def test_point(self):
"""Single data point
"""
for kind in ('linear', 'spline'):
interp_1d = RegularInterpolator(x=(1.,), y=(2.,), kind=kind)
self.assertEqual(interp_1d(1), 2)
self.assertEqual(RegularInterpolator.eval(x=1, xp=(1.,), yp=(2.,), kind=kind), 2)
self.assertEqual(interp_1d(5), 2) # value outside domain
self.assertEqual(RegularInterpolator.eval(x=5, xp=(1.,), yp=(2.,), kind=kind), 2)
assert_allclose(interp_1d(x=(1, 1, 1)), (2, 2, 2))
assert_allclose(interp_1d(x=(0, 1, 2)), (2, 2, 2)) # some values outside domain
assert_allclose(interp_1d(np.array((1, 1, 1))), (2, 2, 2)) # numpy array
assert_allclose(interp_1d(()), ()) # empty table
interp_2d = RegularInterpolator(x=(1.,), y=(2.,), z=(3.,), kind=kind)
self.assertEqual(interp_2d(1, 2), 3)
self.assertEqual(interp_2d(5, 0), 3) # value outside domain
assert_allclose(interp_2d((1, 1, 1), (2, 2, 2)), (3, 3, 3))
test_y = RegularInterpolator.eval(x=(1, 1, 1), y=(2, 2, 2), xp=(1.,), yp=(2.,), zp=(3.,), kind=kind)
assert_allclose(test_y, (3, 3, 3))
assert_allclose(interp_2d((0, 1, 2), (0, 1, 2)), (3, 3, 3)) # some values outside domain
assert_allclose(interp_2d(np.array((1, 1, 1)), np.array((2, 2, 2))), (3, 3, 3)) # numpy array
assert_allclose(interp_2d((), ()), ()) # empty table
def test_2d_2xnel(self):
"""
Should partially fallback to linear interpolation
"""
for kind in ('linear', 'spline'):
interp_2d = RegularInterpolator(x=(1., 3., 10.),
y=(-2.0, 2.0),
z=((2, 2), (3, 3), (4, 4)),
kind=kind)
assert_allclose(interp_2d(1, -2), 2)
assert_allclose(interp_2d(1, 0), 2)
assert_allclose(interp_2d(1, 2), 2)
assert_allclose(interp_2d(3, -2), 3)
assert_allclose(interp_2d(3, 0), 3)
assert_allclose(interp_2d(3, 2), 3)
assert_allclose(interp_2d(x=(3, 3, 3), y=(-2, 0, 2)), (3, 3, 3))
assert_allclose(interp_2d(2, -2), 2.5, atol=0.1)
assert_allclose(interp_2d(2, 0), 2.5, atol=0.1)
assert_allclose(interp_2d(2, 2), 2.5, atol=0.1)
assert_allclose(RegularInterpolator.eval(x=2,
y=2,
xp=(1., 3., 10.),
yp=(-2.0, 2.0),
zp=((2, 2), (3, 3), (4,
4)),
kind=kind),
2.5,
atol=0.1)
interp_2d = RegularInterpolator(x=(1., 3.),
y=(-2.0, 2.0, 10.0),
z=((2, 2, 2), (3, 3, 3)),
kind=kind)
assert_allclose(interp_2d(1, -2), 2)
assert_allclose(interp_2d(1, 0), 2)
assert_allclose(interp_2d(1, 2), 2)
assert_allclose(interp_2d(1, 10), 2)
assert_allclose(interp_2d(3, -2), 3)
assert_allclose(interp_2d(3, 0), 3)
assert_allclose(interp_2d(3, 2), 3)
assert_allclose(interp_2d(3, 10), 3)
assert_allclose(interp_2d(2, -2), 2.5, atol=0.1)
assert_allclose(interp_2d(2, 0), 2.5, atol=0.1)
assert_allclose(interp_2d(2, 2), 2.5, atol=0.1)
assert_allclose(interp_2d(2, 10), 2.5, atol=0.1)
def test_2d_nxnel():
for kind in ('linear', 'spline'):
interp_2d = RegularInterpolator(x=(1., 3., 10.),
y=(-2.0, 0.0, 2.0),
z=((2, 2, 2), (3, 3, 3), (4, 4,
4)),
kind=kind)
assert_allclose(interp_2d(1, -2), 2)
assert_allclose(interp_2d(1, 0), 2)
assert_allclose(interp_2d(1, 2), 2)
assert_allclose(interp_2d(3, -2), 3)
assert_allclose(interp_2d(3, 0), 3)
assert_allclose(interp_2d(3, 2), 3)
assert_allclose(interp_2d(x=(3, 3, 3), y=(-2, 0, 2)), (3, 3, 3))
assert_allclose(interp_2d(2, -2), 2.5, atol=0.1)
assert_allclose(interp_2d(2, 0), 2.5, atol=0.1)
assert_allclose(interp_2d(2, 2), 2.5, atol=0.1)
assert_allclose(RegularInterpolator.eval(x=2,
y=2,
xp=(1., 3., 10.),
yp=(-2.0, 2.0),
zp=((2, 2), (3, 3), (4,
4)),
kind=kind),
2.5,
atol=0.1)
interp_2d = RegularInterpolator(x=(1., 3.),
y=(-2.0, 2.0, 10.0),
z=((2, 2, 2), (3, 3, 3)),
kind=kind)
assert_allclose(interp_2d(1, -2), 2)
assert_allclose(interp_2d(1, 0), 2)
assert_allclose(interp_2d(1, 2), 2)
assert_allclose(interp_2d(1, 10), 2)
assert_allclose(interp_2d(3, -2), 3)
assert_allclose(interp_2d(3, 0), 3)
assert_allclose(interp_2d(3, 2), 3)
assert_allclose(interp_2d(3, 10), 3)
assert_allclose(interp_2d(2, -2), 2.5, atol=0.1)
assert_allclose(interp_2d(2, 0), 2.5, atol=0.1)
assert_allclose(interp_2d(2, 2), 2.5, atol=0.1)
assert_allclose(interp_2d(2, 10), 2.5, atol=0.1)
def test_1d_2el(self):
"""
Should fallback to linear interpolation
"""
for kind in ('linear', 'spline'):
interp_1d = RegularInterpolator(x=(1., 3.),
y=(-2.0, 2.0),
kind=kind)
self.assertEqual(interp_1d(1), -2)
self.assertEqual(interp_1d(1.25), -1.5)
self.assertEqual(interp_1d(1.5), -1)
self.assertEqual(interp_1d(2), 0)
self.assertEqual(interp_1d(2.25), 0.5)
self.assertEqual(interp_1d(2.5), 1)
self.assertEqual(interp_1d(2.75), 1.5)
self.assertEqual(interp_1d(3), 2)
self.assertEqual(interp_1d(5), 2) # value outside domain
self.assertEqual(interp_1d(0), -2) # value outside domain
assert_allclose(interp_1d((1, 1, 1)), (-2, -2, -2))
assert_allclose(interp_1d((0, 1, 4)),
(-2, -2, 2)) # some values outside domain
assert_allclose(interp_1d(np.array((1, 1, 1))),
(-2, -2, -2)) # numpy array
assert_allclose(interp_1d(np.linspace(1, 3, 10)),
np.linspace(-2, 2, 10))
assert_allclose(interp_1d(()), ()) # empty table
def get_ddd_grid(self, energy_list, n):
""" TODO: documentation
"""
energy = []
dist = []
data = []
ddd_e = self.ddd.keys()
ddd_e = sorted(ddd_e)
for e in energy_list:
idx = np.where((np.array(ddd_e) >= e))[0][0] - 1
d_lower = self.ddd[ddd_e[idx]]
d_upper = self.ddd[ddd_e[idx + 1]]
lower_idx = np.where(max(d_lower[1, :]) == d_lower[1, :])[0][0]
upper_idx = np.where(max(d_upper[1, :]) == d_upper[1, :])[0][0]
offset = 1 / (ddd_e[idx + 1] - ddd_e[idx]) * (e - ddd_e[idx + 1])
x_offset = (d_upper[0, upper_idx] - d_lower[0, lower_idx]) * offset
y_offset = 1 + (1 - d_upper[1, upper_idx] / d_lower[1, lower_idx]) * offset
depth = d_upper[0, :] + x_offset
ddd = d_upper[1, :] * y_offset
xi = np.linspace(0, depth[-1], n)
spl = RegularInterpolator(x=depth, y=ddd)
data.extend(spl(xi))
dist.extend(xi)
energy.extend([e] * n)
out = [dist, energy, data]
return np.reshape(np.transpose(out), (len(energy_list), n, 3))
def __init__(self, gd_filename, _dataset=0, dat_filename=None):
"""
:params str gd_filename: full path to bevlet file, including file extension.
:params str dat_filename: optional full path to output file name.
"""
if not os.path.isfile(gd_filename):
raise IOError("Could not find file " + gd_filename)
if _dataset > 2:
print("DOS: Error- only 0,1,2 OER set available. Got:", _dataset)
from pkg_resources import resource_string
model_files = ('OER_furusawa_V79_C12.dat', 'OER_furusawa_HSG_C12.dat',
'OER_barendsen.dat')
model_data = resource_string(
'pytrip', os.path.join('data', model_files[_dataset]))
lines = model_data.decode('ascii').split('\n')
x = np.asarray([float(line.split()[0]) for line in lines if line])
y = np.asarray([float(line.split()[1]) for line in lines if line])
us = RegularInterpolator(x, y, kind='linear')
with open(gd_filename, 'r') as gd_file:
gd_lines = gd_file.readlines()
first = True
ignore_rest = False
if dat_filename is not None:
out_fd = open(dat_filename, 'w')
else:
out_fd = sys.stdout
for line in gd_lines:
if not line[0].isdigit():
tmp_string = "#" + line
if not first:
ignore_rest = True
else:
first = False
if ignore_rest:
tmp_string = "#" + line
else:
let = float(line.split()[7])
oer = us(let)
tmp_string = ""
for item in line.split():
tmp_string = tmp_string + item + " "
tmp_string = tmp_string + str(oer) + "\n"
out_fd.write(tmp_string)
if dat_filename is not None:
out_fd.close()
def test_point(self):
"""Single data point
"""
for kind in ('linear', 'spline'):
interp_1d = RegularInterpolator(x=(1., ), y=(2., ), kind=kind)
self.assertEqual(interp_1d(1), 2)
self.assertEqual(
RegularInterpolator.eval(x=1, xp=(1., ), yp=(2., ), kind=kind),
2)
self.assertEqual(interp_1d(5), 2) # value outside domain
self.assertEqual(
RegularInterpolator.eval(x=5, xp=(1., ), yp=(2., ), kind=kind),
2)
assert_allclose(interp_1d(x=(1, 1, 1)), (2, 2, 2))
assert_allclose(interp_1d(x=(0, 1, 2)),
(2, 2, 2)) # some values outside domain
assert_allclose(interp_1d(np.array((1, 1, 1))),
(2, 2, 2)) # numpy array
assert_allclose(interp_1d(()), ()) # empty table
interp_2d = RegularInterpolator(x=(1., ),
y=(2., ),
z=(3., ),
kind=kind)
self.assertEqual(interp_2d(1, 2), 3)
self.assertEqual(interp_2d(5, 0), 3) # value outside domain
assert_allclose(interp_2d((1, 1, 1), (2, 2, 2)), (3, 3, 3))
test_y = RegularInterpolator.eval(x=(1, 1, 1),
y=(2, 2, 2),
xp=(1., ),
yp=(2., ),
zp=(3., ),
kind=kind)
assert_allclose(test_y, (3, 3, 3))
assert_allclose(interp_2d((0, 1, 2), (0, 1, 2)),
(3, 3, 3)) # some values outside domain
assert_allclose(interp_2d(np.array((1, 1, 1)), np.array(
(2, 2, 2))), (3, 3, 3)) # numpy array
assert_allclose(interp_2d((), ()), ()) # empty table
def import_hlut(self):
""" Imports the Hounsfield lookup table and stores it into self.hlut_data object
self.hlut_data is trained linear interpolator, it can be later called to get interpolated values
"""
fp = open(self.hlut_file, "r")
lines = fp.read()
fp.close()
lines = lines.split('\n')
x_data = []
y_data = []
for line in lines:
a = line.split()
if a:
x_data.append(float(a[0]))
y_data.append(float(a[3]))
self.hlut_data = RegularInterpolator(np.array(x_data), np.array(y_data), kind='linear')
def test_2d_nxnel(self):
for kind in ('linear', 'spline'):
interp_2d = RegularInterpolator(x=(1., 3., 10.),
y=(-2.0, 0.0, 2.0),
z=((2, 2, 2), (3, 3, 3), (4, 4, 4)),
kind=kind)
assert_allclose(interp_2d(1, -2), 2)
assert_allclose(interp_2d(1, 0), 2)
assert_allclose(interp_2d(1, 2), 2)
assert_allclose(interp_2d(3, -2), 3)
assert_allclose(interp_2d(3, 0), 3)
assert_allclose(interp_2d(3, 2), 3)
assert_allclose(interp_2d(x=(3, 3, 3), y=(-2, 0, 2)), (3, 3, 3))
assert_allclose(interp_2d(2, -2), 2.5, atol=0.1)
assert_allclose(interp_2d(2, 0), 2.5, atol=0.1)
assert_allclose(interp_2d(2, 2), 2.5, atol=0.1)
assert_allclose(RegularInterpolator.eval(x=2,
y=2,
xp=(1., 3., 10.),
yp=(-2.0, 2.0),
zp=((2, 2), (3, 3), (4, 4)),
kind=kind),
2.5,
atol=0.1)
interp_2d = RegularInterpolator(x=(1., 3.), y=(-2.0, 2.0, 10.0), z=((2, 2, 2), (3, 3, 3)), kind=kind)
assert_allclose(interp_2d(1, -2), 2)
assert_allclose(interp_2d(1, 0), 2)
assert_allclose(interp_2d(1, 2), 2)
assert_allclose(interp_2d(1, 10), 2)
assert_allclose(interp_2d(3, -2), 3)
assert_allclose(interp_2d(3, 0), 3)
assert_allclose(interp_2d(3, 2), 3)
assert_allclose(interp_2d(3, 10), 3)
assert_allclose(interp_2d(2, -2), 2.5, atol=0.1)
assert_allclose(interp_2d(2, 0), 2.5, atol=0.1)
assert_allclose(interp_2d(2, 2), 2.5, atol=0.1)
assert_allclose(interp_2d(2, 10), 2.5, atol=0.1)
def test_2d_2x2el(self):
"""
Should fallback to linear interpolation
"""
for kind in ('linear', 'spline'):
interp_2d = RegularInterpolator(x=(1., 3.),
y=(-2.0, 2.0),
z=((2, 2), (3, 3)),
kind=kind)
self.assertEqual(interp_2d(1, -2), 2)
self.assertEqual(interp_2d(1, 0), 2)
self.assertEqual(interp_2d(1, 2), 2)
self.assertEqual(interp_2d(3, -2), 3)
self.assertEqual(interp_2d(3, 0), 3)
self.assertEqual(interp_2d(3, 2), 3)
self.assertEqual(interp_2d(2, -2), 2.5)
self.assertEqual(interp_2d(2, 0), 2.5)
self.assertEqual(interp_2d(2, 2), 2.5)
def load_ddd(self, directory):
""" Loads all .ddd files found in 'directory'
:params str directory: directory where the .ddd files are found.
"""
x_data = []
y_data = []
points = [[], []]
ddd = {}
max_dist = []
items = glob.glob(directory)
for item in items:
x_data = []
y_data = []
with open(item, 'r') as f:
data = f.read()
lines = data.split('\n')
for n, line in enumerate(data.split("\n")):
if line.find("energy") is not -1:
energy = float(line.split()[1])
if line.find('!') is -1 and line.find('#') is -1:
break
for i in range(n, len(lines)):
if len(lines[i]) < 3:
continue
point = [float(s) for s in lines[i].split()]
x_data.append(point[0] * 10)
y_data.append(point[1])
ddd[energy] = np.array([x_data, y_data])
max_dist.append([energy, x_data[-1]])
max_dist = np.array(sorted(max_dist, key=lambda x: x[0]))
self.max_dist = RegularInterpolator(x=max_dist[:, 0], y=max_dist[:, 1])
ddd_list = []
for key, value in ddd.items():
points[0].extend(value[0])
points[1].extend(len(value[0]) * [key])
ddd_list.extend(value[1])
self.ddd_list = ddd_list
self.ddd = ddd
self.points = np.transpose(points)
def test_1d_nel(self):
logger.info("Testing data sampled from constant function")
for kind in ('linear', 'spline'):
interp_1d = RegularInterpolator(x=(1., 3., 4., 5., 10.),
y=(2.0, 2.0, 2.0, 2.0, 2.0),
kind=kind)
assert_allclose(interp_1d(np.linspace(-1, 10, 100)),
np.ones(100) * 2.0)
interp_1d = RegularInterpolator(x=(1., 3., 4., 5.),
y=(2.0, 2.0, 2.0, 2.0),
kind=kind)
assert_allclose(interp_1d(np.linspace(-1, 10, 100)),
np.ones(100) * 2.0)
interp_1d = RegularInterpolator(x=(1., 3., 4.),
y=(2.0, 2.0, 2.0),
kind=kind)
assert_allclose(interp_1d(np.linspace(-1, 10, 100)),
np.ones(100) * 2.0)
logger.info("Testing some random data")
data_x = (1., 3., 4., 5., 10.)
data_y = (0.0, 10.0, 5.0, 0.0, -5.0)
interp_1d = RegularInterpolator(x=data_x, y=data_y, kind='linear')
assert_allclose(interp_1d(data_x), data_y)
self.assertEqual(interp_1d(2), 5)
self.assertEqual(interp_1d(2.5), 7.5)
self.assertEqual(interp_1d(3.5), 7.5)
self.assertEqual(interp_1d(7.5), -2.5)
interp_1d = RegularInterpolator(x=data_x, y=data_y, kind='spline')
assert_allclose(interp_1d(data_x), data_y, atol=1e-12)
self.assertGreater(interp_1d(2),
10) # spline interpolation making strange "hops"
self.assertGreater(interp_1d(2.5), 10)
assert_allclose(interp_1d(3.5), 7.5, atol=0.4)
self.assertLess(interp_1d(7.5), -5)
logger.info("Testing data sampled from identity function")
data_y = data_x
interp_1d = RegularInterpolator(x=data_x, y=data_y, kind='spline')
assert_allclose(interp_1d(data_x), data_y, atol=1e-12)
test_x = np.linspace(start=data_x[0], stop=data_x[-1], num=100)
assert_allclose(interp_1d(test_x), test_x, atol=1e-12)
