diff --git a/GUI/View/Realspace/RealspaceBuilder.cpp b/GUI/View/Realspace/RealspaceBuilder.cpp
index 74ec96b73a738f22f97fb098d873405106dd4660..2a512a8bc30bc266af78ef1e1f18a01f1a6d6ede 100644
--- a/GUI/View/Realspace/RealspaceBuilder.cpp
+++ b/GUI/View/Realspace/RealspaceBuilder.cpp
@@ -105,7 +105,7 @@ std::unique_ptr<const double2d_t> layerRoughnessMap(const LayerItem& layerItem,
roughItem->certainInterlayerModel()->createModel();
auto roughness = LayerRoughness(autocorrelation.get(), interlayer.get());
- if (roughness.sigma() == 0)
+ if (roughness.rms() == 0)
return result;
int n = sceneGeometry.numRoughnessPointsAlongAxis;
diff --git a/Resample/Processed/ReSample.cpp b/Resample/Processed/ReSample.cpp
index 9c10b553f74761c3bc801501e1c3751cf3a42881..2d03aa05aaf3dcc6df3a57553274aab7afa5b96d 100644
--- a/Resample/Processed/ReSample.cpp
+++ b/Resample/Processed/ReSample.cpp
@@ -402,7 +402,7 @@ bool ReSample::polarizing() const
bool ReSample::hasRoughness() const
{
for (const auto& slice : m_stack)
- if (slice.topRoughness() && slice.topRoughness()->sigma() > 0)
+ if (slice.topRoughness() && slice.topRoughness()->rms() > 0)
return true;
return false;
}
diff --git a/Resample/Slice/ProfileHelper.cpp b/Resample/Slice/ProfileHelper.cpp
index 0e25c53674886136c9072816f5cd0c1209b7c6f2..c969445e83c3afb840999ec14fb06a6fb352d7ac 100644
--- a/Resample/Slice/ProfileHelper.cpp
+++ b/Resample/Slice/ProfileHelper.cpp
@@ -27,7 +27,7 @@ double Transition(double x, const LayerRoughness* roughness)
if (!roughness)
return Math::Heaviside(x);
- return roughness->interlayerModel()->transient(x, roughness->sigma());
+ return roughness->interlayerModel()->transient(x, roughness->rms());
}
const std::string SLD = "SLD";
@@ -113,9 +113,9 @@ std::pair<double, double> ProfileHelper::defaultLimits() const
const LayerRoughness* bottomRoughness = m_stack.back().topRoughness();
double top_margin =
- topRoughness && topRoughness->sigma() > 0 ? 5.0 * topRoughness->sigma() : default_margin;
- double bottom_margin = bottomRoughness && bottomRoughness->sigma() > 0
- ? 5.0 * bottomRoughness->sigma()
+ topRoughness && topRoughness->rms() > 0 ? 5.0 * topRoughness->rms() : default_margin;
+ double bottom_margin = bottomRoughness && bottomRoughness->rms() > 0
+ ? 5.0 * bottomRoughness->rms()
: default_margin;
double z_min = m_stack.back().hig() - bottom_margin;
double z_max = m_stack.front().low() + top_margin;
diff --git a/Resample/Specular/ComputeFluxMagnetic.cpp b/Resample/Specular/ComputeFluxMagnetic.cpp
index 83e5b8a730254c0b409d43aeeb2bf7da8980bb8c..63b2380f5c5da6a009b218a376f8b5e65c8c8057 100644
--- a/Resample/Specular/ComputeFluxMagnetic.cpp
+++ b/Resample/Specular/ComputeFluxMagnetic.cpp
@@ -182,7 +182,7 @@ std::vector<MatrixFlux> computeTR(const SliceStack& slices, const std::vector<co
for (size_t i = N - 1; i > 0; --i) {
const auto* roughness = slices.bottomRoughness(i);
- const double sigma = roughness ? roughness->sigma() : 0.;
+ const double sigma = roughness ? roughness->rms() : 0.;
const auto* r_model = roughness ? roughness->interlayerModel() : nullptr;
// compute the 2x2 blocks of the transfer matrix
@@ -288,7 +288,7 @@ SpinMatrix Compute::polarizedReflectivity(const SliceStack& slices,
for (size_t i = N - 1; i > 0; --i) {
MatrixFlux tr_i = createCoeff(i - 1);
const auto* roughness = slices.bottomRoughness(i - 1);
- const double sigma = roughness ? roughness->sigma() : 0.;
+ const double sigma = roughness ? roughness->rms() : 0.;
const auto* r_model = roughness ? roughness->interlayerModel() : nullptr;
// compute the 2x2 blocks of the transfer matrix
diff --git a/Resample/Specular/ComputeFluxScalar.cpp b/Resample/Specular/ComputeFluxScalar.cpp
index 7f8a70ddeacf043c87177eb10d8ed4be63b615d7..b4be0a72c9b4250a085e4fe62e48dc4c4c8c2a70 100644
--- a/Resample/Specular/ComputeFluxScalar.cpp
+++ b/Resample/Specular/ComputeFluxScalar.cpp
@@ -84,7 +84,7 @@ std::vector<Spinor> computeTR(const SliceStack& slices, const std::vector<comple
const size_t jthis = X[i - 1];
const size_t jlast = X[i];
const auto* roughness = slices.bottomRoughness(jthis);
- const double sigma = roughness ? roughness->sigma() : 0.;
+ const double sigma = roughness ? roughness->rms() : 0.;
const auto* r_model = roughness ? roughness->interlayerModel() : nullptr;
const auto [slp, slm] = transition(kz[jthis], kz[jlast], sigma, r_model);
@@ -138,7 +138,7 @@ complex_t Compute::scalarReflectivity(const SliceStack& slices, const std::vecto
for (size_t i = N - 1; i > 0; i--) {
const auto* roughness = slices.bottomRoughness(i - 1);
- const double sigma = roughness ? roughness->sigma() : 0.;
+ const double sigma = roughness ? roughness->rms() : 0.;
const auto* r_model = roughness ? roughness->interlayerModel() : nullptr;
const auto [sp, sm] = ::transition(kz[i - 1], kz[i], sigma, r_model);
diff --git a/Sample/Interface/AutocorrelationModels.cpp b/Sample/Interface/AutocorrelationModels.cpp
index 78d7877b5d578353f7dbcea2912aed7e5eda9324..e95b678f6f86fe87e4409e915efba75361673980 100644
--- a/Sample/Interface/AutocorrelationModels.cpp
+++ b/Sample/Interface/AutocorrelationModels.cpp
@@ -28,7 +28,7 @@ AutocorrelationModel::AutocorrelationModel(double maxSpatFrequency)
std::vector<std::string> AutocorrelationModel::validationErrs() const
{
std::vector<std::string> errs;
- requestGt0(errs, m_max_spatial_frequency, "maxSpatFrequency");
+ requestGt0(errs, m_max_spatial_frequency, "maxSpatialFrequency");
return errs;
}
@@ -40,7 +40,7 @@ std::string AutocorrelationModel::pythonArguments() const
//-------------------------------------------------------------------------------------------------
-//! @param sigma: rms of the roughness in nanometers
+//! @param sigma: height scale of the roughness in nanometers (usually close to rms)
//! @param hurstParameter: hurst parameter which describes how jagged the interface,
//! dimensionless (0.0, 1.0], where 0.0 gives more spikes, 1.0 more smoothness
//! @param lateralCorrLength: lateral correlation length of the roughness in nanometers
@@ -115,3 +115,9 @@ double K_CorrelationModel::corrFunction(const R3& r) const
return m_sigma * m_sigma * std::pow(2., 1 - H) / tgamma(H) * std::pow(R / clength, H)
* gsl_sf_bessel_Knu(H, R / clength);
}
+
+double K_CorrelationModel::rms() const
+{
+ // integration of spectral function: rms^2 = Integrate[PSD(f) * 2*pi*f, {f, 0, max_frequency}]
+ return 0;
+}
diff --git a/Sample/Interface/AutocorrelationModels.h b/Sample/Interface/AutocorrelationModels.h
index dc2a99961f10726dd84896ce7ba0e08b29892a83..ab49076278d7e75013cf53fb79feb7731099aff2 100644
--- a/Sample/Interface/AutocorrelationModels.h
+++ b/Sample/Interface/AutocorrelationModels.h
@@ -23,7 +23,7 @@
class AutocorrelationModel : public ICloneable, public INode {
public:
AutocorrelationModel* clone() const override = 0;
- virtual double sigma() const = 0;
+ virtual double rms() const = 0;
virtual double spectralFunction(const R3& k) const = 0;
virtual double corrFunction(const R3& k) const = 0;
@@ -60,10 +60,12 @@ public:
double corrFunction(const R3& r) const override;
+ double rms() const override;
+
//! Sets rms of roughness
void setSigma(double sigma) { m_sigma = sigma; }
//! Returns rms of roughness
- double sigma() const override { return m_sigma; }
+ double sigma() const { return m_sigma; }
//! Sets hurst parameter. It describes how jagged the surface is.
void setHurstParameter(double hurstParameter) { m_hurst_parameter = hurstParameter; }
diff --git a/Sample/Interface/CrosscorrelationModels.cpp b/Sample/Interface/CrosscorrelationModels.cpp
index 7f9e47f789140b88dfa524b14706c542c1b1623f..d821c3e4ce8b1af99376b434f617e0e7a51e72ef 100644
--- a/Sample/Interface/CrosscorrelationModels.cpp
+++ b/Sample/Interface/CrosscorrelationModels.cpp
@@ -27,15 +27,15 @@ double cross_spectrum(const AutocorrelationModel* up, const AutocorrelationModel
{
ASSERT(up && low);
- const double sigma_up = up->sigma();
- const double sigma_low = low->sigma();
+ const double rms_up = up->rms();
+ const double rms_low = low->rms();
- if (sigma_up <= 0 || sigma_low <= 0)
+ if (rms_up <= 0 || rms_low <= 0)
return 0;
const double spectrum_up = up->spectralFunction(qvec);
const double spectrum_low = low->spectralFunction(qvec);
- return 0.5 * ((sigma_low / sigma_up) * spectrum_up + (sigma_up / sigma_low) * spectrum_low);
+ return 0.5 * ((rms_low / rms_up) * spectrum_up + (rms_up / rms_low) * spectrum_low);
}
} // namespace
diff --git a/Sample/Interface/LayerRoughness.h b/Sample/Interface/LayerRoughness.h
index 711445d47578d001aa105634895866643faf7b35..796e868eb442862de08a9fd6045dd9a58be9974f 100644
--- a/Sample/Interface/LayerRoughness.h
+++ b/Sample/Interface/LayerRoughness.h
@@ -35,15 +35,8 @@ public:
std::string className() const final { return "LayerRoughness"; }
std::vector<const INode*> nodeChildren() const override;
-#ifndef SWIG
- bool showInScriptOrGui() const;
-#endif
-
//! Returns rms of roughness
- double sigma() const
- {
- return m_autocorrelation_model->sigma();
- }
+ double rms() const { return m_autocorrelation_model->rms(); }
void setAutocorrelationModel(const AutocorrelationModel* autocorrelation);
const AutocorrelationModel* autocorrelationModel() const
@@ -52,10 +45,7 @@ public:
}
void setInterlayerModel(const InterlayerModel* interlayer);
- const InterlayerModel* interlayerModel() const
- {
- return m_interlayer_model.get();
- }
+ const InterlayerModel* interlayerModel() const { return m_interlayer_model.get(); }
void setCrosscorrelationModel(const CrosscorrelationModel* crosscorrelation);
const CrosscorrelationModel* crosscorrelationModel() const
@@ -63,6 +53,10 @@ public:
return m_crosscorrelation_model.get();
}
+#ifndef SWIG
+ bool showInScriptOrGui() const;
+#endif
+
private:
std::unique_ptr<AutocorrelationModel> m_autocorrelation_model; // never nullptr
std::unique_ptr<InterlayerModel> m_interlayer_model; // never nullptr
diff --git a/Sample/Interface/RoughnessMap.cpp b/Sample/Interface/RoughnessMap.cpp
index d79718316c64e8cddf21420045421dcf1ed5a5ff..187d6a7554d54eec0643047a63761b42ff2d5302 100644
--- a/Sample/Interface/RoughnessMap.cpp
+++ b/Sample/Interface/RoughnessMap.cpp
@@ -70,7 +70,7 @@ double2d_t RoughnessMap::generateMap()
double2d_t RoughnessMap::mapFromHeights() const
{
const size_t z_steps = 3999;
- const double sigma = m_layerRoughness->sigma();
+ const double sigma = m_layerRoughness->rms();
const double sigma_factor = m_layerRoughness->interlayerModel()->sigmaRange();
const double z_limit = sigma * sigma_factor;
const double step = 2 * z_limit / (z_steps - 1);
@@ -196,7 +196,7 @@ double2d_t RoughnessMap::applyHeightsToSpectrum(const double2d_t& h_map,
void RoughnessMap::createMap()
{
- if (m_layerRoughness->sigma() < 1e-10) {
+ if (m_layerRoughness->rms() < 1e-10) {
m_rough_map = double2d_t(m_y_points, std::vector<double>(m_x_points));
return;
}
diff --git a/Sim/Computation/RoughMultiLayerContribution.cpp b/Sim/Computation/RoughMultiLayerContribution.cpp
index 6d8d2e0fe55424ea3d5122c3ddd4838c93431fbd..2ddcea80f7ea4d2f0e29f42719a52f84b725e169 100644
--- a/Sim/Computation/RoughMultiLayerContribution.cpp
+++ b/Sim/Computation/RoughMultiLayerContribution.cpp
@@ -87,7 +87,7 @@ complex_t get_sum8terms(const ReSample& re_sample, size_t i_layer, const Diffuse
const complex_t qz4_B = -qz1_B;
const LayerRoughness* roughness = re_sample.averageSlices().bottomRoughness(i_layer);
- const double sigma = roughness ? roughness->sigma() : 0.;
+ const double sigma = roughness ? roughness->rms() : 0.;
const complex_t term1 = T_i_A * T_f_A * ::h_above(qz1_A * sigma);
const complex_t term2 = T_i_A * R_f_A * ::h_above(qz2_A * sigma);
const complex_t term3 = R_i_A * T_f_A * ::h_above(qz3_A * sigma);
diff --git a/Tests/Unit/Sample/MultiLayerTest.cpp b/Tests/Unit/Sample/MultiLayerTest.cpp
index 29b10dfff5259d5a7285d300a19c0218522908ef..dfd83c098499dfb9f9ade60abd1aefbf8bb59c2c 100644
--- a/Tests/Unit/Sample/MultiLayerTest.cpp
+++ b/Tests/Unit/Sample/MultiLayerTest.cpp
@@ -87,17 +87,17 @@ TEST_F(MultiLayerTest, LayerInterfaces)
const LayerInterface* interface0 = mLayer.layerInterface(0);
EXPECT_TRUE(nullptr != interface0);
EXPECT_TRUE(nullptr != interface0->roughness());
- EXPECT_EQ(interface0->roughness()->sigma(), 0.0);
+ EXPECT_EQ(interface0->roughness()->rms(), 0.0);
const LayerInterface* interface1 = mLayer.layerInterface(1);
EXPECT_TRUE(nullptr != interface1);
EXPECT_TRUE(nullptr != interface1->roughness());
- EXPECT_EQ(interface1->roughness()->sigma(), 0.0);
+ EXPECT_EQ(interface1->roughness()->rms(), 0.0);
const LayerInterface* interface2 = mLayer.layerInterface(2);
EXPECT_TRUE(nullptr != interface2);
EXPECT_TRUE(nullptr != interface2->roughness());
- EXPECT_EQ(interface2->roughness()->sigma(), 0.0);
+ EXPECT_EQ(interface2->roughness()->rms(), 0.0);
}
TEST_F(MultiLayerTest, Clone)
@@ -119,19 +119,19 @@ TEST_F(MultiLayerTest, Clone)
const LayerInterface* interface0 = mLayerClone->layerInterface(0);
EXPECT_TRUE(nullptr != interface0);
EXPECT_TRUE(nullptr != interface0->roughness());
- EXPECT_EQ(interface0->roughness()->sigma(), 0.0);
+ EXPECT_EQ(interface0->roughness()->rms(), 0.0);
EXPECT_TRUE(nullptr == interface0->roughness()->crosscorrelationModel());
const LayerInterface* interface1 = mLayerClone->layerInterface(1);
EXPECT_TRUE(nullptr != interface1);
EXPECT_TRUE(nullptr != interface1->roughness());
- EXPECT_EQ(interface1->roughness()->sigma(), 0.0);
+ EXPECT_EQ(interface1->roughness()->rms(), 0.0);
EXPECT_TRUE(nullptr == interface1->roughness()->crosscorrelationModel());
const LayerInterface* interface2 = mLayerClone->layerInterface(2);
EXPECT_TRUE(nullptr != interface2);
EXPECT_TRUE(nullptr != interface2->roughness());
- EXPECT_EQ(interface2->roughness()->sigma(), 0.0);
+ EXPECT_EQ(interface2->roughness()->rms(), 0.0);
EXPECT_TRUE(nullptr == interface2->roughness()->crosscorrelationModel());
delete mLayerClone;
@@ -157,12 +157,12 @@ TEST_F(MultiLayerTest, WithRoughness)
dynamic_cast<const K_CorrelationModel*>(roughness0->autocorrelationModel());
EXPECT_TRUE(roughness0_AC);
- EXPECT_EQ(1.1, roughness0->sigma());
+ EXPECT_EQ(1.1, roughness0->rms());
EXPECT_EQ(.3, roughness0_AC->hurst());
EXPECT_EQ(0.1, roughness0_AC->lateralCorrLength());
EXPECT_TRUE(roughness1);
- EXPECT_EQ(roughness1->sigma(), 0.0);
+ EXPECT_EQ(roughness1->rms(), 0.0);
}
TEST_F(MultiLayerTest, CloneWithRoughness)
@@ -196,7 +196,7 @@ TEST_F(MultiLayerTest, CloneWithRoughness)
dynamic_cast<const K_CorrelationModel*>(roughness0->autocorrelationModel());
EXPECT_TRUE(roughness0_AC);
- EXPECT_EQ(2.1, roughness0->sigma());
+ EXPECT_EQ(2.1, roughness0->rms());
EXPECT_EQ(.3, roughness0_AC->hurst());
EXPECT_EQ(12.1, roughness0_AC->lateralCorrLength());
@@ -204,7 +204,7 @@ TEST_F(MultiLayerTest, CloneWithRoughness)
dynamic_cast<const K_CorrelationModel*>(roughness1->autocorrelationModel());
EXPECT_TRUE(roughness1_AC);
- EXPECT_EQ(1.1, roughness1->sigma());
+ EXPECT_EQ(1.1, roughness1->rms());
EXPECT_EQ(.3, roughness1_AC->hurst());
EXPECT_EQ(0.1, roughness1_AC->lateralCorrLength());
diff --git a/auto/Wrap/libBornAgainSample.py b/auto/Wrap/libBornAgainSample.py
index 716bd55a52543777f9911861fa3747f823acb823..50d24f07ead2b332927725ad7ac74c16500f389e 100644
--- a/auto/Wrap/libBornAgainSample.py
+++ b/auto/Wrap/libBornAgainSample.py
@@ -4191,9 +4191,9 @@ class LayerRoughness(ISampleNode):
r"""nodeChildren(LayerRoughness self) -> swig_dummy_type_const_inode_vector"""
return _libBornAgainSample.LayerRoughness_nodeChildren(self)
- def sigma(self):
- r"""sigma(LayerRoughness self) -> double"""
- return _libBornAgainSample.LayerRoughness_sigma(self)
+ def rms(self):
+ r"""rms(LayerRoughness self) -> double"""
+ return _libBornAgainSample.LayerRoughness_rms(self)
def setAutocorrelationModel(self, autocorrelation):
r"""setAutocorrelationModel(LayerRoughness self, AutocorrelationModel autocorrelation)"""
@@ -4326,9 +4326,9 @@ class AutocorrelationModel(libBornAgainBase.ICloneable, libBornAgainParam.INode)
r"""clone(AutocorrelationModel self) -> AutocorrelationModel"""
return _libBornAgainSample.AutocorrelationModel_clone(self)
- def sigma(self):
- r"""sigma(AutocorrelationModel self) -> double"""
- return _libBornAgainSample.AutocorrelationModel_sigma(self)
+ def rms(self):
+ r"""rms(AutocorrelationModel self) -> double"""
+ return _libBornAgainSample.AutocorrelationModel_rms(self)
def spectralFunction(self, k):
r"""spectralFunction(AutocorrelationModel self, R3 k) -> double"""
@@ -4387,6 +4387,10 @@ class K_CorrelationModel(AutocorrelationModel):
r"""corrFunction(K_CorrelationModel self, R3 r) -> double"""
return _libBornAgainSample.K_CorrelationModel_corrFunction(self, r)
+ def rms(self):
+ r"""rms(K_CorrelationModel self) -> double"""
+ return _libBornAgainSample.K_CorrelationModel_rms(self)
+
def setSigma(self, sigma):
r"""setSigma(K_CorrelationModel self, double sigma)"""
return _libBornAgainSample.K_CorrelationModel_setSigma(self, sigma)
diff --git a/auto/Wrap/libBornAgainSample_wrap.cpp b/auto/Wrap/libBornAgainSample_wrap.cpp
index 07d646c2cfb211a5f8d68daef6577fc667066c6b..f782b5f0b269462e617650738c699e3e5c70a422 100644
--- a/auto/Wrap/libBornAgainSample_wrap.cpp
+++ b/auto/Wrap/libBornAgainSample_wrap.cpp
@@ -50738,7 +50738,7 @@ fail:
}
-SWIGINTERN PyObject *_wrap_LayerRoughness_sigma(PyObject *self, PyObject *args) {
+SWIGINTERN PyObject *_wrap_LayerRoughness_rms(PyObject *self, PyObject *args) {
PyObject *resultobj = 0;
LayerRoughness *arg1 = (LayerRoughness *) 0 ;
void *argp1 = 0 ;
@@ -50751,12 +50751,12 @@ SWIGINTERN PyObject *_wrap_LayerRoughness_sigma(PyObject *self, PyObject *args)
swig_obj[0] = args;
res1 = SWIG_ConvertPtr(swig_obj[0], &argp1,SWIGTYPE_p_LayerRoughness, 0 | 0 );
if (!SWIG_IsOK(res1)) {
- SWIG_exception_fail(SWIG_ArgError(res1), "in method '" "LayerRoughness_sigma" "', argument " "1"" of type '" "LayerRoughness const *""'");
+ SWIG_exception_fail(SWIG_ArgError(res1), "in method '" "LayerRoughness_rms" "', argument " "1"" of type '" "LayerRoughness const *""'");
}
arg1 = reinterpret_cast< LayerRoughness * >(argp1);
{
try {
- result = (double)((LayerRoughness const *)arg1)->sigma();
+ result = (double)((LayerRoughness const *)arg1)->rms();
} catch (const std::exception& ex) {
// message shown in the Python interpreter
const std::string msg {
@@ -52085,7 +52085,7 @@ fail:
}
-SWIGINTERN PyObject *_wrap_AutocorrelationModel_sigma(PyObject *self, PyObject *args) {
+SWIGINTERN PyObject *_wrap_AutocorrelationModel_rms(PyObject *self, PyObject *args) {
PyObject *resultobj = 0;
AutocorrelationModel *arg1 = (AutocorrelationModel *) 0 ;
void *argp1 = 0 ;
@@ -52098,12 +52098,12 @@ SWIGINTERN PyObject *_wrap_AutocorrelationModel_sigma(PyObject *self, PyObject *
swig_obj[0] = args;
res1 = SWIG_ConvertPtr(swig_obj[0], &argp1,SWIGTYPE_p_AutocorrelationModel, 0 | 0 );
if (!SWIG_IsOK(res1)) {
- SWIG_exception_fail(SWIG_ArgError(res1), "in method '" "AutocorrelationModel_sigma" "', argument " "1"" of type '" "AutocorrelationModel const *""'");
+ SWIG_exception_fail(SWIG_ArgError(res1), "in method '" "AutocorrelationModel_rms" "', argument " "1"" of type '" "AutocorrelationModel const *""'");
}
arg1 = reinterpret_cast< AutocorrelationModel * >(argp1);
{
try {
- result = (double)((AutocorrelationModel const *)arg1)->sigma();
+ result = (double)((AutocorrelationModel const *)arg1)->rms();
} catch (const std::exception& ex) {
// message shown in the Python interpreter
const std::string msg {
@@ -52879,6 +52879,40 @@ fail:
}
+SWIGINTERN PyObject *_wrap_K_CorrelationModel_rms(PyObject *self, PyObject *args) {
+ PyObject *resultobj = 0;
+ K_CorrelationModel *arg1 = (K_CorrelationModel *) 0 ;
+ void *argp1 = 0 ;
+ int res1 = 0 ;
+ PyObject *swig_obj[1] ;
+ double result;
+
+ (void)self;
+ if (!args) SWIG_fail;
+ swig_obj[0] = args;
+ res1 = SWIG_ConvertPtr(swig_obj[0], &argp1,SWIGTYPE_p_K_CorrelationModel, 0 | 0 );
+ if (!SWIG_IsOK(res1)) {
+ SWIG_exception_fail(SWIG_ArgError(res1), "in method '" "K_CorrelationModel_rms" "', argument " "1"" of type '" "K_CorrelationModel const *""'");
+ }
+ arg1 = reinterpret_cast< K_CorrelationModel * >(argp1);
+ {
+ try {
+ result = (double)((K_CorrelationModel const *)arg1)->rms();
+ } catch (const std::exception& ex) {
+ // message shown in the Python interpreter
+ const std::string msg {
+ "BornAgain C++ Exception: " + std::string(ex.what())
+ };
+ SWIG_exception(SWIG_RuntimeError, msg.c_str());
+ }
+ }
+ resultobj = SWIG_From_double(static_cast< double >(result));
+ return resultobj;
+fail:
+ return NULL;
+}
+
+
SWIGINTERN PyObject *_wrap_K_CorrelationModel_setSigma(PyObject *self, PyObject *args) {
PyObject *resultobj = 0;
K_CorrelationModel *arg1 = (K_CorrelationModel *) 0 ;
@@ -74592,7 +74626,7 @@ static PyMethodDef SwigMethods[] = {
{ "LayerRoughness_clone", _wrap_LayerRoughness_clone, METH_O, "LayerRoughness_clone(LayerRoughness self) -> LayerRoughness"},
{ "LayerRoughness_className", _wrap_LayerRoughness_className, METH_O, "LayerRoughness_className(LayerRoughness self) -> std::string"},
{ "LayerRoughness_nodeChildren", _wrap_LayerRoughness_nodeChildren, METH_O, "LayerRoughness_nodeChildren(LayerRoughness self) -> swig_dummy_type_const_inode_vector"},
- { "LayerRoughness_sigma", _wrap_LayerRoughness_sigma, METH_O, "LayerRoughness_sigma(LayerRoughness self) -> double"},
+ { "LayerRoughness_rms", _wrap_LayerRoughness_rms, METH_O, "LayerRoughness_rms(LayerRoughness self) -> double"},
{ "LayerRoughness_setAutocorrelationModel", _wrap_LayerRoughness_setAutocorrelationModel, METH_VARARGS, "LayerRoughness_setAutocorrelationModel(LayerRoughness self, AutocorrelationModel autocorrelation)"},
{ "LayerRoughness_autocorrelationModel", _wrap_LayerRoughness_autocorrelationModel, METH_O, "LayerRoughness_autocorrelationModel(LayerRoughness self) -> AutocorrelationModel"},
{ "LayerRoughness_setInterlayerModel", _wrap_LayerRoughness_setInterlayerModel, METH_VARARGS, "LayerRoughness_setInterlayerModel(LayerRoughness self, InterlayerModel interlayer)"},
@@ -74628,7 +74662,7 @@ static PyMethodDef SwigMethods[] = {
{ "MultiLayer_swigregister", MultiLayer_swigregister, METH_O, NULL},
{ "MultiLayer_swiginit", MultiLayer_swiginit, METH_VARARGS, NULL},
{ "AutocorrelationModel_clone", _wrap_AutocorrelationModel_clone, METH_O, "AutocorrelationModel_clone(AutocorrelationModel self) -> AutocorrelationModel"},
- { "AutocorrelationModel_sigma", _wrap_AutocorrelationModel_sigma, METH_O, "AutocorrelationModel_sigma(AutocorrelationModel self) -> double"},
+ { "AutocorrelationModel_rms", _wrap_AutocorrelationModel_rms, METH_O, "AutocorrelationModel_rms(AutocorrelationModel self) -> double"},
{ "AutocorrelationModel_spectralFunction", _wrap_AutocorrelationModel_spectralFunction, METH_VARARGS, "AutocorrelationModel_spectralFunction(AutocorrelationModel self, R3 k) -> double"},
{ "AutocorrelationModel_corrFunction", _wrap_AutocorrelationModel_corrFunction, METH_VARARGS, "AutocorrelationModel_corrFunction(AutocorrelationModel self, R3 k) -> double"},
{ "AutocorrelationModel_setMaxSpatialFrequency", _wrap_AutocorrelationModel_setMaxSpatialFrequency, METH_VARARGS, "AutocorrelationModel_setMaxSpatialFrequency(AutocorrelationModel self, double val)"},
@@ -74643,6 +74677,7 @@ static PyMethodDef SwigMethods[] = {
{ "K_CorrelationModel_validate", _wrap_K_CorrelationModel_validate, METH_O, "K_CorrelationModel_validate(K_CorrelationModel self) -> std::string"},
{ "K_CorrelationModel_spectralFunction", _wrap_K_CorrelationModel_spectralFunction, METH_VARARGS, "K_CorrelationModel_spectralFunction(K_CorrelationModel self, R3 q) -> double"},
{ "K_CorrelationModel_corrFunction", _wrap_K_CorrelationModel_corrFunction, METH_VARARGS, "K_CorrelationModel_corrFunction(K_CorrelationModel self, R3 r) -> double"},
+ { "K_CorrelationModel_rms", _wrap_K_CorrelationModel_rms, METH_O, "K_CorrelationModel_rms(K_CorrelationModel self) -> double"},
{ "K_CorrelationModel_setSigma", _wrap_K_CorrelationModel_setSigma, METH_VARARGS, "K_CorrelationModel_setSigma(K_CorrelationModel self, double sigma)"},
{ "K_CorrelationModel_sigma", _wrap_K_CorrelationModel_sigma, METH_O, "K_CorrelationModel_sigma(K_CorrelationModel self) -> double"},
{ "K_CorrelationModel_setHurstParameter", _wrap_K_CorrelationModel_setHurstParameter, METH_VARARGS, "K_CorrelationModel_setHurstParameter(K_CorrelationModel self, double hurstParameter)"},