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This includes a utility function to aid IO with bands based on data type, and a wrapper class for working with in-memory GDAL raster datasets and bands for use with GDAL functions in cython. Includes additional validation logic for data type in rasterize. Includes additional test cases for data type support.
140 lines
4.8 KiB
Python
140 lines
4.8 KiB
Python
import logging
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import sys
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import numpy
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import pytest
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import rasterio
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from rasterio.features import shapes, rasterize
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logging.basicConfig(stream=sys.stderr, level=logging.DEBUG)
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def test_rasterize_geometries():
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rows = cols = 10
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transform = (1.0, 0.0, 0.0, 0.0, 1.0, 0.0)
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geometry = {
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'type':'Polygon',
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'coordinates':[[(2, 2), (2, 4.25), (4.25, 4.25), (4.25, 2), (2, 2)]]
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}
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with rasterio.drivers():
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# we expect a subset of the pixels using default mode
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result = rasterize([geometry], out_shape=(rows, cols))
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truth = numpy.zeros((rows, cols))
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truth[2:4, 2:4] = 1
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assert numpy.array_equal(result, truth)
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# we expect all touched pixels
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result = rasterize(
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[geometry], out_shape=(rows, cols), all_touched=True)
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truth = numpy.zeros((rows, cols))
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truth[2:5, 2:5] = 1
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assert numpy.array_equal(result, truth)
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# we expect the pixel value to match the one we pass in
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value = 5
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result = rasterize([(geometry, value)], out_shape=(rows, cols))
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truth = numpy.zeros((rows, cols))
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truth[2:4, 2:4] = value
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assert numpy.array_equal(result, truth)
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# Check the fill and default transform.
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# we expect the pixel value to match the one we pass in
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value = 5
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result = rasterize(
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[(geometry, value)],
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out_shape=(rows, cols),
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fill=1 )
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truth = numpy.ones((rows, cols))
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truth[2:4, 2:4] = value
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assert numpy.array_equal(result, truth)
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def test_rasterize_dtype():
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rows = cols = 10
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transform = (1.0, 0.0, 0.0, 0.0, 1.0, 0.0)
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geometry = {
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'type':'Polygon',
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'coordinates':[[(2, 2), (2, 4.25), (4.25, 4.25), (4.25, 2), (2, 2)]]
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}
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with rasterio.drivers():
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# Supported types should all work properly
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supported_types = (('int16', -32768), ('int32', -2147483648),
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('uint8', 255), ('uint16', 65535), ('uint32', 4294967295),
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('float32', 1.434532), ('float64', -98332.133422114))
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for dtype, default_value in supported_types:
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truth = numpy.zeros((rows, cols), dtype=dtype)
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truth[2:4, 2:4] = default_value
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result = rasterize(
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[geometry],
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out_shape=(rows, cols),
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default_value=default_value,
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dtype=dtype
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)
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assert numpy.array_equal(result, truth)
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assert numpy.dtype(result.dtype) == numpy.dtype(truth.dtype)
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result = rasterize(
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[(geometry, default_value)],
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out_shape=(rows, cols)
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)
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if numpy.dtype(dtype).kind == 'f':
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assert numpy.allclose(result, truth)
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else:
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assert numpy.array_equal(result, truth)
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# Since dtype is auto-detected, it may not match due to upcasting
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# Unsupported types should all raise exceptions
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unsupported_types = (('int8', -127), ('int64', 20439845334323),
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('float16', -9343.232))
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for dtype, default_value in unsupported_types:
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with pytest.raises(ValueError):
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rasterize(
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[geometry],
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out_shape=(rows, cols),
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default_value=default_value,
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dtype=dtype
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)
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with pytest.raises(ValueError):
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rasterize(
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[(geometry, default_value)],
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out_shape=(rows, cols),
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dtype=dtype
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)
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# Mismatched values and dtypes should raise exceptions
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mismatched_types = (('uint8', 3.2423), ('uint8', -2147483648))
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for dtype, default_value in mismatched_types:
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print("Testing mismatched dtype {0} with value {1}".format(dtype, default_value))
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with pytest.raises(ValueError):
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rasterize(
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[geometry],
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out_shape=(rows, cols),
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default_value=default_value,
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dtype=dtype
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)
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with pytest.raises(ValueError):
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rasterize(
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[(geometry, default_value)],
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out_shape=(rows, cols),
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dtype=dtype
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)
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def test_rasterize_geometries_symmetric():
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"""Make sure that rasterize is symmetric with shapes"""
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rows = cols = 10
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transform = (1.0, 0.0, 0.0, 0.0, -1.0, 0.0)
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truth = numpy.zeros((rows, cols), dtype=rasterio.ubyte)
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truth[2:5, 2:5] = 1
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with rasterio.drivers():
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s = shapes(truth, transform=transform)
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result = rasterize(s, out_shape=(rows, cols), transform=transform)
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assert numpy.array_equal(result, truth)
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