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How does NumPy ndarrary.view(…) work internally

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1

I've used the following code to create a view but i want to understand how this view works under the hood,

>>> x = np.array([(1, 2)], dtype=np.int8)
>>> y = x.view(dtype=np.int16)

where to find the source code for ndarray.view(...), I searched through the GitHub code repository but couldn't find it

numpy numpy-ndarray

share|improve this question

edited Mar 24 at 14:36

Brad Solomon

15.9k84198

asked Mar 24 at 13:09

ManoranjanManoranjan

14910

• 
  
  
  

  
  1
  

  
  
  
  
  
  

  
  
  github.com/numpy/numpy/blob/…
  
  – Brad Solomon
  Mar 24 at 14:28
  

  
  
  
  

add a comment | 

1

I've used the following code to create a view but i want to understand how this view works under the hood,

>>> x = np.array([(1, 2)], dtype=np.int8)
>>> y = x.view(dtype=np.int16)

where to find the source code for ndarray.view(...), I searched through the GitHub code repository but couldn't find it

numpy numpy-ndarray

share|improve this question

edited Mar 24 at 14:36

Brad Solomon

15.9k84198

asked Mar 24 at 13:09

ManoranjanManoranjan

14910

• 
  
  
  

  
  1
  

  
  
  
  
  
  

  
  
  github.com/numpy/numpy/blob/…
  
  – Brad Solomon
  Mar 24 at 14:28
  

  
  
  
  

add a comment | 

I've used the following code to create a view but i want to understand how this view works under the hood,

>>> x = np.array([(1, 2)], dtype=np.int8)
>>> y = x.view(dtype=np.int16)

where to find the source code for ndarray.view(...), I searched through the GitHub code repository but couldn't find it

numpy numpy-ndarray

share|improve this question

edited Mar 24 at 14:36

Brad Solomon

15.9k84198

asked Mar 24 at 13:09

ManoranjanManoranjan

14910

>>> x = np.array([(1, 2)], dtype=np.int8)
>>> y = x.view(dtype=np.int16)

share|improve this question

edited Mar 24 at 14:36

Brad Solomon

15.9k84198

asked Mar 24 at 13:09

ManoranjanManoranjan

14910

share|improve this question

edited Mar 24 at 14:36

Brad Solomon

15.9k84198

asked Mar 24 at 13:09

ManoranjanManoranjan

14910

edited Mar 24 at 14:36

Brad Solomon

15.9k84198

edited Mar 24 at 14:36

Brad Solomon

15.9k84198

asked Mar 24 at 13:09

ManoranjanManoranjan

14910

asked Mar 24 at 13:09

ManoranjanManoranjan

14910

1 Answer
1

active

oldest

votes

2

What are you most interested in - the coding mechanics, or how int8 values are repsented as int16?

view creates a new array, with its own shape and dtype, but sharing the data buffer with the source. Most of the code that you'll see has to do with creating that new array, with little to nothing about the specific dtypes.

There are actually two versions of int16, big-ended and little.

In [194]: np.array([(1,2)],np.int8).view('<i2') 
Out[194]: array([[513]], dtype=int16)
In [195]: np.array([(1,2)],np.int8).view('>i2') 
Out[195]: array([[258]], dtype=int16)

np.int8 is a single byte which can represent values up to 256. The values we see depend on how the 2 bytes are combined into 1 number.

In [197]: 2*256+1 
Out[197]: 513
In [198]: 1*256+2 
Out[198]: 258

My guess is that you won't see this level of detail in the numpy C code. It's performed by the C compiler.

share|improve this answer

edited Mar 24 at 16:59

answered Mar 24 at 15:56

hpauljhpaulj

121k790166

• 
  
  
  

  
  

  
  
  
  
  
  

  
  
  Hi @hpaulj, thanks for the reply, basically i am trying to understand the mechanics behind numpy view(...) in general. just like array broadcasting is powered by multi iterators behind the scene, so is this view(...) also backed-up by some iterators or something else, I wanted to see some piece of source code to understand it or some blogs/forums that talks about the internals of how view(...) works under the cover.
  
  – Manoranjan
  Mar 24 at 18:40
  

  
  
  
  


• 
  
  
  

  
  

  
  
  
  
  
  

  
  
  how to get the view that can result the array having alternate elements from the original array, for ex:- array1 = np.array([1,2,3,4,5]) and array2 = array1.view() this gives me all the elements from the original array array1, but how to create a view having only alternate items form the original array, like [1,3,5], is there anyway to achieve using just numpy view(...) ??
  
  – Manoranjan
  Mar 24 at 18:44
  

  
  
  
  


• 
  
  
  

  
  1
  

  
  
  
  
  
  

  
  
  The view method isn't the only way to create a view :) Basic indexing, ie. with a slice, also creates a view: arr[::2]. The view method, as shown in its docs, has two purposes, changing dtype and changing subclass, type.
  
  – hpaulj
  Mar 24 at 19:58
  
  

  
  
  
  

add a comment | 

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2

What are you most interested in - the coding mechanics, or how int8 values are repsented as int16?

view creates a new array, with its own shape and dtype, but sharing the data buffer with the source. Most of the code that you'll see has to do with creating that new array, with little to nothing about the specific dtypes.

There are actually two versions of int16, big-ended and little.

In [194]: np.array([(1,2)],np.int8).view('<i2') 
Out[194]: array([[513]], dtype=int16)
In [195]: np.array([(1,2)],np.int8).view('>i2') 
Out[195]: array([[258]], dtype=int16)

np.int8 is a single byte which can represent values up to 256. The values we see depend on how the 2 bytes are combined into 1 number.

In [197]: 2*256+1 
Out[197]: 513
In [198]: 1*256+2 
Out[198]: 258

My guess is that you won't see this level of detail in the numpy C code. It's performed by the C compiler.

share|improve this answer

edited Mar 24 at 16:59

answered Mar 24 at 15:56

hpauljhpaulj

121k790166

• 
  
  
  

  
  

  
  
  
  
  
  

  
  
  Hi @hpaulj, thanks for the reply, basically i am trying to understand the mechanics behind numpy view(...) in general. just like array broadcasting is powered by multi iterators behind the scene, so is this view(...) also backed-up by some iterators or something else, I wanted to see some piece of source code to understand it or some blogs/forums that talks about the internals of how view(...) works under the cover.
  
  – Manoranjan
  Mar 24 at 18:40
  

  
  
  
  


• 
  
  
  

  
  

  
  
  
  
  
  

  
  
  how to get the view that can result the array having alternate elements from the original array, for ex:- array1 = np.array([1,2,3,4,5]) and array2 = array1.view() this gives me all the elements from the original array array1, but how to create a view having only alternate items form the original array, like [1,3,5], is there anyway to achieve using just numpy view(...) ??
  
  – Manoranjan
  Mar 24 at 18:44
  

  
  
  
  


• 
  
  
  

  
  1
  

  
  
  
  
  
  

  
  
  The view method isn't the only way to create a view :) Basic indexing, ie. with a slice, also creates a view: arr[::2]. The view method, as shown in its docs, has two purposes, changing dtype and changing subclass, type.
  
  – hpaulj
  Mar 24 at 19:58
  
  

  
  
  
  

add a comment | 

2

What are you most interested in - the coding mechanics, or how int8 values are repsented as int16?

view creates a new array, with its own shape and dtype, but sharing the data buffer with the source. Most of the code that you'll see has to do with creating that new array, with little to nothing about the specific dtypes.

There are actually two versions of int16, big-ended and little.

In [194]: np.array([(1,2)],np.int8).view('<i2') 
Out[194]: array([[513]], dtype=int16)
In [195]: np.array([(1,2)],np.int8).view('>i2') 
Out[195]: array([[258]], dtype=int16)

np.int8 is a single byte which can represent values up to 256. The values we see depend on how the 2 bytes are combined into 1 number.

In [197]: 2*256+1 
Out[197]: 513
In [198]: 1*256+2 
Out[198]: 258

My guess is that you won't see this level of detail in the numpy C code. It's performed by the C compiler.

share|improve this answer

edited Mar 24 at 16:59

answered Mar 24 at 15:56

hpauljhpaulj

121k790166

• 
  
  
  

  
  

  
  
  
  
  
  

  
  
  Hi @hpaulj, thanks for the reply, basically i am trying to understand the mechanics behind numpy view(...) in general. just like array broadcasting is powered by multi iterators behind the scene, so is this view(...) also backed-up by some iterators or something else, I wanted to see some piece of source code to understand it or some blogs/forums that talks about the internals of how view(...) works under the cover.
  
  – Manoranjan
  Mar 24 at 18:40
  

  
  
  
  


• 
  
  
  

  
  

  
  
  
  
  
  

  
  
  how to get the view that can result the array having alternate elements from the original array, for ex:- array1 = np.array([1,2,3,4,5]) and array2 = array1.view() this gives me all the elements from the original array array1, but how to create a view having only alternate items form the original array, like [1,3,5], is there anyway to achieve using just numpy view(...) ??
  
  – Manoranjan
  Mar 24 at 18:44
  

  
  
  
  


• 
  
  
  

  
  1
  

  
  
  
  
  
  

  
  
  The view method isn't the only way to create a view :) Basic indexing, ie. with a slice, also creates a view: arr[::2]. The view method, as shown in its docs, has two purposes, changing dtype and changing subclass, type.
  
  – hpaulj
  Mar 24 at 19:58
  
  

  
  
  
  

add a comment | 

What are you most interested in - the coding mechanics, or how int8 values are repsented as int16?

view creates a new array, with its own shape and dtype, but sharing the data buffer with the source. Most of the code that you'll see has to do with creating that new array, with little to nothing about the specific dtypes.

There are actually two versions of int16, big-ended and little.

In [194]: np.array([(1,2)],np.int8).view('<i2') 
Out[194]: array([[513]], dtype=int16)
In [195]: np.array([(1,2)],np.int8).view('>i2') 
Out[195]: array([[258]], dtype=int16)

np.int8 is a single byte which can represent values up to 256. The values we see depend on how the 2 bytes are combined into 1 number.

In [197]: 2*256+1 
Out[197]: 513
In [198]: 1*256+2 
Out[198]: 258

My guess is that you won't see this level of detail in the numpy C code. It's performed by the C compiler.

share|improve this answer

edited Mar 24 at 16:59

answered Mar 24 at 15:56

hpauljhpaulj

121k790166

In [194]: np.array([(1,2)],np.int8).view('<i2') 
Out[194]: array([[513]], dtype=int16)
In [195]: np.array([(1,2)],np.int8).view('>i2') 
Out[195]: array([[258]], dtype=int16)

In [197]: 2*256+1 
Out[197]: 513
In [198]: 1*256+2 
Out[198]: 258

share|improve this answer

edited Mar 24 at 16:59

answered Mar 24 at 15:56

hpauljhpaulj

121k790166

answered Mar 24 at 15:56

hpauljhpaulj

121k790166

answered Mar 24 at 15:56

hpauljhpaulj

121k790166