In [1]:
%matplotlib inline
import pandas as pd
import socket
host = socket.getfqdn()

from core import  load, zoom, calc, save,plots,monitor
In [2]:
#reload funcs after updating ./core/*.py
import importlib
importlib.reload(load)
importlib.reload(zoom)
importlib.reload(calc)
importlib.reload(save)
importlib.reload(plots)
importlib.reload(monitor)
Out[2]:
<module 'core.monitor' from '/ccc/work/cont003/gen7420/odakatin/monitor-sedna/notebook/core/monitor.py'>
In [3]:
# 'month':  = 'JOBID' almost month but not really, 

# If you submit the job with job scheduler, above

#below are list of enviroment variable one can pass
#%env local='2"
# local : if True  run dask local cluster, if not true, put number of workers
# setted in the 'local'
# if no 'local ' given, local will be setted automatically to 'True'
#%env ychunk='2'
#%env tchunk='2'
# controls chunk. 'False' sets no modification from original netcdf file's chunk.  
# ychunk=10 will group the original netcdf file to 10 by 10 
# tchunk=1 will chunk the time coordinate one by one
#%env control=FWC_SSH 
# name of control file to be used for computation/plots/save/ 
#%env file_exp= 
# 'file_exp': Which 'experiment' name is it? 
#.    this corresopnds to intake catalog name without path and .yaml
#%env year=
# for Validation, this correspoinds to path/year/month 's year
# for monitoring, this corresponids to 'date'  having * means do all files in the monitoring directory
# setting it as *0[0-9] &*1[0-9]& *[2-3][0-9], the job can be separated in three lots.
#%env month=
# for monitoring  this corresponds to file path path-XIOS.{month}/
#
#%env save=   proceed saving?   True or False  , Default is setted as True 
#%env plot=   proceed plotting?  True or False , Default is setted as True 
#%env calc=   proceed computation? or just load computed result? True or False , Default is setted as True 
#%env save=False
#%env lazy=False
In [4]:
%%time
# 'savefig': Do we save output in html? or not. keep it true. 
savefig=True
client,cluster,control,catalog_url,month,year,daskreport,outputpath = load.set_control(host)
!mkdir -p $outputpath
!mkdir -p $daskreport
client

local True
using host= irene4695.c-irene.mg1.tgcc.ccc.cea.fr starting dask cluster on local= True workers 16
10000000000
False
rome local cluster starting
This code is running on  irene4695.c-irene.mg1.tgcc.ccc.cea.fr using  SEDNA_DELTA_MONITOR file experiment, read from  ../lib/SEDNA_DELTA_MONITOR.yaml  on year= 2012  on month= 01  outputpath= ../results/SEDNA_DELTA_MONITOR/ daskreport= ../results/dask/6412985irene4695.c-irene.mg1.tgcc.ccc.cea.fr_SEDNA_DELTA_MONITOR_01M_FWC_2D/
CPU times: user 575 ms, sys: 129 ms, total: 704 ms
Wall time: 21.5 s
Out[4]:

Client

Client-acb15a14-1331-11ed-a50c-080038b949a5

Connection method: Cluster object Cluster type: distributed.LocalCluster
Dashboard: http://127.0.0.1:8787/status

Cluster Info

LocalCluster

c6a4ba70

Dashboard: http://127.0.0.1:8787/status Workers: 16
Total threads: 128 Total memory: 251.06 GiB
Status: running Using processes: True

Scheduler Info

Scheduler

Scheduler-d9133c78-7eb9-4e63-a4a9-127cce4b067e

Comm: tcp://127.0.0.1:32875 Workers: 16
Dashboard: http://127.0.0.1:8787/status Total threads: 128
Started: Just now Total memory: 251.06 GiB

Workers

Worker: 0
Comm: tcp://127.0.0.1:46347 Total threads: 8
Dashboard: http://127.0.0.1:43955/status Memory: 15.69 GiB
Nanny: tcp://127.0.0.1:42089
Local directory: /tmp/dask-worker-space/worker-muxlhkfh

Worker: 1
Comm: tcp://127.0.0.1:44852 Total threads: 8
Dashboard: http://127.0.0.1:37132/status Memory: 15.69 GiB
Nanny: tcp://127.0.0.1:41874
Local directory: /tmp/dask-worker-space/worker-ebndkefa

Worker: 2
Comm: tcp://127.0.0.1:36927 Total threads: 8
Dashboard: http://127.0.0.1:41265/status Memory: 15.69 GiB
Nanny: tcp://127.0.0.1:42802
Local directory: /tmp/dask-worker-space/worker-bpu_cwox

Worker: 3
Comm: tcp://127.0.0.1:43840 Total threads: 8
Dashboard: http://127.0.0.1:39076/status Memory: 15.69 GiB
Nanny: tcp://127.0.0.1:39665
Local directory: /tmp/dask-worker-space/worker-xxulyy_2

Worker: 4
Comm: tcp://127.0.0.1:40565 Total threads: 8
Dashboard: http://127.0.0.1:41071/status Memory: 15.69 GiB
Nanny: tcp://127.0.0.1:42041
Local directory: /tmp/dask-worker-space/worker-pg8djy4_

Worker: 5
Comm: tcp://127.0.0.1:38546 Total threads: 8
Dashboard: http://127.0.0.1:39135/status Memory: 15.69 GiB
Nanny: tcp://127.0.0.1:36611
Local directory: /tmp/dask-worker-space/worker-1mef0wz4

Worker: 6
Comm: tcp://127.0.0.1:34827 Total threads: 8
Dashboard: http://127.0.0.1:42782/status Memory: 15.69 GiB
Nanny: tcp://127.0.0.1:39613
Local directory: /tmp/dask-worker-space/worker-01pc8349

Worker: 7
Comm: tcp://127.0.0.1:39868 Total threads: 8
Dashboard: http://127.0.0.1:33343/status Memory: 15.69 GiB
Nanny: tcp://127.0.0.1:33148
Local directory: /tmp/dask-worker-space/worker-86mps9p2

Worker: 8
Comm: tcp://127.0.0.1:42632 Total threads: 8
Dashboard: http://127.0.0.1:36320/status Memory: 15.69 GiB
Nanny: tcp://127.0.0.1:40157
Local directory: /tmp/dask-worker-space/worker-xbolg3j9

Worker: 9
Comm: tcp://127.0.0.1:39718 Total threads: 8
Dashboard: http://127.0.0.1:41774/status Memory: 15.69 GiB
Nanny: tcp://127.0.0.1:40387
Local directory: /tmp/dask-worker-space/worker-0zyv_lrf

Worker: 10
Comm: tcp://127.0.0.1:44009 Total threads: 8
Dashboard: http://127.0.0.1:46065/status Memory: 15.69 GiB
Nanny: tcp://127.0.0.1:46598
Local directory: /tmp/dask-worker-space/worker-mgcyu_s6

Worker: 11
Comm: tcp://127.0.0.1:44161 Total threads: 8
Dashboard: http://127.0.0.1:39393/status Memory: 15.69 GiB
Nanny: tcp://127.0.0.1:39842
Local directory: /tmp/dask-worker-space/worker-vvkgeeg0

Worker: 12
Comm: tcp://127.0.0.1:43742 Total threads: 8
Dashboard: http://127.0.0.1:37967/status Memory: 15.69 GiB
Nanny: tcp://127.0.0.1:33900
Local directory: /tmp/dask-worker-space/worker-xz9a4nrt

Worker: 13
Comm: tcp://127.0.0.1:34509 Total threads: 8
Dashboard: http://127.0.0.1:34980/status Memory: 15.69 GiB
Nanny: tcp://127.0.0.1:39966
Local directory: /tmp/dask-worker-space/worker-h7dsoxis

Worker: 14
Comm: tcp://127.0.0.1:36714 Total threads: 8
Dashboard: http://127.0.0.1:35933/status Memory: 15.69 GiB
Nanny: tcp://127.0.0.1:35695
Local directory: /tmp/dask-worker-space/worker-dq8_sysj

Worker: 15
Comm: tcp://127.0.0.1:46623 Total threads: 8
Dashboard: http://127.0.0.1:35453/status Memory: 15.69 GiB
Nanny: tcp://127.0.0.1:39547
Local directory: /tmp/dask-worker-space/worker-fqjn8tkm

read plotting information from a csv file¶

In [5]:
df=load.controlfile(control)
#Take out 'later' tagged computations
#df=df[~df['Value'].str.contains('later')]
df
Out[5]:
Value Inputs Equation Zone Plot Colourmap MinMax Unit Oldname Unnamed: 10
FWC_2D gridS.vosaline,param.mask,param.e3t,param.e1te2t calc.FWC2D_UFUNC(data) BBFG maps Spectral_r (0,24) m S-1

Computation starts here¶

Each computation consists of

  1. Load NEMO data set
  2. Zoom data set
  3. Compute (or load computed data set)
  4. Save
  5. Plot
  6. Close
In [6]:
%%time
import os
calcswitch=os.environ.get('calc', 'True') 
lazy=os.environ.get('lazy','False' )
loaddata=((df.Inputs != '').any()) 
print('calcswitch=',calcswitch,'df.Inputs != nothing',loaddata, 'lazy=',lazy)
data = load.datas(catalog_url,df.Inputs,month,year,daskreport,lazy=lazy) if ((calcswitch=='True' )*loaddata) else 0 
data

calcswitch= True df.Inputs != nothing True lazy= False
../lib/SEDNA_DELTA_MONITOR.yaml
using param_xios reading  ../lib/SEDNA_DELTA_MONITOR.yaml
using param_xios reading  <bound method DataSourceBase.describe of sources:
  param_xios:
    args:
      combine: nested
      concat_dim: y
      urlpath: /ccc/work/cont003/gen7420/odakatin/CONFIGS/SEDNA/SEDNA-I/SEDNA_Domain_cfg_Tgt_20210423_tsh10m_L1/param_f32/x_*.nc
      xarray_kwargs:
        compat: override
        coords: minimal
        data_vars: minimal
        parallel: true
    description: SEDNA NEMO parameters from MPI output  nav_lon lat fails
    driver: intake_xarray.netcdf.NetCDFSource
    metadata:
      catalog_dir: /ccc/work/cont003/gen7420/odakatin/monitor-sedna/notebook/../lib/
>
{'name': 'param_xios', 'container': 'xarray', 'plugin': ['netcdf'], 'driver': ['netcdf'], 'description': 'SEDNA NEMO parameters from MPI output  nav_lon lat fails', 'direct_access': 'forbid', 'user_parameters': [{'name': 'path', 'description': 'file coordinate', 'type': 'str', 'default': '/ccc/work/cont003/gen7420/odakatin/CONFIGS/SEDNA/MESH/SEDNA_mesh_mask_Tgt_20210423_tsh10m_L1/param'}], 'metadata': {}, 'args': {'urlpath': '/ccc/work/cont003/gen7420/odakatin/CONFIGS/SEDNA/SEDNA-I/SEDNA_Domain_cfg_Tgt_20210423_tsh10m_L1/param_f32/x_*.nc', 'combine': 'nested', 'concat_dim': 'y'}}
0 read gridS ['vosaline']
using load_data_xios_kerchunk reading  gridS
using load_data_xios_kerchunk reading  <bound method DataSourceBase.describe of sources:
  data_xios_kerchunk:
    args:
      consolidated: false
      storage_options:
        fo: file:////ccc/cont003/home/ra5563/ra5563/catalogue/DELTA/201201/gridS_0[0-5][0-9][0-9].json
        target_protocol: file
      urlpath: reference://
    description: CREG025 NEMO outputs from different xios server in kerchunk format
    driver: intake_xarray.xzarr.ZarrSource
    metadata:
      catalog_dir: /ccc/work/cont003/gen7420/odakatin/monitor-sedna/notebook/../lib/
>
      took 35.06680512428284 seconds
0 merging gridS ['vosaline']
param nav_lon will be included in data
param nav_lat will be included in data
param mask will be included in data
param mask2d will be included in data
param e1te2t will be included in data
param e3t will be included in data
CPU times: user 20.7 s, sys: 3.29 s, total: 24 s
Wall time: 57.9 s
Out[6]:
<xarray.Dataset>
Dimensions:        (t: 31, z: 150, y: 6540, x: 6560)
Coordinates:
    time_centered  (t) object dask.array<chunksize=(1,), meta=np.ndarray>
  * t              (t) object 2012-01-01 12:00:00 ... 2012-01-31 12:00:00
  * y              (y) int64 1 2 3 4 5 6 7 ... 6535 6536 6537 6538 6539 6540
  * x              (x) int64 1 2 3 4 5 6 7 ... 6555 6556 6557 6558 6559 6560
  * z              (z) int64 1 2 3 4 5 6 7 8 ... 143 144 145 146 147 148 149 150
    nav_lon        (y, x) float32 dask.array<chunksize=(13, 6560), meta=np.ndarray>
    nav_lat        (y, x) float32 dask.array<chunksize=(13, 6560), meta=np.ndarray>
    mask           (z, y, x) bool dask.array<chunksize=(150, 13, 6560), meta=np.ndarray>
    mask2d         (y, x) bool dask.array<chunksize=(13, 6560), meta=np.ndarray>
    e1te2t         (y, x) float64 dask.array<chunksize=(13, 6560), meta=np.ndarray>
    e3t            (z, y, x) float64 dask.array<chunksize=(150, 13, 6560), meta=np.ndarray>
Data variables:
    vosaline       (t, z, y, x) float32 dask.array<chunksize=(1, 150, 13, 6560), meta=np.ndarray>
Attributes: (12/26)
    CASE:                    DELTA
    CONFIG:                  SEDNA
    Conventions:             CF-1.6
    DOMAIN_dimensions_ids:   [2, 3]
    DOMAIN_halo_size_end:    [0, 0]
    DOMAIN_halo_size_start:  [0, 0]
    ...                      ...
    nj:                      13
    output_frequency:        1d
    start_date:              20090101
    timeStamp:               2022-Jan-17 19:00:16 GMT
    title:                   ocean T grid variables
    uuid:                    d8db76f6-a436-451a-9ab1-72dc892753af
xarray.Dataset
    • t: 31
    • z: 150
    • y: 6540
    • x: 6560
    • time_centered
      (t)
      object
      dask.array<chunksize=(1,), meta=np.ndarray>
      bounds :
      time_centered_bounds
      long_name :
      Time axis
      standard_name :
      time
      time_origin :
      1900-01-01 00:00:00
      Array Chunk
      Bytes 248 B 8 B
      Shape (31,) (1,)
      Count 32 Tasks 31 Chunks
      Type object numpy.ndarray
      		31 1
    • t
      (t)
      object
      2012-01-01 12:00:00 ... 2012-01-...
      axis :
      T
      bounds :
      time_counter_bounds
      long_name :
      Time axis
      standard_name :
      time
      time_origin :
      1900-01-01 00:00:00
      array([cftime.DatetimeNoLeap(2012, 1, 1, 12, 0, 0, 0, has_year_zero=True),
       cftime.DatetimeNoLeap(2012, 1, 2, 12, 0, 0, 0, has_year_zero=True),
       cftime.DatetimeNoLeap(2012, 1, 3, 12, 0, 0, 0, has_year_zero=True),
       cftime.DatetimeNoLeap(2012, 1, 4, 12, 0, 0, 0, has_year_zero=True),
       cftime.DatetimeNoLeap(2012, 1, 5, 12, 0, 0, 0, has_year_zero=True),
       cftime.DatetimeNoLeap(2012, 1, 6, 12, 0, 0, 0, has_year_zero=True),
       cftime.DatetimeNoLeap(2012, 1, 7, 12, 0, 0, 0, has_year_zero=True),
       cftime.DatetimeNoLeap(2012, 1, 8, 12, 0, 0, 0, has_year_zero=True),
       cftime.DatetimeNoLeap(2012, 1, 9, 12, 0, 0, 0, has_year_zero=True),
       cftime.DatetimeNoLeap(2012, 1, 10, 12, 0, 0, 0, has_year_zero=True),
       cftime.DatetimeNoLeap(2012, 1, 11, 12, 0, 0, 0, has_year_zero=True),
       cftime.DatetimeNoLeap(2012, 1, 12, 12, 0, 0, 0, has_year_zero=True),
       cftime.DatetimeNoLeap(2012, 1, 13, 12, 0, 0, 0, has_year_zero=True),
       cftime.DatetimeNoLeap(2012, 1, 14, 12, 0, 0, 0, has_year_zero=True),
       cftime.DatetimeNoLeap(2012, 1, 15, 12, 0, 0, 0, has_year_zero=True),
       cftime.DatetimeNoLeap(2012, 1, 16, 12, 0, 0, 0, has_year_zero=True),
       cftime.DatetimeNoLeap(2012, 1, 17, 12, 0, 0, 0, has_year_zero=True),
       cftime.DatetimeNoLeap(2012, 1, 18, 12, 0, 0, 0, has_year_zero=True),
       cftime.DatetimeNoLeap(2012, 1, 19, 12, 0, 0, 0, has_year_zero=True),
       cftime.DatetimeNoLeap(2012, 1, 20, 12, 0, 0, 0, has_year_zero=True),
       cftime.DatetimeNoLeap(2012, 1, 21, 12, 0, 0, 0, has_year_zero=True),
       cftime.DatetimeNoLeap(2012, 1, 22, 12, 0, 0, 0, has_year_zero=True),
       cftime.DatetimeNoLeap(2012, 1, 23, 12, 0, 0, 0, has_year_zero=True),
       cftime.DatetimeNoLeap(2012, 1, 24, 12, 0, 0, 0, has_year_zero=True),
       cftime.DatetimeNoLeap(2012, 1, 25, 12, 0, 0, 0, has_year_zero=True),
       cftime.DatetimeNoLeap(2012, 1, 26, 12, 0, 0, 0, has_year_zero=True),
       cftime.DatetimeNoLeap(2012, 1, 27, 12, 0, 0, 0, has_year_zero=True),
       cftime.DatetimeNoLeap(2012, 1, 28, 12, 0, 0, 0, has_year_zero=True),
       cftime.DatetimeNoLeap(2012, 1, 29, 12, 0, 0, 0, has_year_zero=True),
       cftime.DatetimeNoLeap(2012, 1, 30, 12, 0, 0, 0, has_year_zero=True),
       cftime.DatetimeNoLeap(2012, 1, 31, 12, 0, 0, 0, has_year_zero=True)],
      dtype=object)
    • y
      (y)
      int64
      1 2 3 4 5 ... 6537 6538 6539 6540
      array([   1,    2,    3, ..., 6538, 6539, 6540])
    • x
      (x)
      int64
      1 2 3 4 5 ... 6557 6558 6559 6560
      array([   1,    2,    3, ..., 6558, 6559, 6560])
    • z
      (z)
      int64
      1 2 3 4 5 6 ... 146 147 148 149 150
      array([  1,   2,   3,   4,   5,   6,   7,   8,   9,  10,  11,  12,  13,  14,
        15,  16,  17,  18,  19,  20,  21,  22,  23,  24,  25,  26,  27,  28,
        29,  30,  31,  32,  33,  34,  35,  36,  37,  38,  39,  40,  41,  42,
        43,  44,  45,  46,  47,  48,  49,  50,  51,  52,  53,  54,  55,  56,
        57,  58,  59,  60,  61,  62,  63,  64,  65,  66,  67,  68,  69,  70,
        71,  72,  73,  74,  75,  76,  77,  78,  79,  80,  81,  82,  83,  84,
        85,  86,  87,  88,  89,  90,  91,  92,  93,  94,  95,  96,  97,  98,
        99, 100, 101, 102, 103, 104, 105, 106, 107, 108, 109, 110, 111, 112,
       113, 114, 115, 116, 117, 118, 119, 120, 121, 122, 123, 124, 125, 126,
       127, 128, 129, 130, 131, 132, 133, 134, 135, 136, 137, 138, 139, 140,
       141, 142, 143, 144, 145, 146, 147, 148, 149, 150])
    • nav_lon
      (y, x)
      float32
      dask.array<chunksize=(13, 6560), meta=np.ndarray>
      Array Chunk
      Bytes 163.66 MiB 333.12 kiB
      Shape (6540, 6560) (13, 6560)
      Count 1632 Tasks 544 Chunks
      Type float32 numpy.ndarray
      		6560 6540
    • nav_lat
      (y, x)
      float32
      dask.array<chunksize=(13, 6560), meta=np.ndarray>
      Array Chunk
      Bytes 163.66 MiB 333.12 kiB
      Shape (6540, 6560) (13, 6560)
      Count 1632 Tasks 544 Chunks
      Type float32 numpy.ndarray
      		6560 6540
    • mask
      (z, y, x)
      bool
      dask.array<chunksize=(150, 13, 6560), meta=np.ndarray>
      Array Chunk
      Bytes 5.99 GiB 12.20 MiB
      Shape (150, 6540, 6560) (150, 13, 6560)
      Count 1632 Tasks 544 Chunks
      Type bool numpy.ndarray
      		6560 6540 150
    • mask2d
      (y, x)
      bool
      dask.array<chunksize=(13, 6560), meta=np.ndarray>
      Array Chunk
      Bytes 40.91 MiB 83.28 kiB
      Shape (6540, 6560) (13, 6560)
      Count 1632 Tasks 544 Chunks
      Type bool numpy.ndarray
      		6560 6540
    • e1te2t
      (y, x)
      float64
      dask.array<chunksize=(13, 6560), meta=np.ndarray>
      Array Chunk
      Bytes 327.32 MiB 666.25 kiB
      Shape (6540, 6560) (13, 6560)
      Count 1632 Tasks 544 Chunks
      Type float64 numpy.ndarray
      		6560 6540
    • e3t
      (z, y, x)
      float64
      dask.array<chunksize=(150, 13, 6560), meta=np.ndarray>
      Array Chunk
      Bytes 47.95 GiB 97.60 MiB
      Shape (150, 6540, 6560) (150, 13, 6560)
      Count 1632 Tasks 544 Chunks
      Type float64 numpy.ndarray
      		6560 6540 150
    • vosaline
      (t, z, y, x)
      float32
      dask.array<chunksize=(1, 150, 13, 6560), meta=np.ndarray>
      cell_methods :
      time: mean (interval: 40 s)
      interval_operation :
      40 s
      interval_write :
      1 d
      long_name :
      salinity
      online_operation :
      average
      standard_name :
      sea_water_practical_salinity
      units :
      1e-3
      Array Chunk
      Bytes 743.18 GiB 48.80 MiB
      Shape (31, 150, 6540, 6560) (1, 150, 13, 6560)
      Count 34272 Tasks 16864 Chunks
      Type float32 numpy.ndarray
      		31 1 6560 6540 150
  • CASE :
    DELTA
    CONFIG :
    SEDNA
    Conventions :
    CF-1.6
    DOMAIN_dimensions_ids :
    [2, 3]
    DOMAIN_halo_size_end :
    [0, 0]
    DOMAIN_halo_size_start :
    [0, 0]
    DOMAIN_number :
    0
    DOMAIN_number_total :
    544
    DOMAIN_position_first :
    [1, 1]
    DOMAIN_position_last :
    [6560, 13]
    DOMAIN_size_global :
    [6560, 6540]
    DOMAIN_size_local :
    [6560, 13]
    DOMAIN_type :
    box
    NCO :
    netCDF Operators version 4.9.1 (Homepage = http://nco.sf.net, Code = http://github.com/nco/nco)
    description :
    ocean T grid variables
    history :
    Tue Jan 18 17:23:11 2022: ncks -4 -L 1 SEDNA-DELTA_1d_gridS_201201-201201_NOZIP_0000.nc /ccc/scratch/cont003/gen7420/talandel/SEDNA/SEDNA-DELTA-S/SPLIT/1d/2012/01/SEDNA-DELTA_1d_gridS_201201-201201_0000.nc Tue Jan 18 17:22:45 2022: ncrcat -n 31,2,1 SEDNA-DELTA_1d_gridS_0000_01.nc SEDNA-DELTA_1d_gridS_201201-201201_NOZIP_0000.nc
    ibegin :
    0
    jbegin :
    0
    name :
    /ccc/scratch/cont003/ra5563/talandel/ONGOING-RUNS/SEDNA-DELTA-XIOS.46/SEDNA-DELTA_1d_gridS
    ni :
    6560
    nj :
    13
    output_frequency :
    1d
    start_date :
    20090101
    timeStamp :
    2022-Jan-17 19:00:16 GMT
    title :
    ocean T grid variables
    uuid :
    d8db76f6-a436-451a-9ab1-72dc892753af
In [7]:
%%time
monitor.auto(df,data,savefig,daskreport,outputpath,file_exp='SEDNA'
            )

#calc= True
#save= True
#plot= False
monitor.optimize_dataset(data)
Value='FWC_2D'
Zone='BBFG'
Plot='maps'
cmap='Spectral_r'
clabel='m'
clim= (0, 24)
outputpath='../results/SEDNA_DELTA_MONITOR/'
nc_outputpath='../nc_results/SEDNA_DELTA_MONITOR/'
filename='SEDNA_maps_BBFG_FWC_2D'
#2 Zooming Data
dtaa= zoom.BBFG(data)

<xarray.Dataset>
Dimensions:        (t: 31, z: 150, y: 5264, x: 6560)
Coordinates:
    time_centered  (t) object dask.array<chunksize=(1,), meta=np.ndarray>
  * t              (t) object 2012-01-01 12:00:00 ... 2012-01-31 12:00:00
  * y              (y) int64 1277 1278 1279 1280 1281 ... 6537 6538 6539 6540
  * x              (x) int64 1 2 3 4 5 6 7 ... 6555 6556 6557 6558 6559 6560
  * z              (z) int64 1 2 3 4 5 6 7 8 ... 143 144 145 146 147 148 149 150
    nav_lon        (y, x) float32 dask.array<chunksize=(8, 6560), meta=np.ndarray>
    nav_lat        (y, x) float32 dask.array<chunksize=(8, 6560), meta=np.ndarray>
    mask           (z, y, x) bool dask.array<chunksize=(150, 8, 6560), meta=np.ndarray>
    mask2d         (y, x) bool dask.array<chunksize=(8, 6560), meta=np.ndarray>
    e1te2t         (y, x) float64 dask.array<chunksize=(8, 6560), meta=np.ndarray>
    e3t            (z, y, x) float64 dask.array<chunksize=(150, 8, 6560), meta=np.ndarray>
Data variables:
    vosaline       (t, z, y, x) float32 dask.array<chunksize=(1, 150, 8, 6560), meta=np.ndarray>
Attributes: (12/26)
    CASE:                    DELTA
    CONFIG:                  SEDNA
    Conventions:             CF-1.6
    DOMAIN_dimensions_ids:   [2, 3]
    DOMAIN_halo_size_end:    [0, 0]
    DOMAIN_halo_size_start:  [0, 0]
    ...                      ...
    nj:                      13
    output_frequency:        1d
    start_date:              20090101
    timeStamp:               2022-Jan-17 19:00:16 GMT
    title:                   ocean T grid variables
    uuid:                    d8db76f6-a436-451a-9ab1-72dc892753af
xarray.Dataset
    • t: 31
    • z: 150
    • y: 5264
    • x: 6560
    • time_centered
      (t)
      object
      dask.array<chunksize=(1,), meta=np.ndarray>
      bounds :
      time_centered_bounds
      long_name :
      Time axis
      standard_name :
      time
      time_origin :
      1900-01-01 00:00:00
      Array Chunk
      Bytes 248 B 8 B
      Shape (31,) (1,)
      Count 32 Tasks 31 Chunks
      Type object numpy.ndarray
      		31 1
    • t
      (t)
      object
      2012-01-01 12:00:00 ... 2012-01-...
      axis :
      T
      bounds :
      time_counter_bounds
      long_name :
      Time axis
      standard_name :
      time
      time_origin :
      1900-01-01 00:00:00
      array([cftime.DatetimeNoLeap(2012, 1, 1, 12, 0, 0, 0, has_year_zero=True),
       cftime.DatetimeNoLeap(2012, 1, 2, 12, 0, 0, 0, has_year_zero=True),
       cftime.DatetimeNoLeap(2012, 1, 3, 12, 0, 0, 0, has_year_zero=True),
       cftime.DatetimeNoLeap(2012, 1, 4, 12, 0, 0, 0, has_year_zero=True),
       cftime.DatetimeNoLeap(2012, 1, 5, 12, 0, 0, 0, has_year_zero=True),
       cftime.DatetimeNoLeap(2012, 1, 6, 12, 0, 0, 0, has_year_zero=True),
       cftime.DatetimeNoLeap(2012, 1, 7, 12, 0, 0, 0, has_year_zero=True),
       cftime.DatetimeNoLeap(2012, 1, 8, 12, 0, 0, 0, has_year_zero=True),
       cftime.DatetimeNoLeap(2012, 1, 9, 12, 0, 0, 0, has_year_zero=True),
       cftime.DatetimeNoLeap(2012, 1, 10, 12, 0, 0, 0, has_year_zero=True),
       cftime.DatetimeNoLeap(2012, 1, 11, 12, 0, 0, 0, has_year_zero=True),
       cftime.DatetimeNoLeap(2012, 1, 12, 12, 0, 0, 0, has_year_zero=True),
       cftime.DatetimeNoLeap(2012, 1, 13, 12, 0, 0, 0, has_year_zero=True),
       cftime.DatetimeNoLeap(2012, 1, 14, 12, 0, 0, 0, has_year_zero=True),
       cftime.DatetimeNoLeap(2012, 1, 15, 12, 0, 0, 0, has_year_zero=True),
       cftime.DatetimeNoLeap(2012, 1, 16, 12, 0, 0, 0, has_year_zero=True),
       cftime.DatetimeNoLeap(2012, 1, 17, 12, 0, 0, 0, has_year_zero=True),
       cftime.DatetimeNoLeap(2012, 1, 18, 12, 0, 0, 0, has_year_zero=True),
       cftime.DatetimeNoLeap(2012, 1, 19, 12, 0, 0, 0, has_year_zero=True),
       cftime.DatetimeNoLeap(2012, 1, 20, 12, 0, 0, 0, has_year_zero=True),
       cftime.DatetimeNoLeap(2012, 1, 21, 12, 0, 0, 0, has_year_zero=True),
       cftime.DatetimeNoLeap(2012, 1, 22, 12, 0, 0, 0, has_year_zero=True),
       cftime.DatetimeNoLeap(2012, 1, 23, 12, 0, 0, 0, has_year_zero=True),
       cftime.DatetimeNoLeap(2012, 1, 24, 12, 0, 0, 0, has_year_zero=True),
       cftime.DatetimeNoLeap(2012, 1, 25, 12, 0, 0, 0, has_year_zero=True),
       cftime.DatetimeNoLeap(2012, 1, 26, 12, 0, 0, 0, has_year_zero=True),
       cftime.DatetimeNoLeap(2012, 1, 27, 12, 0, 0, 0, has_year_zero=True),
       cftime.DatetimeNoLeap(2012, 1, 28, 12, 0, 0, 0, has_year_zero=True),
       cftime.DatetimeNoLeap(2012, 1, 29, 12, 0, 0, 0, has_year_zero=True),
       cftime.DatetimeNoLeap(2012, 1, 30, 12, 0, 0, 0, has_year_zero=True),
       cftime.DatetimeNoLeap(2012, 1, 31, 12, 0, 0, 0, has_year_zero=True)],
      dtype=object)
    • y
      (y)
      int64
      1277 1278 1279 ... 6538 6539 6540
      array([1277, 1278, 1279, ..., 6538, 6539, 6540])
    • x
      (x)
      int64
      1 2 3 4 5 ... 6557 6558 6559 6560
      array([   1,    2,    3, ..., 6558, 6559, 6560])
    • z
      (z)
      int64
      1 2 3 4 5 6 ... 146 147 148 149 150
      array([  1,   2,   3,   4,   5,   6,   7,   8,   9,  10,  11,  12,  13,  14,
        15,  16,  17,  18,  19,  20,  21,  22,  23,  24,  25,  26,  27,  28,
        29,  30,  31,  32,  33,  34,  35,  36,  37,  38,  39,  40,  41,  42,
        43,  44,  45,  46,  47,  48,  49,  50,  51,  52,  53,  54,  55,  56,
        57,  58,  59,  60,  61,  62,  63,  64,  65,  66,  67,  68,  69,  70,
        71,  72,  73,  74,  75,  76,  77,  78,  79,  80,  81,  82,  83,  84,
        85,  86,  87,  88,  89,  90,  91,  92,  93,  94,  95,  96,  97,  98,
        99, 100, 101, 102, 103, 104, 105, 106, 107, 108, 109, 110, 111, 112,
       113, 114, 115, 116, 117, 118, 119, 120, 121, 122, 123, 124, 125, 126,
       127, 128, 129, 130, 131, 132, 133, 134, 135, 136, 137, 138, 139, 140,
       141, 142, 143, 144, 145, 146, 147, 148, 149, 150])
    • nav_lon
      (y, x)
      float32
      dask.array<chunksize=(8, 6560), meta=np.ndarray>
      Array Chunk
      Bytes 131.73 MiB 307.50 kiB
      Shape (5264, 6560) (12, 6560)
      Count 2071 Tasks 439 Chunks
      Type float32 numpy.ndarray
      		6560 5264
    • nav_lat
      (y, x)
      float32
      dask.array<chunksize=(8, 6560), meta=np.ndarray>
      Array Chunk
      Bytes 131.73 MiB 307.50 kiB
      Shape (5264, 6560) (12, 6560)
      Count 2071 Tasks 439 Chunks
      Type float32 numpy.ndarray
      		6560 5264
    • mask
      (z, y, x)
      bool
      dask.array<chunksize=(150, 8, 6560), meta=np.ndarray>
      Array Chunk
      Bytes 4.82 GiB 11.26 MiB
      Shape (150, 5264, 6560) (150, 12, 6560)
      Count 2071 Tasks 439 Chunks
      Type bool numpy.ndarray
      		6560 5264 150
    • mask2d
      (y, x)
      bool
      dask.array<chunksize=(8, 6560), meta=np.ndarray>
      Array Chunk
      Bytes 32.93 MiB 76.88 kiB
      Shape (5264, 6560) (12, 6560)
      Count 2071 Tasks 439 Chunks
      Type bool numpy.ndarray
      		6560 5264
    • e1te2t
      (y, x)
      float64
      dask.array<chunksize=(8, 6560), meta=np.ndarray>
      Array Chunk
      Bytes 263.46 MiB 615.00 kiB
      Shape (5264, 6560) (12, 6560)
      Count 2071 Tasks 439 Chunks
      Type float64 numpy.ndarray
      		6560 5264
    • e3t
      (z, y, x)
      float64
      dask.array<chunksize=(150, 8, 6560), meta=np.ndarray>
      Array Chunk
      Bytes 38.59 GiB 90.09 MiB
      Shape (150, 5264, 6560) (150, 12, 6560)
      Count 2071 Tasks 439 Chunks
      Type float64 numpy.ndarray
      		6560 5264 150
    • vosaline
      (t, z, y, x)
      float32
      dask.array<chunksize=(1, 150, 8, 6560), meta=np.ndarray>
      cell_methods :
      time: mean (interval: 40 s)
      interval_operation :
      40 s
      interval_write :
      1 d
      long_name :
      salinity
      online_operation :
      average
      standard_name :
      sea_water_practical_salinity
      units :
      1e-3
      Array Chunk
      Bytes 598.18 GiB 45.04 MiB
      Shape (31, 150, 5264, 6560) (1, 150, 12, 6560)
      Count 62369 Tasks 13609 Chunks
      Type float32 numpy.ndarray
      		31 1 6560 5264 150
  • CASE :
    DELTA
    CONFIG :
    SEDNA
    Conventions :
    CF-1.6
    DOMAIN_dimensions_ids :
    [2, 3]
    DOMAIN_halo_size_end :
    [0, 0]
    DOMAIN_halo_size_start :
    [0, 0]
    DOMAIN_number :
    0
    DOMAIN_number_total :
    544
    DOMAIN_position_first :
    [1, 1]
    DOMAIN_position_last :
    [6560, 13]
    DOMAIN_size_global :
    [6560, 6540]
    DOMAIN_size_local :
    [6560, 13]
    DOMAIN_type :
    box
    NCO :
    netCDF Operators version 4.9.1 (Homepage = http://nco.sf.net, Code = http://github.com/nco/nco)
    description :
    ocean T grid variables
    history :
    Tue Jan 18 17:23:11 2022: ncks -4 -L 1 SEDNA-DELTA_1d_gridS_201201-201201_NOZIP_0000.nc /ccc/scratch/cont003/gen7420/talandel/SEDNA/SEDNA-DELTA-S/SPLIT/1d/2012/01/SEDNA-DELTA_1d_gridS_201201-201201_0000.nc Tue Jan 18 17:22:45 2022: ncrcat -n 31,2,1 SEDNA-DELTA_1d_gridS_0000_01.nc SEDNA-DELTA_1d_gridS_201201-201201_NOZIP_0000.nc
    ibegin :
    0
    jbegin :
    0
    name :
    /ccc/scratch/cont003/ra5563/talandel/ONGOING-RUNS/SEDNA-DELTA-XIOS.46/SEDNA-DELTA_1d_gridS
    ni :
    6560
    nj :
    13
    output_frequency :
    1d
    start_date :
    20090101
    timeStamp :
    2022-Jan-17 19:00:16 GMT
    title :
    ocean T grid variables
    uuid :
    d8db76f6-a436-451a-9ab1-72dc892753af
#3 Start computing 
dtaa= calc.FWC2D_UFUNC(data)
monitor.optimize_dataset(dtaa)

<xarray.Dataset>
Dimensions:        (t: 31, y: 5264, x: 6560)
Coordinates:
    time_centered  (t) object dask.array<chunksize=(1,), meta=np.ndarray>
  * t              (t) object 2012-01-01 12:00:00 ... 2012-01-31 12:00:00
  * y              (y) int64 1277 1278 1279 1280 1281 ... 6537 6538 6539 6540
  * x              (x) int64 1 2 3 4 5 6 7 ... 6555 6556 6557 6558 6559 6560
    nav_lon        (y, x) float32 dask.array<chunksize=(8, 6560), meta=np.ndarray>
    nav_lat        (y, x) float32 dask.array<chunksize=(8, 6560), meta=np.ndarray>
    mask2d         (y, x) bool dask.array<chunksize=(8, 6560), meta=np.ndarray>
    e1te2t         (y, x) float64 dask.array<chunksize=(8, 6560), meta=np.ndarray>
Data variables:
    FWC2D          (t, y, x) float32 dask.array<chunksize=(1, 8, 6560), meta=np.ndarray>
xarray.Dataset
    • t: 31
    • y: 5264
    • x: 6560
    • time_centered
      (t)
      object
      dask.array<chunksize=(1,), meta=np.ndarray>
      Array Chunk
      Bytes 248 B 8 B
      Shape (31,) (1,)
      Count 32 Tasks 31 Chunks
      Type object numpy.ndarray
      		31 1
    • t
      (t)
      object
      2012-01-01 12:00:00 ... 2012-01-...
      array([cftime.DatetimeNoLeap(2012, 1, 1, 12, 0, 0, 0, has_year_zero=True),
       cftime.DatetimeNoLeap(2012, 1, 2, 12, 0, 0, 0, has_year_zero=True),
       cftime.DatetimeNoLeap(2012, 1, 3, 12, 0, 0, 0, has_year_zero=True),
       cftime.DatetimeNoLeap(2012, 1, 4, 12, 0, 0, 0, has_year_zero=True),
       cftime.DatetimeNoLeap(2012, 1, 5, 12, 0, 0, 0, has_year_zero=True),
       cftime.DatetimeNoLeap(2012, 1, 6, 12, 0, 0, 0, has_year_zero=True),
       cftime.DatetimeNoLeap(2012, 1, 7, 12, 0, 0, 0, has_year_zero=True),
       cftime.DatetimeNoLeap(2012, 1, 8, 12, 0, 0, 0, has_year_zero=True),
       cftime.DatetimeNoLeap(2012, 1, 9, 12, 0, 0, 0, has_year_zero=True),
       cftime.DatetimeNoLeap(2012, 1, 10, 12, 0, 0, 0, has_year_zero=True),
       cftime.DatetimeNoLeap(2012, 1, 11, 12, 0, 0, 0, has_year_zero=True),
       cftime.DatetimeNoLeap(2012, 1, 12, 12, 0, 0, 0, has_year_zero=True),
       cftime.DatetimeNoLeap(2012, 1, 13, 12, 0, 0, 0, has_year_zero=True),
       cftime.DatetimeNoLeap(2012, 1, 14, 12, 0, 0, 0, has_year_zero=True),
       cftime.DatetimeNoLeap(2012, 1, 15, 12, 0, 0, 0, has_year_zero=True),
       cftime.DatetimeNoLeap(2012, 1, 16, 12, 0, 0, 0, has_year_zero=True),
       cftime.DatetimeNoLeap(2012, 1, 17, 12, 0, 0, 0, has_year_zero=True),
       cftime.DatetimeNoLeap(2012, 1, 18, 12, 0, 0, 0, has_year_zero=True),
       cftime.DatetimeNoLeap(2012, 1, 19, 12, 0, 0, 0, has_year_zero=True),
       cftime.DatetimeNoLeap(2012, 1, 20, 12, 0, 0, 0, has_year_zero=True),
       cftime.DatetimeNoLeap(2012, 1, 21, 12, 0, 0, 0, has_year_zero=True),
       cftime.DatetimeNoLeap(2012, 1, 22, 12, 0, 0, 0, has_year_zero=True),
       cftime.DatetimeNoLeap(2012, 1, 23, 12, 0, 0, 0, has_year_zero=True),
       cftime.DatetimeNoLeap(2012, 1, 24, 12, 0, 0, 0, has_year_zero=True),
       cftime.DatetimeNoLeap(2012, 1, 25, 12, 0, 0, 0, has_year_zero=True),
       cftime.DatetimeNoLeap(2012, 1, 26, 12, 0, 0, 0, has_year_zero=True),
       cftime.DatetimeNoLeap(2012, 1, 27, 12, 0, 0, 0, has_year_zero=True),
       cftime.DatetimeNoLeap(2012, 1, 28, 12, 0, 0, 0, has_year_zero=True),
       cftime.DatetimeNoLeap(2012, 1, 29, 12, 0, 0, 0, has_year_zero=True),
       cftime.DatetimeNoLeap(2012, 1, 30, 12, 0, 0, 0, has_year_zero=True),
       cftime.DatetimeNoLeap(2012, 1, 31, 12, 0, 0, 0, has_year_zero=True)],
      dtype=object)
    • y
      (y)
      int64
      1277 1278 1279 ... 6538 6539 6540
      array([1277, 1278, 1279, ..., 6538, 6539, 6540])
    • x
      (x)
      int64
      1 2 3 4 5 ... 6557 6558 6559 6560
      array([   1,    2,    3, ..., 6558, 6559, 6560])
    • nav_lon
      (y, x)
      float32
      dask.array<chunksize=(8, 6560), meta=np.ndarray>
      Array Chunk
      Bytes 131.73 MiB 307.50 kiB
      Shape (5264, 6560) (12, 6560)
      Count 2071 Tasks 439 Chunks
      Type float32 numpy.ndarray
      		6560 5264
    • nav_lat
      (y, x)
      float32
      dask.array<chunksize=(8, 6560), meta=np.ndarray>
      Array Chunk
      Bytes 131.73 MiB 307.50 kiB
      Shape (5264, 6560) (12, 6560)
      Count 2071 Tasks 439 Chunks
      Type float32 numpy.ndarray
      		6560 5264
    • mask2d
      (y, x)
      bool
      dask.array<chunksize=(8, 6560), meta=np.ndarray>
      Array Chunk
      Bytes 32.93 MiB 76.88 kiB
      Shape (5264, 6560) (12, 6560)
      Count 2071 Tasks 439 Chunks
      Type bool numpy.ndarray
      		6560 5264
    • e1te2t
      (y, x)
      float64
      dask.array<chunksize=(8, 6560), meta=np.ndarray>
      Array Chunk
      Bytes 263.46 MiB 615.00 kiB
      Shape (5264, 6560) (12, 6560)
      Count 2071 Tasks 439 Chunks
      Type float64 numpy.ndarray
      		6560 5264
    • FWC2D
      (t, y, x)
      float32
      dask.array<chunksize=(1, 8, 6560), meta=np.ndarray>
      long_name :
      Fresh Water Content 2D
      Array Chunk
      Bytes 3.99 GiB 307.50 kiB
      Shape (31, 5264, 6560) (1, 12, 6560)
      Count 72875 Tasks 13609 Chunks
      Type float32 numpy.ndarray
      		6560 5264 31 
#4 Saving  SEDNA_maps_BBFG_FWC_2D
dtaa=save.datas(data,plot=Plot,path=nc_outputpath,filename=filename)
start saving data
saving data in a file
t (1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1)
0
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
slice(0, 1, None)

2022-08-03 15:41:49,718 - distributed.worker - ERROR - Worker stream died during communication: tcp://127.0.0.1:46347
Traceback (most recent call last):
  File "/ccc/cont003/home/ra5563/ra5563/monitor/lib/python3.10/site-packages/distributed/comm/tcp.py", line 264, in write
    async def write(self, msg, serializers=None, on_error="message"):
asyncio.exceptions.CancelledError

During handling of the above exception, another exception occurred:

Traceback (most recent call last):
  File "/ccc/cont003/home/ra5563/ra5563/monitor/lib/python3.10/asyncio/tasks.py", line 418, in wait_for
    return fut.result()
asyncio.exceptions.CancelledError

The above exception was the direct cause of the following exception:

Traceback (most recent call last):
  File "/ccc/cont003/home/ra5563/ra5563/monitor/lib/python3.10/site-packages/distributed/comm/core.py", line 329, in connect
    await asyncio.wait_for(comm.write(local_info), time_left())
  File "/ccc/cont003/home/ra5563/ra5563/monitor/lib/python3.10/asyncio/tasks.py", line 420, in wait_for
    raise exceptions.TimeoutError() from exc
asyncio.exceptions.TimeoutError

The above exception was the direct cause of the following exception:

Traceback (most recent call last):
  File "/ccc/cont003/home/ra5563/ra5563/monitor/lib/python3.10/site-packages/distributed/worker.py", line 1983, in gather_dep
    response = await get_data_from_worker(
  File "/ccc/cont003/home/ra5563/ra5563/monitor/lib/python3.10/site-packages/distributed/worker.py", line 2725, in get_data_from_worker
    return await retry_operation(_get_data, operation="get_data_from_worker")
  File "/ccc/cont003/home/ra5563/ra5563/monitor/lib/python3.10/site-packages/distributed/utils_comm.py", line 383, in retry_operation
    return await retry(
  File "/ccc/cont003/home/ra5563/ra5563/monitor/lib/python3.10/site-packages/distributed/utils_comm.py", line 368, in retry
    return await coro()
  File "/ccc/cont003/home/ra5563/ra5563/monitor/lib/python3.10/site-packages/distributed/worker.py", line 2702, in _get_data
    comm = await rpc.connect(worker)
  File "/ccc/cont003/home/ra5563/ra5563/monitor/lib/python3.10/site-packages/distributed/core.py", line 1371, in connect
    return await connect_attempt
  File "/ccc/cont003/home/ra5563/ra5563/monitor/lib/python3.10/site-packages/distributed/core.py", line 1307, in _connect
    comm = await connect(
  File "/ccc/cont003/home/ra5563/ra5563/monitor/lib/python3.10/site-packages/distributed/comm/core.py", line 333, in connect
    raise OSError(
OSError: Timed out during handshake while connecting to tcp://127.0.0.1:46347 after 30 s

slice(1, 2, None)
slice(2, 3, None)
slice(3, 4, None)
slice(4, 5, None)
slice(5, 6, None)
slice(6, 7, None)
slice(7, 8, None)
slice(8, 9, None)
slice(9, 10, None)
slice(10, 11, None)
slice(11, 12, None)
slice(12, 13, None)
slice(13, 14, None)
slice(14, 15, None)
slice(15, 16, None)
slice(16, 17, None)
slice(17, 18, None)
slice(18, 19, None)
slice(19, 20, None)
slice(20, 21, None)
slice(21, 22, None)
slice(22, 23, None)
slice(23, 24, None)
slice(24, 25, None)
slice(25, 26, None)
slice(26, 27, None)
slice(27, 28, None)
slice(28, 29, None)
slice(29, 30, None)
slice(30, 31, None)
CPU times: user 15min 6s, sys: 2min 13s, total: 17min 20s
Wall time: 57min 53s