diff --git a/auto/Examples/fit/scatter2d/consecutive_fitting.py b/auto/Examples/fit/scatter2d/consecutive_fitting.py
index d53f842e29f67681b4f243d61672239c0d8022ab..5e294e5fa1d019140083a07deb09a16942fa5f9b 100755
--- a/auto/Examples/fit/scatter2d/consecutive_fitting.py
+++ b/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)
diff --git a/auto/MiniExamples/fit/scatter2d/consecutive_fitting.py b/auto/MiniExamples/fit/scatter2d/consecutive_fitting.py
index 97c1adeb80325d4831912fde77a917ce39b2a13c..9df5d93c4907aabf214c8afa5b0dbae523d01a9a 100755
--- a/auto/MiniExamples/fit/scatter2d/consecutive_fitting.py
+++ b/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)
diff --git a/rawEx/fit/scatter2d/consecutive_fitting.py b/rawEx/fit/scatter2d/consecutive_fitting.py
index 74ab218391ffbc0bd9c7d7db107b4c5e670999d1..471fd42d41032d98686384ad3bdb683ae4dc2511 100755
--- a/rawEx/fit/scatter2d/consecutive_fitting.py
+++ b/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)