""" Base classes for visualization of MolProbity analysis using matplotlib. """ from __future__ import division from libtbx import slots_getstate_setstate class rotarama_plot_mixin (object) : extent = [0, 360, 0, 360] def __init__ (self) : assert hasattr(self, "figure") self._points = [] self._xyz = [] # only used by Phenix GUI (not offline plotting) self.plot = self.figure.add_subplot(111) self.plot.set_position([0.1, 0.1, 0.85, 0.85]) def draw_plot (self, stats, title, points=None, show_labels=True, colormap='jet', contours=None, xyz=None, extent=None, y_marks=None) : import matplotlib.cm self._points = [] self._xyz = [] cm = getattr(matplotlib.cm, colormap) self.plot.clear() if (extent is None) : extent = self.extent else : assert (len(extent) == 4) print extent self.plot.imshow(stats, origin="lower", cmap=cm, extent=extent) if (contours is not None) : self.plot.contour(stats, contours, origin="lower", colors='k', extent=extent) if (y_marks is None) : self.set_labels() else : self.set_labels(y_marks=y_marks) self.plot.set_title(title) if (points is not None) : if (xyz is not None) : assert (len(xyz) == len(points)) for i, (x, y, label, is_outlier) in enumerate(points) : if is_outlier : self.plot.plot((x,),(y,), 'bo', markerfacecolor='red') if show_labels : self.plot.text(x, y, label, color='black') self._points.append((x,y)) if (xyz is not None) : self._xyz.append(xyz[i]) else : self.plot.plot((x,),(y,), 'bo', markerfacecolor='white') self.canvas.draw() class residue_bin (slots_getstate_setstate) : __slots__ = ["residues", "marks", "labels"] def __init__ (self) : self.residues = [] self.marks = [] self.labels = [] def add_residue (self, residue) : self.residues.append(residue) n_res = len(self.residues) if (n_res % 10 == 0) : self.marks.append(n_res) if (residue is not None) : self.labels.append(residue.residue_group_id_str()) else : self.labels.append("") def add_empty (self, n) : for i in range(n) : self.add_residue(None) def n_res (self) : return len(self.residues) def get_residue_range (self) : bin_start = bin_end = None i = 0 while (i < len(self.residues)) : if (self.residues[i] is not None) : bin_start = self.residues[i].residue_group_id_str() break i += 1 i = len(self.residues) - 1 while (i >= 0) : if (self.residues[i] is not None) : bin_end = self.residues[i].residue_group_id_str() break i -= 1 return "%s - %s" % (bin_start, bin_end) def x_values (self) : return range(len(self.residues)) def get_selected (self, index) : return self.residues[index] def get_real_space_plot_values (self) : import numpy y = [] for residue in self.residues : if (residue is not None) : y.append(residue.get_real_space_plot_values()) else : y.append([numpy.NaN] * 4) return numpy.array(y).transpose() def get_outlier_plot_values (self) : import numpy y = [] for residue in self.residues : if (residue is not None) : y.append(residue.get_outlier_plot_values()) else : y.append([numpy.NaN] * 4) return numpy.array(y).transpose() class residue_binner (object) : def __init__ (self, res_list, bin_size=100, one_chain_per_bin=False) : self.bins = [] last_chain = last_resseq = None for i, residue in enumerate(res_list) : if (len(self.bins) == 0) or (self.bins[-1].n_res() == bin_size) : self.bins.append(residue_bin()) chain = residue.chain_id resseq = residue.resseq_as_int() if (last_chain is not None) : # FIXME needs to take icode into account! if (chain != last_chain) or (resseq > (last_resseq + 10)) : if ((chain != last_chain and one_chain_per_bin) or (self.bins[-1].n_res() > (bin_size - 20))) : self.bins.append(residue_bin()) else : self.bins[-1].add_empty(10) elif (resseq > (last_resseq + 1)) and (self.bins[-1].n_res() > 0) : gap = resseq - (last_resseq + 1) i = 0 while (i < gap) and (self.bins[-1].n_res() < bin_size) : self.bins[-1].add_empty(1) i += 1 self.bins[-1].add_residue(residue) last_chain = chain last_resseq = resseq def get_bin (self, i_bin) : return self.bins[i_bin] def get_ranges (self) : return [ bin.get_residue_range() for bin in self.bins ] class multi_criterion_plot_mixin (object) : def __init__ (self, binner, y_limits) : self.binner = binner self.y_limits = y_limits self.disabled = False def plot_range (self, i_bin) : if (self.disabled) : return # TODO: fix y-ticks, x-ticks width residue ID, add legend self.figure.clear() bin = self.binner.get_bin(i_bin) self._current_bin = bin n_plots = 2 x = bin.x_values() # b_iso, cc, fmodel, two_fofc y = bin.get_real_space_plot_values() (b_iso, cc, fmodel, two_fofc) = bin.get_real_space_plot_values() # electron density (observed and calculated) p1 = self.figure.add_subplot(n_plots, 1, n_plots) p1.set_position([0.075, 0.625, 0.825, 0.325]) p1.plot(x, y[2], "-", linewidth=1, color="g") p1.plot(x, y[3], "-", linewidth=1, color="k") p1.set_title("Multi-criterion validation") a1 = p1.get_axes() a1.set_ylabel("Density", fontproperties=self.get_font("label")) a1.set_xlim(-1, 101) rho_min, rho_max = self.y_limits["rho"] a1.set_ylim(rho_min, rho_max) a1.xaxis.set_major_formatter(self.null_fmt) # CC p2 = self.figure.add_subplot(n_plots, 1, n_plots - 1, sharex=p1) p2.set_position([0.075, 0.075, 0.825, 0.55]) p2.plot(x, y[1], "-", linewidth=1, color="b") a2 = p2.get_axes() a2.set_ylabel("Local real-space CC", fontproperties=self.get_font("label"), color='b') a2.set_xlim(-1, 101) cc_min, cc_max = self.y_limits["cc"] a2.set_ylim(cc_min, cc_max) a2.get_yticklabels()[-1].set_visible(False) # B_iso p3 = p2.twinx() p3.set_position([0.075, 0.075, 0.825, 0.55]) p3.plot(x, y[0], "-", linewidth=1, color="r") a3 = p3.get_axes() a3.set_ylabel("B-factor", fontproperties=self.get_font("label"), color='r') b_min, b_max = self.y_limits["b"] a3.set_ylim(b_min, b_max) a3.get_yticklabels()[-1].set_visible(False) # labels a2 = p2.get_axes() a2.set_xlabel("Residue", fontproperties=self.get_font("label")) a2.set_xticks(bin.marks) a2.set_xticklabels(bin.labels, fontproperties=self.get_font("basic")) a2.get_yticklabels()[-1].set_visible(False) # rama, rota, cbeta, clash y2 = bin.get_outlier_plot_values() p2.plot(x, y2[0] * 0.99, "o") p2.plot(x, y2[1] * 0.98, "^") p2.plot(x, y2[2] * 0.97, "s") p2.plot(x, y2[3] * 0.96, "d") self.figure.legend(p2.lines + p3.lines + p1.lines, ["CC", "Ramachandran", "Rotamer", "C-beta", "Bad clash", "B-factor", "Fc", "2mFo-DFc"], prop=self.get_font("legend")) self.canvas.draw()