How to smoothly migrate a project to the latest numpy version

With the continuous development of the field of scientific computing, numpy, as one of the most important scientific computing libraries in Python, is also constantly updated and iterated. Each new version of numpy brings more practical functions and more efficient performance, so we often need to migrate our projects to the latest version of numpy. In this article, we will discuss how to smoothly migrate your project to a latest version of numpy, and we will provide some specific code examples to facilitate readers' understanding.

1. First understand the version changes of numpy

The version changes of numpy are not random. Each new version will bring some new features, fix previous problems, improve performance, etc. . Therefore, before starting the migration, we need to first understand the difference between the numpy version we are using and the target version. This difference may affect our subsequent code modification work.

Currently, the latest version of numpy is 1.20.2. Compared with version 1.16, there are the following major changes:

• Added sparse matrix, Fourier transform and linear Algebra and other new functions.
• Removed some outdated functions or APIs, such as scipy.misc.face function, etc.
• Optimized the performance of certain operations, such as np.in1d, np.isin functions, etc.

2. Analyze your own code and make modifications

After understanding the numpy version changes, we need to analyze our own code to see if it is needed in the new version the place need to change. The main modification points may be as follows:

• Some APIs or functions have been removed in the new version and need to be replaced or eliminated.
• New functions or functions are not available in the old version and need to be added.
• The type or format of some parameters or return values ​​has changed and needs to be modified.

For example, assuming that our project uses the np.info function and calls some scipy.misc.face APIs, then when migrating to version 1.20, we need to do the following Modification:

1. Replace np.info function with np.__version__ function to view the currently used numpy version.
2. Replace the scipy.misc.face function with the skimage.data.face function. The scipy.misc.face function has been removed in the new version.

Another thing to note is changes in type or format. For example, the return value type of the np.mean function has changed in version 1.20, from a floating point type to an integer type. Therefore, when migrating to version 1.20, if we need to use the return value of the np.mean function for floating point calculations, we will need to perform a cast.

The following is a specific example of modification:

import numpy as np
from skimage.io import imshow
from skimage.data import face

img = face(gray=True)
mean_value = np.mean(img) #The old version returns the floating point type
new_img = img - mean_value.astype('int16') #numpy 1.20 returns the integer type, which needs to be forced. Conversion

imshow(new_img)

3. Perform unit testing

After the migration is completed, we need to perform unit testing to ensure that the migrated project runs normally and does not affect the project other functions. Unit testing can help us quickly discover potential problems so that we can fix them in time.

The following is an example of a unit test:

import numpy as np
def test_numpy_version():

assert np.__version__ == '1.20.2', "numpy版本错误"

def test_scipy_face():

from skimage.data import face
from skimage.io import imshow

img = face(gray=True)
imshow(img)

def test_numpy_mean():

from skimage.data import face
from skimage.io import imshow

img = face(gray=True)
mean_value = np.mean(img) 
new_img = img - mean_value.astype('int16') 
assert new_img.dtype == 'int16', "强制类型转换失败"
imshow(new_img)

Through the above unit tests, we can confirm whether the migration is smooth and ensure that the numpy-related functions in the project run normally.

Conclusion

This article provides some methods and tips on how to successfully migrate numpy, and gives some specific code examples. I hope it will be helpful to readers. When migrating, we need to first understand the numpy version changes, analyze our own code and make modifications, and conduct unit testing to ensure smooth project migration and stable operation.

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