Browse Source

#66 more refactoring

zolfa-add_slices_loading
jim 9 years ago
parent
commit
544fd974eb
11 changed files with 459 additions and 411 deletions
  1. +0
    -11
      nd2reader/driver/driver.py
  2. +0
    -397
      nd2reader/driver/v3.py
  3. +0
    -0
      nd2reader/imreader/__init__.py
  4. +0
    -0
      nd2reader/imreader/v2.py
  5. +96
    -0
      nd2reader/imreader/v3.py
  6. +3
    -3
      nd2reader/interface.py
  7. +62
    -0
      nd2reader/model/metadata.py
  8. +1
    -0
      nd2reader/parser/__init__.py
  9. +2
    -0
      nd2reader/parser/parser.py
  10. +0
    -0
      nd2reader/parser/v2.py
  11. +295
    -0
      nd2reader/parser/v3.py

+ 0
- 11
nd2reader/driver/driver.py View File

@ -1,11 +0,0 @@
def get_driver(filename, version):
"""
Instantiates the correct driver for the ND2, which allows us to parse metadata and access images.
:param filename: the path to the ND2
:type filename: str
:param version: the version of the ND2. Note that this is different than the version of NIS Elements used to create the ND2.
:type version: tuple
"""
return 1

+ 0
- 397
nd2reader/driver/v3.py View File

@ -1,397 +0,0 @@
# -*- coding: utf-8 -*-
import array
from datetime import datetime
import numpy as np
import re
import struct
import six
from nd2reader.model.image import Image
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 = six.b("ND2 FILEMAP SIGNATURE NAME 0001!")
CHUNK_MAP_END = six.b("ND2 CHUNK MAP SIGNATURE 0000001!")
def __init__(self, filename):
self._absolute_start = None
self._filename = filename
self._fh = None
self._channels = None
self._channel_count = None
self._chunk_map_start_location = None
self._cursor_position = 0
self._dimension_text = None
self._fields_of_view = None
self._label_map = {}
self._metadata = {}
self._read_map()
self._time_indexes = None
self._parse_metadata()
self._z_levels = None
def get_image(self, index):
channel_offset = index % len(self._metadata.channels)
fov = self._calculate_field_of_view(index)
channel = self._calculate_channel(index)
z_level = self._calculate_z_level(index)
image_group_number = int(index / len(self._metadata.channels))
frame_number = self._calculate_frame_number(image_group_number, fov, z_level)
timestamp, image = self._get_raw_image_data(image_group_number, channel_offset, self._metadata.height, self._metadata.width)
image.add_params(timestamp, frame_number, fov, channel, z_level)
@property
def absolute_start(self):
"""
The date and time when acquisition began.
:rtype: datetime.datetime()
"""
if self._absolute_start is None:
for line in self.metadata[six.b('ImageTextInfo')][six.b('SLxImageTextInfo')].values():
line = line.decode("utf8")
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 (TypeError, ValueError):
pass
try:
absolute_start_12 = datetime.strptime(line, "%m/%d/%Y %I:%M:%S %p")
except (TypeError, 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.")
return self._absolute_start
@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: list
"""
if not self._channels:
self._channels = []
metadata = self.metadata[six.b('ImageMetadataSeq')][six.b('SLxPictureMetadata')][six.b('sPicturePlanes')]
try:
validity = self.metadata[six.b('ImageMetadata')][six.b('SLxExperiment')][six.b('ppNextLevelEx')][six.b('')][0][six.b('ppNextLevelEx')][six.b('')][0][six.b('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[six.b('sPlaneNew')].items()), validity):
if not valid:
continue
self._channels.append(chan[six.b('sDescription')].decode("utf8"))
return self._channels
@property
def 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.
:rtype: list
"""
if self._fields_of_view is None:
self._fields_of_view = self._parse_dimension_text(r""".*?XY\((\d+)\).*?""")
return self._fields_of_view
@property
def frames(self):
"""
The number of cycles.
:rtype: list
"""
if self._time_indexes is None:
self._time_indexes = self._parse_dimension_text(r""".*?T'\((\d+)\).*?""")
return self._time_indexes
@property
def z_levels(self):
"""
The different levels in the Z-plane. Just a sequence from 0 to n.
:rtype: list
"""
if self._z_levels is None:
self._z_levels = self._parse_dimension_text(r""".*?Z\((\d+)\).*?""")
return self._z_levels
def _calculate_field_of_view(self, frame_number):
images_per_cycle = len(self.z_levels) * len(self.channels)
return int((frame_number - (frame_number % images_per_cycle)) / images_per_cycle) % len(self.fields_of_view)
def _calculate_channel(self, frame_number):
return self.channels[frame_number % len(self.channels)]
def _calculate_z_level(self, frame_number):
return self.z_levels[int(((frame_number - (frame_number % len(self.channels))) / len(self.channels)) % len(self.z_levels))]
@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, height, width):
"""
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[six.b("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 = np.reshape(image_group_data[image_data_start::len(self.channels)], (height, width))
# 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(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[six.b('ImageTextInfo')][six.b('SLxImageTextInfo')].values():
if six.b("Dimensions:") in line:
metadata = line
break
else:
raise ValueError("Could not parse metadata dimensions!")
for line in metadata.split(six.b("\r\n")):
if line.startswith(six.b("Dimensions:")):
self._dimension_text = line
break
else:
raise ValueError("Could not parse metadata dimensions!")
return self._dimension_text
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 * len(self.fields_of_view) * len(self.z_levels) + (fov * len(self.z_levels) + z_level)
def _calculate_frame_number(self, image_group_number, fov, z_level):
return (image_group_number - (fov * len(self.z_levels) + z_level)) / (len(self.fields_of_view) * len(self.z_levels))
@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
def _parse_dimension_text(self, pattern):
try:
count = int(re.match(pattern, self._dimensions).group(1))
except AttributeError:
return [0]
except TypeError:
match = re.match(pattern, self._dimensions.decode("utf8"))
if not match:
return [0]
return list(range(int(match.group(1))))
else:
return list(range(count))
@property
def _total_images_per_channel(self):
"""
The total number of images per channel. Warning: this may be inaccurate as it includes "gap" images.
:rtype: int
"""
return self.metadata[six.b('ImageAttributes')][six.b('SLxImageAttributes')][six.b('uiSequenceCount')]
def _parse_metadata(self):
"""
Reads all metadata.
"""
for label in self._label_map.keys():
if label.endswith(six.b("LV!")) or six.b("LV|") in label:
data = self._read_chunk(self._label_map[label])
stop = label.index(six.b("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(six.b("!"), 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(six.b("\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 = six.BytesIO(data)
metadata = {}
for _ in range(count):
self._cursor_position = data.tell()
header = data.read(2)
if not header:
# We've reached the end of some hierarchy of data
break
if six.PY3:
header = header.decode("utf8")
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

nd2reader/driver/__init__.py → nd2reader/imreader/__init__.py View File


nd2reader/driver/v2.py → nd2reader/imreader/v2.py View File


+ 96
- 0
nd2reader/imreader/v3.py View File

@ -0,0 +1,96 @@
# -*- coding: utf-8 -*-
import array
from datetime import datetime
import numpy as np
import re
import struct
import six
from nd2reader.model.image import Image
class V3ImageReader(object):
def __init__(self, metadata):
self._metadata = metadata
def _calculate_field_of_view(self, frame_number):
images_per_cycle = len(self._metadata.z_levels) * len(self._metadata.channels)
return int((frame_number - (frame_number % images_per_cycle)) / images_per_cycle) % len(self._metadata.fields_of_view)
def _calculate_channel(self, frame_number):
return self._metadata.channels[frame_number % len(self._metadata.channels)]
def _calculate_z_level(self, frame_number):
return self._metadata.z_levels[int(((frame_number - (frame_number % len(self._metadata.channels))) / len(self._metadata.channels)) % len(self._metadata.z_levels))]
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 * len(self._metadata.fields_of_view) * len(self._metadata.z_levels) + (fov * len(self._metadata.z_levels) + z_level)
def _calculate_frame_number(self, image_group_number, fov, z_level):
return (image_group_number - (fov * len(self._metadata.z_levels) + z_level)) / (len(self._metadata.fields_of_view) * len(self._metadata.z_levels))
def get_image(self, index):
channel_offset = index % len(self._metadata.channels)
fov = self._calculate_field_of_view(index)
channel = self._calculate_channel(index)
z_level = self._calculate_z_level(index)
image_group_number = int(index / len(self._metadata.channels))
frame_number = self._calculate_frame_number(image_group_number, fov, z_level)
timestamp, image = self._get_raw_image_data(image_group_number, channel_offset, self._metadata.height, self._metadata.width)
image.add_params(timestamp, frame_number, fov, channel, 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
def _get_raw_image_data(self, image_group_number, channel_offset, height, width):
"""
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[six.b("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 = np.reshape(image_group_data[image_data_start::len(self.channels)], (height, width))
# 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(image_data)
return None

+ 3
- 3
nd2reader/interface.py View File

@ -12,8 +12,8 @@ class Nd2(object):
"""
def __init__(self, filename):
version = get_version(filename)
parser = get_parser(filename, version)
major_version, minor_version = get_version(filename)
parser = get_parser(filename, major_version, minor_version)
self._driver = parser.driver
self._metadata = parser.metadata
self._filename = filename
@ -89,7 +89,7 @@ class Nd2(object):
:return: model.ImageSet()
"""
warnings.warn("nd2.image_sets will be removed from the nd2reader library in the near future.", DeprecationWarning)
warnings.warn("Nd2.image_sets will be removed from the nd2reader library in the near future.", DeprecationWarning)
for frame in self.frames:
image_group = ImageGroup()


+ 62
- 0
nd2reader/model/metadata.py View File

@ -0,0 +1,62 @@
class Metadata(object):
""" A simple container for ND2 metadata. """
def __init__(self, channels, date, fields_of_view, frames, z_levels):
self._channels = channels
self._date = date
self._fields_of_view = fields_of_view
self._frames = frames
self._z_levels = z_levels
@property
def date(self):
"""
The date and time when acquisition began.
:rtype: datetime.datetime()
"""
return self._date
@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: list
"""
return self._channels
@property
def 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.
:rtype: list
"""
return self._fields_of_view
@property
def frames(self):
"""
The number of cycles.
:rtype: list
"""
return self._frames
@property
def z_levels(self):
"""
The different levels in the Z-plane. Just a sequence from 0 to n.
:rtype: list
"""
return self._z_levels

+ 1
- 0
nd2reader/parser/__init__.py View File

@ -0,0 +1 @@
from . parser import get_parser

+ 2
- 0
nd2reader/parser/parser.py View File

@ -0,0 +1,2 @@
def get_parser(filename, major_version, minor_version):
parsers = {}

nd2reader/parser.py → nd2reader/parser/v2.py View File


+ 295
- 0
nd2reader/parser/v3.py View File

@ -0,0 +1,295 @@
# -*- coding: utf-8 -*-
import array
from datetime import datetime
from nd2reader.model.metadata import Metadata
import re
import six
import struct
class V3Parser(object):
""" Parses ND2 files and creates a Metadata and ImageReader object. """
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):
self._filename = filename
self._fh = None
self._metadata = None
def _parse_date(self, metadata_dict):
"""
The date and time when acquisition began.
:rtype: datetime.datetime()
"""
for line in metadata_dict[six.b('ImageTextInfo')][six.b('SLxImageTextInfo')].values():
line = line.decode("utf8")
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 (TypeError, ValueError):
pass
try:
absolute_start_12 = datetime.strptime(line, "%m/%d/%Y %I:%M:%S %p")
except (TypeError, 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.")
def _parse_channels(self, metadata_dict):
"""
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: list
"""
channels = []
metadata = metadata_dict[six.b('ImageMetadataSeq')][six.b('SLxPictureMetadata')][six.b('sPicturePlanes')]
try:
validity = metadata_dict[six.b('ImageMetadata')][six.b('SLxExperiment')][six.b('ppNextLevelEx')][six.b('')][0][six.b('ppNextLevelEx')][six.b('')][0][six.b('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[six.b('sPlaneNew')].items()), validity):
if not valid:
continue
channels.append(chan[six.b('sDescription')].decode("utf8"))
return channels
def _parse_fields_of_view(self, metadata_dict):
"""
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: list
"""
return self._parse_dimension(r""".*?XY\((\d+)\).*?""", metadata_dict)
def _parse_frames(self, metadata_dict):
"""
The number of cycles.
:rtype: list
"""
return self._parse_dimension(r""".*?T'\((\d+)\).*?""", metadata_dict)
def _parse_z_levels(self, metadata_dict):
"""
The different levels in the Z-plane. Just a sequence from 0 to n.
:rtype: list
"""
return self._parse_dimension(r""".*?Z\((\d+)\).*?""", metadata_dict)
@property
def _file_handle(self):
if self._fh is None:
self._fh = open(self._filename, "rb")
return self._fh
def _parse_dimension_text(self, metadata_dict):
"""
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
"""
for line in metadata_dict[six.b('ImageTextInfo')][six.b('SLxImageTextInfo')].values():
if six.b("Dimensions:") in line:
metadata = line
break
else:
raise ValueError("Could not parse metadata dimensions!")
for line in metadata.split(six.b("\r\n")):
if line.startswith(six.b("Dimensions:")):
dimension_text = line
break
else:
raise ValueError("Could not parse metadata dimensions!")
return dimension_text
def _parse_dimension(self, pattern, metadata_dict):
try:
dimension_text = self._parse_dimension_text(metadata_dict)
count = int(re.match(pattern, dimension_text).group(1))
except AttributeError:
return [0]
except TypeError:
match = re.match(pattern, dimension_text.decode("utf8"))
if not match:
return [0]
return list(range(int(match.group(1))))
else:
return list(range(count))
def _parse_total_images_per_channel(self, metadata_dict):
"""
The total number of images per channel. Warning: this may be inaccurate as it includes "gap" images.
:rtype: int
"""
return metadata_dict[six.b('ImageAttributes')][six.b('SLxImageAttributes')][six.b('uiSequenceCount')]
def _parse_metadata(self):
"""
Reads all metadata.
"""
metadata_dict = {}
label_map = self._build_label_map()
for label in label_map.keys():
if label.endswith(six.b("LV!")) or six.b("LV|") in label:
data = self._read_chunk(label_map[label])
stop = label.index(six.b("LV"))
metadata_dict[label[:stop]] = self._read_metadata(data, 1)
channels = self._parse_channels(metadata_dict)
date = self._parse_fields_of_view(metadata_dict)
fields_of_view = self._parse_fields_of_view(metadata_dict)
frames = self._parse_frames(metadata_dict)
z_levels = self._parse_z_levels(metadata_dict)
self._metadata = Metadata(channels, date, fields_of_view, frames, z_levels)
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.
:rtype: dict
"""
label_map = {}
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(V3Parser.CHUNK_MAP_START) + 32
while True:
data_start = raw_text.index(six.b("!"), label_start) + 1
key = raw_text[label_start: data_start]
location, length = struct.unpack("QQ", raw_text[data_start: data_start + 16])
if key == V3Parser.CHUNK_MAP_END:
# We've reached the end of the chunk map
break
label_map[key] = location
label_start = data_start + 16
return label_map
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 != V3Parser.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(six.b("\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 = six.BytesIO(data)
metadata = {}
for _ in range(count):
self._cursor_position = data.tell()
header = data.read(2)
if not header:
# We've reached the end of some hierarchy of data
break
if six.PY3:
header = header.decode("utf8")
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

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