Source code for pycbc.tmpltbank.lambda_mapping

# Copyright (C) 2013 Ian W. Harry
# This program is free software; you can redistribute it and/or modify it
# under the terms of the GNU General Public License as published by the
# Free Software Foundation; either version 3 of the License, or (at your
# option) any later version.
# This program is distributed in the hope that it will be useful, but
# WITHOUT ANY WARRANTY; without even the implied warranty of
# Public License for more details.
# You should have received a copy of the GNU General Public License along
# with this program; if not, write to the Free Software Foundation, Inc.,
# 51 Franklin Street, Fifth Floor, Boston, MA  02110-1301, USA.
from __future__ import division
import re
import numpy
from six.moves import range
from lal import MTSUN_SI, PI, CreateREAL8Vector
import lalsimulation

pycbcValidTmpltbankOrders = ['zeroPN','onePN','onePointFivePN','twoPN',\

     * zeroPN: Will only include the dominant term (proportional to chirp mass)
     * onePN: Will only the leading orbit term and first correction at 1PN
     * onePointFivePN: Will include orbit and spin terms to 1.5PN.
     * twoPN: Will include orbit and spin terms to 2PN.
     * twoPointFivePN: Will include orbit and spin terms to 2.5PN.
     * threePN: Will include orbit terms to 3PN and spin terms to 2.5PN.
     * threePointFivePN: Include orbit terms to 3.5PN and spin terms to 2.5PN

[docs]def generate_mapping(order): """ This function will take an order string and return a mapping between components in the metric and the various Lambda components. This must be used (and consistently used) when generating the metric *and* when transforming to/from the xi_i coordinates to the lambda_i coordinates. NOTE: This is not a great way of doing this. It would be nice to clean this up. Hence pulling this function out. The valid PN orders are {} Parameters ---------- order : string A string containing a PN order. Valid values are given above. Returns -------- mapping : dictionary A mapping between the active Lambda terms and index in the metric """ mapping = {} mapping['Lambda0'] = 0 if order == 'zeroPN': return mapping mapping['Lambda2'] = 1 if order == 'onePN': return mapping mapping['Lambda3'] = 2 if order == 'onePointFivePN': return mapping mapping['Lambda4'] = 3 if order == 'twoPN': return mapping mapping['LogLambda5'] = 4 if order == 'twoPointFivePN': return mapping mapping['Lambda6'] = 5 mapping['LogLambda6'] = 6 if order == 'threePN': return mapping mapping['Lambda7'] = 7 if order == 'threePointFivePN': return mapping # For some as-of-yet unknown reason, the tidal terms are not giving correct # match estimates when enabled. So, for now, this order is commented out. #if order == 'tidalTesting': # mapping['Lambda10'] = 8 # mapping['Lambda12'] = 9 # return mapping raise ValueError("Order %s is not understood." %(order))
# Override doc so the PN orders are added automatically to online docs generate_mapping.__doc__ = \ generate_mapping.__doc__.format(pycbcValidOrdersHelpDescriptions)
[docs]def generate_inverse_mapping(order): """Genereate a lambda entry -> PN order map. This function will generate the opposite of generate mapping. So where generate_mapping gives dict[key] = item this will give dict[item] = key. Valid PN orders are: {} Parameters ---------- order : string A string containing a PN order. Valid values are given above. Returns -------- mapping : dictionary An inverse mapping between the active Lambda terms and index in the metric """ mapping = generate_mapping(order) inv_mapping = {} for key,value in mapping.items(): inv_mapping[value] = key return inv_mapping
generate_inverse_mapping.__doc__ = \ generate_inverse_mapping.__doc__.format(pycbcValidOrdersHelpDescriptions)
[docs]def get_ethinca_orders(): """ Returns the dictionary mapping TaylorF2 PN order names to twice-PN orders (powers of v/c) """ ethinca_orders = {"zeroPN" : 0, "onePN" : 2, "onePointFivePN" : 3, "twoPN" : 4, "twoPointFivePN" : 5, "threePN" : 6, "threePointFivePN" : 7 } return ethinca_orders
[docs]def ethinca_order_from_string(order): """ Returns the integer giving twice the post-Newtonian order used by the ethinca calculation. Currently valid only for TaylorF2 metric Parameters ---------- order : string Returns ------- int """ if order in get_ethinca_orders().keys(): return get_ethinca_orders()[order] else: raise ValueError("Order "+str(order)+" is not valid for ethinca" "calculation! Valid orders: "+ str(get_ethinca_orders().keys()))
[docs]def get_chirp_params(mass1, mass2, spin1z, spin2z, f0, order, quadparam1=None, quadparam2=None, lambda1=None, lambda2=None): """ Take a set of masses and spins and convert to the various lambda coordinates that describe the orbital phase. Accepted PN orders are: {} Parameters ---------- mass1 : float or array Mass1 of input(s). mass2 : float or array Mass2 of input(s). spin1z : float or array Parallel spin component(s) of body 1. spin2z : float or array Parallel spin component(s) of body 2. f0 : float This is an arbitrary scaling factor introduced to avoid the potential for numerical overflow when calculating this. Generally the default value (70) is safe here. **IMPORTANT, if you want to calculate the ethinca metric components later this MUST be set equal to f_low.** This value must also be used consistently (ie. don't change its value when calling different functions!). order : string The Post-Newtonian order that is used to translate from masses and spins to the lambda_i coordinate system. Valid orders given above. Returns -------- lambdas : list of floats or numpy.arrays The lambda coordinates for the input system(s) """ # Determine whether array or single value input sngl_inp = False try: num_points = len(mass1) except TypeError: sngl_inp = True # If you care about speed, you aren't calling this function one entry # at a time. mass1 = numpy.array([mass1]) mass2 = numpy.array([mass2]) spin1z = numpy.array([spin1z]) spin2z = numpy.array([spin2z]) if quadparam1 is not None: quadparam1 = numpy.array([quadparam1]) if quadparam2 is not None: quadparam2 = numpy.array([quadparam2]) if lambda1 is not None: lambda1 = numpy.array([lambda1]) if lambda2 is not None: lambda2 = numpy.array([lambda2]) num_points = 1 if quadparam1 is None: quadparam1 = numpy.ones(len(mass1), dtype=float) if quadparam2 is None: quadparam2 = numpy.ones(len(mass1), dtype=float) if lambda1 is None: lambda1 = numpy.zeros(len(mass1), dtype=float) if lambda2 is None: lambda2 = numpy.zeros(len(mass1), dtype=float) mass1_v = CreateREAL8Vector(len(mass1))[:] = mass1[:] mass2_v = CreateREAL8Vector(len(mass1))[:] = mass2[:] spin1z_v = CreateREAL8Vector(len(mass1))[:] = spin1z[:] spin2z_v = CreateREAL8Vector(len(mass1))[:] = spin2z[:] lambda1_v = CreateREAL8Vector(len(mass1))[:] = lambda1[:] lambda2_v = CreateREAL8Vector(len(mass1))[:] = lambda2[:] dquadparam1_v = CreateREAL8Vector(len(mass1))[:] = quadparam1[:] - 1. dquadparam2_v = CreateREAL8Vector(len(mass1))[:] = quadparam2[:] - 1. phasing_arr = lalsimulation.SimInspiralTaylorF2AlignedPhasingArray\ (mass1_v, mass2_v, spin1z_v, spin2z_v, lambda1_v, lambda2_v, dquadparam1_v, dquadparam2_v) vec_len = lalsimulation.PN_PHASING_SERIES_MAX_ORDER + 1; phasing_vs = numpy.zeros([num_points, vec_len]) phasing_vlogvs = numpy.zeros([num_points, vec_len]) phasing_vlogvsqs = numpy.zeros([num_points, vec_len]) lng = len(mass1) jmp = lng * vec_len for idx in range(vec_len): phasing_vs[:,idx] =[lng*idx : lng*(idx+1)] phasing_vlogvs[:,idx] = \[jmp + lng*idx : jmp + lng*(idx+1)] phasing_vlogvsqs[:,idx] = \[2*jmp + lng*idx : 2*jmp + lng*(idx+1)] pim = PI * (mass1 + mass2)*MTSUN_SI pmf = pim * f0 pmf13 = pmf**(1./3.) logpim13 = numpy.log((pim)**(1./3.)) mapping = generate_inverse_mapping(order) lambdas = [] lambda_str = '^Lambda([0-9]+)' loglambda_str = '^LogLambda([0-9]+)' logloglambda_str = '^LogLogLambda([0-9]+)' for idx in range(len(mapping.keys())): # RE magic engage! rematch = re.match(lambda_str, mapping[idx]) if rematch: pn_order = int(rematch.groups()[0]) term = phasing_vs[:,pn_order] term = term + logpim13 * phasing_vlogvs[:,pn_order] lambdas.append(term * pmf13**(-5+pn_order)) continue rematch = re.match(loglambda_str, mapping[idx]) if rematch: pn_order = int(rematch.groups()[0]) lambdas.append((phasing_vlogvs[:,pn_order]) * pmf13**(-5+pn_order)) continue rematch = re.match(logloglambda_str, mapping[idx]) if rematch: raise ValueError("LOGLOG terms are not implemented") #pn_order = int(rematch.groups()[0]) #lambdas.append(phasing_vlogvsqs[:,pn_order] * pmf13**(-5+pn_order)) #continue err_msg = "Failed to parse " + mapping[idx] raise ValueError(err_msg) if sngl_inp: return [l[0] for l in lambdas] else: return lambdas
get_chirp_params.__doc__ = \ get_chirp_params.__doc__.format(pycbcValidOrdersHelpDescriptions)