This package provides modules for manipulating floating point numbers quantization using NumPy.
- Supports multidimensional arrays.
- Supports encoding and decoding between 64/32/16-bits floating point numbers and N-bits unsigned normalized integers.
- Supports encoding and decoding between 64/32/16-bits floating point numbers and N-bits signed normalized integers.
- Supports encoding and decoding between 3d-vectors and N-bits unsigned integers.
- Supports encoding and decoding between Quaternions and N-bits unsigned integers.
fpq works with Python 3.4 or higher.
- NumPy
- Numba
pip install fpq
encoding and decoding between 32-bits floating point numbers and 5-bits unsigned normalized integers.
>>> import numpy as np
>>> from fpq.fp import *
>>> fp = np.array([0., 0.25, 1.], dtype=np.float32)
>>> enc = encode_fp_to_std_unorm(fp, dtype=np.uint8, nbits=5)
>>> enc
array([ 0, 8, 31], dtype=uint8)
>>> dec = decode_std_unorm_to_fp(enc, dtype=np.float32, nbits=5)
>>> dec
array([0. , 0.2580645, 1. ], dtype=float32)
encoding and decoding between 32-bits floating point numbers and 5-bits signed normalized integers.
>>> import numpy as np
>>> from fpq.fp import *
>>> fp = np.array([-1., -0.25, -0., 0., 0.25, 1.], dtype=np.float32)
>>> enc = encode_fp_to_std_snorm(fp, dtype=np.uint8, nbits=5)
>>> enc
array([31, 9, 1, 0, 8, 30], dtype=uint8)
>>> dec = decode_std_snorm_to_fp(enc, dtype=np.float32, nbits=5)
>>> dec
array([-1. , -0.26666668, -0. , 0. , 0.26666668,
1. ], dtype=float32)
>>> enc = encode_fp_to_ogl_snorm(fp, dtype=np.uint8, nbits=5)
>>> enc
array([17, 28, 0, 0, 4, 15], dtype=uint8)
>>> dec = decode_ogl_snorm_to_fp(enc, dtype=np.float32, nbits=5)
>>> dec
array([-1. , -0.26666668, 0. , 0. , 0.26666668,
1. ], dtype=float32)
encoding and decoding between 3d-vectors and 64-bits(2:20:20:22) unsigned integers.
>>> import math
>>> import random
>>> import numpy as np
>>> from fpq.vector import *
>>> v = np.array([vec_random(norm=100.) for _ in range(3)], dtype=np.float64)
>>> v
array([[-54.70386501, -22.45578546, -52.18237577],
[-85.46791152, -5.69032986, 1.21334561],
[ 16.02886205, 1.94634654, -30.35219431]])
>>> enc = encode_vec_to_uint(v, dtype=np.uint64, nbits=20)
>>> enc
array([ 1313110064653969262, 306332797892602581, 11373476070061802081],
dtype=uint64)
>>> dec = decode_uint_to_vec(enc, dtype=np.float64, nbits=20)
>>> dec
array([[-54.69957531, -22.45404536, -52.17828412],
[-85.4662517 , -5.69027392, 1.21334561],
[ 16.02845595, 1.94632843, -30.3514349 ]])
encoding and decoding between Quaternions and 64-bits(2:20:20:20) unsigned integers.
>>> import numpy as np
>>> from fpq.quaternion import *
>>> q = np.array([quat_random() for _ in range(3)], dtype=np.float64)
>>> q
array([[ 0.25679071, -0.15512517, 0.88804262, 0.34838917],
[ 0.71399177, 0.05729705, -0.69728753, -0.02688697],
[-0.66527338, -0.62596543, -0.40672262, -0.01246296]])
>>> enc = encode_quat_to_uint(q, dtype=np.uint64)
>>> enc
array([2724532880236077588, 93422189206870975, 1020620101889574962],
dtype=uint64)
>>> dec = decode_uint_to_quat(enc, dtype=np.float64)
>>> dec
array([[ 0.25679011, -0.15512497, 0.88804308, 0.34838854],
[ 0.71399243, 0.0572969 , -0.69728688, -0.02688637],
[ 0.66527395, 0.62596484, 0.40672258, 0.01246335]])
Here are a few examples.
For users, docs are now available at https://hasenpfote.github.io/fpq/.
D3D: Data Conversion Rules OGL: Normalized Integer Vulkan: Fixed-Point Data Conversions
This software is released under the MIT License, see LICENSE.