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Handle upcoming numpy deprecations #1026

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Jan 16, 2023
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Handle upcoming numpy deprecations #1026

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1 change: 1 addition & 0 deletions CHANGELOG.md
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Original file line number Diff line number Diff line change
Expand Up @@ -6,6 +6,7 @@
- Better parse svs pixel size in tiff and tifffile sources ([#1021](../../pull/1021))
- Add geojson to known mime types ([#1022](../../pull/1022))
- Handle upcoming matplotlib deprecations ([#1025](../../pull/1025))
- Handle upcoming numpy deprecations ([#1026](../../pull/1026))

## 1.19.1

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3 changes: 2 additions & 1 deletion sources/vips/large_image_source_vips/__init__.py
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Expand Up @@ -338,7 +338,8 @@ def addTile(self, tile, x=0, y=0, mask=None, interpretation=None):
newarr = numpy.zeros(
(tile.shape[0], tile.shape[1], tile.shape[2] + 1), dtype=tile.dtype)
newarr[:, :, :tile.shape[2]] = tile
newarr[:, :, -1] = 255
newarr[:, :, -1] = min(numpy.iinfo(
tile.dtype).max, 255) if tile.dtype.kind in 'iu' else 255
tile = newarr
if mask is not None:
if len(mask.shape) == 3:
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28 changes: 14 additions & 14 deletions test/test_source_hdf5.py
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Expand Up @@ -27,25 +27,25 @@
}

possible_data_ranges = [
[0, 1, float],
[0, 2**8, numpy.uint8],
[0, 2**16, numpy.uint16],
[0, 2**32, numpy.uint32],
[-2**7, 2**7, numpy.int8],
[-2**15, 2**15, numpy.int16],
[-2**31, 2**31, numpy.int32],
[-1, 1, float]
[0, 1, 2, float],
[0, 2**8, -1, numpy.uint8],
[0, 2**16, -2, numpy.uint16],
[0, 2**32, -4, numpy.uint32],
[-2**7, 2**7, -1, numpy.int8],
[-2**15, 2**15, -2, numpy.int16],
[-2**31, 2**31, -4, numpy.int32],
[-1, 1, 2, float]
]

max_tile_size = 100
tile_overlap_ratio = 0.5


# https://stackoverflow.com/questions/18915378/rounding-to-significant-figures-in-numpy
def signif(x):
def signif(x, minval, maxval, digits):
if x == 0:
return 0
return round(x, -2)
return max(min(round(x, digits), max(1, maxval - 1)), minval)


def get_dims(x, y, s, max=False):
Expand All @@ -72,7 +72,7 @@ def random_tile(data_range):
tile = numpy.random.rand(*tile_shape)
tile *= (data_range[1] - data_range[0])
tile += data_range[0]
tile = tile.astype(data_range[2]) # apply dtype
tile = tile.astype(data_range[3]) # apply dtype
mask = numpy.random.randint(2, size=tile_shape[:-1])
return (tile, mask)

Expand Down Expand Up @@ -154,10 +154,10 @@ def testImageGeneration(data_range):
# trim unused space from expected
expected = expected[:max_x, :max_y]

# round to -2 precision
# round to specified precision
precision_vector = numpy.vectorize(signif)
expected = precision_vector(expected)
result = precision_vector(result)
expected = precision_vector(expected, data_range[0], data_range[1], data_range[2])
result = precision_vector(result, data_range[0], data_range[1], data_range[2])

# ignore alpha values for now
expected = expected.take(indices=range(0, -1), axis=-1)
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