""" Sensospot Images Creating nice spot images from scans """ __version__ = "0.0.1" from pathlib import Path import sys from sensospot_data import parse_file from sensospot_data.parameters import _search_measurement_params_file from .images import recalculate, get_position, annotate_image, load_array_image, crop from .parameters import get_spot_parameters, get_array_parameters def calulate_pixel_size(data_frame, array_definition): first = get_position(data_frame.iloc[0], actual=False) last = get_position(data_frame.iloc[-1], actual=False) x_dist_pixel = last.x - first.x y_dist_pixel = last.y - first.y ad = array_definition x_dist_um = ad.dist_x * (ad.size_x - 1) y_dist_um = ad.dist_y * (ad.size_y - 1) if x_dist_um == 0: # only one spot in x direction return x_dist_um / x_dist_pixel elif y_dist_um == 0: # only one spot in x direction return y_dist_um / y_dist_pixel # more than one spot in each direction x_pixel_size = x_dist_um / x_dist_pixel y_pixel_size = y_dist_um / y_dist_pixel return (x_pixel_size + y_pixel_size) / 2 def get_example_data_path(input_dir): input_path = Path(input_dir) tif_files = input_path.glob("*.tif") example_tif = next(tif_files) return example_tif.with_suffix(".csv") def get_filename_prefix(input_dir): file_path = get_example_data_path(input_dir) example_name = file_path.stem prefix, well, exposure = example_name.rsplit("_", 2) return prefix def retrieve_spot_parameters(input_dir, scale): parameters_path = _search_measurement_params_file(input_dir) if parameters_path is None: sys.exit(f"Could not find parameter files in {input_dir}") array_parameters = get_array_parameters(parameters_path) spot_parameters = get_spot_parameters(parameters_path, array_parameters) example_data_path = get_example_data_path(input_dir) example_data = parse_file(example_data_path) pixel_size = calulate_pixel_size(example_data, array_parameters) return recalculate(spot_parameters, scale / pixel_size) def search_image_files(input_dir, wells, exposures): input_path = Path(input_dir) prefix = get_filename_prefix(input_path) tmp_pattern = f"{prefix}_*{wells}*_*{exposures}.tif" pattern = tmp_pattern.replace("***", "*").replace("**", "*") return input_path.glob(pattern) def create_file_map(input_dir, wells, exposures): file_map = {} for tif_path in search_image_files(input_dir, wells, exposures): rest, exposure = tif_path.stem.rsplit("_", 1) csv_path = tif_path.parent / f"{rest}_1.csv" if csv_path.is_file(): if csv_path not in file_map: file_map[csv_path] = [] file_map[csv_path].append(tif_path) return file_map def process_image(image_file, spot_parameters, spot_data, scale): img = load_array_image(image_file, scale=scale) annotate_image(img, spot_parameters, spot_data, scale) return img def create_crops(output_path, img, image_path, spot_parameters, array_data, scale): base_name = image_path.stem for index, spot_data in array_data.iterrows(): cropped_img = crop(img, spot_parameters, spot_data, scale) new_path = output_path / f"{base_name}_{index + 1:03}.tif" cropped_img.save(new_path) def process(input_dir, output_dir, scale=3, wells="*", exposures="*", add_single_spots=False): spot_parameters = retrieve_spot_parameters(input_dir, scale) file_map = create_file_map(input_dir, wells, exposures) output_path = Path(output_dir) if not output_path.is_dir(): sys.exit(f"Could not find output directory: {output_dir}") for data_file, image_files in file_map.items(): array_data = parse_file(data_file) print(data_file) for image_path in image_files: img = process_image(image_path, spot_parameters, array_data, scale) img.save(output_path / image_path.name) if add_single_spots: create_crops(output_path, img, image_path, spot_parameters, array_data, scale)