Multiple lib changes

fluents/lib/blmplots.py

@@ -12,8 +12,9 @@ fixme2:
         colorbar, but when adding colors the colorbar shoud be created.
 """
 from fluents import plots
-from scipy import dot,sum,diag,arange,log,mean,newaxis
+from scipy import dot,sum,diag,arange,log,mean,newaxis,sqrt
 from matplotlib import cm
+import pylab as PB
 
 class PcaScorePlot(plots.ScatterPlot):
     """PCA Score plot"""
@@ -27,10 +28,10 @@ class PcaScorePlot(plots.ScatterPlot):
         id_2, = dataset_1.get_identifiers(sel_dim, [ordi])
         plots.ScatterPlot.__init__(self, dataset_1, dataset_2, id_dim, sel_dim, id_1, id_2 ,c='b' ,s=40 , name='pca-scores')
 
-    def set_absicca(self,n):
+    def set_absicca(self, n):
         self.xaxis_data = self._T[:,n]
 
-    def set_ordinate(self,n):
+    def set_ordinate(self, n):
         self.yaxis_data = self._T[:,n]
 
 class PcaLoadingPlot(plots.ScatterPlot):
@@ -50,15 +51,15 @@ class PcaLoadingPlot(plots.ScatterPlot):
              col = 'g'
          plots.ScatterPlot.__init__(self, dataset_1, dataset_2, id_dim, sel_dim, id_1, id_2,c=col,s=20, name='pls-loadings')
 
-     def set_absicca(self,n):
+     def set_absicca(self, n):
          self.xaxis_data = self._P[:,n]
 
-     def set_ordinate(self,n):
+     def set_ordinate(self, n):
          self.yaxis_data = self._P[:,n]
     
 class PlsScorePlot(plots.ScatterPlot):
     """PLS Score plot"""
-    def __init__(self,model, absi=0, ordi=1):
+    def __init__(self, model, absi=0, ordi=1):
         self._T = model.model['T']
         dataset_1 = model.as_dataset('T')
         dataset_2 = dataset_1
@@ -71,16 +72,16 @@ class PlsScorePlot(plots.ScatterPlot):
                                    id_dim, sel_dim, id_1, id_2 ,
                                    c='b' ,s=40 , name='pls-scores')
         
-    def set_absicca(self,n):
+    def set_absicca(self, n):
         self.xaxis_data = self._T[:,n]
 
-    def set_ordinate(self,n):
+    def set_ordinate(self, n):
         self.yaxis_data = self._T[:,n]
 
 
 class PlsLoadingPlot(plots.ScatterPlot):    
     """PLS Loading plot"""
-    def __init__(self,model,absi=0,ordi=1):
+    def __init__(self, model, absi=0, ordi=1):
         self._P = model.model['P']
         dataset_1 = model.as_dataset('P')
         dataset_2 = dataset_1
@@ -97,44 +98,46 @@ class PlsLoadingPlot(plots.ScatterPlot):
                                    id_dim, sel_dim, id_1, id_2,
                                    c=col, s=20,                                   name='loadings')
         
-    def set_absicca(self,n):
+    def set_absicca(self, n):
         self.xaxis_data = self._P[:,n]
         
-    def set_ordinate(self,n):
+    def set_ordinate(self, n):
         self.yaxis_data = self._T[:,n]
 
+
 class LineViewXc(plots.LineViewPlot):
     """A line view of centered raw data
     """
-    def __init__(self, func_class, name='Profiles'):
+    def __init__(self, model, name='Profiles'):
         # copy, center, plot
-        x = func_class._dataset['X'].copy()
+        x = model._dataset['X'].copy()
         x._array = x._array - mean(x._array,0)[newaxis]
         plots.LineViewPlot.__init__(self, x, 1, None, name)
-        
+
+
 class ParalellCoordinates(plots.Plot):
     """Parallell coordinates for score loads with many comp.
     """
-    def __init__(self,model, p = 'loads'):
+    def __init__(self, model, p='loads'):
         pass
 
+
 class PlsQvalScatter(plots.ScatterPlot):
     """A vulcano like plot of loads vs qvals
     """
-    def __init__(self, func_class, pc=0):
-        model = func_class.model
-        if not model.has_key('w_tsq'):
+    def __init__(self, model, pc=0):
+        if not model.model.has_key('w_tsq'):
             return
-        self._W = model['P']
-        dataset_1 = func_class.as_dataset('P')
-        dataset_2 = func_class.as_dataset('w_tsq')
+        self._W = model.model['P']
+        dataset_1 = model.as_dataset('P')
+        dataset_2 = model.as_dataset('w_tsq')
         id_dim = dataset_1.get_dim_name(0) #genes
         sel_dim = dataset_1.get_dim_name(1) #_comp
         sel_dim_2 = dataset_2.get_dim_name(1) #_zero_dim
         id_1, = dataset_1.get_identifiers(sel_dim, [0])
         id_2, = dataset_2.get_identifiers(sel_dim_2, [0])
-        if model.has_key('w_tsq'):
-            col = model['w_tsq'].ravel()
+        if model.model.has_key('w_tsq'):
+            col = model.model['w_tsq'].ravel()
             col = normalise(col)
         else:
             col = 'g'
@@ -143,7 +146,34 @@ class PlsQvalScatter(plots.ScatterPlot):
                                    c=col, s=20, sel_dim_2=sel_dim_2,
                                    name='Load Volcano')
 
+class PredictionErrorPlot(plots.Plot):
+    """A boxplot of prediction error vs. comp. number.
+    """
+    def __init__(self, model, name="Pred. Err."):
+        if not model.model.has_key('sep'):
+            logger.log('notice', 'Model has no calculations of sep')
+            return
+        plots.Plot.__init__(self, name)
+        self._frozen = True
+        self.current_dim = 'johndoe'
+        self.ax = self.fig.add_subplot(111)
+        
+        # draw
+        sep = model.model['sep']
+        aopt = model.model['aopt']
+        bx_plot_lines = self.ax.boxplot(sqrt(sep))
+        aopt_marker = self.ax.axvline(aopt, linewidth=10,
+                                      color='r',zorder=0,
+                                      alpha=.5)
+        
+        # add canvas
+        self.add(self.canvas)
+        self.canvas.show()
 
+    def set_current_selection(self, selection):
+        pass
+    
+    
 class InfluencePlot(plots.ScatterPlot):
     """
     """