diff --git a/core/lls_core/cmds/__main__.py b/core/lls_core/cmds/__main__.py
index c5560d3d..89dc766a 100644
--- a/core/lls_core/cmds/__main__.py
+++ b/core/lls_core/cmds/__main__.py
@@ -36,20 +36,17 @@ class CliDeskewDirection(StrEnum):
"input_image": ["input_image"],
"angle": ["angle"],
"skew": ["skew"],
- "pixel_sizes": ["physical_pixel_sizes"],
- "rois": ["crop", "roi_list"],
- "roi_indices": ["crop", "roi_subset"],
- "z_start": ["crop", "z_range", 0],
- "z_end": ["crop", "z_range", 1],
+ "physical_pixel_sizes": ["physical_pixel_sizes"],
+ "roi_list": ["crop", "roi_list"],
+ "roi_subset": ["crop", "roi_subset"],
+ "z_range": ["crop", "z_range"],
"decon_processing": ["deconvolution", "decon_processing"],
"psf": ["deconvolution", "psf"],
- "psf_num_iter": ["deconvolution", "psf_num_iter"],
+ "decon_num_iter": ["deconvolution", "decon_num_iter"],
"background": ["deconvolution", "background"],
"workflow": ["workflow"],
- "time_start": ["time_range", 0],
- "time_end": ["time_range", 1],
- "channel_start": ["channel_range", 0],
- "channel_end": ["channel_range", 1],
+ "time_range": ["time_range"],
+ "channel_range": ["channel_range"],
"save_dir": ["save_dir"],
"save_name": ["save_name"],
"save_type": ["save_type"],
@@ -144,35 +141,30 @@ def process(
input_image: Path = Argument(None, help="Path to the image file to read, in a format readable by AICSImageIO, for example .tiff or .czi", show_default=False),
skew: CliDeskewDirection = field_from_model(DeskewParams, "skew"),# DeskewParams.make_typer_field("skew"),
angle: float = field_from_model(DeskewParams, "angle") ,
- pixel_sizes: Tuple[float, float, float] = field_from_model(DeskewParams, "physical_pixel_sizes", extra_description="This takes three arguments, corresponding to the Z, Y and X pixel dimensions respectively", default=(
+ physical_pixel_sizes: Tuple[float, float, float] = field_from_model(DeskewParams, "physical_pixel_sizes", extra_description="This takes three arguments, corresponding to the Z, Y and X pixel dimensions respectively", default=(
DefinedPixelSizes.get_default("Z"),
DefinedPixelSizes.get_default("Y"),
DefinedPixelSizes.get_default("X")
)),
- rois: List[Path] = field_from_model(CropParams, "roi_list", description="A list of paths pointing to regions of interest to crop to, in ImageJ format."), #Option([], help="A list of paths pointing to regions of interest to crop to, in ImageJ format."),
- roi_indices: List[int] = field_from_model(CropParams, "roi_subset"),
- # Ideally this and other range values would be defined as Tuples, but these seem to be broken: https://github.com/tiangolo/typer/discussions/667
- z_start: Optional[int] = Option(0, help="The index of the first Z slice to use. All prior Z slices will be discarded.", show_default=False),
- z_end: Optional[int] = Option(None, help="The index of the last Z slice to use. The selected index and all subsequent Z slices will be discarded. Defaults to the last z index of the image.", show_default=False),
-
+ roi_list: List[Path] = field_from_model(CropParams, "roi_list"),
+ roi_subset: List[int] = field_from_model(CropParams, "roi_subset"),
+ z_range: Optional[Tuple[int,int]] = field_from_model(CropParams, "z_range", show_default=False),
+
enable_deconvolution: bool = Option(False, "--deconvolution/--disable-deconvolution", rich_help_panel="Deconvolution"),
decon_processing: DeconvolutionChoice = field_from_model(DeconvolutionParams, "decon_processing", rich_help_panel="Deconvolution"),
- psf: List[Path] = field_from_model(DeconvolutionParams, "psf", description="One or more paths pointing to point spread functions to use for deconvolution. Each file should in a standard image format (.czi, .tiff etc), containing a 3D image array. This option can be used multiple times to provide multiple PSF files.", rich_help_panel="Deconvolution"),
- psf_num_iter: int = field_from_model(DeconvolutionParams, "psf_num_iter", rich_help_panel="Deconvolution"),
+ psf: List[Path] = field_from_model(DeconvolutionParams, "psf", rich_help_panel="Deconvolution"),
+ decon_num_iter: int = field_from_model(DeconvolutionParams, "decon_num_iter", rich_help_panel="Deconvolution"),
background: str = field_from_model(DeconvolutionParams, "background", rich_help_panel="Deconvolution"),
- time_start: Optional[int] = Option(0, help="Index of the first time slice to use (inclusive). Defaults to the first time index of the image.", rich_help_panel="Output"),
- time_end: Optional[int] = Option(None, help="Index of the first time slice to use (exclusive). Defaults to the last time index of the image.", show_default=False, rich_help_panel="Output"),
-
- channel_start: Optional[int] = Option(0, help="Index of the first channel slice to use (inclusive). Defaults to the first channel index of the image.", rich_help_panel="Output"),
- channel_end: Optional[int] = Option(None, help="Index of the first channel slice to use (exclusive). Defaults to the last channel index of the image.", show_default=False, rich_help_panel="Output"),
-
+ time_range: Optional[Tuple[int,int]] = field_from_model(OutputParams, "time_range", rich_help_panel="Output"),
+ channel_range: Optional[Tuple[int,int]] = field_from_model(OutputParams,"channel_range", rich_help_panel="Output"),
+
save_dir: Path = field_from_model(OutputParams, "save_dir", rich_help_panel="Output"),
save_name: Optional[str] = field_from_model(OutputParams, "save_name", rich_help_panel="Output"),
save_type: SaveFileType = field_from_model(OutputParams, "save_type", rich_help_panel="Output"),
- workflow: Optional[Path] = Option(None, help="Path to a Napari Workflow file, in YAML format. If provided, the configured desekewing processing will be added to the chosen workflow.", show_default=False),
+ workflow: Optional[Path] = field_from_model(LatticeData, "workflow", show_default=False),
json_config: Optional[Path] = Option(None, show_default=False, help="Path to a JSON file from which parameters will be read."),
yaml_config: Optional[Path] = Option(None, show_default=False, help="Path to a YAML file from which parameters will be read."),
diff --git a/core/lls_core/cropping.py b/core/lls_core/cropping.py
index 2f0c35a9..f57f2894 100644
--- a/core/lls_core/cropping.py
+++ b/core/lls_core/cropping.py
@@ -43,7 +43,7 @@ def read_imagej_roi(roi_path: PathLike) -> List[Roi]:
if roi_path.suffix == ".zip":
ij_roi = read_roi_zip(roi_path)
elif roi_path.suffix == ".roi":
- ij_roi = read_roi_file(roi_path)
+ ij_roi = read_roi_file(str(roi_path))
else:
raise Exception("ImageJ ROI file needs to be a zip/roi file")
diff --git a/core/lls_core/models/deconvolution.py b/core/lls_core/models/deconvolution.py
index c628afe8..6bfc32ab 100644
--- a/core/lls_core/models/deconvolution.py
+++ b/core/lls_core/models/deconvolution.py
@@ -22,7 +22,7 @@ class DeconvolutionParams(FieldAccessModel):
default=[],
description="List of Point Spread Functions to use for deconvolution. Each of which should be a 3D array. Each PSF can also be provided as a `str` path, in which case they will be loaded from disk as images."
)
- psf_num_iter: NonNegativeInt = Field(
+ decon_num_iter: NonNegativeInt = Field(
default=10,
description="Number of iterations to perform in deconvolution"
)
diff --git a/core/lls_core/models/deskew.py b/core/lls_core/models/deskew.py
index d432443f..de741b47 100644
--- a/core/lls_core/models/deskew.py
+++ b/core/lls_core/models/deskew.py
@@ -191,7 +191,7 @@ def read_image(cls, values: dict):
# If the image was convertible to AICSImage, we should use the metadata from there
if aics:
- values["input_image"] = aics.xarray_dask_data
+ values["input_image"] = aics.xarray_dask_data
# Take pixel sizes from the image metadata, but only if they're defined
# and only if we don't already have them
if all(size is not None for size in aics.physical_pixel_sizes) and values.get("physical_pixel_sizes") is None:
diff --git a/core/lls_core/models/lattice_data.py b/core/lls_core/models/lattice_data.py
index f1322d84..d7e2c98b 100644
--- a/core/lls_core/models/lattice_data.py
+++ b/core/lls_core/models/lattice_data.py
@@ -61,6 +61,7 @@ def read_image(cls, values: dict):
from lls_core.types import is_pathlike
from pathlib import Path
input_image = values.get("input_image")
+ logger.info(f"Processing File {input_image}") # this is handy for debugging
if is_pathlike(input_image):
if values.get("save_name") is None:
values["save_name"] = Path(values["input_image"]).stem
@@ -76,6 +77,21 @@ def read_image(cls, values: dict):
# Use the Deskew version of this validator, to do the actual image loading
return super().read_image(values)
+ @validator("input_image", pre=True, always=True)
+ def incomplete_final_frame(cls, v: DataArray) -> Any:
+ """
+ Check final frame, if acquisition is stopped halfway through it causes failures
+ This validator will remove a bad final frame
+ """
+ final_frame = v.isel(T=-1,C=-1, drop=True)
+ try:
+ final_frame.compute()
+ except ValueError:
+ logger.warning("Final frame is borked. Acquisition probably stopped prematurely. Removing final frame.")
+ v = v.drop_isel(T=-1)
+ return v
+
+
@validator("workflow", pre=True)
def parse_workflow(cls, v: Any):
# Load the workflow from disk if it was provided as a path
@@ -336,24 +352,6 @@ def generate_workflows(
# The user can use any of these arguments as inputs to their tasks
yield lattice_slice.copy_with_data(user_workflow)
- def check_incomplete_acquisition(self, volume: ArrayLike, time_point: int, channel: int):
- """
- Checks for a slice with incomplete data, caused by incomplete acquisition
- """
- import numpy as np
- if not isinstance(volume, DaskArray):
- return volume
- orig_shape = volume.shape
- raw_vol = volume.compute()
- if raw_vol.shape != orig_shape:
- logger.warn(f"Time {time_point}, channel {channel} is incomplete. Actual shape {orig_shape}, got {raw_vol.shape}")
- z_diff, y_diff, x_diff = np.subtract(orig_shape, raw_vol.shape)
- logger.info(f"Padding with{z_diff,y_diff,x_diff}")
- raw_vol = np.pad(raw_vol, ((0, z_diff), (0, y_diff), (0, x_diff)))
- if raw_vol.shape != orig_shape:
- raise Exception(f"Shape of last timepoint still doesn't match. Got {raw_vol.shape}")
- return raw_vol
-
@property
def deskewed_volume(self) -> DaskArray:
from dask.array import zeros
@@ -372,7 +370,7 @@ def _process_crop(self) -> Iterable[ImageSlice]:
deconv_args: dict[Any, Any] = {}
if self.deconvolution is not None:
deconv_args = dict(
- num_iter = self.deconvolution.psf_num_iter,
+ num_iter = self.deconvolution.decon_num_iter,
psf = self.deconvolution.psf[slice.channel].to_numpy(),
decon_processing=self.deconvolution.decon_processing
)
@@ -417,13 +415,13 @@ def _process_non_crop(self) -> Iterable[ImageSlice]:
dxdata=self.dx,
dzpsf=self.dz,
dxpsf=self.dx,
- num_iter=self.deconvolution.psf_num_iter
+ num_iter=self.deconvolution.decon_num_iter
)
else:
data = skimage_decon(
vol_zyx=data,
psf=self.deconvolution.psf[slice.channel].to_numpy(),
- num_iter=self.deconvolution.psf_num_iter,
+ num_iter=self.deconvolution.decon_num_iter,
clip=False,
filter_epsilon=0,
boundary='nearest'
diff --git a/core/tests/test_arg_parser.py b/core/tests/test_arg_parser.py
index 26bb1e20..1505be06 100644
--- a/core/tests/test_arg_parser.py
+++ b/core/tests/test_arg_parser.py
@@ -10,9 +10,9 @@ def test_voxel_parsing():
parser = command.make_parser(ctx)
args, _, _ = parser.parse_args(args=[
"process",
- "input",
+ "input-image",
"--save-name", "output",
"--save-type", "tiff",
- "--pixel-sizes", "1", "1", "1"
+ "--physical-pixel-sizes", "1", "1", "1"
])
- assert args["pixel_sizes"] == ("1", "1", "1")
+ assert args["physical_pixel_sizes"] == ("1", "1", "1")
diff --git a/core/tests/test_cli.py b/core/tests/test_cli.py
index 7d96b33f..38867aba 100644
--- a/core/tests/test_cli.py
+++ b/core/tests/test_cli.py
@@ -62,7 +62,7 @@ def assert_h5(output_dir: Path):
[
[["--save-type", "h5"], assert_h5],
[["--save-type", "tiff"], assert_tiff],
- [["--save-type", "tiff", "--time-start", "0", "--time-end", "1"], assert_tiff],
+ [["--save-type", "tiff", "--time-range", "0", "1"], assert_tiff],
]
)
def test_batch_deskew(flags: List[str], check_fn: Callable[[Path], None]):
diff --git a/plugin/napari_lattice/fields.py b/plugin/napari_lattice/fields.py
index 2360c69f..c46e1b59 100644
--- a/plugin/napari_lattice/fields.py
+++ b/plugin/napari_lattice/fields.py
@@ -375,7 +375,7 @@ class DeconvolutionFields(NapariFieldGroup):
tooltip="PSFs must be in the same order as the image channels",
layout="vertical"
)
- psf_num_iter = field(int, label = "Number of Iterations")
+ decon_num_iter = field(int, label = "Number of Iterations")
background = field(ComboBox).with_choices(
[it.value for it in BackgroundSource]
).with_options(label="Background")
@@ -397,7 +397,7 @@ def _enable_custom_background(self, background: str) -> bool:
fields = [
decon_processing,
psf,
- psf_num_iter,
+ decon_num_iter,
background
]
)
@@ -418,7 +418,7 @@ def _make_model(self) -> Optional[DeconvolutionParams]:
background=background,
# Filter out unset PSFs
psf=[psf for psf in self.psf.value if psf.is_file()],
- psf_num_iter=self.psf_num_iter.value
+ decon_num_iter=self.decon_num_iter.value
)
@magicclass