Mixing
Indexing empty array, Numba vs. Numpy
I was experimenting with the behavior of Numba vs Numpy for array indexing, and I came across something which I do not quite understand; so I hoped someone can point me in the right direction for what is probably a very simple question. Below are two functions, both of which create an empty array using the np.arange command. I then "append" (experimenting with a variety of methods to see how both Numba and Numpy perform/break) to the array using an index of 0, example[0] = 1.
The Numba function with jit runs without error, but the Numpy example gives the error:
IndexError: index 0 is out of bounds for axis 0 with size 0
The Numpy error makes sense, but I am unsure as to why Numba with jit enabled allows the operation without error.
import numba as nb
import numpy as np
@nb.jit()
def funcnumba():
'''
Add item to position 0 using Numba
'''
example = np.arange(0)
example[0] = 1
return example
def funcnumpy():
'''
Add item to position 0 using Numpy. This produces an error which makes sense
'''
example = np.arange(0)
example[0] = 1
return example
print(funcnumba())
print(funcnumpy())
python numpy numba
asked Nov 19 '18 at 14:04
Yeti
145112
add a comment |
I was experimenting with the behavior of Numba vs Numpy for array indexing, and I came across something which I do not quite understand; so I hoped someone can point me in the right direction for what is probably a very simple question. Below are two functions, both of which create an empty array using the np.arange command. I then "append" (experimenting with a variety of methods to see how both Numba and Numpy perform/break) to the array using an index of 0, example[0] = 1.
The Numba function with jit runs without error, but the Numpy example gives the error:
IndexError: index 0 is out of bounds for axis 0 with size 0
The Numpy error makes sense, but I am unsure as to why Numba with jit enabled allows the operation without error.
import numba as nb
import numpy as np
@nb.jit()
def funcnumba():
'''
Add item to position 0 using Numba
'''
example = np.arange(0)
example[0] = 1
return example
def funcnumpy():
'''
Add item to position 0 using Numpy. This produces an error which makes sense
'''
example = np.arange(0)
example[0] = 1
return example
print(funcnumba())
print(funcnumpy())
python numpy numba
asked Nov 19 '18 at 14:04
Yeti
145112
add a comment |
I was experimenting with the behavior of Numba vs Numpy for array indexing, and I came across something which I do not quite understand; so I hoped someone can point me in the right direction for what is probably a very simple question. Below are two functions, both of which create an empty array using the np.arange command. I then "append" (experimenting with a variety of methods to see how both Numba and Numpy perform/break) to the array using an index of 0, example[0] = 1.
The Numba function with jit runs without error, but the Numpy example gives the error:
IndexError: index 0 is out of bounds for axis 0 with size 0
The Numpy error makes sense, but I am unsure as to why Numba with jit enabled allows the operation without error.
import numba as nb
import numpy as np
@nb.jit()
def funcnumba():
'''
Add item to position 0 using Numba
'''
example = np.arange(0)
example[0] = 1
return example
def funcnumpy():
'''
Add item to position 0 using Numpy. This produces an error which makes sense
'''
example = np.arange(0)
example[0] = 1
return example
print(funcnumba())
print(funcnumpy())
python numpy numba
asked Nov 19 '18 at 14:04
Yeti
145112
I was experimenting with the behavior of Numba vs Numpy for array indexing, and I came across something which I do not quite understand; so I hoped someone can point me in the right direction for what is probably a very simple question. Below are two functions, both of which create an empty array using the np.arange command. I then "append" (experimenting with a variety of methods to see how both Numba and Numpy perform/break) to the array using an index of 0, example[0] = 1.
The Numba function with jit runs without error, but the Numpy example gives the error:
IndexError: index 0 is out of bounds for axis 0 with size 0
The Numpy error makes sense, but I am unsure as to why Numba with jit enabled allows the operation without error.
import numba as nb
import numpy as np
@nb.jit()
def funcnumba():
'''
Add item to position 0 using Numba
'''
example = np.arange(0)
example[0] = 1
return example
def funcnumpy():
'''
Add item to position 0 using Numpy. This produces an error which makes sense
'''
example = np.arange(0)
example[0] = 1
return example
print(funcnumba())
print(funcnumpy())
python numpy numba
python numpy numba
asked Nov 19 '18 at 14:04
Yeti
145112
asked Nov 19 '18 at 14:04
Yeti
145112
asked Nov 19 '18 at 14:04
Yeti
145112
asked Nov 19 '18 at 14:04
Yeti
145112
asked Nov 19 '18 at 14:04
Yeti
145112
145112
add a comment |
add a comment |
1 Answer
1
active
oldest
votes
See the Numba documentation on arrays:
Currently there are no bounds checking for array indexing and slicing (...)
That means that you will be writing out of the bounds of the array in this case. Since it is just one element you may be lucky and get away with it, but you can also crash your program or, even worse, silently overwrite some other value. See issue #730 for a discussion about it.
answered Nov 19 '18 at 14:17
jdehesa
22.3k43150
I'm confused, why does it throw the error when there are no bounds checks? Isn't this more to do with numpy treating single item arrays as scalars?
– roganjosh
Nov 19 '18 at 14:19
In fact,np.arange(0)seems to me to be inviting wonky behaviour tbh. Does it have a purpose?
– roganjosh
Nov 19 '18 at 14:22
@roganjosh It's the NumPy function the one that raises the error, not the Numba one (which makes sense, because NumPy does implement bounds checking).np.arange(0)is an empty array, not a single-item one. Wrt its utility, I'd say it's a matter of consistence. I have probably used it when I needed something to work whether the result should be empty or not.
– jdehesa
Nov 19 '18 at 14:29
Oops, that was a bad misread on my part, sorry!
– roganjosh
Nov 19 '18 at 14:31
add a comment |
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1 Answer
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1 Answer
1
active
oldest
votes
active
oldest
votes
active
oldest
votes
See the Numba documentation on arrays:
Currently there are no bounds checking for array indexing and slicing (...)
That means that you will be writing out of the bounds of the array in this case. Since it is just one element you may be lucky and get away with it, but you can also crash your program or, even worse, silently overwrite some other value. See issue #730 for a discussion about it.
answered Nov 19 '18 at 14:17
jdehesa
22.3k43150
I'm confused, why does it throw the error when there are no bounds checks? Isn't this more to do with numpy treating single item arrays as scalars?
– roganjosh
Nov 19 '18 at 14:19
In fact,np.arange(0)seems to me to be inviting wonky behaviour tbh. Does it have a purpose?
– roganjosh
Nov 19 '18 at 14:22
@roganjosh It's the NumPy function the one that raises the error, not the Numba one (which makes sense, because NumPy does implement bounds checking).np.arange(0)is an empty array, not a single-item one. Wrt its utility, I'd say it's a matter of consistence. I have probably used it when I needed something to work whether the result should be empty or not.
– jdehesa
Nov 19 '18 at 14:29
Oops, that was a bad misread on my part, sorry!
– roganjosh
Nov 19 '18 at 14:31
add a comment |
See the Numba documentation on arrays:
Currently there are no bounds checking for array indexing and slicing (...)
That means that you will be writing out of the bounds of the array in this case. Since it is just one element you may be lucky and get away with it, but you can also crash your program or, even worse, silently overwrite some other value. See issue #730 for a discussion about it.
answered Nov 19 '18 at 14:17
jdehesa
22.3k43150
I'm confused, why does it throw the error when there are no bounds checks? Isn't this more to do with numpy treating single item arrays as scalars?
– roganjosh
Nov 19 '18 at 14:19
In fact,np.arange(0)seems to me to be inviting wonky behaviour tbh. Does it have a purpose?
– roganjosh
Nov 19 '18 at 14:22
@roganjosh It's the NumPy function the one that raises the error, not the Numba one (which makes sense, because NumPy does implement bounds checking).np.arange(0)is an empty array, not a single-item one. Wrt its utility, I'd say it's a matter of consistence. I have probably used it when I needed something to work whether the result should be empty or not.
– jdehesa
Nov 19 '18 at 14:29
Oops, that was a bad misread on my part, sorry!
– roganjosh
Nov 19 '18 at 14:31
add a comment |
See the Numba documentation on arrays:
Currently there are no bounds checking for array indexing and slicing (...)
That means that you will be writing out of the bounds of the array in this case. Since it is just one element you may be lucky and get away with it, but you can also crash your program or, even worse, silently overwrite some other value. See issue #730 for a discussion about it.
answered Nov 19 '18 at 14:17
jdehesa
22.3k43150
See the Numba documentation on arrays:
Currently there are no bounds checking for array indexing and slicing (...)
That means that you will be writing out of the bounds of the array in this case. Since it is just one element you may be lucky and get away with it, but you can also crash your program or, even worse, silently overwrite some other value. See issue #730 for a discussion about it.
answered Nov 19 '18 at 14:17
jdehesa
22.3k43150
answered Nov 19 '18 at 14:17
jdehesa
22.3k43150
answered Nov 19 '18 at 14:17
jdehesa
22.3k43150
answered Nov 19 '18 at 14:17
jdehesa
22.3k43150
22.3k43150
I'm confused, why does it throw the error when there are no bounds checks? Isn't this more to do with numpy treating single item arrays as scalars?
– roganjosh
Nov 19 '18 at 14:19
In fact,np.arange(0)seems to me to be inviting wonky behaviour tbh. Does it have a purpose?
– roganjosh
Nov 19 '18 at 14:22
@roganjosh It's the NumPy function the one that raises the error, not the Numba one (which makes sense, because NumPy does implement bounds checking).np.arange(0)is an empty array, not a single-item one. Wrt its utility, I'd say it's a matter of consistence. I have probably used it when I needed something to work whether the result should be empty or not.
– jdehesa
Nov 19 '18 at 14:29
Oops, that was a bad misread on my part, sorry!
– roganjosh
Nov 19 '18 at 14:31
add a comment |
I'm confused, why does it throw the error when there are no bounds checks? Isn't this more to do with numpy treating single item arrays as scalars?
– roganjosh
Nov 19 '18 at 14:19
In fact,np.arange(0)seems to me to be inviting wonky behaviour tbh. Does it have a purpose?
– roganjosh
Nov 19 '18 at 14:22
@roganjosh It's the NumPy function the one that raises the error, not the Numba one (which makes sense, because NumPy does implement bounds checking).np.arange(0)is an empty array, not a single-item one. Wrt its utility, I'd say it's a matter of consistence. I have probably used it when I needed something to work whether the result should be empty or not.
– jdehesa
Nov 19 '18 at 14:29
Oops, that was a bad misread on my part, sorry!
– roganjosh
Nov 19 '18 at 14:31
I'm confused, why does it throw the error when there are no bounds checks? Isn't this more to do with numpy treating single item arrays as scalars?
– roganjosh
Nov 19 '18 at 14:19
I'm confused, why does it throw the error when there are no bounds checks? Isn't this more to do with numpy treating single item arrays as scalars?
– roganjosh
Nov 19 '18 at 14:19
In fact,
np.arange(0) seems to me to be inviting wonky behaviour tbh. Does it have a purpose?– roganjosh
Nov 19 '18 at 14:22
In fact,
np.arange(0) seems to me to be inviting wonky behaviour tbh. Does it have a purpose?– roganjosh
Nov 19 '18 at 14:22
@roganjosh It's the NumPy function the one that raises the error, not the Numba one (which makes sense, because NumPy does implement bounds checking).
np.arange(0) is an empty array, not a single-item one. Wrt its utility, I'd say it's a matter of consistence. I have probably used it when I needed something to work whether the result should be empty or not.– jdehesa
Nov 19 '18 at 14:29
@roganjosh It's the NumPy function the one that raises the error, not the Numba one (which makes sense, because NumPy does implement bounds checking).
np.arange(0) is an empty array, not a single-item one. Wrt its utility, I'd say it's a matter of consistence. I have probably used it when I needed something to work whether the result should be empty or not.– jdehesa
Nov 19 '18 at 14:29
Oops, that was a bad misread on my part, sorry!
– roganjosh
Nov 19 '18 at 14:31
Oops, that was a bad misread on my part, sorry!
– roganjosh
Nov 19 '18 at 14:31
add a comment |
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