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.
 
 
 

164 lines
4.7 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
"""
c_name = self.metadata["channels"][c]
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=[])))
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=[])))
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):
if size > 0:
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']]