首先，让我们制作一些假数据：
# an N x 5 array containing a regular mesh representing the stimulus params
stim_params = np.mgrid[:2, :3, :4, :5, :6].reshape(5, -1).T
# an N x 3 array representing the output values for each simulation run
output_vals = np.arange(720 * 3).reshape(720, 3)
# shuffle the rows for a bit of added realism
shuf = np.random.permutation(stim_params.shape[0])
stim_params = stim_params[shuf]
output_vals = output_vals[shuf]
现在你可以使用np.lexsort获取一组索引，该索引将对模拟参数的 2D 数组的行进行排序，以便每列中的值按升序排列。完成此操作后，您可以将这些索引应用于模拟输出值的行。
# get the number of unique values for each stimulus parameter
params_shape = tuple(np.unique(col).shape[0] for col in stim_params.T)
# get the set of row indices that will sort the stimulus parameters in ascending
# order, starting with the final column
idx = np.lexsort(stim_params[:, ::-1].T)
# sort and reshape the stimulus parameters:
sorted_params = stim_params[idx].T.reshape((5,) + params_shape)
# sort and reshape the output values
sorted_output = output_vals[idx].T.reshape((3,) + params_shape)
我发现最难的部分通常只是试图理解输出的所有不同维度对应的内容：
# array of stimulus parameters, with dimensions (n_params, p1, p2, p3, p4, p5)
print(sorted_params.shape)
# (5, 2, 3, 4, 5, 6)
# to check that the sorting worked as expected, we can look at the values of the
# 5th parameter when all the others are held constant at 0:
print(sorted_params[4, 0, 0, 0, 0, :])
# [0 1 2 3 4 5]
# ... and the 1st parameter when we hold all the others constant:
print(sorted_params[0, :, 0, 0, 0, 0])
# [0, 1]
# ... now let the 1st and 2nd parameters covary:
print(sorted_params[:2, :, :, 0, 0, 0])
# [[[0 0 0]
# [1 1 1]]
# [[0 1 2]
# [0 1 2]]]
希望你明白了。相同的索引逻辑适用于排序的模拟输出：
# array of outputs, with dimensions (n_outputs, p1, p2, p3, p4, p5)
print(sorted_output.shape)
# (3, 2, 3, 4, 5, 6)
# the first output variable whilst holding the first 4 simulation parameters
# constant at 0:
print(sorted_output[0, 0, 0, 0, 0, :])
# [ 0 3 6 9 12 15]
