>>> api（snt，“演示”）
sc.fiji.snt.SNTService 中有 7 种可用方法：
  demoImage(String arg0) -> ImagePlus
  demoImgPlus(String arg0) -> ImgPlus
  demoTree() -> 树
  demoTree(String arg0) -> 树
  demoTreeImage() -> ImagePlus
  demoTrees() -> 列表
  demoTreesSWC() -> 列表

导入 sc.fiji.snt.analysis.*

path = "https://raw.githubusercontent.com/morphonets/misc/master/dataset-demos/csv/demo-trees-coord.csv" // 本地文件的 url 或路径
table = SNTTable.fromFile(path) // 将数据读入表中
headers = ["y", "z"] // 要绘制的列的标题
histogram = SNTChart.getHistogram(table, headers, false) // 表，列。标题，径向图？
histogram.setGMMFitVisible(true) // 将高斯混合模型拟合到数据
histogram.show() // 显示直方图

导入 sc.fiji.snt.analysis.*
导入 sc.fiji.snt.util.*

path = "https://raw.githubusercontent.com/morphonets/misc/master/dataset-demos/csv/demo-trees-coord.csv" // 本地文件的 url 或路径
table = SNTTable.fromFile(path) // 将数据读入表中
SNTChart.showHistogram3D(table.get("y"), table.get("z"), ColorMaps.get("viridis")) // 使用 viridis LUT 显示相同列的 3D 直方图

# 最后测试：2024-07-12，SNTv4.3.0pre
#@上下文上下文
#@DatasetService ds
#@DisplayService显示
#@ImageJ ij
#@LogService日志
#@LUTService lut
#@SNTService snt
#@UIService 用户界面

从 sc.fiji.snt 导入路径、PathAndFillManager、SNT、SNTUI、树
从 sc.fiji.snt.analysis 导入 GroupedTreeStatistics、MultiTreeStatistics、NodeStatistics、TreeStatistics、\
            ConvexHullAnalyzer, PersistenceAnalyzer, ShollAnalyzer, StrahlerAnalyzer, NodeColorMapper,\
            TreeColorMapper, PathProfiler, PathStraightener, RoiConverter, SkeletonConverter, SNTChart, SNTTable
从 sc.fiji.snt.analysis.graph 导入 DirectedWeightedGraph
从 sc.fiji.snt.analysis.sholl.parsers 导入 TreeParser
从 sc.fiji.snt.annotation 导入 AllenCompartment、AllenUtils、VFBUtils、ZBAtlasUtils
从 sc.fiji.snt.io 导入 FlyCircuitLoader、MouseLightLoader、NeuroMorphoLoader
从 sc.fiji.snt.plugin 导入 SkeletonizerCmd、StrahlerCmd
从 sc.fiji.snt.util 导入 BoundingBox、PointInImage、SNTColor、SWCPoint
从 sc.fiji.snt.viewer 导入 Annotation3D、OBJMesh、MultiViewer2D、Viewer2D、Viewer3D

# 文档资源：https://imagej.net/plugins/snt/scripting
# 最新 SNT API：https://javadoc.scijava.org/SNT/

# 推荐。查看器 API：https://javadoc.scijava.org/SNT/index.html?sc/fiji/snt/viewer/Viewer3D.html
# 提示：场景详细信息可以使用 `viewer.logSceneControls()` 打印到控制台或记录
# 当查看器位于最前面时按“L”（助记符：[L]og）
# 如果从独立 RV 打开此模板，则为查看器的实例 ID（例如 2）
# 将自动作为参数传递给 getRecViewer()
查看器 = snt.getRecViewer()

# 添加的代码从这里开始 ####################################################################
导入时间

def do_stuff（查看器）：
    viewer.logSceneControls()

    # Load a neuron from Mouse Secondary Motor Area from the MouseLight database.
    loader = MouseLightLoader("AA0100")
    if not loader.isDatabaseAvailable():
        ui.showDialog("Could not connect to ML database", "Error")
        return
    if not loader.idExists():
        ui.showDialog("Somehow the specified id was not found", "Error")
        return

    # Extract nodes with tag "axon".
    axon = loader.getTree('axon')
    # Do the same for the dendrites.
    dendrites = loader.getTree('dendrites')

    # Load the Allen CCF Mouse Reference Brain and add it to the scene
    viewer.loadRefBrain('mouse')

    # Add both Trees to the scene
    viewer.addTree(axon)
    dendrites.setColor('cyan')
    viewer.addTree(dendrites)

    # Get the OBJMesh object representing the maximum depth Allen CCF compartment
    # containing the soma point and add it to the scene.
    soma_annotation = loader.getSomaCompartment()
    viewer.addMesh(soma_annotation.getMesh())

    # Enforce side perspective.
    viewer.setViewMode('side')

    # Next, we will apply a color mapping to the axon
    # using branch order as the metric and the Ice Lut.
    # Make an instance of TreeColorMapper using SciJava context.
    mapper = TreeColorMapper(context)
    # Get the net.imglib2.display.ColorTable object.
    lut = mapper.getColorTable('Ice.lut')
    # Color code the axon and render in scene.
    # The min and max values returned by the mapper are used
    # to set the range of the color bar legend.
    bounds = viewer.colorCode(axon.getLabel(), TreeColorMapper.STRAHLER_NUMBER, lut)
    viewer.addColorBarLegend(lut, bounds[0], bounds[1])
    # Display a text annotation of the cell ID and metric used by the mapper.
    scene_annotation = "{}: Branch Order".format(axon.getLabel())
    viewer.addLabel(scene_annotation)

    # Begin a rotation animation for the objects in scene.
    viewer.setAnimationEnabled(True)
    # Wait some time before applying a new color mapping
    # based on path distances, using a different LUT (Fire).
    time.sleep(5)

    lut = mapper.getColorTable('Fire.lut')
    # Remap the axon Tree and update scene.
    bounds = viewer.colorCode(axon.getLabel(), TreeColorMapper.PATH_DISTANCE, lut)
    viewer.addColorBarLegend(lut, bounds[0], bounds[1])
    scene_annotation = "{}: Path Distance".format(axon.getLabel())
    viewer.addLabel(scene_annotation)
do_stuff（查看器）

d = getDirectory("选择目录");
文件= getFileList(d);
扩展名=“.tif”；

for( i = 0; i < 文件长度; ++i ) {
    filename = files[i];
    if( endsWith(filename,extension) ) {
        l = lengthOf(filename);
        el = lengthOf(extension);
        basename = substring(filename,0,l-el);
        expected_window_name = "result";
        output_filename = d + File.separator + basename + ".tubes.tif";
        open(filename);
        run("Gaussian Blur...", "sigma=2 scaled stack"); // processing step here
        selectWindow(expected_window_name);
        saveAs("Tiff", output_filename);
    }
}