Source code for nd2reader.raw_metadata

import re
import xmltodict
import six
import numpy as np

from nd2reader.common import read_chunk, read_array, read_metadata, parse_date, get_from_dict_if_exists
from nd2reader.common_raw_metadata import parse_dimension_text_line, parse_if_not_none, parse_roi_shape, parse_roi_type, get_loops_from_data, determine_sampling_interval


[docs]class RawMetadata(object): """RawMetadata class parses and stores the raw metadata that is read from the binary file in dict format. """ def __init__(self, fh, label_map): self._fh = fh self._label_map = label_map self._metadata_parsed = None @property def __dict__(self): """Returns the parsed metadata in dictionary form. Returns: dict: the parsed metadata """ return self.get_parsed_metadata()
[docs] def get_parsed_metadata(self): """Returns the parsed metadata in dictionary form. Returns: dict: the parsed metadata """ if self._metadata_parsed is not None: return self._metadata_parsed frames_per_channel = self._parse_total_images_per_channel() self._metadata_parsed = { "height": parse_if_not_none(self.image_attributes, self._parse_height), "width": parse_if_not_none(self.image_attributes, self._parse_width), "date": parse_if_not_none(self.image_text_info, self._parse_date), "fields_of_view": self._parse_fields_of_view(), "frames": self._parse_frames(), "z_levels": self._parse_z_levels(), "total_images_per_channel": frames_per_channel, "channels": self._parse_channels(), "pixel_microns": parse_if_not_none(self.image_calibration, self._parse_calibration), } self._set_default_if_not_empty('fields_of_view') self._set_default_if_not_empty('frames') self._metadata_parsed['num_frames'] = len(self._metadata_parsed['frames']) self._parse_roi_metadata() self._parse_experiment_metadata() return self._metadata_parsed
def _set_default_if_not_empty(self, entry): total_images = self._metadata_parsed['total_images_per_channel'] \ if self._metadata_parsed['total_images_per_channel'] is not None else 0 if len(self._metadata_parsed[entry]) == 0 and total_images > 0: # if the file is not empty, we always have one of this entry self._metadata_parsed[entry] = [0] def _parse_width_or_height(self, key): try: length = self.image_attributes[six.b('SLxImageAttributes')][six.b(key)] except KeyError: length = None return length def _parse_height(self): return self._parse_width_or_height('uiHeight') def _parse_width(self): return self._parse_width_or_height('uiWidth') def _parse_date(self): try: return parse_date(self.image_text_info[six.b('SLxImageTextInfo')]) except KeyError: return None def _parse_calibration(self): try: return self.image_calibration.get(six.b('SLxCalibration'), {}).get(six.b('dCalibration')) except KeyError: return None def _parse_frames(self): """The number of cycles. Returns: list: list of all the frame numbers """ return self._parse_dimension(r""".*?T'?\((\d+)\).*?""") def _parse_channels(self): """These are labels created by the NIS Elements user. Typically they may a short description of the filter cube used (e.g. 'bright field', 'GFP', etc.) Returns: list: the color channels """ if self.image_metadata_sequence is None: return [] try: metadata = self.image_metadata_sequence[six.b('SLxPictureMetadata')][six.b('sPicturePlanes')] except KeyError: return [] channels = self._process_channels_metadata(metadata) return channels def _process_channels_metadata(self, metadata): validity = self._get_channel_validity_list(metadata) # Channel information is contained in dictionaries with the keys a0, a1...an where the number # indicates the order in which the channel is stored. So by sorting the dicts alphabetically # we get the correct order. channels = [] for valid, (label, chan) in zip(validity, sorted(metadata[six.b('sPlaneNew')].items())): if not valid: continue if chan[six.b('sDescription')] is not None: channels.append(chan[six.b('sDescription')].decode("utf8")) else: channels.append('Unknown') return channels def _get_channel_validity_list(self, metadata): try: validity = self.image_metadata[six.b('SLxExperiment')][six.b('ppNextLevelEx')][six.b('')][0][ six.b('ppNextLevelEx')][six.b('')][0][six.b('pItemValid')] except (KeyError, TypeError): # If none of the channels have been deleted, there is no validity list, so we just make one validity = [True for _ in metadata] return validity def _parse_fields_of_view(self): """The metadata contains information about fields of view, but it contains it even if some fields of view were cropped. We can't find anything that states which fields of view are actually in the image data, so we have to calculate it. There probably is something somewhere, since NIS Elements can figure it out, but we haven't found it yet. """ return self._parse_dimension(r""".*?XY\((\d+)\).*?""") def _parse_z_levels(self): """The different levels in the Z-plane. Returns: list: the z levels, just a sequence from 0 to n. """ return self._parse_dimension(r""".*?Z\((\d+)\).*?""") def _parse_dimension_text(self): """While there are metadata values that represent a lot of what we want to capture, they seem to be unreliable. Sometimes certain elements don't exist, or change their data type randomly. However, the human-readable text is always there and in the same exact format, so we just parse that instead. """ dimension_text = six.b("") if self.image_text_info is None: return dimension_text try: textinfo = self.image_text_info[six.b('SLxImageTextInfo')].values() except KeyError: return dimension_text for line in textinfo: entry = parse_dimension_text_line(line) if entry is not None: return entry return dimension_text def _parse_dimension(self, pattern): dimension_text = self._parse_dimension_text() if dimension_text is None: return [] if six.PY3: dimension_text = dimension_text.decode("utf8") match = re.match(pattern, dimension_text) if not match: return [] count = int(match.group(1)) return list(range(count)) def _parse_total_images_per_channel(self): """The total number of images per channel. Warning: this may be inaccurate as it includes 'gap' images. """ if self.image_attributes is None: return 0 try: total_images = self.image_attributes[six.b('SLxImageAttributes')][six.b('uiSequenceCount')] except KeyError: total_images = None return total_images def _parse_roi_metadata(self): """Parse the raw ROI metadata. """ if self.roi_metadata is None or not six.b('RoiMetadata_v1') in self.roi_metadata: return raw_roi_data = self.roi_metadata[six.b('RoiMetadata_v1')] if not six.b('m_vectGlobal_Size') in raw_roi_data: return number_of_rois = raw_roi_data[six.b('m_vectGlobal_Size')] roi_objects = [] for i in range(number_of_rois): current_roi = raw_roi_data[six.b('m_vectGlobal_%d' % i)] roi_objects.append(self._parse_roi(current_roi)) self._metadata_parsed['rois'] = roi_objects def _parse_roi(self, raw_roi_dict): """Extract the vector animation parameters from the ROI. This includes the position and size at the given timepoints. Args: raw_roi_dict: dictionary of raw roi metadata Returns: dict: the parsed ROI metadata """ number_of_timepoints = raw_roi_dict[six.b('m_vectAnimParams_Size')] roi_dict = { "timepoints": [], "positions": [], "sizes": [], "shape": parse_roi_shape(raw_roi_dict[six.b('m_sInfo')][six.b('m_uiShapeType')]), "type": parse_roi_type(raw_roi_dict[six.b('m_sInfo')][six.b('m_uiInterpType')]) } for i in range(number_of_timepoints): roi_dict = self._parse_vect_anim(roi_dict, raw_roi_dict[six.b('m_vectAnimParams_%d' % i)]) # convert to NumPy arrays roi_dict["timepoints"] = np.array(roi_dict["timepoints"], dtype=np.float) roi_dict["positions"] = np.array(roi_dict["positions"], dtype=np.float) roi_dict["sizes"] = np.array(roi_dict["sizes"], dtype=np.float) return roi_dict def _parse_vect_anim(self, roi_dict, animation_dict): """ Parses a ROI vector animation object and adds it to the global list of timepoints and positions. Args: roi_dict: the raw roi dictionary animation_dict: the raw animation dictionary Returns: dict: the parsed metadata """ roi_dict["timepoints"].append(animation_dict[six.b('m_dTimeMs')]) image_width = self._metadata_parsed["width"] * self._metadata_parsed["pixel_microns"] image_height = self._metadata_parsed["height"] * self._metadata_parsed["pixel_microns"] # positions are taken from the center of the image as a fraction of the half width/height of the image position = np.array((0.5 * image_width * (1 + animation_dict[six.b('m_dCenterX')]), 0.5 * image_height * (1 + animation_dict[six.b('m_dCenterY')]), animation_dict[six.b('m_dCenterZ')])) roi_dict["positions"].append(position) size_dict = animation_dict[six.b('m_sBoxShape')] # sizes are fractions of the half width/height of the image roi_dict["sizes"].append((size_dict[six.b('m_dSizeX')] * 0.25 * image_width, size_dict[six.b('m_dSizeY')] * 0.25 * image_height, size_dict[six.b('m_dSizeZ')])) return roi_dict def _parse_experiment_metadata(self): """Parse the metadata of the ND experiment """ self._metadata_parsed['experiment'] = { 'description': 'unknown', 'loops': [] } if self.image_metadata is None or six.b('SLxExperiment') not in self.image_metadata: return raw_data = self.image_metadata[six.b('SLxExperiment')] if six.b('wsApplicationDesc') in raw_data: self._metadata_parsed['experiment']['description'] = raw_data[six.b('wsApplicationDesc')].decode('utf8') if six.b('uLoopPars') in raw_data: self._metadata_parsed['experiment']['loops'] = self._parse_loop_data(raw_data[six.b('uLoopPars')]) def _parse_loop_data(self, loop_data): """Parse the experimental loop data Args: loop_data: dictionary of experiment loops Returns: list: list of the parsed loops """ loops = get_loops_from_data(loop_data) # take into account the absolute time in ms time_offset = 0 parsed_loops = [] for loop in loops: # duration of this loop duration = get_from_dict_if_exists('dDuration', loop) or 0 interval = determine_sampling_interval(duration, loop) # if duration is not saved, infer it duration = self.get_duration_from_interval_and_loops(duration, interval, loop) # uiLoopType == 6 is a stimulation loop is_stimulation = get_from_dict_if_exists('uiLoopType', loop) == 6 parsed_loop = { 'start': time_offset, 'duration': duration, 'stimulation': is_stimulation, 'sampling_interval': interval } parsed_loops.append(parsed_loop) # increase the time offset time_offset += duration return parsed_loops
[docs] def get_duration_from_interval_and_loops(self, duration, interval, loop): """Infers the duration of the loop from the number of measurements and the interval Args: duration: loop duration in milliseconds duration: measurement interval in milliseconds loop: loop dictionary Returns: float: the loop duration in milliseconds """ if duration == 0 and interval > 0: number_of_loops = get_from_dict_if_exists('uiCount', loop) number_of_loops = number_of_loops if number_of_loops is not None and number_of_loops > 0 else 1 duration = interval * number_of_loops return duration
@property def image_text_info(self): """Textual image information Returns: dict: containing the textual image info """ return read_metadata(read_chunk(self._fh, self._label_map.image_text_info), 1) @property def image_metadata_sequence(self): """Image metadata of the sequence Returns: dict: containing the metadata """ return read_metadata(read_chunk(self._fh, self._label_map.image_metadata_sequence), 1) @property def image_calibration(self): """The amount of pixels per micron. Returns: dict: pixels per micron """ return read_metadata(read_chunk(self._fh, self._label_map.image_calibration), 1) @property def image_attributes(self): """Image attributes Returns: dict: containing the image attributes """ return read_metadata(read_chunk(self._fh, self._label_map.image_attributes), 1) @property def x_data(self): """X data Returns: dict: x_data """ return read_array(self._fh, 'double', self._label_map.x_data) @property def y_data(self): """Y data Returns: dict: y_data """ return read_array(self._fh, 'double', self._label_map.y_data) @property def z_data(self): """Z data Returns: dict: z_data """ return read_array(self._fh, 'double', self._label_map.z_data) @property def roi_metadata(self): """Contains information about the defined ROIs: shape, position and type (reference/background/stimulation). Returns: dict: ROI metadata dictionary """ return read_metadata(read_chunk(self._fh, self._label_map.roi_metadata), 1) @property def pfs_status(self): """Perfect focus system (PFS) status Returns: dict: Perfect focus system (PFS) status """ return read_array(self._fh, 'int', self._label_map.pfs_status) @property def pfs_offset(self): """Perfect focus system (PFS) offset Returns: dict: Perfect focus system (PFS) offset """ return read_array(self._fh, 'int', self._label_map.pfs_offset) @property def camera_exposure_time(self): """Exposure time information Returns: dict: Camera exposure time """ return read_array(self._fh, 'double', self._label_map.camera_exposure_time) @property def lut_data(self): """LUT information Returns: dict: LUT information """ return xmltodict.parse(read_chunk(self._fh, self._label_map.lut_data)) @property def grabber_settings(self): """Grabber settings Returns: dict: Acquisition settings """ return xmltodict.parse(read_chunk(self._fh, self._label_map.grabber_settings)) @property def custom_data(self): """Custom user data Returns: dict: custom user data """ return xmltodict.parse(read_chunk(self._fh, self._label_map.custom_data)) @property def app_info(self): """NIS elements application info Returns: dict: (Version) information of the NIS Elements application """ return xmltodict.parse(read_chunk(self._fh, self._label_map.app_info)) @property def camera_temp(self): """Camera temperature Yields: float: the temperature """ camera_temp = read_array(self._fh, 'double', self._label_map.camera_temp) if camera_temp: for temp in map(lambda x: round(x * 100.0, 2), camera_temp): yield temp @property def acquisition_times(self): """Acquisition times Yields: float: the acquisition time """ acquisition_times = read_array(self._fh, 'double', self._label_map.acquisition_times) if acquisition_times: for acquisition_time in map(lambda x: x / 1000.0, acquisition_times): yield acquisition_time @property def image_metadata(self): """Image metadata Returns: dict: Extra image metadata """ if self._label_map.image_metadata: return read_metadata(read_chunk(self._fh, self._label_map.image_metadata), 1)