from pims import FramesSequenceND, Frame from nd2reader.parser import Parser import numpy as np class ND2Reader(FramesSequenceND): """PIMS wrapper for the ND2 parser """ def __init__(self, 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() @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_frame(self, i): """Return one frame """ fetch_all_channels = 'c' in self.bundle_axes if fetch_all_channels: return self._get_frame_all_channels(i) else: return self.get_frame_2D(self.default_coords['c'], i, self.default_coords['z']) def _get_frame_all_channels(self, i): """Get all color channels for this frame """ frames = None for c in range(len(self.metadata["channels"])): frame = self.get_frame_2D(c, i, self.default_coords['z']) if frames is None: frames = Frame([frame]) else: frames = np.concatenate((frames, [frame]), axis=0) return frames def get_frame_2D(self, c, t, z): """Gets a given frame using the parser """ c_name = self.metadata["channels"][c] return self._parser.get_image_by_attributes(t, 0, c_name, z, self.metadata["height"], self.metadata["width"]) @property def pixel_type(self): """Return the pixel data type """ return self._dtype def _setup_axes(self): """Setup the xyctz axes, iterate over t axis by default """ self._init_axis('x', self.metadata["width"]) self._init_axis('y', self.metadata["height"]) self._init_axis('c', len(self.metadata["channels"])) self._init_axis('t', len(self.metadata["frames"])) self._init_axis('z', len(self.metadata["z_levels"])) # provide the default self.iter_axes = 't' def get_timesteps(self): """Get the timesteps of the experiment """ timesteps = np.array([]) current_time = 0.0 for loop in self.metadata['experiment']['loops']: if loop['stimulation']: 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. return timesteps[:self.metadata['num_frames']]