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378 lines
14 KiB
378 lines
14 KiB
# -*- coding: utf-8 -*-
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import array
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from collections import namedtuple
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from datetime import datetime
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import numpy as np
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import re
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import struct
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from StringIO import StringIO
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field_of_view = namedtuple('FOV', ['number', 'x', 'y', 'z', 'pfs_offset'])
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class Nd2Parser(object):
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"""
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Reads .nd2 files, provides an interface to the metadata, and generates numpy arrays from the image data.
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You should not ever need to instantiate this class manually unless you're a developer.
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"""
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CHUNK_HEADER = 0xabeceda
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CHUNK_MAP_START = "ND2 FILEMAP SIGNATURE NAME 0001!"
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CHUNK_MAP_END = "ND2 CHUNK MAP SIGNATURE 0000001!"
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def __init__(self, filename):
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self._filename = filename
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self._fh = None
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self._chunk_map_start_location = None
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self._cursor_position = 0
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self._dimension_text = None
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self._label_map = {}
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self.metadata = {}
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self._read_map()
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self._parse_metadata()
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@property
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def _file_handle(self):
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if self._fh is None:
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self._fh = open(self._filename, "rb")
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return self._fh
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def _get_raw_image_data(self, image_group_number, channel_offset):
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"""
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Reads the raw bytes and the timestamp of an image.
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:param image_group_number: groups are made of images with the same time index, field of view and z-level.
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:type image_group_number: int
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:param channel_offset: the offset in the array where the bytes for this image are found.
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:type channel_offset: int
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:return: (int, array.array()) or None
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"""
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chunk = self._label_map["ImageDataSeq|%d!" % image_group_number]
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data = self._read_chunk(chunk)
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# All images in the same image group share the same timestamp! So if you have complicated image data,
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# your timestamps may not be entirely accurate. Practically speaking though, they'll only be off by a few
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# seconds unless you're doing something super weird.
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timestamp = struct.unpack("d", data[:8])[0]
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image_group_data = array.array("H", data)
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image_data_start = 4 + channel_offset
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# The images for the various channels are interleaved within the same array. For example, the second image
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# of a four image group will be composed of bytes 2, 6, 10, etc. If you understand why someone would design
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# a data structure that way, please send the author of this library a message.
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image_data = image_group_data[image_data_start::self._channel_count]
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# Skip images that are all zeros! This is important, since NIS Elements creates blank "gap" images if you
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# don't have the same number of images each cycle. We discovered this because we only took GFP images every
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# other cycle to reduce phototoxicity, but NIS Elements still allocated memory as if we were going to take
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# them every cycle.
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if np.any(image_data):
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return timestamp, image_data
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return None
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@property
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def _dimensions(self):
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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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:rtype: str
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"""
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if self._dimension_text is None:
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for line in self.metadata['ImageTextInfo']['SLxImageTextInfo'].values():
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if "Dimensions:" in line:
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metadata = line
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break
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else:
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raise ValueError("Could not parse metadata dimensions!")
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for line in metadata.split("\r\n"):
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if line.startswith("Dimensions:"):
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self._dimension_text = line
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break
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else:
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raise ValueError("Could not parse metadata dimensions!")
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return self._dimension_text
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@property
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def _channels(self):
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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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:rtype: str
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"""
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metadata = self.metadata['ImageMetadataSeq']['SLxPictureMetadata']['sPicturePlanes']
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try:
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validity = self.metadata['ImageMetadata']['SLxExperiment']['ppNextLevelEx'][''][0]['ppNextLevelEx'][''][0]['pItemValid']
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except KeyError:
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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['sPlaneNew'].items()), validity):
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if not valid:
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continue
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yield chan['sDescription']
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def _calculate_image_group_number(self, time_index, fov, z_level):
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"""
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Images are grouped together if they share the same time index, field of view, and z-level.
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:type time_index: int
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:type fov: int
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:type z_level: int
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:rtype: int
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"""
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return time_index * self._field_of_view_count * self._z_level_count + (fov * self._z_level_count + z_level)
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@property
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def _channel_offset(self):
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"""
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Image data is interleaved for each image set. That is, if there are four images in a set, the first image
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will consist of pixels 1, 5, 9, etc, the second will be pixels 2, 6, 10, and so forth.
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:rtype: dict
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"""
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channel_offset = {}
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for n, channel in enumerate(self._channels):
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channel_offset[channel] = n
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return channel_offset
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@property
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def _absolute_start(self):
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"""
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The date and time when acquisition began.
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:rtype: datetime.datetime()
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"""
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for line in self.metadata['ImageTextInfo']['SLxImageTextInfo'].values():
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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 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 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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raise ValueError("This ND2 has no recorded start time. This is probably a bug.")
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@property
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def _channel_count(self):
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"""
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The number of different channels used, including bright field.
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:rtype: int
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"""
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pattern = r""".*?λ\((\d+)\).*?"""
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try:
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count = int(re.match(pattern, self._dimensions).group(1))
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except AttributeError:
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return 1
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else:
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return count
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@property
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def _field_of_view_count(self):
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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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:rtype: int
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"""
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pattern = r""".*?XY\((\d+)\).*?"""
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try:
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count = int(re.match(pattern, self._dimensions).group(1))
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except AttributeError:
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return 1
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else:
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return count
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@property
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def _time_index_count(self):
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"""
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The number of cycles.
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:rtype: int
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"""
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pattern = r""".*?T'\((\d+)\).*?"""
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try:
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count = int(re.match(pattern, self._dimensions).group(1))
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except AttributeError:
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return 1
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else:
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return count
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@property
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def _z_level_count(self):
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"""
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The number of different levels in the Z-plane.
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:rtype: int
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"""
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pattern = r""".*?Z\((\d+)\).*?"""
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try:
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count = int(re.match(pattern, self._dimensions).group(1))
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except AttributeError:
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return 1
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else:
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return count
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@property
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def _image_count(self):
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"""
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The total number of images in the ND2. Warning: this may be inaccurate as it includes "gap" images.
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:rtype: int
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"""
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return self.metadata['ImageAttributes']['SLxImageAttributes']['uiSequenceCount']
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def _parse_metadata(self):
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"""
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Reads all metadata.
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"""
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for label in self._label_map.keys():
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if label.endswith("LV!") or "LV|" in label:
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data = self._read_chunk(self._label_map[label])
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stop = label.index("LV")
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self.metadata[label[:stop]] = self._read_metadata(data, 1)
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def _read_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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"""
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self._file_handle.seek(-8, 2)
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chunk_map_start_location = struct.unpack("Q", self._file_handle.read(8))[0]
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self._file_handle.seek(chunk_map_start_location)
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raw_text = self._file_handle.read(-1)
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label_start = raw_text.index(Nd2Parser.CHUNK_MAP_START) + 32
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while True:
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data_start = raw_text.index("!", label_start) + 1
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key = raw_text[label_start: data_start]
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location, length = struct.unpack("QQ", raw_text[data_start: data_start + 16])
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if key == Nd2Parser.CHUNK_MAP_END:
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# We've reached the end of the chunk map
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break
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self._label_map[key] = location
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label_start = data_start + 16
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def _read_chunk(self, chunk_location):
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"""
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Gets the data for a given chunk pointer
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"""
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self._file_handle.seek(chunk_location)
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# The chunk metadata is always 16 bytes long
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chunk_metadata = self._file_handle.read(16)
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header, relative_offset, data_length = struct.unpack("IIQ", chunk_metadata)
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if header != Nd2Parser.CHUNK_HEADER:
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raise ValueError("The ND2 file seems to be corrupted.")
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# We start at the location of the chunk metadata, skip over the metadata, and then proceed to the
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# start of the actual data field, which is at some arbitrary place after the metadata.
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self._file_handle.seek(chunk_location + 16 + relative_offset)
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return self._file_handle.read(data_length)
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def _parse_unsigned_char(self, data):
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return struct.unpack("B", data.read(1))[0]
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def _parse_unsigned_int(self, data):
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return struct.unpack("I", data.read(4))[0]
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def _parse_unsigned_long(self, data):
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return struct.unpack("Q", data.read(8))[0]
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def _parse_double(self, data):
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return struct.unpack("d", data.read(8))[0]
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def _parse_string(self, data):
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value = data.read(2)
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while not value.endswith("\x00\x00"):
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# the string ends at the first instance of \x00\x00
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value += data.read(2)
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return value.decode("utf16")[:-1].encode("utf8")
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def _parse_char_array(self, data):
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array_length = struct.unpack("Q", data.read(8))[0]
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return array.array("B", data.read(array_length))
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def _parse_metadata_item(self, data):
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"""
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Reads hierarchical data, analogous to a Python dict.
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"""
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new_count, length = struct.unpack("<IQ", data.read(12))
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length -= data.tell() - self._cursor_position
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next_data_length = data.read(length)
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value = self._read_metadata(next_data_length, new_count)
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# Skip some offsets
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data.read(new_count * 8)
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return value
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def _get_value(self, data, data_type):
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"""
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ND2s use various codes to indicate different data types, which we translate here.
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"""
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parser = {1: self._parse_unsigned_char,
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2: self._parse_unsigned_int,
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3: self._parse_unsigned_int,
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5: self._parse_unsigned_long,
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6: self._parse_double,
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8: self._parse_string,
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9: self._parse_char_array,
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11: self._parse_metadata_item}
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return parser[data_type](data)
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def _read_metadata(self, data, count):
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"""
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Iterates over each element some section of the metadata and parses it.
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"""
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data = StringIO(data)
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metadata = {}
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for _ in xrange(count):
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self._cursor_position = data.tell()
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header = data.read(2)
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if not header:
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# We've reached the end of some hierarchy of data
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break
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data_type, name_length = map(ord, header)
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name = data.read(name_length * 2).decode("utf16")[:-1].encode("utf8")
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value = self._get_value(data, data_type)
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if name not in metadata.keys():
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metadata[name] = value
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else:
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if not isinstance(metadata[name], list):
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# We have encountered this key exactly once before. Since we're seeing it again, we know we
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# need to convert it to a list before proceeding.
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metadata[name] = [metadata[name]]
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# We've encountered this key before so we're guaranteed to be dealing with a list. Thus we append
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# the value to the already-existing list.
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metadata[name].append(value)
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return metadata
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