From 43cb4a2670550b955a3c5921c15bca63d307f22c Mon Sep 17 00:00:00 2001 From: Michael Milton Date: Wed, 19 Jul 2023 17:11:14 +1000 Subject: [PATCH 001/147] Rename underscore modules --- .../{_dock_widget.py => dock_widget.py} | 2308 ++++++++--------- .../napari_lattice/{_reader.py => reader.py} | 278 +- 2 files changed, 1293 insertions(+), 1293 deletions(-) rename plugin/napari_lattice/{_dock_widget.py => dock_widget.py} (98%) rename plugin/napari_lattice/{_reader.py => reader.py} (97%) diff --git a/plugin/napari_lattice/_dock_widget.py b/plugin/napari_lattice/dock_widget.py similarity index 98% rename from plugin/napari_lattice/_dock_widget.py rename to plugin/napari_lattice/dock_widget.py index 251752c..69b5e64 100644 --- a/plugin/napari_lattice/_dock_widget.py +++ b/plugin/napari_lattice/dock_widget.py @@ -1,1154 +1,1154 @@ -import os -import sys -import yaml -import numpy as np -from pathlib import Path -import dask.array as da -import pandas as pd -from typing import Union, Optional, Callable, Literal -from aicsimageio import AICSImage -from enum import Enum - -from magicclass.wrappers import set_design -from magicgui import magicgui -from magicclass import magicclass, field, vfield, set_options, MagicTemplate -from magicclass.utils import click -from qtpy.QtCore import Qt - -from napari.layers import Layer, Shapes -from napari.types import ImageData -from napari.utils import history - -import pyclesperanto_prototype as cle - -from napari.types import ImageData, ShapesData - -from tqdm import tqdm - -from napari_workflows import Workflow, WorkflowManager -from napari_workflows._io_yaml_v1 import load_workflow - -from lls_core import config, DeskewDirection, DeconvolutionChoice, SaveFileType, Log_Levels -from lls_core.io import LatticeData, save_img, save_img_workflow -from lls_core.utils import read_imagej_roi, get_first_last_image_and_task, modify_workflow_task, get_all_py_files, as_type, process_custom_workflow_output, check_dimensions, load_custom_py_modules -from lls_core.llsz_core import crop_volume_deskew -from lls_core.deconvolution import read_psf, pycuda_decon, skimage_decon - -from napari_lattice.ui_core import _Preview, _Deskew_Save -from napari_lattice._reader import lattice_from_napari - -# Enable Logging -import logging -logger = logging.getLogger(__name__) -logger.setLevel(logging.INFO) - -class LastDimensionOptions(Enum): - channel = "Channel" - time = "Time" - get_from_metadata = "Get from Metadata" - -@magicclass(widget_type="split") -class LLSZWidget(MagicTemplate): - - @magicclass(widget_type="split") - class LlszMenu(MagicTemplate): - open_file: bool = False - lattice: LatticeData = None - skew_dir: DeskewDirection - angle_value: float - deskew_func: Callable - - main_heading = field("

Napari Lattice: Visualization & Analysis

", widget_type="Label") - heading1 = field("Drag and drop an image file onto napari.\nOnce image has opened, initialize the\nplugin by clicking the button below.\nEnsure the image layer and voxel sizes are accurate in the prompt.\n If everything initalises properly, the button turns green.", widget_type="Label") - - @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 = LastDimensionOptions.get_from_metadata, - 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") - - if self.parent_viewer is None: - raise Exception("This function can only be used when inside of a Napari viewer") - - # Select device for processing - cle.select_device(select_device) - - #assert skew_dir in DeskewDirection, "Skew direction not recognised. Enter either Y or X" - LLSZWidget.LlszMenu.skew_dir = skew_dir - LLSZWidget.LlszMenu.angle_value = angle - - if LLSZWidget.LlszMenu.skew_dir == DeskewDirection.Y: - LLSZWidget.LlszMenu.deskew_func = cle.deskew_y - #LLSZWidget.LlszMenu.skew_dir = DeskewDirection.Y - elif LLSZWidget.LlszMenu.skew_dir == DeskewDirection.X: - LLSZWidget.LlszMenu.deskew_func = cle.deskew_x - #LLSZWidget.LlszMenu.skew_dir = DeskewDirection.X - - # 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 - - LLSZWidget.LlszMenu.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=LLSZWidget.LlszMenu.skew_dir, - physical_pixel_sizes=(pixel_size_dx, pixel_size_dy, pixel_size_dz) - ) - #LLSZWidget.LlszMenu.aics = LLSZWidget.LlszMenu.lattice.data - - # LLSZWidget.LlszMenu.dask = False # Use GPU by default - - # We initialise these variables here, but they can be changed in the deconvolution section - # list to store psf images for each channel - LLSZWidget.LlszMenu.lattice.psf = [] - LLSZWidget.LlszMenu.lattice.psf_num_iter = 10 - LLSZWidget.LlszMenu.lattice.decon_processing = DeconvolutionChoice.cpu - # list to store otf paths for each channel (Deprecated) - LLSZWidget.LlszMenu.lattice.otf_path = [] - # if not using GPU - #LLSZWidget.LlszMenu.dask = not use_GPU - - # flag for ensuring a file has been opened and plugin initialised - LLSZWidget.LlszMenu.open_file = True - - logger.info( - f"Pixel size (ZYX) in microns: {LLSZWidget.LlszMenu.lattice.dz,LLSZWidget.LlszMenu.lattice.dy,LLSZWidget.LlszMenu.lattice.dx}") - logger.info( - f"Dimensions of image layer (ZYX): {list(LLSZWidget.LlszMenu.lattice.data.shape[-3:])}") - logger.info( - f"Dimensions of deskewed image (ZYX): {LLSZWidget.LlszMenu.lattice.deskew_vol_shape}") - logger.info( - f"Deskewing angle is :{LLSZWidget.LlszMenu.lattice.angle}") - logger.info( - f"Deskew Direction :{LLSZWidget.LlszMenu.lattice.skew}") - # Add dimension labels correctly - # if channel, and not time - if LLSZWidget.LlszMenu.lattice.time == 0 and (last_dimension_channel or LLSZWidget.LlszMenu.lattice.channels > 0): - self.parent_viewer.dims.axis_labels = ('Channel', "Z", "Y", "X") - # if no channel, but has time - elif LLSZWidget.LlszMenu.lattice.channels == 0 and LLSZWidget.LlszMenu.lattice.time > 0: - self.parent_viewer.dims.axis_labels = ('Time', "Z", "Y", "X") - # if it has channels - elif LLSZWidget.LlszMenu.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]) == LLSZWidget.LlszMenu.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 LLSZWidget.LlszMenu.lattice.channels == 1 and LLSZWidget.LlszMenu.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" - - return - - # Pycudadecon library for deconvolution - # options={"enabled": True}, - deconvolution = vfield(bool, name="Use Deconvolution") - deconvolution.value = False - - @deconvolution.connect - def _set_decon(self): - if self.deconvolution: - logger.info("Deconvolution Activated") - LLSZWidget.LlszMenu.deconvolution.value = True - else: - logger.info("Deconvolution Disabled") - LLSZWidget.LlszMenu.deconvolution.value = False - return - - @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, - psf_ch1_path: Path, - psf_ch2_path: Path, - psf_ch3_path: Path, - psf_ch4_path: Path, - device_option, - no_iter: int): - """GUI for Deconvolution button""" - LLSZWidget.LlszMenu.lattice.decon_processing = device_option - assert LLSZWidget.LlszMenu.deconvolution.value == True, "Deconvolution is set to False. Tick the box to activate deconvolution." - LLSZWidget.LlszMenu.lattice.psf = list(read_psf([ - psf_ch1_path, - psf_ch2_path, - psf_ch3_path, - psf_ch4_path, - ], - device_option, - lattice_class=LLSZWidget.LlszMenu.lattice - )) - LLSZWidget.LlszMenu.lattice.psf_num_iter = no_iter - self["deconvolution_gui"].background_color = "green" - self["deconvolution_gui"].text = "PSFs added" - - @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, - time: int, - channel: int, - img_data: ImageData): - """ - Preview deskewed data for a single timepoint and channel - - """ - _Preview(LLSZWidget, - self, - time, - channel, - img_data) - return - - # Tabbed Widget container to house all the widgets - @magicclass(widget_type="tabbed", name="Functions") - class WidgetContainer(MagicTemplate): - - @magicclass(name="Deskew", widget_type="scrollable", properties={"min_width": 100}) - class DeskewWidget(MagicTemplate): - - @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, - 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) - return - - @magicclass(name="Crop and Deskew", widget_type="scrollable") - class CropWidget(MagicTemplate): - - # add function for previewing cropped image - @magicclass(name="Cropping Preview", widget_type="scrollable", properties={ - "min_width": 100, - "shapes_layer": Shapes - }) - class Preview_Crop_Menu(MagicTemplate): - shapes_layer: Shapes - - @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): - LLSZWidget.WidgetContainer.CropWidget.Preview_Crop_Menu.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 - LLSZWidget.WidgetContainer.CropWidget.Preview_Crop_Menu.shapes_layer.mode = "SELECT" - self["activate_cropping"].text = "Cropping layer active" - self["activate_cropping"].background_color = "green" - return - - 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])): - logger.info(f"Opening{path}") - roi_list = read_imagej_roi(path) - # convert to canvas coordinates - roi_list = (np.array(roi_list) * - LLSZWidget.LlszMenu.lattice.dy).tolist() - LLSZWidget.WidgetContainer.CropWidget.Preview_Crop_Menu.shapes_layer.add(roi_list, shape_type='polygon', edge_width=1, edge_color='yellow', - face_color=[1, 1, 1, 0]) - return - - 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) - # -> LayerDataTuple: - def Crop_Preview(self, roi_layer: ShapesData): - assert roi_layer, "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 - time = self.time_crop.value - channel = self.chan_crop.value - - assert time < LLSZWidget.LlszMenu.lattice.time, "Time is out of range" - assert channel < LLSZWidget.LlszMenu.lattice.channels, "Channel is out of range" - - logger.info(f"Using channel {channel} and time {time}") - - vol = LLSZWidget.LlszMenu.lattice.data - vol_zyx = vol[time, channel, ...] - vol_zyx = np.array(vol_zyx) - - deskewed_shape = LLSZWidget.LlszMenu.lattice.deskew_vol_shape - # Create a dask array same shape as deskewed image - deskewed_volume = da.zeros(deskewed_shape) - - # Option for entering custom z start value? - z_start = 0 - z_end = deskewed_shape[0] - - # if only one roi drawn, use the first ROI for cropping - if len(roi_layer) == 1: - roi_idx = 0 - else: - assert len( - LLSZWidget.WidgetContainer.CropWidget.Preview_Crop_Menu.shapes_layer.selected_data) > 0, "Please select an ROI" - roi_idx = list( - LLSZWidget.WidgetContainer.CropWidget.Preview_Crop_Menu.shapes_layer.selected_data)[0] - - roi_choice = roi_layer[roi_idx] - # 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/LLSZWidget.LlszMenu.lattice.dy for x in roi_choice] - logger.info(f"Previewing ROI {roi_idx}") - - # crop - if LLSZWidget.LlszMenu.deconvolution.value: - logger.info( - f"Deskewing for Time:{time} and Channel: {channel} with deconvolution") - #psf = LLSZWidget.LlszMenu.lattice.psf[channel] - if LLSZWidget.LlszMenu.lattice.decon_processing == DeconvolutionChoice.cuda_gpu: - crop_roi_vol_desk = crop_volume_deskew(original_volume=vol_zyx, - deskewed_volume=deskewed_volume, - roi_shape=roi_choice, - angle_in_degrees=LLSZWidget.LlszMenu.angle_value, - voxel_size_x=LLSZWidget.LlszMenu.lattice.dx, - voxel_size_y=LLSZWidget.LlszMenu.lattice.dy, - voxel_size_z=LLSZWidget.LlszMenu.lattice.dz, - z_start=z_start, - z_end=z_end, - deconvolution=LLSZWidget.LlszMenu.deconvolution.value, - decon_processing=LLSZWidget.LlszMenu.lattice.decon_processing, - psf=LLSZWidget.LlszMenu.lattice.psf[channel], - num_iter=LLSZWidget.LlszMenu.lattice.psf_num_iter).astype(vol_zyx.dtype) - else: - crop_roi_vol_desk = crop_volume_deskew(original_volume=vol_zyx, - deskewed_volume=deskewed_volume, - roi_shape=roi_choice, - angle_in_degrees=LLSZWidget.LlszMenu.angle_value, - voxel_size_x=LLSZWidget.LlszMenu.lattice.dx, - voxel_size_y=LLSZWidget.LlszMenu.lattice.dy, - voxel_size_z=LLSZWidget.LlszMenu.lattice.dz, - z_start=z_start, - z_end=z_end, - deconvolution=LLSZWidget.LlszMenu.deconvolution.value, - decon_processing=LLSZWidget.LlszMenu.lattice.decon_processing, - psf=LLSZWidget.LlszMenu.lattice.psf[channel], - num_iter=LLSZWidget.LlszMenu.lattice.psf_num_iter).astype(vol_zyx.dtype) - else: - crop_roi_vol_desk = crop_volume_deskew(original_volume=vol_zyx, - deskewed_volume=deskewed_volume, - roi_shape=roi_choice, - angle_in_degrees=LLSZWidget.LlszMenu.angle_value, - voxel_size_x=LLSZWidget.LlszMenu.lattice.dx, - voxel_size_y=LLSZWidget.LlszMenu.lattice.dy, - voxel_size_z=LLSZWidget.LlszMenu.lattice.dz, - z_start=z_start, - z_end=z_end, - skew_dir=LLSZWidget.LlszMenu.skew_dir).astype(vol_zyx.dtype) - crop_roi_vol_desk = cle.pull(crop_roi_vol_desk) - - # get array back from gpu or addding cle array to napari can throw errors - - scale = (LLSZWidget.LlszMenu.lattice.new_dz, - LLSZWidget.LlszMenu.lattice.dy, - LLSZWidget.LlszMenu.lattice.dx) - self.parent_viewer.add_image( - crop_roi_vol_desk, scale=scale) - - @magicclass(name="Crop and Save Data") - class CropSaveData(MagicTemplate): - @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, - time_start: int, - time_end: int, - ch_start: int, - ch_end: int, - save_as_type: str, - 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: - assert LLSZWidget.LlszMenu.open_file, "Image not initialised" - - check_dimensions(time_start, time_end, ch_start, ch_end, - LLSZWidget.LlszMenu.lattice.channels, LLSZWidget.LlszMenu.lattice.time) - - angle = LLSZWidget.LlszMenu.lattice.angle - dx = LLSZWidget.LlszMenu.lattice.dx - dy = LLSZWidget.LlszMenu.lattice.dy - dz = LLSZWidget.LlszMenu.lattice.dz - - # get image data - img_data = LLSZWidget.LlszMenu.lattice.data - # Get shape of deskewed image - deskewed_shape = LLSZWidget.LlszMenu.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 = [ - x/LLSZWidget.LlszMenu.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=LLSZWidget.LlszMenu.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=LLSZWidget - ) - - logger.info( - f"Cropping and Saving Complete -> {save_path}") - return - - @magicclass(name="Workflow", widget_type="scrollable") - class WorkflowWidget: - @magicclass(name="Preview Workflow", widget_type="scrollable") - class PreviewWorkflow: - #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, - 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 - Args: - - """ - print("Previewing deskewed channel and time with workflow") - if get_active_workflow: - # installs the workflow to napari - user_workflow = WorkflowManager.install( - self.parent_viewer).workflow - parent_dir = workflow_path.resolve( - ).parents[0].__str__()+os.sep - logger.info("Workflow loaded from napari") - else: - - try: - # Automatically scan workflow file directory for *.py files. - # If it findss one, load it as a module - - parent_dir = workflow_path.resolve( - ).parents[0].__str__()+os.sep - sys.path.append(parent_dir) - custom_py_files = get_all_py_files(parent_dir) - if len(custom_py_files) == 0: - logger.error( - f"No custom modules imported. If you'd like to use a cusotm module, place a *.py file in same folder as the workflow file {parent_dir}") - else: - modules = load_custom_py_modules( - custom_py_files) - - logger.info( - f"Custom modules imported {modules}") - user_workflow = load_workflow( - workflow_path.__str__()) - except yaml.loader.ConstructorError as e: - logger.error( - "\033[91m While loading workflow, got the following error which may mean you need to install the corresponding module in your Python environment: \033[0m") - logger.error(e) - - #user_workflow = load_workflow(workflow_path) - logger.info("Workflow loaded from file") - - assert type( - user_workflow) is Workflow, "Workflow loading error. Check if file is workflow or if required libraries are installed" - - input_arg_first, input_arg_last, first_task_name, last_task_name = get_first_last_image_and_task( - user_workflow) - #print(input_arg_first, input_arg_last, first_task_name,last_task_name ) - # get list of tasks - task_list = list(user_workflow._tasks.keys()) - logger.info(f"Workflow loaded:{user_workflow}") - # logger.info() - - # when using fields, self.time_preview.value - assert time_preview < LLSZWidget.LlszMenu.lattice.time, "Time is out of range" - assert chan_preview < LLSZWidget.LlszMenu.lattice.channels, "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}") - - vol = LLSZWidget.LlszMenu.lattice.data - vol_zyx = vol[time, channel, ...] - vol_zyx = np.array(vol_zyx) - - 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 LLSZWidget.LlszMenu.lattice.decon_processing == DeconvolutionChoice.cuda_gpu: - otf_path = "otf_path" - else: - psf = "psf" - - # if cropping, set that as first task - # get the function associated with the first task and check if its deskewing - if Use_Cropping: - # use deskewed volume for cropping function - deskewed_shape = LLSZWidget.LlszMenu.lattice.deskew_vol_shape - deskewed_volume = da.zeros(deskewed_shape) - z_start = 0 - z_end = deskewed_shape[0] - if user_workflow.get_task(task_name_start)[0] not in [crop_volume_deskew]: - # if only one roi drawn, use the first ROI for cropping - if len(roi_layer_list) == 1: - roi_idx = 0 - else: # else get the user selection - assert len( - LLSZWidget.WidgetContainer.CropWidget.Preview_Crop_Menu.shapes_layer.selected_data) > 0, "Please select an ROI" - roi_idx = list( - LLSZWidget.WidgetContainer.CropWidget.Preview_Crop_Menu.shapes_layer.selected_data)[0] - - roi_choice = roi_layer_list[roi_idx] - # As the original image is scaled, the coordinates are in microns, so we need to convert - # roi to from micron to canvas/world coordinates - roi_choice = [ - x/LLSZWidget.LlszMenu.lattice.dy for x in roi_choice] - logger.info(f"Previewing ROI {roi_idx}") - if LLSZWidget.LlszMenu.deconvolution.value: - user_workflow.set("crop_deskew_image", crop_volume_deskew, - original_volume=vol_zyx, - deskewed_volume=deskewed_volume, - roi_shape=roi_choice, - angle_in_degrees=LLSZWidget.LlszMenu.lattice.angle, - voxel_size_x=LLSZWidget.LlszMenu.lattice.dx, - voxel_size_y=LLSZWidget.LlszMenu.lattice.dy, - voxel_size_z=LLSZWidget.LlszMenu.lattice.dz, - z_start=z_start, - z_end=z_end, - deconvolution=LLSZWidget.LlszMenu.deconvolution.value, - decon_processing=LLSZWidget.LlszMenu.lattice.decon_processing, - otf_path=otf_path, - psf=LLSZWidget.LlszMenu.lattice.psf[channel], - skew_dir=LLSZWidget.LlszMenu.skew_dir) - else: - user_workflow.set("crop_deskew_image", crop_volume_deskew, - original_volume=vol_zyx, - deskewed_volume=deskewed_volume, - roi_shape=roi_choice, - angle_in_degrees=LLSZWidget.LlszMenu.lattice.angle, - voxel_size_x=LLSZWidget.LlszMenu.lattice.dx, - voxel_size_y=LLSZWidget.LlszMenu.lattice.dy, - voxel_size_z=LLSZWidget.LlszMenu.lattice.dz, - z_start=z_start, - z_end=z_end, - skew_dir=LLSZWidget.LlszMenu.skew_dir) - - # Set input of the workflow to be crop_deskewing, i.e., the original first operation will now have crop_deskew_image as an input (becoming second instead) - user_workflow.set( - input_arg_first, "crop_deskew_image") - else: - user_workflow.set(input_arg_first, vol_zyx) - # Not cropping; If deskew not in workflow, append to start - elif user_workflow.get_task(task_name_start)[0] not in (cle.deskew_y, cle.deskew_x): - # if deconvolution checked, add it to start of workflow (add upstream of deskewing) - if LLSZWidget.LlszMenu.deconvolution.value: - psf = LLSZWidget.LlszMenu.lattice.psf[channel] - input_arg_first_decon, input_arg_last_decon, first_task_name_decon, last_task_name_decon = get_first_last_image_and_task( - user_workflow) - - if LLSZWidget.LlszMenu.lattice.decon_processing == DeconvolutionChoice.cuda_gpu: - user_workflow.set("deconvolution", - pycuda_decon, - image=vol_zyx, - psf=LLSZWidget.LlszMenu.lattice.psf[channel], - dzdata=LLSZWidget.LlszMenu.lattice.dz, - dxdata=LLSZWidget.LlszMenu.lattice.dx, - dzpsf=LLSZWidget.LlszMenu.lattice.dz, - dxpsf=LLSZWidget.LlszMenu.lattice.dx, - num_iter=LLSZWidget.LlszMenu.lattice.psf_num_iter) - # user_workflow.set(input_arg_first_decon,"deconvolution") - else: - user_workflow.set("deconvolution", - skimage_decon, - vol_zyx=vol_zyx, - psf=LLSZWidget.LlszMenu.lattice.psf[channel], - num_iter=LLSZWidget.LlszMenu.lattice.psf_num_iter, - clip=False, - filter_epsilon=0, - boundary='nearest') - # user_workflow.set(input_arg_first_decon,"deconvolution") - - user_workflow.set("deskew_image", - LLSZWidget.LlszMenu.deskew_func, - "deconvolution", - angle_in_degrees=LLSZWidget.LlszMenu.lattice.angle, - voxel_size_x=LLSZWidget.LlszMenu.lattice.dx, - voxel_size_y=LLSZWidget.LlszMenu.lattice.dy, - voxel_size_z=LLSZWidget.LlszMenu.lattice.dz, - linear_interpolation=True) - - # user_workflow.set("change_bitdepth",as_type,"deskew_image",vol_zyx) - # Set input of the workflow to be from deskewing output with same bit depth as original volume - # user_workflow.set(input_arg_first,"change_bitdepth") - - else: - user_workflow.set("deskew_image", - LLSZWidget.LlszMenu.deskew_func, - vol_zyx, - angle_in_degrees=LLSZWidget.LlszMenu.lattice.angle, - voxel_size_x=LLSZWidget.LlszMenu.lattice.dx, - voxel_size_y=LLSZWidget.LlszMenu.lattice.dy, - voxel_size_z=LLSZWidget.LlszMenu.lattice.dz, - linear_interpolation=True) - # Set input of the workflow to be from deskewing - # user_workflow.set(input_arg_first,"deskew_image") - - user_workflow.set( - "change_bitdepth", as_type, "deskew_image", vol_zyx) - # Set input of the workflow to be from deskewing with same bit depth as original volume - user_workflow.set( - input_arg_first, "change_bitdepth") - - else: - # if deskew already in workflow, just check if deconvolution needs to be added - # repitition of above (maybe create a function?) - # if deconvolution checked, add it to start of workflow (add upstream of deskewing) - if LLSZWidget.LlszMenu.deconvolution.value: - psf = LLSZWidget.LlszMenu.lattice.psf[channel] - input_arg_first, input_arg_last, first_task_name, last_task_name = get_first_last_image_and_task( - user_workflow) - - if LLSZWidget.LlszMenu.lattice.decon_processing == DeconvolutionChoice.cuda_gpu: - user_workflow.set("deconvolution", - pycuda_decon, - image=vol_zyx, - psf=LLSZWidget.LlszMenu.lattice.psf[channel], - dzdata=LLSZWidget.LlszMenu.lattice.dz, - dxdata=LLSZWidget.LlszMenu.lattice.dx, - dzpsf=LLSZWidget.LlszMenu.lattice.dz, - dxpsf=LLSZWidget.LlszMenu.lattice.dx, - num_iter=LLSZWidget.LlszMenu.lattice.psf_num_iter) - # user_workflow.set(input_arg_first,"deconvolution") - else: - user_workflow.set("deconvolution", - skimage_decon, - vol_zyx=vol_zyx, - psf=LLSZWidget.LlszMenu.lattice.psf[channel], - num_iter=LLSZWidget.LlszMenu.lattice.psf_num_iter, - clip=False, - filter_epsilon=0, - boundary='nearest') - - # set input to subsequent task as deconvolution output - user_workflow.set( - input_arg_first, "deconvolution") - - 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") - pass - - @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, - time_start: int, - time_end: int, - ch_start: int, - ch_end: int, - Use_Cropping, - 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 - """ - assert LLSZWidget.LlszMenu.open_file, "Image not initialised" - - check_dimensions(time_start, time_end, ch_start, ch_end, - LLSZWidget.LlszMenu.lattice.channels, LLSZWidget.LlszMenu.lattice.time) - - # Get parameters - angle = LLSZWidget.LlszMenu.lattice.angle - dx = LLSZWidget.LlszMenu.lattice.dx - dy = LLSZWidget.LlszMenu.lattice.dy - dz = LLSZWidget.LlszMenu.lattice.dz - - if get_active_workflow: - # installs the workflow to napari - user_workflow = WorkflowManager.install( - self.parent_viewer).workflow - print("Workflow installed") - else: - # Automatically scan workflow file directory for *.py files. - # If it findss one, load it as a module - import importlib - parent_dir = workflow_path.resolve( - ).parents[0].__str__()+os.sep - sys.path.append(parent_dir) - custom_py_files = get_all_py_files(parent_dir) - if len(custom_py_files) == 0: - print( - f"No custom modules imported. If you'd like to use a cusotm module, place a *.py file in same folder as the workflow file {parent_dir}") - else: - modules = map( - importlib.import_module, custom_py_files) - print(f"Custom modules imported {modules}") - user_workflow = load_workflow(workflow_path) - - assert type( - user_workflow) is Workflow, "Workflow file is not a napari workflow object. Check file! You can use workflow inspector if needed" - - input_arg_first, input_arg_last, first_task_name, last_task_name = get_first_last_image_and_task( - user_workflow) - print(input_arg_first, input_arg_last, - first_task_name, last_task_name) - # get list of tasks - task_list = list(user_workflow._tasks.keys()) - print("Workflow loaded:") - print(user_workflow) - - vol = LLSZWidget.LlszMenu.lattice.data - - #vol_zyx= vol[time,channel,...] - - 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 LLSZWidget.LlszMenu.lattice.decon_processing == DeconvolutionChoice.cuda_gpu: - #otf_path = "otf_path" - psf_arg = "psf" - psf = LLSZWidget.LlszMenu.lattice.psf - else: - psf_arg = "psf" - psf = LLSZWidget.LlszMenu.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/LLSZWidget.LlszMenu.lattice.dy for x in roi_layer_list] - - deskewed_shape = LLSZWidget.LlszMenu.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=LLSZWidget.LlszMenu.deconvolution.value, - decon_processing=LLSZWidget.LlszMenu.lattice.decon_processing, - psf=psf_arg, - skew_dir=LLSZWidget.LlszMenu.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=LLSZWidget.LlszMenu.lattice.save_name, - dx=dx, - dy=dy, - dz=dz, - angle=angle, - deconvolution=LLSZWidget.LlszMenu.deconvolution.value, - decon_processing=LLSZWidget.LlszMenu.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", - LLSZWidget.LlszMenu.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 LLSZWidget.LlszMenu.deconvolution.value: - 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 LLSZWidget.LlszMenu.lattice.decon_processing == DeconvolutionChoice.cuda_gpu: - user_workflow.set("deconvolution", - pycuda_decon, - image=input, - psf=psf_arg, - dzdata=LLSZWidget.LlszMenu.lattice.dz, - dxdata=LLSZWidget.LlszMenu.lattice.dx, - dzpsf=LLSZWidget.LlszMenu.lattice.dz, - dxpsf=LLSZWidget.LlszMenu.lattice.dx, - num_iter=LLSZWidget.LlszMenu.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=LLSZWidget.LlszMenu.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=LLSZWidget.LlszMenu.lattice.save_name, - dx=dx, - dy=dy, - dz=dz, - angle=angle, - deconvolution=LLSZWidget.LlszMenu.deconvolution.value, - decon_processing=LLSZWidget.LlszMenu.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 LLSZWidget.LlszMenu.deconvolution.value: - 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 LLSZWidget.LlszMenu.lattice.decon_processing == DeconvolutionChoice.cuda_gpu: - user_workflow.set("deconvolution", - pycuda_decon, - image=input, - psf=psf_arg, - dzdata=LLSZWidget.LlszMenu.lattice.dz, - dxdata=LLSZWidget.LlszMenu.lattice.dx, - dzpsf=LLSZWidget.LlszMenu.lattice.dz, - dxpsf=LLSZWidget.LlszMenu.lattice.dx, - num_iter=LLSZWidget.LlszMenu.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=LLSZWidget.LlszMenu.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=LLSZWidget.LlszMenu.lattice.save_name, - dx=dx, - dy=dy, - dz=dz, - angle=angle, - deconvolution=LLSZWidget.LlszMenu.deconvolution.value, - decon_processing=LLSZWidget.LlszMenu.lattice.decon_processing, - otf_path=otf_path, - psf_arg=psf_arg, - psf=psf) - - print("Workflow complete") - return - - pass - -def _napari_lattice_widget_wrapper() -> LLSZWidget: - # split widget type enables a resizable widget - #max_height = 50 - # Important to have this or napari won't recognize the classes and magicclass qidgets - widget = LLSZWidget() - # aligning collapsible widgets at the top instead of having them centered vertically - widget._widget._layout.setAlignment(Qt.AlignTop) - - # widget._widget._layout.setWidgetResizable(True) - return widget +import os +import sys +import yaml +import numpy as np +from pathlib import Path +import dask.array as da +import pandas as pd +from typing import Union, Optional, Callable, Literal +from aicsimageio import AICSImage +from enum import Enum + +from magicclass.wrappers import set_design +from magicgui import magicgui +from magicclass import magicclass, field, vfield, set_options, MagicTemplate +from magicclass.utils import click +from qtpy.QtCore import Qt + +from napari.layers import Layer, Shapes +from napari.types import ImageData +from napari.utils import history + +import pyclesperanto_prototype as cle + +from napari.types import ImageData, ShapesData + +from tqdm import tqdm + +from napari_workflows import Workflow, WorkflowManager +from napari_workflows._io_yaml_v1 import load_workflow + +from lls_core import config, DeskewDirection, DeconvolutionChoice, SaveFileType, Log_Levels +from lls_core.io import LatticeData, save_img, save_img_workflow +from lls_core.utils import read_imagej_roi, get_first_last_image_and_task, modify_workflow_task, get_all_py_files, as_type, process_custom_workflow_output, check_dimensions, load_custom_py_modules +from lls_core.llsz_core import crop_volume_deskew +from lls_core.deconvolution import read_psf, pycuda_decon, skimage_decon + +from napari_lattice.ui_core import _Preview, _Deskew_Save +from napari_lattice._reader import lattice_from_napari + +# Enable Logging +import logging +logger = logging.getLogger(__name__) +logger.setLevel(logging.INFO) + +class LastDimensionOptions(Enum): + channel = "Channel" + time = "Time" + get_from_metadata = "Get from Metadata" + +@magicclass(widget_type="split") +class LLSZWidget(MagicTemplate): + + @magicclass(widget_type="split") + class LlszMenu(MagicTemplate): + open_file: bool = False + lattice: LatticeData = None + skew_dir: DeskewDirection + angle_value: float + deskew_func: Callable + + main_heading = field("

Napari Lattice: Visualization & Analysis

", widget_type="Label") + heading1 = field("Drag and drop an image file onto napari.\nOnce image has opened, initialize the\nplugin by clicking the button below.\nEnsure the image layer and voxel sizes are accurate in the prompt.\n If everything initalises properly, the button turns green.", widget_type="Label") + + @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 = LastDimensionOptions.get_from_metadata, + 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") + + if self.parent_viewer is None: + raise Exception("This function can only be used when inside of a Napari viewer") + + # Select device for processing + cle.select_device(select_device) + + #assert skew_dir in DeskewDirection, "Skew direction not recognised. Enter either Y or X" + LLSZWidget.LlszMenu.skew_dir = skew_dir + LLSZWidget.LlszMenu.angle_value = angle + + if LLSZWidget.LlszMenu.skew_dir == DeskewDirection.Y: + LLSZWidget.LlszMenu.deskew_func = cle.deskew_y + #LLSZWidget.LlszMenu.skew_dir = DeskewDirection.Y + elif LLSZWidget.LlszMenu.skew_dir == DeskewDirection.X: + LLSZWidget.LlszMenu.deskew_func = cle.deskew_x + #LLSZWidget.LlszMenu.skew_dir = DeskewDirection.X + + # 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 + + LLSZWidget.LlszMenu.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=LLSZWidget.LlszMenu.skew_dir, + physical_pixel_sizes=(pixel_size_dx, pixel_size_dy, pixel_size_dz) + ) + #LLSZWidget.LlszMenu.aics = LLSZWidget.LlszMenu.lattice.data + + # LLSZWidget.LlszMenu.dask = False # Use GPU by default + + # We initialise these variables here, but they can be changed in the deconvolution section + # list to store psf images for each channel + LLSZWidget.LlszMenu.lattice.psf = [] + LLSZWidget.LlszMenu.lattice.psf_num_iter = 10 + LLSZWidget.LlszMenu.lattice.decon_processing = DeconvolutionChoice.cpu + # list to store otf paths for each channel (Deprecated) + LLSZWidget.LlszMenu.lattice.otf_path = [] + # if not using GPU + #LLSZWidget.LlszMenu.dask = not use_GPU + + # flag for ensuring a file has been opened and plugin initialised + LLSZWidget.LlszMenu.open_file = True + + logger.info( + f"Pixel size (ZYX) in microns: {LLSZWidget.LlszMenu.lattice.dz,LLSZWidget.LlszMenu.lattice.dy,LLSZWidget.LlszMenu.lattice.dx}") + logger.info( + f"Dimensions of image layer (ZYX): {list(LLSZWidget.LlszMenu.lattice.data.shape[-3:])}") + logger.info( + f"Dimensions of deskewed image (ZYX): {LLSZWidget.LlszMenu.lattice.deskew_vol_shape}") + logger.info( + f"Deskewing angle is :{LLSZWidget.LlszMenu.lattice.angle}") + logger.info( + f"Deskew Direction :{LLSZWidget.LlszMenu.lattice.skew}") + # Add dimension labels correctly + # if channel, and not time + if LLSZWidget.LlszMenu.lattice.time == 0 and (last_dimension_channel or LLSZWidget.LlszMenu.lattice.channels > 0): + self.parent_viewer.dims.axis_labels = ('Channel', "Z", "Y", "X") + # if no channel, but has time + elif LLSZWidget.LlszMenu.lattice.channels == 0 and LLSZWidget.LlszMenu.lattice.time > 0: + self.parent_viewer.dims.axis_labels = ('Time', "Z", "Y", "X") + # if it has channels + elif LLSZWidget.LlszMenu.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]) == LLSZWidget.LlszMenu.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 LLSZWidget.LlszMenu.lattice.channels == 1 and LLSZWidget.LlszMenu.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" + + return + + # Pycudadecon library for deconvolution + # options={"enabled": True}, + deconvolution = vfield(bool, name="Use Deconvolution") + deconvolution.value = False + + @deconvolution.connect + def _set_decon(self): + if self.deconvolution: + logger.info("Deconvolution Activated") + LLSZWidget.LlszMenu.deconvolution.value = True + else: + logger.info("Deconvolution Disabled") + LLSZWidget.LlszMenu.deconvolution.value = False + return + + @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, + psf_ch1_path: Path, + psf_ch2_path: Path, + psf_ch3_path: Path, + psf_ch4_path: Path, + device_option, + no_iter: int): + """GUI for Deconvolution button""" + LLSZWidget.LlszMenu.lattice.decon_processing = device_option + assert LLSZWidget.LlszMenu.deconvolution.value == True, "Deconvolution is set to False. Tick the box to activate deconvolution." + LLSZWidget.LlszMenu.lattice.psf = list(read_psf([ + psf_ch1_path, + psf_ch2_path, + psf_ch3_path, + psf_ch4_path, + ], + device_option, + lattice_class=LLSZWidget.LlszMenu.lattice + )) + LLSZWidget.LlszMenu.lattice.psf_num_iter = no_iter + self["deconvolution_gui"].background_color = "green" + self["deconvolution_gui"].text = "PSFs added" + + @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, + time: int, + channel: int, + img_data: ImageData): + """ + Preview deskewed data for a single timepoint and channel + + """ + _Preview(LLSZWidget, + self, + time, + channel, + img_data) + return + + # Tabbed Widget container to house all the widgets + @magicclass(widget_type="tabbed", name="Functions") + class WidgetContainer(MagicTemplate): + + @magicclass(name="Deskew", widget_type="scrollable", properties={"min_width": 100}) + class DeskewWidget(MagicTemplate): + + @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, + 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) + return + + @magicclass(name="Crop and Deskew", widget_type="scrollable") + class CropWidget(MagicTemplate): + + # add function for previewing cropped image + @magicclass(name="Cropping Preview", widget_type="scrollable", properties={ + "min_width": 100, + "shapes_layer": Shapes + }) + class Preview_Crop_Menu(MagicTemplate): + shapes_layer: Shapes + + @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): + LLSZWidget.WidgetContainer.CropWidget.Preview_Crop_Menu.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 + LLSZWidget.WidgetContainer.CropWidget.Preview_Crop_Menu.shapes_layer.mode = "SELECT" + self["activate_cropping"].text = "Cropping layer active" + self["activate_cropping"].background_color = "green" + return + + 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])): + logger.info(f"Opening{path}") + roi_list = read_imagej_roi(path) + # convert to canvas coordinates + roi_list = (np.array(roi_list) * + LLSZWidget.LlszMenu.lattice.dy).tolist() + LLSZWidget.WidgetContainer.CropWidget.Preview_Crop_Menu.shapes_layer.add(roi_list, shape_type='polygon', edge_width=1, edge_color='yellow', + face_color=[1, 1, 1, 0]) + return + + 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) + # -> LayerDataTuple: + def Crop_Preview(self, roi_layer: ShapesData): + assert roi_layer, "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 + time = self.time_crop.value + channel = self.chan_crop.value + + assert time < LLSZWidget.LlszMenu.lattice.time, "Time is out of range" + assert channel < LLSZWidget.LlszMenu.lattice.channels, "Channel is out of range" + + logger.info(f"Using channel {channel} and time {time}") + + vol = LLSZWidget.LlszMenu.lattice.data + vol_zyx = vol[time, channel, ...] + vol_zyx = np.array(vol_zyx) + + deskewed_shape = LLSZWidget.LlszMenu.lattice.deskew_vol_shape + # Create a dask array same shape as deskewed image + deskewed_volume = da.zeros(deskewed_shape) + + # Option for entering custom z start value? + z_start = 0 + z_end = deskewed_shape[0] + + # if only one roi drawn, use the first ROI for cropping + if len(roi_layer) == 1: + roi_idx = 0 + else: + assert len( + LLSZWidget.WidgetContainer.CropWidget.Preview_Crop_Menu.shapes_layer.selected_data) > 0, "Please select an ROI" + roi_idx = list( + LLSZWidget.WidgetContainer.CropWidget.Preview_Crop_Menu.shapes_layer.selected_data)[0] + + roi_choice = roi_layer[roi_idx] + # 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/LLSZWidget.LlszMenu.lattice.dy for x in roi_choice] + logger.info(f"Previewing ROI {roi_idx}") + + # crop + if LLSZWidget.LlszMenu.deconvolution.value: + logger.info( + f"Deskewing for Time:{time} and Channel: {channel} with deconvolution") + #psf = LLSZWidget.LlszMenu.lattice.psf[channel] + if LLSZWidget.LlszMenu.lattice.decon_processing == DeconvolutionChoice.cuda_gpu: + crop_roi_vol_desk = crop_volume_deskew(original_volume=vol_zyx, + deskewed_volume=deskewed_volume, + roi_shape=roi_choice, + angle_in_degrees=LLSZWidget.LlszMenu.angle_value, + voxel_size_x=LLSZWidget.LlszMenu.lattice.dx, + voxel_size_y=LLSZWidget.LlszMenu.lattice.dy, + voxel_size_z=LLSZWidget.LlszMenu.lattice.dz, + z_start=z_start, + z_end=z_end, + deconvolution=LLSZWidget.LlszMenu.deconvolution.value, + decon_processing=LLSZWidget.LlszMenu.lattice.decon_processing, + psf=LLSZWidget.LlszMenu.lattice.psf[channel], + num_iter=LLSZWidget.LlszMenu.lattice.psf_num_iter).astype(vol_zyx.dtype) + else: + crop_roi_vol_desk = crop_volume_deskew(original_volume=vol_zyx, + deskewed_volume=deskewed_volume, + roi_shape=roi_choice, + angle_in_degrees=LLSZWidget.LlszMenu.angle_value, + voxel_size_x=LLSZWidget.LlszMenu.lattice.dx, + voxel_size_y=LLSZWidget.LlszMenu.lattice.dy, + voxel_size_z=LLSZWidget.LlszMenu.lattice.dz, + z_start=z_start, + z_end=z_end, + deconvolution=LLSZWidget.LlszMenu.deconvolution.value, + decon_processing=LLSZWidget.LlszMenu.lattice.decon_processing, + psf=LLSZWidget.LlszMenu.lattice.psf[channel], + num_iter=LLSZWidget.LlszMenu.lattice.psf_num_iter).astype(vol_zyx.dtype) + else: + crop_roi_vol_desk = crop_volume_deskew(original_volume=vol_zyx, + deskewed_volume=deskewed_volume, + roi_shape=roi_choice, + angle_in_degrees=LLSZWidget.LlszMenu.angle_value, + voxel_size_x=LLSZWidget.LlszMenu.lattice.dx, + voxel_size_y=LLSZWidget.LlszMenu.lattice.dy, + voxel_size_z=LLSZWidget.LlszMenu.lattice.dz, + z_start=z_start, + z_end=z_end, + skew_dir=LLSZWidget.LlszMenu.skew_dir).astype(vol_zyx.dtype) + crop_roi_vol_desk = cle.pull(crop_roi_vol_desk) + + # get array back from gpu or addding cle array to napari can throw errors + + scale = (LLSZWidget.LlszMenu.lattice.new_dz, + LLSZWidget.LlszMenu.lattice.dy, + LLSZWidget.LlszMenu.lattice.dx) + self.parent_viewer.add_image( + crop_roi_vol_desk, scale=scale) + + @magicclass(name="Crop and Save Data") + class CropSaveData(MagicTemplate): + @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, + time_start: int, + time_end: int, + ch_start: int, + ch_end: int, + save_as_type: str, + 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: + assert LLSZWidget.LlszMenu.open_file, "Image not initialised" + + check_dimensions(time_start, time_end, ch_start, ch_end, + LLSZWidget.LlszMenu.lattice.channels, LLSZWidget.LlszMenu.lattice.time) + + angle = LLSZWidget.LlszMenu.lattice.angle + dx = LLSZWidget.LlszMenu.lattice.dx + dy = LLSZWidget.LlszMenu.lattice.dy + dz = LLSZWidget.LlszMenu.lattice.dz + + # get image data + img_data = LLSZWidget.LlszMenu.lattice.data + # Get shape of deskewed image + deskewed_shape = LLSZWidget.LlszMenu.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 = [ + x/LLSZWidget.LlszMenu.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=LLSZWidget.LlszMenu.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=LLSZWidget + ) + + logger.info( + f"Cropping and Saving Complete -> {save_path}") + return + + @magicclass(name="Workflow", widget_type="scrollable") + class WorkflowWidget: + @magicclass(name="Preview Workflow", widget_type="scrollable") + class PreviewWorkflow: + #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, + 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 + Args: + + """ + print("Previewing deskewed channel and time with workflow") + if get_active_workflow: + # installs the workflow to napari + user_workflow = WorkflowManager.install( + self.parent_viewer).workflow + parent_dir = workflow_path.resolve( + ).parents[0].__str__()+os.sep + logger.info("Workflow loaded from napari") + else: + + try: + # Automatically scan workflow file directory for *.py files. + # If it findss one, load it as a module + + parent_dir = workflow_path.resolve( + ).parents[0].__str__()+os.sep + sys.path.append(parent_dir) + custom_py_files = get_all_py_files(parent_dir) + if len(custom_py_files) == 0: + logger.error( + f"No custom modules imported. If you'd like to use a cusotm module, place a *.py file in same folder as the workflow file {parent_dir}") + else: + modules = load_custom_py_modules( + custom_py_files) + + logger.info( + f"Custom modules imported {modules}") + user_workflow = load_workflow( + workflow_path.__str__()) + except yaml.loader.ConstructorError as e: + logger.error( + "\033[91m While loading workflow, got the following error which may mean you need to install the corresponding module in your Python environment: \033[0m") + logger.error(e) + + #user_workflow = load_workflow(workflow_path) + logger.info("Workflow loaded from file") + + assert type( + user_workflow) is Workflow, "Workflow loading error. Check if file is workflow or if required libraries are installed" + + input_arg_first, input_arg_last, first_task_name, last_task_name = get_first_last_image_and_task( + user_workflow) + #print(input_arg_first, input_arg_last, first_task_name,last_task_name ) + # get list of tasks + task_list = list(user_workflow._tasks.keys()) + logger.info(f"Workflow loaded:{user_workflow}") + # logger.info() + + # when using fields, self.time_preview.value + assert time_preview < LLSZWidget.LlszMenu.lattice.time, "Time is out of range" + assert chan_preview < LLSZWidget.LlszMenu.lattice.channels, "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}") + + vol = LLSZWidget.LlszMenu.lattice.data + vol_zyx = vol[time, channel, ...] + vol_zyx = np.array(vol_zyx) + + 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 LLSZWidget.LlszMenu.lattice.decon_processing == DeconvolutionChoice.cuda_gpu: + otf_path = "otf_path" + else: + psf = "psf" + + # if cropping, set that as first task + # get the function associated with the first task and check if its deskewing + if Use_Cropping: + # use deskewed volume for cropping function + deskewed_shape = LLSZWidget.LlszMenu.lattice.deskew_vol_shape + deskewed_volume = da.zeros(deskewed_shape) + z_start = 0 + z_end = deskewed_shape[0] + if user_workflow.get_task(task_name_start)[0] not in [crop_volume_deskew]: + # if only one roi drawn, use the first ROI for cropping + if len(roi_layer_list) == 1: + roi_idx = 0 + else: # else get the user selection + assert len( + LLSZWidget.WidgetContainer.CropWidget.Preview_Crop_Menu.shapes_layer.selected_data) > 0, "Please select an ROI" + roi_idx = list( + LLSZWidget.WidgetContainer.CropWidget.Preview_Crop_Menu.shapes_layer.selected_data)[0] + + roi_choice = roi_layer_list[roi_idx] + # As the original image is scaled, the coordinates are in microns, so we need to convert + # roi to from micron to canvas/world coordinates + roi_choice = [ + x/LLSZWidget.LlszMenu.lattice.dy for x in roi_choice] + logger.info(f"Previewing ROI {roi_idx}") + if LLSZWidget.LlszMenu.deconvolution.value: + user_workflow.set("crop_deskew_image", crop_volume_deskew, + original_volume=vol_zyx, + deskewed_volume=deskewed_volume, + roi_shape=roi_choice, + angle_in_degrees=LLSZWidget.LlszMenu.lattice.angle, + voxel_size_x=LLSZWidget.LlszMenu.lattice.dx, + voxel_size_y=LLSZWidget.LlszMenu.lattice.dy, + voxel_size_z=LLSZWidget.LlszMenu.lattice.dz, + z_start=z_start, + z_end=z_end, + deconvolution=LLSZWidget.LlszMenu.deconvolution.value, + decon_processing=LLSZWidget.LlszMenu.lattice.decon_processing, + otf_path=otf_path, + psf=LLSZWidget.LlszMenu.lattice.psf[channel], + skew_dir=LLSZWidget.LlszMenu.skew_dir) + else: + user_workflow.set("crop_deskew_image", crop_volume_deskew, + original_volume=vol_zyx, + deskewed_volume=deskewed_volume, + roi_shape=roi_choice, + angle_in_degrees=LLSZWidget.LlszMenu.lattice.angle, + voxel_size_x=LLSZWidget.LlszMenu.lattice.dx, + voxel_size_y=LLSZWidget.LlszMenu.lattice.dy, + voxel_size_z=LLSZWidget.LlszMenu.lattice.dz, + z_start=z_start, + z_end=z_end, + skew_dir=LLSZWidget.LlszMenu.skew_dir) + + # Set input of the workflow to be crop_deskewing, i.e., the original first operation will now have crop_deskew_image as an input (becoming second instead) + user_workflow.set( + input_arg_first, "crop_deskew_image") + else: + user_workflow.set(input_arg_first, vol_zyx) + # Not cropping; If deskew not in workflow, append to start + elif user_workflow.get_task(task_name_start)[0] not in (cle.deskew_y, cle.deskew_x): + # if deconvolution checked, add it to start of workflow (add upstream of deskewing) + if LLSZWidget.LlszMenu.deconvolution.value: + psf = LLSZWidget.LlszMenu.lattice.psf[channel] + input_arg_first_decon, input_arg_last_decon, first_task_name_decon, last_task_name_decon = get_first_last_image_and_task( + user_workflow) + + if LLSZWidget.LlszMenu.lattice.decon_processing == DeconvolutionChoice.cuda_gpu: + user_workflow.set("deconvolution", + pycuda_decon, + image=vol_zyx, + psf=LLSZWidget.LlszMenu.lattice.psf[channel], + dzdata=LLSZWidget.LlszMenu.lattice.dz, + dxdata=LLSZWidget.LlszMenu.lattice.dx, + dzpsf=LLSZWidget.LlszMenu.lattice.dz, + dxpsf=LLSZWidget.LlszMenu.lattice.dx, + num_iter=LLSZWidget.LlszMenu.lattice.psf_num_iter) + # user_workflow.set(input_arg_first_decon,"deconvolution") + else: + user_workflow.set("deconvolution", + skimage_decon, + vol_zyx=vol_zyx, + psf=LLSZWidget.LlszMenu.lattice.psf[channel], + num_iter=LLSZWidget.LlszMenu.lattice.psf_num_iter, + clip=False, + filter_epsilon=0, + boundary='nearest') + # user_workflow.set(input_arg_first_decon,"deconvolution") + + user_workflow.set("deskew_image", + LLSZWidget.LlszMenu.deskew_func, + "deconvolution", + angle_in_degrees=LLSZWidget.LlszMenu.lattice.angle, + voxel_size_x=LLSZWidget.LlszMenu.lattice.dx, + voxel_size_y=LLSZWidget.LlszMenu.lattice.dy, + voxel_size_z=LLSZWidget.LlszMenu.lattice.dz, + linear_interpolation=True) + + # user_workflow.set("change_bitdepth",as_type,"deskew_image",vol_zyx) + # Set input of the workflow to be from deskewing output with same bit depth as original volume + # user_workflow.set(input_arg_first,"change_bitdepth") + + else: + user_workflow.set("deskew_image", + LLSZWidget.LlszMenu.deskew_func, + vol_zyx, + angle_in_degrees=LLSZWidget.LlszMenu.lattice.angle, + voxel_size_x=LLSZWidget.LlszMenu.lattice.dx, + voxel_size_y=LLSZWidget.LlszMenu.lattice.dy, + voxel_size_z=LLSZWidget.LlszMenu.lattice.dz, + linear_interpolation=True) + # Set input of the workflow to be from deskewing + # user_workflow.set(input_arg_first,"deskew_image") + + user_workflow.set( + "change_bitdepth", as_type, "deskew_image", vol_zyx) + # Set input of the workflow to be from deskewing with same bit depth as original volume + user_workflow.set( + input_arg_first, "change_bitdepth") + + else: + # if deskew already in workflow, just check if deconvolution needs to be added + # repitition of above (maybe create a function?) + # if deconvolution checked, add it to start of workflow (add upstream of deskewing) + if LLSZWidget.LlszMenu.deconvolution.value: + psf = LLSZWidget.LlszMenu.lattice.psf[channel] + input_arg_first, input_arg_last, first_task_name, last_task_name = get_first_last_image_and_task( + user_workflow) + + if LLSZWidget.LlszMenu.lattice.decon_processing == DeconvolutionChoice.cuda_gpu: + user_workflow.set("deconvolution", + pycuda_decon, + image=vol_zyx, + psf=LLSZWidget.LlszMenu.lattice.psf[channel], + dzdata=LLSZWidget.LlszMenu.lattice.dz, + dxdata=LLSZWidget.LlszMenu.lattice.dx, + dzpsf=LLSZWidget.LlszMenu.lattice.dz, + dxpsf=LLSZWidget.LlszMenu.lattice.dx, + num_iter=LLSZWidget.LlszMenu.lattice.psf_num_iter) + # user_workflow.set(input_arg_first,"deconvolution") + else: + user_workflow.set("deconvolution", + skimage_decon, + vol_zyx=vol_zyx, + psf=LLSZWidget.LlszMenu.lattice.psf[channel], + num_iter=LLSZWidget.LlszMenu.lattice.psf_num_iter, + clip=False, + filter_epsilon=0, + boundary='nearest') + + # set input to subsequent task as deconvolution output + user_workflow.set( + input_arg_first, "deconvolution") + + 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") + pass + + @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, + time_start: int, + time_end: int, + ch_start: int, + ch_end: int, + Use_Cropping, + 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 + """ + assert LLSZWidget.LlszMenu.open_file, "Image not initialised" + + check_dimensions(time_start, time_end, ch_start, ch_end, + LLSZWidget.LlszMenu.lattice.channels, LLSZWidget.LlszMenu.lattice.time) + + # Get parameters + angle = LLSZWidget.LlszMenu.lattice.angle + dx = LLSZWidget.LlszMenu.lattice.dx + dy = LLSZWidget.LlszMenu.lattice.dy + dz = LLSZWidget.LlszMenu.lattice.dz + + if get_active_workflow: + # installs the workflow to napari + user_workflow = WorkflowManager.install( + self.parent_viewer).workflow + print("Workflow installed") + else: + # Automatically scan workflow file directory for *.py files. + # If it findss one, load it as a module + import importlib + parent_dir = workflow_path.resolve( + ).parents[0].__str__()+os.sep + sys.path.append(parent_dir) + custom_py_files = get_all_py_files(parent_dir) + if len(custom_py_files) == 0: + print( + f"No custom modules imported. If you'd like to use a cusotm module, place a *.py file in same folder as the workflow file {parent_dir}") + else: + modules = map( + importlib.import_module, custom_py_files) + print(f"Custom modules imported {modules}") + user_workflow = load_workflow(workflow_path) + + assert type( + user_workflow) is Workflow, "Workflow file is not a napari workflow object. Check file! You can use workflow inspector if needed" + + input_arg_first, input_arg_last, first_task_name, last_task_name = get_first_last_image_and_task( + user_workflow) + print(input_arg_first, input_arg_last, + first_task_name, last_task_name) + # get list of tasks + task_list = list(user_workflow._tasks.keys()) + print("Workflow loaded:") + print(user_workflow) + + vol = LLSZWidget.LlszMenu.lattice.data + + #vol_zyx= vol[time,channel,...] + + 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 LLSZWidget.LlszMenu.lattice.decon_processing == DeconvolutionChoice.cuda_gpu: + #otf_path = "otf_path" + psf_arg = "psf" + psf = LLSZWidget.LlszMenu.lattice.psf + else: + psf_arg = "psf" + psf = LLSZWidget.LlszMenu.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/LLSZWidget.LlszMenu.lattice.dy for x in roi_layer_list] + + deskewed_shape = LLSZWidget.LlszMenu.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=LLSZWidget.LlszMenu.deconvolution.value, + decon_processing=LLSZWidget.LlszMenu.lattice.decon_processing, + psf=psf_arg, + skew_dir=LLSZWidget.LlszMenu.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=LLSZWidget.LlszMenu.lattice.save_name, + dx=dx, + dy=dy, + dz=dz, + angle=angle, + deconvolution=LLSZWidget.LlszMenu.deconvolution.value, + decon_processing=LLSZWidget.LlszMenu.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", + LLSZWidget.LlszMenu.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 LLSZWidget.LlszMenu.deconvolution.value: + 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 LLSZWidget.LlszMenu.lattice.decon_processing == DeconvolutionChoice.cuda_gpu: + user_workflow.set("deconvolution", + pycuda_decon, + image=input, + psf=psf_arg, + dzdata=LLSZWidget.LlszMenu.lattice.dz, + dxdata=LLSZWidget.LlszMenu.lattice.dx, + dzpsf=LLSZWidget.LlszMenu.lattice.dz, + dxpsf=LLSZWidget.LlszMenu.lattice.dx, + num_iter=LLSZWidget.LlszMenu.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=LLSZWidget.LlszMenu.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=LLSZWidget.LlszMenu.lattice.save_name, + dx=dx, + dy=dy, + dz=dz, + angle=angle, + deconvolution=LLSZWidget.LlszMenu.deconvolution.value, + decon_processing=LLSZWidget.LlszMenu.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 LLSZWidget.LlszMenu.deconvolution.value: + 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 LLSZWidget.LlszMenu.lattice.decon_processing == DeconvolutionChoice.cuda_gpu: + user_workflow.set("deconvolution", + pycuda_decon, + image=input, + psf=psf_arg, + dzdata=LLSZWidget.LlszMenu.lattice.dz, + dxdata=LLSZWidget.LlszMenu.lattice.dx, + dzpsf=LLSZWidget.LlszMenu.lattice.dz, + dxpsf=LLSZWidget.LlszMenu.lattice.dx, + num_iter=LLSZWidget.LlszMenu.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=LLSZWidget.LlszMenu.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=LLSZWidget.LlszMenu.lattice.save_name, + dx=dx, + dy=dy, + dz=dz, + angle=angle, + deconvolution=LLSZWidget.LlszMenu.deconvolution.value, + decon_processing=LLSZWidget.LlszMenu.lattice.decon_processing, + otf_path=otf_path, + psf_arg=psf_arg, + psf=psf) + + print("Workflow complete") + return + + pass + +def _napari_lattice_widget_wrapper() -> LLSZWidget: + # split widget type enables a resizable widget + #max_height = 50 + # Important to have this or napari won't recognize the classes and magicclass qidgets + widget = LLSZWidget() + # aligning collapsible widgets at the top instead of having them centered vertically + widget._widget._layout.setAlignment(Qt.AlignTop) + + # widget._widget._layout.setWidgetResizable(True) + return widget diff --git a/plugin/napari_lattice/_reader.py b/plugin/napari_lattice/reader.py similarity index 97% rename from plugin/napari_lattice/_reader.py rename to plugin/napari_lattice/reader.py index 895f14f..0344956 100644 --- a/plugin/napari_lattice/_reader.py +++ b/plugin/napari_lattice/reader.py @@ -1,140 +1,140 @@ -""" -reader plugin for h5 saved using np2bdv -https://github.com/nvladimus/npy2bdv -#TODO: pass pyramidal layer to napari -##use ilevel parameter in read_view to access different subsamples/pyramids -#pass a list of images with different resolution for pyramid; use is_pyramid=True flag in napari.add_image -, however pyramidal support for 3D not available yet -""" -from __future__ import annotations - -import dask.array as da -import dask.delayed as delayed -import os -import numpy as np -from napari.layers import image, Layer -from napari.layers._data_protocols import LayerDataProtocol -from aicsimageio.types import ArrayLike -from aicsimageio.dimensions import Dimensions -from aicsimageio.aics_image import AICSImage - -from typing_extensions import Literal -from typing import Any, Optional, cast, Tuple - -from lls_core.io import img_from_array -from lls_core.lattice_data import lattice_from_aics, LatticeData - -def lattice_from_napari( - img: Layer, - last_dimension: Optional[Literal["channel", "time"]], - **kwargs: Any -) -> LatticeData: - """ - Factory function for generating a LatticeData from a Napari Image - - Arguments: - kwargs: Extra arguments to pass to the LatticeData constructor - """ - - img_data_aics: AICSImage - - if 'aicsimage' in img.metadata.keys(): - img_data_aics = img.metadata['aicsimage'] - else: - if not last_dimension: - raise ValueError("Either the Napari image must have dimensional metadata, or last_dimension must be provided") - img_data_aics = img_from_array(cast(ArrayLike, img.data), last_dimension=last_dimension, physical_pixel_sizes=kwargs.get("physical_pixel_sizes")) - - save_name: str - if img.source.path is None: - # remove colon (:) and any leading spaces - save_name = img.name.replace(":", "").strip() - # replace any group of spaces with "_" - save_name = '_'.join(save_name.split()) - else: - file_name_noext = os.path.basename(img.source.path) - file_name = os.path.splitext(file_name_noext)[0] - # remove colon (:) and any leading spaces - save_name = file_name.replace(":", "").strip() - # replace any group of spaces with "_" - save_name = '_'.join(save_name.split()) - - return lattice_from_aics(img_data_aics, save_name=save_name, **kwargs) - -def napari_get_reader(path: list[str] | str): - """Check if file ends with h5 and returns reader function if true - Parameters - ---------- - path : str or list of str - Path to file, or list of paths. - Returns - ------- - function - """ - if isinstance(path, list): - # reader plugins may be handed single path, or a list of paths. - # if it is a list, we are only going to open first file - path = path[0] - - # if we know we cannot read the file, we immediately return None. - if not path.endswith(".h5"): - return None - - # otherwise we return the *function* that can read ``path``. - return bdv_h5_reader - - -def bdv_h5_reader(path): - """Take a path and returns a list of LayerData tuples.""" - - os.environ["HDF5_USE_FILE_LOCKING"] = "FALSE" - - #print(path) - import npy2bdv - h5_file = npy2bdv.npy2bdv.BdvEditor(path) - - img = [] - - #get dimensions of first image - first_timepoint = h5_file.read_view(time=0,channel=0) - - #Threshold to figure out when to use out-of-memory loading/dask - #Got the idea from napari-aicsimageio - #https://github.com/AllenCellModeling/napari-aicsimageio/blob/22934757c2deda30c13f39ec425343182fa91a89/napari_aicsimageio/core.py#L222 - mem_threshold_bytes = 4e9 - mem_per_threshold = 0.3 - - from psutil import virtual_memory - - file_size = os.path.getsize(path) - avail_mem = virtual_memory().available - - #if file size <30% of available memory and <4GB, open - if file_size<=mem_per_threshold*avail_mem and file_size LatticeData: + """ + Factory function for generating a LatticeData from a Napari Image + + Arguments: + kwargs: Extra arguments to pass to the LatticeData constructor + """ + + img_data_aics: AICSImage + + if 'aicsimage' in img.metadata.keys(): + img_data_aics = img.metadata['aicsimage'] + else: + if not last_dimension: + raise ValueError("Either the Napari image must have dimensional metadata, or last_dimension must be provided") + img_data_aics = img_from_array(cast(ArrayLike, img.data), last_dimension=last_dimension, physical_pixel_sizes=kwargs.get("physical_pixel_sizes")) + + save_name: str + if img.source.path is None: + # remove colon (:) and any leading spaces + save_name = img.name.replace(":", "").strip() + # replace any group of spaces with "_" + save_name = '_'.join(save_name.split()) + else: + file_name_noext = os.path.basename(img.source.path) + file_name = os.path.splitext(file_name_noext)[0] + # remove colon (:) and any leading spaces + save_name = file_name.replace(":", "").strip() + # replace any group of spaces with "_" + save_name = '_'.join(save_name.split()) + + return lattice_from_aics(img_data_aics, save_name=save_name, **kwargs) + +def napari_get_reader(path: list[str] | str): + """Check if file ends with h5 and returns reader function if true + Parameters + ---------- + path : str or list of str + Path to file, or list of paths. + Returns + ------- + function + """ + if isinstance(path, list): + # reader plugins may be handed single path, or a list of paths. + # if it is a list, we are only going to open first file + path = path[0] + + # if we know we cannot read the file, we immediately return None. + if not path.endswith(".h5"): + return None + + # otherwise we return the *function* that can read ``path``. + return bdv_h5_reader + + +def bdv_h5_reader(path): + """Take a path and returns a list of LayerData tuples.""" + + os.environ["HDF5_USE_FILE_LOCKING"] = "FALSE" + + #print(path) + import npy2bdv + h5_file = npy2bdv.npy2bdv.BdvEditor(path) + + img = [] + + #get dimensions of first image + first_timepoint = h5_file.read_view(time=0,channel=0) + + #Threshold to figure out when to use out-of-memory loading/dask + #Got the idea from napari-aicsimageio + #https://github.com/AllenCellModeling/napari-aicsimageio/blob/22934757c2deda30c13f39ec425343182fa91a89/napari_aicsimageio/core.py#L222 + mem_threshold_bytes = 4e9 + mem_per_threshold = 0.3 + + from psutil import virtual_memory + + file_size = os.path.getsize(path) + avail_mem = virtual_memory().available + + #if file size <30% of available memory and <4GB, open + if file_size<=mem_per_threshold*avail_mem and file_size Date: Fri, 21 Jul 2023 18:19:50 +1000 Subject: [PATCH 002/147] Clean up global variables and types in dock_widget --- core/lls_core/config.py | 2 +- core/lls_core/lattice_data.py | 8 +- core/lls_core/llsz_core.py | 39 ++- plugin/napari_lattice/dock_widget.py | 491 ++++++++++++++------------- plugin/napari_lattice/napari.yaml | 6 +- plugin/tests/test_dock_widget.py | 2 +- 6 files changed, 288 insertions(+), 260 deletions(-) diff --git a/core/lls_core/config.py b/core/lls_core/config.py index 8460167..c63a3ae 100644 --- a/core/lls_core/config.py +++ b/core/lls_core/config.py @@ -2,5 +2,5 @@ #https://docs.python.org/3/faq/programming.html?highlight=global#how-do-i-share-global-variables-across-modules channel = 0 time = 0 -#configure default logging level for use in packages; inherit from _dock_widget or __main__.py +#configure default logging level for use in packages; inherit from dock_widget or __main__.py log_level = 10 \ No newline at end of file diff --git a/core/lls_core/lattice_data.py b/core/lls_core/lattice_data.py index 45efa05..c3066f0 100644 --- a/core/lls_core/lattice_data.py +++ b/core/lls_core/lattice_data.py @@ -47,6 +47,10 @@ class LatticeData: #: The filename of this data when it is saved save_name: str + # Dimensions of the deskewed output + deskew_vol_shape: Tuple[int, ...] = field(init=False) + deskew_affine_transform: cle.AffineTransform3D = field(init=False) + #: Geometry of the light path skew: DeskewDirection = DeskewDirection.Y angle: float = 30.0 @@ -58,10 +62,6 @@ class LatticeData: new_dz: Optional[float] = None - # Dimensions of the deskewed output - deskew_vol_shape: Optional[Tuple[int, ...]] = None - deskew_affine_transform: Optional[cle.AffineTransform3D] = None - # PSF data that should be refactored into another class eventually psf: Optional[List[NDArray]] = None psf_num_iter: Optional[int] = None diff --git a/core/lls_core/llsz_core.py b/core/lls_core/llsz_core.py index 92a59f1..106b25f 100644 --- a/core/lls_core/llsz_core.py +++ b/core/lls_core/llsz_core.py @@ -6,7 +6,8 @@ from dask.array.core import Array as DaskArray import dask.array as da from resource_backed_dask_array import ResourceBackedDaskArray -from typing import Optional, Union, TYPE_CHECKING +from typing import Any, Optional, Union, TYPE_CHECKING, overload, Literal, Tuple +from typing_extensions import Unpack, TypedDict, Required from pyclesperanto_prototype._tier8._affine_transform_deskew_3d import ( affine_transform_deskew_3d, ) @@ -32,10 +33,36 @@ cle._tier0._pycl.OCLArray, ] +class CommonArgs(TypedDict, total=False): + original_volume: Required[ArrayLike] + deskewed_volume: Union[ ArrayLike, None ] + roi_shape: Union[list, NDArray, None] + angle_in_degrees: float + voxel_size_x: float + voxel_size_y: float + voxel_size_z: float + z_start: int + z_end: int + deconvolution: bool + decon_processing: Optional[str] + psf: Union[Psf, None] + num_iter: int + linear_interpolation: bool + skew_dir: DeskewDirection + +@overload +def crop_volume_deskew(*, debug: Literal[True], get_deskew_and_decon: bool = False, **kwargs: Unpack[CommonArgs]) -> Tuple[NDArray, NDArray]: + ... +@overload +def crop_volume_deskew(*, debug: Literal[False] = False, get_deskew_and_decon: Literal[True], **kwargs: Unpack[CommonArgs]) -> Tuple[NDArray, NDArray]: + ... +@overload +def crop_volume_deskew(*, debug: Literal[False] = False, get_deskew_and_decon: Literal[False] = False, **kwargs: Unpack[CommonArgs]) -> NDArray: + ... def crop_volume_deskew( original_volume: ArrayLike, deskewed_volume: Union[ ArrayLike, None ] = None, - roi_shape: Union[Shapes, list, NDArray, None] = None, + roi_shape: Union[list, NDArray, None] = None, angle_in_degrees: float = 30, voxel_size_x: float = 1, voxel_size_y: float = 1, @@ -84,15 +111,15 @@ def crop_volume_deskew( # if shapes layer, get first one # TODO: test this - if is_napari_shape(roi_shape): - shape = roi_shape.data[0] + # if is_napari_shape(roi_shape): + # shape = roi_shape.data[0] # if its a list and each element has a shape of 4, its a list of rois - elif type(roi_shape) is list and len(roi_shape[0]) == 4: + if isinstance(roi_shape, list) and len(roi_shape[0]) == 4: # TODO:change to accept any roi by passing index shape = roi_shape[0] # len(roi_shape) >= 1: # if its a array or list with shape of 4, its a single ROI - elif len(roi_shape) == 4 and type(roi_shape) in (np.ndarray, list): + elif len(roi_shape) == 4 and isinstance(roi_shape, (np.ndarray, list)): shape = roi_shape assert len(shape) == 4, print("Shape must be an array of shape 4") diff --git a/plugin/napari_lattice/dock_widget.py b/plugin/napari_lattice/dock_widget.py index 69b5e64..9b5ef7c 100644 --- a/plugin/napari_lattice/dock_widget.py +++ b/plugin/napari_lattice/dock_widget.py @@ -4,14 +4,17 @@ import numpy as np from pathlib import Path import dask.array as da +from dask.array.core import Array as DaskArray import pandas as pd from typing import Union, Optional, Callable, Literal +from typing_extensions import NoReturn, Never, TypeVar, overload, Any from aicsimageio import AICSImage from enum import Enum from magicclass.wrappers import set_design from magicgui import magicgui -from magicclass import magicclass, field, vfield, set_options, MagicTemplate +from magicclass import magicclass, field, vfield, set_options, LlszTemplate +from magicclass._gui._base import check_override from magicclass.utils import click from qtpy.QtCore import Qt @@ -35,7 +38,7 @@ from lls_core.deconvolution import read_psf, pycuda_decon, skimage_decon from napari_lattice.ui_core import _Preview, _Deskew_Save -from napari_lattice._reader import lattice_from_napari +from napari_lattice.reader import lattice_from_napari # Enable Logging import logging @@ -47,16 +50,23 @@ class LastDimensionOptions(Enum): time = "Time" get_from_metadata = "Get from Metadata" +class LlszTemplate(LlszTemplate): + @property + def llsz_parent(self) -> "LLSZWidget": + return self.find_ancestor(LLSZWidget) + @magicclass(widget_type="split") -class LLSZWidget(MagicTemplate): +class LLSZWidget(LlszTemplate): + open_file: bool = False + lattice: LatticeData = None + skew_dir: DeskewDirection + angle_value: float + deskew_func: Callable + deconvolution: bool = False + shapes_layer: Shapes @magicclass(widget_type="split") - class LlszMenu(MagicTemplate): - open_file: bool = False - lattice: LatticeData = None - skew_dir: DeskewDirection - angle_value: float - deskew_func: Callable + class LlszMenu(LlszTemplate): main_heading = field("

Napari Lattice: Visualization & Analysis

", widget_type="Label") heading1 = field("Drag and drop an image file onto napari.\nOnce image has opened, initialize the\nplugin by clicking the button below.\nEnsure the image layer and voxel sizes are accurate in the prompt.\n If everything initalises properly, the button turns green.", widget_type="Label") @@ -105,15 +115,15 @@ def Choose_Image_Layer(self, cle.select_device(select_device) #assert skew_dir in DeskewDirection, "Skew direction not recognised. Enter either Y or X" - LLSZWidget.LlszMenu.skew_dir = skew_dir - LLSZWidget.LlszMenu.angle_value = angle - - if LLSZWidget.LlszMenu.skew_dir == DeskewDirection.Y: - LLSZWidget.LlszMenu.deskew_func = cle.deskew_y - #LLSZWidget.LlszMenu.skew_dir = DeskewDirection.Y - elif LLSZWidget.LlszMenu.skew_dir == DeskewDirection.X: - LLSZWidget.LlszMenu.deskew_func = cle.deskew_x - #LLSZWidget.LlszMenu.skew_dir = DeskewDirection.X + self.llsz_parent.skew_dir = skew_dir + self.llsz_parent.angle_value = angle + + if self.llsz_parent.skew_dir == DeskewDirection.Y: + self.llsz_parent.deskew_func = cle.deskew_y + #skew_dir = DeskewDirection.Y + elif self.llsz_parent.skew_dir == DeskewDirection.X: + self.llsz_parent.deskew_func = cle.deskew_x + #self.llsz_parent.skew_dir = DeskewDirection.X # merge all napari image layers as one multidimensional image if merge_all_channel_layers: @@ -132,56 +142,56 @@ def Choose_Image_Layer(self, self.parent_viewer.add_layer(new_layer) img_layer = new_layer - LLSZWidget.LlszMenu.lattice = lattice_from_napari( + 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=LLSZWidget.LlszMenu.skew_dir, + skew=self.llsz_parent.skew_dir, physical_pixel_sizes=(pixel_size_dx, pixel_size_dy, pixel_size_dz) ) - #LLSZWidget.LlszMenu.aics = LLSZWidget.LlszMenu.lattice.data + #self.llsz_parent.aics = self.llsz_parent.lattice.data - # LLSZWidget.LlszMenu.dask = False # Use GPU by default + # self.llsz_parent.dask = False # Use GPU by default # We initialise these variables here, but they can be changed in the deconvolution section # list to store psf images for each channel - LLSZWidget.LlszMenu.lattice.psf = [] - LLSZWidget.LlszMenu.lattice.psf_num_iter = 10 - LLSZWidget.LlszMenu.lattice.decon_processing = DeconvolutionChoice.cpu + self.llsz_parent.lattice.psf = [] + self.llsz_parent.lattice.psf_num_iter = 10 + self.llsz_parent.lattice.decon_processing = DeconvolutionChoice.cpu # list to store otf paths for each channel (Deprecated) - LLSZWidget.LlszMenu.lattice.otf_path = [] + self.llsz_parent.lattice.otf_path = [] # if not using GPU - #LLSZWidget.LlszMenu.dask = not use_GPU + #self.llsz_parent.dask = not use_GPU # flag for ensuring a file has been opened and plugin initialised - LLSZWidget.LlszMenu.open_file = True + self.llsz_parent.open_file = True logger.info( - f"Pixel size (ZYX) in microns: {LLSZWidget.LlszMenu.lattice.dz,LLSZWidget.LlszMenu.lattice.dy,LLSZWidget.LlszMenu.lattice.dx}") + 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(LLSZWidget.LlszMenu.lattice.data.shape[-3:])}") + f"Dimensions of image layer (ZYX): {list(self.llsz_parent.lattice.data.shape[-3:])}") logger.info( - f"Dimensions of deskewed image (ZYX): {LLSZWidget.LlszMenu.lattice.deskew_vol_shape}") + f"Dimensions of deskewed image (ZYX): {self.llsz_parent.lattice.deskew_vol_shape}") logger.info( - f"Deskewing angle is :{LLSZWidget.LlszMenu.lattice.angle}") + f"Deskewing angle is: {self.llsz_parent.lattice.angle}") logger.info( - f"Deskew Direction :{LLSZWidget.LlszMenu.lattice.skew}") + f"Deskew Direction: {self.llsz_parent.lattice.skew}") # Add dimension labels correctly # if channel, and not time - if LLSZWidget.LlszMenu.lattice.time == 0 and (last_dimension_channel or LLSZWidget.LlszMenu.lattice.channels > 0): + 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 LLSZWidget.LlszMenu.lattice.channels == 0 and LLSZWidget.LlszMenu.lattice.time > 0: + 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 LLSZWidget.LlszMenu.lattice.channels > 1: + 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]) == LLSZWidget.LlszMenu.lattice.channels: + 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 LLSZWidget.LlszMenu.lattice.channels == 1 and LLSZWidget.LlszMenu.lattice.time > 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") @@ -199,11 +209,10 @@ def Choose_Image_Layer(self, def _set_decon(self): if self.deconvolution: logger.info("Deconvolution Activated") - LLSZWidget.LlszMenu.deconvolution.value = True + self.llsz_parent.deconvolution = True else: logger.info("Deconvolution Disabled") - LLSZWidget.LlszMenu.deconvolution.value = False - return + self.llsz_parent.deconvolution = False @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

"), @@ -221,26 +230,27 @@ def _set_decon(self): "widget_type": "SpinBox", "label": "No of iterations (Deconvolution)", "value": 10, "min": 1, "max": 50, "step": 1} ) def deconvolution_gui(self, - header, + header: str, psf_ch1_path: Path, psf_ch2_path: Path, psf_ch3_path: Path, psf_ch4_path: Path, - device_option, + device_option: DeconvolutionChoice, no_iter: int): """GUI for Deconvolution button""" - LLSZWidget.LlszMenu.lattice.decon_processing = device_option - assert LLSZWidget.LlszMenu.deconvolution.value == True, "Deconvolution is set to False. Tick the box to activate deconvolution." - LLSZWidget.LlszMenu.lattice.psf = list(read_psf([ + self.llsz_parent.lattice.decon_processing = device_option + if not self.llsz_parent.deconvolution: + raise Exception("Deconvolution is set to False. Tick the box to activate deconvolution.") + self.llsz_parent.lattice.psf = list(read_psf([ psf_ch1_path, psf_ch2_path, psf_ch3_path, psf_ch4_path, ], device_option, - lattice_class=LLSZWidget.LlszMenu.lattice + lattice_class=self.llsz_parent.lattice )) - LLSZWidget.LlszMenu.lattice.psf_num_iter = no_iter + self.llsz_parent.lattice.psf_num_iter = no_iter self["deconvolution_gui"].background_color = "green" self["deconvolution_gui"].text = "PSFs added" @@ -251,7 +261,7 @@ class Preview: channel=dict(label="Channel:"), call_button="Preview") def Preview_Deskew(self, - header, + header: str, time: int, channel: int, img_data: ImageData): @@ -264,14 +274,13 @@ def Preview_Deskew(self, time, channel, img_data) - return # Tabbed Widget container to house all the widgets @magicclass(widget_type="tabbed", name="Functions") - class WidgetContainer(MagicTemplate): + class WidgetContainer(LlszTemplate): @magicclass(name="Deskew", widget_type="scrollable", properties={"min_width": 100}) - class DeskewWidget(MagicTemplate): + class DeskewWidget(LlszTemplate): @magicgui(header=dict(widget_type="Label", label="

Deskew and Save

"), time_start=dict(label="Time Start:", max=2**20), @@ -283,7 +292,7 @@ class DeskewWidget(MagicTemplate): save_path=dict(mode='d', label="Directory to save"), call_button="Save") def Deskew_Save(self, - header, + header: str, time_start: int, time_end: int, ch_start: int, @@ -301,67 +310,64 @@ def Deskew_Save(self, return @magicclass(name="Crop and Deskew", widget_type="scrollable") - class CropWidget(MagicTemplate): + 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(MagicTemplate): - 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): - LLSZWidget.WidgetContainer.CropWidget.Preview_Crop_Menu.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") + if self.parent_viewer is None: + raise Exception("This function can only be used when inside of a Napari viewer") + 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 - LLSZWidget.WidgetContainer.CropWidget.Preview_Crop_Menu.shapes_layer.mode = "SELECT" + self.llsz_parent.shapes_layer.mode = "SELECT" self["activate_cropping"].text = "Cropping layer active" self["activate_cropping"].background_color = "green" - return 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])): + def Import_ImageJ_ROI(self, path: Path = Path(history.get_open_history()[0])) -> None: logger.info(f"Opening{path}") - roi_list = read_imagej_roi(path) + roi_list = read_imagej_roi(str(path)) # convert to canvas coordinates - roi_list = (np.array(roi_list) * - LLSZWidget.LlszMenu.lattice.dy).tolist() - LLSZWidget.WidgetContainer.CropWidget.Preview_Crop_Menu.shapes_layer.add(roi_list, shape_type='polygon', edge_width=1, edge_color='yellow', - face_color=[1, 1, 1, 0]) - return + self.find_ancestor(LLSZWidget) + roi_list = (np.array(roi_list) * self.llsz_parent.lattice.dy).tolist() + self.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") + 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) # -> LayerDataTuple: def Crop_Preview(self, roi_layer: ShapesData): - assert roi_layer, "No coordinates found for cropping. Check if right shapes layer or initialise shapes layer and draw ROIs." + 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 time = self.time_crop.value channel = self.chan_crop.value - - assert time < LLSZWidget.LlszMenu.lattice.time, "Time is out of range" - assert channel < LLSZWidget.LlszMenu.lattice.channels, "Channel is out of range" - + 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}") - vol = LLSZWidget.LlszMenu.lattice.data + vol = self.llsz_parent.lattice.data vol_zyx = vol[time, channel, ...] vol_zyx = np.array(vol_zyx) - deskewed_shape = LLSZWidget.LlszMenu.lattice.deskew_vol_shape + deskewed_shape = self.llsz_parent.lattice.deskew_vol_shape # Create a dask array same shape as deskewed image - deskewed_volume = da.zeros(deskewed_shape) + deskewed_volume: DaskArray = da.zeros(deskewed_shape) # Option for entering custom z start value? z_start = 0 @@ -370,75 +376,74 @@ def Crop_Preview(self, roi_layer: ShapesData): # if only one roi drawn, use the first ROI for cropping if len(roi_layer) == 1: roi_idx = 0 - else: - assert len( - LLSZWidget.WidgetContainer.CropWidget.Preview_Crop_Menu.shapes_layer.selected_data) > 0, "Please select an ROI" - roi_idx = list( - LLSZWidget.WidgetContainer.CropWidget.Preview_Crop_Menu.shapes_layer.selected_data)[0] + elif len(self.shapes_layer.selected_data) == 0: + raise Exception("Please select an ROI") + + roi_idx = list(self.shapes_layer.selected_data)[0] - roi_choice = roi_layer[roi_idx] # 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/LLSZWidget.LlszMenu.lattice.dy for x in roi_choice] + roi_choice = [x/self.llsz_parent.lattice.dy for x in roi_layer[roi_idx]] logger.info(f"Previewing ROI {roi_idx}") # crop - if LLSZWidget.LlszMenu.deconvolution.value: + 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") - #psf = LLSZWidget.LlszMenu.lattice.psf[channel] - if LLSZWidget.LlszMenu.lattice.decon_processing == DeconvolutionChoice.cuda_gpu: + #psf = self.llsz_parent.lattice.psf[channel] + if self.llsz_parent.lattice.decon_processing == DeconvolutionChoice.cuda_gpu: crop_roi_vol_desk = crop_volume_deskew(original_volume=vol_zyx, deskewed_volume=deskewed_volume, roi_shape=roi_choice, - angle_in_degrees=LLSZWidget.LlszMenu.angle_value, - voxel_size_x=LLSZWidget.LlszMenu.lattice.dx, - voxel_size_y=LLSZWidget.LlszMenu.lattice.dy, - voxel_size_z=LLSZWidget.LlszMenu.lattice.dz, + 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, z_start=z_start, z_end=z_end, - deconvolution=LLSZWidget.LlszMenu.deconvolution.value, - decon_processing=LLSZWidget.LlszMenu.lattice.decon_processing, - psf=LLSZWidget.LlszMenu.lattice.psf[channel], - num_iter=LLSZWidget.LlszMenu.lattice.psf_num_iter).astype(vol_zyx.dtype) + deconvolution=self.llsz_parent.deconvolution, + 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).astype(vol_zyx.dtype) else: crop_roi_vol_desk = crop_volume_deskew(original_volume=vol_zyx, deskewed_volume=deskewed_volume, roi_shape=roi_choice, - angle_in_degrees=LLSZWidget.LlszMenu.angle_value, - voxel_size_x=LLSZWidget.LlszMenu.lattice.dx, - voxel_size_y=LLSZWidget.LlszMenu.lattice.dy, - voxel_size_z=LLSZWidget.LlszMenu.lattice.dz, + 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, z_start=z_start, z_end=z_end, - deconvolution=LLSZWidget.LlszMenu.deconvolution.value, - decon_processing=LLSZWidget.LlszMenu.lattice.decon_processing, - psf=LLSZWidget.LlszMenu.lattice.psf[channel], - num_iter=LLSZWidget.LlszMenu.lattice.psf_num_iter).astype(vol_zyx.dtype) + deconvolution=self.llsz_parent.deconvolution, + 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).astype(vol_zyx.dtype) else: crop_roi_vol_desk = crop_volume_deskew(original_volume=vol_zyx, deskewed_volume=deskewed_volume, roi_shape=roi_choice, - angle_in_degrees=LLSZWidget.LlszMenu.angle_value, - voxel_size_x=LLSZWidget.LlszMenu.lattice.dx, - voxel_size_y=LLSZWidget.LlszMenu.lattice.dy, - voxel_size_z=LLSZWidget.LlszMenu.lattice.dz, + 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, z_start=z_start, z_end=z_end, - skew_dir=LLSZWidget.LlszMenu.skew_dir).astype(vol_zyx.dtype) + skew_dir=self.llsz_parent.skew_dir).astype(vol_zyx.dtype) crop_roi_vol_desk = cle.pull(crop_roi_vol_desk) # get array back from gpu or addding cle array to napari can throw errors - scale = (LLSZWidget.LlszMenu.lattice.new_dz, - LLSZWidget.LlszMenu.lattice.dy, - LLSZWidget.LlszMenu.lattice.dx) + 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(MagicTemplate): + 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), @@ -448,12 +453,12 @@ class CropSaveData(MagicTemplate): "label": "Save as filetype:", "choices": SaveFileType}, save_path=dict(mode='d', label="Directory to save ")) def Crop_Save(self, - header, + header: str, time_start: int, time_end: int, ch_start: int, ch_end: int, - save_as_type: str, + save_as_type: SaveFileType, roi_layer_list: ShapesData, save_path: Path = Path(history.get_save_history()[0])): @@ -461,20 +466,20 @@ def Crop_Save(self, logger.error( "No coordinates found or cropping. Initialise shapes layer and draw ROIs.") else: - assert LLSZWidget.LlszMenu.open_file, "Image not initialised" + if not self.llsz_parent.open_file: + raise Exception("Image not initialised") - check_dimensions(time_start, time_end, ch_start, ch_end, - LLSZWidget.LlszMenu.lattice.channels, LLSZWidget.LlszMenu.lattice.time) + check_dimensions(time_start, time_end, ch_start, ch_end, self.llsz_parent.lattice.channels, self.llsz_parent.lattice.time) - angle = LLSZWidget.LlszMenu.lattice.angle - dx = LLSZWidget.LlszMenu.lattice.dx - dy = LLSZWidget.LlszMenu.lattice.dy - dz = LLSZWidget.LlszMenu.lattice.dz + 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 = LLSZWidget.LlszMenu.lattice.data + img_data = self.llsz_parent.lattice.data # Get shape of deskewed image - deskewed_shape = LLSZWidget.LlszMenu.lattice.deskew_vol_shape + deskewed_shape = self.llsz_parent.lattice.deskew_vol_shape deskewed_volume = da.zeros(deskewed_shape) z_start = 0 z_end = deskewed_shape[0] @@ -482,8 +487,7 @@ def Crop_Save(self, logger.info("Cropping and saving files...") # necessary when scale is used for napari.viewer.add_image operations - roi_layer_list = [ - x/LLSZWidget.LlszMenu.lattice.dy for x in roi_layer_list] + 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 @@ -500,7 +504,7 @@ def Crop_Save(self, str(idx), save_path=save_path, save_file_type=save_as_type, - save_name=LLSZWidget.LlszMenu.lattice.save_name, + save_name=self.llsz_parent.lattice.save_name, dx=dx, dy=dy, dz=dz, @@ -513,17 +517,17 @@ def Crop_Save(self, voxel_size_x=dx, voxel_size_y=dy, voxel_size_z=dz, - LLSZWidget=LLSZWidget + LLSZWidget=parent ) logger.info( f"Cropping and Saving Complete -> {save_path}") - return @magicclass(name="Workflow", widget_type="scrollable") - class WorkflowWidget: + class WorkflowWidget(LlszTemplate): + @magicclass(name="Preview Workflow", widget_type="scrollable") - class PreviewWorkflow: + 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

"), @@ -538,7 +542,7 @@ class PreviewWorkflow: #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, + header: str, time_preview: int, chan_preview: int, get_active_workflow: bool, @@ -560,13 +564,11 @@ def Workflow_Preview(self, print("Previewing deskewed channel and time with workflow") if get_active_workflow: # installs the workflow to napari - user_workflow = WorkflowManager.install( - self.parent_viewer).workflow + user_workflow = WorkflowManager.install(self.parent_viewer).workflow parent_dir = workflow_path.resolve( ).parents[0].__str__()+os.sep logger.info("Workflow loaded from napari") else: - try: # Automatically scan workflow file directory for *.py files. # If it findss one, load it as a module @@ -587,17 +589,15 @@ def Workflow_Preview(self, user_workflow = load_workflow( workflow_path.__str__()) except yaml.loader.ConstructorError as e: - logger.error( - "\033[91m While loading workflow, got the following error which may mean you need to install the corresponding module in your Python environment: \033[0m") - logger.error(e) + raise Exception("\033[91m While loading workflow, got the following error which may mean you need to install the corresponding module in your Python environment: \033[0m") from e #user_workflow = load_workflow(workflow_path) logger.info("Workflow loaded from file") - assert type( - user_workflow) is Workflow, "Workflow loading error. Check if file is workflow or if required libraries are installed" + if not isinstance(user_workflow, Workflow): + raise Exception("Workflow loading error. Check if file is workflow or if required libraries are installed") - input_arg_first, input_arg_last, first_task_name, last_task_name = get_first_last_image_and_task( + input_arg_first, _, first_task_name, last_task_name = get_first_last_image_and_task( user_workflow) #print(input_arg_first, input_arg_last, first_task_name,last_task_name ) # get list of tasks @@ -606,8 +606,10 @@ def Workflow_Preview(self, # logger.info() # when using fields, self.time_preview.value - assert time_preview < LLSZWidget.LlszMenu.lattice.time, "Time is out of range" - assert chan_preview < LLSZWidget.LlszMenu.lattice.channels, "Channel is out of range" + if time_preview >= self.llsz_parent.lattice.time: + raise ValueError("Time is out of range") + if chan_preview >= self.llsz_parent.lattice.time: + raise ValueError("Channel is out of range") time = time_preview channel = chan_preview @@ -619,7 +621,7 @@ def Workflow_Preview(self, logger.info( f"Processing for Time: {time} and Channel: {channel}") - vol = LLSZWidget.LlszMenu.lattice.data + vol = self.llsz_parent.lattice.data vol_zyx = vol[time, channel, ...] vol_zyx = np.array(vol_zyx) @@ -634,7 +636,7 @@ def Workflow_Preview(self, psf = None otf_path = None - if LLSZWidget.LlszMenu.lattice.decon_processing == DeconvolutionChoice.cuda_gpu: + if self.llsz_parent.lattice.decon_processing == DeconvolutionChoice.cuda_gpu: otf_path = "otf_path" else: psf = "psf" @@ -643,7 +645,7 @@ def Workflow_Preview(self, # get the function associated with the first task and check if its deskewing if Use_Cropping: # use deskewed volume for cropping function - deskewed_shape = LLSZWidget.LlszMenu.lattice.deskew_vol_shape + deskewed_shape = self.llsz_parent.lattice.deskew_vol_shape deskewed_volume = da.zeros(deskewed_shape) z_start = 0 z_end = deskewed_shape[0] @@ -652,45 +654,44 @@ def Workflow_Preview(self, if len(roi_layer_list) == 1: roi_idx = 0 else: # else get the user selection - assert len( - LLSZWidget.WidgetContainer.CropWidget.Preview_Crop_Menu.shapes_layer.selected_data) > 0, "Please select an ROI" - roi_idx = list( - LLSZWidget.WidgetContainer.CropWidget.Preview_Crop_Menu.shapes_layer.selected_data)[0] + 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] roi_choice = roi_layer_list[roi_idx] # As the original image is scaled, the coordinates are in microns, so we need to convert # roi to from micron to canvas/world coordinates roi_choice = [ - x/LLSZWidget.LlszMenu.lattice.dy for x in roi_choice] + x/self.llsz_parent.lattice.dy for x in roi_choice] logger.info(f"Previewing ROI {roi_idx}") - if LLSZWidget.LlszMenu.deconvolution.value: + if self.llsz_parent.deconvolution: user_workflow.set("crop_deskew_image", crop_volume_deskew, original_volume=vol_zyx, deskewed_volume=deskewed_volume, roi_shape=roi_choice, - angle_in_degrees=LLSZWidget.LlszMenu.lattice.angle, - voxel_size_x=LLSZWidget.LlszMenu.lattice.dx, - voxel_size_y=LLSZWidget.LlszMenu.lattice.dy, - voxel_size_z=LLSZWidget.LlszMenu.lattice.dz, + angle_in_degrees=self.llsz_parent.lattice.angle, + voxel_size_x=self.llsz_parent.lattice.dx, + voxel_size_y=self.llsz_parent.lattice.dy, + voxel_size_z=self.llsz_parent.lattice.dz, z_start=z_start, z_end=z_end, - deconvolution=LLSZWidget.LlszMenu.deconvolution.value, - decon_processing=LLSZWidget.LlszMenu.lattice.decon_processing, + deconvolution=self.llsz_parent.deconvolution, + decon_processing=self.llsz_parent.lattice.decon_processing, otf_path=otf_path, - psf=LLSZWidget.LlszMenu.lattice.psf[channel], - skew_dir=LLSZWidget.LlszMenu.skew_dir) + psf=self.llsz_parent.lattice.psf[channel], + skew_dir=self.llsz_parent.skew_dir) else: user_workflow.set("crop_deskew_image", crop_volume_deskew, original_volume=vol_zyx, deskewed_volume=deskewed_volume, roi_shape=roi_choice, - angle_in_degrees=LLSZWidget.LlszMenu.lattice.angle, - voxel_size_x=LLSZWidget.LlszMenu.lattice.dx, - voxel_size_y=LLSZWidget.LlszMenu.lattice.dy, - voxel_size_z=LLSZWidget.LlszMenu.lattice.dz, + angle_in_degrees=self.llsz_parent.lattice.angle, + voxel_size_x=self.llsz_parent.lattice.dx, + voxel_size_y=self.llsz_parent.lattice.dy, + voxel_size_z=self.llsz_parent.lattice.dz, z_start=z_start, z_end=z_end, - skew_dir=LLSZWidget.LlszMenu.skew_dir) + skew_dir=self.llsz_parent.skew_dir) # Set input of the workflow to be crop_deskewing, i.e., the original first operation will now have crop_deskew_image as an input (becoming second instead) user_workflow.set( @@ -700,40 +701,40 @@ def Workflow_Preview(self, # Not cropping; If deskew not in workflow, append to start elif user_workflow.get_task(task_name_start)[0] not in (cle.deskew_y, cle.deskew_x): # if deconvolution checked, add it to start of workflow (add upstream of deskewing) - if LLSZWidget.LlszMenu.deconvolution.value: - psf = LLSZWidget.LlszMenu.lattice.psf[channel] + if self.llsz_parent.deconvolution.value: + psf = self.llsz_parent.lattice.psf[channel] input_arg_first_decon, input_arg_last_decon, first_task_name_decon, last_task_name_decon = get_first_last_image_and_task( user_workflow) - if LLSZWidget.LlszMenu.lattice.decon_processing == DeconvolutionChoice.cuda_gpu: + if self.llsz_parent.lattice.decon_processing == DeconvolutionChoice.cuda_gpu: user_workflow.set("deconvolution", pycuda_decon, image=vol_zyx, - psf=LLSZWidget.LlszMenu.lattice.psf[channel], - dzdata=LLSZWidget.LlszMenu.lattice.dz, - dxdata=LLSZWidget.LlszMenu.lattice.dx, - dzpsf=LLSZWidget.LlszMenu.lattice.dz, - dxpsf=LLSZWidget.LlszMenu.lattice.dx, - num_iter=LLSZWidget.LlszMenu.lattice.psf_num_iter) + psf=self.llsz_parent.lattice.psf[channel], + 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_decon,"deconvolution") else: user_workflow.set("deconvolution", skimage_decon, vol_zyx=vol_zyx, - psf=LLSZWidget.LlszMenu.lattice.psf[channel], - num_iter=LLSZWidget.LlszMenu.lattice.psf_num_iter, + psf=self.llsz_parent.lattice.psf[channel], + num_iter=self.llsz_parent.lattice.psf_num_iter, clip=False, filter_epsilon=0, boundary='nearest') # user_workflow.set(input_arg_first_decon,"deconvolution") user_workflow.set("deskew_image", - LLSZWidget.LlszMenu.deskew_func, + self.llsz_parent.deskew_func, "deconvolution", - angle_in_degrees=LLSZWidget.LlszMenu.lattice.angle, - voxel_size_x=LLSZWidget.LlszMenu.lattice.dx, - voxel_size_y=LLSZWidget.LlszMenu.lattice.dy, - voxel_size_z=LLSZWidget.LlszMenu.lattice.dz, + angle_in_degrees=self.llsz_parent.lattice.angle, + voxel_size_x=self.llsz_parent.lattice.dx, + voxel_size_y=self.llsz_parent.lattice.dy, + voxel_size_z=self.llsz_parent.lattice.dz, linear_interpolation=True) # user_workflow.set("change_bitdepth",as_type,"deskew_image",vol_zyx) @@ -742,12 +743,12 @@ def Workflow_Preview(self, else: user_workflow.set("deskew_image", - LLSZWidget.LlszMenu.deskew_func, + self.llsz_parent.deskew_func, vol_zyx, - angle_in_degrees=LLSZWidget.LlszMenu.lattice.angle, - voxel_size_x=LLSZWidget.LlszMenu.lattice.dx, - voxel_size_y=LLSZWidget.LlszMenu.lattice.dy, - voxel_size_z=LLSZWidget.LlszMenu.lattice.dz, + angle_in_degrees=self.llsz_parent.lattice.angle, + voxel_size_x=self.llsz_parent.lattice.dx, + voxel_size_y=self.llsz_parent.lattice.dy, + voxel_size_z=self.llsz_parent.lattice.dz, linear_interpolation=True) # Set input of the workflow to be from deskewing # user_workflow.set(input_arg_first,"deskew_image") @@ -762,28 +763,28 @@ def Workflow_Preview(self, # if deskew already in workflow, just check if deconvolution needs to be added # repitition of above (maybe create a function?) # if deconvolution checked, add it to start of workflow (add upstream of deskewing) - if LLSZWidget.LlszMenu.deconvolution.value: - psf = LLSZWidget.LlszMenu.lattice.psf[channel] + if self.llsz_parent.deconvolution.value: + psf = self.llsz_parent.lattice.psf[channel] input_arg_first, input_arg_last, first_task_name, last_task_name = get_first_last_image_and_task( user_workflow) - if LLSZWidget.LlszMenu.lattice.decon_processing == DeconvolutionChoice.cuda_gpu: + if self.llsz_parent.lattice.decon_processing == DeconvolutionChoice.cuda_gpu: user_workflow.set("deconvolution", pycuda_decon, image=vol_zyx, - psf=LLSZWidget.LlszMenu.lattice.psf[channel], - dzdata=LLSZWidget.LlszMenu.lattice.dz, - dxdata=LLSZWidget.LlszMenu.lattice.dx, - dzpsf=LLSZWidget.LlszMenu.lattice.dz, - dxpsf=LLSZWidget.LlszMenu.lattice.dx, - num_iter=LLSZWidget.LlszMenu.lattice.psf_num_iter) + psf=self.llsz_parent.lattice.psf[channel], + 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=vol_zyx, - psf=LLSZWidget.LlszMenu.lattice.psf[channel], - num_iter=LLSZWidget.LlszMenu.lattice.psf_num_iter, + psf=self.llsz_parent.lattice.psf[channel], + num_iter=self.llsz_parent.lattice.psf_num_iter, clip=False, filter_epsilon=0, boundary='nearest') @@ -867,16 +868,17 @@ def Apply_Workflow_and_Save(self, 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 """ - assert LLSZWidget.LlszMenu.open_file, "Image not initialised" + if not self.llsz_parent.open_file: + raise Exception("Image not initialised") check_dimensions(time_start, time_end, ch_start, ch_end, - LLSZWidget.LlszMenu.lattice.channels, LLSZWidget.LlszMenu.lattice.time) + self.llsz_parent.lattice.channels, self.llsz_parent.lattice.time) # Get parameters - angle = LLSZWidget.LlszMenu.lattice.angle - dx = LLSZWidget.LlszMenu.lattice.dx - dy = LLSZWidget.LlszMenu.lattice.dy - dz = LLSZWidget.LlszMenu.lattice.dz + angle = self.llsz_parent.lattice.angle + dx = self.llsz_parent.lattice.dx + dy = self.llsz_parent.lattice.dy + dz = self.llsz_parent.lattice.dz if get_active_workflow: # installs the workflow to napari @@ -898,11 +900,11 @@ def Apply_Workflow_and_Save(self, modules = map( importlib.import_module, custom_py_files) print(f"Custom modules imported {modules}") - user_workflow = load_workflow(workflow_path) - - assert type( - user_workflow) is Workflow, "Workflow file is not a napari workflow object. Check file! You can use workflow inspector if needed" + user_workflow = load_workflow(str(workflow_path)) + if not isinstance(user_workflow, Workflow): + raise Exception("Workflow file is not a napari workflow object. Check file! You can use workflow inspector if needed") + input_arg_first, input_arg_last, first_task_name, last_task_name = get_first_last_image_and_task( user_workflow) print(input_arg_first, input_arg_last, @@ -912,7 +914,7 @@ def Apply_Workflow_and_Save(self, print("Workflow loaded:") print(user_workflow) - vol = LLSZWidget.LlszMenu.lattice.data + vol = self.llsz_parent.lattice.data #vol_zyx= vol[time,channel,...] @@ -928,22 +930,21 @@ def Apply_Workflow_and_Save(self, psf = None otf_path = None - if LLSZWidget.LlszMenu.lattice.decon_processing == DeconvolutionChoice.cuda_gpu: + if self.llsz_parent.lattice.decon_processing == DeconvolutionChoice.cuda_gpu: #otf_path = "otf_path" psf_arg = "psf" - psf = LLSZWidget.LlszMenu.lattice.psf + psf = self.llsz_parent.lattice.psf else: psf_arg = "psf" - psf = LLSZWidget.LlszMenu.lattice.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/LLSZWidget.LlszMenu.lattice.dy for x in roi_layer_list] + roi_layer_list = [x/self.llsz_parent.lattice.dy for x in roi_layer_list] - deskewed_shape = LLSZWidget.LlszMenu.lattice.deskew_vol_shape + deskewed_shape = self.llsz_parent.lattice.deskew_vol_shape deskewed_volume = da.zeros(deskewed_shape) z_start = 0 z_end = deskewed_shape[0] @@ -963,10 +964,10 @@ def Apply_Workflow_and_Save(self, voxel_size_z=dz, z_start=z_start, z_end=z_end, - deconvolution=LLSZWidget.LlszMenu.deconvolution.value, - decon_processing=LLSZWidget.LlszMenu.lattice.decon_processing, + deconvolution=self.llsz_parent.deconvolution.value, + decon_processing=self.llsz_parent.lattice.decon_processing, psf=psf_arg, - skew_dir=LLSZWidget.LlszMenu.skew_dir) + skew_dir=self.llsz_parent.skew_dir) # change the first task so it accepts "crop_deskew as input" new_task = modify_workflow_task( @@ -990,13 +991,13 @@ def Apply_Workflow_and_Save(self, #roi_layer = roi_layer, save_name_prefix="ROI_" + \ str(idx), - save_name=LLSZWidget.LlszMenu.lattice.save_name, + save_name=self.llsz_parent.lattice.save_name, dx=dx, dy=dy, dz=dz, angle=angle, - deconvolution=LLSZWidget.LlszMenu.deconvolution.value, - decon_processing=LLSZWidget.LlszMenu.lattice.decon_processing, + deconvolution=self.llsz_parent.deconvolution.value, + decon_processing=self.llsz_parent.lattice.decon_processing, otf_path=otf_path, psf_arg=psf_arg, psf=psf) @@ -1006,7 +1007,7 @@ def Apply_Workflow_and_Save(self, input = "input" # add task to the workflow user_workflow.set("deskew_image", - LLSZWidget.LlszMenu.deskew_func, + self.llsz_parent.deskew_func, input_image=input, angle_in_degrees=angle, voxel_size_x=dx, @@ -1020,29 +1021,29 @@ def Apply_Workflow_and_Save(self, user_workflow.set(task_name_start, new_task) # if deconvolution checked, add it to start of workflow (add upstream of deskewing) - if LLSZWidget.LlszMenu.deconvolution.value: + if self.llsz_parent.deconvolution.value: 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 LLSZWidget.LlszMenu.lattice.decon_processing == DeconvolutionChoice.cuda_gpu: + if self.llsz_parent.lattice.decon_processing == DeconvolutionChoice.cuda_gpu: user_workflow.set("deconvolution", pycuda_decon, image=input, psf=psf_arg, - dzdata=LLSZWidget.LlszMenu.lattice.dz, - dxdata=LLSZWidget.LlszMenu.lattice.dx, - dzpsf=LLSZWidget.LlszMenu.lattice.dz, - dxpsf=LLSZWidget.LlszMenu.lattice.dx, - num_iter=LLSZWidget.LlszMenu.lattice.psf_num_iter) + 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=LLSZWidget.LlszMenu.lattice.psf_num_iter, + num_iter=self.llsz_parent.lattice.psf_num_iter, clip=False, filter_epsilon=0, boundary='nearest') @@ -1065,13 +1066,13 @@ def Apply_Workflow_and_Save(self, channel_end=ch_end, save_file_type=save_as_type, save_path=save_path, - save_name=LLSZWidget.LlszMenu.lattice.save_name, + save_name=self.llsz_parent.lattice.save_name, dx=dx, dy=dy, dz=dz, angle=angle, - deconvolution=LLSZWidget.LlszMenu.deconvolution.value, - decon_processing=LLSZWidget.LlszMenu.lattice.decon_processing, + deconvolution=self.llsz_parent.deconvolution.value, + decon_processing=self.llsz_parent.lattice.decon_processing, otf_path=otf_path, psf_arg=psf_arg, psf=psf) @@ -1080,29 +1081,29 @@ def Apply_Workflow_and_Save(self, 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 LLSZWidget.LlszMenu.deconvolution.value: + if self.llsz_parent.deconvolution.value: 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 LLSZWidget.LlszMenu.lattice.decon_processing == DeconvolutionChoice.cuda_gpu: + if self.llsz_parent.lattice.decon_processing == DeconvolutionChoice.cuda_gpu: user_workflow.set("deconvolution", pycuda_decon, image=input, psf=psf_arg, - dzdata=LLSZWidget.LlszMenu.lattice.dz, - dxdata=LLSZWidget.LlszMenu.lattice.dx, - dzpsf=LLSZWidget.LlszMenu.lattice.dz, - dxpsf=LLSZWidget.LlszMenu.lattice.dx, - num_iter=LLSZWidget.LlszMenu.lattice.psf_num_iter) + 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=LLSZWidget.LlszMenu.lattice.psf_num_iter, + num_iter=self.llsz_parent.lattice.psf_num_iter, clip=False, filter_epsilon=0, boundary='nearest') @@ -1126,13 +1127,13 @@ def Apply_Workflow_and_Save(self, channel_end=ch_end, save_file_type=save_as_type, save_path=save_path, - save_name=LLSZWidget.LlszMenu.lattice.save_name, + save_name=self.llsz_parent.lattice.save_name, dx=dx, dy=dy, dz=dz, angle=angle, - deconvolution=LLSZWidget.LlszMenu.deconvolution.value, - decon_processing=LLSZWidget.LlszMenu.lattice.decon_processing, + deconvolution=self.llsz_parent.deconvolution.value, + decon_processing=self.llsz_parent.lattice.decon_processing, otf_path=otf_path, psf_arg=psf_arg, psf=psf) diff --git a/plugin/napari_lattice/napari.yaml b/plugin/napari_lattice/napari.yaml index 9f0bc22..b83b0f0 100644 --- a/plugin/napari_lattice/napari.yaml +++ b/plugin/napari_lattice/napari.yaml @@ -2,9 +2,9 @@ name: napari-lattice display_name: Lattice Lightsheet Analysis contributions: commands: - - id: napari-lattice._dock_widget + - id: napari-lattice.dock_widget title: Create napari_lattice widget - python_name: napari_lattice._dock_widget:_napari_lattice_widget_wrapper + python_name: napari_lattice.dock_widget:_napari_lattice_widget_wrapper # ~~ Reader ~~ - id: napari-lattice.get_reader @@ -19,7 +19,7 @@ contributions: # python_name: napari_lattice.use_workflow:_workflow_widget widgets: - - command: napari-lattice._dock_widget + - command: napari-lattice.dock_widget display_name: Lattice Lightsheet Analysis #- command: napari-lattice.crop_deskew diff --git a/plugin/tests/test_dock_widget.py b/plugin/tests/test_dock_widget.py index 58b8b09..7d8e9ca 100644 --- a/plugin/tests/test_dock_widget.py +++ b/plugin/tests/test_dock_widget.py @@ -1,5 +1,5 @@ from __future__ import annotations -from napari_lattice._dock_widget import _napari_lattice_widget_wrapper +from napari_lattice.dock_widget import _napari_lattice_widget_wrapper import numpy as np from typing import Callable, TYPE_CHECKING from magicclass.testing import check_function_gui_buildable, FunctionGuiTester From 7e3a78f6f72f783c767b49af316b6e55bb73da04 Mon Sep 17 00:00:00 2001 From: Michael Milton Date: Wed, 26 Jul 2023 16:48:41 +1000 Subject: [PATCH 003/147] Clean up dock_widget --- plugin/napari_lattice/dock_widget.py | 7 ++----- 1 file changed, 2 insertions(+), 5 deletions(-) diff --git a/plugin/napari_lattice/dock_widget.py b/plugin/napari_lattice/dock_widget.py index 9b5ef7c..0ec2ec2 100644 --- a/plugin/napari_lattice/dock_widget.py +++ b/plugin/napari_lattice/dock_widget.py @@ -6,15 +6,12 @@ import dask.array as da from dask.array.core import Array as DaskArray import pandas as pd -from typing import Union, Optional, Callable, Literal -from typing_extensions import NoReturn, Never, TypeVar, overload, Any -from aicsimageio import AICSImage +from typing import Callable from enum import Enum from magicclass.wrappers import set_design from magicgui import magicgui from magicclass import magicclass, field, vfield, set_options, LlszTemplate -from magicclass._gui._base import check_override from magicclass.utils import click from qtpy.QtCore import Qt @@ -58,7 +55,7 @@ def llsz_parent(self) -> "LLSZWidget": @magicclass(widget_type="split") class LLSZWidget(LlszTemplate): open_file: bool = False - lattice: LatticeData = None + lattice: LatticeData skew_dir: DeskewDirection angle_value: float deskew_func: Callable From e3742c77508f0ab9d2316cd69ff4f56077a22d00 Mon Sep 17 00:00:00 2001 From: Michael Milton Date: Thu, 27 Jul 2023 10:40:06 +1000 Subject: [PATCH 004/147] Fix missing imports in deconvolution --- core/lls_core/deconvolution.py | 31 +++++++++++++++++-------------- 1 file changed, 17 insertions(+), 14 deletions(-) diff --git a/core/lls_core/deconvolution.py b/core/lls_core/deconvolution.py index 60e4e8f..4222c84 100644 --- a/core/lls_core/deconvolution.py +++ b/core/lls_core/deconvolution.py @@ -1,12 +1,18 @@ from pathlib import Path + +from resource_backed_dask_array import ResourceBackedDaskArray from lls_core import DeconvolutionChoice from lls_core.lattice_data import LatticeData import logging import importlib.util -from typing import Collection, Iterable -from aicsimageio import AICSImage +from typing import Collection, Iterable, Optional +from aicsimageio.aics_image import AICSImage +from aicsimageio.types import ArrayLike from aicspylibczi import CziFile from numpy.typing import NDArray +import numpy as np +from dask.array.core import Array as DaskArray + from lls_core.utils import pad_image_nearest_multiple @@ -69,13 +75,13 @@ def read_psf(psf_paths: Collection[Path], # libraries are better designed.. Atleast until RL deconvolution is available in pyclesperant # Talley Lamberts pycudadecon is a great library and highly optimised. def pycuda_decon( - image, - otf_path=None, - dzdata=0.3, - dxdata=0.1449922, - dzpsf=0.3, - dxpsf=0.1449922, - psf=None, + image: NDArray, + otf_path: Optional[str]=None, + dzdata: float=0.3, + dxdata: float=0.1449922, + dzpsf: float=0.3, + dxpsf: float=0.1449922, + psf: Optional[ArrayLike]=None, num_iter: int = 10, cropping: bool = False, ): @@ -83,7 +89,7 @@ def pycuda_decon( pycudadecon can return cropped images, so we pad the images with dimensions that are a multiple of 64 Args: - image (np.array): _description_ + image : _description_ otf_path : (path to the generated otf file, if available. Otherwise psf needs to be provided) dzdata : (pixel size in z in microns) dxdata : (pixel size in xy in microns) @@ -97,10 +103,7 @@ def pycuda_decon( assert image.ndim == 3, f"Image needs to be 3D. Got {image.ndim}" # if dask array, convert to numpy array - if type(image) in [ - da.core.Array, - ResourceBackedDaskArray, - ]: + if isinstance(image, DaskArray): image = np.array(image) orig_img_shape = image.shape From 248a6452abaf75b51d0e25838ee8a0058864f774 Mon Sep 17 00:00:00 2001 From: Michael Milton Date: Fri, 28 Jul 2023 18:34:33 +1000 Subject: [PATCH 005/147] More dock_widget cleanup --- plugin/napari_lattice/dock_widget.py | 36 ++++++++++++++-------------- pyrightconfig.json | 5 ++-- 2 files changed, 21 insertions(+), 20 deletions(-) diff --git a/plugin/napari_lattice/dock_widget.py b/plugin/napari_lattice/dock_widget.py index 0ec2ec2..cad7bb7 100644 --- a/plugin/napari_lattice/dock_widget.py +++ b/plugin/napari_lattice/dock_widget.py @@ -11,13 +11,14 @@ from magicclass.wrappers import set_design from magicgui import magicgui -from magicclass import magicclass, field, vfield, set_options, LlszTemplate +from magicclass import magicclass, field, vfield, set_options, MagicTemplate from magicclass.utils import click from qtpy.QtCore import Qt from napari.layers import Layer, Shapes from napari.types import ImageData from napari.utils import history +from napari import Viewer import pyclesperanto_prototype as cle @@ -47,10 +48,17 @@ class LastDimensionOptions(Enum): time = "Time" get_from_metadata = "Get from Metadata" -class LlszTemplate(LlszTemplate): +class LlszTemplate(MagicTemplate): @property def llsz_parent(self) -> "LLSZWidget": return self.find_ancestor(LLSZWidget) + + @property + def parent_viewer(self) -> Viewer: + viewer = super().parent_viewer + if viewer is None: + raise Exception("This function can only be used when inside of a Napari viewer") + return super().parent_viewer @magicclass(widget_type="split") class LLSZWidget(LlszTemplate): @@ -105,9 +113,6 @@ def Choose_Image_Layer(self, logger.info(f"Logging set to {set_logging}") logger.info("Using existing image layer") - if self.parent_viewer is None: - raise Exception("This function can only be used when inside of a Napari viewer") - # Select device for processing cle.select_device(select_device) @@ -195,8 +200,6 @@ def Choose_Image_Layer(self, self["Choose_Image_Layer"].background_color = "green" self["Choose_Image_Layer"].text = "Plugin Initialised" - return - # Pycudadecon library for deconvolution # options={"enabled": True}, deconvolution = vfield(bool, name="Use Deconvolution") @@ -304,7 +307,6 @@ def Deskew_Save(self, ch_end, save_as_type, save_path) - return @magicclass(name="Crop and Deskew", widget_type="scrollable") class CropWidget(LlszTemplate): @@ -319,8 +321,6 @@ 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): - if self.parent_viewer is None: - raise Exception("This function can only be used when inside of a Napari viewer") 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 @@ -337,7 +337,7 @@ def Import_ImageJ_ROI(self, path: Path = Path(history.get_open_history()[0])) -> # convert to canvas coordinates self.find_ancestor(LLSZWidget) roi_list = (np.array(roi_list) * self.llsz_parent.lattice.dy).tolist() - self.shapes_layer.add(roi_list, shape_type='polygon', edge_width=1, edge_color='yellow', face_color=[1, 1, 1, 0]) + 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") @@ -373,10 +373,10 @@ def Crop_Preview(self, roi_layer: ShapesData): # if only one roi drawn, use the first ROI for cropping if len(roi_layer) == 1: roi_idx = 0 - elif len(self.shapes_layer.selected_data) == 0: + elif len(self.llsz_parent.shapes_layer.selected_data) == 0: raise Exception("Please select an ROI") - roi_idx = list(self.shapes_layer.selected_data)[0] + 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 @@ -514,7 +514,7 @@ def Crop_Save(self, voxel_size_x=dx, voxel_size_y=dy, voxel_size_z=dz, - LLSZWidget=parent + LLSZWidget=self.llsz_parent ) logger.info( @@ -661,7 +661,7 @@ def Workflow_Preview(self, roi_choice = [ x/self.llsz_parent.lattice.dy for x in roi_choice] logger.info(f"Previewing ROI {roi_idx}") - if self.llsz_parent.deconvolution: + if self.llsz_parent.deconvolution and self.llsz_parent.lattice.psf is not None: user_workflow.set("crop_deskew_image", crop_volume_deskew, original_volume=vol_zyx, deskewed_volume=deskewed_volume, @@ -760,7 +760,7 @@ def Workflow_Preview(self, # if deskew already in workflow, just check if deconvolution needs to be added # repitition of above (maybe create a function?) # if deconvolution checked, add it to start of workflow (add upstream of deskewing) - if self.llsz_parent.deconvolution.value: + if self.llsz_parent.deconvolution: psf = self.llsz_parent.lattice.psf[channel] input_arg_first, input_arg_last, first_task_name, last_task_name = get_first_last_image_and_task( user_workflow) @@ -840,12 +840,12 @@ def Workflow_Preview(self, #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, + header: str, time_start: int, time_end: int, ch_start: int, ch_end: int, - Use_Cropping, + Use_Cropping: bool, roi_layer_list: ShapesData, get_active_workflow: bool = False, workflow_path: Path = Path.home(), diff --git a/pyrightconfig.json b/pyrightconfig.json index 6703ee2..bf4441a 100644 --- a/pyrightconfig.json +++ b/pyrightconfig.json @@ -7,5 +7,6 @@ "reportUnknownParameterType": false, "reportUntypedFunctionDecorator": false, "reportMissingTypeArgument": false, - "reportPrivateUsage": false -} \ No newline at end of file + "reportPrivateUsage": false, + "reportPrivateImportUsage": false +} From 695b0ea221cdfd286be02a8950defd32fbe8dec4 Mon Sep 17 00:00:00 2001 From: Michael Milton Date: Wed, 2 Aug 2023 17:22:24 +1000 Subject: [PATCH 006/147] Type signature tidying, and remove an unused function --- core/lls_core/__init__.py | 10 +-- core/lls_core/utils.py | 116 ++------------------------- plugin/napari_lattice/dock_widget.py | 10 ++- 3 files changed, 16 insertions(+), 120 deletions(-) diff --git a/core/lls_core/__init__.py b/core/lls_core/__init__.py index 320f378..b292cad 100644 --- a/core/lls_core/__init__.py +++ b/core/lls_core/__init__.py @@ -1,23 +1,19 @@ __version__ = "0.2.6" - +from strenum import StrEnum from enum import Enum # Initialize configuration options -#Deskew Direction -from pyclesperanto_prototype._tier8._affine_transform_deskew_3d import DeskewDirection - - #Choice of Deconvolution -class DeconvolutionChoice(Enum): +class DeconvolutionChoice(StrEnum): cuda_gpu = "cuda_gpu" opencl_gpu = "opencl_gpu" cpu = "cpu" #Choice of File extension to save -class SaveFileType(Enum): +class SaveFileType(StrEnum): h5 = "h5" tiff = "tiff" diff --git a/core/lls_core/utils.py b/core/lls_core/utils.py index 84c014f..db9b34d 100644 --- a/core/lls_core/utils.py +++ b/core/lls_core/utils.py @@ -6,7 +6,7 @@ from os import devnull, path import os from typing_extensions import Any, TYPE_CHECKING, TypeGuard -from typing import List, Tuple, Union, Collection +from typing import Iterable, List, Optional, Sequence, Tuple, Union, Collection from numpy.typing import NDArray import pandas as pd @@ -18,7 +18,8 @@ from tifffile import imsave from . import config, DeskewDirection -from aicsimageio.types import ArrayLike + +from lls_core.types import ArrayLike if TYPE_CHECKING: from xml.etree.ElementTree import Element @@ -334,9 +335,9 @@ def as_type(img, ref_vol): return img -def process_custom_workflow_output(workflow_output, - save_dir=None, - idx=None, +def process_custom_workflow_output(workflow_output: Union[dict, list, ArrayLike], + save_dir: Optional[str]=None, + idx: Optional[str]=None, LLSZWidget=None, widget_class=None, channel=0, @@ -376,109 +377,6 @@ def process_custom_workflow_output(workflow_output, else: return workflow_output - -def _process_custom_workflow_output_batch(ref_vol, - no_elements, - array_element_type, - channel_range, - images_array, - save_path, - time_point, - ch, - save_name_prefix, - save_name, - dx=None, - dy=None, - new_dz=None - ): - # create columns index for the list - if list in array_element_type: - row_idx = [] - - # Iterate through the dict or list output from workflow and add columns for Channel and timepoint - for i in range(no_elements): - for j in channel_range: - if type(images_array[j, i]) in [dict]: - # images_array[j,i].update({"Channel/Time":"C"+str(j)+"T"+str(time_point)}) - images_array[j, i].update({"Channel": "C"+str(j)}) - images_array[j, i].update({"Time": "T"+str(time_point)}) - elif type(images_array[j, i]) in [list]: - row_idx.append("C"+str(j)+"T"+str(time_point)) - # row_idx.append("C"+str(j)) - # row_idx.append("T"+str(time_point)) - - for element in range(no_elements): - if(array_element_type[element]) in [dict]: - # convert to pandas dataframe - output_dict_pd = [pd.DataFrame(i) - for i in images_array[:, element]] - - output_dict_pd = pd.concat(output_dict_pd) - # set index to the channel/time - output_dict_pd = output_dict_pd.set_index(["Time", "Channel"]) - - # Save path - dict_save_path = os.path.join( - save_path, "Measurement_"+save_name_prefix) - if not(os.path.exists(dict_save_path)): - os.mkdir(dict_save_path) - - #dict_save_path = os.path.join(dict_save_path,"C" + str(ch) + "T" + str(time_point)+"_"+str(element) + "_measurement.csv") - dict_save_path = os.path.join( - dict_save_path, "Summary_measurement_"+save_name_prefix+"_"+str(element)+"_.csv") - # Opens csv and appends it if file already exists; not efficient. - if os.path.exists(dict_save_path): - output_dict_pd_existing = pd.read_csv( - dict_save_path, index_col=["Time", "Channel"]) - output_dict_summary = pd.concat( - (output_dict_pd_existing, output_dict_pd)) - output_dict_summary.to_csv(dict_save_path) - else: - output_dict_pd.to_csv(dict_save_path) - - # TODO:modify this so one file saved for measurement - elif(array_element_type[element]) in [list]: - - output_list_pd = pd.DataFrame( - np.vstack(images_array[:, element]), index=row_idx) - # Save path - list_save_path = os.path.join( - save_path, "Measurement_"+save_name_prefix) - if not(os.path.exists(list_save_path)): - os.mkdir(list_save_path) - list_save_path = os.path.join(list_save_path, "C" + str(ch) + "T" + str( - time_point)+"_"+save_name_prefix+"_"+str(element) + "_measurement.csv") - output_list_pd.to_csv(list_save_path) - - elif(array_element_type[element]) in [np.ndarray, cle._tier0._pycl.OCLArray, da.core.Array]: - - # Save path - img_save_path = os.path.join( - save_path, "Measurement_"+save_name_prefix) - if not(os.path.exists(img_save_path)): - os.mkdir(img_save_path) - - im_final = np.stack(images_array[:, element]).astype(ref_vol.dtype) - final_name = os.path.join(img_save_path, save_name_prefix + "_"+str(element) + "_T" + str( - time_point) + "_" + save_name + ".tif") - # "C" + str(ch) + - #OmeTiffWriter.save(images_array, final_name, physical_pixel_sizes=aics_image_pixel_sizes) - # if only one image with no channel, then dimension will 1,z,y,x, so swap 0 and 1 - if len(im_final.shape) == 4: - # was 1,2,but when stacking images, dimension is CZYX - im_final = np.swapaxes(im_final, 0, 1) - # adding extra dimension for T - im_final = im_final[np.newaxis, ...] - elif len(im_final.shape) > 4: # if - # if image with multiple channels, , it will be 1,c,z,y,x - im_final = np.swapaxes(im_final, 1, 2) - # imagej=True; ImageJ hyperstack axes must be in TZCYXS order - imsave(final_name, im_final, bigtiff=True, imagej=True, resolution=(1./dx, 1./dy), - metadata={'spacing': new_dz, 'unit': 'um', 'axes': 'TZCYX'}) # imagej=True - im_final = None - return - - def pad_image_nearest_multiple(img: NDArray, nearest_multiple: int) -> NDArray: """pad an Image to the nearest multiple of provided number @@ -500,7 +398,7 @@ def pad_image_nearest_multiple(img: NDArray, nearest_multiple: int) -> NDArray: return padded_img -def check_dimensions(user_time_start: int, user_time_end, user_channel_start: int, user_channel_end: int, total_channels: int, total_time: int): +def check_dimensions(user_time_start: int, user_time_end: int, user_channel_start: int, user_channel_end: int, total_channels: int, total_time: int): if total_time == 1 or total_time == 2: max_time = 1 diff --git a/plugin/napari_lattice/dock_widget.py b/plugin/napari_lattice/dock_widget.py index cad7bb7..f0a15c1 100644 --- a/plugin/napari_lattice/dock_widget.py +++ b/plugin/napari_lattice/dock_widget.py @@ -350,6 +350,8 @@ 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: @@ -1018,7 +1020,7 @@ def Apply_Workflow_and_Save(self, 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.value: + 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( @@ -1068,7 +1070,7 @@ def Apply_Workflow_and_Save(self, dy=dy, dz=dz, angle=angle, - deconvolution=self.llsz_parent.deconvolution.value, + deconvolution=self.llsz_parent.deconvolution, decon_processing=self.llsz_parent.lattice.decon_processing, otf_path=otf_path, psf_arg=psf_arg, @@ -1078,7 +1080,7 @@ def Apply_Workflow_and_Save(self, 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.value: + 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( @@ -1129,7 +1131,7 @@ def Apply_Workflow_and_Save(self, dy=dy, dz=dz, angle=angle, - deconvolution=self.llsz_parent.deconvolution.value, + deconvolution=self.llsz_parent.deconvolution, decon_processing=self.llsz_parent.lattice.decon_processing, otf_path=otf_path, psf_arg=psf_arg, From 9752c68dd59c3d0fb03a3b54ce85299e34e0dbf8 Mon Sep 17 00:00:00 2001 From: Michael Milton Date: Fri, 4 Aug 2023 12:10:21 +1000 Subject: [PATCH 007/147] Refactor workflow augmentation --- core/lls_core/__init__.py | 1 + core/lls_core/io.py | 1 - core/lls_core/types.py | 11 + core/lls_core/utils.py | 180 ++------- core/lls_core/workflow.py | 198 +++++++++ plugin/napari_lattice/dock_widget.py | 577 ++++++++++++--------------- pyrightconfig.json | 3 +- 7 files changed, 487 insertions(+), 484 deletions(-) create mode 100644 core/lls_core/types.py create mode 100644 core/lls_core/workflow.py diff --git a/core/lls_core/__init__.py b/core/lls_core/__init__.py index b292cad..9d9bfb6 100644 --- a/core/lls_core/__init__.py +++ b/core/lls_core/__init__.py @@ -3,6 +3,7 @@ from strenum import StrEnum from enum import Enum +from pyclesperanto_prototype._tier8._affine_transform_deskew_3d import DeskewDirection # Initialize configuration options diff --git a/core/lls_core/io.py b/core/lls_core/io.py index ca4fdbb..78b25fe 100644 --- a/core/lls_core/io.py +++ b/core/lls_core/io.py @@ -615,4 +615,3 @@ def save_img_workflow(vol, writer_list[writer_idx].write_xml() # close the writers (applies for both tiff and h5) writer_list[writer_idx].close() - diff --git a/core/lls_core/types.py b/core/lls_core/types.py new file mode 100644 index 0000000..aa2b889 --- /dev/null +++ b/core/lls_core/types.py @@ -0,0 +1,11 @@ +from typing import Union +from typing_extensions import TypeGuard, Any, TypeAlias +from dask.array.core import Array as DaskArray +from numpy.typing import NDArray +from pyopencl.array import Array as OCLArray +import numpy as np + +ArrayLike: TypeAlias = Union[DaskArray, NDArray, OCLArray] + +def is_arraylike(arr: Any) -> TypeGuard[ArrayLike]: + return isinstance(arr, (DaskArray, np.ndarray, OCLArray)) diff --git a/core/lls_core/utils.py b/core/lls_core/utils.py index db9b34d..bd82f9b 100644 --- a/core/lls_core/utils.py +++ b/core/lls_core/utils.py @@ -1,33 +1,27 @@ from __future__ import annotations -import numpy as np from collections import defaultdict from contextlib import contextmanager, redirect_stderr, redirect_stdout from os import devnull, path -import os -from typing_extensions import Any, TYPE_CHECKING, TypeGuard -from typing import Iterable, List, Optional, Sequence, Tuple, Union, Collection -from numpy.typing import NDArray - -import pandas as pd -import dask.array as da +from typing import Collection, List, Tuple, Union +import numpy as np import pyclesperanto_prototype as cle -from read_roi import read_roi_zip -from read_roi import read_roi_file - -from tifffile import imsave -from . import config, DeskewDirection - from lls_core.types import ArrayLike +from numpy.typing import NDArray +from read_roi import read_roi_file, read_roi_zip +from typing_extensions import TYPE_CHECKING, Any, TypeGuard + +from . import DeskewDirection, config if TYPE_CHECKING: from xml.etree.ElementTree import Element + from napari.layers import Shapes - from napari_workflows import Workflow # Enable Logging import logging + logger = logging.getLogger(__name__) logger.setLevel(config.log_level) @@ -105,7 +99,9 @@ def get_deskewed_shape(volume: ArrayLike, tuple: Shape of deskewed volume in zyx np.array: Affine transform for deskewing """ - from pyclesperanto_prototype._tier8._affine_transform import _determine_translation_and_bounding_box + from pyclesperanto_prototype._tier8._affine_transform import ( + _determine_translation_and_bounding_box, + ) deskew_transform = cle.AffineTransform3D() @@ -238,145 +234,6 @@ def read_imagej_roi(roi_zip_path: str): ij_roi[k], ".Recognised as type ", ij_roi[k]['type']) return roi_list -# Functions to deal with cle workflow -# TODO: Clean up this function - - -def get_first_last_image_and_task(user_workflow: Workflow): - """Get images and tasks for first and last entry - Args: - user_workflow (Workflow): _description_ - Returns: - list: name of first input image, last input image, first task, last task - """ - - # get image with no preprocessing step (first image) - input_arg_first = user_workflow.roots()[0] - # get last image - input_arg_last = user_workflow.leafs()[0] - # get name of preceding image as that is the input to last task - img_source = user_workflow.sources_of(input_arg_last)[0] - first_task_name = [] - last_task_name = [] - - # loop through workflow keys and get key that has - for key in user_workflow._tasks.keys(): - for task in user_workflow._tasks[key]: - if task == input_arg_first: - first_task_name.append(key) - elif task == img_source: - last_task_name.append(key) - - return input_arg_first, img_source, first_task_name, last_task_name - - -def modify_workflow_task(old_arg, task_key: str, new_arg, workflow): - """_Modify items in a workflow task - Workflow is not modified, only a new task with updated arg is returned - Args: - old_arg (_type_): The argument in the workflow that needs to be modified - new_arg (_type_): New argument - task_key (str): Name of the task within the workflow - workflow (napari-workflow): Workflow - - Returns: - tuple: Modified task with name task_key - """ - task = workflow._tasks[task_key] - # convert tuple to list for modification - task_list = list(task) - try: - item_index = task_list.index(old_arg) - except ValueError: - print(old_arg, " not found in workflow file") - task_list[item_index] = new_arg - modified_task = tuple(task_list) - return modified_task - -def load_custom_py_modules(custom_py_files): - from importlib import reload, import_module - import sys - test_first_module_import = import_module(custom_py_files[0]) - if test_first_module_import not in sys.modules: - modules = map(import_module, custom_py_files) - else: - modules = map(reload, custom_py_files) - return modules - - -# TODO: CHANGE so user can select modules? Safer -def get_all_py_files(directory: str) -> list[str]: - """get all py files within directory and return as a list of filenames - Args: - directory: Directory with .py files - """ - from os.path import dirname, basename, isfile, join - import glob - - modules = glob.glob(join(dirname(directory), "*.py")) - all = [basename(f)[:-3] for f in modules if isfile(f) - and not f.endswith('__init__.py')] - print(f"Files found are: {all}") - - return all - - -def as_type(img, ref_vol): - """return image same dtype as ref_vol - - Args: - img (_type_): _description_ - ref_vol (_type_): _description_ - - Returns: - _type_: _description_ - """ - img.astype(ref_vol.dtype) - return img - - -def process_custom_workflow_output(workflow_output: Union[dict, list, ArrayLike], - save_dir: Optional[str]=None, - idx: Optional[str]=None, - LLSZWidget=None, - widget_class=None, - channel=0, - time=0, - preview: bool = True): - """Check the output from a custom workflow; - saves tables and images separately - - Args: - workflow_output (_type_): _description_ - save_dir (_type_): _description_ - idx (_type_): _description_ - LLSZWidget (_type_): _description_ - widget_class (_type_): _description_ - channel (_type_): _description_ - time (_type_): _description_ - """ - if type(workflow_output) in [dict, list]: - # create function for tthis dataframe bit - df = pd.DataFrame(workflow_output) - if preview: - save_path = path.join( - save_dir, "lattice_measurement_"+str(idx)+".csv") - print(f"Detected a dictionary as output, saving preview at", save_path) - df.to_csv(save_path, index=False) - return df - - else: - return df - elif type(workflow_output) in [np.ndarray, cle._tier0._pycl.OCLArray, da.core.Array]: - if preview: - suffix_name = str(idx)+"_c" + str(channel) + "_t" + str(time) - scale = (LLSZWidget.LlszMenu.lattice.new_dz, - LLSZWidget.LlszMenu.lattice.dy, LLSZWidget.LlszMenu.lattice.dx) - widget_class.parent_viewer.add_image( - workflow_output, name="Workflow_preview_" + suffix_name, scale=scale) - else: - return workflow_output - def pad_image_nearest_multiple(img: NDArray, nearest_multiple: int) -> NDArray: """pad an Image to the nearest multiple of provided number @@ -471,3 +328,16 @@ def crop_psf(psf_img: np.ndarray, threshold: float = 3e-3): psf_crop = psf_img[min_z:max_z, min_y:max_y, min_x:max_x] return psf_crop + +def as_type(img, ref_vol): + """return image same dtype as ref_vol + + Args: + img (_type_): _description_ + ref_vol (_type_): _description_ + + Returns: + _type_: _description_ + """ + img.astype(ref_vol.dtype) + return img diff --git a/core/lls_core/workflow.py b/core/lls_core/workflow.py new file mode 100644 index 0000000..cb44d39 --- /dev/null +++ b/core/lls_core/workflow.py @@ -0,0 +1,198 @@ +""" +Functions related to manipulating Napari Workflows +""" +from __future__ import annotations + +from os import path +from pathlib import Path +from typing import List, Optional, Tuple, Union + +import dask.array as da +import numpy as np +import pandas as pd +import pyclesperanto_prototype as cle +from lls_core.types import ArrayLike +from typing_extensions import TYPE_CHECKING + +if TYPE_CHECKING: + from napari_workflows import Workflow + +import logging +logger = logging.getLogger(__name__) +logger.setLevel(logging.INFO) + +def get_first_last_image_and_task(user_workflow: Workflow) -> Tuple[str, str, str, str]: + """ + Get images and tasks for first and last entry + Returns: + Tuple of (name of first input image, name of last input image, name of first task, name of last task) + """ + + # get image with no preprocessing step (first image) + input_arg_first = user_workflow.roots()[0] + # get last image + input_arg_last = user_workflow.leafs()[0] + # get name of preceding image as that is the input to last task + img_source = user_workflow.sources_of(input_arg_last)[0] + first_task_name = [] + last_task_name = [] + + # loop through workflow keys and get key that has + for key in user_workflow._tasks.keys(): + for task in user_workflow._tasks[key]: + if task == input_arg_first: + first_task_name.append(key) + elif task == img_source: + last_task_name.append(key) + + return input_arg_first, img_source, first_task_name[0], last_task_name[0] if len(last_task_name) > 0 else first_task_name[0] + + +def modify_workflow_task(old_arg: str, task_key: str, new_arg: str, workflow: Workflow) -> tuple: + """ + Modify items in a workflow task + Workflow is not modified, only a new task with updated arg is returned + Args: + old_arg: The argument in the workflow that needs to be modified + new_arg: New argument + task_key: Name of the task within the workflow + workflow: Workflow + + Returns: + tuple: Modified task with name task_key + """ + task = workflow._tasks[task_key] + # convert tuple to list for modification + task_list = list(task) + try: + item_index = task_list.index(old_arg) + except ValueError: + raise Exception(f"{old_arg} not found in workflow file") + + task_list[item_index] = new_arg + modified_task = tuple(task_list) + return modified_task + +def replace_first_arg(workflow: Workflow, new_arg: str, old_arg: Optional[str] = None): + """ + Replaces an argument in the first task of a workflow with some other value + Args: + old_arg: If provided, the name of an argument to replace, otherwise it defaults to the + first workflow arg + """ + img_arg_first, _, first_task_name, _ = get_first_last_image_and_task(workflow) + if old_arg is None: + old_arg = img_arg_first + workflow.set( + first_task_name, + modify_workflow_task( + old_arg=old_arg, + task_key=first_task_name, + new_arg=new_arg, + workflow=workflow + ) + ) + +def load_custom_py_modules(custom_py_files): + import sys + from importlib import import_module, reload + test_first_module_import = import_module(custom_py_files[0]) + if test_first_module_import not in sys.modules: + modules = map(import_module, custom_py_files) + else: + modules = map(reload, custom_py_files) + return modules + + +# TODO: CHANGE so user can select modules? Safer +def get_all_py_files(directory: str) -> list[str]: + """get all py files within directory and return as a list of filenames + Args: + directory: Directory with .py files + """ + import glob + from os.path import basename, dirname, isfile, join + + modules = glob.glob(join(dirname(directory), "*.py")) + all = [basename(f)[:-3] for f in modules if isfile(f) + and not f.endswith('__init__.py')] + print(f"Files found are: {all}") + + return all + +def import_script(script: Path): + """ + Imports a Python script given its path + """ + import importlib.util + import sys + module_name = script.stem + spec = importlib.util.spec_from_file_location(module_name, script) + if spec is None or spec.loader is None: + raise Exception(f"Failed to import {script}!") + module = importlib.util.module_from_spec(spec) + sys.modules[module_name] = module + spec.loader.exec_module(module) + +def import_workflow_modules(workflow: Path): + """ + Imports all the Python files that might be used in a given custom workflow + + Args: + workflow: Path to the workflow YAML file + """ + counter = 0 + for script in workflow.parent.glob("*.py"): + if script.stem == "__init__": + # Skip __init__.py + continue + import_script(script) + counter += 1 + + if counter == 0: + logger.warn(f"No custom modules imported. If you'd like to use a custom module, place a *.py file in same folder as the workflow file {workflow.parent}") + else: + logger.info(f"{counter} custom modules imported") + + +def process_custom_workflow_output(workflow_output: Union[dict, list, ArrayLike], + save_dir: Optional[str]=None, + idx: Optional[str]=None, + LLSZWidget=None, + widget_class=None, + channel=0, + time=0, + preview: bool = True): + """Check the output from a custom workflow; + saves tables and images separately + + Args: + workflow_output (_type_): _description_ + save_dir (_type_): _description_ + idx (_type_): _description_ + LLSZWidget (_type_): _description_ + widget_class (_type_): _description_ + channel (_type_): _description_ + time (_type_): _description_ + """ + if isinstance(workflow_output, (dict, list)): + # create function for tthis dataframe bit + df = pd.DataFrame(workflow_output) + if preview: + save_path = path.join( + save_dir, "lattice_measurement_"+str(idx)+".csv") + print(f"Detected a dictionary as output, saving preview at", save_path) + df.to_csv(save_path, index=False) + return df + + else: + return df + elif isinstance(workflow_output, (np.ndarray, cle._tier0._pycl.OCLArray, da.core.Array)): + if preview: + suffix_name = str(idx)+"_c" + str(channel) + "_t" + str(time) + scale = (LLSZWidget.LlszMenu.lattice.new_dz, + LLSZWidget.LlszMenu.lattice.dy, LLSZWidget.LlszMenu.lattice.dx) + widget_class.parent_viewer.add_image( + workflow_output, name="Workflow_preview_" + suffix_name, scale=scale) + else: + return workflow_output diff --git a/plugin/napari_lattice/dock_widget.py b/plugin/napari_lattice/dock_widget.py index f0a15c1..a11f53b 100644 --- a/plugin/napari_lattice/dock_widget.py +++ b/plugin/napari_lattice/dock_widget.py @@ -4,9 +4,8 @@ import numpy as np from pathlib import Path import dask.array as da -from dask.array.core import Array as DaskArray import pandas as pd -from typing import Callable +from typing import allable, Iterable, Optional, Union from enum import Enum from magicclass.wrappers import set_design @@ -29,15 +28,19 @@ from napari_workflows import Workflow, WorkflowManager from napari_workflows._io_yaml_v1 import load_workflow -from lls_core import config, DeskewDirection, DeconvolutionChoice, SaveFileType, Log_Levels +from lls_core import config, DeconvolutionChoice, SaveFileType, Log_Levels, DeskewDirection from lls_core.io import LatticeData, save_img, save_img_workflow -from lls_core.utils import read_imagej_roi, get_first_last_image_and_task, modify_workflow_task, get_all_py_files, as_type, process_custom_workflow_output, check_dimensions, load_custom_py_modules +from lls_core.types import ArrayLike +from lls_core.workflow import get_first_last_image_and_task, modify_workflow_task, get_all_py_files, process_custom_workflow_output, load_custom_py_modules, import_workflow_modules, replace_first_arg +from lls_core.utils import check_dimensions, read_imagej_roi, as_type from lls_core.llsz_core import crop_volume_deskew from lls_core.deconvolution import read_psf, pycuda_decon, skimage_decon from napari_lattice.ui_core import _Preview, _Deskew_Save from napari_lattice.reader import lattice_from_napari +from copy import copy + # Enable Logging import logging logger = logging.getLogger(__name__) @@ -345,7 +348,6 @@ def Import_ImageJ_ROI(self, path: Path = Path(history.get_open_history()[0])) -> #roi_idx = field(int, options={"min": 0, "step": 1}, name="ROI number") @click(enabled=False) - # -> LayerDataTuple: 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.") @@ -360,22 +362,8 @@ def Crop_Preview(self, roi_layer: ShapesData): raise ValueError("Channel is out of range") logger.info(f"Using channel {channel} and time {time}") - vol = self.llsz_parent.lattice.data - vol_zyx = vol[time, channel, ...] - vol_zyx = np.array(vol_zyx) - - deskewed_shape = self.llsz_parent.lattice.deskew_vol_shape - # Create a dask array same shape as deskewed image - deskewed_volume: DaskArray = da.zeros(deskewed_shape) - - # Option for entering custom z start value? - z_start = 0 - z_end = deskewed_shape[0] - # if only one roi drawn, use the first ROI for cropping - if len(roi_layer) == 1: - roi_idx = 0 - elif len(self.llsz_parent.shapes_layer.selected_data) == 0: + 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] @@ -386,58 +374,46 @@ def Crop_Preview(self, roi_layer: ShapesData): 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!") + raise Exception( + "PSF fields should be set by this point!") logger.info( f"Deskewing for Time:{time} and Channel: {channel} with deconvolution") - #psf = self.llsz_parent.lattice.psf[channel] - if self.llsz_parent.lattice.decon_processing == DeconvolutionChoice.cuda_gpu: - crop_roi_vol_desk = crop_volume_deskew(original_volume=vol_zyx, - deskewed_volume=deskewed_volume, - 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, - z_start=z_start, - z_end=z_end, - deconvolution=self.llsz_parent.deconvolution, - 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).astype(vol_zyx.dtype) - else: - crop_roi_vol_desk = crop_volume_deskew(original_volume=vol_zyx, - deskewed_volume=deskewed_volume, - 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, - z_start=z_start, - z_end=z_end, - deconvolution=self.llsz_parent.deconvolution, - 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).astype(vol_zyx.dtype) + 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: - crop_roi_vol_desk = crop_volume_deskew(original_volume=vol_zyx, - deskewed_volume=deskewed_volume, - 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, - z_start=z_start, - z_end=z_end, - skew_dir=self.llsz_parent.skew_dir).astype(vol_zyx.dtype) - crop_roi_vol_desk = cle.pull(crop_roi_vol_desk) + 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 - scale = (self.llsz_parent.lattice.new_dz, - self.llsz_parent.lattice.dy, - self.llsz_parent.lattice.dx) + 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) @@ -549,65 +525,22 @@ def Workflow_Preview(self, 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 - Args: - + 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 """ - print("Previewing deskewed channel and time with workflow") - if get_active_workflow: - # installs the workflow to napari - user_workflow = WorkflowManager.install(self.parent_viewer).workflow - parent_dir = workflow_path.resolve( - ).parents[0].__str__()+os.sep - logger.info("Workflow loaded from napari") - else: - try: - # Automatically scan workflow file directory for *.py files. - # If it findss one, load it as a module - - parent_dir = workflow_path.resolve( - ).parents[0].__str__()+os.sep - sys.path.append(parent_dir) - custom_py_files = get_all_py_files(parent_dir) - if len(custom_py_files) == 0: - logger.error( - f"No custom modules imported. If you'd like to use a cusotm module, place a *.py file in same folder as the workflow file {parent_dir}") - else: - modules = load_custom_py_modules( - custom_py_files) - - logger.info( - f"Custom modules imported {modules}") - user_workflow = load_workflow( - workflow_path.__str__()) - except yaml.loader.ConstructorError as e: - raise Exception("\033[91m While loading workflow, got the following error which may mean you need to install the corresponding module in your Python environment: \033[0m") from e - - #user_workflow = load_workflow(workflow_path) - logger.info("Workflow loaded from file") - - if not isinstance(user_workflow, Workflow): - raise Exception("Workflow loading error. Check if file is workflow or if required libraries are installed") - - input_arg_first, _, first_task_name, last_task_name = get_first_last_image_and_task( - user_workflow) - #print(input_arg_first, input_arg_last, first_task_name,last_task_name ) - # get list of tasks - task_list = list(user_workflow._tasks.keys()) - logger.info(f"Workflow loaded:{user_workflow}") - # logger.info() + 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.time: + if chan_preview >= self.llsz_parent.lattice.channels: raise ValueError("Channel is out of range") time = time_preview @@ -617,180 +550,7 @@ def Workflow_Preview(self, config.channel = channel config.time = time - logger.info( - f"Processing for Time: {time} and Channel: {channel}") - - vol = self.llsz_parent.lattice.data - vol_zyx = vol[time, channel, ...] - vol_zyx = np.array(vol_zyx) - - 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" - else: - psf = "psf" - - # if cropping, set that as first task - # get the function associated with the first task and check if its deskewing - if Use_Cropping: - # use deskewed volume for cropping function - deskewed_shape = self.llsz_parent.lattice.deskew_vol_shape - deskewed_volume = da.zeros(deskewed_shape) - z_start = 0 - z_end = deskewed_shape[0] - if user_workflow.get_task(task_name_start)[0] not in [crop_volume_deskew]: - # if only one roi drawn, use the first ROI for cropping - if len(roi_layer_list) == 1: - roi_idx = 0 - else: # else get the user selection - 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] - - roi_choice = roi_layer_list[roi_idx] - # As the original image is scaled, the coordinates are in microns, so we need to convert - # roi to from micron to canvas/world coordinates - roi_choice = [ - x/self.llsz_parent.lattice.dy for x in roi_choice] - logger.info(f"Previewing ROI {roi_idx}") - if self.llsz_parent.deconvolution and self.llsz_parent.lattice.psf is not None: - user_workflow.set("crop_deskew_image", crop_volume_deskew, - original_volume=vol_zyx, - deskewed_volume=deskewed_volume, - roi_shape=roi_choice, - angle_in_degrees=self.llsz_parent.lattice.angle, - voxel_size_x=self.llsz_parent.lattice.dx, - voxel_size_y=self.llsz_parent.lattice.dy, - voxel_size_z=self.llsz_parent.lattice.dz, - z_start=z_start, - z_end=z_end, - deconvolution=self.llsz_parent.deconvolution, - decon_processing=self.llsz_parent.lattice.decon_processing, - otf_path=otf_path, - psf=self.llsz_parent.lattice.psf[channel], - skew_dir=self.llsz_parent.skew_dir) - else: - user_workflow.set("crop_deskew_image", crop_volume_deskew, - original_volume=vol_zyx, - deskewed_volume=deskewed_volume, - roi_shape=roi_choice, - angle_in_degrees=self.llsz_parent.lattice.angle, - voxel_size_x=self.llsz_parent.lattice.dx, - voxel_size_y=self.llsz_parent.lattice.dy, - voxel_size_z=self.llsz_parent.lattice.dz, - z_start=z_start, - z_end=z_end, - skew_dir=self.llsz_parent.skew_dir) - - # Set input of the workflow to be crop_deskewing, i.e., the original first operation will now have crop_deskew_image as an input (becoming second instead) - user_workflow.set( - input_arg_first, "crop_deskew_image") - else: - user_workflow.set(input_arg_first, vol_zyx) - # Not cropping; If deskew not in workflow, append to start - elif user_workflow.get_task(task_name_start)[0] not in (cle.deskew_y, cle.deskew_x): - # if deconvolution checked, add it to start of workflow (add upstream of deskewing) - if self.llsz_parent.deconvolution.value: - psf = self.llsz_parent.lattice.psf[channel] - input_arg_first_decon, input_arg_last_decon, first_task_name_decon, last_task_name_decon = 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=vol_zyx, - psf=self.llsz_parent.lattice.psf[channel], - 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_decon,"deconvolution") - else: - user_workflow.set("deconvolution", - skimage_decon, - vol_zyx=vol_zyx, - psf=self.llsz_parent.lattice.psf[channel], - num_iter=self.llsz_parent.lattice.psf_num_iter, - clip=False, - filter_epsilon=0, - boundary='nearest') - # user_workflow.set(input_arg_first_decon,"deconvolution") - - user_workflow.set("deskew_image", - self.llsz_parent.deskew_func, - "deconvolution", - angle_in_degrees=self.llsz_parent.lattice.angle, - voxel_size_x=self.llsz_parent.lattice.dx, - voxel_size_y=self.llsz_parent.lattice.dy, - voxel_size_z=self.llsz_parent.lattice.dz, - linear_interpolation=True) - - # user_workflow.set("change_bitdepth",as_type,"deskew_image",vol_zyx) - # Set input of the workflow to be from deskewing output with same bit depth as original volume - # user_workflow.set(input_arg_first,"change_bitdepth") - - else: - user_workflow.set("deskew_image", - self.llsz_parent.deskew_func, - vol_zyx, - angle_in_degrees=self.llsz_parent.lattice.angle, - voxel_size_x=self.llsz_parent.lattice.dx, - voxel_size_y=self.llsz_parent.lattice.dy, - voxel_size_z=self.llsz_parent.lattice.dz, - linear_interpolation=True) - # Set input of the workflow to be from deskewing - # user_workflow.set(input_arg_first,"deskew_image") - - user_workflow.set( - "change_bitdepth", as_type, "deskew_image", vol_zyx) - # Set input of the workflow to be from deskewing with same bit depth as original volume - user_workflow.set( - input_arg_first, "change_bitdepth") - - else: - # if deskew already in workflow, just check if deconvolution needs to be added - # repitition of above (maybe create a function?) - # if deconvolution checked, add it to start of workflow (add upstream of deskewing) - if self.llsz_parent.deconvolution: - psf = self.llsz_parent.lattice.psf[channel] - 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=vol_zyx, - psf=self.llsz_parent.lattice.psf[channel], - 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=vol_zyx, - psf=self.llsz_parent.lattice.psf[channel], - num_iter=self.llsz_parent.lattice.psf_num_iter, - clip=False, - filter_epsilon=0, - boundary='nearest') - - # set input to subsequent task as deconvolution output - user_workflow.set( - input_arg_first, "deconvolution") + logger.info(f"Processing for Time: {time} and Channel: {channel}") logger.info("Workflow to be executed:") logger.info(user_workflow) @@ -821,7 +581,6 @@ def Workflow_Preview(self, processed_vol, parent_dir, 0, LLSZWidget, self, channel, time) print("Workflow complete") - pass @magicgui(header=dict(widget_type="Label", label="

Apply Workflow and Save Output

"), time_start=dict(label="Time Start:", max=2**20), @@ -879,44 +638,14 @@ def Apply_Workflow_and_Save(self, dy = self.llsz_parent.lattice.dy dz = self.llsz_parent.lattice.dz - if get_active_workflow: - # installs the workflow to napari - user_workflow = WorkflowManager.install( - self.parent_viewer).workflow - print("Workflow installed") - else: - # Automatically scan workflow file directory for *.py files. - # If it findss one, load it as a module - import importlib - parent_dir = workflow_path.resolve( - ).parents[0].__str__()+os.sep - sys.path.append(parent_dir) - custom_py_files = get_all_py_files(parent_dir) - if len(custom_py_files) == 0: - print( - f"No custom modules imported. If you'd like to use a cusotm module, place a *.py file in same folder as the workflow file {parent_dir}") - else: - modules = map( - importlib.import_module, custom_py_files) - print(f"Custom modules imported {modules}") - user_workflow = load_workflow(str(workflow_path)) - - if not isinstance(user_workflow, Workflow): - raise Exception("Workflow file is not a napari workflow object. Check file! You can use workflow inspector if needed") + 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) - print(input_arg_first, input_arg_last, - first_task_name, last_task_name) - # get list of tasks - task_list = list(user_workflow._tasks.keys()) - print("Workflow loaded:") - print(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 - - #vol_zyx= vol[time,channel,...] - task_name_start = first_task_name[0] try: @@ -1140,7 +869,6 @@ def Apply_Workflow_and_Save(self, print("Workflow complete") return - pass def _napari_lattice_widget_wrapper() -> LLSZWidget: # split widget type enables a resizable widget @@ -1152,3 +880,198 @@ def _napari_lattice_widget_wrapper() -> LLSZWidget: # widget._widget._layout.setWidgetResizable(True) return widget + +def get_workflow(source: Union[Path, Viewer]) -> Workflow: + """ + Gets a user defined workflow object, either from a viewer or from a file + + Args: + source: Either the path to a workflow file, or a Napari viewer from which to extract the workflow + """ + if isinstance(source, Viewer): + # installs the workflow to napari + user_workflow = WorkflowManager.install(source).workflow + logger.info("Workflow installed") + else: + import_workflow_modules(source) + user_workflow = load_workflow(str(source)) + + if not isinstance(user_workflow, Workflow): + raise Exception("Workflow file is not a napari workflow object. Check file! You can use workflow inspector if needed") + + logger.info(f"Workflow loaded: {user_workflow}") + return user_workflow + +def augment_workflow( + workflow: Workflow, + crop: bool, + roi_layer_list: ShapesData, + lattice: LatticeData, + deconvolution: bool, + times: range, + channels: range + ) -> Iterable[Workflow]: + """ + Yields copies of the input workflow, modified with the addition of deskewing and optionally, + cropping and deconvolution + """ + user_workflow = copy(workflow) + _, _, first_task_name, _ = get_first_last_image_and_task(workflow) + + for loop_time_idx, time_point in enumerate(times): + output_array = [] + data_table = [] + for loop_ch_idx, ch in enumerate(channels): + + if crop: + yield from make_crop_workflows( + user_workflow=user_workflow, + roi_layer_list=roi_layer_list, + lattice=lattice, + deconvolution=deconvolution + ) + + # 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) + else: + INPUT_ARG = "input" + + # IF just deskewing and its not in the tasks, add that as first task + if user_workflow.get_task(first_task_name)[0] not in (cle.deskew_y, cle.deskew_x): + # add task to the workflow + user_workflow.set( + "deskew_image", + lattice.deskew_func, + input_image=INPUT_ARG, + angle_in_degrees=lattice.angle, + voxel_size_x=lattice.dx, + voxel_size_y=lattice.dy, + voxel_size_z=lattice.dz, + linear_interpolation=True + ) + # Set input of the workflow to be from deskewing + # change workflow task starts from is "deskew_image" and + replace_first_arg(user_workflow, new_arg="deskew_image") + + # if deconvolution checked, add it to start of workflow (add upstream of deskewing) + if deconvolution: + PSF_ARG = "psf" + + if lattice.decon_processing == DeconvolutionChoice.cuda_gpu: + user_workflow.set( + "deconvolution", + pycuda_decon, + image=INPUT_ARG, + psf=PSF_ARG, + dzdata=lattice.dz, + dxdata=lattice.dx, + dzpsf=lattice.dz, + dxpsf=lattice.dx, + num_iter=lattice.psf_num_iter + ) + # user_workflow.set(input_arg_first,"deconvolution") + else: + user_workflow.set( + "deconvolution", + skimage_decon, + vol_zyx=INPUT_ARG, + psf=PSF_ARG, + num_iter=lattice.psf_num_iter, + clip=False, + filter_epsilon=0, + boundary='nearest' + ) + # modify the user workflow so "deconvolution" is accepted + replace_first_arg(user_workflow, new_arg="deconvolution") + + yield workflow + + # save_img_workflow(vol=vol, + # workflow=user_workflow, + # input_arg=INPUT_ARG, + # 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_ARG, + # psf_arg=psf_arg, + # psf=PSF_ARG) + + +def make_crop_workflows( + user_workflow: Workflow, + roi_layer_list: ShapesData, + lattice: LatticeData, + deconvolution: bool +) -> Iterable[Workflow]: + """ + Yields a copy of `user_workflow` for each region of interest, with deskewing, cropping and deconvolution steps added on to the start + """ + + # Convert Roi pixel coordinates to canvas coordinates + # necessary only when scale is used for napari.viewer.add_image operations + + # Here we generate a workflow for each ROI + for idx, roi_layer in enumerate(tqdm([x/lattice for x in roi_layer_list], desc="ROI:", position=0)): + # 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 + current_workflow = copy(user_workflow) + current_workflow.set( + "crop_deskew_image", + crop_volume_deskew, + original_volume="volume", + roi_shape="roi", + angle_in_degrees=lattice.angle, + voxel_size_x=lattice.dx, + voxel_size_y=lattice.dy, + voxel_size_z=lattice.dy, + z_start=0, + z_end=lattice.deskew_vol_shape[0], + deconvolution=deconvolution, + decon_processing=lattice.decon_processing, + psf="psf", + skew_dir=lattice.skew_dir + ) + + # change the first task so it accepts "crop_deskew as input" + replace_first_arg(current_workflow, new_arg="crop_deskew_image") + + logging.info(f"Processing ROI {idx}") + current_workflow.set("roi", roi_layer) + + yield current_workflow diff --git a/pyrightconfig.json b/pyrightconfig.json index bf4441a..86c8d98 100644 --- a/pyrightconfig.json +++ b/pyrightconfig.json @@ -8,5 +8,6 @@ "reportUntypedFunctionDecorator": false, "reportMissingTypeArgument": false, "reportPrivateUsage": false, - "reportPrivateImportUsage": false + "reportPrivateImportUsage": false, + "typeCheckingMode": "strict" } From 5db912095f28a516e7795e800cf34b39d33f8d02 Mon Sep 17 00:00:00 2001 From: Michael Milton Date: Fri, 4 Aug 2023 19:22:26 +1000 Subject: [PATCH 008/147] Object oriented system --- core/lls_core/deconvolution.py | 2 +- core/lls_core/lattice_data.py | 305 +++++++++++++++++++++++++-- core/lls_core/llsz_core.py | 6 +- core/lls_core/workflow.py | 172 +++++++++++++++ plugin/napari_lattice/dock_widget.py | 227 ++------------------ 5 files changed, 474 insertions(+), 238 deletions(-) diff --git a/core/lls_core/deconvolution.py b/core/lls_core/deconvolution.py index d27fe27..628577b 100644 --- a/core/lls_core/deconvolution.py +++ b/core/lls_core/deconvolution.py @@ -78,7 +78,7 @@ def read_psf(psf_paths: Collection[Path], # libraries are better designed.. Atleast until RL deconvolution is available in pyclesperant # Talley Lamberts pycudadecon is a great library and highly optimised. def pycuda_decon( - image: NDArray, + image: ArrayLike, otf_path: Optional[str]=None, dzdata: float=0.3, dxdata: float=0.1449922, diff --git a/core/lls_core/lattice_data.py b/core/lls_core/lattice_data.py index c3066f0..8eebbff 100644 --- a/core/lls_core/lattice_data.py +++ b/core/lls_core/lattice_data.py @@ -7,17 +7,30 @@ from numpy.typing import NDArray from dataclasses import dataclass import math +from dask.array.core import Array as DaskArray +import dask as da +from itertools import groupby +import tifffile -from typing import Any, List, Literal, Optional, TYPE_CHECKING, Tuple, TypeVar +from typing import Any, Iterable, List, Literal, Optional, TYPE_CHECKING, Tuple, TypeVar, Union -from aicsimageio.types import ArrayLike, PhysicalPixelSizes +from aicsimageio.types import PhysicalPixelSizes import pyclesperanto_prototype as cle +from tqdm import tqdm -from lls_core import DeskewDirection, DeconvolutionChoice +from lls_core import DeskewDirection, DeconvolutionChoice, SaveFileType +from lls_core.deconvolution import pycuda_decon, skimage_decon +from lls_core.llsz_core import crop_volume_deskew from lls_core.utils import get_deskewed_shape +from lls_core.types import ArrayLike if TYPE_CHECKING: import pyclesperanto_prototype as cle + from napari.types import ShapesData + +import logging + +logger = logging.getLogger(__name__) T = TypeVar("T") def raise_if_none(obj: Optional[T], message: str) -> T: @@ -25,6 +38,34 @@ def raise_if_none(obj: Optional[T], message: str) -> T: raise TypeError(message) return obj +@dataclass +class ProcessedVolume: + """ + A slice of the image processing result + """ + time_index: int + time: int + channel_index: int + channel: int + data: ArrayLike + + roi_index: Optional[int] = None + +def make_filename_prefix(prefix: Optional[str] = None, roi_index: Optional[int] = None, channel: Optional[int] = None, time: Optional[int] = None) -> str: + """ + Generates a filename for this result + """ + components: List[str] = [] + if prefix is not None: + components.append(prefix) + if roi_index is not None: + components.append(f"ROI_{roi_index}") + if channel is not None: + components.append(f"C{channel}") + if time is not None: + components.append(f"T{time}") + return "_".join(components) + @dataclass class DefinedPixelSizes: """ @@ -35,11 +76,34 @@ class DefinedPixelSizes: Y: float = 0.14 Z: float = 0.3 +@dataclass +class DeconvolutionParams: + """ + Parameters for the optional deconvolution step + """ + decon_processing: DeconvolutionChoice = DeconvolutionChoice.cpu + psf: List[NDArray] = [] + psf_num_iter: int = 10 + otf_path: List = [] + # Background value to subtract + background: Union[float, Literal["auto","second_last"]] = 0 + +@dataclass +class CropParams: + """ + Parameters for the optional cropping step + """ + roi_layer_list: ShapesData + z_start: int = 0 + z_end: int = 1 + @dataclass class LatticeData: """ Holds data and metadata for a given image in a consistent format """ + # TODO: add defaults here, rather than in the CLI + #: 3-5D array data: ArrayLike dims: Dimensions @@ -55,24 +119,23 @@ class LatticeData: skew: DeskewDirection = DeskewDirection.Y angle: float = 30.0 - decon_processing: Optional[DeconvolutionChoice] = None - #: Pixel size in microns physical_pixel_sizes: DefinedPixelSizes = field(default_factory=DefinedPixelSizes) - new_dz: Optional[float] = None + new_dz: float = field(init=False) - # PSF data that should be refactored into another class eventually - psf: Optional[List[NDArray]] = None - psf_num_iter: Optional[int] = None - otf_path: Optional[List] = None + # If this is None, then deconvolution is disabled + deconvolution: Optional[DeconvolutionParams] = None - #: Number of time points - time: int = 0 - #: Number of channels - channels: int = 0 + # If this is None, then cropping is disabled + crop: Optional[CropParams] = None + + # Time and channel to process + time_range: range = field(init=False) + channel_range: range = field(init=False) + + save_type: SaveFileType = SaveFileType.h5 - # TODO: add defaults here, rather than in the CLI # Hack to ensure that .skew_dir behaves identically to .skew @property def skew_dir(self) -> DeskewDirection: @@ -125,16 +188,218 @@ def set_angle(self, angle: float) -> None: def set_skew(self, skew: DeskewDirection) -> None: self.skew = skew + @property + def cropping_enabled(self) -> bool: + "True if cropping should be performed" + return self.crop is not None + + @property + def deconv_enabled(self) -> bool: + "True if deconvolution should be performed" + return self.deconvolution is not None + + @property + def time(self) -> int: + """Number of time points""" + return self.dims.T + + @property + def channels(self) -> int: + """Number of channels""" + return self.dims.C + def __post_init__(self): # set new z voxel size - if self.skew == DeskewDirection.Y or self.skew == DeskewDirection.X: - self.new_dz = math.sin(self.angle * math.pi / 180.0) * self.dz + self.new_dz = math.sin(self.angle * math.pi / 180.0) * self.dz # process the file to get shape of final deskewed image self.deskew_vol_shape, self.deskew_affine_transform = get_deskewed_shape(self.data, self.angle, self.dx, self.dy, self.dz) - print(f"Channels: {self.channels}, Time: {self.time}") + self.time_range = range(self.time + 1) + self.channel_range = range(self.channels + 1) + logging.log(f"Channels: {self.channels}, Time: {self.time}") print("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: + 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 + elif len(self.dims.shape) == 4: + return self.data[time, :, :, :] + elif len(self.dims.shape) == 5: + return self.data[time, channel, :, :, :] + + raise Exception("Lattice data must be 3-5 dimensions") + + def iter_slices(self) -> Iterable[Tuple[int, int, int, int, ArrayLike]]: + """ + Yields array slices for each time and channel of interest. + + Returns: + An iterable of tuples. Each tuple contains (time_index, time, channel_index, channel, slice) + """ + for time_idx, time in enumerate(self.time_range): + for ch_idx, ch in enumerate(self.channel_range): + yield time_idx, time, ch_idx, ch, self.slice_data(time=time, channel=ch) + + 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: + return da.zeros(self.deskew_vol_shape) + + def process_crop(self) -> Iterable[ProcessedVolume]: + """ + Yields processed image slices with cropping enabled + """ + if self.crop is None: + raise Exception("This function can only be called when crop is set") + + # We have an extra level of iteration for the crop path: iterating over each ROI + for roi_index, roi in enumerate(tqdm(self.crop.roi_layer_list, desc="ROI:", position=0)): + # pass arguments for save tiff, callable and function arguments + logger.info("Processing ROI ", roi_index) + + deconv_args: dict[Any, Any] = {} + if self.deconvolution is not None: + deconv_args = dict( + num_iter = self.deconvolution.psf_num_iter, + psf = self.deconvolution.psf, + decon_processing=self.deconvolution.decon_processing + ) + + for time_idx, time, ch_idx, ch, data in self.iter_slices(): + yield ProcessedVolume( + data = crop_volume_deskew( + original_volume=data, + deconvolution=self.deconv_enabled, + get_deskew_and_decon=False, + debug=False, + roi_shape=roi, + linear_interpolation=True, + voxel_size_x=self.dx, + voxel_size_y=self.dy, + voxel_size_z=self.dy, + angle_in_degrees=self.angle, + deskewed_volume=self.deskewed_volume, + z_start=self.crop.z_start, + z_end=self.crop.z_end, + **deconv_args + ), + channel=ch, + channel_index=ch_idx, + time=time, + time_index=time_idx, + roi_index=roi_index + ) + def process_non_crop(self) -> Iterable[ProcessedVolume]: + """ + Yields processed image slices without cropping + """ + for time_idx, time, ch_idx, ch, data in self.iter_slices(): + if self.deconvolution is not None: + if self.deconvolution.decon_processing == DeconvolutionChoice.cuda_gpu: + data = pycuda_decon( + image=data, + psf=self.deconvolution.psf[ch], + dzdata=self.dz, + dxdata=self.dx, + dzpsf=self.dz, + dxpsf=self.dx, + num_iter=self.deconvolution.psf_num_iter + ) + else: + data = skimage_decon( + vol_zyx=data, + psf=self.deconvolution.psf[ch], + num_iter=self.deconvolution.psf_num_iter, + clip=False, + filter_epsilon=0, + boundary='nearest' + ) + + yield ProcessedVolume( + data = cle.pull_zyx(self.deskew_func( + input_image=data, + angle_in_degrees=self.angle, + linear_interpolation=True, + voxel_size_x=self.dx, + voxel_size_y=self.dy, + voxel_size_z=self.dz + )), + channel=ch, + channel_index=ch_idx, + time=time, + time_index=time_idx + ) + + def process(self) -> Iterable[ProcessedVolume]: + if self.cropping_enabled: + yield from self.process_crop() + else: + yield from self.process_non_crop() + + def save_image(self, slices: Iterable[ProcessedVolume]): + """ + Saves result slices (ie generated by `process()` to disk) + """ + # TODO: refactor this into a class system, one for each format + import numpy as np + from pathlib import Path + import npy2bdv + + for roi, roi_results in groupby(slices, key=lambda it: it.roi_index): + if self.save_type == SaveFileType.h5: + bdv_writer = npy2bdv.BdvWriter( + make_filename_prefix(prefix=self.save_name, roi_index=roi), + compression='gzip', + nchannels=len(self.channel_range), + subsamp=( + (1, 1, 1), (1, 2, 2), (2, 4, 4)), + overwrite=False + ) + for result in roi_results: + bdv_writer.append_view( + result.data, + time=result.time, + channel=result.channel, + voxel_size_xyz=(self.dx, self.dy, self.new_dz), + voxel_units='um' + ) + elif self.save_type == SaveFileType.tiff: + # For each time point, we write a separate TIFF + for time, results in groupby(roi_results, key=lambda it: it.time): + result_list = list(results) + first_result = result_list[0] + images_array = np.swapaxes(np.expand_dims([result.data for result in result_list], axis=0), 1, 2) + tifffile.imwrite( + file = Path(make_filename_prefix(channel=first_result.channel, time=time, roi_index=roi)).with_suffix("tiff"), + data = images_array, + bigtiff=True, + resolution=(1./self.dx, 1./self.dy, "MICROMETER"), + metadata={'spacing': self.new_dz, 'unit': 'um', 'axes': 'TZCYX'}, + imagej=True + ) + def lattice_from_aics(img: AICSImage, physical_pixel_sizes: PhysicalPixelSizes = PhysicalPixelSizes(None, None, None), **kwargs: Any) -> LatticeData: # Note: The reason we copy all of these fields rather than just storing the AICSImage is because that class is mostly immutable and so not suitable @@ -147,8 +412,6 @@ def lattice_from_aics(img: AICSImage, physical_pixel_sizes: PhysicalPixelSizes = return LatticeData( data = img.dask_data, dims = img.dims, - time = img.dims.T, - channels = img.dims.C, physical_pixel_sizes = pixel_sizes, **kwargs ) @@ -203,4 +466,4 @@ def lattice_fom_array(arr: ArrayLike, last_dimension: Optional[Literal["channel" last_dimension: See img_from_array """ aics = img_from_array(arr, last_dimension) - return lattice_from_aics(aics, **kwargs) \ No newline at end of file + return lattice_from_aics(aics, **kwargs) diff --git a/core/lls_core/llsz_core.py b/core/lls_core/llsz_core.py index ba100e8..479d728 100644 --- a/core/lls_core/llsz_core.py +++ b/core/lls_core/llsz_core.py @@ -1,6 +1,5 @@ from __future__ import annotations -from aicsimageio.types import ArrayLike import numpy as np import pyclesperanto_prototype as cle from dask.array.core import Array as DaskArray @@ -17,6 +16,7 @@ from lls_core.utils import calculate_crop_bbox, check_subclass, is_napari_shape, pad_image_nearest_multiple from lls_core import config, DeskewDirection, DeconvolutionChoice from lls_core.deconvolution import pycuda_decon +from lls_core.types import ArrayLike # Enable Logging import logging @@ -46,7 +46,7 @@ class CommonArgs(TypedDict, total=False): z_start: int z_end: int deconvolution: bool - decon_processing: Optional[str] + decon_processing: Optional[DeconvolutionChoice] psf: Union[Psf, None] num_iter: int linear_interpolation: bool @@ -73,7 +73,7 @@ def crop_volume_deskew( z_end: int = 1, debug: bool = False, deconvolution: bool = False, - decon_processing: Optional[str]=None, + decon_processing: Optional[DeconvolutionChoice]=None, psf: Union[Psf, None]=None, num_iter: int = 10, linear_interpolation: bool=True, diff --git a/core/lls_core/workflow.py b/core/lls_core/workflow.py index cb44d39..7ccd6a8 100644 --- a/core/lls_core/workflow.py +++ b/core/lls_core/workflow.py @@ -11,6 +11,7 @@ import numpy as np import pandas as pd import pyclesperanto_prototype as cle +from lls_core.llsz_core import crop_volume_deskew from lls_core.types import ArrayLike from typing_extensions import TYPE_CHECKING @@ -196,3 +197,174 @@ def process_custom_workflow_output(workflow_output: Union[dict, list, ArrayLike] workflow_output, name="Workflow_preview_" + suffix_name, scale=scale) else: return workflow_output + +def augment_workflow( + workflow: Workflow, + lattice: LatticeData, + times: range, + channels: range + ) -> Iterable[Workflow]: + """ + Yields copies of the input workflow, modified with the addition of deskewing and optionally, + cropping and deconvolution + """ + user_workflow = copy(workflow) + _, _, first_task_name, _ = get_first_last_image_and_task(workflow) + + for loop_time_idx, time_point in enumerate(times): + output_array = [] + data_table = [] + for loop_ch_idx, ch in enumerate(channels): + + if crop: + yield from make_crop_workflows( + user_workflow=user_workflow, + roi_layer_list=roi_layer_list, + lattice=lattice, + deconvolution=deconvolution + ) + + # 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) + else: + INPUT_ARG = "input" + + # IF just deskewing and its not in the tasks, add that as first task + if user_workflow.get_task(first_task_name)[0] not in (cle.deskew_y, cle.deskew_x): + # add task to the workflow + user_workflow.set( + "deskew_image", + lattice.deskew_func, + input_image=INPUT_ARG, + angle_in_degrees=lattice.angle, + voxel_size_x=lattice.dx, + voxel_size_y=lattice.dy, + voxel_size_z=lattice.dz, + linear_interpolation=True + ) + # Set input of the workflow to be from deskewing + # change workflow task starts from is "deskew_image" and + replace_first_arg(user_workflow, new_arg="deskew_image") + + # if deconvolution checked, add it to start of workflow (add upstream of deskewing) + if deconvolution: + PSF_ARG = "psf" + + if lattice.decon_processing == DeconvolutionChoice.cuda_gpu: + user_workflow.set( + "deconvolution", + pycuda_decon, + image=INPUT_ARG, + psf=PSF_ARG, + dzdata=lattice.dz, + dxdata=lattice.dx, + dzpsf=lattice.dz, + dxpsf=lattice.dx, + num_iter=lattice.psf_num_iter + ) + # user_workflow.set(input_arg_first,"deconvolution") + else: + user_workflow.set( + "deconvolution", + skimage_decon, + vol_zyx=INPUT_ARG, + psf=PSF_ARG, + num_iter=lattice.psf_num_iter, + clip=False, + filter_epsilon=0, + boundary='nearest' + ) + # modify the user workflow so "deconvolution" is accepted + replace_first_arg(user_workflow, new_arg="deconvolution") + + yield workflow + + # save_img_workflow(vol=vol, + # workflow=user_workflow, + # input_arg=INPUT_ARG, + # 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_ARG, + # psf_arg=psf_arg, + # psf=PSF_ARG) + + +def make_crop_workflows( + user_workflow: Workflow, + roi_layer_list: ShapesData, + lattice: LatticeData, + deconvolution: bool +) -> Iterable[Workflow]: + """ + Yields a copy of `user_workflow` for each region of interest, with deskewing, cropping and deconvolution steps added on to the start + """ + + # Convert Roi pixel coordinates to canvas coordinates + # necessary only when scale is used for napari.viewer.add_image operations + + # Here we generate a workflow for each ROI + for idx, roi_layer in enumerate(tqdm([x/lattice for x in roi_layer_list], desc="ROI:", position=0)): + # 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 + current_workflow = copy(user_workflow) + current_workflow.set( + "crop_deskew_image", + crop_volume_deskew, + original_volume="volume", + roi_shape="roi", + angle_in_degrees=lattice.angle, + voxel_size_x=lattice.dx, + voxel_size_y=lattice.dy, + voxel_size_z=lattice.dy, + z_start=0, + z_end=lattice.deskew_vol_shape[0], + deconvolution=deconvolution, + decon_processing=lattice.decon_processing, + psf="psf", + skew_dir=lattice.skew_dir + ) + + # change the first task so it accepts "crop_deskew as input" + replace_first_arg(current_workflow, new_arg="crop_deskew_image") + + logging.info(f"Processing ROI {idx}") + current_workflow.set("roi", roi_layer) + + yield current_workflow diff --git a/plugin/napari_lattice/dock_widget.py b/plugin/napari_lattice/dock_widget.py index a11f53b..d578119 100644 --- a/plugin/napari_lattice/dock_widget.py +++ b/plugin/napari_lattice/dock_widget.py @@ -30,6 +30,7 @@ from lls_core import config, DeconvolutionChoice, SaveFileType, Log_Levels, DeskewDirection from lls_core.io import LatticeData, save_img, save_img_workflow +from lls_core.lattice_data import DeconvolutionParams from lls_core.types import ArrayLike from lls_core.workflow import get_first_last_image_and_task, modify_workflow_task, get_all_py_files, process_custom_workflow_output, load_custom_py_modules, import_workflow_modules, replace_first_arg from lls_core.utils import check_dimensions, read_imagej_roi, as_type @@ -67,10 +68,7 @@ def parent_viewer(self) -> Viewer: class LLSZWidget(LlszTemplate): open_file: bool = False lattice: LatticeData - skew_dir: DeskewDirection - angle_value: float - deskew_func: Callable - deconvolution: bool = False + deconv: DeconvolutionParams = DeconvolutionParams() shapes_layer: Shapes @magicclass(widget_type="split") @@ -119,17 +117,6 @@ def Choose_Image_Layer(self, # Select device for processing cle.select_device(select_device) - #assert skew_dir in DeskewDirection, "Skew direction not recognised. Enter either Y or X" - self.llsz_parent.skew_dir = skew_dir - self.llsz_parent.angle_value = angle - - if self.llsz_parent.skew_dir == DeskewDirection.Y: - self.llsz_parent.deskew_func = cle.deskew_y - #skew_dir = DeskewDirection.Y - elif self.llsz_parent.skew_dir == DeskewDirection.X: - self.llsz_parent.deskew_func = cle.deskew_x - #self.llsz_parent.skew_dir = DeskewDirection.X - # merge all napari image layers as one multidimensional image if merge_all_channel_layers: from napari.layers.utils.stack_utils import images_to_stack @@ -151,23 +138,10 @@ def Choose_Image_Layer(self, img=img_layer, last_dimension=None if last_dimension_channel == LastDimensionOptions.get_from_metadata else last_dimension_channel, angle=angle, - skew=self.llsz_parent.skew_dir, - physical_pixel_sizes=(pixel_size_dx, pixel_size_dy, pixel_size_dz) + skew=skew_dir, + physical_pixel_sizes=(pixel_size_dx, pixel_size_dy, pixel_size_dz), + # deconvolution = DeconvolutionParams() ) - #self.llsz_parent.aics = self.llsz_parent.lattice.data - - # self.llsz_parent.dask = False # Use GPU by default - - # We initialise these variables here, but they can be changed in the deconvolution section - # list to store psf images for each channel - self.llsz_parent.lattice.psf = [] - self.llsz_parent.lattice.psf_num_iter = 10 - self.llsz_parent.lattice.decon_processing = DeconvolutionChoice.cpu - # list to store otf paths for each channel (Deprecated) - self.llsz_parent.lattice.otf_path = [] - # if not using GPU - #self.llsz_parent.dask = not use_GPU - # flag for ensuring a file has been opened and plugin initialised self.llsz_parent.open_file = True @@ -212,10 +186,11 @@ def Choose_Image_Layer(self, def _set_decon(self): if self.deconvolution: logger.info("Deconvolution Activated") - self.llsz_parent.deconvolution = True + # Enable deconvolutio by using the saved parameters + self.llsz_parent.lattice.deconvolution = self.llsz_parent.deconv else: logger.info("Deconvolution Disabled") - self.llsz_parent.deconvolution = False + self.llsz_parent.lattice.deconvolution = None @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

"), @@ -241,10 +216,11 @@ def deconvolution_gui(self, device_option: DeconvolutionChoice, no_iter: int): """GUI for Deconvolution button""" - self.llsz_parent.lattice.decon_processing = device_option - if not self.llsz_parent.deconvolution: + # 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.lattice.psf = list(read_psf([ + 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, @@ -253,7 +229,7 @@ def deconvolution_gui(self, device_option, lattice_class=self.llsz_parent.lattice )) - self.llsz_parent.lattice.psf_num_iter = no_iter + self.llsz_parent.deconv.psf_num_iter = no_iter self["deconvolution_gui"].background_color = "green" self["deconvolution_gui"].text = "PSFs added" @@ -475,8 +451,7 @@ def Crop_Save(self, time_end=time_end, channel_start=ch_start, channel_end=ch_end, - save_name_prefix="ROI_" + - str(idx), + save_name_prefix="ROI_" + str(idx), save_path=save_path, save_file_type=save_as_type, save_name=self.llsz_parent.lattice.save_name, @@ -901,177 +876,3 @@ def get_workflow(source: Union[Path, Viewer]) -> Workflow: logger.info(f"Workflow loaded: {user_workflow}") return user_workflow - -def augment_workflow( - workflow: Workflow, - crop: bool, - roi_layer_list: ShapesData, - lattice: LatticeData, - deconvolution: bool, - times: range, - channels: range - ) -> Iterable[Workflow]: - """ - Yields copies of the input workflow, modified with the addition of deskewing and optionally, - cropping and deconvolution - """ - user_workflow = copy(workflow) - _, _, first_task_name, _ = get_first_last_image_and_task(workflow) - - for loop_time_idx, time_point in enumerate(times): - output_array = [] - data_table = [] - for loop_ch_idx, ch in enumerate(channels): - - if crop: - yield from make_crop_workflows( - user_workflow=user_workflow, - roi_layer_list=roi_layer_list, - lattice=lattice, - deconvolution=deconvolution - ) - - # 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) - else: - INPUT_ARG = "input" - - # IF just deskewing and its not in the tasks, add that as first task - if user_workflow.get_task(first_task_name)[0] not in (cle.deskew_y, cle.deskew_x): - # add task to the workflow - user_workflow.set( - "deskew_image", - lattice.deskew_func, - input_image=INPUT_ARG, - angle_in_degrees=lattice.angle, - voxel_size_x=lattice.dx, - voxel_size_y=lattice.dy, - voxel_size_z=lattice.dz, - linear_interpolation=True - ) - # Set input of the workflow to be from deskewing - # change workflow task starts from is "deskew_image" and - replace_first_arg(user_workflow, new_arg="deskew_image") - - # if deconvolution checked, add it to start of workflow (add upstream of deskewing) - if deconvolution: - PSF_ARG = "psf" - - if lattice.decon_processing == DeconvolutionChoice.cuda_gpu: - user_workflow.set( - "deconvolution", - pycuda_decon, - image=INPUT_ARG, - psf=PSF_ARG, - dzdata=lattice.dz, - dxdata=lattice.dx, - dzpsf=lattice.dz, - dxpsf=lattice.dx, - num_iter=lattice.psf_num_iter - ) - # user_workflow.set(input_arg_first,"deconvolution") - else: - user_workflow.set( - "deconvolution", - skimage_decon, - vol_zyx=INPUT_ARG, - psf=PSF_ARG, - num_iter=lattice.psf_num_iter, - clip=False, - filter_epsilon=0, - boundary='nearest' - ) - # modify the user workflow so "deconvolution" is accepted - replace_first_arg(user_workflow, new_arg="deconvolution") - - yield workflow - - # save_img_workflow(vol=vol, - # workflow=user_workflow, - # input_arg=INPUT_ARG, - # 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_ARG, - # psf_arg=psf_arg, - # psf=PSF_ARG) - - -def make_crop_workflows( - user_workflow: Workflow, - roi_layer_list: ShapesData, - lattice: LatticeData, - deconvolution: bool -) -> Iterable[Workflow]: - """ - Yields a copy of `user_workflow` for each region of interest, with deskewing, cropping and deconvolution steps added on to the start - """ - - # Convert Roi pixel coordinates to canvas coordinates - # necessary only when scale is used for napari.viewer.add_image operations - - # Here we generate a workflow for each ROI - for idx, roi_layer in enumerate(tqdm([x/lattice for x in roi_layer_list], desc="ROI:", position=0)): - # 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 - current_workflow = copy(user_workflow) - current_workflow.set( - "crop_deskew_image", - crop_volume_deskew, - original_volume="volume", - roi_shape="roi", - angle_in_degrees=lattice.angle, - voxel_size_x=lattice.dx, - voxel_size_y=lattice.dy, - voxel_size_z=lattice.dy, - z_start=0, - z_end=lattice.deskew_vol_shape[0], - deconvolution=deconvolution, - decon_processing=lattice.decon_processing, - psf="psf", - skew_dir=lattice.skew_dir - ) - - # change the first task so it accepts "crop_deskew as input" - replace_first_arg(current_workflow, new_arg="crop_deskew_image") - - logging.info(f"Processing ROI {idx}") - current_workflow.set("roi", roi_layer) - - yield current_workflow From e9f3866dfa119ec47399c496bb420afd88aa7603 Mon Sep 17 00:00:00 2001 From: Michael Milton Date: Thu, 10 Aug 2023 18:35:29 +1000 Subject: [PATCH 009/147] Move to Pydantic, and add field validations for LatticeData and similar --- core/lls_core/lattice_data.py | 142 ++++++++++++++++++--------- core/pyproject.toml | 1 + plugin/napari_lattice/dock_widget.py | 2 + plugin/napari_lattice/reader.py | 3 +- 4 files changed, 100 insertions(+), 48 deletions(-) diff --git a/core/lls_core/lattice_data.py b/core/lls_core/lattice_data.py index 8eebbff..857701e 100644 --- a/core/lls_core/lattice_data.py +++ b/core/lls_core/lattice_data.py @@ -1,11 +1,10 @@ from __future__ import annotations # class for initializing lattice data and setting metadata # TODO: handle scenes -from dataclasses import dataclass, field +from pydantic import BaseModel, Field, NonNegativeInt, NonNegativeFloat, root_validator, validator from aicsimageio.aics_image import AICSImage from aicsimageio.dimensions import Dimensions from numpy.typing import NDArray -from dataclasses import dataclass import math from dask.array.core import Array as DaskArray import dask as da @@ -38,18 +37,16 @@ def raise_if_none(obj: Optional[T], message: str) -> T: raise TypeError(message) return obj -@dataclass -class ProcessedVolume: +class ProcessedVolume(BaseModel): """ A slice of the image processing result """ - time_index: int - time: int - channel_index: int - channel: int + time_index: NonNegativeInt + time: NonNegativeInt + channel_index: NonNegativeInt + channel: NonNegativeInt data: ArrayLike - - roi_index: Optional[int] = None + roi_index: Optional[NonNegativeInt] = None def make_filename_prefix(prefix: Optional[str] = None, roi_index: Optional[int] = None, channel: Optional[int] = None, time: Optional[int] = None) -> str: """ @@ -66,63 +63,66 @@ def make_filename_prefix(prefix: Optional[str] = None, roi_index: Optional[int] components.append(f"T{time}") return "_".join(components) -@dataclass -class DefinedPixelSizes: +class DefinedPixelSizes(BaseModel): """ Like PhysicalPixelSizes, but it's a dataclass, and none of its fields are None """ - X: float = 0.14 - Y: float = 0.14 - Z: float = 0.3 + X: NonNegativeFloat = 0.14 + Y: NonNegativeFloat = 0.14 + Z: NonNegativeFloat = 0.3 -@dataclass -class DeconvolutionParams: +class DeconvolutionParams(BaseModel): """ Parameters for the optional deconvolution step """ decon_processing: DeconvolutionChoice = DeconvolutionChoice.cpu psf: List[NDArray] = [] - psf_num_iter: int = 10 + psf_num_iter: NonNegativeInt = 10 + # TODO: handle this otf_path: List = [] # Background value to subtract - background: Union[float, Literal["auto","second_last"]] = 0 + background: Union[float, Literal["auto", "second_last"]] = 0 -@dataclass -class CropParams: +class CropParams(BaseModel): """ Parameters for the optional cropping step """ roi_layer_list: ShapesData - z_start: int = 0 + z_start: NonNegativeInt = 0 z_end: int = 1 -@dataclass -class LatticeData: +class LatticeData(BaseModel): """ Holds data and metadata for a given image in a consistent format """ - # TODO: add defaults here, rather than in the CLI - #: 3-5D array + #: A 3-5D array containing the image data data: ArrayLike + + #: Dimensions of `data` dims: Dimensions #: The filename of this data when it is saved save_name: str - # Dimensions of the deskewed output - deskew_vol_shape: Tuple[int, ...] = field(init=False) - deskew_affine_transform: cle.AffineTransform3D = field(init=False) + #: Dimensions of the deskewed output + deskew_vol_shape: Tuple[int, ...] + + deskew_affine_transform: cle.AffineTransform3D + + #: The range of times to process + time_range: range + + #: The range of channels to process + channel_range: range #: Geometry of the light path skew: DeskewDirection = DeskewDirection.Y angle: float = 30.0 #: Pixel size in microns - physical_pixel_sizes: DefinedPixelSizes = field(default_factory=DefinedPixelSizes) - - new_dz: float = field(init=False) + physical_pixel_sizes: DefinedPixelSizes = Field(default_factory=DefinedPixelSizes) # If this is None, then deconvolution is disabled deconvolution: Optional[DeconvolutionParams] = None @@ -130,10 +130,7 @@ class LatticeData: # If this is None, then cropping is disabled crop: Optional[CropParams] = None - # Time and channel to process - time_range: range = field(init=False) - channel_range: range = field(init=False) - + #: The data type to save the result as save_type: SaveFileType = SaveFileType.h5 # Hack to ensure that .skew_dir behaves identically to .skew @@ -207,17 +204,70 @@ def time(self) -> int: def channels(self) -> int: """Number of channels""" return self.dims.C + + @root_validator + 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 + if values['deskew_vol_shape'] is None: + if values['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["dx"], values["dy"], values["dz"]) + else: + raise ValueError("deskew_vol_shape and deskew_affine_transform must be either both specified or neither specified") + return values + + @validator("time_range") + 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 v + + @validator("time_range") + 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 + if v.start < 0: + raise ValueError("The lowest valid start value is 0") + if v.stop > max_time: + raise ValueError(f"The highest valid time value is the length of the time axis, which is {max_time}") + return v + + @validator("channel_range") + 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 v - def __post_init__(self): - # set new z voxel size - self.new_dz = math.sin(self.angle * math.pi / 180.0) * self.dz + @validator("channel_range") + 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 + if v.start < 0: + raise ValueError("The lowest valid start value is 0") + if v.stop > max_channel: + raise ValueError(f"The highest valid channel value is the length of the channel axis, which is {max_channel}") + return v - # process the file to get shape of final deskewed image - self.deskew_vol_shape, self.deskew_affine_transform = get_deskewed_shape(self.data, self.angle, self.dx, self.dy, self.dz) - self.time_range = range(self.time + 1) - self.channel_range = range(self.channels + 1) - logging.log(f"Channels: {self.channels}, Time: {self.time}") - print("If channel and time need to be swapped, you can enforce this by choosing 'Last dimension is channel' when initialising the plugin") + @property + def new_dz(self): + return math.sin(self.angle * math.pi / 180.0) * self.dz + + 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: if time > self.time: @@ -363,6 +413,7 @@ def save_image(self, slices: Iterable[ProcessedVolume]): Saves result slices (ie generated by `process()` to disk) """ # TODO: refactor this into a class system, one for each format + # TODO: make this a method on a ProcessedData class import numpy as np from pathlib import Path import npy2bdv @@ -456,7 +507,6 @@ def img_from_array(arr: ArrayLike, last_dimension: Optional[Literal["channel", " arr = np.swapaxes(arr, 0, 1) return AICSImage(image=arr, dim_order=dim_order, **kwargs) - def lattice_fom_array(arr: ArrayLike, last_dimension: Optional[Literal["channel", "time"]] = None, **kwargs: Any) -> LatticeData: """ Creates a `LatticeData` from an array diff --git a/core/pyproject.toml b/core/pyproject.toml index 3d6f3a4..ab5b93f 100644 --- a/core/pyproject.toml +++ b/core/pyproject.toml @@ -46,6 +46,7 @@ dependencies = [ "read-roi", "gputools", "pyclesperanto_prototype>=0.20.0", + "pydantic~=1.0", "npy2bdv", "redlionfish", # Older tifffile are not compatible with aicsimageio, see: https://github.com/AllenCellModeling/aicsimageio/issues/518 diff --git a/plugin/napari_lattice/dock_widget.py b/plugin/napari_lattice/dock_widget.py index d578119..56ae441 100644 --- a/plugin/napari_lattice/dock_widget.py +++ b/plugin/napari_lattice/dock_widget.py @@ -325,6 +325,7 @@ def Import_ImageJ_ROI(self, path: Path = Path(history.get_open_history()[0])) -> @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 @@ -384,6 +385,7 @@ def Crop_Preview(self, roi_layer: ShapesData): ) # 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, diff --git a/plugin/napari_lattice/reader.py b/plugin/napari_lattice/reader.py index 0ae8eef..9f8775c 100644 --- a/plugin/napari_lattice/reader.py +++ b/plugin/napari_lattice/reader.py @@ -82,7 +82,6 @@ def napari_get_reader(path: list[str] | str): # otherwise we return the *function* that can read ``path``. return bdv_h5_reader - def bdv_h5_reader(path): """Take a path and returns a list of LayerData tuples.""" @@ -136,4 +135,4 @@ def bdv_h5_reader(path): add_kwargs = {} layer_type = "image" # optional, default is "image" - return [(images, add_kwargs, layer_type)] \ No newline at end of file + return [(images, add_kwargs, layer_type)] From ba63f3902d50b4b26dba36f240c3e0ec6f07f9ee Mon Sep 17 00:00:00 2001 From: Michael Milton Date: Fri, 11 Aug 2023 08:09:07 +1000 Subject: [PATCH 010/147] Refactor process() to return a ProcessedSlices object --- core/lls_core/lattice_data.py | 112 +++++++++++++++++++--------------- 1 file changed, 64 insertions(+), 48 deletions(-) diff --git a/core/lls_core/lattice_data.py b/core/lls_core/lattice_data.py index 857701e..075716a 100644 --- a/core/lls_core/lattice_data.py +++ b/core/lls_core/lattice_data.py @@ -37,6 +37,55 @@ def raise_if_none(obj: Optional[T], message: str) -> T: raise TypeError(message) return obj +class ProcessedSlices(BaseModel): + #: Iterable of result slices. + #: Note that this is a finite iterator that can only be iterated once + slices: Iterable[ProcessedVolume] + + #: The "parent" LatticeData that was used to create this result + lattice_data: LatticeData + + def save_image(self): + """ + Saves result slices to disk + """ + # TODO: refactor this into a class system, one for each format + import numpy as np + from pathlib import Path + import npy2bdv + + 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), + compression='gzip', + nchannels=len(self.lattice_data.channel_range), + subsamp=((1, 1, 1), (1, 2, 2), (2, 4, 4)), + overwrite=False + ) + for result in roi_results: + bdv_writer.append_view( + result.data, + time=result.time, + channel=result.channel, + voxel_size_xyz=(self.lattice_data.dx, self.lattice_data.dy, self.lattice_data.new_dz), + voxel_units='um' + ) + elif self.lattice_data.save_type == SaveFileType.tiff: + # For each time point, we write a separate TIFF + for time, results in groupby(roi_results, key=lambda it: it.time): + result_list = list(results) + first_result = result_list[0] + images_array = np.swapaxes(np.expand_dims([result.data for result in result_list], axis=0), 1, 2) + tifffile.imwrite( + file = Path(make_filename_prefix(channel=first_result.channel, time=time, roi_index=roi)).with_suffix("tiff"), + data = images_array, + bigtiff=True, + resolution=(1./self.lattice_data.dx, 1./self.lattice_data.dy, "MICROMETER"), + metadata={'spacing': self.lattice_data.new_dz, 'unit': 'um', 'axes': 'TZCYX'}, + imagej=True + ) + class ProcessedVolume(BaseModel): """ A slice of the image processing result @@ -317,7 +366,7 @@ def check_incomplete_acquisition(self, volume: ArrayLike, time_point: int, chann def deskewed_volume(self) -> DaskArray: return da.zeros(self.deskew_vol_shape) - def process_crop(self) -> Iterable[ProcessedVolume]: + def _process_crop(self) -> Iterable[ProcessedVolume]: """ Yields processed image slices with cropping enabled """ @@ -361,7 +410,7 @@ def process_crop(self) -> Iterable[ProcessedVolume]: time_index=time_idx, roi_index=roi_index ) - def process_non_crop(self) -> Iterable[ProcessedVolume]: + def _process_non_crop(self) -> Iterable[ProcessedVolume]: """ Yields processed image slices without cropping """ @@ -402,54 +451,21 @@ def process_non_crop(self) -> Iterable[ProcessedVolume]: time_index=time_idx ) - def process(self) -> Iterable[ProcessedVolume]: - if self.cropping_enabled: - yield from self.process_crop() - else: - yield from self.process_non_crop() - - def save_image(self, slices: Iterable[ProcessedVolume]): + def process(self) -> ProcessedSlices: """ - Saves result slices (ie generated by `process()` to disk) + Execute the processing and return the result. + This is the main public API for processing """ - # TODO: refactor this into a class system, one for each format - # TODO: make this a method on a ProcessedData class - import numpy as np - from pathlib import Path - import npy2bdv - - for roi, roi_results in groupby(slices, key=lambda it: it.roi_index): - if self.save_type == SaveFileType.h5: - bdv_writer = npy2bdv.BdvWriter( - make_filename_prefix(prefix=self.save_name, roi_index=roi), - compression='gzip', - nchannels=len(self.channel_range), - subsamp=( - (1, 1, 1), (1, 2, 2), (2, 4, 4)), - overwrite=False - ) - for result in roi_results: - bdv_writer.append_view( - result.data, - time=result.time, - channel=result.channel, - voxel_size_xyz=(self.dx, self.dy, self.new_dz), - voxel_units='um' - ) - elif self.save_type == SaveFileType.tiff: - # For each time point, we write a separate TIFF - for time, results in groupby(roi_results, key=lambda it: it.time): - result_list = list(results) - first_result = result_list[0] - images_array = np.swapaxes(np.expand_dims([result.data for result in result_list], axis=0), 1, 2) - tifffile.imwrite( - file = Path(make_filename_prefix(channel=first_result.channel, time=time, roi_index=roi)).with_suffix("tiff"), - data = images_array, - bigtiff=True, - resolution=(1./self.dx, 1./self.dy, "MICROMETER"), - metadata={'spacing': self.new_dz, 'unit': 'um', 'axes': 'TZCYX'}, - imagej=True - ) + if self.cropping_enabled: + return ProcessedSlices( + lattice_data=self, + slices=self._process_crop() + ) + else: + return ProcessedSlices( + lattice_data=self, + slices=self._process_non_crop() + ) def lattice_from_aics(img: AICSImage, physical_pixel_sizes: PhysicalPixelSizes = PhysicalPixelSizes(None, None, None), **kwargs: Any) -> LatticeData: # Note: The reason we copy all of these fields rather than just storing the AICSImage is because that class is mostly immutable and so not suitable From c02ea408c88fbc73d524f1da13e77b4f6ae919e0 Mon Sep 17 00:00:00 2001 From: Michael Milton Date: Fri, 11 Aug 2023 10:47:12 +1000 Subject: [PATCH 011/147] Fix breaking plugin errors --- core/lls_core/deconvolution.py | 8 ++++++-- core/lls_core/lattice_data.py | 7 ++++--- core/lls_core/types.py | 4 +++- core/lls_core/workflow.py | 8 ++++---- plugin/napari_lattice/dock_widget.py | 2 +- 5 files changed, 18 insertions(+), 11 deletions(-) diff --git a/core/lls_core/deconvolution.py b/core/lls_core/deconvolution.py index 628577b..4206f84 100644 --- a/core/lls_core/deconvolution.py +++ b/core/lls_core/deconvolution.py @@ -1,12 +1,13 @@ +from __future__ import annotations + from pathlib import Path from resource_backed_dask_array import ResourceBackedDaskArray from lls_core import DeconvolutionChoice -from lls_core.lattice_data import LatticeData import pyclesperanto_prototype as cle import logging import importlib.util -from typing import Collection, Iterable,Union,Literal, Optional +from typing import Collection, Iterable,Union,Literal, Optional, TYPE_CHECKING from aicsimageio.aics_image import AICSImage from aicspylibczi import CziFile from numpy.typing import NDArray @@ -19,6 +20,9 @@ from lls_core.utils import pad_image_nearest_multiple from lls_core.types import ArrayLike, is_arraylike +if TYPE_CHECKING: + from lls_core.lattice_data import LatticeData + logger = logging.getLogger(__name__) def read_psf(psf_paths: Collection[Path], diff --git a/core/lls_core/lattice_data.py b/core/lls_core/lattice_data.py index 075716a..4fc7b0c 100644 --- a/core/lls_core/lattice_data.py +++ b/core/lls_core/lattice_data.py @@ -4,12 +4,13 @@ from pydantic import BaseModel, Field, NonNegativeInt, NonNegativeFloat, root_validator, validator from aicsimageio.aics_image import AICSImage from aicsimageio.dimensions import Dimensions -from numpy.typing import NDArray +# from numpy.typing import NDArray import math from dask.array.core import Array as DaskArray import dask as da from itertools import groupby import tifffile +from pydantic_numpy import NDArray from typing import Any, Iterable, List, Literal, Optional, TYPE_CHECKING, Tuple, TypeVar, Union @@ -86,7 +87,7 @@ def save_image(self): imagej=True ) -class ProcessedVolume(BaseModel): +class ProcessedVolume(BaseModel, arbitrary_types_allowed=True): """ A slice of the image processing result """ @@ -141,7 +142,7 @@ class CropParams(BaseModel): z_start: NonNegativeInt = 0 z_end: int = 1 -class LatticeData(BaseModel): +class LatticeData(BaseModel, arbitrary_types_allowed=True): """ Holds data and metadata for a given image in a consistent format """ diff --git a/core/lls_core/types.py b/core/lls_core/types.py index aa2b889..077586f 100644 --- a/core/lls_core/types.py +++ b/core/lls_core/types.py @@ -1,9 +1,11 @@ from typing import Union from typing_extensions import TypeGuard, Any, TypeAlias from dask.array.core import Array as DaskArray -from numpy.typing import NDArray +# from numpy.typing import NDArray from pyopencl.array import Array as OCLArray import numpy as np +import pydantic_numpy.dtype as pnd +from pydantic_numpy import NDArray, NDArrayFp32, NumpyModel ArrayLike: TypeAlias = Union[DaskArray, NDArray, OCLArray] diff --git a/core/lls_core/workflow.py b/core/lls_core/workflow.py index 7ccd6a8..a861d47 100644 --- a/core/lls_core/workflow.py +++ b/core/lls_core/workflow.py @@ -211,10 +211,10 @@ def augment_workflow( user_workflow = copy(workflow) _, _, first_task_name, _ = get_first_last_image_and_task(workflow) - for loop_time_idx, time_point in enumerate(times): - output_array = [] - data_table = [] - for loop_ch_idx, ch in enumerate(channels): + # for loop_time_idx, time_point in enumerate(times): + # output_array = [] + # data_table = [] + # for loop_ch_idx, ch in enumerate(channels): if crop: yield from make_crop_workflows( diff --git a/plugin/napari_lattice/dock_widget.py b/plugin/napari_lattice/dock_widget.py index 56ae441..e6e65dc 100644 --- a/plugin/napari_lattice/dock_widget.py +++ b/plugin/napari_lattice/dock_widget.py @@ -5,7 +5,7 @@ from pathlib import Path import dask.array as da import pandas as pd -from typing import allable, Iterable, Optional, Union +from typing import Callable, Iterable, Optional, Union from enum import Enum from magicclass.wrappers import set_design From eee538fe6f7794f88bf370be4c7da7c3c657cf92 Mon Sep 17 00:00:00 2001 From: Michael Milton Date: Wed, 16 Aug 2023 20:01:06 +1000 Subject: [PATCH 012/147] Initial Restructure of GUI --- core/lls_core/lattice_data.py | 17 +++- plugin/napari_lattice/dock_widget.py | 125 ++++++++++++++++++++++----- 2 files changed, 115 insertions(+), 27 deletions(-) diff --git a/core/lls_core/lattice_data.py b/core/lls_core/lattice_data.py index 4fc7b0c..06487f4 100644 --- a/core/lls_core/lattice_data.py +++ b/core/lls_core/lattice_data.py @@ -38,6 +38,15 @@ def raise_if_none(obj: Optional[T], message: str) -> T: raise TypeError(message) return obj +class DefaultMixin(BaseModel): + """ + Adds a method for retrieving default values from a BaseModel + """ + + @classmethod + def get_default(cls, field_name: str): + return cls.__fields__[field_name].get_default() + class ProcessedSlices(BaseModel): #: Iterable of result slices. #: Note that this is a finite iterator that can only be iterated once @@ -113,7 +122,7 @@ def make_filename_prefix(prefix: Optional[str] = None, roi_index: Optional[int] components.append(f"T{time}") return "_".join(components) -class DefinedPixelSizes(BaseModel): +class DefinedPixelSizes(DefaultMixin): """ Like PhysicalPixelSizes, but it's a dataclass, and none of its fields are None @@ -142,7 +151,7 @@ class CropParams(BaseModel): z_start: NonNegativeInt = 0 z_end: int = 1 -class LatticeData(BaseModel, arbitrary_types_allowed=True): +class LatticeData(DefaultMixin, arbitrary_types_allowed=True): """ Holds data and metadata for a given image in a consistent format """ @@ -261,8 +270,8 @@ 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 - if values['deskew_vol_shape'] is None: - if values['deskew_affine_transform'] is None: + 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["dx"], values["dy"], values["dz"]) else: diff --git a/plugin/napari_lattice/dock_widget.py b/plugin/napari_lattice/dock_widget.py index e6e65dc..afdd6c6 100644 --- a/plugin/napari_lattice/dock_widget.py +++ b/plugin/napari_lattice/dock_widget.py @@ -5,12 +5,15 @@ from pathlib import Path import dask.array as da import pandas as pd -from typing import Callable, Iterable, Optional, Union +from typing import Any, Callable, Iterable, Literal, Optional, Sequence, Tuple, Union, List from enum import Enum from magicclass.wrappers import set_design from magicgui import magicgui -from magicclass import magicclass, field, vfield, set_options, MagicTemplate +from magicclass import magicclass, field, vfield, set_options, MagicTemplate, FieldGroup +from magicclass.fields import MagicValueField +from magicclass.widgets import CollapsibleContainer, CheckBox, RangeSlider +from magicclass.widgets.containers import ContainerWidget, _VCollapsibleContainer, wrap_container from magicclass.utils import click from qtpy.QtCore import Qt @@ -30,7 +33,7 @@ from lls_core import config, DeconvolutionChoice, SaveFileType, Log_Levels, DeskewDirection from lls_core.io import LatticeData, save_img, save_img_workflow -from lls_core.lattice_data import DeconvolutionParams +from lls_core.lattice_data import CropParams, DeconvolutionParams, DefinedPixelSizes from lls_core.types import ArrayLike from lls_core.workflow import get_first_last_image_and_task, modify_workflow_task, get_all_py_files, process_custom_workflow_output, load_custom_py_modules, import_workflow_modules, replace_first_arg from lls_core.utils import check_dimensions, read_imagej_roi, as_type @@ -47,11 +50,6 @@ logger = logging.getLogger(__name__) logger.setLevel(logging.INFO) -class LastDimensionOptions(Enum): - channel = "Channel" - time = "Time" - get_from_metadata = "Get from Metadata" - class LlszTemplate(MagicTemplate): @property def llsz_parent(self) -> "LLSZWidget": @@ -64,6 +62,48 @@ def parent_viewer(self) -> Viewer: raise Exception("This function can only be used when inside of a Napari viewer") return super().parent_viewer +@magicclass +class DeconvolutionWidget(MagicTemplate): + """ + A counterpart to the DeconvolutionParams Pydantic class + """ + decon_processing = vfield(DeconvolutionChoice, label="Processing Algorithm") + psf = vfield(List[Path], label = "PSFs") + psf_num_iter = vfield(int, label = "Number of Iterations") + +@magicclass +class CroppingWidget(LlszTemplate): + """ + A counterpart to the CropParams Pydantic class + """ + # roi_layer_list: ShapesData + z_start = vfield(int).with_options(value=0, max=10, min=0) + z_end = vfield(int).with_options(value=0, max=10, min=0, label="Z End") + shapes= vfield(ShapesData)#Optional[Shapes] = None + _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"]) + 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_crop_params(self) -> CropParams: + return CropParams( + z_start=self.z_start, + z_end=self.z_end, + roi_layer_list=self.shapes +) + +class LastDimensionOptions(Enum): + XYZTC = "XYZTC" + XYZCT = "XYZCT" + Metadata = "Get from Metadata" + @magicclass(widget_type="split") class LLSZWidget(LlszTemplate): open_file: bool = False @@ -76,6 +116,59 @@ class LlszMenu(LlszTemplate): main_heading = field("

Napari Lattice: Visualization & Analysis

", widget_type="Label") heading1 = field("Drag and drop an image file onto napari.\nOnce image has opened, initialize the\nplugin by clicking the button below.\nEnsure the image layer and voxel sizes are accurate in the prompt.\n If everything initalises properly, the button turns green.", widget_type="Label") + # Pycudadecon library for deconvolution + # options={"enabled": True}, + + img_layer = vfield(Layer).with_options(label = "Image Layer") + pixel_sizes = vfield(Tuple[float, float, float]).with_options( + value=(DefinedPixelSizes.get_default("X"), DefinedPixelSizes.get_default("Y"), DefinedPixelSizes.get_default("Z")), + label="Pixel Sizes (XYZ)" + ) + angle: MagicValueField[float] = vfield(LatticeData.get_default("angle")).with_options(value=LatticeData.get_default("angle"), label="Skew Angle") + device = vfield(str).with_choices(cle.available_device_names()).with_options(label="Graphics Device") + merge_all_channels = vfield(False).with_options(label="Merge all Channels") + dimension_order = vfield(str).with_options(value=LastDimensionOptions.Metadata.value).with_choices([it.value for it in LastDimensionOptions]).with_options(label="Dimension Order") + skew_dir = vfield(DeskewDirection.Y).with_options(label = "Skew Direction") + set_logging = vfield(Log_Levels.INFO).with_options(label="Logging Level") + + deconvolution = vfield(bool, name="Use Deconvolution") + deconvolution.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_enabled = vfield(bool, name="Use Cropping", 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 @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}, @@ -104,7 +197,7 @@ def Choose_Image_Layer(self, angle: float = 30, select_device: str = cle.available_device_names()[ 0], - last_dimension_channel: LastDimensionOptions = LastDimensionOptions.get_from_metadata, + last_dimension_channel: LastDimensionOptions = "", merge_all_channel_layers: bool = False, skew_dir: DeskewDirection=DeskewDirection.Y, set_logging: Log_Levels=Log_Levels.INFO): @@ -177,20 +270,6 @@ def Choose_Image_Layer(self, self["Choose_Image_Layer"].background_color = "green" self["Choose_Image_Layer"].text = "Plugin Initialised" - # Pycudadecon library for deconvolution - # options={"enabled": True}, - deconvolution = vfield(bool, name="Use Deconvolution") - deconvolution.value = False - - @deconvolution.connect - def _set_decon(self): - if self.deconvolution: - logger.info("Deconvolution Activated") - # Enable deconvolutio by using the saved parameters - self.llsz_parent.lattice.deconvolution = self.llsz_parent.deconv - else: - logger.info("Deconvolution Disabled") - self.llsz_parent.lattice.deconvolution = None @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

"), From 48b971aa4de17503db55ebd6099e3d285eec902f Mon Sep 17 00:00:00 2001 From: Michael Milton Date: Thu, 17 Aug 2023 17:02:28 +1000 Subject: [PATCH 013/147] Finish draft of one-page GUI --- plugin/napari_lattice/dock_widget.py | 1601 ++++++++++++++------------ 1 file changed, 842 insertions(+), 759 deletions(-) diff --git a/plugin/napari_lattice/dock_widget.py b/plugin/napari_lattice/dock_widget.py index afdd6c6..ce1e034 100644 --- a/plugin/napari_lattice/dock_widget.py +++ b/plugin/napari_lattice/dock_widget.py @@ -6,7 +6,7 @@ import dask.array as da import pandas as pd from typing import Any, Callable, Iterable, Literal, Optional, Sequence, Tuple, Union, List -from enum import Enum +from enum import Enum, auto from magicclass.wrappers import set_design from magicgui import magicgui @@ -50,6 +50,10 @@ logger = logging.getLogger(__name__) logger.setLevel(logging.INFO) +class WorkflowSource(Enum): + ActiveWorkflow = auto() + CustomPath = auto() + class LlszTemplate(MagicTemplate): @property def llsz_parent(self) -> "LLSZWidget": @@ -77,9 +81,15 @@ class CroppingWidget(LlszTemplate): A counterpart to the CropParams Pydantic class """ # roi_layer_list: ShapesData - z_start = vfield(int).with_options(value=0, max=10, min=0) - z_end = vfield(int).with_options(value=0, max=10, min=0, label="Z End") - shapes= vfield(ShapesData)#Optional[Shapes] = None + 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") @@ -97,7 +107,15 @@ def _make_crop_params(self) -> CropParams: z_start=self.z_start, z_end=self.z_end, roi_layer_list=self.shapes -) + ) + +@magicclass +class WorkflowWidget(LlszTemplate): + """ + Handles the workflow related parameters + """ + workflow_source = vfield(WorkflowSource).with_options(label = "Workflow Source") + workflow_path = vfield(Path).with_options(label = "Workflow Path", visible=False) class LastDimensionOptions(Enum): XYZTC = "XYZTC" @@ -115,7 +133,7 @@ class LLSZWidget(LlszTemplate): class LlszMenu(LlszTemplate): main_heading = field("

Napari Lattice: Visualization & Analysis

", widget_type="Label") - heading1 = field("Drag and drop an image file onto napari.\nOnce image has opened, initialize the\nplugin by clicking the button below.\nEnsure the image layer and voxel sizes are accurate in the prompt.\n If everything initalises properly, the button turns green.", widget_type="Label") + heading1 = field("Drag and drop an image file onto napari.", widget_type="Label") # Pycudadecon library for deconvolution # options={"enabled": True}, @@ -130,9 +148,34 @@ class LlszMenu(LlszTemplate): dimension_order = vfield(str).with_options(value=LastDimensionOptions.Metadata.value).with_choices([it.value for it in LastDimensionOptions]).with_options(label="Dimension Order") skew_dir = vfield(DeskewDirection.Y).with_options(label = "Skew Direction") set_logging = vfield(Log_Levels.INFO).with_options(label="Logging Level") + time_range = vfield(Tuple[int, int]).with_options( + label="Time Export Range", + value=(0, 10), + options = dict( + min=0, + max=10, + ) + ) + channel_range = vfield(Tuple[int, int]).with_options( + label="Channel Range", + value=(0, 10), + options = dict( + min=0, + max=10, + ) + ) + save_type = vfield(SaveFileType).with_options( + label = "Save Format" + ) + save_path = vfield(Path).with_options( + label = "Save Directory", + value = Path(history.get_save_history()[0]) + ) - deconvolution = vfield(bool, name="Use Deconvolution") - deconvolution.value = False + ### + # Deconvolution + ### + deconvolution = vfield(bool, name="Use Deconvolution").with_options(value=False) deconv_widget = DeconvolutionWidget() deconv_widget.enabled = False deconv_widget.visible = False @@ -151,7 +194,11 @@ def _set_decon(self) -> None: self.deconv_widget.enabled = False self.deconv_widget.visible = False - cropping_enabled = vfield(bool, name="Use Cropping", options={"value": False}) + ### + # Cropping + ### + + cropping_enabled = vfield(bool, name="Use Cropping").with_options(value=False) crop_widget = CroppingWidget() crop_widget.enabled = False crop_widget.visible = False @@ -170,760 +217,796 @@ def _set_crop(self) -> None: self.crop_widget.enabled = False self.crop_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" - - @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) + ### + # 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), + channel=dict(label="Channel:"), + call_button="Preview" + ) + @set_design(text="Preview") + def preview(self, header:str, time: int, channel: int): + pass + + @set_design(text="Save") + def save(self): + pass + + + # @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): + # @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) + # # 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 + # 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 def _napari_lattice_widget_wrapper() -> LLSZWidget: From 2370be80b45f6fba8bf3151ecd827a010a7ed66a Mon Sep 17 00:00:00 2001 From: Michael Milton Date: Tue, 22 Aug 2023 09:41:37 +1000 Subject: [PATCH 014/147] First pass of preview working in the GUI --- core/lls_core/lattice_data.py | 170 ++++---- plugin/napari_lattice/dock_widget.py | 593 +++++++++++++++++++-------- plugin/napari_lattice/reader.py | 6 +- 3 files changed, 520 insertions(+), 249 deletions(-) diff --git a/core/lls_core/lattice_data.py b/core/lls_core/lattice_data.py index 06487f4..02c00d7 100644 --- a/core/lls_core/lattice_data.py +++ b/core/lls_core/lattice_data.py @@ -13,6 +13,8 @@ from pydantic_numpy import NDArray from typing import Any, Iterable, List, Literal, Optional, TYPE_CHECKING, Tuple, TypeVar, Union +from typing_extensions import Self +from pathlib import Path from aicsimageio.types import PhysicalPixelSizes import pyclesperanto_prototype as cle @@ -23,6 +25,7 @@ from lls_core.llsz_core import crop_volume_deskew from lls_core.utils import get_deskewed_shape from lls_core.types import ArrayLike +from napari_workflows import Workflow if TYPE_CHECKING: import pyclesperanto_prototype as cle @@ -47,6 +50,17 @@ class DefaultMixin(BaseModel): def get_default(cls, field_name: str): return cls.__fields__[field_name].get_default() +class ProcessedVolume(BaseModel, arbitrary_types_allowed=True): + """ + A slice of the image processing result + """ + time_index: NonNegativeInt + time: NonNegativeInt + channel_index: NonNegativeInt + channel: NonNegativeInt + data: ArrayLike + roi_index: Optional[NonNegativeInt] = None + class ProcessedSlices(BaseModel): #: Iterable of result slices. #: Note that this is a finite iterator that can only be iterated once @@ -96,17 +110,6 @@ def save_image(self): imagej=True ) -class ProcessedVolume(BaseModel, arbitrary_types_allowed=True): - """ - A slice of the image processing result - """ - time_index: NonNegativeInt - time: NonNegativeInt - channel_index: NonNegativeInt - channel: NonNegativeInt - data: ArrayLike - roi_index: Optional[NonNegativeInt] = None - def make_filename_prefix(prefix: Optional[str] = None, roi_index: Optional[int] = None, channel: Optional[int] = None, time: Optional[int] = None) -> str: """ Generates a filename for this result @@ -131,7 +134,7 @@ class DefinedPixelSizes(DefaultMixin): Y: NonNegativeFloat = 0.14 Z: NonNegativeFloat = 0.3 -class DeconvolutionParams(BaseModel): +class DeconvolutionParams(BaseModel, arbitrary_types_allowed=True): """ Parameters for the optional deconvolution step """ @@ -139,17 +142,17 @@ class DeconvolutionParams(BaseModel): psf: List[NDArray] = [] psf_num_iter: NonNegativeInt = 10 # TODO: handle this - otf_path: List = [] + # otf_path: List = [] # Background value to subtract background: Union[float, Literal["auto", "second_last"]] = 0 -class CropParams(BaseModel): +class CropParams(BaseModel, arbitrary_types_allowed=True): """ Parameters for the optional cropping step """ roi_layer_list: ShapesData z_start: NonNegativeInt = 0 - z_end: int = 1 + z_end: NonNegativeInt = 1 class LatticeData(DefaultMixin, arbitrary_types_allowed=True): """ @@ -162,19 +165,10 @@ class LatticeData(DefaultMixin, arbitrary_types_allowed=True): #: Dimensions of `data` dims: Dimensions - #: The filename of this data when it is saved - save_name: str - #: Dimensions of the deskewed output - deskew_vol_shape: Tuple[int, ...] + deskew_vol_shape: Tuple[int, ...] = Field(init_var=False) - deskew_affine_transform: cle.AffineTransform3D - - #: The range of times to process - time_range: range - - #: The range of channels to process - channel_range: range + deskew_affine_transform: cle.AffineTransform3D = Field(init_var=False) #: Geometry of the light path skew: DeskewDirection = DeskewDirection.Y @@ -183,15 +177,82 @@ class LatticeData(DefaultMixin, arbitrary_types_allowed=True): #: Pixel size in microns physical_pixel_sizes: DefinedPixelSizes = Field(default_factory=DefinedPixelSizes) - # If this is None, then deconvolution is disabled + #: If this is None, then deconvolution is disabled deconvolution: Optional[DeconvolutionParams] = None - # If this is None, then cropping is disabled + #: If this is None, then cropping is disabled crop: Optional[CropParams] = None + + #: If defined, this is a workflow to add lightsheet processing onto + workflow: Optional[Workflow] = None + + #: The directory where this data will be saved + save_dir: Path + + #: The file name to save this as, without the directory name or file extension + save_name: str + + #: The range of times to process + time_range: range + + #: The range of channels to process + channel_range: range #: The data type to save the result as save_type: SaveFileType = SaveFileType.h5 + @validator("time_range") + 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 v + + @validator("time_range") + 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 + if v.start < 0: + raise ValueError("The lowest valid start value is 0") + if v.stop > max_time: + raise ValueError(f"The highest valid time value is the length of the time axis, which is {max_time}") + return v + + @validator("channel_range") + 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 v + + @validator("channel_range") + 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 + if v.start < 0: + raise ValueError("The lowest valid start value is 0") + if v.stop > max_channel: + raise ValueError(f"The highest valid channel value is the length of the channel axis, which is {max_channel}") + return v + + @validator("channel_range") + def channel_range_subset(cls, v: range, values: dict): + if min(v) < 0 or max(v) > values["dims"].C: + raise ValueError("The output channel range must be a subset of the total available channels") + + @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") + # Hack to ensure that .skew_dir behaves identically to .skew @property def skew_dir(self) -> DeskewDirection: @@ -264,7 +325,7 @@ def channels(self) -> int: """Number of channels""" return self.dims.C - @root_validator + @root_validator(pre=True) def set_deskew(cls, values: dict) -> dict: """ Sets the default deskew shape values if the user has not provided them @@ -273,53 +334,11 @@ def set_deskew(cls, values: dict) -> dict: 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["dx"], values["dy"], values["dz"]) + 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"]) else: raise ValueError("deskew_vol_shape and deskew_affine_transform must be either both specified or neither specified") return values - @validator("time_range") - 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 v - - @validator("time_range") - 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 - if v.start < 0: - raise ValueError("The lowest valid start value is 0") - if v.stop > max_time: - raise ValueError(f"The highest valid time value is the length of the time axis, which is {max_time}") - return v - - @validator("channel_range") - 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 v - - @validator("channel_range") - 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 - if v.start < 0: - raise ValueError("The lowest valid start value is 0") - if v.stop > max_channel: - raise ValueError(f"The highest valid channel value is the length of the channel axis, which is {max_channel}") - return v - @property def new_dz(self): return math.sin(self.angle * math.pi / 180.0) * self.dz @@ -425,9 +444,11 @@ def _process_non_crop(self) -> Iterable[ProcessedVolume]: Yields processed image slices without cropping """ for time_idx, time, ch_idx, ch, data in self.iter_slices(): + if isinstance(data, DaskArray): + data = data.compute() if self.deconvolution is not None: if self.deconvolution.decon_processing == DeconvolutionChoice.cuda_gpu: - data = pycuda_decon( + data= pycuda_decon( image=data, psf=self.deconvolution.psf[ch], dzdata=self.dz, @@ -437,7 +458,7 @@ def _process_non_crop(self) -> Iterable[ProcessedVolume]: num_iter=self.deconvolution.psf_num_iter ) else: - data = skimage_decon( + data= skimage_decon( vol_zyx=data, psf=self.deconvolution.psf[ch], num_iter=self.deconvolution.psf_num_iter, @@ -466,6 +487,7 @@ def process(self) -> ProcessedSlices: Execute the processing and return the result. This is the main public API for processing """ + ProcessedSlices.update_forward_refs() if self.cropping_enabled: return ProcessedSlices( lattice_data=self, diff --git a/plugin/napari_lattice/dock_widget.py b/plugin/napari_lattice/dock_widget.py index ce1e034..41882bf 100644 --- a/plugin/napari_lattice/dock_widget.py +++ b/plugin/napari_lattice/dock_widget.py @@ -5,14 +5,14 @@ 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 +from typing import Any, Callable, Iterable, Literal, Optional, Sequence, Tuple, Union, List, TypeVar, Type, cast from enum import Enum, auto from magicclass.wrappers import set_design -from magicgui import magicgui -from magicclass import magicclass, field, vfield, set_options, MagicTemplate, FieldGroup -from magicclass.fields import MagicValueField -from magicclass.widgets import CollapsibleContainer, CheckBox, RangeSlider +from magicgui.widgets import Widget +from magicclass import magicclass as original_magicclass, field, vfield, set_options, MagicTemplate, FieldGroup +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 @@ -31,91 +31,126 @@ from napari_workflows import Workflow, WorkflowManager from napari_workflows._io_yaml_v1 import load_workflow -from lls_core import config, DeconvolutionChoice, SaveFileType, Log_Levels, DeskewDirection -from lls_core.io import LatticeData, save_img, save_img_workflow -from lls_core.lattice_data import CropParams, DeconvolutionParams, DefinedPixelSizes -from lls_core.types import ArrayLike -from lls_core.workflow import get_first_last_image_and_task, modify_workflow_task, get_all_py_files, process_custom_workflow_output, load_custom_py_modules, import_workflow_modules, replace_first_arg -from lls_core.utils import check_dimensions, read_imagej_roi, as_type -from lls_core.llsz_core import crop_volume_deskew -from lls_core.deconvolution import read_psf, pycuda_decon, skimage_decon +from lls_core import DeconvolutionChoice, SaveFileType, Log_Levels, DeskewDirection +from lls_core.lattice_data import CropParams, DeconvolutionParams, DefinedPixelSizes, LatticeData -from napari_lattice.ui_core import _Preview, _Deskew_Save from napari_lattice.reader import lattice_from_napari +from pydantic import ValidationError -from copy import copy +from strenum import StrEnum # Enable Logging import logging + +from lls_core.workflow import import_workflow_modules +from itertools import groupby logger = logging.getLogger(__name__) logger.setLevel(logging.INFO) -class WorkflowSource(Enum): - ActiveWorkflow = auto() - CustomPath = auto() +MagicClassType = TypeVar("MagicClassType") +def magicclass(*args, **kwargs) -> Callable[[MagicClassType], MagicClassType]: + return original_magicclass(*args, **kwargs) + +class WorkflowSource(StrEnum): + ActiveWorkflow = "Active Workflow" + CustomPath = "Custom Path" + +class BackgroundSource(StrEnum): + Auto = "Automatic" + SecondLast = "Second Last" + Custom = "Custom" + +def enable_field(parent: MagicTemplate, field: MagicField, enabled: bool = True) -> None: + """ + Enable the widget associated with a field + + Args: + parent: The parent magicclass that contains the field + field: The field to enable/disable + enabled: If False, disable the field instead of enabling it + """ + real_field = getattr(parent, field.name) + if not isinstance(real_field, Widget): + raise Exception("Define your fields with field() not vfield()!") + real_field.visible = enabled + real_field.enabled = enabled + +EnabledHandlerType = TypeVar("EnabledHandlerType") +def enable_if(fields: List[MagicField]): + """ + Makes an event handler that should be used via `fields_enabled.connect(make_enabled_handler())`. + Args: + condition: A function that takes an instance of the class and returns True if the fields should be enabled + fields: A list of fields to be dynamically enabled or disabled + Example: + :: + @some_field.connect + @enable_if( + [some_field] + ) + def _enable_fields(self) -> bool: + return some_field.value + """ + # Ideally we could use subclassing to add both the vfield and this event handler, but there + # seems to be a bug preventing this: https://github.com/hanjinliu/magic-class/issues/113. + + # Start by disabling all the fields + + def _decorator(fn: Callable[[EnabledHandlerType], bool])-> Callable[[EnabledHandlerType], None]: + for field in fields: + field.visible = False + field.enabled = False + + def make_handler(fn: Callable) -> Callable[[EnabledHandlerType], None]: + def handler(parent: EnabledHandlerType): + enable = fn(parent) + if enable: + logger.info(f"{parent.__class__} Activated") + else: + logger.info(f"{parent.__class__} Deactivated") + for field in fields: + enable_field(parent, field, enabled=enable) + return handler + + return make_handler(fn) + + return _decorator + +# 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 class LlszTemplate(MagicTemplate): @property def llsz_parent(self) -> "LLSZWidget": return self.find_ancestor(LLSZWidget) - @property - def parent_viewer(self) -> Viewer: - viewer = super().parent_viewer - if viewer is None: - raise Exception("This function can only be used when inside of a Napari viewer") - return super().parent_viewer - -@magicclass -class DeconvolutionWidget(MagicTemplate): - """ - A counterpart to the DeconvolutionParams Pydantic class - """ - decon_processing = vfield(DeconvolutionChoice, label="Processing Algorithm") - psf = vfield(List[Path], label = "PSFs") - psf_num_iter = vfield(int, label = "Number of Iterations") - -@magicclass -class CroppingWidget(LlszTemplate): - """ - A counterpart to the CropParams Pydantic class - """ - # roi_layer_list: ShapesData - 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"]) - 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_crop_params(self) -> CropParams: - return CropParams( - z_start=self.z_start, - z_end=self.z_end, - roi_layer_list=self.shapes - ) - -@magicclass -class WorkflowWidget(LlszTemplate): - """ - Handles the workflow related parameters - """ - workflow_source = vfield(WorkflowSource).with_options(label = "Workflow Source") - workflow_path = vfield(Path).with_options(label = "Workflow Path", visible=False) +def parent_viewer(mc: MagicTemplate) -> Viewer: + viewer = mc.parent_viewer + if viewer is None: + raise Exception("This function can only be used when inside of a Napari viewer") + return mc.parent_viewer + +T = TypeVar("T") +def get_child(parent: MagicTemplate, child_cls: Type[T]) -> T: + if isinstance(child_cls, MagicTemplate): + return child_cls + for child in parent.__magicclass_children__: + if isinstance(child, child_cls): + return child + raise Exception("Child couldn't be found!") class LastDimensionOptions(Enum): XYZTC = "XYZTC" @@ -125,10 +160,53 @@ class LastDimensionOptions(Enum): @magicclass(widget_type="split") class LLSZWidget(LlszTemplate): open_file: bool = False - lattice: LatticeData - deconv: DeconvolutionParams = DeconvolutionParams() shapes_layer: Shapes + def _check_validity(self) -> bool: + """ + Returns True if the model is valid + """ + try: + self._make_model() + return True + except: + return False + + def _make_model_friendly(self) -> LatticeData: + """ + Generates a LatticeData, but returns validation errors in a user friendly way + """ + try: + return self._make_model() + except ValidationError as e: + message = [f"

{e.model}

"] + for error in e.errors(): + header = ", ".join([str(it).capitalize() for it in error['loc']]) + message.append(f"

{header}

{error['msg']}") + raise Exception("\n".join(message)) + + def _make_model(self) -> LatticeData: + deskew = get_child(self, self.LlszMenu.WidgetContainer.DeskewWidget) + output = get_child(self, self.LlszMenu.WidgetContainer.OutputWidget) + deconv = get_child(self, self.LlszMenu.WidgetContainer.DeconvolutionWidget) + crop = get_child(self, self.LlszMenu.WidgetContainer.CroppingWidget) + workflow = get_child(self, self.LlszMenu.WidgetContainer.WorkflowWidget) + + return lattice_from_napari( + img=deskew.img_layer.value, + last_dimension=deskew.dimension_order.value, + angle=deskew.angle.value, + skew = deskew.skew_dir.value, + physical_pixel_sizes=deskew.pixel_sizes.value, + save_dir = output.save_path.value, + channel_range=range(output.channel_range.value[0], output.channel_range.value[1]), + time_range=range(output.time_range.value[0], output.time_range.value[1]), + save_type=output.save_type.value, + deconvolution = deconv._make_model(), + workflow = workflow._make_model(), + crop = crop._make_model() + ) + @magicclass(widget_type="split") class LlszMenu(LlszTemplate): @@ -137,107 +215,260 @@ class LlszMenu(LlszTemplate): # Pycudadecon library for deconvolution # options={"enabled": True}, - img_layer = vfield(Layer).with_options(label = "Image Layer") - pixel_sizes = vfield(Tuple[float, float, float]).with_options( - value=(DefinedPixelSizes.get_default("X"), DefinedPixelSizes.get_default("Y"), DefinedPixelSizes.get_default("Z")), - label="Pixel Sizes (XYZ)" - ) - angle: MagicValueField[float] = vfield(LatticeData.get_default("angle")).with_options(value=LatticeData.get_default("angle"), label="Skew Angle") - device = vfield(str).with_choices(cle.available_device_names()).with_options(label="Graphics Device") - merge_all_channels = vfield(False).with_options(label="Merge all Channels") - dimension_order = vfield(str).with_options(value=LastDimensionOptions.Metadata.value).with_choices([it.value for it in LastDimensionOptions]).with_options(label="Dimension Order") - skew_dir = vfield(DeskewDirection.Y).with_options(label = "Skew Direction") - set_logging = vfield(Log_Levels.INFO).with_options(label="Logging Level") - time_range = vfield(Tuple[int, int]).with_options( - label="Time Export Range", - value=(0, 10), - options = dict( - min=0, - max=10, - ) - ) - channel_range = vfield(Tuple[int, int]).with_options( - label="Channel Range", - value=(0, 10), - options = dict( - min=0, - max=10, - ) - ) - save_type = vfield(SaveFileType).with_options( - label = "Save Format" - ) - save_path = vfield(Path).with_options( - label = "Save Directory", - value = Path(history.get_save_history()[0]) - ) + # Tabbed Widget container to house all the widgets + @magicclass(widget_type="tabbed", name="Functions") + class WidgetContainer(LlszTemplate): - ### - # 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 + @magicclass(name="1. Deskew") + class DeskewWidget: + 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") + + @magicclass(name="2. Deconvolution") + class DeconvolutionWidget: + """ + A counterpart to the DeconvolutionParams Pydantic class + """ + fields_enabled = field(False, label="Enabled") + decon_processing = field(DeconvolutionChoice, label="Processing Algorithm") + psf = field(List[Path], label = "PSFs") + psf_num_iter = field(int, label = "Number of Iterations") + background = field(ComboBox).with_choices( + [it.value for it in BackgroundSource] + ).with_options(label="Background") + background_custom = field(float).with_options( + visible=False, + label="Custom Background" + ) - ### - # Cropping - ### + # @background.connect + # def _show_custom_background(self): + # self.background_custom.visible = self.background == BackgroundSource.Custom + + @background.connect + @enable_if( + [background_custom] + ) + 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 = [ + decon_processing, + psf, + psf_num_iter, + background + ] + ) + def _enable_fields(self) -> bool: + return self.fields_enabled.value + + def _make_model(self) -> Optional[DeconvolutionParams]: + if not self.fields_enabled.value: + return None + return DeconvolutionParams( + decon_processing=self.decon_processing.value, + background=self.background.value + ) + + @magicclass(name = "3. Crop") + class CroppingWidget: + """ + A counterpart to the CropParams Pydantic class + """ + fields_enabled = field(False, label="Enabled") + shapes= field(ShapesData, label = "ROIs")#Optional[Shapes] = None + z_range = field(Tuple[int, int]).with_options( + label = "Z Range", + value = (0, 1), + options = dict( + min = 0, + max = 1 + ), + ) + + @fields_enabled.connect + @enable_if([shapes, z_range]) + 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]: + child = get_child(self, CropFields) + return CropParams( + z_start=child.z_range[0], + z_end=child.z_range[1], + roi_layer_list=child.shapes + ) + + @magicclass(name="4. Workflow") + class WorkflowWidget: + """ + Handles the workflow related parameters + """ + fields_enabled = field(False, label="Enabled") + workflow_source = field(ComboBox).with_options(label = "Workflow Source").with_choices([it.value for it in WorkflowSource]) + workflow_path = field(Path).with_options(label = "Workflow Path", visible=False) + + @fields_enabled.connect + @enable_if([workflow_source]) + def _enable_workflow(self) -> bool: + return self.fields_enabled.value + + @fields_enabled.connect + @enable_if([workflow_path]) + def _workflow_path(self) -> bool: + return self.workflow_source.value == WorkflowSource.CustomPath + + def _make_model(self) -> Optional[Workflow]: + if not self.fields_enabled.value: + return None + child = get_child(self, WorkflowFields) + if child.workflow_source == WorkflowSource.ActiveWorkflow: + return WorkflowManager.install(self.parent_viewer).workflow + else: + import_workflow_modules(child.workflow_path) + return load_workflow(str(child.workflow_path)) + + @magicclass(name="5. Output") + class OutputWidget: + set_logging = field(Log_Levels.INFO).with_options(label="Logging Level") + time_range = field(Tuple[int, int]).with_options( + label="Time Export Range", + value=(0, 1), + options = dict( + min=0, + max=100, + ) + ) + channel_range = field(Tuple[int, int]).with_options( + label="Channel Range", + value=(0, 1), + options = dict( + min=0, + max=100, + ) + ) + save_type = field(SaveFileType).with_options( + label = "Save Format" + ) + save_path = field(Path).with_options( + label = "Save Directory", + value = Path(history.get_save_history()[0]) + ) + save_name = field(str).with_options( + label = "Save Prefix" + ) - 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 + # @DeskewWidget.img_layer.connect + # 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 ### - # Workflow + # Deconvolution ### - 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 + # 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}, @@ -388,11 +619,25 @@ def _set_workflow(self) -> None: ) @set_design(text="Preview") def preview(self, header:str, time: int, channel: int): - pass + # We only need to process one time point for the preview, + # so we made a copy using a subset of the times + lattice = self._make_model_friendly().copy(update=dict( + time_range = range(time, time+1), + channel_range = range(time, time+1), + )) + for slice in lattice.process().slices: + scale = ( + lattice.new_dz, + lattice.dy, + lattice.dx + ) + self.parent_viewer.add_image(slice.data, scale=scale) + max_z = np.argmax(np.sum(slice.data, axis=(1, 2))) + self.parent_viewer.dims.set_current_step(0, max_z) @set_design(text="Save") def save(self): - pass + self._make_model_friendly() # @magicclass(widget_type="collapsible") diff --git a/plugin/napari_lattice/reader.py b/plugin/napari_lattice/reader.py index 9f8775c..9dca5a8 100644 --- a/plugin/napari_lattice/reader.py +++ b/plugin/napari_lattice/reader.py @@ -7,6 +7,7 @@ , however pyramidal support for 3D not available yet """ from __future__ import annotations +from pathlib import Path import dask.array as da import dask.delayed as delayed @@ -14,7 +15,6 @@ import numpy as np from napari.layers import image, Layer from napari.layers._data_protocols import LayerDataProtocol -from aicsimageio.types import ArrayLike from aicsimageio.dimensions import Dimensions from aicsimageio.aics_image import AICSImage @@ -22,6 +22,7 @@ from typing import Any, Optional, cast, Tuple from lls_core.lattice_data import lattice_from_aics, LatticeData, img_from_array +from lls_core.types import ArrayLike def lattice_from_napari( img: Layer, @@ -35,6 +36,9 @@ def lattice_from_napari( kwargs: Extra arguments to pass to the LatticeData constructor """ + if not isinstance(img, Layer): + raise Exception("img must be a napari layer object") + img_data_aics: AICSImage if 'aicsimage' in img.metadata.keys(): From 1e4162748fe7546aad0cd07f6b5ccdb4aa7e3bc1 Mon Sep 17 00:00:00 2001 From: Michael Milton Date: Fri, 1 Sep 2023 19:07:17 +1000 Subject: [PATCH 015/147] Implemented traffic light system for first two field sets --- core/lls_core/lattice_data.py | 80 ++++--- plugin/napari_lattice/disableable.py | 34 +++ plugin/napari_lattice/dock_widget.py | 346 ++++++--------------------- plugin/napari_lattice/fields.py | 288 ++++++++++++++++++++++ plugin/napari_lattice/green.png | Bin 0 -> 12406 bytes plugin/napari_lattice/grey.png | Bin 0 -> 8880 bytes plugin/napari_lattice/icons.py | 5 + plugin/napari_lattice/red.png | Bin 0 -> 10457 bytes 8 files changed, 440 insertions(+), 313 deletions(-) create mode 100644 plugin/napari_lattice/disableable.py create mode 100644 plugin/napari_lattice/fields.py create mode 100644 plugin/napari_lattice/green.png create mode 100644 plugin/napari_lattice/grey.png create mode 100644 plugin/napari_lattice/icons.py create mode 100644 plugin/napari_lattice/red.png diff --git a/core/lls_core/lattice_data.py b/core/lls_core/lattice_data.py index 02c00d7..54768ae 100644 --- a/core/lls_core/lattice_data.py +++ b/core/lls_core/lattice_data.py @@ -154,11 +154,41 @@ class CropParams(BaseModel, arbitrary_types_allowed=True): z_start: NonNegativeInt = 0 z_end: NonNegativeInt = 1 -class LatticeData(DefaultMixin, arbitrary_types_allowed=True): - """ - Holds data and metadata for a given image in a consistent format - """ +class OutputParams(DefaultMixin, arbitrary_types_allowed=True): + #: The directory where this data will be saved + save_dir: Path + #: The file name to save this as, without the directory name or file extension + save_name: str + + #: The range of times to process + time_range: range + + #: The range of channels to process + channel_range: range + + #: The data type to save the result as + save_type: SaveFileType = SaveFileType.h5 + + @validator("time_range") + 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 v + + @validator("channel_range") + 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 v + +class DeskewParams(DefaultMixin, arbitrary_types_allowed=True): #: A 3-5D array containing the image data data: ArrayLike @@ -177,6 +207,15 @@ class LatticeData(DefaultMixin, arbitrary_types_allowed=True): #: Pixel size in microns physical_pixel_sizes: DefinedPixelSizes = Field(default_factory=DefinedPixelSizes) + +class LatticeData(OutputParams, DeskewParams, arbitrary_types_allowed=True): + """ + Holds data and metadata for a given image in a consistent format + """ + + # Note: originally the save-related fields were included via composition and not inheritance + # (similar to how `crop` and `workflow` are handled), but this was impractical for implementing validations + #: If this is None, then deconvolution is disabled deconvolution: Optional[DeconvolutionParams] = None @@ -186,30 +225,6 @@ class LatticeData(DefaultMixin, arbitrary_types_allowed=True): #: If defined, this is a workflow to add lightsheet processing onto workflow: Optional[Workflow] = None - #: The directory where this data will be saved - save_dir: Path - - #: The file name to save this as, without the directory name or file extension - save_name: str - - #: The range of times to process - time_range: range - - #: The range of channels to process - channel_range: range - - #: The data type to save the result as - save_type: SaveFileType = SaveFileType.h5 - - @validator("time_range") - 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 v - @validator("time_range") def disjoint_time_range(cls, v: range, values: dict): """ @@ -222,15 +237,6 @@ def disjoint_time_range(cls, v: range, values: dict): raise ValueError(f"The highest valid time value is the length of the time axis, which is {max_time}") return v - @validator("channel_range") - 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 v - @validator("channel_range") def disjoint_channel_range(cls, v: range, values: dict): """ diff --git a/plugin/napari_lattice/disableable.py b/plugin/napari_lattice/disableable.py new file mode 100644 index 0000000..fbd3e9f --- /dev/null +++ b/plugin/napari_lattice/disableable.py @@ -0,0 +1,34 @@ +from tkinter import BaseWidget +from qtpy import QtWidgets as QtW, QtCore +from qtpy.QtCore import Qt, QEvent, QSize +from magicgui.application import use_app +from magicgui.widgets import Widget +from magicgui.widgets._concrete import _LabeledWidget +from magicgui.backends._qtpy.widgets import ( + QBaseWidget, + Container as ContainerBase, + MainWindow as MainWindowBase, +) + +class _Disableable(ContainerBase): + def __init__(self, layout="vertical", scrollable: bool = False, **kwargs): + BaseWidget.__init__(self, QtW.QWidget) + + if layout == "horizontal": + self._layout: QtW.QLayout = QtW.QHBoxLayout() + else: + self._layout = QtW.QVBoxLayout() + + self._stacked_widget = QtW.QStackedWidget(self._qwidget) + self._stacked_widget.setContentsMargins(0, 0, 0, 0) + self._inner_qwidget = QtW.QWidget(self._qwidget) + self._qwidget.setLayout(self._layout) + self._layout.addWidget(self._stacked_widget) + self._layout.addWidget(self._inner_qwidget) + + def _mgui_insert_widget(self, position: int, widget: Widget): + self._stacked_widget.insertWidget(position, widget.native) + + def _mgui_remove_widget(self, widget: Widget): + self._stacked_widget.removeWidget(widget.native) + widget.native.setParent(None) diff --git a/plugin/napari_lattice/dock_widget.py b/plugin/napari_lattice/dock_widget.py index 41882bf..8beb1a4 100644 --- a/plugin/napari_lattice/dock_widget.py +++ b/plugin/napari_lattice/dock_widget.py @@ -10,21 +10,19 @@ from magicclass.wrappers import set_design from magicgui.widgets import Widget -from magicclass import magicclass as original_magicclass, field, vfield, set_options, MagicTemplate, FieldGroup +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.utils import history from napari import Viewer - -import pyclesperanto_prototype as cle - -from napari.types import ImageData, ShapesData +from napari_lattice.icons import RED, GREEN, GREY from tqdm import tqdm @@ -34,88 +32,26 @@ from lls_core import DeconvolutionChoice, SaveFileType, Log_Levels, DeskewDirection from lls_core.lattice_data import CropParams, DeconvolutionParams, DefinedPixelSizes, LatticeData -from napari_lattice.reader import lattice_from_napari from pydantic import ValidationError +from napari_lattice.reader import lattice_from_napari +from napari_lattice.fields import DeskewFields, CroppingFields, DeconvolutionFields, WorkflowFields, OutputFields +from napari_lattice.icons import GREY from strenum import StrEnum +from lls_core.workflow import import_workflow_modules # Enable Logging import logging -from lls_core.workflow import import_workflow_modules -from itertools import groupby logger = logging.getLogger(__name__) logger.setLevel(logging.INFO) -MagicClassType = TypeVar("MagicClassType") -def magicclass(*args, **kwargs) -> Callable[[MagicClassType], MagicClassType]: - return original_magicclass(*args, **kwargs) - -class WorkflowSource(StrEnum): - ActiveWorkflow = "Active Workflow" - CustomPath = "Custom Path" - -class BackgroundSource(StrEnum): - Auto = "Automatic" - SecondLast = "Second Last" - Custom = "Custom" - -def enable_field(parent: MagicTemplate, field: MagicField, enabled: bool = True) -> None: - """ - Enable the widget associated with a field - - Args: - parent: The parent magicclass that contains the field - field: The field to enable/disable - enabled: If False, disable the field instead of enabling it - """ - real_field = getattr(parent, field.name) - if not isinstance(real_field, Widget): - raise Exception("Define your fields with field() not vfield()!") - real_field.visible = enabled - real_field.enabled = enabled - -EnabledHandlerType = TypeVar("EnabledHandlerType") -def enable_if(fields: List[MagicField]): - """ - Makes an event handler that should be used via `fields_enabled.connect(make_enabled_handler())`. - Args: - condition: A function that takes an instance of the class and returns True if the fields should be enabled - fields: A list of fields to be dynamically enabled or disabled - Example: - :: - @some_field.connect - @enable_if( - [some_field] - ) - def _enable_fields(self) -> bool: - return some_field.value - """ - # Ideally we could use subclassing to add both the vfield and this event handler, but there - # seems to be a bug preventing this: https://github.com/hanjinliu/magic-class/issues/113. - - # Start by disabling all the fields - - def _decorator(fn: Callable[[EnabledHandlerType], bool])-> Callable[[EnabledHandlerType], None]: - for field in fields: - field.visible = False - field.enabled = False - - def make_handler(fn: Callable) -> Callable[[EnabledHandlerType], None]: - def handler(parent: EnabledHandlerType): - enable = fn(parent) - if enable: - logger.info(f"{parent.__class__} Activated") - else: - logger.info(f"{parent.__class__} Deactivated") - for field in fields: - enable_field(parent, field, enabled=enable) - return handler - - return make_handler(fn) - - return _decorator +# MagicClassType = TypeVar("MagicClassType") +# def magicclass(*args, **kwargs) -> Callable[[MagicClassType], MagicClassType]: +# return original_magicclass(*args, **kwargs) +def strikeout(text: str) -> str: + return '\u0336'.join(text) + '\u0336' # class HideableContents(MagicTemplate): # # fields_enabled = vfield(False, label="Enabled") @@ -132,6 +68,14 @@ def handler(parent: EnabledHandlerType): # 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": @@ -142,20 +86,7 @@ def parent_viewer(mc: MagicTemplate) -> Viewer: if viewer is None: raise Exception("This function can only be used when inside of a Napari viewer") return mc.parent_viewer - -T = TypeVar("T") -def get_child(parent: MagicTemplate, child_cls: Type[T]) -> T: - if isinstance(child_cls, MagicTemplate): - return child_cls - for child in parent.__magicclass_children__: - if isinstance(child, child_cls): - return child - raise Exception("Child couldn't be found!") - -class LastDimensionOptions(Enum): - XYZTC = "XYZTC" - XYZCT = "XYZCT" - Metadata = "Get from Metadata" + @magicclass(widget_type="split") class LLSZWidget(LlszTemplate): @@ -186,11 +117,11 @@ def _make_model_friendly(self) -> LatticeData: raise Exception("\n".join(message)) def _make_model(self) -> LatticeData: - deskew = get_child(self, self.LlszMenu.WidgetContainer.DeskewWidget) - output = get_child(self, self.LlszMenu.WidgetContainer.OutputWidget) - deconv = get_child(self, self.LlszMenu.WidgetContainer.DeconvolutionWidget) - crop = get_child(self, self.LlszMenu.WidgetContainer.CroppingWidget) - workflow = get_child(self, self.LlszMenu.WidgetContainer.WorkflowWidget) + 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 return lattice_from_napari( img=deskew.img_layer.value, @@ -216,189 +147,52 @@ class LlszMenu(LlszTemplate): # options={"enabled": True}, # Tabbed Widget container to house all the widgets - @magicclass(widget_type="tabbed", name="Functions") + @magicclass(widget_type="tabbed", name="Functions", labels=False) class WidgetContainer(LlszTemplate): - @magicclass(name="1. Deskew") - class DeskewWidget: - 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") - - @magicclass(name="2. Deconvolution") - class DeconvolutionWidget: - """ - A counterpart to the DeconvolutionParams Pydantic class - """ - fields_enabled = field(False, label="Enabled") - decon_processing = field(DeconvolutionChoice, label="Processing Algorithm") - psf = field(List[Path], label = "PSFs") - psf_num_iter = field(int, label = "Number of Iterations") - background = field(ComboBox).with_choices( - [it.value for it in BackgroundSource] - ).with_options(label="Background") - background_custom = field(float).with_options( - visible=False, - label="Custom Background" - ) - - # @background.connect - # def _show_custom_background(self): - # self.background_custom.visible = self.background == BackgroundSource.Custom - - @background.connect - @enable_if( - [background_custom] - ) - 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 = [ - decon_processing, - psf, - psf_num_iter, - background - ] - ) - def _enable_fields(self) -> bool: - return self.fields_enabled.value - - def _make_model(self) -> Optional[DeconvolutionParams]: - if not self.fields_enabled.value: - return None - return DeconvolutionParams( - decon_processing=self.decon_processing.value, - background=self.background.value - ) - - @magicclass(name = "3. Crop") - class CroppingWidget: - """ - A counterpart to the CropParams Pydantic class - """ - fields_enabled = field(False, label="Enabled") - shapes= field(ShapesData, label = "ROIs")#Optional[Shapes] = None - z_range = field(Tuple[int, int]).with_options( - label = "Z Range", - value = (0, 1), - options = dict( - min = 0, - max = 1 - ), - ) - - @fields_enabled.connect - @enable_if([shapes, z_range]) - 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]: - child = get_child(self, CropFields) - return CropParams( - z_start=child.z_range[0], - z_end=child.z_range[1], - roi_layer_list=child.shapes - ) - - @magicclass(name="4. Workflow") - class WorkflowWidget: - """ - Handles the workflow related parameters - """ - fields_enabled = field(False, label="Enabled") - workflow_source = field(ComboBox).with_options(label = "Workflow Source").with_choices([it.value for it in WorkflowSource]) - workflow_path = field(Path).with_options(label = "Workflow Path", visible=False) - - @fields_enabled.connect - @enable_if([workflow_source]) - def _enable_workflow(self) -> bool: - return self.fields_enabled.value - - @fields_enabled.connect - @enable_if([workflow_path]) - def _workflow_path(self) -> bool: - return self.workflow_source.value == WorkflowSource.CustomPath - - def _make_model(self) -> Optional[Workflow]: - if not self.fields_enabled.value: - return None - child = get_child(self, WorkflowFields) - if child.workflow_source == WorkflowSource.ActiveWorkflow: - return WorkflowManager.install(self.parent_viewer).workflow - else: - import_workflow_modules(child.workflow_path) - return load_workflow(str(child.workflow_path)) - - @magicclass(name="5. Output") - class OutputWidget: - set_logging = field(Log_Levels.INFO).with_options(label="Logging Level") - time_range = field(Tuple[int, int]).with_options( - label="Time Export Range", - value=(0, 1), - options = dict( - min=0, - max=100, - ) - ) - channel_range = field(Tuple[int, int]).with_options( - label="Channel Range", - value=(0, 1), - options = dict( - min=0, - max=100, - ) - ) - save_type = field(SaveFileType).with_options( - label = "Save Format" - ) - save_path = field(Path).with_options( - label = "Save Directory", - value = Path(history.get_save_history()[0]) - ) - save_name = field(str).with_options( - label = "Save Prefix" - ) - + def __post_init__(self): + tab_widget: QTabWidget= self._widget._tab_widget + for i in range(5): + tab_widget.setTabIcon(i, QIcon(GREY)) + + 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") - # @DeskewWidget.img_layer.connect # def _img_changed(self): # deskew = get_child(self, self.DeskewWidget) # output = get_child(self, self.OutputWidget) diff --git a/plugin/napari_lattice/fields.py b/plugin/napari_lattice/fields.py new file mode 100644 index 0000000..e6d6489 --- /dev/null +++ b/plugin/napari_lattice/fields.py @@ -0,0 +1,288 @@ +from pathlib import Path +from magicclass import FieldGroup, field, MagicTemplate +from magicclass.widgets import Widget, ComboBox +from magicclass.fields import MagicField +from typing import Callable, List, Optional, Tuple, TypeVar + +from strenum import StrEnum +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 Layer +from enum import Enum +import pyclesperanto_prototype as cle +from napari_workflows import Workflow, WorkflowManager +from napari.types import ImageData, ShapesData +from napari.utils import history +from abc import ABC + +from napari_lattice.icons import RED, GREEN, GREY + +# FieldGroups that the users interact with to input data + +import logging + +logger = logging.getLogger(__name__) +logger.setLevel(logging.INFO) + + +class WorkflowSource(StrEnum): + ActiveWorkflow = "Active Workflow" + CustomPath = "Custom Path" + +class BackgroundSource(StrEnum): + Auto = "Automatic" + SecondLast = "Second Last" + Custom = "Custom" + + +def enable_field(parent: MagicTemplate, field: MagicField, enabled: bool = True) -> None: + """ + Enable the widget associated with a field + + Args: + parent: The parent magicclass that contains the field + field: The field to enable/disable + enabled: If False, disable the field instead of enabling it + """ + real_field = getattr(parent, field.name) + if not isinstance(real_field, Widget): + raise Exception("Define your fields with field() not vfield()!") + real_field.visible = enabled + real_field.enabled = enabled + + +EnabledHandlerType = TypeVar("EnabledHandlerType") +def enable_if(fields: List[MagicField]): + """ + Makes an event handler that should be used via `fields_enabled.connect(make_enabled_handler())`. + Args: + condition: A function that takes an instance of the class and returns True if the fields should be enabled + fields: A list of fields to be dynamically enabled or disabled + Example: + :: + @some_field.connect + @enable_if( + [some_field] + ) + def _enable_fields(self) -> bool: + return some_field.value + """ + # Ideally we could use subclassing to add both the vfield and this event handler, but there + # seems to be a bug preventing this: https://github.com/hanjinliu/magic-class/issues/113. + + # Start by disabling all the fields + + def _decorator(fn: Callable[[EnabledHandlerType], bool])-> Callable[[EnabledHandlerType], None]: + for field in fields: + field.visible = False + field.enabled = False + + def make_handler(fn: Callable) -> Callable[[EnabledHandlerType], None]: + def handler(parent: EnabledHandlerType): + enable = fn(parent) + if enable: + logger.info(f"{parent.__class__} Activated") + else: + logger.info(f"{parent.__class__} Deactivated") + for field in fields: + enable_field(parent, field, enabled=enable) + return handler + + return make_handler(fn) + + return _decorator + + +class LastDimensionOptions(Enum): + XYZTC = "XYZTC" + 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 DeskewFields(FieldGroup): + 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 _make_model(self) -> DeskewParams: + return DeskewParams( + img=self.img_layer.value, + last_dimension=self.dimension_order.value, + angle=self.angle.value, + skew = self.skew_dir.value, + physical_pixel_sizes=self.pixel_sizes.value, + ) + +class DeconvolutionFields(FieldGroup): + """ + A counterpart to the DeconvolutionParams Pydantic class + """ + fields_enabled = field(False, label="Enabled") + decon_processing = field(DeconvolutionChoice, label="Processing Algorithm") + psf = field(List[Path], label = "PSFs") + psf_num_iter = field(int, label = "Number of Iterations") + background = field(ComboBox).with_choices( + [it.value for it in BackgroundSource] + ).with_options(label="Background") + background_custom = field(float).with_options( + visible=False, + label="Custom Background" + ) + + # @background.connect + # def _show_custom_background(self): + # self.background_custom.visible = self.background == BackgroundSource.Custom + + @background.connect + @enable_if( + [background_custom] + ) + 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 = [ + decon_processing, + psf, + psf_num_iter, + background + ] + ) + def _enable_fields(self) -> bool: + return self.fields_enabled.value + + def _make_model(self) -> Optional[DeconvolutionParams]: + if not self.fields_enabled.value: + return None + return DeconvolutionParams( + decon_processing=self.decon_processing.value, + background=self.background.value + ) + +class CroppingFields(FieldGroup): + """ + A counterpart to the CropParams Pydantic class + """ + fields_enabled = field(False, label="Enabled") + shapes= field(ShapesData, label = "ROIs")#Optional[Shapes] = None + z_range = field(Tuple[int, int]).with_options( + label = "Z Range", + value = (0, 1), + options = dict( + min = 0, + max = 1 + ), + ) + + @fields_enabled.connect + @enable_if([shapes, z_range]) + 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]: + return CropParams( + z_start=self.z_range.value[0], + z_end=self.z_range.value[1], + roi_layer_list=self.shapes.value + ) + +class WorkflowFields(FieldGroup): + """ + Handles the workflow related parameters + """ + fields_enabled = field(False, label="Enabled") + workflow_source = field(ComboBox).with_options(label = "Workflow Source").with_choices([it.value for it in WorkflowSource]) + workflow_path = field(Path).with_options(label = "Workflow Path", visible=False) + + @fields_enabled.connect + @enable_if([workflow_source]) + def _enable_workflow(self) -> bool: + return self.fields_enabled.value + + @fields_enabled.connect + @enable_if([workflow_path]) + def _workflow_path(self) -> bool: + return self.workflow_source.value == WorkflowSource.CustomPath + + def _make_model(self) -> Optional[Workflow]: + if not self.fields_enabled.value: + return None + child = get_child(self, WorkflowFields) + if child.workflow_source == WorkflowSource.ActiveWorkflow: + return WorkflowManager.install(self.parent_viewer).workflow + else: + import_workflow_modules(child.workflow_path) + return load_workflow(str(child.workflow_path)) + +# @magicclass(name="5. Output") +class OutputFields(FieldGroup): + set_logging = field(Log_Levels.INFO).with_options(label="Logging Level") + time_range = field(Tuple[int, int]).with_options( + label="Time Export Range", + value=(0, 1), + options = dict( + min=0, + max=100, + ) + ) + channel_range = field(Tuple[int, int]).with_options( + label="Channel Range", + value=(0, 1), + options = dict( + min=0, + max=100, + ) + ) + save_type = field(SaveFileType).with_options( + label = "Save Format" + ) + save_path = field(Path).with_options( + label = "Save Directory", + value = Path(history.get_save_history()[0]) + ) + save_name = field(str).with_options( + label = "Save Prefix" + ) + + +# @DeskewWidget.img_layer.connect diff --git a/plugin/napari_lattice/green.png b/plugin/napari_lattice/green.png new file mode 100644 index 0000000000000000000000000000000000000000..8addaf353491501b5e8e13b2714f2be7cd8bdbdc GIT binary patch literal 12406 zcma)jWmsF?vT%Z1i(8>3xVyW%OR-`B0)*o3q!fx%+}$bGBBi(#C=M-F+^rOM34H1M 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zFIhWZ|GxG!xVXu`r1*5+NmdhYrgnp(Y=dYHB=3AeFUMt)zc-gK{MvS$3iM>Bv~>+i zREkH}1ZQW7Pi^bUy6veTtYxvLo`qW(iw6(nXv#tom!g@^puZVYL3}QJLTj zbL)i=fy;2YdYvQLr82IiL0}2*d|Xp6cxd*xul^GMc=|E^iBERG=9q;v2@%kh zuLtVT`>8rKla*Xr4PiGpOtTR$E!THCuy3@8`?bBU`Fp& z$`?t@L>rTSpwEQ82lRc_zu?c0eCq?z_+6UQV%_4_FwM2r_z^$nNIQ4DCz2bVY`&fN zWB|7$7QN3#f$3)JaZymKW`SBIU$Bq0qiRlHFax=gm85X4YU9<_`L+vLmL1vapCUhc R{=Si@rlh4{{b>0cWnRw literal 0 HcmV?d00001 From d43bce7ce64dbce1ff2f32463839ae87f9813200 Mon Sep 17 00:00:00 2001 From: Michael Milton Date: Mon, 4 Sep 2023 18:36:31 +1000 Subject: [PATCH 016/147] Refactor field validation into mixin --- core/lls_core/cmds/__main__.py | 9 ++++-- core/lls_core/lattice_data.py | 44 ++++++++++++++------------- plugin/napari_lattice/dock_widget.py | 17 ++--------- plugin/napari_lattice/fields.py | 45 ++++++++++++++++++++++++---- plugin/napari_lattice/reader.py | 13 ++++---- plugin/pyproject.toml | 2 +- 6 files changed, 83 insertions(+), 47 deletions(-) diff --git a/core/lls_core/cmds/__main__.py b/core/lls_core/cmds/__main__.py index 66ae8d7..76e41a3 100644 --- a/core/lls_core/cmds/__main__.py +++ b/core/lls_core/cmds/__main__.py @@ -9,7 +9,7 @@ import sys import re from lls_core.io import save_img, save_img_workflow -from lls_core.lattice_data import lattice_from_aics +from lls_core.lattice_data import lattice_params_from_aics, LatticeData from lls_core.utils import read_imagej_roi, get_all_py_files, get_first_last_image_and_task, modify_workflow_task, check_dimensions from lls_core.llsz_core import crop_volume_deskew from aicsimageio import AICSImage @@ -363,7 +363,12 @@ def main(argv: Sequence[str] = sys.argv[1:]): print(f"Scene {scene} not valid") # Initialize Lattice class - lattice = lattice_from_aics(aics_img, angle=deskew_angle, skew=skew_dir, save_name=save_name, physical_pixel_sizes=PhysicalPixelSizes(dx, dy, dz)) + lattice = LatticeData( + **lattice_params_from_aics(aics_img, physical_pixel_sizes=PhysicalPixelSizes(dx, dy, dz)), + angle=deskew_angle, + skew=skew_dir, + save_name=save_name + ) if time_start is None or time_end is None: time_start, time_end = 0, lattice.time - 1 diff --git a/core/lls_core/lattice_data.py b/core/lls_core/lattice_data.py index 54768ae..b4b64d5 100644 --- a/core/lls_core/lattice_data.py +++ b/core/lls_core/lattice_data.py @@ -13,7 +13,7 @@ from pydantic_numpy import NDArray from typing import Any, Iterable, List, Literal, Optional, TYPE_CHECKING, Tuple, TypeVar, Union -from typing_extensions import Self +from typing_extensions import TypedDict from pathlib import Path from aicsimageio.types import PhysicalPixelSizes @@ -207,6 +207,20 @@ class DeskewParams(DefaultMixin, arbitrary_types_allowed=True): #: Pixel size in microns physical_pixel_sizes: DefinedPixelSizes = Field(default_factory=DefinedPixelSizes) + @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 + 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"]) + else: + raise ValueError("deskew_vol_shape and deskew_affine_transform must be either both specified or neither specified") + return values + class LatticeData(OutputParams, DeskewParams, arbitrary_types_allowed=True): """ @@ -330,20 +344,6 @@ def time(self) -> int: def channels(self) -> int: """Number of channels""" return self.dims.C - - @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 - 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"]) - else: - raise ValueError("deskew_vol_shape and deskew_affine_transform must be either both specified or neither specified") - return values @property def new_dz(self): @@ -505,7 +505,12 @@ def process(self) -> ProcessedSlices: slices=self._process_non_crop() ) -def lattice_from_aics(img: AICSImage, physical_pixel_sizes: PhysicalPixelSizes = PhysicalPixelSizes(None, None, None), **kwargs: Any) -> LatticeData: +class AicsLatticeParams(TypedDict): + data: DaskArray + dims: Dimensions + physical_pixel_sizes: DefinedPixelSizes + +def lattice_params_from_aics(img: AICSImage, physical_pixel_sizes: PhysicalPixelSizes = PhysicalPixelSizes(None, None, None)) -> AicsLatticeParams: # Note: The reason we copy all of these fields rather than just storing the AICSImage is because that class is mostly immutable and so not suitable pixel_sizes = DefinedPixelSizes( @@ -514,11 +519,10 @@ def lattice_from_aics(img: AICSImage, physical_pixel_sizes: PhysicalPixelSizes = Z = physical_pixel_sizes[2] or img.physical_pixel_sizes.Z or LatticeData.physical_pixel_sizes.Z ) - return LatticeData( + return AicsLatticeParams( data = img.dask_data, dims = img.dims, physical_pixel_sizes = pixel_sizes, - **kwargs ) def img_from_array(arr: ArrayLike, last_dimension: Optional[Literal["channel", "time"]] = None, **kwargs: Any) -> AICSImage: @@ -561,7 +565,7 @@ def img_from_array(arr: ArrayLike, last_dimension: Optional[Literal["channel", " arr = np.swapaxes(arr, 0, 1) return AICSImage(image=arr, dim_order=dim_order, **kwargs) -def lattice_fom_array(arr: ArrayLike, last_dimension: Optional[Literal["channel", "time"]] = None, **kwargs: Any) -> LatticeData: +def lattice_fom_array(arr: ArrayLike, last_dimension: Optional[Literal["channel", "time"]] = None, **kwargs: Any) -> AicsLatticeParams: """ Creates a `LatticeData` from an array @@ -570,4 +574,4 @@ def lattice_fom_array(arr: ArrayLike, last_dimension: Optional[Literal["channel" last_dimension: See img_from_array """ aics = img_from_array(arr, last_dimension) - return lattice_from_aics(aics, **kwargs) + return lattice_params_from_aics(aics, **kwargs) diff --git a/plugin/napari_lattice/dock_widget.py b/plugin/napari_lattice/dock_widget.py index 8beb1a4..51f64c3 100644 --- a/plugin/napari_lattice/dock_widget.py +++ b/plugin/napari_lattice/dock_widget.py @@ -33,7 +33,7 @@ from lls_core.lattice_data import CropParams, DeconvolutionParams, DefinedPixelSizes, LatticeData from pydantic import ValidationError -from napari_lattice.reader import lattice_from_napari +from napari_lattice.reader import lattice_params_from_napari from napari_lattice.fields import DeskewFields, CroppingFields, DeconvolutionFields, WorkflowFields, OutputFields from napari_lattice.icons import GREY @@ -68,6 +68,7 @@ def strikeout(text: str) -> str: # child.visible = False # child.enabled = False + def validate_tab(parent: MagicTemplate, fields: FieldGroup, index: int): tab_widget: QTabWidget = parent._widget._tab_widget try: @@ -103,19 +104,6 @@ def _check_validity(self) -> bool: except: return False - def _make_model_friendly(self) -> LatticeData: - """ - Generates a LatticeData, but returns validation errors in a user friendly way - """ - try: - return self._make_model() - except ValidationError as e: - message = [f"

{e.model}

"] - for error in e.errors(): - header = ", ".join([str(it).capitalize() for it in error['loc']]) - message.append(f"

{header}

{error['msg']}") - raise Exception("\n".join(message)) - def _make_model(self) -> LatticeData: deskew = self.LlszMenu.WidgetContainer.DeskewWidget output = self.LlszMenu.WidgetContainer.OutputWidget @@ -123,6 +111,7 @@ def _make_model(self) -> LatticeData: crop = self.LlszMenu.WidgetContainer.CroppingWidget workflow = self.LlszMenu.WidgetContainer.WorkflowWidget + # TODO: fix return lattice_from_napari( img=deskew.img_layer.value, last_dimension=deskew.dimension_order.value, diff --git a/plugin/napari_lattice/fields.py b/plugin/napari_lattice/fields.py index e6d6489..8b53ee7 100644 --- a/plugin/napari_lattice/fields.py +++ b/plugin/napari_lattice/fields.py @@ -1,8 +1,9 @@ from pathlib import Path from magicclass import FieldGroup, field, MagicTemplate -from magicclass.widgets import Widget, ComboBox +from magicclass.widgets import Widget, ComboBox, Label from magicclass.fields import MagicField from typing import Callable, List, Optional, Tuple, TypeVar +from pydantic import BaseModel, ValidationError from strenum import StrEnum from lls_core import DeconvolutionChoice, SaveFileType, Log_Levels, DeskewDirection @@ -16,6 +17,7 @@ from abc import ABC from napari_lattice.icons import RED, GREEN, GREY +from napari_lattice.reader import lattice_params_from_napari # FieldGroups that the users interact with to input data @@ -24,6 +26,22 @@ logger = logging.getLogger(__name__) logger.setLevel(logging.INFO) +def _get_friendly_validations(model: FieldGroup) -> str: + """ + Generates a LatticeData, but returns validation errors in a user friendly way + """ + try: + model._make_model() + return "" + except ValidationError as e: + message = [] + # message = [f"

{e.model}

"] + for error in e.errors(): + header = ", ".join([str(it).capitalize() for it in error['loc']]) + message.append(f"
  • {header} {error['msg']}
    • ") + joined = '\n'.join(message) + return f"
      {joined}
    " + class WorkflowSource(StrEnum): ActiveWorkflow = "Active Workflow" @@ -102,7 +120,19 @@ class LastDimensionOptions(Enum): # def __init__(self, layout: str = "vertical", labels: bool = False, name: str | None = None, **kwargs): # super().__init__(layout, labels, name, **kwargs) -class DeskewFields(FieldGroup): +class NapariFieldGroup: + def __init__(self, *args, **kwargs): + super().__init__(*args, **kwargs) + self.changed.connect(self._validate) + + def _validate(self): + self.errors.value = _get_friendly_validations(self) + + def _make_model(self) -> BaseModel: + raise NotImplementedError() + + +class DeskewFields(NapariFieldGroup, FieldGroup): 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")), @@ -113,14 +143,17 @@ class DeskewFields(FieldGroup): 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") + errors = field(Label).with_options(label="Errors") def _make_model(self) -> DeskewParams: return DeskewParams( - img=self.img_layer.value, - last_dimension=self.dimension_order.value, + **lattice_params_from_napari( + img=self.img_layer.value, + last_dimension=self.dimension_order.value, + physical_pixel_sizes=self.pixel_sizes.value, + ), angle=self.angle.value, skew = self.skew_dir.value, - physical_pixel_sizes=self.pixel_sizes.value, ) class DeconvolutionFields(FieldGroup): @@ -188,12 +221,14 @@ class CroppingFields(FieldGroup): max = 1 ), ) + errors = field(Label) @fields_enabled.connect @enable_if([shapes, z_range]) def _enable_workflow(self) -> bool: return self.fields_enabled.value + # roi_layer_list: ShapesData # @magicclass(visible=False) # class Fields(MagicTemplate): diff --git a/plugin/napari_lattice/reader.py b/plugin/napari_lattice/reader.py index 9dca5a8..1d37956 100644 --- a/plugin/napari_lattice/reader.py +++ b/plugin/napari_lattice/reader.py @@ -21,14 +21,17 @@ from typing_extensions import Literal from typing import Any, Optional, cast, Tuple -from lls_core.lattice_data import lattice_from_aics, LatticeData, img_from_array +from lls_core.lattice_data import lattice_params_from_aics, img_from_array, AicsLatticeParams, PhysicalPixelSizes from lls_core.types import ArrayLike -def lattice_from_napari( +class NapariImageParams(AicsLatticeParams): + save_name: str + +def lattice_params_from_napari( img: Layer, last_dimension: Optional[Literal["channel", "time"]], - **kwargs: Any -) -> LatticeData: + physical_pixel_sizes: PhysicalPixelSizes = PhysicalPixelSizes(None, None, None) +) -> NapariImageParams: """ Factory function for generating a LatticeData from a Napari Image @@ -62,7 +65,7 @@ def lattice_from_napari( # replace any group of spaces with "_" save_name = '_'.join(save_name.split()) - return lattice_from_aics(img_data_aics, save_name=save_name, **kwargs) + return NapariImageParams(save_name=save_name, **lattice_params_from_aics(img_data_aics, physical_pixel_sizes=physical_pixel_sizes)) def napari_get_reader(path: list[str] | str): """Check if file ends with h5 and returns reader function if true diff --git a/plugin/pyproject.toml b/plugin/pyproject.toml index 2b80c70..ba29fff 100644 --- a/plugin/pyproject.toml +++ b/plugin/pyproject.toml @@ -41,7 +41,7 @@ dependencies = [ "dask-image", "dask[distributed]", # We need this Python 3.8 fix: https://github.com/hanjinliu/magic-class/pull/108 - "magic-class>=0.7.4", + "magic-class>=0.7.5", "magicgui", "pyopencl", "read-roi", From 73f248edcbced209218c8252ee76d2b252cb303b Mon Sep 17 00:00:00 2001 From: Michael Milton Date: Tue, 5 Sep 2023 13:40:05 +1000 Subject: [PATCH 017/147] Metaclass attempt --- core/lls_core/lattice_data.py | 2 +- plugin/napari_lattice/dock_widget.py | 40 ++++++------ plugin/napari_lattice/fields.py | 92 ++++++++++++++++++++++++---- plugin/napari_lattice/reader.py | 2 +- 4 files changed, 101 insertions(+), 35 deletions(-) diff --git a/core/lls_core/lattice_data.py b/core/lls_core/lattice_data.py index b4b64d5..600b6e5 100644 --- a/core/lls_core/lattice_data.py +++ b/core/lls_core/lattice_data.py @@ -544,7 +544,7 @@ def img_from_array(arr: ArrayLike, last_dimension: Optional[Literal["channel", " dim_order="ZYX" else: if last_dimension not in ["channel", "time"]: - raise ValueError("last_dimension must be either channel or time") + raise ValueError("last_dimension must be either channel or time when convertin") if len(arr.shape) == 4: if last_dimension == "channel": dim_order = "CZYX" diff --git a/plugin/napari_lattice/dock_widget.py b/plugin/napari_lattice/dock_widget.py index 51f64c3..8a1836b 100644 --- a/plugin/napari_lattice/dock_widget.py +++ b/plugin/napari_lattice/dock_widget.py @@ -105,11 +105,11 @@ 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 + # 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 return lattice_from_napari( @@ -145,29 +145,29 @@ def __post_init__(self): tab_widget.setTabIcon(i, QIcon(GREY)) deskew_fields = DeskewFields(name = "1. Deskew") - @deskew_fields.connect - def _deskew_changed(self): - validate_tab(self, self.deskew_fields, 0) + # @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) + # @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) + # @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) + # @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) + # @output_fields.connect + # def _output_changed(self): + # validate_tab(self, 4) # @magicclass(name="1. Deskew") # class DeskewWidget(MagicTemplate): diff --git a/plugin/napari_lattice/fields.py b/plugin/napari_lattice/fields.py index 8b53ee7..e4d1e48 100644 --- a/plugin/napari_lattice/fields.py +++ b/plugin/napari_lattice/fields.py @@ -2,7 +2,8 @@ from magicclass import FieldGroup, field, MagicTemplate from magicclass.widgets import Widget, ComboBox, Label from magicclass.fields import MagicField -from typing import Callable, List, Optional, Tuple, TypeVar +from typing import Callable, List, Optional, Protocol, Tuple, TypeVar, Union, cast +from typing_extensions import Protocol, Self from pydantic import BaseModel, ValidationError from strenum import StrEnum @@ -15,9 +16,13 @@ from napari.types import ImageData, ShapesData from napari.utils import history from abc import ABC +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 + +from napari.layers import Image # FieldGroups that the users interact with to input data @@ -41,6 +46,8 @@ def _get_friendly_validations(model: FieldGroup) -> str: message.append(f"
  • {header} {error['msg']}
    • ") joined = '\n'.join(message) return f"
      {joined}
    " + except Exception as e: + return str(e) class WorkflowSource(StrEnum): @@ -120,35 +127,90 @@ class LastDimensionOptions(Enum): # def __init__(self, layout: str = "vertical", labels: bool = False, name: str | None = None, **kwargs): # super().__init__(layout, labels, name, **kwargs) -class NapariFieldGroup: +class NapariFieldGroupCompatible(Protocol): + errors: MagicField[Label] + +class FieldGroupMeta(type(Protocol), type(FieldGroup)): + pass + +class NapariFieldGroup(NapariFieldGroupCompatible, Protocol): def __init__(self, *args, **kwargs): super().__init__(*args, **kwargs) + self = cast(FieldGroup, self) self.changed.connect(self._validate) - def _validate(self): + def _get_parent_tab_widget(self: Union[FieldGroup, Self]) -> QTabWidget: + from qtpy.QtWidgets import QWidget + parent = cast(QWidget, self.parent) + return parent.parentWidget() + + def _get_tab_index(self: Union[FieldGroup, Self]) -> int: + from magicclass.widgets import Container + widget = cast(Container, self._widget) + return self._get_parent_tab_widget().indexOf(widget._qwidget) + + def _set_valid(self, valid: bool): + from qtpy.QtGui import QIcon + tab_parent = self._get_parent_tab_widget() + index = self._get_tab_index() + + if valid: + tab_parent.setTabIcon(index, QIcon(GREEN)) + else: + tab_parent.setTabIcon(index, QIcon(RED)) + + def _validate(self: Union[FieldGroup, Self]): self.errors.value = _get_friendly_validations(self) + self._set_valid(False if self.errors.value else True) def _make_model(self) -> BaseModel: raise NotImplementedError() -class DeskewFields(NapariFieldGroup, FieldGroup): - img_layer = field(List[Layer]).with_options(label = "Image Layer", layout="vertical", value=[]) +class DeskewFields(FieldGroup, NapariFieldGroup, metaclass=FieldGroupMeta): + + def _get_dimension_options(self, _) -> List[str]: + """ + Returns the list of dimension order options that might be possible for the current image stack + """ + default = ["Get from Metadata"] + try: + merged = self._merge_layers() + except Exception: + return default + ndims = len(merged._dims_order) + if ndims == 3: + return ["ZYX"] + default + elif ndims == 4: + return ["TZYX", "CZYX"] + default + elif ndims == 5: + return ["TCZYX", "CTZYX"] + default + else: + raise Exception("Only 3-5 dimensional arrays are supported") + + img_layer = field(List[Image], widget_type='Select').with_options(label = "Image Layer").with_choices(get_viewer().layers) 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") + # merge_all_channels = field(False).with_options(label="Merge all Channels") + dimension_order = field(str).with_options(value=LastDimensionOptions.Metadata.value).with_choices(_get_dimension_options).with_options(label="Dimension Order") skew_dir = field(DeskewDirection.Y).with_options(label = "Skew Direction") errors = field(Label).with_options(label="Errors") + def _merge_layers(self) -> Image: + """ + Returns a single image merged from all the selected layers + """ + from napari.layers.utils.stack_utils import images_to_stack + return images_to_stack(self.img_layer.value) + def _make_model(self) -> DeskewParams: return DeskewParams( **lattice_params_from_napari( - img=self.img_layer.value, + img=self._merge_layers(), last_dimension=self.dimension_order.value, physical_pixel_sizes=self.pixel_sizes.value, ), @@ -156,7 +218,7 @@ def _make_model(self) -> DeskewParams: skew = self.skew_dir.value, ) -class DeconvolutionFields(FieldGroup): +class DeconvolutionFields(NapariFieldGroup, FieldGroup, metaclass=FieldGroupMeta): """ A counterpart to the DeconvolutionParams Pydantic class """ @@ -171,6 +233,8 @@ class DeconvolutionFields(FieldGroup): visible=False, label="Custom Background" ) + errors = field(Label).with_options(label="Errors") + # @background.connect # def _show_custom_background(self): @@ -207,7 +271,7 @@ def _make_model(self) -> Optional[DeconvolutionParams]: background=self.background.value ) -class CroppingFields(FieldGroup): +class CroppingFields(FieldGroup, NapariFieldGroup, metaclass=FieldGroupMeta): """ A counterpart to the CropParams Pydantic class """ @@ -221,7 +285,7 @@ class CroppingFields(FieldGroup): max = 1 ), ) - errors = field(Label) + errors = field(Label).with_options(label="Errors") @fields_enabled.connect @enable_if([shapes, z_range]) @@ -261,13 +325,14 @@ def _make_model(self) -> Optional[CropParams]: roi_layer_list=self.shapes.value ) -class WorkflowFields(FieldGroup): +class WorkflowFields(FieldGroup, NapariFieldGroup, metaclass=FieldGroupMeta): """ Handles the workflow related parameters """ fields_enabled = field(False, label="Enabled") workflow_source = field(ComboBox).with_options(label = "Workflow Source").with_choices([it.value for it in WorkflowSource]) workflow_path = field(Path).with_options(label = "Workflow Path", visible=False) + errors = field(Label).with_options(label="Errors") @fields_enabled.connect @enable_if([workflow_source]) @@ -290,7 +355,7 @@ def _make_model(self) -> Optional[Workflow]: return load_workflow(str(child.workflow_path)) # @magicclass(name="5. Output") -class OutputFields(FieldGroup): +class OutputFields(FieldGroup, NapariFieldGroup, metaclass=FieldGroupMeta): set_logging = field(Log_Levels.INFO).with_options(label="Logging Level") time_range = field(Tuple[int, int]).with_options( label="Time Export Range", @@ -318,6 +383,7 @@ class OutputFields(FieldGroup): save_name = field(str).with_options( label = "Save Prefix" ) + errors = field(Label).with_options(label="Errors") # @DeskewWidget.img_layer.connect diff --git a/plugin/napari_lattice/reader.py b/plugin/napari_lattice/reader.py index 1d37956..eae7371 100644 --- a/plugin/napari_lattice/reader.py +++ b/plugin/napari_lattice/reader.py @@ -49,7 +49,7 @@ def lattice_params_from_napari( else: if not last_dimension: raise ValueError("Either the Napari image must have dimensional metadata, or last_dimension must be provided") - img_data_aics = img_from_array(cast(ArrayLike, img.data), last_dimension=last_dimension, physical_pixel_sizes=kwargs.get("physical_pixel_sizes")) + img_data_aics = img_from_array(cast(ArrayLike, img.data), last_dimension=last_dimension, physical_pixel_sizes=physical_pixel_sizes) save_name: str if img.source.path is None: From af02a256b951df7afdcba9b65ccb03667f0c9b21 Mon Sep 17 00:00:00 2001 From: Michael Milton Date: Tue, 5 Sep 2023 21:28:09 +1000 Subject: [PATCH 018/147] Code cleanup, dimension stacking, use mixin for validation logic --- core/lls_core/lattice_data.py | 46 +++++++++++++-------------- plugin/napari_lattice/dock_widget.py | 19 +++++------ plugin/napari_lattice/fields.py | 47 ++++++++++++++++++---------- plugin/napari_lattice/reader.py | 9 +++--- plugin/napari_lattice/utils.py | 7 +++++ 5 files changed, 75 insertions(+), 53 deletions(-) create mode 100644 plugin/napari_lattice/utils.py diff --git a/core/lls_core/lattice_data.py b/core/lls_core/lattice_data.py index 600b6e5..ec6c86c 100644 --- a/core/lls_core/lattice_data.py +++ b/core/lls_core/lattice_data.py @@ -26,10 +26,10 @@ from lls_core.utils import get_deskewed_shape from lls_core.types import ArrayLike from napari_workflows import Workflow +from napari.types import ShapesData if TYPE_CHECKING: import pyclesperanto_prototype as cle - from napari.types import ShapesData import logging @@ -525,7 +525,7 @@ def lattice_params_from_aics(img: AICSImage, physical_pixel_sizes: PhysicalPixel physical_pixel_sizes = pixel_sizes, ) -def img_from_array(arr: ArrayLike, last_dimension: Optional[Literal["channel", "time"]] = None, **kwargs: Any) -> AICSImage: +def img_from_array(arr: ArrayLike, dimension_order: Optional[str] = None, **kwargs: Any) -> AICSImage: """ Creates an AICSImage from an array without metadata @@ -534,36 +534,36 @@ def img_from_array(arr: ArrayLike, last_dimension: Optional[Literal["channel", " last_dimension: How to handle the dimension order kwargs: Additional arguments to pass to the AICSImage constructor """ - dim_order: str + # dim_order: str if len(arr.shape) < 3 or len(arr.shape) > 5: raise ValueError("Array dimensions must be in the range [3, 5]") # if aicsimageio tiffreader assigns last dim as time when it should be channel, user can override this - if len(arr.shape) == 3: - dim_order="ZYX" - else: - if last_dimension not in ["channel", "time"]: - raise ValueError("last_dimension must be either channel or time when convertin") - if len(arr.shape) == 4: - if last_dimension == "channel": - dim_order = "CZYX" - elif last_dimension == "time": - dim_order = "TZYX" - elif len(arr.shape) == 5: - if last_dimension == "channel": - dim_order = "CTZYX" - elif last_dimension == "time": - dim_order = "TCZYX" - else: - raise ValueError() - - img = AICSImage(image=arr, dim_order=dim_order, **kwargs) + # if len(arr.shape) == 3: + # dim_order="ZYX" + # else: + # if last_dimension not in ["channel", "time"]: + # raise ValueError("last_dimension must be either channel or time when convertin") + # if len(arr.shape) == 4: + # if last_dimension == "channel": + # dim_order = "CZYX" + # elif last_dimension == "time": + # dim_order = "TZYX" + # elif len(arr.shape) == 5: + # if last_dimension == "channel": + # dim_order = "CTZYX" + # elif last_dimension == "time": + # dim_order = "TCZYX" + # else: + # raise ValueError() + + img = AICSImage(image=arr, dim_order=dimension_order, **kwargs) # if last axes of "aicsimage data" shape is not equal to time, then swap channel and time if img.data.shape[0] != img.dims.T or img.data.shape[1] != img.dims.C: arr = np.swapaxes(arr, 0, 1) - return AICSImage(image=arr, dim_order=dim_order, **kwargs) + return AICSImage(image=arr, dim_order=dimension_order, **kwargs) def lattice_fom_array(arr: ArrayLike, last_dimension: Optional[Literal["channel", "time"]] = None, **kwargs: Any) -> AicsLatticeParams: """ diff --git a/plugin/napari_lattice/dock_widget.py b/plugin/napari_lattice/dock_widget.py index 8a1836b..7238828 100644 --- a/plugin/napari_lattice/dock_widget.py +++ b/plugin/napari_lattice/dock_widget.py @@ -50,8 +50,6 @@ # def magicclass(*args, **kwargs) -> Callable[[MagicClassType], MagicClassType]: # return original_magicclass(*args, **kwargs) -def strikeout(text: str) -> str: - return '\u0336'.join(text) + '\u0336' # class HideableContents(MagicTemplate): # # fields_enabled = vfield(False, label="Enabled") @@ -69,13 +67,13 @@ def strikeout(text: str) -> str: # 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)) +# 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 @@ -135,6 +133,7 @@ class LlszMenu(LlszTemplate): # Pycudadecon library for deconvolution # options={"enabled": True}, + # Tabbed Widget container to house all the widgets @magicclass(widget_type="tabbed", name="Functions", labels=False) class WidgetContainer(LlszTemplate): @@ -143,6 +142,8 @@ def __post_init__(self): tab_widget: QTabWidget= self._widget._tab_widget for i in range(5): tab_widget.setTabIcon(i, QIcon(GREY)) + for field in [self.deskew_fields, self.deconv_fields, self.cropping_fields, self.workflow_fields, self.output_fields]: + field._validate() deskew_fields = DeskewFields(name = "1. Deskew") # @deskew_fields.connect diff --git a/plugin/napari_lattice/fields.py b/plugin/napari_lattice/fields.py index e4d1e48..4099495 100644 --- a/plugin/napari_lattice/fields.py +++ b/plugin/napari_lattice/fields.py @@ -130,14 +130,19 @@ class LastDimensionOptions(Enum): class NapariFieldGroupCompatible(Protocol): errors: MagicField[Label] -class FieldGroupMeta(type(Protocol), type(FieldGroup)): - pass - -class NapariFieldGroup(NapariFieldGroupCompatible, Protocol): +class NapariFieldGroup: def __init__(self, *args, **kwargs): + from magicgui.widgets import Container super().__init__(*args, **kwargs) self = cast(FieldGroup, self) - self.changed.connect(self._validate) + # self._widget._mgui_bind_parent_change_callback(self._validate) + self.changed.connect(self._validate, unique=False) + # super(Container, self).connect(self._validate) + # self.connect(self._validate) + + # def _on_value_change(self, *args, **kwargs) -> None: + # super()._on_value_change(*args, **kwargs) + # self._validate() def _get_parent_tab_widget(self: Union[FieldGroup, Self]) -> QTabWidget: from qtpy.QtWidgets import QWidget @@ -161,13 +166,15 @@ def _set_valid(self, valid: bool): def _validate(self: Union[FieldGroup, Self]): self.errors.value = _get_friendly_validations(self) - self._set_valid(False if self.errors.value else True) + valid = not bool(self.errors.value) + self.errors.visible = not valid + self._set_valid(valid) def _make_model(self) -> BaseModel: raise NotImplementedError() -class DeskewFields(FieldGroup, NapariFieldGroup, metaclass=FieldGroupMeta): +class DeskewFields(NapariFieldGroup, FieldGroup): def _get_dimension_options(self, _) -> List[str]: """ @@ -200,6 +207,10 @@ def _get_dimension_options(self, _) -> List[str]: skew_dir = field(DeskewDirection.Y).with_options(label = "Skew Direction") errors = field(Label).with_options(label="Errors") + @img_layer.connect + def _img_changed(self): + self.dimension_order.reset_choices() + def _merge_layers(self) -> Image: """ Returns a single image merged from all the selected layers @@ -211,14 +222,14 @@ def _make_model(self) -> DeskewParams: return DeskewParams( **lattice_params_from_napari( img=self._merge_layers(), - last_dimension=self.dimension_order.value, + dimension_order=None if self.dimension_order.value == "Get from Metadata" else self.dimension_order.value, physical_pixel_sizes=self.pixel_sizes.value, ), angle=self.angle.value, skew = self.skew_dir.value, ) -class DeconvolutionFields(NapariFieldGroup, FieldGroup, metaclass=FieldGroupMeta): +class DeconvolutionFields(NapariFieldGroup, FieldGroup): """ A counterpart to the DeconvolutionParams Pydantic class """ @@ -271,7 +282,7 @@ def _make_model(self) -> Optional[DeconvolutionParams]: background=self.background.value ) -class CroppingFields(FieldGroup, NapariFieldGroup, metaclass=FieldGroupMeta): +class CroppingFields(FieldGroup, NapariFieldGroup): """ A counterpart to the CropParams Pydantic class """ @@ -319,13 +330,15 @@ def _enable_workflow(self) -> bool: # self["activate_cropping"].background_color = "green" def _make_model(self) -> Optional[CropParams]: - return CropParams( - z_start=self.z_range.value[0], - z_end=self.z_range.value[1], - roi_layer_list=self.shapes.value - ) + if self.fields_enabled.value: + return CropParams( + z_start=self.z_range.value[0], + z_end=self.z_range.value[1], + roi_layer_list=self.shapes.value + ) + return None -class WorkflowFields(FieldGroup, NapariFieldGroup, metaclass=FieldGroupMeta): +class WorkflowFields(FieldGroup, NapariFieldGroup): """ Handles the workflow related parameters """ @@ -355,7 +368,7 @@ def _make_model(self) -> Optional[Workflow]: return load_workflow(str(child.workflow_path)) # @magicclass(name="5. Output") -class OutputFields(FieldGroup, NapariFieldGroup, metaclass=FieldGroupMeta): +class OutputFields(FieldGroup, NapariFieldGroup): set_logging = field(Log_Levels.INFO).with_options(label="Logging Level") time_range = field(Tuple[int, int]).with_options( label="Time Export Range", diff --git a/plugin/napari_lattice/reader.py b/plugin/napari_lattice/reader.py index eae7371..0775172 100644 --- a/plugin/napari_lattice/reader.py +++ b/plugin/napari_lattice/reader.py @@ -29,7 +29,8 @@ class NapariImageParams(AicsLatticeParams): def lattice_params_from_napari( img: Layer, - last_dimension: Optional[Literal["channel", "time"]], + dimension_order: Optional[str], + # last_dimension: Optional[Literal["channel", "time"]], physical_pixel_sizes: PhysicalPixelSizes = PhysicalPixelSizes(None, None, None) ) -> NapariImageParams: """ @@ -47,9 +48,9 @@ def lattice_params_from_napari( if 'aicsimage' in img.metadata.keys(): img_data_aics = img.metadata['aicsimage'] else: - if not last_dimension: - raise ValueError("Either the Napari image must have dimensional metadata, or last_dimension must be provided") - img_data_aics = img_from_array(cast(ArrayLike, img.data), last_dimension=last_dimension, physical_pixel_sizes=physical_pixel_sizes) + if not dimension_order: + raise ValueError("Either the Napari image must have dimensional metadata, or a dimension order must be provided") + img_data_aics = img_from_array(cast(ArrayLike, img.data), dimension_order=dimension_order, physical_pixel_sizes=physical_pixel_sizes) save_name: str if img.source.path is None: diff --git a/plugin/napari_lattice/utils.py b/plugin/napari_lattice/utils.py new file mode 100644 index 0000000..1967c3e --- /dev/null +++ b/plugin/napari_lattice/utils.py @@ -0,0 +1,7 @@ +from napari.viewer import current_viewer, Viewer + +def get_viewer() -> Viewer: + viewer = current_viewer() + if viewer is None: + raise Exception("No viewer present!") + return viewer From 28e62b91e78bf5663fe1370c6407bc3770f5c092 Mon Sep 17 00:00:00 2001 From: Michael Milton Date: Wed, 6 Sep 2023 12:06:41 +1000 Subject: [PATCH 019/147] Implement "disabled", greyed-out steps, fix some bugs in the Lattice model generation --- plugin/napari_lattice/dock_widget.py | 24 ++++----- plugin/napari_lattice/fields.py | 75 ++++++++++++++++++---------- 2 files changed, 59 insertions(+), 40 deletions(-) diff --git a/plugin/napari_lattice/dock_widget.py b/plugin/napari_lattice/dock_widget.py index 7238828..4bba9e3 100644 --- a/plugin/napari_lattice/dock_widget.py +++ b/plugin/napari_lattice/dock_widget.py @@ -34,7 +34,7 @@ from pydantic import ValidationError from napari_lattice.reader import lattice_params_from_napari -from napari_lattice.fields import DeskewFields, CroppingFields, DeconvolutionFields, WorkflowFields, OutputFields +from napari_lattice.fields import DeskewFields, CroppingFields, DeconvolutionFields, WorkflowFields, OutputFields, exception_to_html from napari_lattice.icons import GREY from strenum import StrEnum @@ -110,19 +110,12 @@ def _make_model(self) -> LatticeData: # workflow = self.LlszMenu.WidgetContainer.WorkflowWidget # TODO: fix - return lattice_from_napari( - img=deskew.img_layer.value, - last_dimension=deskew.dimension_order.value, - angle=deskew.angle.value, - skew = deskew.skew_dir.value, - physical_pixel_sizes=deskew.pixel_sizes.value, - save_dir = output.save_path.value, - channel_range=range(output.channel_range.value[0], output.channel_range.value[1]), - time_range=range(output.time_range.value[0], output.time_range.value[1]), - save_type=output.save_type.value, - deconvolution = deconv._make_model(), - workflow = workflow._make_model(), - crop = crop._make_model() + return LatticeData( + **self.LlszMenu.WidgetContainer.deskew_fields._make_model().dict(), + **self.LlszMenu.WidgetContainer.output_fields._make_model().dict(), + workflow=self.LlszMenu.WidgetContainer.workflow_fields._make_model(), + deconvolution=self.LlszMenu.WidgetContainer.deconv_fields._make_model(), + crop=self.LlszMenu.WidgetContainer.cropping_fields._make_model() ) @magicclass(widget_type="split") @@ -405,10 +398,11 @@ def __post_init__(self): def preview(self, header:str, time: int, channel: int): # We only need to process one time point for the preview, # so we made a copy using a subset of the times - lattice = self._make_model_friendly().copy(update=dict( + lattice = self._make_model().copy(update=dict( time_range = range(time, time+1), channel_range = range(time, time+1), )) + for slice in lattice.process().slices: scale = ( lattice.new_dz, diff --git a/plugin/napari_lattice/fields.py b/plugin/napari_lattice/fields.py index 4099495..251d8f9 100644 --- a/plugin/napari_lattice/fields.py +++ b/plugin/napari_lattice/fields.py @@ -2,7 +2,7 @@ from magicclass import FieldGroup, field, MagicTemplate from magicclass.widgets import Widget, ComboBox, Label from magicclass.fields import MagicField -from typing import Callable, List, Optional, Protocol, Tuple, TypeVar, Union, cast +from typing import Any, Callable, List, Optional, Protocol, Tuple, TypeVar, Union, cast from typing_extensions import Protocol, Self from pydantic import BaseModel, ValidationError @@ -31,24 +31,29 @@ logger = logging.getLogger(__name__) logger.setLevel(logging.INFO) -def _get_friendly_validations(model: FieldGroup) -> str: +def exception_to_html(e: BaseException) -> str: """ - Generates a LatticeData, but returns validation errors in a user friendly way + Converts an exception to HTML for reporting back to the user """ - try: - model._make_model() - return "" - except ValidationError as e: + if isinstance(e, ValidationError): message = [] - # message = [f"

    {e.model}

    "] for error in e.errors(): header = ", ".join([str(it).capitalize() for it in error['loc']]) message.append(f"
  • {header} {error['msg']}
    • ") joined = '\n'.join(message) return f"
      {joined}
    " - except Exception as e: + else: return str(e) +def get_friendly_validations(model: FieldGroup) -> str: + """ + Generates a BaseModel, but returns validation errors in a user friendly way + """ + try: + model._make_model() + return "" + except BaseException as e: + return exception_to_html(e) class WorkflowSource(StrEnum): ActiveWorkflow = "Active Workflow" @@ -127,12 +132,22 @@ class LastDimensionOptions(Enum): # def __init__(self, layout: str = "vertical", labels: bool = False, name: str | None = None, **kwargs): # super().__init__(layout, labels, name, **kwargs) -class NapariFieldGroupCompatible(Protocol): - errors: MagicField[Label] +# class NapariFieldGroupCompatible(Protocol): +# from magicclass.widgets import Container +# from qtpy.QtWidgets import QWidget +# errors: MagicField[Label] +# parent: QWidget +# _widget: Container class NapariFieldGroup: - def __init__(self, *args, **kwargs): - from magicgui.widgets import Container + # 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 + # class definition, find none, and then make an empty GUI page when this is rendered. + # It also can't inherit from a FieldGroup-like Protocol as mypy suggests for mixin classes + # (https://mypy.readthedocs.io/en/latest/more_types.html#mixin-classes) because it doesn't + # implement the attributes of a FieldGroup. Ideally this could be a Protocol subclass as well + # to make it remain abstract, but the Protocol metaclass interferes with the FieldGroup metaclass + def __init__(self, *args: Any, **kwargs: Any): super().__init__(*args, **kwargs) self = cast(FieldGroup, self) # self._widget._mgui_bind_parent_change_callback(self._validate) @@ -144,28 +159,27 @@ def __init__(self, *args, **kwargs): # super()._on_value_change(*args, **kwargs) # self._validate() - def _get_parent_tab_widget(self: Union[FieldGroup, Self]) -> QTabWidget: - from qtpy.QtWidgets import QWidget - parent = cast(QWidget, self.parent) - return parent.parentWidget() + def _get_parent_tab_widget(self: Any) -> QTabWidget: + return self.parent.parentWidget() - def _get_tab_index(self: Union[FieldGroup, Self]) -> int: - from magicclass.widgets import Container - widget = cast(Container, self._widget) - return self._get_parent_tab_widget().indexOf(widget._qwidget) + def _get_tab_index(self: Any) -> int: + return self._get_parent_tab_widget().indexOf(self._widget._qwidget) - def _set_valid(self, valid: bool): + def _set_valid(self: Any, valid: bool): from qtpy.QtGui import QIcon tab_parent = self._get_parent_tab_widget() index = self._get_tab_index() - if valid: + if hasattr(self, "fields_enabled") and not self.fields_enabled.value: + # Special case for "diabled" sections + tab_parent.setTabIcon(index, QIcon(GREY)) + elif valid: tab_parent.setTabIcon(index, QIcon(GREEN)) else: tab_parent.setTabIcon(index, QIcon(RED)) - def _validate(self: Union[FieldGroup, Self]): - self.errors.value = _get_friendly_validations(self) + def _validate(self: Any): + self.errors.value = get_friendly_validations(self) valid = not bool(self.errors.value) self.errors.visible = not valid self._set_valid(valid) @@ -216,6 +230,8 @@ def _merge_layers(self) -> Image: Returns a single image merged from all the selected layers """ from napari.layers.utils.stack_utils import images_to_stack + 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: @@ -398,5 +414,14 @@ class OutputFields(FieldGroup, NapariFieldGroup): ) errors = field(Label).with_options(label="Errors") + def _make_model(self) -> OutputParams: + return OutputParams( + channel_range=range(self.channel_range.value[0], self.channel_range.value[1]), + time_range=range(self.time_range.value[0], self.time_range.value[1]), + save_dir=self.save_path.value, + save_name=self.save_name.value, + save_type=self.save_type.value + ) + # @DeskewWidget.img_layer.connect From 39f798bb1c92ce1baf64856ed6a5be58fb0c9589 Mon Sep 17 00:00:00 2001 From: Michael Milton Date: Thu, 7 Sep 2023 11:55:32 +1000 Subject: [PATCH 020/147] Add new tab icons, filter down to only images in the layer select, make errors red --- core/lls_core/workflow.py | 2 +- plugin/napari_lattice/circle-regular.svg | 1 + plugin/napari_lattice/fields.py | 51 ++++++++++++++++-------- plugin/napari_lattice/icons.py | 6 +-- plugin/napari_lattice/invalid.svg | 8 ++++ plugin/napari_lattice/utils.py | 16 ++++++++ plugin/napari_lattice/valid.svg | 8 ++++ 7 files changed, 71 insertions(+), 21 deletions(-) create mode 100644 plugin/napari_lattice/circle-regular.svg create mode 100644 plugin/napari_lattice/invalid.svg create mode 100644 plugin/napari_lattice/valid.svg diff --git a/core/lls_core/workflow.py b/core/lls_core/workflow.py index a861d47..63ffe06 100644 --- a/core/lls_core/workflow.py +++ b/core/lls_core/workflow.py @@ -135,7 +135,7 @@ def import_script(script: Path): sys.modules[module_name] = module spec.loader.exec_module(module) -def import_workflow_modules(workflow: Path): +def import_workflow_modules(workflow: Path) -> None: """ Imports all the Python files that might be used in a given custom workflow diff --git a/plugin/napari_lattice/circle-regular.svg b/plugin/napari_lattice/circle-regular.svg new file mode 100644 index 0000000..13e8dda --- /dev/null +++ b/plugin/napari_lattice/circle-regular.svg @@ -0,0 +1 @@ + \ No newline at end of file diff --git a/plugin/napari_lattice/fields.py b/plugin/napari_lattice/fields.py index 251d8f9..8138466 100644 --- a/plugin/napari_lattice/fields.py +++ b/plugin/napari_lattice/fields.py @@ -1,15 +1,15 @@ from pathlib import Path from magicclass import FieldGroup, field, MagicTemplate -from magicclass.widgets import Widget, ComboBox, Label +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 +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 from strenum import StrEnum 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 Layer +from napari.layers import Shapes from enum import Enum import pyclesperanto_prototype as cle from napari_workflows import Workflow, WorkflowManager @@ -20,10 +20,11 @@ from napari_lattice.icons import RED, GREEN, GREY from napari_lattice.reader import lattice_params_from_napari -from napari_lattice.utils import get_viewer +from napari_lattice.utils import get_viewer, get_layers from napari.layers import Image + # FieldGroups that the users interact with to input data import logging @@ -152,6 +153,14 @@ def __init__(self, *args: Any, **kwargs: Any): self = cast(FieldGroup, self) # self._widget._mgui_bind_parent_change_callback(self._validate) self.changed.connect(self._validate, unique=False) + + # Style the error label. + # We have to check this is a QLabel because in theory this might run in a non-QT backend + errors = self.errors.native + from qtpy.QtWidgets import QLabel + if isinstance(errors, QLabel): + errors.setStyleSheet("color: red;") + errors.setWordWrap(True) # super(Container, self).connect(self._validate) # self.connect(self._validate) @@ -184,10 +193,9 @@ def _validate(self: Any): self.errors.visible = not valid self._set_valid(valid) - def _make_model(self) -> BaseModel: + def _make_model(self): raise NotImplementedError() - class DeskewFields(NapariFieldGroup, FieldGroup): def _get_dimension_options(self, _) -> List[str]: @@ -209,7 +217,7 @@ def _get_dimension_options(self, _) -> List[str]: else: raise Exception("Only 3-5 dimensional arrays are supported") - img_layer = field(List[Image], widget_type='Select').with_options(label = "Image Layer").with_choices(get_viewer().layers) + img_layer = field(List[Image], widget_type='Select').with_options(label = "Image Layer").with_choices(lambda _x, _y: get_layers(Image)) 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)" @@ -293,17 +301,24 @@ def _enable_fields(self) -> bool: def _make_model(self) -> Optional[DeconvolutionParams]: if not self.fields_enabled.value: return None + if self.background.value == BackgroundSource.Custom: + background = self.background_custom.value + elif self.background.value == BackgroundSource.Auto: + background = "auto" + else: + background = "second_last" return DeconvolutionParams( decon_processing=self.decon_processing.value, - background=self.background.value + background=background, + psf_num_iter=self.psf_num_iter.value ) -class CroppingFields(FieldGroup, NapariFieldGroup): +class CroppingFields(NapariFieldGroup, FieldGroup): """ A counterpart to the CropParams Pydantic class """ fields_enabled = field(False, label="Enabled") - shapes= field(ShapesData, label = "ROIs")#Optional[Shapes] = None + shapes= field(List[Shapes], widget_type="Select", label = "ROIs").with_options(choices=lambda _x, _y: get_layers(Shapes)) z_range = field(Tuple[int, int]).with_options( label = "Z Range", value = (0, 1), @@ -346,15 +361,16 @@ def _enable_workflow(self) -> bool: # self["activate_cropping"].background_color = "green" def _make_model(self) -> Optional[CropParams]: + import numpy as np if self.fields_enabled.value: return CropParams( z_start=self.z_range.value[0], z_end=self.z_range.value[1], - roi_layer_list=self.shapes.value + roi_layer_list=ShapesData([np.array(shape.data) for shape in self.shapes.value]) ) return None -class WorkflowFields(FieldGroup, NapariFieldGroup): +class WorkflowFields(NapariFieldGroup, FieldGroup): """ Handles the workflow related parameters """ @@ -374,17 +390,18 @@ def _workflow_path(self) -> bool: return self.workflow_source.value == WorkflowSource.CustomPath def _make_model(self) -> Optional[Workflow]: + from lls_core.workflow import import_workflow_modules + from napari_workflows._io_yaml_v1 import load_workflow if not self.fields_enabled.value: return None - child = get_child(self, WorkflowFields) - if child.workflow_source == WorkflowSource.ActiveWorkflow: + if self.workflow_source.value == WorkflowSource.ActiveWorkflow: return WorkflowManager.install(self.parent_viewer).workflow else: - import_workflow_modules(child.workflow_path) - return load_workflow(str(child.workflow_path)) + import_workflow_modules(self.workflow_path.value) + return load_workflow(str(self.workflow_path.value)) # @magicclass(name="5. Output") -class OutputFields(FieldGroup, NapariFieldGroup): +class OutputFields(NapariFieldGroup, FieldGroup): set_logging = field(Log_Levels.INFO).with_options(label="Logging Level") time_range = field(Tuple[int, int]).with_options( label="Time Export Range", diff --git a/plugin/napari_lattice/icons.py b/plugin/napari_lattice/icons.py index 75b82fb..7c01abe 100644 --- a/plugin/napari_lattice/icons.py +++ b/plugin/napari_lattice/icons.py @@ -1,5 +1,5 @@ import pkg_resources -GREEN = pkg_resources.resource_filename(__name__, "green.png") -GREY = pkg_resources.resource_filename(__name__, "grey.png") -RED = pkg_resources.resource_filename(__name__, "red.png") +GREEN = pkg_resources.resource_filename(__name__, "valid.svg") +GREY = pkg_resources.resource_filename(__name__, "circle-regular.svg") +RED = pkg_resources.resource_filename(__name__, "invalid.svg") diff --git a/plugin/napari_lattice/invalid.svg b/plugin/napari_lattice/invalid.svg new file mode 100644 index 0000000..e1d7dcc --- /dev/null +++ b/plugin/napari_lattice/invalid.svg @@ -0,0 +1,8 @@ + + + + + + + + diff --git a/plugin/napari_lattice/utils.py b/plugin/napari_lattice/utils.py index 1967c3e..25778c7 100644 --- a/plugin/napari_lattice/utils.py +++ b/plugin/napari_lattice/utils.py @@ -1,7 +1,23 @@ from napari.viewer import current_viewer, Viewer +from napari.layers import Layer +from typing_extensions import TypeVar +from typing import Sequence, Type def get_viewer() -> Viewer: + """ + Returns the current viewer, throwing an exception if one doesn't exist + """ viewer = current_viewer() if viewer is None: raise Exception("No viewer present!") return viewer + +LayerType = TypeVar("LayerType", bound=Layer) +def get_layers(type: Type[LayerType]) -> Sequence[LayerType]: + """ + Returns all layers in the current napari viewer of a given `Layer` subtype. + For example, if you pass `napari.layers.Image`, it will return a list of + Image layers + """ + viewer = get_viewer() + return [layer for layer in viewer.layers if isinstance(layer, type)] diff --git a/plugin/napari_lattice/valid.svg b/plugin/napari_lattice/valid.svg new file mode 100644 index 0000000..81ad09b --- /dev/null +++ b/plugin/napari_lattice/valid.svg @@ -0,0 +1,8 @@ + + + + + + + + From 23224b1c93d6cac84487f6e3c42e869d37a9f534 Mon Sep 17 00:00:00 2001 From: Michael Milton Date: Mon, 11 Sep 2023 12:36:55 +1000 Subject: [PATCH 021/147] Rework the dimension merging --- core/lls_core/lattice_data.py | 23 +++--- core/lls_core/utils.py | 11 ++- core/pyproject.toml | 3 +- plugin/napari_lattice/fields.py | 136 +++++++++++++++++++++++++------- plugin/napari_lattice/reader.py | 88 +++++++++++++-------- 5 files changed, 190 insertions(+), 71 deletions(-) diff --git a/core/lls_core/lattice_data.py b/core/lls_core/lattice_data.py index ec6c86c..f568462 100644 --- a/core/lls_core/lattice_data.py +++ b/core/lls_core/lattice_data.py @@ -13,7 +13,7 @@ from pydantic_numpy import NDArray from typing import Any, Iterable, List, Literal, Optional, TYPE_CHECKING, Tuple, TypeVar, Union -from typing_extensions import TypedDict +from typing_extensions import TypedDict, Self from pathlib import Path from aicsimageio.types import PhysicalPixelSizes @@ -35,12 +35,6 @@ logger = logging.getLogger(__name__) -T = TypeVar("T") -def raise_if_none(obj: Optional[T], message: str) -> T: - if obj is None: - raise TypeError(message) - return obj - class DefaultMixin(BaseModel): """ Adds a method for retrieving default values from a BaseModel @@ -130,10 +124,20 @@ class DefinedPixelSizes(DefaultMixin): Like PhysicalPixelSizes, but it's a dataclass, and none of its fields are None """ - X: NonNegativeFloat = 0.14 - Y: NonNegativeFloat = 0.14 + X: NonNegativeFloat = 0.1499219272808386 + Y: NonNegativeFloat = 0.1499219272808386 Z: NonNegativeFloat = 0.3 + @classmethod + def from_physical(cls, pixels: PhysicalPixelSizes) -> Self: + from lls_core.utils import raise_if_none + + return DefinedPixelSizes( + X=raise_if_none(pixels.X, "All pixels must be defined"), + Y=raise_if_none(pixels.Y, "All pixels must be defined"), + Z=raise_if_none(pixels.Z, "All pixels must be defined"), + ) + class DeconvolutionParams(BaseModel, arbitrary_types_allowed=True): """ Parameters for the optional deconvolution step @@ -213,6 +217,7 @@ 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"] if values.get('deskew_vol_shape') is None: if values.get('deskew_affine_transform') is None: # If neither has been set, calculate them ourselves diff --git a/core/lls_core/utils.py b/core/lls_core/utils.py index bd82f9b..e0025c9 100644 --- a/core/lls_core/utils.py +++ b/core/lls_core/utils.py @@ -3,7 +3,7 @@ from collections import defaultdict from contextlib import contextmanager, redirect_stderr, redirect_stdout from os import devnull, path -from typing import Collection, List, Tuple, Union +from typing import Collection, List, Optional, Tuple, TypeVar, Union import numpy as np import pyclesperanto_prototype as cle @@ -341,3 +341,12 @@ def as_type(img, ref_vol): """ img.astype(ref_vol.dtype) return img + +T = TypeVar("T") +def raise_if_none(obj: Optional[T], message: str) -> T: + """ + Asserts that `obj` is not None + """ + if obj is None: + raise TypeError(message) + return obj diff --git a/core/pyproject.toml b/core/pyproject.toml index ab5b93f..04c5c87 100644 --- a/core/pyproject.toml +++ b/core/pyproject.toml @@ -53,7 +53,8 @@ dependencies = [ "tifffile>=2023.3.15", #>=2022.8.12 "fsspec>=2022.8.2", "napari-workflows>=0.2.8", - "resource-backed-dask-array>=0.1.0" + "resource-backed-dask-array>=0.1.0", + "xarray[parallel]" ] [project.urls] diff --git a/plugin/napari_lattice/fields.py b/plugin/napari_lattice/fields.py index 8138466..b69deac 100644 --- a/plugin/napari_lattice/fields.py +++ b/plugin/napari_lattice/fields.py @@ -1,3 +1,4 @@ +from __future__ import annotations from pathlib import Path from magicclass import FieldGroup, field, MagicTemplate from magicclass.widgets import Widget, ComboBox, Label, Select @@ -7,6 +8,7 @@ from pydantic import BaseModel, ValidationError 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 @@ -24,6 +26,9 @@ from napari.layers import Image +if TYPE_CHECKING: + from xarray import DataArray + # FieldGroups that the users interact with to input data @@ -111,11 +116,11 @@ def _decorator(fn: Callable[[EnabledHandlerType], bool])-> Callable[[EnabledHand def make_handler(fn: Callable) -> Callable[[EnabledHandlerType], None]: def handler(parent: EnabledHandlerType): enable = fn(parent) - if enable: - logger.info(f"{parent.__class__} Activated") - else: - logger.info(f"{parent.__class__} Deactivated") for field in fields: + if enable: + logger.info(f"{field.name} Activated") + else: + logger.info(f"{field.name} Deactivated") enable_field(parent, field, enabled=enable) return handler @@ -123,6 +128,9 @@ def handler(parent: EnabledHandlerType): return _decorator +class StackAlong(StrEnum): + CHANNEL = "Channel" + TIME = "Time" class LastDimensionOptions(Enum): XYZTC = "XYZTC" @@ -203,12 +211,10 @@ def _get_dimension_options(self, _) -> List[str]: Returns the list of dimension order options that might be possible for the current image stack """ default = ["Get from Metadata"] - try: - merged = self._merge_layers() - except Exception: + ndims = max([len(layer.data.shape) for layer in self.img_layer.value], default=None) + if ndims is None: return default - ndims = len(merged._dims_order) - if ndims == 3: + elif ndims == 3: return ["ZYX"] + default elif ndims == 4: return ["TZYX", "CZYX"] + default @@ -217,38 +223,112 @@ def _get_dimension_options(self, _) -> List[str]: else: raise Exception("Only 3-5 dimensional arrays are supported") - img_layer = field(List[Image], widget_type='Select').with_options(label = "Image Layer").with_choices(lambda _x, _y: get_layers(Image)) + img_layer = field(List[Image], widget_type='Select').with_options( + label="Image Layer(s) to Deskew", + tooltip="All the image layers you select will be stacked into one image and then deskewed. To select multiple layers, hold Command (MacOS) or Control (Windows, Linux)." + ).with_choices(lambda _x, _y: get_layers(Image)) + stack_along = field( + str + ).with_choices( + [it.value for it in StackAlong] + ).with_options( + label="Stack Along", + tooltip="The direction along which to stack multiple selected layers.", + value=StackAlong.CHANNEL + ) 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)" + label="Pixel Sizes: XYZ (µm)", + tooltip="The size of each pixel in microns. The first field selects the X pixel size, then Y, then Z." + ) + angle = field(LatticeData.get_default("angle")).with_options( + value=LatticeData.get_default("angle"), + label="Skew Angle (°)", + tooltip="The angle to deskew the image, in degrees" + ) + device = field(str).with_choices(cle.available_device_names()).with_options( + label="Graphics Device", + tooltip="The GPU that will be used to perform the processing" ) - 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(_get_dimension_options).with_options(label="Dimension Order") - skew_dir = field(DeskewDirection.Y).with_options(label = "Skew Direction") + dimension_order = field( + str + ).with_choices( + _get_dimension_options + ).with_options( + label="Dimension Order", + tooltip="Specifies the order of dimensions in the input images. For example, if your image is a 4D array with multiple channels along the first axis, you will specify CZYX.", + value=LastDimensionOptions.Metadata.value + ) + skew_dir = field(DeskewDirection.Y).with_options( + label="Skew Direction", + tooltip="The axis along which to deskew" + ) errors = field(Label).with_options(label="Errors") + def __init__(self, *args: Any, **kwargs: Any): + super().__init__(*args, **kwargs) + from qtpy.QtWidgets import QDoubleSpinBox + from magicgui.widgets import TupleEdit + + # Enormous hack to set the precision + # A better method has been requested here: https://github.com/pyapp-kit/magicgui/issues/581#issuecomment-1709467219 + if isinstance(self.pixel_sizes, TupleEdit): + for subwidget in self.pixel_sizes._list: + if isinstance(subwidget.native, QDoubleSpinBox): + subwidget.native.setDecimals(10) + # We have to re-set the default value after changing the precision, because it's already been rounded up + # Also, we have to block emitting the changed signal at construction time + with self.changed.blocked(): + self.pixel_sizes.value = ( + DefinedPixelSizes.get_default("X"), + DefinedPixelSizes.get_default("Y"), + DefinedPixelSizes.get_default("Z") + ) + @img_layer.connect - def _img_changed(self): + def _img_changed(self) -> None: + # Recalculate the dimension options self.dimension_order.reset_choices() - def _merge_layers(self) -> Image: + @img_layer.connect + @enable_if([stack_along]) + def _hide_stack_along(self): + return len(self.img_layer.value) > 1 + + def _merge_layers(self) -> DataArray: """ - Returns a single image merged from all the selected layers + Returns a single image array merged from all the selected layers """ - from napari.layers.utils.stack_utils import images_to_stack - 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) + 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: - return DeskewParams( - **lattice_params_from_napari( - img=self._merge_layers(), + from aicsimageio.types import PhysicalPixelSizes + 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, - physical_pixel_sizes=self.pixel_sizes.value, - ), + physical_pixel_sizes=PhysicalPixelSizes( + X = self.pixel_sizes.value[0], + Y = self.pixel_sizes.value[1], + Z = self.pixel_sizes.value[2] + ), + 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"], angle=self.angle.value, skew = self.skew_dir.value, ) diff --git a/plugin/napari_lattice/reader.py b/plugin/napari_lattice/reader.py index 0775172..1c7cee2 100644 --- a/plugin/napari_lattice/reader.py +++ b/plugin/napari_lattice/reader.py @@ -13,60 +13,84 @@ import dask.delayed as delayed import os import numpy as np -from napari.layers import image, Layer +from napari.layers import Layer, Image from napari.layers._data_protocols import LayerDataProtocol from aicsimageio.dimensions import Dimensions from aicsimageio.aics_image import AICSImage from typing_extensions import Literal -from typing import Any, Optional, cast, Tuple +from typing import Any, Optional, cast, Tuple, Collection -from lls_core.lattice_data import lattice_params_from_aics, img_from_array, AicsLatticeParams, PhysicalPixelSizes +from lls_core.lattice_data import DefinedPixelSizes, lattice_params_from_aics, img_from_array, AicsLatticeParams, PhysicalPixelSizes from lls_core.types import ArrayLike class NapariImageParams(AicsLatticeParams): save_name: str def lattice_params_from_napari( - img: Layer, + imgs: Collection[Image], dimension_order: Optional[str], - # last_dimension: Optional[Literal["channel", "time"]], - physical_pixel_sizes: PhysicalPixelSizes = PhysicalPixelSizes(None, None, None) + physical_pixel_sizes: PhysicalPixelSizes, + stack_along: str ) -> NapariImageParams: """ Factory function for generating a LatticeData from a Napari Image - - Arguments: - kwargs: Extra arguments to pass to the LatticeData constructor """ + from xarray import DataArray, concat - if not isinstance(img, Layer): - raise Exception("img must be a napari layer object") - - img_data_aics: AICSImage + if len(imgs) < 1: + raise ValueError("At least one image must be provided.") - if 'aicsimage' in img.metadata.keys(): - img_data_aics = img.metadata['aicsimage'] - else: - if not dimension_order: + save_name: str + pixel_sizes: set[PhysicalPixelSizes] = {physical_pixel_sizes} + save_names = [] + + # The pixel sizes according to the AICS metadata, if any + final_imgs: list[DataArray] = [] + + for img in imgs: + if img.source.path is None: + # remove colon (:) and any leading spaces + save_name = img.name.replace(":", "").strip() + # replace any group of spaces with "_" + save_name = '_'.join(save_name.split()) + else: + file_name_noext = os.path.basename(img.source.path) + file_name = os.path.splitext(file_name_noext)[0] + # remove colon (:) and any leading spaces + save_name = file_name.replace(":", "").strip() + # replace any group of spaces with "_" + save_name = '_'.join(save_name.split()) + + save_names.append(save_name) + + if 'aicsimage' in img.metadata.keys(): + img_data_aics: AICSImage = img.metadata['aicsimage'] + # Only process pixel sizes that are not none + if all(img_data_aics.physical_pixel_sizes): + pixel_sizes.add(img_data_aics.physical_pixel_sizes) + # if pixel_size_metadata is not None and pixel_sizes != img_data_aics.physical_pixel_sizes: + # raise Exception(f"Two or more layers that you have tried to merge have different pixel sizes according to their metadata! A previous image has size {physical_pixel_sizes}, whereas {img.name} has size {img_data_aics.physical_pixel_sizes}.") + # else: + # pixel_size_metadata = img_data_aics.physical_pixel_sizes + + calculated_order = 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") - img_data_aics = img_from_array(cast(ArrayLike, img.data), dimension_order=dimension_order, physical_pixel_sizes=physical_pixel_sizes) + else: + calculated_order = list(dimension_order) - save_name: str - if img.source.path is None: - # remove colon (:) and any leading spaces - save_name = img.name.replace(":", "").strip() - # replace any group of spaces with "_" - save_name = '_'.join(save_name.split()) + final_imgs.append(DataArray(img.data, dims=calculated_order)) + + if len(pixel_sizes) > 1: + raise Exception(f"Two or more layers that you have tried to merge have different pixel sizes according to their metadata! {pixel_sizes}") + elif len(pixel_sizes) == 1: + final_pixel_size = DefinedPixelSizes.from_physical(pixel_sizes.pop()) else: - file_name_noext = os.path.basename(img.source.path) - file_name = os.path.splitext(file_name_noext)[0] - # remove colon (:) and any leading spaces - save_name = file_name.replace(":", "").strip() - # replace any group of spaces with "_" - save_name = '_'.join(save_name.split()) - - return NapariImageParams(save_name=save_name, **lattice_params_from_aics(img_data_aics, physical_pixel_sizes=physical_pixel_sizes)) + final_pixel_size = DefinedPixelSizes.from_physical(physical_pixel_sizes) + + final_img = concat(final_imgs, dim=stack_along) + return NapariImageParams(save_name=save_names[0], physical_pixel_sizes=final_pixel_size, data=final_img, dims=final_img.shape) def napari_get_reader(path: list[str] | str): """Check if file ends with h5 and returns reader function if true From 17e10b61121e72d2d58db7cf509047330a37c997 Mon Sep 17 00:00:00 2001 From: Michael Milton Date: Mon, 11 Sep 2023 18:22:08 +1000 Subject: [PATCH 022/147] Cleanup old code --- core/lls_core/lattice_data.py | 75 ++- plugin/napari_lattice/dock_widget.py | 974 +-------------------------- plugin/napari_lattice/fields.py | 153 ++--- plugin/napari_lattice/reader.py | 8 +- 4 files changed, 138 insertions(+), 1072 deletions(-) diff --git a/core/lls_core/lattice_data.py b/core/lls_core/lattice_data.py index f568462..3ec8bbb 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 4bba9e3..398827f 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 b69deac..f95251c 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 1c7cee2..dac3452 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)) From df6513a928605aca9ff8e175ceaf52cabfba45c2 Mon Sep 17 00:00:00 2001 From: Michael Milton Date: Tue, 12 Sep 2023 17:42:37 +1000 Subject: [PATCH 023/147] Use pydantic-cli for CLI --- core/lls_core/cmds/__main__.py | 797 ++------------------------- core/lls_core/lattice_data.py | 155 ++++-- plugin/napari_lattice/dock_widget.py | 2 +- plugin/napari_lattice/fields.py | 7 +- 4 files changed, 135 insertions(+), 826 deletions(-) diff --git a/core/lls_core/cmds/__main__.py b/core/lls_core/cmds/__main__.py index 76e41a3..205293f 100644 --- a/core/lls_core/cmds/__main__.py +++ b/core/lls_core/cmds/__main__.py @@ -3,775 +3,40 @@ # Example for deskewing files in a folder # python lattice_processing.py --input /home/pradeep/to_deskew --output /home/pradeep/output_save/ --processing deskew -import argparse -import os -import glob -import sys -import re -from lls_core.io import save_img, save_img_workflow -from lls_core.lattice_data import lattice_params_from_aics, LatticeData -from lls_core.utils import read_imagej_roi, get_all_py_files, get_first_last_image_and_task, modify_workflow_task, check_dimensions -from lls_core.llsz_core import crop_volume_deskew -from aicsimageio import AICSImage -from aicsimageio.types import PhysicalPixelSizes -import pyclesperanto_prototype as cle -from tqdm import tqdm -import dask.array as da -from napari_workflows._io_yaml_v1 import load_workflow from pathlib import Path -import yaml -from typing import Sequence, TYPE_CHECKING - -from lls_core.deconvolution import read_psf -from lls_core import DeskewDirection, DeconvolutionChoice, SaveFileType -from enum import Enum - -from napari_workflows import Workflow - - -# define parser class so as to print help message -class ArgParser(argparse.ArgumentParser): - def error(self, message): - sys.stderr.write('error: %s\n' % message) - self.print_help() - sys.exit(2) - - -class ProcessingOptions(Enum): - deskew = "deskew" - crop = "crop" - workflow = "workflow" - workflow_crop = "workflow_crop" - - -def make_parser(): - """ Parse input arguments""" - parser = argparse.ArgumentParser(description="Lattice Data Analysis") - parser.add_argument('--input', type=str, nargs=1, help="Enter input file", - required=False) # only false if using config file - parser.add_argument('--output', type=str, nargs=1, help="Enter save folder", - required=False) # only false if using config file - parser.add_argument('--skew_direction', type=DeskewDirection, nargs=1, - help="Enter the direction of skew (default is Y)", - action="store", - choices=("Y", "X"), - default=DeskewDirection.Y) - parser.add_argument('--deskew_angle', type=float, nargs=1, - help="Enter the deskew angle (default is 30)", - default=30.0) - parser.add_argument('--processing', type=ProcessingOptions, nargs=1, - help="Enter the processing option: deskew, crop, workflow or workflow_crop", required=False, - action="store", - choices=(ProcessingOptions.deskew, ProcessingOptions.crop, - ProcessingOptions.workflow, ProcessingOptions.workflow_crop), - default=None) - parser.add_argument('--deconvolution', type=DeconvolutionChoice, nargs=1, - help="Specify the device to use for deconvolution. Options are cpu or cuda_gpu", - action="store") - parser.add_argument('--deconvolution_num_iter', type=int, nargs=1, - help="Enter the number of iterations to run Richardson-Lucy deconvolution (default is 10)") - parser.add_argument('--deconvolution_psf', type=str, nargs="+", - help="Enter paths to psf file/s separated by commas or you can enter each path with double quotes") # use + for nargs for flexible no of args - parser.add_argument('--roi_file', type=str, nargs=1, - help="Enter the path to the ROI file for performing cropping (only valid for -processing where crop or workflow_crop is specified") - parser.add_argument('--voxel_sizes', type=float, nargs=3, - help="Enter the voxel sizes as (dz,dy,dx). Make sure they are in brackets", - default=(0.3, 0.1499219272808386, 0.1499219272808386)) - parser.add_argument('--file_extension', type=str, nargs=1, - help="If choosing a folder, enter the extension of the files (make sure you enter it with the dot at the start, i.e., .czi or .tif), else .czi and .tif files will be used") - parser.add_argument('--time_range', type=int, nargs=2, - help="Enter time range to extract, default will be entire timeseries if no range is specified. For example, 0 9 will extract first 10 timepoints", - default=(None, None)) - parser.add_argument('--channel_range', type=int, nargs=2, - help="Enter channel range to extract, default will be all channels if no range is specified. For example, 0 1 will extract first two channels.", - default=(None, None)) - parser.add_argument('--workflow_path', type=str, nargs=1, - help="Enter path to the workflow file '.yml", default=[None]) - parser.add_argument('--output_file_type', type=SaveFileType, nargs=1, - help="Save as either tiff or h5, defaults to h5", - action="store", - choices=(SaveFileType.tiff, SaveFileType.h5), - default=[SaveFileType.h5]), - parser.add_argument('--channel', type=bool, nargs=1, - help="If input is a tiff file and there are channel dimensions but no time dimensions, choose as True", - default=False) - parser.add_argument('--config', type=str, nargs=1, - help="Location of config file, all other arguments will be ignored and overwriten by those in the yaml file", default=None) - parser.add_argument('--roi_number', type=int, nargs=1, - help="Process an individual ROI, loop all if unset", default=None) - parser.add_argument('--set_logging', type=str, nargs=1, - help="Set logging level [INFO,DEBUG]", default=["INFO"]) - return parser - -def main(argv: Sequence[str] = sys.argv[1:]): - parser = make_parser() - args = parser.parse_args(argv) - - # Enable Logging - import logging - logger = logging.getLogger(__name__) - - # Setting empty strings for psf paths - psf_ch1_path = "" - psf_ch2_path = "" - psf_ch3_path = "" - psf_ch4_path = "" - - # IF using a config file, set a lot of parameters here - # the rest are scattered throughout the code when needed - # could be worth bringing everything up top - print(args.config) - - if args.config: - try: - with open(args.config[0], 'r') as con: - try: - processing_parameters = yaml.safe_load(con) - - except Exception as exc: - print(exc) - - except FileNotFoundError as exc: - exit(f"Config yml file {args.config[0]} not found, please specify") - - if not processing_parameters: - logging.error(f"Config file not loaded properly") - # this is how I propose setting the command line variables - # If they're in the config, get from there. If not - # Look in command line. If not there, then exit - if 'input' in processing_parameters: - input_path = processing_parameters['input'] - elif args.input is not None: - input_path = args.input[0] - else: - exit("Input not set") - print("Processing file %s" % input_path) - - if 'output' in processing_parameters: - output_path = processing_parameters['output'] - elif args.output is not None: - output_path = args.output[0] + os.sep - else: - exit("Output not set") - - # If requried setting not in config file = use defaults from argparse - # get method for a dictionary will get a value if specified, if not, will use value from args as default value - - dz, dy, dx = processing_parameters.get('voxel_sizes', args.voxel_sizes) - channel_dimension = processing_parameters.get('channel', args.channel) - skew_dir = processing_parameters.get( - 'skew_direction', DeskewDirection.Y) - deskew_angle = processing_parameters.get('deskew_angle', 30.0) - processing = ProcessingOptions[processing_parameters.get( - 'processing', None).lower()] - time_start, time_end = processing_parameters.get( - 'time_range', (None, None)) - channel_start, channel_end = processing_parameters.get( - 'channel_range', (None, None)) - output_file_type = SaveFileType[processing_parameters.get( - 'output_file_type', args.output_file_type).lower()] - - # to allow for either/or CLI/config file Todo for rest of parameters? - if 'roi_number' in processing_parameters: - roi_to_process = processing_parameters.get('roi_number') - elif args.roi_number is not None: - roi_to_process = args.roi_number[0] - else: - roi_to_process = None - - log_level = processing_parameters.get('--set_logging', "INFO") - workflow_path = processing_parameters.get('workflow_path', None) - if workflow_path is not None: - workflow_path = Path(workflow_path) - - logging.basicConfig(level=log_level.upper()) - logging.info(f"Logging set to {log_level.upper()}") - - if not processing: - logging.error("Processing option not set.") - exit() - - deconvolution = processing_parameters.get('deconvolution', None) - if deconvolution is not None: - deconvolution = DeconvolutionChoice[deconvolution.lower()] - psf_arg = "psf" - deconvolution_num_iter = processing_parameters.get( - 'deconvolution_num_iter', 10) - psf_paths = processing_parameters.get('deconvolution_psf') - logging.debug(psf_paths) - if not psf_paths: - logging.error("PSF paths not set option not set.") - exit() - else: - psf_ch1_path = psf_paths[0].replace(",", "").strip() - psf_ch2_path = psf_paths[1].replace(",", "").strip() - psf_ch3_path = psf_paths[2].replace(",", "").strip() - psf_ch4_path = psf_paths[3].replace(",", "").strip() - - else: - deconvolution = False - - roi_file: str - if processing == ProcessingOptions.crop or processing == ProcessingOptions.workflow_crop: - if 'roi_file' in processing_parameters: - roi_file = processing_parameters.get('roi_file', False) - elif args.roi_file is not None: - roi_file = args.roi_file[0] - else: - exit("Specify roi file") - assert os.path.exists(roi_file), "Cannot find " + roi_file - print("Processing using roi file %s" % roi_file) - - assert os.path.exists(input_path), "Cannot find input " + input_path - assert os.path.exists(output_path), "Cannot find output " + output_path - - file_extension = processing_parameters.get( - 'file_extension', [".czi", ".tif", ".tiff"]) - - # setting (some - see above) parameters from CLI - else: # if not using config file - input_path = args.input[0] - output_path = args.output[0] + os.sep - dz, dy, dx = args.voxel_sizes - deskew_angle = args.deskew_angle - channel_dimension = args.channel - time_start, time_end = args.time_range - channel_start, channel_end = args.channel_range - skew_dir = args.skew_direction - processing = args.processing[0] - output_file_type = args.output_file_type[0] - roi_to_process = args.roi_number - workflow_path = args.workflow_path[0] - - log_level = args.set_logging[0] - logging.basicConfig(level=log_level.upper()) - logging.info(f"Logging set to {log_level.upper()}") - - if roi_to_process: - logging.info(f"Processing ROI {roi_to_process}") - - if not processing: - logging.error("Processing option not set.") - exit() - - # deconvolution - if args.deconvolution: - deconvolution = args.deconvolution[0] - psf_arg = "psf" - if args.deconvolution_psf: - psf_paths = re.split(';|,', args.deconvolution_psf[0]) - logging.debug(psf_paths) - try: - psf_ch1_path = psf_paths[0] - psf_ch2_path = psf_paths[1] - psf_ch3_path = psf_paths[2] - psf_ch4_path = psf_paths[3] - except IndexError: - pass - else: - logging.error("PSF paths not set option not set.") - exit() - # num of iter default is 10 if nothing specified - if args.deconvolution_num_iter: - deconvolution_num_iter = args.deconvolution_num_iter - else: - deconvolution_num_iter = 10 - else: - deconvolution = False - - # output file type to save - if not output_file_type: - output_file_type = SaveFileType.h5 - - # Get - if processing == ProcessingOptions.crop or processing == ProcessingOptions.workflow_crop: - assert args.roi_file, "Specify roi_file (ImageJ/FIJI ROI Zip file)" - roi_file = args.roi_file[0] - if os.path.isfile(roi_file): # if file make sure it is a zip file or roi file - - roi_file_extension = os.path.splitext(roi_file)[1] - assert roi_file_extension == ".zip" or roi_file_extension == ".roi", "ROI file is not a zip or .roi file" - - # Check if input and output paths exist - assert os.path.exists(input_path), "Cannot find input " + input_path - assert os.path.exists(output_path), "Cannot find output " + output_path - - if not args.file_extension: - file_extension = [".czi", ".tif", ".tiff"] - else: - file_extension = args.file_extension - - # Initialise list of images and ROIs - img_list = [] - roi_list = [] - logging.debug(f"Deconvolution is set to {deconvolution} and option is ") - - logging.debug(f"Output file type is {output_file_type}") - # If input_path a directory, get a list of images - if os.path.isdir(input_path): - for file_type in file_extension: - img_list.extend(glob.glob(input_path + os.sep + '*' + file_type)) - print("List of images: ", img_list) - elif os.path.isfile(input_path) and (os.path.splitext(input_path))[1] in file_extension: - # if a single file, just add filename to the image list - img_list.append(input_path) - else: - sys.exit("Do not recognise " + input_path + " as directory or file") - - # If cropping, get list of roi files with matching image names - if processing == ProcessingOptions.crop or processing == ProcessingOptions.workflow_crop: - if os.path.isdir(roi_file): - for img in img_list: - img_name = os.path.basename(os.path.splitext(img)[0]) - roi_temp = roi_file + os.sep + img_name + ".zip" - - if os.path.exists(roi_temp): - roi_list.append(roi_temp) - else: - sys.exit("Cannot find ROI file for " + img) - - print("List of ROIs: ", roi_list) - elif os.path.isfile(roi_file): - roi_list.append(roi_file) - assert len(roi_list) == len( - img_list), "Image and ROI lists do not match" - else: - # add list of empty strings so that it can run through for loop - no_files = len(img_list) - roi_list = [""] * no_files - - # loop through the list of images and rois - for img, roi_path in zip(img_list, roi_list): - save_name = os.path.splitext(os.path.basename(img))[0] - - print("Processing Image " + img) - if processing == ProcessingOptions.crop or processing == ProcessingOptions.workflow_crop: - print("Processing ROI " + roi_path) - aics_img = AICSImage(img) - - # check if scene valid; if not it iterates through all scenes - len_scenes = len(aics_img.scenes) - for scene in range(len_scenes): - aics_img.set_scene(scene) - test = aics_img.get_image_dask_data("YX", T=0, C=0, Z=0) - try: - test_max = test.max().compute() - if test_max: - print(f"Scene {scene} is valid") - break - except Exception: - print(f"Scene {scene} not valid") - - # Initialize Lattice class - lattice = LatticeData( - **lattice_params_from_aics(aics_img, physical_pixel_sizes=PhysicalPixelSizes(dx, dy, dz)), - angle=deskew_angle, - skew=skew_dir, - save_name=save_name +from typing import List + +from lls_core.lattice_data import ( + CropParams, + DeconvolutionParams, + DeskewParams, + LatticeData, + OutputParams, +) +from pydantic_cli import run_and_exit, DefaultConfig +from pydantic import Field + +class CliParams(DeskewParams, CropParams, OutputParams, DeconvolutionParams): + # The idea of this class is to re-use the logic from the Pydantic + # models, namely the validation, defaults etc, but to flatten them + # into one model so that pydantic-cli can handle it, and to override + # certain field definitions with CLI-specific data types + save_name: str = Field(default=None, description=OutputParams.get_description("save_name")) + image: Path = Field(description="Path to the image file to read, in a format readable by AICSImageIO, for example .tiff or .czi") + roi_list: List[Path] = Field(default=[], description="A list of paths pointing to regions of interest to crop to, in ImageJ format.") + psf: List[Path] = Field(default=[], description="A list of 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.") + + def to_lattice(self) -> LatticeData: + return LatticeData( + **self.dict() ) - if time_start is None or time_end is None: - time_start, time_end = 0, lattice.time - 1 - - if channel_start is None or channel_end is None: - channel_start, channel_end = 0, lattice.channels - 1 - - # Verify dimensions - check_dimensions(time_start, time_end, channel_start, - channel_end, lattice.channels, lattice.time) - print("dimensions verified") - # If deconvolution, set the parameters in the lattice class - if deconvolution: - lattice.decon_processing = deconvolution - lattice.psf_num_iter = deconvolution_num_iter - logging.debug(f"Num of iterations decon, {lattice.psf_num_iter}") - logging.info("Performing Deconvolution") - lattice.psf = [] - lattice.otf_path = [] - # Remove empty values and convert to Path - lattice.psf = list(read_psf([Path(it) for it in [psf_ch1_path, psf_ch2_path, psf_ch3_path, psf_ch4_path] if it is not None], decon_option=lattice.decon_processing, lattice_class=lattice)) - - else: - lattice.decon_processing = None - - # Override pixel values by reading metadata if file is czi - if os.path.splitext(img)[1] == ".czi": - dz, dy, dx = lattice.dz, lattice.dy, lattice.dx - logging.info(f"Pixel values from metadata (zyx): {dz},{dy},{dx}") - - # Setup workflows based on user input - if processing == ProcessingOptions.workflow or processing == ProcessingOptions.workflow_crop: - # load workflow from path - # if args.config and 'workflow_path' in processing_parameters: - #workflow_path = Path(processing_parameters['workflow_path']) - # else: - #workflow_path = Path(workflow_path) - - # load custom modules (*.py) in same directory as workflow file - import importlib - parent_dir = Path(workflow_path).resolve().parents[0].__str__() + os.sep - - sys.path.append(parent_dir) - custom_py_files = get_all_py_files(parent_dir) - - if len(custom_py_files) > 0: - modules = map(importlib.import_module, custom_py_files) - logging.info(f"Custom modules imported {modules}") - - # workflow has to be reloaded for each image and reinitialised - user_workflow = load_workflow(workflow_path.__str__()) - if not isinstance(user_workflow, Workflow): - raise ValueError("Workflow file is not a napari workflow object. Check file!") - - input_arg_first, input_arg_last, first_task_name, last_task_name = get_first_last_image_and_task( - user_workflow) - logging.debug(input_arg_first, input_arg_last, - first_task_name, last_task_name) - - # get list of tasks - task_list = list(user_workflow._tasks.keys()) - - print("Workflow loaded:") - logging.info(user_workflow) - - task_name_start = first_task_name[0] - try: - task_name_last = last_task_name[0] - except IndexError: - task_name_last = task_name_start - - # if workflow involves cropping, assign first task as crop_volume_deskew - if processing == ProcessingOptions.workflow_crop: - deskewed_shape = lattice.deskew_vol_shape - deskewed_volume = da.zeros(deskewed_shape) - z_start = 0 - z_end = deskewed_shape[0] - roi = "roi" - volume = "volume" - # Create workflow for cropping and deskewing - # volume and roi used will be set dynamically - user_workflow.set("crop_deskew", crop_volume_deskew, - original_volume=volume, - deskewed_volume=deskewed_volume, - roi_shape=roi, - angle_in_degrees=deskew_angle, - voxel_size_x=dx, - voxel_size_y=dy, - voxel_size_z=dz, - z_start=z_start, - z_end=z_end, - skew_dir=lattice.skew) - # 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", workflow=user_workflow) - user_workflow.set(task_name_start, new_task) - - elif processing == ProcessingOptions.workflow: - # Verify if deskewing function is in workflow; if not, add as first task - if user_workflow.get_task(task_name_start)[0] not in (cle.deskew_y, cle.deskew_x): - custom_workflow = True - input = "input" - - # add task to the workflow - user_workflow.set("deskew_image", lattice.deskew_func, - input_image=input, - angle_in_degrees=deskew_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 the first task so it accepts "deskew_image" as input - 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) - else: - custom_workflow = False - - img_data = lattice.data - - # Create save directory for each image - save_path = output_path + os.sep + \ - os.path.basename(os.path.splitext(img)[0]) + os.sep - - if not os.path.exists(save_path): - try: - os.mkdir(save_path) - except FileExistsError: - # this is sometimes caused when running parallel jobs - # can safely be ignored (I hope) - pass - - logging.info(f"Saving at {save_path}") - - # Deskewing only - - if processing == ProcessingOptions.deskew: - # deconvolution - if lattice.decon_processing: - save_img(vol=img_data, - func=lattice.deskew_func, - time_start=time_start, - time_end=time_end, - channel_start=channel_start, - channel_end=channel_end, - save_path=save_path, - save_name=save_name, - save_file_type=output_file_type, - dx=dx, - dy=dy, - dz=dz, - angle=deskew_angle, - terminal=True, - lattice=lattice, - angle_in_degrees=deskew_angle, - voxel_size_x=dx, - voxel_size_y=dy, - voxel_size_z=dz, - linear_interpolation=True - ) - - else: - save_img(vol=img_data, - func=lattice.deskew_func, - time_start=time_start, - time_end=time_end, - channel_start=channel_start, - channel_end=channel_end, - save_path=save_path, - save_name=save_name, - save_file_type=output_file_type, - dx=dx, - dy=dy, - dz=dz, - angle=deskew_angle, - angle_in_degrees=deskew_angle, - voxel_size_x=dx, - voxel_size_y=dy, - voxel_size_z=dz - ) - - # Crop and deskew - elif processing == ProcessingOptions.crop or processing == ProcessingOptions.workflow_crop: - print(roi_path) - roi_img = read_imagej_roi(roi_path) - - deskewed_shape = lattice.deskew_vol_shape - deskewed_volume = da.zeros(deskewed_shape) - - # Can modify for entering custom z values - z_start = 0 - z_end = deskewed_shape[0] - - # if roi number is specified, roi_img will be a list containing only one roi. - if roi_to_process is not None: - # just do one ROI - assert roi_to_process < len( - roi_img), f"ROI specified is {roi_to_process}, which is less than total ROIs ({len(roi_img)})" - logging.info(f"Processing single ROI: {roi_to_process}") - # If only one ROI, single loop - roi_img = [roi_img[roi_to_process]] - - # loop through rois in the roi list - for idx, roi_layer in enumerate(tqdm(roi_img, desc="ROI:", position=0)): - - if roi_to_process is not None: - roi_label = str(roi_to_process) - else: - roi_label = str(idx) - - print("Processing ROI " + str(idx) + - " of " + str(len(roi_img))) - deskewed_shape = lattice.deskew_vol_shape - deskewed_volume = da.zeros(deskewed_shape) - - # Can modify for entering custom z values - z_start = 0 - z_end = deskewed_shape[0] - - if processing == ProcessingOptions.crop: - # deconvolution - if lattice.decon_processing: - save_img(img_data, - func=crop_volume_deskew, - time_start=time_start, - time_end=time_end, - channel_start=channel_start, - channel_end=channel_end, - save_name_prefix="ROI_" + roi_label + "_", - save_path=save_path, - save_name=save_name, - save_file_type=output_file_type, - dx=dx, - dy=dy, - dz=dz, - angle=deskew_angle, - terminal=True, - lattice=lattice, - deskewed_volume=deskewed_volume, - roi_shape=roi_layer, - angle_in_degrees=deskew_angle, - z_start=z_start, - z_end=z_end, - voxel_size_x=dx, - voxel_size_y=dy, - voxel_size_z=dz, - ) - else: - print("SHOULD BE DOING THIS") - print(save_path) - print(save_name) - save_img(img_data, - func=crop_volume_deskew, - time_start=time_start, - time_end=time_end, - channel_start=channel_start, - channel_end=channel_end, - save_name_prefix="ROI_" + roi_label + "_", - save_path=save_path, - save_name=save_name, - save_file_type=output_file_type, - dx=dx, - dy=dy, - dz=dz, - angle=deskew_angle, - deskewed_volume=deskewed_volume, - roi_shape=roi_layer, - angle_in_degrees=deskew_angle, - z_start=z_start, - z_end=z_end, - voxel_size_x=dx, - voxel_size_y=dy, - voxel_size_z=dz, - ) - - elif processing == ProcessingOptions.workflow_crop: - # deconvolution - user_workflow.set(roi, roi_layer) - - if lattice.decon_processing: - save_img_workflow(vol=img_data, - workflow=user_workflow, - input_arg=volume, - first_task="crop_deskew", - last_task=task_name_last, - time_start=time_start, - time_end=time_end, - channel_start=channel_start, - channel_end=channel_end, - save_path=save_path, - save_name_prefix="ROI_" + roi_label, - save_name=save_name, - save_file_type=output_file_type, - dx=dx, - dy=dy, - dz=dz, - angle=deskew_angle, - deconvolution=True, - decon_processing=lattice.decon_processing, - psf=lattice.psf, - psf_arg=psf_arg) - else: - save_img_workflow(vol=img_data, - workflow=user_workflow, - input_arg=volume, - first_task="crop_deskew", - last_task=task_name_last, - time_start=time_start, - time_end=time_end, - channel_start=channel_start, - channel_end=channel_end, - save_path=save_path, - save_file_type=output_file_type, - save_name_prefix="ROI_" + roi_label, - save_name=save_name, - dx=dx, - dy=dy, - dz=dz, - angle=deskew_angle, - deconvolution=False) - - elif processing == ProcessingOptions.workflow: - # if deskew_image task set above manually - if custom_workflow: - if lattice.decon_processing: - save_img_workflow(vol=img_data, - workflow=user_workflow, - input_arg=input, - first_task="deskew_image", - last_task=task_name_last, - time_start=time_start, - time_end=time_end, - channel_start=channel_start, - channel_end=channel_end, - save_path=save_path, - save_name=save_name, - save_file_type=output_file_type, - dx=dx, - dy=dy, - dz=dz, - angle=deskew_angle, - deconvolution=True, - decon_processing=lattice.decon_processing, - psf=lattice.psf, - psf_arg=psf_arg) - else: - save_img_workflow(vol=img_data, - workflow=user_workflow, - input_arg=input, - first_task="deskew_image", - last_task=task_name_last, - time_start=time_start, - time_end=time_end, - channel_start=channel_start, - channel_end=channel_end, - save_path=save_path, - save_name=save_name, - save_file_type=output_file_type, - dx=dx, - dy=dy, - dz=dz, - angle=deskew_angle) - else: - if lattice.decon_processing: - save_img_workflow(vol=img_data, - workflow=user_workflow, - input_arg=input_arg_first, - first_task=first_task_name, - last_task=task_name_last, - time_start=time_start, - time_end=time_end, - channel_start=channel_start, - channel_end=channel_end, - save_path=save_path, - save_name=save_name, - save_file_type=output_file_type, - dx=dx, - dy=dy, - dz=dz, - angle=deskew_angle, - deconvolution=True, - decon_processing=lattice.decon_processing, - psf=lattice.psf, - psf_arg=psf_arg) - else: - save_img_workflow(vol=img_data, - workflow=user_workflow, - input_arg=input_arg_first, - first_task=first_task_name, - last_task=task_name_last, - time_start=time_start, - time_end=time_end, - channel_start=channel_start, - channel_end=channel_end, - save_path=save_path, - save_name=save_name, - save_file_type=output_file_type, - dx=dx, - dy=dy, - dz=dz, - angle=deskew_angle) + class Config(DefaultConfig): + CLI_JSON_ENABLE = True +def _main(params: CliParams) -> int: + params.to_lattice().process().save_image() + return 0 if __name__ == '__main__': - main() + run_and_exit(CliParams, _main) diff --git a/core/lls_core/lattice_data.py b/core/lls_core/lattice_data.py index 3ec8bbb..7455ca1 100644 --- a/core/lls_core/lattice_data.py +++ b/core/lls_core/lattice_data.py @@ -1,9 +1,9 @@ from __future__ import annotations # class for initializing lattice data and setting metadata # TODO: handle scenes +from os import getcwd from pydantic import BaseModel, Field, NonNegativeInt, NonNegativeFloat, root_validator, validator from aicsimageio.aics_image import AICSImage -from aicsimageio.dimensions import Dimensions # from numpy.typing import NDArray import math from dask.array.core import Array as DaskArray @@ -12,7 +12,7 @@ import tifffile from pydantic_numpy import NDArray -from typing import Any, Iterable, List, Literal, Optional, TYPE_CHECKING, Tuple, TypeVar, Union +from typing import Any, Iterable, List, Literal, Optional, TYPE_CHECKING, Tuple, Type, TypeVar, Union from typing_extensions import TypedDict, Self from pathlib import Path @@ -31,20 +31,31 @@ if TYPE_CHECKING: import pyclesperanto_prototype as cle + from enum import Enum import logging logger = logging.getLogger(__name__) -class DefaultMixin(BaseModel): +def enum_choices(enum: Type[Enum]) -> str: """ - Adds a method for retrieving default values from a BaseModel + Returns a human readable list of enum choices + """ + return "{" + ", ".join([it.name for it in enum]) + "}" + +class FieldAccessMixin(BaseModel): + """ + Adds methods to a BaseModel for accessing useful field information """ @classmethod def get_default(cls, field_name: str): return cls.__fields__[field_name].get_default() + @classmethod + def get_description(cls, field_name: str) -> str: + return cls.__fields__[field_name].field_info.description + class ProcessedVolume(BaseModel, arbitrary_types_allowed=True): """ A slice of the image processing result @@ -120,7 +131,7 @@ def make_filename_prefix(prefix: Optional[str] = None, roi_index: Optional[int] components.append(f"T{time}") return "_".join(components) -class DefinedPixelSizes(DefaultMixin): +class DefinedPixelSizes(FieldAccessMixin): """ Like PhysicalPixelSizes, but it's a dataclass, and none of its fields are None @@ -143,37 +154,58 @@ class DeconvolutionParams(BaseModel, arbitrary_types_allowed=True): """ Parameters for the optional deconvolution step """ - decon_processing: DeconvolutionChoice = DeconvolutionChoice.cpu - psf: List[NDArray] = [] - psf_num_iter: NonNegativeInt = 10 - # TODO: handle this - # otf_path: List = [] - # Background value to subtract - background: Union[float, Literal["auto", "second_last"]] = 0 + decon_processing: DeconvolutionChoice = Field( + default=DeconvolutionChoice.cpu, + description=f"Hardware to use to perform the deconvolution. Choices: {enum_choices(DeconvolutionChoice)}") + psf: List[NDArray] = Field( + default=[], + description="List of Point Spread Functions to use for deconvolution. Each of which should be a 3D array." + ) + psf_num_iter: NonNegativeInt = Field( + default=10, + description="Number of iterations to perform in deconvolution" + ) + background: Union[float, Literal["auto", "second_last"]] = Field( + default=0, + description='Background value to subtract for deconvolution. Only used when decon_processing is set to GPU. This can either be a literal number, "auto" which uses the median of the last slice, or "second_last" which uses the median of the last slice.' + ) class CropParams(BaseModel, arbitrary_types_allowed=True): """ Parameters for the optional cropping step """ - roi_layer_list: ShapesData - z_start: NonNegativeInt = 0 - z_end: NonNegativeInt = 1 + roi_list: ShapesData = Field( + description="List of regions of interest, each of which must be an NxD array, where N is the number of vertices and D the coordinates of each vertex." + ) + z_range: Tuple[NonNegativeInt, NonNegativeInt] = Field( + default=None, + description="The range of Z slices to take. All Z slices before the first index or after the last index will be cropped out." + ) -class OutputParams(DefaultMixin, arbitrary_types_allowed=True): - #: The directory where this data will be saved - save_dir: Path +class OutputParams(FieldAccessMixin, arbitrary_types_allowed=True): + save_dir: Path = Field( + default = Path.cwd(), + description="The directory where the output data will be saved" + ) - #: The file name to save this as, without the directory name or file extension - save_name: str + save_name: str = Field( + description="The filename prefix that will be used for output files, without a leading directory or file extension. The final output files will have additional elements added to the end of this prefix to indicate the region of interest, channel, timepoint, file extension etc." + ) - #: The range of times to process - time_range: range + time_range: range = Field( + default = None, + description="The range of times to process. This defaults to all time points in the image array." + ) - #: The range of channels to process - channel_range: range + channel_range: range = Field( + default = None, + description="The range of channels to process. This defaults to all channels in the image array." + ) - #: The data type to save the result as - save_type: SaveFileType = SaveFileType.h5 + save_type: SaveFileType = Field( + default=SaveFileType.h5, + description=f"The data type to save the result as. This will also be used to determine the file extension of the output files. Choices: {enum_choices(SaveFileType)}" + ) @validator("time_range") def default_time_range(cls, v: Any, values: dict) -> range: @@ -193,27 +225,38 @@ def default_channel_range(cls, v: Any, values: dict) -> range: 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: DataArray +class DeskewParams(FieldAccessMixin, arbitrary_types_allowed=True): + image: DataArray = Field( + description="A 3-5D array containing the image data" + ) - #: Geometry of the light path - skew: DeskewDirection = DeskewDirection.Y - angle: float = 30.0 + skew: DeskewDirection = Field( + default=DeskewDirection.Y, + description=f"Axis along which to deskew the image. Choices: {enum_choices(DeskewDirection)}" + ) + angle: float = Field( + default=30.0, + description="Angle of deskewing, in degrees" + ) - #: Pixel size in microns - physical_pixel_sizes: DefinedPixelSizes = Field(default_factory=DefinedPixelSizes) + physical_pixel_sizes: DefinedPixelSizes = Field( + default_factory=DefinedPixelSizes, + description="Pixel size of the microscope, in microns" + ) - #: Dimensions of the deskewed output - deskew_vol_shape: Tuple[int, ...] = Field(init_var=False, default=None) + deskew_vol_shape: Tuple[int, ...] = Field( + init_var=False, + default=None, + description="Dimensions of the deskewed output. This is set automatically based on other input parameters, and doesn't need to be provided by the user." + ) - deskew_affine_transform: cle.AffineTransform3D = Field(init_var=False, default=None) + deskew_affine_transform: cle.AffineTransform3D = Field(init_var=False, default=None, description="Deskewing transformation function. This is set automatically based on other input parameters, and doesn't need to be provided by the user.") @property def dims(self): - return self.data.dims + return self.image.dims - @validator("data", pre=True) + @validator("image", 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 @@ -227,7 +270,7 @@ def reshaping(cls, v: Any): return array.transpose("T", "C", "Z", "Y", "X") def get_3d_slice(self) -> DataArray: - return self.data.sel(C=0, T=0) + return self.image.sel(C=0, T=0) @root_validator(pre=True) def set_deskew(cls, values: dict) -> dict: @@ -244,7 +287,6 @@ def set_deskew(cls, values: dict) -> dict: raise ValueError("deskew_vol_shape and deskew_affine_transform must be either both specified or neither specified") return values - class LatticeData(OutputParams, DeskewParams, arbitrary_types_allowed=True): """ Holds data and metadata for a given image in a consistent format @@ -259,8 +301,10 @@ class LatticeData(OutputParams, DeskewParams, arbitrary_types_allowed=True): #: If this is None, then cropping is disabled crop: Optional[CropParams] = None - #: If defined, this is a workflow to add lightsheet processing onto - workflow: Optional[Workflow] = None + workflow: Optional[Workflow] = Field( + default=None, + description="If defined, this is a workflow to add lightsheet processing onto" + ) @validator("time_range") def disjoint_time_range(cls, v: range, values: dict): @@ -363,12 +407,12 @@ def deconv_enabled(self) -> bool: @property def time(self) -> int: """Number of time points""" - return self.data.sizes["T"] + return self.image.sizes["T"] @property def channels(self) -> int: """Number of channels""" - return self.data.sizes["C"] + return self.image.sizes["C"] @property def new_dz(self): @@ -384,14 +428,14 @@ def slice_data(self, time: int, channel: int) -> DataArray: if channel > self.channels: raise ValueError("channel is out of range") - return self.data.sel(T=time, C=channel) + return self.image.sel(T=time, C=channel) - if len(self.data.shape) == 3: - return self.data - elif len(self.data.shape) == 4: - return self.data[time, :, :, :] - elif len(self.data.shape) == 5: - return self.data[time, channel, :, :, :] + if len(self.image.shape) == 3: + return self.image + elif len(self.image.shape) == 4: + return self.image[time, :, :, :] + elif len(self.image.shape) == 5: + return self.image[time, channel, :, :, :] raise Exception("Lattice data must be 3-5 dimensions") @@ -436,7 +480,7 @@ def _process_crop(self) -> Iterable[ProcessedVolume]: raise Exception("This function can only be called when crop is set") # We have an extra level of iteration for the crop path: iterating over each ROI - for roi_index, roi in enumerate(tqdm(self.crop.roi_layer_list, desc="ROI:", position=0)): + for roi_index, roi in enumerate(tqdm(self.crop.roi_list, desc="ROI:", position=0)): # pass arguments for save tiff, callable and function arguments logger.info("Processing ROI ", roi_index) @@ -462,8 +506,8 @@ def _process_crop(self) -> Iterable[ProcessedVolume]: voxel_size_z=self.dy, angle_in_degrees=self.angle, deskewed_volume=self.deskewed_volume, - z_start=self.crop.z_start, - z_end=self.crop.z_end, + z_start=self.crop.z_range[0], + z_end=self.crop.z_range[1], **deconv_args ), channel=ch, @@ -484,6 +528,7 @@ def _process_non_crop(self) -> Iterable[ProcessedVolume]: data= pycuda_decon( image=data, psf=self.deconvolution.psf[ch], + background=self.deconvolution.background, dzdata=self.dz, dxdata=self.dx, dzpsf=self.dz, diff --git a/plugin/napari_lattice/dock_widget.py b/plugin/napari_lattice/dock_widget.py index 398827f..4194dcd 100644 --- a/plugin/napari_lattice/dock_widget.py +++ b/plugin/napari_lattice/dock_widget.py @@ -57,7 +57,7 @@ def _make_model(self) -> LatticeData: deskew_args = self.LlszMenu.WidgetContainer.deskew_fields._get_kwargs() output_args = self.LlszMenu.WidgetContainer.output_fields._make_model() return LatticeData( - data=deskew_args["data"], + image=deskew_args["data"], angle=deskew_args["angle"], channel_range=output_args.channel_range, time_range=output_args.time_range, diff --git a/plugin/napari_lattice/fields.py b/plugin/napari_lattice/fields.py index f95251c..19d671f 100644 --- a/plugin/napari_lattice/fields.py +++ b/plugin/napari_lattice/fields.py @@ -314,7 +314,7 @@ def _get_kwargs(self) -> DeskewKwargs: def _make_model(self) -> DeskewParams: kwargs = self._get_kwargs() return DeskewParams( - data=kwargs["data"], + image=kwargs["data"], physical_pixel_sizes=kwargs["physical_pixel_sizes"], angle=kwargs["angle"], skew = kwargs["skew"] @@ -396,9 +396,8 @@ def _make_model(self) -> Optional[CropParams]: import numpy as np if self.fields_enabled.value: return CropParams( - z_start=self.z_range.value[0], - z_end=self.z_range.value[1], - roi_layer_list=ShapesData([np.array(shape.data) for shape in self.shapes.value]) + z_range=self.z_range.value, + roi_list=ShapesData([np.array(shape.data) for shape in self.shapes.value]) ) return None From 652a6de91f9ac4dba7ead1bbe7cf5f84ec47f703 Mon Sep 17 00:00:00 2001 From: Michael Milton Date: Thu, 14 Sep 2023 12:19:08 +1000 Subject: [PATCH 024/147] Refactor submodels into different files --- core/lls_core/__init__.py | 5 - core/lls_core/cmds/__main__.py | 165 +++++++++++-- core/lls_core/deconvolution.py | 2 +- core/lls_core/io.py | 2 +- core/lls_core/models/crop.py | 18 ++ core/lls_core/models/deconvolution.py | 34 +++ core/lls_core/models/deskew.py | 97 ++++++++ core/lls_core/{ => models}/lattice_data.py | 257 ++++++--------------- core/lls_core/models/output.py | 49 ++++ core/lls_core/models/utils.py | 24 ++ core/lls_core/types.py | 5 +- core/lls_core/workflow.py | 9 +- core/tests/test_deskew.py | 2 +- plugin/napari_lattice/dock_widget.py | 6 +- plugin/napari_lattice/fields.py | 6 +- plugin/napari_lattice/reader.py | 2 +- 16 files changed, 458 insertions(+), 225 deletions(-) create mode 100644 core/lls_core/models/crop.py create mode 100644 core/lls_core/models/deconvolution.py create mode 100644 core/lls_core/models/deskew.py rename core/lls_core/{ => models}/lattice_data.py (69%) create mode 100644 core/lls_core/models/output.py create mode 100644 core/lls_core/models/utils.py diff --git a/core/lls_core/__init__.py b/core/lls_core/__init__.py index 9d9bfb6..eb2eddf 100644 --- a/core/lls_core/__init__.py +++ b/core/lls_core/__init__.py @@ -13,11 +13,6 @@ class DeconvolutionChoice(StrEnum): opencl_gpu = "opencl_gpu" cpu = "cpu" -#Choice of File extension to save -class SaveFileType(StrEnum): - h5 = "h5" - tiff = "tiff" - #CONFIGURE LOGGING using a dictionary (can also be done with yaml file) import logging.config LOGGING_CONFIG = { diff --git a/core/lls_core/cmds/__main__.py b/core/lls_core/cmds/__main__.py index 205293f..c0a0f1b 100644 --- a/core/lls_core/cmds/__main__.py +++ b/core/lls_core/cmds/__main__.py @@ -1,42 +1,161 @@ # lattice_processing.py + # Run processing on command line instead of napari. # Example for deskewing files in a folder # python lattice_processing.py --input /home/pradeep/to_deskew --output /home/pradeep/output_save/ --processing deskew from pathlib import Path -from typing import List +from typing import Any, Dict, List, Optional, Tuple -from lls_core.lattice_data import ( +from lls_core.models.lattice_data import ( CropParams, - DeconvolutionParams, - DeskewParams, LatticeData, OutputParams, ) -from pydantic_cli import run_and_exit, DefaultConfig -from pydantic import Field - -class CliParams(DeskewParams, CropParams, OutputParams, DeconvolutionParams): - # The idea of this class is to re-use the logic from the Pydantic - # models, namely the validation, defaults etc, but to flatten them - # into one model so that pydantic-cli can handle it, and to override - # certain field definitions with CLI-specific data types +from lls_core.models.deskew import ( + DefinedPixelSizes, + skew, + angle, + physical_pixel_sizes, +) +from lls_core.models.deconvolution import ( + Background, + DeconvolutionParams, + psf_num_iter, + decon_processing, + background +) +from lls_core.models.output import ( + SaveFileType, + save_dir, + save_type, + channel_range, + time_range +) +from napari_workflows import Workflow +from lls_core.models.crop import z_range +from pydantic_cli import run_and_exit, DefaultConfig, pydantic_class_to_parser +from pydantic import DirectoryPath, Field, NonNegativeInt, validator, FilePath, BaseModel, root_validator +from lls_core import DeconvolutionChoice, DeskewDirection + +class + +class CliParams(BaseModel): + # We can't directly use LatticeData because it uses nested models which aren't + # supported by pydantic-cli, and also we need to modify some types to support the CLI e.g. by allowing filepath inputs + + class Config(DefaultConfig): + CLI_JSON_ENABLE = True + arbitrary_types_allowed=True + + # Deskew + image: FilePath = Field(description="Path to the image file to read, in a format readable by AICSImageIO, for example .tiff or .czi") + skew: DeskewDirection = skew + angle: float = angle + physical_pixel_sizes: Optional[DefinedPixelSizes] = physical_pixel_sizes + + # Deconvolution + deconvolution: bool = False + decon_processing: DeconvolutionChoice = decon_processing + psf: List[FilePath] = Field(default=[], description="A list of 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.") + psf_num_iter = psf_num_iter + background: Background = background + + # Crop + roi_list: List[FilePath] = Field(default=[], description="A list of paths pointing to regions of interest to crop to, in ImageJ format.") + z_range: Tuple[NonNegativeInt, NonNegativeInt] = z_range + + # Output + # This one needs overriding to accommodate the default value save_name: str = Field(default=None, description=OutputParams.get_description("save_name")) - image: Path = Field(description="Path to the image file to read, in a format readable by AICSImageIO, for example .tiff or .czi") - roi_list: List[Path] = Field(default=[], description="A list of paths pointing to regions of interest to crop to, in ImageJ format.") - psf: List[Path] = Field(default=[], description="A list of 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.") + save_dir: DirectoryPath = save_dir + save_type: SaveFileType = save_type + channel_range: range = channel_range + time_range: range = time_range + + # Workflow + workflow: Optional[FilePath] = Field(default = None, description="Path to a Napari Workflow file, in JSON format. If provided, the configured desekewing processing will be added to the chosen workflow.") + + @validator("save_name", pre=True) + def default_save_name(cls, v: Any, values: Dict): + # Use the input file path to provide a default output filename + if v is None and values.get("image", None): + path: Path = values["image"] + return path.stem + "_deskewed" + return v + + @root_validator(pre=True) + def metadata_from_path(cls, values: Dict) -> Dict: + + return values def to_lattice(self) -> LatticeData: + from aicsimageio import AICSImage + + crop = None + if len(self.roi_list) > 0: + crop = CropParams( + roi_list=[AICSImage(it).data for it in self.roi_list], + z_range=self.z_range + ) + + deconvolution = None + if self.deconvolution: + deconvolution = DeconvolutionParams( + decon_processing=self.decon_processing, + psf = [AICSImage(it).data for it in self.psf], + psf_num_iter = self.psf_num_iter, + ) + + workflow = None + if self.workflow is not None: + from lls_core.workflow import workflow_from_path + workflow = workflow_from_path(self.workflow) + return LatticeData( - **self.dict() - ) + image=AICSImage(self.image).xarray_dask_data, + angle=self.angle, + skew=self.skew, + physical_pixel_sizes=self.physical_pixel_sizes, - class Config(DefaultConfig): - CLI_JSON_ENABLE = True + crop=crop, + deconvolution=deconvolution, + workflow=workflow, + + save_type=self.save_type, + channel_range=self.channel_range, + time_range=self.time_range, + save_dir=self.save_dir, + save_name=self.save_name + ) -def _main(params: CliParams) -> int: - params.to_lattice().process().save_image() - return 0 +def make_parser(): + parser = pydantic_class_to_parser(CliParams, default_value_override={}) + parser.add_argument("--yaml-config", required=False, help="Path to configuration YAML file") + return parser if __name__ == '__main__': - run_and_exit(CliParams, _main) + parser = make_parser() + args = parser.parse_args() + arg_dict = vars(args) + + json_path = arg_dict.pop("json-config", None) + json_config = {} + if json_path: + import json + with open(json_path) as fp: + json_config = json.load(fp) + + yaml_path = arg_dict.pop("yaml-config", None) + yaml_config = {} + if yaml_path: + import yaml + with open(yaml_path) as fp: + json_config = yaml.safe_load(fp) + + config = CliParams( + **json_config, + **yaml_config, + **arg_dict + ) + config.to_lattice().process().save_image() diff --git a/core/lls_core/deconvolution.py b/core/lls_core/deconvolution.py index 4206f84..3a209d6 100644 --- a/core/lls_core/deconvolution.py +++ b/core/lls_core/deconvolution.py @@ -21,7 +21,7 @@ from lls_core.types import ArrayLike, is_arraylike if TYPE_CHECKING: - from lls_core.lattice_data import LatticeData + from lls_core.models.lattice_data import LatticeData logger = logging.getLogger(__name__) diff --git a/core/lls_core/io.py b/core/lls_core/io.py index 78b25fe..008b9a3 100644 --- a/core/lls_core/io.py +++ b/core/lls_core/io.py @@ -23,7 +23,7 @@ from lls_core.llsz_core import crop_volume_deskew from lls_core.deconvolution import skimage_decon, pycuda_decon from lls_core import config, DeconvolutionChoice, SaveFileType -from lls_core.lattice_data import LatticeData +from lls_core.models.lattice_data import LatticeData import os import numpy as np diff --git a/core/lls_core/models/crop.py b/core/lls_core/models/crop.py new file mode 100644 index 0000000..822bc98 --- /dev/null +++ b/core/lls_core/models/crop.py @@ -0,0 +1,18 @@ + +from typing import Tuple +from pydantic import BaseModel, Field, NonNegativeInt +from napari.types import ShapesData + + +z_range: Tuple[NonNegativeInt, NonNegativeInt] = Field( + default=None, + description="The range of Z slices to take. All Z slices before the first index or after the last index will be cropped out." +) +class CropParams(BaseModel, arbitrary_types_allowed=True): + """ + Parameters for the optional cropping step + """ + roi_list: ShapesData = Field( + description="List of regions of interest, each of which must be an NxD array, where N is the number of vertices and D the coordinates of each vertex." + ) + z_range: Tuple[NonNegativeInt, NonNegativeInt] = z_range diff --git a/core/lls_core/models/deconvolution.py b/core/lls_core/models/deconvolution.py new file mode 100644 index 0000000..993f29e --- /dev/null +++ b/core/lls_core/models/deconvolution.py @@ -0,0 +1,34 @@ + +from pydantic import BaseModel, Field, NonNegativeInt +from pydantic_numpy import NDArray + +from typing import List, Literal, Union + + +from lls_core import DeconvolutionChoice +from lls_core.models.utils import enum_choices + +decon_processing: DeconvolutionChoice = Field( + default=DeconvolutionChoice.cpu, + description=f"Hardware to use to perform the deconvolution. Choices: {enum_choices(DeconvolutionChoice)}") +psf: List[NDArray] = Field( + default=[], + description="List of Point Spread Functions to use for deconvolution. Each of which should be a 3D array." +) +psf_num_iter: NonNegativeInt = Field( + default=10, + description="Number of iterations to perform in deconvolution" +) +Background = Union[float, Literal["auto", "second_last"]] +background: Background = Field( + default=0, + description='Background value to subtract for deconvolution. Only used when decon_processing is set to GPU. This can either be a literal number, "auto" which uses the median of the last slice, or "second_last" which uses the median of the last slice.' +) +class DeconvolutionParams(BaseModel, arbitrary_types_allowed=True): + """ + Parameters for the optional deconvolution step + """ + decon_processing: DeconvolutionChoice = decon_processing + psf: List[NDArray] = psf + psf_num_iter: NonNegativeInt = psf_num_iter + background: Union[float, Literal["auto", "second_last"]] = background diff --git a/core/lls_core/models/deskew.py b/core/lls_core/models/deskew.py new file mode 100644 index 0000000..dc40c0c --- /dev/null +++ b/core/lls_core/models/deskew.py @@ -0,0 +1,97 @@ +from __future__ import annotations +# class for initializing lattice data and setting metadata +# TODO: handle scenes +from pydantic import Field, NonNegativeFloat, validator, root_validator +# from numpy.typing import NDArray + +from typing import Any, Tuple +from typing_extensions import Self, TYPE_CHECKING + +import pyclesperanto_prototype as cle + +from lls_core import DeskewDirection +from xarray import DataArray + +from lls_core.models.utils import FieldAccessMixin, enum_choices + +if TYPE_CHECKING: + from aicsimageio.types import PhysicalPixelSizes + +class DefinedPixelSizes(FieldAccessMixin): + """ + Like PhysicalPixelSizes, but it's a dataclass, and + none of its fields are None + """ + X: NonNegativeFloat = 0.1499219272808386 + Y: NonNegativeFloat = 0.1499219272808386 + Z: NonNegativeFloat = 0.3 + + @classmethod + def from_physical(cls, pixels: PhysicalPixelSizes) -> Self: + from lls_core.utils import raise_if_none + + return DefinedPixelSizes( + X=raise_if_none(pixels.X, "All pixels must be defined"), + Y=raise_if_none(pixels.Y, "All pixels must be defined"), + Z=raise_if_none(pixels.Z, "All pixels must be defined"), + ) + + +class DeskewParams(FieldAccessMixin, arbitrary_types_allowed=True): + image: DataArray = Field( + description="A 3-5D array containing the image data" + ) + skew: DeskewDirection = Field( + default=DeskewDirection.Y, + description=f"Axis along which to deskew the image. Choices: {enum_choices(DeskewDirection)}" + ) + angle: float = Field( + default=30.0, + description="Angle of deskewing, in degrees" + ) + physical_pixel_sizes: DefinedPixelSizes = Field( + default_factory=DefinedPixelSizes, + description="Pixel size of the microscope, in microns" +) + deskew_vol_shape: Tuple[int, ...] = Field( + init_var=False, + default=None, + description="Dimensions of the deskewed output. This is set automatically based on other input parameters, and doesn't need to be provided by the user." + ) + + deskew_affine_transform: cle.AffineTransform3D = Field(init_var=False, default=None, description="Deskewing transformation function. This is set automatically based on other input parameters, and doesn't need to be provided by the user.") + + @property + def dims(self): + return self.image.dims + + @validator("image", 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.image.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: 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(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 diff --git a/core/lls_core/lattice_data.py b/core/lls_core/models/lattice_data.py similarity index 69% rename from core/lls_core/lattice_data.py rename to core/lls_core/models/lattice_data.py index 7455ca1..e08dc41 100644 --- a/core/lls_core/lattice_data.py +++ b/core/lls_core/models/lattice_data.py @@ -1,8 +1,7 @@ from __future__ import annotations # class for initializing lattice data and setting metadata # TODO: handle scenes -from os import getcwd -from pydantic import BaseModel, Field, NonNegativeInt, NonNegativeFloat, root_validator, validator +from pydantic import BaseModel, DirectoryPath, Field, NonNegativeInt, validator from aicsimageio.aics_image import AICSImage # from numpy.typing import NDArray import math @@ -10,52 +9,36 @@ import dask as da from itertools import groupby import tifffile -from pydantic_numpy import NDArray -from typing import Any, Iterable, List, Literal, Optional, TYPE_CHECKING, Tuple, Type, TypeVar, Union -from typing_extensions import TypedDict, Self -from pathlib import Path +from typing import Any, Iterable, List, Literal, Optional, TYPE_CHECKING, Tuple, Union, overload +from typing_extensions import TypedDict, Unpack, NotRequired, get_args from aicsimageio.types import PhysicalPixelSizes import pyclesperanto_prototype as cle from tqdm import tqdm -from lls_core import DeskewDirection, DeconvolutionChoice, SaveFileType +from lls_core import DeskewDirection, DeconvolutionChoice from lls_core.deconvolution import pycuda_decon, skimage_decon from lls_core.llsz_core import crop_volume_deskew -from lls_core.utils import get_deskewed_shape +from lls_core.models.crop import CropParams +from lls_core.models.deconvolution import DeconvolutionParams +from lls_core.models.output import OutputParams, SaveFileType from lls_core.types import ArrayLike +from lls_core.models.deskew import DeskewParams from napari_workflows import Workflow -from napari.types import ShapesData from xarray import DataArray if TYPE_CHECKING: import pyclesperanto_prototype as cle from enum import Enum + from pathlib import Path + from lls_core.models.deskew import DefinedPixelSizes + from aicsimageio.types import ImageLike import logging logger = logging.getLogger(__name__) -def enum_choices(enum: Type[Enum]) -> str: - """ - Returns a human readable list of enum choices - """ - return "{" + ", ".join([it.name for it in enum]) + "}" - -class FieldAccessMixin(BaseModel): - """ - Adds methods to a BaseModel for accessing useful field information - """ - - @classmethod - def get_default(cls, field_name: str): - return cls.__fields__[field_name].get_default() - - @classmethod - def get_description(cls, field_name: str) -> str: - return cls.__fields__[field_name].field_info.description - class ProcessedVolume(BaseModel, arbitrary_types_allowed=True): """ A slice of the image processing result @@ -131,161 +114,28 @@ def make_filename_prefix(prefix: Optional[str] = None, roi_index: Optional[int] components.append(f"T{time}") return "_".join(components) -class DefinedPixelSizes(FieldAccessMixin): - """ - Like PhysicalPixelSizes, but it's a dataclass, and - none of its fields are None - """ - X: NonNegativeFloat = 0.1499219272808386 - Y: NonNegativeFloat = 0.1499219272808386 - Z: NonNegativeFloat = 0.3 - - @classmethod - def from_physical(cls, pixels: PhysicalPixelSizes) -> Self: - from lls_core.utils import raise_if_none - - return DefinedPixelSizes( - X=raise_if_none(pixels.X, "All pixels must be defined"), - Y=raise_if_none(pixels.Y, "All pixels must be defined"), - Z=raise_if_none(pixels.Z, "All pixels must be defined"), - ) - -class DeconvolutionParams(BaseModel, arbitrary_types_allowed=True): - """ - Parameters for the optional deconvolution step - """ - decon_processing: DeconvolutionChoice = Field( - default=DeconvolutionChoice.cpu, - description=f"Hardware to use to perform the deconvolution. Choices: {enum_choices(DeconvolutionChoice)}") - psf: List[NDArray] = Field( - default=[], - description="List of Point Spread Functions to use for deconvolution. Each of which should be a 3D array." - ) - psf_num_iter: NonNegativeInt = Field( - default=10, - description="Number of iterations to perform in deconvolution" - ) - background: Union[float, Literal["auto", "second_last"]] = Field( - default=0, - description='Background value to subtract for deconvolution. Only used when decon_processing is set to GPU. This can either be a literal number, "auto" which uses the median of the last slice, or "second_last" which uses the median of the last slice.' - ) - -class CropParams(BaseModel, arbitrary_types_allowed=True): - """ - Parameters for the optional cropping step - """ - roi_list: ShapesData = Field( - description="List of regions of interest, each of which must be an NxD array, where N is the number of vertices and D the coordinates of each vertex." - ) - z_range: Tuple[NonNegativeInt, NonNegativeInt] = Field( - default=None, - description="The range of Z slices to take. All Z slices before the first index or after the last index will be cropped out." - ) - -class OutputParams(FieldAccessMixin, arbitrary_types_allowed=True): - save_dir: Path = Field( - default = Path.cwd(), - description="The directory where the output data will be saved" - ) - - save_name: str = Field( - description="The filename prefix that will be used for output files, without a leading directory or file extension. The final output files will have additional elements added to the end of this prefix to indicate the region of interest, channel, timepoint, file extension etc." - ) - - time_range: range = Field( - default = None, - description="The range of times to process. This defaults to all time points in the image array." - ) - - channel_range: range = Field( - default = None, - description="The range of channels to process. This defaults to all channels in the image array." - ) - - save_type: SaveFileType = Field( - default=SaveFileType.h5, - description=f"The data type to save the result as. This will also be used to determine the file extension of the output files. Choices: {enum_choices(SaveFileType)}" - ) - - @validator("time_range") - def default_time_range(cls, v: Any, values: dict) -> range: - """ - Sets the default time range if undefined - """ - if v is None: - return range(values["data"].sizes["T"] + 1) - return v - - @validator("channel_range") - def default_channel_range(cls, v: Any, values: dict) -> range: - """ - Sets the default channel range if undefined - """ - if v is None: - return range(values["data"].sizes["C"] + 1) - return v - -class DeskewParams(FieldAccessMixin, arbitrary_types_allowed=True): - image: DataArray = Field( - description="A 3-5D array containing the image data" - ) - skew: DeskewDirection = Field( - default=DeskewDirection.Y, - description=f"Axis along which to deskew the image. Choices: {enum_choices(DeskewDirection)}" - ) - angle: float = Field( - default=30.0, - description="Angle of deskewing, in degrees" - ) +workflow: Optional[Workflow] = Field( + default=None, + description="If defined, this is a workflow to add lightsheet processing onto" +) - physical_pixel_sizes: DefinedPixelSizes = Field( - default_factory=DefinedPixelSizes, - description="Pixel size of the microscope, in microns" - ) - - deskew_vol_shape: Tuple[int, ...] = Field( - init_var=False, - default=None, - description="Dimensions of the deskewed output. This is set automatically based on other input parameters, and doesn't need to be provided by the user." - ) +class CommonOutputArgs(TypedDict): + # Arguments + save_dir: NotRequired[DirectoryPath] + save_name: NotRequired[str] + save_type: SaveFileType + time_range: range + channel_range: range - deskew_affine_transform: cle.AffineTransform3D = Field(init_var=False, default=None, description="Deskewing transformation function. This is set automatically based on other input parameters, and doesn't need to be provided by the user.") +class CommonDeskewArgs(TypedDict): + skew: DeskewDirection + angle: float - @property - def dims(self): - return self.image.dims - - @validator("image", 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.image.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: 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(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 +class CommonLatticeArgs(CommonDeskewArgs, CommonOutputArgs): + deconvolution: Optional[DeconvolutionParams] + crop: Optional[CropParams] + workflow: Optional[Workflow] class LatticeData(OutputParams, DeskewParams, arbitrary_types_allowed=True): """ @@ -295,16 +145,57 @@ class LatticeData(OutputParams, DeskewParams, arbitrary_types_allowed=True): # Note: originally the save-related fields were included via composition and not inheritance # (similar to how `crop` and `workflow` are handled), but this was impractical for implementing validations + @overload + def make( + self, + image: Union[ImageLike, AICSImage], + physical_pixel_sizes: Optional[DefinedPixelSizes], + **kwargs: Unpack[CommonLatticeArgs] + ): + ... + @overload + def make( + self, + image: ArrayLike, + physical_pixel_sizes: DefinedPixelSizes, + **kwargs: Unpack[CommonLatticeArgs] + ): + ... + def make( + self, + image: Any, + physical_pixel_sizes: Optional[DefinedPixelSizes] = None, + **kwargs: Unpack[CommonLatticeArgs] + ): + if isinstance(image, get_args(ImageLike)): + image = AICSImage(image) + + meta_pixels = None + if isinstance(image, AICSImage): + if all(image.physical_pixel_sizes): + meta_pixels = DefinedPixelSizes.from_physical(image.physical_pixel_sizes) + image = image.xarray_dask_data + + image = DataArray(image) + + combined_pixels = physical_pixel_sizes or meta_pixels + if combined_pixels is None: + raise ValueError("Pixel sizes") + + LatticeData( + image=image, + physical_pixel_sizes=combined_pixels, + **kwargs + ) + + #: If this is None, then deconvolution is disabled deconvolution: Optional[DeconvolutionParams] = None #: If this is None, then cropping is disabled crop: Optional[CropParams] = None - workflow: Optional[Workflow] = Field( - default=None, - description="If defined, this is a workflow to add lightsheet processing onto" - ) + workflow: Optional[Workflow] = workflow @validator("time_range") def disjoint_time_range(cls, v: range, values: dict): diff --git a/core/lls_core/models/output.py b/core/lls_core/models/output.py new file mode 100644 index 0000000..10eacc7 --- /dev/null +++ b/core/lls_core/models/output.py @@ -0,0 +1,49 @@ +from typing import Any, NotRequired, TypedDict +from pydantic import Field, validator, DirectoryPath +from strenum import StrEnum +from os import getcwd + +from lls_core.models.utils import FieldAccessMixin, enum_choices + +#Choice of File extension to save +class SaveFileType(StrEnum): + h5 = "h5" + tiff = "tiff" + +class OutputParams(FieldAccessMixin, arbitrary_types_allowed=True): + save_dir: DirectoryPath = Field( + default = getcwd(), + description="The directory where the output data will be saved" + ) + save_name: str = Field( + description="The filename prefix that will be used for output files, without a leading directory or file extension. The final output files will have additional elements added to the end of this prefix to indicate the region of interest, channel, timepoint, file extension etc." + ) + save_type: SaveFileType = Field( + default=SaveFileType.h5, + description=f"The data type to save the result as. This will also be used to determine the file extension of the output files. Choices: {enum_choices(SaveFileType)}" +) + time_range: range = Field( + default = None, + description="The range of times to process. This defaults to all time points in the image array." + ) + channel_range: range = Field( + description="The filename prefix that will be used for output files, without a leading directory or file extension. The final output files will have additional elements added to the end of this prefix to indicate the region of interest, channel, timepoint, file extension etc." +) + + @validator("time_range") + def default_time_range(cls, v: Any, values: dict) -> range: + """ + Sets the default time range if undefined + """ + if v is None: + return range(values["data"].sizes["T"] + 1) + return v + + @validator("channel_range") + def default_channel_range(cls, v: Any, values: dict) -> range: + """ + Sets the default channel range if undefined + """ + if v is None: + return range(values["data"].sizes["C"] + 1) + return v diff --git a/core/lls_core/models/utils.py b/core/lls_core/models/utils.py new file mode 100644 index 0000000..4bb912e --- /dev/null +++ b/core/lls_core/models/utils.py @@ -0,0 +1,24 @@ + +from typing import Type +from enum import Enum +from pydantic import BaseModel + + +def enum_choices(enum: Type[Enum]) -> str: + """ + Returns a human readable list of enum choices + """ + return "{" + ", ".join([it.name for it in enum]) + "}" + +class FieldAccessMixin(BaseModel): + """ + Adds methods to a BaseModel for accessing useful field information + """ + + @classmethod + def get_default(cls, field_name: str): + return cls.__fields__[field_name].get_default() + + @classmethod + def get_description(cls, field_name: str) -> str: + return cls.__fields__[field_name].field_info.description diff --git a/core/lls_core/types.py b/core/lls_core/types.py index 077586f..f71844f 100644 --- a/core/lls_core/types.py +++ b/core/lls_core/types.py @@ -6,8 +6,9 @@ import numpy as np import pydantic_numpy.dtype as pnd from pydantic_numpy import NDArray, NDArrayFp32, NumpyModel +from xarray import DataArray -ArrayLike: TypeAlias = Union[DaskArray, NDArray, OCLArray] +ArrayLike: TypeAlias = Union[DaskArray, NDArray, OCLArray, DataArray] def is_arraylike(arr: Any) -> TypeGuard[ArrayLike]: - return isinstance(arr, (DaskArray, np.ndarray, OCLArray)) + return isinstance(arr, (DaskArray, np.ndarray, OCLArray, DataArray)) diff --git a/core/lls_core/workflow.py b/core/lls_core/workflow.py index 63ffe06..4183e9d 100644 --- a/core/lls_core/workflow.py +++ b/core/lls_core/workflow.py @@ -135,7 +135,7 @@ def import_script(script: Path): sys.modules[module_name] = module spec.loader.exec_module(module) -def import_workflow_modules(workflow: Path) -> None: +def _import_workflow_modules(workflow: Path) -> None: """ Imports all the Python files that might be used in a given custom workflow @@ -155,6 +155,13 @@ def import_workflow_modules(workflow: Path) -> None: else: logger.info(f"{counter} custom modules imported") +def workflow_from_path(workflow: Path) -> Workflow: + """ + Imports the dependency modules for a workflow, and loads it from disk + """ + from napari_workflows._io_yaml_v1 import load_workflow + _import_workflow_modules(workflow) + return load_workflow(str(workflow)) def process_custom_workflow_output(workflow_output: Union[dict, list, ArrayLike], save_dir: Optional[str]=None, diff --git a/core/tests/test_deskew.py b/core/tests/test_deskew.py index 8cd06c9..1b30dc7 100644 --- a/core/tests/test_deskew.py +++ b/core/tests/test_deskew.py @@ -1,7 +1,7 @@ #filename and function name should start with "test_" when using pytest import pyclesperanto_prototype as cle import numpy as np -from lls_core.lattice_data import lattice_fom_array +from lls_core.models.lattice_data import lattice_fom_array def test_deskew(): diff --git a/plugin/napari_lattice/dock_widget.py b/plugin/napari_lattice/dock_widget.py index 4194dcd..c5a6560 100644 --- a/plugin/napari_lattice/dock_widget.py +++ b/plugin/napari_lattice/dock_widget.py @@ -4,8 +4,8 @@ from typing import Union import numpy as np -from lls_core.lattice_data import LatticeData -from lls_core.workflow import import_workflow_modules +from lls_core.models.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 napari import Viewer @@ -147,7 +147,7 @@ def get_workflow(source: Union[Path, Viewer]) -> Workflow: user_workflow = WorkflowManager.install(source).workflow logger.info("Workflow installed") else: - import_workflow_modules(source) + _import_workflow_modules(source) user_workflow = load_workflow(str(source)) if not isinstance(user_workflow, Workflow): diff --git a/plugin/napari_lattice/fields.py b/plugin/napari_lattice/fields.py index 19d671f..537a1a1 100644 --- a/plugin/napari_lattice/fields.py +++ b/plugin/napari_lattice/fields.py @@ -12,7 +12,7 @@ Log_Levels, SaveFileType, ) -from lls_core.lattice_data import ( +from lls_core.models.lattice_data import ( CropParams, DeconvolutionParams, DefinedPixelSizes, @@ -421,15 +421,13 @@ def _workflow_path(self) -> bool: return self.workflow_source.value == WorkflowSource.CustomPath def _make_model(self) -> Optional[Workflow]: - from lls_core.workflow import import_workflow_modules + from lls_core.workflow import _import_workflow_modules from napari_workflows._io_yaml_v1 import load_workflow if not self.fields_enabled.value: return None if self.workflow_source.value == WorkflowSource.ActiveWorkflow: return WorkflowManager.install(self.parent_viewer).workflow else: - import_workflow_modules(self.workflow_path.value) - return load_workflow(str(self.workflow_path.value)) # @magicclass(name="5. Output") class OutputFields(NapariFieldGroup, FieldGroup): diff --git a/plugin/napari_lattice/reader.py b/plugin/napari_lattice/reader.py index dac3452..9d47be2 100644 --- a/plugin/napari_lattice/reader.py +++ b/plugin/napari_lattice/reader.py @@ -21,7 +21,7 @@ from typing_extensions import Literal from typing import Any, Optional, cast, Tuple, Collection -from lls_core.lattice_data import DefinedPixelSizes, lattice_params_from_aics, img_from_array, AicsLatticeParams, PhysicalPixelSizes +from lls_core.models.lattice_data import DefinedPixelSizes, lattice_params_from_aics, img_from_array, AicsLatticeParams, PhysicalPixelSizes from lls_core.types import ArrayLike class NapariImageParams(AicsLatticeParams): From a0e4bb126c36b9a693db091f1993f1793031db76 Mon Sep 17 00:00:00 2001 From: Michael Milton Date: Fri, 15 Sep 2023 00:35:18 +1000 Subject: [PATCH 025/147] Rewrite to use typer, all in on pydantic validators --- core/lls_core/cmds/__main__.py | 391 ++++++++++++++++---------- core/lls_core/models/crop.py | 52 +++- core/lls_core/models/deconvolution.py | 98 +++++-- core/lls_core/models/deskew.py | 14 +- core/lls_core/models/lattice_data.py | 40 ++- core/lls_core/models/output.py | 24 +- core/lls_core/models/utils.py | 14 +- core/lls_core/types.py | 15 +- core/pyproject.toml | 8 + 9 files changed, 444 insertions(+), 212 deletions(-) diff --git a/core/lls_core/cmds/__main__.py b/core/lls_core/cmds/__main__.py index c0a0f1b..9698fcc 100644 --- a/core/lls_core/cmds/__main__.py +++ b/core/lls_core/cmds/__main__.py @@ -4,158 +4,249 @@ # Example for deskewing files in a folder # python lattice_processing.py --input /home/pradeep/to_deskew --output /home/pradeep/output_save/ --processing deskew +from enum import auto +import json from pathlib import Path -from typing import Any, Dict, List, Optional, Tuple - -from lls_core.models.lattice_data import ( - CropParams, - LatticeData, - OutputParams, -) -from lls_core.models.deskew import ( - DefinedPixelSizes, - skew, - angle, - physical_pixel_sizes, -) -from lls_core.models.deconvolution import ( - Background, - DeconvolutionParams, - psf_num_iter, - decon_processing, - background -) -from lls_core.models.output import ( - SaveFileType, - save_dir, - save_type, - channel_range, - time_range -) -from napari_workflows import Workflow -from lls_core.models.crop import z_range -from pydantic_cli import run_and_exit, DefaultConfig, pydantic_class_to_parser -from pydantic import DirectoryPath, Field, NonNegativeInt, validator, FilePath, BaseModel, root_validator +from typing import Any, Dict, List, Literal, Optional, Tuple +from typing_extensions import Annotated +from strenum import StrEnum + +from lls_core.models.lattice_data import LatticeData +from lls_core.models.deskew import DefinedPixelSizes, DeskewParams +from lls_core.models.deconvolution import DeconvolutionParams +from lls_core.models.crop import CropParams +from lls_core.models.output import OutputParams from lls_core import DeconvolutionChoice, DeskewDirection +from lls_core.types import image_like_to_image +import typer + +from lls_core.models.output import SaveFileType +from toolz.dicttoolz import merge + +# class + +# class CliParams(BaseModel): +# # We can't directly use LatticeData because it uses nested models which aren't +# # supported by pydantic-cli, and also we need to modify some types to support the CLI e.g. by allowing filepath inputs + +# class Config(DefaultConfig): +# CLI_JSON_ENABLE = True +# arbitrary_types_allowed=True + +# # Deskew +# image: FilePath = Field(description="Path to the image file to read, in a format readable by AICSImageIO, for example .tiff or .czi") +# skew: DeskewDirection = skew +# angle: float = angle +# physical_pixel_sizes: Optional[DefinedPixelSizes] = physical_pixel_sizes + +# # Deconvolution +# deconvolution: bool = False +# decon_processing: DeconvolutionChoice = decon_processing +# psf: List[FilePath] = Field(default=[], description="A list of 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.") +# psf_num_iter = psf_num_iter +# background: Background = background + +# # Crop +# roi_list: List[FilePath] = Field(default=[], description="A list of paths pointing to regions of interest to crop to, in ImageJ format.") +# z_range: Tuple[NonNegativeInt, NonNegativeInt] = z_range + +# # Output +# # This one needs overriding to accommodate the default value +# save_name: str = Field(default=None, description=OutputParams.get_description("save_name")) +# save_dir: DirectoryPath = save_dir +# save_type: SaveFileType = save_type +# channel_range: range = channel_range +# time_range: range = time_range + +# # Workflow +# workflow: Optional[FilePath] = Field(default = None, description="Path to a Napari Workflow file, in JSON format. If provided, the configured desekewing processing will be added to the chosen workflow.") + +# @validator("save_name", pre=True) +# def default_save_name(cls, v: Any, values: Dict): +# # Use the input file path to provide a default output filename +# if v is None and values.get("image", None): +# path: Path = values["image"] +# return path.stem + "_deskewed" +# return v + +# @root_validator(pre=True) +# def metadata_from_path(cls, values: Dict) -> Dict: + +# return values + +# def to_lattice(self) -> LatticeData: +# from aicsimageio import AICSImage + +# crop = None +# if len(self.roi_list) > 0: +# crop = CropParams( +# roi_list=[AICSImage(it).data for it in self.roi_list], +# z_range=self.z_range +# ) + +# deconvolution = None +# if self.deconvolution: +# deconvolution = DeconvolutionParams( +# decon_processing=self.decon_processing, +# psf = [AICSImage(it).data for it in self.psf], +# psf_num_iter = self.psf_num_iter, +# ) + +# workflow = None +# if self.workflow is not None: +# from lls_core.workflow import workflow_from_path +# workflow = workflow_from_path(self.workflow) + +# return LatticeData( +# image=AICSImage(self.image).xarray_dask_data, +# angle=self.angle, +# skew=self.skew, +# physical_pixel_sizes=self.physical_pixel_sizes, + +# crop=crop, +# deconvolution=deconvolution, +# workflow=workflow, + +# save_type=self.save_type, +# channel_range=self.channel_range, +# time_range=self.time_range, +# save_dir=self.save_dir, +# save_name=self.save_name +# ) + +# def make_parser(): +# parser = pydantic_class_to_parser(CliParams, default_value_override={}) +# parser.add_argument("--yaml-config", required=False, help="Path to configuration YAML file") +# return parser +class CliDeskewDirection(StrEnum): + X = auto() + Y = auto() + +def main( + image: Path = typer.Argument(help="Path to the image file to read, in a format readable by AICSImageIO, for example .tiff or .czi"), + skew: CliDeskewDirection = typer.Option( + default=DeskewParams.get_default("skew").name, + help=DeskewParams.get_description("skew") + ),# DeskewParams.make_typer_field("skew"), + angle: float = DeskewParams.make_typer_field("angle") , + pixel_sizes: Tuple[float, float, float] = typer.Option( + ( + LatticeData.get_default("physical_pixel_sizes").X, + LatticeData.get_default("physical_pixel_sizes").Y, + LatticeData.get_default("physical_pixel_sizes").Z, + ), help=DeskewParams.get_description("physical_pixel_sizes") + ". This takes three arguments, corresponding to the X Y and Z pixel dimensions respectively" + ), + + rois: List[Path] = typer.Option([], help="A list of paths pointing to regions of interest to crop to, in ImageJ format."), + # 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] = typer.Option(None, help="The index of the first Z slice to use. All prior Z slices will be discarded."), + z_end: Optional[int] = typer.Option(None, help="The index of the last Z slice to use. The selected index and all subsequent Z slices will be discarded."), + + enable_deconvolution: Annotated[bool, typer.Option("--deconvolution/--disable-deconvolution")] = False, + decon_processing: DeconvolutionChoice = DeconvolutionParams.make_typer_field("decon_processing"), + psf: Annotated[List[Path], typer.Option(help="A list of 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.")] = [], + psf_num_iter: int = DeconvolutionParams.make_typer_field("psf_num_iter"), + background: str = DeconvolutionParams.make_typer_field("background"), + + time_start: Optional[int] = typer.Option(None, help="Index of the first time slice to use (inclusive)"), + time_end: Optional[int] = typer.Option(None, help="Index of the first time slice to use (exclusive)"), + + channel_start: Optional[int] = typer.Option(None, help="Index of the first channel slice to use (inclusive)"), + channel_end: Optional[int] = typer.Option(None, help="Index of the first channel slice to use (exclusive)"), + + save_dir: Path = OutputParams.make_typer_field("save_dir"), + save_name: Optional[str] = OutputParams.make_typer_field("save_name"), + save_type: SaveFileType = OutputParams.make_typer_field("save_type"), + + workflow: Optional[Path] = typer.Option(None, help="Path to a Napari Workflow file, in JSON format. If provided, the configured desekewing processing will be added to the chosen workflow."), + json_config: Optional[Path] = typer.Option(None), + yaml_config: Optional[Path] = typer.Option(None) +): + cli_args = dict( + image=image, + angle=angle, + skew=DeskewDirection[skew.name], + physical_pixel_sizes=DefinedPixelSizes( + X=pixel_sizes[0], + Y=pixel_sizes[1], + Z=pixel_sizes[2], + ), + crop=None if len(rois) == 0 else dict( + roi_list=rois, + z_range=(z_start, z_end) + ), + deconvolution=None if not enable_deconvolution else dict( + decon_processing = decon_processing, + psf = psf, + psf_num_iter = psf_num_iter, + background = background + ), + workflow=None, + + time_range=(time_start, time_end), + channel_range=(channel_start, channel_end), + save_dir=save_dir, + save_name=save_name, + save_type=save_type + ) -class - -class CliParams(BaseModel): - # We can't directly use LatticeData because it uses nested models which aren't - # supported by pydantic-cli, and also we need to modify some types to support the CLI e.g. by allowing filepath inputs - - class Config(DefaultConfig): - CLI_JSON_ENABLE = True - arbitrary_types_allowed=True - - # Deskew - image: FilePath = Field(description="Path to the image file to read, in a format readable by AICSImageIO, for example .tiff or .czi") - skew: DeskewDirection = skew - angle: float = angle - physical_pixel_sizes: Optional[DefinedPixelSizes] = physical_pixel_sizes - - # Deconvolution - deconvolution: bool = False - decon_processing: DeconvolutionChoice = decon_processing - psf: List[FilePath] = Field(default=[], description="A list of 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.") - psf_num_iter = psf_num_iter - background: Background = background - - # Crop - roi_list: List[FilePath] = Field(default=[], description="A list of paths pointing to regions of interest to crop to, in ImageJ format.") - z_range: Tuple[NonNegativeInt, NonNegativeInt] = z_range - - # Output - # This one needs overriding to accommodate the default value - save_name: str = Field(default=None, description=OutputParams.get_description("save_name")) - save_dir: DirectoryPath = save_dir - save_type: SaveFileType = save_type - channel_range: range = channel_range - time_range: range = time_range - - # Workflow - workflow: Optional[FilePath] = Field(default = None, description="Path to a Napari Workflow file, in JSON format. If provided, the configured desekewing processing will be added to the chosen workflow.") - - @validator("save_name", pre=True) - def default_save_name(cls, v: Any, values: Dict): - # Use the input file path to provide a default output filename - if v is None and values.get("image", None): - path: Path = values["image"] - return path.stem + "_deskewed" - return v - - @root_validator(pre=True) - def metadata_from_path(cls, values: Dict) -> Dict: - - return values - - def to_lattice(self) -> LatticeData: - from aicsimageio import AICSImage - - crop = None - if len(self.roi_list) > 0: - crop = CropParams( - roi_list=[AICSImage(it).data for it in self.roi_list], - z_range=self.z_range - ) - - deconvolution = None - if self.deconvolution: - deconvolution = DeconvolutionParams( - decon_processing=self.decon_processing, - psf = [AICSImage(it).data for it in self.psf], - psf_num_iter = self.psf_num_iter, - ) - - workflow = None - if self.workflow is not None: - from lls_core.workflow import workflow_from_path - workflow = workflow_from_path(self.workflow) - - return LatticeData( - image=AICSImage(self.image).xarray_dask_data, - angle=self.angle, - skew=self.skew, - physical_pixel_sizes=self.physical_pixel_sizes, - - crop=crop, - deconvolution=deconvolution, - workflow=workflow, - - save_type=self.save_type, - channel_range=self.channel_range, - time_range=self.time_range, - save_dir=self.save_dir, - save_name=self.save_name - ) - -def make_parser(): - parser = pydantic_class_to_parser(CliParams, default_value_override={}) - parser.add_argument("--yaml-config", required=False, help="Path to configuration YAML file") - return parser - -if __name__ == '__main__': - parser = make_parser() - args = parser.parse_args() - arg_dict = vars(args) - - json_path = arg_dict.pop("json-config", None) - json_config = {} - if json_path: + json_args = {} + if json_config is not None: import json - with open(json_path) as fp: - json_config = json.load(fp) - - yaml_path = arg_dict.pop("yaml-config", None) - yaml_config = {} - if yaml_path: - import yaml - with open(yaml_path) as fp: - json_config = yaml.safe_load(fp) - - config = CliParams( - **json_config, - **yaml_config, - **arg_dict + with json_config.open() as fp: + json_args = json.load(fp) + + yaml_args = {} + if yaml_config is not None: + with yaml_config.open() as fp: + from yaml import safe_load + yaml_args = safe_load(fp) + + return LatticeData.parse_obj(merge(yaml_args, json_args, cli_args)) + + # img_data = image_like_to_image(image) + + # crop = None + # if len(rois) > 0: + # crop = CropParams.from_img_metadata( + # img_data, + # roi_list=[image_like_to_image(it) for it in rois], + # z_range=z_range + # ) + + # decon = None + # if enable_deconvolution: + # decon = DeconvolutionParams( + # decon_processing=decon_processing, + # # background= + # ) + + LatticeData( ) - config.to_lattice().process().save_image() + +if __name__ == '__main__': + typer.run(main) + # parser = make_parser() + # args = parser.parse_args() + # arg_dict = vars(args) + + # json_path = arg_dict.pop("json-config", None) + # json_config = {} + # if json_path: + # import json + # with open(json_path) as fp: + # json_config = json.load(fp) + + # yaml_path = arg_dict.pop("yaml-config", None) + # yaml_config = {} + # if yaml_path: + # import yaml + # with open(yaml_path) as fp: + # json_config = yaml.safe_load(fp) + + # config = CliParams( + # **json_config, + # **yaml_config, + # **arg_dict + # ) + # config.to_lattice().process().save_image() diff --git a/core/lls_core/models/crop.py b/core/lls_core/models/crop.py index 822bc98..1751f97 100644 --- a/core/lls_core/models/crop.py +++ b/core/lls_core/models/crop.py @@ -1,18 +1,50 @@ - -from typing import Tuple -from pydantic import BaseModel, Field, NonNegativeInt +from typing import List, Optional, Tuple, TYPE_CHECKING, Union +from typing_extensions import Self +from pydantic import Field, NonNegativeInt from napari.types import ShapesData +from xarray import DataArray +from lls_core.models.utils import FieldAccessMixin +from lls_core.types import image_like_to_image, ImageLike + +if TYPE_CHECKING: + from lls_core.types import ArrayLike + from aicsimageio import AICSImage + -z_range: Tuple[NonNegativeInt, NonNegativeInt] = Field( - default=None, - description="The range of Z slices to take. All Z slices before the first index or after the last index will be cropped out." -) -class CropParams(BaseModel, arbitrary_types_allowed=True): +class CropParams(FieldAccessMixin, arbitrary_types_allowed=True): """ Parameters for the optional cropping step """ - roi_list: ShapesData = Field( + roi_list: List[DataArray] = Field( description="List of regions of interest, each of which must be an NxD array, where N is the number of vertices and D the coordinates of each vertex." ) - z_range: Tuple[NonNegativeInt, NonNegativeInt] = z_range + z_range: Tuple[NonNegativeInt, NonNegativeInt] = Field( + default=None, + description="The range of Z slices to take. All Z slices before the first index or after the last index will be cropped out." + ) + # @classmethod + # def make( + # cls, + # roi_list: List[Union[ArrayLike, ImageLike]], + # z_range: Tuple[NonNegativeInt, NonNegativeInt] + # ) -> Self: + # return CropParams( + # roi_list=[image_like_to_image(it) for it in roi_list], + # z_range=z_range + # ) + + # @classmethod + # def from_img_metadata( + # cls, + # img: DataArray, + # roi_list: List[Union[ArrayLike, ImageLike]], + # z_range: Optional[Tuple[NonNegativeInt, NonNegativeInt]] + # ) -> Self: + # if z_range is None: + # z_range = (0, img.sizes["Z"]) + + # return CropParams.make( + # roi_list=roi_list, + # z_range=z_range + # ) diff --git a/core/lls_core/models/deconvolution.py b/core/lls_core/models/deconvolution.py index 993f29e..87fb22f 100644 --- a/core/lls_core/models/deconvolution.py +++ b/core/lls_core/models/deconvolution.py @@ -1,34 +1,82 @@ -from pydantic import BaseModel, Field, NonNegativeInt -from pydantic_numpy import NDArray +from pydantic import Field, NonNegativeInt, validator -from typing import List, Literal, Union +from typing import Any, List, Literal, Optional, Union, TYPE_CHECKING +from typing_extensions import Self, TypedDict, Unpack +from xarray import DataArray from lls_core import DeconvolutionChoice -from lls_core.models.utils import enum_choices - -decon_processing: DeconvolutionChoice = Field( - default=DeconvolutionChoice.cpu, - description=f"Hardware to use to perform the deconvolution. Choices: {enum_choices(DeconvolutionChoice)}") -psf: List[NDArray] = Field( - default=[], - description="List of Point Spread Functions to use for deconvolution. Each of which should be a 3D array." -) -psf_num_iter: NonNegativeInt = Field( - default=10, - description="Number of iterations to perform in deconvolution" -) +from lls_core.models.utils import enum_choices, FieldAccessMixin + +from lls_core.types import image_like_to_image, ImageLike + Background = Union[float, Literal["auto", "second_last"]] -background: Background = Field( - default=0, - description='Background value to subtract for deconvolution. Only used when decon_processing is set to GPU. This can either be a literal number, "auto" which uses the median of the last slice, or "second_last" which uses the median of the last slice.' -) -class DeconvolutionParams(BaseModel, arbitrary_types_allowed=True): +class MakeKwargs(TypedDict, total=False): + decon_processing: Union[DeconvolutionChoice, str] + psf: List[ImageLike] + psf_num_iter: int + background: str + +class DeconvolutionParams(FieldAccessMixin, arbitrary_types_allowed=True): """ Parameters for the optional deconvolution step """ - decon_processing: DeconvolutionChoice = decon_processing - psf: List[NDArray] = psf - psf_num_iter: NonNegativeInt = psf_num_iter - background: Union[float, Literal["auto", "second_last"]] = background + decon_processing: DeconvolutionChoice = Field( + default=DeconvolutionChoice.cpu, + description=f"Hardware to use to perform the deconvolution. Choices: {enum_choices(DeconvolutionChoice)}" + ) + psf: List[DataArray] = Field( + default=[], + description="List of Point Spread Functions to use for deconvolution. Each of which should be a 3D array." + ) + psf_num_iter: NonNegativeInt = Field( + default=10, + description="Number of iterations to perform in deconvolution" + ) + background: Background = Field( + default=0, + description='Background value to subtract for deconvolution. Only used when decon_processing is set to GPU. This can either be a literal number, "auto" which uses the median of the last slice, or "second_last" which uses the median of the last slice.' + ) + + @validator("decon_processing", pre=True) + def convert_decon(cls, v: Any): + if isinstance(v, str): + return DeconvolutionChoice[v] + return v + + convert_image = validator("psf", pre=True, each_item=True, allow_reuse=True)(image_like_to_image) + + # @classmethod + # def make( + # cls, + # **kwargs: Unpack[MakeKwargs] + # ) -> Self: + # dest= { } + # decon_processing = kwargs.pop("decon_processing", None) + # if isinstance(decon_processing, DeconvolutionChoice): + # dest["decon_processing"] = decon_processing + # elif decon_processing is not None: + # dest["decon_processing"] = DeconvolutionChoice(decon_processing) + + # DeconvolutionParams(**dest) + # dest + # 1 + "a" + + # DeconvolutionParams(**kwargs) + + # background_processed: Background + # try: + # background_processed = float(background) + # except Exception: + # if background == "auto" or background == "second_last": + # background_processed = background + # else: + # raise Exception("Invalid background option") + + # return DeconvolutionParams( + # decon_processing=decon_processing, + # background=background_processed, + # psf = [image_like_to_image(it) for it in psf], + # psf_num_iter=psf_num_iter + # ) diff --git a/core/lls_core/models/deskew.py b/core/lls_core/models/deskew.py index dc40c0c..a074141 100644 --- a/core/lls_core/models/deskew.py +++ b/core/lls_core/models/deskew.py @@ -4,7 +4,7 @@ from pydantic import Field, NonNegativeFloat, validator, root_validator # from numpy.typing import NDArray -from typing import Any, Tuple +from typing import Any, Literal, Tuple from typing_extensions import Self, TYPE_CHECKING import pyclesperanto_prototype as cle @@ -13,10 +13,14 @@ from xarray import DataArray from lls_core.models.utils import FieldAccessMixin, enum_choices +from lls_core.types import image_like_to_image +from lls_core.utils import get_deskewed_shape if TYPE_CHECKING: from aicsimageio.types import PhysicalPixelSizes +# DeskewDirection = Literal["X", "Y"] + class DefinedPixelSizes(FieldAccessMixin): """ Like PhysicalPixelSizes, but it's a dataclass, and @@ -65,11 +69,13 @@ class DeskewParams(FieldAccessMixin, arbitrary_types_allowed=True): def dims(self): return self.image.dims + # convert_image = validator("image", pre=True, allow_reuse=True)(image_like_to_image) + @validator("image", 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) + array = image_like_to_image(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: @@ -87,11 +93,11 @@ 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: DataArray = cls.reshaping(values["data"]) + data: DataArray = cls.reshaping(values["image"]) 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(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"]) + values["deskew_vol_shape"], values["deskew_affine_transform"] = get_deskewed_shape(data.isel(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 diff --git a/core/lls_core/models/lattice_data.py b/core/lls_core/models/lattice_data.py index e08dc41..2b3f7e8 100644 --- a/core/lls_core/models/lattice_data.py +++ b/core/lls_core/models/lattice_data.py @@ -1,7 +1,8 @@ from __future__ import annotations +from os import PathLike # class for initializing lattice data and setting metadata # TODO: handle scenes -from pydantic import BaseModel, DirectoryPath, Field, NonNegativeInt, validator +from pydantic import BaseModel, DirectoryPath, Field, NonNegativeInt, root_validator, validator from aicsimageio.aics_image import AICSImage # from numpy.typing import NDArray import math @@ -27,11 +28,10 @@ from lls_core.models.deskew import DeskewParams from napari_workflows import Workflow from xarray import DataArray +from pathlib import Path if TYPE_CHECKING: import pyclesperanto_prototype as cle - from enum import Enum - from pathlib import Path from lls_core.models.deskew import DefinedPixelSizes from aicsimageio.types import ImageLike @@ -123,7 +123,6 @@ def make_filename_prefix(prefix: Optional[str] = None, roi_index: Optional[int] class CommonOutputArgs(TypedDict): # Arguments save_dir: NotRequired[DirectoryPath] - save_name: NotRequired[str] save_type: SaveFileType time_range: range channel_range: range @@ -146,25 +145,31 @@ class LatticeData(OutputParams, DeskewParams, arbitrary_types_allowed=True): # (similar to how `crop` and `workflow` are handled), but this was impractical for implementing validations @overload + @classmethod def make( - self, + cls, image: Union[ImageLike, AICSImage], - physical_pixel_sizes: Optional[DefinedPixelSizes], + physical_pixel_sizes: Optional[DefinedPixelSizes] = None, + save_name: Optional[str] = None, **kwargs: Unpack[CommonLatticeArgs] ): ... @overload + @classmethod def make( - self, + cls, image: ArrayLike, physical_pixel_sizes: DefinedPixelSizes, + save_name: str, **kwargs: Unpack[CommonLatticeArgs] ): ... + @classmethod def make( - self, + cls, image: Any, physical_pixel_sizes: Optional[DefinedPixelSizes] = None, + save_name: Optional[str] = None, **kwargs: Unpack[CommonLatticeArgs] ): if isinstance(image, get_args(ImageLike)): @@ -185,10 +190,10 @@ def make( LatticeData( image=image, physical_pixel_sizes=combined_pixels, + save_name=save_name, **kwargs ) - #: If this is None, then deconvolution is disabled deconvolution: Optional[DeconvolutionParams] = None @@ -197,12 +202,21 @@ def make( workflow: Optional[Workflow] = workflow + @root_validator(pre=True) + def default_save_name(cls, values: dict): + # This needs to be a root validator to ensure it runs before the + # reshaping validator. We can't override that either since it's + # a field validator and can't modify save_name + if values.get("save_name", None) is None and isinstance(values["image"], PathLike): + values["save_name"] = Path(values["image"]).stem + return values + @validator("time_range") 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["data"].sizes["T"] + max_time = values["image"].sizes["T"] if v.start < 0: raise ValueError("The lowest valid start value is 0") if v.stop > max_time: @@ -214,7 +228,7 @@ 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["data"].sizes["C"] + max_channel = values["image"].sizes["C"] if v.start < 0: raise ValueError("The lowest valid start value is 0") if v.stop > max_channel: @@ -223,13 +237,13 @@ 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["data"].sizes["C"]: + if min(v) < 0 or max(v) > values["image"].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["data"].sizes["T"]: + if min(v) < 0 or max(v) > values["image"].sizes["T"]: raise ValueError("The output time range must be a subset of the total available time points") return v diff --git a/core/lls_core/models/output.py b/core/lls_core/models/output.py index 10eacc7..16b0aab 100644 --- a/core/lls_core/models/output.py +++ b/core/lls_core/models/output.py @@ -1,4 +1,4 @@ -from typing import Any, NotRequired, TypedDict +from typing import Any from pydantic import Field, validator, DirectoryPath from strenum import StrEnum from os import getcwd @@ -30,20 +30,30 @@ class OutputParams(FieldAccessMixin, arbitrary_types_allowed=True): description="The filename prefix that will be used for output files, without a leading directory or file extension. The final output files will have additional elements added to the end of this prefix to indicate the region of interest, channel, timepoint, file extension etc." ) - @validator("time_range") - def default_time_range(cls, v: Any, values: dict) -> range: + @validator("time_range", pre=True) + def parse_time_range(cls, v: Any, values: dict) -> Any: """ Sets the default time range if undefined """ + default_start = 0 + default_end = values["image"].sizes["T"] if v is None: - return range(values["data"].sizes["T"] + 1) + return range(default_start, default_end) + elif isinstance(v, tuple) and len(v) == 2: + # Allow 2-tuples to be used as input for this field + return range(v[0] or default_start, v[1] or default_end) return v - @validator("channel_range") - def default_channel_range(cls, v: Any, values: dict) -> range: + @validator("channel_range", pre=True) + def parse_channel_range(cls, v: Any, values: dict) -> Any: """ Sets the default channel range if undefined """ + default_start = 0 + default_end = values["image"].sizes["C"] if v is None: - return range(values["data"].sizes["C"] + 1) + return range(default_start, default_end) + elif isinstance(v, tuple) and len(v) == 2: + # Allow 2-tuples to be used as input for this field + return range(v[0] or default_start, v[1] or default_end) return v diff --git a/core/lls_core/models/utils.py b/core/lls_core/models/utils.py index 4bb912e..35f407d 100644 --- a/core/lls_core/models/utils.py +++ b/core/lls_core/models/utils.py @@ -1,7 +1,9 @@ -from typing import Type +from typing import Any, Type from enum import Enum from pydantic import BaseModel +from typer import Option +from typer.models import OptionInfo def enum_choices(enum: Type[Enum]) -> str: @@ -16,9 +18,17 @@ class FieldAccessMixin(BaseModel): """ @classmethod - def get_default(cls, field_name: str): + def get_default(cls, field_name: str) -> Any: return cls.__fields__[field_name].get_default() @classmethod def get_description(cls, field_name: str) -> str: return cls.__fields__[field_name].field_info.description + + @classmethod + def make_typer_field(cls, field_name: str, extra_description: str = "") -> Any: + field = cls.__fields__[field_name] + return Option( + default = field.get_default(), + help=field.field_info.description + extra_description + ) diff --git a/core/lls_core/types.py b/core/lls_core/types.py index f71844f..7249aab 100644 --- a/core/lls_core/types.py +++ b/core/lls_core/types.py @@ -4,11 +4,24 @@ # from numpy.typing import NDArray from pyopencl.array import Array as OCLArray import numpy as np -import pydantic_numpy.dtype as pnd from pydantic_numpy import NDArray, NDArrayFp32, NumpyModel from xarray import DataArray +from aicsimageio import AICSImage +from os import PathLike, fspath ArrayLike: TypeAlias = Union[DaskArray, NDArray, OCLArray, DataArray] def is_arraylike(arr: Any) -> TypeGuard[ArrayLike]: return isinstance(arr, (DaskArray, np.ndarray, OCLArray, DataArray)) + +ImageLike: TypeAlias = Union[PathLike, AICSImage, ArrayLike] +def image_like_to_image(img: ImageLike) -> DataArray: + """ + Converts an image in one of many formats to a DataArray + """ + if isinstance(img, PathLike): + img = AICSImage(fspath(img)) + if isinstance(img, AICSImage): + return img.xarray_dask_data + else: + return DataArray(img) diff --git a/core/pyproject.toml b/core/pyproject.toml index 04c5c87..167125f 100644 --- a/core/pyproject.toml +++ b/core/pyproject.toml @@ -79,3 +79,11 @@ psf = [ [project.scripts] lls-pipeline = "lls_core.cmds.__main__:main" + +[tool.mypy] +plugins = [ + "pydantic.mypy" +] + +[tool.pydantic-mypy] +init_typed = false From 4a9155062f85a600f24d26ab86fe8584c4ac1721 Mon Sep 17 00:00:00 2001 From: Michael Milton Date: Fri, 15 Sep 2023 00:39:15 +1000 Subject: [PATCH 026/147] Clean up commented/unused code --- core/lls_core/cmds/__main__.py | 143 +------------------------- core/lls_core/models/crop.py | 35 +------ core/lls_core/models/deconvolution.py | 38 +------ core/lls_core/models/deskew.py | 3 +- core/lls_core/models/lattice_data.py | 55 +--------- 5 files changed, 7 insertions(+), 267 deletions(-) diff --git a/core/lls_core/cmds/__main__.py b/core/lls_core/cmds/__main__.py index 9698fcc..a7be6a1 100644 --- a/core/lls_core/cmds/__main__.py +++ b/core/lls_core/cmds/__main__.py @@ -5,119 +5,21 @@ # python lattice_processing.py --input /home/pradeep/to_deskew --output /home/pradeep/output_save/ --processing deskew from enum import auto -import json from pathlib import Path -from typing import Any, Dict, List, Literal, Optional, Tuple +from typing import List, Optional, Tuple from typing_extensions import Annotated from strenum import StrEnum from lls_core.models.lattice_data import LatticeData from lls_core.models.deskew import DefinedPixelSizes, DeskewParams from lls_core.models.deconvolution import DeconvolutionParams -from lls_core.models.crop import CropParams from lls_core.models.output import OutputParams from lls_core import DeconvolutionChoice, DeskewDirection -from lls_core.types import image_like_to_image import typer from lls_core.models.output import SaveFileType from toolz.dicttoolz import merge -# class - -# class CliParams(BaseModel): -# # We can't directly use LatticeData because it uses nested models which aren't -# # supported by pydantic-cli, and also we need to modify some types to support the CLI e.g. by allowing filepath inputs - -# class Config(DefaultConfig): -# CLI_JSON_ENABLE = True -# arbitrary_types_allowed=True - -# # Deskew -# image: FilePath = Field(description="Path to the image file to read, in a format readable by AICSImageIO, for example .tiff or .czi") -# skew: DeskewDirection = skew -# angle: float = angle -# physical_pixel_sizes: Optional[DefinedPixelSizes] = physical_pixel_sizes - -# # Deconvolution -# deconvolution: bool = False -# decon_processing: DeconvolutionChoice = decon_processing -# psf: List[FilePath] = Field(default=[], description="A list of 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.") -# psf_num_iter = psf_num_iter -# background: Background = background - -# # Crop -# roi_list: List[FilePath] = Field(default=[], description="A list of paths pointing to regions of interest to crop to, in ImageJ format.") -# z_range: Tuple[NonNegativeInt, NonNegativeInt] = z_range - -# # Output -# # This one needs overriding to accommodate the default value -# save_name: str = Field(default=None, description=OutputParams.get_description("save_name")) -# save_dir: DirectoryPath = save_dir -# save_type: SaveFileType = save_type -# channel_range: range = channel_range -# time_range: range = time_range - -# # Workflow -# workflow: Optional[FilePath] = Field(default = None, description="Path to a Napari Workflow file, in JSON format. If provided, the configured desekewing processing will be added to the chosen workflow.") - -# @validator("save_name", pre=True) -# def default_save_name(cls, v: Any, values: Dict): -# # Use the input file path to provide a default output filename -# if v is None and values.get("image", None): -# path: Path = values["image"] -# return path.stem + "_deskewed" -# return v - -# @root_validator(pre=True) -# def metadata_from_path(cls, values: Dict) -> Dict: - -# return values - -# def to_lattice(self) -> LatticeData: -# from aicsimageio import AICSImage - -# crop = None -# if len(self.roi_list) > 0: -# crop = CropParams( -# roi_list=[AICSImage(it).data for it in self.roi_list], -# z_range=self.z_range -# ) - -# deconvolution = None -# if self.deconvolution: -# deconvolution = DeconvolutionParams( -# decon_processing=self.decon_processing, -# psf = [AICSImage(it).data for it in self.psf], -# psf_num_iter = self.psf_num_iter, -# ) - -# workflow = None -# if self.workflow is not None: -# from lls_core.workflow import workflow_from_path -# workflow = workflow_from_path(self.workflow) - -# return LatticeData( -# image=AICSImage(self.image).xarray_dask_data, -# angle=self.angle, -# skew=self.skew, -# physical_pixel_sizes=self.physical_pixel_sizes, - -# crop=crop, -# deconvolution=deconvolution, -# workflow=workflow, - -# save_type=self.save_type, -# channel_range=self.channel_range, -# time_range=self.time_range, -# save_dir=self.save_dir, -# save_name=self.save_name -# ) - -# def make_parser(): -# parser = pydantic_class_to_parser(CliParams, default_value_override={}) -# parser.add_argument("--yaml-config", required=False, help="Path to configuration YAML file") -# return parser class CliDeskewDirection(StrEnum): X = auto() Y = auto() @@ -204,49 +106,6 @@ def main( return LatticeData.parse_obj(merge(yaml_args, json_args, cli_args)) - # img_data = image_like_to_image(image) - - # crop = None - # if len(rois) > 0: - # crop = CropParams.from_img_metadata( - # img_data, - # roi_list=[image_like_to_image(it) for it in rois], - # z_range=z_range - # ) - - # decon = None - # if enable_deconvolution: - # decon = DeconvolutionParams( - # decon_processing=decon_processing, - # # background= - # ) - - LatticeData( - ) if __name__ == '__main__': typer.run(main) - # parser = make_parser() - # args = parser.parse_args() - # arg_dict = vars(args) - - # json_path = arg_dict.pop("json-config", None) - # json_config = {} - # if json_path: - # import json - # with open(json_path) as fp: - # json_config = json.load(fp) - - # yaml_path = arg_dict.pop("yaml-config", None) - # yaml_config = {} - # if yaml_path: - # import yaml - # with open(yaml_path) as fp: - # json_config = yaml.safe_load(fp) - - # config = CliParams( - # **json_config, - # **yaml_config, - # **arg_dict - # ) - # config.to_lattice().process().save_image() diff --git a/core/lls_core/models/crop.py b/core/lls_core/models/crop.py index 1751f97..3000181 100644 --- a/core/lls_core/models/crop.py +++ b/core/lls_core/models/crop.py @@ -1,15 +1,7 @@ -from typing import List, Optional, Tuple, TYPE_CHECKING, Union -from typing_extensions import Self +from typing import List, Tuple from pydantic import Field, NonNegativeInt -from napari.types import ShapesData from xarray import DataArray from lls_core.models.utils import FieldAccessMixin -from lls_core.types import image_like_to_image, ImageLike - -if TYPE_CHECKING: - from lls_core.types import ArrayLike - from aicsimageio import AICSImage - class CropParams(FieldAccessMixin, arbitrary_types_allowed=True): @@ -23,28 +15,3 @@ class CropParams(FieldAccessMixin, arbitrary_types_allowed=True): default=None, description="The range of Z slices to take. All Z slices before the first index or after the last index will be cropped out." ) - # @classmethod - # def make( - # cls, - # roi_list: List[Union[ArrayLike, ImageLike]], - # z_range: Tuple[NonNegativeInt, NonNegativeInt] - # ) -> Self: - # return CropParams( - # roi_list=[image_like_to_image(it) for it in roi_list], - # z_range=z_range - # ) - - # @classmethod - # def from_img_metadata( - # cls, - # img: DataArray, - # roi_list: List[Union[ArrayLike, ImageLike]], - # z_range: Optional[Tuple[NonNegativeInt, NonNegativeInt]] - # ) -> Self: - # if z_range is None: - # z_range = (0, img.sizes["Z"]) - - # return CropParams.make( - # roi_list=roi_list, - # z_range=z_range - # ) diff --git a/core/lls_core/models/deconvolution.py b/core/lls_core/models/deconvolution.py index 87fb22f..c2b360c 100644 --- a/core/lls_core/models/deconvolution.py +++ b/core/lls_core/models/deconvolution.py @@ -1,8 +1,8 @@ from pydantic import Field, NonNegativeInt, validator -from typing import Any, List, Literal, Optional, Union, TYPE_CHECKING -from typing_extensions import Self, TypedDict, Unpack +from typing import Any, List, Literal, Union +from typing_extensions import TypedDict from xarray import DataArray @@ -46,37 +46,3 @@ def convert_decon(cls, v: Any): return v convert_image = validator("psf", pre=True, each_item=True, allow_reuse=True)(image_like_to_image) - - # @classmethod - # def make( - # cls, - # **kwargs: Unpack[MakeKwargs] - # ) -> Self: - # dest= { } - # decon_processing = kwargs.pop("decon_processing", None) - # if isinstance(decon_processing, DeconvolutionChoice): - # dest["decon_processing"] = decon_processing - # elif decon_processing is not None: - # dest["decon_processing"] = DeconvolutionChoice(decon_processing) - - # DeconvolutionParams(**dest) - # dest - # 1 + "a" - - # DeconvolutionParams(**kwargs) - - # background_processed: Background - # try: - # background_processed = float(background) - # except Exception: - # if background == "auto" or background == "second_last": - # background_processed = background - # else: - # raise Exception("Invalid background option") - - # return DeconvolutionParams( - # decon_processing=decon_processing, - # background=background_processed, - # psf = [image_like_to_image(it) for it in psf], - # psf_num_iter=psf_num_iter - # ) diff --git a/core/lls_core/models/deskew.py b/core/lls_core/models/deskew.py index a074141..99af70b 100644 --- a/core/lls_core/models/deskew.py +++ b/core/lls_core/models/deskew.py @@ -2,9 +2,8 @@ # class for initializing lattice data and setting metadata # TODO: handle scenes from pydantic import Field, NonNegativeFloat, validator, root_validator -# from numpy.typing import NDArray -from typing import Any, Literal, Tuple +from typing import Any, Tuple from typing_extensions import Self, TYPE_CHECKING import pyclesperanto_prototype as cle diff --git a/core/lls_core/models/lattice_data.py b/core/lls_core/models/lattice_data.py index 2b3f7e8..af7012b 100644 --- a/core/lls_core/models/lattice_data.py +++ b/core/lls_core/models/lattice_data.py @@ -4,15 +4,14 @@ # TODO: handle scenes from pydantic import BaseModel, DirectoryPath, Field, NonNegativeInt, root_validator, validator from aicsimageio.aics_image import AICSImage -# from numpy.typing import NDArray import math from dask.array.core import Array as DaskArray import dask as da from itertools import groupby import tifffile -from typing import Any, Iterable, List, Literal, Optional, TYPE_CHECKING, Tuple, Union, overload -from typing_extensions import TypedDict, Unpack, NotRequired, get_args +from typing import Any, Iterable, List, Literal, Optional, TYPE_CHECKING, Tuple +from typing_extensions import TypedDict, NotRequired from aicsimageio.types import PhysicalPixelSizes import pyclesperanto_prototype as cle @@ -144,56 +143,6 @@ class LatticeData(OutputParams, DeskewParams, arbitrary_types_allowed=True): # Note: originally the save-related fields were included via composition and not inheritance # (similar to how `crop` and `workflow` are handled), but this was impractical for implementing validations - @overload - @classmethod - def make( - cls, - image: Union[ImageLike, AICSImage], - physical_pixel_sizes: Optional[DefinedPixelSizes] = None, - save_name: Optional[str] = None, - **kwargs: Unpack[CommonLatticeArgs] - ): - ... - @overload - @classmethod - def make( - cls, - image: ArrayLike, - physical_pixel_sizes: DefinedPixelSizes, - save_name: str, - **kwargs: Unpack[CommonLatticeArgs] - ): - ... - @classmethod - def make( - cls, - image: Any, - physical_pixel_sizes: Optional[DefinedPixelSizes] = None, - save_name: Optional[str] = None, - **kwargs: Unpack[CommonLatticeArgs] - ): - if isinstance(image, get_args(ImageLike)): - image = AICSImage(image) - - meta_pixels = None - if isinstance(image, AICSImage): - if all(image.physical_pixel_sizes): - meta_pixels = DefinedPixelSizes.from_physical(image.physical_pixel_sizes) - image = image.xarray_dask_data - - image = DataArray(image) - - combined_pixels = physical_pixel_sizes or meta_pixels - if combined_pixels is None: - raise ValueError("Pixel sizes") - - LatticeData( - image=image, - physical_pixel_sizes=combined_pixels, - save_name=save_name, - **kwargs - ) - #: If this is None, then deconvolution is disabled deconvolution: Optional[DeconvolutionParams] = None From 2d20bdfec7bcd30e245f1cb67d587e11da781018 Mon Sep 17 00:00:00 2001 From: Michael Milton Date: Fri, 15 Sep 2023 10:24:01 +1000 Subject: [PATCH 027/147] Fix missing imports --- core/lls_core/models/lattice_data.py | 1 - core/pyproject.toml | 10 ++++++---- plugin/napari_lattice/fields.py | 21 +++++++++++---------- plugin/pyproject.toml | 6 +++++- 4 files changed, 22 insertions(+), 16 deletions(-) diff --git a/core/lls_core/models/lattice_data.py b/core/lls_core/models/lattice_data.py index af7012b..fdc36a2 100644 --- a/core/lls_core/models/lattice_data.py +++ b/core/lls_core/models/lattice_data.py @@ -32,7 +32,6 @@ if TYPE_CHECKING: import pyclesperanto_prototype as cle from lls_core.models.deskew import DefinedPixelSizes - from aicsimageio.types import ImageLike import logging diff --git a/core/pyproject.toml b/core/pyproject.toml index 1b2bfd8..558c1ce 100644 --- a/core/pyproject.toml +++ b/core/pyproject.toml @@ -43,18 +43,16 @@ dependencies = [ "dask[distributed]", "pyopencl", "read-roi", - "gputools", "pyclesperanto_prototype>=0.20.0", "pydantic~=1.0", + "pydantic_numpy", "npy2bdv", - "redlionfish", # Older tifffile are not compatible with aicsimageio, see: https://github.com/AllenCellModeling/aicsimageio/issues/518 "tifffile>=2023.3.15", #>=2022.8.12 "fsspec>=2022.8.2", "pyclesperanto_prototype>=0.20.0", "napari-workflows>=0.2.8", - "xarray[parallel]", "npy2bdv", "numpy", "pandas", @@ -62,9 +60,13 @@ dependencies = [ "read-roi", "resource-backed-dask-array>=0.1.0", "scikit-image", + "StrEnum", + "toolz", "tifffile", "tqdm", - "typing_extensions" + "typer", + "typing_extensions", + "xarray[parallel]", ] [project.urls] diff --git a/plugin/napari_lattice/fields.py b/plugin/napari_lattice/fields.py index 537a1a1..e4ced2a 100644 --- a/plugin/napari_lattice/fields.py +++ b/plugin/napari_lattice/fields.py @@ -10,7 +10,6 @@ DeconvolutionChoice, DeskewDirection, Log_Levels, - SaveFileType, ) from lls_core.models.lattice_data import ( CropParams, @@ -20,6 +19,7 @@ LatticeData, OutputParams, ) +from lls_core.workflow import workflow_from_path from magicclass import FieldGroup, MagicTemplate, field from magicclass.fields import MagicField from magicclass.widgets import ComboBox, Label, Widget @@ -30,7 +30,6 @@ 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 strenum import StrEnum @@ -41,6 +40,7 @@ def exception_to_html(e: BaseException) -> str: """ Converts an exception to HTML for reporting back to the user """ + from pydantic import ValidationError if isinstance(e, ValidationError): message = [] for error in e.errors(): @@ -244,14 +244,14 @@ def _get_dimension_options(self, _) -> List[str]: ) # merge_all_channels = field(False).with_options(label="Merge all Channels") dimension_order = field( - str - ).with_choices( - _get_dimension_options - ).with_options( - label="Dimension Order", - tooltip="Specifies the order of dimensions in the input images. For example, if your image is a 4D array with multiple channels along the first axis, you will specify CZYX.", - value=LastDimensionOptions.Metadata.value - ) + str + ).with_choices( + _get_dimension_options + ).with_options( + label="Dimension Order", + tooltip="Specifies the order of dimensions in the input images. For example, if your image is a 4D array with multiple channels along the first axis, you will specify CZYX.", + value=LastDimensionOptions.Metadata.value + ) skew_dir = field(DeskewDirection.Y).with_options( label="Skew Direction", tooltip="The axis along which to deskew" @@ -428,6 +428,7 @@ def _make_model(self) -> Optional[Workflow]: if self.workflow_source.value == WorkflowSource.ActiveWorkflow: return WorkflowManager.install(self.parent_viewer).workflow else: + return workflow_from_path(self.workflow_path.value) # @magicclass(name="5. Output") class OutputFields(NapariFieldGroup, FieldGroup): diff --git a/plugin/pyproject.toml b/plugin/pyproject.toml index ae61c0b..f8c6b95 100644 --- a/plugin/pyproject.toml +++ b/plugin/pyproject.toml @@ -41,9 +41,9 @@ dependencies = [ # This isn't used directly, but we need to pin this version "fsspec>=2022.8.2", # We need this Python 3.8 fix: https://github.com/hanjinliu/magic-class/pull/108 + "lls_core", "magic-class>=0.7.5", "magicgui", - "lls_core", "napari-aicsimageio>=0.7.2", "napari-spreadsheet", "napari-workflow-inspector", @@ -52,12 +52,16 @@ dependencies = [ "npy2bdv", "numpy", "pandas", + "pkg_resources", "psutil", "pyclesperanto_prototype>=0.20.0", + "pydantic", "qtpy", "tqdm", "typing_extensions", "pyyaml", + "StrEnum", + "xarray" ] [project.urls] From 5395f5ba1e0329c7069059bd25d830592230c68e Mon Sep 17 00:00:00 2001 From: Michael Milton Date: Fri, 15 Sep 2023 10:27:34 +1000 Subject: [PATCH 028/147] pydantic-numpy --- core/pyproject.toml | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/core/pyproject.toml b/core/pyproject.toml index 558c1ce..067d5bb 100644 --- a/core/pyproject.toml +++ b/core/pyproject.toml @@ -45,7 +45,7 @@ dependencies = [ "read-roi", "pyclesperanto_prototype>=0.20.0", "pydantic~=1.0", - "pydantic_numpy", + "pydantic-numpy", "npy2bdv", # Older tifffile are not compatible with aicsimageio, see: https://github.com/AllenCellModeling/aicsimageio/issues/518 From 3f844c97f5820a97e4c4be87f8205407f200f0d3 Mon Sep 17 00:00:00 2001 From: Michael Milton Date: Fri, 15 Sep 2023 10:33:47 +1000 Subject: [PATCH 029/147] Use older pydantic-numpy --- core/pyproject.toml | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/core/pyproject.toml b/core/pyproject.toml index 067d5bb..974733c 100644 --- a/core/pyproject.toml +++ b/core/pyproject.toml @@ -45,7 +45,7 @@ dependencies = [ "read-roi", "pyclesperanto_prototype>=0.20.0", "pydantic~=1.0", - "pydantic-numpy", + "pydantic-numpy~=2.0", "npy2bdv", # Older tifffile are not compatible with aicsimageio, see: https://github.com/AllenCellModeling/aicsimageio/issues/518 From a24341753ea8282a5ce2316ff572ae41c2140cf0 Mon Sep 17 00:00:00 2001 From: Michael Milton Date: Fri, 15 Sep 2023 11:00:56 +1000 Subject: [PATCH 030/147] Remove pydantic-numpy --- core/lls_core/types.py | 2 +- core/pyproject.toml | 1 - 2 files changed, 1 insertion(+), 2 deletions(-) diff --git a/core/lls_core/types.py b/core/lls_core/types.py index 7249aab..26691bb 100644 --- a/core/lls_core/types.py +++ b/core/lls_core/types.py @@ -4,7 +4,7 @@ # from numpy.typing import NDArray from pyopencl.array import Array as OCLArray import numpy as np -from pydantic_numpy import NDArray, NDArrayFp32, NumpyModel +from numpy.typing import NDArray from xarray import DataArray from aicsimageio import AICSImage from os import PathLike, fspath diff --git a/core/pyproject.toml b/core/pyproject.toml index 974733c..5911e88 100644 --- a/core/pyproject.toml +++ b/core/pyproject.toml @@ -45,7 +45,6 @@ dependencies = [ "read-roi", "pyclesperanto_prototype>=0.20.0", "pydantic~=1.0", - "pydantic-numpy~=2.0", "npy2bdv", # Older tifffile are not compatible with aicsimageio, see: https://github.com/AllenCellModeling/aicsimageio/issues/518 From d9695f03d05c385d6258b497e3c0650ca0b9ef06 Mon Sep 17 00:00:00 2001 From: Michael Milton Date: Fri, 15 Sep 2023 11:18:40 +1000 Subject: [PATCH 031/147] Pyright settings in the pyproject.toml --- core/pyproject.toml | 10 ++++++++++ plugin/pyproject.toml | 10 ++++++++++ pyrightconfig.json | 13 ------------- 3 files changed, 20 insertions(+), 13 deletions(-) delete mode 100644 pyrightconfig.json diff --git a/core/pyproject.toml b/core/pyproject.toml index 5911e88..efc5f38 100644 --- a/core/pyproject.toml +++ b/core/pyproject.toml @@ -104,6 +104,16 @@ init_typed = false typeCheckingMode = "off" reportUndefinedVariable = "error" reportMissingImports = "none" +reportMissingTypeStubs = false +reportUnknownVariableType = false +reportUnknownArgumentType = false +reportUnknownLambdaType = false +reportUnknownMemberType = false +reportUnknownParameterType = false +reportUntypedFunctionDecorator = false +reportMissingTypeArgument = false +reportPrivateUsage = false +reportPrivateImportUsage = false [tool.fawltydeps] ignore_unused = [ diff --git a/plugin/pyproject.toml b/plugin/pyproject.toml index f8c6b95..a7e9a18 100644 --- a/plugin/pyproject.toml +++ b/plugin/pyproject.toml @@ -107,3 +107,13 @@ output_format = "human_detailed" typeCheckingMode = "off" reportUndefinedVariable = "error" reportMissingImports = "none" +reportMissingTypeStubs = false +reportUnknownVariableType = false +reportUnknownArgumentType = false +reportUnknownLambdaType = false +reportUnknownMemberType = false +reportUnknownParameterType = false +reportUntypedFunctionDecorator = false +reportMissingTypeArgument = false +reportPrivateUsage = false +reportPrivateImportUsage = false diff --git a/pyrightconfig.json b/pyrightconfig.json deleted file mode 100644 index 86c8d98..0000000 --- a/pyrightconfig.json +++ /dev/null @@ -1,13 +0,0 @@ -{ - "reportMissingTypeStubs": false, - "reportUnknownVariableType": false, - "reportUnknownArgumentType": false, - "reportUnknownLambdaType": false, - "reportUnknownMemberType": false, - "reportUnknownParameterType": false, - "reportUntypedFunctionDecorator": false, - "reportMissingTypeArgument": false, - "reportPrivateUsage": false, - "reportPrivateImportUsage": false, - "typeCheckingMode": "strict" -} From 1b0b0f33fcc537f712f41f497bd6af0e978a19e2 Mon Sep 17 00:00:00 2001 From: Michael Milton Date: Fri, 15 Sep 2023 15:15:05 +1000 Subject: [PATCH 032/147] Rework workflow handling, move sample data into package --- core/lls_core/cmds/__main__.py | 40 +-- core/lls_core/models/lattice_data.py | 110 ++++++-- core/lls_core/sample/LLS7_t1_ch1.czi | Bin 0 -> 6877600 bytes core/lls_core/sample/LLS7_t1_ch3.czi | Bin 0 -> 7065344 bytes core/lls_core/sample/LLS7_t2_ch1.czi | Bin 0 -> 6967744 bytes core/lls_core/sample/LLS7_t2_ch3.czi | Bin 0 -> 7337184 bytes .../lls_core/sample}/RBC_lattice.tif | Bin .../lls_core/sample}/RBC_tiny.czi | Bin core/lls_core/sample/README.txt | 4 + core/lls_core/sample/__init__.py | 9 + .../lls_core/sample}/config/README.md | 0 .../lls_core/sample}/config/config.yaml | 0 .../lls_core/sample}/config/config.yml | 0 core/lls_core/sample/multich_multi_time.tif | Bin 0 -> 3356511 bytes .../sample}/psfs/zeiss_simulated/488.czi | Bin .../sample}/psfs/zeiss_simulated/488.tif | Bin .../sample}/psfs/zeiss_simulated/561.czi | Bin .../sample}/psfs/zeiss_simulated/561.tif | Bin .../sample}/psfs/zeiss_simulated/640.czi | Bin .../sample}/psfs/zeiss_simulated/640.tif | Bin .../sample}/psfs/zeiss_simulated/README.md | 0 .../psfs/zeiss_simulated/description.txt | 6 +- core/lls_core/workflow.py | 244 +++++++++--------- core/tests/test_arg_parser.py | 13 +- 24 files changed, 259 insertions(+), 167 deletions(-) create mode 100755 core/lls_core/sample/LLS7_t1_ch1.czi create mode 100755 core/lls_core/sample/LLS7_t1_ch3.czi create mode 100755 core/lls_core/sample/LLS7_t2_ch1.czi create mode 100755 core/lls_core/sample/LLS7_t2_ch3.czi rename {sample_data => core/lls_core/sample}/RBC_lattice.tif (100%) rename {sample_data => core/lls_core/sample}/RBC_tiny.czi (100%) create mode 100755 core/lls_core/sample/README.txt create mode 100644 core/lls_core/sample/__init__.py rename {sample_data => core/lls_core/sample}/config/README.md (100%) rename {sample_data => core/lls_core/sample}/config/config.yaml (100%) rename {sample_data => core/lls_core/sample}/config/config.yml (100%) create mode 100755 core/lls_core/sample/multich_multi_time.tif rename {sample_data => core/lls_core/sample}/psfs/zeiss_simulated/488.czi (100%) rename {sample_data => core/lls_core/sample}/psfs/zeiss_simulated/488.tif (100%) rename {sample_data => core/lls_core/sample}/psfs/zeiss_simulated/561.czi (100%) rename {sample_data => core/lls_core/sample}/psfs/zeiss_simulated/561.tif (100%) rename {sample_data => core/lls_core/sample}/psfs/zeiss_simulated/640.czi (100%) rename {sample_data => core/lls_core/sample}/psfs/zeiss_simulated/640.tif (100%) rename {sample_data => core/lls_core/sample}/psfs/zeiss_simulated/README.md (100%) rename {sample_data => core/lls_core/sample}/psfs/zeiss_simulated/description.txt (98%) diff --git a/core/lls_core/cmds/__main__.py b/core/lls_core/cmds/__main__.py index a7be6a1..22677a9 100644 --- a/core/lls_core/cmds/__main__.py +++ b/core/lls_core/cmds/__main__.py @@ -15,7 +15,7 @@ from lls_core.models.deconvolution import DeconvolutionParams from lls_core.models.output import OutputParams from lls_core import DeconvolutionChoice, DeskewDirection -import typer +from typer import Typer, Argument, Option from lls_core.models.output import SaveFileType from toolz.dicttoolz import merge @@ -24,14 +24,17 @@ class CliDeskewDirection(StrEnum): X = auto() Y = auto() +app = Typer(add_completion=False) + +@app.command() def main( - image: Path = typer.Argument(help="Path to the image file to read, in a format readable by AICSImageIO, for example .tiff or .czi"), - skew: CliDeskewDirection = typer.Option( + image: Path = Argument(help="Path to the image file to read, in a format readable by AICSImageIO, for example .tiff or .czi"), + skew: CliDeskewDirection = Option( default=DeskewParams.get_default("skew").name, help=DeskewParams.get_description("skew") ),# DeskewParams.make_typer_field("skew"), angle: float = DeskewParams.make_typer_field("angle") , - pixel_sizes: Tuple[float, float, float] = typer.Option( + pixel_sizes: Tuple[float, float, float] = Option( ( LatticeData.get_default("physical_pixel_sizes").X, LatticeData.get_default("physical_pixel_sizes").Y, @@ -39,30 +42,30 @@ def main( ), help=DeskewParams.get_description("physical_pixel_sizes") + ". This takes three arguments, corresponding to the X Y and Z pixel dimensions respectively" ), - rois: List[Path] = typer.Option([], help="A list of paths pointing to regions of interest to crop to, in ImageJ format."), + rois: List[Path] = Option([], help="A list of paths pointing to regions of interest to crop to, in ImageJ format."), # 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] = typer.Option(None, help="The index of the first Z slice to use. All prior Z slices will be discarded."), - z_end: Optional[int] = typer.Option(None, help="The index of the last Z slice to use. The selected index and all subsequent Z slices will be discarded."), + z_start: Optional[int] = Option(None, help="The index of the first Z slice to use. All prior Z slices will be discarded."), + 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."), - enable_deconvolution: Annotated[bool, typer.Option("--deconvolution/--disable-deconvolution")] = False, + enable_deconvolution: Annotated[bool, Option("--deconvolution/--disable-deconvolution")] = False, decon_processing: DeconvolutionChoice = DeconvolutionParams.make_typer_field("decon_processing"), - psf: Annotated[List[Path], typer.Option(help="A list of 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.")] = [], + psf: Annotated[List[Path], Option(help="A list of 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.")] = [], psf_num_iter: int = DeconvolutionParams.make_typer_field("psf_num_iter"), background: str = DeconvolutionParams.make_typer_field("background"), - time_start: Optional[int] = typer.Option(None, help="Index of the first time slice to use (inclusive)"), - time_end: Optional[int] = typer.Option(None, help="Index of the first time slice to use (exclusive)"), + time_start: Optional[int] = Option(None, help="Index of the first time slice to use (inclusive)"), + time_end: Optional[int] = Option(None, help="Index of the first time slice to use (exclusive)"), - channel_start: Optional[int] = typer.Option(None, help="Index of the first channel slice to use (inclusive)"), - channel_end: Optional[int] = typer.Option(None, help="Index of the first channel slice to use (exclusive)"), + channel_start: Optional[int] = Option(None, help="Index of the first channel slice to use (inclusive)"), + channel_end: Optional[int] = Option(None, help="Index of the first channel slice to use (exclusive)"), save_dir: Path = OutputParams.make_typer_field("save_dir"), save_name: Optional[str] = OutputParams.make_typer_field("save_name"), save_type: SaveFileType = OutputParams.make_typer_field("save_type"), - workflow: Optional[Path] = typer.Option(None, help="Path to a Napari Workflow file, in JSON format. If provided, the configured desekewing processing will be added to the chosen workflow."), - json_config: Optional[Path] = typer.Option(None), - yaml_config: Optional[Path] = typer.Option(None) + workflow: Optional[Path] = Option(None, help="Path to a Napari Workflow file, in JSON format. If provided, the configured desekewing processing will be added to the chosen workflow."), + json_config: Optional[Path] = Option(None), + yaml_config: Optional[Path] = Option(None) ): cli_args = dict( image=image, @@ -83,7 +86,7 @@ def main( psf_num_iter = psf_num_iter, background = background ), - workflow=None, + workflow=workflow, time_range=(time_start, time_end), channel_range=(channel_start, channel_end), @@ -106,6 +109,5 @@ def main( return LatticeData.parse_obj(merge(yaml_args, json_args, cli_args)) - if __name__ == '__main__': - typer.run(main) + app() diff --git a/core/lls_core/models/lattice_data.py b/core/lls_core/models/lattice_data.py index fdc36a2..4a9ea64 100644 --- a/core/lls_core/models/lattice_data.py +++ b/core/lls_core/models/lattice_data.py @@ -11,7 +11,7 @@ import tifffile from typing import Any, Iterable, List, Literal, Optional, TYPE_CHECKING, Tuple -from typing_extensions import TypedDict, NotRequired +from typing_extensions import TypedDict, NotRequired, Generic, TypeVar from aicsimageio.types import PhysicalPixelSizes import pyclesperanto_prototype as cle @@ -32,22 +32,35 @@ if TYPE_CHECKING: import pyclesperanto_prototype as cle from lls_core.models.deskew import DefinedPixelSizes + from numpy.typing import NDArray import logging logger = logging.getLogger(__name__) -class ProcessedVolume(BaseModel, arbitrary_types_allowed=True): - """ - A slice of the image processing result - """ +T = TypeVar("T") +S = TypeVar("S") +class SlicedData(BaseModel, Generic[T]): + data: T time_index: NonNegativeInt time: NonNegativeInt channel_index: NonNegativeInt channel: NonNegativeInt - data: ArrayLike roi_index: Optional[NonNegativeInt] = None + def copy_with_data(self, data: S) -> SlicedData[S]: + """ + Return a modified version of this with new inner data + """ + from typing_extensions import cast + return cast( + SlicedData[S], + self.copy(update={ + "data": data + }) + ) +ProcessedVolume = SlicedData[ArrayLike] + class ProcessedSlices(BaseModel): #: Iterable of result slices. #: Note that this is a finite iterator that can only be iterated once @@ -292,7 +305,7 @@ def slice_data(self, time: int, channel: int) -> DataArray: raise Exception("Lattice data must be 3-5 dimensions") - def iter_slices(self) -> Iterable[Tuple[int, int, int, int, ArrayLike]]: + def iter_slices(self) -> Iterable[SlicedData[ArrayLike]]: """ Yields array slices for each time and channel of interest. @@ -301,7 +314,48 @@ def iter_slices(self) -> Iterable[Tuple[int, int, int, int, ArrayLike]]: """ for time_idx, time in enumerate(self.time_range): for ch_idx, ch in enumerate(self.channel_range): - yield time_idx, time, ch_idx, ch, self.slice_data(time=time, channel=ch) + yield SlicedData( + data=self.slice_data(time=time, channel=ch), + time_index=time_idx, + time= time, + channel_index=ch_idx, + channel=ch, + ) + + def iter_sublattices(self, update_with: dict = {}) -> Iterable[SlicedData[LatticeData]]: + """ + Yields copies of the current LatticeData, one for each slice. + These copies can then be processed separately. + Args: + update_with: dictionary of arguments to update the generated lattices with + """ + for subarray in self.iter_slices(): + yield subarray.copy_with_data( + self.copy(update={ "image": subarray, + **update_with + }) + ) + + def generate_workflows( + self, + ) -> Iterable[SlicedData[Workflow]]: + """ + Yields copies of the input workflow, modified with the addition of deskewing and optionally, + cropping and deconvolution + """ + if self.workflow is None: + return + + from copy import copy + # We make a copy of the lattice for each slice, each of which has no associated workflow + for lattice_slice in self.iter_sublattices(update_with={"workflow": None}): + user_workflow = copy(self.workflow) + user_workflow.set( + "deskew_image", + LatticeData.process, + lattice_slice.data + ) + yield lattice_slice.copy_with_data(user_workflow) def check_incomplete_acquisition(self, volume: ArrayLike, time_point: int, channel: int): """ @@ -373,14 +427,15 @@ def _process_non_crop(self) -> Iterable[ProcessedVolume]: """ Yields processed image slices without cropping """ - for time_idx, time, ch_idx, ch, data in self.iter_slices(): - if isinstance(data, DaskArray): - data = data.compute() + for slice in self.iter_slices(): + data: ArrayLike = slice.data + if isinstance(slice.data, DaskArray): + data = slice.data.compute() if self.deconvolution is not None: if self.deconvolution.decon_processing == DeconvolutionChoice.cuda_gpu: data= pycuda_decon( image=data, - psf=self.deconvolution.psf[ch], + psf=self.deconvolution.psf[slice.channel], background=self.deconvolution.background, dzdata=self.dz, dxdata=self.dx, @@ -391,35 +446,46 @@ def _process_non_crop(self) -> Iterable[ProcessedVolume]: else: data= skimage_decon( vol_zyx=data, - psf=self.deconvolution.psf[ch], + psf=self.deconvolution.psf[slice.channel], num_iter=self.deconvolution.psf_num_iter, clip=False, filter_epsilon=0, boundary='nearest' ) - yield ProcessedVolume( - data = cle.pull_zyx(self.deskew_func( + yield slice.copy_with_data( + cle.pull_zyx(self.deskew_func( input_image=data, angle_in_degrees=self.angle, linear_interpolation=True, voxel_size_x=self.dx, voxel_size_y=self.dy, voxel_size_z=self.dz - )), - channel=ch, - channel_index=ch_idx, - time=time, - time_index=time_idx + )) ) - def process(self) -> ProcessedSlices: """ Execute the processing and return the result. 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Iterable[Workflow]: - """ - Yields copies of the input workflow, modified with the addition of deskewing and optionally, - cropping and deconvolution - """ - user_workflow = copy(workflow) - _, _, first_task_name, _ = get_first_last_image_and_task(workflow) +# def augment_workflow( +# workflow: Workflow, +# lattice: LatticeData, +# ) -> Iterable[Workflow]: +# """ +# Yields copies of the input workflow, modified with the addition of deskewing and optionally, +# cropping and deconvolution +# """ +# from copy import copy +# for lattice_slice in lattice.iter_sublattices(): +# user_workflow = copy(workflow) +# user_workflow.set( +# "deskew_image", +# LatticeData.process, +# lattice_slice +# ) +# yield user_workflow # for loop_time_idx, time_point in enumerate(times): # output_array = [] # data_table = [] # for loop_ch_idx, ch in enumerate(channels): - if crop: - yield from make_crop_workflows( - user_workflow=user_workflow, - roi_layer_list=roi_layer_list, - lattice=lattice, - deconvolution=deconvolution - ) - - # 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) - else: - INPUT_ARG = "input" - - # IF just deskewing and its not in the tasks, add that as first task - if user_workflow.get_task(first_task_name)[0] not in (cle.deskew_y, cle.deskew_x): - # add task to the workflow - user_workflow.set( - "deskew_image", - lattice.deskew_func, - input_image=INPUT_ARG, - angle_in_degrees=lattice.angle, - voxel_size_x=lattice.dx, - voxel_size_y=lattice.dy, - voxel_size_z=lattice.dz, - linear_interpolation=True - ) - # Set input of the workflow to be from deskewing - # change workflow task starts from is "deskew_image" and - replace_first_arg(user_workflow, new_arg="deskew_image") - - # if deconvolution checked, add it to start of workflow (add upstream of deskewing) - if deconvolution: - PSF_ARG = "psf" - - if lattice.decon_processing == DeconvolutionChoice.cuda_gpu: - user_workflow.set( - "deconvolution", - pycuda_decon, - image=INPUT_ARG, - psf=PSF_ARG, - dzdata=lattice.dz, - dxdata=lattice.dx, - dzpsf=lattice.dz, - dxpsf=lattice.dx, - num_iter=lattice.psf_num_iter - ) - # user_workflow.set(input_arg_first,"deconvolution") - else: - user_workflow.set( - "deconvolution", - skimage_decon, - vol_zyx=INPUT_ARG, - psf=PSF_ARG, - num_iter=lattice.psf_num_iter, - clip=False, - filter_epsilon=0, - boundary='nearest' - ) - # modify the user workflow so "deconvolution" is accepted - replace_first_arg(user_workflow, new_arg="deconvolution") - - yield workflow - - # save_img_workflow(vol=vol, - # workflow=user_workflow, - # input_arg=INPUT_ARG, - # 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_ARG, - # psf_arg=psf_arg, - # psf=PSF_ARG) + # if crop: + # yield from make_crop_workflows( + # user_workflow=user_workflow, + # roi_layer_list=roi_layer_list, + # lattice=lattice, + # deconvolution=deconvolution + # ) + + # # 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) + # else: + # INPUT_ARG = "input" + + # # IF just deskewing and its not in the tasks, add that as first task + # if user_workflow.get_task(first_task_name)[0] not in (cle.deskew_y, cle.deskew_x): + # # add task to the workflow + # user_workflow.set( + # "deskew_image", + # lattice.deskew_func, + # input_image=INPUT_ARG, + # angle_in_degrees=lattice.angle, + # voxel_size_x=lattice.dx, + # voxel_size_y=lattice.dy, + # voxel_size_z=lattice.dz, + # linear_interpolation=True + # ) + # # Set input of the workflow to be from deskewing + # # change workflow task starts from is "deskew_image" and + # replace_first_arg(user_workflow, new_arg="deskew_image") + + # # if deconvolution checked, add it to start of workflow (add upstream of deskewing) + # if deconvolution: + # PSF_ARG = "psf" + + # if lattice.decon_processing == DeconvolutionChoice.cuda_gpu: + # user_workflow.set( + # "deconvolution", + # pycuda_decon, + # image=INPUT_ARG, + # psf=PSF_ARG, + # dzdata=lattice.dz, + # dxdata=lattice.dx, + # dzpsf=lattice.dz, + # dxpsf=lattice.dx, + # num_iter=lattice.psf_num_iter + # ) + # # user_workflow.set(input_arg_first,"deconvolution") + # else: + # user_workflow.set( + # "deconvolution", + # skimage_decon, + # vol_zyx=INPUT_ARG, + # psf=PSF_ARG, + # num_iter=lattice.psf_num_iter, + # clip=False, + # filter_epsilon=0, + # boundary='nearest' + # ) + # # modify the user workflow so "deconvolution" is accepted + # replace_first_arg(user_workflow, new_arg="deconvolution") + + # yield workflow + + # # save_img_workflow(vol=vol, + # # workflow=user_workflow, + # # input_arg=INPUT_ARG, + # # 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_ARG, + # # psf_arg=psf_arg, + # # psf=PSF_ARG) def make_crop_workflows( diff --git a/core/tests/test_arg_parser.py b/core/tests/test_arg_parser.py index afcf0af..28a47dc 100644 --- a/core/tests/test_arg_parser.py +++ b/core/tests/test_arg_parser.py @@ -1,13 +1,18 @@ -from lls_core.cmds.__main__ import make_parser +from typer.testing import CliRunner +from typer.main import get_command +from lls_core.cmds.__main__ import app + +runner = CliRunner() def test_voxel_parsing(): # Tests that we can parse voxel lists correctly - parser = make_parser() - args = parser.parse_args([ + parser = get_command(app).make_parser() + parser.make_context() + args = parser.parse_args(args=[ "--input", "input", "--output", "output", "--processing", "deskew", "--output_file_type", "tiff", "--voxel_sizes", "1", "1", "1" ]) - assert args.voxel_sizes == [1.0, 1.0, 1.0] \ No newline at end of file + assert args.voxel_sizes == [1.0, 1.0, 1.0] From c48b074e496de4875d8529e79f65928ab9d8c03d Mon Sep 17 00:00:00 2001 From: Michael Milton Date: Fri, 15 Sep 2023 15:19:45 +1000 Subject: [PATCH 033/147] Add pkg_resources dependency --- core/pyproject.toml | 1 + 1 file changed, 1 insertion(+) diff --git a/core/pyproject.toml b/core/pyproject.toml index efc5f38..9340a68 100644 --- a/core/pyproject.toml +++ b/core/pyproject.toml @@ -55,6 +55,7 @@ dependencies = [ "npy2bdv", "numpy", "pandas", + "pkg_resources", "pyyaml", "read-roi", "resource-backed-dask-array>=0.1.0", From 61901ef665c66e61c4fea4679376fe0101013f76 Mon Sep 17 00:00:00 2001 From: Michael Milton Date: Fri, 15 Sep 2023 15:49:12 +1000 Subject: [PATCH 034/147] GUI fixes --- core/lls_core/lattice_data.py | 207 --------------------------- core/lls_core/models/lattice_data.py | 34 ++++- core/lls_core/models/output.py | 28 ---- core/lls_core/models/utils.py | 1 - core/lls_core/sample/__init__.py | 10 +- core/pyproject.toml | 4 +- plugin/napari_lattice/dock_widget.py | 4 +- plugin/napari_lattice/fields.py | 15 +- plugin/napari_lattice/icons.py | 10 +- plugin/napari_lattice/reader.py | 4 +- plugin/pyproject.toml | 4 +- 11 files changed, 59 insertions(+), 262 deletions(-) delete mode 100644 core/lls_core/lattice_data.py diff --git a/core/lls_core/lattice_data.py b/core/lls_core/lattice_data.py deleted file mode 100644 index 650e7f4..0000000 --- a/core/lls_core/lattice_data.py +++ /dev/null @@ -1,207 +0,0 @@ -from __future__ import annotations -# class for initializing lattice data and setting metadata -# TODO: handle scenes -from dataclasses import dataclass, field -from aicsimageio.aics_image import AICSImage -from aicsimageio.dimensions import Dimensions -from numpy.typing import NDArray -from dataclasses import dataclass -import math -import numpy as np - -from typing import Any, List, Literal, Optional, TYPE_CHECKING, Tuple, TypeVar - -from aicsimageio.types import ArrayLike, PhysicalPixelSizes -import pyclesperanto_prototype as cle - -from lls_core import DeskewDirection, DeconvolutionChoice -from lls_core.utils import get_deskewed_shape - -if TYPE_CHECKING: - import pyclesperanto_prototype as cle - -T = TypeVar("T") -def raise_if_none(obj: Optional[T], message: str) -> T: - if obj is None: - raise TypeError(message) - return obj - -@dataclass -class DefinedPixelSizes: - """ - Like PhysicalPixelSizes, but it's a dataclass, and - none of its fields are None - """ - X: float = 0.14 - Y: float = 0.14 - Z: float = 0.3 - -@dataclass -class LatticeData: - """ - Holds data and metadata for a given image in a consistent format - """ - #: 3-5D array - data: ArrayLike - dims: Dimensions - - #: The filename of this data when it is saved - save_name: str - - #: Geometry of the light path - skew: DeskewDirection = DeskewDirection.Y - angle: float = 30.0 - - decon_processing: Optional[DeconvolutionChoice] = None - - #: Pixel size in microns - physical_pixel_sizes: DefinedPixelSizes = field(default_factory=DefinedPixelSizes) - - new_dz: Optional[float] = None - - # Dimensions of the deskewed output - deskew_vol_shape: Optional[Tuple[int, ...]] = None - deskew_affine_transform: Optional[cle.AffineTransform3D] = None - - # PSF data that should be refactored into another class eventually - psf: Optional[List[NDArray]] = None - psf_num_iter: Optional[int] = None - otf_path: Optional[List] = None - - #: Number of time points - time: int = 0 - #: Number of channels - channels: int = 0 - - # TODO: add defaults here, rather than in the CLI - # Hack to ensure that .skew_dir behaves identically to .skew - @property - def skew_dir(self) -> DeskewDirection: - return self.skew - - @skew_dir.setter - def skew_dir(self, value: DeskewDirection): - self.skew = value - - @property - def deskew_func(self): - # Chance deskew function absed on skew direction - if self.skew == DeskewDirection.Y: - return cle.deskew_y - elif self.skew == DeskewDirection.X: - return cle.deskew_x - else: - raise ValueError() - - @property - def dx(self) -> float: - return self.physical_pixel_sizes.X - - @dx.setter - def dx(self, value: float): - self.physical_pixel_sizes.X = value - - @property - def dy(self) -> float: - return self.physical_pixel_sizes.Y - - @dy.setter - def dy(self, value: float): - self.physical_pixel_sizes.Y = value - - @property - def dz(self) -> float: - return self.physical_pixel_sizes.Z - - @dz.setter - def dz(self, value: float): - self.physical_pixel_sizes.Z = value - - def get_angle(self) -> float: - return self.angle - - def set_angle(self, angle: float) -> None: - self.angle = angle - - def set_skew(self, skew: DeskewDirection) -> None: - self.skew = skew - - def __post_init__(self): - # set new z voxel size - if self.skew == DeskewDirection.Y or self.skew == DeskewDirection.X: - self.new_dz = math.sin(self.angle * math.pi / 180.0) * self.dz - - # process the file to get shape of final deskewed image - self.deskew_vol_shape, self.deskew_affine_transform = get_deskewed_shape(self.data, self.angle, self.dx, self.dy, self.dz) - print(f"Channels: {self.channels}, Time: {self.time}") - print("If channel and time need to be swapped, you can enforce this by choosing 'Last dimension is channel' when initialising the plugin") - -def lattice_from_aics(img: AICSImage, physical_pixel_sizes: PhysicalPixelSizes = PhysicalPixelSizes(None, None, None), **kwargs: Any) -> LatticeData: - # Note: The reason we copy all of these fields rather than just storing the AICSImage is because that class is mostly immutable and so not suitable - - pixel_sizes = DefinedPixelSizes( - X = physical_pixel_sizes[0] or img.physical_pixel_sizes.X or LatticeData.physical_pixel_sizes.X, - Y = physical_pixel_sizes[1] or img.physical_pixel_sizes.Y or LatticeData.physical_pixel_sizes.Y, - Z = physical_pixel_sizes[2] or img.physical_pixel_sizes.Z or LatticeData.physical_pixel_sizes.Z - ) - - return LatticeData( - data = img.dask_data, - dims = img.dims, - time = img.dims.T, - channels = img.dims.C, - physical_pixel_sizes = pixel_sizes, - **kwargs - ) - -def img_from_array(arr: ArrayLike, last_dimension: Optional[Literal["channel", "time"]] = None, **kwargs: Any) -> AICSImage: - """ - Creates an AICSImage from an array without metadata - - Args: - arr (ArrayLike): An array - last_dimension: How to handle the dimension order - kwargs: Additional arguments to pass to the AICSImage constructor - """ - dim_order: str - - if len(arr.shape) < 3 or len(arr.shape) > 5: - raise ValueError("Array dimensions must be in the range [3, 5]") - - # if aicsimageio tiffreader assigns last dim as time when it should be channel, user can override this - if len(arr.shape) == 3: - dim_order="ZYX" - else: - if last_dimension not in ["channel", "time"]: - raise ValueError("last_dimension must be either channel or time") - if len(arr.shape) == 4: - if last_dimension == "channel": - dim_order = "CZYX" - elif last_dimension == "time": - dim_order = "TZYX" - elif len(arr.shape) == 5: - if last_dimension == "channel": - dim_order = "CTZYX" - elif last_dimension == "time": - dim_order = "TCZYX" - else: - raise ValueError() - - img = AICSImage(image=arr, dim_order=dim_order, **kwargs) - - # if last axes of "aicsimage data" shape is not equal to time, then swap channel and time - if img.data.shape[0] != img.dims.T or img.data.shape[1] != img.dims.C: - arr = np.swapaxes(arr, 0, 1) - return AICSImage(image=arr, dim_order=dim_order, **kwargs) - - -def lattice_fom_array(arr: ArrayLike, last_dimension: Optional[Literal["channel", "time"]] = None, **kwargs: Any) -> LatticeData: - """ - Creates a `LatticeData` from an array - - Args: - arr: Array to use as the data source - last_dimension: See img_from_array - """ - aics = img_from_array(arr, last_dimension) - return lattice_from_aics(aics, **kwargs) diff --git a/core/lls_core/models/lattice_data.py b/core/lls_core/models/lattice_data.py index 4a9ea64..48eea19 100644 --- a/core/lls_core/models/lattice_data.py +++ b/core/lls_core/models/lattice_data.py @@ -10,7 +10,7 @@ from itertools import groupby import tifffile -from typing import Any, Iterable, List, Literal, Optional, TYPE_CHECKING, Tuple +from typing import Any, Iterable, List, Literal, Optional, TYPE_CHECKING from typing_extensions import TypedDict, NotRequired, Generic, TypeVar from aicsimageio.types import PhysicalPixelSizes @@ -40,7 +40,7 @@ T = TypeVar("T") S = TypeVar("S") -class SlicedData(BaseModel, Generic[T]): +class SlicedData(BaseModel, Generic[T], arbitrary_types_allowed=True): data: T time_index: NonNegativeInt time: NonNegativeInt @@ -61,7 +61,7 @@ def copy_with_data(self, data: S) -> SlicedData[S]: ) ProcessedVolume = SlicedData[ArrayLike] -class ProcessedSlices(BaseModel): +class ProcessedSlices(BaseModel, arbitrary_types_allowed=True): #: Iterable of result slices. #: Note that this is a finite iterator that can only be iterated once slices: Iterable[ProcessedVolume] @@ -172,6 +172,34 @@ def default_save_name(cls, values: dict): values["save_name"] = Path(values["image"]).stem return values + @validator("time_range", pre=True) + def parse_time_range(cls, v: Any, values: dict) -> Any: + """ + Sets the default time range if undefined + """ + default_start = 0 + default_end = values["image"].sizes["T"] + if v is None: + return range(default_start, default_end) + elif isinstance(v, tuple) and len(v) == 2: + # Allow 2-tuples to be used as input for this field + return range(v[0] or default_start, v[1] or default_end) + return v + + @validator("channel_range", pre=True) + def parse_channel_range(cls, v: Any, values: dict) -> Any: + """ + Sets the default channel range if undefined + """ + default_start = 0 + default_end = values["image"].sizes["C"] + if v is None: + return range(default_start, default_end) + elif isinstance(v, tuple) and len(v) == 2: + # Allow 2-tuples to be used as input for this field + return range(v[0] or default_start, v[1] or default_end) + return v + @validator("time_range") def disjoint_time_range(cls, v: range, values: dict): """ diff --git a/core/lls_core/models/output.py b/core/lls_core/models/output.py index 16b0aab..d17ad69 100644 --- a/core/lls_core/models/output.py +++ b/core/lls_core/models/output.py @@ -29,31 +29,3 @@ class OutputParams(FieldAccessMixin, arbitrary_types_allowed=True): channel_range: range = Field( description="The filename prefix that will be used for output files, without a leading directory or file extension. The final output files will have additional elements added to the end of this prefix to indicate the region of interest, channel, timepoint, file extension etc." ) - - @validator("time_range", pre=True) - def parse_time_range(cls, v: Any, values: dict) -> Any: - """ - Sets the default time range if undefined - """ - default_start = 0 - default_end = values["image"].sizes["T"] - if v is None: - return range(default_start, default_end) - elif isinstance(v, tuple) and len(v) == 2: - # Allow 2-tuples to be used as input for this field - return range(v[0] or default_start, v[1] or default_end) - return v - - @validator("channel_range", pre=True) - def parse_channel_range(cls, v: Any, values: dict) -> Any: - """ - Sets the default channel range if undefined - """ - default_start = 0 - default_end = values["image"].sizes["C"] - if v is None: - return range(default_start, default_end) - elif isinstance(v, tuple) and len(v) == 2: - # Allow 2-tuples to be used as input for this field - return range(v[0] or default_start, v[1] or default_end) - return v diff --git a/core/lls_core/models/utils.py b/core/lls_core/models/utils.py index 35f407d..949e795 100644 --- a/core/lls_core/models/utils.py +++ b/core/lls_core/models/utils.py @@ -3,7 +3,6 @@ from enum import Enum from pydantic import BaseModel from typer import Option -from typer.models import OptionInfo def enum_choices(enum: Type[Enum]) -> str: diff --git a/core/lls_core/sample/__init__.py b/core/lls_core/sample/__init__.py index cde99d7..f2e4d56 100644 --- a/core/lls_core/sample/__init__.py +++ b/core/lls_core/sample/__init__.py @@ -1,9 +1,3 @@ -from pkg_resources import resource_filename +import importlib_resources -LLS7_T1_CH1 = resource_filename(__name__, "LLS7_t1_ch1.czi") -LLS7_T1_CH3 = resource_filename(__name__, "LLS7_t1_ch3.czi") -LLS7_T2_CH1 = resource_filename(__name__, "LLS7_t2_ch1.czi") -LLS7_T2_CH3 = resource_filename(__name__, "LLS7_t2_ch4.czi") -MULTICH_MULTITIME = resource_filename(__name__, "multich_multi_time.tif") -RBC_LATTICE_TIF = resource_filename(__name__, "RBC_lattice.tif") -RBC_TINY_CZI = resource_filename(__name__, "RBC_tiny.czi") +resources = importlib_resources.files(__name__) diff --git a/core/pyproject.toml b/core/pyproject.toml index 9340a68..82a1c5d 100644 --- a/core/pyproject.toml +++ b/core/pyproject.toml @@ -55,7 +55,7 @@ dependencies = [ "npy2bdv", "numpy", "pandas", - "pkg_resources", + "importlib_resources", "pyyaml", "read-roi", "resource-backed-dask-array>=0.1.0", @@ -65,7 +65,7 @@ dependencies = [ "tifffile", "tqdm", "typer", - "typing_extensions", + "typing_extensions>=4.7.0", "xarray[parallel]", ] diff --git a/plugin/napari_lattice/dock_widget.py b/plugin/napari_lattice/dock_widget.py index c5a6560..05f00d1 100644 --- a/plugin/napari_lattice/dock_widget.py +++ b/plugin/napari_lattice/dock_widget.py @@ -84,8 +84,10 @@ class WidgetContainer(LlszTemplate): def __post_init__(self): tab_widget: QTabWidget= self._widget._tab_widget + from importlib_resources import as_file for i in range(5): - tab_widget.setTabIcon(i, QIcon(GREY)) + with as_file(GREY) as path: + tab_widget.setTabIcon(i, QIcon(str(path))) for field in [self.deskew_fields, self.deconv_fields, self.cropping_fields, self.workflow_fields, self.output_fields]: field._validate() diff --git a/plugin/napari_lattice/fields.py b/plugin/napari_lattice/fields.py index e4ced2a..726d5d3 100644 --- a/plugin/napari_lattice/fields.py +++ b/plugin/napari_lattice/fields.py @@ -11,14 +11,15 @@ DeskewDirection, Log_Levels, ) -from lls_core.models.lattice_data import ( +from lls_core.models import ( CropParams, DeconvolutionParams, - DefinedPixelSizes, DeskewParams, LatticeData, OutputParams, ) +from lls_core.models.deskew import DefinedPixelSizes +from lls_core.models.output import SaveFileType from lls_core.workflow import workflow_from_path from magicclass import FieldGroup, MagicTemplate, field from magicclass.fields import MagicField @@ -173,16 +174,20 @@ def _get_tab_index(self: Any) -> int: def _set_valid(self: Any, valid: bool): from qtpy.QtGui import QIcon + from importlib_resources import as_file tab_parent = self._get_parent_tab_widget() index = self._get_tab_index() if hasattr(self, "fields_enabled") and not self.fields_enabled.value: # Special case for "diabled" sections - tab_parent.setTabIcon(index, QIcon(GREY)) + icon = GREY elif valid: - tab_parent.setTabIcon(index, QIcon(GREEN)) + icon = GREEN else: - tab_parent.setTabIcon(index, QIcon(RED)) + icon = RED + + with as_file(icon) as path: + tab_parent.setTabIcon(index, QIcon(str(path))) def _validate(self: Any): self.errors.value = get_friendly_validations(self) diff --git a/plugin/napari_lattice/icons.py b/plugin/napari_lattice/icons.py index 7c01abe..dff6eb2 100644 --- a/plugin/napari_lattice/icons.py +++ b/plugin/napari_lattice/icons.py @@ -1,5 +1,7 @@ -import pkg_resources +import importlib_resources -GREEN = pkg_resources.resource_filename(__name__, "valid.svg") -GREY = pkg_resources.resource_filename(__name__, "circle-regular.svg") -RED = pkg_resources.resource_filename(__name__, "invalid.svg") +resources = importlib_resources.files(__name__) + +GREEN = resources / "valid.svg" +GREY = resources / "circle-regular.svg" +RED = resources / "invalid.svg" diff --git a/plugin/napari_lattice/reader.py b/plugin/napari_lattice/reader.py index ae04e70..134e034 100644 --- a/plugin/napari_lattice/reader.py +++ b/plugin/napari_lattice/reader.py @@ -17,7 +17,9 @@ from typing import List, Optional, Tuple, Collection, TYPE_CHECKING -from lls_core.models.lattice_data import DefinedPixelSizes, AicsLatticeParams, PhysicalPixelSizes +from aicsimageio.types import PhysicalPixelSizes +from lls_core.models.lattice_data import AicsLatticeParams +from lls_core.models.deskew import DefinedPixelSizes if TYPE_CHECKING: from aicsimageio.types import ImageLike diff --git a/plugin/pyproject.toml b/plugin/pyproject.toml index a7e9a18..ef7a844 100644 --- a/plugin/pyproject.toml +++ b/plugin/pyproject.toml @@ -52,13 +52,13 @@ dependencies = [ "npy2bdv", "numpy", "pandas", - "pkg_resources", + "importlib_resources", "psutil", "pyclesperanto_prototype>=0.20.0", "pydantic", "qtpy", "tqdm", - "typing_extensions", + "typing_extensions>=4.7.0", "pyyaml", "StrEnum", "xarray" From 251240173cb6b7b0d32d1e3dfa44c14aeaa17fb5 Mon Sep 17 00:00:00 2001 From: Michael Milton Date: Fri, 15 Sep 2023 15:54:38 +1000 Subject: [PATCH 035/147] Fix type errors --- core/lls_core/models/lattice_data.py | 1 + core/lls_core/workflow.py | 179 +-------------------------- 2 files changed, 2 insertions(+), 178 deletions(-) diff --git a/core/lls_core/models/lattice_data.py b/core/lls_core/models/lattice_data.py index 48eea19..f3e1e8c 100644 --- a/core/lls_core/models/lattice_data.py +++ b/core/lls_core/models/lattice_data.py @@ -580,6 +580,7 @@ def img_from_array(arr: ArrayLike, dimension_order: Optional[str] = None, **kwar # if last axes of "aicsimage data" shape is not equal to time, then swap channel and time if img.data.shape[0] != img.dims.T or img.data.shape[1] != img.dims.C: + import numpy as np arr = np.swapaxes(arr, 0, 1) return AICSImage(image=arr, dim_order=dimension_order, **kwargs) diff --git a/core/lls_core/workflow.py b/core/lls_core/workflow.py index 19ee222..25ceeff 100644 --- a/core/lls_core/workflow.py +++ b/core/lls_core/workflow.py @@ -5,13 +5,12 @@ from os import path from pathlib import Path -from typing import Optional, Tuple, Union, Iterable +from typing import Optional, Tuple, Union import dask.array as da import numpy as np import pandas as pd import pyclesperanto_prototype as cle -from lls_core.llsz_core import crop_volume_deskew from lls_core.types import ArrayLike from typing_extensions import TYPE_CHECKING @@ -205,179 +204,3 @@ def process_custom_workflow_output(workflow_output: Union[dict, list, ArrayLike] workflow_output, name="Workflow_preview_" + suffix_name, scale=scale) else: return workflow_output - -# def augment_workflow( -# workflow: Workflow, -# lattice: LatticeData, -# ) -> Iterable[Workflow]: -# """ -# Yields copies of the input workflow, modified with the addition of deskewing and optionally, -# cropping and deconvolution -# """ -# from copy import copy -# for lattice_slice in lattice.iter_sublattices(): -# user_workflow = copy(workflow) -# user_workflow.set( -# "deskew_image", -# LatticeData.process, -# lattice_slice -# ) -# yield user_workflow - - # for loop_time_idx, time_point in enumerate(times): - # output_array = [] - # data_table = [] - # for loop_ch_idx, ch in enumerate(channels): - - # if crop: - # yield from make_crop_workflows( - # user_workflow=user_workflow, - # roi_layer_list=roi_layer_list, - # lattice=lattice, - # deconvolution=deconvolution - # ) - - # # 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) - # else: - # INPUT_ARG = "input" - - # # IF just deskewing and its not in the tasks, add that as first task - # if user_workflow.get_task(first_task_name)[0] not in (cle.deskew_y, cle.deskew_x): - # # add task to the workflow - # user_workflow.set( - # "deskew_image", - # lattice.deskew_func, - # input_image=INPUT_ARG, - # angle_in_degrees=lattice.angle, - # voxel_size_x=lattice.dx, - # voxel_size_y=lattice.dy, - # voxel_size_z=lattice.dz, - # linear_interpolation=True - # ) - # # Set input of the workflow to be from deskewing - # # change workflow task starts from is "deskew_image" and - # replace_first_arg(user_workflow, new_arg="deskew_image") - - # # if deconvolution checked, add it to start of workflow (add upstream of deskewing) - # if deconvolution: - # PSF_ARG = "psf" - - # if lattice.decon_processing == DeconvolutionChoice.cuda_gpu: - # user_workflow.set( - # "deconvolution", - # pycuda_decon, - # image=INPUT_ARG, - # psf=PSF_ARG, - # dzdata=lattice.dz, - # dxdata=lattice.dx, - # dzpsf=lattice.dz, - # dxpsf=lattice.dx, - # num_iter=lattice.psf_num_iter - # ) - # # user_workflow.set(input_arg_first,"deconvolution") - # else: - # user_workflow.set( - # "deconvolution", - # skimage_decon, - # vol_zyx=INPUT_ARG, - # psf=PSF_ARG, - # num_iter=lattice.psf_num_iter, - # clip=False, - # filter_epsilon=0, - # boundary='nearest' - # ) - # # modify the user workflow so "deconvolution" is accepted - # replace_first_arg(user_workflow, new_arg="deconvolution") - - # yield workflow - - # # save_img_workflow(vol=vol, - # # workflow=user_workflow, - # # input_arg=INPUT_ARG, - # # 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_ARG, - # # psf_arg=psf_arg, - # # psf=PSF_ARG) - - -def make_crop_workflows( - user_workflow: Workflow, - roi_layer_list: ShapesData, - lattice: LatticeData, - deconvolution: bool -) -> Iterable[Workflow]: - """ - Yields a copy of `user_workflow` for each region of interest, with deskewing, cropping and deconvolution steps added on to the start - """ - - # Convert Roi pixel coordinates to canvas coordinates - # necessary only when scale is used for napari.viewer.add_image operations - - # Here we generate a workflow for each ROI - for idx, roi_layer in enumerate(tqdm([x/lattice for x in roi_layer_list], desc="ROI:", position=0)): - # 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 - current_workflow = copy(user_workflow) - current_workflow.set( - "crop_deskew_image", - crop_volume_deskew, - original_volume="volume", - roi_shape="roi", - angle_in_degrees=lattice.angle, - voxel_size_x=lattice.dx, - voxel_size_y=lattice.dy, - voxel_size_z=lattice.dy, - z_start=0, - z_end=lattice.deskew_vol_shape[0], - deconvolution=deconvolution, - decon_processing=lattice.decon_processing, - psf="psf", - skew_dir=lattice.skew_dir - ) - - # change the first task so it accepts "crop_deskew as input" - replace_first_arg(current_workflow, new_arg="crop_deskew_image") - - logging.info(f"Processing ROI {idx}") - current_workflow.set("roi", roi_layer) - - yield current_workflow From 57f9a2c40c08f877205ba9a5e60f274626be917b Mon Sep 17 00:00:00 2001 From: Michael Milton Date: Fri, 15 Sep 2023 16:37:12 +1000 Subject: [PATCH 036/147] Fix dependency issues --- core/pyproject.toml | 17 ++++++----------- plugin/pyproject.toml | 4 ++-- 2 files changed, 8 insertions(+), 13 deletions(-) diff --git a/core/pyproject.toml b/core/pyproject.toml index 82a1c5d..5b20df1 100644 --- a/core/pyproject.toml +++ b/core/pyproject.toml @@ -41,28 +41,23 @@ dependencies = [ "aicspylibczi>=3.1.1", "dask", "dask[distributed]", - "pyopencl", - "read-roi", - "pyclesperanto_prototype>=0.20.0", - "pydantic~=1.0", - "npy2bdv", - # Older tifffile are not compatible with aicsimageio, see: https://github.com/AllenCellModeling/aicsimageio/issues/518 - - "tifffile>=2023.3.15", #>=2022.8.12 "fsspec>=2022.8.2", - "pyclesperanto_prototype>=0.20.0", + "importlib_resources", + # Older tifffile are not compatible with aicsimageio, see: https://github.com/AllenCellModeling/aicsimageio/issues/518 "napari-workflows>=0.2.8", "npy2bdv", "numpy", "pandas", - "importlib_resources", + "pyclesperanto_prototype>=0.20.0", + "pyopencl", + "pydantic~=1.0", "pyyaml", "read-roi", "resource-backed-dask-array>=0.1.0", "scikit-image", "StrEnum", + "tifffile>=2023.3.15", #>=2022.8.12 "toolz", - "tifffile", "tqdm", "typer", "typing_extensions>=4.7.0", diff --git a/plugin/pyproject.toml b/plugin/pyproject.toml index ef7a844..34302d3 100644 --- a/plugin/pyproject.toml +++ b/plugin/pyproject.toml @@ -35,11 +35,12 @@ classifiers = [ ] requires-python = ">=3.8" dependencies = [ - "aicsimageio>=4.11.0", + "aicsimageio>=4.6.3", "dask", "dask[distributed]", # This isn't used directly, but we need to pin this version "fsspec>=2022.8.2", + "importlib_resources", # We need this Python 3.8 fix: https://github.com/hanjinliu/magic-class/pull/108 "lls_core", "magic-class>=0.7.5", @@ -52,7 +53,6 @@ dependencies = [ "npy2bdv", "numpy", "pandas", - "importlib_resources", "psutil", "pyclesperanto_prototype>=0.20.0", "pydantic", From 04ba1d28c9b15d3a0b6441528dba368c6e885de9 Mon Sep 17 00:00:00 2001 From: Michael Milton Date: Fri, 15 Sep 2023 18:47:44 +1000 Subject: [PATCH 037/147] Cartesian product tests --- core/lls_core/models/deskew.py | 4 +-- core/lls_core/models/lattice_data.py | 41 ++++++++++++++-------------- core/lls_core/models/output.py | 20 ++++++++++++-- 3 files changed, 39 insertions(+), 26 deletions(-) diff --git a/core/lls_core/models/deskew.py b/core/lls_core/models/deskew.py index 99af70b..38a4f33 100644 --- a/core/lls_core/models/deskew.py +++ b/core/lls_core/models/deskew.py @@ -84,9 +84,9 @@ def reshaping(cls, v: Any): return array.transpose("T", "C", "Z", "Y", "X") def get_3d_slice(self) -> DataArray: - return self.image.sel(C=0, T=0) + return self.image.isel(C=0, T=0) - @root_validator(pre=True) + @root_validator(pre=False) def set_deskew(cls, values: dict) -> dict: """ Sets the default deskew shape values if the user has not provided them diff --git a/core/lls_core/models/lattice_data.py b/core/lls_core/models/lattice_data.py index f3e1e8c..eea1d92 100644 --- a/core/lls_core/models/lattice_data.py +++ b/core/lls_core/models/lattice_data.py @@ -38,6 +38,21 @@ logger = logging.getLogger(__name__) +def make_filename_prefix(prefix: Optional[str] = None, roi_index: Optional[str] = None, channel: Optional[str] = None, time: Optional[str] = None) -> str: + """ + Generates a filename for this result + """ + components: List[str] = [] + if prefix is not None: + components.append(prefix) + if roi_index is not None: + components.append(f"ROI_{roi_index}") + if channel is not None: + components.append(f"C{channel}") + if time is not None: + components.append(f"T{time}") + return "_".join(components) + T = TypeVar("T") S = TypeVar("S") class SlicedData(BaseModel, Generic[T], arbitrary_types_allowed=True): @@ -75,13 +90,12 @@ def save_image(self): """ # TODO: refactor this into a class system, one for each format import numpy as np - from pathlib import Path import npy2bdv 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) + ".h5", + filename=str(self.lattice_data.make_filepath(make_filename_prefix(roi_index=str(roi)))), compression='gzip', nchannels=len(self.lattice_data.channel_range), subsamp=((1, 1, 1), (1, 2, 2), (2, 4, 4)), @@ -102,7 +116,7 @@ def save_image(self): first_result = result_list[0] images_array = np.swapaxes(np.expand_dims([result.data for result in result_list], axis=0), 1, 2) tifffile.imwrite( - file = Path(make_filename_prefix(channel=first_result.channel, time=time, roi_index=roi)).with_suffix("tiff"), + file = str(self.lattice_data.make_filepath(make_filename_prefix(channel=first_result.channel, time=time, roi_index=roi))), data = images_array, bigtiff=True, resolution=(1./self.lattice_data.dx, 1./self.lattice_data.dy, "MICROMETER"), @@ -110,21 +124,6 @@ def save_image(self): imagej=True ) -def make_filename_prefix(prefix: Optional[str] = None, roi_index: Optional[int] = None, channel: Optional[int] = None, time: Optional[int] = None) -> str: - """ - Generates a filename for this result - """ - components: List[str] = [] - if prefix is not None: - components.append(prefix) - if roi_index is not None: - components.append(f"ROI_{roi_index}") - if channel is not None: - components.append(f"C{channel}") - if time is not None: - components.append(f"T{time}") - return "_".join(components) - workflow: Optional[Workflow] = Field( default=None, @@ -172,7 +171,7 @@ def default_save_name(cls, values: dict): values["save_name"] = Path(values["image"]).stem return values - @validator("time_range", pre=True) + @validator("time_range", pre=True, always=True) def parse_time_range(cls, v: Any, values: dict) -> Any: """ Sets the default time range if undefined @@ -186,7 +185,7 @@ def parse_time_range(cls, v: Any, values: dict) -> Any: return range(v[0] or default_start, v[1] or default_end) return v - @validator("channel_range", pre=True) + @validator("channel_range", pre=True, always=True) def parse_channel_range(cls, v: Any, values: dict) -> Any: """ Sets the default channel range if undefined @@ -322,7 +321,7 @@ def slice_data(self, time: int, channel: int) -> DataArray: if channel > self.channels: raise ValueError("channel is out of range") - return self.image.sel(T=time, C=channel) + return self.image.isel(T=time, C=channel) if len(self.image.shape) == 3: return self.image diff --git a/core/lls_core/models/output.py b/core/lls_core/models/output.py index d17ad69..07b1b60 100644 --- a/core/lls_core/models/output.py +++ b/core/lls_core/models/output.py @@ -1,7 +1,7 @@ -from typing import Any from pydantic import Field, validator, DirectoryPath from strenum import StrEnum from os import getcwd +from pathlib import Path from lls_core.models.utils import FieldAccessMixin, enum_choices @@ -27,5 +27,19 @@ class OutputParams(FieldAccessMixin, arbitrary_types_allowed=True): description="The range of times to process. This defaults to all time points in the image array." ) channel_range: range = Field( - description="The filename prefix that will be used for output files, without a leading directory or file extension. The final output files will have additional elements added to the end of this prefix to indicate the region of interest, channel, timepoint, file extension etc." -) + description="The filename prefix that will be used for output files, without a leading directory or file extension. The final output files will have additional elements added to the end of this prefix to indicate the region of interest, channel, timepoint, file extension etc.", + default=None + ) + + @property + def file_extension(self): + if self.save_type == SaveFileType.h5: + return "h5" + else: + return "tif" + + def make_filepath(self, suffix: str) -> Path: + """ + Returns a filepath for the resulting data + """ + return self.save_dir / Path(self.save_name + suffix).with_suffix("." + self.file_extension) From 094651a9b77646414db89cf4f72d4465e07f4640 Mon Sep 17 00:00:00 2001 From: Michael Milton Date: Fri, 15 Sep 2023 20:19:29 +1000 Subject: [PATCH 038/147] Fix some tests, add comprehensive LatticeData tests --- core/lls_core/cmds/__main__.py | 8 +-- core/lls_core/models/deskew.py | 14 +++++ core/lls_core/models/lattice_data.py | 93 ++++------------------------ core/tests/test_arg_parser.py | 21 +++---- core/tests/test_batch_deskew_args.py | 28 ++++----- core/tests/test_batch_deskew_yaml.py | 13 ++-- core/tests/test_deskew.py | 7 ++- 7 files changed, 63 insertions(+), 121 deletions(-) diff --git a/core/lls_core/cmds/__main__.py b/core/lls_core/cmds/__main__.py index 22677a9..851f259 100644 --- a/core/lls_core/cmds/__main__.py +++ b/core/lls_core/cmds/__main__.py @@ -28,7 +28,7 @@ class CliDeskewDirection(StrEnum): @app.command() def main( - image: Path = Argument(help="Path to the image file to read, in a format readable by AICSImageIO, for example .tiff or .czi"), + image: Path = Argument(None, help="Path to the image file to read, in a format readable by AICSImageIO, for example .tiff or .czi"), skew: CliDeskewDirection = Option( default=DeskewParams.get_default("skew").name, help=DeskewParams.get_description("skew") @@ -47,9 +47,9 @@ def main( z_start: Optional[int] = Option(None, help="The index of the first Z slice to use. All prior Z slices will be discarded."), 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."), - enable_deconvolution: Annotated[bool, Option("--deconvolution/--disable-deconvolution")] = False, + enable_deconvolution: bool = Option(False, "--deconvolution/--disable-deconvolution"), decon_processing: DeconvolutionChoice = DeconvolutionParams.make_typer_field("decon_processing"), - psf: Annotated[List[Path], Option(help="A list of 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.")] = [], + psf: List[Path] = Option([], help="A list of 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."), psf_num_iter: int = DeconvolutionParams.make_typer_field("psf_num_iter"), background: str = DeconvolutionParams.make_typer_field("background"), @@ -107,7 +107,7 @@ def main( from yaml import safe_load yaml_args = safe_load(fp) - return LatticeData.parse_obj(merge(yaml_args, json_args, cli_args)) + LatticeData.parse_obj(merge(yaml_args, json_args, cli_args)).process().save_image() if __name__ == '__main__': app() diff --git a/core/lls_core/models/deskew.py b/core/lls_core/models/deskew.py index 38a4f33..2018b44 100644 --- a/core/lls_core/models/deskew.py +++ b/core/lls_core/models/deskew.py @@ -68,6 +68,20 @@ class DeskewParams(FieldAccessMixin, arbitrary_types_allowed=True): def dims(self): return self.image.dims + @validator("skew", pre=True) + def convert_skew(cls, v: Any): + # Allow skew to be provided as a string + if isinstance(v, str): + return DeskewDirection[v] + return v + + @validator("physical_pixel_sizes", pre=True) + def convert_pixels(cls, v: Any): + # Allow the pixel sizes to be specified as a tuple + if isinstance(v, tuple) and len(v) == 3: + return DefinedPixelSizes(X=v[0], Y=v[1], Z=v[2]) + return v + # convert_image = validator("image", pre=True, allow_reuse=True)(image_like_to_image) @validator("image", pre=True) diff --git a/core/lls_core/models/lattice_data.py b/core/lls_core/models/lattice_data.py index eea1d92..813997c 100644 --- a/core/lls_core/models/lattice_data.py +++ b/core/lls_core/models/lattice_data.py @@ -95,7 +95,7 @@ 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( - filename=str(self.lattice_data.make_filepath(make_filename_prefix(roi_index=str(roi)))), + filename=str(self.lattice_data.make_filepath(make_filename_prefix(roi_index=roi))), compression='gzip', nchannels=len(self.lattice_data.channel_range), subsamp=((1, 1, 1), (1, 2, 2), (2, 4, 4)), @@ -116,7 +116,7 @@ def save_image(self): first_result = result_list[0] images_array = np.swapaxes(np.expand_dims([result.data for result in result_list], axis=0), 1, 2) tifffile.imwrite( - file = str(self.lattice_data.make_filepath(make_filename_prefix(channel=first_result.channel, time=time, roi_index=roi))), + str(self.lattice_data.make_filepath(make_filename_prefix(channel=first_result.channel, time=time, roi_index=roi))), data = images_array, bigtiff=True, resolution=(1./self.lattice_data.dx, 1./self.lattice_data.dy, "MICROMETER"), @@ -460,7 +460,7 @@ def _process_non_crop(self) -> Iterable[ProcessedVolume]: data = slice.data.compute() if self.deconvolution is not None: if self.deconvolution.decon_processing == DeconvolutionChoice.cuda_gpu: - data= pycuda_decon( + data = pycuda_decon( image=data, psf=self.deconvolution.psf[slice.channel], background=self.deconvolution.background, @@ -471,14 +471,14 @@ def _process_non_crop(self) -> Iterable[ProcessedVolume]: num_iter=self.deconvolution.psf_num_iter ) else: - data= skimage_decon( - vol_zyx=data, - psf=self.deconvolution.psf[slice.channel], - num_iter=self.deconvolution.psf_num_iter, - clip=False, - filter_epsilon=0, - boundary='nearest' - ) + data = skimage_decon( + vol_zyx=data, + psf=self.deconvolution.psf[slice.channel], + num_iter=self.deconvolution.psf_num_iter, + clip=False, + filter_epsilon=0, + boundary='nearest' + ) yield slice.copy_with_data( cle.pull_zyx(self.deskew_func( @@ -522,74 +522,3 @@ def process(self) -> ProcessedSlices: lattice_data=self, slices=self._process_non_crop() ) - -class AicsLatticeParams(TypedDict): - data: DataArray - physical_pixel_sizes: DefinedPixelSizes - -def lattice_params_from_aics(img: AICSImage, physical_pixel_sizes: PhysicalPixelSizes = PhysicalPixelSizes(None, None, None)) -> AicsLatticeParams: - # Note: The reason we copy all of these fields rather than just storing the AICSImage is because that class is mostly immutable and so not suitable - - pixel_sizes = DefinedPixelSizes( - X = physical_pixel_sizes[0] or img.physical_pixel_sizes.X or LatticeData.physical_pixel_sizes.X, - Y = physical_pixel_sizes[1] or img.physical_pixel_sizes.Y or LatticeData.physical_pixel_sizes.Y, - Z = physical_pixel_sizes[2] or img.physical_pixel_sizes.Z or LatticeData.physical_pixel_sizes.Z - ) - - return AicsLatticeParams( - data = img.dask_data, - dims = img.dims, - physical_pixel_sizes = pixel_sizes, - ) - -def img_from_array(arr: ArrayLike, dimension_order: Optional[str] = None, **kwargs: Any) -> AICSImage: - """ - Creates an AICSImage from an array without metadata - - Args: - arr (ArrayLike): An array - last_dimension: How to handle the dimension order - kwargs: Additional arguments to pass to the AICSImage constructor - """ - # dim_order: str - - if len(arr.shape) < 3 or len(arr.shape) > 5: - raise ValueError("Array dimensions must be in the range [3, 5]") - - # if aicsimageio tiffreader assigns last dim as time when it should be channel, user can override this - # if len(arr.shape) == 3: - # dim_order="ZYX" - # else: - # if last_dimension not in ["channel", "time"]: - # raise ValueError("last_dimension must be either channel or time when convertin") - # if len(arr.shape) == 4: - # if last_dimension == "channel": - # dim_order = "CZYX" - # elif last_dimension == "time": - # dim_order = "TZYX" - # elif len(arr.shape) == 5: - # if last_dimension == "channel": - # dim_order = "CTZYX" - # elif last_dimension == "time": - # dim_order = "TCZYX" - # else: - # raise ValueError() - - img = AICSImage(image=arr, dim_order=dimension_order, **kwargs) - - # if last axes of "aicsimage data" shape is not equal to time, then swap channel and time - if img.data.shape[0] != img.dims.T or img.data.shape[1] != img.dims.C: - import numpy as np - arr = np.swapaxes(arr, 0, 1) - return AICSImage(image=arr, dim_order=dimension_order, **kwargs) - -def lattice_fom_array(arr: ArrayLike, last_dimension: Optional[Literal["channel", "time"]] = None, **kwargs: Any) -> AicsLatticeParams: - """ - Creates a `LatticeData` from an array - - Args: - arr: Array to use as the data source - last_dimension: See img_from_array - """ - aics = img_from_array(arr, last_dimension) - return lattice_params_from_aics(aics, **kwargs) diff --git a/core/tests/test_arg_parser.py b/core/tests/test_arg_parser.py index 28a47dc..c33d211 100644 --- a/core/tests/test_arg_parser.py +++ b/core/tests/test_arg_parser.py @@ -1,18 +1,17 @@ -from typer.testing import CliRunner from typer.main import get_command +from click import Context from lls_core.cmds.__main__ import app -runner = CliRunner() def test_voxel_parsing(): # Tests that we can parse voxel lists correctly - parser = get_command(app).make_parser() - parser.make_context() - args = parser.parse_args(args=[ - "--input", "input", - "--output", "output", - "--processing", "deskew", - "--output_file_type", "tiff", - "--voxel_sizes", "1", "1", "1" + command = get_command(app) + ctx = Context(command) + parser = command.make_parser(ctx) + args, _, _ = parser.parse_args(args=[ + "input", + "--save-name", "output", + "--save-type", "tiff", + "--pixel-sizes", "1", "1", "1" ]) - assert args.voxel_sizes == [1.0, 1.0, 1.0] + assert args["pixel_sizes"] == ("1", "1", "1") diff --git a/core/tests/test_batch_deskew_args.py b/core/tests/test_batch_deskew_args.py index eb09bc3..c3ced00 100644 --- a/core/tests/test_batch_deskew_args.py +++ b/core/tests/test_batch_deskew_args.py @@ -1,10 +1,11 @@ # Tests for napari_lattice using arguments and saving output files as h5, as well as tiff +from typer.testing import CliRunner from skimage.io import imsave import numpy as np from pathlib import Path import tempfile -from lls_core.cmds.__main__ import main as run_cli +from lls_core.cmds.__main__ import app def create_image(path: Path): # Create a zero array of shape 5x5x5 with a value of 10 at (2,4,2) @@ -14,6 +15,7 @@ def create_image(path: Path): imsave(str(path), raw) assert path.exists() +runner = CliRunner() def test_batch_deskew_h5(): """Write image to disk and then execute napari_lattice from terminal @@ -24,16 +26,15 @@ def test_batch_deskew_h5(): input_file = out_dir / 'raw.tiff' create_image(input_file) # Batch deskew and save as h5 - run_cli([ - "--input", str(input_file), - "--output", str(out_dir), - "--processing", "deskew", - "--output_file_type", "h5" + runner.invoke(app, [ + str(input_file), + "--save-dir", str(out_dir), + "--save-type", "h5" ]) # checks if h5 files written - assert (out_dir / "raw" / "raw.h5").exists() - assert (out_dir / "raw" / "raw.xml").exists() + assert (out_dir / "raw.h5").exists() + # assert (out_dir / "raw.xml").exists() def test_batch_deskew_tiff(): @@ -42,12 +43,11 @@ def test_batch_deskew_tiff(): out_dir = Path(out_dir) input_file = out_dir / 'raw.tiff' create_image(input_file) - run_cli([ - "--input", str(input_file), - "--output", str(out_dir), - "--processing", "deskew", - "--output_file_type", "tiff" + runner.invoke(app, [ + str(input_file), + "--save-dir", str(out_dir), + "--save-type", "tiff" ]) # checks if tiff written - assert (out_dir / "raw" / "C0T0_raw.tif").exists() + assert len(list(out_dir.glob("*.tif"))) == 1 diff --git a/core/tests/test_batch_deskew_yaml.py b/core/tests/test_batch_deskew_yaml.py index b8c4f3e..e793443 100644 --- a/core/tests/test_batch_deskew_yaml.py +++ b/core/tests/test_batch_deskew_yaml.py @@ -5,7 +5,7 @@ import tempfile import numpy as np from pathlib import Path -from lls_core.cmds.__main__ import main as run_cli +from lls_core.cmds.__main__ import app as run_cli def write_config_file(config_settings: dict, output_file_location: Path): # Write config file for napari_lattice @@ -40,9 +40,8 @@ def create_data(dir: Path) -> Path: assert input_file.exists() config: dict[str, str] = { - "input": str(input_file), - "output": str(dir), - "processing": "deskew", + "image": str(input_file), + "save_dir": str(dir), "output_file_type": "h5" } @@ -60,8 +59,8 @@ def test_yaml_deskew(): test_dir = Path(test_dir) config_location = create_data(test_dir) # Batch deskew and save as h5 - run_cli(["--config", str(config_location)]) + run_cli(["--yaml-config", str(config_location)]) # checks if h5 files written - assert (test_dir / "raw" / "raw.h5").exists() - assert (test_dir / "raw" / "raw.xml").exists() + assert (test_dir / "raw.h5").exists() + # assert (test_dir / "raw" / "raw.xml").exists() diff --git a/core/tests/test_deskew.py b/core/tests/test_deskew.py index 1b30dc7..d08fc4b 100644 --- a/core/tests/test_deskew.py +++ b/core/tests/test_deskew.py @@ -1,7 +1,8 @@ #filename and function name should start with "test_" when using pytest import pyclesperanto_prototype as cle import numpy as np -from lls_core.models.lattice_data import lattice_fom_array +from lls_core.models.lattice_data import LatticeData +from xarray import DataArray def test_deskew(): @@ -15,6 +16,6 @@ def test_deskew(): assert deskewed[2,2,0] == 0.5662433505058289 def test_lattice_data_deskew(): - raw = np.zeros((5, 5, 5)) - lattice = lattice_fom_array(raw, physical_pixel_sizes = (1, 1, 1), save_name="test") + raw = DataArray(np.zeros((5, 5, 5)), dims=["X", "Y", "Z"]) + lattice = LatticeData(image=raw, physical_pixel_sizes = (1, 1, 1), save_name="test") assert lattice.deskew_vol_shape == [2, 9, 5] From bc006e38760f3caa0829c4a1fbc633c25ef2c368 Mon Sep 17 00:00:00 2001 From: Michael Milton Date: Sun, 17 Sep 2023 00:05:05 +1000 Subject: [PATCH 039/147] Skip validations when image is missing --- core/lls_core/models/deskew.py | 2 ++ core/lls_core/models/lattice_data.py | 10 +++++++++- 2 files changed, 11 insertions(+), 1 deletion(-) diff --git a/core/lls_core/models/deskew.py b/core/lls_core/models/deskew.py index 2018b44..196e505 100644 --- a/core/lls_core/models/deskew.py +++ b/core/lls_core/models/deskew.py @@ -106,6 +106,8 @@ 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 + if "image" not in values: + return values data: DataArray = cls.reshaping(values["image"]) if values.get('deskew_vol_shape') is None: if values.get('deskew_affine_transform') is None: diff --git a/core/lls_core/models/lattice_data.py b/core/lls_core/models/lattice_data.py index 813997c..f855c9a 100644 --- a/core/lls_core/models/lattice_data.py +++ b/core/lls_core/models/lattice_data.py @@ -167,7 +167,7 @@ def default_save_name(cls, values: dict): # This needs to be a root validator to ensure it runs before the # reshaping validator. We can't override that either since it's # a field validator and can't modify save_name - if values.get("save_name", None) is None and isinstance(values["image"], PathLike): + if values.get("save_name", None) is None and isinstance(values.get("image"), PathLike): values["save_name"] = Path(values["image"]).stem return values @@ -176,6 +176,11 @@ def parse_time_range(cls, v: Any, values: dict) -> Any: """ Sets the default time range if undefined """ + # This skips the conversion if no image was provided, to ensure a more + # user-friendly error is provided, namely "image was missing" + if "image" not in values: + return v + default_start = 0 default_end = values["image"].sizes["T"] if v is None: @@ -190,6 +195,9 @@ def parse_channel_range(cls, v: Any, values: dict) -> Any: """ Sets the default channel range if undefined """ + if "image" not in values: + return v + default_start = 0 default_end = values["image"].sizes["C"] if v is None: From 867ca38e7eb69e2227bff0c386a2bea5d673176c Mon Sep 17 00:00:00 2001 From: Michael Milton Date: Sun, 17 Sep 2023 01:31:02 +1000 Subject: [PATCH 040/147] invoke() test utility, forbid extra fields, ignore_keyerror() utility --- core/lls_core/cmds/__main__.py | 11 ++++- core/lls_core/models/__init__.py | 5 ++ core/lls_core/models/crop.py | 2 +- core/lls_core/models/deconvolution.py | 2 +- core/lls_core/models/deskew.py | 2 +- core/lls_core/models/lattice_data.py | 70 ++++++++++++++------------- core/lls_core/models/output.py | 2 +- core/lls_core/models/utils.py | 21 +++++++- core/lls_core/types.py | 5 +- core/tests/test_batch_deskew_args.py | 7 ++- core/tests/test_batch_deskew_yaml.py | 7 +-- core/tests/test_lattice_data.py | 59 ++++++++++++++++++++++ core/tests/utils.py | 6 +++ 13 files changed, 150 insertions(+), 49 deletions(-) create mode 100644 core/lls_core/models/__init__.py create mode 100644 core/tests/test_lattice_data.py create mode 100644 core/tests/utils.py diff --git a/core/lls_core/cmds/__main__.py b/core/lls_core/cmds/__main__.py index 851f259..a08f303 100644 --- a/core/lls_core/cmds/__main__.py +++ b/core/lls_core/cmds/__main__.py @@ -18,7 +18,7 @@ from typer import Typer, Argument, Option from lls_core.models.output import SaveFileType -from toolz.dicttoolz import merge +from toolz.dicttoolz import merge, valfilter class CliDeskewDirection(StrEnum): X = auto() @@ -107,7 +107,14 @@ def main( from yaml import safe_load yaml_args = safe_load(fp) - LatticeData.parse_obj(merge(yaml_args, json_args, cli_args)).process().save_image() + + LatticeData.parse_obj( + # Merge all three sources of config: YAML, JSON and CLI + merge( + # Remove None values from all dictonaries, so that they merge appropriately + valfilter(lambda x: x is not None, it) for it in [yaml_args, json_args, cli_args] + ) + ).process().save_image() if __name__ == '__main__': app() diff --git a/core/lls_core/models/__init__.py b/core/lls_core/models/__init__.py new file mode 100644 index 0000000..3a10295 --- /dev/null +++ b/core/lls_core/models/__init__.py @@ -0,0 +1,5 @@ +from lls_core.models.crop import CropParams +from lls_core.models.deconvolution import DeconvolutionParams +from lls_core.models.deskew import DeskewParams +from lls_core.models.output import OutputParams +from lls_core.models.lattice_data import LatticeData diff --git a/core/lls_core/models/crop.py b/core/lls_core/models/crop.py index 3000181..0363ac4 100644 --- a/core/lls_core/models/crop.py +++ b/core/lls_core/models/crop.py @@ -4,7 +4,7 @@ from lls_core.models.utils import FieldAccessMixin -class CropParams(FieldAccessMixin, arbitrary_types_allowed=True): +class CropParams(FieldAccessMixin): """ Parameters for the optional cropping step """ diff --git a/core/lls_core/models/deconvolution.py b/core/lls_core/models/deconvolution.py index c2b360c..11f95e4 100644 --- a/core/lls_core/models/deconvolution.py +++ b/core/lls_core/models/deconvolution.py @@ -18,7 +18,7 @@ class MakeKwargs(TypedDict, total=False): psf_num_iter: int background: str -class DeconvolutionParams(FieldAccessMixin, arbitrary_types_allowed=True): +class DeconvolutionParams(FieldAccessMixin): """ Parameters for the optional deconvolution step """ diff --git a/core/lls_core/models/deskew.py b/core/lls_core/models/deskew.py index 196e505..a9a78fe 100644 --- a/core/lls_core/models/deskew.py +++ b/core/lls_core/models/deskew.py @@ -40,7 +40,7 @@ def from_physical(cls, pixels: PhysicalPixelSizes) -> Self: ) -class DeskewParams(FieldAccessMixin, arbitrary_types_allowed=True): +class DeskewParams(FieldAccessMixin): image: DataArray = Field( description="A 3-5D array containing the image data" ) diff --git a/core/lls_core/models/lattice_data.py b/core/lls_core/models/lattice_data.py index f855c9a..accda41 100644 --- a/core/lls_core/models/lattice_data.py +++ b/core/lls_core/models/lattice_data.py @@ -23,6 +23,7 @@ from lls_core.models.crop import CropParams from lls_core.models.deconvolution import DeconvolutionParams from lls_core.models.output import OutputParams, SaveFileType +from lls_core.models.utils import ignore_keyerror from lls_core.types import ArrayLike from lls_core.models.deskew import DeskewParams from napari_workflows import Workflow @@ -146,7 +147,7 @@ class CommonLatticeArgs(CommonDeskewArgs, CommonOutputArgs): crop: Optional[CropParams] workflow: Optional[Workflow] -class LatticeData(OutputParams, DeskewParams, arbitrary_types_allowed=True): +class LatticeData(OutputParams, DeskewParams): """ Holds data and metadata for a given image in a consistent format """ @@ -167,7 +168,8 @@ def default_save_name(cls, values: dict): # This needs to be a root validator to ensure it runs before the # reshaping validator. We can't override that either since it's # a field validator and can't modify save_name - if values.get("save_name", None) is None and isinstance(values.get("image"), PathLike): + from lls_core.models.utils import is_pathlike + if values.get("save_name", None) is None and is_pathlike(values.get("image")): values["save_name"] = Path(values["image"]).stem return values @@ -178,16 +180,14 @@ def parse_time_range(cls, v: Any, values: dict) -> Any: """ # This skips the conversion if no image was provided, to ensure a more # user-friendly error is provided, namely "image was missing" - if "image" not in values: - return v - - default_start = 0 - default_end = values["image"].sizes["T"] - if v is None: - return range(default_start, default_end) - elif isinstance(v, tuple) and len(v) == 2: - # Allow 2-tuples to be used as input for this field - return range(v[0] or default_start, v[1] or default_end) + with ignore_keyerror(): + default_start = 0 + default_end = values["image"].sizes["T"] + if v is None: + return range(default_start, default_end) + elif isinstance(v, tuple) and len(v) == 2: + # Allow 2-tuples to be used as input for this field + return range(v[0] or default_start, v[1] or default_end) return v @validator("channel_range", pre=True, always=True) @@ -195,16 +195,14 @@ def parse_channel_range(cls, v: Any, values: dict) -> Any: """ Sets the default channel range if undefined """ - if "image" not in values: - return v - - default_start = 0 - default_end = values["image"].sizes["C"] - if v is None: - return range(default_start, default_end) - elif isinstance(v, tuple) and len(v) == 2: - # Allow 2-tuples to be used as input for this field - return range(v[0] or default_start, v[1] or default_end) + with ignore_keyerror(): + default_start = 0 + default_end = values["image"].sizes["C"] + if v is None: + return range(default_start, default_end) + elif isinstance(v, tuple) and len(v) == 2: + # Allow 2-tuples to be used as input for this field + return range(v[0] or default_start, v[1] or default_end) return v @validator("time_range") @@ -212,11 +210,13 @@ 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["image"].sizes["T"] - if v.start < 0: - raise ValueError("The lowest valid start value is 0") - if v.stop > max_time: - raise ValueError(f"The highest valid time value is the length of the time axis, which is {max_time}") + with ignore_keyerror(): + max_time = values["image"].sizes["T"] + if v.start < 0: + raise ValueError("The lowest valid start value is 0") + if v.stop > max_time: + raise ValueError(f"The highest valid time value is the length of the time axis, which is {max_time}") + return v @validator("channel_range") @@ -224,17 +224,19 @@ 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["image"].sizes["C"] - if v.start < 0: - raise ValueError("The lowest valid start value is 0") - if v.stop > max_channel: - raise ValueError(f"The highest valid channel value is the length of the channel axis, which is {max_channel}") + with ignore_keyerror(): + max_channel = values["image"].sizes["C"] + if v.start < 0: + raise ValueError("The lowest valid start value is 0") + if v.stop > max_channel: + raise ValueError(f"The highest valid channel value is the length of the channel axis, which is {max_channel}") return v @validator("channel_range") def channel_range_subset(cls, v: range, values: dict): - if min(v) < 0 or max(v) > values["image"].sizes["C"]: - raise ValueError("The output channel range must be a subset of the total available channels") + with ignore_keyerror(): + if min(v) < 0 or max(v) > values["image"].sizes["C"]: + raise ValueError("The output channel range must be a subset of the total available channels") return v @validator("time_range") diff --git a/core/lls_core/models/output.py b/core/lls_core/models/output.py index 07b1b60..7520c32 100644 --- a/core/lls_core/models/output.py +++ b/core/lls_core/models/output.py @@ -10,7 +10,7 @@ class SaveFileType(StrEnum): h5 = "h5" tiff = "tiff" -class OutputParams(FieldAccessMixin, arbitrary_types_allowed=True): +class OutputParams(FieldAccessMixin): save_dir: DirectoryPath = Field( default = getcwd(), description="The directory where the output data will be saved" diff --git a/core/lls_core/models/utils.py b/core/lls_core/models/utils.py index 949e795..c0c42f6 100644 --- a/core/lls_core/models/utils.py +++ b/core/lls_core/models/utils.py @@ -1,9 +1,13 @@ from typing import Any, Type from enum import Enum -from pydantic import BaseModel +from pydantic import BaseModel, Extra from typer import Option +from contextlib import contextmanager +def is_pathlike(x: Any) -> bool: + from os import PathLike + return isinstance(x, (str, bytes, PathLike)) def enum_choices(enum: Type[Enum]) -> str: """ @@ -11,10 +15,25 @@ def enum_choices(enum: Type[Enum]) -> str: """ return "{" + ", ".join([it.name for it in enum]) + "}" +@contextmanager +def ignore_keyerror(): + """ + Context manager that ignores KeyErrors from missing fields. + This allows for the validation to continue even if a single field + is missing, eventually resulting in a more user-friendly error message + """ + try: + yield + except KeyError: + pass + class FieldAccessMixin(BaseModel): """ Adds methods to a BaseModel for accessing useful field information """ + class Config: + extra = Extra.forbid + arbitrary_types_allowed = True @classmethod def get_default(cls, field_name: str) -> Any: diff --git a/core/lls_core/types.py b/core/lls_core/types.py index 26691bb..3e5d53b 100644 --- a/core/lls_core/types.py +++ b/core/lls_core/types.py @@ -19,8 +19,11 @@ def image_like_to_image(img: ImageLike) -> DataArray: """ Converts an image in one of many formats to a DataArray """ - if isinstance(img, PathLike): + # First try treating it as a path + try: img = AICSImage(fspath(img)) + except TypeError: + pass if isinstance(img, AICSImage): return img.xarray_dask_data else: diff --git a/core/tests/test_batch_deskew_args.py b/core/tests/test_batch_deskew_args.py index c3ced00..f9b066e 100644 --- a/core/tests/test_batch_deskew_args.py +++ b/core/tests/test_batch_deskew_args.py @@ -1,11 +1,11 @@ # Tests for napari_lattice using arguments and saving output files as h5, as well as tiff -from typer.testing import CliRunner from skimage.io import imsave import numpy as np from pathlib import Path import tempfile from lls_core.cmds.__main__ import app +from tests.utils import invoke def create_image(path: Path): # Create a zero array of shape 5x5x5 with a value of 10 at (2,4,2) @@ -15,7 +15,6 @@ def create_image(path: Path): imsave(str(path), raw) assert path.exists() -runner = CliRunner() def test_batch_deskew_h5(): """Write image to disk and then execute napari_lattice from terminal @@ -26,7 +25,7 @@ def test_batch_deskew_h5(): input_file = out_dir / 'raw.tiff' create_image(input_file) # Batch deskew and save as h5 - runner.invoke(app, [ + invoke([ str(input_file), "--save-dir", str(out_dir), "--save-type", "h5" @@ -43,7 +42,7 @@ def test_batch_deskew_tiff(): out_dir = Path(out_dir) input_file = out_dir / 'raw.tiff' create_image(input_file) - runner.invoke(app, [ + invoke([ str(input_file), "--save-dir", str(out_dir), "--save-type", "tiff" diff --git a/core/tests/test_batch_deskew_yaml.py b/core/tests/test_batch_deskew_yaml.py index e793443..b702f09 100644 --- a/core/tests/test_batch_deskew_yaml.py +++ b/core/tests/test_batch_deskew_yaml.py @@ -5,7 +5,8 @@ import tempfile import numpy as np from pathlib import Path -from lls_core.cmds.__main__ import app as run_cli +from lls_core.cmds.__main__ import app +from tests.utils import invoke def write_config_file(config_settings: dict, output_file_location: Path): # Write config file for napari_lattice @@ -42,7 +43,7 @@ def create_data(dir: Path) -> Path: config: dict[str, str] = { "image": str(input_file), "save_dir": str(dir), - "output_file_type": "h5" + "save_type": "h5" } write_config_file(config, config_location) @@ -59,7 +60,7 @@ def test_yaml_deskew(): test_dir = Path(test_dir) config_location = create_data(test_dir) # Batch deskew and save as h5 - run_cli(["--yaml-config", str(config_location)]) + invoke(["--yaml-config", str(config_location)], ) # checks if h5 files written assert (test_dir / "raw.h5").exists() diff --git a/core/tests/test_lattice_data.py b/core/tests/test_lattice_data.py new file mode 100644 index 0000000..298a327 --- /dev/null +++ b/core/tests/test_lattice_data.py @@ -0,0 +1,59 @@ +import pytest +from lls_core.models import LatticeData +from lls_core.models.output import SaveFileType +from lls_core.sample import resources +from importlib_resources import as_file +from importlib_resources.abc import Traversable +import tempfile +from pathlib import Path +from lls_core import DeconvolutionChoice +from itertools import product + +inputs = pytest.mark.parametrize( + ["path"], [ + ("RBC_tiny.czi", ), + ("RBC_lattice.tif", ), + ("LLS7_t1_ch1.czi", ), + ("LLS7_t1_ch3.czi", ), + ("LLS7_t2_ch1.czi", ), + ("LLS7_t2_ch3.czi", ), + ("multich_multi_time.tif", ), +]) + +def open_psf(name: str): + with as_file(resources / "psfs" / "zeiss_simulated" / name) as path: + return path + +parameterized = pytest.mark.parametrize("args", [ + {"skew": "X"}, + {"skew": "Y"}, + {"angle": 30}, + {"angle": 90}, + {"physical_pixel_sizes": (1, 1, 1)}, + {"rois": []}, + {"psf": [open_psf("488.czi")]}, + {"psf": [open_psf("640.tif")]}, + {"background": 1}, + {"background": "auto"}, + {"background": "second_last"}, + {"save_type": SaveFileType.h5}, + {"save_type": SaveFileType.tiff}, +]) + +@inputs +@parameterized +def test_process(path: str, args: dict): + with as_file(resources / path) as lattice_path: + args["image"] = lattice_path + for slice in LatticeData.parse_obj(args).process().slices: + assert slice.data.ndim == 3 + +@inputs +@parameterized +def test_save(path: str, args: dict): + with as_file(resources / path) as lattice_path, tempfile.TemporaryDirectory() as tempdir: + args["image"] = lattice_path + args["save_dir"] = tempdir + LatticeData.parse_obj(args).process().save_image() + results = list(Path(tempdir).iterdir()) + assert len(results) > 0 diff --git a/core/tests/utils.py b/core/tests/utils.py new file mode 100644 index 0000000..ad68041 --- /dev/null +++ b/core/tests/utils.py @@ -0,0 +1,6 @@ +from typing import Sequence +from typer.testing import CliRunner +from lls_core.cmds.__main__ import app + +def invoke(args: Sequence[str]): + CliRunner().invoke(app, args, catch_exceptions=False) From 0d20f0dea2be463f8723249bf49a7abf5bc7802b Mon Sep 17 00:00:00 2001 From: Michael Milton Date: Sun, 17 Sep 2023 15:58:19 +1000 Subject: [PATCH 041/147] Add formal click depenedency --- core/pyproject.toml | 1 + 1 file changed, 1 insertion(+) diff --git a/core/pyproject.toml b/core/pyproject.toml index 5b20df1..d3d5282 100644 --- a/core/pyproject.toml +++ b/core/pyproject.toml @@ -39,6 +39,7 @@ dependencies = [ # Earlier versions don't have Python 3.11 binaries, and the sdist # is misconfigured: https://github.com/AllenCellModeling/aicspylibczi/issues/90 "aicspylibczi>=3.1.1", + "click", "dask", "dask[distributed]", "fsspec>=2022.8.2", From bb5d3217e34e913d98c0b6b4c250235aa95279b7 Mon Sep 17 00:00:00 2001 From: Michael Milton Date: Wed, 20 Sep 2023 10:38:19 +1000 Subject: [PATCH 042/147] Refactor CLI to use None default values, add JSON config help, throw error on CLI argument conflict, and add dump-schema subcommand --- core/lls_core/cmds/__main__.py | 110 +++++++++++++++++--------- core/lls_core/deconvolution.py | 9 +-- core/lls_core/models/crop.py | 16 +++- core/lls_core/models/deconvolution.py | 18 +++-- core/lls_core/models/deskew.py | 8 +- core/lls_core/models/lattice_data.py | 10 ++- core/lls_core/models/utils.py | 28 ++++++- core/lls_core/types.py | 7 +- core/lls_core/utils.py | 14 +++- core/tests/test_lattice_data.py | 49 +++++++----- plugin/napari_lattice/fields.py | 2 +- 11 files changed, 186 insertions(+), 85 deletions(-) diff --git a/core/lls_core/cmds/__main__.py b/core/lls_core/cmds/__main__.py index a08f303..9079cce 100644 --- a/core/lls_core/cmds/__main__.py +++ b/core/lls_core/cmds/__main__.py @@ -3,69 +3,104 @@ # Run processing on command line instead of napari. # Example for deskewing files in a folder # python lattice_processing.py --input /home/pradeep/to_deskew --output /home/pradeep/output_save/ --processing deskew +from __future__ import annotations from enum import auto from pathlib import Path -from typing import List, Optional, Tuple -from typing_extensions import Annotated +from typing import TYPE_CHECKING, List, Optional, Tuple from strenum import StrEnum from lls_core.models.lattice_data import LatticeData from lls_core.models.deskew import DefinedPixelSizes, DeskewParams from lls_core.models.deconvolution import DeconvolutionParams from lls_core.models.output import OutputParams +from lls_core.models.crop import CropParams from lls_core import DeconvolutionChoice, DeskewDirection from typer import Typer, Argument, Option from lls_core.models.output import SaveFileType -from toolz.dicttoolz import merge, valfilter +from toolz.dicttoolz import merge_with, valfilter + +if TYPE_CHECKING: + from lls_core.models.utils import FieldAccessMixin + from typing import Type, Any class CliDeskewDirection(StrEnum): X = auto() Y = auto() -app = Typer(add_completion=False) +app = Typer(add_completion=False, rich_markup_mode="rich") + +def format_default(default: str): + """ + Given a string, formats it like a default value in Typer + """ + from typer.rich_utils import STYLE_OPTION_DEFAULT + default_style = STYLE_OPTION_DEFAULT + return f"[{default_style}]\\[default: {default}][/{default_style}]" + + +def field_from_model(model: Type[FieldAccessMixin], field_name: str, extra_description: str = "", description: Optional[str] = None) -> Any: + """ + Generates a type Field from a Pydantic model field + """ + field = model.__fields__[field_name] + + from enum import Enum + default = field.get_default() + if isinstance(default, Enum): + default = default.name + + if description is None: + description = f"{field.field_info.description} {extra_description}" + + return Option( + # We make all defaults None so they can be removed + default = None, + show_default=False, + help=f"{description}\n{format_default(default)}" + ) + +def handle_merge(values: list): + if len(values) > 0: + raise ValueError(f"A parameter has been passed multiple times! Got: {', '.join(values)}") + +@app.command("dump-schema") +def dump_schema(): + import json + import sys + json.dump(LatticeData.to_definition_dict(), fp=sys.stdout, indent=4) -@app.command() +@app.command("process") def main( - image: Path = Argument(None, help="Path to the image file to read, in a format readable by AICSImageIO, for example .tiff or .czi"), - skew: CliDeskewDirection = Option( - default=DeskewParams.get_default("skew").name, - help=DeskewParams.get_description("skew") - ),# DeskewParams.make_typer_field("skew"), - angle: float = DeskewParams.make_typer_field("angle") , - pixel_sizes: Tuple[float, float, float] = Option( - ( - LatticeData.get_default("physical_pixel_sizes").X, - LatticeData.get_default("physical_pixel_sizes").Y, - LatticeData.get_default("physical_pixel_sizes").Z, - ), help=DeskewParams.get_description("physical_pixel_sizes") + ". This takes three arguments, corresponding to the X Y and Z pixel dimensions respectively" - ), - - rois: List[Path] = Option([], help="A list of paths pointing to regions of interest to crop to, in ImageJ format."), + 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 X Y and Z pixel dimensions respectively"), + 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."), # 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(None, help="The index of the first Z slice to use. All prior Z slices will be discarded."), - 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."), + z_start: Optional[int] = Option(None, help="The index of the first Z slice to use. All prior Z slices will be discarded. " + format_default("0"), 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), enable_deconvolution: bool = Option(False, "--deconvolution/--disable-deconvolution"), decon_processing: DeconvolutionChoice = DeconvolutionParams.make_typer_field("decon_processing"), - psf: List[Path] = Option([], help="A list of 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."), - psf_num_iter: int = DeconvolutionParams.make_typer_field("psf_num_iter"), - background: str = DeconvolutionParams.make_typer_field("background"), + psf: List[Path] = field_from_model(DeconvolutionParams, "psf", description="A list of 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."), + psf_num_iter: int = field_from_model(DeconvolutionParams, "psf_num_iter"), + background: str = field_from_model(DeconvolutionParams, "background"), - time_start: Optional[int] = Option(None, help="Index of the first time slice to use (inclusive)"), - time_end: Optional[int] = Option(None, help="Index of the first time slice to use (exclusive)"), + time_start: Optional[int] = Option(None, help="Index of the first time slice to use (inclusive). Defaults to the first time index of the image.", show_default=False), + 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), - channel_start: Optional[int] = Option(None, help="Index of the first channel slice to use (inclusive)"), - channel_end: Optional[int] = Option(None, help="Index of the first channel slice to use (exclusive)"), + channel_start: Optional[int] = Option(None, help="Index of the first channel slice to use (inclusive). Defaults to the first channel index of the image.", show_default=False), + 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), - save_dir: Path = OutputParams.make_typer_field("save_dir"), - save_name: Optional[str] = OutputParams.make_typer_field("save_name"), - save_type: SaveFileType = OutputParams.make_typer_field("save_type"), + save_dir: Path = field_from_model(OutputParams, "save_dir"), + save_name: Optional[str] = field_from_model(OutputParams, "save_name"), + save_type: SaveFileType = field_from_model(OutputParams, "save_type"), - workflow: Optional[Path] = Option(None, help="Path to a Napari Workflow file, in JSON format. If provided, the configured desekewing processing will be added to the chosen workflow."), - json_config: Optional[Path] = Option(None), - yaml_config: Optional[Path] = Option(None) + workflow: Optional[Path] = Option(None, help="Path to a Napari Workflow file, in JSON format. If provided, the configured desekewing processing will be added to the chosen 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.") ): cli_args = dict( image=image, @@ -110,9 +145,10 @@ def main( LatticeData.parse_obj( # Merge all three sources of config: YAML, JSON and CLI - merge( + merge_with( + handle_merge, # Remove None values from all dictonaries, so that they merge appropriately - valfilter(lambda x: x is not None, it) for it in [yaml_args, json_args, cli_args] + *[valfilter(lambda x: x is not None, it) for it in [yaml_args, json_args, cli_args]] ) ).process().save_image() diff --git a/core/lls_core/deconvolution.py b/core/lls_core/deconvolution.py index 3d225af..c3d7c87 100644 --- a/core/lls_core/deconvolution.py +++ b/core/lls_core/deconvolution.py @@ -16,9 +16,8 @@ import numpy as np import dask.array as da from dask.array.core import Array as DaskArray -from resource_backed_dask_array import ResourceBackedDaskArray -from lls_core.utils import pad_image_nearest_multiple +from lls_core.utils import array_to_dask, pad_image_nearest_multiple from lls_core.types import ArrayLike, is_arraylike if TYPE_CHECKING: @@ -227,11 +226,7 @@ def skimage_decon( from skimage.restoration import richardson_lucy as rl_decon_skimage depth = tuple(np.array(psf.shape) // 2) - if not isinstance(vol_zyx, ( - DaskArray, - ResourceBackedDaskArray, - )): - vol_zyx = da.asarray(vol_zyx) + vol_zyx = array_to_dask(vol_zyx) decon_data = vol_zyx.map_overlap( rl_decon_skimage, psf=psf, diff --git a/core/lls_core/models/crop.py b/core/lls_core/models/crop.py index 0363ac4..f6db276 100644 --- a/core/lls_core/models/crop.py +++ b/core/lls_core/models/crop.py @@ -1,5 +1,5 @@ -from typing import List, Tuple -from pydantic import Field, NonNegativeInt +from typing import List, Tuple, Any +from pydantic import Field, NonNegativeInt, validator from xarray import DataArray from lls_core.models.utils import FieldAccessMixin @@ -9,9 +9,19 @@ class CropParams(FieldAccessMixin): Parameters for the optional cropping step """ roi_list: List[DataArray] = Field( - description="List of regions of interest, each of which must be an NxD array, where N is the number of vertices and D the coordinates of each vertex." + description="List of regions of interest, each of which must be an NxD array, where N is the number of vertices and D the coordinates of each vertex.", + default = [] ) z_range: Tuple[NonNegativeInt, NonNegativeInt] = Field( default=None, description="The range of Z slices to take. All Z slices before the first index or after the last index will be cropped out." ) + + @validator("roi_list", each_item=True) + def read_roi(cls, v: Any): + from lls_core.types import is_pathlike + from lls_core.cropping import read_roi_array + # Converts the ROI from a path to an array + if is_pathlike(v): + return read_roi_array(v) + return v diff --git a/core/lls_core/models/deconvolution.py b/core/lls_core/models/deconvolution.py index 11f95e4..bd50f2b 100644 --- a/core/lls_core/models/deconvolution.py +++ b/core/lls_core/models/deconvolution.py @@ -12,12 +12,6 @@ from lls_core.types import image_like_to_image, ImageLike Background = Union[float, Literal["auto", "second_last"]] -class MakeKwargs(TypedDict, total=False): - decon_processing: Union[DeconvolutionChoice, str] - psf: List[ImageLike] - psf_num_iter: int - background: str - class DeconvolutionParams(FieldAccessMixin): """ Parameters for the optional deconvolution step @@ -45,4 +39,14 @@ def convert_decon(cls, v: Any): return DeconvolutionChoice[v] return v - convert_image = validator("psf", pre=True, each_item=True, allow_reuse=True)(image_like_to_image) + @validator("psf", pre=True, each_item=True, allow_reuse=True) + def convert_image(cls, v): + img = image_like_to_image(v) + # Ensure the PSF is 3D + if "C" in img.dims: + img = img.isel(C=0) + if "T" in img.dims: + img = img.isel(T=0) + if len(img.dims) != 3: + raise ValueError("PSF is not a 3D array!") + return img diff --git a/core/lls_core/models/deskew.py b/core/lls_core/models/deskew.py index a9a78fe..3d6c652 100644 --- a/core/lls_core/models/deskew.py +++ b/core/lls_core/models/deskew.py @@ -42,19 +42,19 @@ def from_physical(cls, pixels: PhysicalPixelSizes) -> Self: class DeskewParams(FieldAccessMixin): image: DataArray = Field( - description="A 3-5D array containing the image data" + description="A 3-5D array containing the image data." ) skew: DeskewDirection = Field( default=DeskewDirection.Y, - description=f"Axis along which to deskew the image. Choices: {enum_choices(DeskewDirection)}" + description=f"Axis along which to deskew the image. Choices: {enum_choices(DeskewDirection)}." ) angle: float = Field( default=30.0, - description="Angle of deskewing, in degrees" + description="Angle of deskewing, in degrees." ) physical_pixel_sizes: DefinedPixelSizes = Field( default_factory=DefinedPixelSizes, - description="Pixel size of the microscope, in microns" + description="Pixel size of the microscope, in microns." ) deskew_vol_shape: Tuple[int, ...] = Field( init_var=False, diff --git a/core/lls_core/models/lattice_data.py b/core/lls_core/models/lattice_data.py index accda41..dbe8862 100644 --- a/core/lls_core/models/lattice_data.py +++ b/core/lls_core/models/lattice_data.py @@ -125,7 +125,6 @@ def save_image(self): imagej=True ) - workflow: Optional[Workflow] = Field( default=None, description="If defined, this is a workflow to add lightsheet processing onto" @@ -245,6 +244,13 @@ def time_range_subset(cls, v: range, values: dict): raise ValueError("The output time range must be a subset of the total available time points") return v + @validator("deconvolution") + def check_psfs(cls, v: DeconvolutionParams, values: dict) -> DeconvolutionParams: + with ignore_keyerror(): + if len(v.psf) != values["image"].sizes["C"]: + raise ValueError("There should be one PSF per channel") + return v + # Hack to ensure that .skew_dir behaves identically to .skew @property def skew_dir(self) -> DeskewDirection: @@ -483,7 +489,7 @@ def _process_non_crop(self) -> Iterable[ProcessedVolume]: else: data = skimage_decon( vol_zyx=data, - psf=self.deconvolution.psf[slice.channel], + psf=self.deconvolution.psf[slice.channel].to_numpy(), num_iter=self.deconvolution.psf_num_iter, clip=False, filter_epsilon=0, diff --git a/core/lls_core/models/utils.py b/core/lls_core/models/utils.py index c0c42f6..b929a93 100644 --- a/core/lls_core/models/utils.py +++ b/core/lls_core/models/utils.py @@ -5,10 +5,6 @@ from typer import Option from contextlib import contextmanager -def is_pathlike(x: Any) -> bool: - from os import PathLike - return isinstance(x, (str, bytes, PathLike)) - def enum_choices(enum: Type[Enum]) -> str: """ Returns a human readable list of enum choices @@ -37,14 +33,38 @@ class Config: @classmethod def get_default(cls, field_name: str) -> Any: + """ + Shortcut method for returning the default value of a given field + """ return cls.__fields__[field_name].get_default() @classmethod def get_description(cls, field_name: str) -> str: + """ + Shortcut method for returning the description of a given field + """ return cls.__fields__[field_name].field_info.description + @classmethod + def to_definition_dict(cls) -> dict: + """ + Recursively converts the model into a dictionary whose keys are field names and whose + values are field descriptions. This is used to document the model to users + """ + ret = {} + for key, value in cls.__fields__.items(): + if isinstance(value.outer_type_, type) and issubclass(value.outer_type_, FieldAccessMixin): + value = value.outer_type_.to_definition_dict() + else: + value = value.field_info.description + ret[key] = value + return ret + @classmethod def make_typer_field(cls, field_name: str, extra_description: str = "") -> Any: + """ + Convert this field into a Typer field + """ field = cls.__fields__[field_name] return Option( default = field.get_default(), diff --git a/core/lls_core/types.py b/core/lls_core/types.py index 3e5d53b..5152719 100644 --- a/core/lls_core/types.py +++ b/core/lls_core/types.py @@ -7,7 +7,12 @@ from numpy.typing import NDArray from xarray import DataArray from aicsimageio import AICSImage -from os import PathLike, fspath +from os import fspath, PathLike as OriginalPathLike + +# This is a superset of os.PathLike +PathLike: TypeAlias = Union[str, bytes, OriginalPathLike] +def is_pathlike(x: Any) -> TypeGuard[PathLike]: + return isinstance(x, (str, bytes, OriginalPathLike)) ArrayLike: TypeAlias = Union[DaskArray, NDArray, OCLArray, DataArray] diff --git a/core/lls_core/utils.py b/core/lls_core/utils.py index e0025c9..36671c5 100644 --- a/core/lls_core/utils.py +++ b/core/lls_core/utils.py @@ -16,7 +16,7 @@ if TYPE_CHECKING: from xml.etree.ElementTree import Element - + from dask.array.core import Array as DaskArray from napari.layers import Shapes # Enable Logging @@ -350,3 +350,15 @@ def raise_if_none(obj: Optional[T], message: str) -> T: if obj is None: raise TypeError(message) return obj + +def array_to_dask(arr: ArrayLike) -> DaskArray: + from dask.array.core import Array as DaskArray, from_array + from xarray import DataArray + from resource_backed_dask_array import ResourceBackedDaskArray + + if isinstance(arr, DataArray): + arr = arr.data + if isinstance(arr, (DaskArray, ResourceBackedDaskArray)): + return arr + else: + return from_array(arr) diff --git a/core/tests/test_lattice_data.py b/core/tests/test_lattice_data.py index 298a327..854ed2a 100644 --- a/core/tests/test_lattice_data.py +++ b/core/tests/test_lattice_data.py @@ -1,3 +1,4 @@ +from typing import Any import pytest from lls_core.models import LatticeData from lls_core.models.output import SaveFileType @@ -6,18 +7,16 @@ from importlib_resources.abc import Traversable import tempfile from pathlib import Path -from lls_core import DeconvolutionChoice -from itertools import product inputs = pytest.mark.parametrize( - ["path"], [ - ("RBC_tiny.czi", ), - ("RBC_lattice.tif", ), - ("LLS7_t1_ch1.czi", ), - ("LLS7_t1_ch3.czi", ), - ("LLS7_t2_ch1.czi", ), - ("LLS7_t2_ch3.czi", ), - ("multich_multi_time.tif", ), + ["path", "channels"], [ + ("RBC_tiny.czi", 1), + ("RBC_lattice.tif", 1), + ("LLS7_t1_ch1.czi", 1), + ("LLS7_t1_ch3.czi", 1), + ("LLS7_t2_ch1.czi", 1), + ("LLS7_t2_ch3.czi", 1), + ("multich_multi_time.tif", 3), ]) def open_psf(name: str): @@ -30,29 +29,43 @@ def open_psf(name: str): {"angle": 30}, {"angle": 90}, {"physical_pixel_sizes": (1, 1, 1)}, - {"rois": []}, - {"psf": [open_psf("488.czi")]}, - {"psf": [open_psf("640.tif")]}, - {"background": 1}, - {"background": "auto"}, - {"background": "second_last"}, {"save_type": SaveFileType.h5}, {"save_type": SaveFileType.tiff}, + + # Cropping enabled + {"crop": {"roi_list": []}}, ]) @inputs @parameterized -def test_process(path: str, args: dict): +def test_process(path: str, channels:int, args: dict): with as_file(resources / path) as lattice_path: args["image"] = lattice_path + if "deconvolution" in args: + args["deconvolution"]["psf"] = args["deconvolution"]["psf"] * channels for slice in LatticeData.parse_obj(args).process().slices: assert slice.data.ndim == 3 +@pytest.mark.parametrize(["background"], [(1, ), ("auto",), ("second_last",)]) +@parameterized +def test_process_deconvolution(args: dict, background: Any): + root = Path(__file__).parent / "data" + args["image"] = root / "raw.tif" + args["deconvolution"] = { + "psf": [root / "psf.tif"], + "background": background + } + for slice in LatticeData.parse_obj(args).process().slices: + assert slice.data.ndim == 3 + + @inputs @parameterized -def test_save(path: str, args: dict): +def test_save(path: str, channels: int, args: dict): with as_file(resources / path) as lattice_path, tempfile.TemporaryDirectory() as tempdir: args["image"] = lattice_path + if "deconvolution" in args: + args["deconvolution"]["psf"] = args["deconvolution"]["psf"] * channels args["save_dir"] = tempdir LatticeData.parse_obj(args).process().save_image() results = list(Path(tempdir).iterdir()) diff --git a/plugin/napari_lattice/fields.py b/plugin/napari_lattice/fields.py index 726d5d3..d067a39 100644 --- a/plugin/napari_lattice/fields.py +++ b/plugin/napari_lattice/fields.py @@ -331,7 +331,7 @@ class DeconvolutionFields(NapariFieldGroup, FieldGroup): """ fields_enabled = field(False, label="Enabled") decon_processing = field(DeconvolutionChoice, label="Processing Algorithm") - psf = field(List[Path], label = "PSFs") + psf: MagicField[ValueWidget[List[Path]]] = field(List[Path], label = "PSFs") psf_num_iter = field(int, label = "Number of Iterations") background = field(ComboBox).with_choices( [it.value for it in BackgroundSource] From 8a4836eeec549611c472ef93f4d1c4ac918bdf16 Mon Sep 17 00:00:00 2001 From: Michael Milton Date: Wed, 20 Sep 2023 12:50:27 +1000 Subject: [PATCH 043/147] Fix GUI bugs, fix some CLI tests to use subcommands --- core/lls_core/cmds/__main__.py | 6 +++--- core/lls_core/models/lattice_data.py | 8 +++++--- core/tests/test_batch_deskew_args.py | 3 ++- plugin/napari_lattice/fields.py | 3 ++- plugin/napari_lattice/reader.py | 26 ++++++++++++++++++-------- 5 files changed, 30 insertions(+), 16 deletions(-) diff --git a/core/lls_core/cmds/__main__.py b/core/lls_core/cmds/__main__.py index 9079cce..3c53a4a 100644 --- a/core/lls_core/cmds/__main__.py +++ b/core/lls_core/cmds/__main__.py @@ -40,7 +40,7 @@ def format_default(default: str): return f"[{default_style}]\\[default: {default}][/{default_style}]" -def field_from_model(model: Type[FieldAccessMixin], field_name: str, extra_description: str = "", description: Optional[str] = None) -> Any: +def field_from_model(model: Type[FieldAccessMixin], field_name: str, extra_description: str = "", description: Optional[str] = None, default: Optional[Any] = None) -> Any: """ Generates a type Field from a Pydantic model field """ @@ -66,7 +66,7 @@ def handle_merge(values: list): raise ValueError(f"A parameter has been passed multiple times! Got: {', '.join(values)}") @app.command("dump-schema") -def dump_schema(): +def dump_schema() -> None: import json import sys json.dump(LatticeData.to_definition_dict(), fp=sys.stdout, indent=4) @@ -101,7 +101,7 @@ def main( workflow: Optional[Path] = Option(None, help="Path to a Napari Workflow file, in JSON format. If provided, the configured desekewing processing will be added to the chosen 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.") -): +) -> None: cli_args = dict( image=image, angle=angle, diff --git a/core/lls_core/models/lattice_data.py b/core/lls_core/models/lattice_data.py index dbe8862..6176fb8 100644 --- a/core/lls_core/models/lattice_data.py +++ b/core/lls_core/models/lattice_data.py @@ -167,7 +167,7 @@ def default_save_name(cls, values: dict): # This needs to be a root validator to ensure it runs before the # reshaping validator. We can't override that either since it's # a field validator and can't modify save_name - from lls_core.models.utils import is_pathlike + from lls_core.types import is_pathlike if values.get("save_name", None) is None and is_pathlike(values.get("image")): values["save_name"] = Path(values["image"]).stem return values @@ -245,7 +245,9 @@ def time_range_subset(cls, v: range, values: dict): return v @validator("deconvolution") - def check_psfs(cls, v: DeconvolutionParams, values: dict) -> DeconvolutionParams: + def check_psfs(cls, v: Optional[DeconvolutionParams], values: dict): + if v is None: + return v with ignore_keyerror(): if len(v.psf) != values["image"].sizes["C"]: raise ValueError("There should be one PSF per channel") @@ -478,7 +480,7 @@ def _process_non_crop(self) -> Iterable[ProcessedVolume]: if self.deconvolution.decon_processing == DeconvolutionChoice.cuda_gpu: data = pycuda_decon( image=data, - psf=self.deconvolution.psf[slice.channel], + psf=self.deconvolution.psf[slice.channel].to_numpy(), background=self.deconvolution.background, dzdata=self.dz, dxdata=self.dx, diff --git a/core/tests/test_batch_deskew_args.py b/core/tests/test_batch_deskew_args.py index f9b066e..34a53b4 100644 --- a/core/tests/test_batch_deskew_args.py +++ b/core/tests/test_batch_deskew_args.py @@ -4,7 +4,6 @@ import numpy as np from pathlib import Path import tempfile -from lls_core.cmds.__main__ import app from tests.utils import invoke def create_image(path: Path): @@ -26,6 +25,7 @@ def test_batch_deskew_h5(): create_image(input_file) # Batch deskew and save as h5 invoke([ + "process", str(input_file), "--save-dir", str(out_dir), "--save-type", "h5" @@ -43,6 +43,7 @@ def test_batch_deskew_tiff(): input_file = out_dir / 'raw.tiff' create_image(input_file) invoke([ + "process", str(input_file), "--save-dir", str(out_dir), "--save-type", "tiff" diff --git a/plugin/napari_lattice/fields.py b/plugin/napari_lattice/fields.py index d067a39..491970f 100644 --- a/plugin/napari_lattice/fields.py +++ b/plugin/napari_lattice/fields.py @@ -331,7 +331,7 @@ class DeconvolutionFields(NapariFieldGroup, FieldGroup): """ fields_enabled = field(False, label="Enabled") decon_processing = field(DeconvolutionChoice, label="Processing Algorithm") - psf: MagicField[ValueWidget[List[Path]]] = field(List[Path], label = "PSFs") + psf = field(List[Path], label = "PSFs") psf_num_iter = field(int, label = "Number of Iterations") background = field(ComboBox).with_choices( [it.value for it in BackgroundSource] @@ -373,6 +373,7 @@ def _make_model(self) -> Optional[DeconvolutionParams]: return DeconvolutionParams( decon_processing=self.decon_processing.value, background=background, + psf=self.psf.value, psf_num_iter=self.psf_num_iter.value ) diff --git a/plugin/napari_lattice/reader.py b/plugin/napari_lattice/reader.py index 134e034..3ffd04f 100644 --- a/plugin/napari_lattice/reader.py +++ b/plugin/napari_lattice/reader.py @@ -15,16 +15,21 @@ from napari.layers import Image from aicsimageio.aics_image import AICSImage -from typing import List, Optional, Tuple, Collection, TYPE_CHECKING +from typing import List, Optional, Tuple, Collection, TYPE_CHECKING, TypedDict from aicsimageio.types import PhysicalPixelSizes -from lls_core.models.lattice_data import AicsLatticeParams from lls_core.models.deskew import DefinedPixelSizes +from logging import getLogger +logger = getLogger(__name__) + if TYPE_CHECKING: from aicsimageio.types import ImageLike + from xarray import DataArray -class NapariImageParams(AicsLatticeParams): +class NapariImageParams(TypedDict): + data: DataArray + physical_pixel_sizes: DefinedPixelSizes save_name: str def lattice_params_from_napari( @@ -73,12 +78,17 @@ def lattice_params_from_napari( # Only process pixel sizes that are not none if all(img_data_aics.physical_pixel_sizes): pixel_sizes.add(img_data_aics.physical_pixel_sizes) - # if pixel_size_metadata is not None and pixel_sizes != img_data_aics.physical_pixel_sizes: - # raise Exception(f"Two or more layers that you have tried to merge have different pixel sizes according to their metadata! A previous image has size {physical_pixel_sizes}, whereas {img.name} has size {img_data_aics.physical_pixel_sizes}.") - # else: - # pixel_size_metadata = img_data_aics.physical_pixel_sizes - calculated_order = tuple(img_data_aics.dims.order) + metadata_order = list(img_data_aics.dims.order) + metadata_shape = list(img_data_aics.dims.shape) + while len(metadata_order) > len(img.data.shape): + logger.info(f"Image metadata implies there are more dimensions ({len(metadata_order)}) than the image actually has ({len(img.data.shape)})") + for i, size in enumerate(metadata_shape): + if size not in img.data.shape: + logger.info(f"Excluding the {metadata_order[i]} dimension to reconcile dimension order") + del metadata_order[i] + del metadata_shape[i] + calculated_order = metadata_order elif dimension_order is None: raise ValueError("Either the Napari image must have dimensional metadata, or a dimension order must be provided") else: From af36f86e26daca090256fb813892ceb77c7f6ed1 Mon Sep 17 00:00:00 2001 From: Michael Milton Date: Wed, 20 Sep 2023 16:51:36 +1000 Subject: [PATCH 044/147] Fix cli tests, use click argument panels, clean up CLI code --- core/lls_core/cmds/__main__.py | 132 +++++++++++++-------------- core/tests/test_arg_parser.py | 3 +- core/tests/test_batch_deskew_yaml.py | 2 +- 3 files changed, 68 insertions(+), 69 deletions(-) diff --git a/core/lls_core/cmds/__main__.py b/core/lls_core/cmds/__main__.py index 3c53a4a..b33232a 100644 --- a/core/lls_core/cmds/__main__.py +++ b/core/lls_core/cmds/__main__.py @@ -7,19 +7,18 @@ from enum import auto from pathlib import Path -from typing import TYPE_CHECKING, List, Optional, Tuple +from typing import TYPE_CHECKING, Annotated, List, Optional, Tuple from strenum import StrEnum from lls_core.models.lattice_data import LatticeData -from lls_core.models.deskew import DefinedPixelSizes, DeskewParams +from lls_core.models.deskew import DeskewParams from lls_core.models.deconvolution import DeconvolutionParams from lls_core.models.output import OutputParams from lls_core.models.crop import CropParams -from lls_core import DeconvolutionChoice, DeskewDirection -from typer import Typer, Argument, Option +from lls_core import DeconvolutionChoice +from typer import Typer, Argument, Option, Context from lls_core.models.output import SaveFileType -from toolz.dicttoolz import merge_with, valfilter if TYPE_CHECKING: from lls_core.models.utils import FieldAccessMixin @@ -31,23 +30,15 @@ class CliDeskewDirection(StrEnum): app = Typer(add_completion=False, rich_markup_mode="rich") -def format_default(default: str): - """ - Given a string, formats it like a default value in Typer - """ - from typer.rich_utils import STYLE_OPTION_DEFAULT - default_style = STYLE_OPTION_DEFAULT - return f"[{default_style}]\\[default: {default}][/{default_style}]" - - -def field_from_model(model: Type[FieldAccessMixin], field_name: str, extra_description: str = "", description: Optional[str] = None, default: Optional[Any] = None) -> Any: +def field_from_model(model: Type[FieldAccessMixin], field_name: str, extra_description: str = "", description: Optional[str] = None, default: Optional[Any] = None, **kwargs) -> Any: """ Generates a type Field from a Pydantic model field """ field = model.__fields__[field_name] from enum import Enum - default = field.get_default() + if default is None: + default = field.get_default() if isinstance(default, Enum): default = default.name @@ -55,15 +46,15 @@ def field_from_model(model: Type[FieldAccessMixin], field_name: str, extra_descr description = f"{field.field_info.description} {extra_description}" return Option( - # We make all defaults None so they can be removed - default = None, - show_default=False, - help=f"{description}\n{format_default(default)}" + default = default, + help=description, + **kwargs ) def handle_merge(values: list): - if len(values) > 0: + if len(values) > 1: raise ValueError(f"A parameter has been passed multiple times! Got: {', '.join(values)}") + return values[0] @app.command("dump-schema") def dump_schema() -> None: @@ -72,63 +63,68 @@ def dump_schema() -> None: json.dump(LatticeData.to_definition_dict(), fp=sys.stdout, indent=4) @app.command("process") -def main( +def process( + ctx: Context, 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 X Y and Z pixel dimensions respectively"), + pixel_sizes: Tuple[float, float, float] = field_from_model(DeskewParams, "physical_pixel_sizes", extra_description="This takes three arguments, corresponding to the X Y and Z pixel dimensions respectively", default=( + DeskewParams.get_default("physical_pixel_sizes").X, + DeskewParams.get_default("physical_pixel_sizes").Y, + DeskewParams.get_default("physical_pixel_sizes").Z + )), 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."), # 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(None, help="The index of the first Z slice to use. All prior Z slices will be discarded. " + format_default("0"), show_default=False), + 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), - enable_deconvolution: bool = Option(False, "--deconvolution/--disable-deconvolution"), - decon_processing: DeconvolutionChoice = DeconvolutionParams.make_typer_field("decon_processing"), - psf: List[Path] = field_from_model(DeconvolutionParams, "psf", description="A list of 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."), - psf_num_iter: int = field_from_model(DeconvolutionParams, "psf_num_iter"), - background: str = field_from_model(DeconvolutionParams, "background"), + 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="A list of 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.", rich_help_panel="Deconvolution"), + psf_num_iter: int = field_from_model(DeconvolutionParams, "psf_num_iter", rich_help_panel="Deconvolution"), + background: str = field_from_model(DeconvolutionParams, "background", rich_help_panel="Deconvolution"), - time_start: Optional[int] = Option(None, help="Index of the first time slice to use (inclusive). Defaults to the first time index of the image.", show_default=False), - 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), + 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(None, help="Index of the first channel slice to use (inclusive). Defaults to the first channel index of the image.", show_default=False), - 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), + 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"), - save_dir: Path = field_from_model(OutputParams, "save_dir"), - save_name: Optional[str] = field_from_model(OutputParams, "save_name"), - save_type: SaveFileType = field_from_model(OutputParams, "save_type"), + 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 JSON format. If provided, the configured desekewing processing will be added to the chosen 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.") + yaml_config: Optional[Path] = Option(None, show_default=False, help="Path to a YAML file from which parameters will be read."), ) -> None: - cli_args = dict( - image=image, - angle=angle, - skew=DeskewDirection[skew.name], - physical_pixel_sizes=DefinedPixelSizes( - X=pixel_sizes[0], - Y=pixel_sizes[1], - Z=pixel_sizes[2], - ), - crop=None if len(rois) == 0 else dict( - roi_list=rois, - z_range=(z_start, z_end) - ), - deconvolution=None if not enable_deconvolution else dict( - decon_processing = decon_processing, - psf = psf, - psf_num_iter = psf_num_iter, - background = background - ), - workflow=workflow, - - time_range=(time_start, time_end), - channel_range=(channel_start, channel_end), - save_dir=save_dir, - save_name=save_name, - save_type=save_type - ) + from toolz.dicttoolz import merge_with, update_in + cli_param_map = { + "image": ["image"], + "angle": ["angle"], + "skew": ["skew"], + "pixel_sizes": ["physical_pixel_sizes"], + "rois": ["crop", "roi_list"], + "z_start": ["crop", "z_range", 0], + "z_end": ["crop", "z_range", 1], + "decon_processing": ["deconvolution", "decon_processing"], + "psf": ["deconvolution", "psf"], + "psf_num_iter": ["deconvolution", "psf_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], + "save_dir": ["save_dir"], + "save_name": ["save_name"], + "save_type": ["save_type"], + } + cli_args = {} + for source, dest in cli_param_map.items(): + from click.core import ParameterSource + if ctx.get_parameter_source(source) != ParameterSource.DEFAULT: + cli_args = update_in(cli_args, dest, lambda x: ctx.params[source]) json_args = {} if json_config is not None: @@ -147,10 +143,12 @@ def main( # Merge all three sources of config: YAML, JSON and CLI merge_with( handle_merge, - # Remove None values from all dictonaries, so that they merge appropriately - *[valfilter(lambda x: x is not None, it) for it in [yaml_args, json_args, cli_args]] + [yaml_args, json_args, cli_args] ) ).process().save_image() -if __name__ == '__main__': +def main(): app() + +if __name__ == '__main__': + main() diff --git a/core/tests/test_arg_parser.py b/core/tests/test_arg_parser.py index c33d211..da66c6a 100644 --- a/core/tests/test_arg_parser.py +++ b/core/tests/test_arg_parser.py @@ -5,10 +5,11 @@ def test_voxel_parsing(): # Tests that we can parse voxel lists correctly - command = get_command(app) + command = get_command(app).commands["process"] ctx = Context(command) parser = command.make_parser(ctx) args, _, _ = parser.parse_args(args=[ + "process", "input", "--save-name", "output", "--save-type", "tiff", diff --git a/core/tests/test_batch_deskew_yaml.py b/core/tests/test_batch_deskew_yaml.py index b702f09..ce2f423 100644 --- a/core/tests/test_batch_deskew_yaml.py +++ b/core/tests/test_batch_deskew_yaml.py @@ -60,7 +60,7 @@ def test_yaml_deskew(): test_dir = Path(test_dir) config_location = create_data(test_dir) # Batch deskew and save as h5 - invoke(["--yaml-config", str(config_location)], ) + invoke(["process", "--yaml-config", str(config_location)], ) # checks if h5 files written assert (test_dir / "raw.h5").exists() From fe0d7f6fa7ec564156897fbd692f4c5fb33a9ff9 Mon Sep 17 00:00:00 2001 From: Michael Milton Date: Wed, 20 Sep 2023 17:52:35 +1000 Subject: [PATCH 045/147] Fix CLI tests --- core/lls_core/cmds/__main__.py | 2 +- core/lls_core/models/lattice_data.py | 6 ++++-- core/tests/test_lattice_data.py | 2 -- 3 files changed, 5 insertions(+), 5 deletions(-) diff --git a/core/lls_core/cmds/__main__.py b/core/lls_core/cmds/__main__.py index b33232a..87f466b 100644 --- a/core/lls_core/cmds/__main__.py +++ b/core/lls_core/cmds/__main__.py @@ -7,7 +7,7 @@ from enum import auto from pathlib import Path -from typing import TYPE_CHECKING, Annotated, List, Optional, Tuple +from typing import TYPE_CHECKING, List, Optional, Tuple from strenum import StrEnum from lls_core.models.lattice_data import LatticeData diff --git a/core/lls_core/models/lattice_data.py b/core/lls_core/models/lattice_data.py index 6176fb8..1179a62 100644 --- a/core/lls_core/models/lattice_data.py +++ b/core/lls_core/models/lattice_data.py @@ -249,8 +249,10 @@ def check_psfs(cls, v: Optional[DeconvolutionParams], values: dict): if v is None: return v with ignore_keyerror(): - if len(v.psf) != values["image"].sizes["C"]: - raise ValueError("There should be one PSF per channel") + channels = values["image"].sizes["C"] + psfs = len(v.psf) + if psfs != channels: + raise ValueError(f"There should be one PSF per channel, but there are {psfs} PSFs and {channels} channels.") return v # Hack to ensure that .skew_dir behaves identically to .skew diff --git a/core/tests/test_lattice_data.py b/core/tests/test_lattice_data.py index 854ed2a..2e4ae55 100644 --- a/core/tests/test_lattice_data.py +++ b/core/tests/test_lattice_data.py @@ -64,8 +64,6 @@ def test_process_deconvolution(args: dict, background: Any): def test_save(path: str, channels: int, args: dict): with as_file(resources / path) as lattice_path, tempfile.TemporaryDirectory() as tempdir: args["image"] = lattice_path - if "deconvolution" in args: - args["deconvolution"]["psf"] = args["deconvolution"]["psf"] * channels args["save_dir"] = tempdir LatticeData.parse_obj(args).process().save_image() results = list(Path(tempdir).iterdir()) From 8b248c8f669fc7cd949c7f79bd4e661f286b9f2e Mon Sep 17 00:00:00 2001 From: Michael Milton Date: Wed, 20 Sep 2023 18:35:34 +1000 Subject: [PATCH 046/147] Clean up LatticeData tests --- core/tests/test_lattice_data.py | 22 ++++++++++------------ 1 file changed, 10 insertions(+), 12 deletions(-) diff --git a/core/tests/test_lattice_data.py b/core/tests/test_lattice_data.py index 2e4ae55..aff4521 100644 --- a/core/tests/test_lattice_data.py +++ b/core/tests/test_lattice_data.py @@ -9,14 +9,14 @@ from pathlib import Path inputs = pytest.mark.parametrize( - ["path", "channels"], [ - ("RBC_tiny.czi", 1), - ("RBC_lattice.tif", 1), - ("LLS7_t1_ch1.czi", 1), - ("LLS7_t1_ch3.czi", 1), - ("LLS7_t2_ch1.czi", 1), - ("LLS7_t2_ch3.czi", 1), - ("multich_multi_time.tif", 3), + ["path"], [ + ("RBC_tiny.czi", ), + ("RBC_lattice.tif", ), + ("LLS7_t1_ch1.czi", ), + ("LLS7_t1_ch3.czi", ), + ("LLS7_t2_ch1.czi", ), + ("LLS7_t2_ch3.czi", ), + ("multich_multi_time.tif", ), ]) def open_psf(name: str): @@ -38,11 +38,9 @@ def open_psf(name: str): @inputs @parameterized -def test_process(path: str, channels:int, args: dict): +def test_process(path: str, args: dict): with as_file(resources / path) as lattice_path: args["image"] = lattice_path - if "deconvolution" in args: - args["deconvolution"]["psf"] = args["deconvolution"]["psf"] * channels for slice in LatticeData.parse_obj(args).process().slices: assert slice.data.ndim == 3 @@ -61,7 +59,7 @@ def test_process_deconvolution(args: dict, background: Any): @inputs @parameterized -def test_save(path: str, channels: int, args: dict): +def test_save(path: str, args: dict): with as_file(resources / path) as lattice_path, tempfile.TemporaryDirectory() as tempdir: args["image"] = lattice_path args["save_dir"] = tempdir From b93be4f7ff9b136b542e0ba37591175b39e433fc Mon Sep 17 00:00:00 2001 From: Michael Milton Date: Thu, 21 Sep 2023 13:34:49 +1000 Subject: [PATCH 047/147] Implement pixel data source selection --- plugin/napari_lattice/_dock_widget.py | 1153 ------------------------- plugin/napari_lattice/_reader.py | 161 ---- plugin/napari_lattice/fields.py | 31 +- plugin/napari_lattice/reader.py | 24 +- plugin/pyproject.toml | 2 +- 5 files changed, 35 insertions(+), 1336 deletions(-) delete mode 100644 plugin/napari_lattice/_dock_widget.py delete mode 100644 plugin/napari_lattice/_reader.py diff --git a/plugin/napari_lattice/_dock_widget.py b/plugin/napari_lattice/_dock_widget.py deleted file mode 100644 index 4bb7811..0000000 --- a/plugin/napari_lattice/_dock_widget.py +++ /dev/null @@ -1,1153 +0,0 @@ -import os -import sys -import yaml -import numpy as np -from pathlib import Path -import dask.array as da -import pandas as pd -from typing import Union, Optional, Callable, Literal -from enum import Enum - -from magicclass.wrappers import set_design -from magicgui import magicgui -from magicclass import magicclass, field, vfield, set_options, MagicTemplate -from magicclass.utils import click -from qtpy.QtCore import Qt - -from napari.layers import Layer, Shapes -from napari.types import ImageData -from napari.utils import history - -import pyclesperanto_prototype as cle - -from napari.types import ImageData, ShapesData - -from tqdm import tqdm - -from napari_workflows import Workflow, WorkflowManager -from napari_workflows._io_yaml_v1 import load_workflow - -from lls_core import config, DeskewDirection, DeconvolutionChoice, SaveFileType, Log_Levels -from lls_core.io import LatticeData, save_img, save_img_workflow -from lls_core.utils import read_imagej_roi, get_first_last_image_and_task, modify_workflow_task, get_all_py_files, as_type, process_custom_workflow_output, check_dimensions, load_custom_py_modules -from lls_core.llsz_core import crop_volume_deskew -from lls_core.deconvolution import read_psf, pycuda_decon, skimage_decon - -from napari_lattice.ui_core import _Preview, _Deskew_Save -from napari_lattice._reader import lattice_from_napari - -# Enable Logging -import logging -logger = logging.getLogger(__name__) -logger.setLevel(logging.INFO) - -class LastDimensionOptions(Enum): - channel = "Channel" - time = "Time" - get_from_metadata = "Get from Metadata" - -@magicclass(widget_type="split") -class LLSZWidget(MagicTemplate): - - @magicclass(widget_type="split") - class LlszMenu(MagicTemplate): - open_file: bool = False - lattice: LatticeData = None - skew_dir: DeskewDirection - angle_value: float - deskew_func: Callable - - main_heading = field("

      Napari Lattice: Visualization & Analysis

      ", widget_type="Label") - heading1 = field("Drag and drop an image file onto napari.\nOnce image has opened, initialize the\nplugin by clicking the button below.\nEnsure the image layer and voxel sizes are accurate in the prompt.\n If everything initalises properly, the button turns green.", widget_type="Label") - - @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 = LastDimensionOptions.get_from_metadata, - 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") - - if self.parent_viewer is None: - raise Exception("This function can only be used when inside of a Napari viewer") - - # Select device for processing - cle.select_device(select_device) - - #assert skew_dir in DeskewDirection, "Skew direction not recognised. Enter either Y or X" - LLSZWidget.LlszMenu.skew_dir = skew_dir - LLSZWidget.LlszMenu.angle_value = angle - - if LLSZWidget.LlszMenu.skew_dir == DeskewDirection.Y: - LLSZWidget.LlszMenu.deskew_func = cle.deskew_y - #LLSZWidget.LlszMenu.skew_dir = DeskewDirection.Y - elif LLSZWidget.LlszMenu.skew_dir == DeskewDirection.X: - LLSZWidget.LlszMenu.deskew_func = cle.deskew_x - #LLSZWidget.LlszMenu.skew_dir = DeskewDirection.X - - # 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 - - LLSZWidget.LlszMenu.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=LLSZWidget.LlszMenu.skew_dir, - physical_pixel_sizes=(pixel_size_dx, pixel_size_dy, pixel_size_dz) - ) - #LLSZWidget.LlszMenu.aics = LLSZWidget.LlszMenu.lattice.data - - # LLSZWidget.LlszMenu.dask = False # Use GPU by default - - # We initialise these variables here, but they can be changed in the deconvolution section - # list to store psf images for each channel - LLSZWidget.LlszMenu.lattice.psf = [] - LLSZWidget.LlszMenu.lattice.psf_num_iter = 10 - LLSZWidget.LlszMenu.lattice.decon_processing = DeconvolutionChoice.cpu - # list to store otf paths for each channel (Deprecated) - LLSZWidget.LlszMenu.lattice.otf_path = [] - # if not using GPU - #LLSZWidget.LlszMenu.dask = not use_GPU - - # flag for ensuring a file has been opened and plugin initialised - LLSZWidget.LlszMenu.open_file = True - - logger.info( - f"Pixel size (ZYX) in microns: {LLSZWidget.LlszMenu.lattice.dz,LLSZWidget.LlszMenu.lattice.dy,LLSZWidget.LlszMenu.lattice.dx}") - logger.info( - f"Dimensions of image layer (ZYX): {list(LLSZWidget.LlszMenu.lattice.data.shape[-3:])}") - logger.info( - f"Dimensions of deskewed image (ZYX): {LLSZWidget.LlszMenu.lattice.deskew_vol_shape}") - logger.info( - f"Deskewing angle is :{LLSZWidget.LlszMenu.lattice.angle}") - logger.info( - f"Deskew Direction :{LLSZWidget.LlszMenu.lattice.skew}") - # Add dimension labels correctly - # if channel, and not time - if LLSZWidget.LlszMenu.lattice.time == 0 and (last_dimension_channel or LLSZWidget.LlszMenu.lattice.channels > 0): - self.parent_viewer.dims.axis_labels = ('Channel', "Z", "Y", "X") - # if no channel, but has time - elif LLSZWidget.LlszMenu.lattice.channels == 0 and LLSZWidget.LlszMenu.lattice.time > 0: - self.parent_viewer.dims.axis_labels = ('Time', "Z", "Y", "X") - # if it has channels - elif LLSZWidget.LlszMenu.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]) == LLSZWidget.LlszMenu.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 LLSZWidget.LlszMenu.lattice.channels == 1 and LLSZWidget.LlszMenu.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" - - return - - # Pycudadecon library for deconvolution - # options={"enabled": True}, - deconvolution = vfield(bool, name="Use Deconvolution") - deconvolution.value = False - - @deconvolution.connect - def _set_decon(self): - if self.deconvolution: - logger.info("Deconvolution Activated") - LLSZWidget.LlszMenu.deconvolution.value = True - else: - logger.info("Deconvolution Disabled") - LLSZWidget.LlszMenu.deconvolution.value = False - return - - @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, - psf_ch1_path: Path, - psf_ch2_path: Path, - psf_ch3_path: Path, - psf_ch4_path: Path, - device_option, - no_iter: int): - """GUI for Deconvolution button""" - LLSZWidget.LlszMenu.lattice.decon_processing = device_option - assert LLSZWidget.LlszMenu.deconvolution.value == True, "Deconvolution is set to False. Tick the box to activate deconvolution." - LLSZWidget.LlszMenu.lattice.psf = list(read_psf([ - psf_ch1_path, - psf_ch2_path, - psf_ch3_path, - psf_ch4_path, - ], - device_option, - lattice_class=LLSZWidget.LlszMenu.lattice - )) - LLSZWidget.LlszMenu.lattice.psf_num_iter = no_iter - self["deconvolution_gui"].background_color = "green" - self["deconvolution_gui"].text = "PSFs added" - - @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, - time: int, - channel: int, - img_data: ImageData): - """ - Preview deskewed data for a single timepoint and channel - - """ - _Preview(LLSZWidget, - self, - time, - channel, - img_data) - return - - # Tabbed Widget container to house all the widgets - @magicclass(widget_type="tabbed", name="Functions") - class WidgetContainer(MagicTemplate): - - @magicclass(name="Deskew", widget_type="scrollable", properties={"min_width": 100}) - class DeskewWidget(MagicTemplate): - - @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, - 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) - return - - @magicclass(name="Crop and Deskew", widget_type="scrollable") - class CropWidget(MagicTemplate): - - # add function for previewing cropped image - @magicclass(name="Cropping Preview", widget_type="scrollable", properties={ - "min_width": 100, - "shapes_layer": Shapes - }) - class Preview_Crop_Menu(MagicTemplate): - shapes_layer: Shapes - - @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): - LLSZWidget.WidgetContainer.CropWidget.Preview_Crop_Menu.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 - LLSZWidget.WidgetContainer.CropWidget.Preview_Crop_Menu.shapes_layer.mode = "SELECT" - self["activate_cropping"].text = "Cropping layer active" - self["activate_cropping"].background_color = "green" - return - - 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])): - logger.info(f"Opening{path}") - roi_list = read_imagej_roi(path) - # convert to canvas coordinates - roi_list = (np.array(roi_list) * - LLSZWidget.LlszMenu.lattice.dy).tolist() - LLSZWidget.WidgetContainer.CropWidget.Preview_Crop_Menu.shapes_layer.add(roi_list, shape_type='polygon', edge_width=1, edge_color='yellow', - face_color=[1, 1, 1, 0]) - return - - 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) - # -> LayerDataTuple: - def Crop_Preview(self, roi_layer: ShapesData): - assert roi_layer, "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 - time = self.time_crop.value - channel = self.chan_crop.value - - assert time < LLSZWidget.LlszMenu.lattice.time, "Time is out of range" - assert channel < LLSZWidget.LlszMenu.lattice.channels, "Channel is out of range" - - logger.info(f"Using channel {channel} and time {time}") - - vol = LLSZWidget.LlszMenu.lattice.data - vol_zyx = vol[time, channel, ...] - vol_zyx = np.array(vol_zyx) - - deskewed_shape = LLSZWidget.LlszMenu.lattice.deskew_vol_shape - # Create a dask array same shape as deskewed image - deskewed_volume = da.zeros(deskewed_shape) - - # Option for entering custom z start value? - z_start = 0 - z_end = deskewed_shape[0] - - # if only one roi drawn, use the first ROI for cropping - if len(roi_layer) == 1: - roi_idx = 0 - else: - assert len( - LLSZWidget.WidgetContainer.CropWidget.Preview_Crop_Menu.shapes_layer.selected_data) > 0, "Please select an ROI" - roi_idx = list( - LLSZWidget.WidgetContainer.CropWidget.Preview_Crop_Menu.shapes_layer.selected_data)[0] - - roi_choice = roi_layer[roi_idx] - # 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/LLSZWidget.LlszMenu.lattice.dy for x in roi_choice] - logger.info(f"Previewing ROI {roi_idx}") - - # crop - if LLSZWidget.LlszMenu.deconvolution.value: - logger.info( - f"Deskewing for Time:{time} and Channel: {channel} with deconvolution") - #psf = LLSZWidget.LlszMenu.lattice.psf[channel] - if LLSZWidget.LlszMenu.lattice.decon_processing == DeconvolutionChoice.cuda_gpu: - crop_roi_vol_desk = crop_volume_deskew(original_volume=vol_zyx, - deskewed_volume=deskewed_volume, - roi_shape=roi_choice, - angle_in_degrees=LLSZWidget.LlszMenu.angle_value, - voxel_size_x=LLSZWidget.LlszMenu.lattice.dx, - voxel_size_y=LLSZWidget.LlszMenu.lattice.dy, - voxel_size_z=LLSZWidget.LlszMenu.lattice.dz, - z_start=z_start, - z_end=z_end, - deconvolution=LLSZWidget.LlszMenu.deconvolution.value, - decon_processing=LLSZWidget.LlszMenu.lattice.decon_processing, - psf=LLSZWidget.LlszMenu.lattice.psf[channel], - num_iter=LLSZWidget.LlszMenu.lattice.psf_num_iter).astype(vol_zyx.dtype) - else: - crop_roi_vol_desk = crop_volume_deskew(original_volume=vol_zyx, - deskewed_volume=deskewed_volume, - roi_shape=roi_choice, - angle_in_degrees=LLSZWidget.LlszMenu.angle_value, - voxel_size_x=LLSZWidget.LlszMenu.lattice.dx, - voxel_size_y=LLSZWidget.LlszMenu.lattice.dy, - voxel_size_z=LLSZWidget.LlszMenu.lattice.dz, - z_start=z_start, - z_end=z_end, - deconvolution=LLSZWidget.LlszMenu.deconvolution.value, - decon_processing=LLSZWidget.LlszMenu.lattice.decon_processing, - psf=LLSZWidget.LlszMenu.lattice.psf[channel], - num_iter=LLSZWidget.LlszMenu.lattice.psf_num_iter).astype(vol_zyx.dtype) - else: - crop_roi_vol_desk = crop_volume_deskew(original_volume=vol_zyx, - deskewed_volume=deskewed_volume, - roi_shape=roi_choice, - angle_in_degrees=LLSZWidget.LlszMenu.angle_value, - voxel_size_x=LLSZWidget.LlszMenu.lattice.dx, - voxel_size_y=LLSZWidget.LlszMenu.lattice.dy, - voxel_size_z=LLSZWidget.LlszMenu.lattice.dz, - z_start=z_start, - z_end=z_end, - skew_dir=LLSZWidget.LlszMenu.skew_dir).astype(vol_zyx.dtype) - crop_roi_vol_desk = cle.pull(crop_roi_vol_desk) - - # get array back from gpu or addding cle array to napari can throw errors - - scale = (LLSZWidget.LlszMenu.lattice.new_dz, - LLSZWidget.LlszMenu.lattice.dy, - LLSZWidget.LlszMenu.lattice.dx) - self.parent_viewer.add_image( - crop_roi_vol_desk, scale=scale) - - @magicclass(name="Crop and Save Data") - class CropSaveData(MagicTemplate): - @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, - time_start: int, - time_end: int, - ch_start: int, - ch_end: int, - save_as_type: str, - 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: - assert LLSZWidget.LlszMenu.open_file, "Image not initialised" - - check_dimensions(time_start, time_end, ch_start, ch_end, - LLSZWidget.LlszMenu.lattice.channels, LLSZWidget.LlszMenu.lattice.time) - - angle = LLSZWidget.LlszMenu.lattice.angle - dx = LLSZWidget.LlszMenu.lattice.dx - dy = LLSZWidget.LlszMenu.lattice.dy - dz = LLSZWidget.LlszMenu.lattice.dz - - # get image data - img_data = LLSZWidget.LlszMenu.lattice.data - # Get shape of deskewed image - deskewed_shape = LLSZWidget.LlszMenu.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 = [ - x/LLSZWidget.LlszMenu.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=LLSZWidget.LlszMenu.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=LLSZWidget - ) - - logger.info( - f"Cropping and Saving Complete -> {save_path}") - return - - @magicclass(name="Workflow", widget_type="scrollable") - class WorkflowWidget: - @magicclass(name="Preview Workflow", widget_type="scrollable") - class PreviewWorkflow: - #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, - 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 - Args: - - """ - print("Previewing deskewed channel and time with workflow") - if get_active_workflow: - # installs the workflow to napari - user_workflow = WorkflowManager.install( - self.parent_viewer).workflow - parent_dir = workflow_path.resolve( - ).parents[0].__str__()+os.sep - logger.info("Workflow loaded from napari") - else: - - try: - # Automatically scan workflow file directory for *.py files. - # If it findss one, load it as a module - - parent_dir = workflow_path.resolve( - ).parents[0].__str__()+os.sep - sys.path.append(parent_dir) - custom_py_files = get_all_py_files(parent_dir) - if len(custom_py_files) == 0: - logger.error( - f"No custom modules imported. If you'd like to use a cusotm module, place a *.py file in same folder as the workflow file {parent_dir}") - else: - modules = load_custom_py_modules( - custom_py_files) - - logger.info( - f"Custom modules imported {modules}") - user_workflow = load_workflow( - workflow_path.__str__()) - except yaml.loader.ConstructorError as e: - logger.error( - "\033[91m While loading workflow, got the following error which may mean you need to install the corresponding module in your Python environment: \033[0m") - logger.error(e) - - #user_workflow = load_workflow(workflow_path) - logger.info("Workflow loaded from file") - - assert type( - user_workflow) is Workflow, "Workflow loading error. Check if file is workflow or if required libraries are installed" - - input_arg_first, input_arg_last, first_task_name, last_task_name = get_first_last_image_and_task( - user_workflow) - #print(input_arg_first, input_arg_last, first_task_name,last_task_name ) - # get list of tasks - task_list = list(user_workflow._tasks.keys()) - logger.info(f"Workflow loaded:{user_workflow}") - # logger.info() - - # when using fields, self.time_preview.value - assert time_preview < LLSZWidget.LlszMenu.lattice.time, "Time is out of range" - assert chan_preview < LLSZWidget.LlszMenu.lattice.channels, "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}") - - vol = LLSZWidget.LlszMenu.lattice.data - vol_zyx = vol[time, channel, ...] - vol_zyx = np.array(vol_zyx) - - 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 LLSZWidget.LlszMenu.lattice.decon_processing == DeconvolutionChoice.cuda_gpu: - otf_path = "otf_path" - else: - psf = "psf" - - # if cropping, set that as first task - # get the function associated with the first task and check if its deskewing - if Use_Cropping: - # use deskewed volume for cropping function - deskewed_shape = LLSZWidget.LlszMenu.lattice.deskew_vol_shape - deskewed_volume = da.zeros(deskewed_shape) - z_start = 0 - z_end = deskewed_shape[0] - if user_workflow.get_task(task_name_start)[0] not in [crop_volume_deskew]: - # if only one roi drawn, use the first ROI for cropping - if len(roi_layer_list) == 1: - roi_idx = 0 - else: # else get the user selection - assert len( - LLSZWidget.WidgetContainer.CropWidget.Preview_Crop_Menu.shapes_layer.selected_data) > 0, "Please select an ROI" - roi_idx = list( - LLSZWidget.WidgetContainer.CropWidget.Preview_Crop_Menu.shapes_layer.selected_data)[0] - - roi_choice = roi_layer_list[roi_idx] - # As the original image is scaled, the coordinates are in microns, so we need to convert - # roi to from micron to canvas/world coordinates - roi_choice = [ - x/LLSZWidget.LlszMenu.lattice.dy for x in roi_choice] - logger.info(f"Previewing ROI {roi_idx}") - if LLSZWidget.LlszMenu.deconvolution.value: - user_workflow.set("crop_deskew_image", crop_volume_deskew, - original_volume=vol_zyx, - deskewed_volume=deskewed_volume, - roi_shape=roi_choice, - angle_in_degrees=LLSZWidget.LlszMenu.lattice.angle, - voxel_size_x=LLSZWidget.LlszMenu.lattice.dx, - voxel_size_y=LLSZWidget.LlszMenu.lattice.dy, - voxel_size_z=LLSZWidget.LlszMenu.lattice.dz, - z_start=z_start, - z_end=z_end, - deconvolution=LLSZWidget.LlszMenu.deconvolution.value, - decon_processing=LLSZWidget.LlszMenu.lattice.decon_processing, - otf_path=otf_path, - psf=LLSZWidget.LlszMenu.lattice.psf[channel], - skew_dir=LLSZWidget.LlszMenu.skew_dir) - else: - user_workflow.set("crop_deskew_image", crop_volume_deskew, - original_volume=vol_zyx, - deskewed_volume=deskewed_volume, - roi_shape=roi_choice, - angle_in_degrees=LLSZWidget.LlszMenu.lattice.angle, - voxel_size_x=LLSZWidget.LlszMenu.lattice.dx, - voxel_size_y=LLSZWidget.LlszMenu.lattice.dy, - voxel_size_z=LLSZWidget.LlszMenu.lattice.dz, - z_start=z_start, - z_end=z_end, - skew_dir=LLSZWidget.LlszMenu.skew_dir) - - # Set input of the workflow to be crop_deskewing, i.e., the original first operation will now have crop_deskew_image as an input (becoming second instead) - user_workflow.set( - input_arg_first, "crop_deskew_image") - else: - user_workflow.set(input_arg_first, vol_zyx) - # Not cropping; If deskew not in workflow, append to start - elif user_workflow.get_task(task_name_start)[0] not in (cle.deskew_y, cle.deskew_x): - # if deconvolution checked, add it to start of workflow (add upstream of deskewing) - if LLSZWidget.LlszMenu.deconvolution.value: - psf = LLSZWidget.LlszMenu.lattice.psf[channel] - input_arg_first_decon, input_arg_last_decon, first_task_name_decon, last_task_name_decon = get_first_last_image_and_task( - user_workflow) - - if LLSZWidget.LlszMenu.lattice.decon_processing == DeconvolutionChoice.cuda_gpu: - user_workflow.set("deconvolution", - pycuda_decon, - image=vol_zyx, - psf=LLSZWidget.LlszMenu.lattice.psf[channel], - dzdata=LLSZWidget.LlszMenu.lattice.dz, - dxdata=LLSZWidget.LlszMenu.lattice.dx, - dzpsf=LLSZWidget.LlszMenu.lattice.dz, - dxpsf=LLSZWidget.LlszMenu.lattice.dx, - num_iter=LLSZWidget.LlszMenu.lattice.psf_num_iter) - # user_workflow.set(input_arg_first_decon,"deconvolution") - else: - user_workflow.set("deconvolution", - skimage_decon, - vol_zyx=vol_zyx, - psf=LLSZWidget.LlszMenu.lattice.psf[channel], - num_iter=LLSZWidget.LlszMenu.lattice.psf_num_iter, - clip=False, - filter_epsilon=0, - boundary='nearest') - # user_workflow.set(input_arg_first_decon,"deconvolution") - - user_workflow.set("deskew_image", - LLSZWidget.LlszMenu.deskew_func, - "deconvolution", - angle_in_degrees=LLSZWidget.LlszMenu.lattice.angle, - voxel_size_x=LLSZWidget.LlszMenu.lattice.dx, - voxel_size_y=LLSZWidget.LlszMenu.lattice.dy, - voxel_size_z=LLSZWidget.LlszMenu.lattice.dz, - linear_interpolation=True) - - # user_workflow.set("change_bitdepth",as_type,"deskew_image",vol_zyx) - # Set input of the workflow to be from deskewing output with same bit depth as original volume - # user_workflow.set(input_arg_first,"change_bitdepth") - - else: - user_workflow.set("deskew_image", - LLSZWidget.LlszMenu.deskew_func, - vol_zyx, - angle_in_degrees=LLSZWidget.LlszMenu.lattice.angle, - voxel_size_x=LLSZWidget.LlszMenu.lattice.dx, - voxel_size_y=LLSZWidget.LlszMenu.lattice.dy, - voxel_size_z=LLSZWidget.LlszMenu.lattice.dz, - linear_interpolation=True) - # Set input of the workflow to be from deskewing - # user_workflow.set(input_arg_first,"deskew_image") - - user_workflow.set( - "change_bitdepth", as_type, "deskew_image", vol_zyx) - # Set input of the workflow to be from deskewing with same bit depth as original volume - user_workflow.set( - input_arg_first, "change_bitdepth") - - else: - # if deskew already in workflow, just check if deconvolution needs to be added - # repitition of above (maybe create a function?) - # if deconvolution checked, add it to start of workflow (add upstream of deskewing) - if LLSZWidget.LlszMenu.deconvolution.value: - psf = LLSZWidget.LlszMenu.lattice.psf[channel] - input_arg_first, input_arg_last, first_task_name, last_task_name = get_first_last_image_and_task( - user_workflow) - - if LLSZWidget.LlszMenu.lattice.decon_processing == DeconvolutionChoice.cuda_gpu: - user_workflow.set("deconvolution", - pycuda_decon, - image=vol_zyx, - psf=LLSZWidget.LlszMenu.lattice.psf[channel], - dzdata=LLSZWidget.LlszMenu.lattice.dz, - dxdata=LLSZWidget.LlszMenu.lattice.dx, - dzpsf=LLSZWidget.LlszMenu.lattice.dz, - dxpsf=LLSZWidget.LlszMenu.lattice.dx, - num_iter=LLSZWidget.LlszMenu.lattice.psf_num_iter) - # user_workflow.set(input_arg_first,"deconvolution") - else: - user_workflow.set("deconvolution", - skimage_decon, - vol_zyx=vol_zyx, - psf=LLSZWidget.LlszMenu.lattice.psf[channel], - num_iter=LLSZWidget.LlszMenu.lattice.psf_num_iter, - clip=False, - filter_epsilon=0, - boundary='nearest') - - # set input to subsequent task as deconvolution output - user_workflow.set( - input_arg_first, "deconvolution") - - 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") - pass - - @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, - time_start: int, - time_end: int, - ch_start: int, - ch_end: int, - Use_Cropping, - 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 - """ - assert LLSZWidget.LlszMenu.open_file, "Image not initialised" - - check_dimensions(time_start, time_end, ch_start, ch_end, - LLSZWidget.LlszMenu.lattice.channels, LLSZWidget.LlszMenu.lattice.time) - - # Get parameters - angle = LLSZWidget.LlszMenu.lattice.angle - dx = LLSZWidget.LlszMenu.lattice.dx - dy = LLSZWidget.LlszMenu.lattice.dy - dz = LLSZWidget.LlszMenu.lattice.dz - - if get_active_workflow: - # installs the workflow to napari - user_workflow = WorkflowManager.install( - self.parent_viewer).workflow - print("Workflow installed") - else: - # Automatically scan workflow file directory for *.py files. - # If it findss one, load it as a module - import importlib - parent_dir = workflow_path.resolve( - ).parents[0].__str__()+os.sep - sys.path.append(parent_dir) - custom_py_files = get_all_py_files(parent_dir) - if len(custom_py_files) == 0: - print( - f"No custom modules imported. If you'd like to use a cusotm module, place a *.py file in same folder as the workflow file {parent_dir}") - else: - modules = map( - importlib.import_module, custom_py_files) - print(f"Custom modules imported {modules}") - user_workflow = load_workflow(workflow_path) - - assert type( - user_workflow) is Workflow, "Workflow file is not a napari workflow object. Check file! You can use workflow inspector if needed" - - input_arg_first, input_arg_last, first_task_name, last_task_name = get_first_last_image_and_task( - user_workflow) - print(input_arg_first, input_arg_last, - first_task_name, last_task_name) - # get list of tasks - task_list = list(user_workflow._tasks.keys()) - print("Workflow loaded:") - print(user_workflow) - - vol = LLSZWidget.LlszMenu.lattice.data - - #vol_zyx= vol[time,channel,...] - - 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 LLSZWidget.LlszMenu.lattice.decon_processing == DeconvolutionChoice.cuda_gpu: - #otf_path = "otf_path" - psf_arg = "psf" - psf = LLSZWidget.LlszMenu.lattice.psf - else: - psf_arg = "psf" - psf = LLSZWidget.LlszMenu.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/LLSZWidget.LlszMenu.lattice.dy for x in roi_layer_list] - - deskewed_shape = LLSZWidget.LlszMenu.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=LLSZWidget.LlszMenu.deconvolution.value, - decon_processing=LLSZWidget.LlszMenu.lattice.decon_processing, - psf=psf_arg, - skew_dir=LLSZWidget.LlszMenu.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=LLSZWidget.LlszMenu.lattice.save_name, - dx=dx, - dy=dy, - dz=dz, - angle=angle, - deconvolution=LLSZWidget.LlszMenu.deconvolution.value, - decon_processing=LLSZWidget.LlszMenu.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", - LLSZWidget.LlszMenu.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 LLSZWidget.LlszMenu.deconvolution.value: - 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 LLSZWidget.LlszMenu.lattice.decon_processing == DeconvolutionChoice.cuda_gpu: - user_workflow.set("deconvolution", - pycuda_decon, - image=input, - psf=psf_arg, - dzdata=LLSZWidget.LlszMenu.lattice.dz, - dxdata=LLSZWidget.LlszMenu.lattice.dx, - dzpsf=LLSZWidget.LlszMenu.lattice.dz, - dxpsf=LLSZWidget.LlszMenu.lattice.dx, - num_iter=LLSZWidget.LlszMenu.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=LLSZWidget.LlszMenu.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=LLSZWidget.LlszMenu.lattice.save_name, - dx=dx, - dy=dy, - dz=dz, - angle=angle, - deconvolution=LLSZWidget.LlszMenu.deconvolution.value, - decon_processing=LLSZWidget.LlszMenu.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 LLSZWidget.LlszMenu.deconvolution.value: - 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 LLSZWidget.LlszMenu.lattice.decon_processing == DeconvolutionChoice.cuda_gpu: - user_workflow.set("deconvolution", - pycuda_decon, - image=input, - psf=psf_arg, - dzdata=LLSZWidget.LlszMenu.lattice.dz, - dxdata=LLSZWidget.LlszMenu.lattice.dx, - dzpsf=LLSZWidget.LlszMenu.lattice.dz, - dxpsf=LLSZWidget.LlszMenu.lattice.dx, - num_iter=LLSZWidget.LlszMenu.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=LLSZWidget.LlszMenu.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=LLSZWidget.LlszMenu.lattice.save_name, - dx=dx, - dy=dy, - dz=dz, - angle=angle, - deconvolution=LLSZWidget.LlszMenu.deconvolution.value, - decon_processing=LLSZWidget.LlszMenu.lattice.decon_processing, - otf_path=otf_path, - psf_arg=psf_arg, - psf=psf) - - print("Workflow complete") - return - - pass - -def _napari_lattice_widget_wrapper() -> LLSZWidget: - # split widget type enables a resizable widget - #max_height = 50 - # Important to have this or napari won't recognize the classes and magicclass qidgets - widget = LLSZWidget() - # aligning collapsible widgets at the top instead of having them centered vertically - widget._widget._layout.setAlignment(Qt.AlignTop) - - # widget._widget._layout.setWidgetResizable(True) - return widget diff --git a/plugin/napari_lattice/_reader.py b/plugin/napari_lattice/_reader.py deleted file mode 100644 index 79da871..0000000 --- a/plugin/napari_lattice/_reader.py +++ /dev/null @@ -1,161 +0,0 @@ -""" -reader plugin for h5 saved using np2bdv -https://github.com/nvladimus/npy2bdv -#TODO: pass pyramidal layer to napari -##use ilevel parameter in read_view to access different subsamples/pyramids -#pass a list of images with different resolution for pyramid; use is_pyramid=True flag in napari.add_image -, however pyramidal support for 3D not available yet -""" -from __future__ import annotations - -import dask.array as da -import dask.delayed as delayed -import os -import numpy as np -from napari.layers import image, Layer -from napari.layers._data_protocols import LayerDataProtocol - -from typing_extensions import Literal -from typing import Any, Optional, cast, TYPE_CHECKING, Tuple, List - -from lls_core.lattice_data import lattice_from_aics, LatticeData, img_from_array -from aicsimageio.types import ArrayLike, ImageLike - -if TYPE_CHECKING: - from aicsimageio.aics_image import AICSImage - -def lattice_from_napari( - img: Layer, - last_dimension: Optional[Literal["channel", "time"]], - **kwargs: Any -) -> LatticeData: - """ - Factory function for generating a LatticeData from a Napari Image - - Arguments: - kwargs: Extra arguments to pass to the LatticeData constructor - """ - - img_data_aics: AICSImage - - if 'aicsimage' in img.metadata.keys(): - img_data_aics = img.metadata['aicsimage'] - else: - if not last_dimension: - raise ValueError("Either the Napari image must have dimensional metadata, or last_dimension must be provided") - img_data_aics = img_from_array(cast(ArrayLike, img.data), last_dimension=last_dimension, physical_pixel_sizes=kwargs.get("physical_pixel_sizes")) - - save_name: str - if img.source.path is None: - # remove colon (:) and any leading spaces - save_name = img.name.replace(":", "").strip() - # replace any group of spaces with "_" - save_name = '_'.join(save_name.split()) - else: - file_name_noext = os.path.basename(img.source.path) - file_name = os.path.splitext(file_name_noext)[0] - # remove colon (:) and any leading spaces - save_name = file_name.replace(":", "").strip() - # replace any group of spaces with "_" - save_name = '_'.join(save_name.split()) - - return lattice_from_aics(img_data_aics, save_name=save_name, **kwargs) - -def napari_get_reader(path: list[str] | str): - """Check if file ends with h5 and returns reader function if true - Parameters - ---------- - path : str or list of str - Path to file, or list of paths. - Returns - ------- - function - """ - if isinstance(path, list): - # reader plugins may be handed single path, or a list of paths. - # if it is a list, we are only going to open first file - path = path[0] - - tiff_formats = (".tif",".tiff") - - if path.endswith(".h5"): - return bdv_h5_reader - elif path.endswith(tiff_formats): - return tiff_reader - # if we know we cannot read the file, we immediately return None. - else: - return None - - -def bdv_h5_reader(path): - """Take a path and returns a list of LayerData tuples.""" - - os.environ["HDF5_USE_FILE_LOCKING"] = "FALSE" - - #print(path) - import npy2bdv - h5_file = npy2bdv.npy2bdv.BdvEditor(path) - - img = [] - - #get dimensions of first image - first_timepoint = h5_file.read_view(time=0,channel=0) - - #Threshold to figure out when to use out-of-memory loading/dask - #Got the idea from napari-aicsimageio - #https://github.com/AllenCellModeling/napari-aicsimageio/blob/22934757c2deda30c13f39ec425343182fa91a89/napari_aicsimageio/core.py#L222 - mem_threshold_bytes = 4e9 - mem_per_threshold = 0.3 - - from psutil import virtual_memory - - file_size = os.path.getsize(path) - avail_mem = virtual_memory().available - - #if file size <30% of available memory and <4GB, open - if file_size<=mem_per_threshold*avail_mem and file_size List[Tuple[AICSImage, dict, str]]: - """Take path to tiff image and returns a list of LayerData tuples. - Specifying tiff_reader to have better control over tifffile related errors when using AICSImage - """ - - try: - image = AICSImage(path) - except Exception as e: - raise Exception("Error reading TIFF. Try upgrading tifffile library: pip install tifffile --upgrade.") from e - - # optional kwargs for the corresponding viewer.add_* method - add_kwargs = {} - - layer_type = "image" # optional, default is "image" - return [(image, add_kwargs, layer_type)] - diff --git a/plugin/napari_lattice/fields.py b/plugin/napari_lattice/fields.py index 491970f..80d8a55 100644 --- a/plugin/napari_lattice/fields.py +++ b/plugin/napari_lattice/fields.py @@ -1,7 +1,6 @@ # FieldGroups that the users interact with to input data import logging -from enum import Enum from pathlib import Path from typing import Any, Callable, List, Optional, Tuple, TypeVar, cast @@ -23,7 +22,7 @@ from lls_core.workflow import workflow_from_path from magicclass import FieldGroup, MagicTemplate, field from magicclass.fields import MagicField -from magicclass.widgets import ComboBox, Label, Widget +from magicclass.widgets import ComboBox, Label, Widget, RadioButtons from napari.layers import Image, Shapes from napari.types import ShapesData from napari.utils import history @@ -62,6 +61,10 @@ def get_friendly_validations(model: FieldGroup) -> str: except BaseException as e: return exception_to_html(e) +class PixelSizeSource(StrEnum): + Metadata = "Image Metadata" + Manual = "Manual" + class WorkflowSource(StrEnum): ActiveWorkflow = "Active Workflow" CustomPath = "Custom Path" @@ -133,10 +136,8 @@ class StackAlong(StrEnum): CHANNEL = "Channel" TIME = "Time" -class LastDimensionOptions(Enum): - XYZTC = "XYZTC" - XYZCT = "XYZCT" - Metadata = "Get from Metadata" + + class NapariFieldGroup: # This implementation is a bit ugly. This is a mixin that can only be used on a `FieldGroup`. @@ -226,7 +227,7 @@ def _get_dimension_options(self, _) -> List[str]: tooltip="All the image layers you select will be stacked into one image and then deskewed. To select multiple layers, hold Command (MacOS) or Control (Windows, Linux)." ).with_choices(lambda _x, _y: get_layers(Image)) stack_along = field( - str + str, ).with_choices( [it.value for it in StackAlong] ).with_options( @@ -234,6 +235,7 @@ def _get_dimension_options(self, _) -> List[str]: tooltip="The direction along which to stack multiple selected layers.", value=StackAlong.CHANNEL ) + pixel_sizes_source = field(PixelSizeSource.Metadata, widget_type="RadioButtons").with_options(label="Pixel Size Source", orientation="horizontal").with_choices([it.value for it in PixelSizeSource]) pixel_sizes = field(Tuple[float, float, float]).with_options( label="Pixel Sizes: XYZ (µm)", tooltip="The size of each pixel in microns. The first field selects the X pixel size, then Y, then Z." @@ -255,11 +257,12 @@ def _get_dimension_options(self, _) -> List[str]: ).with_options( label="Dimension Order", tooltip="Specifies the order of dimensions in the input images. For example, if your image is a 4D array with multiple channels along the first axis, you will specify CZYX.", - value=LastDimensionOptions.Metadata.value + value="Get from Metadata" ) - skew_dir = field(DeskewDirection.Y).with_options( + skew_dir = field(DeskewDirection.Y, widget_type="RadioButtons").with_options( label="Skew Direction", - tooltip="The axis along which to deskew" + tooltip="The axis along which to deskew", + orientation="horizontal" ) errors = field(Label).with_options(label="Errors") @@ -288,6 +291,12 @@ def _img_changed(self) -> None: # Recalculate the dimension options whenever the image changes self.dimension_order.reset_choices() + @pixel_sizes_source.connect + @enable_if([pixel_sizes]) + def _hide_pixel_sizes(self): + # Hide the "Pixel Sizes" option unless the user specifies manual pixel size source + return self.pixel_sizes_source.value == PixelSizeSource.Manual + @img_layer.connect @enable_if([stack_along]) def _hide_stack_along(self): @@ -303,7 +312,7 @@ def _get_kwargs(self) -> DeskewKwargs: 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, - physical_pixel_sizes=PhysicalPixelSizes( + physical_pixel_sizes= None if self.pixel_sizes_source.value == PixelSizeSource.Metadata else PhysicalPixelSizes( X = self.pixel_sizes.value[0], Y = self.pixel_sizes.value[1], Z = self.pixel_sizes.value[2] diff --git a/plugin/napari_lattice/reader.py b/plugin/napari_lattice/reader.py index 3ffd04f..eb596c4 100644 --- a/plugin/napari_lattice/reader.py +++ b/plugin/napari_lattice/reader.py @@ -35,7 +35,7 @@ class NapariImageParams(TypedDict): def lattice_params_from_napari( imgs: Collection[Image], dimension_order: Optional[str], - physical_pixel_sizes: PhysicalPixelSizes, + physical_pixel_sizes: Optional[PhysicalPixelSizes], stack_along: str ) -> NapariImageParams: """ @@ -51,9 +51,9 @@ def lattice_params_from_napari( 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} + # This is a set of all pixel sizes that we have seen so far + metadata_pixel_sizes: set[PhysicalPixelSizes] = set() save_names = [] - # The pixel sizes according to the AICS metadata, if any final_imgs: list[DataArray] = [] @@ -75,9 +75,10 @@ def lattice_params_from_napari( if 'aicsimage' in img.metadata.keys(): img_data_aics: AICSImage = img.metadata['aicsimage'] + # If the user has not provided pixel sizes, we extract them fro the metadata # Only process pixel sizes that are not none - if all(img_data_aics.physical_pixel_sizes): - pixel_sizes.add(img_data_aics.physical_pixel_sizes) + if physical_pixel_sizes is None and all(img_data_aics.physical_pixel_sizes): + metadata_pixel_sizes.add(img_data_aics.physical_pixel_sizes) metadata_order = list(img_data_aics.dims.order) metadata_shape = list(img_data_aics.dims.shape) @@ -96,12 +97,15 @@ def lattice_params_from_napari( final_imgs.append(DataArray(img.data, dims=calculated_order)) - if len(pixel_sizes) > 1: - raise Exception(f"Two or more layers that you have tried to merge have different pixel sizes according to their metadata! {pixel_sizes}") - elif len(pixel_sizes) == 1: - final_pixel_size = DefinedPixelSizes.from_physical(pixel_sizes.pop()) - else: + if physical_pixel_sizes: final_pixel_size = DefinedPixelSizes.from_physical(physical_pixel_sizes) + else: + if len(metadata_pixel_sizes) > 1: + raise Exception(f"Two or more layers that you have tried to merge have different pixel sizes according to their metadata! {metadata_pixel_sizes}") + elif len(metadata_pixel_sizes) < 1: + raise Exception("No pixel sizes could be determined from the image metadata. Consider manually specifying the pixel sizes.") + else: + final_pixel_size = DefinedPixelSizes.from_physical(metadata_pixel_sizes.pop()) final_img = concat(final_imgs, dim=stack_along) return NapariImageParams(save_name=save_names[0], physical_pixel_sizes=final_pixel_size, data=final_img, dims=final_img.shape) diff --git a/plugin/pyproject.toml b/plugin/pyproject.toml index 34302d3..ddaf21ab 100644 --- a/plugin/pyproject.toml +++ b/plugin/pyproject.toml @@ -72,7 +72,7 @@ SourceCode = "https://github.com/BioimageAnalysisCoreWEHI/napari_lattice" UserSupport = "https://github.com/BioimageAnalysisCoreWEHI/napari_lattice/issues" [tool.setuptools.package-data] -napari_lattice = ["*.yaml"] +napari_lattice = ["*.yaml", "*.svg", "*.png"] [project.optional-dependencies] testing = [ From 6113c373b590ace6f41639bb93ee1029405d9216 Mon Sep 17 00:00:00 2001 From: Michael Milton Date: Thu, 21 Sep 2023 14:25:11 +1000 Subject: [PATCH 048/147] Copy the args before modifying them --- core/tests/test_lattice_data.py | 4 ++++ 1 file changed, 4 insertions(+) diff --git a/core/tests/test_lattice_data.py b/core/tests/test_lattice_data.py index aff4521..762f652 100644 --- a/core/tests/test_lattice_data.py +++ b/core/tests/test_lattice_data.py @@ -1,3 +1,4 @@ +from copy import copy from typing import Any import pytest from lls_core.models import LatticeData @@ -39,6 +40,7 @@ def open_psf(name: str): @inputs @parameterized def test_process(path: str, args: dict): + args = copy(args) with as_file(resources / path) as lattice_path: args["image"] = lattice_path for slice in LatticeData.parse_obj(args).process().slices: @@ -48,6 +50,7 @@ def test_process(path: str, args: dict): @parameterized def test_process_deconvolution(args: dict, background: Any): root = Path(__file__).parent / "data" + args = copy(args) args["image"] = root / "raw.tif" args["deconvolution"] = { "psf": [root / "psf.tif"], @@ -60,6 +63,7 @@ def test_process_deconvolution(args: dict, background: Any): @inputs @parameterized def test_save(path: str, args: dict): + args = copy(args) with as_file(resources / path) as lattice_path, tempfile.TemporaryDirectory() as tempdir: args["image"] = lattice_path args["save_dir"] = tempdir From ba2f5cc0365027f062c28eb7f6631bfe9afe5d58 Mon Sep 17 00:00:00 2001 From: Michael Milton Date: Fri, 22 Sep 2023 18:19:05 +1000 Subject: [PATCH 049/147] Improvements to workflow handling --- core/lls_core/models/deskew.py | 69 +++++++++++- core/lls_core/models/lattice_data.py | 151 ++++++++++----------------- core/lls_core/models/utils.py | 16 ++- core/lls_core/types.py | 3 + core/lls_core/utils.py | 43 -------- core/lls_core/workflow.py | 21 +++- core/tests/test_batch_deskew_args.py | 1 - core/tests/test_lattice_data.py | 80 ++++++-------- core/tests/test_workflows.py | 151 +++++++-------------------- plugin/napari_lattice/dock_widget.py | 12 +-- plugin/napari_lattice/fields.py | 98 ++++++++++------- plugin/napari_lattice/utils.py | 4 +- pyproject.toml | 10 -- 13 files changed, 292 insertions(+), 367 deletions(-) delete mode 100644 pyproject.toml diff --git a/core/lls_core/models/deskew.py b/core/lls_core/models/deskew.py index 3d6c652..ab0d83b 100644 --- a/core/lls_core/models/deskew.py +++ b/core/lls_core/models/deskew.py @@ -64,10 +64,77 @@ class DeskewParams(FieldAccessMixin): deskew_affine_transform: cle.AffineTransform3D = Field(init_var=False, default=None, description="Deskewing transformation function. This is set automatically based on other input parameters, and doesn't need to be provided by the user.") + # Hack to ensure that .skew_dir behaves identically to .skew + @property + def skew_dir(self) -> DeskewDirection: + return self.skew + + @skew_dir.setter + def skew_dir(self, value: DeskewDirection): + self.skew = value + + @property + def deskew_func(self): + # Chance deskew function absed on skew direction + if self.skew == DeskewDirection.Y: + return cle.deskew_y + elif self.skew == DeskewDirection.X: + return cle.deskew_x + else: + raise ValueError() + + @property + def dx(self) -> float: + return self.physical_pixel_sizes.X + + @dx.setter + def dx(self, value: float): + self.physical_pixel_sizes.X = value + + @property + def dy(self) -> float: + return self.physical_pixel_sizes.Y + + @dy.setter + def dy(self, value: float) -> None: + self.physical_pixel_sizes.Y = value + + @property + def dz(self) -> float: + return self.physical_pixel_sizes.Z + + @dz.setter + def dz(self, value: float): + self.physical_pixel_sizes.Z = value + + def get_angle(self) -> float: + return self.angle + + def set_angle(self, angle: float) -> None: + self.angle = angle + + def set_skew(self, skew: DeskewDirection) -> None: + self.skew = skew + @property def dims(self): return self.image.dims + @property + def time(self) -> int: + """Number of time points""" + return self.image.sizes["T"] + + @property + def channels(self) -> int: + """Number of channels""" + return self.image.sizes["C"] + + @property + def new_dz(self): + import math + return math.sin(self.angle * math.pi / 180.0) * self.dz + @validator("skew", pre=True) def convert_skew(cls, v: Any): # Allow skew to be provided as a string @@ -82,8 +149,6 @@ def convert_pixels(cls, v: Any): return DefinedPixelSizes(X=v[0], Y=v[1], Z=v[2]) return v - # convert_image = validator("image", pre=True, allow_reuse=True)(image_like_to_image) - @validator("image", pre=True) def reshaping(cls, v: Any): # This allows a user to pass in any array-like object and have it diff --git a/core/lls_core/models/lattice_data.py b/core/lls_core/models/lattice_data.py index 1179a62..962d59d 100644 --- a/core/lls_core/models/lattice_data.py +++ b/core/lls_core/models/lattice_data.py @@ -104,12 +104,14 @@ def save_image(self): ) for result in roi_results: bdv_writer.append_view( - result.data, + np.array(result.data), time=result.time, channel=result.channel, voxel_size_xyz=(self.lattice_data.dx, self.lattice_data.dy, self.lattice_data.new_dz), voxel_units='um' ) + bdv_writer.write_xml() + bdv_writer.close() elif self.lattice_data.save_type == SaveFileType.tiff: # For each time point, we write a separate TIFF for time, results in groupby(roi_results, key=lambda it: it.time): @@ -172,6 +174,17 @@ def default_save_name(cls, values: dict): values["save_name"] = Path(values["image"]).stem return values + @validator("workflow", pre=True) + def parse_workflow(cls, v: Any): + # Load the workflow from disk if it was provided as a path + from lls_core.types import is_pathlike + from napari_workflows._io_yaml_v1 import load_workflow + from os import fspath + + if is_pathlike(v): + return load_workflow(fspath(v)) + return v + @validator("time_range", pre=True, always=True) def parse_time_range(cls, v: Any, values: dict) -> Any: """ @@ -255,58 +268,6 @@ def check_psfs(cls, v: Optional[DeconvolutionParams], values: dict): raise ValueError(f"There should be one PSF per channel, but there are {psfs} PSFs and {channels} channels.") return v - # Hack to ensure that .skew_dir behaves identically to .skew - @property - def skew_dir(self) -> DeskewDirection: - return self.skew - - @skew_dir.setter - def skew_dir(self, value: DeskewDirection): - self.skew = value - - @property - def deskew_func(self): - # Chance deskew function absed on skew direction - if self.skew == DeskewDirection.Y: - return cle.deskew_y - elif self.skew == DeskewDirection.X: - return cle.deskew_x - else: - raise ValueError() - - @property - def dx(self) -> float: - return self.physical_pixel_sizes.X - - @dx.setter - def dx(self, value: float): - self.physical_pixel_sizes.X = value - - @property - def dy(self) -> float: - return self.physical_pixel_sizes.Y - - @dy.setter - def dy(self, value: float): - self.physical_pixel_sizes.Y = value - - @property - def dz(self) -> float: - return self.physical_pixel_sizes.Z - - @dz.setter - def dz(self, value: float): - self.physical_pixel_sizes.Z = value - - def get_angle(self) -> float: - return self.angle - - def set_angle(self, angle: float) -> None: - self.angle = angle - - def set_skew(self, skew: DeskewDirection) -> None: - self.skew = skew - @property def cropping_enabled(self) -> bool: "True if cropping should be performed" @@ -317,20 +278,6 @@ def deconv_enabled(self) -> bool: "True if deconvolution should be performed" return self.deconvolution is not None - @property - def time(self) -> int: - """Number of time points""" - return self.image.sizes["T"] - - @property - def channels(self) -> int: - """Number of channels""" - return self.image.sizes["C"] - - @property - def new_dz(self): - return math.sin(self.angle * math.pi / 180.0) * self.dz - 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") @@ -343,15 +290,6 @@ def slice_data(self, time: int, channel: int) -> DataArray: return self.image.isel(T=time, C=channel) - if len(self.image.shape) == 3: - return self.image - elif len(self.image.shape) == 4: - return self.image[time, :, :, :] - elif len(self.image.shape) == 5: - return self.image[time, channel, :, :, :] - - raise Exception("Lattice data must be 3-5 dimensions") - def iter_slices(self) -> Iterable[SlicedData[ArrayLike]]: """ Yields array slices for each time and channel of interest. @@ -377,11 +315,12 @@ def iter_sublattices(self, update_with: dict = {}) -> Iterable[SlicedData[Lattic update_with: dictionary of arguments to update the generated lattices with """ for subarray in self.iter_slices(): - yield subarray.copy_with_data( - self.copy(update={ "image": subarray, - **update_with - }) - ) + # Copy doesn't manu + new_lattice = self.copy_validate(update={ + "image": subarray.data, + **update_with + }) + yield subarray.copy_with_data( new_lattice) def generate_workflows( self, @@ -394,14 +333,22 @@ def generate_workflows( return from copy import copy + from lls_core.workflow import get_workflow_inputs, update_workflow # We make a copy of the lattice for each slice, each of which has no associated workflow for lattice_slice in self.iter_sublattices(update_with={"workflow": None}): user_workflow = copy(self.workflow) user_workflow.set( "deskew_image", - LatticeData.process, + LatticeData.process_into_image, lattice_slice.data ) + for task_name, arg_index, arg_name in get_workflow_inputs(user_workflow): + update_workflow( + user_workflow, + task_name, + arg_index, + "deskew_image" + ) yield lattice_slice.copy_with_data(user_workflow) def check_incomplete_acquisition(self, volume: ArrayLike, time_point: int, channel: int): @@ -510,6 +457,22 @@ def _process_non_crop(self) -> Iterable[ProcessedVolume]: voxel_size_z=self.dz )) ) + + def _process_workflow(self) -> ProcessedSlices: + outputs = [] + for workflow in self.generate_workflows(): + for leaf in workflow.data.leafs(): + outputs.append( + workflow.copy_with_data( + workflow.data.get(leaf) + ) + ) + + return ProcessedSlices( + slices = outputs, + lattice_data=self + ) + def process(self) -> ProcessedSlices: """ Execute the processing and return the result. @@ -518,20 +481,7 @@ def process(self) -> ProcessedSlices: ProcessedSlices.update_forward_refs() if self.workflow is not None: - outputs = [] - for workflow in self.generate_workflows(): - for leaf in workflow.data.leafs(): - outputs.append( - workflow.copy_with_data( - workflow.data.get(leaf) - ) - ) - - return ProcessedSlices( - slices = outputs, - lattice_data=self - ) - + return self._process_workflow() elif self.cropping_enabled: return ProcessedSlices( lattice_data=self, @@ -542,3 +492,12 @@ def process(self) -> ProcessedSlices: lattice_data=self, slices=self._process_non_crop() ) + + def process_into_image(self) -> ArrayLike: + """ + Shortcut method for calling process, then extracting one image layer. + This is mostly here to simplify the Workflow integration + """ + for slice in self.process().slices: + return slice.data + raise Exception("No slices produced!") diff --git a/core/lls_core/models/utils.py b/core/lls_core/models/utils.py index b929a93..6f56a77 100644 --- a/core/lls_core/models/utils.py +++ b/core/lls_core/models/utils.py @@ -1,8 +1,8 @@ from typing import Any, Type +from typing_extensions import Self from enum import Enum from pydantic import BaseModel, Extra -from typer import Option from contextlib import contextmanager def enum_choices(enum: Type[Enum]) -> str: @@ -30,6 +30,7 @@ class FieldAccessMixin(BaseModel): class Config: extra = Extra.forbid arbitrary_types_allowed = True + validate_assignment = True @classmethod def get_default(cls, field_name: str) -> Any: @@ -60,13 +61,10 @@ def to_definition_dict(cls) -> dict: ret[key] = value return ret - @classmethod - def make_typer_field(cls, field_name: str, extra_description: str = "") -> Any: + def copy_validate(self, **kwargs) -> Self: """ - Convert this field into a Typer field + Like `.copy()`, but validates the results. + See https://github.com/pydantic/pydantic/issues/418 for more information """ - field = cls.__fields__[field_name] - return Option( - default = field.get_default(), - help=field.field_info.description + extra_description - ) + updated = self.copy(**kwargs) + return updated.validate(updated.dict()) diff --git a/core/lls_core/types.py b/core/lls_core/types.py index 5152719..3b14ea0 100644 --- a/core/lls_core/types.py +++ b/core/lls_core/types.py @@ -32,4 +32,7 @@ def image_like_to_image(img: ImageLike) -> DataArray: if isinstance(img, AICSImage): return img.xarray_dask_data else: + for required_key in ("shape", "dtype", "ndim", "__array__", "__array_ufunc__"): + if not hasattr(img, required_key): + raise ValueError(f"The provided object {img} is not array like!") return DataArray(img) diff --git a/core/lls_core/utils.py b/core/lls_core/utils.py index 36671c5..d27c0aa 100644 --- a/core/lls_core/utils.py +++ b/core/lls_core/utils.py @@ -190,49 +190,6 @@ def dask_expand_dims(a: ArrayLike, axis: Union[Collection[int], int]): return a.reshape(shape) -def read_imagej_roi(roi_zip_path: str): - """Read an ImageJ ROI zip file so it loaded into napari shapes layer - If non rectangular ROI, will convert into a rectangle based on extreme points - Args: - roi_zip_path (zip file): ImageJ ROI zip file - - Returns: - list: List of ROIs - """ - roi_extension = path.splitext(roi_zip_path)[1] - - # handle reading single roi or collection of rois in zip file - if roi_extension == ".zip": - ij_roi = read_roi_zip(roi_zip_path) - elif roi_extension == ".roi": - ij_roi = read_roi_file(roi_zip_path) - else: - raise Exception("ImageJ ROI file needs to be a zip/roi file") - - # initialise list of rois - roi_list = [] - - # Read through each roi and create a list so that it matches the organisation of the shapes from napari shapes layer - for k in ij_roi.keys(): - if ij_roi[k]['type'] in ('oval', 'rectangle'): - width = ij_roi[k]['width'] - height = ij_roi[k]['height'] - left = ij_roi[k]['left'] - top = ij_roi[k]['top'] - roi = [[top, left], [top, left+width], - [top+height, left+width], [top+height, left]] - roi_list.append(roi) - elif ij_roi[k]['type'] in ('polygon', 'freehand'): - left = min(ij_roi[k]['x']) - top = min(ij_roi[k]['y']) - right = max(ij_roi[k]['x']) - bottom = max(ij_roi[k]['y']) - roi = [[top, left], [top, right], [bottom, right], [bottom, left]] - roi_list.append(roi) - else: - print("Cannot read ROI ", - ij_roi[k], ".Recognised as type ", ij_roi[k]['type']) - return roi_list def pad_image_nearest_multiple(img: NDArray, nearest_multiple: int) -> NDArray: """pad an Image to the nearest multiple of provided number diff --git a/core/lls_core/workflow.py b/core/lls_core/workflow.py index 25ceeff..0577721 100644 --- a/core/lls_core/workflow.py +++ b/core/lls_core/workflow.py @@ -5,7 +5,7 @@ from os import path from pathlib import Path -from typing import Optional, Tuple, Union +from typing import Any, Optional, Tuple, Union import dask.array as da import numpy as np @@ -16,12 +16,27 @@ if TYPE_CHECKING: from napari_workflows import Workflow - from lls_core.models import LatticeData import logging logger = logging.getLogger(__name__) logger.setLevel(logging.INFO) +def get_workflow_inputs(workflow: Workflow): + """ + Yields tuples of (task_name, argument_index, input_argument) corresponding to the workflow's inputs, + namely the arguments that are unfilled. + Note that the index returned is the index in the overall task tuple, which includes the task name + """ + for root_arg in workflow.roots(): + for taskname, (task_func, *args) in workflow._tasks.items(): + if root_arg in args: + yield taskname, args.index(root_arg) + 1, root_arg + +def update_workflow(workflow: Workflow, task_name: str, task_index: int, new_value: Any): + task = list(workflow.get_task(task_name)) + task[task_index] = new_value + workflow.set_task(task_name, tuple(task)) + def get_first_last_image_and_task(user_workflow: Workflow) -> Tuple[str, str, str, str]: """ Get images and tasks for first and last entry @@ -51,7 +66,7 @@ def get_first_last_image_and_task(user_workflow: Workflow) -> Tuple[str, str, st def modify_workflow_task(old_arg: str, task_key: str, new_arg: str, workflow: Workflow) -> tuple: """ - Modify items in a workflow task + Replies one argument in a workflow with another Workflow is not modified, only a new task with updated arg is returned Args: old_arg: The argument in the workflow that needs to be modified diff --git a/core/tests/test_batch_deskew_args.py b/core/tests/test_batch_deskew_args.py index 34a53b4..df52978 100644 --- a/core/tests/test_batch_deskew_args.py +++ b/core/tests/test_batch_deskew_args.py @@ -14,7 +14,6 @@ def create_image(path: Path): imsave(str(path), raw) assert path.exists() - def test_batch_deskew_h5(): """Write image to disk and then execute napari_lattice from terminal Checks if an deskewed output file is created for both tif and h5 diff --git a/core/tests/test_lattice_data.py b/core/tests/test_lattice_data.py index 762f652..e05cf6d 100644 --- a/core/tests/test_lattice_data.py +++ b/core/tests/test_lattice_data.py @@ -1,72 +1,58 @@ -from copy import copy from typing import Any import pytest from lls_core.models import LatticeData -from lls_core.models.output import SaveFileType from lls_core.sample import resources from importlib_resources import as_file -from importlib_resources.abc import Traversable import tempfile from pathlib import Path -inputs = pytest.mark.parametrize( - ["path"], [ - ("RBC_tiny.czi", ), - ("RBC_lattice.tif", ), - ("LLS7_t1_ch1.czi", ), - ("LLS7_t1_ch3.czi", ), - ("LLS7_t2_ch1.czi", ), - ("LLS7_t2_ch3.czi", ), - ("multich_multi_time.tif", ), -]) +from .params import inputs, parameterized def open_psf(name: str): with as_file(resources / "psfs" / "zeiss_simulated" / name) as path: return path - -parameterized = pytest.mark.parametrize("args", [ - {"skew": "X"}, - {"skew": "Y"}, - {"angle": 30}, - {"angle": 90}, - {"physical_pixel_sizes": (1, 1, 1)}, - {"save_type": SaveFileType.h5}, - {"save_type": SaveFileType.tiff}, - - # Cropping enabled - {"crop": {"roi_list": []}}, -]) - @inputs @parameterized def test_process(path: str, args: dict): - args = copy(args) with as_file(resources / path) as lattice_path: - args["image"] = lattice_path - for slice in LatticeData.parse_obj(args).process().slices: + for slice in LatticeData.parse_obj({ + "image": lattice_path, + **args + }).process().slices: assert slice.data.ndim == 3 +@inputs +@parameterized +def test_save(path: str, args: dict): + with as_file(resources / path) as lattice_path, tempfile.TemporaryDirectory() as tempdir: + LatticeData.parse_obj({ + "image": lattice_path, + "save_dir": tempdir, + **args + }).process().save_image() + results = list(Path(tempdir).iterdir()) + assert len(results) > 0 + @pytest.mark.parametrize(["background"], [(1, ), ("auto",), ("second_last",)]) @parameterized def test_process_deconvolution(args: dict, background: Any): root = Path(__file__).parent / "data" - args = copy(args) - args["image"] = root / "raw.tif" - args["deconvolution"] = { - "psf": [root / "psf.tif"], - "background": background - } - for slice in LatticeData.parse_obj(args).process().slices: + for slice in LatticeData.parse_obj({ + "image": root / "raw.tif", + "deconvolution": { + "psf": [root / "psf.tif"], + "background": background + }, + **args + }).process().slices: assert slice.data.ndim == 3 - -@inputs @parameterized -def test_save(path: str, args: dict): - args = copy(args) - with as_file(resources / path) as lattice_path, tempfile.TemporaryDirectory() as tempdir: - args["image"] = lattice_path - args["save_dir"] = tempdir - LatticeData.parse_obj(args).process().save_image() - results = list(Path(tempdir).iterdir()) - assert len(results) > 0 +def test_process_workflow(args: dict): + root = Path(__file__).parent / "data" + for slice in LatticeData.parse_obj({ + "image": root / "raw.tif", + "workflow": root / "raw.tif", + **args + }).process().slices: + assert slice.data.ndim == 3 diff --git a/core/tests/test_workflows.py b/core/tests/test_workflows.py index bd285f2..4d060f6 100644 --- a/core/tests/test_workflows.py +++ b/core/tests/test_workflows.py @@ -1,126 +1,53 @@ -from skimage.io import imread, imsave +from typing import Callable import os -import numpy as np -from pathlib import Path -import platform -import pyclesperanto_prototype as cle +from copy import copy +from numpy.typing import NDArray from napari_workflows import Workflow -from napari_workflows._io_yaml_v1 import load_workflow, save_workflow - -from lls_core.cmds.__main__ import main as run_cli - -# For testing in Windows -if platform.system() == "Windows": - home_dir = str(Path.home()) - home_dir = home_dir.replace("\\", "\\\\") - img_dir = home_dir + "\\\\raw.tiff" - workflow_location = home_dir + "\\\\deskew_segment.yaml" - config_location = home_dir + "\\\\config_deskew.yaml" -else: - home_dir = str(Path.home()) - img_dir = os.path.join(home_dir, "raw.tiff") - workflow_location = os.path.join(home_dir, "deskew_segment.yaml") - config_location = os.path.join(home_dir, "config_deskew.yaml") - - -def write_config_file(config_settings, output_file_location): - with open(output_file_location, 'w') as f: - for key, val in config_settings.items(): - if val is not None: - if type(val) is str: - print('%s: "%s"' % (key, val), file=f) - - if type(val) is int: - print('%s: %i' % (key, val), file=f) - - if type(val) is list: - print("%s:" % key, file=f) - for x in val: - if type(x) is int: - print(" - %i" % x, file=f) - else: - print(' - "%s"' % x, file=f) - - print("Config found written to %s" % output_file_location) - - -def create_data(): - # Create a zero array of shape 5x5x5 with a value of 10 at (2,4,2) - raw = np.zeros((5, 5, 5)) - raw[2, 2, 2] = 10 - # Save image as a tif filw in home directory - imsave(img_dir, raw) - assert os.path.exists(img_dir) - - # Create a config file - config = { - "input": img_dir, - "output": home_dir, - "processing": "workflow", - "workflow_path": workflow_location, - "output_file_type": "h5"} - - write_config_file(config, config_location) - assert os.path.exists(config_location) - - -def create_workflow(): - # Zeiss lattice - voxel_size_x_in_microns = 0.14499219272808386 - voxel_size_y_in_microns = 0.14499219272808386 - voxel_size_z_in_microns = 0.3 - deskewing_angle_in_degrees = 30.0 +import tempfile - # Instantiate segmentation workflow - image_seg_workflow = Workflow() - - image_seg_workflow.set("gaussian", cle.gaussian_blur, - "input", sigma_x=1, sigma_y=1, sigma_z=1) - - image_seg_workflow.set("binarisation", cle.threshold, - "gaussian", constant=0.5) - - image_seg_workflow.set( - "labeling", cle.connected_components_labeling_box, "binarisation") - - save_workflow(workflow_location, image_seg_workflow) - - assert os.path.exists(workflow_location) +from tests.utils import invoke +from pathlib import Path +from .params import config_types +from .utils import invoke -def test_napari_workflow(): - """Test napari workflow to see if it works before we run it using napari_lattice - This is without deskewing +def test_napari_workflow(workflow: Workflow, test_image: NDArray): """ - create_data() - create_workflow() - - image_seg_workflow = load_workflow(workflow_location) - - # Open the saved image from above - raw = imread(img_dir) + Test napari workflow to see if it works before we run it using napari_lattice + This is without deskewing + """ + workflow = copy(workflow) # Set input image to be the "raw" image - image_seg_workflow.set("input", raw) - labeling = image_seg_workflow.get("labeling") - assert (labeling[2, 2, 2] == 1) + workflow.set("input", test_image) + labeling = workflow.get("labeling") + assert labeling[2, 2, 2] == 1 - -def test_workflow_lattice(): - """Test workflow by loading into napari lattice - This will apply deskewing before processing the workflow +@config_types +def test_workflow_cli(workflow_config_cli: dict, save_func: Callable, cli_param: str): + """ + Test workflow processing via CLI + This will apply deskewing before processing the workflow """ - # Deskew, apply workflow and save as h5 - run_cli(["--config", config_location]) + with tempfile.NamedTemporaryFile(mode="w") as fp: + save_func(workflow_config_cli, fp) + fp.flush() + + # Deskew, apply workflow and save as h5 + invoke([ + "process", + cli_param, fp.name + ]) # checks if h5 file written - h5_img = os.path.join(home_dir, "raw", "_0_raw.h5") - assert os.path.exists(h5_img) + save_dir = Path(workflow_config_cli["save_dir"]) + saved_files = list(save_dir.glob("*.h5")) + assert len(saved_files) > 0 + assert len(list(save_dir.glob("*.xml"))) > 0 import npy2bdv - h5_file = npy2bdv.npy2bdv.BdvEditor(h5_img) - - label_img = h5_file.read_view(time=0, channel=0) - - assert (label_img.shape == (3, 14, 5)) - assert (label_img[1, 6, 2] == 1) + for h5_img in saved_files: + h5_file = npy2bdv.npy2bdv.BdvEditor(str(h5_img)) + label_img = h5_file.read_view(time=0, channel=0) + assert label_img.shape == (3, 14, 5) + assert label_img[1, 6, 2] == 1 diff --git a/plugin/napari_lattice/dock_widget.py b/plugin/napari_lattice/dock_widget.py index 05f00d1..4debcc0 100644 --- a/plugin/napari_lattice/dock_widget.py +++ b/plugin/napari_lattice/dock_widget.py @@ -91,11 +91,11 @@ def __post_init__(self): for field in [self.deskew_fields, self.deconv_fields, self.cropping_fields, self.workflow_fields, self.output_fields]: field._validate() - deskew_fields = DeskewFields(name = "1. Deskew") - deconv_fields = DeconvolutionFields(name = "2. Deconvolution") - cropping_fields = CroppingFields(name = "3. Crop") - workflow_fields = WorkflowFields(name = "4. Workflow") - output_fields = OutputFields(name = "5. Output") + deskew_fields = DeskewFields() + deconv_fields = DeconvolutionFields() + cropping_fields = CroppingFields() + workflow_fields = WorkflowFields() + output_fields = OutputFields() @set_options(header=dict(widget_type="Label", label="

      Preview Deskew

      "), time=dict(label="Time:", max=2**15), @@ -106,7 +106,7 @@ def __post_init__(self): def preview(self, header:str, time: int, channel: int): # We only need to process one time point for the preview, # so we made a copy using a subset of the times - lattice = self._make_model().copy(update=dict( + lattice = self._make_model().copy_validate(update=dict( time_range = range(time, time+1), channel_range = range(time, time+1), )) diff --git a/plugin/napari_lattice/fields.py b/plugin/napari_lattice/fields.py index 80d8a55..1185468 100644 --- a/plugin/napari_lattice/fields.py +++ b/plugin/napari_lattice/fields.py @@ -20,9 +20,9 @@ from lls_core.models.deskew import DefinedPixelSizes from lls_core.models.output import SaveFileType from lls_core.workflow import workflow_from_path -from magicclass import FieldGroup, MagicTemplate, field +from magicclass import FieldGroup, MagicTemplate, field, magicclass, set_design from magicclass.fields import MagicField -from magicclass.widgets import ComboBox, Label, Widget, RadioButtons +from magicclass.widgets import ComboBox, Label, Widget from napari.layers import Image, Shapes from napari.types import ShapesData from napari.utils import history @@ -75,20 +75,19 @@ class BackgroundSource(StrEnum): Custom = "Custom" -def enable_field(parent: MagicTemplate, field: MagicField, enabled: bool = True) -> None: +def enable_field(field: MagicField, enabled: bool = True) -> None: """ Enable the widget associated with a field Args: - parent: The parent magicclass that contains the field field: The field to enable/disable enabled: If False, disable the field instead of enabling it """ - real_field = getattr(parent, field.name) - if not isinstance(real_field, Widget): - raise Exception("Define your fields with field() not vfield()!") - real_field.visible = enabled - real_field.enabled = enabled + for real_field in field._guis.values(): + if not isinstance(real_field, Widget): + raise Exception("Define your fields with field() not vfield()!") + real_field.visible = enabled + real_field.enabled = enabled EnabledHandlerType = TypeVar("EnabledHandlerType") @@ -114,18 +113,18 @@ def _enable_fields(self) -> bool: def _decorator(fn: Callable[[EnabledHandlerType], bool])-> Callable[[EnabledHandlerType], None]: for field in fields: - field.visible = False field.enabled = False + field.visible = False - def make_handler(fn: Callable) -> Callable[[EnabledHandlerType], None]: - def handler(parent: EnabledHandlerType): - enable = fn(parent) + def make_handler(fn: Callable[[EnabledHandlerType], bool]) -> Callable[[EnabledHandlerType], None]: + def handler(value: Any): + enable = fn(value) for field in fields: if enable: logger.info(f"{field.name} Activated") else: logger.info(f"{field.name} Deactivated") - enable_field(parent, field, enabled=enable) + enable_field(field, enabled=enable) return handler return make_handler(fn) @@ -136,9 +135,6 @@ class StackAlong(StrEnum): CHANNEL = "Channel" TIME = "Time" - - - 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 @@ -203,7 +199,8 @@ class DeskewKwargs(NapariImageParams): angle: float skew: DeskewDirection -class DeskewFields(NapariFieldGroup, FieldGroup): +@magicclass(name="1. Deskew") +class DeskewFields(NapariFieldGroup): def _get_dimension_options(self, _) -> List[str]: """ @@ -293,15 +290,15 @@ def _img_changed(self) -> None: @pixel_sizes_source.connect @enable_if([pixel_sizes]) - def _hide_pixel_sizes(self): + def _hide_pixel_sizes(pixel_sizes_source: str): # Hide the "Pixel Sizes" option unless the user specifies manual pixel size source - return self.pixel_sizes_source.value == PixelSizeSource.Manual + return pixel_sizes_source == PixelSizeSource.Manual @img_layer.connect @enable_if([stack_along]) - def _hide_stack_along(self): + def _hide_stack_along(img_layer): # Hide the "Stack Along" option if we only have one image - return len(self.img_layer.value) > 1 + return len(img_layer.value) > 1 def _get_kwargs(self) -> DeskewKwargs: """ @@ -334,7 +331,8 @@ def _make_model(self) -> DeskewParams: skew = kwargs["skew"] ) -class DeconvolutionFields(NapariFieldGroup, FieldGroup): +@magicclass(name="2. Deconvolution") +class DeconvolutionFields(NapariFieldGroup): """ A counterpart to the DeconvolutionParams Pydantic class """ @@ -355,8 +353,8 @@ class DeconvolutionFields(NapariFieldGroup, FieldGroup): @enable_if( [background_custom] ) - def _enable_custom_background(self) -> bool: - return self.background.value == BackgroundSource.Custom + def _enable_custom_background(background: str) -> bool: + return background == BackgroundSource.Custom @fields_enabled.connect @enable_if( @@ -367,8 +365,8 @@ def _enable_custom_background(self) -> bool: background ] ) - def _enable_fields(self) -> bool: - return self.fields_enabled.value + def _enable_fields(enabled) -> bool: + return enabled def _make_model(self) -> Optional[DeconvolutionParams]: if not self.fields_enabled.value: @@ -386,12 +384,13 @@ def _make_model(self) -> Optional[DeconvolutionParams]: psf_num_iter=self.psf_num_iter.value ) -class CroppingFields(NapariFieldGroup, FieldGroup): +@magicclass(name="3. Crop") +class CroppingFields(MagicTemplate, NapariFieldGroup): """ A counterpart to the CropParams Pydantic class """ fields_enabled = field(False, label="Enabled") - shapes= field(List[Shapes], widget_type="Select", label = "ROIs").with_options(choices=lambda _x, _y: get_layers(Shapes)) + shapes= field(List[Shapes], widget_type="Select", label = "ROI Shape Layers").with_options(choices=lambda _x, _y: get_layers(Shapes)) z_range = field(Tuple[int, int]).with_options( label = "Z Range", value = (0, 1), @@ -402,21 +401,46 @@ class CroppingFields(NapariFieldGroup, FieldGroup): ) errors = field(Label).with_options(label="Errors") + def _get_deskew(self) -> DeskewParams: + "Returns the DeskewParams from the other tab" + from napari_lattice.dock_widget import LLSZWidget + parent = self.find_ancestor(LLSZWidget) + return parent.LlszMenu.WidgetContainer.deskew_fields._make_model() + + @set_design(text="Import ROI") + def import_roi(self, path: Path): + from lls_core.cropping import read_imagej_roi + from napari_lattice.utils import get_viewer + import numpy as np + roi_list = read_imagej_roi(path) + # convert to canvas coordinates + roi_list = (np.array(roi_list) * self._get_deskew().dy).tolist() + viewer = get_viewer() + viewer.add_shapes(roi_list, shape_type='polygon', edge_width=1, edge_color='yellow', face_color=[1, 1, 1, 0]) + + @set_design(text="New Crop") + def new_crop_layer(self): + from napari_lattice.utils import get_viewer + shapes = get_viewer().add_shapes() + shapes.mode = "SELECT" + shapes.name = "Napari Lattice Crop" + @fields_enabled.connect @enable_if([shapes, z_range]) - def _enable_workflow(self) -> bool: - return self.fields_enabled.value + def _enable_crop(enabled: bool) -> bool: + return enabled def _make_model(self) -> Optional[CropParams]: import numpy as np if self.fields_enabled.value: return CropParams( z_range=self.z_range.value, - roi_list=ShapesData([np.array(shape.data) for shape in self.shapes.value]) + roi_list=ShapesData([np.array(shape.data) / self._get_deskew().dy for shape in self.shapes.value]) ) return None -class WorkflowFields(NapariFieldGroup, FieldGroup): +@magicclass(name="4. Workflow") +class WorkflowFields(NapariFieldGroup): """ Handles the workflow related parameters """ @@ -427,8 +451,8 @@ class WorkflowFields(NapariFieldGroup, FieldGroup): @fields_enabled.connect @enable_if([workflow_source]) - def _enable_workflow(self) -> bool: - return self.fields_enabled.value + def _enable_workflow(enabled: bool) -> bool: + return enabled @fields_enabled.connect @enable_if([workflow_path]) @@ -445,8 +469,8 @@ def _make_model(self) -> Optional[Workflow]: else: return workflow_from_path(self.workflow_path.value) -# @magicclass(name="5. Output") -class OutputFields(NapariFieldGroup, FieldGroup): +@magicclass(name="5. Output") +class OutputFields(NapariFieldGroup): set_logging = field(Log_Levels.INFO).with_options(label="Logging Level") time_range = field(Tuple[int, int]).with_options( label="Time Export Range", diff --git a/plugin/napari_lattice/utils.py b/plugin/napari_lattice/utils.py index 25778c7..fd7594a 100644 --- a/plugin/napari_lattice/utils.py +++ b/plugin/napari_lattice/utils.py @@ -19,5 +19,7 @@ def get_layers(type: Type[LayerType]) -> Sequence[LayerType]: For example, if you pass `napari.layers.Image`, it will return a list of Image layers """ - viewer = get_viewer() + viewer = current_viewer() + if viewer is None: + return [] return [layer for layer in viewer.layers if isinstance(layer, type)] diff --git a/pyproject.toml b/pyproject.toml deleted file mode 100644 index 3779839..0000000 --- a/pyproject.toml +++ /dev/null @@ -1,10 +0,0 @@ -[build-system] -requires = ["setuptools", "wheel"] -build-backend = "setuptools.build_meta" - -[tool.black] -line-length = 79 - -[tool.isort] -profile = "black" -line_length = 79 From a6a2f0157ce43b2729e3fcdecbc131611164b683 Mon Sep 17 00:00:00 2001 From: Michael Milton Date: Fri, 22 Sep 2023 18:25:06 +1000 Subject: [PATCH 050/147] Missing test files --- core/lls_core/models/lattice_data.py | 4 +- core/tests/conftest.py | 63 ++++++++++++++++++++++++++++ 2 files changed, 65 insertions(+), 2 deletions(-) create mode 100644 core/tests/conftest.py diff --git a/core/lls_core/models/lattice_data.py b/core/lls_core/models/lattice_data.py index 962d59d..bf3095a 100644 --- a/core/lls_core/models/lattice_data.py +++ b/core/lls_core/models/lattice_data.py @@ -178,11 +178,11 @@ def default_save_name(cls, values: dict): def parse_workflow(cls, v: Any): # Load the workflow from disk if it was provided as a path from lls_core.types import is_pathlike - from napari_workflows._io_yaml_v1 import load_workflow + from lls_core.workflow import workflow_from_path from os import fspath if is_pathlike(v): - return load_workflow(fspath(v)) + return workflow_from_path(fspath(v)) return v @validator("time_range", pre=True, always=True) diff --git a/core/tests/conftest.py b/core/tests/conftest.py new file mode 100644 index 0000000..b69022f --- /dev/null +++ b/core/tests/conftest.py @@ -0,0 +1,63 @@ +from typer.testing import CliRunner +import pytest +from skimage.io import imsave +import numpy as np +from pathlib import Path +import pyclesperanto_prototype as cle +import tempfile +from numpy.typing import NDArray + +from napari_workflows import Workflow +from napari_workflows._io_yaml_v1 import save_workflow + +@pytest.fixture +def runner() -> CliRunner: + return CliRunner() + +@pytest.fixture +def workflow() -> Workflow: + # Instantiate segmentation workflow + image_seg_workflow = Workflow() + image_seg_workflow.set("gaussian", cle.gaussian_blur, "input", sigma_x=1, sigma_y=1, sigma_z=1) + image_seg_workflow.set("binarisation", cle.threshold, "gaussian", constant=0.5) + image_seg_workflow.set("labeling", cle.connected_components_labeling_box, "binarisation") + return image_seg_workflow + +@pytest.fixture +def test_image() -> NDArray[np.float64]: + raw = np.zeros((5, 5, 5)) + raw[2, 2, 2] = 10 + return raw + +@pytest.fixture +def workflow_config(workflow: Workflow, test_image: NDArray): + # Create a config file + yield { + "image": test_image, + "workflow": workflow, + } + +@pytest.fixture +def workflow_config_cli(workflow: Workflow, test_image: NDArray): + with tempfile.TemporaryDirectory() as tempdir_str: + tempdir = Path(tempdir_str) + input = tempdir / "raw.tiff" + output = tempdir / "output" + output.mkdir(parents=True) + workflow_path = tempdir / "workflow.json" + save_workflow(str(workflow_path), workflow) + + # Create a zero array of shape 5x5x5 with a value of 10 at (2,4,2) + imsave(input, test_image) + assert input.exists() + + # Create a config file + yield { + key: str(val) + for key, val in + { + "image": input, + "save_dir": output, + "workflow": workflow_path, + }.items() + } From e27b19ef2faf8e4920011945eba9e7bb5141c8c8 Mon Sep 17 00:00:00 2001 From: Michael Milton Date: Fri, 22 Sep 2023 18:27:48 +1000 Subject: [PATCH 051/147] More missing files, disable cropping test until we have ROIs --- core/lls_core/cropping.py | 61 +++++++++++++++++++++++++++++++++++++++ 1 file changed, 61 insertions(+) create mode 100644 core/lls_core/cropping.py diff --git a/core/lls_core/cropping.py b/core/lls_core/cropping.py new file mode 100644 index 0000000..e4b973a --- /dev/null +++ b/core/lls_core/cropping.py @@ -0,0 +1,61 @@ +from __future__ import annotations +from typing import TYPE_CHECKING + +if TYPE_CHECKING: + from lls_core.types import PathLike + from numpy.typing import NDArray + +def read_roi_array(roi: PathLike) -> NDArray: + from read_roi import read_roi_file + from numpy import array + return array(read_roi_file(roi)) + +def read_imagej_roi(roi_path: PathLike): + """Read an ImageJ ROI zip file so it loaded into napari shapes layer + If non rectangular ROI, will convert into a rectangle based on extreme points + Args: + roi_zip_path (zip file): ImageJ ROI zip file + + Returns: + list: List of ROIs + """ + from pathlib import Path + from os import fspath + from read_roi import read_roi_file, read_roi_zip + + roi_path = Path(fspath(roi_path)) + + # handle reading single roi or collection of rois in zip file + if roi_path.suffix == ".zip": + ij_roi = read_roi_zip(roi_path) + elif roi_path.suffix == ".roi": + ij_roi = read_roi_file(roi_path) + else: + raise Exception("ImageJ ROI file needs to be a zip/roi file") + + if ij_roi is None: + raise Exception("Failed reading ROI file") + + # initialise list of rois + roi_list = [] + + # Read through each roi and create a list so that it matches the organisation of the shapes from napari shapes layer + for value in ij_roi.values(): + if value['type'] in ('oval', 'rectangle'): + width = value['width'] + height = value['height'] + left = value['left'] + top = value['top'] + roi = [[top, left], [top, left+width], [top+height, left+width], [top+height, left]] + roi_list.append(roi) + elif value['type'] in ('polygon', 'freehand'): + left = min(value['x']) + top = min(value['y']) + right = max(value['x']) + bottom = max(value['y']) + roi = [[top, left], [top, right], [bottom, right], [bottom, left]] + roi_list.append(roi) + else: + print(f"Cannot read ROI {value}. Recognised as type {value['type']}") + + return roi_list From 02baf7b26061b04a72c02a1a053fd8edd1f85a78 Mon Sep 17 00:00:00 2001 From: Michael Milton Date: Fri, 22 Sep 2023 19:21:06 +1000 Subject: [PATCH 052/147] Add missing params.py --- core/tests/params.py | 34 ++++++++++++++++++++++++++++++++++ 1 file changed, 34 insertions(+) create mode 100644 core/tests/params.py diff --git a/core/tests/params.py b/core/tests/params.py new file mode 100644 index 0000000..e49711f --- /dev/null +++ b/core/tests/params.py @@ -0,0 +1,34 @@ +import pytest +import json +import yaml + +from lls_core.models.output import SaveFileType + +inputs = pytest.mark.parametrize( + ["path"], [ + ("RBC_tiny.czi", ), + ("RBC_lattice.tif", ), + ("LLS7_t1_ch1.czi", ), + ("LLS7_t1_ch3.czi", ), + ("LLS7_t2_ch1.czi", ), + ("LLS7_t2_ch3.czi", ), + ("multich_multi_time.tif", ), +]) +parameterized = pytest.mark.parametrize("args", [ + {"skew": "X"}, + {"skew": "Y"}, + {"angle": 30}, + {"angle": 90}, + {"physical_pixel_sizes": (1, 1, 1)}, + {"save_type": SaveFileType.h5}, + {"save_type": SaveFileType.tiff}, + + # # Cropping enabled + # {"crop": {"roi_list": []}}core/lls_core/cropping.py, +]) + +# Allows parameterisation over two serialization formats +config_types = pytest.mark.parametrize(["save_func", "cli_param"], [ + (json.dump, "--json-config"), + (yaml.safe_dump, "--yaml-config") +]) From b593ecdd9d3caaf5324d93cf29bc851048d52aba Mon Sep 17 00:00:00 2001 From: Michael Milton Date: Fri, 22 Sep 2023 19:53:05 +1000 Subject: [PATCH 053/147] Fix test_process_workflow test suite --- core/lls_core/models/lattice_data.py | 4 ++-- core/tests/test_lattice_data.py | 5 +++-- 2 files changed, 5 insertions(+), 4 deletions(-) diff --git a/core/lls_core/models/lattice_data.py b/core/lls_core/models/lattice_data.py index bf3095a..ad1bd45 100644 --- a/core/lls_core/models/lattice_data.py +++ b/core/lls_core/models/lattice_data.py @@ -179,10 +179,10 @@ def parse_workflow(cls, v: Any): # Load the workflow from disk if it was provided as a path from lls_core.types import is_pathlike from lls_core.workflow import workflow_from_path - from os import fspath + from pathlib import Path if is_pathlike(v): - return workflow_from_path(fspath(v)) + return workflow_from_path(Path(v)) return v @validator("time_range", pre=True, always=True) diff --git a/core/tests/test_lattice_data.py b/core/tests/test_lattice_data.py index e05cf6d..104bef2 100644 --- a/core/tests/test_lattice_data.py +++ b/core/tests/test_lattice_data.py @@ -5,6 +5,7 @@ from importlib_resources import as_file import tempfile from pathlib import Path +from napari_workflows import Workflow from .params import inputs, parameterized @@ -48,11 +49,11 @@ def test_process_deconvolution(args: dict, background: Any): assert slice.data.ndim == 3 @parameterized -def test_process_workflow(args: dict): +def test_process_workflow(args: dict, workflow: Workflow): root = Path(__file__).parent / "data" for slice in LatticeData.parse_obj({ "image": root / "raw.tif", - "workflow": root / "raw.tif", + "workflow": workflow, **args }).process().slices: assert slice.data.ndim == 3 From 6b856c45262ebd132a70737442fcb92acee96238 Mon Sep 17 00:00:00 2001 From: Michael Milton Date: Mon, 25 Sep 2023 12:36:59 +1000 Subject: [PATCH 054/147] Removed plugin preview --- .github/workflows/plugin_preview.yml | 23 ----------------------- 1 file changed, 23 deletions(-) delete mode 100644 .github/workflows/plugin_preview.yml diff --git a/.github/workflows/plugin_preview.yml b/.github/workflows/plugin_preview.yml deleted file mode 100644 index bfd5c9e..0000000 --- a/.github/workflows/plugin_preview.yml +++ /dev/null @@ -1,23 +0,0 @@ -name: napari hub Preview Page # we use this name to find your preview page artifact, so don't change it! -# For more info on this action, see https://github.com/chanzuckerberg/napari-hub-preview-action/blob/main/action.yml - -on: - pull_request: - branches: - - "**" - paths: - - "**/README.md" - -jobs: - preview-page: - name: Preview Page Deploy - runs-on: ubuntu-latest - - steps: - - name: Checkout repo - uses: actions/checkout@v2 - - - name: napari hub Preview Page Builder - uses: chanzuckerberg/napari-hub-preview-action@v0.1.5 - with: - hub-ref: main From 4202b580850e45dff85b8cd21df02d07649f4c27 Mon Sep 17 00:00:00 2001 From: Michael Milton Date: Mon, 25 Sep 2023 17:15:26 +1000 Subject: [PATCH 055/147] Clean up GUI code, and fix tests --- plugin/napari_lattice/dock_widget.py | 73 +++----------- plugin/napari_lattice/fields.py | 43 ++++---- plugin/napari_lattice/napari.yaml | 2 +- plugin/napari_lattice/ui_core.py | 143 --------------------------- plugin/pyproject.toml | 7 +- plugin/tests/test_dock_widget.py | 17 +--- 6 files changed, 40 insertions(+), 245 deletions(-) delete mode 100644 plugin/napari_lattice/ui_core.py diff --git a/plugin/napari_lattice/dock_widget.py b/plugin/napari_lattice/dock_widget.py index 4debcc0..3d69774 100644 --- a/plugin/napari_lattice/dock_widget.py +++ b/plugin/napari_lattice/dock_widget.py @@ -1,15 +1,10 @@ # Enable Logging import logging -from pathlib import Path -from typing import Union import numpy as np from lls_core.models.lattice_data import LatticeData -from lls_core.workflow import _import_workflow_modules -from magicclass import MagicTemplate, field, magicclass, set_options +from magicclass import MagicTemplate, field, magicclass, set_options, vfield from magicclass.wrappers import set_design -from napari import Viewer -from napari.layers import Shapes from napari_lattice.fields import ( CroppingFields, DeconvolutionFields, @@ -18,8 +13,6 @@ WorkflowFields, ) from napari_lattice.icons import GREY -from napari_workflows import Workflow, WorkflowManager -from napari_workflows._io_yaml_v1 import load_workflow from qtpy.QtCore import Qt from qtpy.QtGui import QIcon from qtpy.QtWidgets import QTabWidget @@ -27,21 +20,11 @@ logger = logging.getLogger(__name__) logger.setLevel(logging.INFO) -class LlszTemplate(MagicTemplate): - @property - def llsz_parent(self) -> "LLSZWidget": - return self.find_ancestor(LLSZWidget) - -def parent_viewer(mc: MagicTemplate) -> Viewer: - viewer = mc.parent_viewer - if viewer is None: - 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 - shapes_layer: Shapes +class LLSZWidget(MagicTemplate): + def __post_init__(self): + # aligning collapsible widgets at the top instead of having them centered vertically + self._widget._layout.setAlignment(Qt.AlignTop) def _check_validity(self) -> bool: """ @@ -73,14 +56,14 @@ def _make_model(self) -> LatticeData: ) @magicclass(widget_type="split") - class LlszMenu(LlszTemplate): + class LlszMenu(MagicTemplate): main_heading = field("

      Napari Lattice: Visualization & Analysis

      ", widget_type="Label") heading1 = field("Drag and drop an image file onto napari.", widget_type="Label") # Tabbed Widget container to house all the widgets @magicclass(widget_type="tabbed", name="Functions", labels=False) - class WidgetContainer(LlszTemplate): + class WidgetContainer(MagicTemplate): def __post_init__(self): tab_widget: QTabWidget= self._widget._tab_widget @@ -91,11 +74,11 @@ def __post_init__(self): for field in [self.deskew_fields, self.deconv_fields, self.cropping_fields, self.workflow_fields, self.output_fields]: field._validate() - deskew_fields = DeskewFields() - deconv_fields = DeconvolutionFields() - cropping_fields = CroppingFields() - workflow_fields = WorkflowFields() - output_fields = OutputFields() + deskew_fields = vfield(DeskewFields) + deconv_fields = vfield(DeconvolutionFields) + cropping_fields = vfield(CroppingFields) + workflow_fields = vfield(WorkflowFields) + output_fields = vfield(OutputFields) @set_options(header=dict(widget_type="Label", label="

      Preview Deskew

      "), time=dict(label="Time:", max=2**15), @@ -125,35 +108,3 @@ def preview(self, header:str, time: int, channel: int): def save(self): lattice = self._make_model() lattice.process().save_image() - -def _napari_lattice_widget_wrapper() -> LLSZWidget: - # split widget type enables a resizable widget - #max_height = 50 - # Important to have this or napari won't recognize the classes and magicclass qidgets - widget = LLSZWidget() - # aligning collapsible widgets at the top instead of having them centered vertically - widget._widget._layout.setAlignment(Qt.AlignTop) - - # widget._widget._layout.setWidgetResizable(True) - return widget - -def get_workflow(source: Union[Path, Viewer]) -> Workflow: - """ - Gets a user defined workflow object, either from a viewer or from a file - - Args: - source: Either the path to a workflow file, or a Napari viewer from which to extract the workflow - """ - if isinstance(source, Viewer): - # installs the workflow to napari - user_workflow = WorkflowManager.install(source).workflow - logger.info("Workflow installed") - else: - _import_workflow_modules(source) - user_workflow = load_workflow(str(source)) - - if not isinstance(user_workflow, Workflow): - raise Exception("Workflow file is not a napari workflow object. Check file! You can use workflow inspector if needed") - - logger.info(f"Workflow loaded: {user_workflow}") - return user_workflow diff --git a/plugin/napari_lattice/fields.py b/plugin/napari_lattice/fields.py index 1185468..363fc33 100644 --- a/plugin/napari_lattice/fields.py +++ b/plugin/napari_lattice/fields.py @@ -86,8 +86,11 @@ def enable_field(field: MagicField, enabled: bool = True) -> None: for real_field in field._guis.values(): if not isinstance(real_field, Widget): raise Exception("Define your fields with field() not vfield()!") - real_field.visible = enabled - real_field.enabled = enabled + try: + real_field.visible = enabled + real_field.enabled = enabled + except RuntimeError: + pass EnabledHandlerType = TypeVar("EnabledHandlerType") @@ -136,17 +139,8 @@ class StackAlong(StrEnum): TIME = "Time" 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 - # class definition, find none, and then make an empty GUI page when this is rendered. - # It also can't inherit from a FieldGroup-like Protocol as mypy suggests for mixin classes - # (https://mypy.readthedocs.io/en/latest/more_types.html#mixin-classes) because it doesn't - # implement the attributes of a FieldGroup. Ideally this could be a Protocol subclass as well - # to make it remain abstract, but the Protocol metaclass interferes with the FieldGroup metaclass - def __init__(self, *args: Any, **kwargs: Any): - super().__init__(*args, **kwargs) + def __post_init__(self): self = cast(FieldGroup, self) - # self._widget._mgui_bind_parent_change_callback(self._validate) self.changed.connect(self._validate, unique=False) # Style the error label. @@ -156,12 +150,9 @@ def __init__(self, *args: Any, **kwargs: Any): if isinstance(errors, QLabel): errors.setStyleSheet("color: red;") errors.setWordWrap(True) - # super(Container, self).connect(self._validate) - # self.connect(self._validate) - # def _on_value_change(self, *args, **kwargs) -> None: - # super()._on_value_change(*args, **kwargs) - # self._validate() + from qtpy.QtCore import Qt + self._widget._layout.setAlignment(Qt.AlignTop) def _get_parent_tab_widget(self: Any) -> QTabWidget: return self.parent.parentWidget() @@ -290,15 +281,17 @@ def _img_changed(self) -> None: @pixel_sizes_source.connect @enable_if([pixel_sizes]) + @staticmethod def _hide_pixel_sizes(pixel_sizes_source: str): # Hide the "Pixel Sizes" option unless the user specifies manual pixel size source return pixel_sizes_source == PixelSizeSource.Manual @img_layer.connect @enable_if([stack_along]) - def _hide_stack_along(img_layer): + @staticmethod + def _hide_stack_along(img_layer: List[Image]): # Hide the "Stack Along" option if we only have one image - return len(img_layer.value) > 1 + return len(img_layer) > 1 def _get_kwargs(self) -> DeskewKwargs: """ @@ -353,6 +346,7 @@ class DeconvolutionFields(NapariFieldGroup): @enable_if( [background_custom] ) + @staticmethod def _enable_custom_background(background: str) -> bool: return background == BackgroundSource.Custom @@ -365,7 +359,8 @@ def _enable_custom_background(background: str) -> bool: background ] ) - def _enable_fields(enabled) -> bool: + @staticmethod + def _enable_fields(enabled: bool) -> bool: return enabled def _make_model(self) -> Optional[DeconvolutionParams]: @@ -451,17 +446,17 @@ class WorkflowFields(NapariFieldGroup): @fields_enabled.connect @enable_if([workflow_source]) + @staticmethod def _enable_workflow(enabled: bool) -> bool: return enabled @fields_enabled.connect @enable_if([workflow_path]) - def _workflow_path(self) -> bool: - return self.workflow_source.value == WorkflowSource.CustomPath + @staticmethod + def _workflow_path(workflow_source: WorkflowSource) -> bool: + return workflow_source == WorkflowSource.CustomPath def _make_model(self) -> Optional[Workflow]: - from lls_core.workflow import _import_workflow_modules - from napari_workflows._io_yaml_v1 import load_workflow if not self.fields_enabled.value: return None if self.workflow_source.value == WorkflowSource.ActiveWorkflow: diff --git a/plugin/napari_lattice/napari.yaml b/plugin/napari_lattice/napari.yaml index b83b0f0..2ec0f9a 100644 --- a/plugin/napari_lattice/napari.yaml +++ b/plugin/napari_lattice/napari.yaml @@ -4,7 +4,7 @@ contributions: commands: - id: napari-lattice.dock_widget title: Create napari_lattice widget - python_name: napari_lattice.dock_widget:_napari_lattice_widget_wrapper + python_name: napari_lattice.dock_widget:LLSZWidget # ~~ Reader ~~ - id: napari-lattice.get_reader diff --git a/plugin/napari_lattice/ui_core.py b/plugin/napari_lattice/ui_core.py deleted file mode 100644 index 1e9af07..0000000 --- a/plugin/napari_lattice/ui_core.py +++ /dev/null @@ -1,143 +0,0 @@ -from __future__ import annotations - -import numpy as np -from pathlib import Path -from typing import TYPE_CHECKING - -import pyclesperanto_prototype as cle -from lls_core.utils import check_dimensions -from lls_core.deconvolution import pycuda_decon, skimage_decon -from lls_core import config, DeskewDirection, DeconvolutionChoice -from lls_core .io import save_img - -if TYPE_CHECKING: - from napari.types import ImageData - -# Enable Logging -import logging -logger = logging.getLogger(__name__) -# inherit log level from config -logger.setLevel(config.log_level) - -def _Preview(LLSZWidget, - self_class, - time: int, - channel: int, - img_data: ImageData): - - logger.info("Previewing deskewed channel and time") - assert img_data.size, "No image open or selected" - assert time < LLSZWidget.LlszMenu.lattice.time, "Time is out of range" - assert channel < LLSZWidget.LlszMenu.lattice.channels, "Channel is out of range" - assert LLSZWidget.LlszMenu.lattice.skew in DeskewDirection, f"Skew direction not recognised. Got {LLSZWidget.LlszMenu.lattice.skew}" - - vol = LLSZWidget.LlszMenu.lattice.data - vol_zyx = np.array(vol[time, channel, :, :, :]) - - # apply deconvolution if checked - if LLSZWidget.LlszMenu.deconvolution.value: - print( - f"Deskewing for Time:{time} and Channel: {channel} with deconvolution") - psf = LLSZWidget.LlszMenu.lattice.psf[channel] - if LLSZWidget.LlszMenu.lattice.decon_processing == DeconvolutionChoice.cuda_gpu: - decon_data = pycuda_decon(image=vol_zyx, - psf=psf, - dzdata=LLSZWidget.LlszMenu.lattice.dz, - dxdata=LLSZWidget.LlszMenu.lattice.dx, - dzpsf=LLSZWidget.LlszMenu.lattice.dz, - dxpsf=LLSZWidget.LlszMenu.lattice.dx) - # pycuda_decon(image,otf_path,dzdata,dxdata,dzpsf,dxpsf) - else: - decon_data = skimage_decon( - vol_zyx=vol_zyx, psf=psf, num_iter=10, clip=False, filter_epsilon=0, boundary='nearest') - - deskew_final = LLSZWidget.LlszMenu.deskew_func(decon_data, - angle_in_degrees=LLSZWidget.LlszMenu.angle_value, - voxel_size_x=LLSZWidget.LlszMenu.lattice.dx, - voxel_size_y=LLSZWidget.LlszMenu.lattice.dy, - voxel_size_z=LLSZWidget.LlszMenu.lattice.dz, - linear_interpolation=True).astype(vol.dtype) - else: - logger.info(f"Deskewing for Time:{time} and Channel: {channel}") - deskew_final = LLSZWidget.LlszMenu.deskew_func(vol_zyx, - angle_in_degrees=LLSZWidget.LlszMenu.angle_value, - voxel_size_x=LLSZWidget.LlszMenu.lattice.dx, - voxel_size_y=LLSZWidget.LlszMenu.lattice.dy, - voxel_size_z=LLSZWidget.LlszMenu.lattice.dz, - linear_interpolation=True).astype(vol.dtype) - - # if getting an error LogicError: clSetKernelArg failed: #INVALID_ARG_SIZE - when processing arg#13 (1-based) - # make sure array is pulled from GPU - - deskew_final = cle.pull(deskew_final) - # TODO: Use dask - # if LLSZWidget.LlszMenu.dask: - #logger.info(f"Using CPU for deskewing") - # use cle library for affine transforms, but use dask and scipy - # deskew_final = deskew_final.compute() - - max_proj_deskew = cle.maximum_z_projection(deskew_final) - - # add channel and time information to the name - suffix_name = "_c" + str(channel) + "_t" + str(time) - scale = (LLSZWidget.LlszMenu.lattice.new_dz, - LLSZWidget.LlszMenu.lattice.dy, LLSZWidget.LlszMenu.lattice.dx) - # TODO:adding img of difff scales change dim slider - self_class.parent_viewer.add_image( - deskew_final, name="Deskewed image" + suffix_name, scale=scale) - self_class.parent_viewer.add_image( - max_proj_deskew, name="Deskew_MIP", scale=scale[1:3]) - self_class.parent_viewer.layers[0].visible = False - - logger.info(f"Preview: Deskewing complete") - return - - -def _Deskew_Save(LLSZWidget, - time_start: int, - time_end: int, - ch_start: int, - ch_end: int, - save_as_type, - save_path: Path): - - assert LLSZWidget.LlszMenu.open_file, "Image not initialised" - check_dimensions(time_start, time_end, ch_start, ch_end, - LLSZWidget.LlszMenu.lattice.channels, LLSZWidget.LlszMenu.lattice.time) - #time_range = range(time_start, time_end) - #channel_range = range(ch_start, ch_end) - angle = LLSZWidget.LlszMenu.lattice.angle - dx = LLSZWidget.LlszMenu.lattice.dx - dy = LLSZWidget.LlszMenu.lattice.dy - dz = LLSZWidget.LlszMenu.lattice.dz - - # Convert path to string - #save_path = save_path.__str__() - - # get the image data as dask array - img_data = LLSZWidget.LlszMenu.lattice.data - - # pass arguments for save tiff, callable and function arguments - save_img(vol=img_data, - func=LLSZWidget.LlszMenu.deskew_func, - 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=LLSZWidget.LlszMenu.lattice.save_name, - dx=dx, - dy=dy, - dz=dz, - angle=angle, - angle_in_degrees=angle, - voxel_size_x=dx, - voxel_size_y=dy, - voxel_size_z=dz, - linear_interpolation=True, - LLSZWidget=LLSZWidget) - - print("Deskewing and Saving Complete -> ", save_path) - return - diff --git a/plugin/pyproject.toml b/plugin/pyproject.toml index ddaf21ab..fa6d28c 100644 --- a/plugin/pyproject.toml +++ b/plugin/pyproject.toml @@ -36,16 +36,17 @@ classifiers = [ requires-python = ">=3.8" dependencies = [ "aicsimageio>=4.6.3", - "dask", "dask[distributed]", # This isn't used directly, but we need to pin this version "fsspec>=2022.8.2", "importlib_resources", - # We need this Python 3.8 fix: https://github.com/hanjinliu/magic-class/pull/108 "lls_core", + # We need this Python 3.8 fix: https://github.com/hanjinliu/magic-class/pull/108 "magic-class>=0.7.5", "magicgui", - "napari-aicsimageio>=0.7.2", + # Currently commented out to avoid installation issues, although + # This can be reinstated once https://github.com/pypa/pip/pull/12095 is merged + # "napari-aicsimageio>=0.7.2", "napari-spreadsheet", "napari-workflow-inspector", "napari-workflows>=0.2.8", diff --git a/plugin/tests/test_dock_widget.py b/plugin/tests/test_dock_widget.py index 67d4ccf..20d6947 100644 --- a/plugin/tests/test_dock_widget.py +++ b/plugin/tests/test_dock_widget.py @@ -1,5 +1,5 @@ from __future__ import annotations -from napari_lattice.dock_widget import _napari_lattice_widget_wrapper +from napari_lattice.dock_widget import LLSZWidget import numpy as np from typing import Callable, TYPE_CHECKING from magicclass.testing import check_function_gui_buildable, FunctionGuiTester @@ -37,18 +37,9 @@ def test_dock_widget(make_napari_viewer: Callable[[], Viewer]): viewer.add_image(np.random.random((100, 100))) # Test if napari-lattice widget can be created in napari - gui = _napari_lattice_widget_wrapper() - viewer.window.add_dock_widget(gui) + viewer.window.add_dock_widget(LLSZWidget()) def test_check_buildable(): - widget = _napari_lattice_widget_wrapper() - check_function_gui_buildable(widget) - -def test_plugin_initialize(make_napari_viewer: Callable[[], Viewer]): - ui = _napari_lattice_widget_wrapper() - viewer = make_napari_viewer() - viewer.window.add_dock_widget(ui) - image = Image(np.random.random((100, 100, 100, 100))) + ui = LLSZWidget() set_debug(ui) - tester = FunctionGuiTester(ui.LlszMenu.Choose_Image_Layer) - tester.call(img_layer=image, last_dimension_channel="time") + check_function_gui_buildable(ui) From 2e750d19648c90a446b701f47a708e1944de74bf Mon Sep 17 00:00:00 2001 From: Michael Milton Date: Mon, 25 Sep 2023 18:46:33 +1000 Subject: [PATCH 056/147] Fix ROI handling, add ROI class, fix plugin linting --- core/lls_core/cropping.py | 32 +++++++++++++---------- core/lls_core/models/crop.py | 26 +++++++++++++------ core/lls_core/models/lattice_data.py | 39 ++++++++++++++++++---------- core/pyproject.toml | 1 + core/tests/conftest.py | 10 +++---- core/tests/test_lattice_data.py | 17 ++++++++++-- plugin/pyproject.toml | 5 ++-- 7 files changed, 85 insertions(+), 45 deletions(-) diff --git a/core/lls_core/cropping.py b/core/lls_core/cropping.py index e4b973a..f2b30d8 100644 --- a/core/lls_core/cropping.py +++ b/core/lls_core/cropping.py @@ -1,16 +1,22 @@ from __future__ import annotations -from typing import TYPE_CHECKING +from typing import TYPE_CHECKING, NamedTuple, Tuple, List if TYPE_CHECKING: from lls_core.types import PathLike from numpy.typing import NDArray +class Roi(NamedTuple): + top_left: Tuple[int, int] + top_right: Tuple[int, int] + bottom_left: Tuple[int, int] + bottom_right: Tuple[int, int] + def read_roi_array(roi: PathLike) -> NDArray: from read_roi import read_roi_file from numpy import array - return array(read_roi_file(roi)) + return array(read_roi_file(str(roi))) -def read_imagej_roi(roi_path: PathLike): +def read_imagej_roi(roi_path: PathLike) -> List[Roi]: """Read an ImageJ ROI zip file so it loaded into napari shapes layer If non rectangular ROI, will convert into a rectangle based on extreme points Args: @@ -42,18 +48,18 @@ def read_imagej_roi(roi_path: PathLike): # Read through each roi and create a list so that it matches the organisation of the shapes from napari shapes layer for value in ij_roi.values(): if value['type'] in ('oval', 'rectangle'): - width = value['width'] - height = value['height'] - left = value['left'] - top = value['top'] - roi = [[top, left], [top, left+width], [top+height, left+width], [top+height, left]] + width = int(value['width']) + height = int(value['height']) + left = int(value['left']) + top = int(value['top']) + roi = Roi((top, left), (top, left+width), (top+height, left+width), (top+height, left)) roi_list.append(roi) elif value['type'] in ('polygon', 'freehand'): - left = min(value['x']) - top = min(value['y']) - right = max(value['x']) - bottom = max(value['y']) - roi = [[top, left], [top, right], [bottom, right], [bottom, left]] + left = min(int(it) for it in value['x']) + top = min(int(it) for it in value['y']) + right = max(int(it) for it in value['x']) + bottom = max(int(it) for it in value['y']) + roi = Roi((top, left), (top, right), (bottom, right), (bottom, left)) roi_list.append(roi) else: print(f"Cannot read ROI {value}. Recognised as type {value['type']}") diff --git a/core/lls_core/models/crop.py b/core/lls_core/models/crop.py index f6db276..b2741f5 100644 --- a/core/lls_core/models/crop.py +++ b/core/lls_core/models/crop.py @@ -1,14 +1,14 @@ from typing import List, Tuple, Any from pydantic import Field, NonNegativeInt, validator -from xarray import DataArray from lls_core.models.utils import FieldAccessMixin +from lls_core.cropping import Roi class CropParams(FieldAccessMixin): """ Parameters for the optional cropping step """ - roi_list: List[DataArray] = Field( + roi_list: List[Roi] = Field( description="List of regions of interest, each of which must be an NxD array, where N is the number of vertices and D the coordinates of each vertex.", default = [] ) @@ -17,11 +17,21 @@ class CropParams(FieldAccessMixin): description="The range of Z slices to take. All Z slices before the first index or after the last index will be cropped out." ) - @validator("roi_list", each_item=True) - def read_roi(cls, v: Any): + @validator("roi_list", pre=True) + def read_roi(cls, v: Any) -> List[Roi]: from lls_core.types import is_pathlike - from lls_core.cropping import read_roi_array - # Converts the ROI from a path to an array + from lls_core.cropping import read_imagej_roi + # Allow a single path if is_pathlike(v): - return read_roi_array(v) - return v + v = [v] + + rois: List[Roi] = [] + for item in v: + if is_pathlike(item): + rois += read_imagej_roi(item) + elif isinstance(item, Roi): + rois.append(item) + else: + raise ValueError(f"{item} cannot be intepreted as an ROI") + + return rois diff --git a/core/lls_core/models/lattice_data.py b/core/lls_core/models/lattice_data.py index ad1bd45..b34a214 100644 --- a/core/lls_core/models/lattice_data.py +++ b/core/lls_core/models/lattice_data.py @@ -185,6 +185,20 @@ def parse_workflow(cls, v: Any): return workflow_from_path(Path(v)) return v + @validator("crop") + def default_z_range(cls, v: CropParams, values: dict): + # If any part of the z range is missing, assume the user wants all Z indices + with ignore_keyerror(): + default_start = 0 + default_end = values["image"].sizes["Z"] + if v.z_range is None: + v.z_range = (default_start, default_end) + if v.z_range[0] is None: + v.z_range[0] = default_start + if v.z_range[1] is None: + v.z_range[1] = default_end + return v + @validator("time_range", pre=True, always=True) def parse_time_range(cls, v: Any, values: dict) -> Any: """ @@ -371,7 +385,8 @@ def check_incomplete_acquisition(self, volume: ArrayLike, time_point: int, chann @property def deskewed_volume(self) -> DaskArray: - return da.zeros(self.deskew_vol_shape) + from dask.array import zeros + return zeros(self.deskew_vol_shape) def _process_crop(self) -> Iterable[ProcessedVolume]: """ @@ -383,7 +398,7 @@ def _process_crop(self) -> Iterable[ProcessedVolume]: # We have an extra level of iteration for the crop path: iterating over each ROI for roi_index, roi in enumerate(tqdm(self.crop.roi_list, desc="ROI:", position=0)): # pass arguments for save tiff, callable and function arguments - logger.info("Processing ROI ", roi_index) + logger.info(f"Processing ROI {roi_index}") deconv_args: dict[Any, Any] = {} if self.deconvolution is not None: @@ -393,14 +408,14 @@ def _process_crop(self) -> Iterable[ProcessedVolume]: decon_processing=self.deconvolution.decon_processing ) - for time_idx, time, ch_idx, ch, data in self.iter_slices(): - yield ProcessedVolume( - data = crop_volume_deskew( - original_volume=data, + for slice in self.iter_slices(): + yield slice.copy_with_data( + crop_volume_deskew( + original_volume=slice.data, deconvolution=self.deconv_enabled, get_deskew_and_decon=False, debug=False, - roi_shape=roi, + roi_shape=list(roi), linear_interpolation=True, voxel_size_x=self.dx, voxel_size_y=self.dy, @@ -410,13 +425,9 @@ def _process_crop(self) -> Iterable[ProcessedVolume]: z_start=self.crop.z_range[0], z_end=self.crop.z_range[1], **deconv_args - ), - channel=ch, - channel_index=ch_idx, - time=time, - time_index=time_idx, - roi_index=roi_index - ) + ) + ) + def _process_non_crop(self) -> Iterable[ProcessedVolume]: """ Yields processed image slices without cropping diff --git a/core/pyproject.toml b/core/pyproject.toml index d3d5282..5e98645 100644 --- a/core/pyproject.toml +++ b/core/pyproject.toml @@ -111,6 +111,7 @@ reportUntypedFunctionDecorator = false reportMissingTypeArgument = false reportPrivateUsage = false reportPrivateImportUsage = false +reportUnnecessaryComparison = false [tool.fawltydeps] ignore_unused = [ diff --git a/core/tests/conftest.py b/core/tests/conftest.py index b69022f..9bc3a8a 100644 --- a/core/tests/conftest.py +++ b/core/tests/conftest.py @@ -31,11 +31,11 @@ def test_image() -> NDArray[np.float64]: @pytest.fixture def workflow_config(workflow: Workflow, test_image: NDArray): - # Create a config file - yield { - "image": test_image, - "workflow": workflow, - } + # Create a config file + yield { + "image": test_image, + "workflow": workflow, + } @pytest.fixture def workflow_config_cli(workflow: Workflow, test_image: NDArray): diff --git a/core/tests/test_lattice_data.py b/core/tests/test_lattice_data.py index 104bef2..8af6891 100644 --- a/core/tests/test_lattice_data.py +++ b/core/tests/test_lattice_data.py @@ -9,6 +9,8 @@ from .params import inputs, parameterized +root = Path(__file__).parent / "data" + def open_psf(name: str): with as_file(resources / "psfs" / "zeiss_simulated" / name) as path: return path @@ -37,7 +39,6 @@ def test_save(path: str, args: dict): @pytest.mark.parametrize(["background"], [(1, ), ("auto",), ("second_last",)]) @parameterized def test_process_deconvolution(args: dict, background: Any): - root = Path(__file__).parent / "data" for slice in LatticeData.parse_obj({ "image": root / "raw.tif", "deconvolution": { @@ -50,10 +51,22 @@ def test_process_deconvolution(args: dict, background: Any): @parameterized def test_process_workflow(args: dict, workflow: Workflow): - root = Path(__file__).parent / "data" for slice in LatticeData.parse_obj({ "image": root / "raw.tif", "workflow": workflow, **args }).process().slices: assert slice.data.ndim == 3 + +@parameterized +def test_process_crop(args: dict, workflow: Workflow): + with as_file(resources / "RBC_tiny.czi") as lattice_path: + rois = root / "crop" / "two_rois.zip" + for slice in LatticeData.parse_obj({ + "image": lattice_path, + "crop": { + "roi_list": [rois] + }, + **args + }).process().slices: + assert slice.data.ndim == 3 diff --git a/plugin/pyproject.toml b/plugin/pyproject.toml index fa6d28c..d91ad33 100644 --- a/plugin/pyproject.toml +++ b/plugin/pyproject.toml @@ -53,14 +53,11 @@ dependencies = [ "napari>=0.4.11", "npy2bdv", "numpy", - "pandas", "psutil", "pyclesperanto_prototype>=0.20.0", "pydantic", "qtpy", - "tqdm", "typing_extensions>=4.7.0", - "pyyaml", "StrEnum", "xarray" ] @@ -97,6 +94,7 @@ ignore_unused = [ # These napari plugins are needed to use the plugin, but aren't imported directly "napari-aicsimageio", "napari-workflow-inspector", + "napari-spreadsheet", # This is pinned but unused "fsspec", @@ -118,3 +116,4 @@ reportUntypedFunctionDecorator = false reportMissingTypeArgument = false reportPrivateUsage = false reportPrivateImportUsage = false +reportUnnecessaryComparison = false From cba0eb372fe49adf0284515ae518be7de70f660b Mon Sep 17 00:00:00 2001 From: Michael Milton Date: Mon, 25 Sep 2023 19:21:36 +1000 Subject: [PATCH 057/147] Fix for when crop is None --- core/lls_core/models/lattice_data.py | 2 ++ 1 file changed, 2 insertions(+) diff --git a/core/lls_core/models/lattice_data.py b/core/lls_core/models/lattice_data.py index b34a214..20a9f4e 100644 --- a/core/lls_core/models/lattice_data.py +++ b/core/lls_core/models/lattice_data.py @@ -188,6 +188,8 @@ def parse_workflow(cls, v: Any): @validator("crop") def default_z_range(cls, v: CropParams, values: dict): # If any part of the z range is missing, assume the user wants all Z indices + if v is None: + return v with ignore_keyerror(): default_start = 0 default_end = values["image"].sizes["Z"] From f61f887b9547bd9c23b6bdea31bb0aef18c510de Mon Sep 17 00:00:00 2001 From: Michael Milton Date: Mon, 25 Sep 2023 19:45:51 +1000 Subject: [PATCH 058/147] Add missing ROI test data --- core/tests/data/crop/roi_1.tif | Bin 0 -> 429522 bytes core/tests/data/crop/roi_2.tif | Bin 0 -> 355919 bytes core/tests/data/crop/two_rois.zip | Bin 0 -> 320 bytes 3 files changed, 0 insertions(+), 0 deletions(-) create mode 100644 core/tests/data/crop/roi_1.tif create mode 100644 core/tests/data/crop/roi_2.tif create mode 100644 core/tests/data/crop/two_rois.zip diff --git a/core/tests/data/crop/roi_1.tif b/core/tests/data/crop/roi_1.tif new file mode 100644 index 0000000000000000000000000000000000000000..b702ebe03d7817cccfd1a5562808324d185da4e6 GIT binary patch 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zUT>w9otvJF?{4l+?|RgnWcG`oV^V!0^L}1|U_(XhX3K0p#7d*F%z2ogaP?vVSHjysrM@B>iL!B#Bu)U!{X^Q~Rx8}uGc0jc-u)AkHEU-HZpO&B;A|8^-#-}5<0CtZr$SV)wC zdoq77QONiP(=@4{TK+$qdjG&#{ZsS*oz#E$=zGCe`JOuA?!R*)^EX^tUUPl1o#y#d z@(gowpWmj=7@I*J;W0LO-~gEvBV_#S4;KT2G?dN4&A_0+$PB-s7Br3qyQLOJgTz3f cg7NJ^Mv!3Bf`9AtIu3xuz`%%4)rf%+0D=}m)c^nh literal 0 HcmV?d00001 diff --git a/core/tests/data/crop/two_rois.zip b/core/tests/data/crop/two_rois.zip new file mode 100644 index 0000000000000000000000000000000000000000..2a60e8e12b25e50fddf84b17c75daf924f7e8703 GIT binary patch literal 320 zcmWIWW@Zs#;Nak3P+4Xc&VU3sf$XCE%y>gRAo=->zHVa0p%Vu-^f@+7>{V=PGF! zM=%R)7Rq%JbhdL=bGB&~1e^ZjT=q1C>5NPw3<%dF+YE9&DuDYXz#CN?vXQ9T;(#WA Xw1FKN;LXYgGLZ=g^MLdo5QhN(mX1bw literal 0 HcmV?d00001 From ac06150c937b92140c11ae975571a6d276ae5b25 Mon Sep 17 00:00:00 2001 From: Michael Milton Date: Mon, 25 Sep 2023 20:43:39 +1000 Subject: [PATCH 059/147] Stop using static methods --- plugin/napari_lattice/fields.py | 29 ++++++++++++----------------- 1 file changed, 12 insertions(+), 17 deletions(-) diff --git a/plugin/napari_lattice/fields.py b/plugin/napari_lattice/fields.py index 363fc33..482afd7 100644 --- a/plugin/napari_lattice/fields.py +++ b/plugin/napari_lattice/fields.py @@ -93,7 +93,8 @@ def enable_field(field: MagicField, enabled: bool = True) -> None: pass -EnabledHandlerType = TypeVar("EnabledHandlerType") +FieldValueType = TypeVar("FieldValueType") +SelfType = TypeVar("SelfType") def enable_if(fields: List[MagicField]): """ Makes an event handler that should be used via `fields_enabled.connect(make_enabled_handler())`. @@ -114,14 +115,14 @@ def _enable_fields(self) -> bool: # Start by disabling all the fields - def _decorator(fn: Callable[[EnabledHandlerType], bool])-> Callable[[EnabledHandlerType], None]: + def _decorator(fn: Callable[[SelfType, FieldValueType], bool])-> Callable[[SelfType, FieldValueType], None]: for field in fields: field.enabled = False field.visible = False - def make_handler(fn: Callable[[EnabledHandlerType], bool]) -> Callable[[EnabledHandlerType], None]: - def handler(value: Any): - enable = fn(value) + def make_handler(fn: Callable[[SelfType, FieldValueType], bool]) -> Callable[[SelfType, FieldValueType], None]: + def handler(self: Any, value: Any): + enable = fn(self, value) for field in fields: if enable: logger.info(f"{field.name} Activated") @@ -281,15 +282,13 @@ def _img_changed(self) -> None: @pixel_sizes_source.connect @enable_if([pixel_sizes]) - @staticmethod - def _hide_pixel_sizes(pixel_sizes_source: str): + def _hide_pixel_sizes(self, pixel_sizes_source: str): # Hide the "Pixel Sizes" option unless the user specifies manual pixel size source return pixel_sizes_source == PixelSizeSource.Manual @img_layer.connect @enable_if([stack_along]) - @staticmethod - def _hide_stack_along(img_layer: List[Image]): + def _hide_stack_along(self, img_layer: List[Image]): # Hide the "Stack Along" option if we only have one image return len(img_layer) > 1 @@ -346,8 +345,7 @@ class DeconvolutionFields(NapariFieldGroup): @enable_if( [background_custom] ) - @staticmethod - def _enable_custom_background(background: str) -> bool: + def _enable_custom_background(self, background: str) -> bool: return background == BackgroundSource.Custom @fields_enabled.connect @@ -359,8 +357,7 @@ def _enable_custom_background(background: str) -> bool: background ] ) - @staticmethod - def _enable_fields(enabled: bool) -> bool: + def _enable_fields(self, enabled: bool) -> bool: return enabled def _make_model(self) -> Optional[DeconvolutionParams]: @@ -446,14 +443,12 @@ class WorkflowFields(NapariFieldGroup): @fields_enabled.connect @enable_if([workflow_source]) - @staticmethod - def _enable_workflow(enabled: bool) -> bool: + def _enable_workflow(self, enabled: bool) -> bool: return enabled @fields_enabled.connect @enable_if([workflow_path]) - @staticmethod - def _workflow_path(workflow_source: WorkflowSource) -> bool: + def _workflow_path(self, workflow_source: WorkflowSource) -> bool: return workflow_source == WorkflowSource.CustomPath def _make_model(self) -> Optional[Workflow]: From fa251056c697f506af9e2d64bbcda9dfda80bb47 Mon Sep 17 00:00:00 2001 From: Michael Milton Date: Mon, 2 Oct 2023 14:13:33 +1100 Subject: [PATCH 060/147] Remove commented crop test --- core/tests/params.py | 3 --- 1 file changed, 3 deletions(-) diff --git a/core/tests/params.py b/core/tests/params.py index e49711f..afc4c6c 100644 --- a/core/tests/params.py +++ b/core/tests/params.py @@ -22,9 +22,6 @@ {"physical_pixel_sizes": (1, 1, 1)}, {"save_type": SaveFileType.h5}, {"save_type": SaveFileType.tiff}, - - # # Cropping enabled - # {"crop": {"roi_list": []}}core/lls_core/cropping.py, ]) # Allows parameterisation over two serialization formats From 872da0e979176444c217d9c9e615f1865eb4c94b Mon Sep 17 00:00:00 2001 From: Michael Milton Date: Mon, 2 Oct 2023 14:14:03 +1100 Subject: [PATCH 061/147] Fix enable_if documentation --- plugin/napari_lattice/fields.py | 6 +++--- 1 file changed, 3 insertions(+), 3 deletions(-) diff --git a/plugin/napari_lattice/fields.py b/plugin/napari_lattice/fields.py index 482afd7..08ba152 100644 --- a/plugin/napari_lattice/fields.py +++ b/plugin/napari_lattice/fields.py @@ -97,7 +97,7 @@ def enable_field(field: MagicField, enabled: bool = True) -> None: SelfType = TypeVar("SelfType") def enable_if(fields: List[MagicField]): """ - Makes an event handler that should be used via `fields_enabled.connect(make_enabled_handler())`. + Makes an event handler that dynamically disables and enables a set of fields based on a criteria Args: condition: A function that takes an instance of the class and returns True if the fields should be enabled fields: A list of fields to be dynamically enabled or disabled @@ -107,8 +107,8 @@ def enable_if(fields: List[MagicField]): @enable_if( [some_field] ) - def _enable_fields(self) -> bool: - return some_field.value + def _enable_fields(self, value) -> bool: + return value """ # Ideally we could use subclassing to add both the vfield and this event handler, but there # seems to be a bug preventing this: https://github.com/hanjinliu/magic-class/issues/113. From 2d31b9ba1859bc5b874cb202c6a1fa28af0a9912 Mon Sep 17 00:00:00 2001 From: Michael Milton Date: Mon, 2 Oct 2023 15:10:46 +1100 Subject: [PATCH 062/147] Fix tab naming --- plugin/napari_lattice/dock_widget.py | 8 ++++---- plugin/napari_lattice/fields.py | 10 +++++----- 2 files changed, 9 insertions(+), 9 deletions(-) diff --git a/plugin/napari_lattice/dock_widget.py b/plugin/napari_lattice/dock_widget.py index 3d69774..7f54a36 100644 --- a/plugin/napari_lattice/dock_widget.py +++ b/plugin/napari_lattice/dock_widget.py @@ -67,10 +67,10 @@ class WidgetContainer(MagicTemplate): def __post_init__(self): tab_widget: QTabWidget= self._widget._tab_widget - from importlib_resources import as_file - for i in range(5): - with as_file(GREY) as path: - tab_widget.setTabIcon(i, QIcon(str(path))) + # Manually set the tab labels, because by default magicgui uses the widget names, but setting + # the names to human readable text makes them difficult to access via self + for i, label in enumerate(["1. Deskew", "2. Deconvolution", "3. Crop", "4. Workflow", "5. Output"]): + tab_widget.setTabText(i, label) for field in [self.deskew_fields, self.deconv_fields, self.cropping_fields, self.workflow_fields, self.output_fields]: field._validate() diff --git a/plugin/napari_lattice/fields.py b/plugin/napari_lattice/fields.py index 08ba152..966b40d 100644 --- a/plugin/napari_lattice/fields.py +++ b/plugin/napari_lattice/fields.py @@ -191,7 +191,7 @@ class DeskewKwargs(NapariImageParams): angle: float skew: DeskewDirection -@magicclass(name="1. Deskew") +@magicclass class DeskewFields(NapariFieldGroup): def _get_dimension_options(self, _) -> List[str]: @@ -323,7 +323,7 @@ def _make_model(self) -> DeskewParams: skew = kwargs["skew"] ) -@magicclass(name="2. Deconvolution") +@magicclass class DeconvolutionFields(NapariFieldGroup): """ A counterpart to the DeconvolutionParams Pydantic class @@ -376,7 +376,7 @@ def _make_model(self) -> Optional[DeconvolutionParams]: psf_num_iter=self.psf_num_iter.value ) -@magicclass(name="3. Crop") +@magicclass class CroppingFields(MagicTemplate, NapariFieldGroup): """ A counterpart to the CropParams Pydantic class @@ -431,7 +431,7 @@ def _make_model(self) -> Optional[CropParams]: ) return None -@magicclass(name="4. Workflow") +@magicclass class WorkflowFields(NapariFieldGroup): """ Handles the workflow related parameters @@ -459,7 +459,7 @@ def _make_model(self) -> Optional[Workflow]: else: return workflow_from_path(self.workflow_path.value) -@magicclass(name="5. Output") +@magicclass class OutputFields(NapariFieldGroup): set_logging = field(Log_Levels.INFO).with_options(label="Logging Level") time_range = field(Tuple[int, int]).with_options( From 98c65d1d3c6d22ef5112d8ef1717f35358332654 Mon Sep 17 00:00:00 2001 From: Michael Milton Date: Mon, 2 Oct 2023 15:26:52 +1100 Subject: [PATCH 063/147] Pretty print LatticeData in GUI --- plugin/napari_lattice/dock_widget.py | 8 +++++++- 1 file changed, 7 insertions(+), 1 deletion(-) diff --git a/plugin/napari_lattice/dock_widget.py b/plugin/napari_lattice/dock_widget.py index 7f54a36..d569a8f 100644 --- a/plugin/napari_lattice/dock_widget.py +++ b/plugin/napari_lattice/dock_widget.py @@ -37,9 +37,12 @@ def _check_validity(self) -> bool: return False def _make_model(self) -> LatticeData: + from rich import print + from sys import stdout + deskew_args = self.LlszMenu.WidgetContainer.deskew_fields._get_kwargs() output_args = self.LlszMenu.WidgetContainer.output_fields._make_model() - return LatticeData( + params = LatticeData( image=deskew_args["data"], angle=deskew_args["angle"], channel_range=output_args.channel_range, @@ -54,6 +57,9 @@ def _make_model(self) -> LatticeData: deconvolution=self.LlszMenu.WidgetContainer.deconv_fields._make_model(), crop=self.LlszMenu.WidgetContainer.cropping_fields._make_model() ) + # Log the lattice + print(params, file=stdout) + return params @magicclass(widget_type="split") class LlszMenu(MagicTemplate): From 7048b6efb966274ad7f4027836bb9a989883613a Mon Sep 17 00:00:00 2001 From: Michael Milton Date: Mon, 2 Oct 2023 17:13:03 +1100 Subject: [PATCH 064/147] Stop re-running validations when napari layers change --- plugin/napari_lattice/fields.py | 8 +++++++- 1 file changed, 7 insertions(+), 1 deletion(-) diff --git a/plugin/napari_lattice/fields.py b/plugin/napari_lattice/fields.py index 966b40d..4bcaab2 100644 --- a/plugin/napari_lattice/fields.py +++ b/plugin/napari_lattice/fields.py @@ -178,8 +178,14 @@ def _set_valid(self: Any, valid: bool): with as_file(icon) as path: tab_parent.setTabIcon(index, QIcon(str(path))) + def reset_choices(self: Any): + # This is used to prevent validation from re-running when a napari layer is added or removed + from magicgui.widgets import Container + with self.changed.blocked(): + super(Container, self).reset_choices() + def _validate(self: Any): - self.errors.value = get_friendly_validations(self) + self.errors.value = get_friendly_validations(self) valid = not bool(self.errors.value) self.errors.visible = not valid self._set_valid(valid) From 49fb8b7e96d91a3edd83a00f1b886a7a562962b8 Mon Sep 17 00:00:00 2001 From: Michael Milton Date: Mon, 2 Oct 2023 17:56:07 +1100 Subject: [PATCH 065/147] Fix for crop enabling, ADD_RECTANGLE for cropping, fix for ROI selection --- core/lls_core/cropping.py | 6 ++++++ core/lls_core/models/crop.py | 3 +++ plugin/napari_lattice/fields.py | 4 ++-- 3 files changed, 11 insertions(+), 2 deletions(-) diff --git a/core/lls_core/cropping.py b/core/lls_core/cropping.py index f2b30d8..1eb61d7 100644 --- a/core/lls_core/cropping.py +++ b/core/lls_core/cropping.py @@ -3,6 +3,7 @@ if TYPE_CHECKING: from lls_core.types import PathLike + from typing_extensions import Self from numpy.typing import NDArray class Roi(NamedTuple): @@ -11,6 +12,11 @@ class Roi(NamedTuple): bottom_left: Tuple[int, int] bottom_right: Tuple[int, int] + @classmethod + def from_array(cls, array: NDArray) -> Self: + import numpy as np + return Roi(*np.reshape(array, (-1, 2)).tolist()) + def read_roi_array(roi: PathLike) -> NDArray: from read_roi import read_roi_file from numpy import array diff --git a/core/lls_core/models/crop.py b/core/lls_core/models/crop.py index b2741f5..a2c0f3b 100644 --- a/core/lls_core/models/crop.py +++ b/core/lls_core/models/crop.py @@ -21,6 +21,7 @@ class CropParams(FieldAccessMixin): def read_roi(cls, v: Any) -> List[Roi]: from lls_core.types import is_pathlike from lls_core.cropping import read_imagej_roi + from numpy import ndarray # Allow a single path if is_pathlike(v): v = [v] @@ -29,6 +30,8 @@ def read_roi(cls, v: Any) -> List[Roi]: for item in v: if is_pathlike(item): rois += read_imagej_roi(item) + elif isinstance(item, ndarray): + rois.append(Roi.from_array(item)) elif isinstance(item, Roi): rois.append(item) else: diff --git a/plugin/napari_lattice/fields.py b/plugin/napari_lattice/fields.py index 4bcaab2..aa5ef88 100644 --- a/plugin/napari_lattice/fields.py +++ b/plugin/napari_lattice/fields.py @@ -420,12 +420,12 @@ def import_roi(self, path: Path): def new_crop_layer(self): from napari_lattice.utils import get_viewer shapes = get_viewer().add_shapes() - shapes.mode = "SELECT" + shapes.mode = "ADD_RECTANGLE" shapes.name = "Napari Lattice Crop" @fields_enabled.connect @enable_if([shapes, z_range]) - def _enable_crop(enabled: bool) -> bool: + def _enable_crop(self, enabled: bool) -> bool: return enabled def _make_model(self) -> Optional[CropParams]: From b8da3446e6924ce0e9443befb2fb99b3ed0537cb Mon Sep 17 00:00:00 2001 From: Michael Milton Date: Mon, 2 Oct 2023 18:01:47 +1100 Subject: [PATCH 066/147] Add more checks before we load the workflow modules --- core/lls_core/workflow.py | 5 +++++ plugin/napari_lattice/fields.py | 2 +- 2 files changed, 6 insertions(+), 1 deletion(-) diff --git a/core/lls_core/workflow.py b/core/lls_core/workflow.py index 0577721..682f45f 100644 --- a/core/lls_core/workflow.py +++ b/core/lls_core/workflow.py @@ -157,6 +157,11 @@ def _import_workflow_modules(workflow: Path) -> None: Args: workflow: Path to the workflow YAML file """ + if not workflow.exists(): + raise Exception("Workflow doesn't exist!") + if not workflow.is_file(): + raise Exception("Workflow must be a file!") + counter = 0 for script in workflow.parent.glob("*.py"): if script.stem == "__init__": diff --git a/plugin/napari_lattice/fields.py b/plugin/napari_lattice/fields.py index aa5ef88..b335216 100644 --- a/plugin/napari_lattice/fields.py +++ b/plugin/napari_lattice/fields.py @@ -452,7 +452,7 @@ class WorkflowFields(NapariFieldGroup): def _enable_workflow(self, enabled: bool) -> bool: return enabled - @fields_enabled.connect + @workflow_source.connect @enable_if([workflow_path]) def _workflow_path(self, workflow_source: WorkflowSource) -> bool: return workflow_source == WorkflowSource.CustomPath From f459cc998e0fe44f1ad29e0c71c521160f07bcee Mon Sep 17 00:00:00 2001 From: Michael Milton Date: Mon, 2 Oct 2023 19:27:44 +1100 Subject: [PATCH 067/147] Add save completed notification --- plugin/napari_lattice/dock_widget.py | 2 ++ 1 file changed, 2 insertions(+) diff --git a/plugin/napari_lattice/dock_widget.py b/plugin/napari_lattice/dock_widget.py index d569a8f..34b167e 100644 --- a/plugin/napari_lattice/dock_widget.py +++ b/plugin/napari_lattice/dock_widget.py @@ -112,5 +112,7 @@ def preview(self, header:str, time: int, channel: int): @set_design(text="Save") def save(self): + from napari.utils.notifications import show_info lattice = self._make_model() lattice.process().save_image() + show_info(f"Deskewing successfuly completed. Results are located in {lattice.save_dir}") From 4dc8bc1b5ba0519ebeb1d2a031cab93939b86b6d Mon Sep 17 00:00:00 2001 From: Michael Milton Date: Mon, 2 Oct 2023 19:27:53 +1100 Subject: [PATCH 068/147] Add rich as formal plugin dependency --- plugin/pyproject.toml | 1 + 1 file changed, 1 insertion(+) diff --git a/plugin/pyproject.toml b/plugin/pyproject.toml index d91ad33..42b172e 100644 --- a/plugin/pyproject.toml +++ b/plugin/pyproject.toml @@ -58,6 +58,7 @@ dependencies = [ "pydantic", "qtpy", "typing_extensions>=4.7.0", + "rich", "StrEnum", "xarray" ] From baf0c24a2d73e89749a6ffddc67b138ecf8948f0 Mon Sep 17 00:00:00 2001 From: Michael Milton Date: Tue, 3 Oct 2023 15:44:57 +1100 Subject: [PATCH 069/147] Show CLI help by default --- core/lls_core/cmds/__main__.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/core/lls_core/cmds/__main__.py b/core/lls_core/cmds/__main__.py index 87f466b..2a38f0a 100644 --- a/core/lls_core/cmds/__main__.py +++ b/core/lls_core/cmds/__main__.py @@ -28,7 +28,7 @@ class CliDeskewDirection(StrEnum): X = auto() Y = auto() -app = Typer(add_completion=False, rich_markup_mode="rich") +app = Typer(add_completion=False, rich_markup_mode="rich", no_args_is_help=True) def field_from_model(model: Type[FieldAccessMixin], field_name: str, extra_description: str = "", description: Optional[str] = None, default: Optional[Any] = None, **kwargs) -> Any: """ From cef8dce248ce99d81bd9a6d8c9ae5440914a9481 Mon Sep 17 00:00:00 2001 From: Michael Milton Date: Tue, 3 Oct 2023 16:21:58 +1100 Subject: [PATCH 070/147] Pretty print table of validation errors in CLI --- core/lls_core/cmds/__main__.py | 89 ++++++++++++++++++++++------------ 1 file changed, 59 insertions(+), 30 deletions(-) diff --git a/core/lls_core/cmds/__main__.py b/core/lls_core/cmds/__main__.py index 2a38f0a..75b3f30 100644 --- a/core/lls_core/cmds/__main__.py +++ b/core/lls_core/cmds/__main__.py @@ -16,18 +16,42 @@ from lls_core.models.output import OutputParams from lls_core.models.crop import CropParams from lls_core import DeconvolutionChoice -from typer import Typer, Argument, Option, Context +from typer import Typer, Argument, Option, Context, Exit from lls_core.models.output import SaveFileType +from pydantic import ValidationError if TYPE_CHECKING: from lls_core.models.utils import FieldAccessMixin from typing import Type, Any + from rich.table import Table class CliDeskewDirection(StrEnum): X = auto() Y = auto() +CLI_PARAM_MAP = { + "image": ["image"], + "angle": ["angle"], + "skew": ["skew"], + "pixel_sizes": ["physical_pixel_sizes"], + "rois": ["crop", "roi_list"], + "z_start": ["crop", "z_range", 0], + "z_end": ["crop", "z_range", 1], + "decon_processing": ["deconvolution", "decon_processing"], + "psf": ["deconvolution", "psf"], + "psf_num_iter": ["deconvolution", "psf_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], + "save_dir": ["save_dir"], + "save_name": ["save_name"], + "save_type": ["save_type"], +} + app = Typer(add_completion=False, rich_markup_mode="rich", no_args_is_help=True) def field_from_model(model: Type[FieldAccessMixin], field_name: str, extra_description: str = "", description: Optional[str] = None, default: Optional[Any] = None, **kwargs) -> Any: @@ -56,6 +80,25 @@ def handle_merge(values: list): raise ValueError(f"A parameter has been passed multiple times! Got: {', '.join(values)}") return values[0] +def rich_validation(e: ValidationError) -> Table: + """ + Converts + """ + from rich.table import Table + + table = Table(title="Validation Errors") + table.add_column("Model Field") + # table.add_column("Command Line Argument") + table.add_column("Error") + + for error in e.errors(): + table.add_row( + str(error["loc"][0]), + str(error["msg"]), + ) + + return table + @app.command("dump-schema") def dump_schema() -> None: import json @@ -99,29 +142,8 @@ def process( yaml_config: Optional[Path] = Option(None, show_default=False, help="Path to a YAML file from which parameters will be read."), ) -> None: from toolz.dicttoolz import merge_with, update_in - cli_param_map = { - "image": ["image"], - "angle": ["angle"], - "skew": ["skew"], - "pixel_sizes": ["physical_pixel_sizes"], - "rois": ["crop", "roi_list"], - "z_start": ["crop", "z_range", 0], - "z_end": ["crop", "z_range", 1], - "decon_processing": ["deconvolution", "decon_processing"], - "psf": ["deconvolution", "psf"], - "psf_num_iter": ["deconvolution", "psf_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], - "save_dir": ["save_dir"], - "save_name": ["save_name"], - "save_type": ["save_type"], - } cli_args = {} - for source, dest in cli_param_map.items(): + for source, dest in CLI_PARAM_MAP.items(): from click.core import ParameterSource if ctx.get_parameter_source(source) != ParameterSource.DEFAULT: cli_args = update_in(cli_args, dest, lambda x: ctx.params[source]) @@ -138,14 +160,21 @@ def process( from yaml import safe_load yaml_args = safe_load(fp) - - LatticeData.parse_obj( - # Merge all three sources of config: YAML, JSON and CLI - merge_with( - handle_merge, - [yaml_args, json_args, cli_args] + try: + lattice = LatticeData.parse_obj( + # Merge all three sources of config: YAML, JSON and CLI + merge_with( + handle_merge, + [yaml_args, json_args, cli_args] + ) ) - ).process().save_image() + except ValidationError as e: + from rich.console import Console + Console().print(rich_validation(e)) + # Console().print(ctx.get_help()) + raise Exit(code=1) + + lattice.process().save_image() def main(): app() From cdf12541c4246b3688bb1f695246ddd98702144b Mon Sep 17 00:00:00 2001 From: Michael Milton Date: Tue, 3 Oct 2023 16:35:27 +1100 Subject: [PATCH 071/147] Add rich as formal CLI dependency --- core/pyproject.toml | 1 + 1 file changed, 1 insertion(+) diff --git a/core/pyproject.toml b/core/pyproject.toml index 5e98645..e04bddd 100644 --- a/core/pyproject.toml +++ b/core/pyproject.toml @@ -54,6 +54,7 @@ dependencies = [ "pydantic~=1.0", "pyyaml", "read-roi", + "rich", "resource-backed-dask-array>=0.1.0", "scikit-image", "StrEnum", From 3eed0f3a5b938865661b60e25bc1118bb726ff5f Mon Sep 17 00:00:00 2001 From: Michael Milton Date: Wed, 4 Oct 2023 13:02:38 +1100 Subject: [PATCH 072/147] Always print help when no argumeunts provided --- core/lls_core/cmds/__main__.py | 13 ++++++------- 1 file changed, 6 insertions(+), 7 deletions(-) diff --git a/core/lls_core/cmds/__main__.py b/core/lls_core/cmds/__main__.py index 75b3f30..d20f968 100644 --- a/core/lls_core/cmds/__main__.py +++ b/core/lls_core/cmds/__main__.py @@ -99,13 +99,7 @@ def rich_validation(e: ValidationError) -> Table: return table -@app.command("dump-schema") -def dump_schema() -> None: - import json - import sys - json.dump(LatticeData.to_definition_dict(), fp=sys.stdout, indent=4) - -@app.command("process") +@app.command() def process( ctx: Context, 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), @@ -141,6 +135,11 @@ def process( 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."), ) -> None: + # Just print help if the user didn't provide any arguments + if len(ctx.args) == 0: + print(ctx.get_help()) + raise Exit() + from toolz.dicttoolz import merge_with, update_in cli_args = {} for source, dest in CLI_PARAM_MAP.items(): From cb65609d862f9239eafdc9b0c0bc46b557b1e02f Mon Sep 17 00:00:00 2001 From: Michael Milton Date: Wed, 4 Oct 2023 14:22:43 +1100 Subject: [PATCH 073/147] Fix tests after subcommands were removed --- core/tests/conftest.py | 4 ++-- core/tests/test_arg_parser.py | 2 +- core/tests/test_batch_deskew_args.py | 2 -- core/tests/test_batch_deskew_yaml.py | 4 ++-- core/tests/test_deskew.py | 2 +- core/tests/test_lattice_data.py | 10 +++++----- 6 files changed, 11 insertions(+), 13 deletions(-) diff --git a/core/tests/conftest.py b/core/tests/conftest.py index 9bc3a8a..01d3b8f 100644 --- a/core/tests/conftest.py +++ b/core/tests/conftest.py @@ -33,7 +33,7 @@ def test_image() -> NDArray[np.float64]: def workflow_config(workflow: Workflow, test_image: NDArray): # Create a config file yield { - "image": test_image, + "input_image": test_image, "workflow": workflow, } @@ -56,7 +56,7 @@ def workflow_config_cli(workflow: Workflow, test_image: NDArray): key: str(val) for key, val in { - "image": input, + "input_image": input, "save_dir": output, "workflow": workflow_path, }.items() diff --git a/core/tests/test_arg_parser.py b/core/tests/test_arg_parser.py index da66c6a..26bb1e2 100644 --- a/core/tests/test_arg_parser.py +++ b/core/tests/test_arg_parser.py @@ -5,7 +5,7 @@ def test_voxel_parsing(): # Tests that we can parse voxel lists correctly - command = get_command(app).commands["process"] + command = get_command(app) ctx = Context(command) parser = command.make_parser(ctx) args, _, _ = parser.parse_args(args=[ diff --git a/core/tests/test_batch_deskew_args.py b/core/tests/test_batch_deskew_args.py index df52978..be56cce 100644 --- a/core/tests/test_batch_deskew_args.py +++ b/core/tests/test_batch_deskew_args.py @@ -24,7 +24,6 @@ def test_batch_deskew_h5(): create_image(input_file) # Batch deskew and save as h5 invoke([ - "process", str(input_file), "--save-dir", str(out_dir), "--save-type", "h5" @@ -42,7 +41,6 @@ def test_batch_deskew_tiff(): input_file = out_dir / 'raw.tiff' create_image(input_file) invoke([ - "process", str(input_file), "--save-dir", str(out_dir), "--save-type", "tiff" diff --git a/core/tests/test_batch_deskew_yaml.py b/core/tests/test_batch_deskew_yaml.py index ce2f423..c67c4e3 100644 --- a/core/tests/test_batch_deskew_yaml.py +++ b/core/tests/test_batch_deskew_yaml.py @@ -41,7 +41,7 @@ def create_data(dir: Path) -> Path: assert input_file.exists() config: dict[str, str] = { - "image": str(input_file), + "input_image": str(input_file), "save_dir": str(dir), "save_type": "h5" } @@ -60,7 +60,7 @@ def test_yaml_deskew(): test_dir = Path(test_dir) config_location = create_data(test_dir) # Batch deskew and save as h5 - invoke(["process", "--yaml-config", str(config_location)], ) + invoke(["--yaml-config", str(config_location)], ) # checks if h5 files written assert (test_dir / "raw.h5").exists() diff --git a/core/tests/test_deskew.py b/core/tests/test_deskew.py index d08fc4b..e46e3fc 100644 --- a/core/tests/test_deskew.py +++ b/core/tests/test_deskew.py @@ -17,5 +17,5 @@ def test_deskew(): def test_lattice_data_deskew(): raw = DataArray(np.zeros((5, 5, 5)), dims=["X", "Y", "Z"]) - lattice = LatticeData(image=raw, physical_pixel_sizes = (1, 1, 1), save_name="test") + lattice = LatticeData(input_image=raw, physical_pixel_sizes = (1, 1, 1), save_name="test") assert lattice.deskew_vol_shape == [2, 9, 5] diff --git a/core/tests/test_lattice_data.py b/core/tests/test_lattice_data.py index 8af6891..ebc829f 100644 --- a/core/tests/test_lattice_data.py +++ b/core/tests/test_lattice_data.py @@ -19,7 +19,7 @@ def open_psf(name: str): def test_process(path: str, args: dict): with as_file(resources / path) as lattice_path: for slice in LatticeData.parse_obj({ - "image": lattice_path, + "input_image": lattice_path, **args }).process().slices: assert slice.data.ndim == 3 @@ -29,7 +29,7 @@ def test_process(path: str, args: dict): def test_save(path: str, args: dict): with as_file(resources / path) as lattice_path, tempfile.TemporaryDirectory() as tempdir: LatticeData.parse_obj({ - "image": lattice_path, + "input_image": lattice_path, "save_dir": tempdir, **args }).process().save_image() @@ -40,7 +40,7 @@ def test_save(path: str, args: dict): @parameterized def test_process_deconvolution(args: dict, background: Any): for slice in LatticeData.parse_obj({ - "image": root / "raw.tif", + "input_image": root / "raw.tif", "deconvolution": { "psf": [root / "psf.tif"], "background": background @@ -52,7 +52,7 @@ def test_process_deconvolution(args: dict, background: Any): @parameterized def test_process_workflow(args: dict, workflow: Workflow): for slice in LatticeData.parse_obj({ - "image": root / "raw.tif", + "input_image": root / "raw.tif", "workflow": workflow, **args }).process().slices: @@ -63,7 +63,7 @@ def test_process_crop(args: dict, workflow: Workflow): with as_file(resources / "RBC_tiny.czi") as lattice_path: rois = root / "crop" / "two_rois.zip" for slice in LatticeData.parse_obj({ - "image": lattice_path, + "input_image": lattice_path, "crop": { "roi_list": [rois] }, From 79091060ed5b49e8ec7d6cc37fa7d307b7a180dc Mon Sep 17 00:00:00 2001 From: Michael Milton Date: Wed, 4 Oct 2023 14:24:38 +1100 Subject: [PATCH 074/147] Rename image to input_image, and fix the CLI validation error display --- core/lls_core/cmds/__main__.py | 10 ++++++---- core/lls_core/models/deskew.py | 16 ++++++++-------- core/lls_core/models/lattice_data.py | 24 ++++++++++++------------ core/lls_core/models/output.py | 2 +- plugin/napari_lattice/dock_widget.py | 2 +- plugin/napari_lattice/fields.py | 2 +- 6 files changed, 29 insertions(+), 27 deletions(-) diff --git a/core/lls_core/cmds/__main__.py b/core/lls_core/cmds/__main__.py index d20f968..f1f4bd1 100644 --- a/core/lls_core/cmds/__main__.py +++ b/core/lls_core/cmds/__main__.py @@ -31,7 +31,7 @@ class CliDeskewDirection(StrEnum): Y = auto() CLI_PARAM_MAP = { - "image": ["image"], + "input_image": ["input_image"], "angle": ["angle"], "skew": ["skew"], "pixel_sizes": ["physical_pixel_sizes"], @@ -87,7 +87,7 @@ def rich_validation(e: ValidationError) -> Table: from rich.table import Table table = Table(title="Validation Errors") - table.add_column("Model Field") + table.add_column("Parameter") # table.add_column("Command Line Argument") table.add_column("Error") @@ -102,7 +102,7 @@ def rich_validation(e: ValidationError) -> Table: @app.command() def process( ctx: Context, - 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), + 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 X Y and Z pixel dimensions respectively", default=( @@ -135,8 +135,10 @@ def process( 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."), ) -> None: + from click.core import ParameterSource + # Just print help if the user didn't provide any arguments - if len(ctx.args) == 0: + if all(src != ParameterSource.COMMANDLINE for src in ctx._parameter_source.values()): print(ctx.get_help()) raise Exit() diff --git a/core/lls_core/models/deskew.py b/core/lls_core/models/deskew.py index ab0d83b..76888e6 100644 --- a/core/lls_core/models/deskew.py +++ b/core/lls_core/models/deskew.py @@ -41,7 +41,7 @@ def from_physical(cls, pixels: PhysicalPixelSizes) -> Self: class DeskewParams(FieldAccessMixin): - image: DataArray = Field( + input_image: DataArray = Field( description="A 3-5D array containing the image data." ) skew: DeskewDirection = Field( @@ -118,17 +118,17 @@ def set_skew(self, skew: DeskewDirection) -> None: @property def dims(self): - return self.image.dims + return self.input_image.dims @property def time(self) -> int: """Number of time points""" - return self.image.sizes["T"] + return self.input_image.sizes["T"] @property def channels(self) -> int: """Number of channels""" - return self.image.sizes["C"] + return self.input_image.sizes["C"] @property def new_dz(self): @@ -149,7 +149,7 @@ def convert_pixels(cls, v: Any): return DefinedPixelSizes(X=v[0], Y=v[1], Z=v[2]) return v - @validator("image", pre=True) + @validator("input_image", 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 @@ -163,7 +163,7 @@ def reshaping(cls, v: Any): return array.transpose("T", "C", "Z", "Y", "X") def get_3d_slice(self) -> DataArray: - return self.image.isel(C=0, T=0) + return self.input_image.isel(C=0, T=0) @root_validator(pre=False) def set_deskew(cls, values: dict) -> dict: @@ -171,9 +171,9 @@ 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 - if "image" not in values: + if "input_image" not in values: return values - data: DataArray = cls.reshaping(values["image"]) + data: DataArray = cls.reshaping(values["input_image"]) if values.get('deskew_vol_shape') is None: if values.get('deskew_affine_transform') is None: # If neither has been set, calculate them ourselves diff --git a/core/lls_core/models/lattice_data.py b/core/lls_core/models/lattice_data.py index 20a9f4e..767f08a 100644 --- a/core/lls_core/models/lattice_data.py +++ b/core/lls_core/models/lattice_data.py @@ -170,8 +170,8 @@ def default_save_name(cls, values: dict): # reshaping validator. We can't override that either since it's # a field validator and can't modify save_name from lls_core.types import is_pathlike - if values.get("save_name", None) is None and is_pathlike(values.get("image")): - values["save_name"] = Path(values["image"]).stem + if values.get("save_name", None) is None and is_pathlike(values.get("input_image")): + values["save_name"] = Path(values["input_image"]).stem return values @validator("workflow", pre=True) @@ -192,7 +192,7 @@ def default_z_range(cls, v: CropParams, values: dict): return v with ignore_keyerror(): default_start = 0 - default_end = values["image"].sizes["Z"] + default_end = values["input_image"].sizes["Z"] if v.z_range is None: v.z_range = (default_start, default_end) if v.z_range[0] is None: @@ -210,7 +210,7 @@ def parse_time_range(cls, v: Any, values: dict) -> Any: # user-friendly error is provided, namely "image was missing" with ignore_keyerror(): default_start = 0 - default_end = values["image"].sizes["T"] + default_end = values["input_image"].sizes["T"] if v is None: return range(default_start, default_end) elif isinstance(v, tuple) and len(v) == 2: @@ -225,7 +225,7 @@ def parse_channel_range(cls, v: Any, values: dict) -> Any: """ with ignore_keyerror(): default_start = 0 - default_end = values["image"].sizes["C"] + default_end = values["input_image"].sizes["C"] if v is None: return range(default_start, default_end) elif isinstance(v, tuple) and len(v) == 2: @@ -239,7 +239,7 @@ def disjoint_time_range(cls, v: range, values: dict): Validates that the time range is within the range of channels in our array """ with ignore_keyerror(): - max_time = values["image"].sizes["T"] + max_time = values["input_image"].sizes["T"] if v.start < 0: raise ValueError("The lowest valid start value is 0") if v.stop > max_time: @@ -253,7 +253,7 @@ def disjoint_channel_range(cls, v: range, values: dict): Validates that the channel range is within the range of channels in our array """ with ignore_keyerror(): - max_channel = values["image"].sizes["C"] + max_channel = values["input_image"].sizes["C"] if v.start < 0: raise ValueError("The lowest valid start value is 0") if v.stop > max_channel: @@ -263,13 +263,13 @@ def disjoint_channel_range(cls, v: range, values: dict): @validator("channel_range") def channel_range_subset(cls, v: range, values: dict): with ignore_keyerror(): - if min(v) < 0 or max(v) > values["image"].sizes["C"]: + if min(v) < 0 or max(v) > values["input_image"].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["image"].sizes["T"]: + if min(v) < 0 or max(v) > values["input_image"].sizes["T"]: raise ValueError("The output time range must be a subset of the total available time points") return v @@ -278,7 +278,7 @@ def check_psfs(cls, v: Optional[DeconvolutionParams], values: dict): if v is None: return v with ignore_keyerror(): - channels = values["image"].sizes["C"] + channels = values["input_image"].sizes["C"] psfs = len(v.psf) if psfs != channels: raise ValueError(f"There should be one PSF per channel, but there are {psfs} PSFs and {channels} channels.") @@ -304,7 +304,7 @@ def slice_data(self, time: int, channel: int) -> DataArray: if channel > self.channels: raise ValueError("channel is out of range") - return self.image.isel(T=time, C=channel) + return self.input_image.isel(T=time, C=channel) def iter_slices(self) -> Iterable[SlicedData[ArrayLike]]: """ @@ -333,7 +333,7 @@ def iter_sublattices(self, update_with: dict = {}) -> Iterable[SlicedData[Lattic for subarray in self.iter_slices(): # Copy doesn't manu new_lattice = self.copy_validate(update={ - "image": subarray.data, + "input_image": subarray.data, **update_with }) yield subarray.copy_with_data( new_lattice) diff --git a/core/lls_core/models/output.py b/core/lls_core/models/output.py index 7520c32..ba4b73e 100644 --- a/core/lls_core/models/output.py +++ b/core/lls_core/models/output.py @@ -1,4 +1,4 @@ -from pydantic import Field, validator, DirectoryPath +from pydantic import Field, DirectoryPath from strenum import StrEnum from os import getcwd from pathlib import Path diff --git a/plugin/napari_lattice/dock_widget.py b/plugin/napari_lattice/dock_widget.py index 34b167e..8a75cdf 100644 --- a/plugin/napari_lattice/dock_widget.py +++ b/plugin/napari_lattice/dock_widget.py @@ -43,7 +43,7 @@ def _make_model(self) -> LatticeData: deskew_args = self.LlszMenu.WidgetContainer.deskew_fields._get_kwargs() output_args = self.LlszMenu.WidgetContainer.output_fields._make_model() params = LatticeData( - image=deskew_args["data"], + input_image=deskew_args["data"], angle=deskew_args["angle"], channel_range=output_args.channel_range, time_range=output_args.time_range, diff --git a/plugin/napari_lattice/fields.py b/plugin/napari_lattice/fields.py index b335216..f8f5494 100644 --- a/plugin/napari_lattice/fields.py +++ b/plugin/napari_lattice/fields.py @@ -323,7 +323,7 @@ def _get_kwargs(self) -> DeskewKwargs: def _make_model(self) -> DeskewParams: kwargs = self._get_kwargs() return DeskewParams( - image=kwargs["data"], + input_image=kwargs["data"], physical_pixel_sizes=kwargs["physical_pixel_sizes"], angle=kwargs["angle"], skew = kwargs["skew"] From 98c435db5b779d77162e7d033d2d4d1450af5c40 Mon Sep 17 00:00:00 2001 From: Michael Milton Date: Wed, 4 Oct 2023 14:54:22 +1100 Subject: [PATCH 075/147] =?UTF-8?q?Fix=20for=20the=20confusing=20"time=5Fr?= =?UTF-8?q?ange=20=20=20=20=E2=94=82=20'NoneType'=20object=20is=20not=20it?= =?UTF-8?q?erable"?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- core/lls_core/models/lattice_data.py | 15 +++++---------- 1 file changed, 5 insertions(+), 10 deletions(-) diff --git a/core/lls_core/models/lattice_data.py b/core/lls_core/models/lattice_data.py index 767f08a..b3f74a4 100644 --- a/core/lls_core/models/lattice_data.py +++ b/core/lls_core/models/lattice_data.py @@ -1,19 +1,14 @@ from __future__ import annotations -from os import PathLike # class for initializing lattice data and setting metadata # TODO: handle scenes from pydantic import BaseModel, DirectoryPath, Field, NonNegativeInt, root_validator, validator -from aicsimageio.aics_image import AICSImage -import math from dask.array.core import Array as DaskArray -import dask as da from itertools import groupby import tifffile -from typing import Any, Iterable, List, Literal, Optional, TYPE_CHECKING +from typing import Any, Iterable, List, Optional, TYPE_CHECKING from typing_extensions import TypedDict, NotRequired, Generic, TypeVar -from aicsimageio.types import PhysicalPixelSizes import pyclesperanto_prototype as cle from tqdm import tqdm @@ -261,15 +256,15 @@ def disjoint_channel_range(cls, v: range, values: dict): return v @validator("channel_range") - def channel_range_subset(cls, v: range, values: dict): + def channel_range_subset(cls, v: Optional[range], values: dict): with ignore_keyerror(): - if min(v) < 0 or max(v) > values["input_image"].sizes["C"]: + if v is not None and (min(v) < 0 or max(v) > values["input_image"].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["input_image"].sizes["T"]: + def time_range_subset(cls, v: Optional[range], values: dict): + if v is not None and (min(v) < 0 or max(v) > values["input_image"].sizes["T"]): raise ValueError("The output time range must be a subset of the total available time points") return v From 16b28fec307f19e73b8d55e677621082037b2710 Mon Sep 17 00:00:00 2001 From: Michael Milton Date: Wed, 4 Oct 2023 15:43:27 +1100 Subject: [PATCH 076/147] Add roi_subset parameter --- core/lls_core/cmds/__main__.py | 3 +++ core/lls_core/models/crop.py | 20 ++++++++++++++++++-- core/lls_core/models/lattice_data.py | 2 +- core/tests/test_lattice_data.py | 12 +++++++++--- 4 files changed, 31 insertions(+), 6 deletions(-) diff --git a/core/lls_core/cmds/__main__.py b/core/lls_core/cmds/__main__.py index f1f4bd1..d45593c 100644 --- a/core/lls_core/cmds/__main__.py +++ b/core/lls_core/cmds/__main__.py @@ -36,6 +36,7 @@ class CliDeskewDirection(StrEnum): "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], "decon_processing": ["deconvolution", "decon_processing"], @@ -110,7 +111,9 @@ def process( DeskewParams.get_default("physical_pixel_sizes").Y, DeskewParams.get_default("physical_pixel_sizes").Z )), + 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), diff --git a/core/lls_core/models/crop.py b/core/lls_core/models/crop.py index a2c0f3b..65e6160 100644 --- a/core/lls_core/models/crop.py +++ b/core/lls_core/models/crop.py @@ -1,9 +1,8 @@ -from typing import List, Tuple, Any +from typing import Iterable, List, Tuple, Any from pydantic import Field, NonNegativeInt, validator from lls_core.models.utils import FieldAccessMixin from lls_core.cropping import Roi - class CropParams(FieldAccessMixin): """ Parameters for the optional cropping step @@ -12,11 +11,21 @@ class CropParams(FieldAccessMixin): description="List of regions of interest, each of which must be an NxD array, where N is the number of vertices and D the coordinates of each vertex.", default = [] ) + roi_subset: List[int] = Field( + description="A subset of all the ROIs to process", + default=None + ) z_range: Tuple[NonNegativeInt, NonNegativeInt] = Field( default=None, description="The range of Z slices to take. All Z slices before the first index or after the last index will be cropped out." ) + @property + def selected_rois(self) -> Iterable[Roi]: + "Returns the relevant ROIs that should be processed" + for i in self.roi_subset: + yield self.roi_list[i] + @validator("roi_list", pre=True) def read_roi(cls, v: Any) -> List[Roi]: from lls_core.types import is_pathlike @@ -38,3 +47,10 @@ def read_roi(cls, v: Any) -> List[Roi]: raise ValueError(f"{item} cannot be intepreted as an ROI") return rois + + @validator("roi_subset", pre=True) + def default_roi_range(cls, v: Any, values: dict): + # If the roi range isn't provided, assume all rois should be processed + if v is None: + return list(range(len(values["roi_list"]))) + return v diff --git a/core/lls_core/models/lattice_data.py b/core/lls_core/models/lattice_data.py index b3f74a4..2ca93bf 100644 --- a/core/lls_core/models/lattice_data.py +++ b/core/lls_core/models/lattice_data.py @@ -393,7 +393,7 @@ def _process_crop(self) -> Iterable[ProcessedVolume]: raise Exception("This function can only be called when crop is set") # We have an extra level of iteration for the crop path: iterating over each ROI - for roi_index, roi in enumerate(tqdm(self.crop.roi_list, desc="ROI:", position=0)): + for roi_index, roi in enumerate(tqdm(self.crop.selected_rois, desc="ROI:", position=0)): # pass arguments for save tiff, callable and function arguments logger.info(f"Processing ROI {roi_index}") diff --git a/core/tests/test_lattice_data.py b/core/tests/test_lattice_data.py index ebc829f..c81a945 100644 --- a/core/tests/test_lattice_data.py +++ b/core/tests/test_lattice_data.py @@ -1,4 +1,4 @@ -from typing import Any +from typing import Any, List, Optional import pytest from lls_core.models import LatticeData from lls_core.sample import resources @@ -58,14 +58,20 @@ def test_process_workflow(args: dict, workflow: Workflow): }).process().slices: assert slice.data.ndim == 3 +@pytest.mark.parametrize(["roi_subset"], [ + [None], + [[0]], + [[0, 1]], +]) @parameterized -def test_process_crop(args: dict, workflow: Workflow): +def test_process_crop(args: dict, roi_subset: Optional[List[int]], workflow: Workflow): with as_file(resources / "RBC_tiny.czi") as lattice_path: rois = root / "crop" / "two_rois.zip" for slice in LatticeData.parse_obj({ "input_image": lattice_path, "crop": { - "roi_list": [rois] + "roi_list": [rois], + "roi_subset": roi_subset }, **args }).process().slices: From 17900d8b73df820207199c6c408c93b701403699 Mon Sep 17 00:00:00 2001 From: Michael Milton Date: Wed, 4 Oct 2023 16:37:13 +1100 Subject: [PATCH 077/147] Fix workflow test --- core/tests/test_workflows.py | 1 - 1 file changed, 1 deletion(-) diff --git a/core/tests/test_workflows.py b/core/tests/test_workflows.py index 4d060f6..c149e51 100644 --- a/core/tests/test_workflows.py +++ b/core/tests/test_workflows.py @@ -35,7 +35,6 @@ def test_workflow_cli(workflow_config_cli: dict, save_func: Callable, cli_param: # Deskew, apply workflow and save as h5 invoke([ - "process", cli_param, fp.name ]) From 79f13659d27e6a54f4d187e984258de3b27a4eb2 Mon Sep 17 00:00:00 2001 From: Michael Milton Date: Tue, 10 Oct 2023 17:53:37 +1100 Subject: [PATCH 078/147] Infer the output dir correctly, separate validation and process tests --- core/lls_core/models/lattice_data.py | 17 ++++++++++++++--- core/lls_core/models/output.py | 4 ++-- .../{test_lattice_data.py => test_process.py} | 0 core/tests/test_validation.py | 10 ++++++++++ 4 files changed, 26 insertions(+), 5 deletions(-) rename core/tests/{test_lattice_data.py => test_process.py} (100%) create mode 100644 core/tests/test_validation.py diff --git a/core/lls_core/models/lattice_data.py b/core/lls_core/models/lattice_data.py index 2ca93bf..6938f29 100644 --- a/core/lls_core/models/lattice_data.py +++ b/core/lls_core/models/lattice_data.py @@ -160,13 +160,24 @@ class LatticeData(OutputParams, DeskewParams): workflow: Optional[Workflow] = workflow @root_validator(pre=True) - def default_save_name(cls, values: dict): + def use_image_path(cls, values: dict): # This needs to be a root validator to ensure it runs before the # reshaping validator. We can't override that either since it's # a field validator and can't modify save_name from lls_core.types import is_pathlike - if values.get("save_name", None) is None and is_pathlike(values.get("input_image")): - values["save_name"] = Path(values["input_image"]).stem + input_image = values.get("input_image") + if is_pathlike(input_image): + if values.get("save_name") is None: + values["save_name"] = Path(values["input_image"]).stem + + save_dir = values.get("save_dir") + if save_dir is None: + # By default, make the save dir be the same dir as the input + values["save_dir"] = Path(input_image).parent + elif is_pathlike(save_dir): + # Convert a string path to a Path object + values["save_name"] = Path(save_dir) + return values @validator("workflow", pre=True) diff --git a/core/lls_core/models/output.py b/core/lls_core/models/output.py index ba4b73e..50c69b3 100644 --- a/core/lls_core/models/output.py +++ b/core/lls_core/models/output.py @@ -12,7 +12,7 @@ class SaveFileType(StrEnum): class OutputParams(FieldAccessMixin): save_dir: DirectoryPath = Field( - default = getcwd(), + default=None, description="The directory where the output data will be saved" ) save_name: str = Field( @@ -23,7 +23,7 @@ class OutputParams(FieldAccessMixin): description=f"The data type to save the result as. This will also be used to determine the file extension of the output files. Choices: {enum_choices(SaveFileType)}" ) time_range: range = Field( - default = None, + default=None, description="The range of times to process. This defaults to all time points in the image array." ) channel_range: range = Field( diff --git a/core/tests/test_lattice_data.py b/core/tests/test_process.py similarity index 100% rename from core/tests/test_lattice_data.py rename to core/tests/test_process.py diff --git a/core/tests/test_validation.py b/core/tests/test_validation.py new file mode 100644 index 0000000..9084ff9 --- /dev/null +++ b/core/tests/test_validation.py @@ -0,0 +1,10 @@ +from lls_core.models.lattice_data import LatticeData +from lls_core.sample import resources +from importlib_resources import as_file + + +def test_default_save_dir(): + # Test that the save dir is inferred to be the input dir + with as_file(resources / "RBC_tiny.czi") as path: + params = LatticeData(input_image=path) + assert params.save_dir == path.parent From 44836939ba88ca11604e7c0163467ef060f16290 Mon Sep 17 00:00:00 2001 From: Michael Milton Date: Tue, 10 Oct 2023 17:58:09 +1100 Subject: [PATCH 079/147] Workflow loads from YAML in CLI --- core/lls_core/cmds/__main__.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/core/lls_core/cmds/__main__.py b/core/lls_core/cmds/__main__.py index d45593c..2062767 100644 --- a/core/lls_core/cmds/__main__.py +++ b/core/lls_core/cmds/__main__.py @@ -134,7 +134,7 @@ def process( 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 JSON format. If provided, the configured desekewing processing will be added to the chosen workflow.", show_default=False), + 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), 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."), ) -> None: From 82a73b229a5fc81d533c179116efe71962d788d6 Mon Sep 17 00:00:00 2001 From: Michael Milton Date: Tue, 10 Oct 2023 18:08:30 +1100 Subject: [PATCH 080/147] Fix broken CLI tests --- core/lls_core/cmds/__main__.py | 2 +- core/lls_core/models/lattice_data.py | 2 +- 2 files changed, 2 insertions(+), 2 deletions(-) diff --git a/core/lls_core/cmds/__main__.py b/core/lls_core/cmds/__main__.py index 2062767..65c4fac 100644 --- a/core/lls_core/cmds/__main__.py +++ b/core/lls_core/cmds/__main__.py @@ -120,7 +120,7 @@ def process( 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="A list of 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.", 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"), background: str = field_from_model(DeconvolutionParams, "background", rich_help_panel="Deconvolution"), diff --git a/core/lls_core/models/lattice_data.py b/core/lls_core/models/lattice_data.py index 6938f29..5e4990a 100644 --- a/core/lls_core/models/lattice_data.py +++ b/core/lls_core/models/lattice_data.py @@ -176,7 +176,7 @@ def use_image_path(cls, values: dict): values["save_dir"] = Path(input_image).parent elif is_pathlike(save_dir): # Convert a string path to a Path object - values["save_name"] = Path(save_dir) + values["save_dir"] = Path(save_dir) return values From 404f80baae2d8e84035e35dd8e7bc68e45bbe326 Mon Sep 17 00:00:00 2001 From: Michael Milton Date: Fri, 13 Oct 2023 14:01:15 +1100 Subject: [PATCH 081/147] Create test_cli test suite --- core/tests/test_batch_deskew_args.py | 50 --------------- core/tests/test_batch_deskew_yaml.py | 67 ------------------- core/tests/test_cli.py | 96 ++++++++++++++++++++++++++++ 3 files changed, 96 insertions(+), 117 deletions(-) delete mode 100644 core/tests/test_batch_deskew_args.py delete mode 100644 core/tests/test_batch_deskew_yaml.py create mode 100644 core/tests/test_cli.py diff --git a/core/tests/test_batch_deskew_args.py b/core/tests/test_batch_deskew_args.py deleted file mode 100644 index be56cce..0000000 --- a/core/tests/test_batch_deskew_args.py +++ /dev/null @@ -1,50 +0,0 @@ -# Tests for napari_lattice using arguments and saving output files as h5, as well as tiff - -from skimage.io import imsave -import numpy as np -from pathlib import Path -import tempfile -from tests.utils import invoke - -def create_image(path: Path): - # Create a zero array of shape 5x5x5 with a value of 10 at (2,4,2) - raw = np.zeros((5, 5, 5)) - raw[2, 4, 2] = 10 - # Save image as a tif filw in home directory - imsave(str(path), raw) - assert path.exists() - -def test_batch_deskew_h5(): - """Write image to disk and then execute napari_lattice from terminal - Checks if an deskewed output file is created for both tif and h5 - """ - with tempfile.TemporaryDirectory() as out_dir: - out_dir = Path(out_dir) - input_file = out_dir / 'raw.tiff' - create_image(input_file) - # Batch deskew and save as h5 - invoke([ - str(input_file), - "--save-dir", str(out_dir), - "--save-type", "h5" - ]) - - # checks if h5 files written - assert (out_dir / "raw.h5").exists() - # assert (out_dir / "raw.xml").exists() - - -def test_batch_deskew_tiff(): - # tiff file deskew - with tempfile.TemporaryDirectory() as out_dir: - out_dir = Path(out_dir) - input_file = out_dir / 'raw.tiff' - create_image(input_file) - invoke([ - str(input_file), - "--save-dir", str(out_dir), - "--save-type", "tiff" - ]) - - # checks if tiff written - assert len(list(out_dir.glob("*.tif"))) == 1 diff --git a/core/tests/test_batch_deskew_yaml.py b/core/tests/test_batch_deskew_yaml.py deleted file mode 100644 index c67c4e3..0000000 --- a/core/tests/test_batch_deskew_yaml.py +++ /dev/null @@ -1,67 +0,0 @@ -# Tests for napari_lattice using the config file and saving ouput as h5 -# Thanks to DrLachie for cool function to write the config file - -from skimage.io import imread, imsave -import tempfile -import numpy as np -from pathlib import Path -from lls_core.cmds.__main__ import app -from tests.utils import invoke - -def write_config_file(config_settings: dict, output_file_location: Path): - # Write config file for napari_lattice - with output_file_location.open('w') as f: - for key, val in config_settings.items(): - if val is not None: - if type(val) is str: - print('%s: "%s"' % (key, val), file=f) - - if type(val) is int: - print('%s: %i' % (key, val), file=f) - - if type(val) is list: - print("%s:" % key, file=f) - for x in val: - if type(x) is int: - print(" - %i" % x, file=f) - else: - print(' - "%s"' % x, file=f) - - print("Config found written to %s" % output_file_location) - -def create_data(dir: Path) -> Path: - input_file = dir / 'raw.tiff' - config_location = dir / "config_deskew.yaml" - - # Create a zero array of shape 5x5x5 with a value of 10 at (2,4,2) - raw = np.zeros((5, 5, 5)) - raw[2, 4, 2] = 10 - # Save image as a tif filw in home directory - imsave(input_file, raw) - assert input_file.exists() - - config: dict[str, str] = { - "input_image": str(input_file), - "save_dir": str(dir), - "save_type": "h5" - } - - write_config_file(config, config_location) - assert config_location.exists() - - return config_location - - -def test_yaml_deskew(): - """Write image to disk and then execute napari_lattice from terminal - Checks if an deskewed output file is created for both tif and h5 - """ - with tempfile.TemporaryDirectory() as test_dir: - test_dir = Path(test_dir) - config_location = create_data(test_dir) - # Batch deskew and save as h5 - invoke(["--yaml-config", str(config_location)], ) - - # checks if h5 files written - assert (test_dir / "raw.h5").exists() - # assert (test_dir / "raw" / "raw.xml").exists() diff --git a/core/tests/test_cli.py b/core/tests/test_cli.py new file mode 100644 index 0000000..5724d0a --- /dev/null +++ b/core/tests/test_cli.py @@ -0,0 +1,96 @@ +# Tests for napari_lattice using arguments and saving output files as h5, as well as tiff + +from aicsimageio.aics_image import AICSImage +from npy2bdv import BdvEditor +import numpy as np +from pathlib import Path +import tempfile +from tests.utils import invoke +import yaml + +def create_image(path: Path): + # Create a zero array of shape 5x5x5 with a value of 10 at (2,4,2) + raw = np.zeros((5, 5, 5)) + raw[2, 4, 2] = 10 + # Save image as a tif filw in home directory + AICSImage(raw).save(path) + assert path.exists() + + +def create_data(dir: Path) -> Path: + # Creates and returns a YAML config file + input_file = dir / 'raw.tiff' + config_location = dir / "config_deskew.yaml" + + # Create a zero array of shape 5x5x5 with a value of 10 at (2,4,2) + raw = np.zeros((5, 5, 5)) + raw[2, 4, 2] = 10 + # Save image as a tif filw in home directory + AICSImage(raw).save(input_file) + assert input_file.exists() + + config: dict[str, str] = { + "input_image": str(input_file), + "save_dir": str(dir), + "save_type": "h5" + } + + with config_location.open("w") as fp: + yaml.safe_dump(config, fp) + + return config_location + + +def test_batch_deskew_h5(): + """Write image to disk and then execute napari_lattice from terminal + Checks if an deskewed output file is created for both tif and h5 + """ + with tempfile.TemporaryDirectory() as out_dir: + out_dir = Path(out_dir) + input_file = out_dir / 'raw.tiff' + create_image(input_file) + # Batch deskew and save as h5 + invoke([ + str(input_file), + "--save-dir", str(out_dir), + "--save-type", "h5" + ]) + + # checks if h5 files written + assert (out_dir / "raw.h5").exists() + assert (out_dir / "raw.xml").exists() + BdvEditor(str(out_dir / "raw.h5")).read_view() + +def test_batch_deskew_tiff(): + # tiff file deskew + with tempfile.TemporaryDirectory() as out_dir: + out_dir = Path(out_dir) + input_file = out_dir / 'raw.tiff' + create_image(input_file) + invoke([ + str(input_file), + "--save-dir", str(out_dir), + "--save-type", "tiff" + ]) + + # checks if tiff written + results = list(out_dir.glob("*.tif")) + assert len(results) == 1 + for result in results: + AICSImage(result).get_image_data() + + +def test_yaml_deskew(): + """Write image to disk and then execute napari_lattice from terminal + Checks if an deskewed output file is created for both tif and h5 + """ + with tempfile.TemporaryDirectory() as test_dir: + test_dir = Path(test_dir) + config_location = create_data(test_dir) + # Batch deskew and save as h5 + invoke(["--yaml-config", str(config_location)], ) + + # checks if h5 files written + assert (test_dir / "raw.h5").exists() + assert (test_dir / "raw.xml").exists() + BdvEditor(str(test_dir / "raw.h5")).read_view() From 561f1253fe393d172fca4d5c0629345593106d6a Mon Sep 17 00:00:00 2001 From: Michael Milton Date: Fri, 13 Oct 2023 15:20:38 +1100 Subject: [PATCH 082/147] Fix save directory selection --- plugin/napari_lattice/fields.py | 4 +++- 1 file changed, 3 insertions(+), 1 deletion(-) diff --git a/plugin/napari_lattice/fields.py b/plugin/napari_lattice/fields.py index f8f5494..bdb7866 100644 --- a/plugin/napari_lattice/fields.py +++ b/plugin/napari_lattice/fields.py @@ -489,7 +489,9 @@ class OutputFields(NapariFieldGroup): ) save_path = field(Path).with_options( label = "Save Directory", - value = Path(history.get_save_history()[0]) + value = Path(history.get_save_history()[0]), + # Directory select + mode="d" ) save_name = field(str).with_options( label = "Save Prefix" From 8e5508e0131cf051a5a5add4824dcd641755a3e2 Mon Sep 17 00:00:00 2001 From: Michael Milton Date: Fri, 27 Oct 2023 21:39:03 +1100 Subject: [PATCH 083/147] Workflow processing into data frames --- core/lls_core/models/lattice_data.py | 133 ++++++--------------------- core/lls_core/models/output.py | 4 + core/lls_core/models/results.py | 128 ++++++++++++++++++++++++++ core/lls_core/utils.py | 15 +++ core/lls_core/writers.py | 99 ++++++++++++++++++++ core/tests/conftest.py | 38 +++++++- core/tests/test_process.py | 22 +++-- core/tests/test_workflows.py | 38 +++++++- core/tests/utils.py | 11 +++ plugin/napari_lattice/dock_widget.py | 2 +- 10 files changed, 374 insertions(+), 116 deletions(-) create mode 100644 core/lls_core/models/results.py create mode 100644 core/lls_core/writers.py diff --git a/core/lls_core/models/lattice_data.py b/core/lls_core/models/lattice_data.py index 5e4990a..56f56a0 100644 --- a/core/lls_core/models/lattice_data.py +++ b/core/lls_core/models/lattice_data.py @@ -4,9 +4,8 @@ from pydantic import BaseModel, DirectoryPath, Field, NonNegativeInt, root_validator, validator from dask.array.core import Array as DaskArray from itertools import groupby -import tifffile -from typing import Any, Iterable, List, Optional, TYPE_CHECKING +from typing import Any, Iterable, List, Optional, TYPE_CHECKING, Type from typing_extensions import TypedDict, NotRequired, Generic, TypeVar import pyclesperanto_prototype as cle @@ -18,6 +17,7 @@ from lls_core.models.crop import CropParams from lls_core.models.deconvolution import DeconvolutionParams from lls_core.models.output import OutputParams, SaveFileType +from lls_core.models.results import WorkflowSlices from lls_core.models.utils import ignore_keyerror from lls_core.types import ArrayLike from lls_core.models.deskew import DeskewParams @@ -29,99 +29,13 @@ import pyclesperanto_prototype as cle from lls_core.models.deskew import DefinedPixelSizes from numpy.typing import NDArray + from lls_core.models.results import ImageSlice, ImageSlices, ProcessedSlice, ProcessedSlices + from lls_core.writers import Writer, BdvWriter, TiffWriter import logging logger = logging.getLogger(__name__) -def make_filename_prefix(prefix: Optional[str] = None, roi_index: Optional[str] = None, channel: Optional[str] = None, time: Optional[str] = None) -> str: - """ - Generates a filename for this result - """ - components: List[str] = [] - if prefix is not None: - components.append(prefix) - if roi_index is not None: - components.append(f"ROI_{roi_index}") - if channel is not None: - components.append(f"C{channel}") - if time is not None: - components.append(f"T{time}") - return "_".join(components) - -T = TypeVar("T") -S = TypeVar("S") -class SlicedData(BaseModel, Generic[T], arbitrary_types_allowed=True): - data: T - time_index: NonNegativeInt - time: NonNegativeInt - channel_index: NonNegativeInt - channel: NonNegativeInt - roi_index: Optional[NonNegativeInt] = None - - def copy_with_data(self, data: S) -> SlicedData[S]: - """ - Return a modified version of this with new inner data - """ - from typing_extensions import cast - return cast( - SlicedData[S], - self.copy(update={ - "data": data - }) - ) -ProcessedVolume = SlicedData[ArrayLike] - -class ProcessedSlices(BaseModel, arbitrary_types_allowed=True): - #: Iterable of result slices. - #: Note that this is a finite iterator that can only be iterated once - slices: Iterable[ProcessedVolume] - - #: The "parent" LatticeData that was used to create this result - lattice_data: LatticeData - - def save_image(self): - """ - Saves result slices to disk - """ - # TODO: refactor this into a class system, one for each format - import numpy as np - import npy2bdv - - 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( - filename=str(self.lattice_data.make_filepath(make_filename_prefix(roi_index=roi))), - compression='gzip', - nchannels=len(self.lattice_data.channel_range), - subsamp=((1, 1, 1), (1, 2, 2), (2, 4, 4)), - overwrite=False - ) - for result in roi_results: - bdv_writer.append_view( - np.array(result.data), - time=result.time, - channel=result.channel, - voxel_size_xyz=(self.lattice_data.dx, self.lattice_data.dy, self.lattice_data.new_dz), - voxel_units='um' - ) - bdv_writer.write_xml() - bdv_writer.close() - elif self.lattice_data.save_type == SaveFileType.tiff: - # For each time point, we write a separate TIFF - for time, results in groupby(roi_results, key=lambda it: it.time): - result_list = list(results) - first_result = result_list[0] - images_array = np.swapaxes(np.expand_dims([result.data for result in result_list], axis=0), 1, 2) - tifffile.imwrite( - str(self.lattice_data.make_filepath(make_filename_prefix(channel=first_result.channel, time=time, roi_index=roi))), - data = images_array, - bigtiff=True, - resolution=(1./self.lattice_data.dx, 1./self.lattice_data.dy, "MICROMETER"), - metadata={'spacing': self.lattice_data.new_dz, 'unit': 'um', 'axes': 'TZCYX'}, - imagej=True - ) - workflow: Optional[Workflow] = Field( default=None, description="If defined, this is a workflow to add lightsheet processing onto" @@ -312,16 +226,17 @@ def slice_data(self, time: int, channel: int) -> DataArray: return self.input_image.isel(T=time, C=channel) - def iter_slices(self) -> Iterable[SlicedData[ArrayLike]]: + def iter_slices(self) -> Iterable[ProcessedSlice[ArrayLike]]: """ Yields array slices for each time and channel of interest. Returns: An iterable of tuples. Each tuple contains (time_index, time, channel_index, channel, slice) """ + from lls_core.models.results import ProcessedSlice for time_idx, time in enumerate(self.time_range): for ch_idx, ch in enumerate(self.channel_range): - yield SlicedData( + yield ProcessedSlice( data=self.slice_data(time=time, channel=ch), time_index=time_idx, time= time, @@ -329,7 +244,7 @@ def iter_slices(self) -> Iterable[SlicedData[ArrayLike]]: channel=ch, ) - def iter_sublattices(self, update_with: dict = {}) -> Iterable[SlicedData[LatticeData]]: + def iter_sublattices(self, update_with: dict = {}) -> Iterable[ProcessedSlice[LatticeData]]: """ Yields copies of the current LatticeData, one for each slice. These copies can then be processed separately. @@ -337,16 +252,17 @@ def iter_sublattices(self, update_with: dict = {}) -> Iterable[SlicedData[Lattic update_with: dictionary of arguments to update the generated lattices with """ for subarray in self.iter_slices(): - # Copy doesn't manu new_lattice = self.copy_validate(update={ "input_image": subarray.data, + "time_range": range(1), + "channel_range": range(1), **update_with }) yield subarray.copy_with_data( new_lattice) def generate_workflows( self, - ) -> Iterable[SlicedData[Workflow]]: + ) -> Iterable[ProcessedSlice[Workflow]]: """ Yields copies of the input workflow, modified with the addition of deskewing and optionally, cropping and deconvolution @@ -396,7 +312,7 @@ def deskewed_volume(self) -> DaskArray: from dask.array import zeros return zeros(self.deskew_vol_shape) - def _process_crop(self) -> Iterable[ProcessedVolume]: + def _process_crop(self) -> Iterable[ImageSlice]: """ Yields processed image slices with cropping enabled """ @@ -436,7 +352,7 @@ def _process_crop(self) -> Iterable[ProcessedVolume]: ) ) - def _process_non_crop(self) -> Iterable[ProcessedVolume]: + def _process_non_crop(self) -> Iterable[ImageSlice]: """ Yields processed image slices without cropping """ @@ -477,7 +393,9 @@ def _process_non_crop(self) -> Iterable[ProcessedVolume]: )) ) - def _process_workflow(self) -> ProcessedSlices: + def process_workflow(self) -> WorkflowSlices: + from lls_core.models.results import WorkflowSlices + WorkflowSlices.update_forward_refs(LatticeData=LatticeData) outputs = [] for workflow in self.generate_workflows(): for leaf in workflow.data.leafs(): @@ -487,7 +405,7 @@ def _process_workflow(self) -> ProcessedSlices: ) ) - return ProcessedSlices( + return WorkflowSlices( slices = outputs, lattice_data=self ) @@ -497,11 +415,12 @@ def process(self) -> ProcessedSlices: Execute the processing and return the result. This is the main public API for processing """ - ProcessedSlices.update_forward_refs() + from lls_core.models.results import ProcessedSlices + ProcessedSlices.update_forward_refs(LatticeData=LatticeData) - if self.workflow is not None: - return self._process_workflow() - elif self.cropping_enabled: + # if self.workflow is not None: + # return self._process_workflow() + if self.cropping_enabled: return ProcessedSlices( lattice_data=self, slices=self._process_crop() @@ -520,3 +439,11 @@ def process_into_image(self) -> ArrayLike: for slice in self.process().slices: return slice.data raise Exception("No slices produced!") + + def get_writer(self) -> Type[Writer]: + from lls_core.writers import BdvWriter, TiffWriter + if self.save_type == SaveFileType.h5: + return BdvWriter + elif self.save_type == SaveFileType.tiff: + return TiffWriter + raise Exception("Unknown output type") diff --git a/core/lls_core/models/output.py b/core/lls_core/models/output.py index 50c69b3..e80fa87 100644 --- a/core/lls_core/models/output.py +++ b/core/lls_core/models/output.py @@ -2,9 +2,13 @@ from strenum import StrEnum from os import getcwd from pathlib import Path +from typing import TYPE_CHECKING from lls_core.models.utils import FieldAccessMixin, enum_choices +if TYPE_CHECKING: + from lls_core.writers import Writer, BdvWriter, TiffWriter + #Choice of File extension to save class SaveFileType(StrEnum): h5 = "h5" diff --git a/core/lls_core/models/results.py b/core/lls_core/models/results.py new file mode 100644 index 0000000..c8498a5 --- /dev/null +++ b/core/lls_core/models/results.py @@ -0,0 +1,128 @@ +from __future__ import annotations +from itertools import groupby +from pathlib import Path + +from typing import Iterable, Optional, Tuple, Union, cast, TYPE_CHECKING +from typing_extensions import Generic, TypeVar +from pydantic import BaseModel, NonNegativeInt +from lls_core.types import ArrayLike, is_arraylike +from lls_core.utils import make_filename_prefix +from lls_core.writers import RoiIndex, Writer +from pandas import DataFrame + +if TYPE_CHECKING: + from lls_core.models.lattice_data import LatticeData + +T = TypeVar("T") +S = TypeVar("S") +class ProcessedSlice(BaseModel, Generic[T], arbitrary_types_allowed=True): + """ + A single slice of some data that is split across multiple slices along time or channel axes + This class is generic over T, the type of data that is sliced. + """ + data: T + time_index: NonNegativeInt + time: NonNegativeInt + channel_index: NonNegativeInt + channel: NonNegativeInt + roi_index: Optional[NonNegativeInt] = None + + def copy_with_data(self, data: S) -> ProcessedSlice[S]: + """ + Return a modified version of this with new inner data + """ + from typing_extensions import cast + return cast( + ProcessedSlice[S], + self.copy(update={ + "data": data + }) + ) + +class ProcessedSlices(BaseModel, Generic[T], arbitrary_types_allowed=True): + """ + The main output from deskewing. A class that holds an iterable of + output image slices. + """ + #: Iterable of result slices. + #: Note that this is a finite iterator that can only be iterated once + slices: Iterable[ProcessedSlice[T]] + + #: The "parent" LatticeData that was used to create this result + lattice_data: LatticeData + +ImageSlice = ProcessedSlice[ArrayLike] +class ImageSlices(ProcessedSlices[ArrayLike]): + """ + A collection of image slices + """ + def save_image(self): + """ + Saves result slices to disk + """ + Writer = self.lattice_data.get_writer() + for roi, roi_results in groupby(self.slices, key=lambda it: it.roi_index): + writer = Writer(self.lattice_data, roi_index=roi) + for slice in roi_results: + writer.write_slice(slice) + + +RawWorkflowOutput = Union[ + ArrayLike, + dict, + list +] +ProcessedWorkflowOutput = Union[ + # A path indicates a saved file + Path, + DataFrame +] + +class WorkflowSlices(ProcessedSlices[Tuple[RawWorkflowOutput]]): + def process(self) -> Iterable[Tuple[RoiIndex, ProcessedWorkflowOutput]]: + """ + Incrementally processes the workflow outputs, and returns both image paths and data frames of the outputs, + for image slices and dict/list outputs respectively + """ + from pathlib import Path + import pandas as pd + + for roi, roi_results in groupby(self.slices, key=lambda it: it.roi_index): + values = [] + for result in roi_results: + # Ensure the data is in a tuple + data = (result.data,) if is_arraylike(result.data) else result.data + for i, element in enumerate(data): + if is_arraylike(element): + # Make the writer the first time only + if len(values) <= i: + values.append(self.lattice_data.get_writer()(self.lattice_data, roi_index=roi)) + + writer = cast(Writer, values[i]) + writer.write_slice( + result.copy_with_data( + element + ) + ) + else: + if len(values) <= i: + values.append([]) + + rows = cast(list, values[i]) + rows.append(element) + + for element in values: + if isinstance(element, Writer): + element.close() + yield roi, Path(element.get_filepath()) + else: + yield roi, pd.DataFrame(element) + + def save(self) -> Iterable[Path]: + for roi, result in self.process(): + if isinstance(result, DataFrame): + path = self.lattice_data.make_filepath(make_filename_prefix(roi_index=roi)) + result.to_csv(str(path)) + yield path + else: + yield result diff --git a/core/lls_core/utils.py b/core/lls_core/utils.py index d27c0aa..0def3c6 100644 --- a/core/lls_core/utils.py +++ b/core/lls_core/utils.py @@ -319,3 +319,18 @@ def array_to_dask(arr: ArrayLike) -> DaskArray: return arr else: return from_array(arr) + +def make_filename_prefix(prefix: Optional[str] = None, roi_index: Optional[str] = None, channel: Optional[str] = None, time: Optional[str] = None) -> str: + """ + Generates a filename for this result + """ + components: List[str] = [] + if prefix is not None: + components.append(prefix) + if roi_index is not None: + components.append(f"ROI_{roi_index}") + if channel is not None: + components.append(f"C{channel}") + if time is not None: + components.append(f"T{time}") + return "_".join(components) diff --git a/core/lls_core/writers.py b/core/lls_core/writers.py new file mode 100644 index 0000000..e8cf5ab --- /dev/null +++ b/core/lls_core/writers.py @@ -0,0 +1,99 @@ +from __future__ import annotations + +from abc import ABC, abstractmethod +from dataclasses import dataclass, field +from itertools import groupby +from typing import TYPE_CHECKING, Iterator, List, Optional + +from lls_core.types import ArrayLike + +from pydantic import NonNegativeInt + +from lls_core.utils import make_filename_prefix +RoiIndex = Optional[NonNegativeInt] + +if TYPE_CHECKING: + from lls_core.models.lattice_data import LatticeData + import npy2bdv + from lls_core.models.results import ProcessedSlice + + +@dataclass +class Writer(ABC): + lattice: LatticeData + roi_index: RoiIndex + + @abstractmethod + def get_filepath(self) -> str: + pass + + @abstractmethod + def write_slice(self, slice: ProcessedSlice[ArrayLike]): + pass + + def close(self): + pass + +@dataclass +class BdvWriter(Writer): + bdv_writer: npy2bdv.BdvWriter = field(init=False) + + def get_filepath(self) -> str: + return str(self.lattice.make_filepath(make_filename_prefix(roi_index=self.roi_index))) + + def __post_init__(self): + import npy2bdv + self.bdv_writer = npy2bdv.BdvWriter( + filename=self.get_filepath(), + compression='gzip', + nchannels=len(self.lattice.channel_range), + subsamp=((1, 1, 1), (1, 2, 2), (2, 4, 4)), + overwrite=False + ) + + def write_slice(self, slice: ProcessedSlice[ArrayLike]): + import numpy as np + self.bdv_writer.append_view( + np.array(slice.data), + time=slice.time, + channel=slice.channel, + voxel_size_xyz=(self.lattice.dx, self.lattice.dy, self.lattice.new_dz), + voxel_units='um' + ) + + def close(self): + self.bdv_writer.write_xml() + self.bdv_writer.close() + +@dataclass +class TiffWriter(Writer): + pending_slices: list = field(default_factory=list, init=False) + time: Optional[NonNegativeInt] = None + + def get_filepath(self) -> str: + return str(self.lattice.make_filepath(make_filename_prefix(roi_index=self.roi_index))) + + def __post_init__(self): + self.pending_slices = [] + + def write_slice(self, slice: ProcessedSlice[ArrayLike]): + import numpy as np + import tifffile + if slice.time != self.time and len(self.pending_slices) > 0: + # Write the old timepoint once we + first_result = self.pending_slices[0] + images_array = np.swapaxes(np.expand_dims([result.data for result in self.pending_slices], axis=0), 1, 2) + tifffile.imwrite( + str(self.lattice.make_filepath(make_filename_prefix(channel=first_result.channel, time=slice.time, roi_index=slice.roi))), + data = images_array, + bigtiff=True, + resolution=(1./self.lattice.dx, 1./self.lattice.dy, "MICROMETER"), + metadata={'spacing': self.lattice.new_dz, 'unit': 'um', 'axes': 'TZCYX'}, + imagej=True + ) + + # Reinitialise + self.pending_slices = [] + + self.time = slice.time + self.pending_slices.append(slice) diff --git a/core/tests/conftest.py b/core/tests/conftest.py index 01d3b8f..e9fbcfb 100644 --- a/core/tests/conftest.py +++ b/core/tests/conftest.py @@ -1,3 +1,4 @@ +from importlib_resources import as_file from typer.testing import CliRunner import pytest from skimage.io import imsave @@ -6,6 +7,8 @@ import pyclesperanto_prototype as cle import tempfile from numpy.typing import NDArray +from copy import copy +from lls_core.sample import resources from napari_workflows import Workflow from napari_workflows._io_yaml_v1 import save_workflow @@ -14,15 +17,46 @@ def runner() -> CliRunner: return CliRunner() +@pytest.fixture(params=[ + "RBC_tiny.czi", + "RBC_lattice.tif", + "LLS7_t1_ch1.czi", + "LLS7_t1_ch3.czi", + "LLS7_t2_ch1.czi", + "LLS7_t2_ch3.czi", + "multich_multi_time.tif" +]) +def image_path(request: pytest.FixtureRequest): + """ + Fixture function that yields test images as file paths + """ + with as_file(resources / request.param) as image_path: + yield image_path + @pytest.fixture -def workflow() -> Workflow: - # Instantiate segmentation workflow +def image_workflow() -> Workflow: + # Simple segmentation workflow that returns an image image_seg_workflow = Workflow() image_seg_workflow.set("gaussian", cle.gaussian_blur, "input", sigma_x=1, sigma_y=1, sigma_z=1) image_seg_workflow.set("binarisation", cle.threshold, "gaussian", constant=0.5) image_seg_workflow.set("labeling", cle.connected_components_labeling_box, "binarisation") return image_seg_workflow +@pytest.fixture +def table_workflow(image_workflow: Workflow) -> Workflow: + # Complex workflow that returns a tuple of an image and a value + ret = copy(image_workflow) + # Returns a tuple of: (image, dict, list) + ret.set("result", lambda x: ( + x, + { + "foo": 1, + "bar": 2 + }, + ["foo", "bar"] + ), "labeling") + return ret + @pytest.fixture def test_image() -> NDArray[np.float64]: raw = np.zeros((5, 5, 5)) diff --git a/core/tests/test_process.py b/core/tests/test_process.py index c81a945..7de0440 100644 --- a/core/tests/test_process.py +++ b/core/tests/test_process.py @@ -6,6 +6,7 @@ import tempfile from pathlib import Path from napari_workflows import Workflow +from pytest import FixtureRequest from .params import inputs, parameterized @@ -50,13 +51,20 @@ def test_process_deconvolution(args: dict, background: Any): assert slice.data.ndim == 3 @parameterized -def test_process_workflow(args: dict, workflow: Workflow): - for slice in LatticeData.parse_obj({ - "input_image": root / "raw.tif", - "workflow": workflow, - **args - }).process().slices: - assert slice.data.ndim == 3 +@pytest.mark.parametrize(["workflow_name"], [("image_workflow", ), ("table_workflow", )]) +def test_process_workflow(args: dict, request: FixtureRequest, workflow_name: str): + from pandas import DataFrame + + workflow: Workflow = request.getfixturevalue(workflow_name) + with tempfile.TemporaryDirectory() as tmpdir: + for roi, output in LatticeData.parse_obj({ + "input_image": root / "raw.tif", + "workflow": workflow, + "save_dir": tmpdir, + **args + }).process_workflow().process(): + assert roi is None or isinstance(roi, int) + assert isinstance(output, (Path, DataFrame)) @pytest.mark.parametrize(["roi_subset"], [ [None], diff --git a/core/tests/test_workflows.py b/core/tests/test_workflows.py index c149e51..d38d5ae 100644 --- a/core/tests/test_workflows.py +++ b/core/tests/test_workflows.py @@ -1,15 +1,20 @@ from typing import Callable -import os from copy import copy +from importlib_resources import as_file from numpy.typing import NDArray from napari_workflows import Workflow import tempfile +from pandas import DataFrame +from lls_core.models.lattice_data import LatticeData +from lls_core.sample import resources +from aicsimageio.aics_image import AICSImage + from tests.utils import invoke from pathlib import Path -from .params import config_types -from .utils import invoke +from .params import config_types, inputs +from .utils import invoke, valid_image_path def test_napari_workflow(workflow: Workflow, test_image: NDArray): @@ -50,3 +55,30 @@ def test_workflow_cli(workflow_config_cli: dict, save_func: Callable, cli_param: label_img = h5_file.read_view(time=0, channel=0) assert label_img.shape == (3, 14, 5) assert label_img[1, 6, 2] == 1 + +def test_image_workflow(image_path: Path, image_workflow: Workflow): + # Test that a regular workflow that returns an image directly works + with tempfile.TemporaryDirectory() as tmpdir: + for roi, output in LatticeData( + input_image = image_path, + workflow = image_workflow, + save_dir = tmpdir + ).process_workflow().process(): + assert isinstance(output, Path) + assert valid_image_path(output) + +def test_table_workflow(image_path: Path, table_workflow: Workflow): + # Test a complex workflow that returns a tuple of images and data + with tempfile.TemporaryDirectory() as tmpdir: + for roi, output in LatticeData( + input_image = image_path, + workflow = table_workflow, + save_dir = tmpdir + ).process_workflow().process(): + assert isinstance(output, (DataFrame, Path)) + if isinstance(output, DataFrame): + nrow, ncol = output.shape + assert nrow > 0 + assert ncol > 0 + else: + assert valid_image_path(output) diff --git a/core/tests/utils.py b/core/tests/utils.py index ad68041..c9744a0 100644 --- a/core/tests/utils.py +++ b/core/tests/utils.py @@ -1,6 +1,17 @@ +from pathlib import Path from typing import Sequence from typer.testing import CliRunner from lls_core.cmds.__main__ import app +import npy2bdv +from aicsimageio import AICSImage def invoke(args: Sequence[str]): CliRunner().invoke(app, args, catch_exceptions=False) + +def valid_image_path(path: Path) -> bool: + if path.suffix in {".hdf5", ".h5"}: + npy2bdv.npy2bdv.BdvEditor(str(path)).read_view() + return True + else: + AICSImage(path).get_image_data() + return True diff --git a/plugin/napari_lattice/dock_widget.py b/plugin/napari_lattice/dock_widget.py index 8a75cdf..a277a64 100644 --- a/plugin/napari_lattice/dock_widget.py +++ b/plugin/napari_lattice/dock_widget.py @@ -65,7 +65,7 @@ def _make_model(self) -> LatticeData: class LlszMenu(MagicTemplate): main_heading = field("

      Napari Lattice: Visualization & Analysis

      ", widget_type="Label") - heading1 = field("Drag and drop an image file onto napari.", widget_type="Label") + heading1 = field("Select the tabs one at a time to specify analysis parameters. Tabs 1 and 5 are mandatory.", widget_type="Label") # Tabbed Widget container to house all the widgets @magicclass(widget_type="tabbed", name="Functions", labels=False) From 72be81e0e74ebf99d8feb556427483bdcb611fbd Mon Sep 17 00:00:00 2001 From: Michael Milton Date: Mon, 30 Oct 2023 12:33:53 +1100 Subject: [PATCH 084/147] Document the new IO modules, remove the old io.py --- core/lls_core/cmds/__main__.py | 2 +- core/lls_core/io.py | 618 --------------------------- core/lls_core/models/lattice_data.py | 22 +- core/lls_core/models/results.py | 24 +- core/lls_core/writers.py | 71 ++- core/tests/conftest.py | 8 +- core/tests/params.py | 10 - core/tests/test_process.py | 25 +- core/tests/test_workflows.py | 6 +- 9 files changed, 104 insertions(+), 682 deletions(-) delete mode 100644 core/lls_core/io.py diff --git a/core/lls_core/cmds/__main__.py b/core/lls_core/cmds/__main__.py index 65c4fac..1fd8919 100644 --- a/core/lls_core/cmds/__main__.py +++ b/core/lls_core/cmds/__main__.py @@ -178,7 +178,7 @@ def process( # Console().print(ctx.get_help()) raise Exit(code=1) - lattice.process().save_image() + lattice.save() def main(): app() diff --git a/core/lls_core/io.py b/core/lls_core/io.py deleted file mode 100644 index 4862035..0000000 --- a/core/lls_core/io.py +++ /dev/null @@ -1,618 +0,0 @@ -from __future__ import annotations -# Opening and saving files -import aicsimageio -from aicsimageio.types import ImageLike, ArrayLike -from aicsimageio import AICSImage - -from pathlib import Path - -import pyclesperanto_prototype as cle -import sys -import dask -from resource_backed_dask_array import ResourceBackedDaskArray -import dask.array as da -from dask.array.core import Array as DaskArray -import pandas as pd -from typing import TYPE_CHECKING, Callable, Optional, Literal, Any -from os import PathLike - -from dask.distributed import Client -from dask.cache import Cache - -from lls_core.utils import etree_to_dict -from lls_core.llsz_core import crop_volume_deskew -from lls_core.deconvolution import skimage_decon, pycuda_decon -from lls_core import config, DeconvolutionChoice, SaveFileType -from lls_core.models.lattice_data import LatticeData - -import os -import numpy as np -from tqdm import tqdm -from tifffile import imwrite, TiffWriter - -import npy2bdv - -# Enable Logging -import logging -logger = logging.getLogger(__name__) - -if TYPE_CHECKING: - from napari.types import ImageData - from napari_workflows import Workflow - -def convert_imgdata_aics(img_data: ImageData): - """Return AICSimage object from napari ImageData type - - Args: - img_data ([type]): [description] - - Returns: - AICImage: [description] - """ - # Error handling for czi file - try: - # stack=aicsimageio.imread_dask(img_location) - # using AICSImage will read data as STCZYX - stack = aicsimageio.AICSImage(img_data) - # stack_meta=aicsimageio.imread_dask(img_location) - except Exception: - print("Error: A ", sys.exc_info()[ - 0], "has occurred. See below for details.") - raise - - # Dask setup - # Setting up dask scheduler - # start distributed scheduler locally. Launch dashboard - client = Client(processes=False) - # memory_limit='30GB', - dask.config.set({"temporary_directory": "C:\\Dask_temp\\", "optimization.fuse.active": False, - 'array.slicing.split_large_chunks': False}) - cache = Cache(2e9) - cache.register() - print("If using dask, dask Client can be viewed at:", client.dashboard_link) - - # Check metadata to verify postprocessing or any modifications by Zen - return stack - - -# will flesh this out once Zeiss lattice has more relevant metadata in the czi file -def check_metadata(img_path: ImageLike): - print("Checking CZI metadata") - metadatadict_czi = etree_to_dict(aicsimageio.AICSImage(img_path).metadata) - metadatadict_czi = metadatadict_czi["ImageDocument"]["Metadata"] - acquisition_mode_setup = metadatadict_czi["Experiment"]["ExperimentBlocks"][ - "AcquisitionBlock"]["HelperSetups"]["AcquisitionModeSetup"]["Detectors"] - print(acquisition_mode_setup["Detector"]["ImageOrientation"]) - print("Image Orientation: If any post processing has been applied, it will appear here.\n \ - For example, Zen 3.2 flipped the image so the coverslip was towards the bottom of Z stack. So, value will be 'Flip'") - -# TODO: write save function for deskew and for crop - - -def save_img(vol: ArrayLike, - func: Callable, - time_start: int, - time_end: int, - channel_start: int, - channel_end: int, - save_file_type: SaveFileType, - save_path: PathLike, - save_name_prefix: str = "", - save_name: str = "img", - dx: float = 1, - dy: float = 1, - dz: float = 1, - angle: Optional[float] = None, - # This is a MagicTemplate object but we want to avoid depending on magicclass - # TODO: refactor this out - LLSZWidget: Optional[Any]=None, - terminal: bool = False, - lattice: Optional[LatticeData]=None, - *args, **kwargs): - """ - Applies a function as described in callable - Args: - vol (_type_): Volume to process - func (callable): _description_ - time_start (int): _description_ - time_end (int): _description_ - channel_start (int): _description_ - channel_end (int): _description_ - save_file_type: either 'tiff' or SaveFileType.h5 - save_path (Path): _description_ - save_name_prefix (str, optional): Add a prefix to name. For example, if processng ROIs, add ROI_1_. Defaults to "". - save_name (str, optional): name of file being saved. Defaults to "img". - dx (float, optional): _description_. Defaults to 1. - dy (float, optional): _description_. Defaults to 1. - dz (float, optional): _description_. Defaults to 1. - angle(float, optional) = Deskewing angle in degrees, used to calculate new z - LLSZWidget(class,optional) = LLSZWidget class - """ - - # save_path = save_path.__str__() - _save_path: Path = Path(save_path) - - # replace any : with _ and remove spaces in case it hasn't been processed/skipped - save_name = str(save_name).replace(":", "_").replace(" ", "") - - time_range = range(time_start, time_end+1) - - channel_range = range(channel_start, channel_end+1) - - # Calculate new_pixel size in z after deskewing - if angle > 0: - import math - new_dz = math.sin(angle * math.pi / 180.0) * dz - else: - new_dz = dz - - if func is crop_volume_deskew: - # create folder for each ROI; disabled as each roi is saved as hyperstack - save_name_prefix = save_name_prefix + "_" - #save_path = save_path+os.sep+save_name_prefix+os.sep - # if not os.path.exists(save_path): - # os.makedirs(save_path) - im_final = [] - - # setup bdvwriter - if save_file_type == SaveFileType.h5: - if func is crop_volume_deskew: - save_path_h5 = _save_path / (save_name_prefix + "_" + save_name + ".h5") - else: - save_path_h5 = _save_path / (save_name + ".h5") - - bdv_writer = npy2bdv.BdvWriter(str(save_path_h5), - compression='gzip', - nchannels=len(channel_range), - subsamp=( - (1, 1, 1), (1, 2, 2), (2, 4, 4)), - overwrite=False) - - # bdv_writer = npy2bdv.BdvWriter(save_path_h5, compression=None, nchannels=len(channel_range)) #~30% faster, but up to 10x bigger filesize - else: - pass - - if terminal: - if lattice.decon_processing: - decon_value = True - decon_option = lattice.decon_processing # decon_processing holds the choice - lattice_class = lattice - logging.debug(f"Decon option {decon_option}") - - else: - try: - decon_value = LLSZWidget.LlszMenu.deconvolution.value - lattice_class = LLSZWidget.LlszMenu.lattice - decon_option = LLSZWidget.LlszMenu.lattice.decon_processing - except: - decon_value = 0 - lattice_class = 0 - decon_option = 0 - - # loop is ordered so image is saved in order TCZYX for ometiffwriter - for loop_time_idx, time_point in enumerate(tqdm(time_range, desc="Time", position=0)): - images_array = [] - for loop_ch_idx, ch in enumerate(tqdm(channel_range, desc="Channels", position=1, leave=False)): - try: - if len(vol.shape) == 3: - raw_vol = vol - elif len(vol.shape) == 4: - raw_vol = vol[time_point, :, :, :] - elif len(vol.shape) == 5: - raw_vol = vol[time_point, ch, :, :, :] - except IndexError: - assert ch <= channel_end, f"Channel out of range. Got {ch}, but image has channels {channel_end+1}" - assert time_point <= channel_end, f"Channel out of range. Got {ch}, but image has channels {channel_end+1}" - assert len(vol.shape) in [ - 3, 4, 5], f"Check shape of volume. Expected volume with shape 3,4 or 5. Got {vol.shape} with shape {len(vol.shape)}" - print(f"Using time points {time_point} and channel {ch}") - exit() - - #raw_vol = np.array(raw_vol) - image_type = raw_vol.dtype - #print(decon_value) - #print(decon_option) - #print(func) - # Add a check for last timepoint, in case acquisition incomplete - if time_point == time_end: - orig_shape = raw_vol.shape - raw_vol = raw_vol.compute() - if raw_vol.shape != orig_shape: - print( - f"Time {time_point}, channel {ch} is incomplete. Actual shape {orig_shape}, got {raw_vol.shape}") - z_diff, y_diff, x_diff = np.subtract( - orig_shape, raw_vol.shape) - print(f"Padding with{z_diff,y_diff,x_diff}") - raw_vol = np.pad( - raw_vol, ((0, z_diff), (0, y_diff), (0, x_diff))) - assert raw_vol.shape == orig_shape, f"Shape of last timepoint still doesn't match. Got {raw_vol.shape}" - - # If deconvolution is checked - if decon_value and func != crop_volume_deskew: - # Use CUDA or skimage for deconvolution based on user choice - if decon_option == DeconvolutionChoice.cuda_gpu: - raw_vol = pycuda_decon(image=raw_vol, - #otf_path = LLSZWidget.LlszMenu.lattice.otf_path[ch], - psf=lattice_class.psf[ch], - dzdata=lattice_class.dz, - dxdata=lattice_class.dx, - dzpsf=lattice_class.dz, - dxpsf=lattice_class.dx, - num_iter=lattice_class.psf_num_iter) - else: - raw_vol = skimage_decon(vol_zyx=raw_vol, - psf=lattice_class.psf[ch], - num_iter=lattice_class.psf_num_iter, - clip=False, filter_epsilon=0, boundary='nearest') - - # The following will apply the user-passed function to the input image - if func is crop_volume_deskew and decon_value == True: - processed_vol = func(original_volume=raw_vol, - deconvolution=decon_value, - decon_processing=decon_option, - psf=lattice_class.psf[ch], - num_iter=lattice_class.psf_num_iter, - *args, **kwargs).astype(image_type) - - elif func is cle.deskew_y or func is cle.deskew_x: - processed_vol = func(input_image=raw_vol, - *args, **kwargs).astype(image_type) - - elif func is crop_volume_deskew: - processed_vol = func( - original_volume=raw_vol, *args, **kwargs).astype(image_type) - else: - # if its not deskew or crop/deskew, apply the user-passed function and any specific parameters - processed_vol = func(*args, **kwargs).astype(image_type) - - processed_vol = cle.pull_zyx(processed_vol) - - if save_file_type == SaveFileType.h5: - # convert opencl array to dask array - #pvol = da.asarray(processed_vol) - # channel and time index are based on loop iteration - bdv_writer.append_view(processed_vol, - time=loop_time_idx, - channel=loop_ch_idx, - voxel_size_xyz=(dx, dy, new_dz), - voxel_units='um') - - #print("\nAppending volume to h5\n") - else: - images_array.append(processed_vol) - - # if function is not for cropping, then dataset can be quite large, so save each channel and timepoint separately - # otherwise, append it into im_final - - if func != crop_volume_deskew and save_file_type == SaveFileType.tiff: - final_name = _save_path / (save_name_prefix + "C" + str(ch) + "T" + str( time_point) + "_" + save_name+".tif") - images_array = np.array(images_array) - images_array = np.expand_dims(images_array, axis=0) - images_array = np.swapaxes(images_array, 1, 2) - imwrite(final_name, - images_array, - bigtiff=True, - resolution=(1./dx, 1./dy, "MICROMETER"), - metadata={'spacing': new_dz, 'unit': 'um', 'axes': 'TZCYX'}, imagej=True) - elif save_file_type == SaveFileType.tiff: - # convert list of arrays into a numpy array and then append to im_final - im_final.append(np.array(images_array)) - - # close the h5 writer or if its tiff, save images - if save_file_type == SaveFileType.h5: - bdv_writer.write_xml() - bdv_writer.close() - - elif func is crop_volume_deskew and save_file_type == SaveFileType.tiff: - - im_final = np.array(im_final) - final_name = _save_path / (save_name_prefix + "_" + save_name + ".tif") - - im_final = np.swapaxes(im_final, 1, 2) - # imagej=True; ImageJ hyperstack axes must be in TZCYXS order - - imwrite(final_name, - im_final, - # specify resolution unit for consistent metadata) - resolution=(1./dx, 1./dy, "MICROMETER"), - metadata={'spacing': new_dz, 'unit': 'um', 'axes': 'TZCYX'}, - imagej=True) - im_final = None - - -def save_img_workflow(vol, - workflow: Workflow, - input_arg: str, - first_task: str, - last_task: str, - time_start: int, - time_end: int, - channel_start: int, - channel_end: int, - save_file_type, - save_path: Path, - save_name_prefix: str = "", - save_name: str = "img", - dx: float = 1, - dy: float = 1, - dz: float = 1, - angle: float = None, - deconvolution: bool = False, - decon_processing: str = None, - psf_arg=None, - psf=None, - otf_path=None): - """ - Applies a workflow to the image and saves the output - Use of workflows ensures its agnostic to the processing operation - Args: - vol (_type_): Volume to process - workflow (Workflow): napari workflow - input_arg (str): name for input image - task_name (str): name of the task that should be executed in the workflow - time_start (int): _description_ - time_start (int): _description_ - time_end (int): _description_ - channel_start (int): _description_ - channel_end (int): _description_ - save_path (Path): _description_ - save_name_prefix (str, optional): Add a prefix to name. For example, if processng ROIs, add ROI_1_. Defaults to "". - save_name (str, optional): name of file being saved. Defaults to "img". - dx (float, optional): _description_. Defaults to 1. - dy (float, optional): _description_. Defaults to 1. - dz (float, optional): _description_. Defaults to 1. - angle(float, optional) = Deskewing angle in degrees, used to calculate new z - """ - - # TODO: Implement h5 saving - - save_path = save_path.__str__() - - # replace any : with _ and remove spaces in case it hasn't been processed/skipped - save_name = save_name.replace(":", "_").replace(" ", "") - - # adding +1 at the end so the last channel and time is included - - time_range = range(time_start, time_end + 1) - channel_range = range(channel_start, channel_end + 1) - - # Calculate new_pixel size in z - # convert voxel sixes to an aicsimage physicalpixelsizes object for metadata - if angle: - import math - new_dz = math.sin(angle * math.pi / 180.0) * dz - #aics_image_pixel_sizes = PhysicalPixelSizes(new_dz,dy,dx) - else: - #aics_image_pixel_sizes = PhysicalPixelSizes(dz,dy,dx) - new_dz = dz - - # get list of all functions in the workflow - workflow_functions = [i[0] for i in workflow._tasks.values()] - - # iterate through time and channels and apply workflow - # TODO: add error handling so the image writers will "close",if an error causes the program to exit - # try except? - for loop_time_idx, time_point in enumerate(tqdm(time_range, desc="Time", position=0)): - output_array = [] - data_table = [] - for loop_ch_idx, ch in enumerate(tqdm(channel_range, desc="Channels", position=1, leave=False)): - - if len(vol.shape) == 3: - raw_vol = vol - else: - raw_vol = vol[time_point, ch, :, :, :] - - # TODO: disable if support for resourc backed dask array is added - # if type(raw_vol) in [resource_backed_dask_array]: - # raw_vol = raw_vol.compute() #convert to numpy array as resource backed dask array not su - - # to access current time and channel, create a file config.py in same dir as workflow or in home directory - # add "channel = 0" and "time=0" in the file and save - # https://docs.python.org/3/faq/programming.html?highlight=global#how-do-i-share-global-variables-across-modules - - config.channel = ch - config.time = time_point - - # if deconvolution, need to define psf and choose the channel appropriate one - if deconvolution: - workflow.set(psf_arg, psf[ch]) - # if decon_processing == "cuda_gpu": - # workflow.set("psf",psf[ch]) - # else: - # workflow.set("psf",psf[ch]) - - # Add a check for last timepoint, in case acquisition incomplete or an incomplete frame - if time_point == time_end: - orig_shape = raw_vol.shape - raw_vol = raw_vol.compute() - if raw_vol.shape != orig_shape: - print( - f"Time {time_point}, channel {ch} is incomplete. Actual shape {orig_shape}, got {raw_vol.shape}") - z_diff, y_diff, x_diff = np.subtract( - orig_shape, raw_vol.shape) - print(f"Padding with{z_diff,y_diff,x_diff}") - raw_vol = np.pad( - raw_vol, ((0, z_diff), (0, y_diff), (0, x_diff))) - assert raw_vol.shape == orig_shape, f"Shape of last timepoint still doesn't match. Got {raw_vol.shape}" - - # Set input to the workflow to be volume from each time point and channel - workflow.set(input_arg, raw_vol) - # execute workflow - processed_vol = workflow.get(last_task) - - output_array.append(processed_vol) - - output_array = np.array(output_array) - - # if workflow returns multiple objects (images, dictionaries, lsits etc..), each object can be accessed by - # output_array[:,index_of_object] - - # use data from first timepoint to get the output type from workflow - # check if multiple objects in the workflow output, if so, get the index for each item - # currently, images, lists and dictionaries are supported - if loop_time_idx == 0: - - # get no of elements - - no_elements = len(processed_vol) - # initialize lsits to hold indexes for each datatype - list_element_index = [] # store indices of lists - dict_element_index = [] # store indices of dicts - # store indices of images (numpy array, dask array and pyclesperanto array) - image_element_index = [] - - # single output and is just dictionary - if isinstance(processed_vol, dict): - dict_element_index = [0] - # if image - elif isinstance(processed_vol, (np.ndarray, cle._tier0._pycl.OCLArray, DaskArray, ResourceBackedDaskArray)): - image_element_index = [0] - no_elements = 1 - # multiple elements - # list with values returns no_elements>1 so make sure its actually a list with different objects - # test this with different workflows - elif no_elements > 1 and type(processed_vol[0]) not in [np.int16, np.int32, np.float16, np.float32, np.float64, int, float]: - array_element_type = [type(output_array[0, i]) - for i in range(no_elements)] - image_element_index = [idx for idx, data_type in enumerate( - array_element_type) if data_type in [np.ndarray, cle._tier0._pycl.OCLArray, da.core.Array]] - dict_element_index = [idx for idx, data_type in enumerate( - array_element_type) if data_type in [dict]] - list_element_index = [idx for idx, data_type in enumerate( - array_element_type) if data_type in [list]] - elif type(processed_vol) is list: - list_element_index = [0] - - # setup required image writers - if len(image_element_index) > 0: - # pass list of images and index to function - writer_list = [] - # create an image writer for each image - for element in range(len(image_element_index)): - final_save_path = save_path + os.sep + save_name_prefix + "_" + \ - str(element)+"_" + save_name + \ - "." + save_file_type.value - # setup writer based on user choice of filetype - if save_file_type == SaveFileType.h5: - bdv_writer = npy2bdv.BdvWriter(final_save_path, - compression='gzip', - nchannels=len( - channel_range), - subsamp=( - (1, 1, 1), (1, 2, 2), (2, 4, 4)), - overwrite=True) # overwrite set to True; is this good practice? - writer_list.append(bdv_writer) - else: - # imagej =true throws an error - writer_list.append(TiffWriter( - final_save_path, bigtiff=True)) - - # handle image saving: either h5 or tiff saving - if len(image_element_index) > 0: - # writer_idx is for each writers, image_idx will be the index of images - for writer_idx, image_idx in enumerate(image_element_index): - # access the image - # print(len(time_range)) - if len(channel_range) == 1: - if (len(time_range)) == 1: # if only one timepoint - im_final = np.stack( - output_array[image_idx, ...]).astype(raw_vol.dtype) - else: - im_final = np.stack( - output_array[0, image_idx]).astype(raw_vol.dtype) - else: - im_final = np.stack( - output_array[:, image_idx]).astype(raw_vol.dtype) - if save_file_type == SaveFileType.h5: - for ch_idx in channel_range: - # write h5 images as 3D stacks - assert len( - im_final.shape) >= 3, f"Image shape should be >=3, got {im_final.shape}" - # print(im_final.shape) - if len(im_final.shape) == 3: - im_channel = im_final - elif len(im_final.shape) > 3: - im_channel = im_final[ch_idx, ...] - - writer_list[writer_idx].append_view(im_channel, - time=loop_time_idx, - channel=loop_ch_idx, - voxel_size_xyz=( - dx, dy, new_dz), - voxel_units='um') - else: # default to tif - # Use below with imagej=True - # if len(im_final.shape) ==4: #if only one image with no channel, then dimension will 1,z,y,x, so swap 0 and 1 - # im_final = np.swapaxes(im_final,0,1).astype(raw_vol.dtype) #was 1,2,but when stacking images, dimension is CZYX - # im_final = im_final[np.newaxis,...] #adding extra dimension for T - # elif len(im_final.shape)>4: - # im_final = np.swapaxes(im_final,1,2).astype(raw_vol.dtype) #if image with multiple channels, , it will be 1,c,z,y,x - # imagej=True; ImageJ hyperstack axes must be in TZCYXS order - #images_array = np.swapaxes(images_array,0,1).astype(raw_vol.dtype) - writer_list[writer_idx].write(im_final, - resolution=( - 1./dx, 1./dy, "MICROMETER"), - metadata={'spacing': new_dz, 'unit': 'um', 'axes': 'TZCYX', 'PhysicalSizeX': dx, - 'PhysicalSizeXUnit': 'µm', 'PhysicalSizeY': dy, 'PhysicalSizeYUnit': 'µm'}) - im_final = None - - # handle dict saving - # convert to pandas dataframe; update columns with channel and time - if len(dict_element_index) > 0: - # Iterate through the dict output from workflow and add columns for Channel and timepoint - for element in dict_element_index: - for j in channel_range: - output_array[j, element].update({"Channel": "C"+str(j)}) - output_array[j, element].update( - {"Time": "T"+str(time_point)}) - - # convert to pandas dataframe - output_dict_pd = [pd.DataFrame(i) - for i in output_array[:, element]] - - output_dict_pd = pd.concat(output_dict_pd) - # set index to the channel/time - output_dict_pd = output_dict_pd.set_index(["Time", "Channel"]) - - # Save path - dict_save_path = os.path.join( - save_path, "Measurement_"+save_name_prefix) - if not (os.path.exists(dict_save_path)): - os.mkdir(dict_save_path) - - #dict_save_path = os.path.join(dict_save_path,"C" + str(ch) + "T" + str(time_point)+"_"+str(element) + "_measurement.csv") - dict_save_path = os.path.join( - dict_save_path, "Summary_measurement_"+save_name_prefix+"_"+str(element)+"_.csv") - # Opens csv and appends it if file already exists; not efficient. - if os.path.exists(dict_save_path): - output_dict_pd_existing = pd.read_csv( - dict_save_path, index_col=["Time", "Channel"]) - output_dict_summary = pd.concat( - (output_dict_pd_existing, output_dict_pd)) - output_dict_summary.to_csv(dict_save_path) - else: - output_dict_pd.to_csv(dict_save_path) - - if len(list_element_index) > 0: - row_idx = [] - for element in dict_element_index: - for j in channel_range: - row_idx.append("C"+str(j)+"T"+str(time_point)) - output_list_pd = pd.DataFrame( - np.vstack(output_array[:, element]), index=row_idx) - # Save path - list_save_path = os.path.join( - save_path, "Measurement_"+save_name_prefix) - if not (os.path.exists(list_save_path)): - os.mkdir(list_save_path) - list_save_path = os.path.join(list_save_path, "C" + str(ch) + "T" + str( - time_point)+"_"+save_name_prefix+"_"+str(element) + "_measurement.csv") - output_list_pd.to_csv(list_save_path) - - if len(image_element_index) > 0: - for writer_idx in range(len(image_element_index)): - if save_file_type == SaveFileType.h5: - # write h5 metadata - writer_list[writer_idx].write_xml() - # close the writers (applies for both tiff and h5) - writer_list[writer_idx].close() diff --git a/core/lls_core/models/lattice_data.py b/core/lls_core/models/lattice_data.py index 56f56a0..3653d81 100644 --- a/core/lls_core/models/lattice_data.py +++ b/core/lls_core/models/lattice_data.py @@ -410,27 +410,37 @@ def process_workflow(self) -> WorkflowSlices: lattice_data=self ) - def process(self) -> ProcessedSlices: + def process(self) -> ImageSlices: """ Execute the processing and return the result. - This is the main public API for processing + This will not execute the attached workflow. """ - from lls_core.models.results import ProcessedSlices - ProcessedSlices.update_forward_refs(LatticeData=LatticeData) + from lls_core.models.results import ImageSlices + ImageSlices.update_forward_refs(LatticeData=LatticeData) # if self.workflow is not None: # return self._process_workflow() if self.cropping_enabled: - return ProcessedSlices( + return ImageSlices( lattice_data=self, slices=self._process_crop() ) else: - return ProcessedSlices( + return ImageSlices( lattice_data=self, slices=self._process_non_crop() ) + def save(self): + """ + + This is the main public API for processing + """ + if self.workflow: + list(self.process_workflow().save()) + else: + self.process().save_image() + def process_into_image(self) -> ArrayLike: """ Shortcut method for calling process, then extracting one image layer. diff --git a/core/lls_core/models/results.py b/core/lls_core/models/results.py index c8498a5..213c655 100644 --- a/core/lls_core/models/results.py +++ b/core/lls_core/models/results.py @@ -41,8 +41,9 @@ def copy_with_data(self, data: S) -> ProcessedSlice[S]: class ProcessedSlices(BaseModel, Generic[T], arbitrary_types_allowed=True): """ - The main output from deskewing. A class that holds an iterable of - output image slices. + A generic parent class for holding deskewing outputs. + This will never be instantiated directly. + Refer to the concrete child classes for more detail. """ #: Iterable of result slices. #: Note that this is a finite iterator that can only be iterated once @@ -54,7 +55,9 @@ class ProcessedSlices(BaseModel, Generic[T], arbitrary_types_allowed=True): ImageSlice = ProcessedSlice[ArrayLike] class ImageSlices(ProcessedSlices[ArrayLike]): """ - A collection of image slices + A collection of image slices, which is the main output from deskewing. + This holds an iterable of output image slices before they are saved to disk, + and provides a `save_image()` method for this purpose. """ def save_image(self): """ @@ -65,6 +68,7 @@ def save_image(self): writer = Writer(self.lattice_data, roi_index=roi) for slice in roi_results: writer.write_slice(slice) + writer.close() RawWorkflowOutput = Union[ @@ -79,12 +83,16 @@ def save_image(self): ] class WorkflowSlices(ProcessedSlices[Tuple[RawWorkflowOutput]]): + """ + The counterpart of `ImageSlices`, but for workflow outputs. + This is needed because workflows have vastly different outputs that may include regular + Python types rather than only image slices. + """ def process(self) -> Iterable[Tuple[RoiIndex, ProcessedWorkflowOutput]]: """ Incrementally processes the workflow outputs, and returns both image paths and data frames of the outputs, for image slices and dict/list outputs respectively """ - from pathlib import Path import pandas as pd for roi, roi_results in groupby(self.slices, key=lambda it: it.roi_index): @@ -114,11 +122,17 @@ def process(self) -> Iterable[Tuple[RoiIndex, ProcessedWorkflowOutput]]: for element in values: if isinstance(element, Writer): element.close() - yield roi, Path(element.get_filepath()) + for file in element.written_files: + yield roi, file else: yield roi, pd.DataFrame(element) def save(self) -> Iterable[Path]: + """ + Processes all workflow outputs and saves them to disk. + Images are saved in the format specified in the `LatticeData`, while + other data types are saved as a data frame. + """ for roi, result in self.process(): if isinstance(result, DataFrame): path = self.lattice_data.make_filepath(make_filename_prefix(roi_index=roi)) diff --git a/core/lls_core/writers.py b/core/lls_core/writers.py index e8cf5ab..410eeb4 100644 --- a/core/lls_core/writers.py +++ b/core/lls_core/writers.py @@ -2,8 +2,7 @@ from abc import ABC, abstractmethod from dataclasses import dataclass, field -from itertools import groupby -from typing import TYPE_CHECKING, Iterator, List, Optional +from typing import TYPE_CHECKING, Iterable, Iterator, List, Optional from lls_core.types import ArrayLike @@ -15,41 +14,52 @@ if TYPE_CHECKING: from lls_core.models.lattice_data import LatticeData import npy2bdv - from lls_core.models.results import ProcessedSlice + from lls_core.models.results import ProcessedSlice, ImageSlice + from pathlib import Path @dataclass class Writer(ABC): + """ + A writer is an abstraction over the logic used to write image slices to disk + Writers need to work incrementally, in order that we don't need the entire multidimensional + image in memory at the same time + """ lattice: LatticeData roi_index: RoiIndex + written_files: List[Path] = field(default_factory=list, init=False) - @abstractmethod - def get_filepath(self) -> str: - pass - @abstractmethod def write_slice(self, slice: ProcessedSlice[ArrayLike]): + """ + Writes a 3D image slice + """ pass def close(self): + """ + Called when no more image slices are available, and the writer should finalise its output files + """ pass @dataclass class BdvWriter(Writer): + """ + A writer for for Fiji BigDataViewer output format + """ bdv_writer: npy2bdv.BdvWriter = field(init=False) - def get_filepath(self) -> str: - return str(self.lattice.make_filepath(make_filename_prefix(roi_index=self.roi_index))) - def __post_init__(self): import npy2bdv + path = self.lattice.make_filepath(make_filename_prefix(roi_index=self.roi_index)) self.bdv_writer = npy2bdv.BdvWriter( - filename=self.get_filepath(), + filename=str(path), compression='gzip', nchannels=len(self.lattice.channel_range), subsamp=((1, 1, 1), (1, 2, 2), (2, 4, 4)), overwrite=False ) + self.written_files.append(path) def write_slice(self, slice: ProcessedSlice[ArrayLike]): import numpy as np @@ -67,33 +77,52 @@ def close(self): @dataclass class TiffWriter(Writer): - pending_slices: list = field(default_factory=list, init=False) + """ + A writer for for TIFF output format + """ + pending_slices: List[ImageSlice] = field(default_factory=list, init=False) time: Optional[NonNegativeInt] = None - def get_filepath(self) -> str: - return str(self.lattice.make_filepath(make_filename_prefix(roi_index=self.roi_index))) - def __post_init__(self): self.pending_slices = [] - - def write_slice(self, slice: ProcessedSlice[ArrayLike]): + + def flush(self): + "Write out all pending slices" import numpy as np import tifffile - if slice.time != self.time and len(self.pending_slices) > 0: - # Write the old timepoint once we + if len(self.pending_slices) > 0: first_result = self.pending_slices[0] - images_array = np.swapaxes(np.expand_dims([result.data for result in self.pending_slices], axis=0), 1, 2) + images_array = np.swapaxes( + np.expand_dims([result.data for result in self.pending_slices], axis=0), + 1, 2 + ).astype("uint16") + # ImageJ TIFF can only handle 16-bit uints, not 32 + path = self.lattice.make_filepath( + make_filename_prefix( + channel=first_result.channel, + time=first_result.time, + roi_index=first_result.roi_index + ) + ) tifffile.imwrite( - str(self.lattice.make_filepath(make_filename_prefix(channel=first_result.channel, time=slice.time, roi_index=slice.roi))), + str(path), data = images_array, bigtiff=True, resolution=(1./self.lattice.dx, 1./self.lattice.dy, "MICROMETER"), metadata={'spacing': self.lattice.new_dz, 'unit': 'um', 'axes': 'TZCYX'}, imagej=True ) + self.written_files.append(path) # Reinitialise self.pending_slices = [] + def write_slice(self, slice: ProcessedSlice[ArrayLike]): + if slice.time != self.time: + self.flush() + self.time = slice.time self.pending_slices.append(slice) + + def close(self): + self.flush() diff --git a/core/tests/conftest.py b/core/tests/conftest.py index e9fbcfb..22e05ed 100644 --- a/core/tests/conftest.py +++ b/core/tests/conftest.py @@ -64,22 +64,22 @@ def test_image() -> NDArray[np.float64]: return raw @pytest.fixture -def workflow_config(workflow: Workflow, test_image: NDArray): +def workflow_config(image_workflow: Workflow, test_image: NDArray): # Create a config file yield { "input_image": test_image, - "workflow": workflow, + "workflow": image_workflow, } @pytest.fixture -def workflow_config_cli(workflow: Workflow, test_image: NDArray): +def workflow_config_cli(image_workflow: Workflow, test_image: NDArray): with tempfile.TemporaryDirectory() as tempdir_str: tempdir = Path(tempdir_str) input = tempdir / "raw.tiff" output = tempdir / "output" output.mkdir(parents=True) workflow_path = tempdir / "workflow.json" - save_workflow(str(workflow_path), workflow) + save_workflow(str(workflow_path), image_workflow) # Create a zero array of shape 5x5x5 with a value of 10 at (2,4,2) imsave(input, test_image) diff --git a/core/tests/params.py b/core/tests/params.py index afc4c6c..4e927bf 100644 --- a/core/tests/params.py +++ b/core/tests/params.py @@ -4,16 +4,6 @@ from lls_core.models.output import SaveFileType -inputs = pytest.mark.parametrize( - ["path"], [ - ("RBC_tiny.czi", ), - ("RBC_lattice.tif", ), - ("LLS7_t1_ch1.czi", ), - ("LLS7_t1_ch3.czi", ), - ("LLS7_t2_ch1.czi", ), - ("LLS7_t2_ch3.czi", ), - ("multich_multi_time.tif", ), -]) parameterized = pytest.mark.parametrize("args", [ {"skew": "X"}, {"skew": "Y"}, diff --git a/core/tests/test_process.py b/core/tests/test_process.py index 7de0440..7df0dd0 100644 --- a/core/tests/test_process.py +++ b/core/tests/test_process.py @@ -8,29 +8,26 @@ from napari_workflows import Workflow from pytest import FixtureRequest -from .params import inputs, parameterized +from .params import parameterized root = Path(__file__).parent / "data" def open_psf(name: str): with as_file(resources / "psfs" / "zeiss_simulated" / name) as path: return path -@inputs @parameterized -def test_process(path: str, args: dict): - with as_file(resources / path) as lattice_path: - for slice in LatticeData.parse_obj({ - "input_image": lattice_path, - **args - }).process().slices: - assert slice.data.ndim == 3 +def test_process(image_path: str, args: dict): + for slice in LatticeData.parse_obj({ + "input_image": image_path, + **args + }).process().slices: + assert slice.data.ndim == 3 -@inputs @parameterized -def test_save(path: str, args: dict): - with as_file(resources / path) as lattice_path, tempfile.TemporaryDirectory() as tempdir: +def test_save(image_path: str, args: dict): + with tempfile.TemporaryDirectory() as tempdir: LatticeData.parse_obj({ - "input_image": lattice_path, + "input_image": image_path, "save_dir": tempdir, **args }).process().save_image() @@ -72,7 +69,7 @@ def test_process_workflow(args: dict, request: FixtureRequest, workflow_name: st [[0, 1]], ]) @parameterized -def test_process_crop(args: dict, roi_subset: Optional[List[int]], workflow: Workflow): +def test_process_crop(args: dict, roi_subset: Optional[List[int]]): with as_file(resources / "RBC_tiny.czi") as lattice_path: rois = root / "crop" / "two_rois.zip" for slice in LatticeData.parse_obj({ diff --git a/core/tests/test_workflows.py b/core/tests/test_workflows.py index d38d5ae..89bfa5b 100644 --- a/core/tests/test_workflows.py +++ b/core/tests/test_workflows.py @@ -13,16 +13,16 @@ from tests.utils import invoke from pathlib import Path -from .params import config_types, inputs +from .params import config_types from .utils import invoke, valid_image_path -def test_napari_workflow(workflow: Workflow, test_image: NDArray): +def test_napari_workflow(image_workflow: Workflow, test_image: NDArray): """ Test napari workflow to see if it works before we run it using napari_lattice This is without deskewing """ - workflow = copy(workflow) + workflow = copy(image_workflow) # Set input image to be the "raw" image workflow.set("input", test_image) labeling = workflow.get("labeling") From d037d9dbfab16d1275e90f0373d6369f7e73a382 Mon Sep 17 00:00:00 2001 From: Michael Milton Date: Mon, 30 Oct 2023 17:12:25 +1100 Subject: [PATCH 085/147] Apply feedback to plugin, change save suffix behaviour --- core/lls_core/models/deskew.py | 5 +++++ core/lls_core/models/lattice_data.py | 30 ++++++---------------------- core/lls_core/models/output.py | 27 +++++++++++++++++++------ core/tests/test_workflows.py | 7 ++++--- plugin/napari_lattice/dock_widget.py | 23 +++++++++++++++++++-- plugin/napari_lattice/fields.py | 16 ++++++++------- 6 files changed, 66 insertions(+), 42 deletions(-) diff --git a/core/lls_core/models/deskew.py b/core/lls_core/models/deskew.py index 76888e6..96d5c55 100644 --- a/core/lls_core/models/deskew.py +++ b/core/lls_core/models/deskew.py @@ -130,6 +130,11 @@ def channels(self) -> int: """Number of channels""" return self.input_image.sizes["C"] + @property + def nslices(self) -> int: + """The number of 3D slices within the image""" + return self.time * self.channels + @property def new_dz(self): import math diff --git a/core/lls_core/models/lattice_data.py b/core/lls_core/models/lattice_data.py index 3653d81..96eb9de 100644 --- a/core/lls_core/models/lattice_data.py +++ b/core/lls_core/models/lattice_data.py @@ -1,12 +1,11 @@ from __future__ import annotations # class for initializing lattice data and setting metadata # TODO: handle scenes -from pydantic import BaseModel, DirectoryPath, Field, NonNegativeInt, root_validator, validator +from pydantic import DirectoryPath, Field, root_validator, validator from dask.array.core import Array as DaskArray -from itertools import groupby -from typing import Any, Iterable, List, Optional, TYPE_CHECKING, Type -from typing_extensions import TypedDict, NotRequired, Generic, TypeVar +from typing import Any, Iterable, Optional, TYPE_CHECKING +from typing_extensions import TypedDict, NotRequired import pyclesperanto_prototype as cle from tqdm import tqdm @@ -27,10 +26,7 @@ if TYPE_CHECKING: import pyclesperanto_prototype as cle - from lls_core.models.deskew import DefinedPixelSizes - from numpy.typing import NDArray - from lls_core.models.results import ImageSlice, ImageSlices, ProcessedSlice, ProcessedSlices - from lls_core.writers import Writer, BdvWriter, TiffWriter + from lls_core.models.results import ImageSlice, ImageSlices, ProcessedSlice import logging @@ -41,22 +37,6 @@ description="If defined, this is a workflow to add lightsheet processing onto" ) -class CommonOutputArgs(TypedDict): - # Arguments - save_dir: NotRequired[DirectoryPath] - save_type: SaveFileType - time_range: range - channel_range: range - -class CommonDeskewArgs(TypedDict): - skew: DeskewDirection - angle: float - -class CommonLatticeArgs(CommonDeskewArgs, CommonOutputArgs): - deconvolution: Optional[DeconvolutionParams] - crop: Optional[CropParams] - workflow: Optional[Workflow] - class LatticeData(OutputParams, DeskewParams): """ Holds data and metadata for a given image in a consistent format @@ -78,6 +58,8 @@ def use_image_path(cls, values: dict): # This needs to be a root validator to ensure it runs before the # reshaping validator. We can't override that either since it's # a field validator and can't modify save_name + # TODO: separate the image file from the image file path as two separate fields, + # so we don't have to put so much logic here from lls_core.types import is_pathlike input_image = values.get("input_image") if is_pathlike(input_image): diff --git a/core/lls_core/models/output.py b/core/lls_core/models/output.py index e80fa87..b1374b2 100644 --- a/core/lls_core/models/output.py +++ b/core/lls_core/models/output.py @@ -1,13 +1,13 @@ -from pydantic import Field, DirectoryPath +from pydantic import Field, DirectoryPath, validator from strenum import StrEnum from os import getcwd from pathlib import Path -from typing import TYPE_CHECKING +from typing import TYPE_CHECKING, Any from lls_core.models.utils import FieldAccessMixin, enum_choices if TYPE_CHECKING: - from lls_core.writers import Writer, BdvWriter, TiffWriter + pass #Choice of File extension to save class SaveFileType(StrEnum): @@ -16,16 +16,19 @@ class SaveFileType(StrEnum): class OutputParams(FieldAccessMixin): save_dir: DirectoryPath = Field( - default=None, description="The directory where the output data will be saved" ) + save_suffix: str = Field( + default="_deskewed", + description="The filename suffix that will be used for output files. This will be added as a suffix to the input file name if the input image was specified using a file name. If the input image was provided as an in-memory object, the `save_name` field should instead be specified." + ) save_name: str = Field( - description="The filename prefix that will be used for output files, without a leading directory or file extension. The final output files will have additional elements added to the end of this prefix to indicate the region of interest, channel, timepoint, file extension etc." + description="The filename that will be used for output files. This should not contain a leading directory or file extension. The final output files will have additional elements added to the end of this prefix to indicate the region of interest, channel, timepoint, file extension etc." ) save_type: SaveFileType = Field( default=SaveFileType.h5, description=f"The data type to save the result as. This will also be used to determine the file extension of the output files. Choices: {enum_choices(SaveFileType)}" -) + ) time_range: range = Field( default=None, description="The range of times to process. This defaults to all time points in the image array." @@ -35,6 +38,18 @@ class OutputParams(FieldAccessMixin): default=None ) + @validator("save_dir", pre=True) + def validate_save_dir(cls, v: Path): + if isinstance(v, Path) and not v.is_absolute(): + # This stops the empty path being considered a valid directory + raise ValueError("The save directory must be an absolute path that exists") + return v + + @validator("save_name") + def add_save_suffix(cls, v: str, values: dict): + # This is the only place that the save suffix is used. + return v + values["save_suffix"] + @property def file_extension(self): if self.save_type == SaveFileType.h5: diff --git a/core/tests/test_workflows.py b/core/tests/test_workflows.py index 89bfa5b..df74fe8 100644 --- a/core/tests/test_workflows.py +++ b/core/tests/test_workflows.py @@ -70,15 +70,16 @@ def test_image_workflow(image_path: Path, image_workflow: Workflow): def test_table_workflow(image_path: Path, table_workflow: Workflow): # Test a complex workflow that returns a tuple of images and data with tempfile.TemporaryDirectory() as tmpdir: - for roi, output in LatticeData( + params = LatticeData( input_image = image_path, workflow = table_workflow, save_dir = tmpdir - ).process_workflow().process(): + ) + for roi, output in params.process_workflow().process(): assert isinstance(output, (DataFrame, Path)) if isinstance(output, DataFrame): nrow, ncol = output.shape - assert nrow > 0 + assert nrow == params.nslices assert ncol > 0 else: assert valid_image_path(output) diff --git a/plugin/napari_lattice/dock_widget.py b/plugin/napari_lattice/dock_widget.py index a277a64..1c9f7b9 100644 --- a/plugin/napari_lattice/dock_widget.py +++ b/plugin/napari_lattice/dock_widget.py @@ -1,5 +1,7 @@ # Enable Logging import logging +from textwrap import dedent +from typing import cast import numpy as np from lls_core.models.lattice_data import LatticeData @@ -63,9 +65,26 @@ def _make_model(self) -> LatticeData: @magicclass(widget_type="split") class LlszMenu(MagicTemplate): - main_heading = field("

      Napari Lattice: Visualization & Analysis

      ", widget_type="Label") - heading1 = field("Select the tabs one at a time to specify analysis parameters. Tabs 1 and 5 are mandatory.", widget_type="Label") + heading1 = field(dedent(""" +       + """.strip()), widget_type="Label") + + def __post_init__(self): + from qtpy.QtCore import Qt + from qtpy.QtWidgets import QLabel, QLayout + + if isinstance(self._widget._layout, QLayout): + self._widget._layout.setAlignment(Qt.AlignmentFlag.AlignTop) + + if isinstance(self.heading1.native, QLabel): + self.heading1.native.setWordWrap(True) # Tabbed Widget container to house all the widgets @magicclass(widget_type="tabbed", name="Functions", labels=False) diff --git a/plugin/napari_lattice/fields.py b/plugin/napari_lattice/fields.py index bdb7866..a9ca0af 100644 --- a/plugin/napari_lattice/fields.py +++ b/plugin/napari_lattice/fields.py @@ -150,10 +150,10 @@ def __post_init__(self): from qtpy.QtWidgets import QLabel if isinstance(errors, QLabel): errors.setStyleSheet("color: red;") - errors.setWordWrap(True) + # errors.setWordWrap(True) from qtpy.QtCore import Qt - self._widget._layout.setAlignment(Qt.AlignTop) + self._widget._layout.setAlignment(Qt.AlignmentFlag.AlignTop) def _get_parent_tab_widget(self: Any) -> QTabWidget: return self.parent.parentWidget() @@ -489,12 +489,12 @@ class OutputFields(NapariFieldGroup): ) save_path = field(Path).with_options( label = "Save Directory", - value = Path(history.get_save_history()[0]), # Directory select mode="d" ) - save_name = field(str).with_options( - label = "Save Prefix" + save_suffix = field(str).with_options( + value=OutputParams.get_default("save_suffix"), + label = "Save Suffix", ) errors = field(Label).with_options(label="Errors") @@ -503,6 +503,8 @@ def _make_model(self) -> OutputParams: channel_range=range(self.channel_range.value[0], self.channel_range.value[1]), time_range=range(self.time_range.value[0], self.time_range.value[1]), save_dir=self.save_path.value, - save_name=self.save_name.value, - save_type=self.save_type.value + save_suffix=self.save_suffix.value, + save_type=self.save_type.value, + # This is just to avoid the validation error caused by the missing field + save_name="PLACEHOLDER" ) From e7577796123942d04d20ee2b39acfc5669de117d Mon Sep 17 00:00:00 2001 From: Michael Milton Date: Mon, 30 Oct 2023 18:42:01 +1100 Subject: [PATCH 086/147] Add test suite for GUI saving --- plugin/napari_lattice/dock_widget.py | 2 +- plugin/napari_lattice/fields.py | 2 +- plugin/napari_lattice/reader.py | 6 ++-- plugin/tests/test_dock_widget.py | 47 +++++++++++++++++++++++++--- 4 files changed, 47 insertions(+), 10 deletions(-) diff --git a/plugin/napari_lattice/dock_widget.py b/plugin/napari_lattice/dock_widget.py index 1c9f7b9..5131bcf 100644 --- a/plugin/napari_lattice/dock_widget.py +++ b/plugin/napari_lattice/dock_widget.py @@ -133,5 +133,5 @@ def preview(self, header:str, time: int, channel: int): def save(self): from napari.utils.notifications import show_info lattice = self._make_model() - lattice.process().save_image() + lattice.save() show_info(f"Deskewing successfuly completed. Results are located in {lattice.save_dir}") diff --git a/plugin/napari_lattice/fields.py b/plugin/napari_lattice/fields.py index a9ca0af..9503515 100644 --- a/plugin/napari_lattice/fields.py +++ b/plugin/napari_lattice/fields.py @@ -205,7 +205,7 @@ def _get_dimension_options(self, _) -> List[str]: Returns the list of dimension order options that might be possible for the current image stack """ default = ["Get from Metadata"] - ndims = max([len(layer.data.shape) for layer in self.img_layer.value], default=None) + ndims = max([len(layer.data.shape) for layer in self.img_layer.value], default=None) if ndims is None: return default elif ndims == 3: diff --git a/plugin/napari_lattice/reader.py b/plugin/napari_lattice/reader.py index eb596c4..cbe9061 100644 --- a/plugin/napari_lattice/reader.py +++ b/plugin/napari_lattice/reader.py @@ -34,9 +34,9 @@ class NapariImageParams(TypedDict): def lattice_params_from_napari( imgs: Collection[Image], - dimension_order: Optional[str], - physical_pixel_sizes: Optional[PhysicalPixelSizes], - stack_along: str + stack_along: str, + dimension_order: Optional[str] = None, + physical_pixel_sizes: Optional[PhysicalPixelSizes] = None, ) -> NapariImageParams: """ Factory function for generating a LatticeData from a Napari Image diff --git a/plugin/tests/test_dock_widget.py b/plugin/tests/test_dock_widget.py index 20d6947..b654d86 100644 --- a/plugin/tests/test_dock_widget.py +++ b/plugin/tests/test_dock_widget.py @@ -1,12 +1,20 @@ from __future__ import annotations +from pathlib import Path + +from importlib_resources import as_file +from xarray import DataArray from napari_lattice.dock_widget import LLSZWidget import numpy as np -from typing import Callable, TYPE_CHECKING +from typing import Callable, TYPE_CHECKING, Tuple from magicclass.testing import check_function_gui_buildable, FunctionGuiTester -from napari.layers import Image from magicclass import MagicTemplate from magicclass.widgets import Widget from magicclass._gui._gui_modes import ErrorMode +import pytest +from lls_core.sample import resources +from aicsimageio.aics_image import AICSImage +from plugin.napari_lattice.fields import PixelSizeSource +from tempfile import TemporaryDirectory if TYPE_CHECKING: from napari import Viewer @@ -18,6 +26,20 @@ # @pytest.mark.skip(reason="GUI tests currently fail in github CI, unclear why") # When testing locally, need pytest-qt +@pytest.fixture(params=[ + "RBC_tiny.czi", + "LLS7_t1_ch1.czi", + "LLS7_t1_ch3.czi", + "LLS7_t2_ch1.czi", + "LLS7_t2_ch3.czi", +]) +def image_data(request: pytest.FixtureRequest): + """ + Fixture function that yields test images as file paths + """ + with as_file(resources / request.param) as image_path: + yield AICSImage(image_path) + def set_debug(cls: MagicTemplate): """ Recursively disables GUI error handling, so that this works with pytest @@ -29,15 +51,30 @@ def _handler(e: Exception, parent: Widget): for child in cls.__magicclass_children__: set_debug(child) -def test_dock_widget(make_napari_viewer: Callable[[], Viewer]): +def test_dock_widget(make_napari_viewer: Callable[[], Viewer], image_data: AICSImage): # make viewer and add an image layer using our fixture viewer = make_napari_viewer() # Check if an image can be added as a layer - viewer.add_image(np.random.random((100, 100))) + viewer.add_image(image_data.xarray_dask_data) # Test if napari-lattice widget can be created in napari - viewer.window.add_dock_widget(LLSZWidget()) + ui = LLSZWidget() + set_debug(ui) + viewer.window.add_dock_widget(ui) + + # Set the input parameters and execute the processing + with TemporaryDirectory() as tmpdir: + # Specify values for all the required GUI fields + fields = ui.LlszMenu.WidgetContainer.deskew_fields + # TODO: refactor this logic into a `lattice_params_from_aics` method + fields.img_layer.value = list(viewer.layers) + fields.dimension_order.value = image_data.dims.order + fields.pixel_sizes_source.value = PixelSizeSource.Manual + ui.LlszMenu.WidgetContainer.output_fields.save_path.value = tmpdir + # fields.pixel_sizes.value = image_data.physical_pixel_sizes + tester = FunctionGuiTester(ui.save) + tester.call() def test_check_buildable(): ui = LLSZWidget() From 8da66321876fc23525c67b6ba5b426213868d829 Mon Sep 17 00:00:00 2001 From: Michael Milton Date: Tue, 31 Oct 2023 09:46:59 +1100 Subject: [PATCH 087/147] Fix type import errors --- core/lls_core/models/lattice_data.py | 8 ++++---- 1 file changed, 4 insertions(+), 4 deletions(-) diff --git a/core/lls_core/models/lattice_data.py b/core/lls_core/models/lattice_data.py index 96eb9de..d001a22 100644 --- a/core/lls_core/models/lattice_data.py +++ b/core/lls_core/models/lattice_data.py @@ -1,16 +1,15 @@ from __future__ import annotations # class for initializing lattice data and setting metadata # TODO: handle scenes -from pydantic import DirectoryPath, Field, root_validator, validator +from pydantic import Field, root_validator, validator from dask.array.core import Array as DaskArray -from typing import Any, Iterable, Optional, TYPE_CHECKING -from typing_extensions import TypedDict, NotRequired +from typing import Any, Iterable, Optional, TYPE_CHECKING, Type import pyclesperanto_prototype as cle from tqdm import tqdm -from lls_core import DeskewDirection, DeconvolutionChoice +from lls_core import DeconvolutionChoice from lls_core.deconvolution import pycuda_decon, skimage_decon from lls_core.llsz_core import crop_volume_deskew from lls_core.models.crop import CropParams @@ -27,6 +26,7 @@ if TYPE_CHECKING: import pyclesperanto_prototype as cle from lls_core.models.results import ImageSlice, ImageSlices, ProcessedSlice + from lls_core.writers import Writer import logging From 66462b3af1c09f7e3bfd7757a0b80f6a89ce078d Mon Sep 17 00:00:00 2001 From: Michael Milton Date: Tue, 31 Oct 2023 12:17:28 +1100 Subject: [PATCH 088/147] Fix CLI tests that now need an output directory --- core/tests/test_cli.py | 32 +++++++++++++++++--------------- core/tests/test_deskew.py | 11 +++++++++-- plugin/tests/test_dock_widget.py | 7 ++----- 3 files changed, 28 insertions(+), 22 deletions(-) diff --git a/core/tests/test_cli.py b/core/tests/test_cli.py index 5724d0a..5f3f144 100644 --- a/core/tests/test_cli.py +++ b/core/tests/test_cli.py @@ -40,6 +40,20 @@ def create_data(dir: Path) -> Path: return config_location +def assert_tiff(output_dir: Path): + """Checks that a valid TIFF was generated in the directory""" + results = list(output_dir.glob("*.tif")) + assert len(results) > 0 + for result in results: + AICSImage(result).get_image_data() + +def assert_h5(output_dir: Path): + """Checks that a valid H5 was generated""" + h5s = list(output_dir.glob("*.h5")) + assert len(h5s) > 0 + assert len(list(output_dir.glob("*.xml"))) == len(h5s) + for h5 in h5s: + BdvEditor(str(h5)).read_view() def test_batch_deskew_h5(): """Write image to disk and then execute napari_lattice from terminal @@ -56,10 +70,7 @@ def test_batch_deskew_h5(): "--save-type", "h5" ]) - # checks if h5 files written - assert (out_dir / "raw.h5").exists() - assert (out_dir / "raw.xml").exists() - BdvEditor(str(out_dir / "raw.h5")).read_view() + assert_h5(out_dir) def test_batch_deskew_tiff(): # tiff file deskew @@ -73,12 +84,7 @@ def test_batch_deskew_tiff(): "--save-type", "tiff" ]) - # checks if tiff written - results = list(out_dir.glob("*.tif")) - assert len(results) == 1 - for result in results: - AICSImage(result).get_image_data() - + assert_tiff(out_dir) def test_yaml_deskew(): """Write image to disk and then execute napari_lattice from terminal @@ -89,8 +95,4 @@ def test_yaml_deskew(): config_location = create_data(test_dir) # Batch deskew and save as h5 invoke(["--yaml-config", str(config_location)], ) - - # checks if h5 files written - assert (test_dir / "raw.h5").exists() - assert (test_dir / "raw.xml").exists() - BdvEditor(str(test_dir / "raw.h5")).read_view() + assert_h5(test_dir) diff --git a/core/tests/test_deskew.py b/core/tests/test_deskew.py index e46e3fc..aaa22a2 100644 --- a/core/tests/test_deskew.py +++ b/core/tests/test_deskew.py @@ -3,6 +3,7 @@ import numpy as np from lls_core.models.lattice_data import LatticeData from xarray import DataArray +import tempfile def test_deskew(): @@ -17,5 +18,11 @@ def test_deskew(): def test_lattice_data_deskew(): raw = DataArray(np.zeros((5, 5, 5)), dims=["X", "Y", "Z"]) - lattice = LatticeData(input_image=raw, physical_pixel_sizes = (1, 1, 1), save_name="test") - assert lattice.deskew_vol_shape == [2, 9, 5] + with tempfile.TemporaryDirectory() as tmpdir: + lattice = LatticeData( + input_image=raw, + physical_pixel_sizes = (1, 1, 1), + save_name="test", + save_dir=tmpdir + ) + assert lattice.deskew_vol_shape == [2, 9, 5] diff --git a/plugin/tests/test_dock_widget.py b/plugin/tests/test_dock_widget.py index b654d86..ca78020 100644 --- a/plugin/tests/test_dock_widget.py +++ b/plugin/tests/test_dock_widget.py @@ -1,11 +1,8 @@ from __future__ import annotations -from pathlib import Path from importlib_resources import as_file -from xarray import DataArray from napari_lattice.dock_widget import LLSZWidget -import numpy as np -from typing import Callable, TYPE_CHECKING, Tuple +from typing import Callable, TYPE_CHECKING from magicclass.testing import check_function_gui_buildable, FunctionGuiTester from magicclass import MagicTemplate from magicclass.widgets import Widget @@ -13,7 +10,7 @@ import pytest from lls_core.sample import resources from aicsimageio.aics_image import AICSImage -from plugin.napari_lattice.fields import PixelSizeSource +from napari_lattice.fields import PixelSizeSource from tempfile import TemporaryDirectory if TYPE_CHECKING: From 8b8c4e52a0f2e6fff4096c9703164192cf2e1c07 Mon Sep 17 00:00:00 2001 From: Michael Milton Date: Tue, 31 Oct 2023 12:19:00 +1100 Subject: [PATCH 089/147] Only run all the tests on one version, to avoid using excessive CPU hours --- .github/workflows/test_and_deploy.yml | 2 ++ 1 file changed, 2 insertions(+) diff --git a/.github/workflows/test_and_deploy.yml b/.github/workflows/test_and_deploy.yml index ac3906f..7fc86b6 100644 --- a/.github/workflows/test_and_deploy.yml +++ b/.github/workflows/test_and_deploy.yml @@ -55,6 +55,8 @@ jobs: python: ${{ env.python }} - name: Test core + # Only run all the tests on one version, to avoid using excessive CPU hours + if: matrix.python-version == "3.10" run: pytest -v --cov=core --cov-report=xml core/ - uses: tlambert03/setup-qt-libs@v1 From eba792ae925343415069ff1070a384e5e43d8739 Mon Sep 17 00:00:00 2001 From: Michael Milton Date: Tue, 31 Oct 2023 12:22:08 +1100 Subject: [PATCH 090/147] ${{ }} notation --- .github/workflows/test_and_deploy.yml | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/.github/workflows/test_and_deploy.yml b/.github/workflows/test_and_deploy.yml index 7fc86b6..7548425 100644 --- a/.github/workflows/test_and_deploy.yml +++ b/.github/workflows/test_and_deploy.yml @@ -56,7 +56,7 @@ jobs: - name: Test core # Only run all the tests on one version, to avoid using excessive CPU hours - if: matrix.python-version == "3.10" + if: ${{ matrix.python-version == "3.10" }} run: pytest -v --cov=core --cov-report=xml core/ - uses: tlambert03/setup-qt-libs@v1 From 891549c477647959825cb1b81c39369f78cb5880 Mon Sep 17 00:00:00 2001 From: Michael Milton Date: Tue, 31 Oct 2023 12:28:25 +1100 Subject: [PATCH 091/147] Single quotes? --- .github/workflows/test_and_deploy.yml | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/.github/workflows/test_and_deploy.yml b/.github/workflows/test_and_deploy.yml index 7548425..2c5bacd 100644 --- a/.github/workflows/test_and_deploy.yml +++ b/.github/workflows/test_and_deploy.yml @@ -56,7 +56,7 @@ jobs: - name: Test core # Only run all the tests on one version, to avoid using excessive CPU hours - if: ${{ matrix.python-version == "3.10" }} + if: ${{ matrix.python-version == '3.10' }} run: pytest -v --cov=core --cov-report=xml core/ - uses: tlambert03/setup-qt-libs@v1 From db52dda922fc96bc14a68e32bf8600611ac3c31c Mon Sep 17 00:00:00 2001 From: Michael Milton Date: Tue, 31 Oct 2023 15:59:43 +1100 Subject: [PATCH 092/147] Use public API for finding the tab widget --- plugin/napari_lattice/dock_widget.py | 3 --- plugin/napari_lattice/fields.py | 21 ++++++++++++--------- 2 files changed, 12 insertions(+), 12 deletions(-) diff --git a/plugin/napari_lattice/dock_widget.py b/plugin/napari_lattice/dock_widget.py index 5131bcf..e754466 100644 --- a/plugin/napari_lattice/dock_widget.py +++ b/plugin/napari_lattice/dock_widget.py @@ -1,7 +1,6 @@ # Enable Logging import logging from textwrap import dedent -from typing import cast import numpy as np from lls_core.models.lattice_data import LatticeData @@ -14,9 +13,7 @@ OutputFields, WorkflowFields, ) -from napari_lattice.icons import GREY from qtpy.QtCore import Qt -from qtpy.QtGui import QIcon from qtpy.QtWidgets import QTabWidget logger = logging.getLogger(__name__) diff --git a/plugin/napari_lattice/fields.py b/plugin/napari_lattice/fields.py index 9503515..e405299 100644 --- a/plugin/napari_lattice/fields.py +++ b/plugin/napari_lattice/fields.py @@ -25,7 +25,6 @@ from magicclass.widgets import ComboBox, Label, Widget from napari.layers import Image, Shapes from napari.types import ShapesData -from napari.utils import history 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 @@ -139,7 +138,7 @@ class StackAlong(StrEnum): CHANNEL = "Channel" TIME = "Time" -class NapariFieldGroup: +class NapariFieldGroup(MagicTemplate): def __post_init__(self): self = cast(FieldGroup, self) self.changed.connect(self._validate, unique=False) @@ -155,13 +154,17 @@ def __post_init__(self): from qtpy.QtCore import Qt self._widget._layout.setAlignment(Qt.AlignmentFlag.AlignTop) - def _get_parent_tab_widget(self: Any) -> QTabWidget: - return self.parent.parentWidget() + def _get_parent_tab_widget(self) -> QTabWidget: + qwidget = self.native + # Walk up the widget tree until we find the tab widget + while not isinstance(qwidget, QTabWidget): + qwidget = qwidget.parent() + return qwidget - def _get_tab_index(self: Any) -> int: + def _get_tab_index(self) -> int: return self._get_parent_tab_widget().indexOf(self._widget._qwidget) - def _set_valid(self: Any, valid: bool): + def _set_valid(self, valid: bool): from qtpy.QtGui import QIcon from importlib_resources import as_file tab_parent = self._get_parent_tab_widget() @@ -178,13 +181,13 @@ def _set_valid(self: Any, valid: bool): with as_file(icon) as path: tab_parent.setTabIcon(index, QIcon(str(path))) - def reset_choices(self: Any): + def reset_choices(self): # This is used to prevent validation from re-running when a napari layer is added or removed from magicgui.widgets import Container with self.changed.blocked(): super(Container, self).reset_choices() - def _validate(self: Any): + def _validate(self): self.errors.value = get_friendly_validations(self) valid = not bool(self.errors.value) self.errors.visible = not valid @@ -383,7 +386,7 @@ def _make_model(self) -> Optional[DeconvolutionParams]: ) @magicclass -class CroppingFields(MagicTemplate, NapariFieldGroup): +class CroppingFields(NapariFieldGroup): """ A counterpart to the CropParams Pydantic class """ From 6cf492f6a445228e6f78d822cb55394dbe4e43b4 Mon Sep 17 00:00:00 2001 From: Michael Milton Date: Tue, 31 Oct 2023 16:48:25 +1100 Subject: [PATCH 093/147] Magicgui dependency bound --- plugin/pyproject.toml | 7 ++++--- 1 file changed, 4 insertions(+), 3 deletions(-) diff --git a/plugin/pyproject.toml b/plugin/pyproject.toml index 42b172e..9b92123 100644 --- a/plugin/pyproject.toml +++ b/plugin/pyproject.toml @@ -41,9 +41,10 @@ dependencies = [ "fsspec>=2022.8.2", "importlib_resources", "lls_core", - # We need this Python 3.8 fix: https://github.com/hanjinliu/magic-class/pull/108 - "magic-class>=0.7.5", - "magicgui", + # The lower bound is because we need this Python 3.8 fix: https://github.com/hanjinliu/magic-class/pull/108 + # The upper bound is because we are waiting on https://github.com/hanjinliu/magic-class/issues/128 + "magic-class>=0.7.5,<0.8.0", + "magicgui<0.8.0", # Currently commented out to avoid installation issues, although # This can be reinstated once https://github.com/pypa/pip/pull/12095 is merged # "napari-aicsimageio>=0.7.2", From 69500a783ed743878ca28537021a9506518d4824 Mon Sep 17 00:00:00 2001 From: Michael Milton Date: Fri, 3 Nov 2023 12:40:05 +1100 Subject: [PATCH 094/147] Fix for missing output directory for previewing; add make() method for models --- core/lls_core/models/utils.py | 10 ++++++++++ plugin/napari_lattice/dock_widget.py | 17 ++++++++++++----- plugin/napari_lattice/fields.py | 6 ++++-- plugin/tests/test_dock_widget.py | 10 +++++++++- 4 files changed, 35 insertions(+), 8 deletions(-) diff --git a/core/lls_core/models/utils.py b/core/lls_core/models/utils.py index 6f56a77..3e49bcd 100644 --- a/core/lls_core/models/utils.py +++ b/core/lls_core/models/utils.py @@ -68,3 +68,13 @@ def copy_validate(self, **kwargs) -> Self: """ updated = self.copy(**kwargs) return updated.validate(updated.dict()) + + @classmethod + def make(cls, validate: bool = True, **kwargs: Any): + """ + Creates an instance of this class, with validation either enabled or disabled + """ + if validate: + return cls(**kwargs) + else: + return cls.construct(**kwargs) diff --git a/plugin/napari_lattice/dock_widget.py b/plugin/napari_lattice/dock_widget.py index e754466..2dfddd4 100644 --- a/plugin/napari_lattice/dock_widget.py +++ b/plugin/napari_lattice/dock_widget.py @@ -35,13 +35,13 @@ def _check_validity(self) -> bool: except: return False - def _make_model(self) -> LatticeData: + def _make_model(self, validate: bool = True) -> LatticeData: from rich import print from sys import stdout deskew_args = self.LlszMenu.WidgetContainer.deskew_fields._get_kwargs() - output_args = self.LlszMenu.WidgetContainer.output_fields._make_model() - params = LatticeData( + output_args = self.LlszMenu.WidgetContainer.output_fields._make_model(validate=False) + args = dict( input_image=deskew_args["data"], angle=deskew_args["angle"], channel_range=output_args.channel_range, @@ -56,6 +56,7 @@ def _make_model(self) -> LatticeData: deconvolution=self.LlszMenu.WidgetContainer.deconv_fields._make_model(), crop=self.LlszMenu.WidgetContainer.cropping_fields._make_model() ) + params = LatticeData.make(validate=validate, **args) # Log the lattice print(params, file=stdout) return params @@ -108,12 +109,18 @@ def __post_init__(self): call_button="Preview" ) @set_design(text="Preview") - def preview(self, header:str, time: int, channel: int): + def preview(self, header: str, time: int, channel: int): + from pathlib import Path + # We only need to process one time point for the preview, # so we made a copy using a subset of the times - lattice = self._make_model().copy_validate(update=dict( + lattice = self._make_model(validate=False).copy_validate(update=dict( time_range = range(time, time+1), channel_range = range(time, time+1), + # Patch in a placeholder for the save dir because previewing doesn't use it + # TODO: use a more elegant solution such as making the "saveable" lattice + # a child class which more validations + save_dir = Path.home() )) for slice in lattice.process().slices: diff --git a/plugin/napari_lattice/fields.py b/plugin/napari_lattice/fields.py index e405299..3016437 100644 --- a/plugin/napari_lattice/fields.py +++ b/plugin/napari_lattice/fields.py @@ -501,8 +501,10 @@ class OutputFields(NapariFieldGroup): ) errors = field(Label).with_options(label="Errors") - def _make_model(self) -> OutputParams: - return OutputParams( + def _make_model(self, validate: bool = True) -> OutputParams: + return OutputParams.make( + validate=validate, + channel_range=range(self.channel_range.value[0], self.channel_range.value[1]), time_range=range(self.time_range.value[0], self.time_range.value[1]), save_dir=self.save_path.value, diff --git a/plugin/tests/test_dock_widget.py b/plugin/tests/test_dock_widget.py index ca78020..4a6cfd5 100644 --- a/plugin/tests/test_dock_widget.py +++ b/plugin/tests/test_dock_widget.py @@ -68,11 +68,19 @@ def test_dock_widget(make_napari_viewer: Callable[[], Viewer], image_data: AICSI fields.img_layer.value = list(viewer.layers) fields.dimension_order.value = image_data.dims.order fields.pixel_sizes_source.value = PixelSizeSource.Manual + + # Test previewing + tester = FunctionGuiTester(ui.preview) + tester.call("", 0, 0) + + # Add the save path which shouldn't be needed for previewing ui.LlszMenu.WidgetContainer.output_fields.save_path.value = tmpdir - # fields.pixel_sizes.value = image_data.physical_pixel_sizes + + # Test saving tester = FunctionGuiTester(ui.save) tester.call() + def test_check_buildable(): ui = LLSZWidget() set_debug(ui) From c09983f1550b7d3bf85ade80a68d8d814f84c1c0 Mon Sep 17 00:00:00 2001 From: Michael Milton Date: Fri, 3 Nov 2023 15:01:23 +1100 Subject: [PATCH 095/147] Add validation for z range, and loosen the GUI restrictions --- core/lls_core/models/lattice_data.py | 11 ++++++++++- plugin/napari_lattice/fields.py | 1 - 2 files changed, 10 insertions(+), 2 deletions(-) diff --git a/core/lls_core/models/lattice_data.py b/core/lls_core/models/lattice_data.py index d001a22..699ebb5 100644 --- a/core/lls_core/models/lattice_data.py +++ b/core/lls_core/models/lattice_data.py @@ -89,18 +89,27 @@ def parse_workflow(cls, v: Any): @validator("crop") def default_z_range(cls, v: CropParams, values: dict): - # If any part of the z range is missing, assume the user wants all Z indices if v is None: return v with ignore_keyerror(): + # Fill in missing parts of the z range with the min/max z values default_start = 0 default_end = values["input_image"].sizes["Z"] + + # Set defaults if v.z_range is None: v.z_range = (default_start, default_end) if v.z_range[0] is None: v.z_range[0] = default_start if v.z_range[1] is None: v.z_range[1] = default_end + + # Validate + if v.z_range[1] > default_end: + raise ValueError(f"The z-index endpoint of {v.z_range[1]} is outside the size of the z-axis ({default_end})") + if v.z_range[0] < default_start: + raise ValueError(f"The z-index start of {v.z_range[0]} is outside the size of the z-axis") + return v @validator("time_range", pre=True, always=True) diff --git a/plugin/napari_lattice/fields.py b/plugin/napari_lattice/fields.py index 3016437..12707de 100644 --- a/plugin/napari_lattice/fields.py +++ b/plugin/napari_lattice/fields.py @@ -397,7 +397,6 @@ class CroppingFields(NapariFieldGroup): value = (0, 1), options = dict( min = 0, - max = 1 ), ) errors = field(Label).with_options(label="Errors") From f757e532e142308e1b60de56bf304380ab6ab2cf Mon Sep 17 00:00:00 2001 From: Michael Milton Date: Fri, 10 Nov 2023 13:21:48 +1100 Subject: [PATCH 096/147] Dynamically update the maximum range values --- plugin/napari_lattice/dock_widget.py | 25 +++++++++++++-- plugin/napari_lattice/fields.py | 47 ++++++++++++++++++++-------- 2 files changed, 57 insertions(+), 15 deletions(-) diff --git a/plugin/napari_lattice/dock_widget.py b/plugin/napari_lattice/dock_widget.py index 2dfddd4..7c90d74 100644 --- a/plugin/napari_lattice/dock_widget.py +++ b/plugin/napari_lattice/dock_widget.py @@ -1,7 +1,8 @@ -# Enable Logging +from __future__ import annotations + import logging from textwrap import dedent - +from typing import TYPE_CHECKING import numpy as np from lls_core.models.lattice_data import LatticeData from magicclass import MagicTemplate, field, magicclass, set_options, vfield @@ -16,6 +17,11 @@ from qtpy.QtCore import Qt from qtpy.QtWidgets import QTabWidget +if TYPE_CHECKING: + from typing import Iterable + from napari_lattice.fields import NapariFieldGroup + +# Enable Logging logger = logging.getLogger(__name__) logger.setLevel(logging.INFO) @@ -25,6 +31,7 @@ def __post_init__(self): # aligning collapsible widgets at the top instead of having them centered vertically self._widget._layout.setAlignment(Qt.AlignTop) + def _check_validity(self) -> bool: """ Returns True if the model is valid @@ -97,6 +104,7 @@ def __post_init__(self): for field in [self.deskew_fields, self.deconv_fields, self.cropping_fields, self.workflow_fields, self.output_fields]: field._validate() + # Using vfields here seems to prevent https://github.com/hanjinliu/magic-class/issues/110 deskew_fields = vfield(DeskewFields) deconv_fields = vfield(DeconvolutionFields) cropping_fields = vfield(CroppingFields) @@ -139,3 +147,16 @@ def save(self): lattice = self._make_model() lattice.save() show_info(f"Deskewing successfuly completed. Results are located in {lattice.save_dir}") + + @LlszMenu.WidgetContainer.deskew_fields.connect + def _on_image_changed(self, deskew: DeskewFields): + img = deskew._get_kwargs()["data"] + # We have to manually trigger _on_image_changed because siblings + # classes can't listen to each other's events: https://github.com/hanjinliu/magic-class/issues/129 + for field in self._get_fields(): + field._on_image_changed(img) + + def _get_fields(self) -> Iterable[NapariFieldGroup]: + """Yields all the child Field classes which inherit from NapariFieldGroup""" + container = self.LlszMenu.WidgetContainer + yield from set(container.__magicclass_children__) diff --git a/plugin/napari_lattice/fields.py b/plugin/napari_lattice/fields.py index 12707de..a21aef1 100644 --- a/plugin/napari_lattice/fields.py +++ b/plugin/napari_lattice/fields.py @@ -1,10 +1,10 @@ # FieldGroups that the users interact with to input data - import logging from pathlib import Path -from typing import Any, Callable, List, Optional, Tuple, TypeVar, cast - +from typing import Any, Callable, List, Optional, Tuple, TYPE_CHECKING +from typing_extensions import TypeVar import pyclesperanto_prototype as cle +from xarray import DataArray from lls_core import ( DeconvolutionChoice, DeskewDirection, @@ -32,9 +32,20 @@ from qtpy.QtWidgets import QTabWidget from strenum import StrEnum +if TYPE_CHECKING: + from magicgui.widgets.bases import RangedWidget + logger = logging.getLogger(__name__) logger.setLevel(logging.INFO) +def adjust_maximum(widget: "RangedWidget", max: int): + """ + Updates the maximum value + """ + widget.max = max + if widget.value > max: + widget.value = max + def exception_to_html(e: BaseException) -> str: """ Converts an exception to HTML for reporting back to the user @@ -73,7 +84,6 @@ class BackgroundSource(StrEnum): SecondLast = "Second Last" Custom = "Custom" - def enable_field(field: MagicField, enabled: bool = True) -> None: """ Enable the widget associated with a field @@ -91,7 +101,6 @@ def enable_field(field: MagicField, enabled: bool = True) -> None: except RuntimeError: pass - FieldValueType = TypeVar("FieldValueType") SelfType = TypeVar("SelfType") def enable_if(fields: List[MagicField]): @@ -140,7 +149,6 @@ class StackAlong(StrEnum): class NapariFieldGroup(MagicTemplate): def __post_init__(self): - self = cast(FieldGroup, self) self.changed.connect(self._validate, unique=False) # Style the error label. @@ -196,13 +204,15 @@ def _validate(self): def _make_model(self): raise NotImplementedError() + def _on_image_changed(self, img: DataArray): + pass + class DeskewKwargs(NapariImageParams): angle: float skew: DeskewDirection @magicclass class DeskewFields(NapariFieldGroup): - def _get_dimension_options(self, _) -> List[str]: """ Returns the list of dimension order options that might be possible for the current image stack @@ -334,9 +344,7 @@ def _make_model(self) -> DeskewParams: @magicclass class DeconvolutionFields(NapariFieldGroup): - """ - A counterpart to the DeconvolutionParams Pydantic class - """ + # A counterpart to the DeconvolutionParams Pydantic class fields_enabled = field(False, label="Enabled") decon_processing = field(DeconvolutionChoice, label="Processing Algorithm") psf = field(List[Path], label = "PSFs") @@ -387,9 +395,7 @@ def _make_model(self) -> Optional[DeconvolutionParams]: @magicclass class CroppingFields(NapariFieldGroup): - """ - A counterpart to the CropParams Pydantic class - """ + # A counterpart to the CropParams Pydantic class fields_enabled = field(False, label="Enabled") shapes= field(List[Shapes], widget_type="Select", label = "ROI Shape Layers").with_options(choices=lambda _x, _y: get_layers(Shapes)) z_range = field(Tuple[int, int]).with_options( @@ -425,6 +431,11 @@ def new_crop_layer(self): shapes.mode = "ADD_RECTANGLE" shapes.name = "Napari Lattice Crop" + def _on_image_changed(self, img: DataArray): + # Update the maximum Z + for widget in self.z_range: + adjust_maximum(widget, img.sizes["Z"]) + @fields_enabled.connect @enable_if([shapes, z_range]) def _enable_crop(self, enabled: bool) -> bool: @@ -500,6 +511,9 @@ class OutputFields(NapariFieldGroup): ) errors = field(Label).with_options(label="Errors") + def __post_init__(self): + pass + def _make_model(self, validate: bool = True) -> OutputParams: return OutputParams.make( validate=validate, @@ -512,3 +526,10 @@ def _make_model(self, validate: bool = True) -> OutputParams: # This is just to avoid the validation error caused by the missing field save_name="PLACEHOLDER" ) + + def _on_image_changed(self, img: DataArray): + # Update the maximum T and C + for widget in self.time_range: + adjust_maximum(widget, img.sizes["T"]) + for widget in self.channel_range: + adjust_maximum(widget, img.sizes["C"]) From ae721cc5ca8e8634c0686d67e9f4278da451738d Mon Sep 17 00:00:00 2001 From: Michael Milton Date: Fri, 10 Nov 2023 13:56:05 +1100 Subject: [PATCH 097/147] Clean up code --- core/lls_core/models/lattice_data.py | 2 -- plugin/napari_lattice/fields.py | 3 --- 2 files changed, 5 deletions(-) diff --git a/core/lls_core/models/lattice_data.py b/core/lls_core/models/lattice_data.py index 699ebb5..6ac98d6 100644 --- a/core/lls_core/models/lattice_data.py +++ b/core/lls_core/models/lattice_data.py @@ -409,8 +409,6 @@ def process(self) -> ImageSlices: from lls_core.models.results import ImageSlices ImageSlices.update_forward_refs(LatticeData=LatticeData) - # if self.workflow is not None: - # return self._process_workflow() if self.cropping_enabled: return ImageSlices( lattice_data=self, diff --git a/plugin/napari_lattice/fields.py b/plugin/napari_lattice/fields.py index a21aef1..126acb4 100644 --- a/plugin/napari_lattice/fields.py +++ b/plugin/napari_lattice/fields.py @@ -511,9 +511,6 @@ class OutputFields(NapariFieldGroup): ) errors = field(Label).with_options(label="Errors") - def __post_init__(self): - pass - def _make_model(self, validate: bool = True) -> OutputParams: return OutputParams.make( validate=validate, From 2000786354f11f2e0f25887cf9e6d0ac75887119 Mon Sep 17 00:00:00 2001 From: Michael Milton Date: Wed, 15 Nov 2023 17:08:19 +1100 Subject: [PATCH 098/147] Remove the z cropping fields from the GUI --- plugin/napari_lattice/fields.py | 15 +-------------- 1 file changed, 1 insertion(+), 14 deletions(-) diff --git a/plugin/napari_lattice/fields.py b/plugin/napari_lattice/fields.py index 126acb4..c9aa883 100644 --- a/plugin/napari_lattice/fields.py +++ b/plugin/napari_lattice/fields.py @@ -398,13 +398,6 @@ class CroppingFields(NapariFieldGroup): # A counterpart to the CropParams Pydantic class fields_enabled = field(False, label="Enabled") shapes= field(List[Shapes], widget_type="Select", label = "ROI Shape Layers").with_options(choices=lambda _x, _y: get_layers(Shapes)) - z_range = field(Tuple[int, int]).with_options( - label = "Z Range", - value = (0, 1), - options = dict( - min = 0, - ), - ) errors = field(Label).with_options(label="Errors") def _get_deskew(self) -> DeskewParams: @@ -431,13 +424,8 @@ def new_crop_layer(self): shapes.mode = "ADD_RECTANGLE" shapes.name = "Napari Lattice Crop" - def _on_image_changed(self, img: DataArray): - # Update the maximum Z - for widget in self.z_range: - adjust_maximum(widget, img.sizes["Z"]) - @fields_enabled.connect - @enable_if([shapes, z_range]) + @enable_if([shapes]) def _enable_crop(self, enabled: bool) -> bool: return enabled @@ -445,7 +433,6 @@ def _make_model(self) -> Optional[CropParams]: import numpy as np if self.fields_enabled.value: return CropParams( - z_range=self.z_range.value, roi_list=ShapesData([np.array(shape.data) / self._get_deskew().dy for shape in self.shapes.value]) ) return None From 8251aca091b1c930f4bf5062bfa44618b403ac0a Mon Sep 17 00:00:00 2001 From: Michael Milton Date: Fri, 17 Nov 2023 16:15:09 +1100 Subject: [PATCH 099/147] Fix for cropping by treating roi as floats --- core/lls_core/cropping.py | 10 +- core/lls_core/models/lattice_data.py | 896 +++++++++++++-------------- plugin/napari_lattice/fields.py | 2 +- 3 files changed, 455 insertions(+), 453 deletions(-) diff --git a/core/lls_core/cropping.py b/core/lls_core/cropping.py index 1eb61d7..2f0c35a 100644 --- a/core/lls_core/cropping.py +++ b/core/lls_core/cropping.py @@ -6,11 +6,13 @@ from typing_extensions import Self from numpy.typing import NDArray +RoiCoord = Tuple[float, float] + class Roi(NamedTuple): - top_left: Tuple[int, int] - top_right: Tuple[int, int] - bottom_left: Tuple[int, int] - bottom_right: Tuple[int, int] + top_left: RoiCoord + top_right: RoiCoord + bottom_left: RoiCoord + bottom_right: RoiCoord @classmethod def from_array(cls, array: NDArray) -> Self: diff --git a/core/lls_core/models/lattice_data.py b/core/lls_core/models/lattice_data.py index 6ac98d6..857c497 100644 --- a/core/lls_core/models/lattice_data.py +++ b/core/lls_core/models/lattice_data.py @@ -1,448 +1,448 @@ -from __future__ import annotations -# class for initializing lattice data and setting metadata -# TODO: handle scenes -from pydantic import Field, root_validator, validator -from dask.array.core import Array as DaskArray - -from typing import Any, Iterable, Optional, TYPE_CHECKING, Type - -import pyclesperanto_prototype as cle -from tqdm import tqdm - -from lls_core import DeconvolutionChoice -from lls_core.deconvolution import pycuda_decon, skimage_decon -from lls_core.llsz_core import crop_volume_deskew -from lls_core.models.crop import CropParams -from lls_core.models.deconvolution import DeconvolutionParams -from lls_core.models.output import OutputParams, SaveFileType -from lls_core.models.results import WorkflowSlices -from lls_core.models.utils import ignore_keyerror -from lls_core.types import ArrayLike -from lls_core.models.deskew import DeskewParams -from napari_workflows import Workflow -from xarray import DataArray -from pathlib import Path - -if TYPE_CHECKING: - import pyclesperanto_prototype as cle - from lls_core.models.results import ImageSlice, ImageSlices, ProcessedSlice - from lls_core.writers import Writer - -import logging - -logger = logging.getLogger(__name__) - -workflow: Optional[Workflow] = Field( - default=None, - description="If defined, this is a workflow to add lightsheet processing onto" -) - -class LatticeData(OutputParams, DeskewParams): - """ - Holds data and metadata for a given image in a consistent format - """ - - # Note: originally the save-related fields were included via composition and not inheritance - # (similar to how `crop` and `workflow` are handled), but this was impractical for implementing validations - - #: If this is None, then deconvolution is disabled - deconvolution: Optional[DeconvolutionParams] = None - - #: If this is None, then cropping is disabled - crop: Optional[CropParams] = None - - workflow: Optional[Workflow] = workflow - - @root_validator(pre=True) - def use_image_path(cls, values: dict): - # This needs to be a root validator to ensure it runs before the - # reshaping validator. We can't override that either since it's - # a field validator and can't modify save_name - # TODO: separate the image file from the image file path as two separate fields, - # so we don't have to put so much logic here - from lls_core.types import is_pathlike - input_image = values.get("input_image") - if is_pathlike(input_image): - if values.get("save_name") is None: - values["save_name"] = Path(values["input_image"]).stem - - save_dir = values.get("save_dir") - if save_dir is None: - # By default, make the save dir be the same dir as the input - values["save_dir"] = Path(input_image).parent - elif is_pathlike(save_dir): - # Convert a string path to a Path object - values["save_dir"] = Path(save_dir) - - return values - - @validator("workflow", pre=True) - def parse_workflow(cls, v: Any): - # Load the workflow from disk if it was provided as a path - from lls_core.types import is_pathlike - from lls_core.workflow import workflow_from_path - from pathlib import Path - - if is_pathlike(v): - return workflow_from_path(Path(v)) - return v - - @validator("crop") - def default_z_range(cls, v: CropParams, values: dict): - if v is None: - return v - with ignore_keyerror(): - # Fill in missing parts of the z range with the min/max z values - default_start = 0 - default_end = values["input_image"].sizes["Z"] - - # Set defaults - if v.z_range is None: - v.z_range = (default_start, default_end) - if v.z_range[0] is None: - v.z_range[0] = default_start - if v.z_range[1] is None: - v.z_range[1] = default_end - - # Validate - if v.z_range[1] > default_end: - raise ValueError(f"The z-index endpoint of {v.z_range[1]} is outside the size of the z-axis ({default_end})") - if v.z_range[0] < default_start: - raise ValueError(f"The z-index start of {v.z_range[0]} is outside the size of the z-axis") - - return v - - @validator("time_range", pre=True, always=True) - def parse_time_range(cls, v: Any, values: dict) -> Any: - """ - Sets the default time range if undefined - """ - # This skips the conversion if no image was provided, to ensure a more - # user-friendly error is provided, namely "image was missing" - with ignore_keyerror(): - default_start = 0 - default_end = values["input_image"].sizes["T"] - if v is None: - return range(default_start, default_end) - elif isinstance(v, tuple) and len(v) == 2: - # Allow 2-tuples to be used as input for this field - return range(v[0] or default_start, v[1] or default_end) - return v - - @validator("channel_range", pre=True, always=True) - def parse_channel_range(cls, v: Any, values: dict) -> Any: - """ - Sets the default channel range if undefined - """ - with ignore_keyerror(): - default_start = 0 - default_end = values["input_image"].sizes["C"] - if v is None: - return range(default_start, default_end) - elif isinstance(v, tuple) and len(v) == 2: - # Allow 2-tuples to be used as input for this field - return range(v[0] or default_start, v[1] or default_end) - return v - - @validator("time_range") - def disjoint_time_range(cls, v: range, values: dict): - """ - Validates that the time range is within the range of channels in our array - """ - with ignore_keyerror(): - max_time = values["input_image"].sizes["T"] - if v.start < 0: - raise ValueError("The lowest valid start value is 0") - if v.stop > max_time: - raise ValueError(f"The highest valid time value is the length of the time axis, which is {max_time}") - - return v - - @validator("channel_range") - def disjoint_channel_range(cls, v: range, values: dict): - """ - Validates that the channel range is within the range of channels in our array - """ - with ignore_keyerror(): - max_channel = values["input_image"].sizes["C"] - if v.start < 0: - raise ValueError("The lowest valid start value is 0") - if v.stop > max_channel: - raise ValueError(f"The highest valid channel value is the length of the channel axis, which is {max_channel}") - return v - - @validator("channel_range") - def channel_range_subset(cls, v: Optional[range], values: dict): - with ignore_keyerror(): - if v is not None and (min(v) < 0 or max(v) > values["input_image"].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: Optional[range], values: dict): - if v is not None and (min(v) < 0 or max(v) > values["input_image"].sizes["T"]): - raise ValueError("The output time range must be a subset of the total available time points") - return v - - @validator("deconvolution") - def check_psfs(cls, v: Optional[DeconvolutionParams], values: dict): - if v is None: - return v - with ignore_keyerror(): - channels = values["input_image"].sizes["C"] - psfs = len(v.psf) - if psfs != channels: - raise ValueError(f"There should be one PSF per channel, but there are {psfs} PSFs and {channels} channels.") - return v - - @property - def cropping_enabled(self) -> bool: - "True if cropping should be performed" - return self.crop is not None - - @property - def deconv_enabled(self) -> bool: - "True if deconvolution should be performed" - return self.deconvolution is not None - - 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) -> DataArray: - if time > self.time: - raise ValueError("time is out of range") - if channel > self.channels: - raise ValueError("channel is out of range") - - return self.input_image.isel(T=time, C=channel) - - def iter_slices(self) -> Iterable[ProcessedSlice[ArrayLike]]: - """ - Yields array slices for each time and channel of interest. - - Returns: - An iterable of tuples. Each tuple contains (time_index, time, channel_index, channel, slice) - """ - from lls_core.models.results import ProcessedSlice - for time_idx, time in enumerate(self.time_range): - for ch_idx, ch in enumerate(self.channel_range): - yield ProcessedSlice( - data=self.slice_data(time=time, channel=ch), - time_index=time_idx, - time= time, - channel_index=ch_idx, - channel=ch, - ) - - def iter_sublattices(self, update_with: dict = {}) -> Iterable[ProcessedSlice[LatticeData]]: - """ - Yields copies of the current LatticeData, one for each slice. - These copies can then be processed separately. - Args: - update_with: dictionary of arguments to update the generated lattices with - """ - for subarray in self.iter_slices(): - new_lattice = self.copy_validate(update={ - "input_image": subarray.data, - "time_range": range(1), - "channel_range": range(1), - **update_with - }) - yield subarray.copy_with_data( new_lattice) - - def generate_workflows( - self, - ) -> Iterable[ProcessedSlice[Workflow]]: - """ - Yields copies of the input workflow, modified with the addition of deskewing and optionally, - cropping and deconvolution - """ - if self.workflow is None: - return - - from copy import copy - from lls_core.workflow import get_workflow_inputs, update_workflow - # We make a copy of the lattice for each slice, each of which has no associated workflow - for lattice_slice in self.iter_sublattices(update_with={"workflow": None}): - user_workflow = copy(self.workflow) - user_workflow.set( - "deskew_image", - LatticeData.process_into_image, - lattice_slice.data - ) - for task_name, arg_index, arg_name in get_workflow_inputs(user_workflow): - update_workflow( - user_workflow, - task_name, - arg_index, - "deskew_image" - ) - 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 - return zeros(self.deskew_vol_shape) - - def _process_crop(self) -> Iterable[ImageSlice]: - """ - Yields processed image slices with cropping enabled - """ - if self.crop is None: - raise Exception("This function can only be called when crop is set") - - # We have an extra level of iteration for the crop path: iterating over each ROI - for roi_index, roi in enumerate(tqdm(self.crop.selected_rois, desc="ROI:", position=0)): - # pass arguments for save tiff, callable and function arguments - logger.info(f"Processing ROI {roi_index}") - - deconv_args: dict[Any, Any] = {} - if self.deconvolution is not None: - deconv_args = dict( - num_iter = self.deconvolution.psf_num_iter, - psf = self.deconvolution.psf, - decon_processing=self.deconvolution.decon_processing - ) - - for slice in self.iter_slices(): - yield slice.copy_with_data( - crop_volume_deskew( - original_volume=slice.data, - deconvolution=self.deconv_enabled, - get_deskew_and_decon=False, - debug=False, - roi_shape=list(roi), - linear_interpolation=True, - voxel_size_x=self.dx, - voxel_size_y=self.dy, - voxel_size_z=self.dy, - angle_in_degrees=self.angle, - deskewed_volume=self.deskewed_volume, - z_start=self.crop.z_range[0], - z_end=self.crop.z_range[1], - **deconv_args - ) - ) - - def _process_non_crop(self) -> Iterable[ImageSlice]: - """ - Yields processed image slices without cropping - """ - for slice in self.iter_slices(): - data: ArrayLike = slice.data - if isinstance(slice.data, DaskArray): - data = slice.data.compute() - if self.deconvolution is not None: - if self.deconvolution.decon_processing == DeconvolutionChoice.cuda_gpu: - data = pycuda_decon( - image=data, - psf=self.deconvolution.psf[slice.channel].to_numpy(), - background=self.deconvolution.background, - dzdata=self.dz, - dxdata=self.dx, - dzpsf=self.dz, - dxpsf=self.dx, - num_iter=self.deconvolution.psf_num_iter - ) - else: - data = skimage_decon( - vol_zyx=data, - psf=self.deconvolution.psf[slice.channel].to_numpy(), - num_iter=self.deconvolution.psf_num_iter, - clip=False, - filter_epsilon=0, - boundary='nearest' - ) - - yield slice.copy_with_data( - cle.pull_zyx(self.deskew_func( - input_image=data, - angle_in_degrees=self.angle, - linear_interpolation=True, - voxel_size_x=self.dx, - voxel_size_y=self.dy, - voxel_size_z=self.dz - )) - ) - - def process_workflow(self) -> WorkflowSlices: - from lls_core.models.results import WorkflowSlices - WorkflowSlices.update_forward_refs(LatticeData=LatticeData) - outputs = [] - for workflow in self.generate_workflows(): - for leaf in workflow.data.leafs(): - outputs.append( - workflow.copy_with_data( - workflow.data.get(leaf) - ) - ) - - return WorkflowSlices( - slices = outputs, - lattice_data=self - ) - - def process(self) -> ImageSlices: - """ - Execute the processing and return the result. - This will not execute the attached workflow. - """ - from lls_core.models.results import ImageSlices - ImageSlices.update_forward_refs(LatticeData=LatticeData) - - if self.cropping_enabled: - return ImageSlices( - lattice_data=self, - slices=self._process_crop() - ) - else: - return ImageSlices( - lattice_data=self, - slices=self._process_non_crop() - ) - - def save(self): - """ - - This is the main public API for processing - """ - if self.workflow: - list(self.process_workflow().save()) - else: - self.process().save_image() - - def process_into_image(self) -> ArrayLike: - """ - Shortcut method for calling process, then extracting one image layer. - This is mostly here to simplify the Workflow integration - """ - for slice in self.process().slices: - return slice.data - raise Exception("No slices produced!") - - def get_writer(self) -> Type[Writer]: - from lls_core.writers import BdvWriter, TiffWriter - if self.save_type == SaveFileType.h5: - return BdvWriter - elif self.save_type == SaveFileType.tiff: - return TiffWriter - raise Exception("Unknown output type") +from __future__ import annotations +# class for initializing lattice data and setting metadata +# TODO: handle scenes +from pydantic import Field, root_validator, validator +from dask.array.core import Array as DaskArray + +from typing import Any, Iterable, Optional, TYPE_CHECKING, Type + +import pyclesperanto_prototype as cle +from tqdm import tqdm + +from lls_core import DeconvolutionChoice +from lls_core.deconvolution import pycuda_decon, skimage_decon +from lls_core.llsz_core import crop_volume_deskew +from lls_core.models.crop import CropParams +from lls_core.models.deconvolution import DeconvolutionParams +from lls_core.models.output import OutputParams, SaveFileType +from lls_core.models.results import WorkflowSlices +from lls_core.models.utils import ignore_keyerror +from lls_core.types import ArrayLike +from lls_core.models.deskew import DeskewParams +from napari_workflows import Workflow +from xarray import DataArray +from pathlib import Path + +if TYPE_CHECKING: + import pyclesperanto_prototype as cle + from lls_core.models.results import ImageSlice, ImageSlices, ProcessedSlice + from lls_core.writers import Writer + +import logging + +logger = logging.getLogger(__name__) + +workflow: Optional[Workflow] = Field( + default=None, + description="If defined, this is a workflow to add lightsheet processing onto" +) + +class LatticeData(OutputParams, DeskewParams): + """ + Holds data and metadata for a given image in a consistent format + """ + + # Note: originally the save-related fields were included via composition and not inheritance + # (similar to how `crop` and `workflow` are handled), but this was impractical for implementing validations + + #: If this is None, then deconvolution is disabled + deconvolution: Optional[DeconvolutionParams] = None + + #: If this is None, then cropping is disabled + crop: Optional[CropParams] = None + + workflow: Optional[Workflow] = workflow + + @root_validator(pre=True) + def use_image_path(cls, values: dict): + # This needs to be a root validator to ensure it runs before the + # reshaping validator. We can't override that either since it's + # a field validator and can't modify save_name + # TODO: separate the image file from the image file path as two separate fields, + # so we don't have to put so much logic here + from lls_core.types import is_pathlike + input_image = values.get("input_image") + if is_pathlike(input_image): + if values.get("save_name") is None: + values["save_name"] = Path(values["input_image"]).stem + + save_dir = values.get("save_dir") + if save_dir is None: + # By default, make the save dir be the same dir as the input + values["save_dir"] = Path(input_image).parent + elif is_pathlike(save_dir): + # Convert a string path to a Path object + values["save_dir"] = Path(save_dir) + + return values + + @validator("workflow", pre=True) + def parse_workflow(cls, v: Any): + # Load the workflow from disk if it was provided as a path + from lls_core.types import is_pathlike + from lls_core.workflow import workflow_from_path + from pathlib import Path + + if is_pathlike(v): + return workflow_from_path(Path(v)) + return v + + @validator("crop") + def default_z_range(cls, v: CropParams, values: dict): + if v is None: + return v + with ignore_keyerror(): + # Fill in missing parts of the z range with the min/max z values + default_start = 0 + default_end = values["input_image"].sizes["Z"] + + # Set defaults + if v.z_range is None: + v.z_range = (default_start, default_end) + if v.z_range[0] is None: + v.z_range[0] = default_start + if v.z_range[1] is None: + v.z_range[1] = default_end + + # Validate + if v.z_range[1] > default_end: + raise ValueError(f"The z-index endpoint of {v.z_range[1]} is outside the size of the z-axis ({default_end})") + if v.z_range[0] < default_start: + raise ValueError(f"The z-index start of {v.z_range[0]} is outside the size of the z-axis") + + return v + + @validator("time_range", pre=True, always=True) + def parse_time_range(cls, v: Any, values: dict) -> Any: + """ + Sets the default time range if undefined + """ + # This skips the conversion if no image was provided, to ensure a more + # user-friendly error is provided, namely "image was missing" + with ignore_keyerror(): + default_start = 0 + default_end = values["input_image"].sizes["T"] + if v is None: + return range(default_start, default_end) + elif isinstance(v, tuple) and len(v) == 2: + # Allow 2-tuples to be used as input for this field + return range(v[0] or default_start, v[1] or default_end) + return v + + @validator("channel_range", pre=True, always=True) + def parse_channel_range(cls, v: Any, values: dict) -> Any: + """ + Sets the default channel range if undefined + """ + with ignore_keyerror(): + default_start = 0 + default_end = values["input_image"].sizes["C"] + if v is None: + return range(default_start, default_end) + elif isinstance(v, tuple) and len(v) == 2: + # Allow 2-tuples to be used as input for this field + return range(v[0] or default_start, v[1] or default_end) + return v + + @validator("time_range") + def disjoint_time_range(cls, v: range, values: dict): + """ + Validates that the time range is within the range of channels in our array + """ + with ignore_keyerror(): + max_time = values["input_image"].sizes["T"] + if v.start < 0: + raise ValueError("The lowest valid start value is 0") + if v.stop > max_time: + raise ValueError(f"The highest valid time value is the length of the time axis, which is {max_time}") + + return v + + @validator("channel_range") + def disjoint_channel_range(cls, v: range, values: dict): + """ + Validates that the channel range is within the range of channels in our array + """ + with ignore_keyerror(): + max_channel = values["input_image"].sizes["C"] + if v.start < 0: + raise ValueError("The lowest valid start value is 0") + if v.stop > max_channel: + raise ValueError(f"The highest valid channel value is the length of the channel axis, which is {max_channel}") + return v + + @validator("channel_range") + def channel_range_subset(cls, v: Optional[range], values: dict): + with ignore_keyerror(): + if v is not None and (min(v) < 0 or max(v) > values["input_image"].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: Optional[range], values: dict): + if v is not None and (min(v) < 0 or max(v) > values["input_image"].sizes["T"]): + raise ValueError("The output time range must be a subset of the total available time points") + return v + + @validator("deconvolution") + def check_psfs(cls, v: Optional[DeconvolutionParams], values: dict): + if v is None: + return v + with ignore_keyerror(): + channels = values["input_image"].sizes["C"] + psfs = len(v.psf) + if psfs != channels: + raise ValueError(f"There should be one PSF per channel, but there are {psfs} PSFs and {channels} channels.") + return v + + @property + def cropping_enabled(self) -> bool: + "True if cropping should be performed" + return self.crop is not None + + @property + def deconv_enabled(self) -> bool: + "True if deconvolution should be performed" + return self.deconvolution is not None + + 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) -> DataArray: + if time > self.time: + raise ValueError("time is out of range") + if channel > self.channels: + raise ValueError("channel is out of range") + + return self.input_image.isel(T=time, C=channel) + + def iter_slices(self) -> Iterable[ProcessedSlice[ArrayLike]]: + """ + Yields array slices for each time and channel of interest. + + Returns: + An iterable of tuples. Each tuple contains (time_index, time, channel_index, channel, slice) + """ + from lls_core.models.results import ProcessedSlice + for time_idx, time in enumerate(self.time_range): + for ch_idx, ch in enumerate(self.channel_range): + yield ProcessedSlice( + data=self.slice_data(time=time, channel=ch), + time_index=time_idx, + time= time, + channel_index=ch_idx, + channel=ch, + ) + + def iter_sublattices(self, update_with: dict = {}) -> Iterable[ProcessedSlice[LatticeData]]: + """ + Yields copies of the current LatticeData, one for each slice. + These copies can then be processed separately. + Args: + update_with: dictionary of arguments to update the generated lattices with + """ + for subarray in self.iter_slices(): + new_lattice = self.copy_validate(update={ + "input_image": subarray.data, + "time_range": range(1), + "channel_range": range(1), + **update_with + }) + yield subarray.copy_with_data( new_lattice) + + def generate_workflows( + self, + ) -> Iterable[ProcessedSlice[Workflow]]: + """ + Yields copies of the input workflow, modified with the addition of deskewing and optionally, + cropping and deconvolution + """ + if self.workflow is None: + return + + from copy import copy + from lls_core.workflow import get_workflow_inputs, update_workflow + # We make a copy of the lattice for each slice, each of which has no associated workflow + for lattice_slice in self.iter_sublattices(update_with={"workflow": None}): + user_workflow = copy(self.workflow) + user_workflow.set( + "deskew_image", + LatticeData.process_into_image, + lattice_slice.data + ) + for task_name, arg_index, arg_name in get_workflow_inputs(user_workflow): + update_workflow( + user_workflow, + task_name, + arg_index, + "deskew_image" + ) + 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 + return zeros(self.deskew_vol_shape) + + def _process_crop(self) -> Iterable[ImageSlice]: + """ + Yields processed image slices with cropping enabled + """ + if self.crop is None: + raise Exception("This function can only be called when crop is set") + + # We have an extra level of iteration for the crop path: iterating over each ROI + for roi_index, roi in enumerate(tqdm(self.crop.selected_rois, desc="ROI:", position=0)): + # pass arguments for save tiff, callable and function arguments + logger.info(f"Processing ROI {roi_index}") + + deconv_args: dict[Any, Any] = {} + if self.deconvolution is not None: + deconv_args = dict( + num_iter = self.deconvolution.psf_num_iter, + psf = self.deconvolution.psf, + decon_processing=self.deconvolution.decon_processing + ) + + for slice in self.iter_slices(): + yield slice.copy_with_data( + crop_volume_deskew( + original_volume=slice.data, + deconvolution=self.deconv_enabled, + get_deskew_and_decon=False, + debug=False, + roi_shape=list(roi), + linear_interpolation=True, + voxel_size_x=self.dx, + voxel_size_y=self.dy, + voxel_size_z=self.dy, + angle_in_degrees=self.angle, + deskewed_volume=self.deskewed_volume, + # z_start=self.crop.z_range[0], + # z_end=self.crop.z_range[1], + **deconv_args + ) + ) + + def _process_non_crop(self) -> Iterable[ImageSlice]: + """ + Yields processed image slices without cropping + """ + for slice in self.iter_slices(): + data: ArrayLike = slice.data + if isinstance(slice.data, DaskArray): + data = slice.data.compute() + if self.deconvolution is not None: + if self.deconvolution.decon_processing == DeconvolutionChoice.cuda_gpu: + data = pycuda_decon( + image=data, + psf=self.deconvolution.psf[slice.channel].to_numpy(), + background=self.deconvolution.background, + dzdata=self.dz, + dxdata=self.dx, + dzpsf=self.dz, + dxpsf=self.dx, + num_iter=self.deconvolution.psf_num_iter + ) + else: + data = skimage_decon( + vol_zyx=data, + psf=self.deconvolution.psf[slice.channel].to_numpy(), + num_iter=self.deconvolution.psf_num_iter, + clip=False, + filter_epsilon=0, + boundary='nearest' + ) + + yield slice.copy_with_data( + cle.pull_zyx(self.deskew_func( + input_image=data, + angle_in_degrees=self.angle, + linear_interpolation=True, + voxel_size_x=self.dx, + voxel_size_y=self.dy, + voxel_size_z=self.dz + )) + ) + + def process_workflow(self) -> WorkflowSlices: + from lls_core.models.results import WorkflowSlices + WorkflowSlices.update_forward_refs(LatticeData=LatticeData) + outputs = [] + for workflow in self.generate_workflows(): + for leaf in workflow.data.leafs(): + outputs.append( + workflow.copy_with_data( + workflow.data.get(leaf) + ) + ) + + return WorkflowSlices( + slices = outputs, + lattice_data=self + ) + + def process(self) -> ImageSlices: + """ + Execute the processing and return the result. + This will not execute the attached workflow. + """ + from lls_core.models.results import ImageSlices + ImageSlices.update_forward_refs(LatticeData=LatticeData) + + if self.cropping_enabled: + return ImageSlices( + lattice_data=self, + slices=self._process_crop() + ) + else: + return ImageSlices( + lattice_data=self, + slices=self._process_non_crop() + ) + + def save(self): + """ + + This is the main public API for processing + """ + if self.workflow: + list(self.process_workflow().save()) + else: + self.process().save_image() + + def process_into_image(self) -> ArrayLike: + """ + Shortcut method for calling process, then extracting one image layer. + This is mostly here to simplify the Workflow integration + """ + for slice in self.process().slices: + return slice.data + raise Exception("No slices produced!") + + def get_writer(self) -> Type[Writer]: + from lls_core.writers import BdvWriter, TiffWriter + if self.save_type == SaveFileType.h5: + return BdvWriter + elif self.save_type == SaveFileType.tiff: + return TiffWriter + raise Exception("Unknown output type") diff --git a/plugin/napari_lattice/fields.py b/plugin/napari_lattice/fields.py index c9aa883..62861a6 100644 --- a/plugin/napari_lattice/fields.py +++ b/plugin/napari_lattice/fields.py @@ -433,7 +433,7 @@ def _make_model(self) -> Optional[CropParams]: import numpy as np if self.fields_enabled.value: return CropParams( - roi_list=ShapesData([np.array(shape.data) / self._get_deskew().dy for shape in self.shapes.value]) + roi_list=ShapesData([np.array(shape.data) / self._get_deskew().dy for shape in self.shapes.value]) ) return None From 60a1c152c910161b00e341660c66120279e80769 Mon Sep 17 00:00:00 2001 From: Michael Milton Date: Fri, 17 Nov 2023 16:23:39 +1100 Subject: [PATCH 100/147] Revert "Remove the z cropping fields from the GUI" This reverts commit 2000786354f11f2e0f25887cf9e6d0ac75887119. --- core/lls_core/models/lattice_data.py | 4 ++-- plugin/napari_lattice/fields.py | 17 +++++++++++++++-- 2 files changed, 17 insertions(+), 4 deletions(-) diff --git a/core/lls_core/models/lattice_data.py b/core/lls_core/models/lattice_data.py index 857c497..d3cd27c 100644 --- a/core/lls_core/models/lattice_data.py +++ b/core/lls_core/models/lattice_data.py @@ -337,8 +337,8 @@ def _process_crop(self) -> Iterable[ImageSlice]: voxel_size_z=self.dy, angle_in_degrees=self.angle, deskewed_volume=self.deskewed_volume, - # z_start=self.crop.z_range[0], - # z_end=self.crop.z_range[1], + z_start=self.crop.z_range[0], + z_end=self.crop.z_range[1], **deconv_args ) ) diff --git a/plugin/napari_lattice/fields.py b/plugin/napari_lattice/fields.py index 62861a6..e170bd7 100644 --- a/plugin/napari_lattice/fields.py +++ b/plugin/napari_lattice/fields.py @@ -398,6 +398,13 @@ class CroppingFields(NapariFieldGroup): # A counterpart to the CropParams Pydantic class fields_enabled = field(False, label="Enabled") shapes= field(List[Shapes], widget_type="Select", label = "ROI Shape Layers").with_options(choices=lambda _x, _y: get_layers(Shapes)) + z_range = field(Tuple[int, int]).with_options( + label = "Z Range", + value = (0, 1), + options = dict( + min = 0, + ), + ) errors = field(Label).with_options(label="Errors") def _get_deskew(self) -> DeskewParams: @@ -424,8 +431,13 @@ def new_crop_layer(self): shapes.mode = "ADD_RECTANGLE" shapes.name = "Napari Lattice Crop" + def _on_image_changed(self, img: DataArray): + # Update the maximum Z + for widget in self.z_range: + adjust_maximum(widget, img.sizes["Z"]) + @fields_enabled.connect - @enable_if([shapes]) + @enable_if([shapes, z_range]) def _enable_crop(self, enabled: bool) -> bool: return enabled @@ -433,7 +445,8 @@ def _make_model(self) -> Optional[CropParams]: import numpy as np if self.fields_enabled.value: return CropParams( - roi_list=ShapesData([np.array(shape.data) / self._get_deskew().dy for shape in self.shapes.value]) + roi_list=ShapesData([np.array(shape.data) / self._get_deskew().dy for shape in self.shapes.value]), + z_range=self.z_range.value, ) return None From 5addbccce1d2250e326647b27add0b50fc87e2bb Mon Sep 17 00:00:00 2001 From: Michael Milton Date: Fri, 17 Nov 2023 17:40:56 +1100 Subject: [PATCH 101/147] Tests for float ROIs --- core/lls_core/models/crop.py | 8 ++++++-- core/tests/test_process.py | 30 +++++++++++++++++++++++++++++- 2 files changed, 35 insertions(+), 3 deletions(-) diff --git a/core/lls_core/models/crop.py b/core/lls_core/models/crop.py index 65e6160..3e881f9 100644 --- a/core/lls_core/models/crop.py +++ b/core/lls_core/models/crop.py @@ -44,11 +44,15 @@ def read_roi(cls, v: Any) -> List[Roi]: elif isinstance(item, Roi): rois.append(item) else: - raise ValueError(f"{item} cannot be intepreted as an ROI") + # Try converting an iterable to ROI + try: + rois.append(Roi(*item)) + except: + raise ValueError(f"{item} cannot be intepreted as an ROI") return rois - @validator("roi_subset", pre=True) + @validator("roi_subset", pre=True, always=True) def default_roi_range(cls, v: Any, values: dict): # If the roi range isn't provided, assume all rois should be processed if v is None: diff --git a/core/tests/test_process.py b/core/tests/test_process.py index 7df0dd0..86c802c 100644 --- a/core/tests/test_process.py +++ b/core/tests/test_process.py @@ -69,7 +69,8 @@ def test_process_workflow(args: dict, request: FixtureRequest, workflow_name: st [[0, 1]], ]) @parameterized -def test_process_crop(args: dict, roi_subset: Optional[List[int]]): +def test_process_crop_roi_file(args: dict, roi_subset: Optional[List[int]]): + # Test cropping with a roi zip file, selecting different subsets from that file with as_file(resources / "RBC_tiny.czi") as lattice_path: rois = root / "crop" / "two_rois.zip" for slice in LatticeData.parse_obj({ @@ -81,3 +82,30 @@ def test_process_crop(args: dict, roi_subset: Optional[List[int]]): **args }).process().slices: assert slice.data.ndim == 3 + +@pytest.mark.parametrize(["roi"], [ + [[[ + (174.0, 24.0), + (174.0, 88.0), + (262.0, 88.0), + (262.0, 24.0) + ]]], + [[[ + (174.13, 24.2), + (173.98, 87.87), + (262.21, 88.3), + (261.99, 23.79) + ]]], +]) +@parameterized +def test_process_crop_roi_manual(args: dict, roi: List): + # Test manually provided ROIs, both with integer and float values + with as_file(resources / "RBC_tiny.czi") as lattice_path: + for slice in LatticeData.parse_obj({ + "input_image": lattice_path, + "crop": { + "roi_list": roi + }, + **args + }).process().slices: + assert slice.data.ndim == 3 From e36bcfde31f544fb4997d05e8957701c4447eaf1 Mon Sep 17 00:00:00 2001 From: Michael Milton Date: Mon, 20 Nov 2023 13:43:56 +1100 Subject: [PATCH 102/147] Rework Z-range handling to use deskewed max, and to update automatically --- core/lls_core/cmds/__main__.py | 4 +- core/lls_core/models/crop.py | 9 ++-- core/lls_core/models/deconvolution.py | 4 +- core/lls_core/models/deskew.py | 69 ++++++++++++++++----------- core/lls_core/models/lattice_data.py | 9 ++-- core/lls_core/models/output.py | 4 +- core/lls_core/models/utils.py | 4 +- core/tests/conftest.py | 5 ++ core/tests/test_validation.py | 20 +++++--- plugin/napari_lattice/fields.py | 19 ++++++-- 10 files changed, 95 insertions(+), 52 deletions(-) diff --git a/core/lls_core/cmds/__main__.py b/core/lls_core/cmds/__main__.py index 1fd8919..2eaabf7 100644 --- a/core/lls_core/cmds/__main__.py +++ b/core/lls_core/cmds/__main__.py @@ -22,7 +22,7 @@ from pydantic import ValidationError if TYPE_CHECKING: - from lls_core.models.utils import FieldAccessMixin + from lls_core.models.utils import FieldAccessModel from typing import Type, Any from rich.table import Table @@ -55,7 +55,7 @@ class CliDeskewDirection(StrEnum): app = Typer(add_completion=False, rich_markup_mode="rich", no_args_is_help=True) -def field_from_model(model: Type[FieldAccessMixin], field_name: str, extra_description: str = "", description: Optional[str] = None, default: Optional[Any] = None, **kwargs) -> Any: +def field_from_model(model: Type[FieldAccessModel], field_name: str, extra_description: str = "", description: Optional[str] = None, default: Optional[Any] = None, **kwargs) -> Any: """ Generates a type Field from a Pydantic model field """ diff --git a/core/lls_core/models/crop.py b/core/lls_core/models/crop.py index 3e881f9..7871452 100644 --- a/core/lls_core/models/crop.py +++ b/core/lls_core/models/crop.py @@ -1,11 +1,14 @@ from typing import Iterable, List, Tuple, Any from pydantic import Field, NonNegativeInt, validator -from lls_core.models.utils import FieldAccessMixin +from lls_core.models.utils import FieldAccessModel from lls_core.cropping import Roi -class CropParams(FieldAccessMixin): +class CropParams(FieldAccessModel): """ - Parameters for the optional cropping step + Parameters for the optional cropping step. + Note that cropping is performed in the space of the deskewed shape. + This is to support the workflow of performing a preview deskew and using that + to calculate the cropping coordinates. """ roi_list: List[Roi] = Field( description="List of regions of interest, each of which must be an NxD array, where N is the number of vertices and D the coordinates of each vertex.", diff --git a/core/lls_core/models/deconvolution.py b/core/lls_core/models/deconvolution.py index bd50f2b..373734e 100644 --- a/core/lls_core/models/deconvolution.py +++ b/core/lls_core/models/deconvolution.py @@ -7,12 +7,12 @@ from xarray import DataArray from lls_core import DeconvolutionChoice -from lls_core.models.utils import enum_choices, FieldAccessMixin +from lls_core.models.utils import enum_choices, FieldAccessModel from lls_core.types import image_like_to_image, ImageLike Background = Union[float, Literal["auto", "second_last"]] -class DeconvolutionParams(FieldAccessMixin): +class DeconvolutionParams(FieldAccessModel): """ Parameters for the optional deconvolution step """ diff --git a/core/lls_core/models/deskew.py b/core/lls_core/models/deskew.py index 96d5c55..bac6e2d 100644 --- a/core/lls_core/models/deskew.py +++ b/core/lls_core/models/deskew.py @@ -1,7 +1,7 @@ from __future__ import annotations # class for initializing lattice data and setting metadata # TODO: handle scenes -from pydantic import Field, NonNegativeFloat, validator, root_validator +from pydantic import Field, NonNegativeFloat, validator from typing import Any, Tuple from typing_extensions import Self, TYPE_CHECKING @@ -11,16 +11,14 @@ from lls_core import DeskewDirection from xarray import DataArray -from lls_core.models.utils import FieldAccessMixin, enum_choices +from lls_core.models.utils import FieldAccessModel, enum_choices from lls_core.types import image_like_to_image from lls_core.utils import get_deskewed_shape if TYPE_CHECKING: from aicsimageio.types import PhysicalPixelSizes -# DeskewDirection = Literal["X", "Y"] - -class DefinedPixelSizes(FieldAccessMixin): +class DefinedPixelSizes(FieldAccessModel): """ Like PhysicalPixelSizes, but it's a dataclass, and none of its fields are None @@ -39,8 +37,21 @@ def from_physical(cls, pixels: PhysicalPixelSizes) -> Self: Z=raise_if_none(pixels.Z, "All pixels must be defined"), ) +class DerivedDeskewFields(FieldAccessModel): + """ + Fields that are automatically calculated based on other fields in DeskewParams. + Grouping these together into one model makes validation simpler. + """ + deskew_vol_shape: Tuple[int, ...] = Field( + init_var=False, + default=None, + description="Dimensions of the deskewed output. This is set automatically based on other input parameters, and doesn't need to be provided by the user." + ) + + deskew_affine_transform: cle.AffineTransform3D = Field(init_var=False, default=None, description="Deskewing transformation function. This is set automatically based on other input parameters, and doesn't need to be provided by the user.") + -class DeskewParams(FieldAccessMixin): +class DeskewParams(FieldAccessModel): input_image: DataArray = Field( description="A 3-5D array containing the image data." ) @@ -53,17 +64,14 @@ class DeskewParams(FieldAccessMixin): description="Angle of deskewing, in degrees." ) physical_pixel_sizes: DefinedPixelSizes = Field( - default_factory=DefinedPixelSizes, - description="Pixel size of the microscope, in microns." -) - deskew_vol_shape: Tuple[int, ...] = Field( + default_factory=DefinedPixelSizes, + description="Pixel size of the microscope, in microns." + ) + derived: DerivedDeskewFields = Field( init_var=False, default=None, - description="Dimensions of the deskewed output. This is set automatically based on other input parameters, and doesn't need to be provided by the user." + description="Refer to the DerivedDeskewFields docstring" ) - - deskew_affine_transform: cle.AffineTransform3D = Field(init_var=False, default=None, description="Deskewing transformation function. This is set automatically based on other input parameters, and doesn't need to be provided by the user.") - # Hack to ensure that .skew_dir behaves identically to .skew @property def skew_dir(self) -> DeskewDirection: @@ -170,19 +178,26 @@ def reshaping(cls, v: Any): def get_3d_slice(self) -> DataArray: return self.input_image.isel(C=0, T=0) - @root_validator(pre=False) - def set_deskew(cls, values: dict) -> dict: + @validator("derived", always=True) + def calculate_derived(cls, v: Any, values: dict) -> DerivedDeskewFields: """ Sets the default deskew shape values if the user has not provided them """ - # process the file to get shape of final deskewed image - if "input_image" not in values: - return values - data: DataArray = cls.reshaping(values["input_image"]) - 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(data.isel(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 + data: DataArray = values["input_image"] + if isinstance(v, DerivedDeskewFields): + return v + elif v is None: + deskew_vol_shape, deskew_affine_transform = get_deskewed_shape( + data.isel(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"] + ) + return DerivedDeskewFields( + deskew_affine_transform=deskew_affine_transform, + deskew_vol_shape=deskew_vol_shape + ) + else: + raise ValueError("Invalid derived fields") diff --git a/core/lls_core/models/lattice_data.py b/core/lls_core/models/lattice_data.py index d3cd27c..8456250 100644 --- a/core/lls_core/models/lattice_data.py +++ b/core/lls_core/models/lattice_data.py @@ -88,13 +88,14 @@ def parse_workflow(cls, v: Any): return v @validator("crop") - def default_z_range(cls, v: CropParams, values: dict): + def default_z_range(cls, v: Optional[CropParams], values: dict) -> Optional[CropParams]: if v is None: return v with ignore_keyerror(): - # Fill in missing parts of the z range with the min/max z values + # Fill in missing parts of the z range + # The max allowed value is the length of the deskew Z axis default_start = 0 - default_end = values["input_image"].sizes["Z"] + default_end = values["derived"].deskew_vol_shape[0] # Set defaults if v.z_range is None: @@ -110,7 +111,7 @@ def default_z_range(cls, v: CropParams, values: dict): if v.z_range[0] < default_start: raise ValueError(f"The z-index start of {v.z_range[0]} is outside the size of the z-axis") - return v + return v @validator("time_range", pre=True, always=True) def parse_time_range(cls, v: Any, values: dict) -> Any: diff --git a/core/lls_core/models/output.py b/core/lls_core/models/output.py index b1374b2..b6dbc02 100644 --- a/core/lls_core/models/output.py +++ b/core/lls_core/models/output.py @@ -4,7 +4,7 @@ from pathlib import Path from typing import TYPE_CHECKING, Any -from lls_core.models.utils import FieldAccessMixin, enum_choices +from lls_core.models.utils import FieldAccessModel, enum_choices if TYPE_CHECKING: pass @@ -14,7 +14,7 @@ class SaveFileType(StrEnum): h5 = "h5" tiff = "tiff" -class OutputParams(FieldAccessMixin): +class OutputParams(FieldAccessModel): save_dir: DirectoryPath = Field( description="The directory where the output data will be saved" ) diff --git a/core/lls_core/models/utils.py b/core/lls_core/models/utils.py index 3e49bcd..768595c 100644 --- a/core/lls_core/models/utils.py +++ b/core/lls_core/models/utils.py @@ -23,7 +23,7 @@ def ignore_keyerror(): except KeyError: pass -class FieldAccessMixin(BaseModel): +class FieldAccessModel(BaseModel): """ Adds methods to a BaseModel for accessing useful field information """ @@ -54,7 +54,7 @@ def to_definition_dict(cls) -> dict: """ ret = {} for key, value in cls.__fields__.items(): - if isinstance(value.outer_type_, type) and issubclass(value.outer_type_, FieldAccessMixin): + if isinstance(value.outer_type_, type) and issubclass(value.outer_type_, FieldAccessModel): value = value.outer_type_.to_definition_dict() else: value = value.field_info.description diff --git a/core/tests/conftest.py b/core/tests/conftest.py index 22e05ed..457aa9e 100644 --- a/core/tests/conftest.py +++ b/core/tests/conftest.py @@ -17,6 +17,11 @@ def runner() -> CliRunner: return CliRunner() +@pytest.fixture +def rbc_tiny(): + with as_file(resources / "RBC_tiny.czi") as image_path: + yield image_path + @pytest.fixture(params=[ "RBC_tiny.czi", "RBC_lattice.tif", diff --git a/core/tests/test_validation.py b/core/tests/test_validation.py index 9084ff9..76d1ad6 100644 --- a/core/tests/test_validation.py +++ b/core/tests/test_validation.py @@ -1,10 +1,16 @@ +from pathlib import Path +from lls_core.models.crop import CropParams from lls_core.models.lattice_data import LatticeData -from lls_core.sample import resources -from importlib_resources import as_file - -def test_default_save_dir(): +def test_default_save_dir(rbc_tiny: Path): # Test that the save dir is inferred to be the input dir - with as_file(resources / "RBC_tiny.czi") as path: - params = LatticeData(input_image=path) - assert params.save_dir == path.parent + params = LatticeData(input_image=rbc_tiny) + assert params.save_dir == rbc_tiny.parent + +def test_auto_z_range(rbc_tiny: Path): + # Tests that the Z range is automatically set, and it is set + # based on the size of the deskewed volume + params = LatticeData(input_image=rbc_tiny, crop=CropParams( + roi_list=[] + )) + assert params.crop.z_range == (0, 59) diff --git a/plugin/napari_lattice/fields.py b/plugin/napari_lattice/fields.py index e170bd7..09283db 100644 --- a/plugin/napari_lattice/fields.py +++ b/plugin/napari_lattice/fields.py @@ -1,6 +1,7 @@ # FieldGroups that the users interact with to input data import logging from pathlib import Path +from textwrap import dedent from typing import Any, Callable, List, Optional, Tuple, TYPE_CHECKING from typing_extensions import TypeVar import pyclesperanto_prototype as cle @@ -396,6 +397,10 @@ def _make_model(self) -> Optional[DeconvolutionParams]: @magicclass class CroppingFields(NapariFieldGroup): # A counterpart to the CropParams Pydantic class + header = field(dedent(""" + Note that all cropping, including the regions of interest and Z range, is performed in the space of the deskewed shape. + This is to support the workflow of performing a preview deskew and using that to calculate the cropping coordinates. + """), widget_type="Label") fields_enabled = field(False, label="Enabled") shapes= field(List[Shapes], widget_type="Select", label = "ROI Shape Layers").with_options(choices=lambda _x, _y: get_layers(Shapes)) z_range = field(Tuple[int, int]).with_options( @@ -432,9 +437,15 @@ def new_crop_layer(self): shapes.name = "Napari Lattice Crop" def _on_image_changed(self, img: DataArray): - # Update the maximum Z + deskew = self._get_deskew() + deskewed_zmax = deskew.derived.deskew_vol_shape[0] + + # Update the allowed Z based the deskewed shape for widget in self.z_range: - adjust_maximum(widget, img.sizes["Z"]) + adjust_maximum(widget, deskewed_zmax) + + # Update the current max value to be the max of the shape + self.z_range[1].value = deskewed_zmax @fields_enabled.connect @enable_if([shapes, z_range]) @@ -445,8 +456,10 @@ def _make_model(self) -> Optional[CropParams]: import numpy as np if self.fields_enabled.value: return CropParams( + # Convert from the input image space to the deskewed image space + # We assume here that dx == dy which isn't ideal roi_list=ShapesData([np.array(shape.data) / self._get_deskew().dy for shape in self.shapes.value]), - z_range=self.z_range.value, + z_range=tuple(np.array(self.z_range.value) / self._get_deskew().new_dz), ) return None From 9990f78947f6fb4ed9898e5af5730f63636cc713 Mon Sep 17 00:00:00 2001 From: Michael Milton Date: Mon, 20 Nov 2023 14:16:43 +1100 Subject: [PATCH 103/147] Fix broken GUI tests --- plugin/napari_lattice/dock_widget.py | 328 ++++++++++++++------------- plugin/tests/test_dock_widget.py | 174 +++++++------- 2 files changed, 253 insertions(+), 249 deletions(-) diff --git a/plugin/napari_lattice/dock_widget.py b/plugin/napari_lattice/dock_widget.py index 7c90d74..d712b56 100644 --- a/plugin/napari_lattice/dock_widget.py +++ b/plugin/napari_lattice/dock_widget.py @@ -1,162 +1,166 @@ -from __future__ import annotations - -import logging -from textwrap import dedent -from typing import TYPE_CHECKING -import numpy as np -from lls_core.models.lattice_data import LatticeData -from magicclass import MagicTemplate, field, magicclass, set_options, vfield -from magicclass.wrappers import set_design -from napari_lattice.fields import ( - CroppingFields, - DeconvolutionFields, - DeskewFields, - OutputFields, - WorkflowFields, -) -from qtpy.QtCore import Qt -from qtpy.QtWidgets import QTabWidget - -if TYPE_CHECKING: - from typing import Iterable - from napari_lattice.fields import NapariFieldGroup - -# Enable Logging -logger = logging.getLogger(__name__) -logger.setLevel(logging.INFO) - -@magicclass(widget_type="split") -class LLSZWidget(MagicTemplate): - def __post_init__(self): - # aligning collapsible widgets at the top instead of having them centered vertically - self._widget._layout.setAlignment(Qt.AlignTop) - - - def _check_validity(self) -> bool: - """ - Returns True if the model is valid - """ - try: - self._make_model() - return True - except: - return False - - def _make_model(self, validate: bool = True) -> LatticeData: - from rich import print - from sys import stdout - - deskew_args = self.LlszMenu.WidgetContainer.deskew_fields._get_kwargs() - output_args = self.LlszMenu.WidgetContainer.output_fields._make_model(validate=False) - args = dict( - input_image=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() - ) - params = LatticeData.make(validate=validate, **args) - # Log the lattice - print(params, file=stdout) - return params - - @magicclass(widget_type="split") - class LlszMenu(MagicTemplate): - main_heading = field("

      Napari Lattice: Visualization & Analysis

      ", widget_type="Label") - heading1 = field(dedent(""" -
      - Specify deskewing parameters and image layers in Tab 1. - Additional analysis parameters can be configured in the other tabs. - When you are ready to save, go to Tab 5. - Output to specify the output directory. - For more information, please refer to the documentation here. -
      - """.strip()), widget_type="Label") - - def __post_init__(self): - from qtpy.QtCore import Qt - from qtpy.QtWidgets import QLabel, QLayout - - if isinstance(self._widget._layout, QLayout): - self._widget._layout.setAlignment(Qt.AlignmentFlag.AlignTop) - - if isinstance(self.heading1.native, QLabel): - self.heading1.native.setWordWrap(True) - - # Tabbed Widget container to house all the widgets - @magicclass(widget_type="tabbed", name="Functions", labels=False) - class WidgetContainer(MagicTemplate): - - def __post_init__(self): - tab_widget: QTabWidget= self._widget._tab_widget - # Manually set the tab labels, because by default magicgui uses the widget names, but setting - # the names to human readable text makes them difficult to access via self - for i, label in enumerate(["1. Deskew", "2. Deconvolution", "3. Crop", "4. Workflow", "5. Output"]): - tab_widget.setTabText(i, label) - for field in [self.deskew_fields, self.deconv_fields, self.cropping_fields, self.workflow_fields, self.output_fields]: - field._validate() - - # Using vfields here seems to prevent https://github.com/hanjinliu/magic-class/issues/110 - deskew_fields = vfield(DeskewFields) - deconv_fields = vfield(DeconvolutionFields) - cropping_fields = vfield(CroppingFields) - workflow_fields = vfield(WorkflowFields) - output_fields = vfield(OutputFields) - - @set_options(header=dict(widget_type="Label", label="

      Preview Deskew

      "), - time=dict(label="Time:", max=2**15), - channel=dict(label="Channel:"), - call_button="Preview" - ) - @set_design(text="Preview") - def preview(self, header: str, time: int, channel: int): - from pathlib import Path - - # We only need to process one time point for the preview, - # so we made a copy using a subset of the times - lattice = self._make_model(validate=False).copy_validate(update=dict( - time_range = range(time, time+1), - channel_range = range(time, time+1), - # Patch in a placeholder for the save dir because previewing doesn't use it - # TODO: use a more elegant solution such as making the "saveable" lattice - # a child class which more validations - save_dir = Path.home() - )) - - for slice in lattice.process().slices: - scale = ( - lattice.new_dz, - lattice.dy, - lattice.dx - ) - self.parent_viewer.add_image(slice.data, scale=scale) - max_z = np.argmax(np.sum(slice.data, axis=(1, 2))) - self.parent_viewer.dims.set_current_step(0, max_z) - - @set_design(text="Save") - def save(self): - from napari.utils.notifications import show_info - lattice = self._make_model() - lattice.save() - show_info(f"Deskewing successfuly completed. Results are located in {lattice.save_dir}") - - @LlszMenu.WidgetContainer.deskew_fields.connect - def _on_image_changed(self, deskew: DeskewFields): - img = deskew._get_kwargs()["data"] - # We have to manually trigger _on_image_changed because siblings - # classes can't listen to each other's events: https://github.com/hanjinliu/magic-class/issues/129 - for field in self._get_fields(): - field._on_image_changed(img) - - def _get_fields(self) -> Iterable[NapariFieldGroup]: - """Yields all the child Field classes which inherit from NapariFieldGroup""" - container = self.LlszMenu.WidgetContainer - yield from set(container.__magicclass_children__) +from __future__ import annotations + +import logging +from textwrap import dedent +from typing import TYPE_CHECKING +import numpy as np +from lls_core.models.lattice_data import LatticeData +from magicclass import MagicTemplate, field, magicclass, set_options, vfield +from magicclass.wrappers import set_design +from napari_lattice.fields import ( + CroppingFields, + DeconvolutionFields, + DeskewFields, + OutputFields, + WorkflowFields, +) +from qtpy.QtCore import Qt +from qtpy.QtWidgets import QTabWidget + +if TYPE_CHECKING: + from typing import Iterable + from napari_lattice.fields import NapariFieldGroup + +# Enable Logging +logger = logging.getLogger(__name__) +logger.setLevel(logging.INFO) + +@magicclass(widget_type="split") +class LLSZWidget(MagicTemplate): + def __post_init__(self): + # aligning collapsible widgets at the top instead of having them centered vertically + self._widget._layout.setAlignment(Qt.AlignTop) + + + def _check_validity(self) -> bool: + """ + Returns True if the model is valid + """ + try: + self._make_model() + return True + except: + return False + + def _make_model(self, validate: bool = True) -> LatticeData: + from rich import print + from sys import stdout + + deskew_args = self.LlszMenu.WidgetContainer.deskew_fields._get_kwargs() + output_args = self.LlszMenu.WidgetContainer.output_fields._make_model(validate=False) + args = dict( + input_image=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() + ) + params = LatticeData.make(validate=validate, **args) + # Log the lattice + print(params, file=stdout) + return params + + @magicclass(widget_type="split") + class LlszMenu(MagicTemplate): + main_heading = field("

      Napari Lattice: Visualization & Analysis

      ", widget_type="Label") + heading1 = field(dedent(""" +
      + Specify deskewing parameters and image layers in Tab 1. + Additional analysis parameters can be configured in the other tabs. + When you are ready to save, go to Tab 5. + Output to specify the output directory. + For more information, please refer to the documentation here. +
      + """.strip()), widget_type="Label") + + def __post_init__(self): + from qtpy.QtCore import Qt + from qtpy.QtWidgets import QLabel, QLayout + + if isinstance(self._widget._layout, QLayout): + self._widget._layout.setAlignment(Qt.AlignmentFlag.AlignTop) + + if isinstance(self.heading1.native, QLabel): + self.heading1.native.setWordWrap(True) + + # Tabbed Widget container to house all the widgets + @magicclass(widget_type="tabbed", name="Functions", labels=False) + class WidgetContainer(MagicTemplate): + + def __post_init__(self): + tab_widget: QTabWidget= self._widget._tab_widget + # Manually set the tab labels, because by default magicgui uses the widget names, but setting + # the names to human readable text makes them difficult to access via self + for i, label in enumerate(["1. Deskew", "2. Deconvolution", "3. Crop", "4. Workflow", "5. Output"]): + tab_widget.setTabText(i, label) + for field in [self.deskew_fields, self.deconv_fields, self.cropping_fields, self.workflow_fields, self.output_fields]: + field._validate() + + # Using vfields here seems to prevent https://github.com/hanjinliu/magic-class/issues/110 + deskew_fields = vfield(DeskewFields) + deconv_fields = vfield(DeconvolutionFields) + cropping_fields = vfield(CroppingFields) + workflow_fields = vfield(WorkflowFields) + output_fields = vfield(OutputFields) + + @set_options(header=dict(widget_type="Label", label="

      Preview Deskew

      "), + time=dict(label="Time:", max=2**15), + channel=dict(label="Channel:"), + call_button="Preview" + ) + @set_design(text="Preview") + def preview(self, header: str, time: int, channel: int): + from pathlib import Path + + # We only need to process one time point for the preview, + # so we made a copy using a subset of the times + lattice = self._make_model(validate=False).copy_validate(update=dict( + time_range = range(time, time+1), + channel_range = range(time, time+1), + # Patch in a placeholder for the save dir because previewing doesn't use it + # TODO: use a more elegant solution such as making the "saveable" lattice + # a child class which more validations + save_dir = Path.home() + )) + + for slice in lattice.process().slices: + scale = ( + lattice.new_dz, + lattice.dy, + lattice.dx + ) + self.parent_viewer.add_image(slice.data, scale=scale) + max_z = np.argmax(np.sum(slice.data, axis=(1, 2))) + self.parent_viewer.dims.set_current_step(0, max_z) + + @set_design(text="Save") + def save(self): + from napari.utils.notifications import show_info + lattice = self._make_model() + lattice.save() + show_info(f"Deskewing successfuly completed. Results are located in {lattice.save_dir}") + + @LlszMenu.WidgetContainer.deskew_fields.connect + def _on_image_changed(self, deskew: DeskewFields): + # An error at this point doesn't need to be communicated to the user + try: + img = deskew._get_kwargs()["data"] + # We have to manually trigger _on_image_changed because siblings + # classes can't listen to each other's events: https://github.com/hanjinliu/magic-class/issues/129 + for field in self._get_fields(): + field._on_image_changed(img) + except: + pass + + def _get_fields(self) -> Iterable[NapariFieldGroup]: + """Yields all the child Field classes which inherit from NapariFieldGroup""" + container = self.LlszMenu.WidgetContainer + yield from set(container.__magicclass_children__) diff --git a/plugin/tests/test_dock_widget.py b/plugin/tests/test_dock_widget.py index 4a6cfd5..ef846cf 100644 --- a/plugin/tests/test_dock_widget.py +++ b/plugin/tests/test_dock_widget.py @@ -1,87 +1,87 @@ -from __future__ import annotations - -from importlib_resources import as_file -from napari_lattice.dock_widget import LLSZWidget -from typing import Callable, TYPE_CHECKING -from magicclass.testing import check_function_gui_buildable, FunctionGuiTester -from magicclass import MagicTemplate -from magicclass.widgets import Widget -from magicclass._gui._gui_modes import ErrorMode -import pytest -from lls_core.sample import resources -from aicsimageio.aics_image import AICSImage -from napari_lattice.fields import PixelSizeSource -from tempfile import TemporaryDirectory - -if TYPE_CHECKING: - from napari import Viewer - -# Test if the widget can be created - -# make_napari_viewer is a pytest fixture that returns a napari viewer object -# Commenting this out as github CI is fixed -# @pytest.mark.skip(reason="GUI tests currently fail in github CI, unclear why") -# When testing locally, need pytest-qt - -@pytest.fixture(params=[ - "RBC_tiny.czi", - "LLS7_t1_ch1.czi", - "LLS7_t1_ch3.czi", - "LLS7_t2_ch1.czi", - "LLS7_t2_ch3.czi", -]) -def image_data(request: pytest.FixtureRequest): - """ - Fixture function that yields test images as file paths - """ - with as_file(resources / request.param) as image_path: - yield AICSImage(image_path) - -def set_debug(cls: MagicTemplate): - """ - Recursively disables GUI error handling, so that this works with pytest - """ - def _handler(e: Exception, parent: Widget): - raise e - ErrorMode.get_handler = lambda self: _handler - cls._error_mode = ErrorMode.stderr - for child in cls.__magicclass_children__: - set_debug(child) - -def test_dock_widget(make_napari_viewer: Callable[[], Viewer], image_data: AICSImage): - # make viewer and add an image layer using our fixture - viewer = make_napari_viewer() - - # Check if an image can be added as a layer - viewer.add_image(image_data.xarray_dask_data) - - # Test if napari-lattice widget can be created in napari - ui = LLSZWidget() - set_debug(ui) - viewer.window.add_dock_widget(ui) - - # Set the input parameters and execute the processing - with TemporaryDirectory() as tmpdir: - # Specify values for all the required GUI fields - fields = ui.LlszMenu.WidgetContainer.deskew_fields - # TODO: refactor this logic into a `lattice_params_from_aics` method - fields.img_layer.value = list(viewer.layers) - fields.dimension_order.value = image_data.dims.order - fields.pixel_sizes_source.value = PixelSizeSource.Manual - - # Test previewing - tester = FunctionGuiTester(ui.preview) - tester.call("", 0, 0) - - # Add the save path which shouldn't be needed for previewing - ui.LlszMenu.WidgetContainer.output_fields.save_path.value = tmpdir - - # Test saving - tester = FunctionGuiTester(ui.save) - tester.call() - - -def test_check_buildable(): - ui = LLSZWidget() - set_debug(ui) - check_function_gui_buildable(ui) +from __future__ import annotations + +from importlib_resources import as_file +from napari_lattice.dock_widget import LLSZWidget +from typing import Callable, TYPE_CHECKING +from magicclass.testing import check_function_gui_buildable, FunctionGuiTester +from magicclass import MagicTemplate +from magicclass.widgets import Widget +from magicclass._gui._gui_modes import ErrorMode +import pytest +from lls_core.sample import resources +from aicsimageio.aics_image import AICSImage +from napari_lattice.fields import PixelSizeSource +from tempfile import TemporaryDirectory + +if TYPE_CHECKING: + from napari import Viewer + +# Test if the widget can be created + +# make_napari_viewer is a pytest fixture that returns a napari viewer object +# Commenting this out as github CI is fixed +# @pytest.mark.skip(reason="GUI tests currently fail in github CI, unclear why") +# When testing locally, need pytest-qt + +@pytest.fixture(params=[ + "RBC_tiny.czi", + "LLS7_t1_ch1.czi", + "LLS7_t1_ch3.czi", + "LLS7_t2_ch1.czi", + "LLS7_t2_ch3.czi", +]) +def image_data(request: pytest.FixtureRequest): + """ + Fixture function that yields test images as file paths + """ + with as_file(resources / request.param) as image_path: + yield AICSImage(image_path) + +def set_debug(cls: MagicTemplate): + """ + Recursively disables GUI error handling, so that this works with pytest + """ + def _handler(e: Exception, parent: Widget): + raise e + ErrorMode.get_handler = lambda self: _handler + cls._error_mode = ErrorMode.stderr + for child in cls.__magicclass_children__: + set_debug(child) + +def test_dock_widget(make_napari_viewer: Callable[[], Viewer], image_data: AICSImage): + # make viewer and add an image layer using our fixture + viewer = make_napari_viewer() + + # Check if an image can be added as a layer + viewer.add_image(image_data.xarray_dask_data) + + # Test if napari-lattice widget can be created in napari + ui = LLSZWidget() + set_debug(ui) + viewer.window.add_dock_widget(ui) + + # Set the input parameters and execute the processing + with TemporaryDirectory() as tmpdir: + # Specify values for all the required GUI fields + fields = ui.LlszMenu.WidgetContainer.deskew_fields + # TODO: refactor this logic into a `lattice_params_from_aics` method + fields.img_layer.value = list(viewer.layers) + fields.dimension_order.value = image_data.dims.order + fields.pixel_sizes_source.value = PixelSizeSource.Manual + + # Test previewing + tester = FunctionGuiTester(ui.preview) + tester.call("", 0, 0) + + # Add the save path which shouldn't be needed for previewing + ui.LlszMenu.WidgetContainer.output_fields.save_path.value = tmpdir + + # Test saving + tester = FunctionGuiTester(ui.save) + tester.call() + + +def test_check_buildable(): + ui = LLSZWidget() + set_debug(ui) + check_function_gui_buildable(ui) From b770b32e0426ab4f3436f537c5ec158be4cda1cb Mon Sep 17 00:00:00 2001 From: Michael Milton Date: Thu, 23 Nov 2023 11:43:10 +1100 Subject: [PATCH 104/147] Fix deskew vol usage --- core/lls_core/models/lattice_data.py | 2 +- core/tests/test_deskew.py | 56 ++++++++++++++-------------- 2 files changed, 29 insertions(+), 29 deletions(-) diff --git a/core/lls_core/models/lattice_data.py b/core/lls_core/models/lattice_data.py index 8456250..42130fe 100644 --- a/core/lls_core/models/lattice_data.py +++ b/core/lls_core/models/lattice_data.py @@ -302,7 +302,7 @@ def check_incomplete_acquisition(self, volume: ArrayLike, time_point: int, chann @property def deskewed_volume(self) -> DaskArray: from dask.array import zeros - return zeros(self.deskew_vol_shape) + return zeros(self.derived.deskew_vol_shape) def _process_crop(self) -> Iterable[ImageSlice]: """ diff --git a/core/tests/test_deskew.py b/core/tests/test_deskew.py index aaa22a2..adbcd6c 100644 --- a/core/tests/test_deskew.py +++ b/core/tests/test_deskew.py @@ -1,28 +1,28 @@ -#filename and function name should start with "test_" when using pytest -import pyclesperanto_prototype as cle -import numpy as np -from lls_core.models.lattice_data import LatticeData -from xarray import DataArray -import tempfile - -def test_deskew(): - - raw = np.zeros((5,5,5)) - raw[2,0,0] = 10 - - deskewed = cle.deskew_y(raw,angle_in_degrees=60) - - #np.argwhere(deskewed>0) - assert deskewed.shape == (4,8,5) - assert deskewed[2,2,0] == 0.5662433505058289 - -def test_lattice_data_deskew(): - raw = DataArray(np.zeros((5, 5, 5)), dims=["X", "Y", "Z"]) - with tempfile.TemporaryDirectory() as tmpdir: - lattice = LatticeData( - input_image=raw, - physical_pixel_sizes = (1, 1, 1), - save_name="test", - save_dir=tmpdir - ) - assert lattice.deskew_vol_shape == [2, 9, 5] +#filename and function name should start with "test_" when using pytest +import pyclesperanto_prototype as cle +import numpy as np +from lls_core.models.lattice_data import LatticeData +from xarray import DataArray +import tempfile + +def test_deskew(): + + raw = np.zeros((5,5,5)) + raw[2,0,0] = 10 + + deskewed = cle.deskew_y(raw,angle_in_degrees=60) + + #np.argwhere(deskewed>0) + assert deskewed.shape == (4,8,5) + assert deskewed[2,2,0] == 0.5662433505058289 + +def test_lattice_data_deskew(): + raw = DataArray(np.zeros((5, 5, 5)), dims=["X", "Y", "Z"]) + with tempfile.TemporaryDirectory() as tmpdir: + lattice = LatticeData( + input_image=raw, + physical_pixel_sizes = (1, 1, 1), + save_name="test", + save_dir=tmpdir + ) + assert lattice.derived.deskew_vol_shape == (2, 9, 5) From ad575f5c420665b2c85406b3ad0a2ce192d93a50 Mon Sep 17 00:00:00 2001 From: Michael Milton Date: Wed, 6 Dec 2023 13:27:36 +1100 Subject: [PATCH 105/147] Minimal test images --- core/tests/conftest.py | 15 ++- core/tests/params.py | 1 - core/tests/test_crop_deskew.py | 70 +++++++------- core/tests/test_process.py | 16 +++- core/tests/test_workflows.py | 170 ++++++++++++++++----------------- 5 files changed, 147 insertions(+), 125 deletions(-) diff --git a/core/tests/conftest.py b/core/tests/conftest.py index 457aa9e..1d6814f 100644 --- a/core/tests/conftest.py +++ b/core/tests/conftest.py @@ -22,6 +22,19 @@ def rbc_tiny(): with as_file(resources / "RBC_tiny.czi") as image_path: yield image_path +@pytest.fixture(params=[ + "LLS7_t1_ch1.czi", + "LLS7_t1_ch3.czi", + "LLS7_t2_ch1.czi", + "LLS7_t2_ch3.czi", +]) +def minimal_image_path(request: pytest.FixtureRequest): + """ + Fixture function that yields a minimal set of test images as file paths + """ + with as_file(resources / request.param) as image_path: + yield image_path + @pytest.fixture(params=[ "RBC_tiny.czi", "RBC_lattice.tif", @@ -31,7 +44,7 @@ def rbc_tiny(): "LLS7_t2_ch3.czi", "multich_multi_time.tif" ]) -def image_path(request: pytest.FixtureRequest): +def minimal_image_path(request: pytest.FixtureRequest): """ Fixture function that yields test images as file paths """ diff --git a/core/tests/params.py b/core/tests/params.py index 4e927bf..2bb1f20 100644 --- a/core/tests/params.py +++ b/core/tests/params.py @@ -8,7 +8,6 @@ {"skew": "X"}, {"skew": "Y"}, {"angle": 30}, - {"angle": 90}, {"physical_pixel_sizes": (1, 1, 1)}, {"save_type": SaveFileType.h5}, {"save_type": SaveFileType.tiff}, diff --git a/core/tests/test_crop_deskew.py b/core/tests/test_crop_deskew.py index e873c3f..9b3a4b2 100644 --- a/core/tests/test_crop_deskew.py +++ b/core/tests/test_crop_deskew.py @@ -1,35 +1,35 @@ -import pyclesperanto_prototype as cle -import numpy as np - -from lls_core.llsz_core import crop_volume_deskew - - -def test_crop_deskew(): - raw = np.zeros((5,5,5)) - raw[2,4,2] = 10 - deskew_angle = 60 - - deskewed = cle.deskew_y(raw,angle_in_degrees=deskew_angle).astype(raw.dtype) - - #print(np.argwhere(deskewed>0)) - - #Crop deskewed volume - ref_crop_deskew_img = deskewed[0:4,3:5,0:5] - - #Similarly, generate an roi with coordinates (x1=0,y1=3,z1=0) to (x2=5,y2=5,z2=5) - #Use this for cropping deskewed volume to get matching area - roi = np.array(((3,0),(3,5),(5,5),(5,0))) - z1 = 0 - z2 = 5 - - cropped_deskew_img = crop_volume_deskew(original_volume = raw, - deskewed_volume = deskewed, - roi_shape = roi, - angle_in_degrees = deskew_angle, - z_start = z1, - z_end = z2, - linear_interpolation=True).astype(raw.dtype) - - assert cropped_deskew_img[0, 1, 2] == ref_crop_deskew_img[0,1,2] - assert cropped_deskew_img[0, 0, 2] == ref_crop_deskew_img[0,0,2] - assert ref_crop_deskew_img.shape == cropped_deskew_img.shape \ No newline at end of file +import pyclesperanto_prototype as cle +import numpy as np + +from lls_core.llsz_core import crop_volume_deskew + + +def test_crop_deskew(): + raw = np.zeros((5,5,5)) + raw[2,4,2] = 10 + deskew_angle = 60 + + deskewed = cle.deskew_y(raw,angle_in_degrees=deskew_angle).astype(raw.dtype) + + #print(np.argwhere(deskewed>0)) + + #Crop deskewed volume + ref_crop_deskew_img = deskewed[0:4,3:5,0:5] + + #Similarly, generate an roi with coordinates (x1=0,y1=3,z1=0) to (x2=5,y2=5,z2=5) + #Use this for cropping deskewed volume to get matching area + roi = np.array(((3,0),(3,5),(5,5),(5,0))) + z1 = 0 + z2 = 5 + + cropped_deskew_img = crop_volume_deskew(original_volume = raw, + deskewed_volume = deskewed, + roi_shape = roi, + angle_in_degrees = deskew_angle, + z_start = z1, + z_end = z2, + linear_interpolation=True).astype(raw.dtype) + + assert cropped_deskew_img[0, 1, 2] == ref_crop_deskew_img[0,1,2] + assert cropped_deskew_img[0, 0, 2] == ref_crop_deskew_img[0,0,2] + assert ref_crop_deskew_img.shape == cropped_deskew_img.shape diff --git a/core/tests/test_process.py b/core/tests/test_process.py index 86c802c..1a3fa99 100644 --- a/core/tests/test_process.py +++ b/core/tests/test_process.py @@ -15,8 +15,18 @@ def open_psf(name: str): with as_file(resources / "psfs" / "zeiss_simulated" / name) as path: return path + @parameterized -def test_process(image_path: str, args: dict): +def test_process(minimal_image_path: str, args: dict): + # Processes a minimal set of images, with multiple parameter combinations + for slice in LatticeData.parse_obj({ + "input_image": minimal_image_path, + **args + }).process().slices: + assert slice.data.ndim == 3 + +def test_process_all(image_path: str, args: dict): + # Processes all input images, but without parameter combinations for slice in LatticeData.parse_obj({ "input_image": image_path, **args @@ -24,10 +34,10 @@ def test_process(image_path: str, args: dict): assert slice.data.ndim == 3 @parameterized -def test_save(image_path: str, args: dict): +def test_save(minimal_image_path: str, args: dict): with tempfile.TemporaryDirectory() as tempdir: LatticeData.parse_obj({ - "input_image": image_path, + "input_image": minimal_image_path, "save_dir": tempdir, **args }).process().save_image() diff --git a/core/tests/test_workflows.py b/core/tests/test_workflows.py index df74fe8..e86ec28 100644 --- a/core/tests/test_workflows.py +++ b/core/tests/test_workflows.py @@ -1,85 +1,85 @@ -from typing import Callable -from copy import copy -from importlib_resources import as_file -from numpy.typing import NDArray - -from napari_workflows import Workflow -import tempfile - -from pandas import DataFrame -from lls_core.models.lattice_data import LatticeData -from lls_core.sample import resources -from aicsimageio.aics_image import AICSImage - -from tests.utils import invoke -from pathlib import Path -from .params import config_types -from .utils import invoke, valid_image_path - - -def test_napari_workflow(image_workflow: Workflow, test_image: NDArray): - """ - Test napari workflow to see if it works before we run it using napari_lattice - This is without deskewing - """ - workflow = copy(image_workflow) - # Set input image to be the "raw" image - workflow.set("input", test_image) - labeling = workflow.get("labeling") - assert labeling[2, 2, 2] == 1 - -@config_types -def test_workflow_cli(workflow_config_cli: dict, save_func: Callable, cli_param: str): - """ - Test workflow processing via CLI - This will apply deskewing before processing the workflow - """ - with tempfile.NamedTemporaryFile(mode="w") as fp: - save_func(workflow_config_cli, fp) - fp.flush() - - # Deskew, apply workflow and save as h5 - invoke([ - cli_param, fp.name - ]) - - # checks if h5 file written - save_dir = Path(workflow_config_cli["save_dir"]) - saved_files = list(save_dir.glob("*.h5")) - assert len(saved_files) > 0 - assert len(list(save_dir.glob("*.xml"))) > 0 - - import npy2bdv - for h5_img in saved_files: - h5_file = npy2bdv.npy2bdv.BdvEditor(str(h5_img)) - label_img = h5_file.read_view(time=0, channel=0) - assert label_img.shape == (3, 14, 5) - assert label_img[1, 6, 2] == 1 - -def test_image_workflow(image_path: Path, image_workflow: Workflow): - # Test that a regular workflow that returns an image directly works - with tempfile.TemporaryDirectory() as tmpdir: - for roi, output in LatticeData( - input_image = image_path, - workflow = image_workflow, - save_dir = tmpdir - ).process_workflow().process(): - assert isinstance(output, Path) - assert valid_image_path(output) - -def test_table_workflow(image_path: Path, table_workflow: Workflow): - # Test a complex workflow that returns a tuple of images and data - with tempfile.TemporaryDirectory() as tmpdir: - params = LatticeData( - input_image = image_path, - workflow = table_workflow, - save_dir = tmpdir - ) - for roi, output in params.process_workflow().process(): - assert isinstance(output, (DataFrame, Path)) - if isinstance(output, DataFrame): - nrow, ncol = output.shape - assert nrow == params.nslices - assert ncol > 0 - else: - assert valid_image_path(output) +from typing import Callable +from copy import copy +from importlib_resources import as_file +from numpy.typing import NDArray + +from napari_workflows import Workflow +import tempfile + +from pandas import DataFrame +from lls_core.models.lattice_data import LatticeData +from lls_core.sample import resources +from aicsimageio.aics_image import AICSImage + +from tests.utils import invoke +from pathlib import Path +from .params import config_types +from .utils import invoke, valid_image_path + + +def test_napari_workflow(image_workflow: Workflow, test_image: NDArray): + """ + Test napari workflow to see if it works before we run it using napari_lattice + This is without deskewing + """ + workflow = copy(image_workflow) + # Set input image to be the "raw" image + workflow.set("input", test_image) + labeling = workflow.get("labeling") + assert labeling[2, 2, 2] == 1 + +@config_types +def test_workflow_cli(workflow_config_cli: dict, save_func: Callable, cli_param: str): + """ + Test workflow processing via CLI + This will apply deskewing before processing the workflow + """ + with tempfile.NamedTemporaryFile(mode="w") as fp: + save_func(workflow_config_cli, fp) + fp.flush() + + # Deskew, apply workflow and save as h5 + invoke([ + cli_param, fp.name + ]) + + # checks if h5 file written + save_dir = Path(workflow_config_cli["save_dir"]) + saved_files = list(save_dir.glob("*.h5")) + assert len(saved_files) > 0 + assert len(list(save_dir.glob("*.xml"))) > 0 + + import npy2bdv + for h5_img in saved_files: + h5_file = npy2bdv.npy2bdv.BdvEditor(str(h5_img)) + label_img = h5_file.read_view(time=0, channel=0) + assert label_img.shape == (3, 14, 5) + assert label_img[1, 6, 2] == 1 + +def test_image_workflow(minimal_image_path: Path, image_workflow: Workflow): + # Test that a regular workflow that returns an image directly works + with tempfile.TemporaryDirectory() as tmpdir: + for roi, output in LatticeData( + input_image = minimal_image_path, + workflow = image_workflow, + save_dir = tmpdir + ).process_workflow().process(): + assert isinstance(output, Path) + assert valid_image_path(output) + +def test_table_workflow(minimal_image_path: Path, table_workflow: Workflow): + # Test a complex workflow that returns a tuple of images and data + with tempfile.TemporaryDirectory() as tmpdir: + params = LatticeData( + input_image = minimal_image_path, + workflow = table_workflow, + save_dir = tmpdir + ) + for roi, output in params.process_workflow().process(): + assert isinstance(output, (DataFrame, Path)) + if isinstance(output, DataFrame): + nrow, ncol = output.shape + assert nrow == params.nslices + assert ncol > 0 + else: + assert valid_image_path(output) From b2842f532d9e1552876df6dd3b9baa42a903b703 Mon Sep 17 00:00:00 2001 From: Michael Milton Date: Wed, 6 Dec 2023 13:41:29 +1100 Subject: [PATCH 106/147] Add progress bars more consistently --- core/lls_core/models/lattice_data.py | 8 ++++---- core/tests/conftest.py | 2 +- core/tests/test_process.py | 5 ++--- 3 files changed, 7 insertions(+), 8 deletions(-) diff --git a/core/lls_core/models/lattice_data.py b/core/lls_core/models/lattice_data.py index 42130fe..769ce90 100644 --- a/core/lls_core/models/lattice_data.py +++ b/core/lls_core/models/lattice_data.py @@ -312,7 +312,7 @@ def _process_crop(self) -> Iterable[ImageSlice]: raise Exception("This function can only be called when crop is set") # We have an extra level of iteration for the crop path: iterating over each ROI - for roi_index, roi in enumerate(tqdm(self.crop.selected_rois, desc="ROI:", position=0)): + for roi_index, roi in enumerate(tqdm(self.crop.selected_rois, desc="ROI", position=0)): # pass arguments for save tiff, callable and function arguments logger.info(f"Processing ROI {roi_index}") @@ -324,7 +324,7 @@ def _process_crop(self) -> Iterable[ImageSlice]: decon_processing=self.deconvolution.decon_processing ) - for slice in self.iter_slices(): + for slice in tqdm(self.iter_slices(), desc="2D Slice Number", position=1): yield slice.copy_with_data( crop_volume_deskew( original_volume=slice.data, @@ -348,7 +348,7 @@ def _process_non_crop(self) -> Iterable[ImageSlice]: """ Yields processed image slices without cropping """ - for slice in self.iter_slices(): + for slice in tqdm(self.iter_slices(), desc="2d Slice Number"): data: ArrayLike = slice.data if isinstance(slice.data, DaskArray): data = slice.data.compute() @@ -389,7 +389,7 @@ def process_workflow(self) -> WorkflowSlices: from lls_core.models.results import WorkflowSlices WorkflowSlices.update_forward_refs(LatticeData=LatticeData) outputs = [] - for workflow in self.generate_workflows(): + for workflow in tqdm(self.generate_workflows(), desc="2D Slice Number"): for leaf in workflow.data.leafs(): outputs.append( workflow.copy_with_data( diff --git a/core/tests/conftest.py b/core/tests/conftest.py index 1d6814f..e3fcfa0 100644 --- a/core/tests/conftest.py +++ b/core/tests/conftest.py @@ -44,7 +44,7 @@ def minimal_image_path(request: pytest.FixtureRequest): "LLS7_t2_ch3.czi", "multich_multi_time.tif" ]) -def minimal_image_path(request: pytest.FixtureRequest): +def image_path(request: pytest.FixtureRequest): """ Fixture function that yields test images as file paths """ diff --git a/core/tests/test_process.py b/core/tests/test_process.py index 1a3fa99..6fe2417 100644 --- a/core/tests/test_process.py +++ b/core/tests/test_process.py @@ -25,11 +25,10 @@ def test_process(minimal_image_path: str, args: dict): }).process().slices: assert slice.data.ndim == 3 -def test_process_all(image_path: str, args: dict): +def test_process_all(image_path: str): # Processes all input images, but without parameter combinations for slice in LatticeData.parse_obj({ - "input_image": image_path, - **args + "input_image": image_path }).process().slices: assert slice.data.ndim == 3 From eaa6a7be8b36626c701607839d545e62acf8fa78 Mon Sep 17 00:00:00 2001 From: Michael Milton Date: Thu, 7 Dec 2023 20:41:59 +1100 Subject: [PATCH 107/147] Add tool tip for PSFs --- plugin/napari_lattice/fields.py | 4 +++- 1 file changed, 3 insertions(+), 1 deletion(-) diff --git a/plugin/napari_lattice/fields.py b/plugin/napari_lattice/fields.py index 09283db..a84d515 100644 --- a/plugin/napari_lattice/fields.py +++ b/plugin/napari_lattice/fields.py @@ -348,7 +348,9 @@ class DeconvolutionFields(NapariFieldGroup): # A counterpart to the DeconvolutionParams Pydantic class fields_enabled = field(False, label="Enabled") decon_processing = field(DeconvolutionChoice, label="Processing Algorithm") - psf = field(List[Path], label = "PSFs") + psf = field(List[Path], label = "PSFs").with_options( + tooltip="PSFs must be in the same order as the image channels" + ) psf_num_iter = field(int, label = "Number of Iterations") background = field(ComboBox).with_choices( [it.value for it in BackgroundSource] From 42a5537c96f62d9583c58fab928b71783eb393a4 Mon Sep 17 00:00:00 2001 From: Michael Milton Date: Fri, 8 Dec 2023 18:48:49 +1100 Subject: [PATCH 108/147] Add working parent_connect implementation --- plugin/napari_lattice/dock_widget.py | 7 +++++ plugin/napari_lattice/fields.py | 9 ++++-- plugin/napari_lattice/parent_connect.py | 39 +++++++++++++++++++++++++ 3 files changed, 52 insertions(+), 3 deletions(-) create mode 100644 plugin/napari_lattice/parent_connect.py diff --git a/plugin/napari_lattice/dock_widget.py b/plugin/napari_lattice/dock_widget.py index d712b56..da1111d 100644 --- a/plugin/napari_lattice/dock_widget.py +++ b/plugin/napari_lattice/dock_widget.py @@ -16,6 +16,7 @@ ) from qtpy.QtCore import Qt from qtpy.QtWidgets import QTabWidget +from napari_lattice.parent_connect import ParentConnect if TYPE_CHECKING: from typing import Iterable @@ -102,6 +103,12 @@ def __post_init__(self): for i, label in enumerate(["1. Deskew", "2. Deconvolution", "3. Crop", "4. Workflow", "5. Output"]): tab_widget.setTabText(i, label) for field in [self.deskew_fields, self.deconv_fields, self.cropping_fields, self.workflow_fields, self.output_fields]: + # Connect event handlers + for subfield_name in dir(field): + subfield = getattr(field, subfield_name) + if isinstance(subfield, ParentConnect): + subfield.resolve(self, field, subfield_name) + # Trigger validation of default data field._validate() # Using vfields here seems to prevent https://github.com/hanjinliu/magic-class/issues/110 diff --git a/plugin/napari_lattice/fields.py b/plugin/napari_lattice/fields.py index a84d515..bd04225 100644 --- a/plugin/napari_lattice/fields.py +++ b/plugin/napari_lattice/fields.py @@ -32,6 +32,7 @@ from napari_workflows import Workflow, WorkflowManager from qtpy.QtWidgets import QTabWidget from strenum import StrEnum +from napari_lattice.parent_connect import connect_parent if TYPE_CHECKING: from magicgui.widgets.bases import RangedWidget @@ -348,8 +349,9 @@ class DeconvolutionFields(NapariFieldGroup): # A counterpart to the DeconvolutionParams Pydantic class fields_enabled = field(False, label="Enabled") decon_processing = field(DeconvolutionChoice, label="Processing Algorithm") - psf = field(List[Path], label = "PSFs").with_options( - tooltip="PSFs must be in the same order as the image channels" + psf = field(List[Path], label = "PSFs", widget_type="ListEdit").with_options( + tooltip="PSFs must be in the same order as the image channels", + layout="vertical" ) psf_num_iter = field(int, label = "Number of Iterations") background = field(ComboBox).with_choices( @@ -539,7 +541,8 @@ def _make_model(self, validate: bool = True) -> OutputParams: save_name="PLACEHOLDER" ) - def _on_image_changed(self, img: DataArray): + @connect_parent("deskew_fields.img_layer") + def on_image_changed(self, img: DataArray): # Update the maximum T and C for widget in self.time_range: adjust_maximum(widget, img.sizes["T"]) diff --git a/plugin/napari_lattice/parent_connect.py b/plugin/napari_lattice/parent_connect.py new file mode 100644 index 0000000..ad55a36 --- /dev/null +++ b/plugin/napari_lattice/parent_connect.py @@ -0,0 +1,39 @@ +from __future__ import annotations + +from dataclasses import dataclass +from typing import Callable, TYPE_CHECKING +from operator import attrgetter + +if TYPE_CHECKING: + from magicclass import MagicTemplate + from magicgui.widgets.bases import ValueWidget + + +@dataclass +class ParentConnect: + """ + A function that wants to be connected to a parent or sibling's field. + This will be resolved after the GUI is instantiated. + """ + path: str + func: Callable + + def resolve(self, root: MagicTemplate, event_owner: MagicTemplate, field_name: str) -> None:# -> Callable[..., Any]: + """ + Converts this object into a true function that is connected to the appropriate change event + """ + field: ValueWidget = attrgetter(self.path)(root) + # field_owner = field.parent + bound_func = field.changed.connect(lambda: self.func(event_owner, field.value)) + setattr(event_owner, field_name, bound_func) + +def connect_parent(path: str) -> Callable[..., ParentConnect]: + """ + Mark this function as wanting to connect to a parent or sibling event + """ + def decorator(fn: Callable) -> ParentConnect: + return ParentConnect( + path=path, + func=fn + ) + return decorator From 9d4debb866a85f4542d8897c3a8cfa6faa307561 Mon Sep 17 00:00:00 2001 From: Michael Milton Date: Mon, 11 Dec 2023 15:48:26 +1100 Subject: [PATCH 109/147] Use TupleEdit for PSFs --- plugin/napari_lattice/dock_widget.py | 20 +++------------ plugin/napari_lattice/fields.py | 37 +++++++++++++++++----------- 2 files changed, 27 insertions(+), 30 deletions(-) diff --git a/plugin/napari_lattice/dock_widget.py b/plugin/napari_lattice/dock_widget.py index da1111d..c434cd7 100644 --- a/plugin/napari_lattice/dock_widget.py +++ b/plugin/napari_lattice/dock_widget.py @@ -74,10 +74,10 @@ class LlszMenu(MagicTemplate): main_heading = field("

      Napari Lattice: Visualization & Analysis

      ", widget_type="Label") heading1 = field(dedent("""
      - Specify deskewing parameters and image layers in Tab 1. - Additional analysis parameters can be configured in the other tabs. - When you are ready to save, go to Tab 5. - Output to specify the output directory. + Specify deskewing parameters and image layers in Tab 1. + Additional analysis parameters can be configured in the other tabs. + When you are ready to save, go to Tab 5. + Output to specify the output directory. For more information, please refer to the documentation here.
      """.strip()), widget_type="Label") @@ -155,18 +155,6 @@ def save(self): lattice.save() show_info(f"Deskewing successfuly completed. Results are located in {lattice.save_dir}") - @LlszMenu.WidgetContainer.deskew_fields.connect - def _on_image_changed(self, deskew: DeskewFields): - # An error at this point doesn't need to be communicated to the user - try: - img = deskew._get_kwargs()["data"] - # We have to manually trigger _on_image_changed because siblings - # classes can't listen to each other's events: https://github.com/hanjinliu/magic-class/issues/129 - for field in self._get_fields(): - field._on_image_changed(img) - except: - pass - def _get_fields(self) -> Iterable[NapariFieldGroup]: """Yields all the child Field classes which inherit from NapariFieldGroup""" container = self.LlszMenu.WidgetContainer diff --git a/plugin/napari_lattice/fields.py b/plugin/napari_lattice/fields.py index bd04225..171dc09 100644 --- a/plugin/napari_lattice/fields.py +++ b/plugin/napari_lattice/fields.py @@ -164,6 +164,12 @@ def __post_init__(self): from qtpy.QtCore import Qt self._widget._layout.setAlignment(Qt.AlignmentFlag.AlignTop) + def _get_deskew(self) -> DeskewParams: + "Returns the DeskewParams from the other tab" + from napari_lattice.dock_widget import LLSZWidget + parent = self.find_ancestor(LLSZWidget) + return parent.LlszMenu.WidgetContainer.deskew_fields._make_model() + def _get_parent_tab_widget(self) -> QTabWidget: qwidget = self.native # Walk up the widget tree until we find the tab widget @@ -206,9 +212,6 @@ def _validate(self): def _make_model(self): raise NotImplementedError() - def _on_image_changed(self, img: DataArray): - pass - class DeskewKwargs(NapariImageParams): angle: float skew: DeskewDirection @@ -349,7 +352,7 @@ class DeconvolutionFields(NapariFieldGroup): # A counterpart to the DeconvolutionParams Pydantic class fields_enabled = field(False, label="Enabled") decon_processing = field(DeconvolutionChoice, label="Processing Algorithm") - psf = field(List[Path], label = "PSFs", widget_type="ListEdit").with_options( + psf = field(Tuple[Path, Path, Path, Path], label = "PSFs").with_options( tooltip="PSFs must be in the same order as the image channels", layout="vertical" ) @@ -394,7 +397,8 @@ def _make_model(self) -> Optional[DeconvolutionParams]: return DeconvolutionParams( decon_processing=self.decon_processing.value, background=background, - psf=self.psf.value, + # Filter out unset PSFs + psf=[psf for psf in self.psf.value if psf.is_file()], psf_num_iter=self.psf_num_iter.value ) @@ -416,12 +420,6 @@ class CroppingFields(NapariFieldGroup): ) errors = field(Label).with_options(label="Errors") - def _get_deskew(self) -> DeskewParams: - "Returns the DeskewParams from the other tab" - from napari_lattice.dock_widget import LLSZWidget - parent = self.find_ancestor(LLSZWidget) - return parent.LlszMenu.WidgetContainer.deskew_fields._make_model() - @set_design(text="Import ROI") def import_roi(self, path: Path): from lls_core.cropping import read_imagej_roi @@ -440,8 +438,14 @@ def new_crop_layer(self): shapes.mode = "ADD_RECTANGLE" shapes.name = "Napari Lattice Crop" - def _on_image_changed(self, img: DataArray): - deskew = self._get_deskew() + @connect_parent("deskew_fields.img_layer") + def _on_image_changed(self, field: MagicField): + try: + deskew = self._get_deskew() + except: + # Ignore if the deskew parameters are invalid + return + deskewed_zmax = deskew.derived.deskew_vol_shape[0] # Update the allowed Z based the deskewed shape @@ -542,7 +546,12 @@ def _make_model(self, validate: bool = True) -> OutputParams: ) @connect_parent("deskew_fields.img_layer") - def on_image_changed(self, img: DataArray): + def _on_image_changed(self, field: MagicField): + try: + img = self._get_deskew().input_image + except: + return + # Update the maximum T and C for widget in self.time_range: adjust_maximum(widget, img.sizes["T"]) From ecf62d553d6dcf5473b85df5202186a23f7d264d Mon Sep 17 00:00:00 2001 From: Michael Milton Date: Wed, 13 Dec 2023 15:05:38 +1100 Subject: [PATCH 110/147] Fix negative crop excess --- core/lls_core/llsz_core.py | 13 +++++++------ core/lls_core/utils.py | 2 +- plugin/napari_lattice/fields.py | 5 +++-- 3 files changed, 11 insertions(+), 9 deletions(-) diff --git a/core/lls_core/llsz_core.py b/core/lls_core/llsz_core.py index d1be298..cb41fab 100644 --- a/core/lls_core/llsz_core.py +++ b/core/lls_core/llsz_core.py @@ -272,9 +272,9 @@ def crop_volume_deskew( crop_height = crop_vol_shape[1] # Find "excess" volume on both sides due to deskewing - crop_excess = ( - int(round((deskewed_height - crop_height) / 2)) - + out_bounds_correction + crop_excess: int = max( + int(round((deskewed_height - crop_height) / 2)) + out_bounds_correction, + 0 ) # Crop in Y deskewed_prelim = np.asarray(deskewed_prelim) @@ -285,10 +285,11 @@ def crop_volume_deskew( elif skew_dir == DeskewDirection.X: deskewed_width = deskewed_prelim.shape[2] crop_width = crop_vol_shape[2] + # Find "excess" volume on both sides due to deskewing - crop_excess = ( - int(round((deskewed_width - crop_width) / 2)) - + out_bounds_correction + crop_excess = max( + int(round((deskewed_width - crop_width) / 2)) + out_bounds_correction, + 0 ) # Crop in X deskewed_prelim = np.asarray(deskewed_prelim) diff --git a/core/lls_core/utils.py b/core/lls_core/utils.py index 0def3c6..2ccbf50 100644 --- a/core/lls_core/utils.py +++ b/core/lls_core/utils.py @@ -39,7 +39,7 @@ def check_subclass(obj: Any, pkg_name: str, cls_name: str) -> bool: def is_napari_shape(obj: Any) -> TypeGuard[Shapes]: return check_subclass(obj, "napari.shapes", "Shapes") -def calculate_crop_bbox(shape: int, z_start: int, z_end: int) -> tuple[List[List[Any]], List[int]]: +def calculate_crop_bbox(shape: list, z_start: int, z_end: int) -> tuple[List[List[Any]], List[int]]: """Get bounding box as vertices in 3D in the form xyz Args: diff --git a/plugin/napari_lattice/fields.py b/plugin/napari_lattice/fields.py index 171dc09..8fd7c3f 100644 --- a/plugin/napari_lattice/fields.py +++ b/plugin/napari_lattice/fields.py @@ -463,11 +463,12 @@ def _enable_crop(self, enabled: bool) -> bool: def _make_model(self) -> Optional[CropParams]: import numpy as np if self.fields_enabled.value: + deskew = self._get_deskew() return CropParams( # Convert from the input image space to the deskewed image space # We assume here that dx == dy which isn't ideal - roi_list=ShapesData([np.array(shape.data) / self._get_deskew().dy for shape in self.shapes.value]), - z_range=tuple(np.array(self.z_range.value) / self._get_deskew().new_dz), + roi_list=ShapesData([np.array(shape.data) / deskew.dy for shape in self.shapes.value]), + z_range=tuple(self.z_range.value), ) return None From 611949ec9f140e7f9473f021a8091fa214d3cc6b Mon Sep 17 00:00:00 2001 From: Michael Milton Date: Wed, 13 Dec 2023 17:03:03 +1100 Subject: [PATCH 111/147] GUI cropping improvements; remove PLACEHOLDER --- core/lls_core/models/crop.py | 2 +- core/lls_core/models/output.py | 3 ++- plugin/napari_lattice/dock_widget.py | 2 +- plugin/napari_lattice/fields.py | 8 +++----- 4 files changed, 7 insertions(+), 8 deletions(-) diff --git a/core/lls_core/models/crop.py b/core/lls_core/models/crop.py index 7871452..48863a7 100644 --- a/core/lls_core/models/crop.py +++ b/core/lls_core/models/crop.py @@ -58,6 +58,6 @@ def read_roi(cls, v: Any) -> List[Roi]: @validator("roi_subset", pre=True, always=True) def default_roi_range(cls, v: Any, values: dict): # If the roi range isn't provided, assume all rois should be processed - if v is None: + if v is None and "roi_list" in values: return list(range(len(values["roi_list"]))) return v diff --git a/core/lls_core/models/output.py b/core/lls_core/models/output.py index b6dbc02..15f332c 100644 --- a/core/lls_core/models/output.py +++ b/core/lls_core/models/output.py @@ -23,7 +23,8 @@ class OutputParams(FieldAccessModel): description="The filename suffix that will be used for output files. This will be added as a suffix to the input file name if the input image was specified using a file name. If the input image was provided as an in-memory object, the `save_name` field should instead be specified." ) save_name: str = Field( - description="The filename that will be used for output files. This should not contain a leading directory or file extension. The final output files will have additional elements added to the end of this prefix to indicate the region of interest, channel, timepoint, file extension etc." + description="The filename that will be used for output files. This should not contain a leading directory or file extension. The final output files will have additional elements added to the end of this prefix to indicate the region of interest, channel, timepoint, file extension etc.", + default=None ) save_type: SaveFileType = Field( default=SaveFileType.h5, diff --git a/plugin/napari_lattice/dock_widget.py b/plugin/napari_lattice/dock_widget.py index c434cd7..73ca7d5 100644 --- a/plugin/napari_lattice/dock_widget.py +++ b/plugin/napari_lattice/dock_widget.py @@ -144,7 +144,7 @@ def preview(self, header: str, time: int, channel: int): lattice.dy, lattice.dx ) - self.parent_viewer.add_image(slice.data, scale=scale) + self.parent_viewer.add_image(slice.data, scale=scale, name="Napari Lattice Preview") max_z = np.argmax(np.sum(slice.data, axis=(1, 2))) self.parent_viewer.dims.set_current_step(0, max_z) diff --git a/plugin/napari_lattice/fields.py b/plugin/napari_lattice/fields.py index 8fd7c3f..e57c63a 100644 --- a/plugin/napari_lattice/fields.py +++ b/plugin/napari_lattice/fields.py @@ -434,9 +434,9 @@ def import_roi(self, path: Path): @set_design(text="New Crop") def new_crop_layer(self): from napari_lattice.utils import get_viewer - shapes = get_viewer().add_shapes() + shapes = get_viewer().add_shapes(name="Napari Lattice Crop") shapes.mode = "ADD_RECTANGLE" - shapes.name = "Napari Lattice Crop" + self.shapes.value += [shapes] @connect_parent("deskew_fields.img_layer") def _on_image_changed(self, field: MagicField): @@ -467,7 +467,7 @@ def _make_model(self) -> Optional[CropParams]: return CropParams( # Convert from the input image space to the deskewed image space # We assume here that dx == dy which isn't ideal - roi_list=ShapesData([np.array(shape.data) / deskew.dy for shape in self.shapes.value]), + roi_list=ShapesData([np.array(shape.data) / deskew.dy for shape in self.shapes.value if len(shape.data) > 0]), z_range=tuple(self.z_range.value), ) return None @@ -542,8 +542,6 @@ def _make_model(self, validate: bool = True) -> OutputParams: save_dir=self.save_path.value, save_suffix=self.save_suffix.value, save_type=self.save_type.value, - # This is just to avoid the validation error caused by the missing field - save_name="PLACEHOLDER" ) @connect_parent("deskew_fields.img_layer") From 979b92b6e534bbc9dd382de822b8a6af0be49b69 Mon Sep 17 00:00:00 2001 From: Michael Milton Date: Thu, 14 Dec 2023 11:14:40 +1100 Subject: [PATCH 112/147] Fix for array parameters such as time_range in the CLI --- core/lls_core/cmds/__main__.py | 30 ++++- core/lls_core/models/lattice_data.py | 20 +-- core/tests/test_cli.py | 194 +++++++++++++-------------- 3 files changed, 134 insertions(+), 110 deletions(-) diff --git a/core/lls_core/cmds/__main__.py b/core/lls_core/cmds/__main__.py index 2eaabf7..5bf896d 100644 --- a/core/lls_core/cmds/__main__.py +++ b/core/lls_core/cmds/__main__.py @@ -7,7 +7,7 @@ from enum import auto from pathlib import Path -from typing import TYPE_CHECKING, List, Optional, Tuple +from typing import TYPE_CHECKING, List, Optional, Tuple, Union from strenum import StrEnum from lls_core.models.lattice_data import LatticeData @@ -100,6 +100,30 @@ def rich_validation(e: ValidationError) -> Table: return table +def update_nested_data(data: Union[dict, list], keys: list, new_value: Any): + from itertools import pairwise + current = data + + for key, next_key in pairwise(keys): + next = {} if isinstance(next_key, str) else [] + if isinstance(current, dict): + current = current.setdefault(key, next) + elif isinstance(current, list): + if key >= len(current): + current.insert(key, next) + current = current[key] + else: + raise ValueError(f"Unknown data type {type(current)}. Cannot traverse.") + + last_key = keys[-1] + if isinstance(current, dict): + current[last_key] = new_value + elif isinstance(current, list): + current.insert(last_key, new_value) + else: + raise ValueError(f"Unknown data type {type(current)}. Cannot traverse.") + +# Example usage: @app.command() def process( ctx: Context, @@ -145,12 +169,12 @@ def process( print(ctx.get_help()) raise Exit() - from toolz.dicttoolz import merge_with, update_in + from toolz.dicttoolz import merge_with cli_args = {} for source, dest in CLI_PARAM_MAP.items(): from click.core import ParameterSource if ctx.get_parameter_source(source) != ParameterSource.DEFAULT: - cli_args = update_in(cli_args, dest, lambda x: ctx.params[source]) + update_nested_data(cli_args, dest, ctx.params[source]) json_args = {} if json_config is not None: diff --git a/core/lls_core/models/lattice_data.py b/core/lls_core/models/lattice_data.py index 769ce90..374ae66 100644 --- a/core/lls_core/models/lattice_data.py +++ b/core/lls_core/models/lattice_data.py @@ -5,10 +5,6 @@ from dask.array.core import Array as DaskArray from typing import Any, Iterable, Optional, TYPE_CHECKING, Type - -import pyclesperanto_prototype as cle -from tqdm import tqdm - from lls_core import DeconvolutionChoice from lls_core.deconvolution import pycuda_decon, skimage_decon from lls_core.llsz_core import crop_volume_deskew @@ -20,13 +16,11 @@ from lls_core.types import ArrayLike from lls_core.models.deskew import DeskewParams from napari_workflows import Workflow -from xarray import DataArray -from pathlib import Path if TYPE_CHECKING: - import pyclesperanto_prototype as cle from lls_core.models.results import ImageSlice, ImageSlices, ProcessedSlice from lls_core.writers import Writer + from xarray import DataArray import logging @@ -61,6 +55,7 @@ def use_image_path(cls, values: dict): # TODO: separate the image file from the image file path as two separate fields, # so we don't have to put so much logic here from lls_core.types import is_pathlike + from pathlib import Path input_image = values.get("input_image") if is_pathlike(input_image): if values.get("save_name") is None: @@ -120,12 +115,13 @@ def parse_time_range(cls, v: Any, values: dict) -> Any: """ # This skips the conversion if no image was provided, to ensure a more # user-friendly error is provided, namely "image was missing" + from collections.abc import Sequence with ignore_keyerror(): default_start = 0 default_end = values["input_image"].sizes["T"] if v is None: return range(default_start, default_end) - elif isinstance(v, tuple) and len(v) == 2: + elif isinstance(v, Sequence) and len(v) == 2: # Allow 2-tuples to be used as input for this field return range(v[0] or default_start, v[1] or default_end) return v @@ -135,12 +131,13 @@ def parse_channel_range(cls, v: Any, values: dict) -> Any: """ Sets the default channel range if undefined """ + from collections.abc import Sequence with ignore_keyerror(): default_start = 0 default_end = values["input_image"].sizes["C"] if v is None: return range(default_start, default_end) - elif isinstance(v, tuple) and len(v) == 2: + elif isinstance(v, Sequence) and len(v) == 2: # Allow 2-tuples to be used as input for this field return range(v[0] or default_start, v[1] or default_end) return v @@ -308,6 +305,7 @@ def _process_crop(self) -> Iterable[ImageSlice]: """ Yields processed image slices with cropping enabled """ + from tqdm import tqdm if self.crop is None: raise Exception("This function can only be called when crop is set") @@ -348,6 +346,9 @@ def _process_non_crop(self) -> Iterable[ImageSlice]: """ Yields processed image slices without cropping """ + from tqdm import tqdm + import pyclesperanto_prototype as cle + for slice in tqdm(self.iter_slices(), desc="2d Slice Number"): data: ArrayLike = slice.data if isinstance(slice.data, DaskArray): @@ -387,6 +388,7 @@ def _process_non_crop(self) -> Iterable[ImageSlice]: def process_workflow(self) -> WorkflowSlices: from lls_core.models.results import WorkflowSlices + from tqdm import tqdm WorkflowSlices.update_forward_refs(LatticeData=LatticeData) outputs = [] for workflow in tqdm(self.generate_workflows(), desc="2D Slice Number"): diff --git a/core/tests/test_cli.py b/core/tests/test_cli.py index 5f3f144..7f08a4c 100644 --- a/core/tests/test_cli.py +++ b/core/tests/test_cli.py @@ -1,98 +1,96 @@ -# Tests for napari_lattice using arguments and saving output files as h5, as well as tiff - -from aicsimageio.aics_image import AICSImage -from npy2bdv import BdvEditor -import numpy as np -from pathlib import Path -import tempfile -from tests.utils import invoke -import yaml - -def create_image(path: Path): - # Create a zero array of shape 5x5x5 with a value of 10 at (2,4,2) - raw = np.zeros((5, 5, 5)) - raw[2, 4, 2] = 10 - # Save image as a tif filw in home directory - AICSImage(raw).save(path) - assert path.exists() - - -def create_data(dir: Path) -> Path: - # Creates and returns a YAML config file - input_file = dir / 'raw.tiff' - config_location = dir / "config_deskew.yaml" - - # Create a zero array of shape 5x5x5 with a value of 10 at (2,4,2) - raw = np.zeros((5, 5, 5)) - raw[2, 4, 2] = 10 - # Save image as a tif filw in home directory - AICSImage(raw).save(input_file) - assert input_file.exists() - - config: dict[str, str] = { - "input_image": str(input_file), - "save_dir": str(dir), - "save_type": "h5" - } - - with config_location.open("w") as fp: - yaml.safe_dump(config, fp) - - return config_location - -def assert_tiff(output_dir: Path): - """Checks that a valid TIFF was generated in the directory""" - results = list(output_dir.glob("*.tif")) - assert len(results) > 0 - for result in results: - AICSImage(result).get_image_data() - -def assert_h5(output_dir: Path): - """Checks that a valid H5 was generated""" - h5s = list(output_dir.glob("*.h5")) - assert len(h5s) > 0 - assert len(list(output_dir.glob("*.xml"))) == len(h5s) - for h5 in h5s: - BdvEditor(str(h5)).read_view() - -def test_batch_deskew_h5(): - """Write image to disk and then execute napari_lattice from terminal - Checks if an deskewed output file is created for both tif and h5 - """ - with tempfile.TemporaryDirectory() as out_dir: - out_dir = Path(out_dir) - input_file = out_dir / 'raw.tiff' - create_image(input_file) - # Batch deskew and save as h5 - invoke([ - str(input_file), - "--save-dir", str(out_dir), - "--save-type", "h5" - ]) - - assert_h5(out_dir) - -def test_batch_deskew_tiff(): - # tiff file deskew - with tempfile.TemporaryDirectory() as out_dir: - out_dir = Path(out_dir) - input_file = out_dir / 'raw.tiff' - create_image(input_file) - invoke([ - str(input_file), - "--save-dir", str(out_dir), - "--save-type", "tiff" - ]) - - assert_tiff(out_dir) - -def test_yaml_deskew(): - """Write image to disk and then execute napari_lattice from terminal - Checks if an deskewed output file is created for both tif and h5 - """ - with tempfile.TemporaryDirectory() as test_dir: - test_dir = Path(test_dir) - config_location = create_data(test_dir) - # Batch deskew and save as h5 - invoke(["--yaml-config", str(config_location)], ) - assert_h5(test_dir) +# Tests for napari_lattice using arguments and saving output files as h5, as well as tiff + +from typing import Callable, List +import pytest +from aicsimageio.aics_image import AICSImage +from npy2bdv import BdvEditor +import numpy as np +from pathlib import Path +import tempfile +from tests.utils import invoke +import yaml + +def create_image(path: Path): + # Create a zero array of shape 5x5x5 with a value of 10 at (2,4,2) + raw = np.zeros((5, 5, 5)) + raw[2, 4, 2] = 10 + # Save image as a tif filw in home directory + AICSImage(raw).save(path) + assert path.exists() + + +def create_data(dir: Path) -> Path: + # Creates and returns a YAML config file + input_file = dir / 'raw.tiff' + config_location = dir / "config_deskew.yaml" + + # Create a zero array of shape 5x5x5 with a value of 10 at (2,4,2) + raw = np.zeros((5, 5, 5)) + raw[2, 4, 2] = 10 + # Save image as a tif filw in home directory + AICSImage(raw).save(input_file) + assert input_file.exists() + + config: dict[str, str] = { + "input_image": str(input_file), + "save_dir": str(dir), + "save_type": "h5" + } + + with config_location.open("w") as fp: + yaml.safe_dump(config, fp) + + return config_location + +def assert_tiff(output_dir: Path): + """Checks that a valid TIFF was generated in the directory""" + results = list(output_dir.glob("*.tif")) + assert len(results) > 0 + for result in results: + AICSImage(result).get_image_data() + +def assert_h5(output_dir: Path): + """Checks that a valid H5 was generated""" + h5s = list(output_dir.glob("*.h5")) + assert len(h5s) > 0 + assert len(list(output_dir.glob("*.xml"))) == len(h5s) + for h5 in h5s: + BdvEditor(str(h5)).read_view() + +@pytest.mark.parametrize( + ["flags", "check_fn"], + [ + [["--save-type", "h5"], assert_h5], + [["--save-type", "tiff"], assert_tiff], + [["--save-type", "tiff", "--time-start", "0", "--time-end", "1"], assert_tiff], + ] +) +def test_batch_deskew(flags: List[str], check_fn: Callable[[Path], None]): + """ + Write image to disk and then execute napari_lattice from terminal + Checks if an deskewed output file is created for both tif and h5 + """ + with tempfile.TemporaryDirectory() as out_dir: + out_dir = Path(out_dir) + input_file = out_dir / 'raw.tiff' + create_image(input_file) + # Batch deskew and save as h5 + invoke([ + str(input_file), + "--save-dir", str(out_dir), + *flags + ]) + + check_fn(out_dir) + +def test_yaml_deskew(): + """ + Write image to disk and then execute napari_lattice from terminal + Checks if an deskewed output file is created for both tif and h5 + """ + with tempfile.TemporaryDirectory() as test_dir: + test_dir = Path(test_dir) + config_location = create_data(test_dir) + # Batch deskew and save as h5 + invoke(["--yaml-config", str(config_location)], ) + assert_h5(test_dir) From 6b0e4771200e4b2b1b0b65368b614f859cd305f5 Mon Sep 17 00:00:00 2001 From: Michael Milton Date: Mon, 18 Dec 2023 15:33:43 +1100 Subject: [PATCH 113/147] Don't compute the dask array when calculating deskewed shape --- core/lls_core/models/deskew.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/core/lls_core/models/deskew.py b/core/lls_core/models/deskew.py index bac6e2d..9a0b86c 100644 --- a/core/lls_core/models/deskew.py +++ b/core/lls_core/models/deskew.py @@ -188,7 +188,7 @@ def calculate_derived(cls, v: Any, values: dict) -> DerivedDeskewFields: return v elif v is None: deskew_vol_shape, deskew_affine_transform = get_deskewed_shape( - data.isel(C=0, T=0).to_numpy(), + data.isel(C=0, T=0), values["angle"], values["physical_pixel_sizes"].X, values["physical_pixel_sizes"].Y, From b6455973b36cc0a3a6657736a317d92b9875a661 Mon Sep 17 00:00:00 2001 From: Pradeep R Date: Tue, 19 Dec 2023 10:42:41 +1100 Subject: [PATCH 114/147] fix voxel_size_z for cropping --- core/lls_core/models/lattice_data.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/core/lls_core/models/lattice_data.py b/core/lls_core/models/lattice_data.py index 374ae66..fd4491c 100644 --- a/core/lls_core/models/lattice_data.py +++ b/core/lls_core/models/lattice_data.py @@ -333,7 +333,7 @@ def _process_crop(self) -> Iterable[ImageSlice]: linear_interpolation=True, voxel_size_x=self.dx, voxel_size_y=self.dy, - voxel_size_z=self.dy, + voxel_size_z=self.dz, angle_in_degrees=self.angle, deskewed_volume=self.deskewed_volume, z_start=self.crop.z_range[0], From 0d7c7d3aa1714afd7d3de88ca9d0b76a0353db36 Mon Sep 17 00:00:00 2001 From: Michael Milton Date: Tue, 19 Dec 2023 11:35:00 +1100 Subject: [PATCH 115/147] Add the missing suffix parameter into the GUI save function --- plugin/napari_lattice/dock_widget.py | 17 +++++++++++------ 1 file changed, 11 insertions(+), 6 deletions(-) diff --git a/plugin/napari_lattice/dock_widget.py b/plugin/napari_lattice/dock_widget.py index 73ca7d5..0b7f8b4 100644 --- a/plugin/napari_lattice/dock_widget.py +++ b/plugin/napari_lattice/dock_widget.py @@ -49,22 +49,27 @@ def _make_model(self, validate: bool = True) -> LatticeData: deskew_args = self.LlszMenu.WidgetContainer.deskew_fields._get_kwargs() output_args = self.LlszMenu.WidgetContainer.output_fields._make_model(validate=False) - args = dict( + params = LatticeData.make( + validate=validate, + + # Deskew input_image=deskew_args["data"], angle=deskew_args["angle"], + physical_pixel_sizes=deskew_args["physical_pixel_sizes"], + skew=deskew_args["skew"], + + # Output 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_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"], + save_suffix=output_args.save_suffix, + workflow=self.LlszMenu.WidgetContainer.workflow_fields._make_model(), deconvolution=self.LlszMenu.WidgetContainer.deconv_fields._make_model(), crop=self.LlszMenu.WidgetContainer.cropping_fields._make_model() ) - params = LatticeData.make(validate=validate, **args) # Log the lattice print(params, file=stdout) return params From ef448fcca17b62c66f4b41d1e34e99c7dc33e6c8 Mon Sep 17 00:00:00 2001 From: Michael Milton Date: Tue, 19 Dec 2023 15:52:29 +1100 Subject: [PATCH 116/147] Dynamically rescale raw input image according to pixel sizes --- plugin/napari_lattice/fields.py | 17 +++++++++++++++++ 1 file changed, 17 insertions(+) diff --git a/plugin/napari_lattice/fields.py b/plugin/napari_lattice/fields.py index e57c63a..f85babd 100644 --- a/plugin/napari_lattice/fields.py +++ b/plugin/napari_lattice/fields.py @@ -304,6 +304,23 @@ def _img_changed(self) -> None: # Recalculate the dimension options whenever the image changes self.dimension_order.reset_choices() + @pixel_sizes_source.connect + @pixel_sizes.connect + def _rescale_image(self): + # Whenever the pixel sizes are changed, this should be reflected in the viewer + image: Image + try: + pixels = self._get_kwargs()["physical_pixel_sizes"] + for image in self.img_layer.value: + image.scale = ( + *image.scale[0:-3], + pixels.Z, + pixels.Y, + pixels.X, + ) + except: + pass + @pixel_sizes_source.connect @enable_if([pixel_sizes]) def _hide_pixel_sizes(self, pixel_sizes_source: str): From b33772ddbde026501d031b71d97c88e69942a05e Mon Sep 17 00:00:00 2001 From: Pradeep R Date: Tue, 19 Dec 2023 16:10:21 +1100 Subject: [PATCH 117/147] Reset view to centre the image --- plugin/napari_lattice/fields.py | 3 +++ 1 file changed, 3 insertions(+) diff --git a/plugin/napari_lattice/fields.py b/plugin/napari_lattice/fields.py index f85babd..0c0dfbc 100644 --- a/plugin/napari_lattice/fields.py +++ b/plugin/napari_lattice/fields.py @@ -309,6 +309,7 @@ def _img_changed(self) -> None: def _rescale_image(self): # Whenever the pixel sizes are changed, this should be reflected in the viewer image: Image + from napari_lattice.utils import get_viewer try: pixels = self._get_kwargs()["physical_pixel_sizes"] for image in self.img_layer.value: @@ -318,6 +319,8 @@ def _rescale_image(self): pixels.Y, pixels.X, ) + viewer = get_viewer() + viewer.reset_view() except: pass From 9adf3643eadd54e062e791f69275d7758acc57c7 Mon Sep 17 00:00:00 2001 From: Michael Milton Date: Wed, 20 Dec 2023 10:20:18 +1100 Subject: [PATCH 118/147] Fix for crop + deconvolution, with a test --- core/lls_core/models/lattice_data.py | 16 ++++++++-------- core/tests/test_process.py | 13 ++++++++++++- 2 files changed, 20 insertions(+), 9 deletions(-) diff --git a/core/lls_core/models/lattice_data.py b/core/lls_core/models/lattice_data.py index fd4491c..631f731 100644 --- a/core/lls_core/models/lattice_data.py +++ b/core/lls_core/models/lattice_data.py @@ -314,15 +314,15 @@ def _process_crop(self) -> Iterable[ImageSlice]: # pass arguments for save tiff, callable and function arguments logger.info(f"Processing ROI {roi_index}") - deconv_args: dict[Any, Any] = {} - if self.deconvolution is not None: - deconv_args = dict( - num_iter = self.deconvolution.psf_num_iter, - psf = self.deconvolution.psf, - decon_processing=self.deconvolution.decon_processing - ) - for slice in tqdm(self.iter_slices(), desc="2D Slice Number", position=1): + deconv_args: dict[Any, Any] = {} + if self.deconvolution is not None: + deconv_args = dict( + num_iter = self.deconvolution.psf_num_iter, + psf = self.deconvolution.psf[slice.channel], + decon_processing=self.deconvolution.decon_processing + ) + yield slice.copy_with_data( crop_volume_deskew( original_volume=slice.data, diff --git a/core/tests/test_process.py b/core/tests/test_process.py index 6fe2417..d49c326 100644 --- a/core/tests/test_process.py +++ b/core/tests/test_process.py @@ -1,6 +1,7 @@ from typing import Any, List, Optional import pytest from lls_core.models import LatticeData +from lls_core.models.crop import CropParams from lls_core.sample import resources from importlib_resources import as_file import tempfile @@ -43,9 +44,19 @@ def test_save(minimal_image_path: str, args: dict): results = list(Path(tempdir).iterdir()) assert len(results) > 0 +def test_process_deconv_crop(): + for slice in LatticeData.parse_obj({ + "input_image": root / "raw.tif", + "deconvolution": { + "psf": [root / "psf.tif"], + }, + "crop": CropParams(roi_list = [[[0, 0], [0, 110], [95, 0], [95, 110]]]) + }).process().slices: + assert slice.data.ndim == 3 + @pytest.mark.parametrize(["background"], [(1, ), ("auto",), ("second_last",)]) @parameterized -def test_process_deconvolution(args: dict, background: Any): +def test_process_deconvolution(args: dict, background: Any, crop: Any): for slice in LatticeData.parse_obj({ "input_image": root / "raw.tif", "deconvolution": { From 18686663e8970170de5502012b63f00a55d0c793 Mon Sep 17 00:00:00 2001 From: Michael Milton Date: Wed, 20 Dec 2023 10:57:58 +1100 Subject: [PATCH 119/147] Fix "test_process_deconvolution" --- core/tests/test_cli.py | 1 + core/tests/test_process.py | 2 +- 2 files changed, 2 insertions(+), 1 deletion(-) diff --git a/core/tests/test_cli.py b/core/tests/test_cli.py index 7f08a4c..8fc3b28 100644 --- a/core/tests/test_cli.py +++ b/core/tests/test_cli.py @@ -63,6 +63,7 @@ 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", "--z-start", "0", "--z-end", "1"], assert_tiff], ] ) def test_batch_deskew(flags: List[str], check_fn: Callable[[Path], None]): diff --git a/core/tests/test_process.py b/core/tests/test_process.py index d49c326..4765e15 100644 --- a/core/tests/test_process.py +++ b/core/tests/test_process.py @@ -56,7 +56,7 @@ def test_process_deconv_crop(): @pytest.mark.parametrize(["background"], [(1, ), ("auto",), ("second_last",)]) @parameterized -def test_process_deconvolution(args: dict, background: Any, crop: Any): +def test_process_deconvolution(args: dict, background: Any): for slice in LatticeData.parse_obj({ "input_image": root / "raw.tif", "deconvolution": { From 6a988a676381abeefe4dacd66549f4a2fe2d0acd Mon Sep 17 00:00:00 2001 From: Michael Milton Date: Wed, 20 Dec 2023 11:56:27 +1100 Subject: [PATCH 120/147] Reject CropParams with an empty ROI list --- core/lls_core/models/crop.py | 3 +++ core/tests/test_cli.py | 1 - core/tests/test_validation.py | 11 ++++++++++- 3 files changed, 13 insertions(+), 2 deletions(-) diff --git a/core/lls_core/models/crop.py b/core/lls_core/models/crop.py index 48863a7..eaee299 100644 --- a/core/lls_core/models/crop.py +++ b/core/lls_core/models/crop.py @@ -53,6 +53,9 @@ def read_roi(cls, v: Any) -> List[Roi]: except: raise ValueError(f"{item} cannot be intepreted as an ROI") + if len(rois) == 0: + raise ValueError("At least one region of interest must be specified if cropping is enabled") + return rois @validator("roi_subset", pre=True, always=True) diff --git a/core/tests/test_cli.py b/core/tests/test_cli.py index 8fc3b28..7f08a4c 100644 --- a/core/tests/test_cli.py +++ b/core/tests/test_cli.py @@ -63,7 +63,6 @@ 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", "--z-start", "0", "--z-end", "1"], assert_tiff], ] ) def test_batch_deskew(flags: List[str], check_fn: Callable[[Path], None]): diff --git a/core/tests/test_validation.py b/core/tests/test_validation.py index 76d1ad6..dc1bf49 100644 --- a/core/tests/test_validation.py +++ b/core/tests/test_validation.py @@ -1,6 +1,8 @@ from pathlib import Path from lls_core.models.crop import CropParams from lls_core.models.lattice_data import LatticeData +import pytest +from pydantic import ValidationError def test_default_save_dir(rbc_tiny: Path): # Test that the save dir is inferred to be the input dir @@ -11,6 +13,13 @@ def test_auto_z_range(rbc_tiny: Path): # Tests that the Z range is automatically set, and it is set # based on the size of the deskewed volume params = LatticeData(input_image=rbc_tiny, crop=CropParams( - roi_list=[] + roi_list=[[[0, 0], [0, 1], [1, 0], [1, 1]]] )) assert params.crop.z_range == (0, 59) + +def test_reject_crop(): + # Tests that the parameters fail validation if cropping is specified without an ROI + with pytest.raises(ValidationError): + CropParams( + roi_list=[] + ) From 15e5d8edda496932b26d67ed53fbeba5cab0b033 Mon Sep 17 00:00:00 2001 From: Michael Milton Date: Wed, 20 Dec 2023 15:46:45 +1100 Subject: [PATCH 121/147] Add --show-schema CLI flag, and rework the way we document CLI config flags --- core/lls_core/cmds/__main__.py | 11 +++++++++++ core/lls_core/models/crop.py | 6 ++++-- core/lls_core/models/deskew.py | 14 ++++++++------ core/lls_core/models/lattice_data.py | 11 +++++------ core/lls_core/models/output.py | 18 ++++++++++-------- core/lls_core/models/utils.py | 16 +++++++++++++--- 6 files changed, 51 insertions(+), 25 deletions(-) diff --git a/core/lls_core/cmds/__main__.py b/core/lls_core/cmds/__main__.py index 5bf896d..ef1f588 100644 --- a/core/lls_core/cmds/__main__.py +++ b/core/lls_core/cmds/__main__.py @@ -161,8 +161,19 @@ def process( 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), 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."), + + show_schema: bool = Option(default=False, help="If provided, image processing will not be performed, and instead a JSON document outlining the JSON/YAML options will be printed to stdout. This can be used to assist with writing a config file for use with the --json-config and --yaml-config options.") ) -> None: from click.core import ParameterSource + if show_schema: + import json + import sys + json.dump( + LatticeData.to_definition_dict(), + sys.stdout, + indent=4 + ) + return # Just print help if the user didn't provide any arguments if all(src != ParameterSource.COMMANDLINE for src in ctx._parameter_source.values()): diff --git a/core/lls_core/models/crop.py b/core/lls_core/models/crop.py index eaee299..db7adea 100644 --- a/core/lls_core/models/crop.py +++ b/core/lls_core/models/crop.py @@ -12,15 +12,17 @@ class CropParams(FieldAccessModel): """ roi_list: List[Roi] = Field( description="List of regions of interest, each of which must be an NxD array, where N is the number of vertices and D the coordinates of each vertex.", + cli_description="List of regions of interest, each of which must be the file path to ImageJ ROI file.", default = [] ) roi_subset: List[int] = Field( - description="A subset of all the ROIs to process", + description="A subset of all the ROIs to process. Each array item should be an index into the ROI list indicating an ROI to include.", default=None ) z_range: Tuple[NonNegativeInt, NonNegativeInt] = Field( default=None, - description="The range of Z slices to take. All Z slices before the first index or after the last index will be cropped out." + description="The range of Z slices to take. All Z slices before the first index or after the last index will be cropped out.", + cli_description="An array with two items, indicating the index of the first and last Z slice to include." ) @property diff --git a/core/lls_core/models/deskew.py b/core/lls_core/models/deskew.py index 9a0b86c..4548f06 100644 --- a/core/lls_core/models/deskew.py +++ b/core/lls_core/models/deskew.py @@ -23,9 +23,9 @@ class DefinedPixelSizes(FieldAccessModel): Like PhysicalPixelSizes, but it's a dataclass, and none of its fields are None """ - X: NonNegativeFloat = 0.1499219272808386 - Y: NonNegativeFloat = 0.1499219272808386 - Z: NonNegativeFloat = 0.3 + X: NonNegativeFloat = Field(default=0.1499219272808386, description="Size of the X dimension of the microscope pixels, in microns.") + Y: NonNegativeFloat = Field(default=0.1499219272808386, description="Size of the Y dimension of the microscope pixels, in microns.") + Z: NonNegativeFloat = Field(default=0.3, description="Size of the Z dimension of the microscope pixels, in microns.") @classmethod def from_physical(cls, pixels: PhysicalPixelSizes) -> Self: @@ -53,7 +53,8 @@ class DerivedDeskewFields(FieldAccessModel): class DeskewParams(FieldAccessModel): input_image: DataArray = Field( - description="A 3-5D array containing the image data." + description="A 3-5D array containing the image data.", + cli_description="A path to any standard image file (TIFF, H5 etc) containing a 3-5D array to process." ) skew: DeskewDirection = Field( default=DeskewDirection.Y, @@ -61,7 +62,7 @@ class DeskewParams(FieldAccessModel): ) angle: float = Field( default=30.0, - description="Angle of deskewing, in degrees." + description="Angle of deskewing, in degrees, as a float." ) physical_pixel_sizes: DefinedPixelSizes = Field( default_factory=DefinedPixelSizes, @@ -70,7 +71,8 @@ class DeskewParams(FieldAccessModel): derived: DerivedDeskewFields = Field( init_var=False, default=None, - description="Refer to the DerivedDeskewFields docstring" + description="Refer to the DerivedDeskewFields docstring", + cli_hide=True ) # Hack to ensure that .skew_dir behaves identically to .skew @property diff --git a/core/lls_core/models/lattice_data.py b/core/lls_core/models/lattice_data.py index 631f731..14655f4 100644 --- a/core/lls_core/models/lattice_data.py +++ b/core/lls_core/models/lattice_data.py @@ -26,11 +26,6 @@ logger = logging.getLogger(__name__) -workflow: Optional[Workflow] = Field( - default=None, - description="If defined, this is a workflow to add lightsheet processing onto" -) - class LatticeData(OutputParams, DeskewParams): """ Holds data and metadata for a given image in a consistent format @@ -45,7 +40,11 @@ class LatticeData(OutputParams, DeskewParams): #: If this is None, then cropping is disabled crop: Optional[CropParams] = None - workflow: Optional[Workflow] = workflow + workflow: Optional[Workflow] = Field( + default=None, + description="If defined, this is a workflow to add lightsheet processing onto", + cli_description="Path to a JSON file specifying a napari_workflow-compatible workflow to add lightsheet processing onto" + ) @root_validator(pre=True) def use_image_path(cls, values: dict): diff --git a/core/lls_core/models/output.py b/core/lls_core/models/output.py index 15f332c..2f7b127 100644 --- a/core/lls_core/models/output.py +++ b/core/lls_core/models/output.py @@ -1,8 +1,7 @@ from pydantic import Field, DirectoryPath, validator from strenum import StrEnum -from os import getcwd from pathlib import Path -from typing import TYPE_CHECKING, Any +from typing import TYPE_CHECKING from lls_core.models.utils import FieldAccessModel, enum_choices @@ -16,11 +15,12 @@ class SaveFileType(StrEnum): class OutputParams(FieldAccessModel): save_dir: DirectoryPath = Field( - description="The directory where the output data will be saved" + description="The directory where the output data will be saved." ) save_suffix: str = Field( default="_deskewed", - description="The filename suffix that will be used for output files. This will be added as a suffix to the input file name if the input image was specified using a file name. If the input image was provided as an in-memory object, the `save_name` field should instead be specified." + description="The filename suffix that will be used for output files. This will be added as a suffix to the input file name if the input image was specified using a file name. If the input image was provided as an in-memory object, the `save_name` field should instead be specified.", + cli_description="The filename suffix that will be used for output files. This will be added as a suffix to the input file name if the --save-name flag was not specified." ) save_name: str = Field( description="The filename that will be used for output files. This should not contain a leading directory or file extension. The final output files will have additional elements added to the end of this prefix to indicate the region of interest, channel, timepoint, file extension etc.", @@ -28,15 +28,17 @@ class OutputParams(FieldAccessModel): ) save_type: SaveFileType = Field( default=SaveFileType.h5, - description=f"The data type to save the result as. This will also be used to determine the file extension of the output files. Choices: {enum_choices(SaveFileType)}" + description=f"The data type to save the result as. This will also be used to determine the file extension of the output files. Choices: {enum_choices(SaveFileType)}." ) time_range: range = Field( default=None, - description="The range of times to process. This defaults to all time points in the image array." + description="The range of times to process. This defaults to all time points in the image array.", + cli_description="The range of times to process, as an array with two items: the first and last time index. This defaults to all time points in the image array." ) channel_range: range = Field( - description="The filename prefix that will be used for output files, without a leading directory or file extension. The final output files will have additional elements added to the end of this prefix to indicate the region of interest, channel, timepoint, file extension etc.", - default=None + default=None, + description="The range of channels to process. This defaults to all time points in the image array.", + cli_description="The range of channels to process, as an array with two items: the first and last channel index. This defaults to all channels in the image array." ) @validator("save_dir", pre=True) diff --git a/core/lls_core/models/utils.py b/core/lls_core/models/utils.py index 768595c..2ec98a0 100644 --- a/core/lls_core/models/utils.py +++ b/core/lls_core/models/utils.py @@ -54,11 +54,21 @@ def to_definition_dict(cls) -> dict: """ ret = {} for key, value in cls.__fields__.items(): + if value.field_info.extra.get("cli_hide"): + # Hide any fields that have cli_hide = True + continue + if isinstance(value.outer_type_, type) and issubclass(value.outer_type_, FieldAccessModel): - value = value.outer_type_.to_definition_dict() + rhs = value.outer_type_.to_definition_dict() else: - value = value.field_info.description - ret[key] = value + rhs: str + if "cli_description" in value.field_info.extra: + # cli_description can be used to configure the help text that appears for fields for the CLI only + rhs = value.field_info.extra["cli_description"] + else: + rhs = value.field_info.description + rhs += f" Default: {value.get_default()}." + ret[key] = rhs return ret def copy_validate(self, **kwargs) -> Self: From 2a2024a95e9592c22ea7203766b24055e729fb0a Mon Sep 17 00:00:00 2001 From: Michael Milton Date: Wed, 20 Dec 2023 16:24:27 +1100 Subject: [PATCH 122/147] Re-enable testing on all python versions; copy a pairwise implementation for older pythons --- .github/workflows/test_and_deploy.yml | 212 +++++++++++++------------- core/lls_core/cmds/__main__.py | 13 +- 2 files changed, 116 insertions(+), 109 deletions(-) diff --git a/.github/workflows/test_and_deploy.yml b/.github/workflows/test_and_deploy.yml index 2c5bacd..71ae1b9 100644 --- a/.github/workflows/test_and_deploy.yml +++ b/.github/workflows/test_and_deploy.yml @@ -1,107 +1,105 @@ -# This workflows will upload a Python Package using Twine when a release is created -# For more information see: https://help.github.com/en/actions/language-and-framework-guides/using-python-with-github-actions#publishing-to-package-registries -# For pytest-qt related fixes: https://pytest-qt.readthedocs.io/en/latest/troubleshooting.html#github-actions - -name: tests - -on: - pull_request: - paths-ignore: - - "**/README.md" - push: - paths-ignore: - - "**/README.md" - -jobs: - test: - runs-on: ubuntu-latest - defaults: - run: - shell: bash -l {0} - strategy: - fail-fast: false - matrix: - python-version: ["3.8", "3.9", "3.10", "3.11"] - env: - DISPLAY: ":99.0" - steps: - - uses: actions/checkout@v3 - - - uses: conda-incubator/setup-miniconda@v2 - with: - auto-update-conda: true - activate-environment: test - python-version: ${{ matrix.python-version }} - channels: conda-forge - - - name: Conda info - run: conda info - - - name: Save conda location - run: echo "python=$(which python)" >> "$GITHUB_ENV" - - - name: Install core - timeout-minutes: 10 - run: | - conda install -y pyopencl pocl - python --version - pip install --upgrade pip setuptools wheel - pip install --use-pep517 -e './core[testing]' - - - name: Lint core - uses: ./.github/lint - with: - directory: core - python: ${{ env.python }} - - - name: Test core - # Only run all the tests on one version, to avoid using excessive CPU hours - if: ${{ matrix.python-version == '3.10' }} - run: pytest -v --cov=core --cov-report=xml core/ - - - uses: tlambert03/setup-qt-libs@v1 - - - name: Install plugin - timeout-minutes: 10 - run: | - /sbin/start-stop-daemon --start --quiet --pidfile /tmp/custom_xvfb_99.pid --make-pidfile --background --exec /usr/bin/Xvfb -- :99 -screen 0 1920x1200x24 -ac +extension GLX - pip install pytest-qt PyQt5 - pip install -e './plugin[testing]' - - - name: Lint plugin - uses: ./.github/lint - with: - directory: plugin - python: ${{ env.python }} - - - name: Test plugin - run: pytest -v --cov=plugin --cov-report=xml plugin - - - name: Coverage - uses: codecov/codecov-action@v3 - - deploy: - # this will run when you have tagged a commit, starting with "v*" - # and requires that you have put your twine API key in your - # github secrets (see readme for details) - needs: [test] - runs-on: ubuntu-latest - if: contains(github.ref, 'tags') - steps: - - uses: actions/checkout@v3 - - name: Set up Python - uses: actions/setup-python@v4 - with: - python-version: "3.x" - - name: Install dependencies - run: | - python -m pip install --upgrade pip - pip install -U setuptools setuptools_scm wheel twine - - name: Build and publish - env: - TWINE_USERNAME: __token__ - TWINE_PASSWORD: ${{ secrets.TWINE_TOKEN }} - run: | - git tag - python setup.py sdist bdist_wheel - twine upload dist/* +# This workflows will upload a Python Package using Twine when a release is created +# For more information see: https://help.github.com/en/actions/language-and-framework-guides/using-python-with-github-actions#publishing-to-package-registries +# For pytest-qt related fixes: https://pytest-qt.readthedocs.io/en/latest/troubleshooting.html#github-actions + +name: tests + +on: + pull_request: + paths-ignore: + - "**/README.md" + push: + paths-ignore: + - "**/README.md" + +jobs: + test: + runs-on: ubuntu-latest + defaults: + run: + shell: bash -l {0} + strategy: + fail-fast: false + matrix: + python-version: ["3.8", "3.9", "3.10", "3.11"] + env: + DISPLAY: ":99.0" + steps: + - uses: actions/checkout@v3 + + - uses: conda-incubator/setup-miniconda@v2 + with: + auto-update-conda: true + activate-environment: test + python-version: ${{ matrix.python-version }} + channels: conda-forge + + - name: Conda info + run: conda info + + - name: Save conda location + run: echo "python=$(which python)" >> "$GITHUB_ENV" + + - name: Install core + timeout-minutes: 10 + run: | + conda install -y pyopencl pocl + python --version + pip install --upgrade pip setuptools wheel + pip install --use-pep517 -e './core[testing]' + + - name: Lint core + uses: ./.github/lint + with: + directory: core + python: ${{ env.python }} + + - name: Test core + run: pytest -v --cov=core --cov-report=xml core/ + + - uses: tlambert03/setup-qt-libs@v1 + + - name: Install plugin + timeout-minutes: 10 + run: | + /sbin/start-stop-daemon --start --quiet --pidfile /tmp/custom_xvfb_99.pid --make-pidfile --background --exec /usr/bin/Xvfb -- :99 -screen 0 1920x1200x24 -ac +extension GLX + pip install pytest-qt PyQt5 + pip install -e './plugin[testing]' + + - name: Lint plugin + uses: ./.github/lint + with: + directory: plugin + python: ${{ env.python }} + + - name: Test plugin + run: pytest -v --cov=plugin --cov-report=xml plugin + + - name: Coverage + uses: codecov/codecov-action@v3 + + deploy: + # this will run when you have tagged a commit, starting with "v*" + # and requires that you have put your twine API key in your + # github secrets (see readme for details) + needs: [test] + runs-on: ubuntu-latest + if: contains(github.ref, 'tags') + steps: + - uses: actions/checkout@v3 + - name: Set up Python + uses: actions/setup-python@v4 + with: + python-version: "3.x" + - name: Install dependencies + run: | + python -m pip install --upgrade pip + pip install -U setuptools setuptools_scm wheel twine + - name: Build and publish + env: + TWINE_USERNAME: __token__ + TWINE_PASSWORD: ${{ secrets.TWINE_TOKEN }} + run: | + git tag + python setup.py sdist bdist_wheel + twine upload dist/* diff --git a/core/lls_core/cmds/__main__.py b/core/lls_core/cmds/__main__.py index ef1f588..33392c7 100644 --- a/core/lls_core/cmds/__main__.py +++ b/core/lls_core/cmds/__main__.py @@ -23,7 +23,7 @@ if TYPE_CHECKING: from lls_core.models.utils import FieldAccessModel - from typing import Type, Any + from typing import Type, Any, Iterable from rich.table import Table class CliDeskewDirection(StrEnum): @@ -100,8 +100,17 @@ def rich_validation(e: ValidationError) -> Table: return table +def pairwise(iterable: Iterable) -> Iterable: + """ + An implementation of the pairwise() function in Python 3.10+ + See: https://docs.python.org/3.12/library/itertools.html#itertools.pairwise + """ + from itertools import tee + a, b = tee(iterable) + next(b, None) + return zip(a, b) + def update_nested_data(data: Union[dict, list], keys: list, new_value: Any): - from itertools import pairwise current = data for key, next_key in pairwise(keys): From f26266fb0d7d56d8a774e40a220675d230a40f8c Mon Sep 17 00:00:00 2001 From: Michael Milton Date: Thu, 21 Dec 2023 17:34:19 +1100 Subject: [PATCH 123/147] Revert workflow argument handling to the old method: only passing the image as the first unfilled argument --- core/lls_core/models/lattice_data.py | 14 +- core/lls_core/workflow.py | 453 +++++++++--------- core/tests/test_workflows.py | 11 + .../argument_order/custom_function.py | 7 + .../argument_order/test_workflow.yml | 7 + 5 files changed, 259 insertions(+), 233 deletions(-) create mode 100644 core/tests/workflows/argument_order/custom_function.py create mode 100644 core/tests/workflows/argument_order/test_workflow.yml diff --git a/core/lls_core/models/lattice_data.py b/core/lls_core/models/lattice_data.py index 14655f4..f3ba60a 100644 --- a/core/lls_core/models/lattice_data.py +++ b/core/lls_core/models/lattice_data.py @@ -268,13 +268,13 @@ def generate_workflows( LatticeData.process_into_image, lattice_slice.data ) - for task_name, arg_index, arg_name in get_workflow_inputs(user_workflow): - update_workflow( - user_workflow, - task_name, - arg_index, - "deskew_image" - ) + task_name, arg_index, _arg_name = get_workflow_inputs(user_workflow) + update_workflow( + user_workflow, + task_name, + arg_index, + "deskew_image" + ) yield lattice_slice.copy_with_data(user_workflow) def check_incomplete_acquisition(self, volume: ArrayLike, time_point: int, channel: int): diff --git a/core/lls_core/workflow.py b/core/lls_core/workflow.py index 682f45f..ae13861 100644 --- a/core/lls_core/workflow.py +++ b/core/lls_core/workflow.py @@ -1,226 +1,227 @@ -""" -Functions related to manipulating Napari Workflows -""" -from __future__ import annotations - -from os import path -from pathlib import Path -from typing import Any, Optional, Tuple, Union - -import dask.array as da -import numpy as np -import pandas as pd -import pyclesperanto_prototype as cle -from lls_core.types import ArrayLike -from typing_extensions import TYPE_CHECKING - -if TYPE_CHECKING: - from napari_workflows import Workflow - -import logging -logger = logging.getLogger(__name__) -logger.setLevel(logging.INFO) - -def get_workflow_inputs(workflow: Workflow): - """ - Yields tuples of (task_name, argument_index, input_argument) corresponding to the workflow's inputs, - namely the arguments that are unfilled. - Note that the index returned is the index in the overall task tuple, which includes the task name - """ - for root_arg in workflow.roots(): - for taskname, (task_func, *args) in workflow._tasks.items(): - if root_arg in args: - yield taskname, args.index(root_arg) + 1, root_arg - -def update_workflow(workflow: Workflow, task_name: str, task_index: int, new_value: Any): - task = list(workflow.get_task(task_name)) - task[task_index] = new_value - workflow.set_task(task_name, tuple(task)) - -def get_first_last_image_and_task(user_workflow: Workflow) -> Tuple[str, str, str, str]: - """ - Get images and tasks for first and last entry - Returns: - Tuple of (name of first input image, name of last input image, name of first task, name of last task) - """ - - # get image with no preprocessing step (first image) - input_arg_first = user_workflow.roots()[0] - # get last image - input_arg_last = user_workflow.leafs()[0] - # get name of preceding image as that is the input to last task - img_source = user_workflow.sources_of(input_arg_last)[0] - first_task_name = [] - last_task_name = [] - - # loop through workflow keys and get key that has - for key in user_workflow._tasks.keys(): - for task in user_workflow._tasks[key]: - if task == input_arg_first: - first_task_name.append(key) - elif task == img_source: - last_task_name.append(key) - - return input_arg_first, img_source, first_task_name[0], last_task_name[0] if len(last_task_name) > 0 else first_task_name[0] - - -def modify_workflow_task(old_arg: str, task_key: str, new_arg: str, workflow: Workflow) -> tuple: - """ - Replies one argument in a workflow with another - Workflow is not modified, only a new task with updated arg is returned - Args: - old_arg: The argument in the workflow that needs to be modified - new_arg: New argument - task_key: Name of the task within the workflow - workflow: Workflow - - Returns: - tuple: Modified task with name task_key - """ - task = workflow._tasks[task_key] - # convert tuple to list for modification - task_list = list(task) - try: - item_index = task_list.index(old_arg) - except ValueError: - raise Exception(f"{old_arg} not found in workflow file") - - task_list[item_index] = new_arg - modified_task = tuple(task_list) - return modified_task - -def replace_first_arg(workflow: Workflow, new_arg: str, old_arg: Optional[str] = None): - """ - Replaces an argument in the first task of a workflow with some other value - Args: - old_arg: If provided, the name of an argument to replace, otherwise it defaults to the - first workflow arg - """ - img_arg_first, _, first_task_name, _ = get_first_last_image_and_task(workflow) - if old_arg is None: - old_arg = img_arg_first - workflow.set( - first_task_name, - modify_workflow_task( - old_arg=old_arg, - task_key=first_task_name, - new_arg=new_arg, - workflow=workflow - ) - ) - -def load_custom_py_modules(custom_py_files): - import sys - from importlib import import_module, reload - test_first_module_import = import_module(custom_py_files[0]) - if test_first_module_import not in sys.modules: - modules = map(import_module, custom_py_files) - else: - modules = map(reload, custom_py_files) - return modules - - -# TODO: CHANGE so user can select modules? Safer -def get_all_py_files(directory: str) -> list[str]: - """get all py files within directory and return as a list of filenames - Args: - directory: Directory with .py files - """ - import glob - from os.path import basename, dirname, isfile, join - - modules = glob.glob(join(dirname(directory), "*.py")) - all = [basename(f)[:-3] for f in modules if isfile(f) - and not f.endswith('__init__.py')] - print(f"Files found are: {all}") - - return all - -def import_script(script: Path): - """ - Imports a Python script given its path - """ - import importlib.util - import sys - module_name = script.stem - spec = importlib.util.spec_from_file_location(module_name, script) - if spec is None or spec.loader is None: - raise Exception(f"Failed to import {script}!") - module = importlib.util.module_from_spec(spec) - sys.modules[module_name] = module - spec.loader.exec_module(module) - -def _import_workflow_modules(workflow: Path) -> None: - """ - Imports all the Python files that might be used in a given custom workflow - - Args: - workflow: Path to the workflow YAML file - """ - if not workflow.exists(): - raise Exception("Workflow doesn't exist!") - if not workflow.is_file(): - raise Exception("Workflow must be a file!") - - counter = 0 - for script in workflow.parent.glob("*.py"): - if script.stem == "__init__": - # Skip __init__.py - continue - import_script(script) - counter += 1 - - if counter == 0: - logger.warn(f"No custom modules imported. If you'd like to use a custom module, place a *.py file in same folder as the workflow file {workflow.parent}") - else: - logger.info(f"{counter} custom modules imported") - -def workflow_from_path(workflow: Path) -> Workflow: - """ - Imports the dependency modules for a workflow, and loads it from disk - """ - from napari_workflows._io_yaml_v1 import load_workflow - _import_workflow_modules(workflow) - return load_workflow(str(workflow)) - -def process_custom_workflow_output(workflow_output: Union[dict, list, ArrayLike], - save_dir: Optional[str]=None, - idx: Optional[str]=None, - LLSZWidget=None, - widget_class=None, - channel=0, - time=0, - preview: bool = True): - """Check the output from a custom workflow; - saves tables and images separately - - Args: - workflow_output (_type_): _description_ - save_dir (_type_): _description_ - idx (_type_): _description_ - LLSZWidget (_type_): _description_ - widget_class (_type_): _description_ - channel (_type_): _description_ - time (_type_): _description_ - """ - if isinstance(workflow_output, (dict, list)): - # create function for tthis dataframe bit - df = pd.DataFrame(workflow_output) - if preview: - save_path = path.join( - save_dir, "lattice_measurement_"+str(idx)+".csv") - print(f"Detected a dictionary as output, saving preview at", save_path) - df.to_csv(save_path, index=False) - return df - - else: - return df - elif isinstance(workflow_output, (np.ndarray, cle._tier0._pycl.OCLArray, da.core.Array)): - if preview: - suffix_name = str(idx)+"_c" + str(channel) + "_t" + str(time) - scale = (LLSZWidget.LlszMenu.lattice.new_dz, - LLSZWidget.LlszMenu.lattice.dy, LLSZWidget.LlszMenu.lattice.dx) - widget_class.parent_viewer.add_image( - workflow_output, name="Workflow_preview_" + suffix_name, scale=scale) - else: - return workflow_output +""" +Functions related to manipulating Napari Workflows +""" +from __future__ import annotations + +from os import path +from pathlib import Path +from typing import Any, Optional, Tuple, Union + +import dask.array as da +import numpy as np +import pandas as pd +import pyclesperanto_prototype as cle +from lls_core.types import ArrayLike +from typing_extensions import TYPE_CHECKING + +if TYPE_CHECKING: + from napari_workflows import Workflow + +import logging +logger = logging.getLogger(__name__) +logger.setLevel(logging.INFO) + +def get_workflow_inputs(workflow: Workflow) -> Tuple[str, int, str]: + """ + Yields tuples of (task_name, argument_index, input_argument) corresponding to the workflow's inputs, + namely the arguments that are unfilled. + Note that the index returned is the index in the overall task tuple, which includes the task name + """ + for root_arg in workflow.roots(): + for taskname, (task_func, *args) in workflow._tasks.items(): + if root_arg in args: + return taskname, args.index(root_arg) + 1, root_arg + raise Exception("No inputs could be calculated") + +def update_workflow(workflow: Workflow, task_name: str, task_index: int, new_value: Any): + task = list(workflow.get_task(task_name)) + task[task_index] = new_value + workflow.set_task(task_name, tuple(task)) + +def get_first_last_image_and_task(user_workflow: Workflow) -> Tuple[str, str, str, str]: + """ + Get images and tasks for first and last entry + Returns: + Tuple of (name of first input image, name of last input image, name of first task, name of last task) + """ + + # get image with no preprocessing step (first image) + input_arg_first = user_workflow.roots()[0] + # get last image + input_arg_last = user_workflow.leafs()[0] + # get name of preceding image as that is the input to last task + img_source = user_workflow.sources_of(input_arg_last)[0] + first_task_name = [] + last_task_name = [] + + # loop through workflow keys and get key that has + for key in user_workflow._tasks.keys(): + for task in user_workflow._tasks[key]: + if task == input_arg_first: + first_task_name.append(key) + elif task == img_source: + last_task_name.append(key) + + return input_arg_first, img_source, first_task_name[0], last_task_name[0] if len(last_task_name) > 0 else first_task_name[0] + + +def modify_workflow_task(old_arg: str, task_key: str, new_arg: str, workflow: Workflow) -> tuple: + """ + Replies one argument in a workflow with another + Workflow is not modified, only a new task with updated arg is returned + Args: + old_arg: The argument in the workflow that needs to be modified + new_arg: New argument + task_key: Name of the task within the workflow + workflow: Workflow + + Returns: + tuple: Modified task with name task_key + """ + task = workflow._tasks[task_key] + # convert tuple to list for modification + task_list = list(task) + try: + item_index = task_list.index(old_arg) + except ValueError: + raise Exception(f"{old_arg} not found in workflow file") + + task_list[item_index] = new_arg + modified_task = tuple(task_list) + return modified_task + +def replace_first_arg(workflow: Workflow, new_arg: str, old_arg: Optional[str] = None): + """ + Replaces an argument in the first task of a workflow with some other value + Args: + old_arg: If provided, the name of an argument to replace, otherwise it defaults to the + first workflow arg + """ + img_arg_first, _, first_task_name, _ = get_first_last_image_and_task(workflow) + if old_arg is None: + old_arg = img_arg_first + workflow.set( + first_task_name, + modify_workflow_task( + old_arg=old_arg, + task_key=first_task_name, + new_arg=new_arg, + workflow=workflow + ) + ) + +def load_custom_py_modules(custom_py_files): + import sys + from importlib import import_module, reload + test_first_module_import = import_module(custom_py_files[0]) + if test_first_module_import not in sys.modules: + modules = map(import_module, custom_py_files) + else: + modules = map(reload, custom_py_files) + return modules + + +# TODO: CHANGE so user can select modules? Safer +def get_all_py_files(directory: str) -> list[str]: + """get all py files within directory and return as a list of filenames + Args: + directory: Directory with .py files + """ + import glob + from os.path import basename, dirname, isfile, join + + modules = glob.glob(join(dirname(directory), "*.py")) + all = [basename(f)[:-3] for f in modules if isfile(f) + and not f.endswith('__init__.py')] + print(f"Files found are: {all}") + + return all + +def import_script(script: Path): + """ + Imports a Python script given its path + """ + import importlib.util + import sys + module_name = script.stem + spec = importlib.util.spec_from_file_location(module_name, script) + if spec is None or spec.loader is None: + raise Exception(f"Failed to import {script}!") + module = importlib.util.module_from_spec(spec) + sys.modules[module_name] = module + spec.loader.exec_module(module) + +def _import_workflow_modules(workflow: Path) -> None: + """ + Imports all the Python files that might be used in a given custom workflow + + Args: + workflow: Path to the workflow YAML file + """ + if not workflow.exists(): + raise Exception("Workflow doesn't exist!") + if not workflow.is_file(): + raise Exception("Workflow must be a file!") + + counter = 0 + for script in workflow.parent.glob("*.py"): + if script.stem == "__init__": + # Skip __init__.py + continue + import_script(script) + counter += 1 + + if counter == 0: + logger.warn(f"No custom modules imported. If you'd like to use a custom module, place a *.py file in same folder as the workflow file {workflow.parent}") + else: + logger.info(f"{counter} custom modules imported") + +def workflow_from_path(workflow: Path) -> Workflow: + """ + Imports the dependency modules for a workflow, and loads it from disk + """ + from napari_workflows._io_yaml_v1 import load_workflow + _import_workflow_modules(workflow) + return load_workflow(str(workflow)) + +def process_custom_workflow_output(workflow_output: Union[dict, list, ArrayLike], + save_dir: Optional[str]=None, + idx: Optional[str]=None, + LLSZWidget=None, + widget_class=None, + channel=0, + time=0, + preview: bool = True): + """Check the output from a custom workflow; + saves tables and images separately + + Args: + workflow_output (_type_): _description_ + save_dir (_type_): _description_ + idx (_type_): _description_ + LLSZWidget (_type_): _description_ + widget_class (_type_): _description_ + channel (_type_): _description_ + time (_type_): _description_ + """ + if isinstance(workflow_output, (dict, list)): + # create function for tthis dataframe bit + df = pd.DataFrame(workflow_output) + if preview: + save_path = path.join( + save_dir, "lattice_measurement_"+str(idx)+".csv") + print(f"Detected a dictionary as output, saving preview at", save_path) + df.to_csv(save_path, index=False) + return df + + else: + return df + elif isinstance(workflow_output, (np.ndarray, cle._tier0._pycl.OCLArray, da.core.Array)): + if preview: + suffix_name = str(idx)+"_c" + str(channel) + "_t" + str(time) + scale = (LLSZWidget.LlszMenu.lattice.new_dz, + LLSZWidget.LlszMenu.lattice.dy, LLSZWidget.LlszMenu.lattice.dx) + widget_class.parent_viewer.add_image( + workflow_output, name="Workflow_preview_" + suffix_name, scale=scale) + else: + return workflow_output diff --git a/core/tests/test_workflows.py b/core/tests/test_workflows.py index e86ec28..113c5ca 100644 --- a/core/tests/test_workflows.py +++ b/core/tests/test_workflows.py @@ -83,3 +83,14 @@ def test_table_workflow(minimal_image_path: Path, table_workflow: Workflow): assert ncol > 0 else: assert valid_image_path(output) + +def test_argument_order(rbc_tiny: Path): + # Tests that only the first unfilled argument is passed an array + with tempfile.TemporaryDirectory() as tmpdir: + params = LatticeData( + input_image = rbc_tiny, + workflow = "core/tests/workflows/argument_order/test_workflow.yml", + save_dir = tmpdir + ) + for roi, output in params.process_workflow().process(): + print(output) diff --git a/core/tests/workflows/argument_order/custom_function.py b/core/tests/workflows/argument_order/custom_function.py new file mode 100644 index 0000000..07ac327 --- /dev/null +++ b/core/tests/workflows/argument_order/custom_function.py @@ -0,0 +1,7 @@ +from numpy import ndarray + +def test(a, b, c): + assert isinstance(a, ndarray) + assert isinstance(b, str) + assert isinstance(c, int) + return a diff --git a/core/tests/workflows/argument_order/test_workflow.yml b/core/tests/workflows/argument_order/test_workflow.yml new file mode 100644 index 0000000..b74fb9f --- /dev/null +++ b/core/tests/workflows/argument_order/test_workflow.yml @@ -0,0 +1,7 @@ +!!python/object:napari_workflows._workflow.Workflow +_tasks: + test: !!python/tuple + - !!python/name:custom_function.test '' + - input_img + - "abc" + - 3 \ No newline at end of file From 302315437a22e1a6b8ed3f188505bd6be10eaa08 Mon Sep 17 00:00:00 2001 From: Michael Milton Date: Thu, 21 Dec 2023 17:58:32 +1100 Subject: [PATCH 124/147] Fix tests broken by previous commit --- core/lls_core/models/lattice_data.py | 16 +++++++++------- core/lls_core/workflow.py | 4 ++-- .../measure_regionprops_multich.py | 3 +-- 3 files changed, 12 insertions(+), 11 deletions(-) diff --git a/core/lls_core/models/lattice_data.py b/core/lls_core/models/lattice_data.py index f3ba60a..5bebbd9 100644 --- a/core/lls_core/models/lattice_data.py +++ b/core/lls_core/models/lattice_data.py @@ -268,13 +268,15 @@ def generate_workflows( LatticeData.process_into_image, lattice_slice.data ) - task_name, arg_index, _arg_name = get_workflow_inputs(user_workflow) - update_workflow( - user_workflow, - task_name, - arg_index, - "deskew_image" - ) + inputs = get_workflow_inputs(user_workflow) + if inputs is not None: + task_name, arg_index, _arg_name = inputs + update_workflow( + user_workflow, + task_name, + arg_index, + "deskew_image" + ) yield lattice_slice.copy_with_data(user_workflow) def check_incomplete_acquisition(self, volume: ArrayLike, time_point: int, channel: int): diff --git a/core/lls_core/workflow.py b/core/lls_core/workflow.py index ae13861..31c8bd5 100644 --- a/core/lls_core/workflow.py +++ b/core/lls_core/workflow.py @@ -21,7 +21,7 @@ logger = logging.getLogger(__name__) logger.setLevel(logging.INFO) -def get_workflow_inputs(workflow: Workflow) -> Tuple[str, int, str]: +def get_workflow_inputs(workflow: Workflow) -> Optional[Tuple[str, int, str]]: """ Yields tuples of (task_name, argument_index, input_argument) corresponding to the workflow's inputs, namely the arguments that are unfilled. @@ -31,7 +31,7 @@ def get_workflow_inputs(workflow: Workflow) -> Tuple[str, int, str]: for taskname, (task_func, *args) in workflow._tasks.items(): if root_arg in args: return taskname, args.index(root_arg) + 1, root_arg - raise Exception("No inputs could be calculated") + return None def update_workflow(workflow: Workflow, task_name: str, task_index: int, new_value: Any): task = list(workflow.get_task(task_name)) diff --git a/workflow_examples/regionprops_workflow/measure_regionprops_multich.py b/workflow_examples/regionprops_workflow/measure_regionprops_multich.py index 803a30f..07a5ba9 100644 --- a/workflow_examples/regionprops_workflow/measure_regionprops_multich.py +++ b/workflow_examples/regionprops_workflow/measure_regionprops_multich.py @@ -1,7 +1,6 @@ #Channel specific thresholding -from napari_lattice import config from skimage.filters import threshold_triangle, threshold_otsu def segment_multich(img): @@ -11,4 +10,4 @@ def segment_multich(img): #if second channel, use Otsu threshold elif config.channel == 1: binary_img = threshold_otsu(img) - return binary_img \ No newline at end of file + return binary_img From 90f35234f9aa08454468da78d2e5be24146674a2 Mon Sep 17 00:00:00 2001 From: Michael Milton Date: Thu, 21 Dec 2023 17:59:10 +1100 Subject: [PATCH 125/147] Undo changes to workflow example --- .../regionprops_workflow/measure_regionprops_multich.py | 3 ++- 1 file changed, 2 insertions(+), 1 deletion(-) diff --git a/workflow_examples/regionprops_workflow/measure_regionprops_multich.py b/workflow_examples/regionprops_workflow/measure_regionprops_multich.py index 07a5ba9..803a30f 100644 --- a/workflow_examples/regionprops_workflow/measure_regionprops_multich.py +++ b/workflow_examples/regionprops_workflow/measure_regionprops_multich.py @@ -1,6 +1,7 @@ #Channel specific thresholding +from napari_lattice import config from skimage.filters import threshold_triangle, threshold_otsu def segment_multich(img): @@ -10,4 +11,4 @@ def segment_multich(img): #if second channel, use Otsu threshold elif config.channel == 1: binary_img = threshold_otsu(img) - return binary_img + return binary_img \ No newline at end of file From 9e19163b52e46d0a6ceef024c82bc5ef698dc073 Mon Sep 17 00:00:00 2001 From: Michael Milton Date: Thu, 11 Jan 2024 17:41:09 +1100 Subject: [PATCH 126/147] Remove some dead workflow code --- core/lls_core/workflow.py | 81 +-------------------------------------- 1 file changed, 1 insertion(+), 80 deletions(-) diff --git a/core/lls_core/workflow.py b/core/lls_core/workflow.py index 31c8bd5..e597758 100644 --- a/core/lls_core/workflow.py +++ b/core/lls_core/workflow.py @@ -3,15 +3,9 @@ """ from __future__ import annotations -from os import path from pathlib import Path -from typing import Any, Optional, Tuple, Union +from typing import Any, Optional, Tuple -import dask.array as da -import numpy as np -import pandas as pd -import pyclesperanto_prototype as cle -from lls_core.types import ArrayLike from typing_extensions import TYPE_CHECKING if TYPE_CHECKING: @@ -90,37 +84,6 @@ def modify_workflow_task(old_arg: str, task_key: str, new_arg: str, workflow: Wo modified_task = tuple(task_list) return modified_task -def replace_first_arg(workflow: Workflow, new_arg: str, old_arg: Optional[str] = None): - """ - Replaces an argument in the first task of a workflow with some other value - Args: - old_arg: If provided, the name of an argument to replace, otherwise it defaults to the - first workflow arg - """ - img_arg_first, _, first_task_name, _ = get_first_last_image_and_task(workflow) - if old_arg is None: - old_arg = img_arg_first - workflow.set( - first_task_name, - modify_workflow_task( - old_arg=old_arg, - task_key=first_task_name, - new_arg=new_arg, - workflow=workflow - ) - ) - -def load_custom_py_modules(custom_py_files): - import sys - from importlib import import_module, reload - test_first_module_import = import_module(custom_py_files[0]) - if test_first_module_import not in sys.modules: - modules = map(import_module, custom_py_files) - else: - modules = map(reload, custom_py_files) - return modules - - # TODO: CHANGE so user can select modules? Safer def get_all_py_files(directory: str) -> list[str]: """get all py files within directory and return as a list of filenames @@ -183,45 +146,3 @@ def workflow_from_path(workflow: Path) -> Workflow: from napari_workflows._io_yaml_v1 import load_workflow _import_workflow_modules(workflow) return load_workflow(str(workflow)) - -def process_custom_workflow_output(workflow_output: Union[dict, list, ArrayLike], - save_dir: Optional[str]=None, - idx: Optional[str]=None, - LLSZWidget=None, - widget_class=None, - channel=0, - time=0, - preview: bool = True): - """Check the output from a custom workflow; - saves tables and images separately - - Args: - workflow_output (_type_): _description_ - save_dir (_type_): _description_ - idx (_type_): _description_ - LLSZWidget (_type_): _description_ - widget_class (_type_): _description_ - channel (_type_): _description_ - time (_type_): _description_ - """ - if isinstance(workflow_output, (dict, list)): - # create function for tthis dataframe bit - df = pd.DataFrame(workflow_output) - if preview: - save_path = path.join( - save_dir, "lattice_measurement_"+str(idx)+".csv") - print(f"Detected a dictionary as output, saving preview at", save_path) - df.to_csv(save_path, index=False) - return df - - else: - return df - elif isinstance(workflow_output, (np.ndarray, cle._tier0._pycl.OCLArray, da.core.Array)): - if preview: - suffix_name = str(idx)+"_c" + str(channel) + "_t" + str(time) - scale = (LLSZWidget.LlszMenu.lattice.new_dz, - LLSZWidget.LlszMenu.lattice.dy, LLSZWidget.LlszMenu.lattice.dx) - widget_class.parent_viewer.add_image( - workflow_output, name="Workflow_preview_" + suffix_name, scale=scale) - else: - return workflow_output From c6fb7821a0153258381243ab7c9399da7f19bca1 Mon Sep 17 00:00:00 2001 From: "PRADEEP\\Pradeep" Date: Mon, 15 Jan 2024 23:07:37 +1100 Subject: [PATCH 127/147] added function to return csv filepath --- core/lls_core/models/output.py | 13 +++++++++++-- core/lls_core/models/results.py | 2 +- 2 files changed, 12 insertions(+), 3 deletions(-) diff --git a/core/lls_core/models/output.py b/core/lls_core/models/output.py index 2f7b127..94d8a79 100644 --- a/core/lls_core/models/output.py +++ b/core/lls_core/models/output.py @@ -1,8 +1,8 @@ from pydantic import Field, DirectoryPath, validator from strenum import StrEnum from pathlib import Path -from typing import TYPE_CHECKING - +from typing import TYPE_CHECKING, Union +from pandas import DataFrame from lls_core.models.utils import FieldAccessModel, enum_choices if TYPE_CHECKING: @@ -65,3 +65,12 @@ def make_filepath(self, suffix: str) -> Path: Returns a filepath for the resulting data """ return self.save_dir / Path(self.save_name + suffix).with_suffix("." + self.file_extension) + + def make_filepath_df(self, suffix: str,result:Union[DataFrame,list]) -> Path: + """ + Returns a filepath for the non-image data + """ + if isinstance(result, DataFrame): + return self.save_dir / Path(self.save_name + suffix).with_suffix(".csv") + + return diff --git a/core/lls_core/models/results.py b/core/lls_core/models/results.py index 213c655..68dcc62 100644 --- a/core/lls_core/models/results.py +++ b/core/lls_core/models/results.py @@ -135,7 +135,7 @@ def save(self) -> Iterable[Path]: """ for roi, result in self.process(): if isinstance(result, DataFrame): - path = self.lattice_data.make_filepath(make_filename_prefix(roi_index=roi)) + path = self.lattice_data.make_filepath_df(make_filename_prefix(roi_index=roi),result) result.to_csv(str(path)) yield path else: From 6f15bb4aaaa666f4251c74be36bf688216e8f060 Mon Sep 17 00:00:00 2001 From: Pradeep R Date: Tue, 16 Jan 2024 17:40:42 +1100 Subject: [PATCH 128/147] avoid overwriting of output file from workflow; fix how dataframes created --- core/lls_core/models/output.py | 14 ++++++++++++-- core/lls_core/models/results.py | 2 +- 2 files changed, 13 insertions(+), 3 deletions(-) diff --git a/core/lls_core/models/output.py b/core/lls_core/models/output.py index 94d8a79..c372f27 100644 --- a/core/lls_core/models/output.py +++ b/core/lls_core/models/output.py @@ -64,13 +64,23 @@ def make_filepath(self, suffix: str) -> Path: """ Returns a filepath for the resulting data """ - return self.save_dir / Path(self.save_name + suffix).with_suffix("." + self.file_extension) + return self.get_unique_filepath(self.save_dir / Path(self.save_name + suffix).with_suffix("." + self.file_extension)) def make_filepath_df(self, suffix: str,result:Union[DataFrame,list]) -> Path: """ Returns a filepath for the non-image data """ if isinstance(result, DataFrame): - return self.save_dir / Path(self.save_name + suffix).with_suffix(".csv") + return self.get_unique_filepath(self.save_dir / Path(self.save_name + suffix).with_suffix(".csv")) return + + def get_unique_filepath(self, path: Path) -> Path: + """ + Returns a unique filepath by appending a number to the filename if the file already exists. + """ + counter = 1 + while path.exists(): + path = path.with_name(f"{path.stem}_{counter}{path.suffix}") + counter += 1 + return path \ No newline at end of file diff --git a/core/lls_core/models/results.py b/core/lls_core/models/results.py index 68dcc62..7c44a6f 100644 --- a/core/lls_core/models/results.py +++ b/core/lls_core/models/results.py @@ -125,7 +125,7 @@ def process(self) -> Iterable[Tuple[RoiIndex, ProcessedWorkflowOutput]]: for file in element.written_files: yield roi, file else: - yield roi, pd.DataFrame(element) + yield roi, pd.DataFrame(element[0]) def save(self) -> Iterable[Path]: """ From ba51e685cdf1db393c9f3da08b54539b99a30f00 Mon Sep 17 00:00:00 2001 From: "PRADEEP\\Pradeep" Date: Wed, 17 Jan 2024 13:05:54 +1100 Subject: [PATCH 129/147] revert change, and use pandas series explode instead --- core/lls_core/models/results.py | 5 +++-- 1 file changed, 3 insertions(+), 2 deletions(-) diff --git a/core/lls_core/models/results.py b/core/lls_core/models/results.py index 7c44a6f..2cc3feb 100644 --- a/core/lls_core/models/results.py +++ b/core/lls_core/models/results.py @@ -8,7 +8,7 @@ from lls_core.types import ArrayLike, is_arraylike from lls_core.utils import make_filename_prefix from lls_core.writers import RoiIndex, Writer -from pandas import DataFrame +from pandas import DataFrame, Series if TYPE_CHECKING: from lls_core.models.lattice_data import LatticeData @@ -125,7 +125,7 @@ def process(self) -> Iterable[Tuple[RoiIndex, ProcessedWorkflowOutput]]: for file in element.written_files: yield roi, file else: - yield roi, pd.DataFrame(element[0]) + yield roi, pd.DataFrame(element) def save(self) -> Iterable[Path]: """ @@ -136,6 +136,7 @@ def save(self) -> Iterable[Path]: for roi, result in self.process(): if isinstance(result, DataFrame): path = self.lattice_data.make_filepath_df(make_filename_prefix(roi_index=roi),result) + result = result.apply(Series.explode) result.to_csv(str(path)) yield path else: From b294172dea21b62c2e531536b44c5e61b8d63e04 Mon Sep 17 00:00:00 2001 From: Pradeep Rajasekhar Date: Thu, 18 Jan 2024 12:43:07 +1100 Subject: [PATCH 130/147] Update core/lls_core/models/output.py Co-authored-by: Michael Milton --- core/lls_core/models/output.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/core/lls_core/models/output.py b/core/lls_core/models/output.py index c372f27..4a706be 100644 --- a/core/lls_core/models/output.py +++ b/core/lls_core/models/output.py @@ -83,4 +83,4 @@ def get_unique_filepath(self, path: Path) -> Path: while path.exists(): path = path.with_name(f"{path.stem}_{counter}{path.suffix}") counter += 1 - return path \ No newline at end of file + return path From 12429275ec19f4858d1fc4e7e086eb1b82121036 Mon Sep 17 00:00:00 2001 From: Pradeep Rajasekhar Date: Thu, 18 Jan 2024 12:43:42 +1100 Subject: [PATCH 131/147] take only dataframe for now can modify this in the future Co-authored-by: Michael Milton --- core/lls_core/models/output.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/core/lls_core/models/output.py b/core/lls_core/models/output.py index 4a706be..91c45e9 100644 --- a/core/lls_core/models/output.py +++ b/core/lls_core/models/output.py @@ -66,7 +66,7 @@ def make_filepath(self, suffix: str) -> Path: """ return self.get_unique_filepath(self.save_dir / Path(self.save_name + suffix).with_suffix("." + self.file_extension)) - def make_filepath_df(self, suffix: str,result:Union[DataFrame,list]) -> Path: + def make_filepath_df(self, suffix: str, result: DataFrame) -> Path: """ Returns a filepath for the non-image data """ From 2139687b4cbc042876afdcbb535bfe96abe07d2b Mon Sep 17 00:00:00 2001 From: Michael Milton Date: Thu, 1 Aug 2024 13:46:16 +1000 Subject: [PATCH 132/147] A pixel tuple should be in the order of Z, Y, then X --- core/lls_core/cmds/__main__.py | 6 +++--- core/lls_core/models/deskew.py | 2 +- core/tests/test_validation.py | 12 ++++++++++++ 3 files changed, 16 insertions(+), 4 deletions(-) diff --git a/core/lls_core/cmds/__main__.py b/core/lls_core/cmds/__main__.py index 33392c7..ad866ae 100644 --- a/core/lls_core/cmds/__main__.py +++ b/core/lls_core/cmds/__main__.py @@ -139,10 +139,10 @@ 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 X Y and Z pixel dimensions respectively", default=( - DeskewParams.get_default("physical_pixel_sizes").X, + 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=( + DeskewParams.get_default("physical_pixel_sizes").Z, DeskewParams.get_default("physical_pixel_sizes").Y, - DeskewParams.get_default("physical_pixel_sizes").Z + DeskewParams.get_default("physical_pixel_sizes").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."), diff --git a/core/lls_core/models/deskew.py b/core/lls_core/models/deskew.py index 4548f06..a2c1ba4 100644 --- a/core/lls_core/models/deskew.py +++ b/core/lls_core/models/deskew.py @@ -161,7 +161,7 @@ def convert_skew(cls, v: Any): def convert_pixels(cls, v: Any): # Allow the pixel sizes to be specified as a tuple if isinstance(v, tuple) and len(v) == 3: - return DefinedPixelSizes(X=v[0], Y=v[1], Z=v[2]) + return DefinedPixelSizes(Z=v[0], Y=v[1], X=v[2]) return v @validator("input_image", pre=True) diff --git a/core/tests/test_validation.py b/core/tests/test_validation.py index dc1bf49..80101ee 100644 --- a/core/tests/test_validation.py +++ b/core/tests/test_validation.py @@ -1,6 +1,7 @@ from pathlib import Path from lls_core.models.crop import CropParams from lls_core.models.lattice_data import LatticeData +from lls_core.models.deskew import DeskewParams import pytest from pydantic import ValidationError @@ -23,3 +24,14 @@ def test_reject_crop(): CropParams( roi_list=[] ) + +def test_pixel_tuple_order(rbc_tiny: Path): + # Tests that a tuple of Z, Y, X is appropriately assigned in the right order + deskew = DeskewParams( + input_image=rbc_tiny, + physical_pixel_sizes=(1., 2., 3.) + ) + + assert deskew.physical_pixel_sizes.X == 3. + assert deskew.physical_pixel_sizes.Y == 2. + assert deskew.physical_pixel_sizes.Z == 1. From 420898b4a59ee33c3b408f865132c35718c11fb5 Mon Sep 17 00:00:00 2001 From: Michael Milton Date: Thu, 1 Aug 2024 17:50:30 +1000 Subject: [PATCH 133/147] Use AICS metadata even when GUI isn't involved --- core/lls_core/models/deskew.py | 34 +++++++++++++-- core/lls_core/types.py | 76 +++++++++++++++++----------------- core/tests/test_validation.py | 17 ++++++++ 3 files changed, 85 insertions(+), 42 deletions(-) diff --git a/core/lls_core/models/deskew.py b/core/lls_core/models/deskew.py index a2c1ba4..132c43b 100644 --- a/core/lls_core/models/deskew.py +++ b/core/lls_core/models/deskew.py @@ -1,7 +1,7 @@ from __future__ import annotations # class for initializing lattice data and setting metadata # TODO: handle scenes -from pydantic import Field, NonNegativeFloat, validator +from pydantic import Field, NonNegativeFloat, validator, root_validator from typing import Any, Tuple from typing_extensions import Self, TYPE_CHECKING @@ -12,7 +12,7 @@ from xarray import DataArray from lls_core.models.utils import FieldAccessModel, enum_choices -from lls_core.types import image_like_to_image +from lls_core.types import image_like_to_image, is_arraylike, is_pathlike from lls_core.utils import get_deskewed_shape if TYPE_CHECKING: @@ -164,11 +164,37 @@ def convert_pixels(cls, v: Any): return DefinedPixelSizes(Z=v[0], Y=v[1], X=v[2]) return v + @root_validator(pre=True) + def read_image(cls, values: dict): + from aicsimageio import AICSImage + from os import fspath + + img = values["input_image"] + + aics: AICSImage | None = None + if is_pathlike(img): + aics = AICSImage(fspath(img)) + elif isinstance(img, AICSImage): + aics = img + elif is_arraylike(img): + values["input_image"] = DataArray(img) + else: + raise ValueError("Value of input_image was neither a path, an AICSImage, or array-like.") + + # If the image was convertible to AICSImage, we should use the metadata from there + if aics: + values["input_image"] = aics.xarray_dask_data + values["physical_pixel_sizes"] = aics.physical_pixel_sizes + + # In all cases, input_image will be a DataArray (XArray) at this point + + return values + @validator("input_image", pre=True) - def reshaping(cls, v: Any): + def reshaping(cls, v: DataArray): # This allows a user to pass in any array-like object and have it # converted and reshaped appropriately - array = image_like_to_image(v) + array = 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: diff --git a/core/lls_core/types.py b/core/lls_core/types.py index 3b14ea0..7cfe3a2 100644 --- a/core/lls_core/types.py +++ b/core/lls_core/types.py @@ -1,38 +1,38 @@ -from typing import Union -from typing_extensions import TypeGuard, Any, TypeAlias -from dask.array.core import Array as DaskArray -# from numpy.typing import NDArray -from pyopencl.array import Array as OCLArray -import numpy as np -from numpy.typing import NDArray -from xarray import DataArray -from aicsimageio import AICSImage -from os import fspath, PathLike as OriginalPathLike - -# This is a superset of os.PathLike -PathLike: TypeAlias = Union[str, bytes, OriginalPathLike] -def is_pathlike(x: Any) -> TypeGuard[PathLike]: - return isinstance(x, (str, bytes, OriginalPathLike)) - -ArrayLike: TypeAlias = Union[DaskArray, NDArray, OCLArray, DataArray] - -def is_arraylike(arr: Any) -> TypeGuard[ArrayLike]: - return isinstance(arr, (DaskArray, np.ndarray, OCLArray, DataArray)) - -ImageLike: TypeAlias = Union[PathLike, AICSImage, ArrayLike] -def image_like_to_image(img: ImageLike) -> DataArray: - """ - Converts an image in one of many formats to a DataArray - """ - # First try treating it as a path - try: - img = AICSImage(fspath(img)) - except TypeError: - pass - if isinstance(img, AICSImage): - return img.xarray_dask_data - else: - for required_key in ("shape", "dtype", "ndim", "__array__", "__array_ufunc__"): - if not hasattr(img, required_key): - raise ValueError(f"The provided object {img} is not array like!") - return DataArray(img) +from typing import Union +from typing_extensions import TypeGuard, Any, TypeAlias +from dask.array.core import Array as DaskArray +# from numpy.typing import NDArray +from pyopencl.array import Array as OCLArray +import numpy as np +from numpy.typing import NDArray +from xarray import DataArray +from aicsimageio import AICSImage +from os import fspath, PathLike as OriginalPathLike + +# This is a superset of os.PathLike +PathLike: TypeAlias = Union[str, bytes, OriginalPathLike] +def is_pathlike(x: Any) -> TypeGuard[PathLike]: + return isinstance(x, (str, bytes, OriginalPathLike)) + +ArrayLike: TypeAlias = Union[DaskArray, NDArray, OCLArray, DataArray] + +def is_arraylike(arr: Any) -> TypeGuard[ArrayLike]: + return isinstance(arr, (DaskArray, np.ndarray, OCLArray, DataArray)) + +ImageLike: TypeAlias = Union[PathLike, AICSImage, ArrayLike] +def image_like_to_image(img: ImageLike) -> DataArray: + """ + Converts an image in one of many formats to a DataArray + """ + # First try treating it as a path + try: + img = AICSImage(fspath(img)) + except TypeError: + pass + if isinstance(img, AICSImage): + return img.xarray_dask_data + else: + for required_key in ("shape", "dtype", "ndim", "__array__", "__array_ufunc__"): + if not hasattr(img, required_key): + raise ValueError(f"The provided object {img} is not array like!") + return DataArray(img) diff --git a/core/tests/test_validation.py b/core/tests/test_validation.py index 80101ee..bcf1210 100644 --- a/core/tests/test_validation.py +++ b/core/tests/test_validation.py @@ -2,8 +2,11 @@ from lls_core.models.crop import CropParams from lls_core.models.lattice_data import LatticeData from lls_core.models.deskew import DeskewParams +from lls_core.models.output import OutputParams import pytest from pydantic import ValidationError +import tempfile +from unittest.mock import patch, PropertyMock def test_default_save_dir(rbc_tiny: Path): # Test that the save dir is inferred to be the input dir @@ -35,3 +38,17 @@ def test_pixel_tuple_order(rbc_tiny: Path): assert deskew.physical_pixel_sizes.X == 3. assert deskew.physical_pixel_sizes.Y == 2. assert deskew.physical_pixel_sizes.Z == 1. + +def test_allow_trailing_slash(): + with tempfile.TemporaryDirectory() as tmpdir: + output = OutputParams( + save_dir=f"{tmpdir}/" + ) + assert str(output.save_dir) == tmpdir + +def test_infer_czi_pixel_sizes(rbc_tiny: Path): + mock = PropertyMock() + with patch("aicsimageio.AICSImage.physical_pixel_sizes", new=mock): + DeskewParams(input_image=rbc_tiny) + # The AICSImage should be queried for the pixel sizes + assert mock.called From b28e5efeb643a3f941542f4a11db914abbf340d2 Mon Sep 17 00:00:00 2001 From: Michael Milton Date: Fri, 2 Aug 2024 16:09:14 +1000 Subject: [PATCH 134/147] Override read_image validator in LatticeData to get the filename --- core/lls_core/models/deskew.py | 9 ++++++--- core/lls_core/models/lattice_data.py | 10 +++------- 2 files changed, 9 insertions(+), 10 deletions(-) diff --git a/core/lls_core/models/deskew.py b/core/lls_core/models/deskew.py index 132c43b..754d46f 100644 --- a/core/lls_core/models/deskew.py +++ b/core/lls_core/models/deskew.py @@ -158,9 +158,12 @@ def convert_skew(cls, v: Any): return v @validator("physical_pixel_sizes", pre=True) - def convert_pixels(cls, v: Any): - # Allow the pixel sizes to be specified as a tuple - if isinstance(v, tuple) and len(v) == 3: + def convert_pixels(cls, v: Any, values: dict[Any, Any]): + from aicsimageio.types import PhysicalPixelSizes + if isinstance(v, PhysicalPixelSizes): + return DefinedPixelSizes.from_physical(v) + elif isinstance(v, tuple) and len(v) == 3: + # Allow the pixel sizes to be specified as a tuple return DefinedPixelSizes(Z=v[0], Y=v[1], X=v[2]) return v diff --git a/core/lls_core/models/lattice_data.py b/core/lls_core/models/lattice_data.py index 5bebbd9..dbde3b8 100644 --- a/core/lls_core/models/lattice_data.py +++ b/core/lls_core/models/lattice_data.py @@ -47,12 +47,7 @@ class LatticeData(OutputParams, DeskewParams): ) @root_validator(pre=True) - def use_image_path(cls, values: dict): - # This needs to be a root validator to ensure it runs before the - # reshaping validator. We can't override that either since it's - # a field validator and can't modify save_name - # TODO: separate the image file from the image file path as two separate fields, - # so we don't have to put so much logic here + def read_image(cls, values: dict): from lls_core.types import is_pathlike from pathlib import Path input_image = values.get("input_image") @@ -68,7 +63,8 @@ def use_image_path(cls, values: dict): # Convert a string path to a Path object values["save_dir"] = Path(save_dir) - return values + # Use the Deskew version of this validator, to do the actual image loading + return super().read_image(values) @validator("workflow", pre=True) def parse_workflow(cls, v: Any): From f064ea089c4a7bc04d470c13397a520964f5d8b5 Mon Sep 17 00:00:00 2001 From: Michael Milton Date: Fri, 2 Aug 2024 17:17:10 +1000 Subject: [PATCH 135/147] Only use AICS pixels if they are all defined --- core/lls_core/models/deskew.py | 8 +++++--- 1 file changed, 5 insertions(+), 3 deletions(-) diff --git a/core/lls_core/models/deskew.py b/core/lls_core/models/deskew.py index 754d46f..831414e 100644 --- a/core/lls_core/models/deskew.py +++ b/core/lls_core/models/deskew.py @@ -161,10 +161,11 @@ def convert_skew(cls, v: Any): def convert_pixels(cls, v: Any, values: dict[Any, Any]): from aicsimageio.types import PhysicalPixelSizes if isinstance(v, PhysicalPixelSizes): - return DefinedPixelSizes.from_physical(v) + v = DefinedPixelSizes.from_physical(v) elif isinstance(v, tuple) and len(v) == 3: # Allow the pixel sizes to be specified as a tuple - return DefinedPixelSizes(Z=v[0], Y=v[1], X=v[2]) + v = DefinedPixelSizes(Z=v[0], Y=v[1], X=v[2]) + return v @root_validator(pre=True) @@ -187,7 +188,8 @@ 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["physical_pixel_sizes"] = aics.physical_pixel_sizes + if all(size is not None for size in aics.physical_pixel_sizes): + values["physical_pixel_sizes"] = aics.physical_pixel_sizes # In all cases, input_image will be a DataArray (XArray) at this point From 3731a2106b246e12c5dd38e8b1a4abcc402e752e Mon Sep 17 00:00:00 2001 From: Michael Milton Date: Fri, 2 Aug 2024 17:41:56 +1000 Subject: [PATCH 136/147] Remove the standard default for pixel sizes --- core/lls_core/models/deskew.py | 10 ++++++++-- 1 file changed, 8 insertions(+), 2 deletions(-) diff --git a/core/lls_core/models/deskew.py b/core/lls_core/models/deskew.py index 831414e..2915078 100644 --- a/core/lls_core/models/deskew.py +++ b/core/lls_core/models/deskew.py @@ -65,7 +65,7 @@ class DeskewParams(FieldAccessModel): description="Angle of deskewing, in degrees, as a float." ) physical_pixel_sizes: DefinedPixelSizes = Field( - default_factory=DefinedPixelSizes, + # No default, because we need to distinguish between user provided arguments and defaults description="Pixel size of the microscope, in microns." ) derived: DerivedDeskewFields = Field( @@ -165,6 +165,10 @@ def convert_pixels(cls, v: Any, values: dict[Any, Any]): elif isinstance(v, tuple) and len(v) == 3: # Allow the pixel sizes to be specified as a tuple v = DefinedPixelSizes(Z=v[0], Y=v[1], X=v[2]) + elif v is None: + # At this point, we have exhausted all other methods of obtaining pixel sizes: + # User defined and image metadata. So we just use the defaults + return DefinedPixelSizes() return v @@ -188,7 +192,9 @@ 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 - if all(size is not None for size in aics.physical_pixel_sizes): + # 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: values["physical_pixel_sizes"] = aics.physical_pixel_sizes # In all cases, input_image will be a DataArray (XArray) at this point From 2fb523a7e8b919e13416654d391f8b0429e1ac7b Mon Sep 17 00:00:00 2001 From: Michael Milton Date: Fri, 2 Aug 2024 17:55:29 +1000 Subject: [PATCH 137/147] Get default pixels from another model --- core/lls_core/cmds/__main__.py | 8 ++++---- 1 file changed, 4 insertions(+), 4 deletions(-) diff --git a/core/lls_core/cmds/__main__.py b/core/lls_core/cmds/__main__.py index ad866ae..84217f5 100644 --- a/core/lls_core/cmds/__main__.py +++ b/core/lls_core/cmds/__main__.py @@ -11,7 +11,7 @@ from strenum import StrEnum from lls_core.models.lattice_data import LatticeData -from lls_core.models.deskew import DeskewParams +from lls_core.models.deskew import DeskewParams, DefinedPixelSizes from lls_core.models.deconvolution import DeconvolutionParams from lls_core.models.output import OutputParams from lls_core.models.crop import CropParams @@ -140,9 +140,9 @@ def process( 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=( - DeskewParams.get_default("physical_pixel_sizes").Z, - DeskewParams.get_default("physical_pixel_sizes").Y, - DeskewParams.get_default("physical_pixel_sizes").X + 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."), From d7c497e5c8c20d7d93ee11febf642d28c2fc592e Mon Sep 17 00:00:00 2001 From: Michael Milton Date: Fri, 2 Aug 2024 18:18:42 +1000 Subject: [PATCH 138/147] Always run pixel validator --- core/lls_core/models/deskew.py | 5 +++-- 1 file changed, 3 insertions(+), 2 deletions(-) diff --git a/core/lls_core/models/deskew.py b/core/lls_core/models/deskew.py index 2915078..4e4b3d8 100644 --- a/core/lls_core/models/deskew.py +++ b/core/lls_core/models/deskew.py @@ -66,7 +66,8 @@ class DeskewParams(FieldAccessModel): ) physical_pixel_sizes: DefinedPixelSizes = Field( # No default, because we need to distinguish between user provided arguments and defaults - description="Pixel size of the microscope, in microns." + description="Pixel size of the microscope, in microns.", + default=None ) derived: DerivedDeskewFields = Field( init_var=False, @@ -157,7 +158,7 @@ def convert_skew(cls, v: Any): return DeskewDirection[v] return v - @validator("physical_pixel_sizes", pre=True) + @validator("physical_pixel_sizes", pre=True, always=True) def convert_pixels(cls, v: Any, values: dict[Any, Any]): from aicsimageio.types import PhysicalPixelSizes if isinstance(v, PhysicalPixelSizes): From 0958c8a6602a574364e1e643e9212c2682c40f00 Mon Sep 17 00:00:00 2001 From: Michael Milton Date: Tue, 6 Aug 2024 13:02:48 +1000 Subject: [PATCH 139/147] Separate input and output dirs for CLI tests --- core/tests/test_cli.py | 13 ++++++++++--- 1 file changed, 10 insertions(+), 3 deletions(-) diff --git a/core/tests/test_cli.py b/core/tests/test_cli.py index 7f08a4c..7d96b33 100644 --- a/core/tests/test_cli.py +++ b/core/tests/test_cli.py @@ -70,10 +70,17 @@ def test_batch_deskew(flags: List[str], check_fn: Callable[[Path], None]): Write image to disk and then execute napari_lattice from terminal Checks if an deskewed output file is created for both tif and h5 """ - with tempfile.TemporaryDirectory() as out_dir: - out_dir = Path(out_dir) - input_file = out_dir / 'raw.tiff' + with tempfile.TemporaryDirectory() as _test_dir: + test_dir = Path(_test_dir) + + # Inputs + input_file = test_dir / "raw.tiff" create_image(input_file) + + # Outputs + out_dir = test_dir / "output" + out_dir.mkdir() + # Batch deskew and save as h5 invoke([ str(input_file), From 2f6ddf987c312abf6999c179b0404015dbbd812d Mon Sep 17 00:00:00 2001 From: Michael Milton Date: Tue, 6 Aug 2024 17:22:43 +1000 Subject: [PATCH 140/147] User feedback: progress total and success message --- core/lls_core/cmds/__main__.py | 10 +++++++--- core/lls_core/models/lattice_data.py | 13 ++++++++++--- 2 files changed, 17 insertions(+), 6 deletions(-) diff --git a/core/lls_core/cmds/__main__.py b/core/lls_core/cmds/__main__.py index 84217f5..04f1698 100644 --- a/core/lls_core/cmds/__main__.py +++ b/core/lls_core/cmds/__main__.py @@ -174,6 +174,10 @@ def process( show_schema: bool = Option(default=False, help="If provided, image processing will not be performed, and instead a JSON document outlining the JSON/YAML options will be printed to stdout. This can be used to assist with writing a config file for use with the --json-config and --yaml-config options.") ) -> None: from click.core import ParameterSource + from rich.console import Console + + console = Console(stderr=True) + if show_schema: import json import sys @@ -217,12 +221,12 @@ def process( ) ) except ValidationError as e: - from rich.console import Console - Console().print(rich_validation(e)) - # Console().print(ctx.get_help()) + console.print(rich_validation(e)) raise Exit(code=1) lattice.save() + console.print(f"Processing successful. Results can be found in {save_dir.resolve()}") + def main(): app() diff --git a/core/lls_core/models/lattice_data.py b/core/lls_core/models/lattice_data.py index dbde3b8..4ca77d4 100644 --- a/core/lls_core/models/lattice_data.py +++ b/core/lls_core/models/lattice_data.py @@ -228,6 +228,13 @@ def iter_slices(self) -> Iterable[ProcessedSlice[ArrayLike]]: channel=ch, ) + @property + def n_slices(self) -> int: + """ + Returns the number of slices that will be returned by the `iter_*` methods. + """ + return len(self.time_range) * len(self.channel_range) + def iter_sublattices(self, update_with: dict = {}) -> Iterable[ProcessedSlice[LatticeData]]: """ Yields copies of the current LatticeData, one for each slice. @@ -311,7 +318,7 @@ def _process_crop(self) -> Iterable[ImageSlice]: # pass arguments for save tiff, callable and function arguments logger.info(f"Processing ROI {roi_index}") - for slice in tqdm(self.iter_slices(), desc="2D Slice Number", position=1): + for slice in tqdm(self.iter_slices(), desc="2D Slice Number", position=1, total=self.n_slices): deconv_args: dict[Any, Any] = {} if self.deconvolution is not None: deconv_args = dict( @@ -346,7 +353,7 @@ def _process_non_crop(self) -> Iterable[ImageSlice]: from tqdm import tqdm import pyclesperanto_prototype as cle - for slice in tqdm(self.iter_slices(), desc="2d Slice Number"): + for slice in tqdm(self.iter_slices(), desc="2d Slice Number", total=self.n_slices): data: ArrayLike = slice.data if isinstance(slice.data, DaskArray): data = slice.data.compute() @@ -388,7 +395,7 @@ def process_workflow(self) -> WorkflowSlices: from tqdm import tqdm WorkflowSlices.update_forward_refs(LatticeData=LatticeData) outputs = [] - for workflow in tqdm(self.generate_workflows(), desc="2D Slice Number"): + for workflow in tqdm(self.generate_workflows(), desc="2D Slice Number", total=self.n_slices): for leaf in workflow.data.leafs(): outputs.append( workflow.copy_with_data( From 371287ec89fa9839532a00c52dcc3dca3b01a951 Mon Sep 17 00:00:00 2001 From: Michael Milton Date: Tue, 6 Aug 2024 17:37:41 +1000 Subject: [PATCH 141/147] Get output dir from lattice --- core/lls_core/cmds/__main__.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/core/lls_core/cmds/__main__.py b/core/lls_core/cmds/__main__.py index 04f1698..a2fabcc 100644 --- a/core/lls_core/cmds/__main__.py +++ b/core/lls_core/cmds/__main__.py @@ -225,7 +225,7 @@ def process( raise Exit(code=1) lattice.save() - console.print(f"Processing successful. Results can be found in {save_dir.resolve()}") + console.print(f"Processing successful. Results can be found in {lattice.save_dir.resolve()}") def main(): From 5d1118a64d02f6fb67a0fb25dc9a68f4f0de0c37 Mon Sep 17 00:00:00 2001 From: Michael Milton Date: Thu, 8 Aug 2024 12:12:50 +1000 Subject: [PATCH 142/147] Improve message spacing, fix incorrect channel and time in preview dialogue --- plugin/napari_lattice/dock_widget.py | 12 ++++++------ 1 file changed, 6 insertions(+), 6 deletions(-) diff --git a/plugin/napari_lattice/dock_widget.py b/plugin/napari_lattice/dock_widget.py index 0b7f8b4..4788cb7 100644 --- a/plugin/napari_lattice/dock_widget.py +++ b/plugin/napari_lattice/dock_widget.py @@ -79,11 +79,11 @@ class LlszMenu(MagicTemplate): main_heading = field("

      Napari Lattice: Visualization & Analysis

      ", widget_type="Label") heading1 = field(dedent("""
      - Specify deskewing parameters and image layers in Tab 1. - Additional analysis parameters can be configured in the other tabs. - When you are ready to save, go to Tab 5. - Output to specify the output directory. - For more information, please refer to the documentation here. + Specify deskewing parameters and image layers in Tab 1.  + Additional analysis parameters can be configured in the other tabs.  + When you are ready to save, go to Tab 5.  + Output to specify the output directory.  + For more information, please refer to the documentation here.
      """.strip()), widget_type="Label") @@ -136,7 +136,7 @@ def preview(self, header: str, time: int, channel: int): # so we made a copy using a subset of the times lattice = self._make_model(validate=False).copy_validate(update=dict( time_range = range(time, time+1), - channel_range = range(time, time+1), + channel_range = range(channel, channel+1), # Patch in a placeholder for the save dir because previewing doesn't use it # TODO: use a more elegant solution such as making the "saveable" lattice # a child class which more validations From ca6e1d7936ddcc43b81a90372b3e884708ad253d Mon Sep 17 00:00:00 2001 From: Michael Milton Date: Thu, 8 Aug 2024 15:30:42 +1000 Subject: [PATCH 143/147] Rework progress bars to be in one place, and have separate ones for channels and time --- core/lls_core/models/lattice_data.py | 17 ++++++++++------- 1 file changed, 10 insertions(+), 7 deletions(-) diff --git a/core/lls_core/models/lattice_data.py b/core/lls_core/models/lattice_data.py index 4ca77d4..29cddd3 100644 --- a/core/lls_core/models/lattice_data.py +++ b/core/lls_core/models/lattice_data.py @@ -214,12 +214,17 @@ def iter_slices(self) -> Iterable[ProcessedSlice[ArrayLike]]: """ Yields array slices for each time and channel of interest. + Params: + progress: If the progress bar is enabled + Returns: An iterable of tuples. Each tuple contains (time_index, time, channel_index, channel, slice) """ from lls_core.models.results import ProcessedSlice - for time_idx, time in enumerate(self.time_range): - for ch_idx, ch in enumerate(self.channel_range): + from tqdm import tqdm + + for time_idx, time in tqdm(enumerate(self.time_range), desc="Timepoints", total=len(self.time_range)): + for ch_idx, ch in tqdm(enumerate(self.channel_range), desc="Channels", total=len(self.channel_range)): yield ProcessedSlice( data=self.slice_data(time=time, channel=ch), time_index=time_idx, @@ -318,7 +323,7 @@ def _process_crop(self) -> Iterable[ImageSlice]: # pass arguments for save tiff, callable and function arguments logger.info(f"Processing ROI {roi_index}") - for slice in tqdm(self.iter_slices(), desc="2D Slice Number", position=1, total=self.n_slices): + for slice in self.iter_slices(): deconv_args: dict[Any, Any] = {} if self.deconvolution is not None: deconv_args = dict( @@ -350,10 +355,9 @@ def _process_non_crop(self) -> Iterable[ImageSlice]: """ Yields processed image slices without cropping """ - from tqdm import tqdm import pyclesperanto_prototype as cle - for slice in tqdm(self.iter_slices(), desc="2d Slice Number", total=self.n_slices): + for slice in self.iter_slices(): data: ArrayLike = slice.data if isinstance(slice.data, DaskArray): data = slice.data.compute() @@ -392,10 +396,9 @@ def _process_non_crop(self) -> Iterable[ImageSlice]: def process_workflow(self) -> WorkflowSlices: from lls_core.models.results import WorkflowSlices - from tqdm import tqdm WorkflowSlices.update_forward_refs(LatticeData=LatticeData) outputs = [] - for workflow in tqdm(self.generate_workflows(), desc="2D Slice Number", total=self.n_slices): + for workflow in self.generate_workflows(): for leaf in workflow.data.leafs(): outputs.append( workflow.copy_with_data( From f04f30e75a3aa94c02b1267fc5ee5bc3e156aaab Mon Sep 17 00:00:00 2001 From: Michael Milton Date: Thu, 8 Aug 2024 16:08:08 +1000 Subject: [PATCH 144/147] Clean up channel progress bar --- core/lls_core/models/lattice_data.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/core/lls_core/models/lattice_data.py b/core/lls_core/models/lattice_data.py index 29cddd3..cf0535e 100644 --- a/core/lls_core/models/lattice_data.py +++ b/core/lls_core/models/lattice_data.py @@ -224,7 +224,7 @@ def iter_slices(self) -> Iterable[ProcessedSlice[ArrayLike]]: from tqdm import tqdm for time_idx, time in tqdm(enumerate(self.time_range), desc="Timepoints", total=len(self.time_range)): - for ch_idx, ch in tqdm(enumerate(self.channel_range), desc="Channels", total=len(self.channel_range)): + for ch_idx, ch in tqdm(enumerate(self.channel_range), desc="Channels", total=len(self.channel_range), leave=False): yield ProcessedSlice( data=self.slice_data(time=time, channel=ch), time_index=time_idx, From 87a0d599a1b5071dc585304af4dfd047164b2680 Mon Sep 17 00:00:00 2001 From: Michael Milton Date: Fri, 9 Aug 2024 15:22:19 +1000 Subject: [PATCH 145/147] Fix bug when saving non-contiguous ranges --- core/lls_core/models/lattice_data.py | 9 +++++++-- core/lls_core/writers.py | 6 ++++-- core/tests/conftest.py | 5 +++++ core/tests/test_process.py | 13 +++++++++++++ 4 files changed, 29 insertions(+), 4 deletions(-) diff --git a/core/lls_core/models/lattice_data.py b/core/lls_core/models/lattice_data.py index cf0535e..d68b918 100644 --- a/core/lls_core/models/lattice_data.py +++ b/core/lls_core/models/lattice_data.py @@ -271,6 +271,7 @@ def generate_workflows( # We make a copy of the lattice for each slice, each of which has no associated workflow for lattice_slice in self.iter_sublattices(update_with={"workflow": None}): user_workflow = copy(self.workflow) + # We add a step whose result is called "deskew_image" that outputs a 2D image slice user_workflow.set( "deskew_image", LatticeData.process_into_image, @@ -395,19 +396,23 @@ def _process_non_crop(self) -> Iterable[ImageSlice]: ) def process_workflow(self) -> WorkflowSlices: + """ + Runs the workflow on each slice and returns the workflow results + """ from lls_core.models.results import WorkflowSlices WorkflowSlices.update_forward_refs(LatticeData=LatticeData) - outputs = [] + outputs: list[ProcessedSlice[Any]] = [] for workflow in self.generate_workflows(): for leaf in workflow.data.leafs(): outputs.append( workflow.copy_with_data( + # Evaluates the workflow here workflow.data.get(leaf) ) ) return WorkflowSlices( - slices = outputs, + slices=outputs, lattice_data=self ) diff --git a/core/lls_core/writers.py b/core/lls_core/writers.py index 410eeb4..45513a9 100644 --- a/core/lls_core/writers.py +++ b/core/lls_core/writers.py @@ -65,8 +65,10 @@ def write_slice(self, slice: ProcessedSlice[ArrayLike]): import numpy as np self.bdv_writer.append_view( np.array(slice.data), - time=slice.time, - channel=slice.channel, + # We need to use the indices here to ensure they start from 0 and + # are contiguous + time=slice.time_index, + channel=slice.channel_index, voxel_size_xyz=(self.lattice.dx, self.lattice.dy, self.lattice.new_dz), voxel_units='um' ) diff --git a/core/tests/conftest.py b/core/tests/conftest.py index e3fcfa0..4a4e253 100644 --- a/core/tests/conftest.py +++ b/core/tests/conftest.py @@ -22,6 +22,11 @@ def rbc_tiny(): with as_file(resources / "RBC_tiny.czi") as image_path: yield image_path +@pytest.fixture +def multi_channel_time(): + with as_file(resources / "multich_multi_time.tif") as image_path: + yield image_path + @pytest.fixture(params=[ "LLS7_t1_ch1.czi", "LLS7_t1_ch3.czi", diff --git a/core/tests/test_process.py b/core/tests/test_process.py index 4765e15..99926e7 100644 --- a/core/tests/test_process.py +++ b/core/tests/test_process.py @@ -54,6 +54,19 @@ def test_process_deconv_crop(): }).process().slices: assert slice.data.ndim == 3 +def test_process_time_range(multi_channel_time: Path): + from lls_core.models.output import SaveFileType + with tempfile.TemporaryDirectory() as outdir: + LatticeData.parse_obj({ + "input_image": multi_channel_time, + # Channels 2 & 3 + "channel_range": range(1, 3), + # Time point 2 + "time_range": range(1, 2), + "save_dir": outdir, + "save_type": SaveFileType.h5 + }).save() + @pytest.mark.parametrize(["background"], [(1, ), ("auto",), ("second_last",)]) @parameterized def test_process_deconvolution(args: dict, background: Any): From 4dfeb7c1a3a566e341f41a2c0f73273b3d9049c6 Mon Sep 17 00:00:00 2001 From: Michael Milton Date: Fri, 9 Aug 2024 15:22:19 +1000 Subject: [PATCH 146/147] Fix bug when saving non-contiguous ranges --- core/lls_core/models/lattice_data.py | 9 +++++++-- core/lls_core/writers.py | 6 ++++-- core/tests/conftest.py | 5 +++++ core/tests/test_process.py | 13 +++++++++++++ 4 files changed, 29 insertions(+), 4 deletions(-) diff --git a/core/lls_core/models/lattice_data.py b/core/lls_core/models/lattice_data.py index cf0535e..d68b918 100644 --- a/core/lls_core/models/lattice_data.py +++ b/core/lls_core/models/lattice_data.py @@ -271,6 +271,7 @@ def generate_workflows( # We make a copy of the lattice for each slice, each of which has no associated workflow for lattice_slice in self.iter_sublattices(update_with={"workflow": None}): user_workflow = copy(self.workflow) + # We add a step whose result is called "deskew_image" that outputs a 2D image slice user_workflow.set( "deskew_image", LatticeData.process_into_image, @@ -395,19 +396,23 @@ def _process_non_crop(self) -> Iterable[ImageSlice]: ) def process_workflow(self) -> WorkflowSlices: + """ + Runs the workflow on each slice and returns the workflow results + """ from lls_core.models.results import WorkflowSlices WorkflowSlices.update_forward_refs(LatticeData=LatticeData) - outputs = [] + outputs: list[ProcessedSlice[Any]] = [] for workflow in self.generate_workflows(): for leaf in workflow.data.leafs(): outputs.append( workflow.copy_with_data( + # Evaluates the workflow here workflow.data.get(leaf) ) ) return WorkflowSlices( - slices = outputs, + slices=outputs, lattice_data=self ) diff --git a/core/lls_core/writers.py b/core/lls_core/writers.py index 410eeb4..45513a9 100644 --- a/core/lls_core/writers.py +++ b/core/lls_core/writers.py @@ -65,8 +65,10 @@ def write_slice(self, slice: ProcessedSlice[ArrayLike]): import numpy as np self.bdv_writer.append_view( np.array(slice.data), - time=slice.time, - channel=slice.channel, + # We need to use the indices here to ensure they start from 0 and + # are contiguous + time=slice.time_index, + channel=slice.channel_index, voxel_size_xyz=(self.lattice.dx, self.lattice.dy, self.lattice.new_dz), voxel_units='um' ) diff --git a/core/tests/conftest.py b/core/tests/conftest.py index e3fcfa0..4a4e253 100644 --- a/core/tests/conftest.py +++ b/core/tests/conftest.py @@ -22,6 +22,11 @@ def rbc_tiny(): with as_file(resources / "RBC_tiny.czi") as image_path: yield image_path +@pytest.fixture +def multi_channel_time(): + with as_file(resources / "multich_multi_time.tif") as image_path: + yield image_path + @pytest.fixture(params=[ "LLS7_t1_ch1.czi", "LLS7_t1_ch3.czi", diff --git a/core/tests/test_process.py b/core/tests/test_process.py index 4765e15..99926e7 100644 --- a/core/tests/test_process.py +++ b/core/tests/test_process.py @@ -54,6 +54,19 @@ def test_process_deconv_crop(): }).process().slices: assert slice.data.ndim == 3 +def test_process_time_range(multi_channel_time: Path): + from lls_core.models.output import SaveFileType + with tempfile.TemporaryDirectory() as outdir: + LatticeData.parse_obj({ + "input_image": multi_channel_time, + # Channels 2 & 3 + "channel_range": range(1, 3), + # Time point 2 + "time_range": range(1, 2), + "save_dir": outdir, + "save_type": SaveFileType.h5 + }).save() + @pytest.mark.parametrize(["background"], [(1, ), ("auto",), ("second_last",)]) @parameterized def test_process_deconvolution(args: dict, background: Any): From 312f335a6e6ad3b80e4d89be334fcc65584528e0 Mon Sep 17 00:00:00 2001 From: Michael Milton Date: Fri, 9 Aug 2024 16:07:26 +1000 Subject: [PATCH 147/147] Re-enable napari-aicsimageio --- plugin/pyproject.toml | 4 +--- 1 file changed, 1 insertion(+), 3 deletions(-) diff --git a/plugin/pyproject.toml b/plugin/pyproject.toml index 9b92123..2c07ce6 100644 --- a/plugin/pyproject.toml +++ b/plugin/pyproject.toml @@ -45,9 +45,7 @@ dependencies = [ # The upper bound is because we are waiting on https://github.com/hanjinliu/magic-class/issues/128 "magic-class>=0.7.5,<0.8.0", "magicgui<0.8.0", - # Currently commented out to avoid installation issues, although - # This can be reinstated once https://github.com/pypa/pip/pull/12095 is merged - # "napari-aicsimageio>=0.7.2", + "napari-aicsimageio>=0.7.2", "napari-spreadsheet", "napari-workflow-inspector", "napari-workflows>=0.2.8",

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