class ND2Reader(FramesSequenceND):
"""PIMS wrapper for the ND2 parser.
This is the main class: use this to process your .nd2 files.
"""
class_priority = 12
def __init__(self, filename):
super(ND2Reader, self).__init__()
if not filename.endswith(".nd2"):
raise InvalidFileType("The file %s you want to read with nd2reader does not have extension .nd2." % filename)
self.filename = filename
# first use the parser to parse the file
self._fh = open(filename, "rb")
self._parser = Parser(self._fh)
# Setup metadata
self.metadata = self._parser.metadata
# Set data type
self._dtype = self._parser.get_dtype_from_metadata()
# Setup the axes
self._setup_axes()
# Other properties
self._timesteps = None
@classmethod
def class_exts(cls):
"""Let PIMS open function use this reader for opening .nd2 files
"""
return {'nd2'} | super(ND2Reader, cls).class_exts()
def close(self):
"""Correctly close the file handle
"""
if self._fh is not None:
self._fh.close()
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:
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:
pims.Frame: The requested frame
"""
# This needs to be set to width/height to return an image
x = self.metadata["width"]
y = self.metadata["height"]
return self._parser.get_image_by_attributes(t, v, c, z, y, x)
@property
def parser(self):
"""
Returns the parser object.
Returns:
Parser: the parser object
"""
return self._parser
@property
def pixel_type(self):
"""Return the pixel data type
Returns:
dtype: the pixel data type
"""
return self._dtype
@property
def timesteps(self):
"""Get the timesteps of the experiment
Returns:
np.ndarray: an array of times in milliseconds.
"""
if self._timesteps is None:
return self.get_timesteps()
return self._timesteps
@property
def events(self):
"""Get the events of the experiment
Returns:
iterator of events as dict
"""
return self._get_metadata_property("events")
@property
def frame_rate(self):
"""The (average) frame rate
Returns:
float: the (average) frame rate in frames per second
"""
total_duration = 0.0
for loop in self.metadata['experiment']['loops']:
total_duration += loop['duration']
if total_duration == 0:
total_duration = self.timesteps[-1]
if total_duration == 0:
raise ValueError('Total measurement duration could not be determined from loops')
return self.metadata['num_frames'] / (total_duration/1000.0)
def _get_metadata_property(self, key, default=None):
if self.metadata is None:
return default
if key not in self.metadata:
return default
if self.metadata[key] is None:
return default
return self.metadata[key]
def _setup_axes(self):
"""Setup the xyctz axes, iterate over t axis by default
"""
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)
if len(self.sizes) == 0:
raise EmptyFileError("No axes were found for this .nd2 file.")
# provide the default
self.iter_axes = self._guess_default_iter_axis()
self._register_get_frame(self.get_frame_2D, 'yx')
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):
"""
Guesses the default axis to iterate over based on axis sizes.
Returns:
the axis to iterate over
"""
priority = ['t', 'z', 'c', 'v']
found_axes = []
for axis in priority:
try:
current_size = self.sizes[axis]
except KeyError:
continue
if current_size > 1:
return axis
found_axes.append(axis)
return found_axes[0]
def get_timesteps(self):
"""Get the timesteps of the experiment
Returns:
np.ndarray: an array of times in milliseconds.
"""
if self._timesteps is not None and len(self._timesteps) > 0:
return self._timesteps
self._timesteps = np.array(list(self._parser._raw_metadata.acquisition_times), dtype=np.float) * 1000.0
return self._timesteps
class ND2Reader(FramesSequenceND):
"""PIMS wrapper for the ND2 parser.
This is the main class: use this to process your .nd2 files.
"""
class_priority = 12
def __init__(self, filename):
super(self.__class__, self).__init__()
self.filename = filename
# first use the parser to parse the file
self._fh = open(filename, "rb")
self._parser = Parser(self._fh)
# Setup metadata
self.metadata = self._parser.metadata
# Set data type
self._dtype = self._parser.get_dtype_from_metadata()
# Setup the axes
self._setup_axes()
# Other properties
self._timesteps = None
@classmethod
def class_exts(cls):
"""Let PIMS open function use this reader for opening .nd2 files
"""
return {'nd2'} | super(ND2Reader, cls).class_exts()
def close(self):
"""Correctly close the file handle
"""
if self._fh is not None:
self._fh.close()
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:
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:
numpy.ndarray: The requested frame
"""
try:
c_name = self.metadata["channels"][c]
except KeyError:
c_name = self.metadata["channels"][0]
x = self.metadata["width"] if x <= 0 else x
y = self.metadata["height"] if y <= 0 else y
return self._parser.get_image_by_attributes(t, v, c_name, z, y, x)
@property
def parser(self):
"""
Returns the parser object.
Returns:
Parser: the parser object
"""
return self._parser
@property
def pixel_type(self):
"""Return the pixel data type
Returns:
dtype: the pixel data type
"""
return self._dtype
@property
def timesteps(self):
"""Get the timesteps of the experiment
Returns:
np.ndarray: an array of times in milliseconds.
"""
if self._timesteps is None:
return self.get_timesteps()
return self._timesteps
@property
def frame_rate(self):
"""The (average) frame rate
Returns:
float: the (average) frame rate in frames per second
"""
return 1000. / np.mean(np.diff(self.timesteps))
def _get_metadata_property(self, key, default=None):
if self.metadata is None:
return default
if key not in self.metadata:
return default
if self.metadata[key] is None:
return default
return self.metadata[key]
def _setup_axes(self):
"""Setup the xyctz axes, iterate over t axis by default
"""
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)
if len(self.sizes) == 0:
raise EmptyFileError("No axes were found for this .nd2 file.")
# provide the default
self.iter_axes = self._guess_default_iter_axis()
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):
"""
Guesses the default axis to iterate over based on axis sizes.
Returns:
the axis to iterate over
"""
priority = ['t', 'z', 'c', 'v']
found_axes = []
for axis in priority:
try:
current_size = self.sizes[axis]
except KeyError:
continue
if current_size > 1:
return axis
found_axes.append(axis)
return found_axes[0]
def get_timesteps(self):
"""Get the timesteps of the experiment
Returns:
np.ndarray: an array of times in milliseconds.
"""
if self._timesteps is not None and len(timesteps) > 0:
return self._timesteps
timesteps = np.array([])
current_time = 0.0
for loop in self.metadata['experiment']['loops']:
if loop['stimulation']:
continue
if loop['sampling_interval'] == 0:
# This is a loop were no data is acquired
current_time += loop['duration']
continue
timesteps = np.concatenate(
(timesteps,
np.arange(current_time, current_time + loop['duration'],
loop['sampling_interval'])))
current_time += loop['duration']
# if experiment did not finish, number of timesteps is wrong. Take correct amount of leading timesteps.
self._timesteps = timesteps[:self.metadata['num_frames']]
return self._timesteps
class ND2Reader(FramesSequenceND):
"""PIMS wrapper for the ND2 parser.
This is the main class: use this to process your .nd2 files.
"""
class_priority = 12
def __init__(self, filename):
super(self.__class__, self).__init__()
self.filename = filename
# first use the parser to parse the file
self._fh = open(filename, "rb")
self._parser = Parser(self._fh)
# Setup metadata
self.metadata = self._parser.metadata
# Set data type
self._dtype = self._parser.get_dtype_from_metadata()
# Setup the axes
self._setup_axes()
# Other properties
self._timesteps = None
@classmethod
def class_exts(cls):
"""Let PIMS open function use this reader for opening .nd2 files
"""
return {'nd2'} | super(ND2Reader, cls).class_exts()
def close(self):
"""Correctly close the file handle
"""
if self._fh is not None:
self._fh.close()
def _get_default(self, coord):
try:
return self.default_coords[coord]
except KeyError:
return 0
def get_frame_vczyx(self, v=None, c=None, t=None, z=None, x=None, y=None):
x = self.metadata["width"] if x <= 0 else x
y = self.metadata["height"] if y <= 0 else y
result = []
for v in self._get_possible_coords('v', v):
for c in self._get_possible_coords('c', c):
for z in self._get_possible_coords('z', z):
result.append(
self._parser.get_image_by_attributes(t, v, c, z, y, x))
return np.squeeze(np.array(result, dtype=self._dtype))
def _get_possible_coords(self, dim, default):
if dim in self.sizes:
return [default] if default is not None else range(self.sizes[dim])
return [None]
@property
def parser(self):
"""
Returns the parser object.
Returns:
Parser: the parser object
"""
return self._parser
@property
def pixel_type(self):
"""Return the pixel data type
Returns:
dtype: the pixel data type
"""
return self._dtype
@property
def timesteps(self):
"""Get the timesteps of the experiment
Returns:
np.ndarray: an array of times in milliseconds.
"""
if self._timesteps is None:
return self.get_timesteps()
return self._timesteps
@property
def events(self):
"""Get the events of the experiment
Returns:
iterator of events as dict
"""
return self._get_metadata_property("events")
@property
def frame_rate(self):
"""The (average) frame rate
Returns:
float: the (average) frame rate in frames per second
"""
total_duration = 0.0
for loop in self.metadata['experiment']['loops']:
total_duration += loop['duration']
if total_duration == 0:
raise ValueError(
'Total measurement duration could not be determined from loops'
)
return self.metadata['num_frames'] / (total_duration / 1000.0)
def _get_metadata_property(self, key, default=None):
if self.metadata is None:
return default
if key not in self.metadata:
return default
if self.metadata[key] is None:
return default
return self.metadata[key]
def _setup_axes(self):
"""Setup the xyctz axes, iterate over t axis by default
"""
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)
if len(self.sizes) == 0:
raise EmptyFileError("No axes were found for this .nd2 file.")
# provide the default
self.iter_axes = self._guess_default_iter_axis()
self._register_get_frame(self.get_frame_vczyx, 'vczyx')
self._register_get_frame(self.get_frame_vczyx, 'vzyx')
self._register_get_frame(self.get_frame_vczyx, 'vcyx')
self._register_get_frame(self.get_frame_vczyx, 'vyx')
self._register_get_frame(self.get_frame_vczyx, 'czyx')
self._register_get_frame(self.get_frame_vczyx, 'cyx')
self._register_get_frame(self.get_frame_vczyx, 'zyx')
self._register_get_frame(self.get_frame_vczyx, 'yx')
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):
"""
Guesses the default axis to iterate over based on axis sizes.
Returns:
the axis to iterate over
"""
priority = ['t', 'z', 'c', 'v']
found_axes = []
for axis in priority:
try:
current_size = self.sizes[axis]
except KeyError:
continue
if current_size > 1:
return axis
found_axes.append(axis)
return found_axes[0]
def get_timesteps(self):
"""Get the timesteps of the experiment
Returns:
np.ndarray: an array of times in milliseconds.
"""
if self._timesteps is not None and len(self._timesteps) > 0:
return self._timesteps
self._timesteps = np.array(list(
self._parser._raw_metadata.acquisition_times),
dtype=np.float) * 1000.0
return self._timesteps
class ND2Reader:
"""PIMS wrapper for the ND2 parser.
This is the main class: use this to process your .nd2 files.
"""
class_priority = 12
def __init__(self, filename, memmap=False):
super(self.__class__, self).__init__()
self.filename = filename
# first use the parser to parse the file
self._fh = MemmappableFile(self.filename, memmap=memmap)
self._parser = Parser(self._fh)
# Set data type
self._dtype = self._parser.get_dtype_from_metadata()
# Setup the axes
# self._setup_axes()
# Other properties
# self._timesteps = None
def reopen(self):
# TODO: this is incredibly clunky
fh = MemmappableFile(self.filename, memmap=self._fh.is_memmap)
reader = copy(self)
reader._fh = fh
parser = copy(self._parser)
parser._fh = fh
raw_metadata = copy(parser._raw_metadata)
raw_metadata._fh = fh
parser._raw_metadata = raw_metadata
reader._parser = parser
return reader
# @classmethod
# def class_exts(cls):
# """Let PIMS open function use this reader for opening .nd2 files
# """
# return {"nd2"} | super(ND2Reader, cls).class_exts()
def close(self):
"""Correctly close the file handle"""
if self._fh is not None:
self._fh.close()
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, memmap=False):
"""Gets a given frame using the parser
Args:
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:
numpy.ndarray: The requested frame
"""
try:
c_name = self.metadata["channels"][c]
except KeyError:
c_name = self.metadata["channels"][0]
x = self.metadata["width"] if x <= 0 else x
y = self.metadata["height"] if y <= 0 else y
return self._parser.get_image_by_attributes(
t, v, c_name, z, y, x, memmap=memmap
)
@property
def parser(self):
"""
Returns the parser object.
Returns:
Parser: the parser object
"""
return self._parser
@property
def metadata(self):
return self._parser.metadata
# @property
# def pixel_type(self):
# """Return the pixel data type
# Returns:
# dtype: the pixel data type
# """
# return self._dtype
# @property
# def timesteps(self):
# """Get the timesteps of the experiment
# Returns:
# np.ndarray: an array of times in milliseconds.
# """
# if self._timesteps is None:
# return self.get_timesteps()
# return self._timesteps
# @property
# def frame_rate(self):
# """The (average) frame rate
# Returns:
# float: the (average) frame rate in frames per second
# """
# total_duration = 0.0
# for loop in self.metadata["experiment"]["loops"]:
# total_duration += loop["duration"]
# if total_duration == 0:
# raise ValueError(
# "Total measurement duration could not be determined from loops"
# )
# return self.metadata["num_frames"] / (total_duration / 1000.0)
@property
def sizes(self):
return {
"x": self.metadata["height"],
"y": self.metadata["width"],
"c": len(self.metadata["channels"]),
"v": len(self.metadata["fields_of_view"]),
"t": len(self.metadata["frames"]),
}
def _get_metadata_property(self, key, default=None):
if self.metadata is None:
return default
if key not in self.metadata:
return default
if self.metadata[key] is None:
return default
return self.metadata[key]
# def _setup_axes(self):
# """Setup the xyctz axes, iterate over t axis by default
# """
# 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,
# )
# if len(self.sizes) == 0:
# raise EmptyFileError("No axes were found for this .nd2 file.")
# # provide the default
# self.iter_axes = self._guess_default_iter_axis()
# 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):
# """
# Guesses the default axis to iterate over based on axis sizes.
# Returns:
# the axis to iterate over
# """
# priority = ["t", "z", "c", "v"]
# found_axes = []
# for axis in priority:
# try:
# current_size = self.sizes[axis]
# except KeyError:
# continue
# if current_size > 1:
# return axis
# found_axes.append(axis)
# return found_axes[0]
def get_timesteps(self):
"""Get the timesteps of the experiment
Returns:
np.ndarray: an array of times in milliseconds.
"""
if self._timesteps is not None and len(self._timesteps) > 0:
return self._timesteps
self._timesteps = (
np.array(list(self._parser._raw_metadata.acquisition_times), dtype=np.float)
* 1000.0
)
return self._timesteps