Create mantis-friendly directory to evaluate clustering accuracy

[ngreenwald]

Is your feature request related to a problem? Please describe.
Mantis is super useful in order to evaluate the quality of the clustering reuslts, but requires a specific formatting

Describe the solution you'd like
Add the option to automatically create the required structure at the end of the pixel and cell clustering notebooks. Here's a quick function I wrote. This can get cleaned up, tested, etc

def create_mantis_project(mantis_project_path, img_data_path, mask_output_dir, mask_suffix, mapping_path, 
                          seg_dir, img_sub_folder='normalized'):
    
    if not os.path.exists(mantis_project_path):
        os.makedirs(mantis_project_path)
        
    # create key from cluster number to cluster name
    map_df = pd.read_csv(mapping_path)
    map_df = map_df.loc[:, ['metacluster', 'mc_name']]

    # remove duplicates from df
    map_df = map_df.drop_duplicates()
    map_df = map_df.sort_values(by=['metacluster'])
    
    # rename for mantis names
    map_df = map_df.rename({'metacluster': 'region_id', 'mn_name': 'region_name'}, axis=1)
    
    # get names of fovs with masks
    mask_names = io_utils.list_files(mask_output_dir, mask_suffix)
    fov_names = io_utils.extract_delimited_names(mask_names, delimiter=mask_suffix)
    
    # create a folder with image data, pixel masks, and segmentation mask
    for idx, val in enumerate(fov_names):
        
        # set up paths
        img_source_dir = os.path.join(img_data_path, val, img_sub_folder)
        output_dir = os.path.join(mantis_project_path, val)
        
        # copy image data if not already copied in from previous round of clustering
        if not os.path.exists(output_dir):
            os.makedirs(output_dir)

            # copy all channels into new folder
            chans = io_utils.list_files(img_source_dir, '.tiff')
            for chan in chans:
                shutil.copy(os.path.join(img_source_dir, chan), os.path.join(output_dir, chan))

        # copy mask into new folder
        mask_name = mask_names[idx]
        shutil.copy(os.path.join(mask_output_dir, mask_name), os.path.join(output_dir, 'population{}.tiff'.format(mask_suffix)))
        
        # copy segmentations into directory
        seg_name = val + '_feature_0.tif'
        shutil.copy(os.path.join(seg_dir, seg_name), os.path.join(output_dir, 'cell_segmentation.tiff'))
        
        # copy mapping into directory
        map_df.to_csv(os.path.join(output_dir, 'population{}.csv'.format(mask_suffix)), index=False)

Here's what it looks like when run from the cell clustering nb

tiff_dir = os.path.join(base_dir, “compensated_data_1”)
create_mantis_project(mantis_project_path=os.path.join(base_dir, cell_output_dir, ‘mantis’),
                      img_data_path=tiff_dir,
                      mask_output_dir=os.path.join(base_dir, cell_output_dir),
                      mask_suffix=‘_cell_mask’,
                      mapping_path = os.path.join(base_dir, cell_output_dir, cell_cluster_prefix + ‘_cell_meta_cluster_mapping.csv’),
                     seg_dir=segmentation_dir)