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213 lines
5.9 KiB
213 lines
5.9 KiB
from pims import Frame
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from pims.base_frames import FramesSequenceND
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from nd2reader.exceptions import EmptyFileError, InvalidFileType
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from nd2reader.parser import Parser
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import numpy as np
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class ND2Reader(FramesSequenceND):
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"""PIMS wrapper for the ND2 parser.
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This is the main class: use this to process your .nd2 files.
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"""
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class_priority = 12
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def __init__(self, fh):
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super(ND2Reader, self).__init__()
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self._fh = fh
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self.filename = ""
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self._parser = Parser(self._fh)
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# Setup metadata
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self.metadata = self._parser.metadata
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# Set data type
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self._dtype = self._parser.get_dtype_from_metadata()
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# Setup the axes
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self._setup_axes()
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# Other properties
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self._timesteps = None
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@staticmethod
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def from_file(filename):
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if not filename.endswith(".nd2"):
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raise InvalidFileType("The file %s you want to read with nd2reader does not have extension .nd2." % filename)
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nd2r = ND2Reader(open(filename, "rb"))
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nd2r.filename = filename
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return nd2r
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@classmethod
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def class_exts(cls):
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"""Let PIMS open function use this reader for opening .nd2 files
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"""
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return {'nd2'} | super(ND2Reader, cls).class_exts()
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def close(self):
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"""Correctly close the file handle
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"""
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if self._fh is not None:
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self._fh.close()
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def _get_default(self, coord):
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try:
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return self.default_coords[coord]
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except KeyError:
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return 0
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def get_frame_2D(self, c=0, t=0, z=0, x=0, y=0, v=0):
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"""Gets a given frame using the parser
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Args:
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x: The x-index (pims expects this)
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y: The y-index (pims expects this)
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c: The color channel number
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t: The frame number
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z: The z stack number
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v: The field of view index
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Returns:
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pims.Frame: The requested frame
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"""
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# This needs to be set to width/height to return an image
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x = self.metadata["width"]
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y = self.metadata["height"]
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return self._parser.get_image_by_attributes(t, v, c, z, y, x)
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@property
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def parser(self):
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"""
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Returns the parser object.
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Returns:
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Parser: the parser object
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"""
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return self._parser
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@property
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def pixel_type(self):
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"""Return the pixel data type
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Returns:
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dtype: the pixel data type
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"""
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return self._dtype
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@property
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def timesteps(self):
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"""Get the timesteps of the experiment
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Returns:
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np.ndarray: an array of times in milliseconds.
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"""
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if self._timesteps is None:
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return self.get_timesteps()
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return self._timesteps
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@property
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def events(self):
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"""Get the events of the experiment
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Returns:
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iterator of events as dict
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"""
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return self._get_metadata_property("events")
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@property
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def frame_rate(self):
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"""The (average) frame rate
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Returns:
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float: the (average) frame rate in frames per second
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"""
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total_duration = 0.0
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for loop in self.metadata['experiment']['loops']:
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total_duration += loop['duration']
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if total_duration == 0:
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total_duration = self.timesteps[-1]
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if total_duration == 0:
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raise ValueError('Total measurement duration could not be determined from loops')
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return self.metadata['num_frames'] / (total_duration/1000.0)
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def _get_metadata_property(self, key, default=None):
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if self.metadata is None:
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return default
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if key not in self.metadata:
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return default
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if self.metadata[key] is None:
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return default
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return self.metadata[key]
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def _setup_axes(self):
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"""Setup the xyctz axes, iterate over t axis by default
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"""
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self._init_axis_if_exists('x', self._get_metadata_property("width", default=0))
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self._init_axis_if_exists('y', self._get_metadata_property("height", default=0))
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self._init_axis_if_exists('c', len(self._get_metadata_property("channels", default=[])), min_size=2)
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self._init_axis_if_exists('t', len(self._get_metadata_property("frames", default=[])))
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self._init_axis_if_exists('z', len(self._get_metadata_property("z_levels", default=[])), min_size=2)
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self._init_axis_if_exists('v', len(self._get_metadata_property("fields_of_view", default=[])), min_size=2)
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if len(self.sizes) == 0:
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raise EmptyFileError("No axes were found for this .nd2 file.")
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# provide the default
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self.iter_axes = self._guess_default_iter_axis()
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self._register_get_frame(self.get_frame_2D, 'yx')
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def _init_axis_if_exists(self, axis, size, min_size=1):
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if size >= min_size:
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self._init_axis(axis, size)
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def _guess_default_iter_axis(self):
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"""
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Guesses the default axis to iterate over based on axis sizes.
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Returns:
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the axis to iterate over
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"""
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priority = ['t', 'z', 'c', 'v']
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found_axes = []
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for axis in priority:
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try:
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current_size = self.sizes[axis]
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except KeyError:
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continue
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if current_size > 1:
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return axis
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found_axes.append(axis)
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return found_axes[0]
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def get_timesteps(self):
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"""Get the timesteps of the experiment
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Returns:
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np.ndarray: an array of times in milliseconds.
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"""
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if self._timesteps is not None and len(self._timesteps) > 0:
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return self._timesteps
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self._timesteps = np.array(list(self._parser._raw_metadata.acquisition_times), dtype=np.float) * 1000.0
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return self._timesteps
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