def test_dcm_dt_strings():
for _t in testv:
assert dicom_time(_t["dt"]) == _t["dcm_tm"].split(".")[0]
assert dicom_date(_t["dt"]) == _t["dcm_dt"]
assert dicom_datetime(_t["dt"]) == (_t["dcm_dt"],
_t["dcm_tm"].split(".")[0])
def __attrs_post_init__(self):
self.meta = {
**self.parent.meta, "InstanceDateTime": self.inst_datetime
}
self.tags = {
**self.parent.tags, "InstanceNumber": self.inst_num,
"SOPInstanceUID": self.instuid,
"InstanceCreationDate": dicom_date(self.inst_datetime),
"InstanceCreationTime": dicom_time(self.inst_datetime)
}
def __attrs_post_init__(self):
self.meta = {
**self.parent.meta, "SeriesDateTime": self.series_datetime
}
self.tags = {
**self.parent.tags, "SeriesDescription": self.ser_desc,
"SeriesNumber": self.ser_num,
"SeriesInstanceUID": self.seruid,
"SeriesDate": dicom_date(self.series_datetime),
"SeriesTime": dicom_time(self.series_datetime)
}
for i in range(1, self.n_instances + 1):
self.children.append(MockInstance(parent=self, inst_num=i))
def __attrs_post_init__(self):
name_seed = _R.random()
gender = _R.choice(["M", "F"])
age = _R.randint(18, 85)
# logging.debug(name_seed)
# logging.debug(gender)
# logging.debug(age)
patient_info = GUIDMint.get_sham_id(name=name_seed,
age=age,
gender=gender,
reference_date=self.study_datetime)
hash_str = patient_info["ID"] + self.study_datetime.isoformat()
accession_num = md5(hash_str.encode("UTF-8")).hexdigest()
self.stuid = dcm_mint.uid(patient_info["ID"], accession_num)
self.meta = {"StudyDateTime": self.study_datetime}
self.tags = {
"AccessionNumber": accession_num,
"PatientName": dicom_name(patient_info["Name"]),
"PatientID": patient_info["ID"],
"PatientSex": gender,
"PatientBirthDate": dicom_date(patient_info["BirthDate"]),
"StudyInstanceUID": self.stuid,
"StudyDescription": self.study_description,
"StationName": self.station_name,
"Manufacturer": "Device Manufacturer",
"ManufacturerModelName": "Device Model Name",
"Institution": self.site_name,
"Modality": self.modality,
"StudyDate": dicom_date(self.study_datetime),
"StudyTime": dicom_time(self.study_datetime)
}
distribution = {
# Mod SeriesDesc NSer NInst P of at least one
"CT": [("Localizer", 2, 2, 1, 1), ("Volume", 1, 3, 30, 200),
("Fluoro", 0, 1, 10, 50, 0.2),
("Reformat", 0, 2, 100, 200, 0.2),
("Dose Report", 1, 1, 1, 1)],
"MR": [("Localizer", 1, 3, 1, 1), ("Sequence", 3, 6, 10, 50),
("Diffusion Map", 0, 1, 1, 1, 0.2)],
"CR": [("PA", 1, 1, 1, 1), ("Lateral", 1, 1, 1, 1),
("Bx", 0, 1, 1, 2, 0.2)],
"US": [("Transverse", 3, 6, 1, 1), ("Sagittal", 3, 6, 1, 1),
("Cine", 1, 3, 30, 50), ("Doppler", 0, 3, 1, 3, 0.2),
("Bx", 0, 2, 3, 5, 0.2)]
}
series_defs = distribution[self.modality]
ser_num = 0
for s in series_defs:
if s[0] == 0:
if _R.random() < s[5]:
num_series_this_type = 1
else:
num_series_this_type = 0
else:
num_series_this_type = _R.randint(s[1], s[2])
for i in range(1, num_series_this_type + 1):
ser_num += 1
if num_series_this_type > 1:
ser_desc = "{} {}".format(s[0], i)
else:
ser_desc = s[0]
n_instances = _R.randint(s[3], s[4])
S = MockSeries(parent=self,
ser_num=ser_num,
ser_desc=ser_desc,
n_instances=n_instances)
self.children.append(S)