python - 如何将一维 numpy 数组拆分为块，每个块的长度取决于条件

Question

我是python和一般编程的新手，所以请赐教。

我有两个用于数据的一维数组，时间。对于时间中的每个元素，都对应于数据中的一个元素。时间元素对应于一整天的测量。

我想将数据数组分成 288 个块，每个块对应于时间数组的 5 分钟块，然后取每个数据块的平均值。

时间数组包含从过去特定点开始计数的以秒为单位的元素。N+1和N个元素的差等于两个数据样本之间的时间差。

我的目标是每 300 秒拆分一次时间数组，然后在数据数组中找到相应的元素。两个数据元素之间的采样率并不总是相同的，有时是每 11.3 秒，有时是 22.6，因此不可能使用 np.split 立即拆分数组。

这是我的代码：

matrix = np.zeros((288,1), dtype=float)
mean_timestamps = np.zeros((288,1), dtype=float)
w=0
for i in range(288):
    meanchunk = np.empty((20,1))
    meanchunk[:] = np.nan
    a = w 
    while time[w] - time[a] < 300:     #time difference in seconds
        if w == 4175:
            break
        meanchunk[w-a] = data[w]
        if np.logical_not(np.isnan(data[w])):
            mean_timestamps[i] = time[w]
        else:
            mean_timestamps[i] = np.nan

        matrix[i] = np.nanmean(meanchunk)

    if time[w+1] - time[a] > 300: 
        w = w+1
        break
    else:
        w = w+1
mean_timestamps = mean_timestamps[mean_timestamps !=0]
matrix = matrix[matrix !=0]

问题是生成的数组“mean_timestamps”和“matrix”的长度为 273 而不是 288。

编辑：以下是时间数组的一些元素。

1.386280815216000080e+09
1.386280837911999941e+09
1.386280860608999968e+09
1.386280883305999994e+09
1.386280906003000021e+09
1.386280928697999954e+09
1.386280951394999981e+09
1.386280974092000008e+09
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1.386280996789000034e+09
1.386281019484999895e+09
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1.386281087575000048e+09
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1.386281201056999922e+09
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1.386281269147000074e+09
1.386281280494999886e+09
1.386281291842999935e+09
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1.386281359933000088e+09
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标签： pythonarrayschunks

Answer 1

编辑：

这更接近：从数组的第一个值开始找到每个 300 秒块的结束时间，然后使用布尔索引来提取块并获得平均值。仍然使用常规的 python for 循环。

span = a[-1] - a[0]
nbr_of_intervals = int((span//300) + 1)
ends = (np.zeros(nbr_of_intervals)+300).cumsum() + a[0]
averages = []
begin = a[0]
for end in ends:
    chunk = a[(begin <= a) & (a < end)]
    averages.append(chunk.mean())
    begin = end

---

加入一些广播。

q = a[:,None] >= ends    # shape (485,34) using the array values from the question.
r = q.argmax(axis=0)
r = r[r.nonzero()]       # shape (33,) - indices to split on
chunks = np.split(a,r)
avgs = [ary.mean() for ary in chunks]