adjust fitting settings

auto/Examples/fit/scatter2d/consecutive_fitting.py

@@ -7,7 +7,6 @@ space and roughly find local minimum. The second Minuit2 minimizer will continue
 after that to find precise minimum location.
 """
 
-import numpy as np
 from matplotlib import pyplot as plt
 import bornagain as ba
 from bornagain import ba_fitmonitor, deg, angstrom, nm
@@ -59,7 +58,7 @@ def fake_data():
     simulation = get_simulation(P)
     result = simulation.simulate()
 
-    return result.noisy(0.1, 0.1)
+    return result.noisy(0.3, 0.5)
 
 
 def run_fitting():
@@ -70,17 +69,17 @@ def run_fitting():
 
     fit_objective = ba.FitObjective()
     fit_objective.addFitPair(get_simulation, data, 1)
-    fit_objective.initPrint(10)
+    fit_objective.initPrint(30)
     observer = ba_fitmonitor.PlotterGISAS()
-    fit_objective.initPlot(10, observer)
+    fit_objective.initPlot(30, observer)
     """
     Setting fitting parameters with starting values.
     Here we select starting values being quite far from true values
     to puzzle our minimizer's as much as possible.
     """
     P = ba.Parameters()
-    P.add("height", 1.*nm, min=0.01, max=30, step=0.05*nm)
-    P.add("radius", 20.*nm, min=0.01, max=30, step=0.05*nm)
+    P.add("height", 1.*nm, min=0.01, max=30)
+    P.add("radius", 20.*nm, min=0.01, max=30)
     """
     Now we run first minimization round using the Genetic minimizer.
     The Genetic minimizer is able to explore large parameter space
@@ -88,7 +87,7 @@ def run_fitting():
     """
     minimizer = ba.Minimizer()
     minimizer.setMinimizer("Genetic", "",
-                           "MaxIterations=2;PopSize=300;RandomSeed=1")
+                           "MaxIterations=3;PopSize=500")
     result = minimizer.minimize(fit_objective.evaluate, P)
     fit_objective.finalize(result)
 

auto/MiniExamples/fit/scatter2d/consecutive_fitting.py

@@ -7,7 +7,6 @@ space and roughly find local minimum. The second Minuit2 minimizer will continue
 after that to find precise minimum location.
 """
 
-import numpy as np
 from matplotlib import pyplot as plt
 import bornagain as ba
 from bornagain import ba_fitmonitor, deg, angstrom, nm
@@ -59,7 +58,7 @@ def fake_data():
     simulation = get_simulation(P)
     result = simulation.simulate()
 
-    return result.noisy(0.1, 0.1)
+    return result.noisy(0.3, 0.5)
 
 
 def run_fitting():
@@ -70,17 +69,17 @@ def run_fitting():
 
     fit_objective = ba.FitObjective()
     fit_objective.addFitPair(get_simulation, data, 1)
-    fit_objective.initPrint(10)
+    fit_objective.initPrint(30)
     observer = ba_fitmonitor.PlotterGISAS()
-    plt.close() # (hide plot) fit_objective.initPlot(10, observer)
+    plt.close() # (hide plot) fit_objective.initPlot(30, observer)
     """
     Setting fitting parameters with starting values.
     Here we select starting values being quite far from true values
     to puzzle our minimizer's as much as possible.
     """
     P = ba.Parameters()
-    P.add("height", 1.*nm, min=0.01, max=30, step=0.05*nm)
-    P.add("radius", 20.*nm, min=0.01, max=30, step=0.05*nm)
+    P.add("height", 1.*nm, min=0.01, max=30)
+    P.add("radius", 20.*nm, min=0.01, max=30)
     """
     Now we run first minimization round using the Genetic minimizer.
     The Genetic minimizer is able to explore large parameter space
@@ -88,7 +87,7 @@ def run_fitting():
     """
     minimizer = ba.Minimizer()
     minimizer.setMinimizer("Genetic", "",
-                           "MaxIterations=1;PopSize=10;RandomSeed=1")
+                           "MaxIterations=1;PopSize=10")
     result = minimizer.minimize(fit_objective.evaluate, P)
     fit_objective.finalize(result)
 

rawEx/fit/scatter2d/consecutive_fitting.py

@@ -7,7 +7,6 @@ space and roughly find local minimum. The second Minuit2 minimizer will continue
 after that to find precise minimum location.
 """
 
-import numpy as np
 from matplotlib import pyplot as plt
 import bornagain as ba
 from bornagain import ba_fitmonitor, deg, angstrom, nm
@@ -59,7 +58,7 @@ def fake_data():
     simulation = get_simulation(P)
     result = simulation.simulate()
 
-    return result.noisy(0.1, 0.1)
+    return result.noisy(0.3, 0.5)
 
 
 def run_fitting():
@@ -70,17 +69,17 @@ def run_fitting():
 
     fit_objective = ba.FitObjective()
     fit_objective.addFitPair(get_simulation, data, 1)
-    fit_objective.initPrint(10)
+    fit_objective.initPrint(30)
     observer = ba_fitmonitor.PlotterGISAS()
-    <%= sm ? "plt.close() # (hide plot) " :"" %>fit_objective.initPlot(10, observer)
+    <%= sm ? "plt.close() # (hide plot) " :"" %>fit_objective.initPlot(30, observer)
     """
     Setting fitting parameters with starting values.
     Here we select starting values being quite far from true values
     to puzzle our minimizer's as much as possible.
     """
     P = ba.Parameters()
-    P.add("height", 1.*nm, min=0.01, max=30, step=0.05*nm)
-    P.add("radius", 20.*nm, min=0.01, max=30, step=0.05*nm)
+    P.add("height", 1.*nm, min=0.01, max=30)
+    P.add("radius", 20.*nm, min=0.01, max=30)
     """
     Now we run first minimization round using the Genetic minimizer.
     The Genetic minimizer is able to explore large parameter space
@@ -89,7 +88,7 @@ def run_fitting():
     minimizer = ba.Minimizer()
     minimizer.setMinimizer("Genetic", "",
                            "<%= sm ? "MaxIterations=1;PopSize=10" :
-                           "MaxIterations=2;PopSize=300" %>;RandomSeed=1")
+                           "MaxIterations=3;PopSize=500" %>")
     result = minimizer.minimize(fit_objective.evaluate, P)
     fit_objective.finalize(result)