{
    "cells": [
        {
            "cell_type": "markdown",
            "id": "bdb48610",
            "metadata": {},
            "source": [
                "# EMRI Waveforms in Time and Frequency Domain\n",
                "\n",
                "In this tutorial, we demonstrate how to use the Fast EMRI Waveform package to produce waveforms in the time domain (TD) as described in [arXiv 2104.04582](https://arxiv.org/abs/2104.04582) and in the frequency domain (FD) as described in [arXiv 2307.12585](https://arxiv.org/abs/2307.12585). We explore the representation of EMRI waveforms in both domains using a reference source. We compare the TD and FD waveforms using mismatch and estimate the waveform generation speed. Additionally, we explore the impact of spin and eccentricity on the waveform signal-to-noise ratio. Finally, we demonstrate mass invariance and downsampling using the Frequency Domain.\n",
                "\n",
                "Created by Lorenzo Speri"
            ]
        },
        {
            "cell_type": "code",
            "execution_count": 1,
            "id": "348daf55",
            "metadata": {},
            "outputs": [
                {
                    "data": {
                        "application/vnd.jupyter.widget-view+json": {
                            "model_id": "f18fdcf5a02145b78d58d57e28add56a",
                            "version_major": 2,
                            "version_minor": 0
                        },
                        "text/plain": [
                            "Output()"
                        ]
                    },
                    "metadata": {},
                    "output_type": "display_data"
                },
                {
                    "data": {
                        "text/html": [
                            "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"></pre>\n"
                        ],
                        "text/plain": []
                    },
                    "metadata": {},
                    "output_type": "display_data"
                },
                {
                    "data": {
                        "application/vnd.jupyter.widget-view+json": {
                            "model_id": "0a65d6bc43c140dfaf7c7545a93347ae",
                            "version_major": 2,
                            "version_minor": 0
                        },
                        "text/plain": [
                            "Output()"
                        ]
                    },
                    "metadata": {},
                    "output_type": "display_data"
                },
                {
                    "data": {
                        "text/html": [
                            "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"></pre>\n"
                        ],
                        "text/plain": []
                    },
                    "metadata": {},
                    "output_type": "display_data"
                },
                {
                    "data": {
                        "application/vnd.jupyter.widget-view+json": {
                            "model_id": "aad1518931fe44ea8d94eaff773db924",
                            "version_major": 2,
                            "version_minor": 0
                        },
                        "text/plain": [
                            "Output()"
                        ]
                    },
                    "metadata": {},
                    "output_type": "display_data"
                },
                {
                    "data": {
                        "text/html": [
                            "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"></pre>\n"
                        ],
                        "text/plain": []
                    },
                    "metadata": {},
                    "output_type": "display_data"
                },
                {
                    "data": {
                        "application/vnd.jupyter.widget-view+json": {
                            "model_id": "31b286bcb05c491aa60592e17c05d8b9",
                            "version_major": 2,
                            "version_minor": 0
                        },
                        "text/plain": [
                            "Output()"
                        ]
                    },
                    "metadata": {},
                    "output_type": "display_data"
                },
                {
                    "data": {
                        "text/html": [
                            "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"></pre>\n"
                        ],
                        "text/plain": []
                    },
                    "metadata": {},
                    "output_type": "display_data"
                },
                {
                    "data": {
                        "application/vnd.jupyter.widget-view+json": {
                            "model_id": "efc64a9c880c4c159ea1a11e29bf12c4",
                            "version_major": 2,
                            "version_minor": 0
                        },
                        "text/plain": [
                            "Output()"
                        ]
                    },
                    "metadata": {},
                    "output_type": "display_data"
                },
                {
                    "data": {
                        "text/html": [
                            "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"></pre>\n"
                        ],
                        "text/plain": []
                    },
                    "metadata": {},
                    "output_type": "display_data"
                }
            ],
            "source": [
                "import time\n",
                "import numpy as np\n",
                "import matplotlib.pyplot as plt\n",
                "\n",
                "from few.waveform import GenerateEMRIWaveform\n",
                "from few.utils.constants import MTSUN_SI\n",
                "from few.utils.utility import get_p_at_t, get_fundamental_frequencies\n",
                "from few.utils.fdutils import GetFDWaveformFromFD, GetFDWaveformFromTD\n",
                "from few.trajectory.inspiral import EMRIInspiral\n",
                "from few.trajectory.ode.flux import KerrEccEqFlux\n",
                "\n",
                "from scipy.interpolate import CubicSpline\n",
                "\n",
                "traj_module = EMRIInspiral(func=KerrEccEqFlux)\n",
                "\n",
                "# import ASD\n",
                "data = np.loadtxt(\"./files/LPA.txt\", dtype=np.float64, skiprows=1)\n",
                "data[:, 1] = data[:, 1] ** 2\n",
                "# define PSD function\n",
                "get_sensitivity = CubicSpline(*data.T)\n",
                "\n",
                "\n",
                "# define inner product eq 3 of https://www.nature.com/articles/s41550-022-01849-y\n",
                "def inner_product(x, y, psd):\n",
                "    return 4 * np.real(np.sum(np.conj(x) * y / psd))\n",
                "\n",
                "\n",
                "# non uniform array of frequencies\n",
                "def get_frequency_array(fmin, fmax, deltaf):\n",
                "    p_freq = np.append(0.0, np.arange(fmin, fmax, step=deltaf))\n",
                "    freq = np.hstack((-p_freq[::-1][:-1], p_freq))\n",
                "    return freq"
            ]
        },
        {
            "cell_type": "code",
            "execution_count": 2,
            "id": "15c08219-56eb-430d-b19e-e012b5c59cc7",
            "metadata": {},
            "outputs": [
                {
                    "data": {
                        "application/vnd.jupyter.widget-view+json": {
                            "model_id": "2ebb603a7dfa4b32a73d5f10e871bd66",
                            "version_major": 2,
                            "version_minor": 0
                        },
                        "text/plain": [
                            "Output()"
                        ]
                    },
                    "metadata": {},
                    "output_type": "display_data"
                },
                {
                    "data": {
                        "text/html": [
                            "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"></pre>\n"
                        ],
                        "text/plain": []
                    },
                    "metadata": {},
                    "output_type": "display_data"
                }
            ],
            "source": [
                "# Initialize waveform generators\n",
                "# frequency domain\n",
                "few_gen = GenerateEMRIWaveform(\n",
                "    \"FastKerrEccentricEquatorialFlux\",\n",
                "    sum_kwargs=dict(pad_output=True, output_type=\"fd\", odd_len=True),\n",
                "    return_list=True,\n",
                ")\n",
                "\n",
                "# time domain\n",
                "td_gen = GenerateEMRIWaveform(\n",
                "    \"FastKerrEccentricEquatorialFlux\",\n",
                "    sum_kwargs=dict(pad_output=True, odd_len=True),\n",
                "    return_list=True,\n",
                ")"
            ]
        },
        {
            "cell_type": "code",
            "execution_count": 3,
            "id": "a2779c4c",
            "metadata": {},
            "outputs": [
                {
                    "name": "stdout",
                    "output_type": "stream",
                    "text": [
                        "New p0:  8.550426200258785\n"
                    ]
                }
            ],
            "source": [
                "# define the injection parameters\n",
                "M = 0.5e6  # central object mass\n",
                "a = 0.9  # will be ignored in Schwarzschild waveform\n",
                "mu = 10.0  # secondary object mass\n",
                "p0 = 12.0  # initial semi-latus rectum\n",
                "e0 = 0.1  # eccentricity\n",
                "\n",
                "x0 = 1.0  # will be ignored in Schwarzschild waveform\n",
                "qK = np.pi / 3  # polar spin angle\n",
                "phiK = np.pi / 3  # azimuthal viewing angle\n",
                "qS = np.pi / 3  # polar sky angle\n",
                "phiS = np.pi / 3  # azimuthal viewing angle\n",
                "dist = 1.0  # distance\n",
                "# initial phases\n",
                "Phi_phi0 = np.pi / 3\n",
                "Phi_theta0 = 0.0\n",
                "Phi_r0 = np.pi / 3\n",
                "\n",
                "Tobs = 0.5  # observation time, if the inspiral is shorter, the it will be zero padded\n",
                "dt = 10.0  # time interval\n",
                "eps = 1e-4  # mode content percentage\n",
                "\n",
                "waveform_kwargs = {\n",
                "    \"T\": Tobs,\n",
                "    \"dt\": dt,\n",
                "    \"eps\": eps,\n",
                "}\n",
                "\n",
                "# get the initial p0 given a certain observation\n",
                "p0 = get_p_at_t(\n",
                "    traj_module,\n",
                "    Tobs * 0.999,\n",
                "    [M, mu, a, e0, 1.0],\n",
                "    index_of_p=3,\n",
                "    index_of_a=2,\n",
                "    index_of_e=4,\n",
                "    index_of_x=5,\n",
                "    traj_kwargs={},\n",
                "    xtol=2e-12,\n",
                "    rtol=8.881784197001252e-16,\n",
                "    bounds=None,\n",
                ")\n",
                "print(\"New p0: \", p0)\n",
                "\n",
                "emri_injection_params = [\n",
                "    M,\n",
                "    mu,\n",
                "    a,\n",
                "    p0,\n",
                "    e0,\n",
                "    x0,\n",
                "    dist,\n",
                "    qS,\n",
                "    phiS,\n",
                "    qK,\n",
                "    phiK,\n",
                "    Phi_phi0,\n",
                "    Phi_theta0,\n",
                "    Phi_r0,\n",
                "]"
            ]
        },
        {
            "cell_type": "markdown",
            "id": "54f0a0e4",
            "metadata": {},
            "source": [
                "## Comparison against the Time Domain Waveforms"
            ]
        },
        {
            "cell_type": "code",
            "execution_count": 4,
            "id": "1274f17f",
            "metadata": {},
            "outputs": [
                {
                    "name": "stdout",
                    "output_type": "stream",
                    "text": [
                        "Time taken to generate the TD signal:  0.5409879684448242 seconds\n"
                    ]
                }
            ],
            "source": [
                "# create TD signal\n",
                "data_channels_td = td_gen(*emri_injection_params, **waveform_kwargs)\n",
                "\n",
                "# time the generation of the TD signal\n",
                "start = time.time()\n",
                "data_channels_td = td_gen(*emri_injection_params, **waveform_kwargs)\n",
                "end = time.time()\n",
                "print(\"Time taken to generate the TD signal: \", end - start, \"seconds\")\n",
                "\n",
                "# take the FFT of the plus polarization and shift it\n",
                "fft_TD = np.fft.fftshift(np.fft.fft(data_channels_td[0])) * dt\n",
                "freq = np.fft.fftshift(np.fft.fftfreq(len(data_channels_td[0]), dt))\n",
                "\n",
                "# define the positive frequencies\n",
                "positive_frequency_mask = freq >= 0.0"
            ]
        },
        {
            "cell_type": "code",
            "execution_count": 5,
            "id": "d85e84b8",
            "metadata": {},
            "outputs": [
                {
                    "name": "stderr",
                    "output_type": "stream",
                    "text": [
                        "/tmp/ipykernel_86035/1463989769.py:8: UserWarning: No artists with labels found to put in legend.  Note that artists whose label start with an underscore are ignored when legend() is called with no argument.\n",
                        "  plt.legend()\n"
                    ]
                },
                {
                    "data": {
                        "image/png": "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",
                        "text/plain": [
                            "<Figure size 640x480 with 1 Axes>"
                        ]
                    },
                    "metadata": {},
                    "output_type": "display_data"
                }
            ],
            "source": [
                "plt.figure()\n",
                "plt.loglog(freq[positive_frequency_mask], np.abs(fft_TD[positive_frequency_mask]) ** 2)\n",
                "plt.loglog(\n",
                "    freq[positive_frequency_mask], get_sensitivity(freq[positive_frequency_mask])\n",
                ")\n",
                "plt.ylabel(r\"$| {\\rm DFT} [h_{+}]|^2$\")\n",
                "plt.xlabel(r\"$f$ [Hz]\")\n",
                "plt.legend()\n",
                "plt.xlim(1e-4, 1e-1)\n",
                "plt.show()"
            ]
        },
        {
            "cell_type": "code",
            "execution_count": 6,
            "id": "a9025a56",
            "metadata": {},
            "outputs": [
                {
                    "name": "stdout",
                    "output_type": "stream",
                    "text": [
                        "Time taken to generate the FD signal:  0.8279085159301758 seconds\n",
                        "mismatch 0.00038772305336887136\n",
                        "TD SNR 59.73202391463132\n",
                        "FD SNR 59.731584513238005\n"
                    ]
                }
            ],
            "source": [
                "# you can specify the frequencies or obtain them directly from the waveform\n",
                "fd_kwargs = waveform_kwargs.copy()\n",
                "fd_kwargs[\"f_arr\"] = freq  # get_frequency_array(1e-5, 1e-1, 1e-4)\n",
                "fd_kwargs[\"mask_positive\"] = True\n",
                "\n",
                "# get FD waveform\n",
                "hf = few_gen(*emri_injection_params, **fd_kwargs)\n",
                "# time the generation of the FD signal\n",
                "start = time.time()\n",
                "hf = few_gen(*emri_injection_params, **fd_kwargs)\n",
                "end = time.time()\n",
                "print(\"Time taken to generate the FD signal: \", end - start, \"seconds\")\n",
                "# to get the frequencies:\n",
                "freq_fd = few_gen.waveform_generator.create_waveform.frequency\n",
                "\n",
                "# mismatch\n",
                "psd = get_sensitivity(freq[positive_frequency_mask]) / np.diff(freq)[0]\n",
                "td_td = inner_product(\n",
                "    fft_TD[positive_frequency_mask], fft_TD[positive_frequency_mask], psd\n",
                ")\n",
                "fd_fd = inner_product(hf[0], hf[0], psd)\n",
                "Mism = np.abs(\n",
                "    1\n",
                "    - inner_product(fft_TD[positive_frequency_mask], hf[0], psd)\n",
                "    / np.sqrt(td_td * fd_fd)\n",
                ")\n",
                "print(\"mismatch\", Mism)\n",
                "# SNR\n",
                "print(\"TD SNR\", np.sqrt(td_td))\n",
                "print(\"FD SNR\", np.sqrt(fd_fd))"
            ]
        },
        {
            "cell_type": "code",
            "execution_count": 7,
            "id": "b1798432",
            "metadata": {},
            "outputs": [
                {
                    "data": {
                        "image/png": "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",
                        "text/plain": [
                            "<Figure size 640x480 with 1 Axes>"
                        ]
                    },
                    "metadata": {},
                    "output_type": "display_data"
                }
            ],
            "source": [
                "# FD plot\n",
                "plt.figure()\n",
                "plt.loglog(\n",
                "    freq[positive_frequency_mask],\n",
                "    np.abs(fft_TD[positive_frequency_mask]) ** 2,\n",
                "    label=\"DFT of TD waveform\",\n",
                ")\n",
                "plt.loglog(freq[positive_frequency_mask], np.abs(hf[0]) ** 2, \"--\", label=\"FD waveform\")\n",
                "plt.loglog(\n",
                "    freq[positive_frequency_mask],\n",
                "    get_sensitivity(freq[positive_frequency_mask]),\n",
                "    \"k:\",\n",
                "    label=\"LISA sensitivity\",\n",
                ")\n",
                "plt.ylabel(r\"$| \\tilde{h}_{+} (f)|^2$\", fontsize=16)\n",
                "plt.grid()\n",
                "plt.xlabel(r\"$f$ [Hz]\", fontsize=16)\n",
                "plt.legend(loc=\"lower left\")\n",
                "plt.ylim([0.5e-41, 1e-32])\n",
                "plt.xlim([1e-4, 1e-1])\n",
                "plt.show()\n",
                "# plt.savefig('figures/FD_TD_frequency.pdf', bbox_inches='tight')"
            ]
        },
        {
            "cell_type": "code",
            "execution_count": 8,
            "id": "46335af4",
            "metadata": {},
            "outputs": [],
            "source": [
                "fd_kwargs_temp = waveform_kwargs.copy()\n",
                "fd_kwargs_temp[\"f_arr\"] = freq\n",
                "# do not mask the positive frequencies\n",
                "fd_kwargs_temp[\"mask_positive\"] = False\n",
                "hf_temp = few_gen(*emri_injection_params, **fd_kwargs_temp)\n",
                "\n",
                "# check the consistency of the waveform\n",
                "assert np.sum(hf_temp[0][positive_frequency_mask] - hf[0]) == 0.0\n",
                "\n",
                "# define the hf to invert\n",
                "hf_to_ifft = np.append(\n",
                "    hf_temp[0][positive_frequency_mask], hf_temp[0][~positive_frequency_mask]\n",
                ")"
            ]
        },
        {
            "cell_type": "code",
            "execution_count": 9,
            "id": "4c3ad8c5",
            "metadata": {},
            "outputs": [
                {
                    "name": "stderr",
                    "output_type": "stream",
                    "text": [
                        "/home/few/.local/few-venv/lib/python3.12/site-packages/matplotlib/cbook.py:1709: ComplexWarning: Casting complex values to real discards the imaginary part\n",
                        "  return math.isfinite(val)\n",
                        "/home/few/.local/few-venv/lib/python3.12/site-packages/matplotlib/cbook.py:1345: ComplexWarning: Casting complex values to real discards the imaginary part\n",
                        "  return np.asarray(x, float)\n"
                    ]
                },
                {
                    "data": {
                        "image/png": "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",
                        "text/plain": [
                            "<Figure size 640x480 with 1 Axes>"
                        ]
                    },
                    "metadata": {},
                    "output_type": "display_data"
                }
            ],
            "source": [
                "plt.figure()\n",
                "# TD plot\n",
                "time_array = np.arange(0, len(data_channels_td[0])) * dt\n",
                "plt.plot(time_array, data_channels_td[0], label=\"TD waveform\")\n",
                "ifft_fd = np.fft.ifft(hf_to_ifft / dt)\n",
                "plt.plot(time_array, ifft_fd, \"--\", label=\"Inverse DFT FD waveform\")\n",
                "plt.ylabel(r\"$h_{+}(t)$\")\n",
                "plt.xlabel(r\"$t$ [s]\")\n",
                "\n",
                "t0 = time_array[-1] * 0.7\n",
                "space_t = 10e3\n",
                "plt.xlim([t0, t0 + space_t / 2])\n",
                "plt.ylim([-4e-22, 6e-22])\n",
                "plt.legend(loc=\"upper center\")\n",
                "plt.show()"
            ]
        },
        {
            "cell_type": "markdown",
            "id": "70c0e265",
            "metadata": {},
            "source": []
        },
        {
            "cell_type": "code",
            "execution_count": null,
            "id": "e3d46791",
            "metadata": {},
            "outputs": [
                {
                    "ename": "ImportError",
                    "evalue": "cannot import name 'tukey' from 'scipy.signal' (/home/few/.local/few-venv/lib/python3.12/site-packages/scipy/signal/__init__.py)",
                    "output_type": "error",
                    "traceback": [
                        "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
                        "\u001b[0;31mImportError\u001b[0m                               Traceback (most recent call last)",
                        "Cell \u001b[0;32mIn[10], line 1\u001b[0m\n\u001b[0;32m----> 1\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;21;01mscipy\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01msignal\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;28;01mimport\u001b[39;00m tukey\n\u001b[1;32m      3\u001b[0m \u001b[38;5;66;03m# no windowing\u001b[39;00m\n\u001b[1;32m      4\u001b[0m window \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mNone\u001b[39;00m  \u001b[38;5;66;03m# np.asarray(hann(len(data_channels_td[0])))\u001b[39;00m\n",
                        "\u001b[0;31mImportError\u001b[0m: cannot import name 'tukey' from 'scipy.signal' (/home/few/.local/few-venv/lib/python3.12/site-packages/scipy/signal/__init__.py)"
                    ]
                }
            ],
            "source": [
                "from scipy.signal.windows import tukey\n",
                "\n",
                "# no windowing\n",
                "window = None  # np.asarray(hann(len(data_channels_td[0])))\n",
                "fft_td_gen = GetFDWaveformFromTD(td_gen, positive_frequency_mask, dt, window=window)\n",
                "fd_gen = GetFDWaveformFromFD(few_gen, positive_frequency_mask, dt, window=window)\n",
                "fd_kwargs_nomask = fd_kwargs.copy()\n",
                "del fd_kwargs_nomask[\"mask_positive\"]\n",
                "np.all(fd_gen(*emri_injection_params, **fd_kwargs_nomask)[0] == hf[0])\n",
                "\n",
                "# add windowing\n",
                "window = np.asarray(\n",
                "    tukey(len(data_channels_td[0]), 0.01)\n",
                ")  # np.asarray(data_channels_td[0]==0.0,dtype=float)#\n",
                "fft_td_gen = GetFDWaveformFromTD(td_gen, positive_frequency_mask, dt, window=window)\n",
                "fd_gen = GetFDWaveformFromFD(few_gen, positive_frequency_mask, dt, window=window)\n",
                "\n",
                "hf = fd_gen(*emri_injection_params, **fd_kwargs_nomask)\n",
                "fft_TD = fft_td_gen(*emri_injection_params, **fd_kwargs_nomask)\n",
                "\n",
                "# mismatch\n",
                "psd = get_sensitivity(freq[positive_frequency_mask]) / np.diff(freq)[0]\n",
                "td_td = inner_product(fft_TD[0], fft_TD[0], psd)\n",
                "fd_fd = inner_product(hf[0], hf[0], psd)\n",
                "Mism = np.abs(1 - inner_product(fft_TD[0], hf[0], psd) / np.sqrt(td_td * fd_fd))\n",
                "print(\"mismatch\", Mism)\n",
                "# SNR\n",
                "print(\"TD SNR\", np.sqrt(td_td))\n",
                "print(\"FD SNR\", np.sqrt(fd_fd))"
            ]
        },
        {
            "cell_type": "code",
            "execution_count": null,
            "id": "e726515a",
            "metadata": {},
            "outputs": [],
            "source": [
                "# FD plot\n",
                "plt.figure()\n",
                "plt.loglog(\n",
                "    freq[positive_frequency_mask], np.abs(fft_TD[0]) ** 2, label=\"DFT of TD waveform\"\n",
                ")\n",
                "plt.loglog(freq[positive_frequency_mask], np.abs(hf[0]) ** 2, \"--\", label=\"FD waveform\")\n",
                "plt.loglog(\n",
                "    freq[positive_frequency_mask],\n",
                "    get_sensitivity(freq[positive_frequency_mask]),\n",
                "    \"k:\",\n",
                "    label=\"LISA sensitivity\",\n",
                ")\n",
                "plt.ylabel(r\"$| \\tilde{h}_{+}(f)|^2$\", fontsize=16)\n",
                "plt.xlabel(r\"$f$ [Hz]\", fontsize=16)\n",
                "# plt.legend(loc='upper left')\n",
                "plt.grid()\n",
                "plt.ylim([0.5e-41, 1e-32])\n",
                "plt.xlim([1e-4, 1e-1])\n",
                "plt.show()\n",
                "# plt.savefig('figures/FD_TD_frequency_windowed.pdf', bbox_inches='tight')"
            ]
        },
        {
            "cell_type": "code",
            "execution_count": null,
            "id": "83171fdf",
            "metadata": {},
            "outputs": [],
            "source": [
                "# TD plot\n",
                "time_array = np.arange(0, len(data_channels_td[0])) * dt\n",
                "\n",
                "plt.figure()\n",
                "\n",
                "plt.plot(time_array, data_channels_td[0] * window, label=\"TD waveform\")\n",
                "plt.plot(time_array, ifft_fd, \"--\", label=\"FD waveform\")\n",
                "plt.ylabel(r\"$h_{+}(t)$\")\n",
                "plt.xlabel(r\"$t$ [s]\")\n",
                "\n",
                "t0 = time_array[-1] * 0.7\n",
                "space_t = 10e3\n",
                "plt.xlim([t0, t0 + space_t / 2])\n",
                "plt.ylim([-4e-22, 6e-22])\n",
                "plt.legend(loc=\"upper center\")\n",
                "plt.show()"
            ]
        },
        {
            "cell_type": "code",
            "execution_count": null,
            "id": "828c4a6f",
            "metadata": {},
            "outputs": [],
            "source": [
                "# you can specify the frequencies or obtain them directly from the waveform\n",
                "freq_temp = get_frequency_array(1e-4, 5e-2, np.diff(freq)[0])\n",
                "\n",
                "print(freq_temp)\n",
                "fd_kwargs_red = waveform_kwargs.copy()\n",
                "fd_kwargs_red[\"f_arr\"] = freq_temp\n",
                "fd_kwargs_red[\"mask_positive\"] = False\n",
                "\n",
                "# get FD waveform\n",
                "hf = few_gen(*emri_injection_params, **fd_kwargs_red)\n",
                "# time the generation of the FD signal\n",
                "start = time.time()\n",
                "hf = few_gen(*emri_injection_params, **fd_kwargs_red)\n",
                "end = time.time()\n",
                "print(\"Time taken to generate the FD signal: \", end - start, \"seconds\")\n",
                "# to get the frequencies:\n",
                "freq_fd = few_gen.waveform_generator.create_waveform.frequency\n",
                "# freq_temp = freq_temp[freq_temp>=0.0]\n",
                "print(\"freq_fd\", freq_fd.shape, \"h shape\", hf[0].shape)\n",
                "# mismatch\n",
                "# SNR\n",
                "print(\"TD SNR\", np.sqrt(td_td))\n",
                "print(\"FD SNR\", np.sqrt(fd_fd))\n",
                "# FD plot\n",
                "plt.figure()\n",
                "plt.loglog(freq_temp, np.abs(hf[0]) ** 2, \"-\", label=\"FD waveform\")\n",
                "plt.loglog(freq_temp, get_sensitivity(freq_temp), \"k:\", label=\"LISA sensitivity\")\n",
                "plt.ylabel(r\"$| \\tilde{h}_{+} (f)|^2$\", fontsize=16)\n",
                "plt.grid()\n",
                "plt.xlabel(r\"$f$ [Hz]\", fontsize=16)\n",
                "plt.legend(loc=\"lower left\")\n",
                "plt.ylim([0.5e-41, 1e-32])\n",
                "plt.xlim([1e-4, 1e-1])\n",
                "plt.show()\n",
                "# plt.savefig('figures/FD_TD_frequency.pdf', bbox_inches='tight')"
            ]
        },
        {
            "cell_type": "markdown",
            "id": "c244ddac",
            "metadata": {},
            "source": [
                "# Signal to noise ratio as a function of eccentricity and spin"
            ]
        },
        {
            "cell_type": "code",
            "execution_count": null,
            "id": "71486c23",
            "metadata": {},
            "outputs": [],
            "source": [
                "def calculate_snr_mismatch(\n",
                "    mode,\n",
                "    emri_injection_params,\n",
                "    waveform_kwargs,\n",
                "    fd_kwargs,\n",
                "    freq,\n",
                "    positive_frequency_mask,\n",
                "    dt,\n",
                "):\n",
                "    # Update fd_kwargs and td_kwargs with the current mode\n",
                "    fd_kwargs = fd_kwargs.copy()\n",
                "    fd_kwargs.pop(\"eps\")\n",
                "    fd_kwargs[\"mode_selection\"] = [mode]\n",
                "    hf_mode = few_gen(*emri_injection_params, **fd_kwargs)\n",
                "\n",
                "    td_kwargs2 = waveform_kwargs.copy()\n",
                "    td_kwargs2.pop(\"eps\")\n",
                "    td_kwargs2[\"mode_selection\"] = [mode]\n",
                "    data_channels_td_mode = td_gen(*emri_injection_params, **td_kwargs2)\n",
                "\n",
                "    # Take the FFT of the plus polarization and shift it\n",
                "    fft_TD_mode = np.fft.fftshift(np.fft.fft(data_channels_td_mode[0])) * dt\n",
                "\n",
                "    # Calculate PSD\n",
                "    psd = get_sensitivity(freq[positive_frequency_mask]) / np.diff(freq)[0]\n",
                "\n",
                "    # Calculate inner products\n",
                "    td_td = inner_product(\n",
                "        fft_TD_mode[positive_frequency_mask], fft_TD_mode[positive_frequency_mask], psd\n",
                "    )\n",
                "    fd_fd = inner_product(hf_mode[0], hf_mode[0], psd)\n",
                "    Mism = np.abs(\n",
                "        1\n",
                "        - inner_product(fft_TD_mode[positive_frequency_mask], hf_mode[0], psd)\n",
                "        / np.sqrt(td_td * fd_fd)\n",
                "    )\n",
                "\n",
                "    # calculated frequency\n",
                "    OmegaPhi, OmegaTheta, OmegaR = get_fundamental_frequencies(\n",
                "        emri_injection_params[2],\n",
                "        emri_injection_params[3],\n",
                "        emri_injection_params[4],\n",
                "        emri_injection_params[5],\n",
                "    )\n",
                "    harmonic_frequency = (OmegaPhi * mode[1] + OmegaR * mode[2]) / (\n",
                "        emri_injection_params[0] * MTSUN_SI * 2 * np.pi\n",
                "    )\n",
                "    return np.sqrt(td_td), Mism, harmonic_frequency\n",
                "\n",
                "\n",
                "# Initialize data storage\n",
                "data_out = []\n",
                "\n",
                "# mode vector\n",
                "eccentricity_vector = [0.1, 0.3, 0.7]\n",
                "max_n_vector = [10, 18, 26]\n",
                "spin_vector = [0.0, 0.9]\n",
                "\n",
                "for a in spin_vector:\n",
                "    for l_set, m_set in zip([2], [2]):\n",
                "        temp = emri_injection_params.copy()\n",
                "        for e_temp, max_n in zip(eccentricity_vector, max_n_vector):\n",
                "            modes = [(l_set, m_set, ii) for ii in range(-3, max_n)]\n",
                "            p_temp = get_p_at_t(\n",
                "                traj_module,\n",
                "                Tobs * 0.99,\n",
                "                [M, mu, a, e_temp, 1.0],\n",
                "                index_of_p=3,\n",
                "                index_of_a=2,\n",
                "                index_of_e=4,\n",
                "                index_of_x=5,\n",
                "                traj_kwargs={},\n",
                "                xtol=2e-12,\n",
                "                rtol=8.881784197001252e-16,\n",
                "                bounds=None,\n",
                "            )\n",
                "            temp[3] = p_temp\n",
                "            temp[4] = e_temp\n",
                "            temp[2] = a\n",
                "            out = np.asarray(\n",
                "                [\n",
                "                    calculate_snr_mismatch(\n",
                "                        mode,\n",
                "                        temp,\n",
                "                        waveform_kwargs,\n",
                "                        fd_kwargs,\n",
                "                        freq,\n",
                "                        positive_frequency_mask,\n",
                "                        dt,\n",
                "                    )\n",
                "                    for mode in modes\n",
                "                ]\n",
                "            )\n",
                "            snr, Mism, harmonic_frequency = out.T\n",
                "            data_out.append((harmonic_frequency, snr, l_set, m_set, e_temp, a))"
            ]
        },
        {
            "cell_type": "code",
            "execution_count": null,
            "id": "d289715c",
            "metadata": {},
            "outputs": [],
            "source": [
                "# Plot the data\n",
                "colors = {0.1: \"royalblue\", 0.3: \"seagreen\", 0.5: \"crimson\", 0.7: \"darkviolet\"}\n",
                "\n",
                "plt.figure(figsize=(8, 5))\n",
                "for harmonic_frequency, snr, l_set, m_set, e_temp, a in data_out:\n",
                "    color = colors[e_temp]\n",
                "    if a == 0.9:\n",
                "        plt.plot(\n",
                "            harmonic_frequency,\n",
                "            20.0 * snr / np.sum(snr**2) ** 0.5,\n",
                "            \":P\",\n",
                "            label=f\"e = {e_temp}, a = {a}\",\n",
                "            color=color,\n",
                "        )\n",
                "        # plt.text(harmonic_frequency[-1], 20.0 * snr[-1]/np.sum(snr**2)**0.5, f\"({l_set},{m_set})\", fontsize=8)\n",
                "    if a == 0.0:\n",
                "        plt.plot(\n",
                "            harmonic_frequency,\n",
                "            20.0 * snr / np.sum(snr**2) ** 0.5,\n",
                "            \"--o\",\n",
                "            label=f\"e = {e_temp}, a = {a}\",\n",
                "            color=color,\n",
                "        )\n",
                "        # for ii in range(len(harmonic_frequency)):\n",
                "        #     plt.text(harmonic_frequency[ii], 20.0 * snr[ii]/np.sum(snr**2)**0.5, f\"n={ii-3}\", fontsize=8)\n",
                "\n",
                "plt.xlabel(\"Initial Harmonic Frequency [Hz]\")\n",
                "plt.ylabel(\"20 x SNR / Total SNR\")\n",
                "plt.title(\n",
                "    f\"SNR per each harmonic (m,n) = ({2},n) for M={M:.2e}, mu={mu:.2e} and plunge in {Tobs} years\"\n",
                ")\n",
                "plt.xlim(0, 0.012)\n",
                "plt.grid()\n",
                "plt.legend()\n",
                "plt.show()"
            ]
        },
        {
            "cell_type": "code",
            "execution_count": null,
            "id": "21fbee3c",
            "metadata": {},
            "outputs": [],
            "source": [
                "# Initialize data storage\n",
                "data_out = []\n",
                "\n",
                "# mode vector\n",
                "a = 0.0\n",
                "Tobs = 0.1  # observation time, if the inspiral is shorter, the it will be zero padded\n",
                "eccentricity_vector = [0.1, 0.5]\n",
                "max_n_vector = [10, 26]\n",
                "eta_vector = [1e-4, 1e-6]  # mass ratio values\n",
                "\n",
                "for eta in eta_vector:\n",
                "    for e_temp, max_n in zip(eccentricity_vector, max_n_vector):\n",
                "        temp = emri_injection_params.copy()\n",
                "        mu = eta * M\n",
                "        p_temp = get_p_at_t(\n",
                "            traj_module,\n",
                "            Tobs * 0.99,\n",
                "            [M, mu, a, e_temp, 1.0],\n",
                "            index_of_p=3,\n",
                "            index_of_a=2,\n",
                "            index_of_e=4,\n",
                "            index_of_x=5,\n",
                "            traj_kwargs={},\n",
                "            xtol=2e-6,\n",
                "            rtol=8.881784197001252e-6,\n",
                "        )\n",
                "        temp[3] = p_temp\n",
                "        temp[4] = e_temp\n",
                "        temp[1] = mu\n",
                "        for l_set, m_set in zip([2], [2]):\n",
                "            modes = [(l_set, m_set, ii) for ii in range(-3, max_n)]\n",
                "            out = np.asarray(\n",
                "                [\n",
                "                    calculate_snr_mismatch(\n",
                "                        mode,\n",
                "                        temp,\n",
                "                        waveform_kwargs,\n",
                "                        fd_kwargs,\n",
                "                        freq,\n",
                "                        positive_frequency_mask,\n",
                "                        dt,\n",
                "                    )\n",
                "                    for mode in modes\n",
                "                ]\n",
                "            )\n",
                "            snr, Mism, harmonic_frequency = out.T\n",
                "            data_out.append((harmonic_frequency, snr, l_set, m_set, e_temp, eta))"
            ]
        },
        {
            "cell_type": "code",
            "execution_count": null,
            "id": "0f0e3429",
            "metadata": {},
            "outputs": [],
            "source": [
                "# Plot the data\n",
                "colors = {0.1: \"royalblue\", 0.3: \"seagreen\", 0.5: \"crimson\", 0.7: \"darkviolet\"}\n",
                "\n",
                "plt.figure(figsize=(8, 5))\n",
                "for harmonic_frequency, snr, l_set, m_set, e_temp, eta_temp in data_out:\n",
                "    color = colors[e_temp]\n",
                "    if eta_temp == 1e-4:\n",
                "        plt.plot(\n",
                "            harmonic_frequency,\n",
                "            20.0 * snr / np.sum(snr**2) ** 0.5,\n",
                "            \"-P\",\n",
                "            label=f\"e = {e_temp}, mu/M={eta_temp:.1e}\",\n",
                "            color=color,\n",
                "        )\n",
                "    if eta_temp == 1e-6:\n",
                "        plt.plot(\n",
                "            harmonic_frequency,\n",
                "            20.0 * snr / np.sum(snr**2) ** 0.5,\n",
                "            \":X\",\n",
                "            label=f\"e = {e_temp}, mu/M={eta_temp:.1e}\",\n",
                "            color=color,\n",
                "        )\n",
                "\n",
                "plt.xlabel(\"Initial Harmonic Frequency [Hz]\")\n",
                "plt.ylabel(\"20 x SNR / Total SNR\")\n",
                "plt.title(\n",
                "    f\"SNR per each harmonic (m,n) = ({2},n) for M={M:.2e} and plunge in {Tobs} years\"\n",
                ")\n",
                "plt.xlim(0, 0.03)\n",
                "plt.grid()\n",
                "plt.legend()\n",
                "plt.show()"
            ]
        },
        {
            "cell_type": "markdown",
            "id": "c0ef9908",
            "metadata": {},
            "source": [
                "## Speed test as a function of the parameter space"
            ]
        },
        {
            "cell_type": "code",
            "execution_count": null,
            "id": "ae62804f",
            "metadata": {},
            "outputs": [],
            "source": [
                "# create a function that times the FD and TD waveform generation for different input parameters\n",
                "\n",
                "\n",
                "def time_waveform_generation(fd_waveform_func, td_waveform_func, input_params, kwargs):\n",
                "    \"\"\"\n",
                "    Times the FD and TD waveform generation for different input parameters.\n",
                "\n",
                "    Parameters:\n",
                "    fd_waveform_func (function): Function to generate FD waveform.\n",
                "    td_waveform_func (function): Function to generate TD waveform.\n",
                "    input_params (list): List of dictionaries containing input parameters for the waveform functions.\n",
                "\n",
                "    Returns:\n",
                "    list: List of dictionaries containing input parameters and their corresponding FD and TD generation times.\n",
                "    \"\"\"\n",
                "    results = []\n",
                "\n",
                "    for params in input_params:\n",
                "        # Time FD waveform generation\n",
                "        start_time = time.time()\n",
                "        fd_waveform_func(*params, **kwargs)\n",
                "        fd_time = time.time() - start_time\n",
                "\n",
                "        # Time TD waveform generation\n",
                "        start_time = time.time()\n",
                "        td_waveform_func(*params, **kwargs)\n",
                "        td_time = time.time() - start_time\n",
                "\n",
                "        # Store the results\n",
                "        result = {\"input_params\": params, \"fd_time\": fd_time, \"td_time\": td_time}\n",
                "        results.append(result)\n",
                "\n",
                "    return results\n",
                "\n",
                "\n",
                "timing_results = []\n",
                "vec_par = []\n",
                "Tobs = 2.0\n",
                "# create a list of input parameters for M, mu, a, p0, e0, x0\n",
                "for el in np.linspace(0.1, 0.7, num=5):\n",
                "    # Tobs = np.random.uniform(0.1, 0.9)\n",
                "    temp = emri_injection_params.copy()\n",
                "    temp[4] = el\n",
                "    temp[3] = get_p_at_t(\n",
                "        traj_module,\n",
                "        Tobs * 0.99,\n",
                "        [M, mu, a, temp[4], 1.0],\n",
                "        index_of_p=3,\n",
                "        index_of_a=2,\n",
                "        index_of_e=4,\n",
                "        index_of_x=5,\n",
                "        traj_kwargs={},\n",
                "        xtol=2e-6,\n",
                "        rtol=8.881784197001252e-6,\n",
                "    )\n",
                "    vec_par.append(temp.copy())\n",
                "\n",
                "\n",
                "waveform_kwargs = {\n",
                "    \"T\": Tobs,\n",
                "    \"dt\": dt,\n",
                "    \"eps\": eps,\n",
                "}\n",
                "timing_results = time_waveform_generation(few_gen, td_gen, vec_par, waveform_kwargs)\n",
                "\n",
                "print(timing_results)"
            ]
        },
        {
            "cell_type": "code",
            "execution_count": null,
            "id": "8e4fc1d1",
            "metadata": {},
            "outputs": [],
            "source": [
                "for lab in [\"fd_time\", \"td_time\"]:\n",
                "    timing = [el[lab] for el in timing_results]\n",
                "    ecc = [el[\"input_params\"][4] for el in timing_results]\n",
                "    plt.plot(ecc, timing, \"o\", label=lab, alpha=0.5)\n",
                "plt.xlabel(\"eccentricity\")\n",
                "plt.ylabel(\"Time [s]\")\n",
                "plt.legend()\n",
                "plt.show()"
            ]
        },
        {
            "cell_type": "markdown",
            "id": "862289f6",
            "metadata": {},
            "source": [
                "## Mass invariance\n",
                "If we fix the mass ratio of an EMRI system the frequency domain waveform is invariant under a total mass change as long as we consider dimensionless frequencies. We show this here as a check of our frequency domain implementation."
            ]
        },
        {
            "cell_type": "code",
            "execution_count": null,
            "id": "88efb70a",
            "metadata": {},
            "outputs": [],
            "source": [
                "list_h = []\n",
                "list_f = []\n",
                "T = 4.0\n",
                "dt = 10.0\n",
                "# array of total masses\n",
                "Mvec = 10 ** np.linspace(5.0, 6.5, num=3)\n",
                "\n",
                "for M in Mvec:\n",
                "    # fix mass ratio\n",
                "    mu = 5e-5 * M\n",
                "\n",
                "    # rescale time\n",
                "    Tnew = T * (M / 1e6)\n",
                "\n",
                "    # generate wave\n",
                "    list_h.append(\n",
                "        few_gen(\n",
                "            M,\n",
                "            mu,\n",
                "            a,\n",
                "            p0,\n",
                "            e0,\n",
                "            x0,\n",
                "            dist,\n",
                "            qS,\n",
                "            phiS,\n",
                "            qK,\n",
                "            phiK,\n",
                "            Phi_phi0,\n",
                "            Phi_theta0,\n",
                "            Phi_r0,\n",
                "            T=10.0,\n",
                "            dt=dt,\n",
                "            mode_selection=[(2, 2, 0)],\n",
                "            mask_positive=True,\n",
                "        )\n",
                "    )\n",
                "\n",
                "    # dimensionless frequency\n",
                "    list_f.append(few_gen.waveform_generator.create_waveform.frequency * M * MTSUN_SI)"
            ]
        },
        {
            "cell_type": "code",
            "execution_count": null,
            "id": "36b31e6c",
            "metadata": {},
            "outputs": [],
            "source": [
                "plt.figure()\n",
                "for ii in range(len(Mvec)):\n",
                "    Tnew = 10.0 * Mvec[ii] / 1e6\n",
                "    tmp_mu = 1e-5 * Mvec[ii]\n",
                "\n",
                "    ff = list_f[ii]\n",
                "    ff = ff[ff >= 0.0]\n",
                "    h2 = np.abs(list_h[ii][0] / (tmp_mu * Tnew)) ** 2\n",
                "    plt.loglog(ff, h2, \"--\", label=f\"mu = {tmp_mu:.2}, T = {Tnew:.2}\", alpha=0.5)\n",
                "\n",
                "plt.xlim([1e-4, 1e-1])\n",
                "plt.legend()\n",
                "plt.show()"
            ]
        },
        {
            "cell_type": "markdown",
            "id": "755bde74",
            "metadata": {},
            "source": [
                "## Downsampled FD Waveforms\n",
                "One of the main advantages of the frequency domain formulation is that we can downsample the frequencies to reduce the computational cost of the waveform. This is illustrated in the following cells where we perform different levels of downsampling."
            ]
        },
        {
            "cell_type": "code",
            "execution_count": null,
            "id": "556743b8",
            "metadata": {},
            "outputs": [],
            "source": [
                "M, mu, p0, e0 = (\n",
                "    3670041.7362535275,\n",
                "    292.0583167470244,\n",
                "    13.709101864726545,\n",
                "    0.5794130830706371,\n",
                ")  # 1e6, 10.0, 13.709101864726545, 0.5794130830706371 #\n",
                "\n",
                "x0 = 1.0  # will be ignored in Schwarzschild waveform\n",
                "qK = np.pi / 3  # polar spin angle\n",
                "phiK = np.pi / 3  # azimuthal viewing angle\n",
                "qS = np.pi / 3  # polar sky angle\n",
                "phiS = np.pi / 3  # azimuthal viewing angle\n",
                "dist = 1.0  # distance\n",
                "# initial phases\n",
                "Phi_phi0 = np.pi / 3\n",
                "Phi_theta0 = 0.0\n",
                "Phi_r0 = np.pi / 3\n",
                "\n",
                "Tobs = 4.0  # observation time, if the inspiral is shorter, the it will be zero padded\n",
                "dt = 10.0  # time interval\n",
                "eps = 1e-2  # mode content percentage\n",
                "mode_selection = [(2, 2, 0)]\n",
                "\n",
                "waveform_kwargs = {\n",
                "    \"T\": Tobs,\n",
                "    \"dt\": dt,\n",
                "    # you can uncomment the following ling if you want to show a mode\n",
                "    #     \"mode_selection\" : mode_selection,\n",
                "    #     \"include_minus_m\": True\n",
                "    \"eps\": eps,\n",
                "}\n",
                "\n",
                "# get the initial p0\n",
                "p0 = get_p_at_t(\n",
                "    traj_module,\n",
                "    Tobs * 0.99,\n",
                "    [M, mu, 0.0, e0, 1.0],\n",
                "    index_of_p=3,\n",
                "    index_of_a=2,\n",
                "    index_of_e=4,\n",
                "    index_of_x=5,\n",
                "    traj_kwargs={},\n",
                "    xtol=2e-12,\n",
                "    rtol=8.881784197001252e-16,\n",
                "    bounds=None,\n",
                ")\n",
                "\n",
                "\n",
                "emri_injection_params = [\n",
                "    M,\n",
                "    mu,\n",
                "    a,\n",
                "    p0,\n",
                "    e0,\n",
                "    x0,\n",
                "    dist,\n",
                "    qS,\n",
                "    phiS,\n",
                "    qK,\n",
                "    phiK,\n",
                "    Phi_phi0,\n",
                "    Phi_theta0,\n",
                "    Phi_r0,\n",
                "]"
            ]
        },
        {
            "cell_type": "code",
            "execution_count": null,
            "id": "f4da894d",
            "metadata": {},
            "outputs": [],
            "source": [
                "# FD plot\n",
                "plt.figure()\n",
                "\n",
                "alpha = [1.0, 0.9, 0.8, 0.2]\n",
                "linest = [\"-\", \"--\", \"-.\", \":\"]\n",
                "\n",
                "for upp, aa, ls in zip([1, 100, 10000], alpha, linest):\n",
                "    # you can specify the frequencies or obtain them directly from the waveform\n",
                "    fd_kwargs = waveform_kwargs.copy()\n",
                "    fd_kwargs[\"mask_positive\"] = True\n",
                "    # get FD waveform\n",
                "    hf = few_gen(*emri_injection_params, **fd_kwargs)\n",
                "    freq_fd = few_gen.waveform_generator.create_waveform.frequency\n",
                "    positive_frequency_mask = freq_fd >= 0.0\n",
                "    mask_non_zero = hf[0] != complex(0.0)\n",
                "    end_f = few_gen.waveform_generator.create_waveform.frequency[\n",
                "        positive_frequency_mask\n",
                "    ][mask_non_zero].max()\n",
                "\n",
                "    if upp != 1:\n",
                "        num = int(len(freq_fd[positive_frequency_mask][mask_non_zero]) / upp)\n",
                "        p_freq = np.linspace(0.0, end_f * 1.01, num=num)\n",
                "        print(\"max frequency\", end_f)\n",
                "        newfreq = np.hstack((-p_freq[::-1][:-1], p_freq))\n",
                "\n",
                "        # you can specify the frequencies or obtain them directly from the waveform\n",
                "        fd_kwargs = waveform_kwargs.copy()\n",
                "        fd_kwargs[\"f_arr\"] = newfreq\n",
                "        fd_kwargs[\"mask_positive\"] = True\n",
                "\n",
                "        # get FD waveform\n",
                "        hf = few_gen(*emri_injection_params, **fd_kwargs)\n",
                "        # to get the frequencies:\n",
                "        freq_fd = few_gen.waveform_generator.create_waveform.frequency\n",
                "        positive_frequency_mask = freq_fd >= 0.0\n",
                "\n",
                "    Nf = len(freq_fd[positive_frequency_mask])\n",
                "    plt.loglog(\n",
                "        freq_fd[positive_frequency_mask],\n",
                "        freq_fd[positive_frequency_mask] ** 2 * np.abs(hf[0]) ** 2,\n",
                "        ls,\n",
                "        label=f\"$N_f = $ {Nf}\",\n",
                "        alpha=aa,\n",
                "    )\n",
                "\n",
                "\n",
                "ff = 10 ** np.linspace(-5, -1, num=100)\n",
                "plt.loglog(ff, ff * get_sensitivity(ff), \"k:\", label=\"LISA sensitivity\")\n",
                "\n",
                "plt.ylabel(r\"$|f\\, \\tilde{h}_{+}(f)|^2$\", fontsize=16)\n",
                "plt.xlabel(r\"$f$ [Hz]\", fontsize=16)\n",
                "plt.legend(loc=\"lower left\")\n",
                "plt.xlim(1e-4, 3e-3)\n",
                "plt.grid()\n",
                "plt.ylim([1e-49, 1e-34])\n",
                "plt.tight_layout()\n",
                "plt.show()\n",
                "\n",
                "# plt.savefig('figures/spectrum_downsampled.pdf')"
            ]
        },
        {
            "cell_type": "code",
            "execution_count": null,
            "id": "b7a8afad",
            "metadata": {},
            "outputs": [],
            "source": []
        }
    ],
    "metadata": {
        "kernelspec": {
            "display_name": "few-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": "3.12.3"
        }
    },
    "nbformat": 4,
    "nbformat_minor": 5
}