a = {'Batch_folder': '', 0: [], 'image path': '', '3D stack': False, 'multichannel': False, 'Dense regions deconvolution': False, 'Cluster computation': False, 'Segmentation': False, 'Napari correction': False, 'x': '0', 'y': '0', 'z': '0', 'c': '0', 't': '0', 'cyto_model_name': 'cyto3', 'cytoplasm channel': '0', 'cytoplasm diameter': '30', 'nucleus_model_name': 'nuclei', 'nucleus channel': '0', 'nucleus diameter': '30', 'Segment only nuclei': False, 'show segmentation': False, 'saving path': '', 'filename': 'cell_segmentation.png', 'threshold': '', 'threshold penalty': '1', 'channel to compute': '', 'voxel_size_z': 'z', 'voxel_size_y': 'y', 'voxel_size_x': 'x', 'spot_size_z': 'z', 'spot_size_y': 'y', 'spot_size_x': 'x', 'log_kernel_size_z': 'z', 'log_kernel_size_y': 'y', 'log_kernel_size_x': 'x', 'minimum_distance_z': 'z', 'minimum_distance_y': 'y', 'minimum_distance_x': 'x', 'nucleus channel signal': '(0,)', 'alpha': '0.5', 'beta': '1', 'gamma': '5', 'deconvolution_kernel_z': 'z', 'deconvolution_kernel_y': 'y', 'deconvolution_kernel_x': 'x', 'cluster size': '400', 'min number of spots': '5', 'Interactive threshold selector': False, 'spots_extraction_folder': '', 'spots_filename': 'spots_extraction', 'do_spots_csv': False, 'do_spots_excel': False, 'do_spots_feather': False, 'output_folder': '', 'batch_name': '', 'save segmentation': False, 'save detection': False, 'extract spots': False, 'csv': False, 'xlsx': False, 'feather': True, 2: 'Output'}
for k in a.keys() :
    print(k, ' : ', type(k))