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from pims.base_frames import FramesSequenceND
from nd2reader.exceptions import EmptyFileError
from nd2reader.parser import Parser
import numpy as np
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