Minor style fixes in python examples

Examples/python/fitting/ex05_FitWithMasks/fit_with_masks.py

@@ -23,11 +23,11 @@ def get_sample(params):
     cylinder_ff = ba.FormFactorCylinder(radius, height)
     cylinder = ba.Particle(m_particle, cylinder_ff)
 
-    particle_layout = ba.ParticleLayout()
-    particle_layout.addParticle(cylinder)
+    layout = ba.ParticleLayout()
+    layout.addParticle(cylinder)
 
     air_layer = ba.Layer(m_air)
-    air_layer.addLayout(particle_layout)
+    air_layer.addLayout(layout)
 
     substrate_layer = ba.Layer(m_substrate, 0)
     multi_layer = ba.MultiLayer()
@@ -59,10 +59,9 @@ def create_real_data():
     """
     params = {'radius': 5.0*nm, 'height': 10.0*nm}
 
+    # retrieving simulated data in the form of numpy array
     simulation = get_simulation(params, add_masks=False)
     simulation.runSimulation()
-
-    # retrieving simulated data in the form of numpy array
     real_data = simulation.result().array()
 
     # spoiling simulated data with the noise to produce "real" data
@@ -81,7 +80,7 @@ def add_mask_to_simulation(simulation):
     with the mask value either "True" (detector bin is excluded from the simulation)
     or False (will be simulated).
 
-    Every subsequent mask override previously defined masks in this area.
+    Every subsequent mask overrides previously defined mask in this area.
 
     In the code below we put masks in such way that simulated image will look like
     a Pac-Man from ancient arcade game.

Examples/python/fitting/ex07_FitAlongSlices/fit_along_slices.py

@@ -2,7 +2,6 @@
 Fitting example: fit along slices
 """
 
-import numpy as np
 from matplotlib import pyplot as plt
 import bornagain as ba
 from bornagain import deg, angstrom, nm
@@ -25,11 +24,11 @@ def get_sample(params):
     cylinder_ff = ba.FormFactorCylinder(radius, height)
     cylinder = ba.Particle(m_particle, cylinder_ff)
 
-    particle_layout = ba.ParticleLayout()
-    particle_layout.addParticle(cylinder)
+    layout = ba.ParticleLayout()
+    layout.addParticle(cylinder)
 
     air_layer = ba.Layer(m_air)
-    air_layer.addLayout(particle_layout)
+    air_layer.addLayout(layout)
 
     substrate_layer = ba.Layer(m_substrate, 0)
     multi_layer = ba.MultiLayer()
@@ -43,15 +42,16 @@ def get_simulation(params, add_masks=True):
     Create and return GISAXS simulation with beam and detector defined
     """
     simulation = ba.GISASSimulation()
-    simulation.setDetectorParameters(100, -1.0*deg, 1.0*deg,
-                                     100, 0.0*deg, 2.0*deg)
+    simulation.setDetectorParameters(100, -1.0*deg, 1.0*deg, 100, 0.0*deg, 2.0*deg)
     simulation.setBeamParameters(1.0*angstrom, 0.2*deg, 0.0*deg)
     simulation.setBeamIntensity(1e+08)
     simulation.setSample(get_sample(params))
     if add_masks:
-        # At this point we mask all the detector and then unmask two areas
-        # corresponding to the vertical and horizontal lines. This will make
-        # simulation/fitting to be performed along slices only.
+        """
+        At this point we mask all the detector and then unmask two areas
+        corresponding to the vertical and horizontal lines. This will make
+        simulation/fitting to be performed along slices only.
+        """
         simulation.maskAll()
         simulation.addMask(ba.HorizontalLine(alpha_slice_value*deg), False)
         simulation.addMask(ba.VerticalLine(phi_slice_value*deg), False)
@@ -62,22 +62,18 @@ def create_real_data():
     """
     Generating "real" data by adding noise to the simulated data.
     """
+    # initial values which we will have to find later during the fit
     params = {'radius': 5.0*nm, 'height': 10.0*nm}
 
+    # retrieving simulated data in the form of numpy array
     simulation = get_simulation(params, add_masks=False)
+    simulation.setBackground(ba.PoissonNoiseBackground())
     simulation.runSimulation()
 
-    # retrieving simulated data in the form of numpy array
-    real_data = simulation.result().array()
-
-    # spoiling simulated data with the noise to produce "real" data
-    noise_factor = 0.1
-    noisy = np.random.normal(real_data, noise_factor*np.sqrt(real_data))
-    noisy[noisy < 0.1] = 0.1
-    return noisy
+    return simulation.result().array()
 
 
-class PlotObserver():
+class PlotObserver:
     """
     Draws fit progress every nth iteration. Here we plot slices along real
     and simulated images to see fit progress.
@@ -87,8 +83,15 @@ class PlotObserver():
         self.fig = plt.figure(figsize=(10.25, 7.69))
         self.fig.canvas.draw()
 
-    def plot_real_data(self, data, nplot):
-        plt.subplot(2, 2, nplot)
+    def __call__(self, fit_objective):
+        self.update(fit_objective)
+
+    @staticmethod
+    def plot_real_data(data):
+        """
+        Plot experimental data as colormap with horizontal/vertical lines
+        representing slices on top.
+        """
         plt.subplots_adjust(wspace=0.2, hspace=0.2)
         ba.plot_histogram(data, title="Experimental data")
         # line representing vertical slice
@@ -100,8 +103,11 @@ class PlotObserver():
                  [alpha_slice_value, alpha_slice_value],
                  color='gray', linestyle='-', linewidth=1)
 
-    def plot_slices(self, slices, title, nplot):
-        plt.subplot(2, 2, nplot)
+    @staticmethod
+    def plot_slices(slices, title):
+        """
+        Plots vertical and horizontal projections.
+        """
         plt.subplots_adjust(wspace=0.2, hspace=0.3)
         for label, slice in slices:
             plt.semilogy(slice.getBinCenters(),
@@ -111,8 +117,11 @@ class PlotObserver():
         plt.legend(loc='upper right')
         plt.title(title)
 
-    def display_fit_parameters(self, fit_objective, nplot):
-        plt.subplot(2, 2, nplot)
+    @staticmethod
+    def display_fit_parameters(fit_objective):
+        """
+        Displays fit parameters, chi and iteration number.
+        """
         plt.title('Parameters')
         plt.axis('off')
 
@@ -121,45 +130,49 @@ class PlotObserver():
         plt.text(0.01, 0.85, "Iterations  " + '{:d}'.
                  format(iteration_info.iterationCount()))
         plt.text(0.01, 0.75, "Chi2       " + '{:8.4f}'.format(iteration_info.chi2()))
-        index = 0
-        params = iteration_info.parameterMap()
-        for key in params:
+        for index, params in enumerate(iteration_info.parameters()):
             plt.text(0.01, 0.55 - index * 0.1,
-                     '{:30.30s}: {:6.3f}'.format(key, params[key]))
-            index = index + 1
+                     '{:30.30s}: {:6.3f}'.format(params.name(), params.value))
+
 
         plt.tight_layout()
         plt.draw()
         plt.pause(0.01)
 
     def update(self, fit_objective):
+        """
+        Callback to access fit_objective on every n'th iteration.
+        """
         self.fig.clf()
 
         real_data = fit_objective.experimentalData().histogram2d()
         simul_data = fit_objective.simulationResult().histogram2d()
 
         # plot real data
-        self.plot_real_data(real_data, nplot=1)
+        plt.subplot(2, 2, 1)
+        self.plot_real_data(real_data)
 
         # horizontal slices
-        slices =[
+        slices = [
             ("real", real_data.projectionX(alpha_slice_value)),
             ("simul", simul_data.projectionX(alpha_slice_value))
             ]
-        title = ( "Horizontal slice at alpha =" +
-                  '{:3.1f}'.format(alpha_slice_value) )
-        self.plot_slices(slices, title, nplot=2)
+        title = ("Horizontal slice at alpha =" + '{:3.1f}'.format(alpha_slice_value))
+        plt.subplot(2, 2, 2)
+        self.plot_slices(slices, title)
 
         # vertical slices
-        slices =[
+        slices = [
             ("real", real_data.projectionY(phi_slice_value)),
             ("simul", simul_data.projectionY(phi_slice_value))
             ]
         title = "Vertical slice at phi =" + '{:3.1f}'.format(phi_slice_value)
-        self.plot_slices(slices, title, nplot=3)
+        plt.subplot(2, 2, 3)
+        self.plot_slices(slices, title)
 
         # display fit parameters
-        self.display_fit_parameters(fit_objective, nplot=4)
+        plt.subplot(2, 2, 4)
+        self.display_fit_parameters(fit_objective)
 
 
 def run_fitting():
@@ -175,7 +188,7 @@ def run_fitting():
 
     # creating custom observer which will draw fit progress
     plotter = PlotObserver()
-    fit_objective.initPlot(10, plotter.update)
+    fit_objective.initPlot(10, plotter)
 
     params = ba.Parameters()
     params.add("radius", 6.*nm, min=4.0, max=8.0)

Examples/python/fitting/ex08_SimultaneousFitOfTwoDatasets/multiple_datasets.py

 """
-Fitting example: demonstrates how to fit two datasets simultaneously.
+Fitting example: simultaneous fit of two datasets
 """
 
 import numpy as np
@@ -21,16 +21,16 @@ def get_sample(params):
     m_substrate = ba.HomogeneousMaterial("Substrate", 6e-6, 2e-8)
     m_particle = ba.HomogeneousMaterial("Particle", 6e-4, 2e-8)
 
-    formFactor = ba.FormFactorHemiEllipsoid(radius_a, radius_b, height)
-    hemiEllipsoid = ba.Particle(m_particle, formFactor)
+    formfactor = ba.FormFactorHemiEllipsoid(radius_a, radius_b, height)
+    particle = ba.Particle(m_particle, formfactor)
 
-    particle_layout = ba.ParticleLayout()
-    particle_layout.addParticle(hemiEllipsoid)
+    layout = ba.ParticleLayout()
+    layout.addParticle(particle)
 
     air_layer = ba.Layer(m_air)
-    air_layer.addLayout(particle_layout)
+    air_layer.addLayout(layout)
 
-    substrate_layer = ba.Layer(m_substrate, 0)
+    substrate_layer = ba.Layer(m_substrate)
     multi_layer = ba.MultiLayer()
     multi_layer.addLayer(air_layer)
     multi_layer.addLayer(substrate_layer)
@@ -87,11 +87,15 @@ class PlotObserver():
     Draws fit progress every nth iteration. Real data, simulated data
     and chi2 map will be shown for both datasets.
     """
-    def __init__(self, draw_every_nth=10):
+    def __init__(self):
         self.fig = plt.figure(figsize=(12.8, 10.24))
         self.fig.canvas.draw()
 
-    def plot_datasets(self, fit_objective, canvas):
+    def __call__(self, fit_objective):
+        self.update(fit_objective)
+
+    @staticmethod
+    def plot_dataset(fit_objective, canvas):
         for i_dataset in range(0, fit_objective.fitObjectCount()):
             real_data = fit_objective.experimentalData(i_dataset)
             simul_data = fit_objective.simulationResult(i_dataset)
@@ -101,17 +105,36 @@ class PlotObserver():
 
             plt.subplot(canvas[i_dataset*3])
             ba.plot_colormap(real_data, title="\"Real\" data - #"+str(i_dataset+1),
-                               zmin=1.0, zmax=zmax, zlabel="")
+                             zmin=1.0, zmax=zmax, zlabel="")
             plt.subplot(canvas[1+i_dataset*3])
             ba.plot_colormap(simul_data, title="Simulated data - #"+str(i_dataset+1),
-                               zmin=1.0, zmax=zmax, zlabel="")
+                             zmin=1.0, zmax=zmax, zlabel="")
             plt.subplot(canvas[2+i_dataset*3])
             ba.plot_colormap(chi2_map, title="Chi2 map - #"+str(i_dataset+1),
-                               zmin=0.001, zmax=10.0, zlabel="")
+                             zmin=0.001, zmax=10.0, zlabel="")
+
+    @staticmethod
+    def display_fit_parameters(fit_objective):
+        """
+        Displays fit parameters, chi and iteration number.
+        """
+        plt.title('Parameters')
+        plt.axis('off')
 
-    def plot_fit_parameters(self, fit_objective, canvas):
-        # fit parameters
-        plt.subplot(canvas[6:])
+        iteration_info = fit_objective.iterationInfo()
+
+        plt.text(0.01, 0.85, "Iterations  " + '{:d}'.
+                 format(iteration_info.iterationCount()))
+        plt.text(0.01, 0.75, "Chi2       " + '{:8.4f}'.format(iteration_info.chi2()))
+        for index, params in enumerate(iteration_info.parameters()):
+            plt.text(0.01, 0.55 - index * 0.1,
+                     '{:30.30s}: {:6.3f}'.format(params.name(), params.value))
+
+    @staticmethod
+    def plot_fit_parameters(fit_objective):
+        """
+        Displays fit parameters, chi and iteration number.
+        """
         plt.axis('off')
 
         iteration_info = fit_objective.iterationInfo()
@@ -119,15 +142,11 @@ class PlotObserver():
         plt.text(0.01, 0.95, "Iterations  " + '{:d}'.
                  format(iteration_info.iterationCount()))
         plt.text(0.01, 0.70, "Chi2       " + '{:8.4f}'.format(iteration_info.chi2()))
-        index = 0
-        params = iteration_info.parameterMap()
-        for key in params:
+        for index, params in enumerate(iteration_info.parameters()):
             plt.text(0.01, 0.30 - index * 0.3,
-                     '{:30.30s}: {:6.3f}'.format(key, params[key]))
-            index = index + 1
-
+                     '{:30.30s}: {:6.3f}'.format(params.name(), params.value))
 
-    def update(self, fit_suite):
+    def update(self, fit_objective):
         self.fig.clf()
 
         # we divide figure to have 3x3 subplots, with two first rows occupying
@@ -136,14 +155,14 @@ class PlotObserver():
             3, 3, width_ratios=[1, 1, 1], height_ratios=[4, 4, 1])
         canvas.update(left=0.05, right=0.95, hspace=0.5, wspace=0.2)
 
-        self.plot_datasets(fit_suite, canvas)
-        self.plot_fit_parameters(fit_suite, canvas)
+        self.plot_dataset(fit_objective, canvas)
+        plt.subplot(canvas[6:])
+        self.plot_fit_parameters(fit_objective)
 
         plt.draw()
         plt.pause(0.01)
 
 
-
 def run_fitting():
     """
     main function to run fitting
@@ -166,7 +185,6 @@ def run_fitting():
     params.add("radius_b", 6.*nm, vary=False)
     params.add("height", 4.*nm, min=2.0, max=10.0)
 
-    print("begin")
     minimizer = ba.Minimizer()
     result = minimizer.minimize(fit_objective.evaluate, params)
     fit_objective.finalize(result)