feat: more examples

.gitignore

@@ -160,4 +160,5 @@ cython_debug/
 #.idea/
 
 */node_modules/
-examples/datasets
+examples/datasets/
+datasets/

examples/histogram.py

@@ -0,0 +1,51 @@
+import argparse
+import webbrowser
+import neuroglancer
+import numpy as np
+import neuroglancer.cli
+
+
+def add_image_layer(state):
+    shape = (40,) * 3
+    dimensions = neuroglancer.CoordinateSpace(
+        names=["x", "y", "z"], units="nm", scales=[40, 40, 40]
+    )
+    data = np.zeros(shape=shape, dtype=np.uint8)
+    data[:10] = 0
+    data[10:20] = 1
+    data[20:30] = 2
+    data[30:] = 3
+    local_volume = neuroglancer.LocalVolume(data, dimensions)
+    state.layers.append(
+        name="image",
+        layer=neuroglancer.ImageLayer(
+            source=local_volume,
+            volume_rendering_mode="max",
+            volume_rendering_depth_samples=40,
+        ),
+        shader="""
+#uicontrol invlerp normalized(range=[1,2])
+void main() {
+  emitGrayscale(normalized());
+    }
+    """,
+    )
+
+
+def launch_nglancer():
+    ap = argparse.ArgumentParser()
+    neuroglancer.cli.add_server_arguments(ap)
+    args = ap.parse_args()
+    neuroglancer.cli.handle_server_arguments(args)
+    viewer = neuroglancer.Viewer()
+    return viewer
+
+
+def main():
+    viewer = launch_nglancer()
+    with viewer.txn() as s:
+        add_image_layer(s)
+    webbrowser.open_new(viewer.get_viewer_url())
+
+if __name__ == "__main__":
+    main()

examples/max_projection.py

@@ -26,7 +26,7 @@ def add_image_layer(state):
     )
     local_volume = neuroglancer.LocalVolume(create_volume(), dimensions)
     state.layers["image"] = neuroglancer.ImageLayer(
-        source=local_volume, volume_rendering_mode="MAX", shader=get_shader()
+        source=local_volume, volume_rendering_mode="max", shader=get_shader()
     )
     state.layout = "3d"
     state.show_axis_lines = False

examples/minimal_max.py

@@ -0,0 +1,48 @@
+import argparse
+import webbrowser
+import neuroglancer
+import numpy as np
+import neuroglancer.cli
+
+
+def add_image_layer(state):
+    shape = (50,) * 3
+    dimensions = neuroglancer.CoordinateSpace(
+        names=["x", "y", "z"], units="nm", scales=[40, 40, 40]
+    )
+    data = np.full(shape=shape, fill_value=255, dtype=np.uint8)
+    local_volume = neuroglancer.LocalVolume(data, dimensions)
+    state.layers.append(
+        name="image",
+        layer=neuroglancer.ImageLayer(
+            source=local_volume,
+            volume_rendering_mode="max",
+            volume_rendering_depth_samples=400,
+        ),
+        shader="""
+void main() {
+    emitIntensity(1.0);
+    emitRGBA(vec4(1.0, 1.0, 1.0, 0.01));
+    }
+    """,
+    )
+    state.layout = "3d"
+
+
+def launch_nglancer():
+    ap = argparse.ArgumentParser()
+    neuroglancer.cli.add_server_arguments(ap)
+    args = ap.parse_args()
+    neuroglancer.cli.handle_server_arguments(args)
+    viewer = neuroglancer.Viewer()
+    return viewer
+
+
+def main():
+    viewer = launch_nglancer()
+    with viewer.txn() as s:
+        add_image_layer(s)
+    webbrowser.open_new(viewer.get_viewer_url())
+
+if __name__ == "__main__":
+    main()

examples/serve_default_dir.py

@@ -1,10 +1,12 @@
 from neuroglancer_utils.create_datasets.create_full_cube import create_cube
 from neuroglancer_utils.create_datasets.create_sphere import create_sphere
 from neuroglancer_utils.create_datasets.create_allen_multi import create_allen_multi
+from neuroglancer_utils.create_datasets.create_cdf_example import create_cdf_example
 from neuroglancer_utils.local_server import create_server
 
 create_cube()
 create_sphere()
 create_allen_multi()
 create_server(directory="datasets")
+create_cdf_example()
 input("Press enter to continue")

neuroglancer_utils/create_datasets/create_cdf_example.py

@@ -0,0 +1,20 @@
+from cloudvolume import CloudVolume
+import numpy as np
+
+
+def create_cdf_example(output_path="file://datasets/cdf"):
+    shape = (40,) * 3
+    data = np.zeros(shape=shape, dtype=np.uint8)
+    data[:10] = 0
+    data[10:20] = 1
+    data[20:30] = 2
+    data[30:] = 3
+    CloudVolume.from_numpy(
+        data,
+        vol_path=output_path,
+        resolution=(40, 40, 40),
+        chunk_size=(40, 40, 40),
+        layer_type="image",
+        progress=True,
+        compress=False,
+    )