Combining tiles
[8]:
import glob
import distributed
import numpy as np
dirname = "/glade/campaign/cgd/oce/people/bachman/ETP_1_20_tides/SHELF"
Request resources
[2]:
import ncar_jobqueue
cluster = ncar_jobqueue.NCARCluster(
project="ncgd0011",
#scheduler_options=dict(dashboard_address=":9797"),
cores=1, # The number of cores you want
memory="12GB", # Amount of memory
processes=1, # How many processes
queue="casper", # The type of queue to utilize (/glade/u/apps/dav/opt/usr/bin/execcasper)
local_directory="$TMPDIR", # Use your local directory
resource_spec="select=1:ncpus=1:mem=12GB", # Specify resources
walltime="04:00:00",
)
cluster
/glade/u/home/dcherian/miniconda3/envs/pump/lib/python3.10/site-packages/dask_jobqueue/core.py:20: FutureWarning: tmpfile is deprecated and will be removed in a future release. Please use dask.utils.tmpfile instead.
from distributed.utils import tmpfile
/glade/u/home/dcherian/miniconda3/envs/pump/lib/python3.10/site-packages/distributed/node.py:179: UserWarning: Port 8787 is already in use.
Perhaps you already have a cluster running?
Hosting the HTTP server on port 42221 instead
warnings.warn(
[15]:
client = distributed.Client(cluster)
client
[15]:
Client
Client-4f4fb284-2301-11ed-ad92-3cecef1acbfa
| Connection method: Cluster object | Cluster type: dask_jobqueue.PBSCluster |
| Dashboard: https://jupyterhub.hpc.ucar.edu/stable/user/dcherian/proxy/42221/status |
Cluster Info
PBSCluster
06d36d7c
| Dashboard: https://jupyterhub.hpc.ucar.edu/stable/user/dcherian/proxy/42221/status | Workers: 0 |
| Total threads: 0 | Total memory: 0 B |
Scheduler Info
Scheduler
Scheduler-c90756ae-e4be-4602-89f2-cf116b242062
| Comm: tcp://10.12.206.63:35854 | Workers: 0 |
| Dashboard: https://jupyterhub.hpc.ucar.edu/stable/user/dcherian/proxy/42221/status | Total threads: 0 |
| Started: 5 minutes ago | Total memory: 0 B |
Workers
[16]:
cluster.scale(2)
Note
The files are stored under paths that look like
'/glade/campaign/cgd/oce/people/bachman/ETP_1_20_tides/SHELF/ocean_shelf__1993_001.nc.0118',
'/glade/campaign/cgd/oce/people/bachman/ETP_1_20_tides/SHELF/ocean_shelf__1993_001.nc.0119',
'/glade/campaign/cgd/oce/people/bachman/ETP_1_20_tides/SHELF/ocean_shelf__1993_001.nc.0120',
`/glade/campaign/cgd/oce/people/bachman/ETP_1_20_tides/SHELF/ocean_shelf__1993_001.nc.0121',
...
Test out combining
Here’s what the files for a single day looks like.
[12]:
pattern = "1993_001"
files = sorted(glob.glob(f"{dirname}/*{pattern}*"))[:30]
files
[12]:
['/glade/campaign/cgd/oce/people/bachman/ETP_1_20_tides/SHELF/ocean_shelf__1993_001.nc.0118',
'/glade/campaign/cgd/oce/people/bachman/ETP_1_20_tides/SHELF/ocean_shelf__1993_001.nc.0119',
'/glade/campaign/cgd/oce/people/bachman/ETP_1_20_tides/SHELF/ocean_shelf__1993_001.nc.0120',
'/glade/campaign/cgd/oce/people/bachman/ETP_1_20_tides/SHELF/ocean_shelf__1993_001.nc.0121',
'/glade/campaign/cgd/oce/people/bachman/ETP_1_20_tides/SHELF/ocean_shelf__1993_001.nc.0122',
'/glade/campaign/cgd/oce/people/bachman/ETP_1_20_tides/SHELF/ocean_shelf__1993_001.nc.0123',
'/glade/campaign/cgd/oce/people/bachman/ETP_1_20_tides/SHELF/ocean_shelf__1993_001.nc.0136',
'/glade/campaign/cgd/oce/people/bachman/ETP_1_20_tides/SHELF/ocean_shelf__1993_001.nc.0137',
'/glade/campaign/cgd/oce/people/bachman/ETP_1_20_tides/SHELF/ocean_shelf__1993_001.nc.0138',
'/glade/campaign/cgd/oce/people/bachman/ETP_1_20_tides/SHELF/ocean_shelf__1993_001.nc.0139',
'/glade/campaign/cgd/oce/people/bachman/ETP_1_20_tides/SHELF/ocean_shelf__1993_001.nc.0140',
'/glade/campaign/cgd/oce/people/bachman/ETP_1_20_tides/SHELF/ocean_shelf__1993_001.nc.0141',
'/glade/campaign/cgd/oce/people/bachman/ETP_1_20_tides/SHELF/ocean_shelf__1993_001.nc.0154',
'/glade/campaign/cgd/oce/people/bachman/ETP_1_20_tides/SHELF/ocean_shelf__1993_001.nc.0155',
'/glade/campaign/cgd/oce/people/bachman/ETP_1_20_tides/SHELF/ocean_shelf__1993_001.nc.0156',
'/glade/campaign/cgd/oce/people/bachman/ETP_1_20_tides/SHELF/ocean_shelf__1993_001.nc.0157',
'/glade/campaign/cgd/oce/people/bachman/ETP_1_20_tides/SHELF/ocean_shelf__1993_001.nc.0158',
'/glade/campaign/cgd/oce/people/bachman/ETP_1_20_tides/SHELF/ocean_shelf__1993_001.nc.0159',
'/glade/campaign/cgd/oce/people/bachman/ETP_1_20_tides/SHELF/ocean_shelf__1993_001.nc.0172',
'/glade/campaign/cgd/oce/people/bachman/ETP_1_20_tides/SHELF/ocean_shelf__1993_001.nc.0173',
'/glade/campaign/cgd/oce/people/bachman/ETP_1_20_tides/SHELF/ocean_shelf__1993_001.nc.0174',
'/glade/campaign/cgd/oce/people/bachman/ETP_1_20_tides/SHELF/ocean_shelf__1993_001.nc.0175',
'/glade/campaign/cgd/oce/people/bachman/ETP_1_20_tides/SHELF/ocean_shelf__1993_001.nc.0176',
'/glade/campaign/cgd/oce/people/bachman/ETP_1_20_tides/SHELF/ocean_shelf__1993_001.nc.0177',
'/glade/campaign/cgd/oce/people/bachman/ETP_1_20_tides/SHELF/ocean_shelf__1993_001.nc.0190',
'/glade/campaign/cgd/oce/people/bachman/ETP_1_20_tides/SHELF/ocean_shelf__1993_001.nc.0191',
'/glade/campaign/cgd/oce/people/bachman/ETP_1_20_tides/SHELF/ocean_shelf__1993_001.nc.0192',
'/glade/campaign/cgd/oce/people/bachman/ETP_1_20_tides/SHELF/ocean_shelf__1993_001.nc.0193',
'/glade/campaign/cgd/oce/people/bachman/ETP_1_20_tides/SHELF/ocean_shelf__1993_001.nc.0194',
'/glade/campaign/cgd/oce/people/bachman/ETP_1_20_tides/SHELF/ocean_shelf__1993_001.nc.0195']
Lets read the raw files using read_raw_files
[ ]:
from mom6_tools.sections import read_raw_files
dsets = read_raw_files(files, parallel=True)
This returns a 1D list of Datasets
[18]:
dsets
[18]:
[<xarray.Dataset>
Dimensions: (xq: 51, yh: 35, z_l: 140, z_i: 141, time: 8, nv: 2, xh: 51,
yq: 35)
Coordinates:
* xq (xq) float64 263.0 263.1 263.1 263.1 ... 265.4 265.4 265.5
* yh (yh) float64 2.975 3.025 3.075 3.125 ... 4.575 4.625 4.675
* z_l (z_l) float64 1.25 3.75 6.25 ... 6.324e+03 6.574e+03
* z_i (z_i) float64 0.0 2.5 5.0 ... 6.199e+03 6.449e+03 6.699e+03
* time (time) object 1992-12-30 01:30:00 ... 1992-12-30 22:30:00
* nv (nv) float64 1.0 2.0
* xh (xh) float64 263.0 263.0 263.1 263.1 ... 265.4 265.4 265.5
* yq (yq) float64 3.0 3.05 3.1 3.15 3.2 ... 4.5 4.55 4.6 4.65 4.7
Data variables: (12/16)
uo (time, z_l, yh, xq) float32 dask.array<chunksize=(8, 140, 35, 51), meta=np.ndarray>
vo (time, z_l, yq, xh) float32 dask.array<chunksize=(8, 140, 35, 51), meta=np.ndarray>
thetao (time, z_l, yh, xh) float32 dask.array<chunksize=(8, 140, 35, 51), meta=np.ndarray>
so (time, z_l, yh, xh) float32 dask.array<chunksize=(8, 140, 35, 51), meta=np.ndarray>
Tflx_dia_diff (time, z_i, yh, xh) float32 dask.array<chunksize=(8, 141, 35, 51), meta=np.ndarray>
Sflx_dia_diff (time, z_i, yh, xh) float32 dask.array<chunksize=(8, 141, 35, 51), meta=np.ndarray>
... ...
Kv_u (time, z_l, yh, xq) float32 dask.array<chunksize=(8, 140, 35, 51), meta=np.ndarray>
Kd_BBL (time, z_i, yh, xh) float32 dask.array<chunksize=(8, 141, 35, 51), meta=np.ndarray>
average_T1 (time) object dask.array<chunksize=(8,), meta=np.ndarray>
average_T2 (time) object dask.array<chunksize=(8,), meta=np.ndarray>
average_DT (time) timedelta64[ns] dask.array<chunksize=(8,), meta=np.ndarray>
time_bnds (time, nv) timedelta64[ns] dask.array<chunksize=(8, 2), meta=np.ndarray>
Attributes:
NumFilesInSet: 0
title: MOM6 diagnostic fields table for CESM case: ETP.003
grid_type: regular
grid_tile: N/A,
<xarray.Dataset>
Dimensions: (xq: 56, yh: 35, z_l: 140, z_i: 141, time: 8, nv: 2, xh: 55,
yq: 35)
Coordinates:
* xq (xq) float64 265.5 265.6 265.6 265.6 ... 268.1 268.2 268.2
* yh (yh) float64 2.975 3.025 3.075 3.125 ... 4.575 4.625 4.675
* z_l (z_l) float64 1.25 3.75 6.25 ... 6.324e+03 6.574e+03
* z_i (z_i) float64 0.0 2.5 5.0 ... 6.199e+03 6.449e+03 6.699e+03
* time (time) object 1992-12-30 01:30:00 ... 1992-12-30 22:30:00
* nv (nv) float64 1.0 2.0
* xh (xh) float64 265.5 265.6 265.6 265.7 ... 268.1 268.2 268.2
* yq (yq) float64 3.0 3.05 3.1 3.15 3.2 ... 4.5 4.55 4.6 4.65 4.7
Data variables: (12/16)
uo (time, z_l, yh, xq) float32 dask.array<chunksize=(8, 140, 35, 56), meta=np.ndarray>
vo (time, z_l, yq, xh) float32 dask.array<chunksize=(8, 140, 35, 55), meta=np.ndarray>
thetao (time, z_l, yh, xh) float32 dask.array<chunksize=(8, 140, 35, 55), meta=np.ndarray>
so (time, z_l, yh, xh) float32 dask.array<chunksize=(8, 140, 35, 55), meta=np.ndarray>
Tflx_dia_diff (time, z_i, yh, xh) float32 dask.array<chunksize=(8, 141, 35, 55), meta=np.ndarray>
Sflx_dia_diff (time, z_i, yh, xh) float32 dask.array<chunksize=(8, 141, 35, 55), meta=np.ndarray>
... ...
Kv_u (time, z_l, yh, xq) float32 dask.array<chunksize=(8, 140, 35, 56), meta=np.ndarray>
Kd_BBL (time, z_i, yh, xh) float32 dask.array<chunksize=(8, 141, 35, 55), meta=np.ndarray>
average_T1 (time) object dask.array<chunksize=(8,), meta=np.ndarray>
average_T2 (time) object dask.array<chunksize=(8,), meta=np.ndarray>
average_DT (time) timedelta64[ns] dask.array<chunksize=(8,), meta=np.ndarray>
time_bnds (time, nv) timedelta64[ns] dask.array<chunksize=(8, 2), meta=np.ndarray>
Attributes:
NumFilesInSet: 0
title: MOM6 diagnostic fields table for CESM case: ETP.003
grid_type: regular
grid_tile: N/A,
<xarray.Dataset>
Dimensions: (xq: 56, yh: 35, z_l: 140, z_i: 141, time: 8, nv: 2, xh: 55,
yq: 35)
Coordinates:
* xq (xq) float64 268.2 268.3 268.4 268.4 ... 270.9 270.9 271.0
* yh (yh) float64 2.975 3.025 3.075 3.125 ... 4.575 4.625 4.675
* z_l (z_l) float64 1.25 3.75 6.25 ... 6.324e+03 6.574e+03
* z_i (z_i) float64 0.0 2.5 5.0 ... 6.199e+03 6.449e+03 6.699e+03
* time (time) object 1992-12-30 01:30:00 ... 1992-12-30 22:30:00
* nv (nv) float64 1.0 2.0
* xh (xh) float64 268.3 268.3 268.4 268.4 ... 270.9 270.9 271.0
* yq (yq) float64 3.0 3.05 3.1 3.15 3.2 ... 4.5 4.55 4.6 4.65 4.7
Data variables: (12/16)
uo (time, z_l, yh, xq) float32 dask.array<chunksize=(8, 140, 35, 56), meta=np.ndarray>
vo (time, z_l, yq, xh) float32 dask.array<chunksize=(8, 140, 35, 55), meta=np.ndarray>
thetao (time, z_l, yh, xh) float32 dask.array<chunksize=(8, 140, 35, 55), meta=np.ndarray>
so (time, z_l, yh, xh) float32 dask.array<chunksize=(8, 140, 35, 55), meta=np.ndarray>
Tflx_dia_diff (time, z_i, yh, xh) float32 dask.array<chunksize=(8, 141, 35, 55), meta=np.ndarray>
Sflx_dia_diff (time, z_i, yh, xh) float32 dask.array<chunksize=(8, 141, 35, 55), meta=np.ndarray>
... ...
Kv_u (time, z_l, yh, xq) float32 dask.array<chunksize=(8, 140, 35, 56), meta=np.ndarray>
Kd_BBL (time, z_i, yh, xh) float32 dask.array<chunksize=(8, 141, 35, 55), meta=np.ndarray>
average_T1 (time) object dask.array<chunksize=(8,), meta=np.ndarray>
average_T2 (time) object dask.array<chunksize=(8,), meta=np.ndarray>
average_DT (time) timedelta64[ns] dask.array<chunksize=(8,), meta=np.ndarray>
time_bnds (time, nv) timedelta64[ns] dask.array<chunksize=(8, 2), meta=np.ndarray>
Attributes:
NumFilesInSet: 0
title: MOM6 diagnostic fields table for CESM case: ETP.003
grid_type: regular
grid_tile: N/A,
<xarray.Dataset>
Dimensions: (xq: 57, yh: 35, z_l: 140, z_i: 141, time: 8, nv: 2, xh: 56,
yq: 35)
Coordinates:
* xq (xq) float64 271.0 271.1 271.1 271.1 ... 273.7 273.8 273.8
* yh (yh) float64 2.975 3.025 3.075 3.125 ... 4.575 4.625 4.675
* z_l (z_l) float64 1.25 3.75 6.25 ... 6.324e+03 6.574e+03
* z_i (z_i) float64 0.0 2.5 5.0 ... 6.199e+03 6.449e+03 6.699e+03
* time (time) object 1992-12-30 01:30:00 ... 1992-12-30 22:30:00
* nv (nv) float64 1.0 2.0
* xh (xh) float64 271.0 271.1 271.1 271.2 ... 273.7 273.7 273.8
* yq (yq) float64 3.0 3.05 3.1 3.15 3.2 ... 4.5 4.55 4.6 4.65 4.7
Data variables: (12/16)
uo (time, z_l, yh, xq) float32 dask.array<chunksize=(8, 140, 35, 57), meta=np.ndarray>
vo (time, z_l, yq, xh) float32 dask.array<chunksize=(8, 140, 35, 56), meta=np.ndarray>
thetao (time, z_l, yh, xh) float32 dask.array<chunksize=(8, 140, 35, 56), meta=np.ndarray>
so (time, z_l, yh, xh) float32 dask.array<chunksize=(8, 140, 35, 56), meta=np.ndarray>
Tflx_dia_diff (time, z_i, yh, xh) float32 dask.array<chunksize=(8, 141, 35, 56), meta=np.ndarray>
Sflx_dia_diff (time, z_i, yh, xh) float32 dask.array<chunksize=(8, 141, 35, 56), meta=np.ndarray>
... ...
Kv_u (time, z_l, yh, xq) float32 dask.array<chunksize=(8, 140, 35, 57), meta=np.ndarray>
Kd_BBL (time, z_i, yh, xh) float32 dask.array<chunksize=(8, 141, 35, 56), meta=np.ndarray>
average_T1 (time) object dask.array<chunksize=(8,), meta=np.ndarray>
average_T2 (time) object dask.array<chunksize=(8,), meta=np.ndarray>
average_DT (time) timedelta64[ns] dask.array<chunksize=(8,), meta=np.ndarray>
time_bnds (time, nv) timedelta64[ns] dask.array<chunksize=(8, 2), meta=np.ndarray>
Attributes:
NumFilesInSet: 0
title: MOM6 diagnostic fields table for CESM case: ETP.003
grid_type: regular
grid_tile: N/A,
<xarray.Dataset>
Dimensions: (xq: 57, yh: 35, z_l: 140, z_i: 141, time: 8, nv: 2, xh: 56,
yq: 35)
Coordinates:
* xq (xq) float64 273.8 273.9 273.9 273.9 ... 276.5 276.6 276.6
* yh (yh) float64 2.975 3.025 3.075 3.125 ... 4.575 4.625 4.675
* z_l (z_l) float64 1.25 3.75 6.25 ... 6.324e+03 6.574e+03
* z_i (z_i) float64 0.0 2.5 5.0 ... 6.199e+03 6.449e+03 6.699e+03
* time (time) object 1992-12-30 01:30:00 ... 1992-12-30 22:30:00
* nv (nv) float64 1.0 2.0
* xh (xh) float64 273.8 273.9 273.9 274.0 ... 276.5 276.5 276.6
* yq (yq) float64 3.0 3.05 3.1 3.15 3.2 ... 4.5 4.55 4.6 4.65 4.7
Data variables: (12/16)
uo (time, z_l, yh, xq) float32 dask.array<chunksize=(8, 140, 35, 57), meta=np.ndarray>
vo (time, z_l, yq, xh) float32 dask.array<chunksize=(8, 140, 35, 56), meta=np.ndarray>
thetao (time, z_l, yh, xh) float32 dask.array<chunksize=(8, 140, 35, 56), meta=np.ndarray>
so (time, z_l, yh, xh) float32 dask.array<chunksize=(8, 140, 35, 56), meta=np.ndarray>
Tflx_dia_diff (time, z_i, yh, xh) float32 dask.array<chunksize=(8, 141, 35, 56), meta=np.ndarray>
Sflx_dia_diff (time, z_i, yh, xh) float32 dask.array<chunksize=(8, 141, 35, 56), meta=np.ndarray>
... ...
Kv_u (time, z_l, yh, xq) float32 dask.array<chunksize=(8, 140, 35, 57), meta=np.ndarray>
Kd_BBL (time, z_i, yh, xh) float32 dask.array<chunksize=(8, 141, 35, 56), meta=np.ndarray>
average_T1 (time) object dask.array<chunksize=(8,), meta=np.ndarray>
average_T2 (time) object dask.array<chunksize=(8,), meta=np.ndarray>
average_DT (time) timedelta64[ns] dask.array<chunksize=(8,), meta=np.ndarray>
time_bnds (time, nv) timedelta64[ns] dask.array<chunksize=(8, 2), meta=np.ndarray>
Attributes:
NumFilesInSet: 0
title: MOM6 diagnostic fields table for CESM case: ETP.003
grid_type: regular
grid_tile: N/A,
<xarray.Dataset>
Dimensions: (xq: 9, yh: 35, z_l: 140, z_i: 141, time: 8, nv: 2, xh: 8,
yq: 35)
Coordinates:
* xq (xq) float64 276.6 276.6 276.7 276.8 ... 276.9 276.9 277.0
* yh (yh) float64 2.975 3.025 3.075 3.125 ... 4.575 4.625 4.675
* z_l (z_l) float64 1.25 3.75 6.25 ... 6.324e+03 6.574e+03
* z_i (z_i) float64 0.0 2.5 5.0 ... 6.199e+03 6.449e+03 6.699e+03
* time (time) object 1992-12-30 01:30:00 ... 1992-12-30 22:30:00
* nv (nv) float64 1.0 2.0
* xh (xh) float64 276.6 276.7 276.7 276.8 276.8 276.9 276.9 277.0
* yq (yq) float64 3.0 3.05 3.1 3.15 3.2 ... 4.5 4.55 4.6 4.65 4.7
Data variables: (12/16)
uo (time, z_l, yh, xq) float32 dask.array<chunksize=(8, 140, 35, 9), meta=np.ndarray>
vo (time, z_l, yq, xh) float32 dask.array<chunksize=(8, 140, 35, 8), meta=np.ndarray>
thetao (time, z_l, yh, xh) float32 dask.array<chunksize=(8, 140, 35, 8), meta=np.ndarray>
so (time, z_l, yh, xh) float32 dask.array<chunksize=(8, 140, 35, 8), meta=np.ndarray>
Tflx_dia_diff (time, z_i, yh, xh) float32 dask.array<chunksize=(8, 141, 35, 8), meta=np.ndarray>
Sflx_dia_diff (time, z_i, yh, xh) float32 dask.array<chunksize=(8, 141, 35, 8), meta=np.ndarray>
... ...
Kv_u (time, z_l, yh, xq) float32 dask.array<chunksize=(8, 140, 35, 9), meta=np.ndarray>
Kd_BBL (time, z_i, yh, xh) float32 dask.array<chunksize=(8, 141, 35, 8), meta=np.ndarray>
average_T1 (time) object dask.array<chunksize=(8,), meta=np.ndarray>
average_T2 (time) object dask.array<chunksize=(8,), meta=np.ndarray>
average_DT (time) timedelta64[ns] dask.array<chunksize=(8,), meta=np.ndarray>
time_bnds (time, nv) timedelta64[ns] dask.array<chunksize=(8, 2), meta=np.ndarray>
Attributes:
NumFilesInSet: 0
title: MOM6 diagnostic fields table for CESM case: ETP.003
grid_type: regular
grid_tile: N/A,
<xarray.Dataset>
Dimensions: (xq: 51, yh: 56, z_l: 140, z_i: 141, time: 8, nv: 2, xh: 51,
yq: 57)
Coordinates:
* xq (xq) float64 263.0 263.1 263.1 263.1 ... 265.4 265.4 265.5
* yh (yh) float64 4.725 4.775 4.825 4.875 ... 7.375 7.425 7.475
* z_l (z_l) float64 1.25 3.75 6.25 ... 6.324e+03 6.574e+03
* z_i (z_i) float64 0.0 2.5 5.0 ... 6.199e+03 6.449e+03 6.699e+03
* time (time) object 1992-12-30 01:30:00 ... 1992-12-30 22:30:00
* nv (nv) float64 1.0 2.0
* xh (xh) float64 263.0 263.0 263.1 263.1 ... 265.4 265.4 265.5
* yq (yq) float64 4.7 4.75 4.8 4.85 4.9 ... 7.3 7.35 7.4 7.45 7.5
Data variables: (12/16)
uo (time, z_l, yh, xq) float32 dask.array<chunksize=(8, 140, 56, 51), meta=np.ndarray>
vo (time, z_l, yq, xh) float32 dask.array<chunksize=(8, 140, 57, 51), meta=np.ndarray>
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so (time, z_l, yh, xh) float32 dask.array<chunksize=(8, 140, 56, 51), meta=np.ndarray>
Tflx_dia_diff (time, z_i, yh, xh) float32 dask.array<chunksize=(8, 141, 56, 51), meta=np.ndarray>
Sflx_dia_diff (time, z_i, yh, xh) float32 dask.array<chunksize=(8, 141, 56, 51), meta=np.ndarray>
... ...
Kv_u (time, z_l, yh, xq) float32 dask.array<chunksize=(8, 140, 56, 51), meta=np.ndarray>
Kd_BBL (time, z_i, yh, xh) float32 dask.array<chunksize=(8, 141, 56, 51), meta=np.ndarray>
average_T1 (time) object dask.array<chunksize=(8,), meta=np.ndarray>
average_T2 (time) object dask.array<chunksize=(8,), meta=np.ndarray>
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vo (time, z_l, yq, xh) float32 dask.array<chunksize=(8, 140, 57, 55), meta=np.ndarray>
thetao (time, z_l, yh, xh) float32 dask.array<chunksize=(8, 140, 56, 55), meta=np.ndarray>
so (time, z_l, yh, xh) float32 dask.array<chunksize=(8, 140, 56, 55), meta=np.ndarray>
Tflx_dia_diff (time, z_i, yh, xh) float32 dask.array<chunksize=(8, 141, 56, 55), meta=np.ndarray>
Sflx_dia_diff (time, z_i, yh, xh) float32 dask.array<chunksize=(8, 141, 56, 55), meta=np.ndarray>
... ...
Kv_u (time, z_l, yh, xq) float32 dask.array<chunksize=(8, 140, 56, 56), meta=np.ndarray>
Kd_BBL (time, z_i, yh, xh) float32 dask.array<chunksize=(8, 141, 56, 55), meta=np.ndarray>
average_T1 (time) object dask.array<chunksize=(8,), meta=np.ndarray>
average_T2 (time) object dask.array<chunksize=(8,), meta=np.ndarray>
average_DT (time) timedelta64[ns] dask.array<chunksize=(8,), meta=np.ndarray>
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vo (time, z_l, yq, xh) float32 dask.array<chunksize=(8, 140, 57, 55), meta=np.ndarray>
thetao (time, z_l, yh, xh) float32 dask.array<chunksize=(8, 140, 56, 55), meta=np.ndarray>
so (time, z_l, yh, xh) float32 dask.array<chunksize=(8, 140, 56, 55), meta=np.ndarray>
Tflx_dia_diff (time, z_i, yh, xh) float32 dask.array<chunksize=(8, 141, 56, 55), meta=np.ndarray>
Sflx_dia_diff (time, z_i, yh, xh) float32 dask.array<chunksize=(8, 141, 56, 55), meta=np.ndarray>
... ...
Kv_u (time, z_l, yh, xq) float32 dask.array<chunksize=(8, 140, 56, 56), meta=np.ndarray>
Kd_BBL (time, z_i, yh, xh) float32 dask.array<chunksize=(8, 141, 56, 55), meta=np.ndarray>
average_T1 (time) object dask.array<chunksize=(8,), meta=np.ndarray>
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average_DT (time) timedelta64[ns] dask.array<chunksize=(8,), meta=np.ndarray>
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vo (time, z_l, yq, xh) float32 dask.array<chunksize=(8, 140, 57, 56), meta=np.ndarray>
thetao (time, z_l, yh, xh) float32 dask.array<chunksize=(8, 140, 56, 56), meta=np.ndarray>
so (time, z_l, yh, xh) float32 dask.array<chunksize=(8, 140, 56, 56), meta=np.ndarray>
Tflx_dia_diff (time, z_i, yh, xh) float32 dask.array<chunksize=(8, 141, 56, 56), meta=np.ndarray>
Sflx_dia_diff (time, z_i, yh, xh) float32 dask.array<chunksize=(8, 141, 56, 56), meta=np.ndarray>
... ...
Kv_u (time, z_l, yh, xq) float32 dask.array<chunksize=(8, 140, 56, 57), meta=np.ndarray>
Kd_BBL (time, z_i, yh, xh) float32 dask.array<chunksize=(8, 141, 56, 56), meta=np.ndarray>
average_T1 (time) object dask.array<chunksize=(8,), meta=np.ndarray>
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average_DT (time) timedelta64[ns] dask.array<chunksize=(8,), meta=np.ndarray>
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vo (time, z_l, yq, xh) float32 dask.array<chunksize=(8, 140, 57, 56), meta=np.ndarray>
thetao (time, z_l, yh, xh) float32 dask.array<chunksize=(8, 140, 56, 56), meta=np.ndarray>
so (time, z_l, yh, xh) float32 dask.array<chunksize=(8, 140, 56, 56), meta=np.ndarray>
Tflx_dia_diff (time, z_i, yh, xh) float32 dask.array<chunksize=(8, 141, 56, 56), meta=np.ndarray>
Sflx_dia_diff (time, z_i, yh, xh) float32 dask.array<chunksize=(8, 141, 56, 56), meta=np.ndarray>
... ...
Kv_u (time, z_l, yh, xq) float32 dask.array<chunksize=(8, 140, 56, 57), meta=np.ndarray>
Kd_BBL (time, z_i, yh, xh) float32 dask.array<chunksize=(8, 141, 56, 56), meta=np.ndarray>
average_T1 (time) object dask.array<chunksize=(8,), meta=np.ndarray>
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average_DT (time) timedelta64[ns] dask.array<chunksize=(8,), meta=np.ndarray>
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vo (time, z_l, yq, xh) float32 dask.array<chunksize=(8, 140, 57, 8), meta=np.ndarray>
thetao (time, z_l, yh, xh) float32 dask.array<chunksize=(8, 140, 56, 8), meta=np.ndarray>
so (time, z_l, yh, xh) float32 dask.array<chunksize=(8, 140, 56, 8), meta=np.ndarray>
Tflx_dia_diff (time, z_i, yh, xh) float32 dask.array<chunksize=(8, 141, 56, 8), meta=np.ndarray>
Sflx_dia_diff (time, z_i, yh, xh) float32 dask.array<chunksize=(8, 141, 56, 8), meta=np.ndarray>
... ...
Kv_u (time, z_l, yh, xq) float32 dask.array<chunksize=(8, 140, 56, 9), meta=np.ndarray>
Kd_BBL (time, z_i, yh, xh) float32 dask.array<chunksize=(8, 141, 56, 8), meta=np.ndarray>
average_T1 (time) object dask.array<chunksize=(8,), meta=np.ndarray>
average_T2 (time) object dask.array<chunksize=(8,), meta=np.ndarray>
average_DT (time) timedelta64[ns] dask.array<chunksize=(8,), meta=np.ndarray>
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vo (time, z_l, yq, xh) float32 dask.array<chunksize=(8, 140, 57, 51), meta=np.ndarray>
thetao (time, z_l, yh, xh) float32 dask.array<chunksize=(8, 140, 56, 51), meta=np.ndarray>
so (time, z_l, yh, xh) float32 dask.array<chunksize=(8, 140, 56, 51), meta=np.ndarray>
Tflx_dia_diff (time, z_i, yh, xh) float32 dask.array<chunksize=(8, 141, 56, 51), meta=np.ndarray>
Sflx_dia_diff (time, z_i, yh, xh) float32 dask.array<chunksize=(8, 141, 56, 51), meta=np.ndarray>
... ...
Kv_u (time, z_l, yh, xq) float32 dask.array<chunksize=(8, 140, 56, 51), meta=np.ndarray>
Kd_BBL (time, z_i, yh, xh) float32 dask.array<chunksize=(8, 141, 56, 51), meta=np.ndarray>
average_T1 (time) object dask.array<chunksize=(8,), meta=np.ndarray>
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vo (time, z_l, yq, xh) float32 dask.array<chunksize=(8, 140, 57, 55), meta=np.ndarray>
thetao (time, z_l, yh, xh) float32 dask.array<chunksize=(8, 140, 56, 55), meta=np.ndarray>
so (time, z_l, yh, xh) float32 dask.array<chunksize=(8, 140, 56, 55), meta=np.ndarray>
Tflx_dia_diff (time, z_i, yh, xh) float32 dask.array<chunksize=(8, 141, 56, 55), meta=np.ndarray>
Sflx_dia_diff (time, z_i, yh, xh) float32 dask.array<chunksize=(8, 141, 56, 55), meta=np.ndarray>
... ...
Kv_u (time, z_l, yh, xq) float32 dask.array<chunksize=(8, 140, 56, 56), meta=np.ndarray>
Kd_BBL (time, z_i, yh, xh) float32 dask.array<chunksize=(8, 141, 56, 55), meta=np.ndarray>
average_T1 (time) object dask.array<chunksize=(8,), meta=np.ndarray>
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vo (time, z_l, yq, xh) float32 dask.array<chunksize=(8, 140, 57, 55), meta=np.ndarray>
thetao (time, z_l, yh, xh) float32 dask.array<chunksize=(8, 140, 56, 55), meta=np.ndarray>
so (time, z_l, yh, xh) float32 dask.array<chunksize=(8, 140, 56, 55), meta=np.ndarray>
Tflx_dia_diff (time, z_i, yh, xh) float32 dask.array<chunksize=(8, 141, 56, 55), meta=np.ndarray>
Sflx_dia_diff (time, z_i, yh, xh) float32 dask.array<chunksize=(8, 141, 56, 55), meta=np.ndarray>
... ...
Kv_u (time, z_l, yh, xq) float32 dask.array<chunksize=(8, 140, 56, 56), meta=np.ndarray>
Kd_BBL (time, z_i, yh, xh) float32 dask.array<chunksize=(8, 141, 56, 55), meta=np.ndarray>
average_T1 (time) object dask.array<chunksize=(8,), meta=np.ndarray>
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vo (time, z_l, yq, xh) float32 dask.array<chunksize=(8, 140, 57, 56), meta=np.ndarray>
thetao (time, z_l, yh, xh) float32 dask.array<chunksize=(8, 140, 56, 56), meta=np.ndarray>
so (time, z_l, yh, xh) float32 dask.array<chunksize=(8, 140, 56, 56), meta=np.ndarray>
Tflx_dia_diff (time, z_i, yh, xh) float32 dask.array<chunksize=(8, 141, 56, 56), meta=np.ndarray>
Sflx_dia_diff (time, z_i, yh, xh) float32 dask.array<chunksize=(8, 141, 56, 56), meta=np.ndarray>
... ...
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vo (time, z_l, yq, xh) float32 dask.array<chunksize=(8, 140, 57, 56), meta=np.ndarray>
thetao (time, z_l, yh, xh) float32 dask.array<chunksize=(8, 140, 56, 56), meta=np.ndarray>
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Sflx_dia_diff (time, z_i, yh, xh) float32 dask.array<chunksize=(8, 141, 56, 56), meta=np.ndarray>
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Kv_u (time, z_l, yh, xq) float32 dask.array<chunksize=(8, 140, 56, 57), meta=np.ndarray>
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average_T1 (time) object dask.array<chunksize=(8,), meta=np.ndarray>
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thetao (time, z_l, yh, xh) float32 dask.array<chunksize=(8, 140, 56, 8), meta=np.ndarray>
so (time, z_l, yh, xh) float32 dask.array<chunksize=(8, 140, 56, 8), meta=np.ndarray>
Tflx_dia_diff (time, z_i, yh, xh) float32 dask.array<chunksize=(8, 141, 56, 8), meta=np.ndarray>
Sflx_dia_diff (time, z_i, yh, xh) float32 dask.array<chunksize=(8, 141, 56, 8), meta=np.ndarray>
... ...
Kv_u (time, z_l, yh, xq) float32 dask.array<chunksize=(8, 140, 56, 9), meta=np.ndarray>
Kd_BBL (time, z_i, yh, xh) float32 dask.array<chunksize=(8, 141, 56, 8), meta=np.ndarray>
average_T1 (time) object dask.array<chunksize=(8,), meta=np.ndarray>
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vo (time, z_l, yq, xh) float32 dask.array<chunksize=(8, 140, 57, 51), meta=np.ndarray>
thetao (time, z_l, yh, xh) float32 dask.array<chunksize=(8, 140, 56, 51), meta=np.ndarray>
so (time, z_l, yh, xh) float32 dask.array<chunksize=(8, 140, 56, 51), meta=np.ndarray>
Tflx_dia_diff (time, z_i, yh, xh) float32 dask.array<chunksize=(8, 141, 56, 51), meta=np.ndarray>
Sflx_dia_diff (time, z_i, yh, xh) float32 dask.array<chunksize=(8, 141, 56, 51), meta=np.ndarray>
... ...
Kv_u (time, z_l, yh, xq) float32 dask.array<chunksize=(8, 140, 56, 51), meta=np.ndarray>
Kd_BBL (time, z_i, yh, xh) float32 dask.array<chunksize=(8, 141, 56, 51), meta=np.ndarray>
average_T1 (time) object dask.array<chunksize=(8,), meta=np.ndarray>
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vo (time, z_l, yq, xh) float32 dask.array<chunksize=(8, 140, 57, 55), meta=np.ndarray>
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so (time, z_l, yh, xh) float32 dask.array<chunksize=(8, 140, 56, 55), meta=np.ndarray>
Tflx_dia_diff (time, z_i, yh, xh) float32 dask.array<chunksize=(8, 141, 56, 55), meta=np.ndarray>
Sflx_dia_diff (time, z_i, yh, xh) float32 dask.array<chunksize=(8, 141, 56, 55), meta=np.ndarray>
... ...
Kv_u (time, z_l, yh, xq) float32 dask.array<chunksize=(8, 140, 56, 56), meta=np.ndarray>
Kd_BBL (time, z_i, yh, xh) float32 dask.array<chunksize=(8, 141, 56, 55), meta=np.ndarray>
average_T1 (time) object dask.array<chunksize=(8,), meta=np.ndarray>
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vo (time, z_l, yq, xh) float32 dask.array<chunksize=(8, 140, 57, 55), meta=np.ndarray>
thetao (time, z_l, yh, xh) float32 dask.array<chunksize=(8, 140, 56, 55), meta=np.ndarray>
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Tflx_dia_diff (time, z_i, yh, xh) float32 dask.array<chunksize=(8, 141, 56, 55), meta=np.ndarray>
Sflx_dia_diff (time, z_i, yh, xh) float32 dask.array<chunksize=(8, 141, 56, 55), meta=np.ndarray>
... ...
Kv_u (time, z_l, yh, xq) float32 dask.array<chunksize=(8, 140, 56, 56), meta=np.ndarray>
Kd_BBL (time, z_i, yh, xh) float32 dask.array<chunksize=(8, 141, 56, 55), meta=np.ndarray>
average_T1 (time) object dask.array<chunksize=(8,), meta=np.ndarray>
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vo (time, z_l, yq, xh) float32 dask.array<chunksize=(8, 140, 57, 56), meta=np.ndarray>
thetao (time, z_l, yh, xh) float32 dask.array<chunksize=(8, 140, 56, 56), meta=np.ndarray>
so (time, z_l, yh, xh) float32 dask.array<chunksize=(8, 140, 56, 56), meta=np.ndarray>
Tflx_dia_diff (time, z_i, yh, xh) float32 dask.array<chunksize=(8, 141, 56, 56), meta=np.ndarray>
Sflx_dia_diff (time, z_i, yh, xh) float32 dask.array<chunksize=(8, 141, 56, 56), meta=np.ndarray>
... ...
Kv_u (time, z_l, yh, xq) float32 dask.array<chunksize=(8, 140, 56, 57), meta=np.ndarray>
Kd_BBL (time, z_i, yh, xh) float32 dask.array<chunksize=(8, 141, 56, 56), meta=np.ndarray>
average_T1 (time) object dask.array<chunksize=(8,), meta=np.ndarray>
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vo (time, z_l, yq, xh) float32 dask.array<chunksize=(8, 140, 57, 56), meta=np.ndarray>
thetao (time, z_l, yh, xh) float32 dask.array<chunksize=(8, 140, 56, 56), meta=np.ndarray>
so (time, z_l, yh, xh) float32 dask.array<chunksize=(8, 140, 56, 56), meta=np.ndarray>
Tflx_dia_diff (time, z_i, yh, xh) float32 dask.array<chunksize=(8, 141, 56, 56), meta=np.ndarray>
Sflx_dia_diff (time, z_i, yh, xh) float32 dask.array<chunksize=(8, 141, 56, 56), meta=np.ndarray>
... ...
Kv_u (time, z_l, yh, xq) float32 dask.array<chunksize=(8, 140, 56, 57), meta=np.ndarray>
Kd_BBL (time, z_i, yh, xh) float32 dask.array<chunksize=(8, 141, 56, 56), meta=np.ndarray>
average_T1 (time) object dask.array<chunksize=(8,), meta=np.ndarray>
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vo (time, z_l, yq, xh) float32 dask.array<chunksize=(8, 140, 57, 8), meta=np.ndarray>
thetao (time, z_l, yh, xh) float32 dask.array<chunksize=(8, 140, 56, 8), meta=np.ndarray>
so (time, z_l, yh, xh) float32 dask.array<chunksize=(8, 140, 56, 8), meta=np.ndarray>
Tflx_dia_diff (time, z_i, yh, xh) float32 dask.array<chunksize=(8, 141, 56, 8), meta=np.ndarray>
Sflx_dia_diff (time, z_i, yh, xh) float32 dask.array<chunksize=(8, 141, 56, 8), meta=np.ndarray>
... ...
Kv_u (time, z_l, yh, xq) float32 dask.array<chunksize=(8, 140, 56, 9), meta=np.ndarray>
Kd_BBL (time, z_i, yh, xh) float32 dask.array<chunksize=(8, 141, 56, 8), meta=np.ndarray>
average_T1 (time) object dask.array<chunksize=(8,), meta=np.ndarray>
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vo (time, z_l, yq, xh) float32 dask.array<chunksize=(8, 140, 39, 51), meta=np.ndarray>
thetao (time, z_l, yh, xh) float32 dask.array<chunksize=(8, 140, 38, 51), meta=np.ndarray>
so (time, z_l, yh, xh) float32 dask.array<chunksize=(8, 140, 38, 51), meta=np.ndarray>
Tflx_dia_diff (time, z_i, yh, xh) float32 dask.array<chunksize=(8, 141, 38, 51), meta=np.ndarray>
Sflx_dia_diff (time, z_i, yh, xh) float32 dask.array<chunksize=(8, 141, 38, 51), meta=np.ndarray>
... ...
Kv_u (time, z_l, yh, xq) float32 dask.array<chunksize=(8, 140, 38, 51), meta=np.ndarray>
Kd_BBL (time, z_i, yh, xh) float32 dask.array<chunksize=(8, 141, 38, 51), meta=np.ndarray>
average_T1 (time) object dask.array<chunksize=(8,), meta=np.ndarray>
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* xq (xq) float64 265.5 265.6 265.6 265.6 ... 268.1 268.2 268.2
* yh (yh) float64 13.12 13.18 13.23 13.27 ... 14.88 14.93 14.98
* z_l (z_l) float64 1.25 3.75 6.25 ... 6.324e+03 6.574e+03
* z_i (z_i) float64 0.0 2.5 5.0 ... 6.199e+03 6.449e+03 6.699e+03
* time (time) object 1992-12-30 01:30:00 ... 1992-12-30 22:30:00
* nv (nv) float64 1.0 2.0
* xh (xh) float64 265.5 265.6 265.6 265.7 ... 268.1 268.2 268.2
* yq (yq) float64 13.1 13.15 13.2 13.25 ... 14.85 14.9 14.95 15.0
Data variables: (12/16)
uo (time, z_l, yh, xq) float32 dask.array<chunksize=(8, 140, 38, 56), meta=np.ndarray>
vo (time, z_l, yq, xh) float32 dask.array<chunksize=(8, 140, 39, 55), meta=np.ndarray>
thetao (time, z_l, yh, xh) float32 dask.array<chunksize=(8, 140, 38, 55), meta=np.ndarray>
so (time, z_l, yh, xh) float32 dask.array<chunksize=(8, 140, 38, 55), meta=np.ndarray>
Tflx_dia_diff (time, z_i, yh, xh) float32 dask.array<chunksize=(8, 141, 38, 55), meta=np.ndarray>
Sflx_dia_diff (time, z_i, yh, xh) float32 dask.array<chunksize=(8, 141, 38, 55), meta=np.ndarray>
... ...
Kv_u (time, z_l, yh, xq) float32 dask.array<chunksize=(8, 140, 38, 56), meta=np.ndarray>
Kd_BBL (time, z_i, yh, xh) float32 dask.array<chunksize=(8, 141, 38, 55), meta=np.ndarray>
average_T1 (time) object dask.array<chunksize=(8,), meta=np.ndarray>
average_T2 (time) object dask.array<chunksize=(8,), meta=np.ndarray>
average_DT (time) timedelta64[ns] dask.array<chunksize=(8,), meta=np.ndarray>
time_bnds (time, nv) timedelta64[ns] dask.array<chunksize=(8, 2), meta=np.ndarray>
Attributes:
NumFilesInSet: 0
title: MOM6 diagnostic fields table for CESM case: ETP.003
grid_type: regular
grid_tile: N/A,
<xarray.Dataset>
Dimensions: (xq: 56, yh: 38, z_l: 140, z_i: 141, time: 8, nv: 2, xh: 55,
yq: 39)
Coordinates:
* xq (xq) float64 268.2 268.3 268.4 268.4 ... 270.9 270.9 271.0
* yh (yh) float64 13.12 13.18 13.23 13.27 ... 14.88 14.93 14.98
* z_l (z_l) float64 1.25 3.75 6.25 ... 6.324e+03 6.574e+03
* z_i (z_i) float64 0.0 2.5 5.0 ... 6.199e+03 6.449e+03 6.699e+03
* time (time) object 1992-12-30 01:30:00 ... 1992-12-30 22:30:00
* nv (nv) float64 1.0 2.0
* xh (xh) float64 268.3 268.3 268.4 268.4 ... 270.9 270.9 271.0
* yq (yq) float64 13.1 13.15 13.2 13.25 ... 14.85 14.9 14.95 15.0
Data variables: (12/16)
uo (time, z_l, yh, xq) float32 dask.array<chunksize=(8, 140, 38, 56), meta=np.ndarray>
vo (time, z_l, yq, xh) float32 dask.array<chunksize=(8, 140, 39, 55), meta=np.ndarray>
thetao (time, z_l, yh, xh) float32 dask.array<chunksize=(8, 140, 38, 55), meta=np.ndarray>
so (time, z_l, yh, xh) float32 dask.array<chunksize=(8, 140, 38, 55), meta=np.ndarray>
Tflx_dia_diff (time, z_i, yh, xh) float32 dask.array<chunksize=(8, 141, 38, 55), meta=np.ndarray>
Sflx_dia_diff (time, z_i, yh, xh) float32 dask.array<chunksize=(8, 141, 38, 55), meta=np.ndarray>
... ...
Kv_u (time, z_l, yh, xq) float32 dask.array<chunksize=(8, 140, 38, 56), meta=np.ndarray>
Kd_BBL (time, z_i, yh, xh) float32 dask.array<chunksize=(8, 141, 38, 55), meta=np.ndarray>
average_T1 (time) object dask.array<chunksize=(8,), meta=np.ndarray>
average_T2 (time) object dask.array<chunksize=(8,), meta=np.ndarray>
average_DT (time) timedelta64[ns] dask.array<chunksize=(8,), meta=np.ndarray>
time_bnds (time, nv) timedelta64[ns] dask.array<chunksize=(8, 2), meta=np.ndarray>
Attributes:
NumFilesInSet: 0
title: MOM6 diagnostic fields table for CESM case: ETP.003
grid_type: regular
grid_tile: N/A,
<xarray.Dataset>
Dimensions: (xq: 57, yh: 38, z_l: 140, z_i: 141, time: 8, nv: 2, xh: 56,
yq: 39)
Coordinates:
* xq (xq) float64 271.0 271.1 271.1 271.1 ... 273.7 273.8 273.8
* yh (yh) float64 13.12 13.18 13.23 13.27 ... 14.88 14.93 14.98
* z_l (z_l) float64 1.25 3.75 6.25 ... 6.324e+03 6.574e+03
* z_i (z_i) float64 0.0 2.5 5.0 ... 6.199e+03 6.449e+03 6.699e+03
* time (time) object 1992-12-30 01:30:00 ... 1992-12-30 22:30:00
* nv (nv) float64 1.0 2.0
* xh (xh) float64 271.0 271.1 271.1 271.2 ... 273.7 273.7 273.8
* yq (yq) float64 13.1 13.15 13.2 13.25 ... 14.85 14.9 14.95 15.0
Data variables: (12/16)
uo (time, z_l, yh, xq) float32 dask.array<chunksize=(8, 140, 38, 57), meta=np.ndarray>
vo (time, z_l, yq, xh) float32 dask.array<chunksize=(8, 140, 39, 56), meta=np.ndarray>
thetao (time, z_l, yh, xh) float32 dask.array<chunksize=(8, 140, 38, 56), meta=np.ndarray>
so (time, z_l, yh, xh) float32 dask.array<chunksize=(8, 140, 38, 56), meta=np.ndarray>
Tflx_dia_diff (time, z_i, yh, xh) float32 dask.array<chunksize=(8, 141, 38, 56), meta=np.ndarray>
Sflx_dia_diff (time, z_i, yh, xh) float32 dask.array<chunksize=(8, 141, 38, 56), meta=np.ndarray>
... ...
Kv_u (time, z_l, yh, xq) float32 dask.array<chunksize=(8, 140, 38, 57), meta=np.ndarray>
Kd_BBL (time, z_i, yh, xh) float32 dask.array<chunksize=(8, 141, 38, 56), meta=np.ndarray>
average_T1 (time) object dask.array<chunksize=(8,), meta=np.ndarray>
average_T2 (time) object dask.array<chunksize=(8,), meta=np.ndarray>
average_DT (time) timedelta64[ns] dask.array<chunksize=(8,), meta=np.ndarray>
time_bnds (time, nv) timedelta64[ns] dask.array<chunksize=(8, 2), meta=np.ndarray>
Attributes:
NumFilesInSet: 0
title: MOM6 diagnostic fields table for CESM case: ETP.003
grid_type: regular
grid_tile: N/A,
<xarray.Dataset>
Dimensions: (xq: 57, yh: 38, z_l: 140, z_i: 141, time: 8, nv: 2, xh: 56,
yq: 39)
Coordinates:
* xq (xq) float64 273.8 273.9 273.9 273.9 ... 276.5 276.6 276.6
* yh (yh) float64 13.12 13.18 13.23 13.27 ... 14.88 14.93 14.98
* z_l (z_l) float64 1.25 3.75 6.25 ... 6.324e+03 6.574e+03
* z_i (z_i) float64 0.0 2.5 5.0 ... 6.199e+03 6.449e+03 6.699e+03
* time (time) object 1992-12-30 01:30:00 ... 1992-12-30 22:30:00
* nv (nv) float64 1.0 2.0
* xh (xh) float64 273.8 273.9 273.9 274.0 ... 276.5 276.5 276.6
* yq (yq) float64 13.1 13.15 13.2 13.25 ... 14.85 14.9 14.95 15.0
Data variables: (12/16)
uo (time, z_l, yh, xq) float32 dask.array<chunksize=(8, 140, 38, 57), meta=np.ndarray>
vo (time, z_l, yq, xh) float32 dask.array<chunksize=(8, 140, 39, 56), meta=np.ndarray>
thetao (time, z_l, yh, xh) float32 dask.array<chunksize=(8, 140, 38, 56), meta=np.ndarray>
so (time, z_l, yh, xh) float32 dask.array<chunksize=(8, 140, 38, 56), meta=np.ndarray>
Tflx_dia_diff (time, z_i, yh, xh) float32 dask.array<chunksize=(8, 141, 38, 56), meta=np.ndarray>
Sflx_dia_diff (time, z_i, yh, xh) float32 dask.array<chunksize=(8, 141, 38, 56), meta=np.ndarray>
... ...
Kv_u (time, z_l, yh, xq) float32 dask.array<chunksize=(8, 140, 38, 57), meta=np.ndarray>
Kd_BBL (time, z_i, yh, xh) float32 dask.array<chunksize=(8, 141, 38, 56), meta=np.ndarray>
average_T1 (time) object dask.array<chunksize=(8,), meta=np.ndarray>
average_T2 (time) object dask.array<chunksize=(8,), meta=np.ndarray>
average_DT (time) timedelta64[ns] dask.array<chunksize=(8,), meta=np.ndarray>
time_bnds (time, nv) timedelta64[ns] dask.array<chunksize=(8, 2), meta=np.ndarray>
Attributes:
NumFilesInSet: 0
title: MOM6 diagnostic fields table for CESM case: ETP.003
grid_type: regular
grid_tile: N/A,
<xarray.Dataset>
Dimensions: (xq: 9, yh: 38, z_l: 140, z_i: 141, time: 8, nv: 2, xh: 8,
yq: 39)
Coordinates:
* xq (xq) float64 276.6 276.6 276.7 276.8 ... 276.9 276.9 277.0
* yh (yh) float64 13.12 13.18 13.23 13.27 ... 14.88 14.93 14.98
* z_l (z_l) float64 1.25 3.75 6.25 ... 6.324e+03 6.574e+03
* z_i (z_i) float64 0.0 2.5 5.0 ... 6.199e+03 6.449e+03 6.699e+03
* time (time) object 1992-12-30 01:30:00 ... 1992-12-30 22:30:00
* nv (nv) float64 1.0 2.0
* xh (xh) float64 276.6 276.7 276.7 276.8 276.8 276.9 276.9 277.0
* yq (yq) float64 13.1 13.15 13.2 13.25 ... 14.85 14.9 14.95 15.0
Data variables: (12/16)
uo (time, z_l, yh, xq) float32 dask.array<chunksize=(8, 140, 38, 9), meta=np.ndarray>
vo (time, z_l, yq, xh) float32 dask.array<chunksize=(8, 140, 39, 8), meta=np.ndarray>
thetao (time, z_l, yh, xh) float32 dask.array<chunksize=(8, 140, 38, 8), meta=np.ndarray>
so (time, z_l, yh, xh) float32 dask.array<chunksize=(8, 140, 38, 8), meta=np.ndarray>
Tflx_dia_diff (time, z_i, yh, xh) float32 dask.array<chunksize=(8, 141, 38, 8), meta=np.ndarray>
Sflx_dia_diff (time, z_i, yh, xh) float32 dask.array<chunksize=(8, 141, 38, 8), meta=np.ndarray>
... ...
Kv_u (time, z_l, yh, xq) float32 dask.array<chunksize=(8, 140, 38, 9), meta=np.ndarray>
Kd_BBL (time, z_i, yh, xh) float32 dask.array<chunksize=(8, 141, 38, 8), meta=np.ndarray>
average_T1 (time) object dask.array<chunksize=(8,), meta=np.ndarray>
average_T2 (time) object dask.array<chunksize=(8,), meta=np.ndarray>
average_DT (time) timedelta64[ns] dask.array<chunksize=(8,), meta=np.ndarray>
time_bnds (time, nv) timedelta64[ns] dask.array<chunksize=(8, 2), meta=np.ndarray>
Attributes:
NumFilesInSet: 0
title: MOM6 diagnostic fields table for CESM case: ETP.003
grid_type: regular
grid_tile: N/A]
Now visualize the bounding boxes for each tile using a single variable
[22]:
from mom6_sections import visualize_tile
for ds in dsets:
visualize_tile(ds.uo)
Now lets combine those tiles into a single Dataset. For that we will first reshape our 1D list of Datasets into 2D tiles matching the figure above (6 along x, 5 along y)
[31]:
from mom6_tools.sections import tile_raw_files
tiled = tile_raw_files(dsets, x=6, y=5)
print(f"ncols={len(tiled)}, nrows={len(tiled[0])}")
ncols=5, nrows=6
[32]:
from mom6_tools.sections import combine_manual
[33]:
combined = combine_manual(tiled)
combined
[33]:
<xarray.Dataset>
Dimensions: (xq: 281, yh: 241, z_l: 140, time: 8, xh: 281, yq: 241,
z_i: 141)
Coordinates:
* xq (xq) float64 263.0 263.1 263.1 263.1 ... 276.9 276.9 277.0
* yh (yh) float64 2.975 3.025 3.075 3.125 ... 14.88 14.93 14.98
* z_l (z_l) float64 1.25 3.75 6.25 ... 6.324e+03 6.574e+03
* time (time) object 1992-12-30 01:30:00 ... 1992-12-30 22:30:00
* xh (xh) float64 263.0 263.0 263.1 263.1 ... 276.9 276.9 277.0
* yq (yq) float64 3.0 3.05 3.1 3.15 3.2 ... 14.85 14.9 14.95 15.0
* z_i (z_i) float64 0.0 2.5 5.0 ... 6.199e+03 6.449e+03 6.699e+03
Data variables:
uo (time, z_l, yh, xq) float32 dask.array<chunksize=(8, 140, 35, 50), meta=np.ndarray>
vo (time, z_l, yq, xh) float32 dask.array<chunksize=(8, 140, 34, 51), meta=np.ndarray>
thetao (time, z_l, yh, xh) float32 dask.array<chunksize=(8, 140, 35, 51), meta=np.ndarray>
so (time, z_l, yh, xh) float32 dask.array<chunksize=(8, 140, 35, 51), meta=np.ndarray>
Tflx_dia_diff (time, z_i, yh, xh) float32 dask.array<chunksize=(8, 141, 35, 51), meta=np.ndarray>
Sflx_dia_diff (time, z_i, yh, xh) float32 dask.array<chunksize=(8, 141, 35, 51), meta=np.ndarray>
Kd_heat (time, z_i, yh, xh) float32 dask.array<chunksize=(8, 141, 35, 51), meta=np.ndarray>
Kd_salt (time, z_i, yh, xh) float32 dask.array<chunksize=(8, 141, 35, 51), meta=np.ndarray>
Kd_ePBL (time, z_i, yh, xh) float32 dask.array<chunksize=(8, 141, 35, 51), meta=np.ndarray>
Kd_shear (time, z_i, yh, xh) float32 dask.array<chunksize=(8, 141, 35, 51), meta=np.ndarray>
Kv_u (time, z_l, yh, xq) float32 dask.array<chunksize=(8, 140, 35, 50), meta=np.ndarray>
Kd_BBL (time, z_i, yh, xh) float32 dask.array<chunksize=(8, 141, 35, 51), meta=np.ndarray>Make sure there are no artifacts
[39]:
import cf_xarray
combined.uo.cf.isel(Z=0, T=0).plot(robust=True)
[39]:
<matplotlib.collections.QuadMesh at 0x2b38bd1e19f0>
[40]:
combined.uo.cf.isel(Z=0, T=0).cf.diff("X").plot(robust=True)
[40]:
<matplotlib.collections.QuadMesh at 0x2b38bd607a00>
Combine files in parallel
The idea is that we’ll use
dask.delayedto execute one task per day i.e. we parallelize across output filesThis task will
synchronously read in all files for that day using
read_raw_files,assemble them to one xarray.Dataset using
tile_raw_filesandcombine_manualwrite that Dataset to a new file with compression enabled
The following function does all these steps
[6]:
def all_steps(pattern, dirname, scheduler="sync"):
import glob
import dask
from mom_tools.sections import combine_manual, read_raw_files, tile_raw_files
# compressor = zarr.Blosc(cname="zstd", clevel=3, shuffle=2)
# encoding = {var: {"compressor": compressor} for var in concat}
# complevel=4 makes no difference
compr_dict = dict(zlib=True, complevel=1, _FillValue=None)
globstr = f"{dirname}/*{pattern}*"
files = sorted(glob.glob(f"{dirname}/*{pattern}*"))
with dask.config.set(scheduler=scheduler):
# only parallelize if not being executed in a delayed task
raw_files_list = read_raw_files(files, parallel= scheduler != "sync")
if not raw_files_list:
raise ValueError(f"bad pattern: {pattern}; reading {globstr}")
nfiles = len(raw_files_list)
# make sure number of files is what I expect
if nfiles != 30:
raise ValueError(f"wrong number of files {nfiles} for pattern: {pattern}")
# reshape into 2D list of lists
# then concatenate along rows, then columns
combined1 = combine_manual(tile_raw_files(raw_files_list[:30], 6, 5))
# write to file
name = f"{dirname}/compressed/ocean_shelf_{pattern}.nc"
combined1.to_netcdf(
name,
unlimited_dims=["time"],
encoding=dict.fromkeys(combined1.variables, compr_dict),
)
return pattern
Test
[ ]:
ds = all_steps("1993_001", dirname)
Combine
Now determine all unique year_day files
[41]:
allfiles = sorted(glob.glob(f"{dirname}/*_*.nc.*"))
patterns = np.unique([file[-16:-8] for file in allfiles])
patterns
[41]:
array(['1993_001', '1993_002', '1993_003', ..., '2018_363', '2018_364',
'2018_365'], dtype='<U8')
Now construct a list of delayed tasks
[40]:
tasks = [dask.delayed(all_steps)(pattern, dirname) for pattern in patterns]
And execute!
[40]:
results = dask.compute(*tasks, scheduler=client);