numfi
numfi is a numpy.ndarray subclass that does fixedpoint arithmetic.
Feature:

Automatically perform fixedpoint arithmetic through overloaded operators

Maximum compatibility with numpy and other library, just like a normal numpy.ndarray

Optimized calculation speed by minimizing quantization as much as possible
Install
Prerequisite: python3 and numpy
pip install numfi
or you can just copy numfi.py and do whatever you want, after all it's only 200 lines of code
Quick start
from numfi import numfi
# numfi(array=[], signed=1, bits_word=32, bits_frac=16, rounding='round', overflow='wrap')
x = numfi(np.random.rand(3),1,16,8)
# numfi.__repr__() return brief description of numfi object: x => s16/8r/s
# s for 'signed', followed by word bits and fraction bits, r/s for 'round' and 'saturate` for rounding/overflow method
# any arithmetic operation with numfi will return a numfi object with proper precision and value
# By overloading operators, numfi object can do fixedpoint arithmetic easily:
y = x + 1
y = [1]  x
y = x * np.random.rand(3)
y = numfi([1,0,0.1234],1,21,15) / x
y = x
y = x ** 0.5
y = x % 3
y = x & 0b101
y = x  0b100
y = x ^ 0b001
y = x << 4
y = x >> 2
y = x > 0.5
y = x >= 0.5
y = x == x
y = x <= np.ones(3)
y = x < [1,1,1]
...
# By inheriting from numpy.ndarray, numfi object can be used just like normal numpy array, and return same numfi object back
y = np.sin(x)
y = x[x>1]
y = x.sum()
y = x.reshape(3,1)
plt.plot(x)
pandas.DataFrame(x)
numpy.convolve(x,np.ones(4))
numpy.fft.fft(x,n=512)
for i in x:
print(i)
...
Document
Details can be found here: https://numfi.readthedocs.io/en/latest/?
License
The project is licensed under the MIT license.