testtest/nd2reader/reader.py

from pims import Frame
from pims.base_frames import FramesSequenceND

from nd2reader.exceptions import EmptyFileError, InvalidFileType
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.
    """

    _fh = None
    class_priority = 12

    def __init__(self, fh):
        """
        Arguments:
            fh {str} -- absolute path to .nd2 file
            fh {IO} -- input buffer handler (opened with "rb" mode)
        """
        super(ND2Reader, self).__init__()

        self.filename = ""

        if isinstance(fh, str):
            if not fh.endswith(".nd2"):
                raise InvalidFileType(
                    ("The file %s you want to read with nd2reader" % fh)
                    + " does not have extension .nd2."
                )
            self.filename = fh
            fh = open(fh, "rb")

        self._fh = fh

        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