def test_optical_density_histogram(self):
"""
Test the optical density calculation is correct and stuffed
with numpy.inf when the intensity is zero.
"""
bins = (200, 60)
size = np.array([[-1, 1], [-1, 1]]) * 30 * u.cm
intensity_results = cpr.synthetic_radiograph(self.sim_results,
size=size,
bins=bins)
od_results = cpr.synthetic_radiograph(self.sim_results,
size=size,
bins=bins,
optical_density=True)
assert np.allclose(intensity_results[0], od_results[0])
assert np.allclose(intensity_results[1], od_results[1])
intensity = intensity_results[2]
zero_mask = intensity == 0
i0 = np.mean(intensity[~zero_mask])
od = -np.log10(intensity / i0)
assert np.allclose(od[~zero_mask], od_results[2][~zero_mask])
assert np.all(np.isposinf(od_results[2][zero_mask]))
def test_warns(self):
"""
Test warning when less than half the particles reach the detector plane.
"""
sim_results = self.sim_results.copy()
sim_results["nparticles"] *= 3
with pytest.warns(RuntimeWarning):
cpr.synthetic_radiograph(sim_results)
def run_1D_example(name):
"""
Run a simulation through an example with parameters optimized to
sum up to a lineout along x. The goal is to run a relatively fast
sim with a quasi-1D field grid that can then be summed to get good
enough statistics to use as a test.
"""
grid = _test_grid(name, L=1 * u.mm, num=50)
# Cartesian
source = (0 * u.mm, -10 * u.mm, 0 * u.mm)
detector = (0 * u.mm, 200 * u.mm, 0 * u.mm)
# Expect warnings because these fields aren't well-behaved at the edges
with pytest.warns(
RuntimeWarning,
match="Fields should go to zero at edges of grid to avoid "):
sim = cpr.Tracker(grid, source, detector, verbose=False)
sim.create_particles(1e4, 3 * u.MeV, max_theta=0.1 * u.deg)
sim.run()
size = np.array([[-1, 1], [-1, 1]]) * 10 * u.cm
bins = [200, 60]
hax, vax, values = cpr.synthetic_radiograph(sim, size=size, bins=bins)
values = np.mean(values[:, 20:40], axis=1)
return hax, values
def test_intensity_histogram(self, args, kwargs, expected):
"""Test several valid use cases."""
results = cpr.synthetic_radiograph(*args, **kwargs)
assert len(results) == 3
x = results[0]
assert isinstance(x, u.Quantity)
assert x.unit == u.m
assert x.shape == (expected["bins"][0], )
assert np.isclose(np.min(x), expected["xrange"][0], rtol=1e4)
assert np.isclose(np.max(x), expected["xrange"][1], rtol=1e4)
y = results[1]
assert isinstance(y, u.Quantity)
assert y.unit == u.m
assert y.shape == (expected["bins"][1], )
assert np.isclose(np.min(y), expected["yrange"][0], rtol=1e4)
assert np.isclose(np.max(y), expected["yrange"][1], rtol=1e4)
histogram = results[2]
assert isinstance(histogram, np.ndarray)
assert histogram.shape == expected["bins"]
def test_add_wire_mesh():
# ************************************************************
# Test various input configurations
# ************************************************************
# Test a circular mesh
run_mesh_example(extent=1 * u.mm)
# Test providing hdir
run_mesh_example(mesh_hdir=np.array([0.5, 0, 0.5]))
# Test providing hdir and vdir
run_mesh_example(mesh_hdir=np.array([0.5, 0, 0.5]),
mesh_vdir=np.array([0, 0.1, 1]))
# ************************************************************
# Test invalid inputs
# ************************************************************
# Test invalid extent (too many elements)
with pytest.raises(ValueError):
run_mesh_example(extent=(1 * u.mm, 2 * u.mm, 3 * u.mm))
# Test wire mesh completely blocks all particles (in this case because
# the wire diameter is absurdly large)
with pytest.raises(ValueError):
run_mesh_example(wire_diameter=5 * u.mm)
# Test if wire mesh is not between the source and object
with pytest.raises(ValueError):
run_mesh_example(location=np.array([0, 3, 0]) * u.mm)
# ************************************************************
# Test that mesh is the right size in the detector plane, and that
# the wire spacing images correctly.
# This is actually a good overall test of the whole proton radiography
# particle tracing algorithm.
# ************************************************************
loc = np.array([0, -2, 0]) * u.mm
extent = (1 * u.mm, 1 * u.mm)
wire_diameter = 30 * u.um
nwires = 9
sim = run_mesh_example(
problem="empty",
nparticles=1e5,
location=loc,
extent=extent,
wire_diameter=wire_diameter,
nwires=nwires,
)
# Calculate the width that the grid SHOULD have on the image plane
src_to_mesh = np.linalg.norm(loc.si.value - sim.source)
mesh_to_det = np.linalg.norm(sim.detector - loc.si.value)
mag = 1 + mesh_to_det / src_to_mesh
true_width = mag * extent[0].to(u.mm).value
true_spacing = true_width / (nwires - 1)
# Create a synthetic radiograph
size = np.array([[-1, 1], [-1, 1]]) * 2 * u.cm
bins = [100, 50]
# Expect a warning because many particles are off the radiograph
# (Chose max_theta so corners are covered)
with pytest.warns(RuntimeWarning):
h, v, i = cpr.synthetic_radiograph(sim, size=size, bins=bins)
# Sum up the vertical direction
line = np.sum(i, axis=1)
# Determine the points that are on gridlines: where 1/line is above the
# median by a lot
ind = np.argwhere(1 / line > 2 * np.median(1 / line))
hwhere = h.to(u.mm).value[ind]
measured_width = np.max(hwhere) - np.min(hwhere)
# Calculate the max spatial frequency (should be close to the grid spacing)
dx = np.abs(size[0][1] - size[0][0]).to(u.mm).value / bins[0]
fnyquist = int(bins[0] / 2)
freqs = np.fft.fftfreq(h.size, d=dx)
freqs = freqs[:fnyquist]
# Calculate the positive frequency power spectrum
pspect = np.abs(np.fft.fft(1 / line))**2
pspect = pspect[:fnyquist]
pspect = np.where(np.abs(freqs) < 0.1, 0,
pspect) # Mask the low frequencies
# Measured spacing is the inverse of the maximum spatial frequency
measured_spacing = 1 / freqs[np.argmax(pspect)]
# This test is somewhat tricky, so here's a matplotlib plot
# that can be uncommented for debugging
"""
fig, ax = plt.subplots(nrows=3, figsize=(4,15))
ax[0].pcolormesh(h.to(u.mm).value, v.to(u.mm).value, i.T, cmap='Blues_r')
ax[0].set_aspect('equal')
ax[0].axvline(x=np.max(hwhere), color='red')
ax[0].axvline(x=np.min(hwhere), color='red')
ax[1].plot(h.to(u.mm).value, 1/line)
ax[1].axhline(y=np.median(1/line))
ax[1].axvline(x=np.max(hwhere), color='red')
ax[1].axvline(x=np.min(hwhere), color='red')
ax[2].plot(freqs, pspect)
"""
# Verify that the edges of the mesh are imaged correctly
assert np.isclose(measured_width, true_width, 1)
# Verify that the spacing is correct by checking the FFT
assert np.isclose(measured_spacing, true_spacing, 0.5)
def test_gaussian_sphere_analytical_comparison():
"""
This test runs a known example problem and compares to a theoretical
model for small deflections.
Still under construction (comparing the actual form of the radiograph
is possible but tricky to implement).
"""
# The Gaussian sphere problem for small deflection potentials
# is solved in Kugland2012relation, and the equations referenced
# below are from that paper.
# https://doi.org/10.1063/1.4750234
a = (1 * u.mm / 3).to(u.mm).value
phi0 = 1.4e5
W = 15e6
l = 10
L = 200
# Define and run the problem
# Setting b to be much larger than the problem so that the field is not
# cut off at the edges. This is required to be directly
# comparable to the theoretical result.
grid = _test_grid(
"electrostatic_gaussian_sphere",
num=100,
phi0=phi0 * u.V,
a=a * u.mm,
b=20 * u.mm,
)
source = (0 * u.mm, -l * u.mm, 0 * u.mm)
detector = (0 * u.mm, L * u.mm, 0 * u.mm)
with pytest.warns(
RuntimeWarning,
match="Fields should go to zero at edges of grid to avoid "):
sim = cpr.Tracker(grid, source, detector, verbose=False)
sim.create_particles(1e3, W * u.eV, max_theta=12 * u.deg)
sim.run()
size = np.array([[-1, 1], [-1, 1]]) * 4 * u.cm
bins = [100, 100]
h, v, i = cpr.synthetic_radiograph(sim, size=size, bins=bins)
h = h.to(u.mm).value / sim.mag
v = v.to(u.mm).value / sim.mag
r0 = h
# Calculate a lineout across the center of the plane (y=0)
v0 = np.argmin(np.abs(v))
line = np.mean(i[:, v0 - 6:v0 + 6], axis=1)
# Zero the edge of the radiograph
line += -np.mean(line)
line *= 1 / np.max(np.abs(line))
# Calculate the theoretical deflection angles (Eq. 28)
theory = phi0 / W * np.sqrt(np.pi) * (r0 / a) * np.exp(-((r0 / a)**2))
max_deflection = np.max(np.abs(theory))
mu = np.sqrt(np.pi) * (phi0 / W) * (l / a)
# sim_mu = sim.max_deflection.to(u.rad).value*(l/a)
# Calculate the theoretical inversion (Eq. 31 )
theory_deflect = -2 * mu * (1 - (r0 / a)**2) * np.exp(-((r0 / a)**2))
theory_deflect *= 1 / np.max(np.abs(theory_deflect))
# Uncomment for debug
"""
print(f"Theory max deflection: {max_deflection:.6f}")
print(f"Theory mu: {mu:.3f}")
print(f"Sim max deflection: {sim.max_deflection.to(u.rad).value:.6f}")
print(f"Sim mu: {sim_mu:.3f}")
import matplotlib.pyplot as plt
print(f"Theory max deflection: {max_deflection:.6f}")
print(f"Theory mu: {mu:.3f}")
print(f"Sim max deflection: {sim.max_deflection.to(u.rad).value:.6f}")
print(f"Sim mu: {sim_mu:.3f}")
fig, ax = plt.subplots()
ax.pcolormesh(h, v, i.T, shading='auto', cmap='Blues_r')
ax.set_aspect('equal')
fig, ax = plt.subplots()
ax.plot(h, line )
ax.plot(h, theory_deflect)
"""
assert np.isclose(max_deflection,
sim.max_deflection.to(u.rad).value,
atol=1e-3)
def test_raises(self, args, kwargs, _raises):
"""Test scenarios the raise an Exception."""
with pytest.raises(_raises):
cpr.synthetic_radiograph(*args, **kwargs)
def test_input_validation():
"""
Intentionally raise a number of errors.
"""
# ************************************************************************
# During initialization
# ************************************************************************
grid = _test_grid("electrostatic_gaussian_sphere")
source = (-10 * u.mm, 90 * u.deg, 45 * u.deg)
detector = (100 * u.mm, 90 * u.deg, 45 * u.deg)
# Check that an error is raised when an input grid has a nan or infty value
# First check NaN
Ex = grid["E_x"]
Ex[0, 0, 0] = np.nan * u.V / u.m
grid.add_quantities(E_x=Ex)
with pytest.raises(ValueError):
sim = cpr.Tracker(grid, source, detector, verbose=False)
Ex[0, 0, 0] = 0 * u.V / u.m
Ex[0, 0, 0] = np.inf * u.V / u.m # Reset element for the rest of the tests
grid.add_quantities(E_x=Ex)
with pytest.raises(ValueError):
sim = cpr.Tracker(grid, source, detector, verbose=False)
Ex[0, 0, 0] = 0 * u.V / u.m
# Check what happens if a value is large relative to the rest of the array
Ex[0, 0, 0] = 0.5 * np.max(Ex)
grid.add_quantities(E_x=Ex)
# with pytest.raises(ValueError):
with pytest.warns(RuntimeWarning):
sim = cpr.Tracker(grid, source, detector, verbose=False)
Ex[0, 0, 0] = 0 * u.V / u.m
# Raise error when source-to-detector vector doesn't pass through the
# field grid
source_bad = (10 * u.mm, -10 * u.mm, 0 * u.mm)
detector_bad = (10 * u.mm, 100 * u.mm, 0 * u.mm)
with pytest.raises(ValueError):
sim = cpr.Tracker(grid, source_bad, detector_bad, verbose=False)
# Test raises warning when one (or more) of the required fields is missing
grid_bad = CartesianGrid(-1 * u.mm, 1 * u.mm, num=50)
with pytest.warns(RuntimeWarning,
match="is not specified for the provided grid."):
sim = cpr.Tracker(grid_bad, source, detector, verbose=True)
# ************************************************************************
# During create_particles
# ************************************************************************
sim = cpr.Tracker(grid, source, detector, verbose=False)
sim.create_particles(1e3, 15 * u.MeV, max_theta=0.99 * np.pi / 2 * u.rad)
# ************************************************************************
# During runtime
# ************************************************************************
sim = cpr.Tracker(grid, source, detector, verbose=False)
sim.create_particles(1e3, 15 * u.MeV)
# Test an invalid field weighting keyword
with pytest.raises(ValueError):
sim.run(field_weighting="not a valid field weighting")
# ************************************************************************
# During runtime
# ************************************************************************
# SYNTHETIC RADIOGRAPH ERRORS
sim.run()
# Choose a very small synthetic radiograph size that misses most of the
# particles
with pytest.warns(
RuntimeWarning,
match="of the particles are shown on this synthetic radiograph."):
size = np.array([[-1, 1], [-1, 1]]) * 1 * u.mm
hax, vax, values = cpr.synthetic_radiograph(sim, size=size)