end = '2019-01-06/00:00:00'
+
+    # Load position data for THEMIS thd probe for the specified time range
+    state(trange=[date_start, date_end], probe='d', get_support_data=True)
+
+    # Transform the position data from GEI to GSM coordinates
+    cotrans(name_in='thd_pos', name_out='thd_pos_gsm',
+            coord_in='gei', coord_out='gsm')
+
+    # Get the position data in GSM coordinates
+    pos_gsm_data = get_data('thd_pos_gsm')
+
+    # Convert position from kilometers to Earth radii
+    pos_gsm = pos_gsm_data.y / 6378.0
+
+    # Define solar wind dynamic pressure
+    dynp = 2.0
+
+    # Call the mpause_t96 function with the position data
+    xmgnp, ymgnp, zmgnp, id, distan = mpause_t96(
+        pd=dynp, xgsm=pos_gsm[:, 0], ygsm=pos_gsm[:, 1], zgsm=pos_gsm[:, 2])
+
+    # Plot xmgnp vs ymgnp
+    plt.figure()
+    plt.plot(xmgnp, ymgnp)
+    plt.xlim(20, -60)
+    plt.ylim(-30, 30)
+    plt.xlabel('X (Re)')
+    plt.ylabel('Y (Re)')
+    plt.title('Magnetopause Boundary')
+    plt.grid(True)
+    plt.show()
+
+# To run this function, uncomment the following line:
+# ex_mpause_t96()

pyspedas_examples/notebooks/BARREL_background_model.ipynb

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+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## BARREL Create Background Model\n",
+    "\n",
+    "\n",
+    "### Setup\n",
+    "Start by importing libraries and loading data from a potentially interesting event.\n",
+    "\n",
+    "In this guide, we are going to use interactive plots, so `%matplotlib ipympl` should be set."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "%matplotlib ipympl\n",
+    "import pyspedas, pytplot, pprint, numpy\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "For our example, we will look at data form flight 1G from January 17th - 19th, 2013.\n",
+    "\n",
+    "FSPC and SSPC data can be downloaded with the `pyspedas.barrel` helper functions:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "trange=['2013-01-17', '2013-01-19']\n",
+    "\n",
+    "pyspedas.barrel.fspc(\n",
+    "    trange=trange,\n",
+    "    probe='1g'\n",
+    ")\n",
+    "\n",
+    "pyspedas.barrel.sspc(\n",
+    "    trange=trange,\n",
+    "    probe='1g'\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Background subtraction\n",
+    "Plot FSPC1 for the loaded data and visually determine the start and stop locations for the background selection."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "pytplot.tplot('brl1G_SSPC')\n",
+    "pytplot.tplot('brl1G_FSPC1')"
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "By moving the mouse cursor over a quiet area of the plot, we can estimate values for the start and stop times.\n",
+    "In this way we can find one or more periods of time to use for background calculation.\n",
+    "\n",
+    "These start and stop times can be stored in a list of tuples:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "background_periods=[(\"2013-01-17/17:25\", \"2013-01-17/20:35\"), (\"2013-01-18/09:35\", \"2013-01-18/12:04\")]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Similarly, we can use the plot to estimate the time period of the event that we are interested in:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "event_period=(\"2013-01-17/01:54\", \"2013-01-17/03:24\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Next we will extract the SSPC data and send it to the `pyspedas.barrel.average_event_spectrum` function. This will return a background subtracted average spectrum of the event. This data is stored in a new tplot variable with the x axis set to show the energy levels."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "ts, cnts, energy_levels = pytplot.get_data('brl1G_SSPC')\n",
+    "spec = pyspedas.barrel.average_event_spectrum(ts, cnts, energy_levels, background_periods, event_period)\n",
+    "pytplot.store_data(\"brl1G_Event_Spec\", data={'x':energy_levels, 'y':spec})\n",
+    "pytplot.options(\"brl1G_Event_Spec\", opt_dict={\"name\": \"Average Event Spectrum\", \"ytitle\": \"cnts/keV/sec\"})\n",
+    "pytplot.tplot(\"brl1G_Event_Spec\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "In addition to plotting the event spectrum, we can generate a background-subtracted spectrogram using the `pyspedas.barrel.background_subtracted_spectrogram` function. "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#The background subtracted spectrogram function only takes the background time periods, not the event time periods.\n",
+    "bg_sub_spectrogram = pyspedas.barrel.background_subtracted_spectrogram(ts, cnts, energy_levels, background_periods)\n",
+    "pytplot.store_data(\"brl1G_SSPC_BKG_SUB\", data={'x':ts, 'y':bg_sub_spectrogram, 'v':energy_levels})\n",
+    "pytplot.options(\"brl1G_SSPC_BKG_SUB\", \"name\", \"Background Subtracted SSPC\")\n",
+    "\n",
+    "#If the option for the spectrogram plot isn't set, it will plot a stack of line plots\n",
+    "pytplot.options(\"brl1G_SSPC_BKG_SUB\", \"Spec\", 1) \n",
+    "\n",
+    "#We can guess at the y axis range by looking at the event stectum above. Setting the upper limit to 500keV will capture all of the counts\n",
+    "pytplot.options(\"brl1G_SSPC_BKG_SUB\", \"yrange\", [0, 500])\n",
+    "\n",
+    "#Use the estimated event period to set the time range for the plot\n",
+    "pytplot.tlimit(list(event_period))\n",
+    "\n",
+    "pytplot.tplot(\"brl1G_SSPC_BKG_SUB\")"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": ".venv",
+   "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": "3.11.3"
+  },
+  "orig_nbformat": 4
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}