def plot_histogram(hist, zmin=None, zmax=None): ba.plot_histogram(hist, xlabel=r'$\varphi_f ^{\circ}$', ylabel=r'$\alpha_f ^{\circ}$', zlabel="", zmin=zmin, zmax=zmax)
def plot(result): """ Runs different plotting functions one by one to demonstrate trivial data presentation tasks. """ fig = plt.figure(figsize=(12.80, 10.24)) plt.subplot(2, 2, 1) ba.plot_histogram(result) plt.title("Intensity as colormap") plt.subplot(2, 2, 2) plot_cropped_map(result) plt.title("Cropping") plt.subplot(2, 2, 3) plot_relative_difference(result) plt.title("Relative difference") plt.subplot(2, 2, 4) plot_slices(result) plt.title("Various slicing of 2D into 1D") save_to_file(result) plt.subplots_adjust(left=0.07, right=0.97, top=0.9, bottom=0.1, hspace=0.25) plt.show()
def plot_relative_difference(hist): """ Creates noisy histogram made of original histogram, then creates and plots a relative difference histogram: (noisy-hist)/hist """ noisy = get_noisy_image(hist) diff = noisy.relativeDifferenceHistogram(hist) ba.plot_histogram(diff, zmin=1e-03, zmax=10)
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 plt.plot([phi_slice_value, phi_slice_value], [data.getYmin(), data.getYmax()], color='gray', linestyle='-', linewidth=1) # line representing horizontal slice plt.plot([data.getXmin(), data.getXmax()], [alpha_slice_value, alpha_slice_value], color='gray', linestyle='-', linewidth=1)
def plot_peaks(hist): peaks = ba.FindPeaks(hist, 3, "nomarkov", 1e-03) xpeaks = [peak[0] for peak in peaks] ypeaks = [peak[1] for peak in peaks] print(peaks) # print("xpeaks:", xpeaks) # print("ypeaks:", ypeaks) print("xpeaks=[", ', '.join('{:4.2f}'.format(k) for k in xpeaks), "]") print("ypeaks=[", ', '.join('{:4.2f}'.format(k) for k in ypeaks), "]") ba.plot_histogram(hist, cmap="jet", zmin=1e+02, zmax=1e+07) plt.plot(xpeaks, ypeaks, linestyle='None', marker='x', color='white', markersize=10)
def plot_real_data(self, data, nplot): plt.subplot(2, 2, nplot) plt.subplots_adjust(wspace=0.2, hspace=0.2) ba.plot_histogram(data, title="Experimental data") # line representing vertical slice plt.plot([phi_slice_value, phi_slice_value], [data.getYmin(), data.getYmax()], color='gray', linestyle='-', linewidth=1) # line representing horizontal slice plt.plot([data.getXmin(), data.getXmax()], [alpha_slice_value, alpha_slice_value], color='gray', linestyle='-', linewidth=1)
def convert(filename): img = fabio.open(filename) print(img.header) data = img.data.astype("float64") nx, ny = 1024, 1024 hist = ba.Histogram2D(nx, 0.0, nx * PIX_SIZE * 1000., ny, 0.0, ny * PIX_SIZE * 1000.) hist.setContent(data) plt.figure() plt.imshow(img.data, norm=colors.LogNorm(100, 1e+07)) plt.figure() ba.plot_histogram(hist) #hist.save("004_230_P144_im_full.int.gz") plt.show()
def test_plot_utils(): simulation = SimulationBuilder().build_simulation() # simulation.runSimulation() result = simulation.result() fig = plt.figure(figsize=(12.80, 10.24)) plt.subplot(2, 2, 1) arr = np.zeros((5, 3)) ba.plot_array(arr) plt.subplot(2, 2, 2) arr = np.array([[1, 2, 3, 4, 5], [6, 7, 8, 9, 10], [11, 12, 13, 14, 15]]) ba.plot_array(arr) plt.subplot(2, 2, 3) ba.plot_colormap(result) plt.subplot(2, 2, 4) ba.plot_histogram(result.histogram2d()) fig.tight_layout()
def run_simulation(): sample = get_sample() simulation = get_simulation() simulation.setSample(sample) simulation.runSimulation() h2 = simulation.result().histogram2d() for i in range(0, h2.getTotalNumberOfBins()): cont = h2.getBinContent(i) x = h2.getXaxisIndex(i) y = h2.getYaxisIndex(i) # if (x>=600 and x<=603 and y>=493 and y<=495): # pass if (x>=593 and x <=606 and y>=0 and y<=617): cont = cont*0.01 if cont == 0.0: cont = 1e-03 h2.setBinContent(i, cont) ba.plot_histogram(h2) np.savetxt("experimental_data.txt", h2.array()) plt.show() return simulation.result()
simulation.getOptions().setMonteCarloIntegration(True, 100) return simulation def run_simulation(): """ Runs simulation and returns intensity map. """ simulation = get_simulation() simulation.setSample(get_sample()) simulation.setTerminalProgressMonitor() simulation.runSimulation() return simulation.result() if __name__ == '__main__': result = run_simulation().histogram2d() ba.plot_histogram(result, cmap='jet', aspect='auto') peaks = ba.FindPeaks(result, 2, "nomarkov", 0.001) xpeaks = [peak[0] for peak in peaks] ypeaks = [peak[1] for peak in peaks] print(peaks) plt.plot(xpeaks, ypeaks, linestyle='None', marker='x', color='white', markersize=10) plt.show()
200, 0.0*deg, 0.6*deg) simulation.setBeamParameters(1.34*angstrom, 0.4*deg, 0.0*deg) simulation.setBeamIntensity(1e+08) simulation.getOptions().setMonteCarloIntegration(True, 100) return simulation def run_simulation(): """ Runs simulation and returns intensity map. """ simulation = get_simulation() simulation.setSample(get_sample()) simulation.setTerminalProgressMonitor() simulation.runSimulation() return simulation.result() if __name__ == '__main__': result = run_simulation().histogram2d() ba.plot_histogram(result) peaks = ba.FindPeaks(result, 2, "nomarkov", 0.001) xpeaks = [peak[0] for peak in peaks] ypeaks = [peak[1] for peak in peaks] print(peaks) plt.plot(xpeaks, ypeaks, linestyle='None', marker='x', color='white', markersize=10) plt.show()
def plot_histogram(hist, zmin=None, zmax=None): ba.plot_histogram(hist, xlabel=r'$\varphi_f ^{\circ}$', ylabel=r'$\alpha_f ^{\circ}$', zlabel="", zmin=zmin, zmax=zmax, cmap='jet', aspect='auto')
def plot_cropped_map(hist): """ Plot cropped version of intensity data """ crop = hist.crop(-1.0 * deg, 0.5 * deg, 1.0 * deg, 1.0 * deg) ba.plot_histogram(crop)