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Added logging for when a value is not finite. Added constexp for min,…
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…max,step defaults for counts
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Arthur Glowacki committed Aug 9, 2024
1 parent e1f39b1 commit 514672e
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Showing 17 changed files with 106 additions and 40 deletions.
15 changes: 9 additions & 6 deletions src/core/defines.h
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Original file line number Diff line number Diff line change
Expand Up @@ -111,16 +111,19 @@ T_real parse_input_real(std::string value)
}
}

#define CALIBRATION_CURVE_SIZE 92
constexpr auto CALIBRATION_CURVE_SIZE = 92;

//namespace keys
//{

#define AVOGADRO 6.02204531e23
#define HC_ANGSTROMS 12398.52
#define RE 2.817938070e-13 // in cm
#define ENERGY_RES_OFFSET 150.0
#define ENERGY_RES_SQRT 12.0
constexpr auto AVOGADRO = 6.02204531e23;
constexpr auto HC_ANGSTROMS = 12398.52;
constexpr auto RE = 2.817938070e-13; // in cm
constexpr auto ENERGY_RES_OFFSET = 150.0;
constexpr auto ENERGY_RES_SQRT = 12.0;
constexpr auto MIN_COUNTS_LIMIT_LOG = -11.0;
constexpr auto MAX_COUNTS_LIMIT_LOG = 20.0;
constexpr auto STEP_COUNTS_LIMIT_LOG = 0.0005;

/**
* @brief String defines for fit parameters string value pair.
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2 changes: 1 addition & 1 deletion src/core/process_whole.cpp
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Original file line number Diff line number Diff line change
Expand Up @@ -386,7 +386,7 @@ void load_and_fit_quatification_datasets(data_struct::Analysis_Job<double>* anal
{
if (false == override_params->fit_params.contains(itr2.first))
{
fit_params.add_parameter(Fit_Param<double>(itr2.first, 1.0e-11, 20.0, quantification_standard->element_counts.at(fit_itr.first).at(itr2.first), 0.0005, E_Bound_Type::LIMITED_LO_HI));
fit_params.add_parameter(Fit_Param<double>(itr2.first, MIN_COUNTS_LIMIT_LOG, MAX_COUNTS_LIMIT_LOG, quantification_standard->element_counts.at(fit_itr.first).at(itr2.first), STEP_COUNTS_LIMIT_LOG, E_Bound_Type::LIMITED_LO_HI));
}
}
fitting::routines::Matrix_Optimized_Fit_Routine<double>* f_routine = (fitting::routines::Matrix_Optimized_Fit_Routine<double>*)fit_routine;
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16 changes: 16 additions & 0 deletions src/data_struct/fit_parameters.cpp
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Original file line number Diff line number Diff line change
Expand Up @@ -260,18 +260,34 @@ void Fit_Parameters<T_real>::update_values(const Fit_Parameters<T_real> *overri
{
itr.second.value = override_fit_params->at(itr.first).value;
}
else
{
logW << itr.first << " value = " << itr.second.value << " . Not updating value\n";
}
if( std::isfinite(override_fit_params->at(itr.first).min_val) )
{
itr.second.min_val = override_fit_params->at(itr.first).min_val;
}
else
{
logW << itr.first << " min_val = " << itr.second.min_val << " . Not updating min_val\n";
}
if( std::isfinite(override_fit_params->at(itr.first).max_val) )
{
itr.second.max_val = override_fit_params->at(itr.first).max_val;
}
else
{
logW << itr.first << " max_val = " << itr.second.max_val << " . Not updating max_val\n";
}
if( override_fit_params->at(itr.first).bound_type != E_Bound_Type::NOT_INIT)
{
itr.second.bound_type = override_fit_params->at(itr.first).bound_type;
}
else
{
logW << itr.first << " bound_type == E_Bound_Type::NOT_INIT. Not updating bound_type\n";
}
}
}
}
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32 changes: 32 additions & 0 deletions src/data_struct/spectra.h
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Original file line number Diff line number Diff line change
Expand Up @@ -172,21 +172,37 @@ class Spectra : public ArrayTr<_T>
{
_elapsed_livetime += val;
}
else
{
logW << " Spectra _elapsed_livetime = " << val << " . Not updating!\n";
}
val = spectra.elapsed_realtime();
if(std::isfinite(val))
{
_elapsed_realtime += val;
}
else
{
logW << " Spectra _elapsed_realtime = " << val << " . Not updating!\n";
}
val = spectra.input_counts();
if(std::isfinite(val))
{
_input_counts += val;
}
else
{
logW << " Spectra _input_counts = " << val << " . Not updating!\n";
}
val = spectra.output_counts();
if(std::isfinite(val))
{
_output_counts += val;
}
else
{
logW << " Spectra _output_counts = " << val << " . Not updating!\n";
}
}

// Note: T_real is different template typename, this is to convert double to float or the other way around.
Expand All @@ -204,21 +220,37 @@ class Spectra : public ArrayTr<_T>
{
_elapsed_livetime += val;
}
else
{
logW << " Spectra _elapsed_livetime = " << val << " . Not updating!\n";
}
val = spectra->elapsed_realtime();
if (std::isfinite(val))
{
_elapsed_realtime += val;
}
else
{
logW << " Spectra _elapsed_realtime = " << val << " . Not updating!\n";
}
val = spectra->input_counts();
if (std::isfinite(val))
{
_input_counts += val;
}
else
{
logW << " Spectra _input_counts = " << val << " . Not updating!\n";
}
val = spectra->output_counts();
if (std::isfinite(val))
{
_output_counts += val;
}
else
{
logW << " Spectra _output_counts = " << val << " . Not updating!\n";
}
}
}

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28 changes: 21 additions & 7 deletions src/fitting/models/gaussian_model.cpp
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Original file line number Diff line number Diff line change
Expand Up @@ -104,11 +104,11 @@ Fit_Parameters<T_real> Gaussian_Model<T_real>::_generate_default_fit_parameters(
fit_params.add_parameter(Fit_Param<T_real>(STR_FWHM_FANOPRIME, (T_real)0.000001, (T_real)0.05, (T_real)0.00012, (T_real)0.000001, E_Bound_Type::LIMITED_LO_HI));

fit_params.add_parameter(Fit_Param<T_real>(STR_COHERENT_SCT_ENERGY, (T_real)9.4, (T_real)10.4, (T_real)9.99, (T_real)0.001, E_Bound_Type::LIMITED_LO_HI));
fit_params.add_parameter(Fit_Param<T_real>(STR_COHERENT_SCT_AMPLITUDE, (T_real)0.000001, (T_real)10.0, (T_real)5.0, (T_real)0.0005, E_Bound_Type::LIMITED_LO_HI));
fit_params.add_parameter(Fit_Param<T_real>(STR_COHERENT_SCT_AMPLITUDE, MIN_COUNTS_LIMIT_LOG, MAX_COUNTS_LIMIT_LOG, (T_real)5.0, STEP_COUNTS_LIMIT_LOG, E_Bound_Type::LIMITED_LO_HI));

fit_params.add_parameter(Fit_Param<T_real>(STR_COMPTON_ANGLE, (T_real)0.0, (T_real)359.0, (T_real)90.0, (T_real)0.1, E_Bound_Type::LIMITED_LO_HI));
fit_params.add_parameter(Fit_Param<T_real>(STR_COMPTON_FWHM_CORR, (T_real)1.0, (T_real)4.0, (T_real)1.0, (T_real)0.1, E_Bound_Type::LIMITED_LO_HI));
fit_params.add_parameter(Fit_Param<T_real>(STR_COMPTON_AMPLITUDE, (T_real)0.00001, (T_real)10.00, (T_real)5.0, (T_real)0.0005, E_Bound_Type::LIMITED_LO_HI));
fit_params.add_parameter(Fit_Param<T_real>(STR_COMPTON_AMPLITUDE, MIN_COUNTS_LIMIT_LOG, MAX_COUNTS_LIMIT_LOG, (T_real)5.0, STEP_COUNTS_LIMIT_LOG, E_Bound_Type::LIMITED_LO_HI));
fit_params.add_parameter(Fit_Param<T_real>(STR_COMPTON_F_STEP, (T_real)0.0, (T_real)1.0, (T_real)0.0, (T_real)0.1, E_Bound_Type::FIXED));
fit_params.add_parameter(Fit_Param<T_real>(STR_COMPTON_F_TAIL, (T_real)0.0, (T_real)1.0, (T_real)0.1, (T_real)0.1, E_Bound_Type::LIMITED_LO));
fit_params.add_parameter(Fit_Param<T_real>(STR_COMPTON_GAMMA, (T_real)0.1, (T_real)10., (T_real)1.0, (T_real)0.1, E_Bound_Type::FIXED));
Expand Down Expand Up @@ -575,6 +575,7 @@ const Spectra<T_real> Gaussian_Model<T_real>::model_spectrum_element(const Fit_P

if (false == std::isfinite(pre_faktor))
{
logE << "Prefactor = " << pre_faktor << "\n";
spectra_model = (ArrayTr<T_real>)(spectra_model).unaryExpr([](T_real v) { return std::numeric_limits<T_real>::quiet_NaN(); });
return spectra_model;
}
Expand Down Expand Up @@ -725,24 +726,35 @@ const ArrayTr<T_real> Gaussian_Model<T_real>::peak(T_real gain, T_real sigma, co
template<typename T_real>
const ArrayTr<T_real> Gaussian_Model<T_real>::step(T_real gain, T_real sigma, const ArrayTr<T_real>& delta_energy, T_real peak_E) const
{
//delta_energy = delta_energy.unaryExpr([sigma](T_real v) { return (T_real)std::erfc(v /( M_SQRT2*sigma)); } );
//return (gain / (T_real)2.0 / peak_E * delta_energy );
ArrayTr<T_real>counts = delta_energy.unaryExpr([gain, sigma, peak_E](T_real v) { return gain / (T_real)2.0 / peak_E * std::erfc(v / ((T_real)(M_SQRT2)*sigma)); });
/*
ArrayTr<T_real>counts(delta_energy.size());
for (unsigned int i = 0; i < delta_energy.size(); i++)
{
counts[i] = gain / (T_real)2.0 / peak_E * std::erfc((T_real)delta_energy[i] / ((T_real)(M_SQRT2) * sigma));
}
*/
return counts;

}

// ----------------------------------------------------------------------------

template<typename T_real>
const ArrayTr<T_real> Gaussian_Model<T_real>::tail(T_real gain, T_real sigma, ArrayTr<T_real> delta_energy, T_real gamma) const
const ArrayTr<T_real> Gaussian_Model<T_real>::tail(T_real gain, T_real sigma, const ArrayTr<T_real> &delta_energy, T_real gamma) const
{
delta_energy = delta_energy.unaryExpr([sigma,gamma](T_real v) { return (v < (T_real)0.0) ? std::exp(v/ (gamma * sigma)) * std::erfc(v / ((T_real)(M_SQRT2)*sigma) + ((T_real)1.0/(gamma*(T_real)(M_SQRT2)))) : std::erfc(v / ((T_real)(M_SQRT2)*sigma) + ((T_real)1.0/(gamma*(T_real)(M_SQRT2)))); } );
return( gain / (T_real)2.0 / gamma / sigma / exp((T_real)-0.5/pow(gamma, (T_real)2.0)) * delta_energy);
T_real val = pow(gamma, (T_real)2.0);
val = exp((T_real)-0.5 / val);
val = sigma / val;
val = gamma / val;
val = (T_real)2.0 / val;
val = gain / val;

return delta_energy.unaryExpr([val,sigma,gamma](T_real v) { return (v < (T_real)0.0) ?
std::exp(v/ (gamma * sigma)) * val * std::erfc(v / ((T_real)(M_SQRT2)*sigma) + ((T_real)1.0/(gamma*(T_real)(M_SQRT2))))
:
val * std::erfc(v / ((T_real)(M_SQRT2)*sigma) + ((T_real)1.0/(gamma*(T_real)(M_SQRT2)))); } );

}

// ----------------------------------------------------------------------------
Expand All @@ -755,6 +767,7 @@ const ArrayTr<T_real> Gaussian_Model<T_real>::elastic_peak(const Fit_Parameters<
T_real sigma = std::sqrt( std::pow( (fitp->at(STR_FWHM_OFFSET).value / (T_real)2.3548), (T_real)2.0 ) + fitp->at(STR_COHERENT_SCT_ENERGY).value * (T_real)2.96 * fitp->at(STR_FWHM_FANOPRIME).value );
if(false == std::isfinite(sigma))
{
logE << "sigma = " << sigma << "\n";
counts = (ArrayTr<T_real>)(counts).unaryExpr([](T_real v) { return std::numeric_limits<T_real>::quiet_NaN(); });
return counts;
}
Expand Down Expand Up @@ -787,6 +800,7 @@ const ArrayTr<T_real> Gaussian_Model<T_real>::compton_peak(const Fit_Parameters<
T_real sigma = std::sqrt( std::pow( (fitp->at(STR_FWHM_OFFSET).value/(T_real)2.3548), (T_real)62.0) + compton_E * (T_real)2.96 * fitp->at(STR_FWHM_FANOPRIME).value );
if(false == std::isfinite(sigma))
{
logE << "sigma = " << sigma << "\n";
counts = (ArrayTr<T_real>)(counts).unaryExpr([](T_real v) { return std::numeric_limits<T_real>::quiet_NaN(); });
return counts;
}
Expand Down
2 changes: 1 addition & 1 deletion src/fitting/models/gaussian_model.h
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Original file line number Diff line number Diff line change
Expand Up @@ -114,7 +114,7 @@ class DLL_EXPORT Gaussian_Model: public Base_Model<T_real>
*/
const ArrayTr<T_real> step(T_real gain, T_real sigma, const ArrayTr<T_real>& delta_energy, T_real peak_E) const;

const ArrayTr<T_real> tail(T_real gain, T_real sigma, ArrayTr<T_real> delta_energy, T_real gamma) const;
const ArrayTr<T_real> tail(T_real gain, T_real sigma, const ArrayTr<T_real>& delta_energy, T_real gamma) const;

virtual const ArrayTr<T_real> elastic_peak(const Fit_Parameters<T_real>* const fitp, const ArrayTr<T_real>& ev, T_real gain) const;

Expand Down
2 changes: 1 addition & 1 deletion src/fitting/optimizers/lmfit_optimizer.cpp
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Original file line number Diff line number Diff line change
Expand Up @@ -185,7 +185,7 @@ void quantification_residuals_lmfit( const T_real *par, int m_dat, const void *d
{
if (std::isfinite(result_map[itr.first]) == false)
{
fvec[idx] = itr.second.e_cal_ratio;
fvec[idx] = itr.second.e_cal_ratio * 10.0;
}
else
{
Expand Down
22 changes: 8 additions & 14 deletions src/fitting/optimizers/mpfit_optimizer.cpp
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Original file line number Diff line number Diff line change
Expand Up @@ -84,22 +84,16 @@ int residuals_mpfit(int m, int params_size, T_real *params, T_real *dy, T_real *
// Add background
ud->spectra_model += ud->spectra_background;
// Remove nan's and inf's
ud->spectra_model = (ArrayTr<T_real>)ud->spectra_model.unaryExpr([ud](T_real v) { return std::isfinite(v) ? v : ud->normalizer; });
//ud->spectra_model = (ArrayTr<T_real>)ud->spectra_model.unaryExpr([ud](T_real v) { return std::isfinite(v) ? v : ud->normalizer; });

T_real sum = 0.0;
//Calculate residuals
for (int i=0; i<m; i++)
{
//T_real n_raw = ud->spectra[i] / ud->norm_arr[i];
//T_real n_model = ud->spectra_model[i] / ud->norm_arr[i];
//dy[i] = pow((n_raw - n_model), (T_real)2.0) * ud->weights[i];

T_real n_raw = ud->spectra[i] / ud->normalizer;
T_real n_model = ud->spectra_model[i] / ud->normalizer;
dy[i] = pow((n_raw - n_model), (T_real)2.0) * ud->weights[i];

dy[i] = pow((ud->spectra[i] - ud->spectra_model[i]), (T_real)2.0) * ud->weights[i];
sum += dy[i];

//dy[i] = pow((ud->spectra[i] - ud->spectra_model[i]), (T_real)2.0) * ud->weights[i];
if (std::isfinite(dy[i]) == false)
if (std::isfinite(dy[i]) == false)
{
if(first)
{
Expand All @@ -119,7 +113,7 @@ int residuals_mpfit(int m, int params_size, T_real *params, T_real *dy, T_real *
dy[i] = ud->normalizer;
}
}
logI << "f = " << sum << "\n";
ud->cur_itr++;
if (ud->status_callback != nullptr)
{
Expand Down Expand Up @@ -202,7 +196,7 @@ int quantification_residuals_mpfit(int m, int params_size, T_real *params, T_rea
{
if (std::isfinite(result_map[itr.first]) == false)
{
dy[idx] = itr.second.e_cal_ratio;
dy[idx] = itr.second.e_cal_ratio * 10.0;
}
else
{
Expand Down Expand Up @@ -391,7 +385,7 @@ void MPFit_Optimizer<T_real>::_fill_limits(Fit_Parameters<T_real> *fit_params ,
break;
}

par[fit.opt_array_index].step = fit.step_size; // 0 = auto ,Step size for finite difference
par[fit.opt_array_index].step = 0; // 0 = auto ,Step size for finite difference
par[fit.opt_array_index].parname = 0;
par[fit.opt_array_index].relstep = 0; // Relative step size for finite difference
par[fit.opt_array_index].side = 0; // Sidedness of finite difference derivative
Expand Down
2 changes: 1 addition & 1 deletion src/fitting/routines/hybrid_param_nnls_fit_routine.cpp
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Original file line number Diff line number Diff line change
Expand Up @@ -179,7 +179,7 @@ OPTIMIZER_OUTCOME Hybrid_Param_NNLS_Fit_Routine<T_real>::fit_spectra_parameters(
out_fit_params.append_and_update(fit_params);
for (const auto& itr : *elements_to_fit)
{
out_fit_params.add_parameter(Fit_Param<T_real>(itr.first, 1.0e-11, 20.0, log10(out_counts[itr.first]), 0.0005, E_Bound_Type::LIMITED_LO_HI));
out_fit_params.add_parameter(Fit_Param<T_real>(itr.first, MIN_COUNTS_LIMIT_LOG, MAX_COUNTS_LIMIT_LOG, log10(out_counts[itr.first]), STEP_COUNTS_LIMIT_LOG, E_Bound_Type::LIMITED_LO_HI));
}
}
}
Expand Down
6 changes: 3 additions & 3 deletions src/fitting/routines/matrix_optimized_fit_routine.cpp
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Original file line number Diff line number Diff line change
Expand Up @@ -125,7 +125,7 @@ std::unordered_map<std::string, Spectra<T_real>> Matrix_Optimized_Fit_Routine<T_
// Set value to 0.0 . This is the pre_faktor in gauss_tails_model. we do 10.0 ^ pre_faktor = 1.0
if (false == fit_parameters.contains(itr.first))
{
Fit_Param<T_real> fp(itr.first, (T_real)-10.0, (T_real)20.0, (T_real)0.0, (T_real)0.0005, E_Bound_Type::LIMITED_LO_HI);
Fit_Param<T_real> fp(itr.first, MIN_COUNTS_LIMIT_LOG, MAX_COUNTS_LIMIT_LOG, (T_real)0.0, STEP_COUNTS_LIMIT_LOG, E_Bound_Type::LIMITED_LO_HI);
fit_parameters[itr.first] = fp;
}
else
Expand Down Expand Up @@ -189,7 +189,7 @@ std::unordered_map<std::string, Spectra<T_real>> Matrix_Optimized_Fit_Routine<T_
{
if (false == fit_parameters.contains(itr.first))
{
Fit_Param<T_real> fp(itr.first, (T_real)-10.0, (T_real)20.0, (T_real)0.0, (T_real)0.0005, E_Bound_Type::LIMITED_LO_HI);
Fit_Param<T_real> fp(itr.first, MIN_COUNTS_LIMIT_LOG, MAX_COUNTS_LIMIT_LOG, (T_real)0.0, STEP_COUNTS_LIMIT_LOG, E_Bound_Type::LIMITED_LO_HI);
fit_parameters[itr.first] = fp;
}
else
Expand Down Expand Up @@ -222,7 +222,7 @@ std::unordered_map<std::string, Spectra<T_real>> Matrix_Optimized_Fit_Routine<T_
// Set value to 0.0 . This is the pre_faktor in gauss_tails_model. we do 10.0 ^ pre_faktor = 1.0
if( false == fit_parameters.contains(itr.first) )
{
Fit_Param<T_real> fp(itr.first, (T_real)-10.0, (T_real)20.0, (T_real)0.0, (T_real)0.0005, E_Bound_Type::LIMITED_LO_HI);
Fit_Param<T_real> fp(itr.first, MIN_COUNTS_LIMIT_LOG, MAX_COUNTS_LIMIT_LOG, (T_real)0.0, STEP_COUNTS_LIMIT_LOG, E_Bound_Type::LIMITED_LO_HI);
fit_parameters[itr.first] = fp;
}
else
Expand Down
2 changes: 1 addition & 1 deletion src/fitting/routines/param_optimized_fit_routine.cpp
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Original file line number Diff line number Diff line change
Expand Up @@ -106,7 +106,7 @@ void Param_Optimized_Fit_Routine<T_real>::_add_elements_to_fit_parameters(Fit_Pa
//if element counts is not in fit params structure, add it
if( false == fit_params->contains(el_itr.first) )
{
Fit_Param<T_real> fp(element->full_name(), (T_real)-11.0, 20, e_guess, (T_real)0.0005, E_Bound_Type::LIMITED_LO_HI);
Fit_Param<T_real> fp(element->full_name(), MIN_COUNTS_LIMIT_LOG, MAX_COUNTS_LIMIT_LOG, e_guess, STEP_COUNTS_LIMIT_LOG, E_Bound_Type::LIMITED_LO_HI);
(*fit_params)[el_itr.first] = fp;
}
if(spectra != nullptr && energies.size() > 0)
Expand Down
5 changes: 5 additions & 0 deletions src/io/file/mca_io.h
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Original file line number Diff line number Diff line change
Expand Up @@ -104,6 +104,7 @@ DLL_EXPORT bool load_integrated_spectra(std::string path, data_struct::Spectra<T
}
catch (std::exception& e)
{
logE << e.what() << ", Defaulting " << tag << " to 0.0\n";
pv_map[tag] = 0.;
}
}
Expand Down Expand Up @@ -178,6 +179,7 @@ DLL_EXPORT bool load_integrated_spectra(std::string path, data_struct::Spectra<T
}
catch (std::exception& e)
{
logE << e.what() << ", Defaulting " << tag << " to 0.0\n";
pv_map[tag] = 0.;
}
}
Expand All @@ -195,6 +197,7 @@ DLL_EXPORT bool load_integrated_spectra(std::string path, data_struct::Spectra<T
}
catch (std::exception& e)
{
logE << e.what() << ", Defaulting " << tag << " to 0.0\n";
pv_map[tag] = 0.;
}
}
Expand All @@ -212,6 +215,7 @@ DLL_EXPORT bool load_integrated_spectra(std::string path, data_struct::Spectra<T
}
catch (std::exception& e)
{
logE << e.what() << ", Defaulting " << tag << " to 0.0\n";
pv_map[tag] = 0.;
}
}
Expand Down Expand Up @@ -260,6 +264,7 @@ DLL_EXPORT bool load_integrated_spectra(std::string path, data_struct::Spectra<T
}
catch (std::exception& e)
{
logE << e.what() << ", Defaulting " << tag << " to 0.0\n";
pv_map[pv_name] = 0.;
}
}
Expand Down
4 changes: 2 additions & 2 deletions src/pybindings/main.cpp
View file
Original file line number Diff line number Diff line change
Expand Up @@ -65,11 +65,11 @@ auto fit_spectra(fitting::routines::Base_Fit_Routine<float>* fit_route,
float val = out_counts.at(itr.first);
if (val != 0.0)
{
fit_params[itr.first] = data_struct::Fit_Param<float>(itr.first, 1.0e-11, 20.0 log10(val), 0.0005, E_Bound_Type::LIMITED_LO_HI);
fit_params[itr.first] = data_struct::Fit_Param<float>(itr.first, MIN_COUNTS_LIMIT_LOG, MAX_COUNTS_LIMIT_LOG, log10(val), STEP_COUNTS_LIMIT_LOG, E_Bound_Type::LIMITED_LO_HI);
}
else
{
fit_params[itr.first] = data_struct::Fit_Param<float>(itr.first, 1.0e-11, 20.0, val, 0.0005, E_Bound_Type::LIMITED_LO_HI);
fit_params[itr.first] = data_struct::Fit_Param<float>(itr.first, MIN_COUNTS_LIMIT_LOG, MAX_COUNTS_LIMIT_LOG, val, STEP_COUNTS_LIMIT_LOG, E_Bound_Type::LIMITED_LO_HI);
}

}
Expand Down
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