TEMPy.graphics package

Submodules

TEMPy.graphics.show_plot module

class Plot

Bases: object

A class to create analysis output

PrintOutChimeraAttributeFileSCCC_Score(code_structure, sccc_list, listRB, out_path=None)

Print out a Chimera attribute file that can be used for visual inspection of the information after Segment based cross-correlation (SCCC) calculation.

Arguments:
code_structure

name of the structure instance

sccc_list

SCCC score for each of the segment.

listRB

list of segment used for the SCCC calculation.

out_path

set output path for attribute file

PrintOutChimeraCmdClusterAnalysis(cluster_output, path_dir, targetMap_location, file_name='chimera_cluster_color', load_map=True)

Print out a Chimera command file that can be used for visual inspection of the information after the hierarchical clustering analysis.”.

Arguments:
cluster_output

List that contains the model and the related cluster index.

path_dir

path to ensemble directory

targetMap_location

path to target map location

file_name

Output file name

load_map

True will add the loading option to the command file.

PrintOutClusterAnalysis(cluster_output, file_name='cluster.out', write=False)

Print our a txt file that contains the clustering information after hierarchical clustering analysis.”. Arguments:

cluster_output

List that contains the model and the related cluster index.

file_name

Output file name

write

True will save the file.

SCCCHeatMap_fromSCCCList(sccc_list, trans=False)
Return a matrix from a list of score.txt files as:

x= Structure Instances and y= Structure Instances segments scored

Arguments:
sccc_list

list of list of SCCC scores

trans

True will transpose the matrix.

SCCCHeatMap_fromSCCCfiles(list_file, trans=False)
Return a matrix from a list of score.txt files as:

x= Structure Instances and y= Structure Instances segments scored

Arguments:
list_file

list of files

trans

True will transpose the matrix.

ShowGeneralMatrix(mxGen, file_name='HeatMap', save=False, range=0, 1, figsize=7, 5, cmap=None)

Heat Map plot of a matrix. Arguments:

mxGen

Generic Matrix. Use SCCCHeatMap_fromSCCCfiles or SCCCHeatMap_fromSCCCList to generate a matrix from a set of segment assessed with SCCC score.

name

Output file name (.pdf)

save

True will save a pdf file of the plot.

range

set the min and max score.

cmap

color palette to use. Choose form the one available in matplotlib or use cmp_Rainbow.

ShowHierarchicalClusterings(ranked_ensemble, mxRMSD, rms_cutoff, name='HierClustPlt', save=False, cluster_index=False, figsize=4, 4, reverse=False)
Plot the Calpha RMSD hierarchical clustering of the multiple “fits”.
Arguments:
ranked_ensemble

Input list of Structure Instances. It is list of fits obtained with Cluster.rank_fit_ensemble function.

mxRMSD

Pairwise RMSD matrix for all Structure Instance in the ensemble obtained as one Cluster.RMSD_ensemble function.

rms_cutoff

float, the Calpha RMSD cutoff based on which you want to cluster the solutions. For example 3.5 (for 3.5 A). Suggested value the mean of the pairwise RMSD matrix.

name

Output file name (.pdf)

save

True will save a pdf file of the plot.

cluster_index

True will return a list that contains the model and the related cluster index.

ShowRMSDmatrix(mxRMSD, name='RMSDmatrix', save=False)

Plot the pairwise RMSD matrix for all Structure Instance in the ensemble.

Arguments:
mxRMSD

Pairwise RMSD matrix for all Structure Instance in the ensemble obtained as one Cluster.RMSD_ensemble function.

name

Output file name (.pdf)

save

True will save a pdf file of the plot.

cmp_Rainbow()

return rainbow color map.

lineplot(dict_points, outfile, xlabel=None, ylabel=None, xlim=None, ylim=None, legend_loc='upper left', line=True, marker=True, leg_pos=1.2, lstyle=True)

Module contents