First import of chemometrics utils

fluents/lib/blmplots.py

@@ -0,0 +1,158 @@
+"""Specialised plots for functions defined in blmfuncs.py.
+
+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
+        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 matplotlib import cm
+
+class PcaScorePlot(plots.ScatterPlot):
+    """PCA Score plot"""
+    def __init__(self, model, absi=0, ordi=1):
+        self._T = model.model['T']
+        dataset_1 = model.as_dataset('T')
+        dataset_2 = dataset_1
+        id_dim = dataset_1.get_dim_name(0)
+        sel_dim = dataset_1.get_dim_name(1)
+        id_1, = dataset_1.get_identifiers(sel_dim, [absi])
+        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):
+        self.xaxis_data = self._T[:,n]
+
+    def set_ordinate(self,n):
+        self.yaxis_data = self._T[:,n]
+
+class PcaLoadingPlot(plots.ScatterPlot):
+     """PCA Loading plot"""
+     def __init__(self, model, absi=0, ordi=1):
+         self._P = model.model['P']
+         dataset_1 = model.as_dataset('P')
+         dataset_2 = dataset_1
+         id_dim = dataset_1.get_dim_name(0)
+         sel_dim = dataset_1.get_dim_name(1)
+         id_1, = dataset_1.get_identifiers(sel_dim, [absi])
+         id_2, = dataset_1.get_identifiers(sel_dim, [ordi])
+         if model.model.has_key('p_tsq'):
+             col = model.model['p_tsq'].ravel()
+             col = normalise(col)
+         else:
+             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):
+         self.xaxis_data = self._P[:,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):
+        self._T = model.model['T']
+        dataset_1 = model.as_dataset('T')
+        dataset_2 = dataset_1
+        id_dim = dataset_1.get_dim_name(0)
+        sel_dim = dataset_1.get_dim_name(1)
+        id_1, = dataset_1.get_identifiers(sel_dim, [absi])
+        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='pls-scores')
+        
+    def set_absicca(self,n):
+        self.xaxis_data = self._T[:,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):
+        self._P = model.model['P']
+        dataset_1 = model.as_dataset('P')
+        dataset_2 = dataset_1
+        id_dim = dataset_1.get_dim_name(0)
+        sel_dim = dataset_1.get_dim_name(1)
+        id_1, = dataset_1.get_identifiers(sel_dim, [absi])
+        id_2, = dataset_1.get_identifiers(sel_dim, [ordi])
+        if model.model.has_key('w_tsq'):
+            col = model.model['w_tsq'].ravel()
+            col = normalise(col)
+        else:
+            col = 'g'
+        plots.ScatterPlot.__init__(self, dataset_1, dataset_2,
+                                   id_dim, sel_dim, id_1, id_2,
+                                   c=col, s=20,                                   name='loadings')
+        
+    def set_absicca(self,n):
+        self.xaxis_data = self._P[:,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'):
+        # copy, center, plot
+        x = func_class._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'):
+        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'):
+            return
+        self._W = model['P']
+        dataset_1 = func_class.as_dataset('P')
+        dataset_2 = func_class.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()
+            col = normalise(col)
+        else:
+            col = 'g'
+        plots.ScatterPlot.__init__(self, dataset_1, dataset_2,
+                                   id_dim, sel_dim, id_1, id_2,
+                                   c=col, s=20, sel_dim_2=sel_dim_2,
+                                   name='Load Volcano')
+
+
+class InfluencePlot(plots.ScatterPlot):
+    """
+    """
+    pass
+        
+
+def normalise(x):
+    """Scale vector x to [0,1]
+    """
+    x = x - x.min()
+    x = x/x.max()
+    return x