From 0bf2c054b62b6dc4f116efbea0c6d9d0ab64374b Mon Sep 17 00:00:00 2001 From: Rafael Bicudo Ribeiro <33603183+rafaelbicudo@users.noreply.github.com> Date: Mon, 2 Oct 2023 17:07:53 -0300 Subject: [PATCH] Revert "Usage + Examples sections" --- docs/.buildinfo | 4 - docs/.doctrees/clusttraj.doctree | Bin 237845 -> 0 bytes docs/.doctrees/environment.pickle | Bin 408416 -> 0 bytes docs/.doctrees/index.doctree | Bin 4676 -> 0 bytes docs/.doctrees/install.doctree | Bin 11492 -> 0 bytes docs/.doctrees/intro.doctree | Bin 6767 -> 0 bytes docs/source/examples.rst | 237 -------------------- docs/source/images/anomalous_dendrogram.pdf | Bin 20457 -> 0 bytes docs/source/images/clusters.pdf | Bin 3500 -> 0 bytes docs/source/images/clusters_dendrogram.pdf | Bin 20493 -> 0 bytes docs/source/images/clusters_evo.pdf | Bin 12721 -> 0 bytes docs/source/usage.rst | 70 ------ 12 files changed, 311 deletions(-) delete mode 100644 docs/.buildinfo delete mode 100644 docs/.doctrees/clusttraj.doctree delete mode 100644 docs/.doctrees/environment.pickle delete mode 100644 docs/.doctrees/index.doctree delete mode 100644 docs/.doctrees/install.doctree delete mode 100644 docs/.doctrees/intro.doctree delete mode 100644 docs/source/examples.rst delete mode 100644 docs/source/images/anomalous_dendrogram.pdf delete mode 100644 docs/source/images/clusters.pdf delete mode 100644 docs/source/images/clusters_dendrogram.pdf delete mode 100644 docs/source/images/clusters_evo.pdf delete mode 100644 docs/source/usage.rst diff --git a/docs/.buildinfo b/docs/.buildinfo deleted file mode 100644 index d9a7f55..0000000 --- a/docs/.buildinfo +++ /dev/null @@ -1,4 +0,0 @@ -# Sphinx build info version 1 -# This file hashes the configuration used when building these files. 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dynamics simulation. The ``h2o_traj.xyz`` file has the molecular trajectory of 5 water molecules: - -.. code-block:: console - - ╰─○ head h2o_traj.xyz - - 15 - Frame 1 - O 5.79224 23.34640 11.44660 - H 4.98409 23.01319 11.83659 - H 5.84046 22.91839 10.59179 - O 5.59517 22.31815 8.86907 - H 5.09795 21.51188 8.73143 - H 6.50540 22.06908 8.70880 - O 2.78349 22.26232 10.73374 - H 2.46676 23.16482 10.69619 - - -To perform the clustering procedure we need to provide the trajectory file and the RMSD threshold distance. This cutoff distance establishs the maximum accepted distance between clusters and can be determined in two ways. - -Manual threshold -^^^^^^^^^^^^^^^^ - -We can fix the maximum RMSD deviation between units in the same cluster up to a certain number, `e.g.`, 2.0 Angstrons: - -.. code-block:: console - - python -m clusttraj h2o_traj.xyz -rmsd 2.0 - -As a result, we obtained 4 output files, `i.e.`, ``distmat.npy``, ``clusters.dat``, ``clusters.out`` and ``clusttraj.log``. - -- ``distmat.npy`` file has the condensed distance matrix in the ``numpy`` file format. - -- ``clusters.dat`` file has the labels of each configuration in the trajectory file. - -.. code-block:: console - - ╰─○ head clusters.dat - 3 - 3 - 1 - 2 - 1 - 1 - 2 - 1 - 1 - 3 - -- ``clusters.out`` file has the simulation details and the cluster sizes. - - -.. code-block:: console - - ╰─○ cat clusters.out - - Full command: /Users/Rafael/Coisas/Doutorado/clusttraj/clusttraj/clusttraj/__main__.py h2o_traj.xyz -rmsd 2.0 -i distmat.npy - - Clusterized from trajectory file: h2o_traj.xyz - Method: average - RMSD criterion: 2.0 - Ignoring hydrogens?: False - - Distance matrix was read from: distmat.npy - The classification of each configuration was written in: clusters.dat - A total 100 snapshots were read and 3 cluster(s) was(were) found. - The cluster sizes are: - Cluster Size - 1 44 - 2 22 - 3 34 - -- ``clusttraj.log`` file has the system output and the cluster sizes. - -.. code-block:: console - - ╰─○ cat clusttraj.log - 2023-09-30 16:39:02,100 INFO [distmat.py:28] Reading condensed distance matrix from distmat.npy - - 2023-09-30 16:39:02,102 INFO [classify.py:97] Clustering using 'average' method to join the clusters - - 2023-09-30 16:39:02,103 INFO [classify.py:105] Saving clustering classification to clusters.dat - - 2023-09-30 16:39:02,105 INFO [main.py:75]
A total 100 snapshots were read and 3 cluster(s) was(were) found. - The cluster sizes are: - Cluster Size - 1 44 - 2 22 - 3 34 - -Automatic threshold -^^^^^^^^^^^^^^^^^^^ - -Instead of manually fixing the maximum RMSD, we can run the ``-ss`` flag to determine the threshold as the value that maximizes the silhouette coefficient. The coefficient varies between -1 and 1, such that higher values indicate a better clustering procedure. Further details can be found `here `_. - -.. code-block:: console - - python -m clusttraj h2o_traj.xyz -ss -i distmat.npy -p - -Since we already computed the distance matrix, we can provide it as input using the ``-i`` flag. Additionally, the ``-p`` flag generates 3 new output files for visualization. - -- ``clusters.pdf`` plots the multidimensional scaling (MDS) of the distance matrix. - -.. image:: images/clusters.pdf - :width: 300pt - -- ``clusters_dendrogram.pdf`` plots the hierarchical clustering dendrogram. - -.. image:: images/clusters_dendrogram.pdf - :width: 300pt - -- ``clusters_evo.pdf`` plots the evolution of cluster populations during the simulation. - -.. image:: images/clusters_evo.pdf - :width: 300pt - -The highest silhouette score is printed in the ``clusttraj.log`` file, along with the corresponding RMSD threshold: - -.. code-block:: console - - ╰─○ cat clusttraj.log - 2023-09-30 17:04:14,908 INFO [distmat.py:28] Reading condensed distance matrix from distmat.npy - - 2023-09-30 17:04:14,916 INFO [classify.py:27] Clustering using 'average' method to join the clusters - - 2023-09-30 17:04:15,064 INFO [classify.py:61] Highest silhouette score: 0.21741836027295444 - - 2023-09-30 17:04:15,065 INFO [classify.py:65] The following RMSD threshold values yielded the same optimial silhouette score: 2.160840752745414, 2.2608407527454135 - - 2023-09-30 17:04:15,065 INFO [classify.py:68] The smallest RMSD of 2.160840752745414 has been adopted - - 2023-09-30 17:04:15,065 INFO [classify.py:76] Saving clustering classification to clusters.dat - - 2023-09-30 17:04:21,562 INFO [main.py:75]
A total 100 snapshots were read and 2 cluster(s) was(were) found. - The cluster sizes are: - Cluster Size - 1 44 - 2 56 - -To determine the optimal threshold the silhouette coefficient is computed for all values in in the `linkage matrix `_ with the default step of 0.1. In this case more than one value yields the same optimal threshold (2.16 and 2.26), and the smallest one is adopted to enhance the within cluster similarity. - -Working with distance methods -^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ - -To investigate the performance of different cluster distance methods we can use the ``-m`` flag. - -Ward -++++ - -In the case of following the Ward variance minimization algorithm: - -.. code-block:: console - - python -m clusttraj -ss -i distmat.npy -p -m ward -f - -In this approach the ``ward`` method is adopted instead of the default ``average`` method. The ``-f`` flag is also included to force the output overwrite with the new data. From the log file: - -.. code-block:: console - - ╰─○ cat clusttraj.log - 2023-09-30 18:07:18,729 INFO [distmat.py:28] Reading condensed distance matrix from distmat.npy - - 2023-09-30 18:07:18,730 INFO [classify.py:27] Clustering using 'ward' method to join the clusters - - 2023-09-30 18:07:18,943 INFO [classify.py:61] Highest silhouette score: 0.23037242401157293 - - 2023-09-30 18:07:18,943 INFO [classify.py:65] The following RMSD threshold values yielded the same optimial silhouette score: 6.060840752745413, 6.160840752745413, 6.260840752745413, 6.360840752745413, 6.460840752745413, 6.5608407527454125, 6.660840752745413, 6.760840752745413, 6.860840752745412, 6.960840752745413, 7.0608407527454125, 7.160840752745413, 7.260840752745413, 7.360840752745412, 7.460840752745413, 7.5608407527454125, 7.660840752745413, 7.760840752745413, 7.860840752745412, 7.960840752745413, 8.060840752745412, 8.160840752745411, 8.260840752745413, 8.360840752745412, 8.460840752745412, 8.560840752745412, 8.660840752745411, 8.760840752745413, 8.860840752745412, 8.960840752745412, 9.060840752745412, 9.160840752745413, 9.260840752745413, 9.360840752745412, 9.460840752745412, 9.560840752745412, 9.660840752745411, 9.760840752745413, 9.860840752745412, 9.960840752745412, 10.060840752745412, 10.160840752745411, 10.260840752745413, 10.360840752745412, 10.460840752745412, 10.560840752745412, 10.660840752745411, 10.760840752745413 - - 2023-09-30 18:07:18,943 INFO [classify.py:68] The smallest RMSD of 6.060840752745413 has been adopted - - 2023-09-30 18:07:18,943 INFO [classify.py:76] Saving clustering classification to clusters.dat - - 2023-09-30 18:07:25,197 INFO [main.py:75]
A total 100 snapshots were read and 2 cluster(s) was(were) found. - The cluster sizes are: - Cluster Size - 1 46 - 2 54 - - -The ``ward`` method also resulted in two clusters with similar populations (46/54 vs 44/56) and with higher silhouette coefficient (0.230 vs 0.217). On the other hand, the smallest RMSD threshold is 6.06, indicating higher deviation between the geometries in each cluster. - -Median -++++++ - -To adopt the ``median`` method we can run: - -.. code-block:: console - - python -m clusttraj h2o_traj.xyz -ss -i distmat.npy -p -m median -f - - ╰─○ cat clusttraj.log - 2023-09-30 18:23:54,842 INFO [distmat.py:28] Reading condensed distance matrix from distmat.npy - - 2023-09-30 18:23:54,843 INFO [classify.py:27] Clustering using 'median' method to join the clusters - - 2023-09-30 18:23:54,870 INFO [classify.py:61] Highest silhouette score: 0.07527635729544939 - - 2023-09-30 18:23:54,870 INFO [classify.py:65] The following RMSD threshold values yielded the same optimial silhouette score: 1.8608407527454136, 1.9608407527454137, 2.060840752745414 - - 2023-09-30 18:23:54,870 INFO [classify.py:68] The smallest RMSD of 1.8608407527454136 has been adopted - - 2023-09-30 18:23:54,870 INFO [classify.py:76] Saving clustering classification to clusters.dat - - 2023-09-30 18:24:00,293 INFO [main.py:75]
A total 100 snapshots were read and 2 cluster(s) was(were) found. - The cluster sizes are: - Cluster Size - 1 99 - 2 1 - - -In this case the highest silhouette score of 0.075 indicates that the points are located near the edge of the clusters. The distribution of population among the 2 clusters (1/99) also indicates the limitations of the method. Finally, visual inspection of the dendrogram shows anomalous behavior. - -.. image:: images/anomalous_dendrogram.pdf - :width: 300pt - -.. .. raw:: html - -.. - - - - - - - - - - - diff --git a/docs/source/images/anomalous_dendrogram.pdf b/docs/source/images/anomalous_dendrogram.pdf deleted file mode 100644 index ff943861a16264b185cb84ac168b54ee56af388c..0000000000000000000000000000000000000000 GIT binary patch literal 0 HcmV?d00001 literal 20457 zcmeIa1yoes_b{x4G$^G=4c$yKFcQ+810vliFyIg(qS7JVAtI^LpeRU4HwqXiQW6q^ zfC>^4?;X(RG5G&Jzu$V-_pSA=7uH$#%)aNG9cSOO_t|%!%cU)^CtX3(3ql-pQgp?5fZzvpZA%XiTPy}7g8NAd*2cP8>)LvNi~&6Iiop1`-X0*ZiVJ{K z_WM=t`&9*G3IgjoSlT#Z>_Nzb?gm;`7i_IPK%)DD|4R<^X9i?27*`K>5EODifgC`M zEd~If4t{Ic+BjOux_SdN;95Wt2r*$e1PK!XA)yFiQ85HuR0L!SkZOqqy8RFY+Sc9G z6Kf4f2FLn8lEDeyuJAIe9}V2Ic5YKONeUD?w`sAE6))9;4p(L8(lLW9CuJT@ zAvh^c>v+DPdwARH^r<6o3X5v3OsVbJ(S^|4nODVq*SA00?5u8mX`@{JwEWQ`WPM}Z zw!8(tedP*t17TXce%XT?-Pl3(vBO~}_5LNkBcj7G4d%iPrkUF$c2ZMLeZnVMl?2AI 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zQNGTukfVds0T8|XnnB_(1qt$VaYdkj$3+0!{Obo?8y@T!SLinkR$cf9k$%C@Xgmt2 z?WH2 zh9;u^#RUUQ?5ci50&xGUVK~A+^AQ1ptNH;K2^%x`_nUtxG!c9^TLr_wvFd6V7P$M> zFgakNSHZAo@Lg~<4EGQ3QD8ctuJG>ANn;O|$% zFeqSE;oqNi@p#z2u7b(^qZ=p!3LH~b^&? - -Instead of fixing the RMSD, one can use the ``-ss`` flag to determine the threshold as the value that maximize the `silhouette coefficient `_ metric: - -.. code-block:: console - - clusttraj trajectory.xyz -ss - -Additional options are available for specifying the input and output files and selecting how the clustering is done. The possible methods used for the agglomerative clustering are the ones available in the linkage method of SciPy's hierarchical clustering. A list with the possible methods (selected with ``-m``) and the description of each of them can be found `here `_. - -The default method for the linkage is ``average``, since `it was found `_ to have a good compromise with the number of clusters and the actual similarity. To learn more about how the clustering is performed using this algorithm, see `UPGMA `_. - -If the ``-n`` option is used, the hydrogens are ignored when performing the Kabsch algorithm to find the superposition and calculating the RMSD. This is useful to avoid clustering identical structures with just a methyl group rotated as different. - -The ``-e`` or ``--reorder`` option, tries to reorder the atoms to increase the overlap and reduce the RMSD. The algorithm can be selected with ``--reorder-alg``, between qml (default), hungarian, brute or distance. For more information about the implementation, see the `RMSD `_ package. - -The reorder option can be used together with the ``-ns`` option, that receives an integer with the number of atoms of the solute. When the ``-ns`` option is used, the script will first superpose the configurations considering only the solute atoms and then reorder considering only the solvent atoms (the atoms in the input that are after the ns atoms). For solute-solvent systems, the use of ``-ns`` is strongly encouraged. - -To use an already saved distance matrix, specify the file containing the distance matrix in the condensed form with the ``-i`` option. The options ``-i`` and ``-od`` are mutually exclusive, since ``-od`` specifies the name of the output distance matrix file to be stored. - -The ``-p`` flag specifies that pdf plots of some information will be saved. -In this case, the filenames will start with the same name used for the clusters output (specified with the ``-oc`` option). When the option is used, the following is saved to disk: - -- A plot with the `multidimensional scaling `_ representation of the distance matrix, colored with the clustering information -- The `dendrogram `_ -- The cluster classification evolution, that shows how during the trajectory, the configurations were classificated. This might be useful to analyze the quality of your sampling. - -If the ``-cc`` option is specified (along with a format supported by OpenBabel) the configurations belonging to the same cluster are superposed and printed to a file. -The superpositions are done considering the `medoid `_ of the cluster as reference. The medoid is printed as the first structure in the clustered strcuture files. - -If you did not consider the hydrogens while building the distance matrix, remember to use the ``-n`` option even if with ``-i`` in this case, since the superposition is done considering the flag. - -Threading and parallelization ------------------------------ - -The ``-np`` option specified the number of processes to be used to calculate the distance matrix. - -Since this is the most time consuming task of the clustering, and due to being a embarassingly parallel problem, it was parallelized using a Python `multiprocessing pool `_. -The default value for ``-np`` is 4. - -When using ``-np`` make sure you also set the correct number of threads for ``numpy``. -If you want to use just the ``multiprocessing`` parallelization (recommended) use the following bash commands to set the number of ``numpy`` threads to one: - -.. code-block:: console - - export OMP_NUM_THREADS=1 - export OPENBLAS_NUM_THREADS=1 - export MKL_NUM_THREADS=1 - export VECLIB_MAXIMUM_THREADS=1 - export NUMEXPR_NUM_THREADS=1