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196 lines
5.5 KiB
196 lines
5.5 KiB
from pims import FramesSequenceND, Frame
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from nd2reader.exceptions import EmptyFileError
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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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def __init__(self, filename):
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super(self.__class__, self).__init__()
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self.filename = filename
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# first use the parser to parse the file
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self._fh = open(filename, "rb")
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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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@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):
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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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Returns:
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numpy.ndarray: The requested frame
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"""
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try:
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c_name = self.metadata["channels"][c]
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except KeyError:
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c_name = self.metadata["channels"][0]
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x = self.metadata["width"] if x <= 0 else x
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y = self.metadata["height"] if y <= 0 else y
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return self._parser.get_image_by_attributes(t, 0, c_name, 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 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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return 1000. / np.mean(np.diff(self.timesteps))
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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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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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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']
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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:
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return self._timesteps
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timesteps = np.array([])
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current_time = 0.0
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for loop in self.metadata['experiment']['loops']:
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if loop['stimulation']:
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continue
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if loop['sampling_interval'] == 0:
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# This is a loop were no data is acquired
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current_time += loop['duration']
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continue
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timesteps = np.concatenate(
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(timesteps, np.arange(current_time, current_time + loop['duration'], loop['sampling_interval'])))
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current_time += loop['duration']
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# if experiment did not finish, number of timesteps is wrong. Take correct amount of leading timesteps.
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self._timesteps = timesteps[:self.metadata['num_frames']]
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return self._timesteps
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