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auto-replace buffers (#974)
* remove isolated_buffer * remove isolated_buffer from mixin * basics works for positions data * replaceable buffers for all positions related features * image data buffer can change * resizeable buffers for volume * black * buffer resize condition checked only if new value is an array * gc for buffer managers * uniform colors WIP * switching color modes works! * typo * balck * update tests for color_mode * update examples * backend tests passing * default for all uniforms is True * update examples * forgot * update test * example tests passing * dereferencing test and fixes * simplify texture array tests a bit * image replace buffer tests pass yay * forgot a file * comments, check image graphic * add image reshaping example * add buffer replace imgui thing for manual testing * black * dont call wgpu_obj.destroy(), seems to work and clear VRAM with normal dereferencing * slower changes * update * update example * fixes and tweaks for test * remove unecessary stuff * update * docstrings * fix example * update example * update example * update docs
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‎docs/source/api/graphics/LineGraphic.rst‎

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LineGraphic.axes
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LineGraphic.block_events
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LineGraphic.cmap
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LineGraphic.color_mode
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LineGraphic.colors
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LineGraphic.data
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LineGraphic.deleted

‎docs/source/api/graphics/ScatterGraphic.rst‎

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ScatterGraphic.axes
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ScatterGraphic.block_events
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ScatterGraphic.cmap
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ScatterGraphic.color_mode
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ScatterGraphic.colors
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ScatterGraphic.data
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ScatterGraphic.deleted

‎examples/events/cmap_event.py‎

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xs = np.linspace(0, 4 * np.pi, 100)
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ys = np.sin(xs)
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figure["sine"].add_line(np.column_stack([xs, ys]))
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figure["sine"].add_line(np.column_stack([xs, ys]), color_mode="vertex")
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# make a 2D gaussian cloud
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cloud_data = np.random.normal(0, scale=3, size=1000).reshape(500, 2)

‎examples/gridplot/multigraphic_gridplot.py‎

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@@ -106,7 +106,7 @@ def make_circle(center, radius: float, n_points: int = 75) -> np.ndarray:
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gaussian_cloud2 = np.random.multivariate_normal(mean, covariance, n_points)
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# add the scatter graphics to the figure
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figure["scatter"].add_scatter(data=gaussian_cloud, sizes=2, cmap="jet")
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figure["scatter"].add_scatter(data=gaussian_cloud, sizes=2, cmap="jet", color_mode="vertex")
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figure["scatter"].add_scatter(data=gaussian_cloud2, colors="r", sizes=2)
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figure.show()

‎examples/guis/imgui_basic.py‎

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figure = fpl.Figure(size=(700, 560))
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# make some scatter points at every 10th point
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figure[0, 0].add_scatter(data[::10], colors="cyan", sizes=15, name="sine-scatter", uniform_color=True)
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figure[0, 0].add_scatter(data[::10], colors="cyan", sizes=15, name="sine-scatter")
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# place a line above the scatter
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figure[0, 0].add_line(data, thickness=3, colors="r", name="sine-wave", uniform_color=True)
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figure[0, 0].add_line(data, thickness=3, colors="r", name="sine-wave")
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class ImguiExample(EdgeWindow):

‎examples/image/image_reshaping.py‎

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"""
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Image reshaping
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===============
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An example that shows replacement of the image data with new data of a different shape. Under the hood, this creates a
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new buffer and a new array of Textures on the GPU that replace the older Textures. Creating a new buffer and textures
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has a performance cost, so you should do this only if you need to or if the performance drawback is not a concern for
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your use case.
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Note that the vmin-vmax is reset when you replace the buffers.
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"""
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# test_example = false
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# sphinx_gallery_pygfx_docs = 'animate'
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import numpy as np
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import fastplotlib as fpl
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# create some data, diagonal sinusoidal bands
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xs = np.linspace(0, 2300, 2300, dtype=np.float16)
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full_data = np.vstack([np.cos(np.sqrt(xs + (np.pi / 2) * i)) * i for i in range(2_300)])
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figure = fpl.Figure()
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image = figure[0, 0].add_image(full_data)
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figure.show()
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i, j = 1, 1
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def update():
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global i, j
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# set the new image data as a subset of the full data
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row = np.abs(np.sin(i)) * 2300
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col = np.abs(np.cos(i)) * 2300
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image.data = full_data[: int(row), : int(col)]
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i += 0.01
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j += 0.01
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figure.add_animations(update)
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# NOTE: fpl.loop.run() should not be used for interactive sessions
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# See the "JupyterLab and IPython" section in the user guide
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if __name__ == "__main__":
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print(__doc__)
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fpl.loop.run()

‎examples/line/line_cmap.py‎

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data=sine_data,
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thickness=10,
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cmap="plasma",
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cmap_transform=sine_data[:, 1]
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cmap_transform=sine_data[:, 1],
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)
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# qualitative colormaps, useful for cluster labels or other types of categorical labels
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data=cosine_data,
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thickness=10,
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cmap="tab10",
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cmap_transform=labels
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cmap_transform=labels,
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)
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figure.show()

‎examples/line/line_cmap_more.py‎

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# set colormap by mapping data using a transform
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# here we map the color using the y-values of the sine data
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# i.e., the color is a function of sine(x)
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line2 = figure[0, 0].add_line(sine, thickness=10, cmap="jet", cmap_transform=sine[:, 1], offset=(0, 4, 0))
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line2 = figure[0, 0].add_line(
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sine,
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thickness=10,
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cmap="jet",
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cmap_transform=sine[:, 1],
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offset=(0, 4, 0),
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)
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# make a line and change the cmap afterward, here we are using the cosine instead fot the transform
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line3 = figure[0, 0].add_line(sine, thickness=10, cmap="jet", cmap_transform=cosine[:, 1], offset=(0, 6, 0))
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line3 = figure[0, 0].add_line(
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sine,
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thickness=10,
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cmap="jet",
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cmap_transform=cosine[:, 1],
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offset=(0, 6, 0)
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)
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# change the cmap
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line3.cmap = "bwr"
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# use quantitative colormaps with categorical cmap_transforms
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labels = [0] * 25 + [1] * 5 + [2] * 50 + [3] * 20
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line4 = figure[0, 0].add_line(sine, thickness=10, cmap="tab10", cmap_transform=labels, offset=(0, 8, 0))
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line4 = figure[0, 0].add_line(
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sine,
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thickness=10,
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cmap="tab10",
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cmap_transform=labels,
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offset=(0, 8, 0),
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)
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# some text labels
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for i in range(5):

‎examples/line/line_colorslice.py‎

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sine = figure[0, 0].add_line(
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data=sine_data,
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thickness=5,
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colors="magenta"
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colors="magenta",
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color_mode="vertex", # initialize with same color across vertices, but we will change the per-vertex colors later
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)
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# you can also use colormaps for lines!
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data=zeros_data,
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thickness=8,
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colors="w",
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color_mode="vertex", # initialize with same color across vertices, but we will change the per-vertex colors later
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offset=(0, 10, 0)
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)
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‎examples/line_collection/line_collection_slicing.py‎

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multi_data,
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thickness=[2, 10, 2, 5, 5, 5, 8, 8, 8, 9, 3, 3, 3, 4, 4],
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separation=4,
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color_mode="vertex", # this will allow us to set per-vertex colors on each line
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metadatas=list(range(15)), # some metadata
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names=list("abcdefghijklmno"), # unique name for each line
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)

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