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# -*- coding: utf-8 -*-
import array
from collections import namedtuple
from datetime import datetime
import numpy as np
import re
import struct
from StringIO import StringIO
field_of_view = namedtuple('FOV', ['number', 'x', 'y', 'z', 'pfs_offset'])
class Nd2Parser(object):
"""
Reads .nd2 files, provides an interface to the metadata, and generates numpy arrays from the image data.
You should not ever need to instantiate this class manually unless you're a developer.
"""
CHUNK_HEADER = 0xabeceda
CHUNK_MAP_START = "ND2 FILEMAP SIGNATURE NAME 0001!"
CHUNK_MAP_END = "ND2 CHUNK MAP SIGNATURE 0000001!"
def __init__(self, filename):
self._filename = filename
self._fh = None
self._chunk_map_start_location = None
self._cursor_position = 0
self._dimension_text = None
self._label_map = {}
self.metadata = {}
self._read_map()
self._parse_metadata()
@property
def _file_handle(self):
if self._fh is None:
self._fh = open(self._filename, "rb")
return self._fh
def _get_raw_image_data(self, image_group_number, channel_offset):
"""
Reads the raw bytes and the timestamp of an image.
:param image_group_number: groups are made of images with the same time index, field of view and z-level.
:type image_group_number: int
:param channel_offset: the offset in the array where the bytes for this image are found.
:type channel_offset: int
:return: (int, array.array()) or None
"""
chunk = self._label_map["ImageDataSeq|%d!" % image_group_number]
data = self._read_chunk(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.
image_data = image_group_data[image_data_start::self._channel_count]
# 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
return None
@property
def _dimensions(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.
:rtype: str
"""
if self._dimension_text is None:
for line in self.metadata['ImageTextInfo']['SLxImageTextInfo'].values():
if "Dimensions:" in line:
metadata = line
break
else:
raise ValueError("Could not parse metadata dimensions!")
for line in metadata.split("\r\n"):
if line.startswith("Dimensions:"):
self._dimension_text = line
break
else:
raise ValueError("Could not parse metadata dimensions!")
return self._dimension_text
@property
def _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.)
:rtype: str
"""
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']
def _calculate_image_group_number(self, time_index, fov, z_level):
"""
Images are grouped together if they share the same time index, field of view, and z-level.
:type time_index: int
:type fov: int
:type z_level: int
:rtype: int
"""
return time_index * self._field_of_view_count * self._z_level_count + (fov * self._z_level_count + z_level)
@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.
:rtype: dict
"""
channel_offset = {}
for n, channel in enumerate(self._channels):
channel_offset[channel] = n
return channel_offset
@property
def _absolute_start(self):
"""
The date and time when acquisition began.
:rtype: datetime.datetime()
"""
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
return absolute_start_12 if absolute_start_12 else absolute_start_24
raise ValueError("This ND2 has no recorded start time. This is probably a bug.")
@property
def _channel_count(self):
"""
The number of different channels used, including bright field.
:rtype: int
"""
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.
:rtype: int
"""
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 cycles.
: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):
"""
The number of different levels in the Z-plane.
:rtype: int
"""
pattern = r""".*?Z\((\d+)\).*?"""
try:
count = int(re.match(pattern, self._dimensions).group(1))
except AttributeError:
return 1
else:
return count
@property
def _image_count(self):
"""
The total number of images in the ND2. Warning: this may be inaccurate as it includes "gap" images.
:rtype: int
"""
return self.metadata['ImageAttributes']['SLxImageAttributes']['uiSequenceCount']
def _parse_metadata(self):
"""
Reads all metadata.
"""
for label in self._label_map.keys():
if label.endswith("LV!") or "LV|" in label:
data = self._read_chunk(self._label_map[label])
stop = label.index("LV")
self.metadata[label[:stop]] = self._read_metadata(data, 1)
def _read_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.
"""
self._file_handle.seek(-8, 2)
chunk_map_start_location = struct.unpack("Q", self._file_handle.read(8))[0]
self._file_handle.seek(chunk_map_start_location)
raw_text = self._file_handle.read(-1)
label_start = raw_text.index(Nd2Parser.CHUNK_MAP_START) + 32
while True:
data_start = raw_text.index("!", label_start) + 1
key = raw_text[label_start: data_start]
location, length = struct.unpack("QQ", raw_text[data_start: data_start + 16])
if key == Nd2Parser.CHUNK_MAP_END:
# We've reached the end of the chunk map
break
self._label_map[key] = location
label_start = data_start + 16
def _read_chunk(self, chunk_location):
"""
Gets the data for a given chunk pointer
"""
self._file_handle.seek(chunk_location)
# The chunk metadata is always 16 bytes long
chunk_metadata = self._file_handle.read(16)
header, relative_offset, data_length = struct.unpack("IIQ", chunk_metadata)
if header != Nd2Parser.CHUNK_HEADER:
raise ValueError("The ND2 file seems to be corrupted.")
# We start at the location of the chunk metadata, skip over the metadata, and then proceed to the
# start of the actual data field, which is at some arbitrary place after the metadata.
self._file_handle.seek(chunk_location + 16 + relative_offset)
return self._file_handle.read(data_length)
def _parse_unsigned_char(self, data):
return struct.unpack("B", data.read(1))[0]
def _parse_unsigned_int(self, data):
return struct.unpack("I", data.read(4))[0]
def _parse_unsigned_long(self, data):
return struct.unpack("Q", data.read(8))[0]
def _parse_double(self, data):
return struct.unpack("d", data.read(8))[0]
def _parse_string(self, data):
value = data.read(2)
while not value.endswith("\x00\x00"):
# the string ends at the first instance of \x00\x00
value += data.read(2)
return value.decode("utf16")[:-1].encode("utf8")
def _parse_char_array(self, data):
array_length = struct.unpack("Q", data.read(8))[0]
return array.array("B", data.read(array_length))
def _parse_metadata_item(self, data):
"""
Reads hierarchical data, analogous to a Python dict.
"""
new_count, length = struct.unpack("<IQ", data.read(12))
length -= data.tell() - self._cursor_position
next_data_length = data.read(length)
value = self._read_metadata(next_data_length, new_count)
# Skip some offsets
data.read(new_count * 8)
return value
def _get_value(self, data, data_type):
"""
ND2s use various codes to indicate different data types, which we translate here.
"""
parser = {1: self._parse_unsigned_char,
2: self._parse_unsigned_int,
3: self._parse_unsigned_int,
5: self._parse_unsigned_long,
6: self._parse_double,
8: self._parse_string,
9: self._parse_char_array,
11: self._parse_metadata_item}
return parser[data_type](data)
def _read_metadata(self, data, count):
"""
Iterates over each element some section of the metadata and parses it.
"""
data = StringIO(data)
metadata = {}
for _ in xrange(count):
self._cursor_position = data.tell()
header = data.read(2)
if not header:
# We've reached the end of some hierarchy of data
break
data_type, name_length = map(ord, header)
name = data.read(name_length * 2).decode("utf16")[:-1].encode("utf8")
value = self._get_value(data, data_type)
if name not in metadata.keys():
metadata[name] = value
else:
if not isinstance(metadata[name], list):
# We have encountered this key exactly once before. Since we're seeing it again, we know we
# need to convert it to a list before proceeding.
metadata[name] = [metadata[name]]
# We've encountered this key before so we're guaranteed to be dealing with a list. Thus we append
# the value to the already-existing list.
metadata[name].append(value)
return metadata