psico.plotting

Plotting with matplotlib

3. 2011-2012 Thomas Holder, MPI for Developmental Biology

License: BSD-2-Clause

Functions

area_plot([selection, filename, quiet, _self])	Surface area plot.
contact_map_plot([selection, metric, state, ...])	Plot a contact map.
get_model_color(model, *[, _self])	API only.
iterate_plot(selection, expr_y[, expr_x, ...])	Plot atomic properties.
pca_plot(aln_object[, ref, state, ...])	Principal Component Analysis on a set of superposed conformations, given by an alignment object.
rms_plot([selection, ref1, ref2, state1, ...])	Scatterplot with RMSD for all models/states in selection against ref1 and ref2.

psico.plotting.area_plot(selection='all', filename=None, *, quiet=1, _self=...)

Surface area plot. Every model-state is one data point.

Uses get_area, so the same settings and flags apply! In particular, the dot_solvent setting controls whether SAS or SES is computed, and the “ignore” flag controls which atoms are considered solvent.

Arguments

selection = string: atom selection {default: all}

psico.plotting.contact_map_plot(selection='guide', metric='euclidean', *, state=-1, filename=None, quiet=1, _self=...)

Plot a contact map.

psico.plotting.get_model_color(model, *, _self=...)

API only. Get model color as #xxxxxx to be used with matplotlib.

psico.plotting.iterate_plot(selection, expr_y, expr_x=None, scatter=0, filename=None, space=None, quiet=1, *, _self=...)

Plot atomic properties.

Arguments

selection = string: atom selection

expr_y = string: python expression for y values

expr_x = string: python expression for x values {default: None}

scatter = 0/1: make line plot or scatter plot {default: 0, line plot}

Example

# C-alpha b-factors iterate_plot name CA, b, resv

psico.plotting.pca_plot(aln_object, ref='all', state=0, maxlabels=20, size=20, invert='', which=(0, 1), alpha=0.75, filename=None, quiet=1, load_b=0, *, _self=...)

Principal Component Analysis on a set of superposed conformations, given by an alignment object. By default all states in all objects are considered. Generates a 2d-plot of the first two principal components.

Usage

pca_plot aln_object [, ref [, state [, maxlabels ]]]

Arguments

aln_object = string: name of alignment object, defines the selection and the atom mapping between objects

ref = string: object names for which to calculate PCA for {default: all}

state = integer: if state=0 use all states {default: 0}

maxlabels = integer: label dots in plot if maxlabels<0 or number of models not more than maxlabels {default: 20}

size = float: size of plot points in px^2 {default: 20}

invert = string: invert plotting axes x, y or xy {default: ‘’}

which = (int,int): indices of principal components to plot {default: (0,1)}

alpha = float: opacity of plotting points

filename = string: if given, plot to file {default: None}

Example

fetch 1ake 4ake 1dvr 1ak2, async=0 split_chains extra_fit (_) and name CA, reference=1ake_A, cycles=0, object=aln pca_plot aln, 1ake_* 4ake_*

fetch 1ubq 2k39, async=0 align 2k39, 1ubq and guide, cycles=0, object=aln2 color blue, 1ubq color orange, 2k39 pca_plot aln2, filename=pca-ubq.pdf

psico.plotting.rms_plot(selection='guide', ref1=None, ref2=None, state1=1, state2=-1, match='align', cur=0, maxlabels=20, size=20, alpha=0.75, filename=None, quiet=1, *, _self=...)

Scatterplot with RMSD for all models/states in selection against ref1 and ref2.

Arguments

selection = string: atom selection {default: guide}

ref1 = string: atom selection {default: first model in selection} ref2 = string: atom selection {default: last model in selection}

state1 = int: object state for ref1 state2 = int: object state for ref2

match = string: in, like, align, none or the name of an alignment object (see “local_rms” help for details) {default: align}

cur = 0/1: if 1, use rms_cur instead of rms (no fitting) {default: 0}