+"""
+Copyright 2023
+The University of Manchester & Diamond Light Source
+Licensed under the Apache License, Version 2.0.
+
+
+These are modules for generation of 3D and 4D (dynamic extensions) phantoms and
+their analytical data.
+
+API Summary:
+
+* Model - generates a 3D phantom from the Library (Phantom3DLibrary)
+* ModelSub - generates a vertical subset (cutoff) for model from the Library (Phantom3DLibrary)
+* ModelTemporal - generates a (3D + t) 4D model from the Library (Phantom3DLibrary)
+* ModelTemporalSub - generates a vertical subset (cutoff) of 4D model from the Library (Phantom3DLibrary)
+* ModelSino - generates 3D projection data for a model from the Library (Phantom3DLibrary)
+* ModelSinoSub - generates a vertical subset (cutoff) for 3D projection data for a model from the Library (Phantom3DLibrary)
+* ModelSinoTemporal - generates (3D + t) 4D projection data for a temporal model from the Library (Phantom3DLibrary)
+* ModelSinoTemporalSub - generates (3D + t) 4D projection data (subset) for a temporal model from the Library (Phantom3DLibrary)
+* Object - generates a 3D phantom from a given object described in the dictionary.
+* ObjectSino - generates a 3D projection data from a given object described in the dictionary.
+"""
+
+import ctypes
+import numpy as np
+from numbers import Number
+from enum import Enum
+from typing import Union, Tuple
+from pathlib import Path
+
+import tomophantom.ctypes.external as external
+
+__all__ = [
+ "Model",
+ "ModelSub",
+ "ModelTemporal",
+ "ModelTemporalSub",
+ "ModelSino",
+ "ModelSinoSub",
+ "ModelSinoTemporal",
+ "ModelSinoTemporalSub",
+ "Object",
+ "ObjectSino",
+]
+
+
+class Objects3D(Enum):
+ """Enumeration with the available objects for 3D phantoms"""
+
+ GAUSSIAN = "gaussian"
+ PARABOLOID = "paraboloid"
+ ELLIPSOID = "ellipsoid"
+ CONE = "cone"
+ CUBOID = "cuboid"
+ ELLIPCYLINDER = "elliptical_cylinder"
+
+
+def _check_params3d(model_no: int, models_library_path: Path) -> np.ndarray:
+ """Check parameters before executing the generation script.
+
+ Args:
+ model_no (int): Model number from the Phantom3DLibrary.dat library file.
+ models_library_path (Path): A path to the library file.
+
+ Returns:
+ list: a list of integers
+ """
+ params = np.ascontiguousarray(np.zeros([12], dtype=ctypes.c_int))
+ external.c_checkParams3D(
+ params,
+ model_no,
+ models_library_path,
+ )
+ return params
+
+
+[docs]def Model(
+
model_no: int,
+
phantom_size: Union[int, Tuple[int, int, int]],
+
models_library_path: Path,
+
) -> np.ndarray:
+
"""Generates a 3D phantom based on the model no. in the library file Phantom3DLibrary.dat.
+
+
Args:
+
model_no (int): Model number from the Phantom3DLibrary.dat library file.
+
phantom_size (int, Tuple(int)): A scalar or a tuple with phantom dimensions.
+
models_library_path (Path): A path to the library file.
+
+
Returns:
+
np.ndarray: The generated 3D phantom.
+
"""
+
if type(phantom_size) == tuple:
+
N1, N2, N3 = [int(i) for i in phantom_size]
+
else:
+
N1 = N2 = N3 = phantom_size
+
+
# check the validity of model's parameters
+
params = np.ascontiguousarray(np.zeros([12], dtype=ctypes.c_int))
+
params = _check_params3d(model_no, models_library_path)
+
__testParams3D(params) # check parameters and terminate if incorrect
+
+
if params[3] == 1:
+
phantom = np.zeros([N1, N2, N3], dtype="float32", order="C")
+
else:
+
raise ValueError(
+
"The selected model is temporal (3D+time), use 'ModelTemporal' function instead"
+
)
+
external.c_model3d(
+
np.ascontiguousarray(phantom),
+
model_no,
+
N1,
+
N2,
+
N3,
+
0,
+
N1,
+
models_library_path,
+
)
+
return phantom
+
+
+[docs]def ModelSub(
+
model_no: int,
+
phantom_size: Union[int, Tuple[int, int, int]],
+
sub_index: Tuple[int, int],
+
models_library_path: Path,
+
) -> np.ndarray:
+
"""Generates a subset of 3D phantom (vertical cutoff) based on the model no. in
+
the library file Phantom3DLibrary.dat.
+
+
Args:
+
model_no (int): Model number from the Phantom3DLibrary.dat library file.
+
phantom_size (int, Tuple(int)): A scalar or a tuple with phantom dimensions.
+
sub_index (Tuple[int, int]): a tuple containing 2 indeces (lower, upper) to select a vertical subset.
+
models_library_path (Path): A path to the library file.
+
+
Returns:
+
np.ndarray: The generated 3D phantom.
+
"""
+
if type(phantom_size) == tuple:
+
N1, N2, N3 = [int(i) for i in phantom_size]
+
else:
+
N1 = N2 = N3 = phantom_size
+
+
Z1, Z2 = [int(i) for i in sub_index]
+
+
rangecheck = Z2 > Z1
+
if not rangecheck:
+
raise ValueError("Upper index must be larger than the lower one")
+
rangecheck = Z1 >= 0 and Z1 < N3
+
if not rangecheck:
+
raise ValueError("Range of the lower index is incorrect")
+
rangecheck = Z2 >= 0 and Z2 <= N3
+
if not rangecheck:
+
raise ValueError("Range of the higher index is incorrect")
+
+
sub_size = Z2 - Z1 # the size of the vertical slab
+
+
# check the validity of model's parameters
+
params = np.ascontiguousarray(np.zeros([12], dtype=ctypes.c_int))
+
params = _check_params3d(model_no, models_library_path)
+
__testParams3D(params) # check parameters and terminate if incorrect
+
+
if params[3] == 1:
+
phantom = np.zeros([sub_size, N2, N3], dtype="float32", order="C")
+
else:
+
raise ValueError(
+
"The selected model is temporal (3D+time), use 'ModelTemporal' function instead"
+
)
+
external.c_model3d(
+
np.ascontiguousarray(phantom),
+
model_no,
+
N1,
+
N2,
+
N3,
+
Z1,
+
Z2,
+
models_library_path,
+
)
+
return phantom
+
+
+[docs]def ModelTemporal(
+
model_no: int,
+
phantom_size: Union[int, Tuple[int, int, int]],
+
models_library_path: Path,
+
) -> np.ndarray:
+
"""Generates 4D (3D+time) phantom based on the model no. in
+
the library file Phantom3DLibrary.dat.
+
+
Args:
+
model_no (int): Model number from the Phantom3DLibrary.dat library file.
+
phantom_size (int, Tuple(int)): A scalar or a tuple with phantom dimensions.
+
models_library_path (Path): A path to the library file.
+
+
Returns:
+
np.ndarray: The generated 4D phantom.
+
"""
+
if type(phantom_size) == tuple:
+
N1, N2, N3 = [int(i) for i in phantom_size]
+
else:
+
N1 = N2 = N3 = phantom_size
+
+
# check the validity of model's parameters
+
params = np.ascontiguousarray(np.zeros([12], dtype=ctypes.c_int))
+
params = _check_params3d(model_no, models_library_path)
+
__testParams3D(params) # check parameters and terminate if incorrect
+
+
if params[3] == 1:
+
raise ValueError(
+
"The selected model is stationary (3D), use 'Model' function instead"
+
)
+
else:
+
phantom = np.zeros([params[3], N1, N2, N3], dtype="float32", order="C")
+
external.c_model3d(
+
np.ascontiguousarray(phantom),
+
model_no,
+
N1,
+
N2,
+
N3,
+
0,
+
N1,
+
models_library_path,
+
)
+
return phantom
+
+
+[docs]def ModelTemporalSub(
+
model_no: int,
+
phantom_size: Union[int, Tuple[int, int, int]],
+
sub_index: Tuple[int, int],
+
models_library_path: Path,
+
) -> np.ndarray:
+
"""Generates a subset of 4D phantom (vertical cutoff) based on the model no. in
+
the library file Phantom3DLibrary.dat.
+
+
Args:
+
model_no (int): Model number from the Phantom3DLibrary.dat library file.
+
phantom_size (int, Tuple(int)): A scalar or a tuple with phantom dimensions.
+
sub_index (Tuple[int, int]): a tuple containing 2 indeces (lower, upper) to select a vertical subset.
+
models_library_path (Path): A path to the library file.
+
+
Returns:
+
np.ndarray: The generated 4D phantom.
+
"""
+
if type(phantom_size) == tuple:
+
N1, N2, N3 = [int(i) for i in phantom_size]
+
else:
+
N1 = N2 = N3 = phantom_size
+
+
Z1, Z2 = [int(i) for i in sub_index]
+
+
rangecheck = Z2 > Z1
+
if not rangecheck:
+
raise ValueError("Upper index must be larger than the lower one")
+
rangecheck = Z1 >= 0 and Z1 < N3
+
if not rangecheck:
+
raise ValueError("Range of the lower index is incorrect")
+
rangecheck = Z2 >= 0 and Z2 <= N3
+
if not rangecheck:
+
raise ValueError("Range of the higher index is incorrect")
+
+
sub_size = Z2 - Z1 # the size of the vertical slab
+
+
# check the validity of model's parameters
+
params = np.ascontiguousarray(np.zeros([12], dtype=ctypes.c_int))
+
params = _check_params3d(model_no, models_library_path)
+
__testParams3D(params) # check parameters and terminate if incorrect
+
+
if params[3] == 1:
+
raise ValueError(
+
"The selected model is temporal (3D+time), use 'ModelTemporal' function instead"
+
)
+
else:
+
phantom = np.zeros([params[3], sub_size, N2, N3], dtype="float32", order="C")
+
external.c_model3d(
+
np.ascontiguousarray(phantom),
+
model_no,
+
N1,
+
N2,
+
N3,
+
Z1,
+
Z2,
+
models_library_path,
+
)
+
return phantom
+
+
+[docs]def ModelSino(
+
model_no: int,
+
phantom_size: int,
+
detector_horiz: int,
+
detector_vert: int,
+
angles: np.ndarray,
+
models_library_path: Path,
+
) -> np.ndarray:
+
"""Generates a 3D analytical sinogram for corresponding models in
+
the library file Phantom3DLibrary.dat.
+
+
Args:
+
model_no (int): Model number from the Phantom3DLibrary.dat library file.
+
phantom_size (int): A scalar for squared phantom dimensions (2D slice).
+
detector_horiz (int): Size of the horizontal detector in pixels.
+
detector_vert (int): Size of the vertical detector in pixels.
+
angles (np.ndarray): Angles vector in degrees.
+
models_library_path (Path): A path to the library file.
+
+
Returns:
+
np.ndarray: The generated 3D projection data of the following shape [detector_vert, AngTot, detector_horiz].
+
"""
+
if type(phantom_size) == tuple:
+
raise ValueError(
+
"Please give a scalar for the phantom size (2d slice), if one needs non-cubic data generator please see ModelSinoSub function"
+
)
+
+
# check the validity of model's parameters
+
params = np.ascontiguousarray(np.zeros([12], dtype=ctypes.c_int))
+
params = _check_params3d(model_no, models_library_path)
+
__testParams3D(params) # check parameters and terminate if incorrect
+
+
angles_total = len(angles)
+
if params[3] == 1:
+
# execute the model building function
+
sino3d = np.zeros(
+
[angles_total, detector_vert, detector_horiz], dtype="float32", order="C"
+
)
+
else:
+
raise ValueError(
+
"The selected model is temporal (4D), use 'ModelTemporalSino' function instead"
+
)
+
external.c_model_sino3d(
+
np.ascontiguousarray(sino3d),
+
model_no,
+
detector_horiz,
+
detector_vert,
+
0,
+
detector_vert,
+
phantom_size,
+
np.ascontiguousarray(angles),
+
angles_total,
+
models_library_path,
+
)
+
return np.swapaxes(sino3d, 0, 1)
+
+
+[docs]def ModelSinoSub(
+
model_no: int,
+
phantom_size: int,
+
detector_horiz: int,
+
detector_vert: int,
+
sub_index: tuple,
+
angles: np.ndarray,
+
models_library_path: Path,
+
) -> np.ndarray:
+
"""Generates a 3D analytical sinogram with vertical cutoff using sub_index tuple for corresponding models in
+
the library file Phantom3DLibrary.dat.
+
+
Args:
+
model_no (int): Model number from the Phantom3DLibrary.dat library file.
+
phantom_size (int): A scalar for squared phantom dimensions (2D slice).
+
detector_horiz (int): Size of the horizontal detector in pixels.
+
detector_vert (int): Size of the vertical detector in pixels.
+
sub_index (Tuple[int, int]): a tuple containing 2 indeces (lower, upper) to select a vertical subset.
+
angles (np.ndarray): Angles vector in degrees.
+
models_library_path (Path): A path to the library file.
+
+
Returns:
+
np.ndarray: The generated 3D projection data of the following shape [detector_vert, AngTot, detector_horiz].
+
"""
+
if type(phantom_size) == tuple:
+
raise ValueError(
+
"Please give a scalar for phantom size, projection data cannot be obtained for non-cubic phantom"
+
)
+
+
Z1, Z2 = [int(i) for i in sub_index]
+
+
rangecheck = Z2 > Z1
+
if not rangecheck:
+
raise ValueError("Upper index must be larger than the lower one")
+
rangecheck = Z1 >= 0 and Z1 < detector_vert
+
if not rangecheck:
+
raise ValueError("Range of the lower index is incorrect")
+
rangecheck = Z2 >= 0 and Z2 <= detector_vert
+
if not rangecheck:
+
raise ValueError("Range of the higher index is incorrect")
+
+
sub_size = Z2 - Z1 # the size of the vertical slab
+
+
# check the validity of model's parameters
+
params = np.ascontiguousarray(np.zeros([12], dtype=ctypes.c_int))
+
params = _check_params3d(model_no, models_library_path)
+
__testParams3D(params) # check parameters and terminate if incorrect
+
+
angles_total = len(angles)
+
if params[3] == 1:
+
# execute the model building function
+
sino3d = np.zeros(
+
[angles_total, sub_size, detector_horiz], dtype="float32", order="C"
+
)
+
else:
+
raise ValueError(
+
"The selected model is temporal (4D), use 'ModelTemporalSino' function instead"
+
)
+
external.c_model_sino3d(
+
np.ascontiguousarray(sino3d),
+
model_no,
+
detector_horiz,
+
detector_vert,
+
Z1,
+
Z2,
+
phantom_size,
+
np.ascontiguousarray(angles),
+
angles_total,
+
models_library_path,
+
)
+
return np.swapaxes(sino3d, 0, 1)
+
+
+[docs]def ModelSinoTemporal(
+
model_no: int,
+
phantom_size: int,
+
detector_horiz: int,
+
detector_vert: int,
+
angles: np.ndarray,
+
models_library_path: Path,
+
) -> np.ndarray:
+
"""Generates a 4D (3D+time) analytical sinogram for corresponding models in
+
the library file Phantom3DLibrary.dat.
+
+
Args:
+
model_no (int): Temporal model number from the Phantom3DLibrary.dat library file.
+
phantom_size (int): A scalar for squared phantom dimensions (2D slice).
+
detector_horiz (int): Size of the horizontal detector in pixels.
+
detector_vert (int): Size of the vertical detector in pixels.
+
angles (np.ndarray): Angles vector in degrees.
+
models_library_path (Path): A path to the library file.
+
+
Returns:
+
np.ndarray: The generated 4D projection data of the following shape [time_frames, detector_vert, AngTot, detector_horiz].
+
"""
+
+
if type(phantom_size) == tuple:
+
raise ValueError(
+
"Please give a scalar for phantom size, projection data cannot be obtained for non-cubic phantom"
+
)
+
+
# check the validity of model's parameters
+
params = np.ascontiguousarray(np.zeros([12], dtype=ctypes.c_int))
+
params = _check_params3d(model_no, models_library_path)
+
__testParams3D(params) # check parameters and terminate if incorrect
+
+
angles_total = len(angles)
+
if params[3] == 1:
+
raise ValueError(
+
"The selected model is stationary (3D), use 'ModelSino' function instead"
+
)
+
else:
+
sino4d = np.zeros(
+
[params[3], angles_total, detector_vert, detector_horiz],
+
dtype="float32",
+
order="C",
+
)
+
+
external.c_model_sino3d(
+
np.ascontiguousarray(sino4d),
+
model_no,
+
detector_horiz,
+
detector_vert,
+
0,
+
detector_vert,
+
phantom_size,
+
np.ascontiguousarray(angles),
+
angles_total,
+
models_library_path,
+
)
+
return np.swapaxes(sino4d, 1, 2)
+
+
+[docs]def ModelSinoTemporalSub(
+
model_no: int,
+
phantom_size: int,
+
detector_horiz: int,
+
detector_vert: int,
+
sub_index: tuple,
+
angles: np.ndarray,
+
models_library_path: Path,
+
) -> np.ndarray:
+
"""Generates a 4D analytical sinogram with vertical cutoff using sub_index tuple for corresponding models in
+
the library file Phantom3DLibrary.dat.
+
+
Args:
+
model_no (int): Temporal model number from the Phantom3DLibrary.dat library file.
+
phantom_size (int): A scalar for squared phantom dimensions (2D slice).
+
detector_horiz (int): Size of the horizontal detector in pixels.
+
detector_vert (int): Size of the vertical detector in pixels.
+
sub_index (Tuple[int, int]): a tuple containing 2 indeces (lower, upper) to select a vertical subset.
+
angles (np.ndarray): Angles vector in degrees.
+
models_library_path (Path): A path to the library file.
+
+
Returns:
+
np.ndarray: The generated 3D projection data of the following shape [time, detector_vert, AngTot, detector_horiz].
+
"""
+
if type(phantom_size) == tuple:
+
raise ValueError(
+
"Please give a scalar for phantom size, projection data cannot be obtained for non-cubic phantom"
+
)
+
+
Z1, Z2 = [int(i) for i in sub_index]
+
+
rangecheck = Z2 > Z1
+
if not rangecheck:
+
raise ValueError("Upper index must be larger than the lower one")
+
rangecheck = Z1 >= 0 and Z1 < detector_vert
+
if not rangecheck:
+
raise ValueError("Range of the lower index is incorrect")
+
rangecheck = Z2 >= 0 and Z2 <= detector_vert
+
if not rangecheck:
+
raise ValueError("Range of the higher index is incorrect")
+
+
sub_size = Z2 - Z1 # the size of the vertical slab
+
+
# check the validity of model's parameters
+
params = np.ascontiguousarray(np.zeros([12], dtype=ctypes.c_int))
+
params = _check_params3d(model_no, models_library_path)
+
__testParams3D(params) # check parameters and terminate if incorrect
+
+
angles_total = len(angles)
+
if params[3] == 1:
+
raise ValueError(
+
"The selected model is stationary (3D), use 'ModelSino' function instead"
+
)
+
else:
+
sino4d = np.zeros(
+
[params[3], angles_total, sub_size, detector_horiz],
+
dtype="float32",
+
order="C",
+
)
+
+
external.c_model_sino3d(
+
np.ascontiguousarray(sino4d),
+
model_no,
+
detector_horiz,
+
detector_vert,
+
Z1,
+
Z2,
+
phantom_size,
+
np.ascontiguousarray(angles),
+
angles_total,
+
models_library_path,
+
)
+
return np.swapaxes(sino4d, 1, 2)
+
+
+[docs]def Object(
+
phantom_size: Union[int, Tuple[int, int, int]], obj_params: Union[list, dict]
+
) -> np.ndarray:
+
"""Generates a 3D object for the standalone geometrical figure
+
that is parametrised in the "obj_params" dictionary.
+
+
Args:
+
phantom_size (int, Tuple(int)): A scalar or a tuple with phantom dimensions.
+
obj_params (a list of dicts or dict): A dictionary with parameters of an object, see demos.
+
+
Returns:
+
np.ndarray: The generated 3D object.
+
"""
+
+
if type(phantom_size) == tuple:
+
N1, N2, N3 = [int(i) for i in phantom_size]
+
else:
+
N1 = N2 = N3 = phantom_size
+
+
if type(obj_params) is dict:
+
obj_params = [obj_params]
+
+
object3d = np.zeros([N1, N2, N3], dtype="float32", order="C")
+
# unpacking obj_params dictionary
+
for obj in obj_params:
+
if __testParams3d(obj):
+
objectName = obj["Obj"].value
+
C0 = obj["C0"]
+
x0 = obj["x0"]
+
y0 = obj["y0"]
+
z0 = obj["z0"]
+
a = obj["a"]
+
b = obj["b"]
+
c = obj["c"]
+
phi1 = obj["phi1"]
+
phi2 = 0.0
+
phi3 = 0.0
+
tt = 0
+
external.c_object3d(
+
np.ascontiguousarray(object3d),
+
N1,
+
N2,
+
N3,
+
0,
+
N1,
+
objectName,
+
C0,
+
x0,
+
y0,
+
z0,
+
a,
+
b,
+
c,
+
phi1,
+
phi2,
+
phi3,
+
tt,
+
)
+
return object3d
+
+
+[docs]def ObjectSino(
+
phantom_size: int,
+
detector_horiz: int,
+
detector_vert: int,
+
angles: np.ndarray,
+
obj_params: Union[list, dict],
+
) -> np.ndarray:
+
"""Generates a 3D analytical projection data for the standalone geometrical figure
+
that is parametrised in the "obj_params" dictionary.
+
+
Args:
+
phantom_size (int): A scalar for phantom dimensions.
+
detector_horiz (int): Size of the horizontal detector in pixels.
+
detector_vert (int): Size of the vertical detector in pixels.
+
angles (np.ndarray): Angles vector in degrees.
+
obj_params (a list of dicts or dict): A dictionary with parameters of an object, see demos.
+
+
Returns:
+
np.ndarray: The generated 3D projection data for an object.
+
"""
+
if type(phantom_size) == tuple:
+
raise ValueError(
+
"Please give a scalar for phantom size, projection data cannot be obtained for non-cubic phantom"
+
)
+
+
if type(obj_params) is dict:
+
obj_params = [obj_params]
+
+
angles_total = len(angles)
+
tt = 0
+
sino3d = np.zeros(
+
[angles_total, detector_vert, detector_horiz], dtype="float32", order="C"
+
)
+
# unpacking obj_params dictionary
+
for obj in obj_params:
+
if __testParams3d(obj):
+
objectName = obj["Obj"].value
+
C0 = obj["C0"]
+
x0 = obj["x0"]
+
y0 = obj["y0"]
+
z0 = obj["z0"]
+
a = obj["a"]
+
b = obj["b"]
+
c = obj["c"]
+
phi1 = obj["phi1"]
+
phi2 = 0.0
+
phi3 = 0.0
+
tt = 0
+
external.c_object_sino3d(
+
np.ascontiguousarray(sino3d),
+
detector_horiz,
+
detector_vert,
+
0,
+
detector_vert,
+
phantom_size,
+
angles,
+
angles_total,
+
objectName,
+
C0,
+
x0,
+
y0,
+
z0,
+
a,
+
b,
+
c,
+
phi1,
+
phi2,
+
phi3,
+
tt,
+
)
+
return np.swapaxes(sino3d, 0, 1)
+
+
+def __testParams3d(obj):
+ """Performs a simple type check of the input parameters and a range check"""
+ if not type(obj) is dict:
+ raise TypeError("obj is not a dict {0}".format(type(obj)))
+ # type check
+ for k, v in obj.items():
+ if not isinstance(v, Number):
+ if not k == "Obj":
+ raise TypeError(k, "is not a Number")
+ typecheck = True
+
+ # range check
+ rangecheck = obj["x0"] >= -1 and obj["x0"] <= 1
+ if not rangecheck:
+ raise ValueError("x0 is out of range. Must be between -1 and 1")
+ rangecheck = rangecheck and obj["y0"] >= -1 and obj["y0"] <= 1
+ if not rangecheck:
+ raise ValueError("y0 is out of range. Must be between -1 and 1")
+ rangecheck = rangecheck and obj["z0"] >= -1 and obj["z0"] <= 1
+ if not rangecheck:
+ raise ValueError("z0 is out of range. Must be between -1 and 1")
+ rangecheck = rangecheck and obj["a"] > 0 and obj["a"] <= 2
+ if not rangecheck:
+ raise ValueError("a (object size) must be positive in [0,2] range")
+ rangecheck = rangecheck and obj["b"] > 0 and obj["b"] <= 2
+ if not rangecheck:
+ raise ValueError("b (object size) must be positive in [0,2] range")
+ rangecheck = rangecheck and obj["c"] > 0 and obj["c"] <= 2
+ if not rangecheck:
+ raise ValueError("c (object size) must be positive in [0,2] range")
+ return rangecheck and typecheck
+
+
+def __testParams3D(obj):
+ if obj[0] == 0:
+ raise TypeError(
+ "Check if the library file <Phantom3DLibrary.dat> exists, the given path is correct and the syntax is valid"
+ )
+ if obj[1] == 0:
+ raise TypeError(
+ "The given model is not found, check available models in <Phantom3DLibrary.dat> file"
+ )
+ if obj[2] == 0:
+ raise TypeError(
+ "Components number cannot be negative, check <Phantom3DLibrary.dat> file"
+ )
+ if obj[3] == 0:
+ raise TypeError(
+ "TimeSteps cannot be negative, check <Phantom3DLibrary.dat> file"
+ )
+ if obj[4] == 0:
+ raise TypeError("Unknown name of the object, check <Phantom3DLibrary.dat> file")
+ if obj[5] == 0:
+ raise TypeError(
+ "C0 should not be equal to zero, check <Phantom3DLibrary.dat> file"
+ )
+ if obj[6] == 0:
+ raise TypeError(
+ "x0 (object position) must be in [-1,1] range, check <Phantom3DLibrary.dat> file"
+ )
+ if obj[7] == 0:
+ raise TypeError(
+ "y0 (object position) must be in [-1,1] range, check <Phantom3DLibrary.dat> file"
+ )
+ if obj[8] == 0:
+ raise TypeError(
+ "z0 (object position) must be in [-1,1] range, check <Phantom3DLibrary.dat> file"
+ )
+ if obj[9] == 0:
+ raise TypeError(
+ "a (object size) must be positive in [0,2] range, check <Phantom3DLibrary.dat> file"
+ )
+ if obj[10] == 0:
+ raise TypeError(
+ "b (object size) must be positive in [0,2] range, check <Phantom3DLibrary.dat> file"
+ )
+ if obj[11] == 0:
+ raise TypeError(
+ "c (object size) must be positive in [0,2] range, check <Phantom3DLibrary.dat> file"
+ )
+ return 0
+
+
+
+
+
+
+
+
+
+
+
+
+