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115 lines
3.9 KiB
ReStructuredText
115 lines
3.9 KiB
ReStructuredText
Plotting
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========
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Rasterio reads raster data into numpy arrays so plotting a single band as
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two dimensional data can be accomplished directly with ``pyplot``.
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.. code-block:: python
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>>> import rasterio
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>>> from matplotlib import pyplot
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>>> src = rasterio.open("tests/data/RGB.byte.tif")
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>>> pyplot.imshow(src.read(1), cmap='pink')
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<matplotlib.image.AxesImage object at 0x...>
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>>> pyplot.show()
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.. image:: http://farm6.staticflickr.com/5032/13938576006_b99b23271b_o_d.png
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Rasterio also provides :func:`rasterio.plot.show` to perform common tasks such as
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displaying multi-band images as RGB and labeling the axes with proper geo-referenced extents.
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The first argument to :func:`~rasterio.plot.show` represent the data source to be plotted. This can be one of
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* A dataset object opened in 'r' mode
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* A single band of a source, represented by a ``(src, band_index)`` tuple
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* A :class:`numpy.ndarray`, 2D or 3D. If the array is 3D, ensure that it is in rasterio band order.
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Thus the following operations for 3-band RGB data are equivalent. Note that when passing arrays,
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you can pass in a transform in order to get extent labels.
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.. code-block:: python
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>>> from rasterio.plot import show
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>>> show(src)
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<matplotlib.axes._subplots.AxesSubplot object at 0x...>
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>>> show(src.read(), transform=src.transform)
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<matplotlib.axes._subplots.AxesSubplot object at 0x...>
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.. image:: ../img/rgb.jpg
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and similarly for single band plots. Note that you can pass in ``cmap`` to
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specify a matplotlib color ramp. Any kwargs passed to :func:`~rasterio.plot.show` will be passed
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through to the underlying pyplot functions.
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.. code-block:: python
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>>> show((src, 2), cmap='viridis')
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<matplotlib.axes._subplots.AxesSubplot object at 0x...>
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>>> show(src.read(2), transform=src.transform, cmap='viridis')
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<matplotlib.axes._subplots.AxesSubplot object at 0x...>
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.. image:: ../img/singleband.jpg
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You can create a figure with multiple subplots by passing the ``show(..., ax=ax1)``
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argument. Also note that this example demonstrates setting the overall figure size
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and sets a title for each subplot.
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.. code-block:: python
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>>> fig, (axr, axg, axb) = pyplot.subplots(1,3, figsize=(21,7))
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>>> show((src, 1), ax=axr, cmap='Reds', title='red channel')
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<matplotlib.axes._subplots.AxesSubplot object at 0x...>
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>>> show((src, 2), ax=axg, cmap='Greens', title='green channel')
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<matplotlib.axes._subplots.AxesSubplot object at 0x...>
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>>> show((src, 3), ax=axb, cmap='Blues', title='blue channel')
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<matplotlib.axes._subplots.AxesSubplot object at 0x...>
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>>> pyplot.show()
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.. image:: ../img/subplots.jpg
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For single-band rasters, there is also an option to generate contours.
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.. code-block:: python
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>>> fig, ax = pyplot.subplots(1, figsize=(12, 12))
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>>> show((src, 1), cmap='Greys_r', interpolation='none', ax=ax)
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<matplotlib.axes._subplots.AxesSubplot object at 0x...>
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>>> show((src, 1), contour=True, ax=ax)
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<matplotlib.axes._subplots.AxesSubplot object at 0x...>
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>>> pyplot.show()
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.. image:: ../img/contours.jpg
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Rasterio also provides a :func:`~rasterio.plot.show_hist` function for generating histograms of
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single or multiband rasters:
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.. code-block:: python
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>>> from rasterio.plot import show_hist
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>>> show_hist(
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... src, bins=50, lw=0.0, stacked=False, alpha=0.3,
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... histtype='stepfilled', title="Histogram")
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.. image:: ../img/hist.jpg
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The :func:`~rasterio.plot.show_hist` function also takes an ``ax`` argument to allow subplot configurations
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.. code-block:: python
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>>> fig, (axrgb, axhist) = pyplot.subplots(1, 2, figsize=(14,7))
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>>> show(src, ax=axrgb)
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<matplotlib.axes._subplots.AxesSubplot object at 0x...>
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>>> show_hist(src, bins=50, histtype='stepfilled',
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... lw=0.0, stacked=False, alpha=0.3, ax=axhist)
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>>> pyplot.show()
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.. image:: ../img/rgb_hist.jpg
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