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Merge pull request #16781 from anntzer/imtut
Update image tutorial wrt. removal of native png handler.
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tutorials/introductory/images.py

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cells below those that create the plot will change the plot - it is a
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live object in memory.
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This tutorial will use matplotlib's imperative-style plotting
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This tutorial will use Matplotlib's imperative-style plotting
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interface, pyplot. This interface maintains global state, and is very
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useful for quickly and easily experimenting with various plot
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settings. The alternative is the object-oriented interface, which is also
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# .. _importing_data:
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#
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# Importing image data into Numpy arrays
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# ===============================================
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# ======================================
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#
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# Loading image data is supported by the `Pillow
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# <https://pillow.readthedocs.io/en/latest/>`_ library. Natively, Matplotlib
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# only supports PNG images. The commands shown below fall back on Pillow if
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# the native read fails.
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# Matplotlib relies on the Pillow_ library to load image data.
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#
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# The image used in this example is a PNG file, but keep that Pillow
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# requirement in mind for your own data.
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# .. _Pillow: https://pillow.readthedocs.io/en/latest/
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#
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# Here's the image we're going to play with:
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#
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# similar. An RGBA (where A is alpha, or transparency), has 4 values
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# per inner list, and a simple luminance image just has one value (and
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# is thus only a 2-D array, not a 3-D array). For RGB and RGBA images,
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# matplotlib supports float32 and uint8 data types. For grayscale,
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# matplotlib supports only float32. If your array data does not meet
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# Matplotlib supports float32 and uint8 data types. For grayscale,
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# Matplotlib supports only float32. If your array data does not meet
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# one of these descriptions, you need to rescale it.
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#
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# .. _plotting_data:

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