Adding documentation for the PNBMS frame fixing method

docs/tutorial_abd.rst

@@ -294,8 +294,12 @@ that are easier to understand/analyze. Effectively, mapping to this frame amount
 in the center-of-mass frame, with no instananeous memory, and its angular velocity in the z-direction. For example,
 for a remnant black hole, this corresponds to making the coordinates match those of the usual Kerr metric and is
 therefore incredibly useful (and necessary) for fitting QNMs to NR waveforms.
+There is another choice of frame which we call the Post-Newtonian BMS frame (or
+PNBMS frame). In this frame, the waveform will agree with the Post-Newtonian
+description of the system at early times.
 
-The function ``scri.asymptotic_bondi_data.map_to_superrest_frame`` maps to this exact frame.
+The function :meth:`scri.asymptotic_bondi_data.map_to_superrest_frame.map_to_superrest_frame` can map
+to one of these frames.
 In particular, it takes as input:
 
 * ``t_0``, the time at which to map to the superrest frame;
@@ -333,7 +337,7 @@ It also can perform a number of other important post-processing steps, such as:
 
 We recommend including all of these post-processing steps when processing SpECTRE CCE output.
 
-To obtain the strain :math:`h` from the ``abd`` object, one can use the function ``scri.asymptotic_bondi_data.map_to_superrest_frame.MT_to_WM`` via ``h = MT_to_WM(2.0*abd.sigma.bar)``. 
+To obtain the strain :math:`h` from the ``abd`` object, one can use the function :meth:`scri.asymptotic_bondi_data.map_to_superrest_frame.MT_to_WM` via ``h = MT_to_WM(2.0*abd.sigma.bar)``. 
 This is because the strain :math:`h` is related to the shear :math:`\sigma` via :math:`h=2\overline{\sigma}`.
 
 Example usage of this function could be:
@@ -357,10 +361,16 @@ Example usage of this function could be:
 For more on the :meth:`scri.WaveformModes` class, i.e., what :math:`h` is in the above code, see https://github.com/moble/scri/blob/main/docs/tutorial_waveformmodes.rst.
 
 The CCE output is sometimes very densely sampled in time, which causes the BMS
-transformation to the superrest frame to take a long time (more than 10
+transformation to the superrest frame or PNBMS frame to take a long time (more than 10
 minutes). An effective way around this is to down sample the CCE data, compute
-the BMS transformation to the superrest frame with the down sampled data, and
-then apply to the full data. This can be done with the following two functions
+the BMS transformation to the appropriate frame with the down sampled data, and
+then apply to the full data.
+
+--------------------------
+Fixing the superrest frame
+--------------------------
+The superrest frame of the CCE waveforms can be fixed using the following two
+functions.
 
 .. code-block:: python
 
@@ -387,6 +397,136 @@ then apply to the full data. This can be done with the following two functions
                            frame_rotation=BMS.frame_rotation.components,
                            boost_velocity=BMS.boost_velocity,)
 
+------------------------------------------
+Fixing the PNBMS frame using PN CoM charge
+------------------------------------------
+
+The PNBMS frame fixing of CCE waveforms requires ``target_PsiM_input`` (the PN 
+Moreschi supermomentum), and ``target_strain_input`` (the PN strain). These
+post-Newtonian waveform modes can be generated using the `PostNewtonian.jl 
+<https://github.com/moble/PostNewtonian.jl>`_ module. We can generate these
+waveform modes and provide them as input arguments to the
+``map_to_superrest_frame`` function for fixing the PNBMS frame. The generation
+of these waveform modes and fixing the frame can be performed using the
+following functions.
+
+.. code-block:: python
+
+  from sxs.julia import PNWaveform
+  import numpy as np
+  import sxs
+  import scri
+  from scri.pn.boosted_comcharge import PN_charges, analytical_CoM_func
+
+  def generate_h_and_Psi_M_from_abd(abd, params, t0_pnbms, pnbms_padding):
+      """This function computes the target strain and target PsiM waveform
+      required for PNBMS frame fixing.
+
+      Parameters
+      ----------
+      abd : AsymptoticBondiData
+      AsymptoticBondiData object from which the PN waveforms will be computed.
+      params: tuple
+      List of intrinsic parameters corresponding to the simulation. Its
+      content should follow the form:
+      params = M1, M2, chi1, chi2
+      where M1, M2 are floats,
+      chi1 : list of floats (n,3)
+      chi2 : list of floats (n,3)
+      t0_pnbms : float
+      When to map to the PNBMS frame.
+      pnbms_padding : float
+      Amount by which to pad around t0 to speed up computations.
+      This also determines the range over which certain BMS charges will be
+      computed.
+
+      Returns
+      -------
+      h_pn_scri, Psi_M_scri : scri.WaveformModes object
+      """
+      # First we create a window for frame-fixing using t0 and padding time.
+      # The window is padded by an additional 200M on both sides of t0.
+      t_left = t0_pnbms - (pnbms_padding + 200)
+      t_right = t0_pnbms + (pnbms_padding + 200)
+
+      left_idx = np.abs(abd.t - t_left).argmin()
+      right_idx = np.abs(abd.t - t_right).argmin()
+
+      h = abd.h[left_idx:right_idx]
+      t = h.t - h.t[0]
+
+      # The convention for choosing the phase from h_{21} mode and the factor of
+      # pi/2 is described in <https://arxiv.org/abs/2603.24661>.
+      h_21 = h.data[:, h.index(2, 1)]
+      θ = -1 * np.unwrap(np.angle(-h_21) - np.pi / 2)
+      ω = np.gradient(θ, t)[0]
+
+      R_i = np.array([np.cos(θ[0] / 2), 0, 0, np.sin(θ[0] / 2)]).T
+
+      M1, M2, chi1, chi2 = params
+
+      h_PN = PNWaveform(M1, M2, chi1, chi2, ω, modes_function=sxs.julia.h_bang,
+          R_i=R_i, saveat=t)
+      Psi_M_PN = PNWaveform(M1, M2, chi1, chi2, ω,
+          modes_function=sxs.julia.Ψ_M_bang, R_i=R_i, saveat=t)
+
+      # The PNWaveform function needs the kwarg times to start from t=0. Thus, we
+      # translate the time array of PN waveform modes to match with the inspiral
+      # window constructed from the abd object at the end.
+      h_PN.t += t0_pnbms - (pnbms_padding + 200)
+      Psi_M_PN.t += t0_pnbms - (pnbms_padding + 200)
+
+      # The sxs.WaveformModes objects should be converted to scri.WaveformModes
+      # before passing to map_to_superrest_frame.
+      h_pn_scri = scri.WaveformModes.from_sxs(h_PN)
+      Psi_M_scri = scri.WaveformModes.from_sxs(Psi_M_PN)
+
+      return h_pn_scri, Psi_M_scri
+
+  def load_waveform_and_fix_to_pnbms(
+      filename: str,
+      params,
+      dt: float = 1.0,
+      t0_pnbms: float = 1800.0,
+      pnbms_padding: float = 200.0,
+      ) -> scri.AsymptoticBondiData:
+      abd = load_waveform(filename, dt)
+      # params should take the same signature as it does in the previous
+      # function.
+      M1, M2, chi1, chi2 = params
+      M = M1 + M2
+      ν = M1 * M2 / M**2
+
+      # We use the parameters of the simulation, and the window information
+      # (t0_pnbms and pnbms_padding) to generate PN strain and Moreschi
+      # supermomentum modes data during that window.
+      h_PN, Psi_M_PN = generate_h_and_Psi_M_from_abd(abd, params, t0_pnbms, pnbms_padding)
+
+      # Compute the BMS transformation to the PNBMS frame and the transformed
+      # abd object.
+      abd_prime, BMS, _ = abd.map_to_superrest_frame(
+          t_0=t0_pnbms,
+          padding_time=pnbms_padding,
+          target_PsiM_input=Psi_M_PN,
+          target_strain_input=h_PN,
+          Gfun=analytical_CoM_func,
+          Gparams0=np.zeros(8),
+          Gargsfun=PN_charges(M, ν),
+      )
+
+      return abd_prime
+
+
+This is the recent method for fixing the PNBMS frame described in Khairnar et
+al. `<https://arxiv.org/abs/2603.24661>`_. The function ``map_to_superrest_frame``
+takes 3 additional keyword arguments - ``Gfun``, ``Gparams0`` and ``Gargsfun``. ``Gfun``
+is the analtyical function used for fitting the CoM charge, ``Gparams0`` is the
+initial guess for the fitting parameters, and ``Gargsfun`` are additional
+arguments passed to Gfun. See the documentation of
+:meth:`scri.asymptotic_bondi_data.map_to_superrest_frame.com_transformation_to_map_to_superrest_frame`
+in :meth:`scri.asymptotic_bondi_data.map_to_superrest_frame` for additional
+details on the signature of ``Gfun`` and ``Gargsfun``.
+
 The parameter `dt` can be used to choose the time sampling for the
 waveform ABD object you get back, with default of `1M`. Reducing the time
 sampling for the waveform ABD is also useful if the CCE data was written too