https://github.com/POVME/POVME
首先按照教程安装povme3.0，每次使用的时候，用一个单独的python27环境，source 路径/miniconda2/bin/activate
（我GPU工作站设置了alias povme，直接输入povme即到python27环境）
第一步：将同一个蛋白（不同轨迹）align好，生成pdb轨迹
gmx trjconv -s tpr 为reference structure，align的时候align到tpr结构上，而不是第一帧
得到一个pdb轨迹
第二步：生成povme参数文件 *.ini
简化版（斜体为必须设置的，其他参数默认）：
GridSpacing 1.0 （相邻点之间的距离，单位为埃。降低这个数字可以提高精度，但会牺牲计算时间）
InclusionSphere 65.0 98.0 50.0 16.0 （添加一个由点组成的球体到包围区域，以(65.0 98.0 50.0)为中心，半径为16.0。(65.0 98.0 50.0)是提供的样品PDB轨迹文件中酶活性位点的位置(4NSS.pdb)）
PDBFileName 4NSS.pdb
DistanceCutoff 1.09 （默认值为1.09埃，因为这是氢原子的范德华半径。）
ConvexHullExclusion first （first/max/each，详见下面解释）
SeedSphere 67.0 102.0 57.0 4.0 （如果设置了该seed球，则测的即必须包含这个区域（如果有多个口袋的话）；不设置，则测口袋总体积，即使有多个不相邻的口袋。不要随便用这个参数，当该设置中所含的体积里面没有口袋时，运算会报错）
ContiguousPointsCriteria 3 （如果两个口袋卷至少共享这个ContiguousPointsCriteria的相邻点，则认为它们是“邻接的”，用默认即可）
NumProcessors 16
OutputFilenamePrefix ./POVME_test_run/example_ （所有POVME输出的文件都以这个前缀开头。POVME自动创建所需的目录(./POVME_test_run/在本例中)）
#### The minimum input required for POVME to run is the input trajectory name and inclusion region.
第三步：看输出结果
We recommend that you visualize the results using VMD. Open the POVME output using VMD with the following command:
vmd -m POVME_test_run/example_volume_trajectory.pdb 4NSS.pdb
Under the Graphics-->Representations menu in VMD, show the 0: POVME_volume_trajectory.pdb molecule using the Drawing Method "VDW" (and consider reducing the sphere Scalevalue to something like0.3). Now press the play button in the bottom right corner of the VMD Main window to watch the pocket trajectory (For this short trajectory, it is probably better to advance manually through the frames).
第四步：PCA分析和聚类
（未完成。。）
详细参数：
# POVME 3.0 Sample Input File
# First, we need to define a point field that entirely encompasses all trajectory pockets.
首先，我们需要定义一个完全包含所有轨迹口袋的点场。
GridSpacing 1.0 # The distance, in Angstroms, between adjacent points. Making this number lower improves accuracy at the expense of computing time.
#相邻点之间的距离，单位为埃。降低这个数字可以提高精度，但会牺牲计算时间。
InclusionSphere 65.0 98.0 50.0 16.0 # Add a sphere of points to the pocket- encompassing region, centered on (65.0 98.0 50.0) with radius 16.0. (65.0 98.0 50.0) is the location of the enzymatic active site in the sample PDB trajectory file provided (4NSS.pdb).
添加一个由点组成的球体到包围区域，以(65.0 98.0 50.0)为中心，半径为16.0。(65.0 98.0 50.0)是提供的样品PDB轨迹文件中酶活性位点的位置(4NSS.pdb)。
#InclusionSphere -100.0 -100.0 -100.0 10.0 # Add a second sphere of points. Note that this sphere is included only for demonstration purposes. The point (-100.0 -100.0 -100.0) is actually far from the 4NSS.pdb structure.
添加第二个点球体。请注意，包含此范围仅用于演示目的。这个点(-100.0 -100.0 -100.0)实际上离4NSS pdb的结构很远。
#InclusionBox 100.0 100.0 100.0 10.0 10.0 10.0 # Add a rectangular prism ("box") of points to the pocket-encompassing region, centered on (100.0 100.0 100.0) and spanning 10.0 Angstroms in the x, y, and z directions, respectively. Again, this box is far from the 4NSS.pdb structure and is included only for demonstration purposes.
#添加一个矩形棱柱(“方框”)到包含口袋的区域，以(100.0 100.0 100.0)为中心，在x、y和z方向上分别跨越10.0埃。同样，这个盒子离4NSS很远，仅用于演示目的。
InclusionCylinder 65.0 98.0 50.0 1.0 1.0 1.0 16.0 10.0 # Add a cylinder of points centered at [65.0 98.0 50.0], with its length along the axis [1.0 1.0 1.0], a radius of 16.0 angstroms, and a height of 10.0 angstroms.
#添加一个以[65.0 98.0 50.0]为中心的点圆柱体，其长度沿轴[1.0 1.0 1.0]，半径16.0埃，高度10.0埃。
#ExclusionSphere -100.0 -100.0 -100.0 10.0 # Remove all points from the pocket- encompassing region that fall within a sphere centered at (-100.0 -100.0 -100.0) with radius 10.0.
#从环绕口袋的区域中移除所有位于半径为10.0圆心(-100.0 -100.0 -100.0)的球体内的点。
#ExclusionBox 100.0 100.0 100.0 10.0 10.0 10.0 # Remove all points from the pocket-encompassing region that fall within a box, centered at (100.0 100.0 100.0) and spanning 10.0 Angstroms in the x, y, and z directions, respectively.
#从包含口袋的区域中移除位于方框内的所有点，在x、y和z方向上分别以(100.0 100.0 100.0)为中心和跨越10.0埃。
# Saving and loading the POVME points of the pocket-encompassing region.
#SavePoints true # You can optionally save the point field to a PDB file. As you can imagine, identifying just the right set of inclusion and exclusion spheres and boxes to encompass the binding pocket is challenging. On approach is to define an initial geometry, visualize that geometry together with the protein using a program like VMD, and then iteratively add new inclusion and exclusion regions as required. The ability to save the points for visualization is helpful. Additionally, if your point field contains many points, generating the field may be computationally intensive. If you want to use the same field for multiple POVME runs, using a saved copy of the field rather than repeatedly generating it from scratch is more efficient. Note that POVME also saves a NPY file, which contains the same data as the PDB file but can be loaded much faster in subsequent POVME runs. The point-field PDB file is saved to {PREFIX}point_field.pdb (see OutputFilenamePrefix below). Additionally, if you specify a contiguous- pocket seed region (see ContiguousPocketSeedSphere and ContiguousPocketSeedBox below), POVME will also save those points to {PREFIX}contiguous_pocket_seed_points.pdb for visualization.
您可以选择将POINT field 保存到PDB文件中。正如你可以想象的那样，确定正确的包含和排除球体和盒子来包含binding口袋是一项挑战。方法是定义一个初始的几何图形，使用像VMD这样的程序将该几何图形与蛋白质可视化，然后根据需要迭代地添加新的包含和排除区域。保存点以便可视化的能力很有帮助。此外，如果点场包含许多点，则生成的场可能会非常密集。如果您希望在多个POVME运行时使用相同的字段，那么使用保存的字段副本而不是从头重复生成该字段更有效。注意，POVME还保存了一个NPY文件，该文件包含与PDB文件相同的数据，但在随后的POVME运行中可以更快地加载。点字段PDB文件被保存到{PREFIX}point_field.pdb(参见下面的OutputFilenamePrefix)。另外，如果您指定一个连续的口袋种子区域(参见下面的 ContiguousPocketSeedSphere和ContiguousPocketSeedBox )，POVME也会将这些点保存为{PREFIX}contiguous_pocket_seed_points.pdb来可视化。
#LoadPointsFilename points.pdb.npy # You can optionally load previous point fields if you don't want to generate them using the comamnds above. Note that you should use the .pdb.npy file, not the pdb file.
加载之前生成的point field 文件
# Load the PDB trajectory file
PDBFileName 4NSS.pdb # Load in the PDB trajectory file with the pocket you want to analyze.Tell POVME how to identify points that are within the binding pocket.
DistanceCutoff 1.09 # Any point that comes within this distance of any receptor atom's van der Waals surface will not be considered part of the pocket volume. 1.09 Angstroms was chosen as the default value because that is the van der Waals radius of a hydrogen atom.
任何靠近任何受体原子的范德华表面这个距离内的点都不会被认为是口袋体积的一部分。默认值为1.09埃，因为这是氢原子的范德华半径。
ConvexHullExclusion first # The convex hull is a method of determining where a pocket "ends" on the outside of the protein. Portions of the inclusion region which lie outside of the convex hull are removed. In previous versions, this would be recalculated for each individual frame, however this led to significant numerical noise as the motion of the protein in different frames could radically redefine the convex hull. A consistent convex hull can now be used in all frames by giving the "first" or "max" keywords here. "first" applied the convex hull from the first frame to all others."max" draws a convex hull around all the frames superimposed simultaneously (and may take a while on large trajectories). To reproduce previous behavior, the "each" keyword may be used. Any other keyword will not use the convex hull exclusion method.
可写参数：first max each none
凸壳是一种确定pocket在蛋白质外部“末端”位置的方法。所述凸包外部的部分包含区域被移除。在以前的版本中，对于每个单独的帧，这将被重新计算，但是这导致了显著的数值噪声，因为蛋白质在不同帧中的运动可能会从根本上重新定义凸包。一个一致的凸壳现在可以使用在所有帧通过给出“first”或“max”关键字在这里。“first”将第一帧中的凸包应用到所有其他帧中。“max”在同时叠加的所有帧周围画一个凸壳(在大轨迹上可能需要一段时间)。要重现以前的行为，可以使用“each”关键字。任何其他关键字都不会使用凸包排除方法。
SeedSphere 67.0 102.0 57.0 4.0 # It's possible your pocket-encompassing point field defined above might include more than one pocket in at least some of the frames of your trajectory. You can instruct POVME to remove any points that are not contiguous with a user-defined "contiguous pocket seed region." This region, which is typically just a small sphere placed in the center of your primary pocket of interest, tells POVME which pocket to measure. If no such regions are specified, POVME will calculate the total volume accross all pockets covered by your pocket-encompassing point field, even if they are not contiguous.
如果设置了该seed球，则测的即必须包含这个区域（如果有多个口袋的话）；不设置，则测口袋总体积，即使有多个不相邻的口袋。
在你的轨迹的至少一些框架中，上面定义的包含口袋的点场可能包含多个口袋。您可以指示POVME删除不与用户定义的“contiguous pocket seed region”相邻的任何points。这个区域，通常是放在你感兴趣的主要口袋中心的一个小球体，告诉POVME要测量哪个口袋（如果有多个口袋的话）。如果没有指定这样的区域，POVME将计算包含口袋的点场所覆盖的所有口袋的总体积，即使它们不是相邻的。
ContiguousPointsCriteria 3 # Two pocket volumes are considered "contiguous" if they share at least this number neighboring points in common. Note that points that are "kitty-corner" from each other count as neighbors.
如果两个口袋卷至少共享这个ContiguousPointsCriteria的相邻点，则认为它们是“邻接的”。注意，点是“kitty-corner”从对方算作相邻。
# Tell POVME how to perform the calculations.
NumProcessors 1 # POVME can use multiple processors on Unix-based systems.
# Tell POVME how to save the output
OutputFilenamePrefix ./POVME_test_run/example_ # All the files POVME outputs will start with this prefix. POVME automatically creates any required directory (./POVME_test_run/ in this case).
所有POVME输出的文件都以这个前缀开头。POVME自动创建所需的目录(./POVME_test_run/在本例中)。
CompressOutput false # If you're short on disk space, POVME can automatically compress all output files using gz compression.
安装POVME3.0
wget https://repo.continuum.io/miniconda/Miniconda2-latest-Linux-x86_64.sh
bash Miniconda2-latest-Linux-x86_64.sh -b -p miniconda2
source miniconda2/bin/activate
pip install povme
https://github.com/POVME/POVME
cd basic_example
POVME3.py sample_input.ini
This example shows the "classic" operation of POVME, using a geometrically-defined inclusion sphere. If you open the "sample_input.ini" text file, you will find the operating parameters. The minimum input required for POVME to run is the input trajectory name and inclusion region.
Once this runs, you will have an output directory named POVME_test_run.
Ligand-defined inclusion region example
cd ligand_example/
POVME3.py sample_POVME_input.ini
To visualize:
vmd -m POVME_test_run/POVME_volume_trajectory.pdb 1BYQ_every250.pdb
POVME 3.0 now allows users to define the inclusion region of a pocket using a ligand residue name. The pocket will then be defined in all grid points within 3 Angstroms of the ligand atoms in the loaded PDB trajectory. Note that this residue name must match the one given in the input PDB trajectory.
Clustering and PCA example
cd analysis_workflow_example/
source runWorkflow.sh
The bulk of the new capabilities of POVME 3.0 are in separate scripts. Three of these are showcased in the analysis workflow example.
This example runs POVME on 5 trajectories taken from the POVME 3.0 paper's HSP90 simulations. Each of these trajectory PDB files has 5 frames, and has had the ligand removed. After running POVME on these trajectories, three post-processing scripts are run:
binding_site_overlap.py calculates the similarity of all of the analyzed frames
cluster.py processes the binding_site_overlap matrix and performs hierarchical clustering.
1. This example is programmed to yield five clusters, as specified in the "-n" argument to cluster.py // 这个示例被编程为生成5个集群，这在cluster.py的“-n”参数中指定
2. A heatmap showing which frames belong to which cluster is displayed when cluster.py finishes running. //py结束运行时将显示一个热图，显示哪个帧属于哪个集群。
3. Combined and individual-simulation transition maps are displayed. //将显示组合和单独模拟的转换映射。
4. The most representative frames from each cluster are output in the 3-post_analysis/ALL/cluster#subdirectories. //每个集群中最具代表性的帧输出在3-post_analysis/ALL/cluster#子目录中。
5. The average pocket shape of each cluster can be visualized in vmd by running vmd -e visualizeAll.vmd in the 3-post_analysis/ALL subdirectory, and showing the second representation in each loaded object .//通过在3-post_analysis/ALL子目录运行vmd -e visualizeAll.vmd，可以在vmd中显示每个簇的平均 pocket shape，并在每个加载的对象中显示 second representation。
6. Text files of the cluster members and representatives are written, with each line corresponding to one cluster //text文件中写cluter成员和代表的结构，每一行对应一个集群
pocketPointsPca.py runs principal component analysis of the pocket points in the frames
1. Scatterplots of each simulations position in PC space are shown//给出了计算机空间中各模拟位置的散点图
2. A plot of the explained variance for each PC is shown//每个PC的方差解释图显示
3. The first 10 principal components can be visualized by running vmd -e loadAllPcs.vmd //显示前10个主成分
运行流程：
(base) $ source runWorkflow.sh ---------------------------------
START
---------------------------------
PDBFILENAME ../1-trajectories/4R3M_every50ns_aligned.pdb
GRIDSPACING 1.0
INCLUSIONSPHERE 38.2 -47.5 63.7 15
CONTIGUOUSPOINTSCRITERIA 3
DISTANCECUTOFF 1.09
CONVEXHULLEXCLUSION none
OUTPUTFILENAMEPREFIX ./4R3M_every50ns/4R3M_every50ns_
COMPRESSOUTPUT false
NUMPROCESSORS 1
If you use POVME in your research, please cite the following reference: Durrant, J. D., C. A. de Oliveira, et al. (2011). "POVME: An algorithm for measuring binding-pocket volumes." J Mol Graph Model 29(5): 773-776.
Parameters:
OUTPUTFILENAMEPREFIX: ./4R3M_every50ns/4R3M_every50ns_
POINTSINCLUDEREGIONS: sphere at (38.2, -47.5, 63.7), radius = 15.0
NUMFRAMES: -1
OutputFrameFilenamePrefix: ./4R3M_every50ns/4R3M_every50ns_frameInfo/4R3M_every50ns_
MAXGROWITERATIONS: 10000000000.0
GRIDSPACING: 1.0
NUMPROCESSORS: 1
DISTANCECUTOFF: 1.09
COMPRESSOUTPUT: False
PDBFILENAME: ../1-trajectories/4R3M_every50ns_aligned.pdb
CONVEXHULLEXCLUSION: none
SAVEREGIONS: False
CONTIGUOUSPOINTSCRITERIA: 3
OUTPUTBASENAME: 4R3M_every50ns_
---------------------------------
PARAMETERS DEFINED
---------------------------------
---------------------------------
ABOUT TO LOAD RECEPTORS
---------------------------------
Reading frames from ../1-trajectories/4R3M_every50ns_aligned.pdb
Further processing frame 1
Further processing frame 2
Further processing frame 3
Further processing frame 4
Further processing frame 5
---------------------------------
RECEPTORS LOADED
---------------------------------
Generating the pocket-encompassing point field
Saving the point field as a PDB and NPY file
Point field saved to ./4R3M_every50ns/4R3M_every50ns_frameInfo/4R3M_every50ns_inclusion.pdb to permit visualization Point field saved to ./4R3M_every50ns/4R3M_every50ns_frameInfo/4R3M_every50ns_inclusion.npy to optionally load for the volume calculation
Calculating the pocket volume of each frame
---------------------------------
STARTING CALC VOLUME
---------------------------------
Frame 1: Volume 2399.0 A^3 Surf. A. 1136.0 A^2
---------------------------------
FINISHING CALC VOLUME
---------------------------------
---------------------------------
STARTING CALC VOLUME
---------------------------------
Frame 2: Volume 2038.0 A^3 Surf. A. 1172.0 A^2
---------------------------------
FINISHING CALC VOLUME
---------------------------------
---------------------------------
STARTING CALC VOLUME
---------------------------------
Frame 3: Volume 1925.0 A^3 Surf. A. 1153.0 A^2
---------------------------------
FINISHING CALC VOLUME
---------------------------------
---------------------------------
STARTING CALC VOLUME
---------------------------------
Frame 4: Volume 2121.0 A^3 Surf. A. 1147.0 A^2
---------------------------------
FINISHING CALC VOLUME
---------------------------------
---------------------------------
STARTING CALC VOLUME
---------------------------------
Frame 5: Volume 2255.0 A^3 Surf. A. 1177.0 A^2
---------------------------------
FINISHING CALC VOLUME
---------------------------------
---------------------------------
VOLUMES CALCULATED
---------------------------------
FRAME | VOLUME (A^3) | SURF. A. (A^2)
-------------+--------------+----------------
1 | 2399.0 | 1136.0
2 | 2038.0 | 1172.0
3 | 1925.0 | 1153.0
4 | 2121.0 | 1147.0
5 | 2255.0 | 1177.0
Execution time = 83.1753201485 sec
---------------------------------
ABOUT TO CALCULATE OCCUPANCY AVERAGE
---------------------------------
---------------------------------
CALCULATED OCCUPANCY AVERAGE
---------------------------------
---------------------------------
ABOUT TO CALCULATE COLOR MAPS
---------------------------------
---------------------------------
CALCULATED COLOR MAPS
---------------------------------
---------------------------------
接下来的4个轨迹略，每个轨迹5帧
['../../2-POVME_analysis/4R3M_every50ns/4R3M_every50ns_frameInfo/4R3M_every50ns_frame_1.npy'
, '../../2-POVME_analysis/1UYF_every50ns/1UYF_every50ns_frameInfo/1UYF_every50ns_frame_1.npy', '../../2-POVME_analysis/1UYL_every50ns/1UYL_every50ns_frameInfo/1UYL_every50ns_frame_1.npy', '../../2-POVME_analysis/3D0B_every50ns/3D0B_every50ns_frameInfo/3D0B_every50ns_frame_1.npy', '../../2-POVME_analysis/1UYI_every50ns/1UYI_every50ns_frameInfo/1UYI_every50ns_frame_1.npy', '../../2-POVME_analysis/4R3M_every50ns/4R3M_every50ns_frameInfo/4R3M_every50ns_frame_2.npy', '../../2-POVME_analysis/1UYF_every50ns/1UYF_every50ns_frameInfo/1UYF_every50ns_frame_2.npy', '../../2-POVME_analysis/1UYL_every50ns/1UYL_every50ns_frameInfo/1UYL_every50ns_frame_2.npy', '../../2-POVME_analysis/3D0B_every50ns/3D0B_every50ns_frameInfo/3D0B_every50ns_frame_2.npy', '../../2-POVME_analysis/1UYI_every50ns/1UYI_every50ns_frameInfo/1UYI_every50ns_frame_2.npy', '../../2-POVME_analysis/4R3M_every50ns/4R3M_every50ns_frameInfo/4R3M_every50ns_frame_3.npy', '../../2-POVME_analysis/1UYF_every50ns/1UYF_every50ns_frameInfo/1UYF_every50ns_frame_3.npy', '../../2-POVME_analysis/1UYL_every50ns/1UYL_every50ns_frameInfo/1UYL_every50ns_frame_3.npy', '../../2-POVME_analysis/3D0B_every50ns/3D0B_every50ns_frameInfo/3D0B_every50ns_frame_3.npy', '../../2-POVME_analysis/1UYI_every50ns/1UYI_every50ns_frameInfo/1UYI_every50ns_frame_3.npy', '../../2-POVME_analysis/4R3M_every50ns/4R3M_every50ns_frameInfo/4R3M_every50ns_frame_4.npy', '../../2-POVME_analysis/1UYF_every50ns/1UYF_every50ns_frameInfo/1UYF_every50ns_frame_4.npy', '../../2-POVME_analysis/1UYL_every50ns/1UYL_every50ns_frameInfo/1UYL_every50ns_frame_4.npy', '../../2-POVME_analysis/3D0B_every50ns/3D0B_every50ns_frameInfo/3D0B_every50ns_frame_4.npy', '../../2-POVME_analysis/1UYI_every50ns/1UYI_every50ns_frameInfo/1UYI_every50ns_frame_4.npy', '../../2-POVME_analysis/4R3M_every50ns/4R3M_every50ns_frameInfo/4R3M_every50ns_frame_5.npy', '../../2-POVME_analysis/1UYF_every50ns/1UYF_every50ns_frameInfo/1UYF_every50ns_frame_5.npy', '../../2-POVME_analysis/1UYL_every50ns/1UYL_every50ns_frameInfo/1UYL_every50ns_frame_5.npy', '../../2-POVME_analysis/3D0B_every50ns/3D0B_every50ns_frameInfo/3D0B_every50ns_frame_5.npy', '../../2-POVME_analysis/1UYI_every50ns/1UYI_every50ns_frameInfo/1UYI_every50ns_frame_5.npy']The number of frames found was: 25 所有的五条轨迹25帧
Starting Tanimoto calculations
Overlap Matrix for Tanimoto calculation
[[1. 0.62900448 0.64765217 0.59501348 0.66082691 0.64882943
0.62778152 0.50920051 0.64109882 0.74176218 0.64160972 0.66573329
0.48633702 0.67280453 0.65585088 0.60796869 0.67383146 0.50098945
0.64149636 0.7079489 0.56489576 0.62705055 0.56037358 0.67741935
0.64851826]
[0.62900448 1. 0.65356004 0.695747 0.74812874 0.55058573
0.69299182 0.49346926 0.64285714 0.73632742 0.5311263 0.69069935
0.47880381 0.69395924 0.67008124 0.56946403 0.74684001 0.47969627
0.67925207 0.70893372 0.54429101 0.70376956 0.5378712 0.66357474
0.66725601]
[0.64765217 0.65356004 1. 0.62482615 0.6469553 0.5600713
0.65291262 0.56429047 0.63038793 0.69622042 0.5225 0.65073788
0.51325948 0.70431287 0.6398892 0.5645614 0.68756531 0.51535606
0.67670251 0.68152866 0.54075813 0.64857437 0.58823529 0.66642883
0.67958748]
[0.59501348 0.695747 0.62482615 1. 0.70594673 0.53978873
0.6518724 0.50803342 0.6281407 0.668637 0.50583245 0.6364587
0.47801837 0.65541741 0.68194741 0.54454073 0.6650619 0.47211286
0.66370107 0.67572336 0.52339861 0.6363016 0.5421123 0.63732024
0.66021808]
[0.66082691 0.74812874 0.6469553 0.70594673 1. 0.58947368
0.69208939 0.51531764 0.64684554 0.72531418 0.54733406 0.65937169
0.50768717 0.67300108 0.71021292 0.59129815 0.70105263 0.49666889
0.67178571 0.70430108 0.57721345 0.65586207 0.55398587 0.67406877
0.66819626]
[0.64882943 0.55058573 0.5600713 0.53978873 0.58947368 1.
0.55807464 0.47963649 0.59841629 0.60843158 0.66512605 0.58087707
0.48221205 0.62072808 0.57082153 0.6120155 0.5830721 0.47951977
0.6001462 0.58773181 0.56564551 0.53864569 0.50365726 0.59525547
0.5790789 ]
[0.62778152 0.69299182 0.65291262 0.6518724 0.69208939 0.55807464
1. 0.49734126 0.64242639 0.71203639 0.53094233 0.74272198
0.46046364 0.66456583 0.66735751 0.54761905 0.71091854 0.46638924
0.66167247 0.71070293 0.5357377 0.72730484 0.52411784 0.66445299
0.71499822]
[0.50920051 0.49346926 0.56429047 0.50803342 0.51531764 0.47963649
0.49734126 1. 0.4659164 0.49798074 0.44208754 0.4935106
0.67952522 0.50482005 0.46926166 0.49799331 0.50386399 0.64959766
0.49713193 0.51492063 0.54539363 0.50265045 0.67279942 0.4849636
0.50380469]
[0.64109882 0.64285714 0.63038793 0.6281407 0.64684554 0.59841629
0.64242639 0.4659164 1. 0.70007289 0.56882129 0.6453824
0.46138415 0.7118003 0.66148844 0.54910394 0.6953547 0.4711051
0.69124767 0.6898572 0.52081911 0.6340694 0.50841751 0.71223565
0.70293353]
[0.74176218 0.73632742 0.69622042 0.668637 0.72531418 0.60843158
0.71203639 0.49798074 0.70007289 1. 0.596 0.72039943
0.46971139 0.77501853 0.71136924 0.58980583 0.78556034 0.48138639
0.72433597 0.76931447 0.54114958 0.69243986 0.54143646 0.74563319
0.73240443]
[0.64160972 0.5311263 0.5225 0.50583245 0.54733406 0.66512605
0.53094233 0.44208754 0.56882129 0.596 1. 0.58644946
0.421875 0.59056886 0.54448017 0.58232303 0.56996103 0.42802102
0.57206045 0.55297065 0.49874507 0.5276907 0.46175243 0.59356287
0.5565186 ]
[0.66573329 0.69069935 0.65073788 0.6364587 0.65937169 0.58087707
0.74272198 0.4935106 0.6453824 0.72039943 0.58644946 1.
0.45285439 0.6780086 0.63786575 0.57559775 0.73573466 0.4778906
0.66430595 0.705 0.53833333 0.72548318 0.53355372 0.6665483
0.69828203]
[0.48633702 0.47880381 0.51325948 0.47801837 0.50768717 0.48221205
0.46046364 0.67952522 0.46138415 0.46971139 0.421875 0.45285439
1. 0.47039581 0.45989134 0.49044807 0.46247655 0.72191235
0.47049234 0.4763285 0.56914894 0.46643445 0.67067217 0.46011673
0.46793426]
[0.67280453 0.69395924 0.70431287 0.65541741 0.67300108 0.62072808
0.66456583 0.50482005 0.7118003 0.77501853 0.59056886 0.6780086
0.47039581 1. 0.66302817 0.58502674 0.75453885 0.48441645
0.75573956 0.72327273 0.53126045 0.66527488 0.53825593 0.75906344
0.71335031]
[0.65585088 0.67008124 0.6398892 0.68194741 0.71021292 0.57082153
0.66735751 0.46926166 0.66148844 0.71136924 0.54448017 0.63786575
0.45989134 0.66302817 1. 0.59231853 0.68267581 0.4729686
0.65780266 0.67899512 0.5376735 0.63218005 0.51506317 0.67706487
0.67120894]
[0.60796869 0.56946403 0.5645614 0.54454073 0.59129815 0.6120155
0.54761905 0.49799331 0.54910394 0.58980583 0.58232303 0.57559775
0.49044807 0.58502674 0.59231853 1. 0.58384668 0.4998245
0.55924502 0.57137907 0.65694813 0.55111713 0.50736554 0.55897795
0.54632807]
[0.67383146 0.74684001 0.68756531 0.6650619 0.70105263 0.5830721
0.71091854 0.50386399 0.6953547 0.78556034 0.56996103 0.73573466
0.46247655 0.75453885 0.68267581 0.58384668 1. 0.47717185
0.73107143 0.74689386 0.5461039 0.7037037 0.54287556 0.72058301
0.73598297]
[0.50098945 0.47969627 0.51535606 0.47211286 0.49666889 0.47951977
0.46638924 0.64959766 0.4711051 0.48138639 0.42802102 0.4778906
0.72191235 0.48441645 0.4729686 0.4998245 0.47717185 1.
0.46941446 0.49557812 0.58810811 0.49870045 0.72211426 0.4748438
0.48074413]
[0.64149636 0.67925207 0.67670251 0.66370107 0.67178571 0.6001462
0.66167247 0.49713193 0.69124767 0.72433597 0.57206045 0.66430595
0.47049234 0.75573956 0.65780266 0.55924502 0.73107143 0.46941446
1. 0.7113031 0.5105194 0.65894955 0.53710483 0.72945966
0.71630948]
[0.7079489 0.70893372 0.68152866 0.67572336 0.70430108 0.58773181
0.71070293 0.51492063 0.6898572 0.76931447 0.55297065 0.705
0.4763285 0.72327273 0.67899512 0.57137907 0.74689386 0.49557812
0.7113031 1. 0.55322688 0.6764099 0.55140496 0.72869629
0.72192321]
[0.56489576 0.54429101 0.54075813 0.52339861 0.57721345 0.56564551
0.5357377 0.54539363 0.52081911 0.54114958 0.49874507 0.53833333
0.56914894 0.53126045 0.5376735 0.65694813 0.5461039 0.58810811
0.5105194 0.55322688 1. 0.53866232 0.56885813 0.51020408
0.52481104]
[0.62705055 0.70376956 0.64857437 0.6363016 0.65586207 0.53864569
0.72730484 0.50265045 0.6340694 0.69243986 0.5276907 0.72548318
0.46643445 0.66527488 0.63218005 0.55111713 0.7037037 0.49870045
0.65894955 0.6764099 0.53866232 1. 0.55409836 0.65424431
0.66815835]
[0.56037358 0.5378712 0.58823529 0.5421123 0.55398587 0.50365726
0.52411784 0.67279942 0.50841751 0.54143646 0.46175243 0.53355372
0.67067217 0.53825593 0.51506317 0.50736554 0.54287556 0.72211426
0.53710483 0.55140496 0.56885813 0.55409836 1. 0.53528628
0.53522054]
[0.67741935 0.66357474 0.66642883 0.63732024 0.67406877 0.59525547
0.66445299 0.4849636 0.71223565 0.74563319 0.59356287 0.6665483
0.46011673 0.75906344 0.67706487 0.55897795 0.72058301 0.4748438
0.72945966 0.72869629 0.51020408 0.65424431 0.53528628 1.
0.697733 ]
[0.64851826 0.66725601 0.67958748 0.66021808 0.66819626 0.5790789
0.71499822 0.50380469 0.70293353 0.73240443 0.5565186 0.69828203
0.46793426 0.71335031 0.67120894 0.54632807 0.73598297 0.48074413
0.71630948 0.72192321 0.52481104 0.66815835 0.53522054 0.697733
1. ]]
Starting Tversky calculations
Overlap Matrix for Tversky calculation
[[1. 0.76115048 0.77615673 0.73614006 0.7861609 0.72780325
0.77615673 0.66902876 0.74906211 0.86327637 0.70446019 0.79283035
0.62317632 0.79199667 0.79199667 0.71238016 0.82325969 0.63318049
0.77198833 0.8315965 0.70029179 0.78074198 0.70029179 0.79658191
0.78449354]
[0.78369099 1. 0.79184549 0.82145923 0.85793991 0.66566524
0.8360515 0.66480687 0.76094421 0.87253219 0.63347639 0.8223176
0.62532189 0.81845494 0.81416309 0.69313305 0.88755365 0.62360515
0.81072961 0.84463519 0.69356223 0.84935622 0.69184549 0.79828326
0.80987124]
[0.79640719 0.78913601 1. 0.76860565 0.786142 0.67194183
0.80538922 0.7245509 0.75064157 0.84302823 0.6257485 0.79213003
0.65397776 0.82420873 0.79041916 0.68819504 0.84431138 0.65312233
0.8075278 0.82378101 0.68947819 0.8075278 0.73139435 0.79897348
0.81736527]
[0.75631692 0.81970021 0.76959315 1. 0.82869379 0.65653105
0.80513919 0.67708779 0.74946467 0.8235546 0.61284797 0.78201285
0.62398287 0.79014989 0.82184154 0.67280514 0.82826552 0.61627409
0.7987152 0.82012848 0.67537473 0.7987152 0.69464668 0.77815846
0.80385439]
[0.80563862 0.85390859 0.78513456 0.82656984 1. 0.69372063
0.83340453 0.68261427 0.76206749 0.86287911 0.64459633 0.79794959
0.64886801 0.80179411 0.84066638 0.70824434 0.85348142 0.6369073
0.80350278 0.83938488 0.71849637 0.8124733 0.70354549 0.80392994
0.80862879]
[0.85672228 0.76104024 0.77085378 0.75220805 0.79685967 1.
0.78508342 0.69921492 0.77870461 0.83562316 0.7767419 0.79293425
0.671737 0.81992149 0.79097154 0.7747792 0.82139352 0.66633955
0.80569185 0.80863592 0.76104024 0.77281649 0.70951914 0.80029441
0.79587831]
[0.76657061 0.80197612 0.77521614 0.77398106 0.8032112 0.65870729
1. 0.65459037 0.74557431 0.83779333 0.621655 0.84026348
0.59695348 0.78139152 0.79538905 0.66282421 0.8443804 0.59983532
0.78180321 0.82832441 0.67270482 0.84767394 0.66652944 0.7826266
0.82626595]
[0.68066158 0.65691264 0.71840543 0.67048346 0.67769296 0.6043257
0.67430025 1. 0.61450382 0.6798134 0.55682782 0.66115352
0.7769296 0.66624258 0.64418999 0.63146735 0.69126378 0.75318066
0.66157761 0.68787108 0.69041561 0.6836302 0.79092451 0.64970314
0.67387617]
[0.81644707 0.80554294 0.79736483 0.79509314 0.81054066 0.72103589
0.82280781 0.65833712 1. 0.8727851 0.67969105 0.81281236
0.62698773 0.85506588 0.83189459 0.69604725 0.87732849 0.63334848
0.84325307 0.85597456 0.69332122 0.82189914 0.68605179 0.85688323
0.86006361]
[0.84050325 0.82508117 0.79991883 0.78043831 0.81980519 0.6911526
0.82589286 0.65056818 0.77962662 1. 0.66517857 0.81980519
0.60105519 0.84862013 0.82021104 0.69034091 0.88758117 0.60876623
0.81899351 0.86079545 0.67248377 0.81777597 0.67613636 0.83157468
0.83198052]
[0.87792208 0.76675325 0.76 0.74337662 0.7838961 0.82233766
0.78441558 0.68207792 0.77714286 0.85142857 1. 0.82285714
0.63116883 0.81974026 0.79168831 0.77350649 0.83584416 0.63480519
0.80623377 0.8025974 0.7225974 0.78701299 0.68987013 0.8238961
0.80051948]
[0.8059322 0.81186441 0.78474576 0.77372881 0.79152542 0.68474576
0.86483051 0.66059322 0.75805085 0.8559322 0.67118644 1.
0.59830508 0.80211864 0.78516949 0.69364407 0.87415254 0.61822034
0.79491525 0.83644068 0.68432203 0.85889831 0.68389831 0.79533898
0.82669492]
[0.68894009 0.67142857 0.70460829 0.67142857 0.7 0.63087558
0.66820276 0.84423963 0.6359447 0.68248848 0.55990783 0.65069124
1. 0.66267281 0.66313364 0.65069124 0.68156682 0.83502304
0.66497696 0.68156682 0.73963134 0.67880184 0.82304147 0.65391705
0.66912442]
[0.8172043 0.82021505 0.8288172 0.79354839 0.80731183 0.71870968
0.81634409 0.67569892 0.80946237 0.89935484 0.67870968 0.81419355
0.61849462 1. 0.80989247 0.70580645 0.89376344 0.6283871
0.86365591 0.85548387 0.68344086 0.82236559 0.69290323 0.86451613
0.84344086]
[0.79232694 0.7910759 0.7706422 0.80025021 0.8206839 0.67222686
0.80567139 0.63344454 0.76355296 0.84278565 0.63552961 0.77272727
0.6000834 0.7852377 1. 0.70100083 0.82985822 0.60925771
0.78398666 0.81150959 0.67848207 0.78482068 0.66305254 0.79649708
0.80108424]
[0.805752 0.76143329 0.75860443 0.74068835 0.78170674 0.74446016
0.75907591 0.70202735 0.7223008 0.8019802 0.70202735 0.77180575
0.66572372 0.77369165 0.79255068 1. 0.80433758 0.67138142
0.75436115 0.77746346 0.81801037 0.76756247 0.69825554 0.75294672
0.75058934]
[0.78779418 0.82489031 0.78739529 0.77143997 0.79696849 0.66773036
0.81810929 0.6501795 0.77024332 0.8723574 0.64180295 0.82289589
0.58994815 0.82887914 0.79377742 0.68049462 1. 0.60031911
0.81651376 0.8392501 0.67092142 0.81850818 0.67171919 0.8085361
0.82728361]
[0.70585502 0.67518587 0.70957249 0.6686803 0.69284387 0.63104089
0.67704461 0.82527881 0.64776952 0.69702602 0.56784387 0.67797398
0.84200743 0.67890335 0.67890335 0.66171004 0.69934944 1.
0.66682156 0.70306691 0.75836431 0.71328996 0.8633829 0.67100372
0.68447955]
[0.79145299 0.80726496 0.80683761 0.79700855 0.80384615 0.7017094
0.81153846 0.66666667 0.79316239 0.86239316 0.66324786 0.8017094
0.61666667 0.85811966 0.8034188 0.68376068 0.87478632 0.61324786
1. 0.84444444 0.66367521 0.81495726 0.68974359 0.84230769
0.84273504]
[0.82642916 0.81524441 0.7978459 0.79328915 0.81400166 0.68268434
0.83347142 0.67191384 0.78044739 0.87862469 0.64001657 0.81772991
0.61267606 0.82394366 0.8061309 0.68309859 0.87158244 0.62676056
0.81855841 1. 0.68889809 0.81483016 0.69096935 0.82891466
0.83347142]
[0.74501109 0.71662971 0.71485588 0.69933481 0.745898 0.68780488
0.72461197 0.72195122 0.6767184 0.73481153 0.61685144 0.71618625
0.71175166 0.70465632 0.72150776 0.76940133 0.745898 0.72372506
0.6886918 0.73747228 1. 0.73215078 0.72904656 0.68736142
0.70820399]
[0.76107273 0.80414466 0.76716782 0.75782202 0.77285656 0.63998375
0.83665177 0.65501829 0.73506705 0.81877286 0.61560341 0.82364892
0.59853718 0.77691995 0.76472978 0.66151971 0.8338074 0.62373019
0.77488826 0.79926859 0.6708655 1. 0.68671272 0.77041853
0.78870378]
[0.73716542 0.70732778 0.75032909 0.71171566 0.72268539 0.63448881
0.7103993 0.81834138 0.6625713 0.73102238 0.58271172 0.70820535
0.78367705 0.70688899 0.69767442 0.64984642 0.73892058 0.81526986
0.70820535 0.73189996 0.72136902 0.74155331 1. 0.70557262
0.71347082]
[0.81911702 0.79725675 0.80068581 0.77882555 0.80668667 0.69909987
0.81483069 0.65666524 0.8084012 0.87826832 0.6798114 0.80454351
0.60822975 0.86155165 0.81868838 0.68452636 0.86883841 0.61894556
0.84483498 0.85769396 0.66438063 0.81268753 0.68924132 1.
0.83111873]
[0.78909853 0.79119497 0.80125786 0.7870021 0.79371069 0.68008386
0.84150943 0.66624738 0.79371069 0.85953878 0.64612159 0.81802935
0.60880503 0.82222222 0.80545073 0.66750524 0.86960168 0.61761006
0.82683438 0.84360587 0.66960168 0.81383648 0.68176101 0.8129979
1. ]]
Given index file (-i) and prefix-to-trajectory mapping (-t or -T). Clustering will return prefix and frame numbers of cluster members, and will extract representatives./home/amax/Downloads/miniconda2/lib/python2.7/site-packages/scipy/cluster/hierarchy.py:490:
ClusterWarning: scipy.cluster: The symmetric non-negative hollow observation matrix looks suspiciously like an uncondensed distance matrix return linkage(y, method='average', metric='euclidean')
Matrix-index-to-trajectory-frame mapping given. Writing out trajectory frames to cluster_reps.csv and cluster_members.csv.Extracting trajectory frames
Generating difference maps
[16, 4, 2, 2, 1]
5 2 3
[4]+ Stopped python ../analyzeClusterMembershipInGroups.py
Traceback (most recent call last):
File "/home/amax/Downloads/miniconda2/bin/pocketPointsPca.py", line 9, in <module>
from sklearn.preprocessing import StandardScaler
ImportError: No module named sklearn.preprocessing
[('1UYI_every50ns', 5), ('3D0B_every50ns', 5), ('1UYL_every50ns', 5), ('4R3M_every50ns', 5),
('1UYF_every50ns', 5)]
