update examples add imshow

README.md

@@ -40,7 +40,8 @@ qapp.exec()
     - `tensorstore.TensorStore`
     - `zarr`
   - You can add support for your own storage class by subclassing `ndv.DataWrapper`
-    and implementing a couple methods
+    and implementing a couple methods. (This doesn't require modifying ndv,
+    but contributions of new wrappers are welcome!)
 
 See examples for each of these array types in [examples](./examples/)
 

examples/custom_store.py

@@ -4,7 +4,7 @@
 
 import numpy as np
 
-from ndv import DataWrapper
+import ndv
 
 if TYPE_CHECKING:
     from ndv import Indices, Sizes
@@ -16,10 +16,10 @@ def __init__(self, shape: tuple[int, ...]) -> None:
         self._data = np.random.randint(0, 256, shape)
 
     def __getitem__(self, item: Any) -> np.ndarray:
-        return self._data[item]
+        return self._data[item]  # type: ignore [no-any-return]
 
 
-class MyWrapper(DataWrapper[MyArrayThing]):
+class MyWrapper(ndv.DataWrapper[MyArrayThing]):
     @classmethod
     def supports(cls, data: Any) -> bool:
         if isinstance(data, MyArrayThing):
@@ -36,13 +36,5 @@ def isel(self, indexers: Indices) -> Any:
         return self.data[idx]
 
 
-if __name__ == "__main__":
-    from qtpy import QtWidgets
-
-    from ndv import NDViewer
-
-    qapp = QtWidgets.QApplication([])
-    data = MyArrayThing((10, 3, 512, 512))
-    v = NDViewer(data, channel_axis=1)
-    v.show()
-    qapp.exec()
+data = MyArrayThing((10, 3, 512, 512))
+ndv.imshow(data)

examples/dask_arr.py

@@ -6,6 +6,7 @@
     from dask.array.core import map_blocks
 except ImportError:
     raise ImportError("Please `pip install dask[array]` to run this example.")
+import ndv
 
 frame_size = (1024, 1024)
 
@@ -21,12 +22,4 @@ def _dask_block(block_id: tuple[int, int, int, int, int]) -> np.ndarray | None:
 chunks += [(x,) for x in frame_size]
 dask_arr = map_blocks(_dask_block, chunks=chunks, dtype=np.uint8)
 
-if __name__ == "__main__":
-    from qtpy import QtWidgets
-
-    from ndv import NDViewer
-
-    qapp = QtWidgets.QApplication([])
-    v = NDViewer(dask_arr)
-    v.show()
-    qapp.exec()
+v = ndv.imshow(dask_arr)

examples/jax_arr.py

@@ -4,18 +4,7 @@
     import jax.numpy as jnp
 except ImportError:
     raise ImportError("Please install jax to run this example")
-from numpy_arr import generate_5d_sine_wave
+import ndv
 
-# Example usage
-array_shape = (10, 3, 5, 512, 512)  # Specify the desired dimensions
-sine_wave_5d = jnp.asarray(generate_5d_sine_wave(array_shape))
-
-if __name__ == "__main__":
-    from qtpy import QtWidgets
-
-    from ndv import NDViewer
-
-    qapp = QtWidgets.QApplication([])
-    v = NDViewer(sine_wave_5d, channel_axis=1)
-    v.show()
-    qapp.exec()
+jax_arr = jnp.asarray(ndv.data.nd_sine_wave())
+v = ndv.imshow(jax_arr)

examples/numpy_arr.py

@@ -1,64 +1,11 @@
 from __future__ import annotations
 
-import numpy as np
-
-
-def generate_5d_sine_wave(
-    shape: tuple[int, int, int, int, int],
-    amplitude: float = 240,
-    base_frequency: float = 5,
-) -> np.ndarray:
-    """5D dataset."""
-    # Unpack the dimensions
-    angle_dim, freq_dim, phase_dim, ny, nx = shape
-
-    # Create an empty array to hold the data
-    output = np.zeros(shape)
-
-    # Define spatial coordinates for the last two dimensions
-    half_per = base_frequency * np.pi
-    x = np.linspace(-half_per, half_per, nx)
-    y = np.linspace(-half_per, half_per, ny)
-    y, x = np.meshgrid(y, x)
-
-    # Iterate through each parameter in the higher dimensions
-    for phase_idx in range(phase_dim):
-        for freq_idx in range(freq_dim):
-            for angle_idx in range(angle_dim):
-                # Calculate phase and frequency
-                phase = np.pi / phase_dim * phase_idx
-                frequency = 1 + (freq_idx * 0.1)  # Increasing frequency with each step
-
-                # Calculate angle
-                angle = np.pi / angle_dim * angle_idx
-                # Rotate x and y coordinates
-                xr = np.cos(angle) * x - np.sin(angle) * y
-                np.sin(angle) * x + np.cos(angle) * y
-
-                # Compute the sine wave
-                sine_wave = (amplitude * 0.5) * np.sin(frequency * xr + phase)
-                sine_wave += amplitude * 0.5
-
-                # Assign to the output array
-                output[angle_idx, freq_idx, phase_idx] = sine_wave
-
-    return output
-
+import ndv
 
 try:
-    from skimage import data
-
-    img = data.cells3d()
-except Exception:
-    img = generate_5d_sine_wave((10, 3, 8, 512, 512))
-
-
-if __name__ == "__main__":
-    from qtpy import QtWidgets
-
-    from ndv import NDViewer
+    img = ndv.data.cells3d()
+except Exception as e:
+    print(e)
+    img = ndv.data.nd_sine_wave((10, 3, 8, 512, 512))
 
-    qapp = QtWidgets.QApplication([])
-    v = NDViewer(img)
-    v.show()
-    qapp.exec()
+ndv.imshow(img)

examples/pyopencl_arr.py

@@ -4,26 +4,14 @@
     import pyopencl as cl
     import pyopencl.array as cl_array
 except ImportError:
-    raise ImportError("Please install jax to run this example")
-from numpy_arr import generate_5d_sine_wave
+    raise ImportError("Please install pyopencl to run this example")
+import ndv
 
 # Set up OpenCL context and queue
 context = cl.create_some_context(interactive=False)
 queue = cl.CommandQueue(context)
 
 
-# Example usage
-array_shape = (10, 3, 5, 512, 512)  # Specify the desired dimensions
-sine_wave_5d = generate_5d_sine_wave(array_shape)
-cl_sine_wave = cl_array.to_device(queue, sine_wave_5d)
+gpu_data = cl_array.to_device(queue, ndv.data.nd_sine_wave())
 
-
-if __name__ == "__main__":
-    from qtpy import QtWidgets
-
-    from ndv import NDViewer
-
-    qapp = QtWidgets.QApplication([])
-    v = NDViewer(cl_sine_wave, channel_axis=1)
-    v.show()
-    qapp.exec()
+ndv.imshow(gpu_data)

examples/sparse_arr.py

@@ -7,6 +7,8 @@
 
 import numpy as np
 
+import ndv
+
 shape = (256, 4, 512, 512)
 N = int(np.prod(shape) * 0.001)
 coords = np.random.randint(low=0, high=shape, size=(N, len(shape))).T
@@ -16,13 +18,4 @@
 # Create the sparse array from the coordinates and data
 sparse_array = sparse.COO(coords, data, shape=shape)
 
-
-if __name__ == "__main__":
-    from qtpy import QtWidgets
-
-    from ndv import NDViewer
-
-    qapp = QtWidgets.QApplication([])
-    v = NDViewer(sparse_array, channel_axis=1)
-    v.show()
-    qapp.exec()
+ndv.imshow(sparse_array)

examples/tensorstore_arr.py

@@ -1,23 +1,28 @@
 from __future__ import annotations
 
-import numpy as np
-import tensorstore as ts
-from qtpy import QtWidgets
+try:
+    import tensorstore as ts
+except ImportError:
+    raise ImportError("Please install tensorstore to run this example")
 
-from ndv import NDViewer
 
-shape = (10, 4, 3, 512, 512)
+import ndv
+
+data = ndv.data.cells3d()
+
 ts_array = ts.open(
-    {"driver": "zarr", "kvstore": {"driver": "memory"}},
+    {
+        "driver": "zarr",
+        "kvstore": {"driver": "memory"},
+        "transform": {
+            # tensorstore supports labeled dimensions
+            "input_labels": ["z", "c", "y", "x"],
+        },
+    },
     create=True,
-    shape=shape,
-    dtype=ts.uint8,
+    shape=data.shape,
+    dtype=data.dtype,
 ).result()
-ts_array[:] = np.random.randint(0, 255, size=shape, dtype=np.uint8)
-ts_array = ts_array[ts.d[:].label["t", "c", "z", "y", "x"]]
+ts_array[:] = ndv.data.cells3d()
 
-if __name__ == "__main__":
-    qapp = QtWidgets.QApplication([])
-    v = NDViewer(ts_array)
-    v.show()
-    qapp.exec()
+ndv.imshow(ts_array)

examples/torch_arr.py

@@ -4,17 +4,18 @@
     import torch
 except ImportError:
     raise ImportError("Please install torch to run this example")
-from numpy_arr import generate_5d_sine_wave
-from qtpy import QtWidgets
 
-from ndv import NDViewer
+import warnings
+
+import ndv
+
+warnings.filterwarnings("ignore", "Named tensors")  # Named tensors are experimental
 
 # Example usage
-array_shape = (10, 3, 5, 512, 512)  # Specify the desired dimensions
-sine_wave_5d = torch.asarray(generate_5d_sine_wave(array_shape))
+try:
+    torch_data = torch.tensor(ndv.data.nd_sine_wave(), names=("t", "c", "z", "y", "x"))
+except TypeError:
+    print("Named tensors are not supported in your version of PyTorch")
+    torch_data = torch.tensor(ndv.data.nd_sine_wave())
 
-if __name__ == "__main__":
-    qapp = QtWidgets.QApplication([])
-    v = NDViewer(sine_wave_5d, channel_axis=1)
-    v.show()
-    qapp.exec()
+ndv.imshow(torch_data)

examples/xarray_arr.py

@@ -1,14 +1,10 @@
 from __future__ import annotations
 
-import xarray as xr
-from qtpy import QtWidgets
-
-from ndv import NDViewer
+try:
+    import xarray as xr
+except ImportError:
+    raise ImportError("Please install xarray to run this example")
+import ndv
 
 da = xr.tutorial.open_dataset("air_temperature").air
-
-if __name__ == "__main__":
-    qapp = QtWidgets.QApplication([])
-    v = NDViewer(da, colormaps=["thermal"], channel_mode="composite")
-    v.show()
-    qapp.exec()
+ndv.imshow(da, cmap="thermal")

examples/zarr_arr.py

@@ -1,16 +1,15 @@
 from __future__ import annotations
 
-import zarr
-import zarr.storage
-from qtpy import QtWidgets
+import ndv
+
+try:
+    import zarr
+    import zarr.storage
+except ImportError:
+    raise ImportError("Please `pip install zarr aiohttp` to run this example")
 
-from ndv import NDViewer
 
 URL = "https://s3.embl.de/i2k-2020/ngff-example-data/v0.4/tczyx.ome.zarr"
 zarr_arr = zarr.open(URL, mode="r")
 
-if __name__ == "__main__":
-    qapp = QtWidgets.QApplication([])
-    v = NDViewer(zarr_arr["s0"])
-    v.show()
-    qapp.exec()
+ndv.imshow(zarr_arr["s0"])

pyproject.toml

@@ -106,6 +106,7 @@ pretty = true
 minversion = "7.0"
 testpaths = ["tests"]
 filterwarnings = ["error"]
+markers = ["allow_leaks: mark test to allow widget leaks"]
 
 # https://coverage.readthedocs.io/
 [tool.coverage.report]

src/ndv/__init__.py

@@ -11,10 +11,12 @@
 
 from typing import TYPE_CHECKING
 
+from . import data
+from .util import imshow
 from .viewer._data_wrapper import DataWrapper
-from .viewer._stack_viewer import NDViewer
+from .viewer._viewer import NDViewer
 
-__all__ = ["NDViewer", "DataWrapper"]
+__all__ = ["NDViewer", "DataWrapper", "imshow", "data"]
 
 
 if TYPE_CHECKING: