"""
A module to produce a contact map chord diagram
"""
from __future__ import division
__author__ = "Felix Simkovic"
__date__ = "13 Feb 2017"
__version__ = 0.1
import matplotlib.patches
import matplotlib.pyplot
import numpy
from conkit.plot._Figure import Figure
from conkit.plot._plottools import ColorDefinitions, points_on_circle
[docs]class ContactMapChordFigure(Figure):
"""A Figure object specifically for a Contact Map chord diagram
This figure will illustrate the contacts linking the residues
in the target sequence. This plot is a very common representation
of contacts. With this figure, you can illustrate intra-molecular.
Color scheme:
========== =========== ========== =========== ========== =========== ========== =========== ========== ===========
Amino acid Hex code Amino acid Hex code Amino acid Hex code Amino acid Hex code Amino acid Hex code
========== =========== ========== =========== ========== =========== ========== =========== ========== ===========
Ala ``#882D17`` Arg ``#B3446C`` Asn ``#F99379`` Asp ``#875692`` Cys ``#F3C300``
Gln ``#F6A600`` Glu ``#F38400`` Gly ``#BE0032`` His ``#C2B280`` Ile ``#848482``
Leu ``#E68FAC`` Lys ``#008856`` Met ``#0067A5`` Phe ``#A1CAF1`` Pro ``#604E97``
Ser ``#DCD300`` Thr ``#8DB600`` Trp ``#E25822`` Tyr ``#2B3D26`` Val ``#654522``
Unk ``#000000``
========== =========== ========== =========== ========== =========== ========== =========== ========== ===========
Attributes
----------
hierarchy : :obj:`ContactMap <conkit.core.ContactMap>`
The default contact map hierarchy
Examples
--------
>>> import conkit
>>> cmap = conkit.io.read('toxd/toxd.mat', 'ccmpred').top_map
>>> conkit.plot.ContactMapChordFigure(cmap)
"""
def __init__(self, hierarchy, **kwargs):
"""A new contact map plot
Parameters
----------
hierarchy : :obj:`ContactMap <conkit.core.ContactMap>`
The default contact map hierarchy
**kwargs
General :obj:`Figure <conkit.plot._Figure.Figure>` keyword arguments
"""
super(ContactMapChordFigure, self).__init__(**kwargs)
self._hierarchy = None
self.hierarchy = hierarchy
self._draw()
def __repr__(self):
return "{0}(file_name=\"{1}\")".format(
self.__class__.__name__, self.file_name
)
@property
def hierarchy(self):
"""The default contact map hierarchy"""
return self._hierarchy
@hierarchy.setter
def hierarchy(self, hierarchy):
"""Define the default contact map hierarchy"""
if hierarchy:
Figure._check_hierarchy(hierarchy, "ContactMap")
if hierarchy.sequence:
Figure._check_hierarchy(hierarchy.sequence, "Sequence")
self._hierarchy = hierarchy
def _draw(self):
"""Draw the actual plot"""
# Re-normalize the data for the lines
hierarchy = self.hierarchy.rescale()
# Obtain the data from the hierarchy
self_data = numpy.asarray([(c.res1, c.res1_seq, c.res2, c.res2_seq, c.raw_score)
for c in hierarchy])
_drange = numpy.append(self_data.T[1], self_data.T[3]).astype(numpy.int64)
self_data_range = numpy.arange(_drange.min(), _drange.max() + 1)
# The number of points on the outer circle and their coordinates
npoints = self_data_range.shape[0]
coords = numpy.asarray(points_on_circle(npoints))
# Instantiate the figure
fig, ax = matplotlib.pyplot.subplots()
# Calculate and plot the Bezier curves
bezier_path = numpy.arange(0, 1.01, 0.01)
for c in self_data:
x1, y1 = coords[int(c[1]) - self_data_range.min()]
x2, y2 = coords[int(c[3]) - self_data_range.min()]
xb, yb = [0, 0] # Midpoint the curve is supposed to approach
x = (1 - bezier_path) ** 2 * x1 + 2 * (1 - bezier_path) * bezier_path * xb + bezier_path ** 2 * x2
y = (1 - bezier_path) ** 2 * y1 + 2 * (1 - bezier_path) * bezier_path * yb + bezier_path ** 2 * y2
ax.plot(x, y, color="#000000", alpha=float(c[4]), linestyle="-", zorder=0)
# Get the amino acids if available
# - get the residue data from the original data array
residue_data = numpy.append(self_data[:, [1, 0]], self_data[:, [3, 2]])
residue_data = residue_data.reshape(self_data.T[0].shape[0] * 2, 2)
# - compute a default color list
color_codes = dict([(k, ColorDefinitions.AA_ENCODING['X']) for k in self_data_range])
# - fill default dict with data we have
for k, v in numpy.vstack({tuple(row) for row in residue_data}):
color_codes[int(k)] = ColorDefinitions.AA_ENCODING[v]
# - create a color list
colors = [color_codes[k] for k in sorted(color_codes.keys())]
# Plot the residue points
ax.scatter(coords.T[0], coords.T[1], marker='o', color=colors, edgecolors="none", zorder=1)
# Annotate some residue
# TODO: Use _plottools module to process this
label_data = set([int(x) for x in zip(*residue_data)[0]])
label_coords = numpy.zeros((npoints, 2))
space = 2 * numpy.pi / npoints
for i in numpy.arange(npoints):
label_coords[i] = [
(npoints + npoints / 10) * numpy.cos(space * i) - npoints / 20,
(npoints + npoints / 10) * numpy.sin(space * i) - npoints / 40
]
for r in sorted(label_data)[::int(npoints / (npoints / 10))]:
i = r - self_data_range.min()
xy = x, y = coords[i]
xytext = label_coords[i]
ax.annotate(r, xy=xy, xytext=xytext)
ax.scatter(x, y, marker='o', facecolors="none", edgecolors="#000000", zorder=2)
# Arrow for the start
arrow_x, arrow_y = (npoints + npoints / 5, 0)
ax.arrow(arrow_x, arrow_y, 0, npoints / 10, head_width=1.5, color="#000000")
# Prettify the plot
ax.set_xlim(-arrow_x, arrow_x + 2)
ax.set_ylim(-arrow_x, arrow_x)
ax.axis("off")
# Make both axes identical in length and remove whitespace around the plot
aspectratio = Figure._correct_aspect(ax, 1.0)
ax.set(aspect=aspectratio)
fig.tight_layout()
fig.savefig(self.file_name, bbox_inches='tight', dpi=self.dpi)