Streaming data from NASA's Earth Surface Minteral Dust Source Investigation (EMIT)¶

This is a proof of concept notebook to demonstrate how earthaccess can facilitate the use of cloud hosted data from NASA using xarray and holoviews. For a formal tutorial on EMIT please visit the official repository where things are explained in detail. EMIT Science Tutorial

Prerequisites

NASA EDL credentials
Openscapes Conda environment installed
For direct access this notebook should run in AWS

IMPORTANT: This notebook should run out of AWS but is not recommended as streaming HDF5 data is slow out of region

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from pprint import pprint

import earthaccess
import xarray as xr

print(f"using earthaccess version {earthaccess.__version__}")

auth = earthaccess.login()
from pprint import pprint

import earthaccess
import xarray as xr

print(f"using earthaccess version {earthaccess.__version__}")

auth = earthaccess.login()

using earthaccess version 0.14.0

Searching for the dataset with `.search_datasets()`¶

Note: API docs can be found at earthaccess

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results = earthaccess.search_datasets(short_name="EMITL2ARFL", cloud_hosted=True)

# Let's print our datasets
for dataset in results:
    pprint(dataset.summary())
results = earthaccess.search_datasets(short_name="EMITL2ARFL", cloud_hosted=True)

# Let's print our datasets
for dataset in results:
    pprint(dataset.summary())

{'cloud-info': {'Region': 'us-west-2',
                'S3BucketAndObjectPrefixNames': ['s3://lp-prod-protected/EMITL2ARFL.001',
                                                 's3://lp-prod-public/EMITL2ARFL.001'],
                'S3CredentialsAPIDocumentationURL': 'https://data.lpdaac.earthdatacloud.nasa.gov/s3credentialsREADME',
                'S3CredentialsAPIEndpoint': 'https://data.lpdaac.earthdatacloud.nasa.gov/s3credentials'},
 'concept-id': 'C2408750690-LPCLOUD',
 'file-type': "[{'FormatType': 'Native', 'AverageFileSize': 1.8, 'Format': "
              "'netCDF-4', 'TotalCollectionFileSizeBeginDate': "
              "'2022-08-09T00:00:00.000Z', 'FormatDescription': 'Network "
              "Common Data Format Version 4', 'AverageFileSizeUnit': 'GB', "
              "'Media': ['Earthdata Cloud', 'HTTPS']}]",
 'get-data': ['https://search.earthdata.nasa.gov/search/granules?p=C2408750690-LPCLOUD',
              'https://appeears.earthdatacloud.nasa.gov/'],
 'short-name': 'EMITL2ARFL',
 'version': '001'}

Searching for the data with `.search_data()` over Ecuador¶

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# ~Ecuador = -82.05,-3.17,-76.94,-0.52
granules = earthaccess.search_data(
    short_name="EMITL2ARFL",
    bounding_box=(-82.05, -3.17, -76.94, -0.52),
    count=10,
)
print(len(granules))
# ~Ecuador = -82.05,-3.17,-76.94,-0.52
granules = earthaccess.search_data(
    short_name="EMITL2ARFL",
    bounding_box=(-82.05, -3.17, -76.94, -0.52),
    count=10,
)
print(len(granules))

`earthaccess` can print a preview of the data using the metadata from CMR¶

Note: there is a bug in earthaccess where the reported size of the granules are always 0, fix is coming next week

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granules[7]
granules[7]

Out[4]:

Data: EMIT_L2A_RFL_001_20230304T151234_2306310_003.nc EMIT_L2A_RFLUNCERT_001_20230304T151234_2306310_003.nc EMIT_L2A_MASK_001_20230304T151234_2306310_003.nc

Size: 3578.78 MB

Cloud Hosted: True

Streaming data from S3 with fsspec¶

Opening the data with earthaccess.open() and accessing the NetCDF as if it was local

If we run this code in AWS(us-west-2), earthaccess can use direct S3 links. If we run it out of AWS, earthaccess can only use HTTPS links. Direct S3 access for NASA data is only allowed in region.

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# open() accepts a list of results or a list of links
file_handlers = earthaccess.open(granules)
file_handlers
# open() accepts a list of results or a list of links
file_handlers = earthaccess.open(granules)
file_handlers

Out[5]:

[<File-like object HTTPFileSystem, https://data.lpdaac.earthdatacloud.nasa.gov/lp-prod-protected/EMITL2ARFL.001/EMIT_L2A_RFL_001_20230217T204403_2304813_036/EMIT_L2A_RFL_001_20230217T204403_2304813_036.nc>,
 <File-like object HTTPFileSystem, https://data.lpdaac.earthdatacloud.nasa.gov/lp-prod-protected/EMITL2ARFL.001/EMIT_L2A_RFL_001_20230217T204403_2304813_036/EMIT_L2A_MASK_001_20230217T204403_2304813_036.nc>,
 <File-like object HTTPFileSystem, https://data.lpdaac.earthdatacloud.nasa.gov/lp-prod-protected/EMITL2ARFL.001/EMIT_L2A_RFL_001_20230217T204403_2304813_036/EMIT_L2A_RFLUNCERT_001_20230217T204403_2304813_036.nc>,
 <File-like object HTTPFileSystem, https://data.lpdaac.earthdatacloud.nasa.gov/lp-prod-protected/EMITL2ARFL.001/EMIT_L2A_RFL_001_20230220T195555_2305113_042/EMIT_L2A_RFL_001_20230220T195555_2305113_042.nc>,
 <File-like object HTTPFileSystem, https://data.lpdaac.earthdatacloud.nasa.gov/lp-prod-protected/EMITL2ARFL.001/EMIT_L2A_RFL_001_20230220T195555_2305113_042/EMIT_L2A_RFLUNCERT_001_20230220T195555_2305113_042.nc>,
 <File-like object HTTPFileSystem, https://data.lpdaac.earthdatacloud.nasa.gov/lp-prod-protected/EMITL2ARFL.001/EMIT_L2A_RFL_001_20230220T195555_2305113_042/EMIT_L2A_MASK_001_20230220T195555_2305113_042.nc>,
 <File-like object HTTPFileSystem, https://data.lpdaac.earthdatacloud.nasa.gov/lp-prod-protected/EMITL2ARFL.001/EMIT_L2A_RFL_001_20230224T182202_2305512_038/EMIT_L2A_RFL_001_20230224T182202_2305512_038.nc>,
 <File-like object HTTPFileSystem, https://data.lpdaac.earthdatacloud.nasa.gov/lp-prod-protected/EMITL2ARFL.001/EMIT_L2A_RFL_001_20230224T182202_2305512_038/EMIT_L2A_RFLUNCERT_001_20230224T182202_2305512_038.nc>,
 <File-like object HTTPFileSystem, https://data.lpdaac.earthdatacloud.nasa.gov/lp-prod-protected/EMITL2ARFL.001/EMIT_L2A_RFL_001_20230224T182202_2305512_038/EMIT_L2A_MASK_001_20230224T182202_2305512_038.nc>,
 <File-like object HTTPFileSystem, https://data.lpdaac.earthdatacloud.nasa.gov/lp-prod-protected/EMITL2ARFL.001/EMIT_L2A_RFL_001_20230225T173354_2305611_028/EMIT_L2A_RFL_001_20230225T173354_2305611_028.nc>,
 <File-like object HTTPFileSystem, https://data.lpdaac.earthdatacloud.nasa.gov/lp-prod-protected/EMITL2ARFL.001/EMIT_L2A_RFL_001_20230225T173354_2305611_028/EMIT_L2A_RFLUNCERT_001_20230225T173354_2305611_028.nc>,
 <File-like object HTTPFileSystem, https://data.lpdaac.earthdatacloud.nasa.gov/lp-prod-protected/EMITL2ARFL.001/EMIT_L2A_RFL_001_20230225T173354_2305611_028/EMIT_L2A_MASK_001_20230225T173354_2305611_028.nc>,
 <File-like object HTTPFileSystem, https://data.lpdaac.earthdatacloud.nasa.gov/lp-prod-protected/EMITL2ARFL.001/EMIT_L2A_RFL_001_20230228T164727_2305911_024/EMIT_L2A_RFL_001_20230228T164727_2305911_024.nc>,
 <File-like object HTTPFileSystem, https://data.lpdaac.earthdatacloud.nasa.gov/lp-prod-protected/EMITL2ARFL.001/EMIT_L2A_RFL_001_20230228T164727_2305911_024/EMIT_L2A_RFLUNCERT_001_20230228T164727_2305911_024.nc>,
 <File-like object HTTPFileSystem, https://data.lpdaac.earthdatacloud.nasa.gov/lp-prod-protected/EMITL2ARFL.001/EMIT_L2A_RFL_001_20230228T164727_2305911_024/EMIT_L2A_MASK_001_20230228T164727_2305911_024.nc>,
 <File-like object HTTPFileSystem, https://data.lpdaac.earthdatacloud.nasa.gov/lp-prod-protected/EMITL2ARFL.001/EMIT_L2A_RFL_001_20230228T164739_2305911_025/EMIT_L2A_RFL_001_20230228T164739_2305911_025.nc>,
 <File-like object HTTPFileSystem, https://data.lpdaac.earthdatacloud.nasa.gov/lp-prod-protected/EMITL2ARFL.001/EMIT_L2A_RFL_001_20230228T164739_2305911_025/EMIT_L2A_MASK_001_20230228T164739_2305911_025.nc>,
 <File-like object HTTPFileSystem, https://data.lpdaac.earthdatacloud.nasa.gov/lp-prod-protected/EMITL2ARFL.001/EMIT_L2A_RFL_001_20230228T164739_2305911_025/EMIT_L2A_RFLUNCERT_001_20230228T164739_2305911_025.nc>,
 <File-like object HTTPFileSystem, https://data.lpdaac.earthdatacloud.nasa.gov/lp-prod-protected/EMITL2ARFL.001/EMIT_L2A_RFL_001_20230304T151222_2306310_002/EMIT_L2A_RFL_001_20230304T151222_2306310_002.nc>,
 <File-like object HTTPFileSystem, https://data.lpdaac.earthdatacloud.nasa.gov/lp-prod-protected/EMITL2ARFL.001/EMIT_L2A_RFL_001_20230304T151222_2306310_002/EMIT_L2A_RFLUNCERT_001_20230304T151222_2306310_002.nc>,
 <File-like object HTTPFileSystem, https://data.lpdaac.earthdatacloud.nasa.gov/lp-prod-protected/EMITL2ARFL.001/EMIT_L2A_RFL_001_20230304T151222_2306310_002/EMIT_L2A_MASK_001_20230304T151222_2306310_002.nc>,
 <File-like object HTTPFileSystem, https://data.lpdaac.earthdatacloud.nasa.gov/lp-prod-protected/EMITL2ARFL.001/EMIT_L2A_RFL_001_20230304T151234_2306310_003/EMIT_L2A_RFL_001_20230304T151234_2306310_003.nc>,
 <File-like object HTTPFileSystem, https://data.lpdaac.earthdatacloud.nasa.gov/lp-prod-protected/EMITL2ARFL.001/EMIT_L2A_RFL_001_20230304T151234_2306310_003/EMIT_L2A_RFLUNCERT_001_20230304T151234_2306310_003.nc>,
 <File-like object HTTPFileSystem, https://data.lpdaac.earthdatacloud.nasa.gov/lp-prod-protected/EMITL2ARFL.001/EMIT_L2A_RFL_001_20230304T151234_2306310_003/EMIT_L2A_MASK_001_20230304T151234_2306310_003.nc>,
 <File-like object HTTPFileSystem, https://data.lpdaac.earthdatacloud.nasa.gov/lp-prod-protected/EMITL2ARFL.001/EMIT_L2A_RFL_001_20230423T192457_2311313_035/EMIT_L2A_RFL_001_20230423T192457_2311313_035.nc>,
 <File-like object HTTPFileSystem, https://data.lpdaac.earthdatacloud.nasa.gov/lp-prod-protected/EMITL2ARFL.001/EMIT_L2A_RFL_001_20230423T192457_2311313_035/EMIT_L2A_RFLUNCERT_001_20230423T192457_2311313_035.nc>,
 <File-like object HTTPFileSystem, https://data.lpdaac.earthdatacloud.nasa.gov/lp-prod-protected/EMITL2ARFL.001/EMIT_L2A_RFL_001_20230423T192457_2311313_035/EMIT_L2A_MASK_001_20230423T192457_2311313_035.nc>,
 <File-like object HTTPFileSystem, https://data.lpdaac.earthdatacloud.nasa.gov/lp-prod-protected/EMITL2ARFL.001/EMIT_L2A_RFL_001_20230424T183609_2311412_034/EMIT_L2A_RFL_001_20230424T183609_2311412_034.nc>,
 <File-like object HTTPFileSystem, https://data.lpdaac.earthdatacloud.nasa.gov/lp-prod-protected/EMITL2ARFL.001/EMIT_L2A_RFL_001_20230424T183609_2311412_034/EMIT_L2A_RFLUNCERT_001_20230424T183609_2311412_034.nc>,
 <File-like object HTTPFileSystem, https://data.lpdaac.earthdatacloud.nasa.gov/lp-prod-protected/EMITL2ARFL.001/EMIT_L2A_RFL_001_20230424T183609_2311412_034/EMIT_L2A_MASK_001_20230424T183609_2311412_034.nc>]

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%%time

# we can use any file from the array
file_p = file_handlers[4]

refl = xr.open_dataset(file_p)
wvl = xr.open_dataset(file_p, group="sensor_band_parameters")
loc = xr.open_dataset(file_p, group="location")
ds = xr.merge([refl, loc])
ds = ds.assign_coords(
    {
        "downtrack": (["downtrack"], refl.downtrack.data),
        "crosstrack": (["crosstrack"], refl.crosstrack.data),
        **wvl.variables,
    }
)

ds
%%time

# we can use any file from the array
file_p = file_handlers[4]

refl = xr.open_dataset(file_p)
wvl = xr.open_dataset(file_p, group="sensor_band_parameters")
loc = xr.open_dataset(file_p, group="location")
ds = xr.merge([refl, loc])
ds = ds.assign_coords(
    {
        "downtrack": (["downtrack"], refl.downtrack.data),
        "crosstrack": (["crosstrack"], refl.crosstrack.data),
        **wvl.variables,
    }
)

ds

CPU times: user 507 ms, sys: 285 ms, total: 792 ms
Wall time: 55.9 s

Out[6]:

<xarray.Dataset> Size: 2GB
Dimensions:                  (downtrack: 1280, crosstrack: 1242, bands: 285,
                              ortho_y: 2035, ortho_x: 1801)
Coordinates:
  * downtrack                (downtrack) int64 10kB 0 1 2 3 ... 1277 1278 1279
  * crosstrack               (crosstrack) int64 10kB 0 1 2 3 ... 1239 1240 1241
    wavelengths              (bands) float32 1kB ...
    fwhm                     (bands) float32 1kB ...
    good_wavelengths         (bands) float32 1kB ...
Dimensions without coordinates: bands, ortho_y, ortho_x
Data variables:
    reflectance_uncertainty  (downtrack, crosstrack, bands) float32 2GB ...
    lon                      (downtrack, crosstrack) float64 13MB ...
    lat                      (downtrack, crosstrack) float64 13MB ...
    elev                     (downtrack, crosstrack) float64 13MB ...
    glt_x                    (ortho_y, ortho_x) float64 29MB ...
    glt_y                    (ortho_y, ortho_x) float64 29MB ...
Attributes: (12/38)
    ncei_template_version:             NCEI_NetCDF_Swath_Template_v2.0
    summary:                           The Earth Surface Mineral Dust Source ...
    keywords:                          Imaging Spectroscopy, minerals, EMIT, ...
    Conventions:                       CF-1.63
    sensor:                            EMIT (Earth Surface Mineral Dust Sourc...
    instrument:                        EMIT
    ...                                ...
    southernmost_latitude:             -4.024231286940166
    spatialResolution:                 0.000542232520256367
    spatial_ref:                       GEOGCS["WGS 84",DATUM["WGS_1984",SPHER...
    geotransform:                      [-8.23355732e+01  5.42232520e-04 -0.00...
    day_night_flag:                    Day
    title:                             EMIT L2A Estimated Surface Reflectance...

xarray.Dataset

Dimensions:
- downtrack: 1280
- crosstrack: 1242
- bands: 285
- ortho_y: 2035
- ortho_x: 1801
Coordinates: (5)
- downtrack
  (downtrack)
  int64
  0 1 2 3 4 ... 1276 1277 1278 1279
```
array([   0,    1,    2, ..., 1277, 1278, 1279], shape=(1280,))
```
- crosstrack
  (crosstrack)
  int64
  0 1 2 3 4 ... 1238 1239 1240 1241
```
array([   0,    1,    2, ..., 1239, 1240, 1241], shape=(1242,))
```
- wavelengths
  (bands)
  float32
  ...
  long_name :
  Wavelength Centers
  units :
  nm
```
[285 values with dtype=float32]
```
- fwhm
  (bands)
  float32
  ...
  long_name :
  Full Width at Half Max
  units :
  nm
```
[285 values with dtype=float32]
```
- good_wavelengths
  (bands)
  float32
  ...
  long_name :
  Wavelengths where reflectance is useable: 1 = good data, 0 = bad data
  units :
  unitless
```
[285 values with dtype=float32]
```
Data variables: (6)
- reflectance_uncertainty
  (downtrack, crosstrack, bands)
  float32
  ...
  long_name :
  Surface Reflectance Uncertainty
  units :
  unitless
```
[453081600 values with dtype=float32]
```
- lon
  (downtrack, crosstrack)
  float64
  ...
  long_name :
  Longitude (WGS-84)
  units :
  degrees east
```
[1589760 values with dtype=float64]
```
- lat
  (downtrack, crosstrack)
  float64
  ...
  long_name :
  Latitude (WGS-84)
  units :
  degrees north
```
[1589760 values with dtype=float64]
```
- elev
  (downtrack, crosstrack)
  float64
  ...
  long_name :
  Surface Elevation
  units :
  m
```
[1589760 values with dtype=float64]
```
- glt_x
  (ortho_y, ortho_x)
  float64
  ...
  long_name :
  GLT Sample Lookup
  units :
  pixel location
```
[3665035 values with dtype=float64]
```
- glt_y
  (ortho_y, ortho_x)
  float64
  ...
  long_name :
  GLT Line Lookup
  units :
  pixel location
```
[3665035 values with dtype=float64]
```

Indexes: (2)

downtrack

PandasIndex

PandasIndex(Index([   0,    1,    2,    3,    4,    5,    6,    7,    8,    9,
       ...
       1270, 1271, 1272, 1273, 1274, 1275, 1276, 1277, 1278, 1279],
      dtype='int64', name='downtrack', length=1280))

crosstrack

PandasIndex

PandasIndex(Index([   0,    1,    2,    3,    4,    5,    6,    7,    8,    9,
       ...
       1232, 1233, 1234, 1235, 1236, 1237, 1238, 1239, 1240, 1241],
      dtype='int64', name='crosstrack', length=1242))

Attributes: (38)
ncei_template_version :
NCEI_NetCDF_Swath_Template_v2.0
summary :
The Earth Surface Mineral Dust Source Investigation (EMIT) is an Earth Ventures-Instrument (EVI-4) Mission that maps the surface mineralogy of arid dust source regions via imaging spectroscopy in the visible and short-wave infrared (VSWIR). Installed on the International Space Station (ISS), the EMIT instrument is a Dyson imaging spectrometer that uses contiguous spectroscopic measurements from 410 to 2450 nm to resolve absoprtion features of iron oxides, clays, sulfates, carbonates, and other dust-forming minerals. During its one-year mission, EMIT will observe the sunlit Earth's dust source regions that occur within +/-52° latitude and produce maps of the source regions that can be used to improve forecasts of the role of mineral dust in the radiative forcing (warming or cooling) of the atmosphere.\n\nThis file contains L2A estimated surface reflectance uncertainties and geolocation data. Reflectance uncertainty estimates are created using an Optimal Estimation technique - see ATBD for details. Reflectance uncertainty values are reported as fractions (relative to 1). Geolocation data (latitude, longitude, height) and a lookup table to project the data are also included.
keywords :
Imaging Spectroscopy, minerals, EMIT, dust, radiative forcing
Conventions :
CF-1.63
sensor :
EMIT (Earth Surface Mineral Dust Source Investigation)
instrument :
EMIT
platform :
ISS
institution :
NASA Jet Propulsion Laboratory/California Institute of Technology
license :
https://science.nasa.gov/earth-science/earth-science-data/data-information-policy/
naming_authority :
LPDAAC
date_created :
2023-03-12T05:18:53Z
keywords_vocabulary :
NASA Global Change Master Directory (GCMD) Science Keywords
stdname_vocabulary :
NetCDF Climate and Forecast (CF) Metadata Convention
creator_name :
Jet Propulsion Laboratory/California Institute of Technology
creator_url :
https://earth.jpl.nasa.gov/emit/
project :
Earth Surface Mineral Dust Source Investigation
project_url :
https://emit.jpl.nasa.gov/
publisher_name :
NASA LPDAAC
publisher_url :
https://lpdaac.usgs.gov
publisher_email :
lpdaac@usgs.gov
identifier_product_doi_authority :
https://doi.org
flight_line :
emit20230220t195555_o05113_s002
time_coverage_start :
2023-02-20T19:55:55+0000
time_coverage_end :
2023-02-20T19:56:07+0000
software_build_version :
010610
software_delivery_version :
010610
product_version :
V001
history :
PGE Run Command: {python /beegfs/store/emit/ops/repos/emit-sds-l2a/spectrum_quality.py /tmp/emit/ops/emit20230220t195555_emit.L2AReflectance_20230222t112607/output/emit20230220t195555_rfl /tmp/emit/ops/emit20230220t195555_emit.L2AReflectance_20230222t112607/output/emit20230220t195555_rfl_quality.txt}, PGE Input Files: {radiance_file=/beegfs/store/emit/ops/data/acquisitions/20230220/emit20230220t195555/l1b/emit20230220t195555_o05113_s002_l1b_rdn_b0106_v01.img, pixel_locations_file=/beegfs/store/emit/ops/data/acquisitions/20230220/emit20230220t195555/l1b/emit20230220t195555_o05113_s002_l1b_loc_b0106_v01.img, observation_parameters_file=/beegfs/store/emit/ops/data/acquisitions/20230220/emit20230220t195555/l1b/emit20230220t195555_o05113_s002_l1b_obs_b0106_v01.img, surface_model_config=/beegfs/store/emit/ops/repos/emit-sds-l2a/surface/surface_20221020.json}
crosstrack_orientation :
as seen on ground
easternmost_longitude :
-81.35901247186709
northernmost_latitude :
-2.92078810821846
westernmost_longitude :
-82.3355732408488
southernmost_latitude :
-4.024231286940166
spatialResolution :
0.000542232520256367
spatial_ref :
GEOGCS["WGS 84",DATUM["WGS_1984",SPHEROID["WGS 84",6378137,298.257223563,AUTHORITY["EPSG","7030"]],AUTHORITY["EPSG","6326"]],PRIMEM["Greenwich",0,AUTHORITY["EPSG","8901"]],UNIT["degree",0.0174532925199433,AUTHORITY["EPSG","9122"]],AXIS["Latitude",NORTH],AXIS["Longitude",EAST],AUTHORITY["EPSG","4326"]]
geotransform :
[-8.23355732e+01 5.42232520e-04 -0.00000000e+00 -2.92078811e+00 -0.00000000e+00 -5.42232520e-04]
day_night_flag :
Day
title :
EMIT L2A Estimated Surface Reflectance Uncertainty 60 m V001

Plotting non orthorectified data¶

Use the following code to plot the Panel widget when you run this code on AWS us-west-2

import holoviews as hv
import hvplot.xarray
import numpy as np
import panel as pn

pn.extension()

# Find band nearest to value of 850 nm (NIR)
b850 = np.nanargmin(abs(ds["wavelengths"].values - 850))
ref_unc = ds["reflectance_uncertainty"]
image = ref_unc.sel(bands=b850).hvplot("crosstrack", "downtrack", cmap="viridis")
stream = hv.streams.Tap(source=image, x=255, y=484)


def wavelengths_histogram(x, y):
    histo = ref_unc.sel(crosstrack=x, downtrack=y, method="nearest").hvplot(
        x="wavelengths", color="green"
    )
    return histo


tap_dmap = hv.DynamicMap(wavelengths_histogram, streams=[stream])
pn.Column(image, tap_dmap)

Streaming data from NASA's Earth Surface Minteral Dust Source Investigation (EMIT)¶

Searching for the dataset with .search_datasets()¶

Searching for the data with .search_data() over Ecuador¶

earthaccess can print a preview of the data using the metadata from CMR¶

Streaming data from S3 with fsspec¶

Plotting non orthorectified data¶

Searching for the dataset with `.search_datasets()`¶

Searching for the data with `.search_data()` over Ecuador¶

`earthaccess` can print a preview of the data using the metadata from CMR¶