image-processing - 如何沿xarray.DataArray的时间维度对每个图像使用带有numpy.digitize的apply_ufunc？

Question

为了清楚起见，我已经基本上重新表述了我之前的问题。根据 Ryan 在单独 channel 上的建议， numpy.digitize 看起来是实现我目标的正确工具。

我有一个形状为 x、y 和时间的 xarray.DataArray。我试图弄清楚我应该为 apply_ufunc 提供哪些值函数的 'input_core_dims' 和 'output_core_dims' 参数以应用 numpy.digitize到时间序列中的每个图像。

直观地说，我希望输出维度为 ['time', 'x', 'y']。我认为输入核心尺寸应该是x和 y因为我要播numpy.digitize时间维度上的函数。然而这行不通。通过将 numpy.digitize 应用于我的时间序列中的第一个 numpy 数组，我得到了正确的结果:

[84]

blues
<xarray.DataArray 'reflectance' (time: 44, y: 1082, x: 1084)>
dask.array<shape=(44, 1082, 1084), dtype=uint16, chunksize=(44, 1082, 1084)>
Coordinates:
    band     int64 1
  * y        (y) float64 9.705e+05 9.705e+05 9.705e+05 ... 9.673e+05 9.672e+05
  * x        (x) float64 4.889e+05 4.889e+05 4.889e+05 ... 4.922e+05 4.922e+05
  * time     (time) datetime64[ns] 2018-10-12 2018-10-16 ... 2019-05-26
Attributes:
    transform:   (3.0, 0.0, 488907.0, 0.0, -3.0, 970494.0)
    crs:         +init=epsg:32630
    res:         (3.0, 3.0)
    is_tiled:    1
    nodatavals:  (1.0, 1.0, 1.0, 1.0)
    scales:      (1.0, 1.0, 1.0, 1.0)
    offsets:     (0.0, 0.0, 0.0, 0.0)

[79]
#correct result
np.digitize(np.array(blues[0]), bin_arr)
array([[14, 15, 15, ..., 16, 17, 16],
       [14, 13, 14, ..., 16, 16, 15],
       [15, 14, 15, ..., 16, 16, 15],
       ...,
       [16, 18, 18, ..., 15, 16, 15],
       [17, 18, 18, ..., 16, 17, 16],
       [17, 17, 17, ..., 17, 18, 17]])

但我对 apply_ufunc的理解是不正确的。将 input_core_dims 更改为 [['x','y']] 或 ['time'] 不会产生正确的数字化结果

bin_arr = np.linspace(configs.rmin, configs.rmax, 50)
blues = t_series['reflectance'].sel(band=1).chunk({'time':-1})
result = xr.apply_ufunc(partial(np.digitize, bins=bin_arr), blues, input_core_dims=[['time']], dask="parallelized", output_dtypes=[blues.dtype])

#wrong values, correct shape
np.array(result)[0]

array([[14, 16, 15, ..., 48, 18, 15],
       [15, 16, 16, ..., 49, 18, 15],
       [15, 16, 16, ..., 49, 18, 14],
       ...,
       [16, 21, 17, ..., 50, 19, 15],
       [17, 21, 17, ..., 50, 19, 16],
       [16, 21, 18, ..., 50, 20, 17]])

bin_arr = np.linspace(configs.rmin, configs.rmax, 50)
blues = t_series['reflectance'].sel(band=1).chunk({'time':-1})
result = xr.apply_ufunc(partial(np.digitize, bins=bin_arr), blues, input_core_dims=[['x','y']], dask="parallelized", output_dtypes=[blues.dtype])


#wrong values, correct shape
np.array(result)[0]

array([[14, 14, 15, ..., 16, 17, 17],
       [15, 13, 14, ..., 18, 18, 17],
       [15, 14, 15, ..., 18, 18, 17],
       ...,
       [16, 16, 16, ..., 15, 16, 17],
       [17, 16, 16, ..., 16, 17, 18],
       [16, 15, 15, ..., 15, 16, 17]])

这些结果中的每一个都具有正确的形状，但具有错误的值，这意味着将数字化函数应用于错误的轴，结果将重新调整为输入的形状。

同样奇怪的是 apply_ufunc的结果显示为 xarray 时删除 input_core_dim。但是在内部，当您将其转换为 numpy 数组时，维度仍然存在

[85]

result
<xarray.DataArray 'reflectance' (y: 1082, x: 1084)>
dask.array<shape=(1082, 1084), dtype=uint16, chunksize=(1082, 1084)>
Coordinates:
    band     int64 1
  * y        (y) float64 9.705e+05 9.705e+05 9.705e+05 ... 9.673e+05 9.672e+05
  * x        (x) float64 4.889e+05 4.889e+05 4.889e+05 ... 4.922e+05 4.922e+05

[87]
# the shape of the xarray and numpy array do not match after apply_ufunc
np.array(result).shape
(1082, 1084, 44) 

另外，当我尝试将 output_core_dims 参数指定为 [['time', 'x', 'y']] 时为了纠正这个问题，我收到一个错误，看起来你不能让一个维度既是输入核心维度又是输出核心维度

[67]

bin_arr = np.linspace(configs.rmin, configs.rmax, 50)
blues = t_series['reflectance'].sel(band=1).chunk({'time':-1})
result = xr.apply_ufunc(partial(np.digitize, bins=bin_arr), blues, input_core_dims=[['time']], output_core_dims=[['time','x','y']], dask="parallelized", output_dtypes=[blues.dtype])
---------------------------------------------------------------------------
ValueError                                Traceback (most recent call last)
 in 
      5 bin_arr = np.linspace(configs.rmin, configs.rmax, 50)
      6 blues = t_series['reflectance'].sel(band=1).chunk({'time':-1})
----> 7 result = xr.apply_ufunc(partial(np.digitize, bins=bin_arr), blues, input_core_dims=[['time']], output_core_dims=[['time','x','y']], dask="parallelized", output_dtypes=[blues.dtype])

~/miniconda3/envs/pyatsa/lib/python3.7/site-packages/xarray/core/computation.py in apply_ufunc(func, input_core_dims, output_core_dims, exclude_dims, vectorize, join, dataset_join, dataset_fill_value, keep_attrs, kwargs, dask, output_dtypes, output_sizes, *args)
    967                                      join=join,
    968                                      exclude_dims=exclude_dims,
--> 969                                      keep_attrs=keep_attrs)
    970     elif any(isinstance(a, Variable) for a in args):
    971         return variables_vfunc(*args)

~/miniconda3/envs/pyatsa/lib/python3.7/site-packages/xarray/core/computation.py in apply_dataarray_vfunc(func, signature, join, exclude_dims, keep_attrs, *args)
    215 
    216     data_vars = [getattr(a, 'variable', a) for a in args]
--> 217     result_var = func(*data_vars)
    218 
    219     if signature.num_outputs > 1:

~/miniconda3/envs/pyatsa/lib/python3.7/site-packages/xarray/core/computation.py in apply_variable_ufunc(func, signature, exclude_dims, dask, output_dtypes, output_sizes, keep_attrs, *args)
    539                   if isinstance(arg, Variable)
    540                   else arg
--> 541                   for arg, core_dims in zip(args, signature.input_core_dims)]
    542 
    543     if any(isinstance(array, dask_array_type) for array in input_data):

~/miniconda3/envs/pyatsa/lib/python3.7/site-packages/xarray/core/computation.py in (.0)
    539                   if isinstance(arg, Variable)
    540                   else arg
--> 541                   for arg, core_dims in zip(args, signature.input_core_dims)]
    542 
    543     if any(isinstance(array, dask_array_type) for array in input_data):

~/miniconda3/envs/pyatsa/lib/python3.7/site-packages/xarray/core/computation.py in broadcast_compat_data(variable, broadcast_dims, core_dims)
    493                          'dimensions %r on an input variable: these are core '
    494                          'dimensions on other input or output variables'
--> 495                          % unexpected_dims)
    496 
    497     # for consistency with numpy, keep broadcast dimensions to the left

ValueError: operand to apply_ufunc encountered unexpected dimensions ['y', 'x'] on an input variable: these are core dimensions on other input or output variables

非常感谢任何帮助，我想了解我如何滥用 input_core_dim 和 output_core_dim 参数。

Answer 1

您要申请 digitize在逐点的基础上。这是 apply_ufunc 最简单的用例.不需要特殊参数。

Numpy 版本

import numpy as np
import xarray as xr

ny, nx = 100, 100
nt = 44
data = xr.DataArray(np.random.randn(nt,ny,nx),
                        dims=['time', 'y', 'x'],
                        name='blue reflectance')

rmin, rmax, nbins = -4, 4, 50
bins = np.linspace(rmin, rmax, nbins)

data_digitized = xr.apply_ufunc(np.digitize, data, bins)

这将返回一个 DataArray 像

<xarray.DataArray 'blue reflectance' (time: 44, y: 100, x: 100)>
array([[[34, 17, ..., 27, 15],
         ....
        [21, 24, ..., 23, 29]]])
Dimensions without coordinates: time, y, x

根据 numpy.digitize 中描述的约定，其中的值是 bin 索引。文档。

Dask版

要使其在 dask 数组上延迟运行，您有两个选择

# create chunked dask version of data
data_chunked = data.chunk({'time': 1})

# use dask's version of digitize
import dask.array as da
xr.apply_ufunc(da.digitize, data_chunked, bins, dask='allowed')

# use xarray's built-in `parallelized` option on the numpy function
# (I needed to define a wrapper function to make this work,
# but I don't fully understand why.)
def wrap_digitize(data):
    return np.digitize(data, bins)
xr.apply_ufunc(wrap_digitize, data_chunked,
               dask='parallelized', output_dtypes=['i8'])