From 2fa072272e4643d51e3eaf3f4c356f0bd89d7b83 Mon Sep 17 00:00:00 2001 From: Matthias Maier Date: Mon, 11 May 2020 14:48:19 -0500 Subject: [PATCH] Address typos brought up in review comments Update include/deal.II/hp/fe_collection.h Update include/deal.II/numerics/smoothness_estimator.h Update include/deal.II/numerics/smoothness_estimator.h Update include/deal.II/numerics/smoothness_estimator.h Update include/deal.II/numerics/smoothness_estimator.h Update include/deal.II/numerics/smoothness_estimator.h Update include/deal.II/numerics/smoothness_estimator.h Update include/deal.II/numerics/smoothness_estimator.h Update include/deal.II/numerics/smoothness_estimator.h Update include/deal.II/numerics/smoothness_estimator.h Co-authored-by: Martin Kronbichler --- include/deal.II/hp/fe_collection.h | 3 +-- .../deal.II/numerics/smoothness_estimator.h | 18 +++++++++--------- 2 files changed, 10 insertions(+), 11 deletions(-) diff --git a/include/deal.II/hp/fe_collection.h b/include/deal.II/hp/fe_collection.h index d7cdb068d9..cb56f8c69b 100644 --- a/include/deal.II/hp/fe_collection.h +++ b/include/deal.II/hp/fe_collection.h @@ -921,8 +921,7 @@ namespace hp unsigned int max = 0; for (unsigned int i = 0; i < finite_elements.size(); ++i) - if (finite_elements[i]->degree > max) - max = finite_elements[i]->degree; + max = std::max(max, finite_elements[i]->degree); return max; } diff --git a/include/deal.II/numerics/smoothness_estimator.h b/include/deal.II/numerics/smoothness_estimator.h index e73762196d..f0d2bfb760 100644 --- a/include/deal.II/numerics/smoothness_estimator.h +++ b/include/deal.II/numerics/smoothness_estimator.h @@ -84,7 +84,7 @@ namespace SmoothnessEstimator * reasons, we will perform the calculation of these matrices and coefficients * only on the reference cell $\hat K$. We only have to calculate the * transformation matrices once this way. However, results are only applicable - * if mapping from the reference cell to the actual cell is linear. We use + * if the mapping from the reference cell to the actual cell is affine. We use * the class FESeries::Legendre to determine all coefficients $a_k$. * * A function is analytic, i.e., representable by a power series, if and only @@ -141,7 +141,7 @@ namespace SmoothnessEstimator * coefficients of some functions may have a repeating pattern of zero * coefficients (i.e. for functions that are locally symmetric or * antisymmetric about the midpoint of the element in any coordinate - * direction). Thus this parameters allows to ingore small (in absolute + * direction). Thus this parameters allows to ignore small (in absolute * value) coefficients within the linear regression fit. In case there are * less than two nonzero coefficients, the returned value for this cell will * be $\sigma=\infty$. @@ -196,7 +196,7 @@ namespace SmoothnessEstimator * Note that Legendre coefficients of some functions may have a repeating * pattern of zero coefficients (i.e. for functions that are locally * symmetric or antisymmetric about the midpoint of the element in any - * coordinate direction). Thus this parameters allows to ingore small (in + * coordinate direction). Thus this parameters allows to ignore small (in * absolute value) coefficients within the linear regression fit. In case * there are less than two nonzero coefficients for a coordinate direction, * this direction will be skipped. If all coefficients are zero, the @@ -266,7 +266,7 @@ namespace SmoothnessEstimator * FESeries::Fourier to determine all coefficients $a_{\bf k}$. * * If the finite element approximation on cell $K$ is part of the Hilbert - * space $H^s(K)$, then the following integral must exit for both finite + * space $H^s(K)$, then the following integral must exist for both the finite * element and spectral representation of our solution * @f{eqnarray*} * \| \nabla^s u_h({\bf x}) \|_{L^2(K)}^2 &=& @@ -294,7 +294,7 @@ namespace SmoothnessEstimator * {-\left(s + \frac d2 + \epsilon \right)} \right) * @f] * - * The next step is that we have to estimate how fast these coefficients + * The next step is to estimate how fast these coefficients * decay with $\|{\bf k}\|_2$. Thus, we perform a least-squares fit * @f[ * \min_{\alpha,\sigma} @@ -378,7 +378,7 @@ namespace SmoothnessEstimator * A series expansion object @p fe_fourier has to be supplied, which needs * to be constructed with the same FECollection object as the @p dof_handler. * - * The parameter @p smallest_abs_coefficient allows to ingore small (in + * The parameter @p smallest_abs_coefficient allows to ignore small (in * absolute value) coefficients within the linear regression fit. In case * there are less than two nonzero coefficients for a coordinate direction, * this direction will be skipped. If all coefficients are zero, the @@ -417,7 +417,7 @@ namespace SmoothnessEstimator * provided to this function. Note that its size is $p+1$, where $p$ is the * polynomial degree of the FE basis on a given element. The default * implementation will use all Fourier coefficients in each coordinate - * direction, i.e. set all elements of the vector to `true`. + * direction, i.e., set all the elements of the vector to `true`. * * For a provided solution vector @p solution defined on a DoFHandler * @p dof_handler, this function returns a vector @p smoothness_indicators @@ -427,9 +427,9 @@ namespace SmoothnessEstimator * A series expansion object @p fe_fourier has to be supplied, which needs * to be constructed with the same FECollection object as the @p dof_handler. * - * The parameter @p smallest_abs_coefficient allows to ingore small (in + * The parameter @p smallest_abs_coefficient allows to ignore small (in * absolute value) coefficients within the linear regression fit. In case - * there are less than two nonzero coefficients for a coordinate direction, + * there are fewer than two nonzero coefficients for a coordinate direction, * this direction will be skipped. If all coefficients are zero, the * returned value for this cell will be $\sigma=\infty$. * -- 2.39.5