Large-scale cloud masking, grouping and mosaic in Descartes Labs platform

I am looking to create a stack of cloud-free sentinel-2 imagery by day for a large portion of California for all of 2020 in the Descartes Lab platform. I realize that this is a very large amount of imagery and will require significant memory to store and so I’m looking for methods to mosaic imagery by day. I’m new to Descartes and I think using the groupby and mosaic methods could work but not sure how to implement the groupby function on a masked image collection.

First, I establish a small area of interest in Northern California and dates from January to March of 2020 (I am using a small area and small time range to test out the workflow):

import descarteslabs as dl
import numpy as np

# Define a bounding box in N CA, in a GeoJSON
aoi = {
    "type": "Polygon",
    "coordinates": [
        [
            [-121.8430, 38.9676], 
            [-121.6991, 38.9676], 
            [-121.6991, 38.8590], 
            [-121.8430, 38.8590], 
        ]
    ],
}

start_datetime = "2019-01-01"
end_datetime = "2019-03-30"

Next, I create a Sentinel-2 scene stack with desired bands:

sentinel_scenes, sentinel_ctx = dl.scenes.search(
    aoi,
    products="sentinel-2:L1C",
    start_datetime = start_datetime,
    end_datetime = end_datetime,
#    cloud_fraction=0.7,
    limit = 500,
)

sentinel_all_stack = sentinel_scenes.stack('red green blue red-edge nir swir1 derived:ndvi', 
                                           sentinel_ctx, 
                                           processing_level='surface')

Then I create a Descartes Labs Sentinel-2 cloudmask stack for masking:

dlcloud_scenes, dlcloud_ctx = dl.scenes.search(
    aoi,
    products=["sentinel-2:L1C:dlcloud:v1"],
    start_datetime=start_datetime, end_datetime=end_datetime,
    limit=None
)


dlcloud_valid_cloudfree_stack = dlcloud_scenes.stack('valid_cloudfree', 
                                                     sentinel_ctx , 
                                                     data_type='Byte')

Next, I mask the Sentinel-2 collection with the Descartes Labs cloudmask:

dlcloud_valid_cloudfree_stack_rep = np.repeat(a=dlcloud_valid_cloudfree_stack,
                                          repeats=7,
                                          axis=1)

# Mask the pixel values where valid_cloud_free is 0
cloudfree_stack = np.ma.masked_where(dlcloud_valid_cloudfree_stack_rep==0, sentinel_all_stack)

From here, I would like to group the imagery by day. I tried:

for (year, month, day), day_scenes in imagery.groupby(
    "properties.date.year", "properties.date.month", "properties.date.day"):
    print("{}: {} scenes".format(month, np.array(day_scenes.stack("blue green red red-edge nir swir1 derived:ndvi", ctx)).shape))

And I get the error: ‘MaskedArray’ object has no attribute ‘groupby’

I would like to mosaic all images grouped by same day afterwards. All ideas are very welcome!

import descarteslabs as dl
import descarteslabs.workflows as wf
import numpy as np

# Define a bounding box in N CA, in a GeoJSON
aoi_geo = {
    "type": "Polygon",
    "coordinates": [
        [
            [-121.8430, 38.9676], 
            [-121.6991, 38.9676], 
            [-121.6991, 38.8590], 
            [-121.8430, 38.8590], 
        ]
    ],
}

aoi = wf.GeoContext(
    geometry=aoi_geo,
    resolution=10.0,
    crs='EPSG:3857',
    bounds_crs='EPSG:4326')

start_datetime = "2019-01-01"
end_datetime = "2019-02-28"

s2_stack = (wf.ImageCollection.from_id('sentinel-2:L1C', 
                               start_datetime=start_datetime, 
                               end_datetime=end_datetime)
            .pick_bands('red green blue red-edge nir swir1 derived:ndvi')
           )

s2_cloud_mask = (wf.ImageCollection.from_id('sentinel-2:L1C:dlcloud:v1', 
                               start_datetime=start_datetime, 
                               end_datetime=end_datetime)
                .pick_bands('valid_cloudfree')
                )

s2_stack = s2_stack.concat_bands(s2_cloud_mask)

s2_masked = s2_stack.map(lambda img: img.mask(img.pick_bands('valid_cloudfree')==0))

s2_daily = (s2_masked
            .groupby(dates=('year', 'month', 'day'))
            .mosaic()
            .pick_bands('red green blue red-edge nir swir1 derived:ndvi')
)

s2_data = s2_daily.compute(aoi)