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