unify command line args with config file args

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"],
@@ -145,30 +142,25 @@ 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", 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_subset: List[int] = field_from_model(CropParams, "roi_subset"),
+    z_range: Optional[Tuple[int,int]] = Option(None, help="The index of the first Z slice to use. All prior Z slices will be discarded.", 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"),
+    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]] = Option(None, help="Start frame and stop frame for time cropping - Defaults to full range.", rich_help_panel="Output"),
+    channel_range: Optional[Tuple[int,int]] = Option(None,help="Index of the first channel (inclusive) and last slice (exclusive) to use. Defaults to full 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"),

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"
     )

core/lls_core/models/lattice_data.py

@@ -345,7 +345,7 @@ 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
                     )
@@ -390,13 +390,13 @@ 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'