SIRENA functions¶

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int addFirstRow(ReconstructInitSIRENA *reconstruct_init, fitsfile **inLibObject, double samprate, int runF0orB0val, gsl_vector *E, gsl_vector *PHEIGHT, gsl_matrix *PULSE, gsl_matrix *PULSEB0, gsl_matrix *MF, gsl_matrix *MFB0, gsl_matrix *COVAR, gsl_matrix *WEIGHT, gsl_matrix *PULSEMaxLengthFixedFilter, gsl_matrix *PULSEMaxLengthFixedFilter_B0)¶

Located in file: tasksSIRENA.cpp

This function writes the first row of the library (without intermediate AB-related values, because it would be necessary to have at least two rows=energies in the library). It also writes the FIXFILTT and FIXFILTF HDUs with the optimal filters in the time and frequency domain with fixed legnths (base-2 values) and the PRCLOFWN HDU with the precalculated values for optimal filtering and :option:`OFNoise` = **WEIGHTN.

Declare variables
Write in the first row of the library FITS file some columns ENERGY, PHEIGHT, PULSE, PULSEB0, MF, MFB0 with the info provided by the input GSL vectors E, PHEIGHT, PULSE, PULSEB0, MF and MFB0.
Write in the first row of the library FITS file PLSMXLFF column if option:largeFilter > OFLength with the info provided by the input GSL vector PULSEMaxLengthFixedFilter
Write in the first row of the library FITS file COVARM and WEIGHTM columns if addCOVAR/addINTCOVAR = yes with the info provided by the input GSL vectors COVAR and WEIGHT
Writing HDUs with fixed filters in time (FIXFILTT) and frequency (FIXFILTF), Tx and Fx columns respectively (calculating the optimal filters, calculus_optimalFilter()). In time domain Tx columns are real numbers but in frequency domain Fx columns are complex numbers (so real parts are written in the first half of the column and imaginary parts in the second one)
Calculate and write the pre-calculated values by using the noise weight matrix from noise intervals (M’WM)^{-1}M’W for different lengths, OFWNx columns in PRCLOFWN

Members/Variables

ReconstructInitSIRENA** reconstruct_init

Member of ReconstructInitSIRENA structure to initialize the reconstruction parameters (pointer and values).

fitsfile** inLibObject

FITS object containing information of the library FITS file

double samprate

Sampling rate

int runF0orB0val

If FilterMethod = F0 => runF0orB0val = 1. If FilterMethod = B0 => runF0orB0val = 0

gsl_vector* E

First energy to be included in the library

gsl_vector* PHEIGHT

Pulse height associated to the first energy to be included in the library

gsl_matrix* PULSE

Pulse template associated to the first energy to be included in the library

gsl_matrix* PULSEB0

Pulse template without baseline associated to the first energy to be included in the library

gsl_matrix* MF

Matched filter associated to the first energy to be included in the library

gsl_matrix* MFB0

Matched filter (baseline subtracted) associated to the first energy to be included in the library

gsl_matrix* COVAR

Covariance matrix associated to the first energy to be included in the library

gsl_matrix* WEIGHT

Weight matrix associated to the first energy to be included in the library

gsl_matrix* PULSEMaxLengthFixedFilter

Pulse template whose length is largeFilter associated to the first energy to be included in the library

gsl_matrix* PULSEMaxLengthFixedFilter_B0

Pulse template whose length is largeFilter associated to the first energy to be included in the library (baseline subtracted)

int align(double samprate, gsl_vector **vector1, gsl_vector **vector2)¶

Located in file: tasksSIRENA.cpp

Based on :cite:`GilPita2005`

This function aligns vector1 with vector2 (by delaying or moving forward vector2) assuming that vector1 and vector2 are shifted replicas of the same function.

From the discrete function \(x[n] (n=0,...,N-1,N)\) and according to the time shifting property of the Fourier transform:

\[\begin{split}& x[n] <------> X[f]\\ & x[n-m] <------> X[f] exp(-j2\cdot\pi\cdot m/N)\end{split}\]

If \(\mathit{Shift} = m\) then \(\mathit{PhaseDueToTheShift}= 2\pi m/N\) and thus, \(m = \mathit{PhaseDueToTheShift}\cdot N/(2\pi)\)

Declare variables
FFT of vector1
FFT of vector2
(Phases of the FFT_vector1 and FFT_vector2) \(*size/(2\pi)\)
Shift between the input vectors
shiftdouble into shiftint (because we are working with samples)
Move forward or delay vector1 depending on positive or negative shift

Members/Variables

double samprate

Sampling rate

gsl_vector** vector1

GSL vector with input vector

gsl_vector** vector2

GSL with input vector which is delayed or moved forward to be aligned with vector1

int calculateEnergy(gsl_vector *vector, gsl_vector *filter, gsl_vector_complex *filterFFT, int indexEalpha, int indexEbeta, ReconstructInitSIRENA *reconstruct_init, double samprate, gsl_vector *Dab, gsl_matrix *PRCLCOV, gsl_matrix *PRCLOFWN, double *calculatedEnergy, double *tstartNewDev, int *lagsShift, int LowRes, int productSize, int tooshortPulse_NoLags)¶

Located in file: tasksSIRENA.cpp

This function calculates the energy of a pulse (vector) basically depending on the EnergyMethod, OFNoise, and the FilterDomain selected from input parameters.

OPTFILT/0PAD and NSD (= I2R or I2RFITTED): Optimal filter = Wiener filter (see Optimal Filtering by using the noise spectral density)

Once the filter template has been created (filter or filterFFT), pulse height analysis is performed by aligning the template with a pulse and multiplying each point in the template by the corresponding point in the pulse. The sum of these products is the energy.

In the practice, the alignment of the pulse relative to the trigger is not completely accurate, so a number of n lags could be used in order to find the peak value of the energy. The n peak values are fitted to a parabola to find the most accurate energy (LagsOrNot) and a corrected starting time.

OPTFILT and WEIGHTN (= I2R or I2RFITTED) (see Optimal Filtering by using the noise weight matrix from noise intervals)

INTCOVAR and COVAR (see Covariance matrices and Covariance matrices 0(n))

Members/Variables

gsl_vector* vector

Pulse whose energy has to be determined

gsl_vector* filter

Optimal filter in time domain

gsl_vector_complex* filterFFT

Optimal filter in frequency domain

int indexEalpha

Index of the energy lower than the energy of the pulse which is being analyzed

int indexEbeta

Index of the energy higher than the energy of the pulse which is being analyzed

ReconstructInitSIRENA** reconstruct_init

Member of ReconstructInitSIRENA structure to initialize the reconstruction parameters (pointer and values)

double samprate

Sampling rate in Hz

gsl_vector* Dab

DAB column in the library

gsl_vector* PRCLCOV

PCOVx column in the library

gsl_vector* PRCLOFWN

OFWNx column in the library

double* calculatedEnergy

Calculated energy in eV.

double tstartNewDev

Addional deviation of the starting time (if LagsOrNot = 1)

int lagsShift

Number of samples shifted to find the maximum of the parabola

int LowREs

1 if the low resolution energy estimator (without lags) is going to be calculated

int productSize

Size of the scalar product to be calculated

int tooshortPulse_NoLags

Pulse too short to apply lags (1) or not (0)

int calculateIntParams(ReconstructInitSIRENA *reconstruct_init, int indexa, int indexb, double samprate, int runF0orB0val, gsl_matrix *modelsaux, gsl_matrix *covarianceaux, gsl_matrix *weightaux, gsl_vector *energycolumn, gsl_matrix **Wabaux, gsl_matrix **TVaux, gsl_vector **tEcolumn, gsl_matrix **XMaux, gsl_matrix **YVaux, gsl_matrix **ZVaux, gsl_vector **rEcolumn, gsl_matrix **Dabaux, gsl_matrix **Sabaux, gsl_matrix **PrecalCOVaux, gsl_matrix **optimalfiltersabFREQaux, gsl_matrix **optimalfiltersabTIMEaux, gsl_matrix *modelsMaxLengthFixedFilteraux, gsl_matrix **DabMaxLengthFixedFilteraux)¶

Located in file: tasksSIRENA.cpp

This function calculates some intermediate scalars, vectors and matrices (WAB, TV, tE, XM, YV, ZV, rE, PAB and DAB) for the interpolation and covariance methods. See Covariance matrices reconstruction method. It is used in readAddSortParams() .

Declare variables and allocate GSL vectors and matrices
Calculate intermediate scalars, vectors and matrices
Free allocated GSL vectors and matrices

Members/Variables

ReconstructInitSIRENA** reconstruct_init

Member of ReconstructInitSIRENA structure to initialize the reconstruction parameters (pointer and values).

int indexa

Lower index of the library to calculate the intermediate params (\(\alpha\))

int indexb

Higher index of the library to calculate the intermediate params (\(\beta\))

double samprate

Sampling rate

int runF0orB0val

If FilterMethod = F0 => runF0orB0val = 1. If FilterMethod = B0 => runF0orB0val = 0

gsl_matrix* modelsaux

GSL input matrix with model template

gsl_matrix* covarianceaux

GSL input matrix with covariance matrix

gsl_matrix* weightaux

GSL input matrix with weight matrix

gsl_vector* energycolumn

GSL input vector with list of energies

gsl_matrix** WAB

Input/output intermediate parameter

gsl_matrix** TVaux

Input/output intermediate parameter

gsl_vector** tEcolumn

Input/output intermediate parameter

gsl_matrix** XMaux

Input/output intermediate parameter

gsl_matrix** YVaux

Input/output intermediate parameter

gsl_matrix** ZVaux

Input/output intermediate parameter

gsl_vector** rEcolumn

Input/output intermediate parameter

gsl_matrix** Dabaux

Input/output intermediate parameter

gsl_matrix** Sabaux

Input/output intermediate parameter

gsl_matrix** PrecalCOVaux

Input/output intermediate parameter

gsl_matrix** optimalfiltersabFREQaux

Input/output intermediate parameter

gsl_matrix** optimalfiltersabTIMEaux

Input/output intermediate parameter

gsl_matrix* modelsMaxLengthFixedFilteraux

Input/output intermediate parameter

gsl_matrix** PabMaxLengthFixedFilteraux

Input/output intermediate parameter

int calculateTemplate(ReconstructInitSIRENA *reconstruct_init, PulsesCollection *pulsesAll, PulsesCollection *pulsesInRecord, double samprate, gsl_vector **pulseaverage, gsl_vector **pulseaverage_B0, double *pulseaverageHeight, gsl_matrix **covariance, gsl_matrix **weight, gsl_vector **pulseaverageMaxLengthFixedFilter, gsl_vector **pulseaverageMaxLengthFixedFilter_B0)¶

Located in file: tasksSIRENA.cpp

This function calculates the template (PULSE column in the library) of non piled-up pulses. Just in case in the detection process some piled-up pulses have not been distinguished as different pulses, a pulseheights histogram is built. This function uses the pulseheights histogram (built by using the PHEIGHT column of the library), Tstart and quality to select the non piled-up pulses.

Declare and initialize variables
Before building the histogram, select the pulseheights of the pulses well separated from other pulses whose quality = 0
Create the pulseheights histogram
Calculate the pulseaverage only taking into account the valid pulses:
- Check if the pulse is piled-up or not
- Non piled-up pulses => Average them
Calculate covariance and weight matrices
Free allocated GSL vectors

Members/Variables

ReconstructInitSIRENA** reconstruct_init

Member of ReconstructInitSIRENA structure to initialize the reconstruction parameters (pointer and values).

PulsesCollection* pulsesAll

Collection of pulses found in the previous records

PulsesCollection* pulsesInRecord

Collection of pulses found in the current record

double samprate

Sampling rate

gsl_vector** pulseaverage

GSL vector with the pulseaverage (template) of the non piled-up pulses

gsl_vector** pulseaverage_B0

GSL vector with the pulseaverage (template) of the non piled-up pulses (baseline subtracted)

double* pulseaverageHeight

Height value of the pulseaverage

gsl_matrix** covariance

GSL matrix with covariance matrix

gsl_matrix** weight

GSL matrix with weight matrix (inverse of covariance matrix)

gsl_vector** pulseaverageMaxLengthFixedFilter

GSL vector with the pulseaverage (template) whose length is largeFilter of the non piled-up pulses

gsl_vector** pulseaverageMaxLengthFixedFilter_B0

GSL vector with the pulseaverage (template) whose length is largeFilter of the non piled-up pulses (baseline subtracted)

int calculus_optimalFilter(int TorF, int intermediate, int opmode, gsl_vector *matchedfiltergsl, long mf_size, double samprate, int runF0orB0val, gsl_vector *freqgsl, gsl_vector *csdgsl, gsl_vector **optimal_filtergsl, gsl_vector **of_f, gsl_vector **of_FFT, gsl_vector_complex **of_FFT_complex)¶

Located in file: tasksSIRENA.cpp

See description also at optimal filter chapter

This function calculates the optimal filter for a pulse whose matched filter (normalized template) is provided as input parameter, matchedfiltergsl. An optimal filter is just a matched filter that has been adjusted based on the noise spectrum of the system.

It is assumed that all pulses are scaled versions of a template. In the frequency domain (as noise can be frequency dependent), the raw data can be expressed as \(D(f)=E\cdot S(f)+N(f)\), where \(S(f)\) is the normalized model pulse shape in the frequency domain, \(N(f)\) is the power spectrum of the noise and \(E\) is the scalar amplitude for the photon energy.

The second assumption is that the noise is stationary, i.e., it does not vary with time. The amplitude of each pulse can then be estimated by minimizing (weighted least-squares sense) the difference between the noisy data and the model pulse shape, being the \(\chi^2\) condition to be minimized:

\[\chi^2 = \int \frac{(D(f)-E \cdot S(f))^2}{\langle\lvert N(f)\lvert ^2\rangle} df\]

In the time domain, the amplitude is the best weighted (optimally filtered) sum of the values in the pulse

\[E = k \int d(t)\cdot of(t)\]

where \(of(t)\) is the time domain expression of optimal filter which in frequency domain

\[OF(f) = \frac{S^*(f)}{\langle\lvert N(f)\lvert ^2\rangle}\]

and \(k\) is the normalization factor to give \(E\) in units of energy

\[k = \int \frac{S(f)\cdot S^{*}(f)}{\langle\lvert N(f)\lvert ^2\rangle} df\]

Steps:

FFT calculus of the matched filter (filter template)
- Declare variables
- Complex FFT values for positive and negative frequencies
- FFT calculus
- Generation of the frequencies (positive and negative)
- Magnitude and argument for positive and negative frequencies
- Free allocated GSL vectors
\(N(f)\)
To divide \(MatchedFilter(f)/N^2(f)\) => \(MatchedFilter(f)\) and \(N(f)\) must have the same number of points
- if (mf_size < freqgsl->size)
  if ((freqgsl->size)%mf_size == 0) => Decimate noise samples
  
  else => It is necessary to work only with the positive frequencies so as not to handle the \(f=0\) => \(N(f)\) interpolation (interpolatePOS())
- else if (mf_size > freqgsl->size) => Error: Noise spectrum must have more samples than pulse spectrum
- else if (mf_size == freqgsl->size) => It is not necessary to do anything
\(OptimalFilter = MatchedFilter'(f)/N^2(f)\)
Calculus of the normalization factor
Apply the normalization factor
Inverse FFT (to get the expression of the optimal filter in time domain)
- Complex \(OptimalFilter(f)\) => Taking into account magnitude \(MatchedFilter(f)/N^2(f)\) and phase given by \(MatchedFilter(f)\)
Free allocated GSL vectors

Members/Variables

int TorF

If FilterDomain = T => TorF = 0; If FilterDomain = F => TorF = 1

int intermediate

If intermediate = 0 => Do not write an intermediate file; If intermediate = 1 => Write an intermediate file

int opmode

If 0 => CALIBRATION run (library creation); if 1 => RECONSTRUCTION run (energy determination)

gsl_vector* matchedfiltergsl

Matched filter associated to the pulse (in general, from the interpolation between two matched filters of the library)

long mf_size

Matched filter size (samples)

double samprate

Sampling rate

int runF0orB0val

If FilterMethod = F0 => runF0orB0val = 1. If FilterMethod = B0 => runF0orB0val = 0.

gsl_vector* freqgsl

Frequency axis of the current noise spectral density (input)

gsl_vector* csdgsl

Current noise spectral density (input)

gsl_vector* * optimal_filtergsl

Optimal filter in time domain (output)

gsl_vector** of_f

Frequency axis of the optimal filter spectrum (output)

gsl_vector** of_FFT

Optimal filter spectrum (absolute values) (output)

gsl_vector_complex** of_FFT_complex

Optimal filter spectrum (complex values) (output)

int callSIRENA_Filei(char *inputFile, SixtStdKeywords *keywords, ReconstructInitSIRENA *reconstruct_init_sirena, struct Parameters par, double sampling_rate, int *trig_reclength, PulsesCollection *pulsesAll, TesEventFile *outfile)¶

Located in file: initSIRENA.c

This function calls SIRENA to build a library or reconstruct energies.

Steps:

Open record file
initializeReconstructionSIRENA
Build up TesRecord to read the file
Iterate of records and run SIRENA:
- reconstructRecordSIRENA
- Save events to the event_list
- Copy trigger keywords to event file
- Close file

Members/Variables

char* inputFile

Input file name

SixtStdKeywords* keywords

Sixt standard keywords structure

ReconstructInitSIRENA* reconstruct_init_sirena

Parameters to run SIRENA

struct Parameters par

Input parameters

double sampling_rate

Sampling rate

int* trig_reclength

Necessary if SIRENA is going to run in THREADING mode

PulsesCollection* pulsesAll

Structure containing the detected pulses

TesEventFile* outfile

Output events FITS file

int callSIRENA(char *inputFile, SixtStdKeywords *keywords, ReconstructInitSIRENA *reconstruct_init_sirena, struct Parameters par, double sampling_rate, int *trig_reclength, PulsesCollection *pulsesAll, TesEventFile *outfile)¶

Located in file: initSIRENA.c

This function calls SIRENA to build a library or reconstruct energies no matter if inputFile is only a FITS file or more (inputFile can start with ‘@’ or not).

Members/Variables

char* inputFile

Input file name

SixtStdKeywords* keywords

Sixt standard keywords structure

ReconstructInitSIRENA* reconstruct_init_sirena

Parameters to run SIRENA

struct Parameters par

Input parameters

double sampling_rate

Sampling rate

int* trig_reclength

Necessary if SIRENA is going to run in THREADING mode

PulsesCollection* pulsesAll

Structure containing the detected pulses

TesEventFile* outfile

Output events FITS file

int checkXmls(struct Parameters *const par)¶

Located in file: initSIRENA.c

This function checks if the XML file used to build the library is the same to be used to recconstruct (by checking the checksums)

Members/Variables

struct Parameters* const par

Structure containing the input parameters specified in teslib.par or tesrecons.par

int convertI2R(char *EnergyMethod, double Ibias, double Imin, double Imax, double ADU_CNV, double ADU_BIAS, double I_BIAS, double Ifit, double V0, double RL, double L, gsl_vector **invector, int real_data)¶

Located in file: tasksSIRENA.cpp

This funcion converts the current space into a quasi-resistance space (see Quasi Resistance Space for I2R and I2RFITTED modes). The input invector filled in with current values is filled in here with I2R or I2RFITTED quasi-resistances at the output.

If the ADU_CNV keyword is in the input FITS file and invector contains the ADC column data from the input FITS file:

\(I(A) = I\_BIAS+ADU\_CNV*(ADC-ADU\_BIAS)\) being \(ADC=I(adu)\) and ADU_CNV, ADU_BIAS and I_BIAS are keywords in the input FITS file

Conversion according to EnergyMethod = I2R:

\(DeltaI = I\)

\(R/R0 = [1 - (abs(DeltaI)/I\_BIAS)/(1+abs(DeltaI)/I\_BIAS)]\cdot10^5\)

Conversion according to EnergyMethod = I2RFITTED:

\(R/V0 = -10^5/(I_{fit}+ADC)\) being \(I_{fit}\) value an input parameter

If the ADU_CNV keyword is NOT in the input FITS file and invector contains the ADC column data from the input FITS file:

\(aducnv = (IMAX-IMIN)/65534\) (IMIN and IMAX are keywords in the input FITS file and 65534 the number of quantification leves)

\(I(A) = ADC*aducnv+IMIN\) being \(ADC=I(adu)\)

Conversion according to EnergyMethod = I2R:

\(DeltaI = I\)

\(R/R0 = [1 - (abs(DeltaI)/I0\_START)/(1+abs(DeltaI)/I0\_START)]\cdot10^5\)

Conversion according to EnergyMethod = I2RFITTED

\(R/V0 = -10^5/(I_{fit}+ADC)\) being \(I_{fit}\) value an input parameter

Conversion I2RDER is also available:

\(R = (V0-I \cdot R_L-L \cdot dI/dt)/I\)

The \(10^5\) scaling factor has been included in the quasi resistance space (both I2R and I2RFITTED transformations) to avoid rounding errors when working with very small numbers.

Members/Variables

char* EnergyMethod

Quasi-resistance energy calculation method: I2R or I2RFITTED, EnergyMethod

double Ibias

Initial bias current (I0_START column)

double Imin

Current corresponding to 0 ADU (IMIN keyword)

double Imax

Current corresponding to maximum ADU (IMAX keyword)

double ADU_CNV

Conversion factor (A/adu) (ADU_CNV keyword)

double ADU_BIAS

Bias current (adu) (ADU_BIAS keyword)

double I_BIAS

Bias current (A) (I_BIAS keyword)

double Ifit

Constant to apply the I2RFITTED conversion (adu)

double V0

Constant voltage bias

double RL

Effective load resistor

double L

Effective inductance

gsl_vector* invector

GSL vector with input signal values (ADC column of the input FITS file)

int createDetectFile(ReconstructInitSIRENA *reconstruct_init, double samprate, fitsfile **dtcObject, int inputPulselength)¶

Located in file: tasksSIRENA.cpp

This function creates an intermediate FITS file with some useful info (during the development phase) if the intermediate input parameter is set to 1.

The intermediate FITS file will contain 2 HDUs:

PULSES HDU will contain some info about the found pulses: TSTART, I0 (the pulse itself), TEND, TAURISE, TAUFALL and QUALITY

TESTINFO HDU will contain columns FILDER (the low-pass filtered and differentiated records) and THRESHOLD

If file exists => Check clobber for overwritting. If it does not, then create it.

Members/Variables

ReconstructInitSIRENA** reconstruct_init

Member of ReconstructInitSIRENA structure to initialize the reconstruction parameters (pointer and values).

double samprate

Sampling rate

fitsfile dtcObject

Object which contains information of the intermediate FITS file (used also by writeTestInfo() and writePulses()).

int inputPulseLength

OFLength input parameter

int createHisto(gsl_vector *invector, int nbins, gsl_vector **xhistogsl, gsl_vector **yhistogsl)¶

Located in file: tasksSIRENA.cpp

This function builds the histogram of the input vector.

Histogram x-axis values are the different input vector values (pulseheights)

Histogram y-axis values are the the number of cases per unit of the variable on the horizontal axis

Declare variables
It will work with the positive elements of the input vector => invectoraux2
Check if all the values of invector are the same => Histogram of only one bin
Obtain invector_max and invector_min
Obtain binSize
Create histogram axis
Free allocated GSL vectors

Members/Variables

gsl_vector* invector

GSL input vector

int nbins

Number of bins to build the histogram

gsl_vector** xhistogsl

GSL vector with output histogram x-axis

gsl_vector** yhistogsl

GSL vector with output histogram y-axis

int createLibrary(ReconstructInitSIRENA *reconstruct_init, bool *appendToLibrary, fitsfile **inLibObject)¶

Located in file: tasksSIRENA.cpp

This function creates the pulse templates library FITS file, if it does not exist yet. Otherwise, it opens it (to add a new row).

If it exists => Open it and set appendToLibrary = true

If it does not exist => Create it and set appendToLibrary = false

Write keyword EVENTCNT = 1 in the LIBRARY extension

Write the whole list of input parameters in HISTORY in the Primary extension (by usin ‘HDpar_stamp’)

Members/Variables

ReconstructInitSIRENA** reconstruct_init

Member of ReconstructInitSIRENA structure to initialize the reconstruction parameters (pointer and values)

bool appendToLibrary

Used by the function writeLibrary()

fitsfile** inLibObject

Object which contains information of the library FITS file (used also by writeLibrary())

int createTPSreprFile()¶

Located in file: gennoisespec.cpp

This function creates the gennoisespec output FITS file.

Steps:

Create the noise representation file (if it does not exist already)
Create the extensions NOISE, NOISEALL and WEIGHTMS
Write keywords

int differentiate(gsl_vector **invector, int szVct)¶

Located in file: pulseprocess.cpp

This function applies the derivative method \(x_i-x_{i-1}\) to the input vector.

The derivative method provides more sensitivity to handle with piled-up pulses. Moreover, little variations of the baseline will not affect.

Members/Variables

gsl_vector** invector

Input/Ouput GSL vector (non-differentiate input vector/differentiate input vector)

int szVct

Size of invector

int eigenVV(gsl_matrix *matrixin, gsl_matrix **eigenvectors, gsl_vector **eigenvalues)¶

Located in file: tasksSIRENA.cpp

This funcion provides the principal eigenvectors and eigenvalues of the input matrix (at the moment, the first two eigenvalues and eigenvectors). The eigenvalues and eigenvectors are sorted in descending order and only the principal components are provided.

Calculate the eigenvectors and the eigenvalues
Sort the eigenvectors and the eigenvalues in descending order
Choose the main eigenvectors and eigenvalues (the principal components analysis). At the moment, the first two eigenvectors and eigenvalues

Members/Variables

gsl_matrix* matrixin

Input GSL matrix

gsl_matrix** eigenvectors

Subset of eigenvectors of ‘matrixin’ chosen by PCA (the first two ones)

gsl_vector** eigenvalues

Subset of eigenvalues of ‘matrixin’ chosen by PCA (the first two ones)

void exit_error(const char *const func, string msg, int status)¶

Located in file: genutils.cpp

This function prints out error messages and exits program.

Members/Variables

const char* const func

Function name whose error is printed

string msg

Error message to be printed

int status

Status

int FFT(gsl_vector *invector, gsl_vector_complex *outvector, double STD)¶

Located in file: genutils.cpp

This function calculates the FFT of the elements of a vector.

GSL library (overview of FFTs):

For physical applications it is important to remember that the index appearing in the DFT does not correspond directly to a physical frequency. If the time-step of the DFT is \(\Delta\) then the frequency domain includes both positive and negative frequencies, ranging from \(-1/(2\Delta)\) through 0 to \(+1/(2\Delta)\). The positive frequencies are stored from the beginning of the array up to the middle, and the negative frequencies are stored backwards from the end of the array.

Here is a table which shows the layout of the array data, and the correspondence between the time domain data z, and the frequency domain data x.

index	z	x = FFT(z)
0	\(z(t = 0)\)	\(x(f = 0)\)
1	\(z(t = 1)\)	\(x(f = 1/(n\Delta))\)
2	\(z(t = 2)\)	\(x(f = 2/(n\Delta))\)
[…]	[……..]	[………………]
n/2	\(z(t = n/2)\)	\(x(f = +1/(2\Delta),-1/(2\Delta))\)
[…]	[……..]	[………………]
n-3	\(z(t = n-3)\)	\(x(f = -3/(n\Delta))\)
n-2	\(z(t = n-2)\)	\(x(f = -2/(n\Delta))\)
n-1	\(z(t = n-1)\)	\(x(f = -1/(n\Delta))\)

The frequency axis will be built as f = i/STD = i/(size/samprate) with i varying from 0 to size/2-1 (n=size and \(\Delta=1/samprate\) sec/sample).

Members/Variables

gsl_vector* invector

Input GSL vector

gsl_vector_complex* outvector

Output GSL complex vector with the FFT of invector

double STD

SelectedTimeDuration = (Size of invector)/samprate

int FFTinverse(gsl_vector_complex *invector, gsl_vector *outvector, double STD)¶

Located in file: genutils.cpp

This function calculates the inverse FFT of the elements of a vector.

Members/Variables

gsl_vector_complex* invector

Input GSL complex vector

gsl_vector* outvector

Output GSL vector with the inverse FFT of invector

double STD

SelectedTimeDuration = (Size of invector)/samprate

int filderLibrary(ReconstructInitSIRENA **reconstruct_init, double samprate)¶

Located in file: tasksSIRENA.cpp

This function calculates the (low-pass filter and) derivative of the models (pulse_templates) in the library (only necessary if first record), and it stores the pulse_templates_filder and the maxDERs and samp1DERs in the reconstruct_init structure.

The maximum of the (low-pass filtered and) differentiated pulse has to be compared to the maxDERs to select the appropriate model. Or, the 1st sample out of the differentiated pulse has to be compared to the samp1DERs to select the appropriate model.

Check if it is the first record
(Low-pass filter and) differentiate the models (pulse_templates) of the library
Store the (low-pass filtered) derivatives in pulse_templates_filder
Calculate the maximum of the (low-pass filtered and) differentiated models (maxDERs)
Locate the 1st sample of the (low-pass filtered and) differentiated models (samp1DERs)

Members/Variables

ReconstructInitSIRENA** reconstruct_init

Member of ReconstructInitSIRENA structure to initialize the reconstruction parameters (pointer and values).

double samprate

Sampling rate

bool fileExists(const std::string &name)¶

Located in file: genutils.cpp

This function checks for file existence returning a boolean value.

Members/Variables

const std::string& name

File name

int fillReconstructInitSIRENAGrading(struct Parameters par, AdvDet *det, ReconstructInitSIRENA **reconstruct_init_sirena)¶

Located in file: initSIRENA.c

This function reads the grading data from the XML file and store it in the member grading of the reconstruct_init_sirena.

It also checks if prebuff_0pad input parameter value (preBuffer when 0PAD) is possible depending on the prebuffer values in the XML file.

reconstruct_init_sirena->grading number of rows = Number of grades in the XML file reconstruct_init_sirena->grading number of columns = 3 (0->pre, 1->filter length inlcuding prebuffer, 2->prebuffer values)

Members/Variables

struct Parameters par

Input parameters

AdvDet* det

Pixel detector

ReconstructInitSIRENA** reconstruct_init

Member of ReconstructInitSIRENA structure to initialize the reconstruction parameters (pointer and values).

int filterByWavelets(ReconstructInitSIRENA *reconstruct_init, gsl_vector **invector, int length, int *onlyOnce)¶

Located in file: tasksSIRENA.cpp

This function filters the input/output signal invector, reducing the noise level.

Steps:

It is only going to work with n elements of :cpp:member:’invector’
Discrete Wavelet Transform
Sorting coefficients
Hard thresholding: n-nc coefficients are deleted (those with low energy)
Inverse DWT

Members/Variables

ReconstructInitSIRENA** reconstruct_init

Member of ReconstructInitSIRENA structure to initialize the reconstruction parameters (pointer and values).

gsl_vector** invector

Input/output signal

int length

Length of the wavelet transform

int* onlyOnce

In order to control the times to be executed

int findInterval(int tail_duration, gsl_vector *invector, gsl_vector *startpulse, int npin, int pulse_length, int nPF, int interval, int *ni, gsl_vector **startinterval)¶

Located in file: gennoisespec.cpp

This function finds the pulse-free intervals when the input vector has pulses. The pulse-free intervals must have a minimum length (intervalMinBins). The interval with pulse is \(Tstart,Tend+nPF*pulse \_ length\) (being \(Tend=n*pulse \_ length\)).

Steps:

Declare variables
Processing if the input vector has more pulses
- It looks for pulse-free intervals between pulses
Processing if there are no more pulses in the input vector
- It looks for pulse-free intervals at the end of the event and the search for more pulse-free intervals is finished

Members/Variables

int tail_duration

Length of the tail of a previous pulse

gsl_vector* invector

Input vector WITH pulses

gsl_vector* startpulse

Vector with the Tstart of all the pulses of the input vector (samples)

int npin

Number of pulses in the input vector

int pulse_length

Pulse length (samples)

int nPF

Number of pulse lengths after ending the pulse to start the pulse-free interval

int interval

Minimum length of the interval (samples)

int ni

Number of pulse-free intervals in the input vector

gsl_vector** startinterval

Vector with the starting time of each pulse-free interval (samples)

int findIntervalN(gsl_vector *invector, int interval, int *ni, gsl_vector **startinterval)¶

Located in file: gennoisespec.cpp

This function finds the pulse-free intervals when the input vector has NO pulses. The pulse-free intervals must have a minimum length (intervalMinBins).

Members/Variables

gsl_vector* invector

Input vector WITHOUT pulses

int interval

Minimum length of the interval (samples)

int* ni

Number of pulse-free intervals in the input vector

gsl_vector** startinterval

Vector with the starting time of each pulse-free interval (samples)

int findMeanSigma(gsl_vector *invector, double *mean, double *sigma)¶

Located in file: pulseprocess.cpp

This function calculates the mean and the standard deviation of the input vector.

Members/Variables

gsl_vector* invector

Input GSL vector

double* mean

Mean of the elements of invector

double* sigma

Standard deviation of the elements of invector

int findPulsesCAL(gsl_vector *vectorin, gsl_vector *vectorinDER, gsl_vector **tstart, gsl_vector **quality, gsl_vector **pulseheight, gsl_vector **maxDERgsl, int *nPulses, double *threshold, double scalefactor, double samplingRate, int samplesup, double nsgms, double lb, double lrs, ReconstructInitSIRENA *reconstruct_init, double stopcriteriamkc, double kappamkc)¶

Located in file: pulseprocess.cpp

This function is going to find the pulses in a record (in the CALibration mode) by using the function findTstartCAL().

Steps:

Declare variables
Establish the threshold (call medianKappaClipping())
Find pulses (call findTstartCAL())
If at least a pulse is found
- Get pulseheight of each found pulse (in order to be used to build the pulse templates library)
Free allocated GSL vectors

Members/Variables

gsl_vector* vectorin

Not filtered record

gsl_vector* vectorinDER

Derivative of the (low-pass filtered) vectorin

gsl_vector** tstart

Starting time of the found pulses into the record (in samples)

gsl_vector** quality

Quality of the found pulses into the record

gsl_vector** pulseheight

Pulse height of the found pulses into the record

gsl_vector** maxDERgsl

Maximum of the derivative of the found (low-pass filtered) pulses into the record

int* nPulses

Number of found pulses

double* threshold

Threshold used to find the pulses (output parameter because it is necessary out of the function)

double scalefactor

Scale factor to calculate the LPF box-car length (scaleFactor)

double samplingRate

Sampling rate

int samplesup

Number of consecutive samples over the threshold to locate a pulse (samplesUp)

double nsgms

Number of Sigmas to establish the threshold (nSgms)

double lb

Vector containing the baseline averaging length used for each pulse

double lrs

Running sum length (LrsT in samples)

ReconstructInitSIRENA* reconstruct_init

Member of ReconstructInitSIRENA structure to initialize the reconstruction parameters (pointer and values).

double stopcriteriamkc

Used in medianKappaClipping() (%)

double kappamkc

Used in medianKappaClipping()

int findPulsesNoise(gsl_vector *vectorin, gsl_vector *vectorinDER, gsl_vector **tstart, gsl_vector **quality, int *nPulses, double *threshold, double scalefactor, int sizepulsebins, double samplingRate, int samplesup, double nsgms, double stopcriteriamkc, double kappamkc)¶

Located in file: gennoisespec.cpp

This function is going to find the pulses in a record by using the function findTstartNoise()

Steps:

Declare variables
Establish the threshold (call medianKappaClipping())
Find pulses (call findTstartNoise())
Free allocated GSL vectors

Members/Variables

gsl_vector* vectorin

Not filtered record

gsl_vector* vectorinDER

Derivative of the low-pass filtered vectorin

gsl_vector** tstart

Starting time of the found pulses into the record (samples)

gsl_vector** quality

Quality of the found pulses into the record

int* nPulses

Number of found pulses

double* threshold

Threshold used to find the pulses (output parameter because it is necessary out of the function)

double scalefactor

Scale factor to calculate the LPF box-car length

int sizepulsebins

Size of the pulse (samples)

double samplingRate

Sampling rate

int samplesup

Number of consecutive samples over the threshold to locate a pulse

double nsgms

Number of Sigmas to establish the threshold

double stopCriteriamkc

Used in medianKappaClipping_noiseSigma() (%)

double kappamkc

Used in medianKappaClipping_noiseSigma()

int FindSecondaries(int maxPulsesPerRecord, gsl_vector *adjustedDerivative, double adaptativethreshold, ReconstructInitSIRENA *reconstruct_init, int tstartFirstEvent, int *numberPulses, gsl_vector **tstartgsl, gsl_vector **flagTruncated, gsl_vector **maxDERgsl, gsl_vector **lagsgsl)¶

Located in file: pulseprocess.cpp

This function runs after InitialTriggering() to find all the events (except the first one) in the first derivative of the (low-pass filtered) record by using the Adjusted Derivative detection method.

Steps:

Declare variables
Establishing the criteria of the slope of the derivative depending on the sampling rate
It is necessary to find the tstarts…

It looks for an event and if a pulse is found, it looks for another event
- It looks for an event since the beginning (or the previous event) to the end of the record. The first condition to detect an event is that the adjustedDerivative was over the threshold
  Select the model of the found pulse from the libary by using the 1st sample of the derivative (samp1DER)
  
  Dot product between the detected pulse and the pulse template in 3 different lags
  
  If maximum of the dot product found => Stop calculating dot products in more lags
  
  If maximum of the dot product not found => Calculate dot products in more lags (number of lags is limited to 5)
  
  If maximum of the dot product not found => tstart is the first sample crossing above the threshold (without jitter)
  
  Average of the first 4 samples of the derivative
  
  Find model in order to subtract
  
  If maximum of the dot product found => Parabola analytically defined => Locate the maximum => New tstart (with jitter)
  
  Iterative process in order to extract the best template from the library:
  
  samp1DER correction
  
  Find the model from the libary by using the corrected samp1DER
  
  Dot product in 3 lags
  
  Locate the maximum of the parabola
  
  samp1DER correction
  
  Find model in order to subtract
  
  Template correction
  
  Average of the first 4 samples of the derivative
  
  The second condition to detect an event is meeting the criteria of the slope of the derivative
- Subtract the model from the adjusted derivative
  Select the model of the found event from the libary by using the first sample of the derivative
  
  Subtract
… Or to use the tstart provided as input parameters
- Obtain the maxDERs of the events whose tstarts have been provided (by using the maximum of the derivative to find the model)
Free allocated GSL vectors

Members/Variables

int maxPulsesPerRecord

Expected maximum number of events per record in order to not allocate the GSL variables with the size of the input vector (EventListSize)

gsl_vector* adjustedDerivative

First derivative of the (low-pass filtered) record

double adaptativethreshold

Threshold

double samprate

Sampling rate

ReconstructInitSIRENA* reconstruct_init

Member of ReconstructInitSIRENA structure to initialize the reconstruction parameters (pointer and values).

int tstartFirstEvent

Tstart of the first event of the record (in samples) found by InitialTriggering()

int* numberPulses

Number of found events

gsl_vector** tstartgsl

Starting time of the found events (in samples)

gsl_vector** flagTruncated

Flag indicating if the event is truncated (inside this function only initial truncated pulses are classified)

gsl_vector** maxDERgsl

Maximum of the derivative of the event

gsl_vector** samp1DERgsl

Average of the first 4 samples of the derivative of the event

gsl_vector** lagsgsl

Number of necessary lags to establish the tstart (currently limited to 5)

int FindSecondariesSTC(int maxPulsesPerRecord, gsl_vector *adjustedDerivative, double adaptativethreshold, ReconstructInitSIRENA *reconstruct_init, int tstartFirstEvent, int *numberPulses, gsl_vector **tstartgsl, gsl_vector **flagTruncated, gsl_vector **maxDERgsl, gsl_vector **lagsgsl)¶

Located in file: pulseprocess.cpp

This function runs after InitialTriggering() to find all the events (except the first one) in the first derivative of the (low-pass filtered) record by using the Single Threshold Crossing method.

Steps:

Declare variables
It is necessary to find the tstarts…

It looks for an event and if a pulse is found, it looks for another event
- It looks for an event since the beginning (or the previous event) to the end of the record. The condition to detect an event is that the adjustedDerivative was over the threshold at least samplesUp consecutive samples
… Or to use the tstart provided as input parameters
- Obtain the maxDERs of the events whose tstarts have been provided
Free allocated GSL vectors

Members/Variables

int maxPulsesPerRecord

Expected maximum number of events per record in order to not allocate the GSL variables with the size of the input vector (EventListSize)

gsl_vector* adjustedDerivative

First derivative of the (low-pass filtered) record

double adaptativethreshold

Threshold

double samprate

Sampling rate

ReconstructInitSIRENA* reconstruct_init

Member of ReconstructInitSIRENA structure to initialize the reconstruction parameters (pointer and values).

int tstartFirstEvent

Tstart of the first event of the record (in samples) found by InitialTriggering()

int* numberPulses

Number of found events

gsl_vector** tstartgsl

Starting time of the found events (in samples)

gsl_vector** flagTruncated

Flag indicating if the event is truncated (inside this function only initial truncated pulses are classified)

gsl_vector** maxDERgsl

Maximum of the derivative of the event

gsl_vector** samp1DERgsl

Average of the first 4 samples of the derivative of the event

int findTstartCAL(int maxPulsesPerRecord, gsl_vector *der, double adaptativethreshold, int nSamplesUp, ReconstructInitSIRENA *reconstruct_init, int *numberPulses, gsl_vector **tstartgsl, gsl_vector **flagTruncated, gsl_vector **maxDERgsl)¶

Located in file: pulseprocess.cpp

This function scans all the values of the derivative of the (low-pass filtered) record until it finds nSamplesUp consecutive values (due to the noise more than 1 value is required) over the threshold. To look for more pulses, it finds nSamplesUp consecutive values (due to the noise) under the threshold and then, it starts to scan again.

Steps:

Declare variables
Allocate GSL vectors
It is possible to find the tstarts…
- Obtain tstart of each pulse in the derivative:
  - If \(der_i>threshold\) and foundPulse=false, it looks for nSamplesUp consecutive samples over the threshold
    - If not, it looks again for a pulse crossing over the threshold
    - If yes, a pulse is found (truncated if it is at the beginning)
  - If \(der_i>threshold\) and foundPulse=true, it looks for a sample under the threshold
    - If not, it looks again for a sample under the threshold
    - If yes, it looks for nSamplesUp consecutive samples under the threshold and again it starts to look for a pulse
… Or to use the tstart provided as input parameters

Obtain the maxDERs of the pulses whose tstarts have been provided

Members/Variables

int maxPulsesPerRecord

Expected maximum number of pulses per record in order to not allocate the GSL variables with the size of the input vector (EventListSize)

gsl_vector* der

First derivative of the (low-pass filtered) record

double adaptativethreshold

Threshold

int nSamplesUp

Number of consecutive samples over the threshold to ‘find’ a pulse (samplesUp)

ReconstructInitSIRENA* reconstruct_init

Member of ReconstructInitSIRENA structure to initialize the reconstruction parameters (pointer and values).

int* numberPulses

Number of found pulses

gsl_vector** tstartgsl

Pulses tstart (in samples)

gsl_vector** flagTruncated

Flag indicating if the pulse is truncated

gsl_vector** maxDERgsl

Maximum of the first derivative of the (low-pass filtered) record inside each found pulse

int findTstartNoise(int maxPulsesPerRecord, gsl_vector *der, double adaptativethreshold, int nSamplesUp, int *numberPulses, gsl_vector **tstartgsl, gsl_vector **flagTruncated, gsl_vector **maxDERgsl)¶

Located in file: gennoisespec.cpp.

This function finds the pulses tstarts in the input vector (first derivative of the filtered record).

This function scans all values the derivative of the (low-pass filtered) record until it finds nSamplesUp consecutive values (due to the noise more than 1 value is required) over the threshold. To look for more pulses, it finds nSamplesUp consecutive values (due to the noise) under the threshold and then, it starts to scan again.

Steps:

Declare variables
Allocate GSL vectors
Obtain tstart of each pulse in the derivative:
- If \(der_i > threshold\) and foundPulse=false, it looks for nSamplesUp consecutive samples over the threshold
  
  If not, it looks again for a pulse crossing over the threshold
  
  If yes, a pulse is found (truncated if it is at the beginning)
- If \(der_i > threshold\) and foundPulse=true, it looks for a sample under the threshold
  
  If not, it looks again for a sample under the threshold
  
  If yes, it looks for nSamplesUp consecutive samples under the threshold and again it starts to look for a pulse

Members/Variables

int maxPulsesPerRecord

Expected maximum number of pulses per record in order to not allocate the GSL variables with the size of the input vector

gsl_vector* der

First derivative of the (low-pass filtered) record

double adaptativethreshold

Threshold

int nSamplesUp

Number of consecutive samples over the threshold to ‘find’ a pulse

int* numberPulses

Number of found pulses

gsl_vector** tstartgsl

Pulses tstart (samples)

gsl_vector** flagTruncated

Flag indicating if the pulse is truncated (inside this function only initial truncated pulses are classified)

gsl_vector** maxDERgsl

Maximum of the first derivative of the (low-pass filtered) record inside each found pulse

int find_Esboundary(double maxDER, gsl_vector *maxDERs, ReconstructInitSIRENA *reconstruct_init, int *indexEalpha, int *indexEbeta, double *Ealpha, double *Ebeta, double margin)¶

Located in file: tasksSIRENA.cpp.

This function provides the indexes of the two energies which straddle the pulse energy, by comparing the maximum value of the pulse derivative (maxDER) to the list of maximums in the library (maxDERs).

It finds the two embracing maxDERs in the calibration library:

If maxDER is lower than the lowest maxDERs in the library => indexEalpha = indexEbeta = 0

If maxDER is higher than the highest maxDERs in the library => indexEalpha = indexEbeta = Number of templates-1

Members/Variables

double maxDER

Max value of the derivative of the (filtered) pulse whose embracing energies are being sought

gsl_vector* maxDERs

GSL vector with the maximum values of the derivatives of the templates in the library to be compared with the pulse being analysed

ReconstructInitSIRENA* reconstruct_init

Member of ReconstructInitSIRENA structure to initialize the reconstruction parameters (pointer and values). In particular, this function uses the info in the library about the energies

int* indexEalpha

Index of the energy lower than the energy of the pulse which is being analyzed

int* indexEbeta

Index of the energy higher than the energy of the pulse which is being analyzed

double* Ealpha

Energy (in eV) which straddle the maxDER in the lower limit

double* Ebeta

Energy (in eV) which straddle the maxDER in the higher limit

double margin

Margin to be applied when several energies in the library to choose the proper filter (hardcoded in LibraryCollection in integraSIRENA.cpp)

int find_matchedfilterSAB(double maxDER, gsl_vector *maxDERs, int preBuffer, ReconstructInitSIRENA *reconstruct_init, gsl_vector **matchedfilterFound, gsl_vector **DabFound, double *Ealpha, double *Ebeta, double margin)¶

Located in file: tasksSIRENA.cpp

This function selects the proper matched filter (normalized template) from the calibration library from column SAB (or from column MF if only one energy included in the library) by comparing the maximum value of the pulse derivative (maxDER) to the list of maximums in the library (maxDERs) for the SAB interpolation method (see optimal filter chapter). It also selects the proper row from the column DAB.

It finds the two embracing maxDERs in the calibration library:

If maxDER is lower than the lowest maxDERs in the library => The data with the lowest maxDERs (first row) in the library are chosen

If maxDER is higher than the highest maxDERs in the library => The data of the penultimate row in the library are chosen

Members/Variables

int runF0orB0val

If FilterMethod = F0 => runF0orB0val = 1. If FilterMethod = B0 => runF0orB0val = 0

double maxDER

Max value of the derivative of the (filtered) pulse whose matched filter is being sought

gsl_vector* maxDERs

GSL vector with the maximum values of the derivatives of the templates in the library to be compared with the pulse being analysed

int preBuffer

preBuffer to work with in the particular pulse

ReconstructInitSIRENA* reconstruct_init

Member of ReconstructInitSIRENA structure to initialize the reconstruction parameters (pointer and values).

gsl_vector** matchedfilterFound

GSL vector with the matched filter selected

gsl_vector** DabFound

DAB column from the library

double* Ealpha

Energy (in eV) which straddle the maxDER in the lower limit

double* Ebeta

Energy (in eV) which straddle the maxDER in the higher limit

double margin

Margin to be applied when several energies in the library to choose the proper filter (hardcoded in LibraryCollection in integraSIRENA.cpp)

int find_model_energies(double energy, ReconstructInitSIRENA *reconstruct_init, gsl_vector **modelFound)¶

Located in file: pulseprocess.cpp

This function uses energy in order to choose the proper pulse template (pulse_templates_B0) of the calibration library.

In general, it finds the two energies wich straddle energy in the calibration library and interpolates (interpolate_model()):

If energy is lower than the lowest energy in the library => The model with the lowest energy in the library is chosen

If energy is higher than the highest energy in the library => The model with the highest energy in the library is chosen

Members/Variables

double energy

Energy of the pulse whose pulse template is being sought

ReconstructInitSIRENA* reconstruct_init

Member of ReconstructInitSIRENA structure to initialize the reconstruction parameters (pointer and values). In particular, this function uses the energies of the models (energies) and their templates (pulse_templates), the number of templates in the library (ntemplates), the template duration (template_duration) and the pulse_templates_B0.

gsl_vector** modelFound

Found template of the pulse whose energy is energy

int find_model_maxDERs(double maxDER, ReconstructInitSIRENA *reconstruct_init, gsl_vector **modelFound)¶

Located in file: pulseprocess.cpp

This function uses the maximum of the derivative of the (filtered) pulse (maxDER) in order to choose the proper pulse template (pulse_templates_filder) of the calibration library.

In general, it finds the two maxDER which straddle maxDER in the calibration library and interpolates (interpolate_model()):

If maxDER is lower than the lowest maxDERs in the library => The model with the lowest maxDERs in the library is chosen

If maxDER is higher than the highest maxDERs in the library => The model with the highest maxDERs in the library is chosen

Members/Variables

double maxDER

Maximum of the derivative of the (filtered) pulse whose pulse template is being sought

ReconstructInitSIRENA* reconstruct_init

Member of ReconstructInitSIRENA structure to initialize the reconstruction parameters (pointer and values). In particular, this function uses the number of templates in the library (ntemplates), the template duration (template_duration), the filtered and differentiated templates (pulse_templates_filder) and the maxDERs of the templates

gsl_vector** modelFound

Found template of the pulse whose maximum of the derivative of the filtered version is maxDER

int find_model_samp1DERs(double samp1DER, ReconstructInitSIRENA *reconstruct_init, gsl_vector **modelFound)¶

Located in file: pulseprocess.cpp

This function uses the 1st sample of the derivative of the filtered pulse (samp1DER) in order to choose the proper pulse template (pulse_templates_filder) of the calibration library.

It finds the two samp1DER closer in the calibration library and interpolates (interpolate_model())

If samp1DER is lower than the lowest samp1DER in the library => The model with the lowest samp1DER in the library is chosen

If samp1DER is higher than the highest samp1DER in the library => The model with the highest samp1DER in the library is chosen

Members/Variables

double samp1DER

1st sample of the derivative of the filtered pulse whose pulse template is being sought

ReconstructInitSIRENA* reconstruct_init

Member of ReconstructInitSIRENA structure to initialize the reconstruction parameters (pointer and values). In particular, this function uses the 1st samples of the derivative of the models (samp1DERs) and their derived templates (pulse_templates_filder), the number of templates in the library (ntemplates) and the template duration (template_duration).

gsl_vector** modelFound

Found template of the pulse whose 1st sample of the derivative of the filtered pulse is samp1DER

int find_optimalfilterSAB(double maxDER, gsl_vector *maxDERs, ReconstructInitSIRENA *reconstruct_init, gsl_vector **optimalfilterFound, gsl_vector **DabFound, double *Ealpha, double *Ebeta, double margin)¶

Located in file: tasksSIRENA.cpp

This function selects the proper optimal filter from the calibration library columns ABTx or ABFx (or from Tx or Fx**columns if only one energy included in the library) by comparing the maximum value of the pulse derivative (:cpp:member:`maxDER`) to the list of maximums in the library (:cpp:member:`maxDERs`). It also selects the proper row from the column **DAB.

It finds the two embracing maxDERs in the calibration library:

If maxDER is lower than the lowest maxDERs in the library => The data with the lowest maxDERs (first row) in the library are chosen

If maxDER is higher than the highest maxDERs in the library => The data of the penultimate row in the library are chosen

Members/Variables

double maxDER

Max value of the derivative of the (filtered) pulse whose optimal filter is being sought

gsl_vector* maxDERs

GSL vector with the maximum values of the derivatives of the templates in the library to be compared with the pulse being analysed

ReconstructInitSIRENA* reconstruct_init

Member of ReconstructInitSIRENA structure to initialize the reconstruction parameters (pointer and values). In particular, this function uses the info in the library (optimal_filters)

gsl_vector** optimalfilterFound

GSL vector with the optimal filter selected

gsl_vector** DabFound

DAB column from the library

double* Ealpha

Energy (in eV) which straddle the maxDER in the lower limit

double* Ebeta

Energy (in eV) which straddle the maxDER in the higher limit

double margin

Margin to be applied when several energies in the library to choose the proper filter (hardcoded in LibraryCollection in integraSIRENA.cpp)

int find_prclofwn(double maxDER, gsl_vector *maxDERs, ReconstructInitSIRENA *reconstruct_init, gsl_vector **PRCLOFWNFound, double *Ealpha, double *Ebeta, double margin)¶

Located in file: tasksSIRENA.cpp

When EnergyMethod = OPTFILT and OFNoise = WEIGHTN this function selects the proper precalculated values (OFWNx) from the calibration PRCLOFWN HDU of the library by comparing the maximum value of the pulse derivative (maxDER) to the list of maximums in the library (maxDERs) for reconstruct_init->OFLib = 1.

It finds the two embracing maxDERs in the calibration library:

If maxDER is lower than the lowest maxDERs in the library => The data with the lowest maxDERs (first row) in the library are chosen

If maxDER is higher than the highest maxDERs in the library => The data of the penultimate row in the library are chosen

Members/Variables

double maxDER

Max value of the derivative of the (filtered) pulse whose optimal filter is being sought

gsl_vector* maxDERs

GSL vector with the maximum values of the derivatives of the templates in the library to be compared with the pulse being analysed

ReconstructInitSIRENA* reconstruct_init

Member of ReconstructInitSIRENA structure to initialize the reconstruction parameters (pointer and values).

gsl_vector** PRCLOFWNFound

GSL vector with some precalculated selected

double* Ealpha

Energy (in eV) which straddle the maxDER in the lower limit

double* Ebeta

Energy (in eV) which straddle the maxDER in the higher limit

double margin

Margin to be applied when several energies in the library to choose the proper filter (hardcoded in LibraryCollection in integraSIRENA.cpp)

int find_prclcov(double maxDER, gsl_vector *maxDERs, ReconstructInitSIRENA *reconstruct_init, gsl_vector **PRCLCOVFound, gsl_vector **DabFound, double *Ealpha, double *Ebeta, double margin)¶

Located in file: tasksSIRENA.cpp

When EnergyMethod = COVAR this function selects the proper precalculated values (PCOVx) from the PRCLCOV HDU of the calibration library by comparing the maximum value of the pulse derivative (maxDER) to the list of maximums in the library (maxDERs) for the reconstruct_init->OFLib = 1. It also selects the proper row from the column PAB.

It finds the two embracing maxDERs in the calibration library:

If maxDER is lower than the lowest maxDERs in the library => The data with the lowest maxDERs (first row) in the library are chosen

If maxDER is higher than the highest maxDERs in the library => The data of the penultimate row in the library are chosen

Members/Variables

double maxDER

Max value of the derivative of the (filtered) pulse whose optimal filter is being sought

gsl_vector* maxDERs

GSL vector with the maximum values of the derivatives of the templates in the library to be compared with the pulse being analysed

ReconstructInitSIRENA* reconstruct_init

Member of ReconstructInitSIRENA structure to initialize the reconstruction parameters (pointer and values).

gsl_vector** PRCLCOVFound

GSL vector with the precalculated values selected

gsl_vector** DabFound

DAB column from the library

double* Ealpha

Energy (in eV) which straddle the maxDER in the lower limit

double* Ebeta

Energy (in eV) which straddle the maxDER in the higher limit

double margin

Margin to be applied when several energies in the library to choose the proper filter (hardcoded in LibraryCollection in integraSIRENA.cpp)

extern_C_void freeOptimalFilterSIRENA(OptimalFilterSIRENA *OFilterColl)¶

Located in file: integraSIRENA.cpp

Destructor of OptimalFilterSIRENA structure.

Members/Variables

OptimalFilterSIRENA* OFilterColl

Instance of OptimalFilterSIRENA structure

extern_C_void freeReconstructInitSIRENA(ReconstructInitSIRENA *reconstruct_init)¶

Located in file: integraSIRENA.cpp

Destructor of ReconstructInitSIRENA structure.

Members/Variables

ReconstructInitSIRENA* reconstruct_init

Instance of ReconstructInitSIRENA structure

extern_C_void freePulsesCollection(PulsesCollection *PulsesColl)¶

Located in file: integraSIRENA.cpp

Destructor of PulsesCollection structure.

Members/Variables

PulsesCollection* PulsesColl

Instance of PulsesCollection structure

int fromGslMatrix(void **buffer, gsl_matrix **matrix, int type)¶

Located in file: inoututils.cpp

The function puts the values of the input GSL matrix into an output buffer.

Members/Variables

void** buffer

Output buffer

gsl_matrix** matrix

Input GSL matrix

int type

FITS type (TINT, TSHORT, TDOUBLE, etc.)

int fromGslVector(void **buffer, gsl_vector **array, int type)¶

Located in file: inoututils.cpp

The function puts the values of the input GSL vector into an output buffer.

Members/Variables

void** buffer

Output buffer

gsl_vector** array

Input GSL vector

int type

FITS type (TINT, TSHORT, TDOUBLE, etc.)

int gennoisespec_main()¶

Located in file: gennoisespec.cpp

This function calculates the current noise spectral density. If there are pulses in a record, the pulses are rejected and it is going to look for pulse-free intervals of a given size (intervalMinBins). If there are no pulses in a record, the event is divided into pulse-free intervals of a given size (intervalMinBins). It is going to look for pulse-free intervals, calculate their FFT(not filtered data) and average them.

Another feature is calculate the weight matrix of the noise (in fact, weight matrices of noise of different lengths).

The output FITS file (_noisespec) contains three columns in two extensions, NOISE and NOISEALL:

FREQ: Frequency
CSD: Current noise spectral density: Amount of current per unit (density) of frequency (spectral), as a function of the frequency
SIGMACSD: Standard error of the mean (filled out with 0’s at the moment)

There is also other extension, WEIGHTMS, where the weight matrices of the noise are stored.

Steps:

Reading all programm parameters by using PIL
Open input FITS file
Check if input FITS file have been simulated with TESSIM or XIFUSIM
To calculate aducnv (conversion factor between arbitrary units and A)…
…or read ADU_CNV, I_BIAS and ADU_BIAS
Get structure of input FITS file columns
Read info to transform to resistance space (if it is necessary)
Read and check other input keywords
Read other necessary keywords from ANY HDU
Calculate the sampling rate
- By using keywords in input FITS file (from DELTAT or TCLOCK``+``DEC_FAC or NUMROW``+``P_ROW)
- If necessary read the sampling rate from input FITS file (from the HISTORY in the Primary HDU)
- If not possible, provide an error message to include DELTAT (inverse of sampling rate) in the input FITS file
Initialize variables and transform from seconds to samples
Declare variables
Create structure to run Iteration
- Read columns (TIME and ADC)
Called iteration function: inDataIterator()
Close input FITS file
Generate CSD representation
- Applying medianKappaClipping_noiseSigma() in order to remove the noise intervals with a high sigma (if rmNoiseInterval = yes)
- FFT calculus (EventSamplesFFT)
- Add to mean FFT samples
- Current noise spectral density
- Extra normalization (further than the FFT normalization factor,1/n) in order to get the apropriate noise level provided by Peille (54 pA/rHz)
Load in noiseIntervals only those intervals with a proper sigma (if rmNoiseInterval = yes) and NumMeanSamples = cnt (in order not to change excesively the code when weightMS)
Generate WEIGHT representation
Create output FITS File: GENNOISESPEC representation file
Write extensions NOISE, NOISEALL and WEIGHTMS (call writeTPSreprExten())
Free allocated GSL vectors
Close output FITS file
Free memory
Finalize the task

The parameters (struct Parameters par) read by getpar_noiseSpec() are:

char inFile

Name of the input FITS file

char outFile

Name of the output FITS file

int intervalMinSamples

Length of a pulse-free interval to use (samples) = intervalMinBins

int nplPF

Number of pulse lengths after ending the pulse (Tend) to start the pulse-free interval

int nintervals

Number of pulse-free intervals to use to calculate the Noise Spectral Density

double scaleFactor

Scale factor to apply in order to calculate the LPF box-car length

int samplesUp

Consecutive samples over the threshold to locate a pulse

double nSgms

Number of Sigmas to establish the threshold

int pulse_length

Pulse length (samples)

double LrsT

Running sum length (seconds)

double LbT

Baseline averaging length (seconds)

char weightMS

Calculate and write the weight matrices if weightMS=yes

char EnergyMethod

Transform to resistance space (I2R, I2RFITTED) or not (OPTFILT)

double Ifit

Constant to apply the I2RFITTED conversion (adu)

char clobber

Re-write output files if clobber=yes

int matrixSize

Size of noise matrix if only one to be created

char rmNoiseIntervals

Remove some noise intervals before calculating the noise spectrum if rmNoiseIntervals=yes

int getB(gsl_vector *vectorin, gsl_vector *tstart, int nPulses, gsl_vector **lb, int sizepulse, gsl_vector **B, gsl_vector **rmsB)¶

Located in file: pulseprocess.cpp

This function calculates the sum, B, of lb digitized data samples of a pulse-free interval immediately before each pulse. If the pulse-free interval before the current pulse is lower than lb, B is calculated with the available number of samples. If there is not a pulse-free interval before the pulse, it is looked for it after the current pulse. The number of samples of the pulse-free interval used to calculate B is stored in the lb vector.

Steps:

First of all, the auxiliary variable Baux is initialized to -999 and all the elements of the lb vector are equal to the LbT input parameter in samples. Then, the code is divided into 2 if statements:

When the current pulse is the first pulse into the record:
- \(tstart \geq lb\) => Sum lb samples
- \(0<tstart<lb\) => Sum the available number of samples (although the available number of samples was lower than lb)
- \(tstart=0\) => If there is not a pulse-free interval before the pulse, it is looked for it after the current pulse
When the current pulse is not the first pulse into the record:
- \(tstart_i-tend_{i-1} \geq lb\) => Sum lb samples
- \(0<tstart_i-tend{i-1}<lb\) => Sum the available number of samples (although the available number of samples was lower than lb)
- If there is not a pulse-free interval before the pulse, it is looked for it after the current pulse

If Baux is still -999, a pulse-free interval can not be found to apply the running sum filter. This has to be taken into account, out of the function, to try to get a usable B.

Members/Variables

gsl_vector* vectorin

Input record

gsl_vector* tstart

Starting time of the pulses into the record

int nPulses

Number of pulses into the record

gsl_vector** lb

Vector containing the baseline averaging length used for each pulse

int sizepulse

Size of the pulse in samples

gsl_vector** B

In general, sum of the Lb digitized data samples (LbT input parameters in samples) of a pulse-free interval immediately before the current pulse

gsl_vector** rmsB

In general, rms of the baseline related to a pulse-free interval immediately before the current pulse

LibraryCollection *getLibraryCollection(ReconstructInitSIRENA *reconstruct_init, gsl_vector *pBi, gsl_vector *posti, int *const status)¶

Located in file: integraSIRENA.cpp

This function creates and retrieves a LibraryCollection from a file.

Create LibraryCollection structure
Open FITS file in READONLY mode (move to the first HDU) and get number of templates (rows)
Allocate library structure
Get PULSE and MF column numbers (depending the different options)
Get template duration
Allocate library structure (cont.)
Get matched filter duration
Read different columns and populate the LibraryCollection structure
Added new code to handle the new HDUs FIXFILTF, FIXFILTT, PRCLCOV and PRCLOFWN
Free allocated GSL vectors and matrices

Members/Variables

ReconstructInitSIRENA* reconstruct_init

Instance of ReconstructInitSIRENA structure

gsl_vector posti

Vector with the post values read from the XML file

int* const status

Input/output status

NoiseSpec *getNoiseSpec(ReconstructInitSIRENA *reconstruct_init, int *const status)¶

Located in file: integraSIRENA.cpp

This function creates and retrieves a NoiseSpec from a file.

Create NoiseSpec structure
Open FITS file, move to the NOISE, NOISEALL and WEIGHTMS HDUs and get necessary keywords
Allocate NoiseSpec structure
Get noise spectrum (CSD), and noise frequencies (FREQ) column numbers
Read column CSD and save it into the structure
Read column FREQ and save it into the structure
Read columns Wx with the noise weight matrix from noise intervals and save them into the structure
Return noise spectrum

Members/Variables

ReconstructInitSIRENA* reconstruct_init

Instance of ReconstructInitSIRENA structure

int* const status

Input/Output status

int getpar_noiseSpec(struct Parameters *const par)¶

Located in file: gennoisespec.cpp

This function gets the input parameter from the command line or their default values from the gennoisespec.par file

Members/Variables

struct Parameters* const par

Structure containing the input parameters specified in gennoisespec.par

int getpar_teslib(struct Parameters *const par)¶

Located in file: teslib.c

This function gets the input parameter from the command line or their default values from the teslib.par file

Members/Variables

struct Parameters* const par

Structure containing the input parameters specified in teslib.par

int getpar_tesrecons(struct Parameters *const par)¶

Located in file: tesrecons.c

This function gets the input parameter from the command line or their default values from the tesrecons.par file

Members/Variables

struct Parameters* const par

Structure containing the input parameters specified in tesrecons.par

int getPulseHeight(gsl_vector *vectorin, double tstart, double tstartnext, int lastPulse, double lrs, double lb, double B, int sizepulse, double *pulseheight)¶

Located in file: pulseprocess.cpp

This function estimates the pulse height of a pulse by using a running sum filter. It extracts from the record, vectorin, the pulse whose pulse height is going to be estimated by using RS_filter().

Steps:

Declare variables
Extracting from the record the pulse whose pulse height is going to be estimated
Apply the running sum filter

Members/Variables

gsl_vector* vectorin

Not filtered record

double tstart

Starting time of the pulse whose pulse height is going to be estimated

double tstartnext

Starting time of the next pulse whose pulse height is going to be estimated

int lastPulse

It is 1 if the pulse is the last one into the record or the only one

double lrs

Running sum length (equal to the LrsT input parameter in samples)

double lb

Baseline averaging length used for the pulse whose pulse height is going to be estimated

double B

In general, sum of the Lb digitized data samples (LbT input parameters in samples) of a pulse-free interval immediately before the current pulse

int sizepulse

Size of the pulse in samples

double* pulseheight

Estimated pulse height of the pulse

int getSamplingrate_trigreclength_Filei(char *inputFile, struct Parameters par, double *samplingrate, int *trigreclength)¶

Located in file: initSIRENA.c

This function gets the sampling rate and the trig_reclength from an inputs FITS file inputFile.

Steps:

Open FITS file

Check if input FITS file have been simulated with TESSIM or XIFUSIM

Check if input XML file and XMl file to build the library to be used to reconstruct are the same

Get the sampling rate from the HISTORY keyword from the input FITS file and check with sampling rate from XML file

If xifusim file => Get ‘trig_reclength’ from the HISTORY keyword from the input FITS file ‘trig_reclength’ is necessary if SIRENA is going to run in THREADING mode

Close FITS file

Free memory

Members/Variables

char* inputFile

Input file name

struct Parameters par

Input parameters

double* samplingrate

(In) Sampling rate from XML file => (Out) Sampling rate

int* trigreclength

Necessary if SIRENA is going to run in THREADING mode

int getSamplingrate_trigreclength(char *inputFile, struct Parameters par, double *samplingrate, int *trigreclength, int *numfits)¶: Located in file: initSIRENA.c

This function gets the sampling rate and the trig_reclength no matter if inputFile is only a FITS file or more (inputFile can start with ‘@’ or not, input file or files can have been simulated with TESSIM or XIFUSIM).

Members/Variables

char* inputFile

Input file name

struct Parameters par

Input parameters

double* samplingrate

(In) Sampling rate from XML file => (Out) Sampling rate

int* trigreclength

Necessary if SIRENA is going to run in THREADING mode

int* numfits

Number of FITS files to work with

void gsl_vector_complex_absIFCA(gsl_vector *cvnew, gsl_vector_complex *cv)¶

Located in file: genutils.cpp

This function calculates the magnitude of the complex elements of a vector (real part).

Members/Variables

gsl_vector_complex* cv

Input GSL complex vector

gsl_vector* cvnew

Output GSL vector with the absolute values of the elements of cv

void gsl_vector_complex_argIFCA(gsl_vector *varg, gsl_vector_complex *vin)¶

Located in file: genutils.cpp

This function calculates the arguments of the complex elements of a vector.

Members/Variables

gsl_vector_complex* vin

Input GSL complex vector

gsl_vector* varg

Output GSL vector with the arguments of the elements of vin

void gsl_vector_complex_scaleIFCA(gsl_vector_complex *cv, gsl_complex z)¶

Located in file: genutils.cpp

This function multiplies the complex elements of a vector by a complex number.

Members/Variables

gsl_vector_complex* cv

Input/Output (scaled) GSL complex vector

gsl_complex z

Input GSL complex number

void gsl_vector_sqrtIFCA(gsl_vector *cvnew, gsl_vector *cv)¶

Located in file: genutils.cpp

This function calculates the square root of the elements of a vector.

Members/Variables

gsl_vector* cv

Input GSL complex vector

gsl_vector* cvnew

Output GSL vector with the square root values of the elements of cv

int gsl_vector_Sumsubvector(gsl_vector *invector, long offset, long n, double *sum)¶

Located in file: genutils.cpp

This function returns the sum of some elements of the input vector.

The starting element of the sum is offset from the start of the input vector. It will sum up n elements.

offset can take values from 0 to invector->size

Members/Variables

gsl_vector* invector

Input GSL vector

long offset

It is the first element to be summed

long n

Number of elements in the sum

double* sum

Calculated output value (sum of the corresponding elements)

int inDataIterator(long totalrows, long offset, long firstrow, long nrows, int ncols, iteratorCol *cols, void *user_strct)¶

Located in file: gennoisespec.cpp

This function takes the optimum number of rows to read the input FITS file and works iteratively

Steps:

Declare variables
Allocate input GSL vectors
Read iterator
Processing each record
- Information has been read by blocks (with nrows per block)
- Just in case the last record has been filled out with 0’s => Last record discarded
- Convert to the resistance space if necessary
- To avoid taking into account the pulse tails at the beginning of a record as part of a pulse-free interval
- Low-pass filtering
- Differentiate
- Finding the pulses: Pulses tstarts are found (call findPulsesNoise())
- Finding the pulse-free intervals in each record
  
  If there are pulses => Call findInterval()
  
  No pulses => The whole event is going to be used (DIVIDING into intervals of intervalMinBins size) => Call findIntervalN()
- Calculating the mean and sigma of the intervals without pulses together => BSLN0 and NOISESTD
Preparing the CSD calculus (not filtered data)
Free allocated GSL vectors

Members/Variables

long totalrows

Total number of rows processed

long offset

If positive, this number of rows at the beginning of the table (or pixels in the image) will be skipped and will not be passed to the work function

long firstrow

First row to read

long nrows

It specifies the number of table rows that are to be passed to the work function on each iteration. If nrows = 0 then CFITSIO will calculate the optimum number for greatest efficiency. If nrows is negative, then all the rows or pixels will be passed at one time, and the work function will only be called once. If any variable length arrays are being processed, then the nrows value is ignored, and the iterator will always process one row of the table at a time

int ncols

Number of columns

iteratorCol* cols

Structure of iteration

void* user_strct

This is a user supplied pointer that can be used to pass ancillary information from the driver routine to the work function. It may point to a single number, an array, or to a structure containing an arbitrary set of parameters

extern_C_void initializeReconstructionSIRENA(ReconstructInitSIRENA *reconstruct_init, char *const record_file, fitsfile *fptr, char *const library_file, char *const event_file, int flength_0pad, int prebuff_0pad, double scaleFactor, int samplesUp, int samplesDown, double nSgms, int detectSP, int opmode, char *detectionMode, double LrsT, double LbT, char *const noise_file, char *filter_domain, char *filter_method, char *energy_method, double filtEev, double Ifit, char *ofnoise, int lagsornot, int nLags, int Fitting35, int ofiter, char oflib, char *ofinterp, char *oflength_strategy, int oflength, char preBuffer, double monoenergy, char addCOVAR, char addINTCOVAR, char addOFWN, int largeFilter, int interm, char *const detectFile, int errorT, int Sum0Filt, char clobber, int maxPulsesPerRecord, double SaturationValue, char *const tstartPulse1, int tstartPulse2, int tstartPulse3, double energyPCA1, double energyPCA2, char *const XMLFile, int *const status)¶

Located in file: integraSIRENA.cpp

This function initializes the structure ReconstructInitSIRENA with the variables required for SIRENA reconstruction. The values are taken from the input parameters.

Load LibraryCollection structure if library file exists
Load NoiseSpec structure
Fill in the matrix tstartPulse1_i if tstartPulse1 = nameFile Start time (in samples) of the first pulse (0 if detection should be performed by the system; greater than 0 if provided by the user) or file name containing the tstart (in seconds) of every pulse, tstartPulse1
Fill in reconstruct_init

Members/Variables

ReconstructInitSIRENA* reconstruct_init

Member of ReconstructInitSIRENA structure to initialize the reconstruction parameters (pointer and values)

char* const record_file

Filename of input data file with records, RecordFile

fitsfile* fptr

FITS object with pointer to data file

char* const library_file

File name of calibration library, LibraryFile

char* const event_file

File name of output events (with reconstructed energy), TesEventFile

int flength_0pad

0-padding filter length, flength_0pad

int prebuff_0pad

preBuffer used when 0-padding, prebuff_0pad

double scaleFactor

Detection scale factor for initial filtering, scaleFactor

int samplesUp

Number of samples for threshold trespassing, samplesUp

int samplesDown

Number of samples below the threshold to look for other pulse, samplesDown

double nSgms

Number of standard deviations in the kappa-clipping process for threshold estimation, nSgms

int detectSP

Detect secondary pulses (1) or not (0), detectSP

int opmode

Calibration run (0) or energy reconstruction run (1)

char* detectionMode

Adjusted Derivative (AD) or Single Threshold Crossing (STC), detectionMode

double LrsT

Running sum length for the RS raw energy estimation (seconds), LrsT

double LbT

Baseline averaging length (seconds), LbT

char* const noise_file

Noise file, NoiseFile

char* filter_domain

Filtering Domain: Time (T) or Frequency (F), FilterDomain

char* filter_method

Filtering Method: F0 (deleting the zero frequency bin) or F0 (deleting the baseline), FilterMethod

char* energy_method

Energy calculation Method: OPTFILT, INTCOVAR, COVAR, I2R or I2RFITTED, EnergyMethod

double filtEev

Energy of the filters of the library to be used to calculate energy (only for OPTFILT, I2R and I2RFITTED), filtEeV

double Ifit

Constant to apply the I2RFITTED conversion

char* ofnoise

For optimal filtering: NSD or WEIGHTN, OFNoise

int lagsornot

Lags (1) or no lags (0), LagsOrNot

int nLags

Number of lags (positive odd number)

int Fitting35

Number of lags to analytically calculate a parabola (3) or to fit a parabola (5)

int ofiter

Iterate (1) or not iterate (0), OFIter

char oflib

Work or not with a library with optimal filters (1/0)

char* ofinterp

Optimal Filter by using the Matched Filter or the DAB as matched filter (MF/DAB) It has been fixed in tesrecons as SAB

char* oflength_strategy

Optimal Filter length Strategy: FREE, BYGRADE or FIXED, OFStrategy

int oflength

Optimal Filter length (taken into account if OFStrategy = FIXED), OFLength

char preBuffer

Some samples added or not before the starting time of a pulse (number of added samples read from the xml file)

double monoenergy

Monochromatic energy of input file in eV (only for library creation), monoenergy

int addCOVAR

Add or not pre-calculated values related to COVAR reconstruction method in the library file (yes/no) (only for library creation), addCOVAR

int addINTCOVAR

Add or not pre-calculated values related to INTCOVAR reconstruction method in the library file (yes/no) (only for library creation), addINTCOVAR

int addOFWN

Add or not pre-calculated values related to Optimal Filtering by using Weight Noise matrix in the library file (yes/no) (only for library creation), addOFWN

int largeFilter

Length of the longest fixed filters (only for library creation), largeFilter

int interm

Write or not intermediate files (1/0), intermediate

char* const detectFile

Intermediate detections file (if intermediate = 1), detectFile

int errorT

Additional error (in samples) added to the detected time (logically, it changes the reconstructed energies)

int Sum0Filt

0-padding: Subtract the sum of the filter (1) or not (0)

char clobber

Overwrite or not output files if exist (yes/no), clobber

int maxPulsesPerRecord

Default size of the event list, EventListSize

double SaturationValue

Saturation level of the ADC curves

int tstartPulse1

Start time (in samples) of the first pulse (0 if detection should be performed by the system; greater than 0 if provided by the user) or file name containing the tstart (in seconds) of every pulse, tstartPulse1

int tstartPulse2

Tstart (samples) of the second pulse, tstartPulse2

int tstartPulse3

Tstart (samples) of the third pulse (if 0 => PAIRS, if not 0 => TRIOS), tstartPulse3

double energyPCA1

First energy (only for EnergyMethod = PCA)

double energyPCA2

Second energy (only for EnergyMethod = PCA)

char * const XMLFile

File name of the XML input file with instrument definition

int* const status

Input/Output status

int InitialTriggering(gsl_vector *derivative, double nSgms, double scalefactor, double samplingRate, double stopcriteriamkc, double kappamkc, bool *triggerCondition, int *tstart, int *flagTruncated, double *threshold, int tstartProvided)¶

Located in file: pulseprocess.cpp

This function finds the first pulse in the input vector, first derivative of the (low-pass filtered) record.

Steps:

Declare variables
Stablish the threshold
It is necessary to find the tstart of the first pulse…

Obtain tstart of the first pulse in the derivative if \(derivative_i>threshold\)
… Or to use the tstart provided as input parameter

Members/Variables

gsl_vector* derivative

First derivative of the (low-pass filtered) record

double nSgms

Number of Sigmas to establish the threshold (nSgms)

double scalefactor

Scale factor to calculate the LPF box-car length (scaleFactor)

double samplingRate

Sampling rate

double stopcriteriamkc

Used in medianKappaClipping() (%)

double kappamkc

Used in medianKappaClipping()

bool* triggerCondition

True => The algorithm has found the first event

False => The algorithm has not found any event

int* tstart

First event tstart (in samples)

int* flagTruncated

Flag indicating if the event is truncated

double* threshold

Calculated threshold (output parameter because it is necessary out of the function)

int tstartProvided

Tstart of the first pulse provided as input parameter

extern_C_void IntegrafreeTesEventListSIRENA(TesEventList *event_list)¶

Located in file integraSIRENA.cpp

This function frees the structure in the input parameter.

Members/Variables

TesEventList* event_list

Instance of TesEventList structure that contains the information of the reconstructed pulses

int interactivePars(inparam *taskPars, int np, string task)¶

Located in file inoututils.cpp

This function reads input parameters interactively (provided by the user or taken as default values). Used in tool gennoisespec.

Members/Variables

inparam* taskPars

Instance of inparam structure storing input parameters

int np

Number of parameters

string task

Tool name

int interpolatePOS(gsl_vector *x_in, gsl_vector *y_in, long size, double step, gsl_vector **x_out, gsl_vector **y_out)¶

Located in file: tasksSIRENA.cpp

This function interpolates an input vector (x_in, y_in), creating an output vector (x_out, y_out) with the size and frequency step given. POS comes from the fact that the input spectrum only has positive frequencies (in order to not handle the f=0 bin).

Declare and initialize variables
GSL method applied for interpolatation
Generate the interpolated output vector
Free memory

Members/Variables

gsl_vector* x_in

GSL input vector with the abscissas of the vector which is going to be interpolated

gsl_vector* y_in

GSL input vector with the ordinates of the vector which is going to be interpolated

long size

Size of the interpolated output vector

double step

Frequency step of the interpolated output vector

gsl_vector** x_out

GSL output vector with the abscissas of the interpolated vector

gsl_vector** y_out

GSL output vector with the ordinates of the interpolated vector

int interpolate_model(gsl_vector **modelFound, double p_model, gsl_vector *modelIn1, double p_modelIn1, gsl_vector *modelIn2, double p_modelIn2)¶

Located in file: pulseprocess.cpp

This function interpolates the pulse model, \(p(t,E)\), between two models of the pulse models library, \(p(t,E_1)\) and \(p(t,E_2)\), being \(E_1<E<E_2\).

According to the interpolation method:

\[p(t,E)={\frac{E_2-E}{E_2-E_1}}p(t,E_1)+{\frac{E-E_1}{E_2-E_1}}p(t,E_2)\]

Members/Variables

gsl_vector** modelFound

Found model of the pulse whose energy or maxDER is p_model

double p_model

Parameter (energy or maxDER) of the pulse whose model is being sought

gsl_vector* modelIn1

Model of the pulse whose parameter (energy or maxDER) is immediately lower than p_model in the library FITS file

double p_modelIn1

Parameter (energy or maxDER) immediately lower than p_model in the library FITS file

gsl_vector* modelIn2

Model of the pulse whose parameter (energy or maxDER) is immediately greater than p_model in the library FITS file

double p_modelIn2

Parameter (energy or maxDER) immediately greater than p_model in the library FITS file

bool isNumber(string s)¶

Located in file: genutils.cpp

This function returns TRUE if the input string is a number or FALSE if not.

Members/Variables

string s

Input string

int loadRecord(TesRecord *record, double *time_record, gsl_vector **adc_double)¶

Located in file: tasksSIRENA.cpp

This fucntion loads the structure record into the adc_double GSL vector.

It checks if the record has been filled out with 0’s => It only loads the first values (which are different from 0).

Members/Variables

TesRecord* record

Member of TesRecord structure that contains the input record

double time_record

Starting time of the record (output)

gsl_vector** adc_double

Storage of the record to be processed (input/output)

int lpf_boxcar(gsl_vector **invector, int szVct, int sampleRate)¶

Located in file: pulseprocess.cpp

This function implements a low pass filtering as a box-car function in time.

The box-car function is a temporal average window:

\[x_{i-1}=\sum_{0}^{n-1}\frac{I_i}{n}\]

\[x_i=\sum_{1}^{n}\frac{I_i}{n}\]

If the cut frequency of the filter is \(\mathit{f_c}\), the box-car length (n) is

\[\frac{1}{f_c}samprate\]

Steps:

Declare variables
Define the LPF (frequency domain) and the box-car function (time domain)
It is going to work with a longer vector to not have fake results for the last boxLength windows
Apply the box-car window by shifting it along the (lengthened) input vector
Free allocated GSL vectors

The function returns:

1: Function cannot run

3: Cut-off frequency too high => Equivalent to not filter

4: Cut-off frequency too low

Members/Variables

gsl_vector** invector

Input/Output GSL vector (non-filtered input vector/filtered input vector)

int szVct

Size of invector

int sampleRate

Sampling rate (samples/s)

int matrix2vector(gsl_matrix *matrixin, gsl_vector **vectorout)¶

Located in file: tasksSIRENA.cpp

This function converts an input square matrix \([n \times n]\) into an output \(n^2\) vector. It puts the first row of the matrix (\(n\) elements) in the first \(n\) elements of the vector (from \(0\) to \(n-1\)), the second row of the matrix in the elements from \(n\) to \(2n-1\) of the vector and so on.

Members/Variables

gsl_matrix* matrixin

GSL input square matrix \([n \times n]\)

gsl_vector** vectorout

GSL output vector whose length is \(n^2\)

int medianKappaClipping(gsl_vector *invector, double kappa, double stopCriteria, double nSigmas, int boxLPF, double *threshold)¶

Located in file: pulseprocess.cpp

This function calculates a threshold in the first derivative of the record by using a Kappa-clipping method (replacing points beyond \(mean\pm kappa \cdot sigma\) with the median).

Mean and sigma are calculated and values of invector out of \((mean+kappa \cdot sigma,mean-kappa \cdot sigma)\) are replaced with the median (it is trying to look for the baseline). And this process is iteratively repeated until there are no points beyond \(mean \pm kappa \cdot sigma\). Finally, the threshold is calculated as \(mean+nSigmas \cdot sigma\) (‘+’ is used because if there are pulses in the input invector they are always positive).

Steps:

Declare variables
Calculate the median
Iterate until there are no points out of the maximum excursion ( \(kappa \cdot sigma\))
Establish the threshold as mean+nSigmas*sigma

Members/Variables

gsl_vector* invector

First derivative of the (filtered) record

double kappa

Value to establish the range around of the mean

double stopCriteria

It is given in %

double nSigmas

Times sigma to calculate threshold as \(mean+nSigmas \cdot sigma\)

int boxLPF

Length of the low-pass filtering box-car

double* threshold

Calculated threshold

int medianKappaClipping_noiseSigma(gsl_vector *invector, double kappa, double stopCriteria, double nSigmas, double *mean, double *sigma)¶

Located in file: gennoisespec.cpp

This function provides the mean and the sigma of an input vector (with noise sigmas) by using a Kappa-clipping method (replacing points beyond \(mean\pm kappa \cdot sigma\) with the median).

First, mean and sigma are calculated and invector values out of \((mean+kappa \cdot sigma,mean-kappa \cdot sigma)\) are replaced with the median (it is trying to look for the baseline). And this process is iteratively repeated until there are no points beyond \(mean \pm kappa \cdot sigma\). Finally, the mean and sigma of the resulting vector are provided.

Steps:

Declare variables
Calculate the median
Iterate until there are no points out of the maximum excursion ( \(kappa \cdot sigma\))
Calculate mean and sigma

Members/Variables

gsl_vector* invector

First derivative of the (filtered) record

double kappa

Value to establish the range around of the mean

double stopCriteria

It is given in %

double nSigmas

Times sigma to calculate threshold as \(mean+nSigmas \cdot sigma\)

double* mean

Mean value of the invector (no points beyond \(mean \pm kappa \cdot sigma\))

double* sigma

Sigma value of the invector (no points beyond \(mean \pm kappa \cdot sigma\))

void MyAssert(int expr, char *msg)¶

Located in file: initSIRENA.c

This function displays an error message if the condition in expr is true.

Members/Variables

int expr

Condition to be true in order to display the error message

char* msg

Message to be displayed

extern_C_OptimalFilterSIRENA *newOptimalFilterSIRENA(int *const status)¶

Located in file: integraSIRENA.cpp

Constructor. It returns a pointer to an empty OptimalFilterSIRENA data structure.

Members/Variables

int* const status

Input/output status

extern_C_PulsesCollection *newPulsesCollection(int *const status)¶

Located in file: integraSIRENA.cpp

Constructor. It returns a pointer to an empty PulsesCollection data structure.

Members/Variables

int* const status

Input/output status

extern_C_ReconstructInitSIRENA *newReconstructInitSIRENA(int *const status)¶

Located in file integraSIRENA.cpp

Constructor. It returns a pointer to an empty ReconstructInitSIRENA data structure.

Members/Variables

int* const status

Input/output status

int noDetect(gsl_vector *der, ReconstructInitSIRENA *reconstruct_init, int *numberPulses, gsl_vector **tstartgsl, gsl_vector **flagTruncated, gsl_vector **maxDERgsl, gsl_vector **samp1DERgsl)¶

Located in file: pulseprocess.cpp

This function runs if the starting time of the pulses are agiven as input parameters (tstartPulse1 != 0). It looks for the maximum of the derivative of the pulse and the average of the first 4 samples of the derivative of the pulse.

Members/Variables

gsl_vector* der

First derivative of the (low-pass filtered) record

ReconstructInitSIRENA* reconstruct_init

Member of ReconstructInitSIRENA structure to initialize the reconstruction parameters (pointer and values)

int* numberPulses

Number of events

gsl_vector** tstartgsl

Starting time of the events (in samples)

gsl_vector** flagTruncated

Flag indicating if the event is truncated (inside this function only initial truncated pulses are classified)

gsl_vector** maxDERgsl

Maximum of the derivative of the event

gsl_vector** samp1DERgsl

Average of the first 4 samples of the derivative of the event

int obtainRiseFallTimes(gsl_vector *recordNOTFILTERED, double samprate, gsl_vector *tstartgsl, gsl_vector *tendgsl, gsl_vector *Bgsl, gsl_vector *Lbgsl, int numPulses, gsl_vector **tauRisegsl, gsl_vector **tauFallgsl)¶

Located in file: tasksSIRENA.cpp

This function provides an estimation of the rise and fall time of the detected pulses in a record.

Find the maximum of each pulse: amax
Baseline of each pulse: abase
Find the first sample in the rising part above the 10% and 50%: t10 and t50
- Line by using 2 points: (t10,a10) and (t50,a50)
- t0 (t0,abase)
- tmax (tmax,amax)
- Rise time = tmax-t0
Find the first sample in the decreasing part below the 50% and 10%: t50 and t10
- Line by using 2 points: (t50,a50) and (t10,a10)
- t0 (t0,abase)
- tmax (tmax,amax)
- Fall time = t0-tmax

Members/Variables

gsl_vector* recordNOTFILTERED

Record neither low-pass filtered nor differentiated

double samprate

Sampling rate

gsl_vector* tstartgsl

Starting time of the detected pulses in the record (samples)

gsl_vector* tendgsl

Ending time of the detected pulses in the record (samples)

gsl_vector* Bgsl

In general, sum of the Lb digitized data samples of a pulse-free interval immediately before each pulse

gsl_vector* Lbgsl

Number of samples added in Bgsl for each pulse

int numPulses

Number of detected pulses in the record

gsl_vector** tauRisegsl

Rise time of the detected pulses in the record (seconds)

gsl_vector** tauFallgsl

Fall time of the detected pulses in the record (seconds)

int parabola3Pts(gsl_vector *x, gsl_vector *y, double *a, double *b, double *c)¶

Located in file: genutils.cpp

This function calculates the equation of a parabola given 3 points.

Members/Variables

gsl_vector* x

Input GSL with x vector

gsl_vector* y

Input GSL with y vector

double* a

Fit coefficient of the quadratic term

double* b

Fit coefficient of the linear term

double* c

Fit coefficient (independent term)

int polyFit(gsl_vector *x_fit, gsl_vector *y_fit, double *a, double *b, double *c)¶

Located in file: genutils.cpp

This function makes a polynomial fitting \(ax^2+bx+c\) using the regression quadratic analysis. To measure how well model agrees with the data, the chi-square merit function is used, which in this case is

\[\chi^2 (a,b,c)= \sum_{i=1}^{N}\left(\frac{y_i-a-bx_i-c{x_i}^2}{\sigma_i}\right)^2\]

This equation is minimized to determine a, b and c. Then

\[\begin{split}\begin{array}{ccc} S_{(x,x)}=\sum{{x_i}^2}-\frac{(\sum{x_i})^2}{N} & S_{(x^2,y)}=\sum{{x_i}^2y_i}-\frac{\sum{{x_i}^2}\cdot\sum{y_i}}{N}\\ S_{(x,y)}=\sum{x_iy_i}-\frac{\sum{x_i}\cdot\sum{y_i}}{N} & S_{(x^2,x^2)}=\sum{{x_i}^4}-\frac{\left(\sum{{x_i}^2}\right)^2}{N}\\ S_{(x,x^2)}=\sum{{x_i}^3}-\frac{\sum{x_i}\cdot\sum{{x_i}^2}}{N} & \\ \end{array}\end{split}\]

\[a = \frac{S_{(x^2,y)}S_{(x,x)}-S_{(x,y)}S_{(x,x^2)}}{S_{(x,x)}S_{(x^2,x^2)} -{\vert S_{(x,x^2)} \vert}^2}\]

\[b = \frac{S_{(x,y)}S_{(x^2,x^2)}-S_{(x^2,y)}S_{(x,x^2)}}{S_{(x,x)}S_{(x^2,x^2)} -{\vert S_{(x,x^2)} \vert}^2}\]

\[c = \frac{\sum{y_i}}{N}-b\frac{\sum{x_i}}{N}-a\frac{\sum{{x_i}^2}}{N}\]

Members/Variables

gsl_vector* x_fit

Input GSL with x vector

gsl_vector* y_fit

Input GSL with y vector

double* a

Fit coefficient of the quadratic term

double* b

Fit coefficient of the linear term

double* c

Fit coefficient (independent term)

int polyFitLinear(gsl_vector *x_fit, gsl_vector *y_fit, double *a, double *b)¶

Located in file: genutils.cpp

This function makes a linear fitting \(ax+b\) using the regression linear analysis. To measure how well model agrees with the data, the chi-square merit function is used, which in this case is

\[\chi^2 (a,b)= \sum_{i=1}^{N}\left(\frac{y_i-a-bx_i}{\sigma_i}\right)^2\]

This equation is minimized to determine a and b. Then

\[a = \frac{N \sum{x_i y_i}- \sum{x_i}\sum{y_i}}{N \sum{x_i^2} - {\left(\sum{x_i}\right)}^2}\]

\[b = \frac{\sum{y_i}}{N}-a \frac{\sum{x_i}}{N}\]

Members/Variables

gsl_vector* x_fit

Input GSL with x vector

gsl_vector* y_fit

Input GSL with y vector

double* a

Fit coefficient of the linear term

double* b

Fit coefficient (independent term)

void print_error(const char *const func, string message, int status)¶

Located in file: genutils.cpp

This function prints out error messages.

Members/Variables

const char* const func

Function name whose error is printed

string msg

Error message to be printed

int status

Status

int procRecord(ReconstructInitSIRENA **reconstruct_init, double tstartRecord, double samprate, fitsfile *dtcObject, gsl_vector *record, gsl_vector *recordWithoutConvert2R, PulsesCollection *foundPulses, long num_previousDetectedPulses, int pixid, gsl_vector *phid, int oscillations, int nrecord, double tstartPrevPulse)¶

Located in file: tasksSIRENA.cpp

This function processes the input record (detecting the pulses):

Declare and initialize variables
Allocate GSL vectors
(Low-pass filtering and) differentiation
If there are weird oscillations in the record, it is not processed => numPulses = 0
Find the events (pulses) in the record
- If production mode:
  - No detect if tstartPulse1 != 0: ‘noDetect’
  - Detect (tstartPulse1 != 0):
    
    ‘InitialTriggering’
    
    ‘FindSecondaries’ (detectionMode = AD) or ‘FindSecondariesSTC’ (detectionMode = STC)
- If calibration mode: ‘findPulsesCAL’
Calculate the end time of the found pulses and check if the pulse is saturated
Calculate the baseline (mean and standard deviation) before a pulse (in general before) => To be written in BSLN and RMSBSLN columns in the output FITS file
Obtain the approximate rise and fall times of each pulse
Load the found pulses data in the input/output foundPulses structure
Write test info (if reconstruct_init->intermediate = 1)
Write pulses info in intermediate output FITS file (if reconstruct_init->intermediate = 1)
Free allocated GSL vectors

Members/Variables

ReconstructInitSIRENA** reconstruct_init

Member of ReconstructInitSIRENA structure to initialize the reconstruction parameters (pointer and values)

double tstartRecord

Starting time of the record (in order to calculate absolute times)

double samprate

Sampling rate (in order to low-pass filter)

fitsfile* dtcObject

Object which contains information of the intermediate FITS file (to be written if intermediate = 1)

gsl_vector* record

GSL vector with signal values of input record

gsl_vector* recordWithoutConvert2R

GSL vector with original signal values of input record (without being converted to R space)

PulsesCollection* foundPulses

Input/output structure where the info about found pulses is stored

long num_previousDetectedPulses

Number of previous detected pulses (to know the index to get the proper element from tstartPulse1_i in case tstartPulse1 was a file name)

int pixid

Pixel ID (from the input file) to be propagated

gsl_vector* phid

Photon ID (from the input file) to be propagated

int oscillations

1 (there are weird oscillations in the record) or 0 (record without weird oscillations)

int nrecord

Current record index

double tstartPrevPulse

tstart of the previous pulse (last pulse of the previous record) (seconds)

int pulseGrading(ReconstructInitSIRENA *reconstruct_init, int tstart, int grade1, int grade2, int *pulseGrade, long *OFlength, int nrecord)¶

Located in file: tasksSIRENA.cpp

This function provides the pulse grade (Rejected=-1, HighRes=1, MidRes=2, LimRes=3, LowRes=4) and the optimal filter length by taking into account the info read from the XML file and the OFStrategy (FREE, BYGRADE or FIXED). (Pileup=-2 not used)

Members/Variables

ReconstructInitSIRENA** reconstruct_init

Member of ReconstructInitSIRENA structure to initialize the reconstruction parameters (pointer and values).

int tstart

Start time (samples)

int grade1

Pulse duration (length of optimal filter applied)

int grade2

Difference between the start time of the pulse and the start time of the previous pulse

int* pulseGrade

Pulse grade (output)

long* OFlength

Optimal filter length (= OFLength only if OFStrategy = FIXED and OFLength <= grade1) (output)

int nrecord

Current record index (to know the particular record where there could be more than one pulse => message)

int readAddSortParams(ReconstructInitSIRENA *reconstruct_init, fitsfile **inLibObject, double samprate, int eventcntLib, double estenergy, gsl_vector *pulsetemplate, gsl_vector *pulsetemplate_B0, gsl_matrix *covariance, gsl_matrix *weight, gsl_vector *pulsetemplateMaxLengthFixedFilter, gsl_vector *pulsetemplateMaxLengthFixedFilter_B0)¶

Located in file: tasksSIRENA.cpp

This function reads the library data, add new data (a new row) and sort the data according to an energy-ascending order.

Declare variables
Load values already in the library
Add new values
Realign
Add intermeadiate values
Recalculate intermediate values of some new pairs
Write values in the library
Free allocated GSL vectors

Members/Variables

ReconstructInitSIRENA** reconstruct_init

Member of ReconstructInitSIRENA structure to initialize the reconstruction parameters (pointer and values).

fitsfile** inLibObject

FITS object containing information of the library FITS file

double samprate

Sampling rate

int eventcntLib

Number of templates in the library

double estenergy

Pulse height of the template whose energy is going to be added to the library

gsl_vector* pulsetemplate

GSL vector with the pulse template whose energy is going to be added to the library

gsl_vector* pulsetemplate_B0

GSL vector with the pulse template whose energy is going to be added to the library (without baseline)

gsl_matrix* covariance

GSL matrix with covariance matrix of the energy which is going to be added to the library

gsl_matrix* weight

GSL matrix with weight matrix of the energy which is going to be added to the library

gsl_vector* pulsetemplateMaxLengthFixedFilter

GSL vector with the largeFilter-length template whose energy is going to be added to the library

gsl_vector* pulsetemplateMaxLengthFixedFilter_B0

GSL vector with the largeFilter-length template whose energy is going to be added to the library (without baseline)

int readFitsComplex(IOData obj, gsl_matrix **result)¶

Located in file: inoututils.cpp

This function reads values of a complex column of a FITS file. After that, the function puts them into a GSL matrix for an easier processing.

Members/Variables

IOData obj

Input object for complex FITS column

gsl_matrix** result

Output GSL matrix

int readFitsSimple(IOData obj, gsl_vector **result)¶

Located in file: inoututils.cpp

This function reads values of a simple column of a FITS file. After that, the function puts them into a GSL vector for an easier processing.

Members/Variables

IOData obj

Input object for simple FITS column

gsl_vector** result

Output GSL vector

extern_C_void reconstructRecordSIRENA(TesRecord *record, int trig_reclength, TesEventList *event_list, ReconstructInitSIRENA *reconstruct_init, int lastRecord, int nRecord, PulsesCollection **pulsesAll, int *const status)¶

Located in file: integraSIRENA.cpp

This function is the main wrapper function to detect, grade and calculate the energy of the pulses in the input records.

Inititalize PulsesCollection structure
Check consistency of some input parameters
If first record, read the necessary keywords and columns from the input file in order to convert from current to quasi-resistance space
In case of running with threading
Detect pulses in input record (runDetect()).
If reconstruction and not PCA:
- Filter and calculate energy of pulses (runEnergy())
Fill in the pulsesAll structure
Populate output event list with pulses energies, arrival time and grading

Members/Variables

TesRecord* record

Instance of TesRecord structure that contains the input record

int trig_reclength

Record size (just in case threading and input files with different ADC lengths but the same record size indeed)

TesEventList* event_list

Instance of TesEventList structure that contains the information of the reconstructed pulses

ReconstructInitSIRENA* reconstruct_init

Member of ReconstructInitSIRENA structure to initialize the reconstruction parameters (pointer and values)

int lastRecord

If record being analyzed is the last one, lastRecord = 1. Otherwise it is equal to 0

int nRecord

Input record number

PulsesCollection** pulsesAll

Member of PulsesCollection structure to successively store all the pulses used to create the library. Re-populated after each processed record.

int* const status

Input/output status

int RS_filter(gsl_vector *vector, double lrs, double lb, double B, double *pulseheight)¶

Located in file: pulseprocess.cpp

This function uses the running sum filter to find the pulse height. It always works in time domain.

A running sum filter, RS, is the sum of lrs digitized data samples. It is continuously updated upon the arrival of new data point. Simultaneously a baseline filter, B, is the sum of lb digitized data samples without pulses. The algorithm looks for the time when RS/lrs reaches its maximum. At that time RS is stored, \(RS_{max}\), and the baseline is scaled with lrs, Bp ( \(Bp=B \cdot lrs/lb\)). Then, the pulse height related to the pulse pseudoenergy is given by:

\[Pulse height=\frac{RS_{max}-B_p}{lrs}\]

Members/Variables

gsl_vector* vector

Not filtered pulse (extracted from the record in getPulseHeight())

double lrs

Running sum length (samples)

double lb

Baseline averaging length (samples)

double B

In general, sum of the lb digitized data samples of a pulse-free interval immediately before the current pulse

double* pulseheight

Pulseheight of the input pulse

void runDetect(TesRecord *record, int trig_reclength, int lastRecord, int nrecord, PulsesCollection *pulsesAll, ReconstructInitSIRENA **reconstruct_init, PulsesCollection **pulsesInRecord)¶

Located in file: tasksSIRENA.cpp

This function is responsible for the detection in SIRENA, record by record. It is used both for library creation and energy reconstruction runnings.

Conditions:

If first record and reconstruction mode => Run filderLibrary()

If last record and calibration mode => Run calculateTemplate() and writeLibrary()

If intermediate = 1 => writeTestInfo() and writePulses()

If calibration mode => Find pulses by using findPulsesCAL()

If reconstruction mode => Find pulses by InitialTriggering() and FindSecondaries() or FindSecondariesSTC()

Steps:

Create library file if it is necessary: calibration and last record (run createLibrary())

Create intermediate output FITS file if required (createDetectFile())

(Filter and) differentiate the models of the library (only for the first record in reconstruction mode). Run (filderLibrary())

Store the input record in invector (loadRecord())

Detect weird oscillations in some GSFC records providing a warning (no pulses detected in that record)

Convert I into R if EnergyMethod = I2R or I2RFITTED (convertI2R())

Process each record (proceRecord()):

(Low-pass filter and) differentiate

Find pulses

Load the found pulses data in the input/output foundPulses structure

Write test info in intermediate output FITS file if intermediate = 1 (writeTestInfo())

Write pulses info in intermediate output FITS file if intermediate = 1 (writePulses())

From this point forward, I2R and I2RFITTED are completely equivalent to OPTFILT

If last record in calibration mode run:

calculateTemplate() (and weightMatrix())

writeLibrary()

If last record and PCA:

In order to not have restrictions when providing (*reconstruct_init)->energyPCAx

Covariance data

Eigenvalues and eigenvectors

RSxN (S=2)

AE straight line: Pto0(x,y) and Pto10(x,y)

Calculus of the rotation angle

Rotation

Histograms of the two clusters (two energies)

Conversion factor from arbitrary unit to eV

Energy calculation

Close intermediate output FITS file if it is necessary

Members/Variables

TesRecord* record

Member of TesRecord structure that contains the input record

int trig_reclength

Record size (just in case threading and input files with different ADC lengths but the same record size indeed)

int lastRecord

Integer to verify whether record is the last one (=1) to be read (and thus if library file will be created)

PulsesCollection* pulsesAll

Member of PulsesCollection structure to successively store all the pulses used to create the library. Re-populated after each processed record

ReconstructInitSIRENA** reconstruct_init

Member of ReconstructInitSIRENA structure to initialize the reconstruction parameters (pointer and values)

PulsesCollection** pulsesInRecord

Member of PulsesCollection structure to store all the pulses found in the input record

void runEnergy(TesRecord *record, int lastRecord, int nrecord, int trig_reclength, ReconstructInitSIRENA **reconstruct_init, PulsesCollection **pulsesInRecord, PulsesCollection *pulsesAll)¶

Located in file: tasksSIRENA.cpp

This function calculates the pulse energy applying different methods (from EnergyMethod and OFNoise). It only runs in RECONSTRUCTION mode (except to EnergyMethod = PCA).

Declare variables
Store the record in invector (loadRecord())
Subtract the baseline if EnergyMethod = OPTFILT and runF0orB0val = 1 (FilterMethod = B0)
Subtract the baseline if EnergyMethod = INTCOVAR
Check Quality
For each pulse:
- Establish the pulse grade (for example VeryHighRes=1, HighRes=2, IntRes=3, MedRes=4, LimRes=5, LowRes=6, Rejected=-1) and the optimal filter length
- Pulse: Load the proper piece of the record in pulse
- Get the low resolution energy estimator by filtering with a 8-samples-length (with lags) filter:
  
  Load the low resolution pulse in pulse_lowres
  
  Get the filter
  
  Calculate the low resolution estimator
- If OFIter = 1, in the first iteration ( numiteration = 0) the values of maxDER and maxDERs are used in find_matchedfilterSAB(), find_optimalfilterSAB() or find_Esboundary() getting the values of the energies which straddle the maxDER (Ealpha and Ebeta). It will have more iterations if the calculated energy is out of [Ealpha, Ebeta]. If energy is in [Ealpha, Ebeta] the iterative process stops.
  If EnergyMethod = OPTFILT (or I2R, I2RFITTED) and reconstruct_init->OFLib = 0 and OFNoise = NSD:
  
  Find the matched filter and load it in filter (find_matchedfilterSAB())
  
  Calculate the optimal filter
  
  If EnergyMethod = OPTFILT (or I2R, I2RFITTED) and reconstruct_init->OFLib = 1 and OFNoise = NSD:
  
  If it is necessary, choose the base-2 system value closest (lower than or equal) to the pulse length
  
  Find the optimal filter and load it in filter (find_optimalfilterSAB())
  
  If EnergyMethod = INTCOVAR or COVAR:
  
  Get the indexes of the two energies which straddle the pulse (find_Esboundary())
  
  If EnergyMethod = COVAR and reconstruct_init->OFLib = 1:
  
  Choose the base-2 system value closest (lower than or equal) to the pulse length
  
  find_prclcov() to find the appropriate values of the PRCLCOV HDU (PCOVx columns)
  
  If EnergyMethod = OPTFILT (or I2R, I2RFITTED) and reconstruct_init->OFLib = 1 and OFNoise = WEIGHTN:
  
  Choose the base-2 system value closest (lower than or equal) to the pulse length
  
  find_prclofwn() to find the appropriate values of the PRCLOFWN HDU (OFWNx columns)
  
  Subtract the sum of the filter if EnergyMethod = OPTFILT, OFNoise = NSD, FilterDomain = T, 0-padding and Sum0Filt =1
  
  Calculate the energy of each pulse
  
  If using lags, it is necessary to modify the tstart of the pulse
- In order to subtract the pulse model, it has to be located in the tstart with jitter and know its values in the digitized samples
- Subtract the pulse model from the record
- Write info of the pulse in the output intemediate file if intermediate = 1
Free allocated GSL vectors

Members/Variables

TesRecord** record

Structure that contains the input ADC record

int lastRecord

If record being analyzed is the last one, lastRecord = 1. Otherwise it is equal to 0

int nRecord

Input record number

int trig_reclength

Record size (just in case threading and input files with different ADC lengths but the same record size indeed)

ReconstructInitSIRENA** reconstruct_init

Member of ReconstructInitSIRENA structure to initialize the reconstruction parameters (pointer and values)

PulsesCollection* pulsesInRecord

Collection of pulses found in the current record

PulsesCollection* pulsesAll

Member of PulsesCollection structure to store all the pulses found in the input FITS file. To know the index to get the proper element from tstartPulse1_i in case tstartPulse1 was a file name

void th_runEnergy(TesRecord *record, int nrecord, int trig_reclength, ReconstructInitSIRENA **reconstruct_init, PulsesCollection **pulsesInRecord, PulsesCollection *pulsesAll)¶

Located in file: tasksSIRENA.cpp

This function is responsible for the reconstruction in SIRENA (instead of runEnergy()) when the THREADING running option has been chosen (hardcoded at this moment).

int shiftm(gsl_vector *vectorin, gsl_vector *vectorout, int m)¶

Located in file: tasksSIRENA.cpp

This function returns as vectorout the vectorin delayed m samples.

Members/Variables

int m

Delay in samples

gsl_vector* vectorin

GSL vector with input vector

gsl_vector* vectorout

GSL with input vector (vectorin) delayed m samples

int shift_m(gsl_vector *vectorin, gsl_vector *vectorout, int m)¶

Located in file: tasksSIRENA.cpp

This function returns as vectorout the vectorin moved forward by m samples.

Members/Variables

int m

Advance in samples

gsl_vector* vectorin

GSL vector with input vector

gsl_vector* vectorout

GSL with input vector (vectorin) moved forward m samples

char *subString(const char *input, int offset, int len, char *dest)¶

Located in file: initSIRENA.cpp

This function extracts some elements from an array of characters.

Members/Variables

const char* input

Array of characters from which some elements are extracted

int offset

Offset

int len

Length (number of elements to extract)

char* dest

Array of characters into which the extracted characters are written

int teslib_main()¶

Located in file: teslib.c

This function is mainly a wrapper to pass a data file to the SIRENA tasks in order to build a library that will be used to reconstruct the energies.

Steps:

Register HEATOOL
Reading all programm parameters by using PIL
Check preBuffer values if the library already exists
Read XML info
getSamplingrate_trigreclength() => Obtain the trig_reclength and the sampling rate
Sixt standard keywords structure
Open output FITS file
Initialize data structures
Read the grading data from the XML file and store it in reconstruct_init_sirena->grading
Build up TesEventList
Call SIRENA to build the library
Save GTI extension to event file
Free memory

The user must supply the following input parameters (teslib.par file).

Parameters:

char RecordFile

Record FITS file

If RecordFile starts with ‘@’ it provides a file text containing several record input FITS files

char TesEventFile

Output event list file

char LibraryFile

File with calibration library

char NoiseFile

Noise FITS file with noise spectrum

char XMLFile

XML input FITS file with instrument definition

char preBuffer

Some samples added or not before the starting time of a pulse (number of added samples read from the XML file) SIRENA’s format XML file (grading=>pre,post and pB) or new format XML file (grading=>pre,post and filtlen)

pre=494, post=8192, pB=1000 pre=494, post=7192, filtlen=8192
preBuffer=filtlen-post

int EventListSize

Default size of the event list per record

char clobber

Overwrite or not output files if exist (yes/no)

char history

Write program parameters into output file

double scaleFactor

Detection scale factor for initial filtering

int samplesUp

Number of consecutive samples up for threshold trespassing

double nSgms

Number of quiescent-signal standard deviations to establish the threshold through the kappa-clipping algorithm

double LrsT

Running sum length for the RS raw energy estimation (seconds)

double LbT

Baseline averaging length (seconds)

double monoenergy

Monochromatic energy of the pulses in the input FITS file in eV

char addCOVAR

Add or not pre-calculated values related to COVAR reconstruction method in the library file (yes/no)

char addINTCOVAR

Add or not pre-calculated values related to INTCOVAR reconstruction method in the library file (yes/no)

char addOFWN

Add or not or not pre-calculated values in the library file related to Optimal Filtering by using Weight Noise matrix in the library file(yes/no)

int largeFilter

Length of the longest fixed filter

char EnergyMethod

Energy calculation Method: OPTFILT, I2R or I2RFITTED

double Ifit

Constant to apply the I2RFITTED conversion

char FilterMethod

Filtering Method: F0 (deleting the zero frequency bin) or B0 (deleting the baseline)

int intermediate

Write or not intermediate files (1/0)

char detectFile

Intermediate detections file (if intermediate = 1)

char tstartPulse1

Integer number: Sample where the first pulse starts or nameFile: File where the tstart (seconds) of every pulse is

int tstartPulse2

Tstart (samples) of the second pulse

int tstartPulse3

Tstart (samples) of the third pulse (if 0 => PAIRS, if not 0 => TRIOS)

int tesrecons_main()¶

Located in file: tesrecons.c

This function is mainly a wrapper to pass a data file to the SIRENA tasks in order to reconstruct the energies.

Steps:

Register HEATOOL
Reading all programm parameters by using PIL
Read XML info
getSamplingrate_trigreclength() => Obtain the trig_reclength and the sampling rate
Sixt standard keywords structure
Open output FITS file
Initialize data structures for pulse filtering
Read the grading data from the XML file and store it in reconstruct_init_sirena->grading
Build up TesEventList
Call SIRENA to build reconstruct the energies
Save GTI extension to event file
Free memory

The user must supply the following input parameters (tesrecons.par file).

Parameters:

char RecordFile

Record FITS file

If RecordFile starts with ‘@’ it provides a file text containing several record input FITS files

char TesEventFile

Output event list file

char LibraryFile

File with calibration library

char XMLFile

XML input FITS file with instrument definition

char preBuffer

Some samples added or not before the starting time of a pulse (number of added samples read from the XML file) SIRENA’s format XML file (grading=>pre,post and pB) or new format XML file (grading=>pre,post and filtlen)

pre=494, post=8192, pB=1000 pre=494, post=7192, filtlen=8192
preBuffer=filtlen-post

int EventListSize

Default size of the event list per record

char clobber

Overwrite or not output files if exist (yes/no)

char history

Write program parameters into output file

double scaleFactor

Detection scale factor for initial filtering

int samplesUp

Number of consecutive samples up for threshold trespassing

int samplesDown

Number of consecutive samples below the threshold to look for other pulse

double nSgms

Number of quiescent-signal standard deviations to establish the threshold through the kappa-clipping algorithm

char detectionMode

Adjusted Derivative (AD) or Single Threshold Crossing (STC)

int detectSP

Detect secondary pulses (1) or not (0)

double LbT

Baseline averaging length (seconds)

int intermediate

Write or not intermediate files (1/0)

char detectFile

Intermediate detections file (if intermediate = 1)

char FilterDomain

Filtering Domain: Time (T) or Frequency (F)

char FilterMethod

Filtering Method: F0 (deleting the zero frequency bin) or B0 (deleting the baseline)

char EnergyMethod

Energy calculation Method: OPTFILT, INTCOVAR, COVAR, I2R or I2RFITTED

double filtEeV

Energy of the filters of the library to be used to calculate energy (only for OPTFILT, I2R and I2RFITTED)

double Ifit

Constant to apply the I2RFITTED conversion

char OFNoise

Noise to use with Optimal Filtering: NSD or WEIGHTN

int LagsOrNot

Lags or no lags (1/0)

int nLags

Number of lags (positive odd number)

int Fitting35

Number of lags to analytically calculate a parabola (3) or to fit a parabola (5)

int OFIter

Iterate or not iterate (1/0)

int OFLib

Work or not with a library with optimal filters (yes/no)

char OFStrategy

Optimal Filter length Strategy: FREE, BYGRADE or FIXED

int OFLength

Optimal Filter length (taken into account if OFStrategy = FIXED)

int flength_0pad

0-padding filter length

int prebuff_0pad

preBuffer when 0-padding

int errorT

Additional error (in samples) added to the detected time (Logically, it changes the reconstructed energies )

int Sum0Filt

0-padding: Subtract the sum of the filter (1) or not (0)

char tstartPulse1

Integer number: Sample where the first pulse starts or nameFile: File where the tstart (seconds) of every pulse is

int tstartPulse2

Tstart (samples) of the second pulse

int tstartPulse3

Tstart (samples) of the third pulse (if 0 => PAIRS, if not 0 => TRIOS)

double energyPCA1

First energy (only for PCA)

double energyPCA2

Second energy (only for PCA)

int th_runDetect(TesRecord *record, int trig_reclength, int lastRecord, int nrecord, PulsesCollection *pulsesAll, ReconstructInitSIRENA **reconstruct_init, PulsesCollection **pulsesInRecord)¶

Located in file: tasksSIRENA.cpp

This function is responsible for the detection in SIRENA (instead of runDetect()) when the THREADING running option has been chosen (hardcoded at this moment). It is used both for library creation and energy reconstruction runnings.

int toGslMatrix(void **buffer, gsl_matrix **matrix, long numCol, int numRow, int type, int eventini)¶

Located in file: inoututils.cpp

The function puts the values of the input buffer into an output GSL matrix. Columns and rows are input parameters.

Members/Variables

void** buffer

Input buffer with data

gsl_matrix** matrix

Output GSL matrix

long numCol

Number of columns

int numRow

Number of rows

int type

FITS type (TINT, TSHORT, TDOUBLE, etc.)

int eventini

Initial event to start writing

int toGslVector(void **buffer, gsl_vector **array, long nevent, int eventini, int type)¶

Located in file: inoututils.cpp

The function puts the values of the input buffer into an output GSL vector.

Members/Variables

void** buffer

Input buffer with data

gsl_vector** array

Output GSL vector

long nevent

Number of elements to store

int eventini

Initial element number

int type

FITS type (TINT, TSHORT, TDOUBLE, etc.)

int vector2matrix(gsl_vector *vectorin, gsl_matrix **matrixout)¶

Located in file: tasksSIRENA.cpp

This function converts an input \(n^2\) vector into an output square matrix \([n \times n]\). It puts the first \(n\) elements of the vector in the first row of the matrix, the second group of \(n\) elements (from \(n\) to \(2n-1\)) of the vector in the second row and so on.

Members/Variables

gsl_vector** vectorin

GSL input vector whose length is \(n^2\)

gsl_matrix* matrixout

GSL output square matrix \([n \times n]\)

int weightMatrix(ReconstructInitSIRENA *reconstruct_init, bool saturatedPulses, PulsesCollection *pulsesAll, PulsesCollection *pulsesInRecord, long nonpileupPulses, gsl_vector *nonpileup, gsl_vector *pulseaverage, gsl_matrix **covariance, gsl_matrix **weight)¶

Located in file: tasksSIRENA.cpp

:cite:`Fixsen2004`

This function calculates the weight matrix by using the non piled-up pulses found in all the records, stored in pulsesAll (previous records) and pulsesInRecord (current record). The weight matrix of each energy (and other intermediate values) will be stored in the library by the function fillInLibraryData().

Definitions:

\(S_i^p\): Value of the ith-sample of the pulse number p

\(M_i^p\): Value of the ith-sample of the model number p (model= pulseaverage):

\[M_i = <S_i> = (1/N)\sum_{p=1}^{N}S_i^p\]

N: number of non piled-up pulses

\[\begin{split}& D_i = S_i - M_i \\ & V_{ij} = <D_iD_j> = E[(S_i-M_i)(S_j-M_j)] = (1/N)\sum_{p=1}^{N}(S_i^p-M_i^p)(S_j^p-M_j^p) \\ & V = \left[\begin{matrix} <D_1D_1> & <D_1D_2> & ... & <D_1D_n> \\ <D_2D_1> & <D_2D_2> & ... & <D_2D_n> \\ .... & .... & ... & .... \\ <D_nD_1> & <D_nD_2> & ... & <D_nD_n>\end{matrix}\right]\end{split}\]

where n is the OFLength and thus \(V = [n \times n]\).

The weight matrix \(W = [V]^{-1}\).

Steps:

Calculate the elements of the diagonal of the covariance matrix

Calculate the elements out of the diagonal of the covariance matrix

If saturated pulses => Covariance matrix is a singular matrix => Non invertible

In order to allow the covariance matrix to be inverted => Replacing 0’s (0’s are due to the saturated values, equal in the pulse and in the model)

Elements of the diagonal: Generating a random double \(f_1\) between a range (fMin,fMax) (-NoiseStd,NoiseStd) to replace 0’s with \(f_1^2\)

Elements out of the diagonal: Generating two random doubles \(f_1\) and \(f_2\) between a range (fMin,fMax) (-NoiseStd,NoiseStd) to replace 0’s with \(f_1 \cdot f_2\)

Calculate the weight matrix

Members/Variables

ReconstructInitSIRENA** reconstruct_init

Member of ReconstructInitSIRENA structure to initialize the reconstruction parameters (pointer and values)

bool saturatedPulses

If true, all the pulses (calibration mode => all the pulses have the same energy) are saturated

PulsesCollection* pulsesAll

Collection of pulses found in the previous records

PulsesCollection* pulsesInRecord

Collection of pulses found in the current record

long nonpileupPulses

Number of non piled-up pulses

gsl_vector* nonpileup

GSL vector containing info about all the pulses informing if they are piled-up or not

gsl_vector** pulseaverage

GSL vector with the pulseaverage (= template = model) of the non piled-up pulses

gsl_matrix** covariance

GSL matrix with covariance matrix

gsl_matrix** weight

GSL matrix with weight matrix

int weightMatrixNoise(gsl_matrix *intervalMatrix, gsl_matrix **weight)¶

Located in file: gennoisespec.cpp

This function calculates the weight matrix of the noise

\(D_i\): Pulse free interval \(V\): Covariance matrix

\(V_{ij} = E[DiDj]-E[Di]E[Dj]\)

\(Di^p\): Value of the pth-sample of the pulse-free interval i \(N\): Number of samples

\[\begin{split}& V_{ij} = <D_iD_j> = E[D_iD_j] = (1/N)sum_{p=1}^{N}(Di^p)(Dj^p) \\ & V = \left[\begin{matrix} <D_1D_1> & <D_1D_2> & ... & <D_1D_n> \\ <D_2D_1> & <D_2D_2> & ... & <D_2D_n> \\ .... & .... & ... & .... \\ <D_nD_1> & <D_nD_2> & ... & <D_nD_n>\end{matrix}\right]\end{split}\]

where n is the OFLength and thus \(V = [n \times n]\).

The weight matrix \(W = 1/V\).

Steps:

Calculate the elements of the diagonal of the covariance matrix
Calculate the elements out of the diagonal of the covariance matrix
Calculate the weight matrix

Members/Variables

gsl_matrix* intervalMatrix

GSL matrix containing pulse-free intervals whose baseline is 0 (baseline previously subtracted) [nintervals x intervalMinSamples]

gsl_matrix** weight

GSL matrix with weight matrix

int writeFilterHDU(ReconstructInitSIRENA **reconstruct_init, int pulse_index, double energy, gsl_vector *optimalfilter, fitsfile **dtcObject)¶

Located in file: tasksSIRENA.cpp

This function runs in RECONSTRUCTION mode and writes the optimal filter info (in the FILTER HDU) for each pulse if intermediate = 1 and either reconstruct_init->OFLib = 0 or reconstruct_init->OFLib = 1, filtEeV = 0 and the the number of energies in the library FITS file is greater than 1.

Declare variables
Open intermediate FITS file
If (reconstruct_init->OFLib = 0) or (reconstruct_init->OFLib = 1, filtEeV = 0 and the the number of energies in the library FITS file is greater than 1):
- Create the FILTER HDU if it is the first pulse
- Write data:
  
  OPTIMALF or OPTIMALFF column (in time or frequency domain)
  
  OFLENGTH column
Write ENERGY column in PULSES HDU
Close intermediate output FITS file if it is necessary
Free memory

Members/Variables

ReconstructInitSIRENA** reconstruct_init

Member of ReconstructInitSIRENA structure to initialize the reconstruction parameters (pointer and values).

int pulse_index

Index of the pulse whose info is going to be written (to know if it is the first pulse)

double energy

Estimated energy (eV)

gsl_vector* optimalfilter

Optimal filter (in time or frequency domain)

fitsfile** dtcObject

Fitsfile object for intermeadiate file name

int writeFitsComplex(IOData obj, gsl_matrix *matrix)¶

Located in file: inoututils.cpp

This function reads values of a GSL matrix. After that, the function puts them into a complex column of the output FITS file.

Members/Variables

IOData obj

Object for FITS column to be written

gsl_matrix* matrix

Input GSL matrix with data

int writeFitsSimple(IOData obj, gsl_vector *vector)¶

Located in file: inoututils.cpp

This function reads values of a GSL vector. After that, the function puts them into a column of the output FITS file.

Members/Variables

IOData obj

Object for FITS column to be written

gsl_vector* vector

Input GSL vector with data

int writeLibrary(ReconstructInitSIRENA *reconstruct_init, double samprate, double estenergy, gsl_vector *pulsetemplate, gsl_vector *pulsetemplate_B0, gsl_matrix *covariance, gsl_matrix *weight, bool appendToLibrary, fitsfile **inLibObject, gsl_vector *pulsetemplateMaxLengthFixedFilter, gsl_vector *pulsetemplateMaxLengthFixedFilter_B0)¶

Located in file: tasksSIRENA.cpp

This function writes the library (reordering if it is necesary and calculating some intermediate parameters)

Adding a new row to the library if appendToLibrary = true (readAddSortParams())

Write the first row of the library if appendToLibrary = false (addFirstRow())

In both cases, the keywords CREADATE and SIRENAV with the date and SIRENA version are written

Members/Variables

ReconstructInitSIRENA** reconstruct_init

Member of ReconstructInitSIRENA structure to initialize the reconstruction parameters (pointer and values)

double samprate

Sampling rate

double estenergy

Pulse height of the template whose energy is going to be added to the library

gsl_vector* pulsetemplate

GSL vector with the pulse template whose energy is going to be added to the library

gsl_vector* pulsetemplate_B0

GSL vector with the pulse template whose energy is going to be added to the library (without baseline)

gsl_matrix** covariance

GSL matrix with covariance matrix

gsl_matrix** weight

GSL matrix with weight matrix

bool appendToLibrary

true if adding a new row to the library and false if it is the first row to be added

fitsfile** inLibObject

FITS object containing information of the library FITS file

gsl_vector* pulsetemplateMaxLengthFixedFilter

GSL vector with the largeFilter-length pulse template whose energy is going to be added to the library

gsl_vector* pulsetemplateMaxLengthFixedFilter_B0

GSL vector with the largeFilter-length pulse template whose energy is going to be added to the library (without baseline)

void writeLog(FILE *fileRef, string type, int verbosity, string message)¶

Located in file: inoututils.cpp

This function includes the processing of the each level of message in the log file and the output screen:

Verbosity = 0 => The log file and the output screen include Errors

Verbosity = 1 => The log file and the output screen include Errors and Warnings

Verbosity = 2 => The log file and the output screen include Errors, Warnings and Alerts

Verbosity = 3 => The log file and the output screen include Errors, Warnings, Alerts and Log messages

Members/Variables

FILE* fileRef

File reference to log file

string type

String to indicate error type “Error”, “Warning”, “Alert”,”Log” or “OK”

int verbosity

Integer value for verbosity

string message

String message to print

int writePulses(ReconstructInitSIRENA **reconstruct_init, double samprate, double initialtime, gsl_vector *invectorNOTFIL, int numPulsesRecord, gsl_vector *tstart, gsl_vector *tend, gsl_vector *quality, gsl_vector *taurise, gsl_vector *taufall, fitsfile *dtcObject)¶

Located in file: tasksSIRENA.cpp

This function writes the data of the pulses found in the record in the intermediate FITS file (in the PULSES HDU). The pulses info given is: TSTART, I0 (the pulse itself), TEND, TAURISE, TAUFALL and QUALITY.

Members/Variables

ReconstructInitSIRENA** reconstruct_init

Member of ReconstructInitSIRENA structure to initialize the reconstruction parameters (pointer and values).

double samprate

Sampling rate (to convert samples to seconds)

double initialtime

Starting time of the record (in order to calculate absolute times)

gsl_vector* invectorNOTFIL

GSL vector with the original record (neither low-pass filtered nor differentiated)

int numPulsesRecord

Number of pulses found in the record

gsl_vector* tstart

GSL vector with the start times of the found pulses

gsl_vector* tend

GSL vector with the end times of the found pulses

gsl_vector* quality

GSL vector with the quality of the found pulses

0 => Standard (good) pulses

1 => Truncated pulses at the beginning

2 => Truncated pulses at the end

10 => Saturated pulses

11 => Truncated and saturated pulses

gsl_vector* taurise

GSL vector with the rise time constants of the found pulses (to be done)

gsl_vector* taufall

GSL vector with the fall time constants of the found pulses (to be done)

fitsfile* dtcObject

Object which contains information of the intermediate FITS file

int writeTestInfo(ReconstructInitSIRENA *reconstruct_init, gsl_vector *recordDERIVATIVE, double threshold, fitsfile *dtcObject)¶

Located in file: tasksSIRENA.cpp

This function writes the TESTINFO HDU in the intermediate FITS file. The written columns are FILDER (low-pass filtered and differentiated record) and THRESHOLD.

Members/Variables

ReconstructInitSIRENA** reconstruct_init

Member of ReconstructInitSIRENA structure to initialize the reconstruction parameters (pointer and values)

gsl_vector* recordDERIVATIVE

GSL vector with input record (low-pass filtered and) differentiated

double threshold

Threshold value used to find pulses

fitsfile dtcObject

Object which contains information of the intermediate FITS file

int writeTPSreprExten()¶

Located in file: gennoisespec.cpp

This function writes the noisespec output FITS file.

Steps:

Allocate GSL vectors
Write the data in the output FITS file (print only half of FFT to prevent aliasing)
NOISE HDU only contains positive frequencies (=> Multiply by 2 the amplitude)
NOISEALL HDU contains negative and positive frequencies => It is the HDU read to build the optimal filters
WEIGHTMS HDU

SIRENA functions¶

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