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)
