add padding function to imgScalePadding()
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@@ -1,15 +0,0 @@
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from __future__ import annotations
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from typing import List, Type
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import numpy
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# list of numarray data types
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integer_types: List[Type] = [
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numpy.int8, numpy.uint8, numpy.int16, numpy.uint16,
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numpy.int32, numpy.uint32, numpy.int64, numpy.uint64]
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float_types: List[Type] = [numpy.float32, numpy.float64]
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complex_types: List[Type] = [numpy.complex64, numpy.complex128]
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types: List[Type] = integer_types + float_types
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1 1 1 1 1 1 1
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1 1 1 1 1 1 1
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1 1 1 1 1 1 1
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1 1 1 1 1 1 1
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1 1 1 1 1 1 1
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1 1 1 1 1 1 1
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1 1 1 1 1 1 1
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1 1 1 0 1 1 1
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1 1 0 0 0 1 1
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1 0 1 0 1 0 1
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0 0 0 1 0 0 0
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1 0 1 0 1 0 1
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1 1 0 0 0 1 1
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1 1 1 0 1 1 1
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1 0 1 1 1 0 1
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0 0 0 1 0 0 0
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1 0 0 1 0 0 1
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1 1 1 1 1 1 1
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1 0 0 1 0 0 1
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0 0 0 1 0 0 0
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1 0 1 1 1 0 1
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@@ -1,294 +0,0 @@
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1 1 1 1 1 1 1
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1 1 1 1 1 1 1
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1 1 1 1 1 1 1
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1 1 1 1 1 1 1
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1 1 1 1 1 1 1
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1 1 1 1 1 1 1
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1 1 1 1 1 1 1
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1 1 1 1 1 1 1
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1 1 1 1 1 1 1
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1 1 1 1 1 1 1
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1 1 1 1 1 1 1
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1 1 1 1 1 1 1
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1 1 1 1 1 1 1
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1 1 1 1 1 1 1
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1 1 1 1 1 1 1
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2 2 2 2 2 2 2
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3 3 3 3 3 3 3
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4 4 4 4 4 4 4
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5 5 5 5 5 5 5
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6 6 6 6 6 6 6
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7 7 7 7 7 7 7
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1 1 1 1 1 1 1
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1 1 1 1 1 1 1
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1 1 1 1 1 1 1
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1 1 1 1 1 1 1
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1 1 1 1 1 1 1
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1 1 1 1 1 1 1
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1 1 1 1 1 1 1
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1 2 3 4 5 6 7
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1 1 1 1 1 1 1
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1 1 1 1 1 1 1
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1 1 1 1 1 1 1
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1 1 1 1 1 1 1
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1 1 1 1 1 1 1
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1 1 1 1 1 1 1
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1 1 1 1 1 1 1
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1 1 1 1 1 1 1
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1 1 1 1 1 1 1
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1 1 1 1 1 1 1
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1 1 1 1 1 1 1
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1 1 1 1 1 1 1
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1 1 1 1 1 1 1
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1 1 1 1 1 1 1
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1 2 3 4 5 6 7
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8 1 2 3 4 5 6
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9 8 1 2 3 4 5
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10 9 8 1 2 3 4
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11 10 9 8 1 2 3
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12 11 10 9 8 1 2
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13 12 11 10 9 8 1
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1 2 3 4 5 6 7
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1 2 3 4 5 6 7
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1 2 3 4 5 6 7
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1 2 3 4 5 6 7
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1 2 3 4 5 6 7
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1 2 3 4 5 6 7
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1 2 3 4 5 6 7
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1 1 1 1 1 1 1
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1 1 1 1 1 1 1
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1 1 1 1 1 1 1
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1 1 1 1 1 1 1
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1 1 1 1 1 1 1
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1 1 1 1 1 1 1
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1 1 1 1 1 1 1
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1 1 1 1 1 1 1
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1 1 1 1 1 1 1
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1 1 1 1 1 1 1
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1 1 1 1 1 1 1
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1 1 1 1 1 1 1
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1 1 1 1 1 1 1
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1 1 1 1 1 1 1
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1 2 1 2 1 2 1
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2 1 2 1 2 1 2
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1 2 1 2 1 2 1
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2 1 2 1 2 1 2
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1 2 1 2 1 2 1
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2 1 2 1 2 1 2
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1 2 1 2 1 2 1
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1 2 3 4 5 6 7
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2 3 4 5 6 7 8
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3 4 5 6 7 8 9
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4 5 6 7 8 9 10
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5 6 7 8 9 10 11
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6 7 8 9 10 11 12
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7 8 9 10 11 12 13
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1 1 1 1 1 1 1
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1 1 1 1 1 1 1
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1 1 1 1 1 1 1
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1 1 1 1 1 1 1
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1 1 1 1 1 1 1
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1 1 1 1 1 1 1
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1 1 1 1 1 1 1
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1 1 1 0 2 2 2
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1 1 0 0 0 2 2
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1 0 3 0 2 0 4
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0 0 0 2 0 0 0
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5 0 2 0 6 0 7
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2 2 0 0 0 7 7
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2 2 2 0 7 7 7
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1 1 1 0 2 2 2
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1 1 0 0 0 2 2
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3 0 1 0 4 0 2
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0 0 0 1 0 0 0
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5 0 6 0 1 0 7
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5 5 0 0 0 1 1
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5 5 5 0 1 1 1
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1 1 1 0 2 2 2
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3 3 0 0 0 4 4
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5 0 6 0 7 0 8
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0 0 0 9 0 0 0
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10 0 11 0 12 0 13
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14 14 0 0 0 15 15
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16 16 16 0 17 17 17
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1 1 1 0 2 3 3
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1 1 0 0 0 3 3
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1 0 4 0 3 0 3
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0 0 0 3 0 0 0
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3 0 3 0 5 0 6
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3 3 0 0 0 6 6
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3 3 7 0 6 6 6
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1 2 3 0 4 5 6
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7 8 0 0 0 9 10
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11 0 12 0 13 0 14
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0 0 0 15 0 0 0
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16 0 17 0 18 0 19
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20 21 0 0 0 22 23
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24 25 26 0 27 28 29
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1 1 1 0 2 2 2
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1 1 0 0 0 2 2
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1 0 3 0 2 0 2
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0 0 0 2 0 0 0
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2 0 2 0 4 0 5
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2 2 0 0 0 5 5
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2 2 2 0 5 5 5
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1 1 1 0 2 2 2
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1 1 0 0 0 2 2
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1 0 3 0 4 0 2
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0 0 0 5 0 0 0
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6 0 7 0 8 0 9
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6 6 0 0 0 9 9
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6 6 6 0 9 9 9
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1 2 3 0 4 5 6
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7 1 0 0 0 4 5
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8 0 1 0 9 0 4
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0 0 0 1 0 0 0
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10 0 11 0 1 0 12
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13 10 0 0 0 1 14
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15 13 10 0 16 17 1
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1 2 3 0 4 5 6
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1 2 0 0 0 5 6
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1 0 7 0 8 0 6
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0 0 0 9 0 0 0
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10 14 0 0 0 15 13
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10 14 16 0 17 15 13
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1 1 1 0 1 1 1
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1 1 0 0 0 1 1
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1 0 1 0 1 0 1
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0 0 0 1 0 0 0
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1 0 1 0 1 0 1
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1 1 0 0 0 1 1
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1 1 1 0 1 1 1
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1 1 2 0 3 3 3
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1 1 0 0 0 3 3
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1 0 1 0 4 0 3
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0 0 0 1 0 0 0
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5 0 6 0 1 0 1
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5 5 0 0 0 1 1
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5 5 5 0 7 1 1
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1 2 1 0 1 3 1
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2 1 0 0 0 1 3
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1 0 1 0 1 0 1
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0 0 0 1 0 0 0
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1 0 1 0 1 0 1
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4 1 0 0 0 1 5
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1 4 1 0 1 5 1
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1 2 3 0 4 5 6
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2 3 0 0 0 6 7
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3 0 8 0 6 0 9
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0 0 0 6 0 0 0
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13 6 0 0 0 12 14
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1 1 1 0 2 2 2
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1 1 0 0 0 2 2
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1 0 1 0 3 0 2
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0 0 0 1 0 0 0
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4 0 5 0 1 0 1
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4 4 0 0 0 1 1
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4 4 4 0 1 1 1
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1 0 2 2 2 0 3
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0 0 0 2 0 0 0
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5 0 0 5 0 0 6
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0 0 0 7 0 0 0
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1 0 2 2 2 0 3
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0 0 0 2 0 0 0
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0 0 0 7 0 0 0
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1 0 2 2 2 0 3
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0 0 0 4 0 0 0
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9 0 0 10 0 0 11
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0 0 0 12 0 0 0
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13 0 14 14 14 0 15
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1 0 2 3 3 0 4
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0 0 0 3 0 0 0
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5 0 0 3 0 0 6
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5 5 3 3 3 6 6
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5 0 0 3 0 0 6
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0 0 0 3 0 0 0
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1 0 2 3 4 0 5
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0 0 0 6 0 0 0
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17 0 0 18 0 0 19
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0 0 0 20 0 0 0
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21 0 22 23 24 0 25
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1 0 2 2 2 0 3
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0 0 0 2 0 0 0
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2 0 0 2 0 0 2
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2 2 2 2 2 2 2
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2 0 0 2 0 0 2
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0 0 0 2 0 0 0
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1 0 2 2 2 0 3
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0 0 0 2 0 0 0
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2 0 0 2 0 0 2
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2 2 2 2 2 2 2
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2 0 0 2 0 0 2
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0 0 0 2 0 0 0
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4 0 2 2 2 0 5
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1 0 2 3 4 0 5
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0 0 0 2 0 0 0
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9 6 10 11 7 12 13
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14 0 0 10 0 0 12
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0 0 0 15 0 0 0
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1 0 2 3 4 0 5
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0 0 0 3 0 0 0
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6 0 0 3 0 0 7
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6 8 9 3 10 11 7
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6 0 0 3 0 0 7
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0 0 0 3 0 0 0
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1 0 2 2 2 0 3
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0 0 0 2 0 0 0
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2 0 0 2 0 0 2
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2 2 2 2 2 2 2
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2 0 0 2 0 0 2
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0 0 0 2 0 0 0
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4 0 2 2 2 0 5
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1 0 2 2 3 0 4
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0 0 0 2 0 0 0
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5 0 0 2 0 0 6
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5 0 0 2 0 0 6
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0 0 0 2 0 0 0
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1 0 2 3 2 0 4
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0 0 0 2 0 0 0
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5 0 0 6 0 0 7
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0 0 0 11 0 0 0
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0 0 0 4 0 0 0
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10 0 0 12 0 0 14
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0 0 0 15 0 0 0
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1 0 2 2 2 0 3
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0 0 0 2 0 0 0
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2 0 0 2 0 0 2
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2 2 2 2 2 2 2
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2 0 0 2 0 0 2
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@@ -1,42 +0,0 @@
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0 0 1
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1 1 1
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1 0 0
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1 0 0
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1 1 1
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0 0 0
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0 1 0
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1 0 0
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0 1 0
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0 1 1
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1 0 1
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0 1 0
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1 0 1
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0 0 1
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0 1 0
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1 0 0
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1 1 0
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1 1 1
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0 1 1
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Before Width: | Height: | Size: 2.1 KiB |
@@ -1,94 +0,0 @@
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import numpy as np
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from numpy.testing import assert_allclose
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from scipy import ndimage
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from scipy.ndimage import _ctest
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from scipy.ndimage import _cytest
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from scipy._lib._ccallback import LowLevelCallable
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FILTER1D_FUNCTIONS = [
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lambda filter_size: _ctest.filter1d(filter_size),
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lambda filter_size: _cytest.filter1d(filter_size, with_signature=False),
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lambda filter_size: LowLevelCallable(_cytest.filter1d(filter_size, with_signature=True)),
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lambda filter_size: LowLevelCallable.from_cython(_cytest, "_filter1d",
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_cytest.filter1d_capsule(filter_size)),
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]
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FILTER2D_FUNCTIONS = [
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lambda weights: _ctest.filter2d(weights),
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lambda weights: _cytest.filter2d(weights, with_signature=False),
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lambda weights: LowLevelCallable(_cytest.filter2d(weights, with_signature=True)),
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lambda weights: LowLevelCallable.from_cython(_cytest, "_filter2d", _cytest.filter2d_capsule(weights)),
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]
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TRANSFORM_FUNCTIONS = [
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lambda shift: _ctest.transform(shift),
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lambda shift: _cytest.transform(shift, with_signature=False),
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lambda shift: LowLevelCallable(_cytest.transform(shift, with_signature=True)),
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lambda shift: LowLevelCallable.from_cython(_cytest, "_transform", _cytest.transform_capsule(shift)),
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]
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def test_generic_filter():
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def filter2d(footprint_elements, weights):
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return (weights*footprint_elements).sum()
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def check(j):
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func = FILTER2D_FUNCTIONS[j]
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im = np.ones((20, 20))
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im[:10,:10] = 0
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footprint = np.array([[0, 1, 0], [1, 1, 1], [0, 1, 0]])
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footprint_size = np.count_nonzero(footprint)
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weights = np.ones(footprint_size)/footprint_size
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res = ndimage.generic_filter(im, func(weights),
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footprint=footprint)
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std = ndimage.generic_filter(im, filter2d, footprint=footprint,
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extra_arguments=(weights,))
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assert_allclose(res, std, err_msg="#{} failed".format(j))
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for j, func in enumerate(FILTER2D_FUNCTIONS):
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check(j)
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def test_generic_filter1d():
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def filter1d(input_line, output_line, filter_size):
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for i in range(output_line.size):
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output_line[i] = 0
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for j in range(filter_size):
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output_line[i] += input_line[i+j]
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output_line /= filter_size
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def check(j):
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func = FILTER1D_FUNCTIONS[j]
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im = np.tile(np.hstack((np.zeros(10), np.ones(10))), (10, 1))
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filter_size = 3
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res = ndimage.generic_filter1d(im, func(filter_size),
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filter_size)
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std = ndimage.generic_filter1d(im, filter1d, filter_size,
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extra_arguments=(filter_size,))
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assert_allclose(res, std, err_msg="#{} failed".format(j))
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for j, func in enumerate(FILTER1D_FUNCTIONS):
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check(j)
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def test_geometric_transform():
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def transform(output_coordinates, shift):
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return output_coordinates[0] - shift, output_coordinates[1] - shift
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def check(j):
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func = TRANSFORM_FUNCTIONS[j]
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im = np.arange(12).reshape(4, 3).astype(np.float64)
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shift = 0.5
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res = ndimage.geometric_transform(im, func(shift))
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std = ndimage.geometric_transform(im, transform, extra_arguments=(shift,))
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assert_allclose(res, std, err_msg="#{} failed".format(j))
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for j, func in enumerate(TRANSFORM_FUNCTIONS):
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check(j)
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@@ -1,66 +0,0 @@
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""" Testing data types for ndimage calls
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"""
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import sys
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import numpy as np
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from numpy.testing import assert_array_almost_equal, assert_
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import pytest
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from scipy import ndimage
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def test_map_coordinates_dts():
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# check that ndimage accepts different data types for interpolation
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data = np.array([[4, 1, 3, 2],
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[7, 6, 8, 5],
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[3, 5, 3, 6]])
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shifted_data = np.array([[0, 0, 0, 0],
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[0, 4, 1, 3],
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[0, 7, 6, 8]])
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idx = np.indices(data.shape)
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dts = (np.uint8, np.uint16, np.uint32, np.uint64,
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np.int8, np.int16, np.int32, np.int64,
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np.intp, np.uintp, np.float32, np.float64)
|
||||
for order in range(0, 6):
|
||||
for data_dt in dts:
|
||||
these_data = data.astype(data_dt)
|
||||
for coord_dt in dts:
|
||||
# affine mapping
|
||||
mat = np.eye(2, dtype=coord_dt)
|
||||
off = np.zeros((2,), dtype=coord_dt)
|
||||
out = ndimage.affine_transform(these_data, mat, off)
|
||||
assert_array_almost_equal(these_data, out)
|
||||
# map coordinates
|
||||
coords_m1 = idx.astype(coord_dt) - 1
|
||||
coords_p10 = idx.astype(coord_dt) + 10
|
||||
out = ndimage.map_coordinates(these_data, coords_m1, order=order)
|
||||
assert_array_almost_equal(out, shifted_data)
|
||||
# check constant fill works
|
||||
out = ndimage.map_coordinates(these_data, coords_p10, order=order)
|
||||
assert_array_almost_equal(out, np.zeros((3,4)))
|
||||
# check shift and zoom
|
||||
out = ndimage.shift(these_data, 1)
|
||||
assert_array_almost_equal(out, shifted_data)
|
||||
out = ndimage.zoom(these_data, 1)
|
||||
assert_array_almost_equal(these_data, out)
|
||||
|
||||
|
||||
@pytest.mark.xfail(not sys.platform == 'darwin', reason="runs only on darwin")
|
||||
def test_uint64_max():
|
||||
# Test interpolation respects uint64 max. Reported to fail at least on
|
||||
# win32 (due to the 32 bit visual C compiler using signed int64 when
|
||||
# converting between uint64 to double) and Debian on s390x.
|
||||
# Interpolation is always done in double precision floating point, so
|
||||
# we use the largest uint64 value for which int(float(big)) still fits
|
||||
# in a uint64.
|
||||
big = 2**64 - 1025
|
||||
arr = np.array([big, big, big], dtype=np.uint64)
|
||||
# Tests geometric transform (map_coordinates, affine_transform)
|
||||
inds = np.indices(arr.shape) - 0.1
|
||||
x = ndimage.map_coordinates(arr, inds)
|
||||
assert_(x[1] == int(float(big)))
|
||||
assert_(x[2] == int(float(big)))
|
||||
# Tests zoom / shift
|
||||
x = ndimage.shift(arr, 0.1)
|
||||
assert_(x[1] == int(float(big)))
|
||||
assert_(x[2] == int(float(big)))
|
||||
File diff suppressed because it is too large
Load Diff
@@ -1,151 +0,0 @@
|
||||
import numpy
|
||||
from numpy import fft
|
||||
from numpy.testing import (assert_almost_equal, assert_array_almost_equal,
|
||||
assert_equal)
|
||||
|
||||
import pytest
|
||||
|
||||
from scipy import ndimage
|
||||
|
||||
|
||||
class TestNdimageFourier:
|
||||
|
||||
@pytest.mark.parametrize('shape', [(32, 16), (31, 15), (1, 10)])
|
||||
@pytest.mark.parametrize('dtype, dec',
|
||||
[(numpy.float32, 6), (numpy.float64, 14)])
|
||||
def test_fourier_gaussian_real01(self, shape, dtype, dec):
|
||||
a = numpy.zeros(shape, dtype)
|
||||
a[0, 0] = 1.0
|
||||
a = fft.rfft(a, shape[0], 0)
|
||||
a = fft.fft(a, shape[1], 1)
|
||||
a = ndimage.fourier_gaussian(a, [5.0, 2.5], shape[0], 0)
|
||||
a = fft.ifft(a, shape[1], 1)
|
||||
a = fft.irfft(a, shape[0], 0)
|
||||
assert_almost_equal(ndimage.sum(a), 1, decimal=dec)
|
||||
|
||||
@pytest.mark.parametrize('shape', [(32, 16), (31, 15)])
|
||||
@pytest.mark.parametrize('dtype, dec',
|
||||
[(numpy.complex64, 6), (numpy.complex128, 14)])
|
||||
def test_fourier_gaussian_complex01(self, shape, dtype, dec):
|
||||
a = numpy.zeros(shape, dtype)
|
||||
a[0, 0] = 1.0
|
||||
a = fft.fft(a, shape[0], 0)
|
||||
a = fft.fft(a, shape[1], 1)
|
||||
a = ndimage.fourier_gaussian(a, [5.0, 2.5], -1, 0)
|
||||
a = fft.ifft(a, shape[1], 1)
|
||||
a = fft.ifft(a, shape[0], 0)
|
||||
assert_almost_equal(ndimage.sum(a.real), 1.0, decimal=dec)
|
||||
|
||||
@pytest.mark.parametrize('shape', [(32, 16), (31, 15), (1, 10)])
|
||||
@pytest.mark.parametrize('dtype, dec',
|
||||
[(numpy.float32, 6), (numpy.float64, 14)])
|
||||
def test_fourier_uniform_real01(self, shape, dtype, dec):
|
||||
a = numpy.zeros(shape, dtype)
|
||||
a[0, 0] = 1.0
|
||||
a = fft.rfft(a, shape[0], 0)
|
||||
a = fft.fft(a, shape[1], 1)
|
||||
a = ndimage.fourier_uniform(a, [5.0, 2.5], shape[0], 0)
|
||||
a = fft.ifft(a, shape[1], 1)
|
||||
a = fft.irfft(a, shape[0], 0)
|
||||
assert_almost_equal(ndimage.sum(a), 1.0, decimal=dec)
|
||||
|
||||
@pytest.mark.parametrize('shape', [(32, 16), (31, 15)])
|
||||
@pytest.mark.parametrize('dtype, dec',
|
||||
[(numpy.complex64, 6), (numpy.complex128, 14)])
|
||||
def test_fourier_uniform_complex01(self, shape, dtype, dec):
|
||||
a = numpy.zeros(shape, dtype)
|
||||
a[0, 0] = 1.0
|
||||
a = fft.fft(a, shape[0], 0)
|
||||
a = fft.fft(a, shape[1], 1)
|
||||
a = ndimage.fourier_uniform(a, [5.0, 2.5], -1, 0)
|
||||
a = fft.ifft(a, shape[1], 1)
|
||||
a = fft.ifft(a, shape[0], 0)
|
||||
assert_almost_equal(ndimage.sum(a.real), 1.0, decimal=dec)
|
||||
|
||||
@pytest.mark.parametrize('shape', [(32, 16), (31, 15)])
|
||||
@pytest.mark.parametrize('dtype, dec',
|
||||
[(numpy.float32, 4), (numpy.float64, 11)])
|
||||
def test_fourier_shift_real01(self, shape, dtype, dec):
|
||||
expected = numpy.arange(shape[0] * shape[1], dtype=dtype)
|
||||
expected.shape = shape
|
||||
a = fft.rfft(expected, shape[0], 0)
|
||||
a = fft.fft(a, shape[1], 1)
|
||||
a = ndimage.fourier_shift(a, [1, 1], shape[0], 0)
|
||||
a = fft.ifft(a, shape[1], 1)
|
||||
a = fft.irfft(a, shape[0], 0)
|
||||
assert_array_almost_equal(a[1:, 1:], expected[:-1, :-1],
|
||||
decimal=dec)
|
||||
assert_array_almost_equal(a.imag, numpy.zeros(shape),
|
||||
decimal=dec)
|
||||
|
||||
@pytest.mark.parametrize('shape', [(32, 16), (31, 15)])
|
||||
@pytest.mark.parametrize('dtype, dec',
|
||||
[(numpy.complex64, 6), (numpy.complex128, 11)])
|
||||
def test_fourier_shift_complex01(self, shape, dtype, dec):
|
||||
expected = numpy.arange(shape[0] * shape[1], dtype=dtype)
|
||||
expected.shape = shape
|
||||
a = fft.fft(expected, shape[0], 0)
|
||||
a = fft.fft(a, shape[1], 1)
|
||||
a = ndimage.fourier_shift(a, [1, 1], -1, 0)
|
||||
a = fft.ifft(a, shape[1], 1)
|
||||
a = fft.ifft(a, shape[0], 0)
|
||||
assert_array_almost_equal(a.real[1:, 1:], expected[:-1, :-1],
|
||||
decimal=dec)
|
||||
assert_array_almost_equal(a.imag, numpy.zeros(shape),
|
||||
decimal=dec)
|
||||
|
||||
@pytest.mark.parametrize('shape', [(32, 16), (31, 15), (1, 10)])
|
||||
@pytest.mark.parametrize('dtype, dec',
|
||||
[(numpy.float32, 5), (numpy.float64, 14)])
|
||||
def test_fourier_ellipsoid_real01(self, shape, dtype, dec):
|
||||
a = numpy.zeros(shape, dtype)
|
||||
a[0, 0] = 1.0
|
||||
a = fft.rfft(a, shape[0], 0)
|
||||
a = fft.fft(a, shape[1], 1)
|
||||
a = ndimage.fourier_ellipsoid(a, [5.0, 2.5],
|
||||
shape[0], 0)
|
||||
a = fft.ifft(a, shape[1], 1)
|
||||
a = fft.irfft(a, shape[0], 0)
|
||||
assert_almost_equal(ndimage.sum(a), 1.0, decimal=dec)
|
||||
|
||||
@pytest.mark.parametrize('shape', [(32, 16), (31, 15)])
|
||||
@pytest.mark.parametrize('dtype, dec',
|
||||
[(numpy.complex64, 5), (numpy.complex128, 14)])
|
||||
def test_fourier_ellipsoid_complex01(self, shape, dtype, dec):
|
||||
a = numpy.zeros(shape, dtype)
|
||||
a[0, 0] = 1.0
|
||||
a = fft.fft(a, shape[0], 0)
|
||||
a = fft.fft(a, shape[1], 1)
|
||||
a = ndimage.fourier_ellipsoid(a, [5.0, 2.5], -1, 0)
|
||||
a = fft.ifft(a, shape[1], 1)
|
||||
a = fft.ifft(a, shape[0], 0)
|
||||
assert_almost_equal(ndimage.sum(a.real), 1.0, decimal=dec)
|
||||
|
||||
def test_fourier_ellipsoid_unimplemented_ndim(self):
|
||||
# arrays with ndim > 3 raise NotImplementedError
|
||||
x = numpy.ones((4, 6, 8, 10), dtype=numpy.complex128)
|
||||
with pytest.raises(NotImplementedError):
|
||||
a = ndimage.fourier_ellipsoid(x, 3)
|
||||
|
||||
def test_fourier_ellipsoid_1d_complex(self):
|
||||
# expected result of 1d ellipsoid is the same as for fourier_uniform
|
||||
for shape in [(32, ), (31, )]:
|
||||
for type_, dec in zip([numpy.complex64, numpy.complex128],
|
||||
[5, 14]):
|
||||
x = numpy.ones(shape, dtype=type_)
|
||||
a = ndimage.fourier_ellipsoid(x, 5, -1, 0)
|
||||
b = ndimage.fourier_uniform(x, 5, -1, 0)
|
||||
assert_array_almost_equal(a, b, decimal=dec)
|
||||
|
||||
@pytest.mark.parametrize('shape', [(0, ), (0, 10), (10, 0)])
|
||||
@pytest.mark.parametrize('dtype',
|
||||
[numpy.float32, numpy.float64,
|
||||
numpy.complex64, numpy.complex128])
|
||||
@pytest.mark.parametrize('test_func',
|
||||
[ndimage.fourier_ellipsoid,
|
||||
ndimage.fourier_gaussian,
|
||||
ndimage.fourier_uniform])
|
||||
def test_fourier_zero_length_dims(self, shape, dtype, test_func):
|
||||
a = numpy.ones(shape, dtype)
|
||||
b = test_func(a, 3)
|
||||
assert_equal(a, b)
|
||||
File diff suppressed because it is too large
Load Diff
File diff suppressed because it is too large
Load Diff
File diff suppressed because it is too large
Load Diff
@@ -1,65 +0,0 @@
|
||||
"""Tests for spline filtering."""
|
||||
import numpy as np
|
||||
import pytest
|
||||
|
||||
from numpy.testing import assert_almost_equal
|
||||
|
||||
from scipy import ndimage
|
||||
|
||||
|
||||
def get_spline_knot_values(order):
|
||||
"""Knot values to the right of a B-spline's center."""
|
||||
knot_values = {0: [1],
|
||||
1: [1],
|
||||
2: [6, 1],
|
||||
3: [4, 1],
|
||||
4: [230, 76, 1],
|
||||
5: [66, 26, 1]}
|
||||
|
||||
return knot_values[order]
|
||||
|
||||
|
||||
def make_spline_knot_matrix(n, order, mode='mirror'):
|
||||
"""Matrix to invert to find the spline coefficients."""
|
||||
knot_values = get_spline_knot_values(order)
|
||||
|
||||
matrix = np.zeros((n, n))
|
||||
for diag, knot_value in enumerate(knot_values):
|
||||
indices = np.arange(diag, n)
|
||||
if diag == 0:
|
||||
matrix[indices, indices] = knot_value
|
||||
else:
|
||||
matrix[indices, indices - diag] = knot_value
|
||||
matrix[indices - diag, indices] = knot_value
|
||||
|
||||
knot_values_sum = knot_values[0] + 2 * sum(knot_values[1:])
|
||||
|
||||
if mode == 'mirror':
|
||||
start, step = 1, 1
|
||||
elif mode == 'reflect':
|
||||
start, step = 0, 1
|
||||
elif mode == 'grid-wrap':
|
||||
start, step = -1, -1
|
||||
else:
|
||||
raise ValueError('unsupported mode {}'.format(mode))
|
||||
|
||||
for row in range(len(knot_values) - 1):
|
||||
for idx, knot_value in enumerate(knot_values[row + 1:]):
|
||||
matrix[row, start + step*idx] += knot_value
|
||||
matrix[-row - 1, -start - 1 - step*idx] += knot_value
|
||||
|
||||
return matrix / knot_values_sum
|
||||
|
||||
|
||||
@pytest.mark.parametrize('order', [0, 1, 2, 3, 4, 5])
|
||||
@pytest.mark.parametrize('mode', ['mirror', 'grid-wrap', 'reflect'])
|
||||
def test_spline_filter_vs_matrix_solution(order, mode):
|
||||
n = 100
|
||||
eye = np.eye(n, dtype=float)
|
||||
spline_filter_axis_0 = ndimage.spline_filter1d(eye, axis=0, order=order,
|
||||
mode=mode)
|
||||
spline_filter_axis_1 = ndimage.spline_filter1d(eye, axis=1, order=order,
|
||||
mode=mode)
|
||||
matrix = make_spline_knot_matrix(n, order, mode=mode)
|
||||
assert_almost_equal(eye, np.dot(spline_filter_axis_0, matrix))
|
||||
assert_almost_equal(eye, np.dot(spline_filter_axis_1, matrix.T))
|
||||
Reference in New Issue
Block a user