numpy lookup table
You can make a copy of your array 'a' that remains unmodified while in the 'for' loop: a = np.array([[1, 1, 2, 2], [0, 1, 2, 2], [1, 3, 2, 3]]) lookup = [(0 ...,Since you are interested in 3D points, you could think about using some data-structure that is optimized for storing spatial data, such as a KD-tree. This is ... , You can use Kd-tree, here is a demo: import numpy as np from scipy import spatial H, W = 200, 100 np.random.seed(1) a ..., Via np.vectorize , with series s and array a : np.vectorize(s.get)(a)., You can just use image to index into lut if lut is 1D. Here's a starter on indexing in NumPy:, You can just use image to index into lut if lut is 1D. Here's a starter on indexing in NumPy:, MYLUT[MYARRAY] should work: MYLUT = np.array([0,2,5,7,8,12,-1,229]) MYARRAY = np.random.randint(0,8,8).reshape(4,2) MYARRAY ..., You can use numpy.searchsorted : def get_index(arr, val): index = np.searchsorted(arr, val) if arr[index] == val: return index In [20]: arr = fq[: ..., Using Boolean array indexing: res = a[(a[:, 3] == 1) & (a[:, 1] > 20)] # array([[ 21, 30, 105, 1], # [ 1, 21, 5, 1]])., import numpy as np lookup = np.array([[ 1. , 3.14 , 4.14 ], [ 2. , 2.71818, 3.7 ], [ 3. ... Build a dictionary from key to row number in the lookup table:
| 相關軟體 Python 資訊 | |
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 numpy lookup table 相關參考資料
Change array values using lookup table - Stack Overflow
You can make a copy of your array 'a' that remains unmodified while in the 'for' loop: a = np.array([[1, 1, 2, 2], [0, 1, 2, 2], [1, 3, 2, 3]]) lookup = [(0 ... https://stackoverflow.com Efficient lookup table for collection of numpy arrays - Stack Overflow
Since you are interested in 3D points, you could think about using some data-structure that is optimized for storing spatial data, such as a KD-tree. This is ... https://stackoverflow.com fastest way to get lookup table indices with numpy - Stack Overflow
You can use Kd-tree, here is a demo: import numpy as np from scipy import spatial H, W = 200, 100 np.random.seed(1) a ... https://stackoverflow.com How to apply a Pandas lookup table to a numpy array? - Stack Overflow
Via np.vectorize , with series s and array a : np.vectorize(s.get)(a). https://stackoverflow.com Is there a convenient way to apply a lookup table to a large ...
You can just use image to index into lut if lut is 1D. Here's a starter on indexing in NumPy: https://stackoverflow.com Is there a convenient way to apply a lookup table to a large array ...
You can just use image to index into lut if lut is 1D. Here's a starter on indexing in NumPy: https://stackoverflow.com Most efficient way to apply Look Up Tables to NUMPY array in ...
MYLUT[MYARRAY] should work: MYLUT = np.array([0,2,5,7,8,12,-1,229]) MYARRAY = np.random.randint(0,8,8).reshape(4,2) MYARRAY ... https://stackoverflow.com Numpy array as lookup table - Stack Overflow
You can use numpy.searchsorted : def get_index(arr, val): index = np.searchsorted(arr, val) if arr[index] == val: return index In [20]: arr = fq[: ... https://stackoverflow.com numpy array lookup table - Stack Overflow
Using Boolean array indexing: res = a[(a[:, 3] == 1) & (a[:, 1] > 20)] # array([[ 21, 30, 105, 1], # [ 1, 21, 5, 1]]). https://stackoverflow.com Using Numpy arrays as lookup tables - Stack Overflow
import numpy as np lookup = np.array([[ 1. , 3.14 , 4.14 ], [ 2. , 2.71818, 3.7 ], [ 3. ... Build a dictionary from key to row number in the lookup table: https://stackoverflow.com |