"""Functions to create artificial nd2 data for testing purposes
|
|
"""
|
|
import six
|
|
import numpy as np
|
|
import struct
|
|
|
|
|
|
class ArtificialND2(object):
|
|
"""Artificial ND2 class (for testing purposes)
|
|
"""
|
|
|
|
def __init__(self, file, version=(3, 0)):
|
|
self._fh = open(file, 'wb')
|
|
self.write_version(version)
|
|
|
|
def __enter__(self):
|
|
return self
|
|
|
|
def __exit__(self, exc_type, exc_value, traceback):
|
|
self.close()
|
|
|
|
@property
|
|
def file_handle(self):
|
|
"""The file handle to the binary file
|
|
|
|
Returns:
|
|
file: the file handle
|
|
|
|
"""
|
|
return self._fh
|
|
|
|
def close(self):
|
|
"""Correctly close the file handle
|
|
"""
|
|
if self._fh is not None:
|
|
self._fh.close()
|
|
|
|
def write_version(self, version=(3, 0)):
|
|
"""Write file header
|
|
"""
|
|
# write 16 empty bytes
|
|
self._fh.write(bytearray(16))
|
|
|
|
# write version info
|
|
version_info = six.b('ND2 FILE SIGNATURE CHUNK NAME01!Ver%s.%s' % version)
|
|
self._fh.write(version_info)
|
|
|
|
@staticmethod
|
|
def create_label_map_bytes():
|
|
"""Construct a binary label map
|
|
|
|
Returns:
|
|
tuple: (binary data, dictionary data)
|
|
|
|
"""
|
|
raw_text = six.b('')
|
|
labels = {
|
|
'image_attributes': "ImageAttributesLV!",
|
|
'image_text_info': "ImageTextInfoLV!",
|
|
'image_metadata': "ImageMetadataLV!",
|
|
'image_metadata_sequence': "ImageMetadataSeqLV|0!",
|
|
'image_calibration': "ImageCalibrationLV|0!",
|
|
'x_data': "CustomData|X!",
|
|
'y_data': "CustomData|Y!",
|
|
'z_data': "CustomData|Z!",
|
|
'roi_metadata': "CustomData|RoiMetadata_v1!",
|
|
'pfs_status': "CustomData|PFS_STATUS!",
|
|
'pfs_offset': "CustomData|PFS_OFFSET!",
|
|
'guid': "CustomData|GUIDStore!",
|
|
'description': "CustomData|CustomDescriptionV1_0!",
|
|
'camera_exposure_time': "CustomData|Camera_ExposureTime1!",
|
|
'camera_temp': "CustomData|CameraTemp1!",
|
|
'acquisition_times': "CustomData|AcqTimesCache!",
|
|
'acquisition_times_2': "CustomData|AcqTimes2Cache!",
|
|
'acquisition_frames': "CustomData|AcqFramesCache!",
|
|
'lut_data': "CustomDataVar|LUTDataV1_0!",
|
|
'grabber_settings': "CustomDataVar|GrabberCameraSettingsV1_0!",
|
|
'custom_data': "CustomDataVar|CustomDataV2_0!",
|
|
'app_info': "CustomDataVar|AppInfo_V1_0!",
|
|
'image_frame_0': "ImageDataSeq|0!"
|
|
}
|
|
data = {}
|
|
|
|
# generate random positions and lengths
|
|
lengths = np.random.random_integers(1, 999, len(labels))
|
|
positions = np.subtract(np.cumsum(lengths), lengths[0])
|
|
|
|
for length, pos, label in zip(lengths, positions, labels):
|
|
raw_text += six.b(labels[label])
|
|
raw_text += struct.pack('QQ', pos, length)
|
|
data[label] = (pos, length)
|
|
|
|
return raw_text, data
|