You can not select more than 25 topics
Topics must start with a letter or number, can include dashes ('-') and can be up to 35 characters long.
168 lines
4.8 KiB
168 lines
4.8 KiB
from pims import FramesSequenceND, Frame
|
|
|
|
from nd2reader.exceptions import EmptyFileError
|
|
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.
|
|
"""
|
|
|
|
def __init__(self, filename):
|
|
super(self.__class__, self).__init__()
|
|
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_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):
|
|
"""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
|
|
|
|
Returns:
|
|
numpy.ndarray: The requested frame
|
|
|
|
"""
|
|
try:
|
|
c_name = self.metadata["channels"][c]
|
|
except KeyError:
|
|
c_name = self.metadata["channels"][0]
|
|
|
|
x = self.metadata["width"] if x <= 0 else x
|
|
y = self.metadata["height"] if y <= 0 else y
|
|
return self._parser.get_image_by_attributes(t, 0, c_name, 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
|
|
|
|
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=[])), min_size=2)
|
|
self._init_axis_if_exists('z', len(self._get_metadata_property("z_levels", 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()
|
|
|
|
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']
|
|
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.
|
|
|
|
"""
|
|
timesteps = np.array([])
|
|
current_time = 0.0
|
|
for loop in self.metadata['experiment']['loops']:
|
|
if loop['stimulation']:
|
|
continue
|
|
|
|
if loop['sampling_interval'] == 0:
|
|
# This is a loop were no data is acquired
|
|
current_time += loop['duration']
|
|
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']]
|