numfi 0.5.0

a numpy.ndarray subclass that does fixed-point arithmetic

numfi

numfi is a numpy.ndarray subclass that does fixed-point arithmetic.

Feature:

• Automatically perform fixed-point arithmetic through overloaded operators

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

• Optimized calculation speed, and keep bit precision as much as possible

• mimic the behavior of matlab's fixed-point toolbox

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 300 lines of code

Quick start

from numfi import numfi as fi
import numpy as np

# numfi(array=[], s=1, w=16, f=None, RoundingMethod='Nearest', OverflowAction='Saturate')
x = fi([1,0,0.1234],1,16,8) 
# print(numfi) return brief description of numfi object: x => s16/8-N/S
# s for 'signed', u for 'unsigned', followed by word bits(16) and fraction bits(8), N/S for 'Nearest' 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 fixed-point arithmetic easily:

# normal arithmetic operation work with float form of x
y = x + 1
y = [1] - x
y = x * [3,0,-3]
y = fi([1,0,0.1234],1,21,15) / x
y = -x
y = x ** 0.5
y = x % 3
# comparison return np.array of bool, just like normal np.array
y = x > 0.5
y = x >= fi([1,0,0.1234],1,21,15)
y = x == x
y = x <= np.ones(3)
y = x < [1,1,1]
# bitwise operation work with integer form of x
y = x & 0b101 
y = 0b100 | x   # order of operands doesn't matter
y = x ^ x       # two numfi object can also be used in bitwise operations
y = x << 4
y = x >> 2
...

# 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)
np.convolve(x[0],np.ones(3))
np.fft.fft(x,n=512)
plt.plot(x)
pandas.DataFrame(x)
f, t, Sxx = scipy.signal.spectrogram(x,nperseg=256,noverlap=128)
plt.pcolormesh(t, f, Sxx, shading='gouraud')
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.