对一维numpy数组进行下采样

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1 2 6 2 1

3 3
3 1.5

import numpy as np
a = np.array([1.,2,6,2,1,7])
R = 3
a.reshape(-1, R)
=> array([[ 1.,  2.,  6.],
         [ 2.,  1.,  7.]])
a.reshape(-1, R).mean(axis=1)
=> array([ 3.        ,  3.33333333])

import math, scipy
b = np.append(a, [ 4 ])
b.shape
=> (7,)
pad_size = math.ceil(float(b.size)/R)*R - b.size
b_padded = np.append(b, np.zeros(pad_size)*np.NaN)
b_padded.shape
=> (9,)
scipy.nanmean(b_padded.reshape(-1,R), axis=1)
=> array([ 3.        ,  3.33333333,  4.])

input_arr = np.arange(531)
R = 150 (number of split)
split_arr = np.linspace(0, len(input_arr), num=R+1, dtype=int)
dwnsmpl_subarr = np.split(input_arr, split_arr[1:])
dwnsmpl_arr = np.array( list( np.mean(item) for item in dwnsmpl_subarr[:-1] ) )

import numpy as np
import matplotlib.pyplot as plt
from scipy.signal import resample
from scipy.interpolate import interp1d
def ResampleLinear1D(original, targetLen):
    original = np.array(original, dtype=np.float)
    index_arr = np.linspace(0, len(original)-1, num=targetLen, dtype=np.float)
    index_floor = np.array(index_arr, dtype=np.int) #Round down
    index_ceil = index_floor + 1
    index_rem = index_arr - index_floor #Remain
    val1 = original[index_floor]
    val2 = original[index_ceil % len(original)]
    interp = val1 * (1.0-index_rem) + val2 * index_rem
    assert(len(interp) == targetLen)
    return interp
if __name__=="__main__":
    original = np.sin(np.arange(256)/10.0)
    targetLen = 100
    # Method 1: Use scipy interp1d (linear interpolation)
    # This is the simplest conceptually as it just uses linear interpolation. Scipy
    # also offers a range of other interpolation methods.
    f = interp1d(np.arange(256), original, 'linear')
    plt.plot(np.apply_along_axis(f, 0, np.linspace(0, 255, num=targetLen)))
    # Method 2: Use numpy to do linear interpolation
    # If you don't have scipy, you can do it in numpy with the above function
    plt.plot(ResampleLinear1D(original, targetLen))

对一维numpy数组进行下采样

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