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feat: main public API to limits tuning
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| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -0,0 +1,120 @@ | ||
| import argparse | ||
| import json | ||
| from pathlib import Path | ||
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| from cryoet_data_portal import Client, Tomogram | ||
| from neuroglancer import viewer_state | ||
| from neuroglancer.url_state import to_url | ||
| from scipy.spatial.transform import Rotation | ||
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| from cryoet_data_portal_neuroglancer.io import load_omezarr_data | ||
| from cryoet_data_portal_neuroglancer.precompute.contrast_limits import ( | ||
| compute_contrast_limits, | ||
| ) | ||
| from cryoet_data_portal_neuroglancer.state_generator import combine_json_layers, generate_image_layer | ||
|
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||
| # Set up logging - level is info | ||
| # logging.basicConfig(level=logging.INFO, force=True) | ||
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| OUTPUT_FOLDER = "/media/starfish/LargeSSD/data/cryoET/data/FromAPI" | ||
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| id_to_path_map = { | ||
| 773: "773/Position_513.zarr", | ||
| } | ||
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| id_to_source_map = { | ||
| 773: ( | ||
| "https://files.cryoetdataportal.cziscience.com/10004/Position_513/Tomograms/VoxelSpacing7.560/CanonicalTomogram/Position_513.zarr", | ||
| (756e-10, 756e-10, 756e-10), | ||
| ), | ||
| } | ||
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| def grab_tomogram(id_: int, zarr_path: Path): | ||
| client = Client() | ||
| if not zarr_path.exists(): | ||
| zarr_path.mkdir(parents=True, exist_ok=True) | ||
| tomogram = Tomogram.get_by_id(client, id_) | ||
| tomogram.download_omezarr(str(zarr_path.parent.resolve())) | ||
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| def run_contrast_limit_calculations_from_api(input_data_path): | ||
| data = load_omezarr_data(input_data_path, resolution_level=-1, persist=False) | ||
| data_shape = data.shape | ||
| data_size_dict = {"z": data_shape[0], "y": data_shape[1], "x": data_shape[2]} | ||
|
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| limits_dict = {} | ||
| # Ensure the main public API is working | ||
| gmm_limits = compute_contrast_limits( | ||
| data, | ||
| method="gmm", | ||
| ) | ||
| cdf_limits = compute_contrast_limits( | ||
| data, | ||
| method="cdf", | ||
| ) | ||
| limits_dict["gmm"] = gmm_limits | ||
| limits_dict["cdf"] = cdf_limits | ||
| limits_dict["2d"] = gmm_limits | ||
| limits_dict["size"] = data_size_dict | ||
| return limits_dict | ||
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| def create_state(id_, contrast_limit_dict, output_folder): | ||
| source, scale = id_to_source_map[id_] | ||
| # One state layer for each contrast limit method | ||
| layers_list = [] | ||
| ignored_keys = ["size", "closest_method", "distance_to_human", "2d"] | ||
|
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||
| # set the most promising contrast limits as visible, rest not | ||
| for key, limits in contrast_limit_dict.items(): | ||
| if key in ignored_keys: | ||
| continue | ||
| is_visible = key == "gmm" | ||
| layer_info = generate_image_layer( | ||
| source=source, | ||
| scale=scale, | ||
| size=contrast_limit_dict["size"], | ||
| name=f"{id_}_{key}", | ||
| twodee_contrast_limits=limits, | ||
| threedee_contrast_limits=limits, | ||
| has_volume_rendering_shader=True, | ||
| volume_rendering_is_visible=True, | ||
| is_visible=is_visible, | ||
| ) | ||
| layer_info["_projectionScale"] = 2000 | ||
| layers_list.append(layer_info) | ||
| json_state = combine_json_layers( | ||
| layers_list, | ||
| scale, | ||
| projection_quaternion=Rotation.from_euler(seq="xyz", angles=(0, 0, 0), degrees=True).as_quat(), | ||
| show_axis_lines=False, | ||
| ) | ||
| with open(output_folder / f"{id_}_state.json", "w") as f: | ||
| json.dump(json_state, f, indent=4) | ||
| return json_state | ||
|
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| def main(output_folder): | ||
| url_list = [] | ||
| for id_, path in id_to_path_map.items(): | ||
| path = Path(output_folder) / path | ||
| grab_tomogram(id_, path) | ||
| limits = run_contrast_limit_calculations_from_api(path) | ||
| state = create_state(id_, limits, Path(output_folder) / "results") | ||
| viewer_state_obj = viewer_state.ViewerState(state) | ||
| url_from_json = to_url( | ||
| viewer_state_obj, | ||
| prefix="https://neuroglancer-demo.appspot.com/", | ||
| ) | ||
| url_list.append(url_from_json) | ||
| print(f"Wrote {len(url_list)} urls to {Path(output_folder) / 'urls.txt'}") | ||
| with open(Path(output_folder) / "urls.txt", "w") as f: | ||
| f.write("\n\n\n".join(url_list)) | ||
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| if __name__ == "__main__": | ||
| parser = argparse.ArgumentParser() | ||
| parser.add_argument("--output_folder", type=str, default=OUTPUT_FOLDER) | ||
| args, _ = parser.parse_known_args() | ||
| main(args.output_folder) |