cvfitCLI_example.py cleaned

Author: Remis Lape

cvfitCLI_example.py

@@ -1,72 +1,68 @@
-#! /usr/bin/python
-
-__author__="remislp"
-__date__ ="$29-Nov-2014 15:40:02$"
-
-from cvfit import fitting
-from cvfit import plots
-from cvfit.fitting import SingleFitSession, MultipleFitSession
-
-if __name__ == "__main__":
-    
-    sets, fname = fitting.load_data(example=True)
-    print('File {0} loaded'.format(fname))
-    print('{0:d} sets found.'.format(len(sets)))
-    
-    #sets = fitting.set_weights(sets)
-    for i in range(len(sets)):
-        print ('\nSet #{0:d}:'.format(i+1))
-        print (sets[i])
-    #settings = fitting.general_settings()
-    
-    #eqname = fitting.choose_equation()
-    #print (eqname)
-    eqname = 'Hill'
-    if eqname == 'Hill' or eqname == 'Langmuir':
-        from cvfit.equations import Hill as eqfit
-         
-    # initiate fitting sessions, fit data and print results
-    fitsessions = MultipleFitSession()
-    for set in sets:
-        equation = eqfit(eqname)
-        fs = SingleFitSession(set, equation)
-        fs.fit()
-        fs.calculate_errors()
-        print('\n*************************************************')
-        print('\t' + fs.data.title + ' fit finished')
-        print(fs.string_estimates())
-        print(fs.string_liklimits())
-        #plots.plot_hill_fit_result_single(fname, fsession.data, fsession.eq, plotguess=True)
-        fitsessions.add(fs)
-    print('\n*************************************************')
-    print('\tAverage of all fits:')
-    print(fitsessions.string_average_estimates())
-    
-    # plot fitted
-    fplots = fitsessions.prepare_fplot('fit')
-    fig = plots.plot(sets, fig=None, 
-        fplotsets=fplots, fplotline='b-',
-        logX=True, logY=False, legend=True)
-    
-    # normalise to fitted maxima and replot
-    for fs in fitsessions.list:
-        fs.eq.normalise(fs.data)
-    fplots = fitsessions.prepare_fplot('norm')
-    plots.plot(sets, fig=None, fplotsets=fplots, fplotline='b-',
-        logX=True, logY=False, legend=True, norm=True)
-    
-    # pool data and fit pooled
-    fitsessions.pool(norm=True)
-    fitsessions.pooled.fit()
-    fitsessions.pooled.calculate_errors()
-    fitsessions.pooled.data.average_pooled()
-    print('\n***********************\n**************************')
-    print('\tNormalised and pooled data fit finished')
-    print(fitsessions.pooled.string_estimates())
-    print(fitsessions.pooled.string_liklimits())
-    
-    # plot pooled data fit
-    fplots = fitsessions.prepare_fplot('pooled')
-    plots.plot([fitsessions.pooled.data], fig=None, fplotsets=fplots, 
-        fplotline='b-', logX=True, logY=False, legend=False, pooled=True)
+#! /usr/bin/python
+
+__author__="remislp"
+__date__ ="$29-Nov-2014 15:40:02$"
+
+from cvfit import fitting
+from cvfit import plots
+from cvfit.fitting import SingleFitSession, MultipleFitSession
+
+if __name__ == "__main__":
+    
+    # load data set
+    sets, fname = fitting.load_data(example=True)
+    print('File {0} loaded'.format(fname))
+    print('{0:d} sets found.'.format(len(sets)))
+    for i in range(len(sets)):
+        print ('\nSet #{0:d}:'.format(i+1))
+        print (sets[i])
+
+    # load equation
+    eqname = 'Hill'
+    if eqname == 'Hill' or eqname == 'Langmuir':
+        from cvfit.equations import Hill as eqfit
+         
+    # initiate fitting sessions, fit data and print results
+    fitsessions = MultipleFitSession()
+    for set in sets:
+        equation = eqfit(eqname)
+        fs = SingleFitSession(set, equation)
+        fs.fit()
+        fs.calculate_errors()
+        print('\n*************************************************')
+        print('\t' + fs.data.title + ' fit finished')
+        print(fs.string_estimates())
+        print(fs.string_liklimits())
+        fitsessions.add(fs)
+    print('\n*************************************************')
+    print('\tAverage of all fits:')
+    print(fitsessions.string_average_estimates())
+    
+    # plot fitted
+    fplots = fitsessions.prepare_fplot('fit')
+    fig = plots.plot(sets, fig=None, 
+        fplotsets=fplots, fplotline='b-',
+        logX=True, logY=False, legend=True)
+    
+    # normalise to fitted maxima and replot
+    for fs in fitsessions.list:
+        fs.eq.normalise(fs.data)
+    fplots = fitsessions.prepare_fplot('norm')
+    plots.plot(sets, fig=None, fplotsets=fplots, fplotline='b-',
+        logX=True, logY=False, legend=True, norm=True)
+    
+    # pool data and fit pooled
+    fitsessions.pool(norm=True)
+    fitsessions.pooled.fit()
+    fitsessions.pooled.calculate_errors()
+    fitsessions.pooled.data.average_pooled()
+    print('\n***********************\n**************************')
+    print('\tNormalised and pooled data fit finished')
+    print(fitsessions.pooled.string_estimates())
+    print(fitsessions.pooled.string_liklimits())
+    
+    # plot pooled data fit
+    fplots = fitsessions.prepare_fplot('pooled')
+    plots.plot([fitsessions.pooled.data], fig=None, fplotsets=fplots, 
+        fplotline='b-', logX=True, logY=False, legend=False, pooled=True)