spin on a/o, colorbar on keypress
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63be80aa92
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@ -15,94 +15,169 @@ from fluents import plots
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from scipy import dot,sum,diag,arange,log,mean,newaxis,sqrt
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from scipy import dot,sum,diag,arange,log,mean,newaxis,sqrt
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from matplotlib import cm
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from matplotlib import cm
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import pylab as PB
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import pylab as PB
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import gtk
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class PcaScorePlot(plots.ScatterPlot):
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"""PCA Score plot"""
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class BlmScatterPlot(plots.ScatterPlot):
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def __init__(self, model, absi=0, ordi=1):
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"""Scatter plot used for scores and loadings in bilinear models."""
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self._T = model.model['T']
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dataset_1 = model.as_dataset('T')
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def __init__(self, title, model, absi=0, ordi=1, part_name='T', color_by=None):
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dataset_2 = dataset_1
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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._colorbar = None
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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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id_dim = dataset_1.get_dim_name(0)
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sel_dim = dataset_1.get_dim_name(1)
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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_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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id_2, = dataset_1.get_identifiers(sel_dim, [ordi])
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plots.ScatterPlot.__init__(self, dataset_1, dataset_2, id_dim, sel_dim, id_1, id_2 ,c='b' ,s=40 , name='pca-scores')
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def set_absicca(self, n):
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col = 'b'
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self.xaxis_data = self._T[:,n]
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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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def set_ordinate(self, n):
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self.add_pc_spin_buttons(self._T.shape[1], absi, ordi)
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self.yaxis_data = self._T[:,n]
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self._key_press = self.canvas.mpl_connect(
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'key_press_event', self._on_key_press)
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class PcaLoadingPlot(plots.ScatterPlot):
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def _on_key_press(self, event):
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"""PCA Loading plot"""
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if event.key=='c':
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def __init__(self, model, absi=0, ordi=1):
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self.toggle_colorbar()
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self._P = model.model['P']
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dataset_1 = model.as_dataset('P')
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dataset_2 = dataset_1
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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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if model.model.has_key('p_tsq'):
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col = model.model['p_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, id_dim, sel_dim, id_1, id_2,c=col,s=20, name='pls-loadings')
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def set_absicca(self, n):
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def set_facecolor(self, colors):
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self.xaxis_data = self._P[:,n]
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"""Set patch facecolors.
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"""
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pass
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def set_ordinate(self, n):
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def set_alphas(self, alphas):
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self.yaxis_data = self._P[:,n]
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"""Set alpha channel for all patches."""
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pass
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class PlsScorePlot(plots.ScatterPlot):
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"""PLS Score plot"""
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def __init__(self, model, absi=0, ordi=1):
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self._T = model.model['T']
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dataset_1 = model.as_dataset('T')
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dataset_2 = dataset_1
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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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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='b' ,s=40 , name='pls-scores')
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def set_absicca(self, n):
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self.xaxis_data = self._T[:,n]
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def set_ordinate(self, n):
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def set_sizes(self, sizes):
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self.yaxis_data = self._T[:,n]
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"""Set patch sizes."""
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pass
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def toggle_colorbar(self):
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class PlsLoadingPlot(plots.ScatterPlot):
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if self._colorbar==None:
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"""PLS Loading plot"""
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if self.sc._A!=None: # we need colormapping
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def __init__(self, model, absi=0, ordi=1):
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# get axes original position
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self._P = model.model['P']
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self._ax_last_pos = self.ax.get_position()
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dataset_1 = model.as_dataset('P')
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self._colorbar = self.fig.colorbar(self.sc)
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dataset_2 = dataset_1
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self._colorbar.draw_all()
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id_dim = dataset_1.get_dim_name(0)
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self.canvas.draw()
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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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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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else:
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col = 'g'
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# remove colorbar
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plots.ScatterPlot.__init__(self, dataset_1, dataset_2,
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# remove, axes, observers, colorbar instance, and restore viewlims
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id_dim, sel_dim, id_1, id_2,
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cb, ax = self.sc.colorbar
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c=col, s=20, name='loadings')
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self.fig.delaxes(ax)
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self.sc.observers = [obs for obs in self.sc.observers if obs !=self._colorbar]
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self._colorbar = None
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self.sc.colorbar = None
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self.ax.set_position(self._ax_last_pos)
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self.canvas.draw()
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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)
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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)
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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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if self.use_blit==True:
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self.canvas.restore_region(self._clean_bck)
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self.ax.draw_artist(self.sc)
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self.canvas.blit()
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else:
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self.canvas.draw_idle()
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def set_absicca(self, n):
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def set_ordinate(self, sb):
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self.xaxis_data = self._P[:,n]
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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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if self.use_blit==True:
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self.canvas.restore_region(self._clean_bck)
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self.ax.draw_artist(self.sc)
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self.canvas.blit()
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else:
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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.ax.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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def set_ordinate(self, n):
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self.yaxis_data = self._T[:,n]
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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 = "Pca 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 LineViewXc(plots.LineViewPlot):
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class LineViewXc(plots.LineViewPlot):
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@ -127,7 +202,7 @@ class PlsQvalScatter(plots.ScatterPlot):
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"""
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"""
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def __init__(self, model, pc=0):
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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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if not model.model.has_key('w_tsq'):
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return
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return None
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self._W = model.model['P']
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self._W = model.model['P']
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dataset_1 = model.as_dataset('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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dataset_2 = model.as_dataset('w_tsq')
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@ -146,13 +221,14 @@ class PlsQvalScatter(plots.ScatterPlot):
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c=col, s=20, sel_dim_2=sel_dim_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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name='Load Volcano')
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class PredictionErrorPlot(plots.Plot):
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class PredictionErrorPlot(plots.Plot):
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"""A boxplot of prediction error vs. comp. number.
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"""A boxplot of prediction error vs. comp. number.
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"""
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"""
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def __init__(self, model, name="Pred. Err."):
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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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if not model.model.has_key('sep'):
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logger.log('notice', 'Model has no calculations of sep')
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logger.log('notice', 'Model has no calculations of sep')
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return
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return None
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plots.Plot.__init__(self, name)
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plots.Plot.__init__(self, name)
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self._frozen = True
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self._frozen = True
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self.current_dim = 'johndoe'
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self.current_dim = 'johndoe'
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@ -180,6 +256,15 @@ class InfluencePlot(plots.ScatterPlot):
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pass
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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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def normalise(x):
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"""Scale vector x to [0,1]
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"""Scale vector x to [0,1]
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"""
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"""
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