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Added support for fitting mass spectrometry populations, few other minor changes
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| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -0,0 +1,119 @@ | ||
| import os | ||
| import numpy as np | ||
|
|
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| _MATPLOTLIB_BACKEND = None #None for default | ||
|
|
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| class MSExperiment(): | ||
| def __init__(self, file): | ||
| self.title = os.path.splitext(os.path.basename(file))[0] | ||
| self.chisq = None | ||
|
|
||
| fh = open(file) | ||
| self.T = float(fh.readline().split()[2]) | ||
| PConc = map(float,fh.readline().split()[2:]) | ||
| LConc = map(float,fh.readline().split()[2:]) | ||
| fh.close() | ||
|
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||
| self.npoints = len(PConc) | ||
| assert len(LConc) == self.npoints | ||
|
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| data = np.genfromtxt( file, usecols=xrange(1,self.npoints+1), unpack=True ) | ||
| assert len(data) == self.npoints | ||
| self.npops = len(data[0])/2 | ||
| for i in xrange(len(data)): | ||
| assert len(data[i])/2 == self.npops | ||
|
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| self.Concentrations,self.PopIntens,self.PopErrors,self.PopFits = [],[],[],[] | ||
| for i in xrange(self.npoints): | ||
| self.Concentrations.append({}) | ||
| self.Concentrations[i]['Lattice'] = PConc[i] / 1.0E6 | ||
| self.Concentrations[i]['Ligand'] = LConc[i] / 1.0E6 | ||
| self.PopIntens.append( data[i][:self.npops] ) | ||
| self.PopErrors.append( data[i][self.npops:] ) | ||
| self.PopFits.append( [0.0]*self.npops ) | ||
|
|
||
| def make_plot(self,hardcopy=False,hardcopydir='.',hardcopyprefix='', hardcopytype='png'): | ||
| try: | ||
| if _MATPLOTLIB_BACKEND != None: | ||
| import matplotlib | ||
| matplotlib.use(_MATPLOTLIB_BACKEND) | ||
| import matplotlib.pyplot as pyplot | ||
| except: | ||
| pyplot = None | ||
|
|
||
| if pyplot == None: return | ||
| if hardcopy: fig = pyplot.figure() | ||
|
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| pyplot.clf() | ||
| pyplot.title(self.title) | ||
| pyplot.ylabel("Experimental - Fit Abundance") | ||
| pyplot.xlabel("Mass Populations") | ||
|
|
||
| ax1 = pyplot.subplot(3,1,1) | ||
| ax2 = pyplot.subplot(3,1,2) | ||
| ax3 = pyplot.subplot(3,1,3) | ||
|
|
||
| xax_positions,xax_labels = [],[] | ||
| width,space,left = 0.25,0.5,0.0 | ||
| for i in xrange(self.npops): | ||
| bars1 = ax1.bar( [left + (j*width) for j in xrange(self.npoints)], [self.PopIntens[j][i] for j in xrange(self.npoints)], width=width, edgecolor='r' ) | ||
| bars2 = ax2.bar( [left + (j*width) for j in xrange(self.npoints)], [self.PopFits[j][i] for j in xrange(self.npoints)], width=width, edgecolor='r' ) | ||
| bars3 = ax3.bar( [left + (j*width) for j in xrange(self.npoints)], [self.PopIntens[j][i] - self.PopFits[j][i] for j in xrange(self.npoints)], width=width, edgecolor='r' ) | ||
| xax_positions.append( left + (self.npoints*width)/2.0 ) | ||
| xax_labels.append("%i"%i) | ||
| left += (j*width)+space | ||
|
|
||
| for j in xrange(self.npoints): | ||
| #color = float(j) / self.npoints | ||
| #bars1[j].set_color( (0, 0, color) ) | ||
| #bars2[j].set_color( (0, color, 0) ) | ||
| #bars3[j].set_color( (color, 0, 0) ) | ||
| color = 1.0 - (float(j) / self.npoints) | ||
| bars1[j].set_color( (color, color, 1) ) | ||
| bars2[j].set_color( (color, 1, color) ) | ||
| bars3[j].set_color( (1, color, color) ) | ||
|
|
||
| bars1[j].set_edgecolor( (0,0,0) ) | ||
| bars2[j].set_edgecolor( (0,0,0) ) | ||
| bars3[j].set_edgecolor( (0,0,0) ) | ||
|
|
||
| ax1.set_ylabel("Experimental") | ||
| ax1.set_xticks( xax_positions ) | ||
| ax1.set_xticklabels( xax_labels) | ||
| ax2.set_ylabel("Fit") | ||
| ax2.set_xticks( xax_positions ) | ||
| ax2.set_xticklabels( xax_labels) | ||
| ax3.set_ylabel("Residuals") | ||
| ax3.set_xticks( xax_positions ) | ||
| ax3.set_xticklabels( xax_labels) | ||
|
|
||
| pyplot.draw() | ||
|
|
||
| if hardcopy: | ||
| fig.savefig( os.path.join(hardcopydir,"%s%s.%s"%(hardcopyprefix,self.title,hardcopytype)), bbox_inches='tight') | ||
| pyplot.close(fig) | ||
| else: | ||
| pyplot.show() | ||
|
|
||
| def export_to_file(self,path): | ||
| fh = open(path, 'w') | ||
| fh.write("# Experimental data\n") | ||
| for i in xrange(self.npoints): | ||
| fh.write("%i %s\n"%(i,"\t".join(["%f"%f for f in self.PopIntens[i]]))) | ||
| fh.write("# Fit data\n") | ||
| for i in xrange(self.npoints): | ||
| fh.write("%i %s\n"%(i,"\t".join(["%f"%f for f in self.PopFits[i]]))) | ||
| fh.close() | ||
|
|
||
| def get_chisq(self, data, writeback=False): | ||
| self.chisq = 0.0 | ||
| for i in xrange(self.npoints): | ||
| for j in xrange(self.npops): | ||
| self.chisq += (data[i][j] - self.PopIntens[i][j])**2 / self.PopErrors[i][j]**2 | ||
|
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| if writeback: | ||
| self.PopFits = data | ||
|
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| self.chisq = self.chisq / (self.npoints * self.npops) | ||
|
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| return self.chisq |