### Accessing simulation results

This is an extended example for the further treatment of simulation results: accessing the results, plotting, cropping, slicing and exporting. This serves as a supporting example to the Accessing simulation results tutorial.

• The standard Cylinders in DWBA sample is used for running the simulation.
• The simulation results are retrieved as a Histogram2D object and then processed in various functions to achieve a resulting image.
  1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131  #!/usr/bin/env python3 """ Extended example for simulation results treatment (cropping, slicing, exporting) """ import math import random import bornagain as ba from bornagain import angstrom, ba_plot as bp, deg, nm, std_samples from matplotlib import pyplot as plt def get_sample(): return std_samples.cylinders() def get_simulation(sample): """ Returns a GISAXS simulation with beam and detector defined. """ beam = ba.Beam(1e5, 1*angstrom, 0.2*deg) n = bp.simargs['n'] det = ba.SphericalDetector(n, -2*deg, 2*deg, n, 0, 2*deg) simulation = ba.ScatteringSimulation(beam, sample, det) return simulation def get_noisy_image(field): """ Returns clone of input field filled with additional noise """ result = field.clone() noise_factor = 2.0 for i in range(0, result.size()): amplitude = field.valAt(i) sigma = noise_factor*math.sqrt(amplitude) noisy_amplitude = random.gauss(amplitude, sigma) result.setAt(i, noisy_amplitude) return result def plot_histogram(field, **kwargs): bp.plot_histogram(field, xlabel=r'$\varphi_f ^{\circ}$', ylabel=r'$\alpha_{\rm f} ^{\circ}$', zlabel="", **kwargs) def plot_slices(field): """ Plot 1D slices along y-axis at certain x-axis values. """ noisy = get_noisy_image(field) plt.yscale('log') # projection along Y, slice at fixed x-value proj1 = noisy.yProjection(0) plt.plot(proj1.axis(0).binCenters(), proj1.flatVector(), label=r'$\varphi=0.0^{\circ}$') # projection along Y, slice at fixed x-value proj2 = noisy.yProjection(0.5) # slice at fixed value plt.plot(proj2.axis(0).binCenters(), proj2.flatVector(), label=r'$\varphi=0.5^{\circ}$') # projection along Y for all X values between [xlow, xup], averaged proj3 = noisy.yProjection(0.41, 0.59) plt.plot(proj3.axis(0).binCenters(), proj3.flatVector(), label=r'$<\varphi>=0.5^{\circ}$') plt.xlim(proj1.axis(0).min(), proj1.axis(0).max()) plt.ylim(proj2.minVal(), proj1.maxVal()*10) plt.xlabel(r'$\alpha_{\rm f} ^{\circ}$', fontsize=16) plt.legend(loc='upper right') plt.tight_layout() def plot(field): """ Demonstrates modified data plots. """ plt.figure(figsize=(12.80, 10.24)) print("Subplot 1") plt.subplot(2, 2, 1) bp.plot_histogram(field) plt.title("Intensity as heatmap") print("Subplot 2") plt.subplot(2, 2, 2) crop = field.crop(-1, 0.5, 1, 1) bp.plot_histogram(crop) plt.title("Cropping") print("Subplot 3") plt.subplot(2, 2, 3) noisy = get_noisy_image(field) reldiff = ba.relativeDifferenceField(noisy, field).npArray() bp.plot_array(reldiff, intensity_min=1e-03, intensity_max=10) plt.title("Relative difference") print("Subplot 4") plt.subplot(2, 2, 4) plot_slices(field) plt.title("Various slicing of 2D into 1D") print("Layout") plt.tight_layout() if __name__ == '__main__': bp.parse_args(sim_n=200) sample = get_sample() simulation = get_simulation(sample) print("Simulate") result = simulation.simulate() if bp.datfile: print("Save results") ba.IOFactory.writeSimulationResult(result, bp.datfile + ".int.gz") # Other supported extensions are .tif and .txt. # Besides compression .gz, we support .bz2, and uncompressed. print("Get datafield") field = result.datafield() print("Plot") plot(field) bp.show_or_export() 
Examples/varia/AccessingSimulationResults.py