{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "7f0aebb9",
   "metadata": {},
   "source": [
    "# PNPedia interface\n",
    "\n",
    "This notebook demonstrates how to use `PyART.analytic.pnpedia.PNPedia` to load and manipulate PN quantities."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "933a4c69",
   "metadata": {},
   "source": [
    "### Loading and plotting a PN quantity "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c7730d46",
   "metadata": {},
   "source": [
    "We begin by importing the necessary modules and creating an instance of `PNPedia`. We then load the energy of a non-spinning binary system on circular orbits, and plot it as a function of the PN parameter `x`.\n",
    "\n",
    "We will use the`get_pn_quantity()` method. Calling `.to_function()` on that object then returns a callable together with the symbolic argument order it expects."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "59e8a8e7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 900x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 900x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import sys\n",
    "from pathlib import Path\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "\n",
    "\n",
    "def find_repo_root(start: Path) -> Path:\n",
    "    for candidate in (start, *start.parents):\n",
    "        if (candidate / \"pyproject.toml\").exists() and (candidate / \"PyART\").exists():\n",
    "            return candidate\n",
    "    raise FileNotFoundError(\n",
    "        \"Could not locate the PyART repository root from the notebook working directory.\"\n",
    "    )\n",
    "\n",
    "\n",
    "repo_root = find_repo_root(Path.cwd().resolve())\n",
    "if str(repo_root) not in sys.path:\n",
    "    sys.path.insert(0, str(repo_root))\n",
    "\n",
    "from PyART.analytic.pnpedia import PNPedia\n",
    "\n",
    "# Clone or reuse a local PNPedia checkout under the repository root.\n",
    "pnpedia_path = repo_root / \"PNPedia\"\n",
    "npd = PNPedia(path=str(pnpedia_path), download=not pnpedia_path.exists())\n",
    "\n",
    "# PN variable x range for circular inspiral (small x region)\n",
    "x = np.linspace(0.001, 0.35, 400)\n",
    "\n",
    "# Evaluate with geometric-like normalized constants and symmetric mass ratio nu=0.25.\n",
    "# Set G=c=m=b0=1 for demonstration, then tune as required for a specific use case.\n",
    "value_map = {\"G\": 1.0, \"b0\": 1.0, \"c\": 1.0, \"m\": 1.0, \"nu\": 0.25, \"x\": x}\n",
    "\n",
    "energy_orders = [0, 1, 2, 3, 4]\n",
    "angular_momentum_orders = [0, 1, 2, 3, 4]\n",
    "\n",
    "\n",
    "def evaluate_expression(expression):\n",
    "    analytic_func, variables = expression.to_function()\n",
    "    args = [value_map[str(symbol)] for symbol in variables]\n",
    "    return analytic_func(*args), variables\n",
    "\n",
    "\n",
    "# 1) Plot E(x) for multiple PN truncations\n",
    "plt.figure(figsize=(9, 6))\n",
    "for order in energy_orders:\n",
    "    energy_expr = npd.get_pn_quantity(\n",
    "        \"energy_circular_nonspinning_binding\", order=order\n",
    "    )\n",
    "    energy, _ = evaluate_expression(energy_expr)\n",
    "    plt.plot(x, energy, lw=1.5, label=f\"{order}PN\")\n",
    "\n",
    "plt.xlabel(\"x (post-Newtonian parameter)\")\n",
    "plt.ylabel(\"binding energy (dimensionless, G=c=m=1)\")\n",
    "plt.title(\"PNPedia: E_binding(x) for nonspinning circular binaries\")\n",
    "plt.grid(True)\n",
    "plt.legend(ncol=2, fontsize=\"small\")\n",
    "plt.tight_layout()\n",
    "plt.show()\n",
    "\n",
    "# 2) Plot J(x) for multiple PN truncations\n",
    "plt.figure(figsize=(9, 6))\n",
    "for order in angular_momentum_orders:\n",
    "    angular_momentum_expr = npd.get_pn_quantity(\n",
    "        \"angular_momentum_circular_nonspinning_conservative\", order=order\n",
    "    )\n",
    "    angmom, _ = evaluate_expression(angular_momentum_expr)\n",
    "    plt.plot(x, angmom, lw=1.5, label=f\"{order}PN\")\n",
    "\n",
    "plt.xlabel(\"x (post-Newtonian parameter)\")\n",
    "plt.ylabel(\"angular momentum (dimensionless, G=c=m=1)\")\n",
    "plt.title(\"PNPedia: J_binding(x) for nonspinning circular binaries\")\n",
    "plt.grid(True)\n",
    "plt.legend(ncol=2, fontsize=\"small\")\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5c43f43d",
   "metadata": {},
   "source": [
    "The `PNPedia` entries contain also metadata with pointers to documentaton, notation and endorsers of the expressions themselves"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "cbd586f9",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "arxiv_references: ['arXiv:2504.13245v1', 'arXiv:1401.4548v2', 'arXiv:1711.00283v2']\n",
      "notation:\n",
      "  Pi: $\\pi \\approx 3.14$\n",
      "  EulerGamma: the Euler's constant $\\gamma_\\text{E} \\approx 0.58$\n",
      "  G: Newton's constant of gravitation\n",
      "  c: the speed of light\n",
      "  x: the dimensionless orbital frequency $x = G m \\omega /c^3$, where $\\omega$ is the dimensionful orbital frequency\n",
      "  \\[Nu]: the symmetric mass ratio, $\\nu = \\frac{m_1 m_2}{(m_1 + m_2)^2}$\n",
      "  b_0: an arbitary constant linked to the choice of spactime foliation\n",
      "endorsers: ['David Trestini', 'Loïc Honet']\n"
     ]
    }
   ],
   "source": [
    "energy_entry = npd.get_entry(\"energy_circular_nonspinning_binding\")\n",
    "metadata = energy_entry.metadata\n",
    "for key, value in metadata.items():\n",
    "    if key == 'notation':\n",
    "        print(f\"{key}:\")\n",
    "        for key2, value2 in value.items():\n",
    "            print(f\"  {key2}: {value2}\")\n",
    "    else:\n",
    "        print(f\"{key}: {value}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "119a7ee9",
   "metadata": {},
   "source": [
    "### Compare the analytical PN binding energy to a NR curve"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "451a9923",
   "metadata": {},
   "source": [
    "\n",
    "The binding energy vs angular momentum plot is a standard check for the consistency of PN results with NR simulations,\n",
    "as for quasi-circular orbits the full dynamics can be represented by these two gauge-invariant quantities.\n",
    "\n",
    "Note: the NR comparison we perform is rather \"naive\". One should in principle shift the NR curve\n",
    "such that the final state (remnant) coincides with the last point of the NR curve, see\n",
    "the appendix of https://arxiv.org/pdf/1506.08457"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "0f838c17",
   "metadata": {},
   "outputs": [],
   "source": [
    "from PyART.catalogs import sxs\n",
    "\n",
    "# NR curve from the SXS catalog\n",
    "sxs_waveform = sxs.Waveform_SXS(\n",
    "    ID=\"0180\",\n",
    "    download=True,\n",
    "    ignore_deprecation=True,\n",
    "    downloads=[\"hlm\", \"metadata\", \"horizons\"],\n",
    "    load=[\"hlm\", \"metadata\", \"horizons\"],\n",
    ")\n",
    "t_max, _, _, _, mrg_idx = sxs_waveform.find_max(return_idx=True, mode=(2, 2))\n",
    "M_adm_0 = sxs_waveform.metadata[\"E0byM\"]\n",
    "J_adm_0 = sxs_waveform.metadata[\"Jz0\"]\n",
    "m1 = sxs_waveform.metadata[\"m1\"]\n",
    "m2 = sxs_waveform.metadata[\"m2\"]\n",
    "modes = [(2, 2), (2, 1), (3, 3), (4, 4), (2, -2), (2, -1), (3, -3), (4, -4)]\n",
    "eb, e, jorb = sxs_waveform.ej_from_hlm(M_adm_0, J_adm_0, m1, m2, modes=modes)\n",
    "\n",
    "# identify NR merger\n",
    "e_mrg = eb[mrg_idx]\n",
    "j_mrg = jorb[mrg_idx]\n",
    "\n",
    "fig, ax = plt.subplots(2, 1, sharex=True)\n",
    "ax0, ax1 = ax\n",
    "ax0.plot(jorb, eb, \"k-\", lw=2, label=\"NR (SXS:BBH:0180)\")\n",
    "ax0.scatter(j_mrg, e_mrg, color=\"k\", label=\"Merger\")\n",
    "\n",
    "# Flip arrays to have increasing J/nu for interpolation.\n",
    "jorb = jorb[::-1]\n",
    "eb = eb[::-1]\n",
    "\n",
    "# PN curve (NPN order)\n",
    "for order, color in zip(energy_orders, [\"C0\", \"C1\", \"C2\", \"C3\", \"C4\"]):\n",
    "    energy_expr = npd.get_pn_quantity(\n",
    "        \"energy_circular_nonspinning_binding\", order=order\n",
    "    )\n",
    "    angular_momentum_expr = npd.get_pn_quantity(\n",
    "        \"angular_momentum_circular_nonspinning_conservative\", order=order\n",
    "    )\n",
    "\n",
    "    E, _ = evaluate_expression(energy_expr)\n",
    "    J, _ = evaluate_expression(angular_momentum_expr)\n",
    "\n",
    "    ax0.plot(\n",
    "        J / value_map[\"nu\"],\n",
    "        E / value_map[\"nu\"],\n",
    "        \"--\",\n",
    "        color=color,\n",
    "        lw=2,\n",
    "        label=f\"{order}PN\",\n",
    "    )\n",
    "\n",
    "    # Plot residuals after interpolating both curves on a shared J grid.\n",
    "    jmin, jmax = np.min(J / value_map[\"nu\"]), np.max(jorb)\n",
    "    jint = np.linspace(jmin, jmax, 500)\n",
    "    idx_min = np.argmin(np.abs(J / value_map[\"nu\"] - jmin))\n",
    "    J_plot = J[: idx_min - 1][::-1]\n",
    "    E_plot = E[: idx_min - 1][::-1]\n",
    "\n",
    "    eb_nr_int = np.interp(jint, jorb, eb)\n",
    "    eb_pn_int = np.interp(jint, J_plot / value_map[\"nu\"], E_plot / value_map[\"nu\"])\n",
    "    deltaE = np.abs(eb_nr_int - eb_pn_int)\n",
    "    ax1.semilogy(jint, deltaE, \"-\", lw=2, color=color, label=f\"{order}PN\")\n",
    "\n",
    "ax0.set_xlim(np.min(jorb) * 0.8, np.max(jorb) * 1.1)\n",
    "ax0.set_ylim(np.min(eb) * 1.2, np.max(eb) * 0.1)\n",
    "ax0.set_ylabel(r\"$E_b/\\nu$\")\n",
    "# legend out of plot, on top and horizontal\n",
    "ax0.legend(loc=\"upper center\",\n",
    "           bbox_to_anchor=(0.5, 1.3),\n",
    "           ncol=4, fontsize=\"small\",\n",
    "           fancybox=False,\n",
    "           edgecolor='none'\n",
    "        )\n",
    "ax1.set_xlabel(r\"$j/M^2$\")\n",
    "ax1.set_ylabel(r\"$|E_b^{NR} - E_b^{PN}|$\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0724f83f",
   "metadata": {},
   "source": [
    "### Inspect `AnalyticalExpression`s, manipulate and evaluate them"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8ff931b9",
   "metadata": {},
   "source": [
    "\n",
    "The `AnalyticExpression` class wraps SymPy expressions and provides a small interface for differentiation, PN truncation, and numerical evaluation."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "75356627",
   "metadata": {},
   "outputs": [],
   "source": [
    "analytic_E = npd.get_pn_quantity(\"energy_circular_nonspinning_binding\", order=4)\n",
    "analytic_E_dvt = analytic_E.derivative(\"x\")\n",
    "\n",
    "analytic_E_values, _ = evaluate_expression(analytic_E)\n",
    "analytic_E_dvt_values, _ = evaluate_expression(analytic_E_dvt)\n",
    "\n",
    "# Plot the numerical derivative and the analytical one.\n",
    "numerical_dvt = np.gradient(analytic_E_values, x)\n",
    "plt.figure(figsize=(9, 6))\n",
    "plt.plot(x, numerical_dvt, label=\"numerical dE/dx\", lw=2)\n",
    "plt.plot(x, analytic_E_dvt_values, label=\"analytical dE/dx\", lw=2, ls=\"--\")\n",
    "plt.ylabel(\"dE/dx (dimensionless, G=c=m=1)\")\n",
    "plt.title(\"PNPedia: dE/dx for nonspinning circular binaries at 4PN order\")\n",
    "plt.grid(True)\n",
    "plt.legend()\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "ac916543",
   "metadata": {},
   "outputs": [],
   "source": [
    "# AnalyticExpression objects can be added, subtracted, multiplied, divided, etc.\n",
    "# to create new derived quantities, exactly like standard SymPy expressions.\n",
    "# further, they also inherit methods like .to_latex() for easy conversion\n",
    "\n",
    "from PyART.analytic import AnalyticExpression\n",
    "\n",
    "simple_expression_1 = AnalyticExpression(\"x**2 + 3*x + 5 + 5*q\", var=[\"x\", \"q\"])\n",
    "simple_expression_2 = AnalyticExpression(\"2*x**3 - x + 1\", var=[\"x\"])\n",
    "\n",
    "derived_expression = simple_expression_1 + simple_expression_2\n",
    "\n",
    "print(derived_expression.expr)\n",
    "print(derived_expression.truncate(\"x\", 2).expr)  # truncate to x^2 order\n",
    "print(derived_expression.to_latex())  # convert to LaTeX string"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "90881d34",
   "metadata": {},
   "source": [
    "### EOB Waveform factorization and resummation"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "37caa6ff",
   "metadata": {},
   "source": [
    "As an additional practical example of the use of the PNPedia parser for GW modeling, we now factorize and resum the multipolar waveform $h_{22}$ as described in [Damour, Iyer, Nagar 2009](https://arxiv.org/abs/0711.2628) and [Damour, Nagar 2010](https://arxiv.org/abs/1009.5998). This is a key step in the EOB waveform construction, and allows for improved accuracy and convergence of the waveform."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "e95dfe90",
   "metadata": {},
   "outputs": [],
   "source": [
    "from PyART.analytic import PNPedia\n",
    "from PyART.analytic import AnalyticExpression\n",
    "import sympy as sp\n",
    "from sympy import gamma\n",
    "\n",
    "# create a PNPedia and object\n",
    "pnpedia = PNPedia(path='./PNPedia', download=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "89f5ee88",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Compute the binding energy and angular momentum, these will be the source terms\n",
    "Eb = pnpedia.get_pn_quantity(\"energy_circular_nonspinning_binding\")\n",
    "jorb = pnpedia.get_pn_quantity(\"angular_momentum_circular_nonspinning_conservative\")\n",
    "nu = sp.symbols(\"nu\")  # symmetric mass ratio\n",
    "x = sp.symbols(\"x\")  # PN expansion parameter\n",
    "# get the real and effective energies\n",
    "Hreal = Eb + 1\n",
    "Heff = 1 + (Hreal**2 - 1)/(2*nu)\n",
    "# truncate expands and then truncates to the specified order\n",
    "Heff = Heff.truncate(\"x\", 5)\n",
    "\n",
    "# inspect the expression\n",
    "sp.collect(Hreal, x)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c2b39667",
   "metadata": {},
   "source": [
    "Now, we write $h_{22}$ as a product of source, tail and residual amplitude and phase factors:\n",
    "\n",
    "$$ h_{22} = h_{22}^{N} \\hat{S}_{\\text{eff}} T_{22} e^{i \\delta_{22}} f_{22} $$\n",
    "\n",
    "where $h_{22}^{N}$ is the Newtonian leading order term, $\\hat{S}_{\\text{eff}}$ is the effective source term, $T_{22}$ is the tail factor, $\\delta_{22}$ is the residual phase correction, and $f_{22}$ is the residual amplitude correction.\n",
    "\n",
    "Below, we compute the tail factor and the residual amplitude correction up to 3PN order."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "0bf80cfd",
   "metadata": {},
   "outputs": [],
   "source": [
    "def tail_term(ell, emm, Hreal, x, order):\n",
    "    \"\"\"\n",
    "    Compute the tail term for a given multipole (ell, emm),\n",
    "    Eq. 19 of https://arxiv.org/pdf/0811.2069\n",
    "\n",
    "    Assume c=G=1 and M=1 as well\n",
    "    Note that we need to work with the loggamma fnction to get the correct\n",
    "    expansion, if not sympy will complain about poles (for... whaever reason)\n",
    "    \"\"\"\n",
    "\n",
    "    G, c, M = sp.symbols(\"G c M\")\n",
    "    x_pos = sp.Symbol(\"x_p\", positive=True)\n",
    "    omega = x_pos**(sp.Rational(3,2))\n",
    "    kap = emm * omega\n",
    "    \n",
    "    # in Hreal assume c=G=M=1 as well\n",
    "    H_pos = Hreal.expr.subs({G:1, M:1, c:1, x: x_pos})\n",
    "    hathatk = sp.Integer(emm) * omega * H_pos\n",
    "    hathatk = sp.series(hathatk, x, 0, order+1).removeO()\n",
    "    r0 = 2/sp.sqrt(sp.exp(1))\n",
    "    eps = sp.Symbol(\"_eps_tail\")\n",
    "    n = ell + 1\n",
    "\n",
    "    # we compute the tail in pieces\n",
    "\n",
    "    # first piece, where we treat the loggamma function\n",
    "    logfirst = sp.series(sp.loggamma(n + eps), eps, 0, order+2).removeO()\n",
    "    log_tail_first = logfirst.subs(eps, -2 * sp.I * hathatk)\n",
    "    log_tail_first = sp.series(log_tail_first, x_pos, 0, order+1).removeO()\n",
    "    tail_first = sp.exp(log_tail_first)\n",
    "    tail_first = sp.series(tail_first, x_pos, 0, order+1).removeO()\n",
    "\n",
    "    # second piece\n",
    "    tail_second = sp.exp(sp.pi * hathatk) * sp.exp(2 * sp.I * hathatk * sp.log(2 * kap * r0))\n",
    "    tail_second = sp.series(tail_second, x_pos, 0, order+1).removeO()\n",
    "\n",
    "    # denominator piece\n",
    "    tail_denominator = gamma(n)\n",
    "\n",
    "    # product of the pieces\n",
    "    tail = tail_first * tail_second / tail_denominator\n",
    "    tail = sp.series(tail, x_pos, 0, order+1).removeO()\n",
    "    tail = tail.subs(x_pos, x)\n",
    "    return AnalyticExpression(tail, var=x)\n",
    "\n",
    "def tail_term_amp(ell, emm, Hreal, x, order):\n",
    "    \"\"\"\n",
    "    Compute the tail term amplitude for a given multipole (ell, emm),\n",
    "    Eq. 38 of https://arxiv.org/pdf/1801.02366\n",
    "    \"\"\"\n",
    "    x_pos = sp.Symbol(\"x_p\", positive=True)\n",
    "    omega = x_pos**(sp.Rational(3,2))\n",
    "    G, c, M = sp.symbols(\"G c M\")\n",
    "    H_pos = Hreal.expr.subs({G:1, M:1, c:1, x: x_pos})\n",
    "    emm = sp.Integer(emm)\n",
    "    ell = sp.Integer(ell)\n",
    "    hathatk = sp.Integer(emm) * omega * H_pos\n",
    "    hathatk = sp.series(hathatk, x, 0, order+1).removeO()\n",
    "\n",
    "    prod = 1\n",
    "    for ss in range(1, ell+1):\n",
    "        ss = sp.Integer(ss)\n",
    "        tmp = (ss**2 + (2*hathatk)**2)\n",
    "        tmp = sp.series(tmp, x_pos, 0, order+1).removeO()\n",
    "        prod *= tmp\n",
    "    num = sp.series(4 * sp.pi * hathatk * prod, x_pos, 0, order+1).removeO()\n",
    "    denom = sp.series(sp.factorial(ell)**2 * (1 - sp.exp(-4 * sp.pi * hathatk)), x_pos, 0, order+1).removeO()\n",
    "    tail = sp.series(num/denom, x_pos, 0, order+1).removeO()\n",
    "    tail = sp.sqrt(tail)\n",
    "    tail = sp.series(tail, x_pos, 0, order+1).removeO()\n",
    "    tail = tail.subs(x_pos, x)\n",
    "    return AnalyticExpression(tail, var=x)\n",
    "\n",
    "def get_amplitude(expr, x, order):\n",
    "    \"\"\"\n",
    "    Get the amplitude of a given expression, by taking the absolute value and\n",
    "    then truncating to the specified order\n",
    "    \"\"\"\n",
    "    x_pos = sp.Symbol(\"x_pos\", posiitive=True)\n",
    "    # make also m and nu positive!\n",
    "    m_pos, nu_pos = sp.symbols(\"m_pos nu_pos\", positive=True)\n",
    "    # drop useless terms\n",
    "    expr_dummy = expr.subs({x: x_pos, sp.symbols(\"m\"): m_pos, sp.symbols(\"nu\"): nu_pos})\n",
    "    expr_dummy = sp.series(expr_dummy, x_pos, 0, order+1).removeO()\n",
    "    amp = sp.Abs(expr_dummy)\n",
    "    amp_expanded = sp.series(amp, x_pos, 0, order+1).removeO()\n",
    "    amp_expanded = amp_expanded.subs({x_pos: x, m_pos: sp.symbols(\"m\"), nu_pos: sp.symbols(\"nu\")})\n",
    "    return AnalyticExpression(amp_expanded, var=x)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "f8d37ba5",
   "metadata": {},
   "outputs": [],
   "source": [
    "# let's get the 22 mode\n",
    "h22 = pnpedia.get_pn_quantity(\"h_2_2_circular_nonspinning_without_waveform\")\n",
    "h22_newt = pnpedia.get_pn_quantity(\"h_2_2_circular_nonspinning_without_waveform\", order=0, variable=x)\n",
    "# max pn order for the 22 mode\n",
    "max_order, span = h22.pn_order(x)\n",
    "min_order = max_order-span\n",
    "# get the h22 amplitude, this will take a while to compute\n",
    "hath22 = sp.collect(h22/h22_newt, x)\n",
    "hath22 = get_amplitude(hath22, x, 4)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "2569f1c1",
   "metadata": {},
   "outputs": [],
   "source": [
    "# get the tail term for the 22, this will take a while to compute\n",
    "t22 = tail_term(2, 2, Hreal, x, 3)\n",
    "ht22 = get_amplitude(t22, x, 3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "8e85e0a2",
   "metadata": {},
   "outputs": [],
   "source": [
    "# now compute the residual amplitude\n",
    "# this expression agrees, up to the desired order, with Eq. 31 of https://arxiv.org/pdf/0811.2069\n",
    "G, c, M, m = sp.symbols(\"G c M m\")\n",
    "res22 = AnalyticExpression((hath22/(ht22)/Heff.expr).subs({G:1, M:1, c:1, m:1}), var=x)\n",
    "sp.collect(sp.simplify(res22.truncate(\"x\", 3).expr), x)"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "pyart-dev-3.11",
   "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": "3.11.11"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}