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| Original file line number | Diff line number | Diff line change |
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| import glob | ||
| import os | ||
| from enum import Enum | ||
| from pathlib import Path | ||
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| import typer | ||
| from rich.panel import Panel | ||
| from rich.progress import Progress | ||
| import numpy as np | ||
| import shapely | ||
| import signal | ||
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| class GracefulKiller: | ||
| kill_now = False | ||
| def __init__(self): | ||
| signal.signal(signal.SIGINT, self.exit_gracefully) | ||
| signal.signal(signal.SIGTERM, self.exit_gracefully) | ||
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| def exit_gracefully(self, *args): | ||
| print("Recieved SIGTERM") | ||
| self.kill_now = True | ||
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| app = typer.Typer() | ||
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| def get_image_fn(dummy): | ||
| import serialem as sem | ||
| sem.ConnectToSEM(48888,"192.168.122.149") | ||
| image = np.asarray(sem.bufferImage("P")).copy() | ||
| return image | ||
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| @app.command() | ||
| def get_image(): | ||
| import napari | ||
| from napari.layers import Image | ||
| from magicgui import magicgui | ||
| from multiprocessing import Pool | ||
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| @magicgui(call_button="Get image") | ||
| def my_widget() -> Image: | ||
| pool = Pool(processes = 1) | ||
| image = pool.map(get_image_fn,[0])[0] | ||
| return Image(image) | ||
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| viewer = napari.Viewer() | ||
| viewer.window.add_dock_widget(my_widget) | ||
| napari.run() | ||
| print("Done") | ||
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| @app.command() | ||
| def setup_areas( | ||
| session_name: str = typer.Option(None, help="Name of the session"), | ||
| directory: str = typer.Option(None, help="Directory to save session in"), | ||
| ): | ||
| session_o = load_session(session_name, directory) | ||
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| num_items = serialem.ReportNumTableItems() | ||
| maps = [] | ||
| map_coordinates = [] | ||
| map_navids = [] | ||
| for i in range(1,int(num_items)+1): | ||
| nav_item_info = serialem.ReportOtherItem(i) | ||
| nav_id = int(nav_item_info[0]) | ||
| nav_note = serialem.GetVariable("navNote") | ||
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| if nav_note == "decolace_acquisition_map": | ||
| serialem.LoadOtherMap(i,"A") | ||
| image = np.asarray(serialem.bufferImage("A")).copy() | ||
| maps.append(image) | ||
| map_navids.append(nav_id) | ||
| map_coordinates.append(nav_item_info[1:3]) | ||
| import napari | ||
| from napari.layers import Shapes | ||
| from magicgui import magicgui | ||
| viewer = napari.view_image(np.array(maps)) | ||
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| @magicgui(shapes={'label': 'Setup areas'}) | ||
| def my_widget(shapes: Shapes): | ||
| points = [] | ||
| areas = shapes.data | ||
| for area in areas: | ||
| map_id = area[0,0] | ||
| if np.sum(area[:,0] - map_id) != 0: | ||
| raise("Error: Map ID is not the same for all points in the polygon") | ||
| name = f"area{len(session_o.active_grid.state['acquisition_areas'])+2}" | ||
| polygon = shapely.geometry.Polygon(area[:,1:3]) | ||
| aa = AcquisitionAreaSingle(name,Path(session_o.active_grid.directory,name).as_posix(),beam_radius=session_o.state["beam_radius"],tilt=session_o.active_grid.state["tilt"]) | ||
| aa.initialize_from_napari(map_navids[int(map_id)], [polygon.centroid.y, polygon.centroid.x], area[:,1:3]) | ||
| aa.calculate_acquisition_positions_from_napari() | ||
| aa.write_to_disk() | ||
| session_o.active_grid.state["acquisition_areas"].append([aa.name,aa.directory]) | ||
| session_o.active_grid.write_to_disk() | ||
| session_o.active_grid.save_navigator() | ||
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| viewer.window.add_dock_widget(my_widget) | ||
| napari.run() | ||
| print("Done") | ||
| typer.Exit() | ||
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| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -0,0 +1,48 @@ | ||
| import typer | ||
| import logging | ||
| from rich.logging import RichHandler | ||
| from rich import print | ||
| from pathlib import Path | ||
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| app = typer.Typer() | ||
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| @app.command() | ||
| def split_particles_into_optical_groups( | ||
| ctx: typer.Context, | ||
| tmpackage_star_file: Path, | ||
| parameters_star_file: Path, | ||
| split_by_session: bool = True, | ||
| beam_image_shift_threshold: float = 1.0 | ||
| ): | ||
| import starfile | ||
| import pandas as pd | ||
| import sqlite3 | ||
| from sklearn.cluster import KMeans | ||
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| particle_info = starfile.read(tmpackage_star_file) | ||
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| aa_fd = pd.DataFrame([aa.model_dump() for aa in ctx.obj.acquisition_areas]) | ||
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| aa_fd["image_folder"] = aa_fd["cistem_project"].map(lambda x: str(Path(x).parent / "Assets" / "Images" )) | ||
| aa_fd["session"] = aa_fd["frames_folder"].map(lambda x: str(Path(x).parent.parent.parent.name)) | ||
| particle_info["image_folder"] = particle_info["cisTEMOriginalImageFilename"].str.strip("'").map(lambda x: str(Path(x).parent)) | ||
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| particle_info["cisTEMOriginalImageFilename"] = particle_info["cisTEMOriginalImageFilename"].str.strip("'") | ||
| particle_info = particle_info.join(aa_fd.set_index("image_folder"), on="image_folder", rsuffix="_aa") | ||
| for i, cistem_project in enumerate(particle_info["cistem_project"].unique()): | ||
| db = sqlite3.connect(cistem_project) | ||
| microgaph_info = pd.read_sql_query("SELECT IMAGE_ASSETS.FILENAME, IMAGE_SHIFT_X, IMAGE_SHIFT_Y FROM IMAGE_ASSETS INNER JOIN MOVIE_ASSETS ON IMAGE_ASSETS.PARENT_MOVIE_ID=MOVIE_ASSETS.MOVIE_ASSET_ID INNER JOIN MOVIE_ASSETS_METADATA ON MOVIE_ASSETS.MOVIE_ASSET_ID=MOVIE_ASSETS_METADATA.MOVIE_ASSET_ID", db) | ||
| if i == 0: | ||
| particle_info = particle_info.merge(microgaph_info, right_on="FILENAME", left_on="cisTEMOriginalImageFilename",suffixes=(None,None),how="left") | ||
| particle_info.set_index("cisTEMOriginalImageFilename", inplace=True) | ||
| else: | ||
| particle_info.update(microgaph_info.set_index("FILENAME")) | ||
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| particle_info["image_shift_label"] = particle_info.apply(lambda x: f"{x['session']}_{x['IMAGE_SHIFT_X'] // 1}_{x['IMAGE_SHIFT_Y'] // 1}", axis=1) | ||
| particle_info.reset_index(inplace=True) | ||
| print(particle_info["image_shift_label"].value_counts()) | ||
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