From e9c6d35fce83a83d0384605e0d38eb405c38a371 Mon Sep 17 00:00:00 2001 From: NJ-Thomson Date: Fri, 29 Nov 2024 20:25:56 +0000 Subject: [PATCH] Added images to docs. --- docs/create_docs.sh | 0 docs/images/Density.png | Bin 0 -> 532954 bytes docs/images/JSD_pdb.png | Bin 0 -> 272644 bytes docs/images/SSI_pdb.png | Bin 0 -> 267547 bytes docs/images/Torsions.jpg | Bin 0 -> 60549 bytes docs/images/WaterFeatures.jpg | Bin 0 -> 295434 bytes docs/images/Waters_and_Density.png | Bin 0 -> 793821 bytes docs/images/bb-clusts.png | Bin 0 -> 21154 bytes docs/images/bb-dists.png | Bin 0 -> 293236 bytes ...eceptor_bb-distances-distributions_jsd.pdf | Bin 0 -> 232359 bytes ...receptor_combined-clusters_bb-torsions.pdf | Bin 0 -> 9380 bytes docs/images/receptor_sc-torsions_jsd.pdf | Bin 0 -> 17740 bytes docs/images/receptor_sc-torsions_ssi.pdf | Bin 0 -> 16854 bytes docs/images/sc-jsd.png | Bin 0 -> 37303 bytes docs/images/sc-ssi.png | Bin 0 -> 32957 bytes docs/tut-2-preprocessing.rst | 6 +++- 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z9K>i#R=@I|1L8B4lf23r~Hdd2A@0O-ZMW|mUxfR!O*O>q4ayWrsW5k zT(qAZhX{R!`pMU)pNlcZTo8MO@407spP~xloAl0imVbZ?=N}q%sw;kLUyNNmS711u z(hd6<69XG!LL|2WQFRd3f*ES6DDUxoGdyq}wS4B$$7%fZutiVD@gNobT-DHjQ1}1) s_KN@a{N``p=07s0e`Y`DurFwp@9EYLM60cU*Svy0NXUtoi|PmbAA}B(Jpcdz literal 0 HcmV?d00001 diff --git a/docs/tut-2-preprocessing.rst b/docs/tut-2-preprocessing.rst index 740aaee2..e7f7fbd2 100644 --- a/docs/tut-2-preprocessing.rst +++ b/docs/tut-2-preprocessing.rst @@ -169,6 +169,10 @@ relatively rigid with sites that are spatially static, for example internal water cavities in membrane proteins. Here we demonstrate the preprocessing for water density, however the same procedure would be used for ions. +.. image:: images/Density.png + :height: 300px + :align: center + :alt: Density of protein Files and Directories --------------------- @@ -236,7 +240,7 @@ This helps us avoid memory errors with large python arrays. out_name_water_a+".gro", out_name_water_a+"_aligned.xtc", out_name_water_b+".gro", out_name_water_b+".xtc", atomgroup="OH2", write_grid_as="TIP3P", - out_name="traj/water_grid_ab_", + out_name="ab_grid_", use_memmap=True, memmap='traj/combined.mymemmap' ) diff --git a/docs/tut-3-featurization.rst b/docs/tut-3-featurization.rst index 2a09b484..0c47feb6 100644 --- a/docs/tut-3-featurization.rst +++ b/docs/tut-3-featurization.rst @@ -9,6 +9,12 @@ Note that all reader functions load the names of the features and their values separately. +.. image:: images/Torsions.jpg + :height: 300px + :align: center + :alt: Torsion angles. + + Basic Example ************* @@ -133,6 +139,12 @@ molecules occupy that are of interest. Water pocket featurization extracts a distribution that represents whether or not a specific protein cavity is occupied by a water molecule, and what that water molecule's orientation (polarisation) is. + +.. image:: images/WaterFeatures.jpg + :width: 300px + :align: center + :alt: Water features derived from density. + .. code:: python from pensa.features import read_water_features @@ -140,7 +152,7 @@ by a water molecule, and what that water molecule's orientation (polarisation) i For the pdb visualisation, the trajectory needs to be fit to the first frame of the simulation so that the density and protein align with each other. -Here we featurize the top 3 most probable water sites (top_waters = 3). +Here we featurize the top 2 most probable water sites (top_waters = 2). Orientation of the waters (water_data - spherical coordinates [radians]) is a timeseries distribution. When water is not present at the site, the orientation is recorded as 10000.0 to represent an empty state. If write=True, we can @@ -161,7 +173,7 @@ water water_feat, water_data = read_water_features( structure_input = struc, xtc_input = xtc, - top_waters = 1, + top_waters = 2, atomgroup = "OH2", write = True, write_grid_as="TIP3P", @@ -176,11 +188,11 @@ This way, sites are the same across both ensembles and can be compared. struc = "traj/condition-a_water.gro" xtc = "traj/condition-a_water_aligned.xtc" - grid = "traj/water_grid_ab_OH2_density.dx" + grid = "ab_grid_OH2_density.dx" water_feat, water_data = read_water_features( structure_input = struc, xtc_input = xtc, - top_waters = 5, + top_waters = 2, atomgroup = "OH2", grid_input = grid ) @@ -206,10 +218,10 @@ written (write=True) using the default density conversion "Angstrom^{-3}" in MDA atom_feat, atom_data = read_atom_features( structure_input = struc, xtc_input = xtc, - top_atoms = 1, + top_atoms = 2, atomgroup = "SOD", element = "Na", write = True, out_name = "features/11426_dyn_151_sodium" ) - \ No newline at end of file + diff --git a/docs/tut-4-comparison.rst b/docs/tut-4-comparison.rst index e0d90ac1..da789efe 100644 --- a/docs/tut-4-comparison.rst +++ b/docs/tut-4-comparison.rst @@ -87,6 +87,19 @@ distance per residue in the "B factor" field of a PDB file. y_label='max. JS dist. of BB torsions' ) + +.. image:: images/JSD_pdb.png + :height: 300px + :align: center + :alt: JSD pbd b-factor visualisaton file. + + +.. image:: images/sc-jsd.png + :height: 300px + :align: center + :alt: Jensen-Shannon Distance PDF output. + + Let's now save the resulting data in CSV files. .. code:: python @@ -159,6 +172,18 @@ We can plot the results in the same way as we did for the backbone analysis. ) +.. image:: images/SSI_pdb.png + :height: 300px + :align: center + :alt: SSI pbd b-factor visualisaton file. + +.. image:: images/sc-ssi.png + :height: 300px + :align: center + :alt: State-Specific Information PDF output. + + + Comparing Distances ------------------- @@ -194,3 +219,11 @@ divergence, Kolmogorov-Smirnov statistic etc. instead). names_bbdist, jsd_bbdist, "plots/receptor_jsd-bbdist.pdf", vmin = 0.0, vmax = 1.0, cbar_label='JSD' ) + + + +.. image:: images/bb-dists.png + :height: 300px + :align: center + :alt: JSD distances pbf plot. + diff --git a/docs/tut-5-dimensionality.rst b/docs/tut-5-dimensionality.rst index 5b939969..9333d9c6 100644 --- a/docs/tut-5-dimensionality.rst +++ b/docs/tut-5-dimensionality.rst @@ -199,6 +199,14 @@ We now perform the actual clustering on the combined data. ) cidx, cond, oidx, wss, centroids = cc + + + +.. image:: images/bb-clusts.png + :height: 300px + :align: center + :alt: BB torsion cluster pbf plot. + ... and save the results to a CSV file. .. code:: python

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