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Fix numpy warnings #510
Fix numpy warnings #510
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Original file line number | Diff line number | Diff line change |
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@@ -6,7 +6,7 @@ | |
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import numpy as np | ||
from numpy import arcsin, pi, cos, size, array | ||
from numpy.linalg import linalg | ||
There was a problem hiding this comment. Choose a reason for hiding this commentThe reason will be displayed to describe this comment to others. Learn more. numpy depricated this import structure for the new one I modified |
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import numpy.linalg as linalg | ||
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from kwave.data import Vector | ||
from kwave.kgrid import kWaveGrid | ||
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@@ -1,5 +1,5 @@ | ||
from typing import Tuple, Union, Optional, List | ||
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from beartype import beartype as typechecker | ||
import numpy as np | ||
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@@ -75,6 +75,7 @@ def matlab_find(arr: Union[List[int], np.ndarray], val: int = 0, mode: str = "ne | |
return np.expand_dims(arr, -1) # compatibility, n => [n, 1] | ||
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@typechecker | ||
There was a problem hiding this comment. Choose a reason for hiding this commentThe reason will be displayed to describe this comment to others. Learn more. This change is not required for this PR but I just added it while I was in the file |
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def matlab_mask(arr: np.ndarray, mask: np.ndarray, diff: Optional[int] = None) -> np.ndarray: | ||
""" | ||
Applies a mask to an array and returns the masked elements. | ||
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@@ -89,6 +90,9 @@ def matlab_mask(arr: np.ndarray, mask: np.ndarray, diff: Optional[int] = None) - | |
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""" | ||
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if mask.dtype == "uint8": | ||
There was a problem hiding this comment. Choose a reason for hiding this commentThe reason will be displayed to describe this comment to others. Learn more. what if we convert uint8 to uint16 and int8 to int16? There was a problem hiding this comment. Choose a reason for hiding this commentThe reason will be displayed to describe this comment to others. Learn more. I think if we promote from uint8 to uint16 we will have the same issue with negative indexing and value wrapping. I have tried it to see. There was a problem hiding this comment. Choose a reason for hiding this commentThe reason will be displayed to describe this comment to others. Learn more. Exaclty, current "value -1 is out of bounds of uint8 type" There was a problem hiding this comment. Choose a reason for hiding this commentThe reason will be displayed to describe this comment to others. Learn more. >>> x
array([0], dtype=uint8)
>>> x - 1
array([255], dtype=uint8)
>>> np.version
<module 'numpy.version' from '/Users/walters/miniconda3/lib/python3.12/site-packages/numpy/version.py'>
>>> np.__version__
'1.26.4' Here is the legacy behavior There was a problem hiding this comment. Choose a reason for hiding this commentThe reason will be displayed to describe this comment to others. Learn more. Here is the new behavior: >>> import numpy as np
>>> x = np.array([0], dtype='uint8')
>>> x - 1
array([255], dtype=uint8)
>>> np.__version__
'2.1.3' looks like my assumption was wrong There was a problem hiding this comment. Choose a reason for hiding this commentThe reason will be displayed to describe this comment to others. Learn more. Seems to be an issue with type promotion with the + operator. >>> import numpy as np
>>> x = np.array([0], dtype='uint8')
>>> x - 1
array([255], dtype=uint8)
>>> x.ravel(order='F') + -1
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
OverflowError: Python integer -1 out of bounds for uint8
>>> x.ravel(order='F').astype("int8") + -1
array([-1], dtype=int8)
>>> x + -1
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
OverflowError: Python integer -1 out of bounds for uint8
>>> np.__version__
'2.1.3' There was a problem hiding this comment. Choose a reason for hiding this commentThe reason will be displayed to describe this comment to others. Learn more. This is all related to this: |
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mask = mask.astype(np.int16) | ||
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if diff is None: | ||
return np.expand_dims(arr.ravel(order="F")[mask.ravel(order="F")], axis=-1) # compatibility, n => [n, 1] | ||
else: | ||
There was a problem hiding this comment. Choose a reason for hiding this commentThe reason will be displayed to describe this comment to others. Learn more. I think there was an issue with unit8 roll-over to positive indexes. This is solved with negative indexing in python and therefore using int16 is acceptable. |
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The reason will be displayed to describe this comment to others. Learn more.
Again here for one of the operations, the unit8 type was not being correctly promoted for values larger than 255 so I bumped the type.