159 lines
5.5 KiB
Python
159 lines
5.5 KiB
Python
"""Specialised plots for functions defined in blmfuncs.py.
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fixme:
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-- Im normalsing all color mapping input vectors to [0,1]. This will
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destroy informative numerical values in colorbar (but we
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are not showing these anyway). A better fix would be to let the
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colorbar listen to the scalarmappable instance and corect itself, but
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I did not get that to work ...
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fixme2:
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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 fluents import plots
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from scipy import dot,sum,diag,arange,log,mean,newaxis
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from matplotlib import cm
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class PcaScorePlot(plots.ScatterPlot):
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"""PCA 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, 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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self.xaxis_data = self._T[:,n]
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def set_ordinate(self,n):
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self.yaxis_data = self._T[:,n]
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class PcaLoadingPlot(plots.ScatterPlot):
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"""PCA Loading plot"""
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def __init__(self, model, absi=0, ordi=1):
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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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self.xaxis_data = self._P[:,n]
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def set_ordinate(self,n):
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self.yaxis_data = self._P[:,n]
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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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self.yaxis_data = self._T[:,n]
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class PlsLoadingPlot(plots.ScatterPlot):
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"""PLS Loading plot"""
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def __init__(self,model,absi=0,ordi=1):
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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('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, name='loadings')
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def set_absicca(self,n):
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self.xaxis_data = self._P[:,n]
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def set_ordinate(self,n):
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self.yaxis_data = self._T[:,n]
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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, func_class, name='Profiles'):
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# copy, center, plot
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x = func_class._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, func_class, pc=0):
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model = func_class.model
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if not model.has_key('w_tsq'):
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return
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self._W = model['P']
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dataset_1 = func_class.as_dataset('P')
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dataset_2 = func_class.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.has_key('w_tsq'):
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col = 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 InfluencePlot(plots.ScatterPlot):
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"""
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"""
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pass
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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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