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correct comp update

This commit is contained in:
Arnar Flatberg 2007-03-14 16:17:21 +00:00
parent 00dd3e8d9d
commit 212da78933
1 changed files with 65 additions and 35 deletions
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100
fluents/lib/blmplots.py
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@ -1,22 +1,16 @@
"""Specialised plots for functions defined in blmfuncs.py. """Specialised plots for functions defined in blmfuncs.py.
fixme: fixme:
-- Im normalsing all color mapping input vectors to [0,1]. This will
destroy informative numerical values in colorbar (but we
are not showing these anyway). A better fix would be to let the
colorbar listen to the scalarmappable instance and corect itself, but
I did not get that to work ...
fixme2:
-- If scatterplot is not inited with a colorvector there will be no -- If scatterplot is not inited with a colorvector there will be no
colorbar, but when adding colors the colorbar shoud be created. colorbar, but when adding colors the colorbar shoud be created.
""" """
from fluents import plots
from scipy import dot,sum,diag,arange,log,mean,newaxis,sqrt
from matplotlib import cm
import pylab as PB
import gtk
from matplotlib import cm
import gtk
import fluents
from fluents import plots
import scipy
from scipy import dot,sum,diag,arange,log,mean,newaxis,sqrt
class BlmScatterPlot(plots.ScatterPlot): class BlmScatterPlot(plots.ScatterPlot):
"""Scatter plot used for scores and loadings in bilinear models.""" """Scatter plot used for scores and loadings in bilinear models."""
@ -31,6 +25,7 @@ class BlmScatterPlot(plots.ScatterPlot):
self._absi = absi self._absi = absi
self._ordi = ordi self._ordi = ordi
self._colorbar = None self._colorbar = None
self._cmap = cm.jet
dataset_1 = model.as_dataset(part_name) dataset_1 = model.as_dataset(part_name)
id_dim = dataset_1.get_dim_name(0) id_dim = dataset_1.get_dim_name(0)
sel_dim = dataset_1.get_dim_name(1) sel_dim = dataset_1.get_dim_name(1)
@ -42,11 +37,46 @@ class BlmScatterPlot(plots.ScatterPlot):
col = model.model[color_by].ravel() col = model.model[color_by].ravel()
plots.ScatterPlot.__init__(self, dataset_1, dataset_1, id_dim, sel_dim, id_1, id_2 ,c=col ,s=40 , name=title) plots.ScatterPlot.__init__(self, dataset_1, dataset_1, id_dim, sel_dim, id_1, id_2 ,c=col ,s=40 , name=title)
self.sc.set_cmap(self._cmap)
self.add_pc_spin_buttons(self._T.shape[1], absi, ordi) self.add_pc_spin_buttons(self._T.shape[1], absi, ordi)
self._key_press = self.canvas.mpl_connect( self._key_press = self.canvas.mpl_connect(
'key_press_event', self._on_key_press) 'key_press_event', self._on_key_press)
def _update_color_from_dataset(self, data):
"""Overriding scatter for testing of colormaps.
"""
is_category = False
array = data.asarray()
#only support for 2d-arrays:
try:
m, n = array.shape
except:
raise ValueError, "No support for more than 2 dimensions."
# is dataset a vector or matrix?
if not n==1:
# we have a category dataset
if isinstance(data, fluents.dataset.CategoryDataset):
is_category = True
map_vec = scipy.dot(array, scipy.diag(scipy.arange(n))).sum(1)
else:
map_vec = array.sum(1)
else:
map_vec = array.ravel()
# update facecolors
self.sc.set_array(map_vec)
self.sc.set_clim(map_vec.min(), map_vec.max())
if is_category:
cmap = cm.Paired
else:
cmap = cm.jet
self.sc.set_cmap(cmap)
self.sc.update_scalarmappable() #sets facecolors from array
self.canvas.draw()
def _on_key_press(self, event): def _on_key_press(self, event):
if event.key=='c': if event.key=='c':
self.toggle_colorbar() self.toggle_colorbar()
@ -68,7 +98,7 @@ class BlmScatterPlot(plots.ScatterPlot):
if self._colorbar==None: if self._colorbar==None:
if self.sc._A!=None: # we need colormapping if self.sc._A!=None: # we need colormapping
# get axes original position # get axes original position
self._ax_last_pos = self.ax.get_position() self._ax_last_pos = self.axes.get_position()
self._colorbar = self.fig.colorbar(self.sc) self._colorbar = self.fig.colorbar(self.sc)
self._colorbar.draw_all() self._colorbar.draw_all()
self.canvas.draw() self.canvas.draw()
@ -80,18 +110,18 @@ class BlmScatterPlot(plots.ScatterPlot):
self.sc.observers = [obs for obs in self.sc.observers if obs !=self._colorbar] self.sc.observers = [obs for obs in self.sc.observers if obs !=self._colorbar]
self._colorbar = None self._colorbar = None
self.sc.colorbar = None self.sc.colorbar = None
self.ax.set_position(self._ax_last_pos) self.axes.set_position(self._ax_last_pos)
self.canvas.draw() self.canvas.draw()
def add_pc_spin_buttons(self, amax, absi, ordi): def add_pc_spin_buttons(self, amax, absi, ordi):
sb_a = gtk.SpinButton(climb_rate=1) sb_a = gtk.SpinButton(climb_rate=1)
sb_a.set_range(1, amax) sb_a.set_range(1, amax)
sb_a.set_value(absi) sb_a.set_value(absi+1)
sb_a.set_increments(1, 5) sb_a.set_increments(1, 5)
sb_a.connect('value_changed', self.set_absicca) sb_a.connect('value_changed', self.set_absicca)
sb_o = gtk.SpinButton(climb_rate=1) sb_o = gtk.SpinButton(climb_rate=1)
sb_o.set_range(1, amax) sb_o.set_range(1, amax)
sb_o.set_value(ordi) sb_o.set_value(ordi+1)
sb_o.set_increments(1, 5) sb_o.set_increments(1, 5)
sb_o.connect('value_changed', self.set_ordinate) sb_o.connect('value_changed', self.set_ordinate)
hbox = gtk.HBox() hbox = gtk.HBox()
@ -115,12 +145,12 @@ class BlmScatterPlot(plots.ScatterPlot):
self.xaxis_data = xy[:,0] self.xaxis_data = xy[:,0]
self.yaxis_data = xy[:,1] self.yaxis_data = xy[:,1]
self.sc._offsets = xy self.sc._offsets = xy
if self.use_blit==True: self.selection_collection._offsets = xy
self.canvas.restore_region(self._clean_bck) self.canvas.draw_idle()
self.ax.draw_artist(self.sc) pad = abs(self.xaxis_data.min()-self.xaxis_data.max())*0.05
self.canvas.blit() new_lims = (self.xaxis_data.min()+pad, self.xaxis_data.max()+pad)
else: self.axes.set_xlim(new_lims, emit=True)
self.canvas.draw_idle() self.canvas.draw_idle()
def set_ordinate(self, sb): def set_ordinate(self, sb):
self._ordi = sb.get_value_as_int() - 1 self._ordi = sb.get_value_as_int() - 1
@ -128,20 +158,19 @@ class BlmScatterPlot(plots.ScatterPlot):
self.xaxis_data = xy[:,0] self.xaxis_data = xy[:,0]
self.yaxis_data = xy[:,1] self.yaxis_data = xy[:,1]
self.sc._offsets = xy self.sc._offsets = xy
if self.use_blit==True: self.selection_collection._offsets = xy
self.canvas.restore_region(self._clean_bck) pad = abs(self.yaxis_data.min()-self.yaxis_data.max())*0.05
self.ax.draw_artist(self.sc) new_lims = (self.yaxis_data.min()+pad, self.yaxis_data.max()+pad)
self.canvas.blit() self.axes.set_ylim(new_lims, emit=True)
else: self.canvas.draw_idle()
self.canvas.draw_idle()
def show_labels(self, index=None): def show_labels(self, index=None):
if self._text_labels == None: if self._text_labels == None:
x = self.xaxis_data x = self.xaxis_data
y = self.yaxis_data y = self.yaxis_data
self._text_labels = {} self._text_labels = {}
for name, n in self.dataset_1[self.current_dim].items(): for name, n in self.dataset_1[self.current_dim].items():
txt = self.ax.text(x[n],y[n], name) txt = self.axes.text(x[n],y[n], name)
txt.set_visible(False) txt.set_visible(False)
self._text_labels[n] = txt self._text_labels[n] = txt
if index!=None: if index!=None:
@ -156,6 +185,7 @@ class BlmScatterPlot(plots.ScatterPlot):
txt.set_visible(False) txt.set_visible(False)
self.canvas.draw() self.canvas.draw()
class PcaScorePlot(BlmScatterPlot): class PcaScorePlot(BlmScatterPlot):
def __init__(self, model, absi=0, ordi=1): def __init__(self, model, absi=0, ordi=1):
title = "Pca scores (%s)" %model._dataset['X'].get_name() title = "Pca scores (%s)" %model._dataset['X'].get_name()
@ -213,7 +243,7 @@ class PlsQvalScatter(plots.ScatterPlot):
id_2, = dataset_2.get_identifiers(sel_dim_2, [0]) id_2, = dataset_2.get_identifiers(sel_dim_2, [0])
if model.model.has_key('w_tsq'): if model.model.has_key('w_tsq'):
col = model.model['w_tsq'].ravel() col = model.model['w_tsq'].ravel()
col = normalise(col) #col = normalise(col)
else: else:
col = 'g' col = 'g'
plots.ScatterPlot.__init__(self, dataset_1, dataset_2, plots.ScatterPlot.__init__(self, dataset_1, dataset_2,
@ -232,13 +262,13 @@ class PredictionErrorPlot(plots.Plot):
plots.Plot.__init__(self, name) plots.Plot.__init__(self, name)
self._frozen = True self._frozen = True
self.current_dim = 'johndoe' self.current_dim = 'johndoe'
self.ax = self.fig.add_subplot(111) self.axes = self.fig.add_subplot(111)
# draw # draw
sep = model.model['sep'] sep = model.model['sep']
aopt = model.model['aopt'] aopt = model.model['aopt']
bx_plot_lines = self.ax.boxplot(sqrt(sep)) bx_plot_lines = self.axes.boxplot(sqrt(sep))
aopt_marker = self.ax.axvline(aopt, linewidth=10, aopt_marker = self.axes.axvline(aopt, linewidth=10,
color='r',zorder=0, color='r',zorder=0,
alpha=.5) alpha=.5)