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- set dtype according to pixel type in file

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- cziread bugfix
- add reader for broken files saved by Fiji
- make ndread work for arrays with less dimensions than 5
- relative imports
- remove some old functions
- make bfread check if it can open the file
This commit is contained in:
Wim Pomp
2023-08-14 17:01:03 +02:00
parent 0bd22d766a
commit bdd7a5399c
10 changed files with 143 additions and 78 deletions
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72
ndbioimage/__init__.py
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@@ -17,13 +17,11 @@ from parfor import parfor
from tiffwrite import IJTiffFile
from numbers import Number
from argparse import ArgumentParser
from typing import List
from pathlib import Path
from importlib.metadata import version
from traceback import print_exc
from ndbioimage.transforms import Transform, Transforms
from ndbioimage.jvm import JVM
from .transforms import Transform, Transforms
from .jvm import JVM
try:
__version__ = version(Path(__file__).parent.name)
@@ -42,8 +40,13 @@ ureg.default_format = '~P'
set_application_registry(ureg)
class ReaderNotFoundError(Exception):
pass
class ImTransforms(Transforms):
""" Transforms class with methods to calculate channel transforms from bead files etc. """
def __init__(self, path, cyllens, file=None, transforms=None):
super().__init__()
self.cyllens = cyllens
@@ -179,6 +182,7 @@ class ImTransforms(Transforms):
class ImShiftTransforms(Transforms):
""" Class to handle drift in xy. The image this is applied to must have a channeltransform already, which is then
replaced by this class. """
def __init__(self, im, shifts=None):
""" im: Calculate shifts from channel-transformed images
im, t x 2 array Sets shifts from array, one row per frame
@@ -242,16 +246,19 @@ class ImShiftTransforms(Transforms):
@parfor(range(1, im.shape['t']), (im, im0), desc='Calculating image shifts.')
def fun(t, im, im0):
return Transform(im0, im[:, 0, t].squeeze().transpose(2, 0, 1), 'translation')
transforms = [Transform()] + fun
self.shifts = np.array([t.parameters[4:] for t in transforms])
self.set_transforms(transforms, im.transform)
def calulate_shifts(self, im):
""" Calculate shifts relative to the previous frame """
@parfor(range(1, im.shape['t']), (im,), desc='Calculating image shifts.')
def fun(t, im):
return Transform(im[:, 0, t-1].squeeze().transpose(2, 0, 1), im[:, 0, t].squeeze().transpose(2, 0, 1),
return Transform(im[:, 0, t - 1].squeeze().transpose(2, 0, 1), im[:, 0, t].squeeze().transpose(2, 0, 1),
'translation')
transforms = [Transform()] + fun
self.shifts = np.cumsum([t.parameters[4:] for t in transforms])
self.set_transforms(transforms, im.transform)
@@ -285,40 +292,12 @@ class DequeDict(OrderedDict):
self.__truncate__()
def tolist(item) -> List:
if hasattr(item, 'items'):
return item
elif isinstance(item, str):
return [item]
try:
iter(item)
return list(item)
except TypeError:
return list((item,))
def find(obj, **kwargs):
for item in obj:
if all([getattr(item, key) == value for key, value in kwargs.items()]):
return item
def find_rec(obj, **kwargs):
if isinstance(obj, list):
for item in obj:
ans = find_rec(item, **kwargs)
if ans:
return ans
elif isinstance(obj, ome_types._base_type.OMEType):
if all([hasattr(obj, key) for key in kwargs.keys()]) and all(
[getattr(obj, key) == value for key, value in kwargs.items()]):
return obj
for k, v in obj.__dict__.items():
ans = find_rec(v, **kwargs)
if ans:
return ans
def try_default(fun, default, *args, **kwargs):
try:
return fun(*args, **kwargs)
@@ -326,9 +305,10 @@ def try_default(fun, default, *args, **kwargs):
return default
def ome_subprocess(path):
def get_ome(path):
from .readers.bfread import jars
try:
jvm = JVM()
jvm = JVM(jars)
ome_meta = jvm.metadata_tools.createOMEXMLMetadata()
reader = jvm.image_reader()
reader.setMetadataStore(ome_meta)
@@ -450,7 +430,7 @@ class Imread(np.lib.mixins.NDArrayOperatorsMixin, metaclass=ABCMeta):
def get_ome(path):
""" Use java BioFormats to make an ome metadata structure. """
with multiprocessing.get_context('spawn').Pool(1) as pool:
ome = pool.map(ome_subprocess, (path,))[0]
ome = pool.map(get_ome, (path,))[0]
return ome
def __new__(cls, path=None, *args, **kwargs):
@@ -479,6 +459,7 @@ class Imread(np.lib.mixins.NDArrayOperatorsMixin, metaclass=ABCMeta):
subclass.do_copy = set(do_copy).union(set(subclass_do_copy))
return super().__new__(subclass)
raise ReaderNotFoundError(f'No reader found for {path}.')
@staticmethod
def split_path_series(path):
@@ -507,7 +488,6 @@ class Imread(np.lib.mixins.NDArrayOperatorsMixin, metaclass=ABCMeta):
self.transform = transform
self.drift = drift
self.beadfile = beadfile
self.dtype = dtype
self.reader = None
self.pcf = None
@@ -523,14 +503,13 @@ class Imread(np.lib.mixins.NDArrayOperatorsMixin, metaclass=ABCMeta):
self.frameoffset = 0, 0 # how far apart the centers of frame and sensor are
self.flags = dict(C_CONTIGUOUS=False, F_CONTIGUOUS=False, OWNDATA=False, WRITEABLE=False,
ALIGNED=False, WRITEBACKIFCOPY=False, UPDATEIFCOPY=False)
self.open()
# extract some metadata from ome
instrument = self.ome.instruments[0] if self.ome.instruments else None
image = self.ome.images[0]
pixels = image.pixels
self.shape = pixels.size_x, pixels.size_y, pixels.size_c, pixels.size_z, pixels.size_t
self.dtype = pixels.type.value if dtype is None else dtype
self.pxsize = pixels.physical_size_x_quantity
try:
self.exposuretime = tuple(find(image.pixels.planes, the_c=c).exposure_time_quantity
@@ -556,7 +535,7 @@ class Imread(np.lib.mixins.NDArrayOperatorsMixin, metaclass=ABCMeta):
try:
self.binning = [int(i) for i in image.pixels.channels[0].detector_settings.binning.value.split('x')]
self.pxsize *= self.binning[0]
except (AttributeError, IndexError):
except (AttributeError, IndexError, ValueError):
self.binning = None
self.cnamelist = [channel.name for channel in image.pixels.channels]
try:
@@ -718,6 +697,7 @@ class Imread(np.lib.mixins.NDArrayOperatorsMixin, metaclass=ABCMeta):
s = [f"path/filename: {self.path}",
f"series/pos: {self.series}",
f"reader: {self.__class__.__module__.split('.')[-1]}",
f"dtype: {self.dtype}",
f"shape ({self.axes}):".ljust(15) + f"{' x '.join(str(i) for i in self.shape)}"]
if self.pxsize_um:
s.append(f'pixel size: {1000 * self.pxsize_um:.2f} nm')
@@ -730,10 +710,10 @@ class Imread(np.lib.mixins.NDArrayOperatorsMixin, metaclass=ABCMeta):
if self.binning:
s.append('binning: {}x{}'.format(*self.binning))
if self.laserwavelengths:
s.append('laser colors: ' + ' | '.join([' & '.join(len(w)*('{:.0f}',)).format(*w)
s.append('laser colors: ' + ' | '.join([' & '.join(len(w) * ('{:.0f}',)).format(*w)
for w in self.laserwavelengths]) + ' nm')
if self.laserpowers:
s.append('laser powers: ' + ' | '.join([' & '.join(len(p)*('{:.3g}',)).format(*[100 * i for i in p])
s.append('laser powers: ' + ' | '.join([' & '.join(len(p) * ('{:.3g}',)).format(*[100 * i for i in p])
for p in self.laserpowers]) + ' %')
if self.objective:
s.append('objective: {}'.format(self.objective.model))
@@ -819,6 +799,7 @@ class Imread(np.lib.mixins.NDArrayOperatorsMixin, metaclass=ABCMeta):
for k in b:
if k not in a:
yield k
for idx in unique_yield(list(self.cache.keys()),
product(range(self.shape['c']), range(self.shape['z']), range(self.shape['t']))):
xyczt = (slice(None), slice(None)) + idx
@@ -1226,6 +1207,7 @@ class Imread(np.lib.mixins.NDArrayOperatorsMixin, metaclass=ABCMeta):
@cached_property
def piezoval(self):
""" gives the height of the piezo and focus motor, only available when CylLensGUI was used """
def upack(idx):
time = list()
val = list()
@@ -1291,7 +1273,7 @@ class Imread(np.lib.mixins.NDArrayOperatorsMixin, metaclass=ABCMeta):
return
return
def save_as_tiff(self, fname=None, c=None, z=None, t=None, split=False, bar=True, pixel_type='uint16'):
def save_as_tiff(self, fname=None, c=None, z=None, t=None, split=False, bar=True, pixel_type='uint16', **kwargs):
""" saves the image as a tiff-file
split: split channels into different files """
if fname is None:
@@ -1319,7 +1301,7 @@ class Imread(np.lib.mixins.NDArrayOperatorsMixin, metaclass=ABCMeta):
shape = [len(i) for i in n]
at_least_one = False
with IJTiffFile(fname.with_suffix('.tif'), shape, pixel_type,
pxsize=self.pxsize_um, deltaz=self.deltaz_um) as tif:
pxsize=self.pxsize_um, deltaz=self.deltaz_um, **kwargs) as tif:
for i, m in tqdm(zip(product(*[range(s) for s in shape]), product(*n)),
total=np.prod(shape), desc='Saving tiff', disable=not bar):
if np.any(self(*m)) or not at_least_one:
@@ -1353,4 +1335,4 @@ def main():
im.save_as_tiff(out, args.channel, args.zslice, args.time, args.split)
from ndbioimage.readers import *
from .readers import *