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344 lines
12 KiB
344 lines
12 KiB
# -*- coding: utf-8 -*-
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from datetime import datetime
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from nd2reader.model.metadata import Metadata
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from nd2reader.model.label import LabelMap
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from nd2reader.model.roi import Roi
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from nd2reader.parser.base import BaseParser
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from nd2reader.driver.v3 import V3Driver
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from nd2reader.common.v3 import read_chunk, read_array, read_metadata
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import re
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import six
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import struct
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import xmltodict
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def ignore_missing(func):
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def wrapper(*args, **kwargs):
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try:
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return func(*args, **kwargs)
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except:
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return None
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return wrapper
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class V3RawMetadata(object):
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def __init__(self, fh, label_map):
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self._fh = fh
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self._label_map = label_map
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@property
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@ignore_missing
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def image_text_info(self):
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return read_metadata(read_chunk(self._fh, self._label_map.image_text_info), 1)
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@property
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@ignore_missing
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def image_metadata_sequence(self):
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return read_metadata(read_chunk(self._fh, self._label_map.image_metadata_sequence), 1)
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@property
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@ignore_missing
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def image_calibration(self):
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return read_metadata(read_chunk(self._fh, self._label_map.image_calibration), 1)
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@property
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@ignore_missing
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def image_attributes(self):
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return read_metadata(read_chunk(self._fh, self._label_map.image_attributes), 1)
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@property
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@ignore_missing
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def x_data(self):
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return read_array(self._fh, 'double', self._label_map.x_data)
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@property
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@ignore_missing
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def y_data(self):
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return read_array(self._fh, 'double', self._label_map.y_data)
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@property
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@ignore_missing
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def z_data(self):
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return read_array(self._fh, 'double', self._label_map.z_data)
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@property
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@ignore_missing
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def roi_metadata(self):
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return read_metadata(read_chunk(self._fh, self._label_map.roi_metadata), 1)
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@property
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@ignore_missing
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def pfs_status(self):
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return read_array(self._fh, 'int', self._label_map.pfs_status)
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@property
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@ignore_missing
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def pfs_offset(self):
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return read_array(self._fh, 'int', self._label_map.pfs_offset)
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@property
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@ignore_missing
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def camera_exposure_time(self):
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return read_array(self._fh, 'double', self._label_map.camera_exposure_time)
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@property
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@ignore_missing
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def lut_data(self):
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return xmltodict.parse(read_chunk(self._fh, self._label_map.lut_data))
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@property
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@ignore_missing
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def grabber_settings(self):
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return xmltodict.parse(read_chunk(self._fh, self._label_map.grabber_settings))
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@property
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@ignore_missing
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def custom_data(self):
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return xmltodict.parse(read_chunk(self._fh, self._label_map.custom_data))
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@property
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@ignore_missing
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def app_info(self):
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return xmltodict.parse(read_chunk(self._fh, self._label_map.app_info))
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@property
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@ignore_missing
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def camera_temp(self):
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camera_temp = read_array(self._fh, 'double', self._label_map.camera_temp)
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if camera_temp:
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for temp in map(lambda x: round(x * 100.0, 2), camera_temp):
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yield temp
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@property
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@ignore_missing
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def acquisition_times(self):
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acquisition_times = read_array(self._fh, 'double', self._label_map.acquisition_times)
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if acquisition_times:
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for acquisition_time in map(lambda x: x / 1000.0, acquisition_times):
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yield acquisition_time
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@property
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@ignore_missing
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def image_metadata(self):
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if self._label_map.image_metadata:
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return read_metadata(read_chunk(self._fh, self._label_map.image_metadata), 1)
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class V3Parser(BaseParser):
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""" Parses ND2 files and creates a Metadata and driver object. """
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CHUNK_HEADER = 0xabeceda
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CHUNK_MAP_START = six.b("ND2 FILEMAP SIGNATURE NAME 0001!")
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CHUNK_MAP_END = six.b("ND2 CHUNK MAP SIGNATURE 0000001!")
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def __init__(self, fh):
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"""
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:type fh: file
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"""
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if six.PY3:
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super().__init__(fh)
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else:
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super(V3Parser, self).__init__(fh)
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self._label_map = self._build_label_map()
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self.raw_metadata = V3RawMetadata(self._fh, self._label_map)
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self._parse_metadata()
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self._parse_roi_metadata()
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@property
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def driver(self):
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"""
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Provides an object that knows how to look up and read images based on an index.
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"""
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return V3Driver(self.metadata, self._label_map, self._fh)
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def _parse_metadata(self):
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"""
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Reads all metadata and instantiates the Metadata object.
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"""
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height = self.raw_metadata.image_attributes[six.b('SLxImageAttributes')][six.b('uiHeight')]
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width = self.raw_metadata.image_attributes[six.b('SLxImageAttributes')][six.b('uiWidth')]
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date = self._parse_date(self.raw_metadata)
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fields_of_view = self._parse_fields_of_view(self.raw_metadata)
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frames = self._parse_frames(self.raw_metadata)
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z_levels = self._parse_z_levels(self.raw_metadata)
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total_images_per_channel = self._parse_total_images_per_channel(self.raw_metadata)
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channels = self._parse_channels(self.raw_metadata)
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pixel_microns = self.raw_metadata.image_calibration.get(six.b('SLxCalibration'), {}).get(six.b('dCalibration'))
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self.metadata = Metadata(height, width, channels, date, fields_of_view, frames, z_levels,
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total_images_per_channel, pixel_microns)
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def _parse_date(self, raw_metadata):
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"""
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The date and time when acquisition began.
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:type raw_metadata: V3RawMetadata
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:rtype: datetime.datetime() or None
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"""
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for line in raw_metadata.image_text_info[six.b('SLxImageTextInfo')].values():
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line = line.decode("utf8")
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absolute_start_12 = None
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absolute_start_24 = None
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# ND2s seem to randomly switch between 12- and 24-hour representations.
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try:
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absolute_start_24 = datetime.strptime(line, "%m/%d/%Y %H:%M:%S")
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except (TypeError, ValueError):
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pass
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try:
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absolute_start_12 = datetime.strptime(line, "%m/%d/%Y %I:%M:%S %p")
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except (TypeError, ValueError):
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pass
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if not absolute_start_12 and not absolute_start_24:
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continue
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return absolute_start_12 if absolute_start_12 else absolute_start_24
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return None
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def _parse_channels(self, raw_metadata):
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"""
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These are labels created by the NIS Elements user. Typically they may a short description of the filter cube
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used (e.g. "bright field", "GFP", etc.)
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:type raw_metadata: V3RawMetadata
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:rtype: list
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"""
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channels = []
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metadata = raw_metadata.image_metadata_sequence[six.b('SLxPictureMetadata')][six.b('sPicturePlanes')]
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try:
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validity = raw_metadata.image_metadata[six.b('SLxExperiment')][six.b('ppNextLevelEx')][six.b('')][0][
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six.b('ppNextLevelEx')][six.b('')][0][six.b('pItemValid')]
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except (KeyError, TypeError):
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# If none of the channels have been deleted, there is no validity list, so we just make one
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validity = [True for _ in metadata]
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# Channel information is contained in dictionaries with the keys a0, a1...an where the number
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# indicates the order in which the channel is stored. So by sorting the dicts alphabetically
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# we get the correct order.
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for (label, chan), valid in zip(sorted(metadata[six.b('sPlaneNew')].items()), validity):
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if not valid:
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continue
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channels.append(chan[six.b('sDescription')].decode("utf8"))
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return channels
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def _parse_fields_of_view(self, raw_metadata):
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"""
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The metadata contains information about fields of view, but it contains it even if some fields
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of view were cropped. We can't find anything that states which fields of view are actually
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in the image data, so we have to calculate it. There probably is something somewhere, since
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NIS Elements can figure it out, but we haven't found it yet.
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:type raw_metadata: V3RawMetadata
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:rtype: list
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"""
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return self._parse_dimension(r""".*?XY\((\d+)\).*?""", raw_metadata)
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def _parse_frames(self, raw_metadata):
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"""
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The number of cycles.
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:type raw_metadata: V3RawMetadata
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:rtype: list
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"""
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return self._parse_dimension(r""".*?T'?\((\d+)\).*?""", raw_metadata)
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def _parse_z_levels(self, raw_metadata):
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"""
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The different levels in the Z-plane. Just a sequence from 0 to n.
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:type raw_metadata: V3RawMetadata
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:rtype: list
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"""
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return self._parse_dimension(r""".*?Z\((\d+)\).*?""", raw_metadata)
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def _parse_dimension_text(self, raw_metadata):
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"""
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While there are metadata values that represent a lot of what we want to capture, they seem to be unreliable.
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Sometimes certain elements don't exist, or change their data type randomly. However, the human-readable text
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is always there and in the same exact format, so we just parse that instead.
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:type raw_metadata: V3RawMetadata
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:rtype: str
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"""
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for line in raw_metadata.image_text_info[six.b('SLxImageTextInfo')].values():
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if six.b("Dimensions:") in line:
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metadata = line
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break
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else:
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return six.b("")
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for line in metadata.split(six.b("\r\n")):
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if line.startswith(six.b("Dimensions:")):
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dimension_text = line
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break
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else:
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return six.b("")
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return dimension_text
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def _parse_dimension(self, pattern, raw_metadata):
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"""
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:param pattern: a valid regex pattern
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:type pattern: str
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:type raw_metadata: V3RawMetadata
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:rtype: list of int
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"""
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dimension_text = self._parse_dimension_text(raw_metadata)
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if six.PY3:
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dimension_text = dimension_text.decode("utf8")
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match = re.match(pattern, dimension_text)
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if not match:
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return [0]
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count = int(match.group(1))
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return list(range(count))
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def _parse_total_images_per_channel(self, raw_metadata):
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"""
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The total number of images per channel. Warning: this may be inaccurate as it includes "gap" images.
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:type raw_metadata: V3RawMetadata
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:rtype: int
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"""
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return raw_metadata.image_attributes[six.b('SLxImageAttributes')][six.b('uiSequenceCount')]
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def _parse_roi_metadata(self):
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"""
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Parse the raw ROI metadata.
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:return:
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"""
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if not six.b('RoiMetadata_v1') in self.raw_metadata.roi_metadata:
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self.roi_metadata = None
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return
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raw_roi_data = self.raw_metadata.roi_metadata[six.b('RoiMetadata_v1')]
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number_of_rois = raw_roi_data[six.b('m_vectGlobal_Size')]
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roi_objects = []
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for i in range(number_of_rois):
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current_roi = raw_roi_data[six.b('m_vectGlobal_%d' % i)]
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roi_objects.append(Roi(current_roi, self.metadata))
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self.roi_metadata = roi_objects
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def _build_label_map(self):
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"""
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Every label ends with an exclamation point, however, we can't directly search for those to find all the labels
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as some of the bytes contain the value 33, which is the ASCII code for "!". So we iteratively find each label,
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grab the subsequent data (always 16 bytes long), advance to the next label and repeat.
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:rtype: LabelMap
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"""
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self._fh.seek(-8, 2)
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chunk_map_start_location = struct.unpack("Q", self._fh.read(8))[0]
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self._fh.seek(chunk_map_start_location)
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raw_text = self._fh.read(-1)
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return LabelMap(raw_text)
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