open_mfdataset xarray 找不到坐标
open_mfdataset with xarray failing to find coordinates
我正在尝试下载一堆 GOES-16 辐射数据并在 xarray 中一起打开它们以使用 xr.open_mfdataset() 函数进行分析。这些 netcdf 文件有一个坐标 t，这是我尝试用作连接的时间戳，但是当我尝试这样做时出现错误 ValueError: Could not find any dimension coordinates to use to order the datasets for concatenation。这是我的代码以及下载两个示例 .nc 文件的链接。
下载两个文件：
wget https://noaa-goes16.s3.amazonaws.com/ABI-L1b-RadF/2019/141/02/OR_ABI-L1b-RadF-M6C14_G16_s20191410240370_e20191410250078_c20191410250143.nc
wget https://noaa-goes16.s3.amazonaws.com/ABI-L1b-RadF/2019/141/03/OR_ABI-L1b-RadF-M6C14_G16_s20191410310370_e20191410320078_c20191410320142.nc
代码：
import xarray as xr
ds_sst = xr.open_mfdataset("OR_ABI-L1b-RadF*nc", concat_dim='t',combine='by_coords')
我可以做些什么来使这项工作正常进行，以便我可以同时打开其中的几十个文件？
改用combine='nested'。
来自 Xarray documentation 按坐标组合：
Attempt to auto-magically combine the given datasets into one by using
dimension coordinates.
't' 不是维度坐标，因此 xarray 魔法在这种情况下不起作用，因为 xarray 的 combine_by_coords 会在导入的 netcdfs 之间寻找匹配的维度坐标。
在这种情况下，您需要更具体：使用 combine = 'nested' 并使用 concat_dim='t' 指定新的维度名称。由于已经有一个名为 't' 的坐标，xarray 会自动将其提升为维度坐标。
ds_sst = xr.open_mfdataset("OR_ABI-L1b-RadF*nc", concat_dim='t', combine='nested')
生成的数据集如下所示。
<xarray.Dataset>
Dimensions: (band: 1, num_star_looks: 24, number_of_image_bounds: 2, number_of_time_bounds: 2, t: 2, x: 5424, y: 5424)
Coordinates:
band_wavelength_star_look (num_star_looks) float32 dask.array<chunksize=(24,), meta=np.ndarray>
x_image float32 0.0
y_image float32 0.0
band_wavelength (band) float32 dask.array<chunksize=(1,), meta=np.ndarray>
band_id (band) int8 dask.array<chunksize=(1,), meta=np.ndarray>
t_star_look (num_star_looks) datetime64[ns] dask.array<chunksize=(24,), meta=np.ndarray>
* y (y) float32 0.151844 ... -0.151844
* x (x) float32 -0.151844 ... 0.151844
* t (t) datetime64[ns] 2019-05-21T02:45:22.400760064 2019-05-21T03:15:22.406056960
Dimensions without coordinates: band, num_star_looks, number_of_image_bounds, number_of_time_bounds
Data variables:
Rad (t, y, x) float32 dask.array<chunksize=(1, 5424, 5424), meta=np.ndarray>
DQF (t, y, x) float32 dask.array<chunksize=(1, 5424, 5424), meta=np.ndarray>
time_bounds (t, number_of_time_bounds) datetime64[ns] dask.array<chunksize=(1, 2), meta=np.ndarray>
goes_imager_projection (t) int32 -2147483647 -2147483647
y_image_bounds (t, number_of_image_bounds) float32 dask.array<chunksize=(1, 2), meta=np.ndarray>
x_image_bounds (t, number_of_image_bounds) float32 dask.array<chunksize=(1, 2), meta=np.ndarray>
nominal_satellite_subpoint_lat (t) float64 0.0 0.0
nominal_satellite_subpoint_lon (t) float64 -75.2 -75.2
nominal_satellite_height (t) float64 3.579e+04 3.579e+04
geospatial_lat_lon_extent (t) float32 9.96921e+36 9.96921e+36
yaw_flip_flag (t) float64 0.0 0.0
esun (t) float64 nan nan
kappa0 (t) float64 nan nan
planck_fk1 (t) float64 8.51e+03 8.51e+03
planck_fk2 (t) float64 1.286e+03 1.286e+03
planck_bc1 (t) float64 0.2252 0.2252
planck_bc2 (t) float64 0.9992 0.9992
valid_pixel_count (t) float64 2.305e+07 2.305e+07
missing_pixel_count (t) float64 268.0 290.0
saturated_pixel_count (t) float64 0.0 0.0
undersaturated_pixel_count (t) float64 0.0 0.0
focal_plane_temperature_threshold_exceeded_count (t) float64 0.0 0.0
min_radiance_value_of_valid_pixels (t) float64 8.217 8.472
max_radiance_value_of_valid_pixels (t) float64 125.5 123.2
mean_radiance_value_of_valid_pixels (t) float64 82.01 81.96
std_dev_radiance_value_of_valid_pixels (t) float64 24.64 24.53
maximum_focal_plane_temperature (t) float64 62.12 62.12
focal_plane_temperature_threshold_increasing (t) float64 81.0 81.0
focal_plane_temperature_threshold_decreasing (t) float64 81.0 81.0
percent_uncorrectable_L0_errors (t) float64 0.0 0.0
earth_sun_distance_anomaly_in_AU (t) float64 1.012 1.012
algorithm_dynamic_input_data_container (t) int32 -2147483647 -2147483647
processing_parm_version_container (t) int32 -2147483647 -2147483647
algorithm_product_version_container (t) int32 -2147483647 -2147483647
star_id (t, num_star_looks) float32 dask.array<chunksize=(1, 24), meta=np.ndarray>
Attributes:
naming_authority: gov.nesdis.noaa
Conventions: CF-1.7
Metadata_Conventions: Unidata Dataset Discovery v1.0
standard_name_vocabulary: CF Standard Name Table (v35, 20 July 2016)
institution: DOC/NOAA/NESDIS > U.S. Department of Commerce,...
project: GOES
production_site: WCDAS
production_environment: OE
spatial_resolution: 2km at nadir
orbital_slot: GOES-East
platform_ID: G16
instrument_type: GOES R Series Advanced Baseline Imager
scene_id: Full Disk
instrument_ID: FM1
title: ABI L1b Radiances
summary: Single emissive band ABI L1b Radiance Products...
keywords: SPECTRAL/ENGINEERING > INFRARED WAVELENGTHS > ...
keywords_vocabulary: NASA Global Change Master Directory (GCMD) Ear...
iso_series_metadata_id: a70be540-c38b-11e0-962b-0800200c9a66
license: Unclassified data. Access is restricted to ap...
processing_level: National Aeronautics and Space Administration ...
cdm_data_type: Image
dataset_name: OR_ABI-L1b-RadF-M6C14_G16_s20191410240370_e201...
production_data_source: Realtime
timeline_id: ABI Mode 6
date_created: 2019-05-21T02:50:14.3Z
time_coverage_start: 2019-05-21T02:40:37.0Z
time_coverage_end: 2019-05-21T02:50:07.8Z
id: abb3657a-03c0-47a9-a1ba-f3196c07c5a9
或者，您可以定义一个函数，将坐标 't' 提升为维度坐标，并将其传递给 open_mfdataset 中的 preprocess 参数。此函数在与其他函数连接之前应用于每个导入的 NetCDF。
def preprocessing(ds):
return ds.expand_dims(dim='t')
ds_sst = xr.open_mfdataset("OR_ABI-L1b-RadF*nc", concat_dim='t',combine='by_coords', preprocess = preprocessing)
结果同上
我正在尝试下载一堆 GOES-16 辐射数据并在 xarray 中一起打开它们以使用 xr.open_mfdataset() 函数进行分析。这些 netcdf 文件有一个坐标 t，这是我尝试用作连接的时间戳，但是当我尝试这样做时出现错误 ValueError: Could not find any dimension coordinates to use to order the datasets for concatenation。这是我的代码以及下载两个示例 .nc 文件的链接。
下载两个文件：
wget https://noaa-goes16.s3.amazonaws.com/ABI-L1b-RadF/2019/141/02/OR_ABI-L1b-RadF-M6C14_G16_s20191410240370_e20191410250078_c20191410250143.nc
wget https://noaa-goes16.s3.amazonaws.com/ABI-L1b-RadF/2019/141/03/OR_ABI-L1b-RadF-M6C14_G16_s20191410310370_e20191410320078_c20191410320142.nc
代码：
import xarray as xr
ds_sst = xr.open_mfdataset("OR_ABI-L1b-RadF*nc", concat_dim='t',combine='by_coords')
我可以做些什么来使这项工作正常进行，以便我可以同时打开其中的几十个文件？
改用combine='nested'。
来自 Xarray documentation 按坐标组合：
Attempt to auto-magically combine the given datasets into one by using dimension coordinates.
't' 不是维度坐标，因此 xarray 魔法在这种情况下不起作用，因为 xarray 的 combine_by_coords 会在导入的 netcdfs 之间寻找匹配的维度坐标。
在这种情况下，您需要更具体：使用 combine = 'nested' 并使用 concat_dim='t' 指定新的维度名称。由于已经有一个名为 't' 的坐标，xarray 会自动将其提升为维度坐标。
ds_sst = xr.open_mfdataset("OR_ABI-L1b-RadF*nc", concat_dim='t', combine='nested')
生成的数据集如下所示。
<xarray.Dataset>
Dimensions: (band: 1, num_star_looks: 24, number_of_image_bounds: 2, number_of_time_bounds: 2, t: 2, x: 5424, y: 5424)
Coordinates:
band_wavelength_star_look (num_star_looks) float32 dask.array<chunksize=(24,), meta=np.ndarray>
x_image float32 0.0
y_image float32 0.0
band_wavelength (band) float32 dask.array<chunksize=(1,), meta=np.ndarray>
band_id (band) int8 dask.array<chunksize=(1,), meta=np.ndarray>
t_star_look (num_star_looks) datetime64[ns] dask.array<chunksize=(24,), meta=np.ndarray>
* y (y) float32 0.151844 ... -0.151844
* x (x) float32 -0.151844 ... 0.151844
* t (t) datetime64[ns] 2019-05-21T02:45:22.400760064 2019-05-21T03:15:22.406056960
Dimensions without coordinates: band, num_star_looks, number_of_image_bounds, number_of_time_bounds
Data variables:
Rad (t, y, x) float32 dask.array<chunksize=(1, 5424, 5424), meta=np.ndarray>
DQF (t, y, x) float32 dask.array<chunksize=(1, 5424, 5424), meta=np.ndarray>
time_bounds (t, number_of_time_bounds) datetime64[ns] dask.array<chunksize=(1, 2), meta=np.ndarray>
goes_imager_projection (t) int32 -2147483647 -2147483647
y_image_bounds (t, number_of_image_bounds) float32 dask.array<chunksize=(1, 2), meta=np.ndarray>
x_image_bounds (t, number_of_image_bounds) float32 dask.array<chunksize=(1, 2), meta=np.ndarray>
nominal_satellite_subpoint_lat (t) float64 0.0 0.0
nominal_satellite_subpoint_lon (t) float64 -75.2 -75.2
nominal_satellite_height (t) float64 3.579e+04 3.579e+04
geospatial_lat_lon_extent (t) float32 9.96921e+36 9.96921e+36
yaw_flip_flag (t) float64 0.0 0.0
esun (t) float64 nan nan
kappa0 (t) float64 nan nan
planck_fk1 (t) float64 8.51e+03 8.51e+03
planck_fk2 (t) float64 1.286e+03 1.286e+03
planck_bc1 (t) float64 0.2252 0.2252
planck_bc2 (t) float64 0.9992 0.9992
valid_pixel_count (t) float64 2.305e+07 2.305e+07
missing_pixel_count (t) float64 268.0 290.0
saturated_pixel_count (t) float64 0.0 0.0
undersaturated_pixel_count (t) float64 0.0 0.0
focal_plane_temperature_threshold_exceeded_count (t) float64 0.0 0.0
min_radiance_value_of_valid_pixels (t) float64 8.217 8.472
max_radiance_value_of_valid_pixels (t) float64 125.5 123.2
mean_radiance_value_of_valid_pixels (t) float64 82.01 81.96
std_dev_radiance_value_of_valid_pixels (t) float64 24.64 24.53
maximum_focal_plane_temperature (t) float64 62.12 62.12
focal_plane_temperature_threshold_increasing (t) float64 81.0 81.0
focal_plane_temperature_threshold_decreasing (t) float64 81.0 81.0
percent_uncorrectable_L0_errors (t) float64 0.0 0.0
earth_sun_distance_anomaly_in_AU (t) float64 1.012 1.012
algorithm_dynamic_input_data_container (t) int32 -2147483647 -2147483647
processing_parm_version_container (t) int32 -2147483647 -2147483647
algorithm_product_version_container (t) int32 -2147483647 -2147483647
star_id (t, num_star_looks) float32 dask.array<chunksize=(1, 24), meta=np.ndarray>
Attributes:
naming_authority: gov.nesdis.noaa
Conventions: CF-1.7
Metadata_Conventions: Unidata Dataset Discovery v1.0
standard_name_vocabulary: CF Standard Name Table (v35, 20 July 2016)
institution: DOC/NOAA/NESDIS > U.S. Department of Commerce,...
project: GOES
production_site: WCDAS
production_environment: OE
spatial_resolution: 2km at nadir
orbital_slot: GOES-East
platform_ID: G16
instrument_type: GOES R Series Advanced Baseline Imager
scene_id: Full Disk
instrument_ID: FM1
title: ABI L1b Radiances
summary: Single emissive band ABI L1b Radiance Products...
keywords: SPECTRAL/ENGINEERING > INFRARED WAVELENGTHS > ...
keywords_vocabulary: NASA Global Change Master Directory (GCMD) Ear...
iso_series_metadata_id: a70be540-c38b-11e0-962b-0800200c9a66
license: Unclassified data. Access is restricted to ap...
processing_level: National Aeronautics and Space Administration ...
cdm_data_type: Image
dataset_name: OR_ABI-L1b-RadF-M6C14_G16_s20191410240370_e201...
production_data_source: Realtime
timeline_id: ABI Mode 6
date_created: 2019-05-21T02:50:14.3Z
time_coverage_start: 2019-05-21T02:40:37.0Z
time_coverage_end: 2019-05-21T02:50:07.8Z
id: abb3657a-03c0-47a9-a1ba-f3196c07c5a9
或者，您可以定义一个函数，将坐标 't' 提升为维度坐标，并将其传递给 open_mfdataset 中的 preprocess 参数。此函数在与其他函数连接之前应用于每个导入的 NetCDF。
def preprocessing(ds):
return ds.expand_dims(dim='t')
ds_sst = xr.open_mfdataset("OR_ABI-L1b-RadF*nc", concat_dim='t',combine='by_coords', preprocess = preprocessing)
结果同上