diff --git a/conda/recipes/mantiddocs/build.sh b/conda/recipes/mantiddocs/build.sh index c9bd48a49212..f1de26c09ff2 100644 --- a/conda/recipes/mantiddocs/build.sh +++ b/conda/recipes/mantiddocs/build.sh @@ -37,7 +37,7 @@ cmake --build . --target StandardTestData export STANDARD_TEST_DATA_DIR=$SRC_DIR/build/ExternalData/Testing/Data echo 'datasearch.directories = '$STANDARD_TEST_DATA_DIR'/UnitTest/;'$STANDARD_TEST_DATA_DIR'/DocTest/' >> $PREFIX/bin/Mantid.properties -# Set QT_APA_PLATFORM=offscreen so we do not need to run with xvfb. Xvfb hides a lot of debug output +# Set QT_QPA_PLATFORM=offscreen so we do not need to run with xvfb. Xvfb hides a lot of debug output export QT_QPA_PLATFORM=offscreen cmake --build . --target docs-qthelp diff --git a/docs/source/algorithms/CalculateDIFC-v1.rst b/docs/source/algorithms/CalculateDIFC-v1.rst index 277956bc4863..d46ec692b7e2 100644 --- a/docs/source/algorithms/CalculateDIFC-v1.rst +++ b/docs/source/algorithms/CalculateDIFC-v1.rst @@ -18,7 +18,7 @@ an instrument. Or if OffsetMode is `Signed` :math:`DIFC` will be calculated with the following equation for logarithmically binned data: -.. math:: DIFC = \frac{m_n}{h}&(L1 + L2)&2sin\theta & * & (1+|BinWidth|)^{-offset} +.. math:: DIFC = \frac{m_n}{h} \cdot (L1 + L2) 2 \sin(\theta) \cdot (1+|BinWidth|)^{-offset} DIFC is used in the equation diff --git a/docs/source/algorithms/ConvertDiffCal-v1.rst b/docs/source/algorithms/ConvertDiffCal-v1.rst index c947aa6d6800..f9612bdb4e70 100644 --- a/docs/source/algorithms/ConvertDiffCal-v1.rst +++ b/docs/source/algorithms/ConvertDiffCal-v1.rst @@ -35,11 +35,11 @@ the following equations: Update existing calibration: -.. math:: DIFC = DIFC_{old} & * & (1+|BinWidth|)^{-offset} +.. math:: DIFC = DIFC_{old} \cdot (1+|BinWidth|)^{-offset} Calculate :math:`DIFC` from geometry of the experiment: -.. math:: DIFC = \frac{m_n}{h}&(L1 + L2)&2sin\theta & * & (1+|BinWidth|)^{-offset} +.. math:: DIFC = \frac{m_n}{h} \cdot (L1 + L2) 2 \sin(\theta) \cdot (1+|BinWidth|)^{-offset} The calculations for signed mode is appropriate for full-pattern cross-correlation with logarithmically binned data