def mephysto(filepath, index):
filepath_found = wildcard_file_resolution(filepath)
(axis, reading, scan_curvetype,
scan_depth) = load_single_item(filepath_found, int(index))
second_column_header = ["Reading"]
if scan_curvetype == "PDD":
first_column_header = ["Depth Profile", "Depth (mm)"]
second_column_header = [None] + second_column_header
elif scan_curvetype == "INPLANE_PROFILE":
first_column_header = ["Inplane Profile", "y (mm)"]
second_column_header = (["Depth = {} mm".format(scan_depth)] +
second_column_header)
elif scan_curvetype == "CROSSPLANE_PROFILE":
first_column_header = ["Crossplane Profile", "x (mm)"]
second_column_header = (["Depth = {} mm".format(scan_depth)] +
second_column_header)
else:
raise ValueError("Unexpected Profile Type")
first_column = np.concatenate([first_column_header, axis])
second_column = np.concatenate([second_column_header, reading])
return np.vstack([first_column, second_column]).T
def depth_dose(dicom_path, depth_adjust, averaging_distance=0):
dicom_path_found = wildcard_file_resolution(dicom_path)
ds = pydicom.read_file(dicom_path_found, force=True)
depth, depth_dose_values = extract_depth_dose(ds, depth_adjust,
averaging_distance)
return np.vstack([depth, depth_dose_values]).T
def crossplane_profile(dicom_path,
depth_adjust,
depth_lookup,
averaging_distance=0):
dicom_path_found = wildcard_file_resolution(dicom_path)
ds = pydicom.read_file(dicom_path_found, force=True)
_, _, crossplane, crossplane_dose = extract_profiles(
ds, depth_adjust, depth_lookup, averaging_distance)
return np.vstack([crossplane, crossplane_dose]).T
def arbitrary_profile(dicom_path, depth_adjust, inplane, crossplane, depth):
dicom_path_found = wildcard_file_resolution(dicom_path)
inplane_ref = np.invert(np.isnan(inplane))
crossplane_ref = np.invert(np.isnan(crossplane))
depth_ref = np.invert(np.isnan(depth))
reference = inplane_ref & crossplane_ref & depth_ref
ds = pydicom.read_file(dicom_path_found, force=True)
dose = arbitrary_profile_from_dicom_dose(ds, depth_adjust,
inplane[reference],
crossplane[reference],
depth[reference])
result = np.ones_like(inplane) * np.nan
result[reference] = dose
return np.vstack([result]).T