# -*- coding: utf-8 -*- import array from datetime import datetime import logging from nd2reader.model import Image, ImageSet from nd2reader.parser import Nd2Parser import re import struct log = logging.getLogger(__name__) log.addHandler(logging.StreamHandler()) log.setLevel(logging.WARN) class Nd2(Nd2Parser): def __init__(self, filename, image_sets=False): super(Nd2, self).__init__(filename) self._use_image_sets = image_sets def __iter__(self): if self._use_image_sets: return self.image_sets() else: return self.images() def images(self): for i in range(self._image_count): for fov in range(self.field_of_view_count): for z_level in range(self.z_level_count): for channel in self.channels: image = self.get_image(i, fov, channel.name, z_level) if image.is_valid: yield image def image_sets(self): for time_index in xrange(self.time_index_count): image_set = ImageSet() for fov in range(self.field_of_view_count): for channel_name in self.channels: for z_level in xrange(self.z_level_count): image = self.get_image(time_index, fov, channel_name, z_level) if image.is_valid: image_set.add(image) yield image_set def get_image(self, time_index, fov, channel_name, z_level): image_set_number = self._calculate_image_set_number(time_index, fov, z_level) timestamp, raw_image_data = self._get_raw_image_data(image_set_number, self._channel_offset[channel_name]) return Image(timestamp, raw_image_data, fov, channel_name, z_level, self.height, self.width) @property def channels(self): metadata = self.metadata['ImageMetadataSeq']['SLxPictureMetadata']['sPicturePlanes'] try: validity = self.metadata['ImageMetadata']['SLxExperiment']['ppNextLevelEx'][''][0]['ppNextLevelEx'][''][0]['pItemValid'] except KeyError: # If none of the channels have been deleted, there is no validity list, so we just make one validity = [True for _ in 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. for (label, chan), valid in zip(sorted(metadata['sPlaneNew'].items()), validity): if not valid: continue yield chan['sDescription'] @property def height(self): """ :return: height of each image, in pixels """ return self.metadata['ImageAttributes']['SLxImageAttributes']['uiHeight'] @property def width(self): """ :return: width of each image, in pixels """ return self.metadata['ImageAttributes']['SLxImageAttributes']['uiWidth'] @property def absolute_start(self): if self._absolute_start is None: for line in self.metadata['ImageTextInfo']['SLxImageTextInfo'].values(): absolute_start_12 = None absolute_start_24 = None # ND2s seem to randomly switch between 12- and 24-hour representations. try: absolute_start_24 = datetime.strptime(line, "%m/%d/%Y %H:%M:%S") except ValueError: pass try: absolute_start_12 = datetime.strptime(line, "%m/%d/%Y %I:%M:%S %p") except ValueError: pass if not absolute_start_12 and not absolute_start_24: continue self._absolute_start = absolute_start_12 if absolute_start_12 else absolute_start_24 return self._absolute_start @property def channel_count(self): pattern = r""".*?λ\((\d+)\).*?""" try: count = int(re.match(pattern, self._dimensions).group(1)) except AttributeError: return 1 else: return count @property def field_of_view_count(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. """ pattern = r""".*?XY\((\d+)\).*?""" try: count = int(re.match(pattern, self._dimensions).group(1)) except AttributeError: return 1 else: return count @property def time_index_count(self): """ The number of image sets. If images were acquired using some kind of cycle, all images at each step in the program will have the same timestamp (even though they may have varied by a few seconds in reality). For example, if you have four fields of view that you're constantly monitoring, and you take a bright field and GFP image of each, and you repeat that process 100 times, you'll have 800 individual images. But there will only be 400 time indexes. :rtype: int """ pattern = r""".*?T'\((\d+)\).*?""" try: count = int(re.match(pattern, self._dimensions).group(1)) except AttributeError: return 1 else: return count @property def z_level_count(self): pattern = r""".*?Z\((\d+)\).*?""" try: count = int(re.match(pattern, self._dimensions).group(1)) except AttributeError: return 1 else: return count @property def _channel_offset(self): """ 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. Why this would be the case is beyond me, but that's how it works. """ channel_offset = {} for n, channel in enumerate(self.channels): self._channel_offset[channel.name] = n return channel_offset def _get_raw_image_data(self, image_set_number, channel_offset): chunk = self._label_map["ImageDataSeq|%d!" % image_set_number] data = self._read_chunk(chunk) timestamp = struct.unpack("d", data[:8])[0] # The images for the various channels are interleaved within each other. image_data = array.array("H", data) image_data_start = 4 + channel_offset return timestamp, image_data[image_data_start::self.channel_count] def _calculate_image_set_number(self, time_index, fov, z_level): return time_index * self.field_of_view_count * self.z_level_count + (fov * self.z_level_count + z_level)