From faf6bc7e49458d50da6c989172f6b205bee64823 Mon Sep 17 00:00:00 2001 From: Mahfuza Humayra Mohona Date: Thu, 26 Sep 2024 10:53:32 +0600 Subject: [PATCH 1/3] remove duplicate level for trajectory-similarity --- doc/source/examples/analysis/reduced_dimensions/README.rst | 7 +++---- .../examples/analysis/trajectory_similarity/README.rst | 2 +- 2 files changed, 4 insertions(+), 5 deletions(-) diff --git a/doc/source/examples/analysis/reduced_dimensions/README.rst b/doc/source/examples/analysis/reduced_dimensions/README.rst index 2e07091b7..843148265 100644 --- a/doc/source/examples/analysis/reduced_dimensions/README.rst +++ b/doc/source/examples/analysis/reduced_dimensions/README.rst @@ -13,12 +13,11 @@ low-dimensional representations of the conformational space explored over the trajectory. MDAnalysis implements two methods for dimensionality reduction. -**Principal component analysis** is a common linear dimensionality reduction technique that maps the coordinates in each frame of your trajectory to a linear combination of orthogonal vectors. The vectors are called *principal components*, and they are ordered such that the first principal component accounts for the most variance in the original data (i.e. the largest uncorrelated motion in your trajectory), and each successive component accounts for less and less variance. Trajectory coordinates can be transformed onto a lower-dimensional space (*essential subspace*) constructed from these principal components in order to compare conformations. Your trajectory can also be projected onto each principal component in order to visualise the motion described by that component. +#. **Principal component analysis** is a common linear dimensionality reduction technique that maps the coordinates in each frame of your trajectory to a linear combination of orthogonal vectors. The vectors are called *principal components*, and they are ordered such that the first principal component accounts for the most variance in the original data (i.e. the largest uncorrelated motion in your trajectory), and each successive component accounts for less and less variance. Trajectory coordinates can be transformed onto a lower-dimensional space (*essential subspace*) constructed from these principal components in order to compare conformations. Your trajectory can also be projected onto each principal component in order to visualise the motion described by that component. -**Diffusion maps** are a non-linear dimensionality reduction technique that embeds the coordinates of each frame onto a lower-dimensional space, such that the distance between each frame in the lower-dimensional space represents their "diffusion distance", or similarity. It integrates local information about the similarity of each point to its neighours, into a global geometry of the intrinsic manifold. This means that this technique is not suitable for trajectories where the transitions between conformational states is not well-sampled (e.g. replica exchange simulations), as the regions may become disconnected and a meaningful global geometry cannot be approximated. Unlike PCA, there is no explicit mapping between the components of the lower-dimensional space and the original atomic coordinates; no physical interpretation of the eigenvectors is immediately available. +#. **Diffusion maps** are a non-linear dimensionality reduction technique that embeds the coordinates of each frame onto a lower-dimensional space, such that the distance between each frame in the lower-dimensional space represents their "diffusion distance", or similarity. It integrates local information about the similarity of each point to its neighours, into a global geometry of the intrinsic manifold. This means that this technique is not suitable for trajectories where the transitions between conformational states is not well-sampled (e.g. replica exchange simulations), as the regions may become disconnected and a meaningful global geometry cannot be approximated. Unlike PCA, there is no explicit mapping between the components of the lower-dimensional space and the original atomic coordinates; no physical interpretation of the eigenvectors is immediately available. - -For computing similarity, see the tutorials in :ref:`trajectory-similarity`. +For computing similarity, see the tutorials in :ref:`trajectory-similarity-analysis`. .. toctree:: :maxdepth: 1 diff --git a/doc/source/examples/analysis/trajectory_similarity/README.rst b/doc/source/examples/analysis/trajectory_similarity/README.rst index 5ad50c554..2e86d1a81 100644 --- a/doc/source/examples/analysis/trajectory_similarity/README.rst +++ b/doc/source/examples/analysis/trajectory_similarity/README.rst @@ -1,5 +1,5 @@ .. -*- coding: utf-8 -*- -.. _trajectory-similarity: +.. _trajectory-similarity-analysis: ====================== Trajectory similarity From 0d34fb433467685d092cda2ff578c71546d5253b Mon Sep 17 00:00:00 2001 From: Mahfuza Humayra Mohona Date: Mon, 14 Oct 2024 00:52:25 +0600 Subject: [PATCH 2/3] change tag --- doc/source/examples/analysis/reduced_dimensions/README.rst | 2 +- doc/source/examples/analysis/trajectory_similarity/README.rst | 2 +- 2 files changed, 2 insertions(+), 2 deletions(-) diff --git a/doc/source/examples/analysis/reduced_dimensions/README.rst b/doc/source/examples/analysis/reduced_dimensions/README.rst index 843148265..1541da7f6 100644 --- a/doc/source/examples/analysis/reduced_dimensions/README.rst +++ b/doc/source/examples/analysis/reduced_dimensions/README.rst @@ -17,7 +17,7 @@ reduction. #. **Diffusion maps** are a non-linear dimensionality reduction technique that embeds the coordinates of each frame onto a lower-dimensional space, such that the distance between each frame in the lower-dimensional space represents their "diffusion distance", or similarity. It integrates local information about the similarity of each point to its neighours, into a global geometry of the intrinsic manifold. This means that this technique is not suitable for trajectories where the transitions between conformational states is not well-sampled (e.g. replica exchange simulations), as the regions may become disconnected and a meaningful global geometry cannot be approximated. Unlike PCA, there is no explicit mapping between the components of the lower-dimensional space and the original atomic coordinates; no physical interpretation of the eigenvectors is immediately available. -For computing similarity, see the tutorials in :ref:`trajectory-similarity-analysis`. +For computing similarity, see the tutorials in :ref:`_trajectory-similarity-overview`. .. toctree:: :maxdepth: 1 diff --git a/doc/source/examples/analysis/trajectory_similarity/README.rst b/doc/source/examples/analysis/trajectory_similarity/README.rst index 2e86d1a81..c6ded6605 100644 --- a/doc/source/examples/analysis/trajectory_similarity/README.rst +++ b/doc/source/examples/analysis/trajectory_similarity/README.rst @@ -1,5 +1,5 @@ .. -*- coding: utf-8 -*- -.. _trajectory-similarity-analysis: +.. _trajectory-similarity-overview: ====================== Trajectory similarity From 5a271d153b09be37857be947a361332c426eac3d Mon Sep 17 00:00:00 2001 From: Oliver Beckstein Date: Mon, 24 Mar 2025 12:47:50 -0700 Subject: [PATCH 3/3] Update doc/source/examples/analysis/reduced_dimensions/README.rst --- doc/source/examples/analysis/reduced_dimensions/README.rst | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/doc/source/examples/analysis/reduced_dimensions/README.rst b/doc/source/examples/analysis/reduced_dimensions/README.rst index 1541da7f6..4564ae890 100644 --- a/doc/source/examples/analysis/reduced_dimensions/README.rst +++ b/doc/source/examples/analysis/reduced_dimensions/README.rst @@ -17,7 +17,7 @@ reduction. #. **Diffusion maps** are a non-linear dimensionality reduction technique that embeds the coordinates of each frame onto a lower-dimensional space, such that the distance between each frame in the lower-dimensional space represents their "diffusion distance", or similarity. It integrates local information about the similarity of each point to its neighours, into a global geometry of the intrinsic manifold. This means that this technique is not suitable for trajectories where the transitions between conformational states is not well-sampled (e.g. replica exchange simulations), as the regions may become disconnected and a meaningful global geometry cannot be approximated. Unlike PCA, there is no explicit mapping between the components of the lower-dimensional space and the original atomic coordinates; no physical interpretation of the eigenvectors is immediately available. -For computing similarity, see the tutorials in :ref:`_trajectory-similarity-overview`. +For computing similarity, see the tutorials in :ref:`trajectory-similarity-overview`. .. toctree:: :maxdepth: 1