提高numpy映射操作的性能

Question

我有一个大小不等的数组(4,X,Y),其中第一个维度代表(R,G,B,A)四联体.我的目标是将每个X*YRGBA四元组转换为X*Y浮点值,给定一个匹配它们的字典.

我目前的代码如下:

codeTable = {
    (255, 255, 255, 127): 5.5,
    (128, 128, 128, 255): 6.5,
    (0  , 0  , 0  , 0  ): 7.5,
}

for i in range(0, rows):
    for j in range(0, cols):
        new_data[i,j] = codeTable.get(tuple(data[:,i,j]), -9999)

哪个data是大小的numpy数组(4, rows, cols),并且new_data大小(rows, cols).

代码工作正常,但需要很长时间.我该如何优化这段代码？

这是一个完整的例子:

import numpy

codeTable = {
    (253, 254, 255, 127): 5.5,
    (128, 129, 130, 255): 6.5,
    (0  , 0  , 0  , 0  ): 7.5,
}

# test data
rows = 2
cols = 2
data = numpy.array([
    [[253, 0], [128,   0], [128,  0]],
    [[254, 0], [129, 144], [129,  0]],
    [[255, 0], [130, 243], [130,  5]],
    [[127, 0], [255, 120], [255,  5]],
])

new_data = numpy.zeros((rows,cols), numpy.float32)

for i in range(0, rows):
    for j in range(0, cols):
        new_data[i,j] = codeTable.get(tuple(data[:,i,j]), -9999)

# expected result for `new_data`:
# array([[  5.50000000e+00,   7.50000000e+00],
#        [  6.50000000e+00,  -9.99900000e+03],
#        [  6.50000000e+00,  -9.99900000e+03], dtype=float32)

Answer 1

这是一种返回预期结果的方法，但由于数据量如此小，很难知道这对您来说是否会更快。然而，由于我避免了双重 for 循环，我想您会看到相当不错的加速。

import numpy
import pandas as pd


codeTable = {
    (253, 254, 255, 127): 5.5,
    (128, 129, 130, 255): 6.5,
    (0  , 0  , 0  , 0  ): 7.5,
}

# test data
rows = 3
cols = 2
data = numpy.array([
    [[253, 0], [128,   0], [128,  0]],
    [[254, 0], [129, 144], [129,  0]],
    [[255, 0], [130, 243], [130,  5]],
    [[127, 0], [255, 120], [255,  5]],
])

new_data = numpy.zeros((rows,cols), numpy.float32)

for i in range(0, rows):
    for j in range(0, cols):
        new_data[i,j] = codeTable.get(tuple(data[:,i,j]), -9999)

def create_output(data):
    # Reshape your two data sources to be a bit more sane
    reshaped_data = data.reshape((4, -1))
    df = pd.DataFrame(reshaped_data).T

    reshaped_codeTable = []
    for key in codeTable.keys():
        reshaped = list(key) + [codeTable[key]]
        reshaped_codeTable.append(reshaped)
    ct = pd.DataFrame(reshaped_codeTable)

    # Merge on the data, replace missing merges with -9999
    result = df.merge(ct, how='left')
    newest_data = result[4].fillna(-9999)

    # Reshape
    output = newest_data.reshape(rows, cols)
    return output

output = create_output(data)
print(output)
# array([[  5.50000000e+00,   7.50000000e+00],
#        [  6.50000000e+00,  -9.99900000e+03],
#        [  6.50000000e+00,  -9.99900000e+03])

print(numpy.array_equal(new_data, output))
# True