掩碼插值回傳常量值

我想沿第一個維度插入一個 3D 陣列。

就資料而言，這意味著我想在地理值中插入缺失時間，換句話說，讓這個影片稍微平滑一點：

我通過呼叫來做到這一點：

new = ma.apply_along_axis(func1d=masked_interpolation, axis=0, arr=dst_data, x=missing_bands, xp=known_bands)

其中插值函式如下：

def masked_interpolation(data, x, xp, propagate_mask=True):
    import math
    import numpy as np
    import numpy.ma as ma

    # The x-coordinates (missing times) at which to evaluate the interpolated values.
    assert len(x) >= 1

    # The x-coordinates (existing times) of the data points (where returns a tuple because each element of the tuple refers to a dimension.)
    assert len(xp) >= 2

    # The y-coordinates (value at existing times) of the data points, that is the valid entries
    fp = np.take(data, xp)
    assert len(fp) >= 2

    # Returns the one-dimensional piecewise linear interpolant to a function with given discrete data points (xp, fp), evaluated at x.
    new_y = np.interp(x, xp, fp.filled(np.nan))

    # interpolate mask & apply to interpolated data
    if propagate_mask:
        new_mask = data.mask[:]
        new_mask[new_mask]  = 1
        new_mask[~new_mask] = 0
        # the mask y values at existing times
        new_fp = np.take(new_mask, xp)
        new_mask = np.interp(x, xp, new_fp)
        new_y = np.ma.masked_array(new_y, new_mask > 0.5)

    print(new_y) # ----> that seems legit
    data[x] = new_y # ----> from here it goes wrong
    return data

列印new_y時，插值看起來是一致的（分布在 [0,1] 區間，我想要的）。但是，當我列印最終輸出（new陣列）時，它肯定更平滑（更多條帶），但所有非屏蔽值都更改為 -0.1（沒有任何意義）：

將其寫入光柵檔案的代碼是：

# Writing the new raster
meta = source.meta
meta.update({'count' : dst_shape[0] })
meta.update({'nodata' : source.nodata})
meta.update(fill_value = source.nodata)
assert new.shape == (meta['count'],meta['height'],meta['width'])
with rasterio.open(outputFile, "w", **meta) as dst:
    dst.write(new.filled(fill_value=source.nodata))

uj5u.com熱心網友回復：

弄清楚這一點非常棘手。發生的情況是插值函式必須用 nan 填充，以便插值起作用，然后用有限值替換剩余的 nan（例如，當整個 fp 向量為 nan 時）。然后應用插值掩碼無論如何都會隱藏這些值。這是怎么回事：

def masked_interpolation(data, x, xp, propagate_mask=True):
    import math
    import numpy as np
    import numpy.ma as ma

    # The x-coordinates (missing times) at which to evaluate the interpolated values.
    assert len(x) >= 1
    # The x-coordinates (existing times) of the data points (where returns a tuple because each element of the tuple refers to a dimension.)
    assert len(xp) >= 2
    # The y-coordinates (value at existing times) of the data points, that is the valid entries
    fp = np.take(data, xp)
    assert len(fp) >= 2

    # Returns the one-dimensional piecewise linear interpolant to a function with given discrete data points (xp, fp), evaluated at x.
    new_y = np.interp(x, xp, fp.filled(np.nan))
    np.nan_to_num(new_y, copy=False)

    # interpolate mask & apply to interpolated data
    if propagate_mask:
        new_mask = data.mask[:]
        new_mask[new_mask]  = 1
        new_mask[~new_mask] = 0
        # the mask y values at existing times
        new_fp = np.take(new_mask, xp)
        new_mask = np.interp(x, xp, new_fp)
        new_y = np.ma.masked_array(new_y, new_mask > 0.5)

    data[x] = new_y
    return data

導致：

標籤：Python 麻木的 插值 光栅

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