\ Include subset of NN classifications

scripts/extract_eurec4a_timeperiod_NNclassifications.py

@@ -0,0 +1,33 @@
+"""
+Extract EUREC4A timeperiod from infrared neural network classification dataset
+"""
+import xarray as xr
+import numcodecs
+
+import dask
+from dask_jobqueue import SLURMCluster
+from dask.distributed import Client, LocalCluster, comm
+print(dask.__version__)
+dask.config.config.get('distributed').get('dashboard').update({'link':'{JUPYTERHUB_SERVICE_PREFIX}/proxy/{port}/status'})
+cluster = SLURMCluster(project="mh0010")
+client = Client(cluster)
+
+
+ds=xr.open_zarr("/mnt/lustre02/work/mh0010/m300408/CharacterizationOfMesoscalePatterns/Data/Level_1/GOES16_CH13_classifications_2018-2020_NDJFM_30min.zarr/")
+
+# Select EUREC4A time period
+ds=ds.sel(time=slice('2020-01-01','2020-03-01'))
+
+del ds.time.attrs['description']
+
+encoding={
+    'mask':{'dtype':bool,'compressor':numcodecs.Blosc("zstd",shuffle=numcodecs.Blosc.BITSHUFFLE)},
+    'time':{'dtype':'float64'}
+         }
+
+ds['mask'] = (ds.mask.astype(float)>0).astype(bool)
+ds['pattern'] = ds.pattern.astype('str')
+
+cluster.scale(10)
+ds2.to_zarr("../auxiliary_data/GOES16_CH13_classifications_EUREC4A_30min.zarr", encoding=encoding, consolidated=True)
+cluster.scale(0)

scripts/reprocess.sh

@@ -52,9 +52,14 @@ python create_level3.py -e ${classification} -m daily
 
 # Calculate agreement between different classifications
 python calculate_agreement.py -t 0.1
-python calculate_agreement.py -t 0.25
 python calculate_agreement.py -t 0.5
 
+# Get subset of neural network classifications
+# (does not need to be executed since subset is included)
+# python extract_eurec4a_timeperiod_NNclassifications.py
+
+# Run notebooks for analysis
+
 # Prepare data for zenodo upload
 bash create_zenodo_datazip.sh ${classification}