From b84b52d93d9b40d4ed4303924dfbbbe18c19241f Mon Sep 17 00:00:00 2001 From: Gabriele Girelli Date: Sat, 15 Aug 2020 11:58:10 +0200 Subject: [PATCH] Regenerated Reader --- nd2reader/reader.py | 378 ++++++++++++++++---------------------------- 1 file changed, 135 insertions(+), 243 deletions(-) diff --git a/nd2reader/reader.py b/nd2reader/reader.py index f99101d..8246125 100644 --- a/nd2reader/reader.py +++ b/nd2reader/reader.py @@ -1,315 +1,207 @@ -# -*- coding: utf-8 -*- -import struct +from pims import Frame +from pims.base_frames import FramesSequenceND -import array -import six -import warnings -from pims.base_frames import Frame +from nd2reader.exceptions import EmptyFileError, InvalidFileType +from nd2reader.parser import Parser import numpy as np -from nd2reader.common import get_version, read_chunk -from nd2reader.exceptions import InvalidVersionError -from nd2reader.label_map import LabelMap -from nd2reader.raw_metadata import RawMetadata +class ND2Reader(FramesSequenceND): + """PIMS wrapper for the ND2 parser. + This is the main class: use this to process your .nd2 files. + """ -class Parser(object): - """Parses ND2 files and creates a Metadata and driver object. + class_priority = 12 - """ - CHUNK_HEADER = 0xabeceda - CHUNK_MAP_START = six.b("ND2 FILEMAP SIGNATURE NAME 0001!") - CHUNK_MAP_END = six.b("ND2 CHUNK MAP SIGNATURE 0000001!") + def __init__(self, filename): + super(ND2Reader, self).__init__() - supported_file_versions = {(3, None): True} + if not filename.endswith(".nd2"): + raise InvalidFileType("The file %s you want to read with nd2reader does not have extension .nd2." % filename) - def __init__(self, fh): - self._fh = fh - self._label_map = None - self._raw_metadata = None - self.metadata = None + self.filename = filename - # First check the file version - self.supported = self._check_version_supported() + # first use the parser to parse the file + self._fh = open(filename, "rb") + self._parser = Parser(self._fh) - # Parse the metadata - self._parse_metadata() + # Setup metadata + self.metadata = self._parser.metadata - def calculate_image_properties(self, index): - """Calculate FOV, channels and z_levels + # Set data type + self._dtype = self._parser.get_dtype_from_metadata() - Args: - index(int): the index (which is simply the order in which the image was acquired) + # Setup the axes + self._setup_axes() - Returns: - tuple: tuple of the field of view, the channel and the z level + # Other properties + self._timesteps = None - """ - field_of_view = self._calculate_field_of_view(index) - channel = self._calculate_channel(index) - z_level = self._calculate_z_level(index) - return field_of_view, channel, z_level + @classmethod + def class_exts(cls): + """Let PIMS open function use this reader for opening .nd2 files - def get_image(self, index): """ - Creates an Image object and adds its metadata, based on the index (which is simply the order in which the image - was acquired). May return None if the ND2 contains multiple channels and not all were taken in each cycle (for - example, if you take bright field images every minute, and GFP images every five minutes, there will be some - indexes that do not contain an image. The reason for this is complicated, but suffice it to say that we hope to - eliminate this possibility in future releases. For now, you'll need to check if your image is None if you're - doing anything out of the ordinary. + return {'nd2'} | super(ND2Reader, cls).class_exts() - Args: - index(int): the index (which is simply the order in which the image was acquired) - - Returns: - Frame: the image + def close(self): + """Correctly close the file handle """ - field_of_view, channel, z_level = self.calculate_image_properties(index) - channel_offset = index % len(self.metadata["channels"]) - image_group_number = int(index / len(self.metadata["channels"])) - frame_number = self._calculate_frame_number(image_group_number, field_of_view, z_level) - try: - timestamp, image = self._get_raw_image_data(image_group_number, channel_offset, self.metadata["height"], - self.metadata["width"]) - except (TypeError): - return Frame([], frame_no=frame_number, metadata=self._get_frame_metadata()) - else: - return Frame(image, frame_no=frame_number, metadata=self._get_frame_metadata()) + if self._fh is not None: + self._fh.close() - def get_image_by_attributes(self, frame_number, field_of_view, channel, z_level, height, width): - """Gets an image based on its attributes alone + def _get_default(self, coord): + try: + return self.default_coords[coord] + except KeyError: + return 0 + def get_frame_2D(self, c=0, t=0, z=0, x=0, y=0, v=0): + """Gets a given frame using the parser Args: - frame_number: the frame number - field_of_view: the field of view - channel_name: the color channel name - z_level: the z level - height: the height of the image - width: the width of the image - + x: The x-index (pims expects this) + y: The y-index (pims expects this) + c: The color channel number + t: The frame number + z: The z stack number + v: The field of view index Returns: - Frame: the requested image - + pims.Frame: The requested frame """ - frame_number = 0 if frame_number is None else frame_number - field_of_view = 0 if field_of_view is None else field_of_view - channel = 0 if channel is None else channel - z_level = 0 if z_level is None else z_level + # This needs to be set to width/height to return an image + x = self.metadata["width"] + y = self.metadata["height"] - image_group_number = self._calculate_image_group_number(frame_number, field_of_view, z_level) - try: - timestamp, raw_image_data = self._get_raw_image_data(image_group_number, channel, - height, width) - except (TypeError): - return Frame([], frame_no=frame_number, metadata=self._get_frame_metadata()) - else: - return Frame(raw_image_data, frame_no=frame_number, metadata=self._get_frame_metadata()) - - @staticmethod - def get_dtype_from_metadata(): - """Determine the data type from the metadata. - - For now, always use float64 to prevent unexpected overflow errors when manipulating the data (calculating sums/ - means/etc.) + return self._parser.get_image_by_attributes(t, v, c, z, y, x) + @property + def parser(self): """ - return np.float64 - - def _check_version_supported(self): - """Checks if the ND2 file version is supported by this reader. - + Returns the parser object. Returns: - bool: True on supported + Parser: the parser object """ - major_version, minor_version = get_version(self._fh) - supported = self.supported_file_versions.get( - (major_version, minor_version)) or self.supported_file_versions.get((major_version, None)) - - if not supported: - print("Warning: No parser is available for your current ND2 version (%d.%d). " % ( - major_version, minor_version) + "This might lead to unexpected behaviour.") + return self._parser - return supported + @property + def pixel_type(self): + """Return the pixel data type - def _parse_metadata(self): - """Reads all metadata and instantiates the Metadata object. + Returns: + dtype: the pixel data type """ - # Retrieve raw metadata from the label mapping - self._label_map = self._build_label_map() - self._raw_metadata = RawMetadata(self._fh, self._label_map) - self.metadata = self._raw_metadata.__dict__ - self.acquisition_times = self._raw_metadata.acquisition_times + return self._dtype - def _build_label_map(self): - """ - Every label ends with an exclamation point, however, we can't directly search for those to find all the labels - as some of the bytes contain the value 33, which is the ASCII code for "!". So we iteratively find each label, - grab the subsequent data (always 16 bytes long), advance to the next label and repeat. + @property + def timesteps(self): + """Get the timesteps of the experiment Returns: - LabelMap: the computed label map + np.ndarray: an array of times in milliseconds. """ - # go 8 bytes back from file end - self._fh.seek(-8, 2) - chunk_map_start_location = struct.unpack("Q", self._fh.read(8))[0] - self._fh.seek(chunk_map_start_location) - raw_text = self._fh.read(-1) - return LabelMap(raw_text) - - def _calculate_field_of_view(self, index): - """Determines what field of view was being imaged for a given image. + if self._timesteps is None: + return self.get_timesteps() + return self._timesteps - Args: - index(int): the index (which is simply the order in which the image was acquired) + @property + def events(self): + """Get the events of the experiment Returns: - int: the field of view + iterator of events as dict """ - images_per_cycle = len(self.metadata["z_levels"]) * len(self.metadata["channels"]) - return int((index - (index % images_per_cycle)) / images_per_cycle) % len(self.metadata["fields_of_view"]) - - def _calculate_channel(self, index): - """Determines what channel a particular image is. - Args: - index(int): the index (which is simply the order in which the image was acquired) + return self._get_metadata_property("events") + @property + def frame_rate(self): + """The (average) frame rate + Returns: - string: the name of the color channel - + float: the (average) frame rate in frames per second """ - return self.metadata["channels"][index % len(self.metadata["channels"])] + total_duration = 0.0 - def _calculate_z_level(self, index): - """Determines the plane in the z-axis a given image was taken in. + for loop in self.metadata['experiment']['loops']: + total_duration += loop['duration'] - In the future, this will be replaced with the actual offset in micrometers. + if total_duration == 0: + total_duration = self.timesteps[-1] - Args: - index(int): the index (which is simply the order in which the image was acquired) + if total_duration == 0: + raise ValueError('Total measurement duration could not be determined from loops') - Returns: - The z level + return self.metadata['num_frames'] / (total_duration/1000.0) - """ - return self.metadata["z_levels"][int( - ((index - (index % len(self.metadata["channels"]))) / len(self.metadata["channels"])) % len( - self.metadata["z_levels"]))] + def _get_metadata_property(self, key, default=None): + if self.metadata is None: + return default - def _calculate_image_group_number(self, frame_number, fov, z_level): - """ - Images are grouped together if they share the same time index, field of view, and z-level. + if key not in self.metadata: + return default - Args: - frame_number: the time index - fov: the field of view number - z_level: the z level number + if self.metadata[key] is None: + return default - Returns: - int: the image group number + return self.metadata[key] + + def _setup_axes(self): + """Setup the xyctz axes, iterate over t axis by default """ - z_length = len(self.metadata['z_levels']) - z_length = z_length if z_length > 0 else 1 - fields_of_view = len(self.metadata["fields_of_view"]) - fields_of_view = fields_of_view if fields_of_view > 0 else 1 + self._init_axis_if_exists('x', self._get_metadata_property("width", default=0)) + self._init_axis_if_exists('y', self._get_metadata_property("height", default=0)) + self._init_axis_if_exists('c', len(self._get_metadata_property("channels", default=[])), min_size=2) + self._init_axis_if_exists('t', len(self._get_metadata_property("frames", default=[]))) + self._init_axis_if_exists('z', len(self._get_metadata_property("z_levels", default=[])), min_size=2) + self._init_axis_if_exists('v', len(self._get_metadata_property("fields_of_view", default=[])), min_size=2) - return frame_number * fields_of_view * z_length + (fov * z_length + z_level) + if len(self.sizes) == 0: + raise EmptyFileError("No axes were found for this .nd2 file.") - def _calculate_frame_number(self, image_group_number, field_of_view, z_level): - """ - Images are in the same frame if they share the same group number and field of view and are taken sequentially. + # provide the default + self.iter_axes = self._guess_default_iter_axis() - Args: - image_group_number: the image group number (see _calculate_image_group_number) - field_of_view: the field of view number - z_level: the z level number + self._register_get_frame(self.get_frame_2D, 'yx') - Returns: + def _init_axis_if_exists(self, axis, size, min_size=1): + if size >= min_size: + self._init_axis(axis, size) + def _guess_default_iter_axis(self): """ - return (image_group_number - (field_of_view * len(self.metadata["z_levels"]) + z_level)) / ( - len(self.metadata["fields_of_view"]) * len(self.metadata["z_levels"])) - - @property - def _channel_offset(self): + Guesses the default axis to iterate over based on axis sizes. + Returns: + the axis to iterate over """ - Image data is interleaved for each image set. That is, if there are four images in a set, the first image - will consist of pixels 1, 5, 9, etc, the second will be pixels 2, 6, 10, and so forth. + priority = ['t', 'z', 'c', 'v'] + found_axes = [] + for axis in priority: + try: + current_size = self.sizes[axis] + except KeyError: + continue - Returns: - dict: the channel offset for each channel + if current_size > 1: + return axis - """ - return {channel: n for n, channel in enumerate(self.metadata["channels"])} - - def _remove_unwanted_bytes(self, image_group_data, image_data_start, height, width): - # Remove unwanted 0-bytes that can appear in stitched images - number_of_true_channels = int(len(image_group_data[4:]) / (height * width)) - unwanted_bytes_len = (len(image_group_data[image_data_start:]))%(height*width) - if unwanted_bytes_len: - warnings.warn('Identified unwanted bytes in the ND2 file, possibly stitched.') - byte_ids = range(image_data_start+height*number_of_true_channels, len(image_group_data)-unwanted_bytes_len+1, height*number_of_true_channels) - if all([0 == image_group_data[byte_ids[i]+i] for i in range(len(byte_ids))]): - warnings.warn('All unwanted bytes are zero-bytes, correctly removed.') - for i in range(len(byte_ids)): - del image_group_data[byte_ids[i]] - - def _get_raw_image_data(self, image_group_number, channel_offset, height, width): - """Reads the raw bytes and the timestamp of an image. + found_axes.append(axis) - Args: - image_group_number: the image group number (see _calculate_image_group_number) - channel_offset: the number of the color channel - height: the height of the image - width: the width of the image + return found_axes[0] + + def get_timesteps(self): + """Get the timesteps of the experiment Returns: + np.ndarray: an array of times in milliseconds. """ - chunk = self._label_map.get_image_data_location(image_group_number) - data = read_chunk(self._fh, chunk) - - # All images in the same image group share the same timestamp! So if you have complicated image data, - # your timestamps may not be entirely accurate. Practically speaking though, they'll only be off by a few - # seconds unless you're doing something super weird. - timestamp = struct.unpack("d", data[:8])[0] - image_group_data = array.array("H", data) - image_data_start = 4 + channel_offset - - # The images for the various channels are interleaved within the same array. For example, the second image - # of a four image group will be composed of bytes 2, 6, 10, etc. If you understand why someone would design - # a data structure that way, please send the author of this library a message. - number_of_true_channels = int(len(image_group_data[4:]) / (height * width)) - self._remove_unwanted_bytes(image_group_data, image_data_start, height, width) - try: - image_data = np.reshape(image_group_data[image_data_start::number_of_true_channels], (height, width)) - except ValueError: - image_data = np.reshape(image_group_data[image_data_start::number_of_true_channels], (height, int(round(len(image_group_data[image_data_start::number_of_true_channels])/height)))) - - # Skip images that are all zeros! This is important, since NIS Elements creates blank "gap" images if you - # don't have the same number of images each cycle. We discovered this because we only took GFP images every - # other cycle to reduce phototoxicity, but NIS Elements still allocated memory as if we were going to take - # them every cycle. - if np.any(image_data): - return timestamp, image_data - - # If a blank "gap" image is encountered, generate an array of corresponding height and width to avoid - # errors with ND2-files with missing frames. Array is filled with nan to reflect that data is missing. - else: - empty_frame = np.full((height, width), np.nan) - warnings.warn('ND2 file contains gap frames which are represented by np.nan-filled arrays; to convert to zeros use e.g. np.nan_to_num(array)') - return timestamp, image_data - - def _get_frame_metadata(self): - """Get the metadata for one frame + if self._timesteps is not None and len(self._timesteps) > 0: + return self._timesteps - Returns: - dict: a dictionary containing the parsed metadata + self._timesteps = np.array(list(self._parser._raw_metadata.acquisition_times), dtype=np.float) * 1000.0 - """ - return self.metadata + return self._timesteps