void LALInferenceDumptemplateFreqDomain(LALInferenceVariables *currentParams,
LALInferenceModel *model,
const char *filename)
/* de-bugging function writing (frequency-domain) template to a CSV file */
/* File contains real & imaginary parts of plus & cross components. */
/* Template amplitude is scaled to 1Mpc distance. */
{
FILE *outfile=NULL;
REAL8 f;
UINT4 i;
REAL8 deltaF = model->deltaF;
LALInferenceCopyVariables(currentParams, model->params);
model->templt(model);
if (model->domain == LAL_SIM_DOMAIN_TIME)
LALInferenceExecuteFT(model);
outfile = fopen(filename, "w");
/*fprintf(outfile, "f PSD dataRe dataIm signalPlusRe signalPlusIm signalCrossRe signalCrossIm\n");*/
fprintf(outfile, "\"f\",\"signalPlusRe\",\"signalPlusIm\",\"signalCrossRe\",\"signalCrossIm\"\n");
for (i=0; i<model->freqhPlus->data->length; ++i){
f = ((double) i) * deltaF;
fprintf(outfile, "%f,%e,%e,%e,%e\n",
f,
creal(model->freqhPlus->data->data[i]),
cimag(model->freqhPlus->data->data[i]),
creal(model->freqhCross->data->data[i]),
cimag(model->freqhCross->data->data[i]));
}
fclose(outfile);
fprintf(stdout, " wrote (frequency-domain) template to CSV file \"%s\".\n", filename);
}
void LALInferenceDumptemplateTimeDomain(LALInferenceVariables *currentParams,
LALInferenceModel *model,
const char *filename)
/* de-bugging function writing (frequency-domain) template to a CSV file */
/* File contains real & imaginary parts of plus & cross components. */
/* Template amplitude is scaled to 1Mpc distance. */
{
FILE *outfile=NULL;
REAL8 deltaT, t, epoch; // deltaF - set but not used
UINT4 i;
LALInferenceCopyVariables(currentParams, model->params);
model->templt(model);
if (model->domain == LAL_SIM_DOMAIN_FREQUENCY)
LALInferenceExecuteInvFT(model);
outfile = fopen(filename, "w");
fprintf(outfile, "\"t\",\"signalPlus\",\"signalCross\"\n");
deltaT = model->deltaT;
epoch = XLALGPSGetREAL8(&model->timehPlus->epoch);
for (i=0; i<model->timehPlus->data->length; ++i){
t = epoch + ((double) i) * deltaT;
fprintf(outfile, "%f,%e,%e\n",
t,
model->timehPlus->data->data[i],
model->timehCross->data->data[i]);
}
fclose(outfile);
fprintf(stdout, " wrote (time-domain) template to CSV file \"%s\".\n", filename);
}
/**
* \brief The log likelihood function
*
* This function calculates natural logarithm of the likelihood of a signal model (specified by a given set of
* parameters) given the data from a set of detectors.
*
* The likelihood is the joint likelihood of chunks of data over which the noise is assumed stationary and Gaussian. For
* each chunk a Gaussian likelihood for the noise and data has been marginalised over the unknown noise standard
* deviation using a Jeffreys prior on the standard deviation. Given the data consisting of independent real and
* imaginary parts this gives a Students-t distribution for each chunk (of length \f$m\f$) with \f$m/2\f$ degrees of
* freedom:
* \f[
* p(\mathbf{\theta}|\mathbf{B}) = \prod_{j=1}^M \frac{(m_j-1)!}{2\pi^{m_j}}
* \left( \sum_{k=k_0}^{k_0+(m_j-1)} |B_k - y(\mathbf{\theta})_k|^2
* \right)^{-m_j},
* \f]
* where \f$\mathbf{B}\f$ is a vector of the complex data, \f$y(\mathbf{\theta})\f$ is the model for a set of parameters
* \f$\mathbf{\theta}\f$, \f$M\f$ is the total number of independent data chunks with lengths \f$m_j\f$ and \f$k_0 =
* \sum_{i=1}^j 1 + m_{i-1}\f$ (with \f$m_0 = 0\f$) is the index of the first data point in each chunk. The product of
* this for each detector will give the full joint likelihood. In the calculation here the unnecessary proportionality
* factors are left out (this would effect the actual value of the marginal likelihood/evidence, but since we are only
* interested in evidence ratios/Bayes factors these factors would cancel out anyway. See \cite DupuisWoan2005 for a
* more detailed description.
*
* In this function data in chunks smaller than a certain minimum length \c chunkMin are ignored.
*
* \param vars [in] The parameter values
* \param data [in] The detector data and initial signal phase template
* \param get_model [in] The signal template/model function
*
* \return The natural logarithm of the likelihood function
*/
REAL8 pulsar_log_likelihood( LALInferenceVariables *vars, LALInferenceIFOData *data,
LALInferenceTemplateFunction get_model){
REAL8 loglike = 0.; /* the log likelihood */
UINT4 i = 0;
REAL8Vector *freqFactors = *(REAL8Vector **)LALInferenceGetVariable( data->dataParams, "freqfactors" );
LALInferenceIFOData *datatemp1 = data, *datatemp2 = data, *datatemp3 = data;
/* copy model parameters to data parameters */
while( datatemp1 ){
LALInferenceCopyVariables( vars, datatemp1->modelParams );
datatemp1 = datatemp1->next;
}
/* get pulsar model */
while( datatemp2 ){
get_model( datatemp2 );
for( i = 0; i < freqFactors->length; i++ ) { datatemp2 = datatemp2->next; }
}
while ( datatemp3 ){
UINT4 j = 0, count = 0, cl = 0;
UINT4 length = 0, chunkMin;
REAL8 chunkLength = 0.;
REAL8 logliketmp = 0.;
REAL8 sumModel = 0., sumDataModel = 0.;
REAL8 chiSquare = 0.;
COMPLEX16 B, M;
REAL8Vector *sumDat = NULL;
UINT4Vector *chunkLengths = NULL;
sumDat = *(REAL8Vector **)LALInferenceGetVariable( datatemp3->dataParams, "sumData" );
chunkLengths = *(UINT4Vector **)LALInferenceGetVariable( datatemp3->dataParams, "chunkLength" );
chunkMin = *(INT4*)LALInferenceGetVariable( datatemp3->dataParams, "chunkMin" );
length = datatemp3->compTimeData->data->length;
for( i = 0 ; i < length ; i += chunkLength ){
chunkLength = (REAL8)chunkLengths->data[count];
/* skip section of data if its length is less than the minimum allowed chunk length */
if( chunkLength < chunkMin ){
count++;
continue;
}
sumModel = 0.;
sumDataModel = 0.;
cl = i + (INT4)chunkLength;
for( j = i ; j < cl ; j++ ){
B = datatemp3->compTimeData->data->data[j];
M = datatemp3->compModelData->data->data[j];
/* sum over the model */
sumModel += creal(M)*creal(M) + cimag(M)*cimag(M);
/* sum over that data and model */
sumDataModel += creal(B)*creal(M) + cimag(B)*cimag(M);
}
chiSquare = sumDat->data[count];
chiSquare -= 2.*sumDataModel;
chiSquare += sumModel;
logliketmp -= chunkLength*log(chiSquare) + LAL_LN2 * (chunkLength-1.) + gsl_sf_lnfact(chunkLength);
count++;
}
loglike += logliketmp;
datatemp3 = datatemp3->next;
}
return loglike;
}