B d(@sldZddlZddlmZddlmZddlmZddlmZGdddej Z Gd d d ej Z dd gZ dS) z|This modules provides classes for evaluating distributions for mchirp and q (i.e., mass ratio) from uniform component mass. N)interp1d)hyp2f1) power_law)boundedcs&eZdZdZdZdfdd ZZS)MchirpfromUniformMass1Mass2a/A distribution for chirp mass from uniform component mass + constraints given by chirp mass. This is a special case for UniformPowerLaw with index 1. For more details see UniformPowerLaw. The parameters (i.e. `**params`) are independent of each other. Instances of this class can be called like a function. By default, `logpdf` will be called, but this can be changed by setting the class's `__call__` method to its pdf method. Derivation for the probability density function: .. math:: P(m_1,m_2)dm_1dm_2 = P(\mathcal{M}_c,q)d\mathcal{M}_cdq Where :math:`\mathcal{M}_c` is chirp mass and :math:`q` is mass ratio, :math:`m_1` and :math:`m_2` are component masses. The jacobian to transform chirp mass and mass ratio to component masses is .. math:: \frac{\partial(m_1,m_2)}{\partial(\mathcal{M}_c,q)} = \ \mathcal{M}_c \left(\frac{1+q}{q^3}\right)^{2/5} (https://github.com/gwastro/pycbc/blob/master/pycbc/transforms.py#L416.) Because :math:`P(m_1,m_2) = const`, then .. math:: P(\mathcal{M}_c,q) = P(\mathcal{M}_c)P(q)\propto \mathcal{M}_c \left(\frac{1+q}{q^3}\right)^{2/5}`. Therefore, .. math:: P(\mathcal{M}_c) \propto \mathcal{M}_c and .. math:: P(q) \propto \left(\frac{1+q}{q^3}\right)^{2/5} Examples -------- Generate 10000 random numbers from this distribution in [5,100] >>> from pycbc import distributions as dist >>> minmc = 5, maxmc = 100, size = 10000 >>> mc = dist.MchirpfromUniformMass1Mass2(value=(minmc,maxmc)).rvs(size) The settings in the configuration file for pycbc_inference should be .. code-block:: ini [variable_params] mchirp = [prior-mchirp] name = mchirp_from_uniform_mass1_mass2 min-mchirp = 10 max-mchirp = 80 Parameters ---------- \**params : The keyword arguments should provide the names of parameters and their corresponding bounds, as either tuples or a `boundaries.Bounds` instance. Zmchirp_from_uniform_mass1_mass2c stt|jfddi|dS)Ndimr)superr__init__)selfrparams) __class__e/work/yifan.wang/ringdown/master-ringdown-env/lib/python3.7/site-packages/pycbc/distributions/mass.pyr esz$MchirpfromUniformMass1Mass2.__init__)r)__name__ __module__ __qualname____doc__namer __classcell__rr)r rrsFrcsveZdZdZdZfddZeddZeddZd d Z d d Z d dZ ddZ dddZ efddZZS)QfromUniformMass1Mass2aA distribution for mass ratio (i.e., q) from uniform component mass + constraints given by q. The parameters (i.e. `**params`) are independent of each other. Instances of this class can be called like a function. By default, `logpdf` will be called, but this can be changed by setting the class's `__call__` method to its pdf method. For mathematical derivation see the documentation above in the class `MchirpfromUniformMass1Mass2`. Parameters ---------- \**params : The keyword arguments should provide the names of parameters and their corresponding bounds, as either tuples or a `boundaries.Bounds` instance. Examples -------- Generate 10000 random numbers from this distribution in [1,8] >>> from pycbc import distributions as dist >>> minq = 1, maxq = 8, size = 10000 >>> q = dist.QfromUniformMass1Mass2(value=(minq,maxq)).rvs(size) Zq_from_uniform_mass1_mass2c svtt|jf|d|_d|_xD|jD]:}|j|||j|d|||j|d_q&Wt |j|_dS)Ng?gr) r rr _norm_lognorm_params _cdf_param_boundsnumpylog)r r p)r rrr s  zQfromUniformMass1Mass2.__init__cCs|jS)z6float: The normalization of the multi-dimensional pdf.)r)r rrrnormszQfromUniformMass1Mass2.normcCs|jS)z$float: The log of the normalization.)r)r rrrlognormszQfromUniformMass1Mass2.lognormc sbx(|jD]}|krtd|qW|krZ|jtfdd|jD}t|SdSdS)zReturns the pdf at the given values. The keyword arguments must contain all of parameters in self's params. Unrecognized arguments are ignored. z&Missing parameter {} to construct pdf.cs(g|] }d|d|dqS)g?g?g333333?r).0r)kwargsrr sz/QfromUniformMass1Mass2._pdf..gN)rkeys ValueErrorformatrrprodfloat)r r#rZpdfr)r#r_pdfs  zQfromUniformMass1Mass2._pdfcKsPx(|jD]}||krtd|qW||krDt|jf|Stj SdS)zReturns the log of the pdf at the given values. The keyword arguments must contain all of parameters in self's params. Unrecognized arguments are ignored. z)Missing parameter {} to construct logpdf.N)rr%r&r'rrr*inf)r r#rrrr_logpdfs  zQfromUniformMass1Mass2._logpdfcCs8||jkr&d|dtddd| Std|dS)aF>>> from sympy import * >>> x = Symbol('x') >>> integrate((1+x)**(2/5)/x**(6/5)) Output: _ -0.2 |_ /-0.4, -0.2 | I*pi\ -5.0*x * | | | x*e | 2 1 \ 0.8 | / ggɿgٿg?z{} is not contructed yet.N)rrr&r')r paramvaluerrrrs z!QfromUniformMass1Mass2._cdf_paramcCst|dks$t|dkr,td||jkr|j|d}|j|d}tj||ddd}t||||ddd}|||||||||||Std |d S) zxReturn the inverse cdf to map the unit interval to parameter bounds. Note that value should be uniform in [0,1].rrz4q_from_uniform_m1_m2 cdfinv requires input in [0,1].iT)numZendpointZcubic)kindZ bounds_errorz{} is not contructed yet.N) rarrayanyr&rrZlinspacerrr')r r-r. lower_bound upper_boundZq_arrayZq_invcdf_interprrr _cdfinv_params $   z$QfromUniformMass1Mass2._cdfinv_paramrNcCsj|dk r|tfg}ndd|jD}tj||d}x2|D]*\}}tjjdd|d}|||||<q8W|S)aGives a set of random values drawn from this distribution. Parameters ---------- size : {1, int} The number of values to generate; default is 1. param : {None, string} If provided, will just return values for the given parameter. Otherwise, returns random values for each parameter. Returns ------- structured array The random values in a numpy structured array. If a param was specified, the array will only have an element corresponding to the given parameter. Otherwise, the array will have an element for each parameter in self's params. NcSsg|] }|tfqSr)r))r"rrrrr$sz.QfromUniformMass1Mass2.rvs..)dtyperr)size)r)r rZzerosrandomuniformr5)r r7r-r6Zarrr_Zuniformcdfvaluerrrrvss zQfromUniformMass1Mass2.rvscstt|j|||ddS)aReturns a distribution based on a configuration file. The parameters for the distribution are retrieved from the section titled "[`section`-`variable_args`]" in the config file. Example: .. code-block:: ini [variable_params] q = [prior-q] name = q_from_uniform_mass1_mass2 min-q = 1 max-q = 8 Parameters ---------- cp : pycbc.workflow.WorkflowConfigParser A parsed configuration file that contains the distribution options. section : str Name of the section in the configuration file. variable_args : str The names of the parameters for this distribution, separated by ``VARARGS_DELIM``. These must appear in the "tag" part of the section header. Returns ------- QfromUniformMass1Mass2 A distribution instance from the pycbc.distributions.bounded module. T)Zbounds_required)r r from_config)clscpsectionZ variable_args)r rrr<s# z"QfromUniformMass1Mass2.from_config)rN)rrrrrr propertyr r!r*r,rr5r; classmethodr<rrr)r rris   r)rrZscipy.interpolaterZ scipy.specialrZpycbc.distributionsrrZUniformPowerLawrZ BoundedDistr__all__rrrrs    N<