from magicgui import magicgui import napari import skimage.io as skio import scipy.ndimage as ndi import numpy as np import pandas as pd from dask_image.imread import imread from dask_image.ndfourier import fourier_shift import dask.array as da from dask.array.image import imread as daimread from dask import delayed import json import os import functools import pandas as pd import matplotlib.pyplot as plt from napari.types import ImageData, PointsData import widgets from magicgui import magicgui import functools def gene_mask(arr,g): v = da.equal(arr,g) return v def dask_reshape(arr,gmax): gene_map = [gene_mask(arr-1,g) for g in range(gmax)] x=da.stack(1*gene_map,axis=0) return x def getTLBF(df:pd.DataFrame,im_shape): # TODO: THIS NEEDS TO BE CHANGED WITH THE NEW CSV FORMAT shift_r = [0] shift_r.extend(df['shift_y'].to_list()) shift_c = [0] shift_c.extend(df['shift_x'].to_list()) dx_tl = np.max(np.maximum(shift_c,0)) dy_tl = np.max(np.maximum(shift_r,0)) dx_br = np.min(np.minimum(shift_c,0)) dy_br = np.min(np.minimum(shift_r,0)) tl = np.ceil([dy_tl,dx_tl]).astype(int) br = np.floor([im_shape[0]+dy_br,im_shape[1]+dx_br]).astype(int) return shift_r,shift_c,tl, br,dx_tl,dy_tl def load_img_and_shift(fn,shift,tl,br): _img = skio.imread(fn) #just perform the shift and then trim shifted_img = ndi.shift(_img,shift) cropped_img = shifted_img#[tl[0]:br[0],tl[1]:br[1]] return cropped_img if __name__=="__main__": config = json.load(open('config.json')) ir_upper=config["ir_upper"] #Has to 9 or less raw_data_dir = config["raw_data_dir"] analysis_dir = config["analysis_dir"] stagepos_file = os.path.join(raw_data_dir,'stagePos_Round#1.xlsx') if os.path.isfile(stagepos_file): stage = pd.read_excel(stagepos_file) else: stagepos_file = os.path.join(raw_data_dir,'stage_position_1.csv') stage = pd.read_csv(stagepos_file) z_lower = config["z_lower"] z_num = config["z_upper"]#len(stage.columns)-5 _fovs = [i for i in range(1,len(stage))] fovs = list(map(lambda x: f'{x:03d}',_fovs)) if 'Var1_ 1' in stage.columns: #Backwards compatability with old stagepos files x_loc = stage['Var1_ 5'] y_loc = stage['Var1_ 4'] z_spacing = np.abs(stage['Var1_ 6'][0]-stage['Var1_ 7'][0]) elif 'x_pos' in stage.columns: x_loc = stage['y_pos'] y_loc = stage['x_pos'] z_spacing = np.abs(stage['z_slice_0'][0]-stage['z_slice_1'][0]) else: x_loc = stage['stage_pos_y'] y_loc = stage['stage_pos_x'] z_spacing = np.abs(stage['z_position_1'][0]-stage['z_position_2'][0]) x_loc = x_loc-x_loc.iloc[0] y_loc = y_loc-y_loc.iloc[0] stage2pix_scaling=config["stage2pix_scaling"] # nikon/pix stage2z_scaling = config["stage2pix_scaling"] #nikon/z viewer = napari.Viewer() for idx,fov in enumerate(fovs): shift_r=np.full((ir_upper,1),0) shift_c=np.full((ir_upper,1),0) tl,br = 0,0 pattern_img = config["file_pattern"].format(fov=fov) image_root = f'/decoding/decoded_images/decoded_{fov}'+'_{:02d}.npy' alignment_root = f'/aligned/shift_{fov}.csv' if config["decoded_img"]: shift_name =analysis_dir+alignment_root codebook_name = os.path.join(config["raw_data_dir"],config["codebook_name"]) codebook_df = pd.read_csv(codebook_name,skiprows=3) codebook_df=codebook_df.rename(columns={c:c.strip() for c in codebook_df.columns},errors='raise') name =analysis_dir+image_root.format(z_lower) if not os.path.isfile(name): print(f"{name} does not exist") continue sample = np.load(name) if os.path.isfile(shift_name): df= pd.read_csv(shift_name) shift_r,shift_c,tl, br, dx_tl,dy_tl= getTLBF(df,sample.shape) num_genes = sample.max() lazy_npload = delayed(np.load) lazy_reshapefn = delayed(dask_reshape) _zs = np.arange(z_lower,z_num+1) lazy_decodes = [lazy_npload(analysis_dir+image_root.format(z)) for z in _zs] dask_arrays = [ da.from_delayed(delayed_reader, shape=sample.shape, dtype=sample.dtype) for delayed_reader in lazy_decodes ] lazy_reshapes = [lazy_reshapefn(da,num_genes) for da in dask_arrays] dask_arrays = [ da.from_delayed(lr, shape=(num_genes,*sample.shape), dtype=sample.dtype) for lr in lazy_reshapes ] stack = da.stack(dask_arrays, axis=0) stack = da.broadcast_to(stack,(1,stack.shape[0],stack.shape[1],stack.shape[2],stack.shape[3])) stack = da.transpose(stack,[2,0,1,3,4]) #add decoded image viewer.add_image(stack,translate=(x_loc.iloc[idx]/stage2pix_scaling,-y_loc[idx]/stage2pix_scaling),name=f'decoded',scale=[1,1,z_spacing/stage2z_scaling,-1,1],blending='additive') #add 473 volume channel = 473 channel_format = f'{channel}nm, Raw/' irs = [imread(raw_data_dir + channel_format + pattern_img.format(ir=ir)) for ir in range(1,ir_upper)] stack = da.stack(irs) viewer.add_image(stack, translate=((x_loc[idx])/stage2pix_scaling, (-y_loc[idx])/stage2pix_scaling), name=f'fov:{fov}, {channel}nm volume', opacity=0.5, scale=[1,z_spacing/stage2z_scaling,-1,1], contrast_limits=[0,2**16]) #add 561 volume channel = 561 channel_format = f'{channel}nm, Raw/' irs = [ daimread(raw_data_dir + channel_format + pattern_img.format(ir=ir),functools.partial(load_img_and_shift, shift = (shift_r[ir-1],shift_c[ir-1]),tl=tl,br=br ) ) for ir in range(1,ir_upper) ] # irs = [imread(raw_data_dir + channel_format + pattern_img.format(ir)) for ir in range(1,ir_upper)] stack = da.stack(irs) viewer.add_image(stack, translate=( x_loc.iloc[idx]/stage2pix_scaling, -y_loc[idx]/stage2pix_scaling), name=f'fov:{fov}, {channel}nm volume', opacity=0.5, scale=[1,z_spacing/stage2z_scaling,-1,1], contrast_limits=[0,2**16]) #add 647 volume channel = 647 channel_format = f'{channel}nm, Raw/' irs = [ daimread(raw_data_dir + channel_format + pattern_img.format(ir=ir),functools.partial(load_img_and_shift, shift = (shift_r[ir-1],shift_c[ir-1]),tl=tl,br=br ) ) for ir in range(1,ir_upper) ] stack = da.stack(irs) viewer.add_image(stack, translate=(x_loc.iloc[idx]/stage2pix_scaling,-y_loc[idx]/stage2pix_scaling), name=f'fov:{fov}, {channel}nm volume', opacity=0.5, scale=[1,z_spacing/stage2z_scaling,-1,1], contrast_limits=[0,2**16], colormap='yellow') #add 750 volume channel = 750 channel_format = f'{channel}nm, Raw/' irs = [ daimread(raw_data_dir + channel_format + pattern_img.format(ir=ir),functools.partial(load_img_and_shift, shift = (shift_r[ir-1],shift_c[ir-1]),tl=tl,br=br ) ) for ir in range(1,ir_upper) ] stack = da.stack(irs) viewer.add_image(stack, translate=(x_loc.iloc[idx]/stage2pix_scaling,-y_loc[idx]/stage2pix_scaling), name=f'fov:{fov}, {channel}nm volume', opacity=0.5, scale=[1,z_spacing/stage2z_scaling,-1,1], contrast_limits=[0,2**16], colormap='green') viewer.dims.axis_labels = ['GN','IR', 'Z', 'Y', 'X'] if config["decoded_img"]: barcode_viewer=widgets.FancyGUI(viewer,codebook_df,fovs=fovs,x_loc=x_loc.to_numpy()/stage2pix_scaling,y_loc=y_loc.to_numpy()/stage2pix_scaling,img_scale=[1,z_spacing/stage2z_scaling,-1,1]) viewer.window.add_dock_widget(barcode_viewer,area='right') viewer.reset_view()# start the event loop and show the viewer napari.run()