{ "cells": [ { "cell_type": "markdown", "id": "59e8aa60-a073-43a1-aba6-846f6b8b30be", "metadata": {}, "source": [ "# Plasma Line\n", "\n", "This will show a simple example of how to create a plasma line using the object." ] }, { "cell_type": "code", "execution_count": 1, "id": "facfe874-f159-47aa-aa83-dc3eb5584144", "metadata": {}, "outputs": [], "source": [ "import numpy as np\n", "import matplotlib.pyplot as plt\n", "from matplotlib.colors import LogNorm\n", "from ISRSpectrum import PLspecinit" ] }, { "cell_type": "markdown", "id": "23a2b7f7-e671-4a85-b695-84a3be6d709d", "metadata": {}, "source": [ "## Ionosphere Profile\n", "\n", "Create a simple ionosphere profile containing, with only the electron density, ion temperature and electron temperature. If a more accurate model is desired createa new example profile function." ] }, { "cell_type": "code", "execution_count": 2, "id": "b105a515-9881-4720-9097-4a7287447056", "metadata": {}, "outputs": [], "source": [ "# %% Test functions\n", "def chapman_func(z, H_0, Z_0, N_0):\n", " \"\"\"This function will return the Chapman function for a given altitude vector z. All of the height values are assumed km.\n", "\n", " Parameters\n", " ----------\n", " z : ndarray\n", " An array of z values in km.\n", " H_0 : float\n", " A single float of the scale height in km.\n", " Z_0 : float\n", " The peak density location.\n", " N_0 : float\n", " The peak electron density.\n", "\n", " Returns\n", " -------\n", " Ne : ndarray\n", " Electron density as a function of z in m^{-3}\n", " \"\"\"\n", " z1 = (z - Z_0) / H_0\n", " Ne = N_0 * np.exp(0.5 * (1 - z1 - np.exp(-z1)))\n", " return Ne\n", "\n", "\n", "def temp_profile(z, T0=1000.0, z0=100.0):\n", " \"\"\"This function creates a temperature profile using arc tan functions for test purposes.\n", "\n", " Parameters\n", " ----------\n", " z : ndarray\n", " An array of z values in km.\n", " T0 : ndarray\n", " The value of the lowest temperature in K.\n", " z0 : ndarray\n", " The middle value of the atan functions along alitutude. In km.\n", "\n", " Returns\n", " -------\n", " Te : ndarray\n", " The electron temperature profile in K. 1700*(atan((z-z0)2*exp(1)/400-exp(1))+1)/2 +T0\n", " Ti : ndarray\n", " The ion temperature profile in K. 500*(atan((z-z0)2*exp(1)/400-exp(1))+1)/2 +T0\n", " \"\"\"\n", " zall = (z - z0) * 2.0 * np.exp(1) / 400.0 - np.exp(1)\n", " atanshp = (np.tanh(zall) + 1.0) / 2\n", " Te = 1700 * atanshp + T0\n", " Ti = 500 * atanshp + T0\n", "\n", " return (Te, Ti)\n", "\n", "\n", "def example_profile():\n", " \"\"\"Creates profiles for ion temperature, electron density and electron temperature.\n", " Returns\n", " -------\n", " z : ndarray\n", " Height in km.\n", " Ne : ndarray\n", " Electron density as a function of z in m^{-3}\n", " Te : ndarray\n", " The electron temperature profile in K. 1700*(atan((z-z0)2*exp(1)/400-exp(1))+1)/2 +T0\n", " Ti : ndarray\n", " The ion temperature profile in K. 500*(atan((z-z0)2*exp(1)/400-exp(1))+1)/2 +T0\n", " \"\"\"\n", " z = np.linspace(100, 1000, 100)\n", " N_0 = 1e12\n", " z_0 = 250.0\n", " H_0 = 50.0\n", " Ne_profile = chapman_func(z, H_0, z_0, N_0)\n", " (Te_prof, Ti_prof) = temp_profile(z)\n", " return (z, Ne_profile, Te_prof, Ti_prof)" ] }, { "cell_type": "markdown", "id": "0d1f2127-bf4a-4ae3-aa74-2b1cacb21b8d", "metadata": {}, "source": [ "## " ] }, { "cell_type": "markdown", "id": "af029121-cfa1-41a7-a175-4849d1bb1825", "metadata": {}, "source": [ "## Form Spectra at Each Altitude\n", "\n", "Create a PLspec object. Since this is made with the intent to use this with a polyphase filter bank implementation of the plasma line the frequency space is broken up into channels. The number of channels determines the number of polyphase filter banks that will be used to create a plasma line signal. The nfreq_pfb parameter is the frequency resolution of these sub bands to create a plasma line spectrum, either upshifted or downshifted.\n", "\n", "The overall sampling frequency is 25 MHz, while the number of channels is 250 and the resolution of the frequency in each channel is 1024. This gives a channel width of 100 kHz. From inspection of plasma line data from Millstone Hill the bandwidth of an upshifted or down shifted line is less than 20 kHz, FWHM. With frequency sampling resolution of 1024 perchannel this is something like 200 samples along the main part of line. \n", "\n" ] }, { "cell_type": "code", "execution_count": 3, "id": "29f896ac-606e-4d37-abc8-d37c723b2ec8", "metadata": {}, "outputs": [], "source": [ "z, Ne, Te, Ti = example_profile()\n", "\n", "fs = 25e6\n", "nfreqchans = 250\n", "nfreq_pfb = 1024\n", "nfreqall = nfreqchans * nfreq_pfb\n", "# create the PLspec object\n", "pls = PLspecinit(\n", " centerFrequency=440.2 * 1e6,\n", " bMag=0.4e-4,\n", " dFlag=False,\n", " fs=fs,\n", " nchans=nfreqchans,\n", " nfreq_pfb=nfreq_pfb,\n", ")\n", "\n", "pl_cf = np.zeros((len(z), 2))\n", "spec = np.zeros((nfreqall, len(z)))\n", "\n", "# go through each of the altitudes and create the plasma line picture.\n", "for inum, (irng, ine, ite, iti) in enumerate(zip(z, Ne, Te, Ti)):\n", " data_vec = np.array([[ine, iti], [ine, ite]])\n", "\n", " pld = pls.get_ul_spec(\n", " data_vec, 0.0, alphadeg=90.0, rcsflag=True, freqflag=True, Tpe=1.0, posflag=True\n", " )\n", " (f_lo, sp_lo, f_hi, sp_hi, rcs, freqs, pos_l, pos_h) = pld\n", " pl_cf[inum] = freqs\n", "\n", " for isplo, isphi, iposl, iposh in zip(sp_lo, sp_hi, pos_l, pos_h):\n", " spec[iposl, inum] += isplo\n", " spec[iposh, inum] += isphi" ] }, { "cell_type": "markdown", "id": "e0b3e1fc-2388-424c-8a68-1fcaf43f57ba", "metadata": {}, "source": [ "## Final Plotting\n", "\n", "Plot the spectra along with the plasma center frequencies." ] }, { "cell_type": "code", "execution_count": 4, "id": "474552cd-cc07-499c-8fce-699a457b5d3a", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 4, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": "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", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "pcmnorm = norm = LogNorm(vmin=1e-7, vmax=3e-5)\n", "fig, (ax1, ax2) = plt.subplots(1, 2, sharey=True, figsize=[12.8, 4.8])\n", "ax1.plot(pl_cf * 1e-6, z)\n", "ax1.grid(True)\n", "ax1.set_xlabel(\"Frequency in MHz\")\n", "ax1.set_ylabel(\"Altitude in km\")\n", "ax1.set_title(\"Plasma Frequency by Altitude.\")\n", "\n", "# Add the machine epsilon to avoid issues with log.\n", "d_eps = np.finfo(spec.dtype).eps\n", "\n", "# define a meshgrid for the altitude and frequency space.\n", "freqar = np.fft.fftshift(np.fft.fftfreq(nfreqall, 1 / fs))\n", "freqm, zm = np.meshgrid(freqar, z)\n", "\n", "pcm = ax2.pcolormesh(freqm * 1e-6, zm, spec.T + d_eps, norm=pcmnorm)\n", "ax2.set_xlabel(\"Frequency in MHz\")\n", "ax2.set_title(\"Plasma Line Spectra by Altitude.\")\n", "fig.colorbar(pcm, ax=ax2)\n", "# fig.savefig(\"plfreqs.png\",dpi=300)" ] } ], "metadata": { "kernelspec": { "display_name": "Python 3 (ipykernel)", "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": "3.10.13" }, "widgets": { "application/vnd.jupyter.widget-state+json": { "state": {}, "version_major": 2, "version_minor": 0 } } }, "nbformat": 4, "nbformat_minor": 5 }