diff --git a/Framework/MDAlgorithms/src/LoadDNSSCD.cpp b/Framework/MDAlgorithms/src/LoadDNSSCD.cpp
index 9065ec8b9dc8b3e675ff463f1efed40f42ef7e70..f872d42be75bbb11828fa12b1bb323fd0ae1b43a 100644
--- a/Framework/MDAlgorithms/src/LoadDNSSCD.cpp
+++ b/Framework/MDAlgorithms/src/LoadDNSSCD.cpp
@@ -553,7 +553,6 @@ void LoadDNSSCD::fillOutputWorkspace(double wavelength) {
double signal = ds.signal[i][channel];
signal_t error = std::sqrt(signal);
double tof2 = static_cast<double>(channel)*ds.chwidth + 0.5*ds.chwidth; // bin centers
- g_log.debug() << "TOF2 = " << tof2 << std::endl;
double dE = 0.0;
if (nchannels > 1) {
double v2 = 1e+06*l2/tof2;
@@ -561,7 +560,6 @@ void LoadDNSSCD::fillOutputWorkspace(double wavelength) {
}
if (dE > dEmin) {
double kf = std::sqrt(ki*ki - 2.0e-20*PhysicalConstants::NeutronMass*dE*PhysicalConstants::meV/(PhysicalConstants::h_bar*PhysicalConstants::h_bar));
- g_log.debug() << "dE = " << dE << std::endl;
double tlab = std::atan2(ki - kf*cos(2.0*theta), kf*sin(2.0*theta));
double omega = (ds.huber - ds.deterota) * deg2rad - tlab;
V3D uphi(-cos(omega), 0, -sin(omega));
@@ -691,8 +689,6 @@ void LoadDNSSCD::read_data(const std::string fname,
boost::trim(line);
const int cols = splitIntoColumns(columns, line);
if (cols > 0){
- g_log.debug() << "Number of columns = " << cols << std::endl;
- g_log.debug() << std::endl;
ds.detID.push_back(std::stoi(columns.front()));
columns.pop_front();
std::vector<double> signal;
diff --git a/docs/source/algorithms/LoadDNSSCD-v1.rst b/docs/source/algorithms/LoadDNSSCD-v1.rst
index d3b24caaf87f879236a4aa96b3150f23a7673dff..9a31751194ada704bb191f62dee8c60962f9f1ad 100644
--- a/docs/source/algorithms/LoadDNSSCD-v1.rst
+++ b/docs/source/algorithms/LoadDNSSCD-v1.rst
@@ -25,7 +25,7 @@ As a result, two workspaces are created:
- `NormalizationWorkspace` contains the choosen normalization data (either monitor counts or experiment duration time).
-Both workspaces have :math:`(H,K,L)` dimensions. The metadata are loaded into time series sample logs.
+Both workspaces have :math:`(H,K,L,dE)` dimensions. The metadata are loaded into time series sample logs.
.. note::
@@ -36,7 +36,7 @@ Restrictions
- This algorithm only supports the *DNS* instrument in its configuration with one detector bank (polarisation analysis).
-- This algorithm does not support DNS TOF mode data. It is meant only for single crystal diffraction experiments with 1 TOF channel.
+- This algorithm does not allow to merge datasets with different number of TOF channels.
Data replication
@@ -91,7 +91,7 @@ Usage
dimension.getUnits()))
# print information about the table workspace
- print ("TableWorkspace '{0}' has {1} row in the column '{2}'.".format(huber_ws.getName(),
+ print ("TableWorkspace '{0}' has {1} row in the column '{2}'.".format(huber_ws.name(),
huber_ws.rowCount(),
huber_ws.getColumnNames()[0]))
print("It contains sample rotation angle {} degrees".format(huber_ws.cell(0, 0)))
@@ -100,11 +100,12 @@ Usage
.. testoutput:: LoadDNSSCDEx1
- Output Workspace Type is: MDEventWorkspace<MDEvent,3>
- It has 24 events and 3 dimensions:
+ Output Workspace Type is: MDEventWorkspace<MDEvent,4>
+ It has 24 events and 4 dimensions:
Dimension 0 has name: H, id: H, Range: -15.22 to 15.22 r.l.u
Dimension 1 has name: K, id: K, Range: -15.22 to 15.22 r.l.u
Dimension 2 has name: L, id: L, Range: -41.95 to 41.95 r.l.u
+ Dimension 3 has name: DeltaE, id: DeltaE, Range: -10.00 to 4.64 r.l.u
TableWorkspace 'huber_ws' has 1 row in the column 'Huber(degrees)'.
It contains sample rotation angle 79.0 degrees
@@ -147,10 +148,10 @@ Usage
.. testoutput:: LoadDNSSCDEx2
- Output Workspace Type is: MDEventWorkspace<MDEvent,3>
- It has 10 events and 3 dimensions.
- Normalization Workspace Type is: MDEventWorkspace<MDEvent,3>
- It has 10 events and 3 dimensions.
+ Output Workspace Type is: MDEventWorkspace<MDEvent,4>
+ It has 10 events and 4 dimensions.
+ Normalization Workspace Type is: MDEventWorkspace<MDEvent,4>
+ It has 10 events and 4 dimensions.
**Example 3 - Load sample rotation angles from the table**
@@ -186,17 +187,18 @@ Usage
print("It has {0} events and {1} dimensions.".format(ws.getNEvents(), ws.getNumDims()))
# setting for the BinMD algorithm
- bvec0 = '[100],unit,1,0,0'
- bvec1 = '[001],unit,0,0,1'
- bvec2 = '[010],unit,0,1,0'
- extents = '-2,1.5,-0.2,6.1,-10,10'
- bins = '10,10,1'
+ bvec0 = '[100],unit,1,0,0,0'
+ bvec1 = '[001],unit,0,0,1,0'
+ bvec2 = '[010],unit,0,1,0,0'
+ bvec3 = 'dE,meV,0,0,0,1'
+ extents = '-2,1.5,-0.2,6.1,-10,10,-10,4.6'
+ bins = '10,10,1,1'
# bin the data
- data_raw = BinMD(ws, AxisAligned='0', BasisVector0=bvec0, BasisVector1=bvec1,
- BasisVector2=bvec2, OutputExtents=extents, OutputBins=bins, NormalizeBasisVectors='0')
+ data_raw = BinMD(ws, AxisAligned='0', BasisVector0=bvec0, BasisVector1=bvec1, BasisVector2=bvec2,
+ BasisVector3=bvec3, OutputExtents=extents, OutputBins=bins, NormalizeBasisVectors='0')
# bin normalization
- data_norm = BinMD(ws_norm, AxisAligned='0', BasisVector0=bvec0, BasisVector1=bvec1,
- BasisVector2=bvec2, OutputExtents=extents, OutputBins=bins, NormalizeBasisVectors='0')
+ data_norm = BinMD(ws_norm, AxisAligned='0', BasisVector0=bvec0, BasisVector1=bvec1, BasisVector2=bvec2,
+ BasisVector3=bvec3, OutputExtents=extents, OutputBins=bins, NormalizeBasisVectors='0')
# normalize data
data = data_raw/data_norm
@@ -204,16 +206,17 @@ Usage
print("Reduced Workspace Type is: {}".format(data.id()))
print("It has {} dimensions.".format(data.getNumDims()))
s = data.getSignalArray()
- print("Signal at some points: {0:.4f}, {1:.4f}, {2:.4f}".format(s[7,1][0], s[7,2][0], s[7,3][0]))
+ print("Signal at some points: {0:.4f}, {1:.4f}, {2:.4f}".format(
+ float(s[7,1][0]), float(s[7,2][0]), float(s[7,3][0])))
**Output:**
.. testoutput:: LoadDNSSCDEx3
- Output Workspace Type is: MDEventWorkspace<MDEvent,3>
- It has 240 events and 3 dimensions.
+ Output Workspace Type is: MDEventWorkspace<MDEvent,4>
+ It has 240 events and 4 dimensions.
Reduced Workspace Type is: MDHistoWorkspace
- It has 3 dimensions.
+ It has 4 dimensions.
Signal at some points: 0.0035, 0.0033, 0.0035
.. categories::