diff --git a/core/lls_core/lattice_data.py b/core/lls_core/lattice_data.py
index f568462d..3ec8bbb8 100644
--- a/core/lls_core/lattice_data.py
+++ b/core/lls_core/lattice_data.py
@@ -27,6 +27,7 @@
from lls_core.types import ArrayLike
from napari_workflows import Workflow
from napari.types import ShapesData
+from xarray import DataArray
if TYPE_CHECKING:
import pyclesperanto_prototype as cle
@@ -75,7 +76,7 @@ def save_image(self):
for roi, roi_results in groupby(self.slices, key=lambda it: it.roi_index):
if self.lattice_data.save_type == SaveFileType.h5:
bdv_writer = npy2bdv.BdvWriter(
- make_filename_prefix(prefix=self.lattice_data.save_name, roi_index=roi),
+ make_filename_prefix(prefix=self.lattice_data.save_name, roi_index=roi) + ".h5",
compression='gzip',
nchannels=len(self.lattice_data.channel_range),
subsamp=((1, 1, 1), (1, 2, 2), (2, 4, 4)),
@@ -180,7 +181,7 @@ def default_time_range(cls, v: Any, values: dict) -> range:
Sets the default time range if undefined
"""
if v is None:
- return range(values["dims"].T + 1)
+ return range(values["data"].sizes["T"] + 1)
return v
@validator("channel_range")
@@ -189,20 +190,12 @@ def default_channel_range(cls, v: Any, values: dict) -> range:
Sets the default channel range if undefined
"""
if v is None:
- return range(values["dims"].C + 1)
+ return range(values["data"].sizes["C"] + 1)
return v
class DeskewParams(DefaultMixin, arbitrary_types_allowed=True):
#: A 3-5D array containing the image data
- data: ArrayLike
-
- #: Dimensions of `data`
- dims: Dimensions
-
- #: Dimensions of the deskewed output
- deskew_vol_shape: Tuple[int, ...] = Field(init_var=False)
-
- deskew_affine_transform: cle.AffineTransform3D = Field(init_var=False)
+ data: DataArray
#: Geometry of the light path
skew: DeskewDirection = DeskewDirection.Y
@@ -211,17 +204,42 @@ class DeskewParams(DefaultMixin, arbitrary_types_allowed=True):
#: Pixel size in microns
physical_pixel_sizes: DefinedPixelSizes = Field(default_factory=DefinedPixelSizes)
+ #: Dimensions of the deskewed output
+ deskew_vol_shape: Tuple[int, ...] = Field(init_var=False, default=None)
+
+ deskew_affine_transform: cle.AffineTransform3D = Field(init_var=False, default=None)
+
+ @property
+ def dims(self):
+ return self.data.dims
+
+ @validator("data", pre=True)
+ def reshaping(cls, v: Any):
+ # This allows a user to pass in any array-like object and have it
+ # converted and reshaped appropriately
+ array = DataArray(v)
+ if not set(array.dims).issuperset({"X", "Y", "Z"}):
+ raise ValueError("The input array must at least have XYZ coordinates")
+ if "T" not in array.dims:
+ array = array.expand_dims("T")
+ if "C" not in array.dims:
+ array = array.expand_dims("C")
+ return array.transpose("T", "C", "Z", "Y", "X")
+
+ def get_3d_slice(self) -> DataArray:
+ return self.data.sel(C=0, T=0)
+
@root_validator(pre=True)
def set_deskew(cls, values: dict) -> dict:
"""
Sets the default deskew shape values if the user has not provided them
"""
# process the file to get shape of final deskewed image
- data = values["data"]
+ data: DataArray = cls.reshaping(values["data"])
if values.get('deskew_vol_shape') is None:
if values.get('deskew_affine_transform') is None:
# If neither has been set, calculate them ourselves
- values["deskew_vol_shape"], values["deskew_affine_transform"] = get_deskewed_shape(values["data"], values["angle"], values["physical_pixel_sizes"].X, values["physical_pixel_sizes"].Y, values["physical_pixel_sizes"].Z, values["skew"])
+ values["deskew_vol_shape"], values["deskew_affine_transform"] = get_deskewed_shape(data.sel(C=0, T=0).to_numpy(), values["angle"], values["physical_pixel_sizes"].X, values["physical_pixel_sizes"].Y, values["physical_pixel_sizes"].Z, values["skew"])
else:
raise ValueError("deskew_vol_shape and deskew_affine_transform must be either both specified or neither specified")
return values
@@ -249,7 +267,7 @@ def disjoint_time_range(cls, v: range, values: dict):
"""
Validates that the time range is within the range of channels in our array
"""
- max_time = values["dims"].T
+ max_time = values["data"].sizes["T"]
if v.start < 0:
raise ValueError("The lowest valid start value is 0")
if v.stop > max_time:
@@ -261,7 +279,7 @@ def disjoint_channel_range(cls, v: range, values: dict):
"""
Validates that the channel range is within the range of channels in our array
"""
- max_channel = values["dims"].T
+ max_channel = values["data"].sizes["C"]
if v.start < 0:
raise ValueError("The lowest valid start value is 0")
if v.stop > max_channel:
@@ -270,13 +288,15 @@ def disjoint_channel_range(cls, v: range, values: dict):
@validator("channel_range")
def channel_range_subset(cls, v: range, values: dict):
- if min(v) < 0 or max(v) > values["dims"].C:
+ if min(v) < 0 or max(v) > values["data"].sizes["C"]:
raise ValueError("The output channel range must be a subset of the total available channels")
+ return v
@validator("time_range")
def time_range_subset(cls, v: range, values: dict):
- if min(v) < 0 or max(v) > values["dims"].T:
- raise ValueError("The output time range must be a subset of the total available time points")
+ if min(v) < 0 or max(v) > values["data"].sizes["T"]:
+ raise ValueError("The output time range must be a subset of the total available time points")
+ return v
# Hack to ensure that .skew_dir behaves identically to .skew
@property
@@ -343,12 +363,12 @@ def deconv_enabled(self) -> bool:
@property
def time(self) -> int:
"""Number of time points"""
- return self.dims.T
+ return self.data.sizes["T"]
@property
def channels(self) -> int:
"""Number of channels"""
- return self.dims.C
+ return self.data.sizes["C"]
@property
def new_dz(self):
@@ -358,17 +378,19 @@ def __post_init__(self):
logger.info(f"Channels: {self.channels}, Time: {self.time}")
logger.info("If channel and time need to be swapped, you can enforce this by choosing 'Last dimension is channel' when initialising the plugin")
- def slice_data(self, time: int, channel: int) -> ArrayLike:
+ def slice_data(self, time: int, channel: int) -> DataArray:
if time > self.time:
raise ValueError("time is out of range")
if channel > self.channels:
raise ValueError("channel is out of range")
- if len(self.dims.shape) == 3:
+ return self.data.sel(T=time, C=channel)
+
+ if len(self.data.shape) == 3:
return self.data
- elif len(self.dims.shape) == 4:
+ elif len(self.data.shape) == 4:
return self.data[time, :, :, :]
- elif len(self.dims.shape) == 5:
+ elif len(self.data.shape) == 5:
return self.data[time, channel, :, :, :]
raise Exception("Lattice data must be 3-5 dimensions")
@@ -511,8 +533,7 @@ def process(self) -> ProcessedSlices:
)
class AicsLatticeParams(TypedDict):
- data: DaskArray
- dims: Dimensions
+ data: DataArray
physical_pixel_sizes: DefinedPixelSizes
def lattice_params_from_aics(img: AICSImage, physical_pixel_sizes: PhysicalPixelSizes = PhysicalPixelSizes(None, None, None)) -> AicsLatticeParams:
diff --git a/plugin/napari_lattice/dock_widget.py b/plugin/napari_lattice/dock_widget.py
index 4bba9e39..398827f9 100644
--- a/plugin/napari_lattice/dock_widget.py
+++ b/plugin/napari_lattice/dock_widget.py
@@ -1,80 +1,32 @@
-import os
-import sys
-import yaml
-import numpy as np
+# Enable Logging
+import logging
from pathlib import Path
-import dask.array as da
-import pandas as pd
-from typing import Any, Callable, Iterable, Literal, Optional, Sequence, Tuple, Union, List, TypeVar, Type, cast
-from enum import Enum, auto
+from typing import Union
+import numpy as np
+from lls_core.lattice_data import LatticeData
+from lls_core.workflow import import_workflow_modules
+from magicclass import MagicTemplate, field, magicclass, set_options
from magicclass.wrappers import set_design
-from magicgui.widgets import Widget
-from magicclass import magicclass, field, vfield, set_options, MagicTemplate, FieldGroup, Icon
-from magicclass.fields import MagicValueField, MagicField
-from magicclass.widgets import CollapsibleContainer, CheckBox, RangeSlider, ComboBox
-from magicclass.widgets.containers import ContainerWidget, _VCollapsibleContainer, wrap_container
-from magicclass.utils import click
-from qtpy.QtCore import Qt
-from qtpy.QtGui import QIcon
-from qtpy.QtWidgets import QTabWidget
-
-from napari.layers import Layer, Shapes
-from napari.types import ImageData
from napari import Viewer
-from napari_lattice.icons import RED, GREEN, GREY
-
-from tqdm import tqdm
-
+from napari.layers import Shapes
+from napari_lattice.fields import (
+ CroppingFields,
+ DeconvolutionFields,
+ DeskewFields,
+ OutputFields,
+ WorkflowFields,
+)
+from napari_lattice.icons import GREY
from napari_workflows import Workflow, WorkflowManager
from napari_workflows._io_yaml_v1 import load_workflow
-
-from lls_core import DeconvolutionChoice, SaveFileType, Log_Levels, DeskewDirection
-from lls_core.lattice_data import CropParams, DeconvolutionParams, DefinedPixelSizes, LatticeData
-
-from pydantic import ValidationError
-from napari_lattice.reader import lattice_params_from_napari
-from napari_lattice.fields import DeskewFields, CroppingFields, DeconvolutionFields, WorkflowFields, OutputFields, exception_to_html
-from napari_lattice.icons import GREY
-
-from strenum import StrEnum
-
-from lls_core.workflow import import_workflow_modules
-# Enable Logging
-import logging
+from qtpy.QtCore import Qt
+from qtpy.QtGui import QIcon
+from qtpy.QtWidgets import QTabWidget
logger = logging.getLogger(__name__)
logger.setLevel(logging.INFO)
-# MagicClassType = TypeVar("MagicClassType")
-# def magicclass(*args, **kwargs) -> Callable[[MagicClassType], MagicClassType]:
-# return original_magicclass(*args, **kwargs)
-
-# class HideableContents(MagicTemplate):
-# # fields_enabled = vfield(False, label="Enabled")
-
-# # @fields_enabled.connect
-# def _enabled_changed(self) -> None:
-# if self.fields_enabled:
-# logger.info(f"{self.__class__} Activated")
-# for child in self.__magicclass_children__:
-# child.visible = True
-# child.enabled = True
-# else:
-# logger.info(f"{self.__class__} Deactivated")
-# for child in self.__magicclass_children__:
-# child.visible = False
-# child.enabled = False
-
-
-# def validate_tab(parent: MagicTemplate, fields: FieldGroup, index: int):
-# tab_widget: QTabWidget = parent._widget._tab_widget
-# try:
-# fields._make_model()
-# tab_widget.setTabIcon(index, QIcon(GREEN))
-# except ValidationError:
-# tab_widget.setTabIcon(index, QIcon(RED))
-
class LlszTemplate(MagicTemplate):
@property
def llsz_parent(self) -> "LLSZWidget":
@@ -86,7 +38,6 @@ def parent_viewer(mc: MagicTemplate) -> Viewer:
raise Exception("This function can only be used when inside of a Napari viewer")
return mc.parent_viewer
-
@magicclass(widget_type="split")
class LLSZWidget(LlszTemplate):
open_file: bool = False
@@ -103,16 +54,19 @@ def _check_validity(self) -> bool:
return False
def _make_model(self) -> LatticeData:
- # deskew = self.LlszMenu.WidgetContainer.DeskewWidget
- # output = self.LlszMenu.WidgetContainer.OutputWidget
- # deconv = self.LlszMenu.WidgetContainer.DeconvolutionWidget
- # crop = self.LlszMenu.WidgetContainer.CroppingWidget
- # workflow = self.LlszMenu.WidgetContainer.WorkflowWidget
-
- # TODO: fix
+ deskew_args = self.LlszMenu.WidgetContainer.deskew_fields._get_kwargs()
+ output_args = self.LlszMenu.WidgetContainer.output_fields._make_model()
return LatticeData(
- **self.LlszMenu.WidgetContainer.deskew_fields._make_model().dict(),
- **self.LlszMenu.WidgetContainer.output_fields._make_model().dict(),
+ data=deskew_args["data"],
+ angle=deskew_args["angle"],
+ channel_range=output_args.channel_range,
+ time_range=output_args.time_range,
+ save_dir=output_args.save_dir,
+ # We let the user specify a prefix, but if they don't, we can use the default
+ save_name=output_args.save_name or deskew_args["save_name"] ,
+ save_type=output_args.save_type,
+ physical_pixel_sizes=deskew_args["physical_pixel_sizes"],
+ skew=deskew_args["skew"],
workflow=self.LlszMenu.WidgetContainer.workflow_fields._make_model(),
deconvolution=self.LlszMenu.WidgetContainer.deconv_fields._make_model(),
crop=self.LlszMenu.WidgetContainer.cropping_fields._make_model()
@@ -123,9 +77,6 @@ class LlszMenu(LlszTemplate):
main_heading = field("Napari Lattice: Visualization & Analysis
", widget_type="Label")
heading1 = field("Drag and drop an image file onto napari.", widget_type="Label")
- # Pycudadecon library for deconvolution
- # options={"enabled": True},
-
# Tabbed Widget container to house all the widgets
@magicclass(widget_type="tabbed", name="Functions", labels=False)
@@ -139,255 +90,10 @@ def __post_init__(self):
field._validate()
deskew_fields = DeskewFields(name = "1. Deskew")
- # @deskew_fields.connect
- # def _deskew_changed(self):
- # validate_tab(self, self.deskew_fields, 0)
-
deconv_fields = DeconvolutionFields(name = "2. Deconvolution")
- # @deconv_fields.connect
- # def _deconv_changed(self):
- # validate_tab(self, self.deconv_fields, 1)
-
cropping_fields = CroppingFields(name = "3. Crop")
- # @cropping_fields.connect
- # def _cropping_changed(self):
- # validate_tab(self, 2)
-
workflow_fields = WorkflowFields(name = "4. Workflow")
- # @workflow_fields.connect
- # def _workflow_changed(self):
- # validate_tab(self, 3)
-
output_fields = OutputFields(name = "5. Output")
- # @output_fields.connect
- # def _output_changed(self):
- # validate_tab(self, 4)
-
- # @magicclass(name="1. Deskew")
- # class DeskewWidget(MagicTemplate):
- # img_layer = field(List[Layer]).with_options(label = "Image Layer", layout="vertical", value=[])
- # pixel_sizes = field(Tuple[float, float, float]).with_options(
- # value=(DefinedPixelSizes.get_default("X"), DefinedPixelSizes.get_default("Y"), DefinedPixelSizes.get_default("Z")),
- # label="Pixel Sizes (XYZ)"
- # )
- # angle = field(LatticeData.get_default("angle")).with_options(value=LatticeData.get_default("angle"), label="Skew Angle")
- # device = field(str).with_choices(cle.available_device_names()).with_options(label="Graphics Device")
- # merge_all_channels = field(False).with_options(label="Merge all Channels")
- # dimension_order = field(str).with_options(value=LastDimensionOptions.Metadata.value).with_choices([it.value for it in LastDimensionOptions]).with_options(label="Dimension Order")
- # skew_dir = field(DeskewDirection.Y).with_options(label = "Skew Direction")
-
- # def _img_changed(self):
- # deskew = get_child(self, self.DeskewWidget)
- # output = get_child(self, self.OutputWidget)
- # output.channel_range.options["max"] = deskew.img_layer.value._dims_displayed
- ###
- # Deconvolution
- ###
- # deconvolution = vfield(bool, name="Use Deconvolution").with_options(value=False)
- # deconv_widget = DeconvolutionWidget()
- # deconv_widget.enabled = False
- # deconv_widget.visible = False
-
- # @deconvolution.connect
- # def _set_decon(self) -> None:
- # if self.deconvolution:
- # logger.info("Deconvolution Activated")
- # # Enable deconvolutio by using the saved parameters
- # # self.llsz_parent.lattice.deconvolution = self.llsz_parent.deconv
- # self.deconv_widget.enabled = True
- # self.deconv_widget.visible = True
- # else:
- # logger.info("Deconvolution Disabled")
- # # self.llsz_parent.lattice.deconvolution = None
- # self.deconv_widget.enabled = False
- # self.deconv_widget.visible = False
-
- # ###
- # # Cropping
- # ###
-
- # cropping_enabled = vfield(bool, name="Use Cropping").with_options(value=False)
- # crop_widget = CroppingWidget()
- # crop_widget.enabled = False
- # crop_widget.visible = False
-
- # @cropping_enabled.connect
- # def _set_crop(self) -> None:
- # if self.cropping_enabled:
- # logger.info("Cropping Activated")
- # # Enable deconvolutio by using the saved parameters
- # # self.llsz_parent.lattice.deconvolution = self.llsz_parent.deconv
- # self.crop_widget.enabled = True
- # self.crop_widget.visible = True
- # else:
- # logger.info("Deconvolution Disabled")
- # # self.llsz_parent.lattice.deconvolution = None
- # self.crop_widget.enabled = False
- # self.crop_widget.visible = False
-
- # ###
- # # Workflow
- # ###
- # workflow_enabled = vfield(bool, name="Use Workflow").with_options(value=False)
- # workflow_widget = WorkflowWidget()
- # workflow_widget.enabled = False
- # workflow_widget.visible = False
-
- # @workflow_enabled.connect
- # def _set_workflow(self) -> None:
- # if self.workflow_enabled:
- # logger.info("Workflow Activated")
- # # Enable deconvolutio by using the saved parameters
- # # self.llsz_parent.lattice.deconvolution = self.llsz_parent.deconv
- # self.workflow_widget.enabled = True
- # self.workflow_widget.visible = True
- # else:
- # logger.info("Deconvolution Disabled")
- # # self.llsz_parent.lattice.deconvolution = None
- # self.workflow_widget.enabled = False
- # self.workflow_widget.visible = False
-
- # @set_design(background_color="magenta", font_family="Consolas", visible=True, text="Initialize Plugin", max_height=75, font_size=13)
- # @set_options(pixel_size_dx={"widget_type": "FloatSpinBox", "value": 0.1449922, "step": 0.000000001},
- # pixel_size_dy={"widget_type": "FloatSpinBox",
- # "value": 0.1449922, "step": 0.000000001},
- # pixel_size_dz={"widget_type": "FloatSpinBox",
- # "value": 0.3, "step": 0.000000001},
- # angle={"widget_type": "FloatSpinBox",
- # "value": 30, "step": 0.1},
- # select_device={"widget_type": "ComboBox", "choices": cle.available_device_names(
- # ), "value": cle.available_device_names()[0]},
- # last_dimension_channel={"widget_type": "ComboBox",
- # "label": "Set Last dimension (channel/time)", "tooltip": "If the last dimension is initialised incorrectly, you can assign it as either channel/time"},
- # merge_all_channel_layers={"widget_type": "CheckBox", "value": True, "label": "Merge all napari layers as channels",
- # "tooltip": "Use this option if the channels are in separate layers. napari-lattice requires all channels to be in same layer"},
- # skew_dir={"widget_type": "ComboBox", "choices": DeskewDirection, "value": DeskewDirection.Y,
- # "label": "Direction of skew (Y or X)", "tooltip": "Skew direction when image is acquired. Ask your microscopist for details"},
- # set_logging={"widget_type": "ComboBox", "choices": Log_Levels, "value": Log_Levels.INFO,
- # "label": "Log Level", "tooltip": "Only use for debugging. Leave it as INFO for regular operation"}
- # )
- # def Choose_Image_Layer(self,
- # img_layer: Layer,
- # pixel_size_dx: float = 0.1449922,
- # pixel_size_dy: float = 0.1449922,
- # pixel_size_dz: float = 0.3,
- # angle: float = 30,
- # select_device: str = cle.available_device_names()[
- # 0],
- # last_dimension_channel: LastDimensionOptions = "",
- # merge_all_channel_layers: bool = False,
- # skew_dir: DeskewDirection=DeskewDirection.Y,
- # set_logging: Log_Levels=Log_Levels.INFO):
-
- # logger.setLevel(set_logging.value)
- # config.log_level = set_logging.value
- # logger.info(f"Logging set to {set_logging}")
- # logger.info("Using existing image layer")
-
- # # Select device for processing
- # cle.select_device(select_device)
-
- # # merge all napari image layers as one multidimensional image
- # if merge_all_channel_layers:
- # from napari.layers.utils.stack_utils import images_to_stack
- # # get list of napari layers as a list
- # layer_list = list(self.parent_viewer.layers)
- # # if more than one layer
- # if len(layer_list) > 1:
- # # convert the list of images into a stack
- # new_layer = images_to_stack(layer_list)
- # # select all current layers
- # self.parent_viewer.layers.select_all()
- # # remove selected layers
- # self.parent_viewer.layers.remove_selected()
- # # add the new composite image layer
- # self.parent_viewer.add_layer(new_layer)
- # img_layer = new_layer
-
- # self.llsz_parent.lattice = lattice_from_napari(
- # img=img_layer,
- # last_dimension=None if last_dimension_channel == LastDimensionOptions.get_from_metadata else last_dimension_channel,
- # angle=angle,
- # skew=skew_dir,
- # physical_pixel_sizes=(pixel_size_dx, pixel_size_dy, pixel_size_dz),
- # # deconvolution = DeconvolutionParams()
- # )
- # # flag for ensuring a file has been opened and plugin initialised
- # self.llsz_parent.open_file = True
-
- # logger.info(
- # f"Pixel size (ZYX) in microns: {self.llsz_parent.lattice.dz,self.llsz_parent.lattice.dy,self.llsz_parent.lattice.dx}")
- # logger.info(
- # f"Dimensions of image layer (ZYX): {list(self.llsz_parent.lattice.data.shape[-3:])}")
- # logger.info(
- # f"Dimensions of deskewed image (ZYX): {self.llsz_parent.lattice.deskew_vol_shape}")
- # logger.info(
- # f"Deskewing angle is: {self.llsz_parent.lattice.angle}")
- # logger.info(
- # f"Deskew Direction: {self.llsz_parent.lattice.skew}")
- # # Add dimension labels correctly
- # # if channel, and not time
- # if self.llsz_parent.lattice.time == 0 and (last_dimension_channel or self.llsz_parent.lattice.channels > 0):
- # self.parent_viewer.dims.axis_labels = ('Channel', "Z", "Y", "X")
- # # if no channel, but has time
- # elif self.llsz_parent.lattice.channels == 0 and self.llsz_parent.lattice.time > 0:
- # self.parent_viewer.dims.axis_labels = ('Time', "Z", "Y", "X")
- # # if it has channels
- # elif self.llsz_parent.lattice.channels > 1:
- # # If merge to stack is used, channel slider goes to the bottom
- # if int(self.parent_viewer.dims.dict()["range"][0][1]) == self.llsz_parent.lattice.channels:
- # self.parent_viewer.dims.axis_labels = ('Channel', "Time", "Z", "Y", "X")
- # else:
- # self.parent_viewer.dims.axis_labels = ('Time', "Channel", "Z", "Y", "X")
- # # if channels initialized by aicsimagio, then channels is 1
- # elif self.llsz_parent.lattice.channels == 1 and self.llsz_parent.lattice.time > 1:
- # self.parent_viewer.dims.axis_labels = ('Time', "Z", "Y", "X")
-
- # logger.info(f"Initialised")
- # self["Choose_Image_Layer"].background_color = "green"
- # self["Choose_Image_Layer"].text = "Plugin Initialised"
-
-
- # @set_design(background_color="magenta", font_family="Consolas", visible=True, text="Click to select PSFs for deconvolution", max_height=75, font_size=11)
- # @set_options(header=dict(widget_type="Label", label="Enter path to the PSF images
"),
- # psf_ch1_path={"widget_type": "FileEdit",
- # "label": "Channel 1:"},
- # psf_ch2_path={"widget_type": "FileEdit",
- # "label": "Channel 2"},
- # psf_ch3_path={"widget_type": "FileEdit",
- # "label": "Channel 3"},
- # psf_ch4_path={"widget_type": "FileEdit",
- # "label": "Channel 4"},
- # device_option={
- # "widget_type": "ComboBox", "label": "Choose processing device", "choices": DeconvolutionChoice},
- # no_iter={
- # "widget_type": "SpinBox", "label": "No of iterations (Deconvolution)", "value": 10, "min": 1, "max": 50, "step": 1}
- # )
- # def deconvolution_gui(self,
- # header: str,
- # psf_ch1_path: Path,
- # psf_ch2_path: Path,
- # psf_ch3_path: Path,
- # psf_ch4_path: Path,
- # device_option: DeconvolutionChoice,
- # no_iter: int):
- # """GUI for Deconvolution button"""
- # # Force deconvolution to be true if we do this
- # if not self.llsz_parent.lattice.deconvolution:
- # raise Exception("Deconvolution is set to False. Tick the box to activate deconvolution.")
- # self.llsz_parent.deconv.decon_processing = device_option
- # self.llsz_parent.deconv.psf = list(read_psf([
- # psf_ch1_path,
- # psf_ch2_path,
- # psf_ch3_path,
- # psf_ch4_path,
- # ],
- # device_option,
- # lattice_class=self.llsz_parent.lattice
- # ))
- # self.llsz_parent.deconv.psf_num_iter = no_iter
- # self["deconvolution_gui"].background_color = "green"
- # self["deconvolution_gui"].text = "PSFs added"
@set_options(header=dict(widget_type="Label", label="Preview Deskew
"),
time=dict(label="Time:", max=2**15),
@@ -415,622 +121,8 @@ def preview(self, header:str, time: int, channel: int):
@set_design(text="Save")
def save(self):
- self._make_model_friendly()
-
-
- # @magicclass(widget_type="collapsible")
- # class Preview:
- # @magicgui(header=dict(widget_type="Label", label="Preview Deskew
"),
- # time=dict(label="Time:", max=2**15),
- # channel=dict(label="Channel:"),
- # call_button="Preview")
- # def Preview_Deskew(self,
- # header: str,
- # time: int,
- # channel: int,
- # img_data: ImageData):
- # """
- # Preview deskewed data for a single timepoint and channel
-
- # """
- # _Preview(LLSZWidget,
- # self,
- # time,
- # channel,
- # img_data)
-
- # Tabbed Widget container to house all the widgets
- # @magicclass(widget_type="tabbed", name="Functions")
- # class WidgetContainer(LlszTemplate):
-
- # @magicclass(name="Deskew", widget_type="scrollable", properties={"min_width": 100})
- # class DeskewWidget(LlszTemplate):
-
- # @magicgui(header=dict(widget_type="Label", label="Deskew and Save
"),
- # time_start=dict(label="Time Start:", max=2**20),
- # time_end=dict(label="Time End:", value=1, max=2**20),
- # ch_start=dict(label="Channel Start:"),
- # ch_end=dict(label="Channel End:", value=1),
- # save_as_type={
- # "label": "Save as filetype:", "choices": SaveFileType, "value": SaveFileType.h5},
- # save_path=dict(mode='d', label="Directory to save"),
- # call_button="Save")
- # def Deskew_Save(self,
- # header: str,
- # time_start: int,
- # time_end: int,
- # ch_start: int,
- # ch_end: int,
- # save_as_type: str,
- # save_path: Path = Path(history.get_save_history()[0])):
- # """ Widget to Deskew and Save Data"""
- # _Deskew_Save(LLSZWidget,
- # time_start,
- # time_end,
- # ch_start,
- # ch_end,
- # save_as_type,
- # save_path)
-
- # @magicclass(name="Crop and Deskew", widget_type="scrollable")
- # class CropWidget(LlszTemplate):
-
- # # add function for previewing cropped image
- # @magicclass(name="Cropping Preview", widget_type="scrollable", properties={
- # "min_width": 100,
- # "shapes_layer": Shapes
- # })
- # class Preview_Crop_Menu(LlszTemplate):
-
- # @set_design(font_size=10, text="Click to activate Cropping Layer", background_color="magenta")
- # @click(enables=["Import_ImageJ_ROI", "Crop_Preview"])
- # def activate_cropping(self):
- # self.llsz_parent.shapes_layer = self.parent_viewer.add_shapes(shape_type='polygon', edge_width=1, edge_color='white',
- # face_color=[1, 1, 1, 0], name="Cropping BBOX layer")
- # # TO select ROIs if needed
- # self.llsz_parent.shapes_layer.mode = "SELECT"
- # self["activate_cropping"].text = "Cropping layer active"
- # self["activate_cropping"].background_color = "green"
-
- # heading2 = field("You can either import ImageJ ROI (.zip) files or manually define ROIs using the shape layer", widget_type="Label")
-
- # @click(enabled=False)
- # def Import_ImageJ_ROI(self, path: Path = Path(history.get_open_history()[0])) -> None:
- # logger.info(f"Opening{path}")
- # roi_list = read_imagej_roi(str(path))
- # # convert to canvas coordinates
- # self.find_ancestor(LLSZWidget)
- # roi_list = (np.array(roi_list) * self.llsz_parent.lattice.dy).tolist()
- # self.llsz_parent.shapes_layer.add(roi_list, shape_type='polygon', edge_width=1, edge_color='yellow', face_color=[1, 1, 1, 0])
-
- # time_crop = field(int, options={"min": 0, "step": 1, "max": 2**20}, name="Time")
- # chan_crop = field(int, options={"min": 0, "step": 1}, name="Channels")
- # heading_roi = field("If there are multiple ROIs, select the ROI before clicking button below", widget_type="Label")
- # #roi_idx = field(int, options={"min": 0, "step": 1}, name="ROI number")
-
- # @click(enabled=False)
- # def Crop_Preview(self, roi_layer: ShapesData):
-
- # if not roi_layer:
- # raise Exception("No coordinates found for cropping. Check if right shapes layer or initialise shapes layer and draw ROIs.")
- # # TODO: Add assertion to check if bbox layer or coordinates
-
- # # Slice out the image of interest to preview
- # time = self.time_crop.value
- # channel = self.chan_crop.value
- # if time >= self.llsz_parent.lattice.time:
- # raise ValueError("Time is out of range")
- # if time >= self.llsz_parent.lattice.time:
- # raise ValueError("Channel is out of range")
- # logger.info(f"Using channel {channel} and time {time}")
-
- # # if only one roi drawn, use the first ROI for cropping
- # if len(self.llsz_parent.shapes_layer.selected_data) == 0:
- # raise Exception("Please select an ROI")
-
- # roi_idx = list(self.llsz_parent.shapes_layer.selected_data)[0]
-
- # # As the original image is scaled, the coordinates are in microns, so we need to convert
- # # roi from micron to canvas/world coordinates
- # roi_choice = [x/self.llsz_parent.lattice.dy for x in roi_layer[roi_idx]]
- # logger.info(f"Previewing ROI {roi_idx}")
-
- # # crop
-
- # # Set the deconvolution options
- # if self.llsz_parent.deconvolution:
- # if not self.llsz_parent.lattice.psf or not self.llsz_parent.lattice.psf_num_iter or not self.llsz_parent.lattice.decon_processing:
- # raise Exception(
- # "PSF fields should be set by this point!")
- # logger.info(
- # f"Deskewing for Time:{time} and Channel: {channel} with deconvolution")
- # decon_kwargs = dict(
- # decon_processing=self.llsz_parent.lattice.decon_processing.value,
- # psf=self.llsz_parent.lattice.psf[channel],
- # num_iter=self.llsz_parent.lattice.psf_num_iter
- # )
- # else:
- # decon_kwargs = dict()
-
- # crop_roi_vol_desk = cle.pull(
- # crop_volume_deskew(
- # original_volume=np.array(self.llsz_parent.lattice.data[time, channel, ...]),
- # roi_shape=roi_choice,
- # angle_in_degrees=self.llsz_parent.angle_value,
- # voxel_size_x=self.llsz_parent.lattice.dx,
- # voxel_size_y=self.llsz_parent.lattice.dy,
- # voxel_size_z=self.llsz_parent.lattice.dz,
- # deconvolution=self.llsz_parent.deconvolution,
- # # Option for entering custom z start value?
- # z_start=0,
- # z_end=self.llsz_parent.lattice.deskew_vol_shape[0],
- # skew_dir=self.llsz_parent.skew_dir,
- # **decon_kwargs
- # ).astype(self.llsz_parent.lattice.data.dtype)
- # )
-
- # # get array back from gpu or addding cle array to napari can throw errors
- # image = next(self.llsz_parent.lattice.process())
-
- # scale = (
- # self.llsz_parent.lattice.new_dz,
- # self.llsz_parent.lattice.dy,
- # self.llsz_parent.lattice.dx
- # )
- # self.parent_viewer.add_image(
- # crop_roi_vol_desk, scale=scale)
-
- # @magicclass(name="Crop and Save Data")
- # class CropSaveData(LlszTemplate):
- # @magicgui(header=dict(widget_type="Label", label="Crop and Save Data
"),
- # time_start=dict(label="Time Start:"),
- # time_end=dict(label="Time End:", value=1),
- # ch_start=dict(label="Channel Start:"),
- # ch_end=dict(label="Channel End:", value=1),
- # save_as_type={
- # "label": "Save as filetype:", "choices": SaveFileType},
- # save_path=dict(mode='d', label="Directory to save "))
- # def Crop_Save(self,
- # header: str,
- # time_start: int,
- # time_end: int,
- # ch_start: int,
- # ch_end: int,
- # save_as_type: SaveFileType,
- # roi_layer_list: ShapesData,
- # save_path: Path = Path(history.get_save_history()[0])):
-
- # if not roi_layer_list:
- # logger.error(
- # "No coordinates found or cropping. Initialise shapes layer and draw ROIs.")
- # else:
- # if not self.llsz_parent.open_file:
- # raise Exception("Image not initialised")
-
- # check_dimensions(time_start, time_end, ch_start, ch_end, self.llsz_parent.lattice.channels, self.llsz_parent.lattice.time)
-
- # angle = self.llsz_parent.lattice.angle
- # dx = self.llsz_parent.lattice.dx
- # dy = self.llsz_parent.lattice.dy
- # dz = self.llsz_parent.lattice.dz
-
- # # get image data
- # img_data = self.llsz_parent.lattice.data
- # # Get shape of deskewed image
- # deskewed_shape = self.llsz_parent.lattice.deskew_vol_shape
- # deskewed_volume = da.zeros(deskewed_shape)
- # z_start = 0
- # z_end = deskewed_shape[0]
-
- # logger.info("Cropping and saving files...")
-
- # # necessary when scale is used for napari.viewer.add_image operations
- # roi_layer_list = ShapesData([x/self.llsz_parent.lattice.dy for x in roi_layer_list])
-
- # for idx, roi_layer in enumerate(tqdm(roi_layer_list, desc="ROI:", position=0)):
- # # pass arguments for save tiff, callable and function arguments
- # logger.info("Processing ROI ", idx)
- # # pass parameters for the crop_volume_deskew function
-
- # save_img(vol=img_data,
- # func=crop_volume_deskew,
- # time_start=time_start,
- # time_end=time_end,
- # channel_start=ch_start,
- # channel_end=ch_end,
- # save_name_prefix="ROI_" + str(idx),
- # save_path=save_path,
- # save_file_type=save_as_type,
- # save_name=self.llsz_parent.lattice.save_name,
- # dx=dx,
- # dy=dy,
- # dz=dz,
- # angle=angle,
- # deskewed_volume=deskewed_volume,
- # roi_shape=roi_layer,
- # angle_in_degrees=angle,
- # z_start=z_start,
- # z_end=z_end,
- # voxel_size_x=dx,
- # voxel_size_y=dy,
- # voxel_size_z=dz,
- # LLSZWidget=self.llsz_parent
- # )
-
- # logger.info(
- # f"Cropping and Saving Complete -> {save_path}")
-
- # @magicclass(name="Workflow", widget_type="scrollable")
- # class WorkflowWidget(LlszTemplate):
-
- # @magicclass(name="Preview Workflow", widget_type="scrollable")
- # class PreviewWorkflow(LlszTemplate):
- # #time_preview= field(int, options={"min": 0, "step": 1}, name="Time")
- # #chan_preview = field(int, options={"min": 0, "step": 1}, name="Channels")
- # @magicgui(header=dict(widget_type="Label", label="Preview Workflow
"),
- # time_preview=dict(label="Time:", max=2**20),
- # chan_preview=dict(label="Channel:"),
- # get_active_workflow=dict(
- # widget_type="Checkbox", label="Get active workflow in napari-workflow", value=False),
- # workflow_path=dict(
- # mode='r', label="Load custom workflow (.yaml/yml)"),
- # Use_Cropping=dict(
- # widget_type="Checkbox", label="Crop Data", value=False),
- # #custom_module=dict(widget_type="Checkbox",label="Load custom module (looks for *.py files in the workflow directory)",value = False),
- # call_button="Apply and Preview Workflow")
- # def Workflow_Preview(self,
- # header: str,
- # time_preview: int,
- # chan_preview: int,
- # get_active_workflow: bool,
- # Use_Cropping: bool,
- # roi_layer_list: ShapesData,
- # workflow_path: Path = Path.home()):
- # """
- # Apply napari_workflows to the processing pipeline
- # User can define a pipeline which can be inspected in napari workflow inspector
- # and then execute it by ticking the get active workflow checkbox,
- # OR
- # Use a predefined workflow
-
- # In both cases, if deskewing is not present as first step, it will be added on
- # and rest of the task will be made followers
- # """
- # logger.info("Previewing deskewed channel and time with workflow")
- # user_workflow = get_workflow(self.parent_viewer if get_active_workflow else workflow_path)
-
- # # when using fields, self.time_preview.value
- # if time_preview >= self.llsz_parent.lattice.time:
- # raise ValueError("Time is out of range")
- # if chan_preview >= self.llsz_parent.lattice.channels:
- # raise ValueError("Channel is out of range")
-
- # time = time_preview
- # channel = chan_preview
-
- # # to access current time and channel and pass it to workflow file
- # config.channel = channel
- # config.time = time
-
- # logger.info(f"Processing for Time: {time} and Channel: {channel}")
-
- # logger.info("Workflow to be executed:")
- # logger.info(user_workflow)
- # # Execute workflow
- # processed_vol = user_workflow.get(task_name_last)
-
- # # check if a measurement table (usually a dictionary or list) or a tuple with different data types
- # # The function below saves the tables and adds any images to napari window
- # if type(processed_vol) in [dict, list, tuple]:
- # if (len(processed_vol) > 1):
- # df = pd.DataFrame()
- # for idx, i in enumerate(processed_vol):
- # df_temp = process_custom_workflow_output(
- # i, parent_dir, idx, LLSZWidget, self, channel, time, preview=True)
- # final_df = pd.concat([df, df_temp])
- # # append dataframes from every loop and have table command outside loop?
- # # TODO: Figure out why table is not displaying
- # from napari_spreadsheet import _widget
- # table_viewer = _widget.TableViewerWidget(
- # show=True)
- # table_viewer.add_spreadsheet(final_df)
- # # widgets.Table(value=final_df).show()
-
- # else:
- # # add image to napari window
- # # TODO: check if its an image napari supports?
- # process_custom_workflow_output(
- # processed_vol, parent_dir, 0, LLSZWidget, self, channel, time)
-
- # print("Workflow complete")
-
- # @magicgui(header=dict(widget_type="Label", label="Apply Workflow and Save Output
"),
- # time_start=dict(label="Time Start:", max=2**20),
- # time_end=dict(label="Time End:",
- # value=1, max=2**20),
- # ch_start=dict(label="Channel Start:"),
- # ch_end=dict(label="Channel End:", value=1),
- # Use_Cropping=dict(
- # widget_type="Checkbox", label="Crop Data", value=False),
- # get_active_workflow=dict(
- # widget_type="Checkbox", label="Get active workflow in napari-workflow", value=False),
- # workflow_path=dict(
- # mode='r', label="Load custom workflow (.yaml/yml)"),
- # save_as_type={
- # "label": "Save as filetype:", "choices": SaveFileType},
- # save_path=dict(
- # mode='d', label="Directory to save "),
- # #custom_module=dict(widget_type="Checkbox",label="Load custom module (same dir as workflow)",value = False),
- # call_button="Apply Workflow and Save Result")
- # def Apply_Workflow_and_Save(self,
- # header: str,
- # time_start: int,
- # time_end: int,
- # ch_start: int,
- # ch_end: int,
- # Use_Cropping: bool,
- # roi_layer_list: ShapesData,
- # get_active_workflow: bool = False,
- # workflow_path: Path = Path.home(),
- # save_as_type: str = SaveFileType.tiff,
- # save_path: Path = Path(history.get_save_history()[0])):
- # """
- # Apply a user-defined analysis workflow using napari-workflows
-
- # Args:
- # time_start (int): Start Time
- # time_end (int): End Time
- # ch_start (int): Start Channel
- # ch_end (int): End Channel
- # Use_Cropping (_type_): Use cropping based on ROIs in the shapes layer
- # roi_layer_list (ShapesData): Shapes layer to use for cropping; can be a list of shapes
- # get_active_workflow (bool, optional): Gets active workflow in napari. Defaults to False.
- # workflow_path (Path, optional): User can also choose a custom workflow defined in a yaml file.
- # save_path (Path, optional): Path to save resulting data
- # """
- # if not self.llsz_parent.open_file:
- # raise Exception("Image not initialised")
-
- # check_dimensions(time_start, time_end, ch_start, ch_end,
- # self.llsz_parent.lattice.channels, self.llsz_parent.lattice.time)
-
- # # Get parameters
- # angle = self.llsz_parent.lattice.angle
- # dx = self.llsz_parent.lattice.dx
- # dy = self.llsz_parent.lattice.dy
- # dz = self.llsz_parent.lattice.dz
-
- # user_workflow = get_workflow(self.parent_viewer if get_active_workflow else workflow_path)
-
- # input_arg_first, input_arg_last, first_task_name, last_task_name = get_first_last_image_and_task(
- # user_workflow)
- # logger.info(f"{input_arg_first=}, {input_arg_last=}, {first_task_name=}, {last_task_name=}")
- # logger.info(f"Workflow loaded: {user_workflow}")
-
- # vol = self.llsz_parent.lattice.data
- # task_name_start = first_task_name[0]
-
- # try:
- # task_name_last = last_task_name[0]
- # except IndexError:
- # task_name_last = task_name_start
-
- # # variables to hold task name, initialize it as None
- # # if gpu, set otf_path, otherwise use psf
- # psf = None
- # otf_path = None
-
- # if self.llsz_parent.lattice.decon_processing == DeconvolutionChoice.cuda_gpu:
- # #otf_path = "otf_path"
- # psf_arg = "psf"
- # psf = self.llsz_parent.lattice.psf
- # else:
- # psf_arg = "psf"
- # psf = self.llsz_parent.lattice.psf
- # # if cropping, set that as first task
-
- # if Use_Cropping:
- # # convert Roi pixel coordinates to canvas coordinates
- # # necessary only when scale is used for napari.viewer.add_image operations
- # roi_layer_list = [x/self.llsz_parent.lattice.dy for x in roi_layer_list]
-
- # deskewed_shape = self.llsz_parent.lattice.deskew_vol_shape
- # deskewed_volume = da.zeros(deskewed_shape)
- # z_start = 0
- # z_end = deskewed_shape[0]
- # roi = "roi"
- # volume = "volume"
- # # Check if decon ticked, if so set as first and crop as second?
-
- # # Create workflow for cropping and deskewing
- # # volume and roi used will be set dynamically
- # user_workflow.set("crop_deskew_image", crop_volume_deskew,
- # original_volume=volume,
- # deskewed_volume=deskewed_volume,
- # roi_shape=roi,
- # angle_in_degrees=angle,
- # voxel_size_x=dx,
- # voxel_size_y=dy,
- # voxel_size_z=dz,
- # z_start=z_start,
- # z_end=z_end,
- # deconvolution=self.llsz_parent.deconvolution.value,
- # decon_processing=self.llsz_parent.lattice.decon_processing,
- # psf=psf_arg,
- # skew_dir=self.llsz_parent.skew_dir)
-
- # # change the first task so it accepts "crop_deskew as input"
- # new_task = modify_workflow_task(
- # old_arg=input_arg_first, task_key=task_name_start, new_arg="crop_deskew_image", workflow=user_workflow)
- # user_workflow.set(task_name_start, new_task)
-
- # for idx, roi_layer in enumerate(tqdm(roi_layer_list, desc="ROI:", position=0)):
- # print("Processing ROI ", idx)
- # user_workflow.set(roi, roi_layer)
- # save_img_workflow(vol=vol,
- # workflow=user_workflow,
- # input_arg=volume,
- # first_task="crop_deskew_image",
- # last_task=task_name_last,
- # time_start=time_start,
- # time_end=time_end,
- # channel_start=ch_start,
- # channel_end=ch_end,
- # save_file_type=save_as_type,
- # save_path=save_path,
- # #roi_layer = roi_layer,
- # save_name_prefix="ROI_" + \
- # str(idx),
- # save_name=self.llsz_parent.lattice.save_name,
- # dx=dx,
- # dy=dy,
- # dz=dz,
- # angle=angle,
- # deconvolution=self.llsz_parent.deconvolution.value,
- # decon_processing=self.llsz_parent.lattice.decon_processing,
- # otf_path=otf_path,
- # psf_arg=psf_arg,
- # psf=psf)
-
- # # IF just deskewing and its not in the tasks, add that as first task
- # elif user_workflow.get_task(task_name_start)[0] not in (cle.deskew_y, cle.deskew_x):
- # input = "input"
- # # add task to the workflow
- # user_workflow.set("deskew_image",
- # self.llsz_parent.deskew_func,
- # input_image=input,
- # angle_in_degrees=angle,
- # voxel_size_x=dx,
- # voxel_size_y=dy,
- # voxel_size_z=dz,
- # linear_interpolation=True)
- # # Set input of the workflow to be from deskewing
- # # change workflow task starts from is "deskew_image" and
- # new_task = modify_workflow_task(
- # old_arg=input_arg_first, task_key=task_name_start, new_arg="deskew_image", workflow=user_workflow)
- # user_workflow.set(task_name_start, new_task)
-
- # # if deconvolution checked, add it to start of workflow (add upstream of deskewing)
- # if self.llsz_parent.deconvolution:
- # psf = "psf"
- # otf_path = "otf_path"
- # input_arg_first, input_arg_last, first_task_name, last_task_name = get_first_last_image_and_task(
- # user_workflow)
-
- # if self.llsz_parent.lattice.decon_processing == DeconvolutionChoice.cuda_gpu:
- # user_workflow.set("deconvolution",
- # pycuda_decon,
- # image=input,
- # psf=psf_arg,
- # dzdata=self.llsz_parent.lattice.dz,
- # dxdata=self.llsz_parent.lattice.dx,
- # dzpsf=self.llsz_parent.lattice.dz,
- # dxpsf=self.llsz_parent.lattice.dx,
- # num_iter=self.llsz_parent.lattice.psf_num_iter)
- # # user_workflow.set(input_arg_first,"deconvolution")
- # else:
- # user_workflow.set("deconvolution",
- # skimage_decon,
- # vol_zyx=input,
- # psf=psf_arg,
- # num_iter=self.llsz_parent.lattice.psf_num_iter,
- # clip=False,
- # filter_epsilon=0,
- # boundary='nearest')
- # # modify the user workflow so "deconvolution" is accepted
- # new_task = modify_workflow_task(
- # old_arg=input_arg_first, task_key=task_name_start, new_arg="deconvolution", workflow=user_workflow)
- # user_workflow.set(task_name_start, new_task)
- # input_arg_first, input_arg_last, first_task_name, last_task_name = get_first_last_image_and_task(
- # user_workflow)
- # task_name_start = first_task_name[0]
-
- # save_img_workflow(vol=vol,
- # workflow=user_workflow,
- # input_arg=input,
- # first_task=task_name_start,
- # last_task=task_name_last,
- # time_start=time_start,
- # time_end=time_end,
- # channel_start=ch_start,
- # channel_end=ch_end,
- # save_file_type=save_as_type,
- # save_path=save_path,
- # save_name=self.llsz_parent.lattice.save_name,
- # dx=dx,
- # dy=dy,
- # dz=dz,
- # angle=angle,
- # deconvolution=self.llsz_parent.deconvolution,
- # decon_processing=self.llsz_parent.lattice.decon_processing,
- # otf_path=otf_path,
- # psf_arg=psf_arg,
- # psf=psf)
-
- # # If deskewing is already as a task, then set the first argument to input so we can modify that later
- # else:
- # # if deskewing is already first task, then check if deconvolution needed
- # # if deconvolution checked, add it to start of workflow (add upstream of deskewing)
- # if self.llsz_parent.deconvolution:
- # psf = "psf"
- # otf_path = "otf_path"
- # input_arg_first, input_arg_last, first_task_name, last_task_name = get_first_last_image_and_task(
- # user_workflow)
-
- # if self.llsz_parent.lattice.decon_processing == DeconvolutionChoice.cuda_gpu:
- # user_workflow.set("deconvolution",
- # pycuda_decon,
- # image=input,
- # psf=psf_arg,
- # dzdata=self.llsz_parent.lattice.dz,
- # dxdata=self.llsz_parent.lattice.dx,
- # dzpsf=self.llsz_parent.lattice.dz,
- # dxpsf=self.llsz_parent.lattice.dx,
- # num_iter=self.llsz_parent.lattice.psf_num_iter)
- # # user_workflow.set(input_arg_first,"deconvolution")
- # else:
- # user_workflow.set("deconvolution",
- # skimage_decon,
- # vol_zyx=input,
- # psf=psf_arg,
- # num_iter=self.llsz_parent.lattice.psf_num_iter,
- # clip=False,
- # filter_epsilon=0,
- # boundary='nearest')
- # # modify the user workflow so "deconvolution" is accepted
- # new_task = modify_workflow_task(
- # old_arg=input_arg_first, task_key=task_name_start, new_arg="deconvolution", workflow=user_workflow)
- # user_workflow.set(task_name_start, new_task)
- # input_arg_first, input_arg_last, first_task_name, last_task_name = get_first_last_image_and_task(
- # user_workflow)
- # task_name_start = first_task_name[0]
-
- # # we pass first argument as input
- # save_img_workflow(vol=vol,
- # workflow=user_workflow,
- # input_arg=input_arg_first,
- # first_task=task_name_start,
- # last_task=task_name_last,
- # time_start=time_start,
- # time_end=time_end,
- # channel_start=ch_start,
- # channel_end=ch_end,
- # save_file_type=save_as_type,
- # save_path=save_path,
- # save_name=self.llsz_parent.lattice.save_name,
- # dx=dx,
- # dy=dy,
- # dz=dz,
- # angle=angle,
- # deconvolution=self.llsz_parent.deconvolution,
- # decon_processing=self.llsz_parent.lattice.decon_processing,
- # otf_path=otf_path,
- # psf_arg=psf_arg,
- # psf=psf)
-
- # print("Workflow complete")
- # return
-
+ lattice = self._make_model()
+ lattice.process().save_image()
def _napari_lattice_widget_wrapper() -> LLSZWidget:
# split widget type enables a resizable widget
diff --git a/plugin/napari_lattice/fields.py b/plugin/napari_lattice/fields.py
index b69deac1..f95251cf 100644
--- a/plugin/napari_lattice/fields.py
+++ b/plugin/napari_lattice/fields.py
@@ -1,38 +1,38 @@
-from __future__ import annotations
-from pathlib import Path
-from magicclass import FieldGroup, field, MagicTemplate
-from magicclass.widgets import Widget, ComboBox, Label, Select
-from magicclass.fields import MagicField
-from typing import Any, Callable, List, Optional, Protocol, Tuple, TypeVar, Union, cast, TYPE_CHECKING
-from typing_extensions import Protocol, Self
-from pydantic import BaseModel, ValidationError
+# FieldGroups that the users interact with to input data
-from strenum import StrEnum
-from enum import auto
-from lls_core import DeconvolutionChoice, SaveFileType, Log_Levels, DeskewDirection
-from lls_core.lattice_data import CropParams, DeconvolutionParams, DefinedPixelSizes, LatticeData, OutputParams, DeskewParams
-from napari.layers import Shapes
+import logging
from enum import Enum
+from pathlib import Path
+from typing import Any, Callable, List, Optional, Tuple, TypeVar, cast
+
import pyclesperanto_prototype as cle
-from napari_workflows import Workflow, WorkflowManager
-from napari.types import ImageData, ShapesData
+from lls_core import (
+ DeconvolutionChoice,
+ DeskewDirection,
+ Log_Levels,
+ SaveFileType,
+)
+from lls_core.lattice_data import (
+ CropParams,
+ DeconvolutionParams,
+ DefinedPixelSizes,
+ DeskewParams,
+ LatticeData,
+ OutputParams,
+)
+from magicclass import FieldGroup, MagicTemplate, field
+from magicclass.fields import MagicField
+from magicclass.widgets import ComboBox, Label, Widget
+from napari.layers import Image, Shapes
+from napari.types import ShapesData
from napari.utils import history
-from abc import ABC
+from napari_lattice.icons import GREEN, GREY, RED
+from napari_lattice.reader import NapariImageParams, lattice_params_from_napari
+from napari_lattice.utils import get_layers
+from napari_workflows import Workflow, WorkflowManager
+from pydantic import ValidationError
from qtpy.QtWidgets import QTabWidget
-
-from napari_lattice.icons import RED, GREEN, GREY
-from napari_lattice.reader import lattice_params_from_napari
-from napari_lattice.utils import get_viewer, get_layers
-
-from napari.layers import Image
-
-if TYPE_CHECKING:
- from xarray import DataArray
-
-
-# FieldGroups that the users interact with to input data
-
-import logging
+from strenum import StrEnum
logger = logging.getLogger(__name__)
logger.setLevel(logging.INFO)
@@ -137,17 +137,6 @@ class LastDimensionOptions(Enum):
XYZCT = "XYZCT"
Metadata = "Get from Metadata"
-# class NapariFields(FieldGroup, ABC):
-# def __init__(self, layout: str = "vertical", labels: bool = False, name: str | None = None, **kwargs):
-# super().__init__(layout, labels, name, **kwargs)
-
-# class NapariFieldGroupCompatible(Protocol):
-# from magicclass.widgets import Container
-# from qtpy.QtWidgets import QWidget
-# errors: MagicField[Label]
-# parent: QWidget
-# _widget: Container
-
class NapariFieldGroup:
# This implementation is a bit ugly. This is a mixin that can only be used on a `FieldGroup`.
# However, it can't inherit from FieldGroup because then the metaclass would look for fields in this
@@ -204,6 +193,10 @@ def _validate(self: Any):
def _make_model(self):
raise NotImplementedError()
+class DeskewKwargs(NapariImageParams):
+ angle: float
+ skew: DeskewDirection
+
class DeskewFields(NapariFieldGroup, FieldGroup):
def _get_dimension_options(self, _) -> List[str]:
@@ -267,8 +260,8 @@ def _get_dimension_options(self, _) -> List[str]:
def __init__(self, *args: Any, **kwargs: Any):
super().__init__(*args, **kwargs)
- from qtpy.QtWidgets import QDoubleSpinBox
from magicgui.widgets import TupleEdit
+ from qtpy.QtWidgets import QDoubleSpinBox
# Enormous hack to set the precision
# A better method has been requested here: https://github.com/pyapp-kit/magicgui/issues/581#issuecomment-1709467219
@@ -287,34 +280,21 @@ def __init__(self, *args: Any, **kwargs: Any):
@img_layer.connect
def _img_changed(self) -> None:
- # Recalculate the dimension options
+ # Recalculate the dimension options whenever the image changes
self.dimension_order.reset_choices()
@img_layer.connect
@enable_if([stack_along])
def _hide_stack_along(self):
+ # Hide the "Stack Along" option if we only have one image
return len(self.img_layer.value) > 1
- def _merge_layers(self) -> DataArray:
+ def _get_kwargs(self) -> DeskewKwargs:
"""
- Returns a single image array merged from all the selected layers
+ Returns the LatticeData fields that the Deskew tab can provide
"""
- from xarray import DataArray, concat
- layers = [DataArray(it, dims=self.dimension_order.value.split()) for it in self.img_layer.value]
- if len(layers) == 0:
- raise Exception("At least one image layer must be selected")
- elif len(layers) == 1:
- return layers[0]
- else:
- dim = "C" if self.stack_along.value == StackAlong.CHANNEL else "T"
- return concat(layers, dim=dim)
-
- # if len(self.img_layer.value) == 0:
- # raise Exception("At least one image layer must be selected.")
- # return images_to_stack(self.img_layer.value)
-
- def _make_model(self) -> DeskewParams:
from aicsimageio.types import PhysicalPixelSizes
+ DeskewParams.update_forward_refs()
params = lattice_params_from_napari(
imgs=self.img_layer.value,
dimension_order=None if self.dimension_order.value == "Get from Metadata" else self.dimension_order.value,
@@ -325,14 +305,21 @@ def _make_model(self) -> DeskewParams:
),
stack_along="C" if self.stack_along.value == StackAlong.CHANNEL else "T"
)
- return DeskewParams(
- data=params["data"],
- dims=params["dims"],
- physical_pixel_sizes=params["physical_pixel_sizes"],
+ return DeskewKwargs(
+ **params,
angle=self.angle.value,
skew = self.skew_dir.value,
)
+ def _make_model(self) -> DeskewParams:
+ kwargs = self._get_kwargs()
+ return DeskewParams(
+ data=kwargs["data"],
+ physical_pixel_sizes=kwargs["physical_pixel_sizes"],
+ angle=kwargs["angle"],
+ skew = kwargs["skew"]
+ )
+
class DeconvolutionFields(NapariFieldGroup, FieldGroup):
"""
A counterpart to the DeconvolutionParams Pydantic class
@@ -350,11 +337,6 @@ class DeconvolutionFields(NapariFieldGroup, FieldGroup):
)
errors = field(Label).with_options(label="Errors")
-
- # @background.connect
- # def _show_custom_background(self):
- # self.background_custom.visible = self.background == BackgroundSource.Custom
-
@background.connect
@enable_if(
[background_custom]
@@ -362,10 +344,6 @@ class DeconvolutionFields(NapariFieldGroup, FieldGroup):
def _enable_custom_background(self) -> bool:
return self.background.value == BackgroundSource.Custom
- # @fields_enabled.connect
- # def _enable_fields(self) -> bool:
- # self.decon_processing.visible = self.fields_enabled
-
@fields_enabled.connect
@enable_if(
fields = [
@@ -414,32 +392,6 @@ class CroppingFields(NapariFieldGroup, FieldGroup):
def _enable_workflow(self) -> bool:
return self.fields_enabled.value
-
- # roi_layer_list: ShapesData
- # @magicclass(visible=False)
- # class Fields(MagicTemplate):
- # shapes= vfield(ShapesData, label = "ROIs")#Optional[Shapes] = None
- # z_range = vfield(Tuple[int, int]).with_options(
- # label = "Z Range",
- # value = (0, 1),
- # options = dict(
- # min = 0,
- # max = 1
- # ),
- # )
- # _shapes_layer: Optional[Shapes] = None
-
- # @set_design(font_size=10, text="Click to activate Cropping Layer", background_color="magenta")
- # @click(enables=["Import_ImageJ_ROI", "Crop_Preview"])
- # @set_design(text="New Cropping Layer")
- # def activate_cropping(self):
- # self._shapes_layer = self.parent_viewer.add_shapes(shape_type='polygon', edge_width=1, edge_color='white',
- # face_color=[1, 1, 1, 0], name="Cropping BBOX layer")
- # # TO select ROIs if needed
- # self._shapes_layer.mode = "SELECT"
- # self["activate_cropping"].text = "Cropping layer active"
- # self["activate_cropping"].background_color = "green"
-
def _make_model(self) -> Optional[CropParams]:
import numpy as np
if self.fields_enabled.value:
@@ -519,6 +471,3 @@ def _make_model(self) -> OutputParams:
save_name=self.save_name.value,
save_type=self.save_type.value
)
-
-
-# @DeskewWidget.img_layer.connect
diff --git a/plugin/napari_lattice/reader.py b/plugin/napari_lattice/reader.py
index 1c7cee27..dac3452b 100644
--- a/plugin/napari_lattice/reader.py
+++ b/plugin/napari_lattice/reader.py
@@ -41,6 +41,10 @@ def lattice_params_from_napari(
if len(imgs) < 1:
raise ValueError("At least one image must be provided.")
+ if len(set(len(it.data.shape) for it in imgs)) > 1:
+ size_message = ",".join(f"{img.name}: {len(img.data.shape)}" for img in imgs)
+ raise ValueError(f"The input images have multiple different dimensions, which napari lattice doesn't support: {size_message}")
+
save_name: str
pixel_sizes: set[PhysicalPixelSizes] = {physical_pixel_sizes}
save_names = []
@@ -74,11 +78,11 @@ def lattice_params_from_napari(
# else:
# pixel_size_metadata = img_data_aics.physical_pixel_sizes
- calculated_order = img_data_aics.dims.order
+ calculated_order = tuple(img_data_aics.dims.order)
elif dimension_order is None:
raise ValueError("Either the Napari image must have dimensional metadata, or a dimension order must be provided")
else:
- calculated_order = list(dimension_order)
+ calculated_order = tuple(dimension_order)
final_imgs.append(DataArray(img.data, dims=calculated_order))