298 lines
10 KiB
Python
298 lines
10 KiB
Python
"""Specialised plots for functions defined in blmfuncs.py.
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fixme:
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-- If scatterplot is not inited with a colorvector there will be no
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colorbar, but when adding colors the colorbar shoud be created.
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"""
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from matplotlib import cm
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import gtk
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import fluents
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from fluents import plots
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import scipy
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from scipy import dot,sum,diag,arange,log,mean,newaxis,sqrt,apply_along_axis
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class BlmScatterPlot(plots.ScatterPlot):
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"""Scatter plot used for scores and loadings in bilinear models."""
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def __init__(self, title, model, absi=0, ordi=1, part_name='T', color_by=None):
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if model.model.has_key(part_name)!=True:
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raise ValueError("Model part: %s not found in model" %mod_param)
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self._T = model.model[part_name]
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if self._T.shape[1]==1:
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logger.log('notice', 'Scores have only one component')
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absi= ordi = 0
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self._absi = absi
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self._ordi = ordi
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self._cmap = cm.jet
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dataset_1 = model.as_dataset(part_name)
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id_dim = dataset_1.get_dim_name(0)
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sel_dim = dataset_1.get_dim_name(1)
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id_1, = dataset_1.get_identifiers(sel_dim, [absi])
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id_2, = dataset_1.get_identifiers(sel_dim, [ordi])
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col = 'b'
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if model.model.has_key(color_by):
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col = model.model[color_by].ravel()
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plots.ScatterPlot.__init__(self, dataset_1, dataset_1, id_dim, sel_dim, id_1, id_2 ,c=col ,s=40 , name=title)
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self._mappable.set_cmap(self._cmap)
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self.sc = self._mappable
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self.add_pc_spin_buttons(self._T.shape[1], absi, ordi)
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def _update_color_from_dataset(self, data):
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"""Overriding scatter for testing of colormaps.
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"""
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is_category = False
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array = data.asarray()
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#only support for 2d-arrays:
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try:
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m, n = array.shape
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except:
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raise ValueError, "No support for more than 2 dimensions."
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# is dataset a vector or matrix?
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if not n==1:
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# we have a category dataset
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if isinstance(data, fluents.dataset.CategoryDataset):
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is_category = True
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map_vec = scipy.dot(array, scipy.diag(scipy.arange(n))).sum(1)
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else:
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map_vec = array.sum(1)
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else:
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map_vec = array.ravel()
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# update facecolors
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self.sc.set_array(map_vec)
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self.sc.set_clim(map_vec.min(), map_vec.max())
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if is_category:
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cmap = cm.Paired
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else:
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cmap = cm.jet
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self.sc.set_cmap(cmap)
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self.sc.update_scalarmappable() #sets facecolors from array
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self.canvas.draw()
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def set_facecolor(self, colors):
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"""Set patch facecolors.
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"""
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pass
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def set_alphas(self, alphas):
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"""Set alpha channel for all patches."""
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pass
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def set_sizes(self, sizes):
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"""Set patch sizes."""
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pass
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def add_pc_spin_buttons(self, amax, absi, ordi):
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sb_a = gtk.SpinButton(climb_rate=1)
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sb_a.set_range(1, amax)
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sb_a.set_value(absi+1)
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sb_a.set_increments(1, 5)
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sb_a.connect('value_changed', self.set_absicca)
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sb_o = gtk.SpinButton(climb_rate=1)
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sb_o.set_range(1, amax)
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sb_o.set_value(ordi+1)
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sb_o.set_increments(1, 5)
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sb_o.connect('value_changed', self.set_ordinate)
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hbox = gtk.HBox()
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gtk_label_a = gtk.Label("A:")
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gtk_label_o = gtk.Label(" O:")
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toolitem = gtk.ToolItem()
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toolitem.set_expand(False)
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toolitem.set_border_width(2)
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toolitem.add(hbox)
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hbox.pack_start(gtk_label_a)
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hbox.pack_start(sb_a)
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hbox.pack_start(gtk_label_o)
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hbox.pack_start(sb_o)
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self._toolbar.insert(toolitem, -1)
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toolitem.set_tooltip(self._toolbar.tooltips, "Set Principal component")
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self._toolbar.show_all() #do i need this?
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def set_absicca(self, sb):
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self._absi = sb.get_value_as_int() - 1
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xy = self._T[:,[self._absi, self._ordi]]
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self.xaxis_data = xy[:,0]
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self.yaxis_data = xy[:,1]
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self.sc._offsets = xy
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self.selection_collection._offsets = xy
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self.canvas.draw_idle()
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pad = abs(self.xaxis_data.min()-self.xaxis_data.max())*0.05
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new_lims = (self.xaxis_data.min()+pad, self.xaxis_data.max()+pad)
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self.axes.set_xlim(new_lims, emit=True)
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self.canvas.draw_idle()
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def set_ordinate(self, sb):
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self._ordi = sb.get_value_as_int() - 1
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xy = self._T[:,[self._absi, self._ordi]]
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self.xaxis_data = xy[:,0]
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self.yaxis_data = xy[:,1]
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self.sc._offsets = xy
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self.selection_collection._offsets = xy
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pad = abs(self.yaxis_data.min()-self.yaxis_data.max())*0.05
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new_lims = (self.yaxis_data.min()+pad, self.yaxis_data.max()+pad)
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self.axes.set_ylim(new_lims, emit=True)
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self.canvas.draw_idle()
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def show_labels(self, index=None):
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if self._text_labels == None:
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x = self.xaxis_data
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y = self.yaxis_data
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self._text_labels = {}
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for name, n in self.dataset_1[self.current_dim].items():
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txt = self.axes.text(x[n],y[n], name)
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txt.set_visible(False)
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self._text_labels[n] = txt
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if index!=None:
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self.hide_labels()
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for indx,txt in self._text_labels.items():
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if indx in index:
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txt.set_visible(True)
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self.canvas.draw()
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def hide_labels(self):
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for txt in self._text_labels.values():
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txt.set_visible(False)
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self.canvas.draw()
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class PcaScorePlot(BlmScatterPlot):
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def __init__(self, model, absi=0, ordi=1):
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title = "Pca scores (%s)" %model._dataset['X'].get_name()
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BlmScatterPlot.__init__(self, title, model, absi, ordi, 'T')
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class PcaLoadingPlot(BlmScatterPlot):
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def __init__(self, model, absi=0, ordi=1):
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title = "Pca loadings (%s)" %model._dataset['X'].get_name()
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BlmScatterPlot.__init__(self, title, model, absi, ordi, part_name='P', color_by='p_tsq')
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class PlsScorePlot(BlmScatterPlot):
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def __init__(self, model, absi=0, ordi=1):
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title = "Pls scores (%s)" %model._dataset['X'].get_name()
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BlmScatterPlot.__init__(self, title, model, absi, ordi, 'T')
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class PlsLoadingPlot(BlmScatterPlot):
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def __init__(self, model, absi=0, ordi=1):
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title = "Pls loadings (%s)" %model._dataset['X'].get_name()
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BlmScatterPlot.__init__(self, title, model, absi, ordi, part_name='P', color_by='w_tsq')
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class PlsCorrelationLoadingPlot(BlmScatterPlot):
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def __init__(self, model, absi=0, ordi=1):
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title = "Pls correlation loadings (%s)" %model._dataset['X'].get_name()
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BlmScatterPlot.__init__(self, title, model, absi, ordi, part_name='CP')
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class LineViewXc(plots.LineViewPlot):
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"""A line view of centered raw data
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"""
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def __init__(self, model, name='Profiles'):
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# copy, center, plot
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x = model._dataset['X'].copy()
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x._array = x._array - mean(x._array,0)[newaxis]
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plots.LineViewPlot.__init__(self, x, 1, None, name)
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class ParalellCoordinates(plots.Plot):
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"""Parallell coordinates for score loads with many comp.
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"""
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def __init__(self, model, p='loads'):
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pass
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class PlsQvalScatter(plots.ScatterPlot):
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"""A vulcano like plot of loads vs qvals
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"""
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def __init__(self, model, pc=0):
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if not model.model.has_key('w_tsq'):
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return None
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self._W = model.model['P']
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dataset_1 = model.as_dataset('P')
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dataset_2 = model.as_dataset('w_tsq')
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id_dim = dataset_1.get_dim_name(0) #genes
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sel_dim = dataset_1.get_dim_name(1) #_comp
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sel_dim_2 = dataset_2.get_dim_name(1) #_zero_dim
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id_1, = dataset_1.get_identifiers(sel_dim, [0])
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id_2, = dataset_2.get_identifiers(sel_dim_2, [0])
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if model.model.has_key('w_tsq'):
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col = model.model['w_tsq'].ravel()
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#col = normalise(col)
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else:
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col = 'g'
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plots.ScatterPlot.__init__(self, dataset_1, dataset_2,
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id_dim, sel_dim, id_1, id_2,
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c=col, s=20, sel_dim_2=sel_dim_2,
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name='Load Volcano')
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class PredictionErrorPlot(plots.Plot):
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"""A boxplot of prediction error vs. comp. number.
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"""
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def __init__(self, model, name="Prediction Error"):
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if not model.model.has_key('sep'):
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logger.log('notice', 'Model has no calculations of sep')
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return None
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plots.Plot.__init__(self, name)
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self._frozen = True
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self.current_dim = 'johndoe'
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self.axes = self.fig.add_subplot(111)
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# draw
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sep = model.model['sep']
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aopt = model.model['aopt']
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bx_plot_lines = self.axes.boxplot(sqrt(sep))
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aopt_marker = self.axes.axvline(aopt, linewidth=10,
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color='r',zorder=0,
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alpha=.5)
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# add canvas
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self.add(self.canvas)
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self.canvas.show()
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def set_current_selection(self, selection):
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pass
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class TRBiplot(plots.ScatterPlot):
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def __init__(self, model, absi=0, ordi=1):
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title = "Target rotation biplot(%s)" %model._dataset['X'].get_name()
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BlmScatterPlot.__init__(self, title, model, absi, ordi, 'B')
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B = model.model.get('B')
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# normalize B
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Bnorm = scipy.apply_along_axis(scipy.linalg.norm, 1, B)
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x = model._dataset['X'].copy()
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Xc = x._array - mean(x._array,0)[newaxis]
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w_rot = B/Bnorm
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t_rot = dot(Xc, w_rot)
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class InfluencePlot(plots.ScatterPlot):
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"""
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"""
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pass
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class RMSEPPlot(plots.BarPlot):
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def __init__(self, model, name="RMSEP"):
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if not model.model.has_key('rmsep'):
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logger.log('notice', 'Model has no calculations of sep')
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return
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dataset = model.as_dataset('rmsep')
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plots.BarPlot.__init__(self, dataset, name=name)
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def normalise(x):
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"""Scale vector x to [0,1]
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"""
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x = x - x.min()
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x = x/x.max()
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return x
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