%matplotlib inline
import pandas as pd
import socket
host = socket.getfqdn()

from core import  load, zoom, calc, save,plots,monitor

#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)

<module 'core.monitor' from '/ccc/work/cont003/gen7420/odakatin/monitor-sedna/notebook/core/monitor.py'>

If you submit the job with job scheduler; 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 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. For DELTA experiment, year corresponds to really 'year'

• %env month=

  for monitoring this corresponds to file path path-XIOS.{month}/

For DELTA experiment, year corresponds to really '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

save output file used for plotting

• #%env lazy=False

using kerchunked file -> False, not using kerhcunk -> True

• #%env control=FWC_SSH

name of control file to be used for computation/plots/save/ We have number of M_xxx.csv

Monitor.sh automate all of above enviroment settings.

Monitor.sh calls M_MLD_2D

and AWTD.sh, Fluxnet.sh, Siconc.sh, IceClim.sh, FWC_SSH.sh, Integrals.sh , Sections.sh

• AWTD.sh

M_AWTMD

• Fluxnet.sh

M_Fluxnet

• Siconc.sh

M_Ice_quantities

• IceClim.sh

M_IceClim M_IceConce M_IceThick

• FWC_SSH.sh

M_FWC_2D M_FWC_integrals M_FWC_SSH M_SSH_anomaly

• Integrals.sh

M_Mean_temp_velo M_Mooring

• Sections.sh

M_Sectionx M_Sectiony

# For debugging this cell can help turn this cell to 'code' and modify variables manually. %env file_exp=SEDNA_DELTA_MONITOR %env year=2012 %env month=01 #0[1-2] %env ychunk=10 %env ychunk=False %env save=False %env plot=True %env calc=True # %env lazy=False #False %env control=M_Fluxnet #M_Sectiony ok with ychunk=False local=True lazy=False

%%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= irene5307.c-irene.mg1.tgcc.ccc.cea.fr starting dask cluster on local= True workers 16
10000000000
rome local cluster starting
This code is running on  irene5307.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= 03  outputpath= ../results/SEDNA_DELTA_MONITOR/ daskreport= ../results/dask/6464054irene5307.c-irene.mg1.tgcc.ccc.cea.fr_SEDNA_DELTA_MONITOR_03M_Sectiony/
CPU times: user 579 ms, sys: 139 ms, total: 718 ms
Wall time: 21.1 s

Client

Client-b61b1233-184e-11ed-be8b-080038b93dd9

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

Cluster Info

LocalCluster

984025e3

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-52378e84-7a99-48fa-9c45-2357b86e1788

Comm: tcp://127.0.0.1:35734	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://127.0.0.1:37315	Total threads: 8
Dashboard: http://127.0.0.1:35575/status	Memory: 15.69 GiB
Nanny: tcp://127.0.0.1:35047
Local directory: /tmp/dask-worker-space/worker-ktd2c8bm

Worker: 1

Comm: tcp://127.0.0.1:38034	Total threads: 8
Dashboard: http://127.0.0.1:34644/status	Memory: 15.69 GiB
Nanny: tcp://127.0.0.1:33063
Local directory: /tmp/dask-worker-space/worker-4b2zj8ha

Worker: 2

Comm: tcp://127.0.0.1:40885	Total threads: 8
Dashboard: http://127.0.0.1:38685/status	Memory: 15.69 GiB
Nanny: tcp://127.0.0.1:39692
Local directory: /tmp/dask-worker-space/worker-blk9ywjc

Worker: 3

Comm: tcp://127.0.0.1:44078	Total threads: 8
Dashboard: http://127.0.0.1:41701/status	Memory: 15.69 GiB
Nanny: tcp://127.0.0.1:33111
Local directory: /tmp/dask-worker-space/worker-jwl8sscj

Worker: 4

Comm: tcp://127.0.0.1:39655	Total threads: 8
Dashboard: http://127.0.0.1:38147/status	Memory: 15.69 GiB
Nanny: tcp://127.0.0.1:39187
Local directory: /tmp/dask-worker-space/worker-ls9v65kn

Worker: 5

Comm: tcp://127.0.0.1:40865	Total threads: 8
Dashboard: http://127.0.0.1:41349/status	Memory: 15.69 GiB
Nanny: tcp://127.0.0.1:42354
Local directory: /tmp/dask-worker-space/worker-q2xeydfe

Worker: 6

Comm: tcp://127.0.0.1:33346	Total threads: 8
Dashboard: http://127.0.0.1:33668/status	Memory: 15.69 GiB
Nanny: tcp://127.0.0.1:41969
Local directory: /tmp/dask-worker-space/worker-uvg_wbpe

Worker: 7

Comm: tcp://127.0.0.1:33822	Total threads: 8
Dashboard: http://127.0.0.1:33448/status	Memory: 15.69 GiB
Nanny: tcp://127.0.0.1:41097
Local directory: /tmp/dask-worker-space/worker-0mr4qg4o

Worker: 8

Comm: tcp://127.0.0.1:40295	Total threads: 8
Dashboard: http://127.0.0.1:44777/status	Memory: 15.69 GiB
Nanny: tcp://127.0.0.1:37479
Local directory: /tmp/dask-worker-space/worker-oqm22ffi

Worker: 9

Comm: tcp://127.0.0.1:39790	Total threads: 8
Dashboard: http://127.0.0.1:40666/status	Memory: 15.69 GiB
Nanny: tcp://127.0.0.1:43717
Local directory: /tmp/dask-worker-space/worker-nvgwc4sp

Worker: 10

Comm: tcp://127.0.0.1:40521	Total threads: 8
Dashboard: http://127.0.0.1:34651/status	Memory: 15.69 GiB
Nanny: tcp://127.0.0.1:44510
Local directory: /tmp/dask-worker-space/worker-ct5zagkf

Worker: 11

Comm: tcp://127.0.0.1:42652	Total threads: 8
Dashboard: http://127.0.0.1:37289/status	Memory: 15.69 GiB
Nanny: tcp://127.0.0.1:35279
Local directory: /tmp/dask-worker-space/worker-ys1y0yqy

Worker: 12

Comm: tcp://127.0.0.1:45712	Total threads: 8
Dashboard: http://127.0.0.1:40300/status	Memory: 15.69 GiB
Nanny: tcp://127.0.0.1:37023
Local directory: /tmp/dask-worker-space/worker-gr1sn071

Worker: 13

Comm: tcp://127.0.0.1:41137	Total threads: 8
Dashboard: http://127.0.0.1:38306/status	Memory: 15.69 GiB
Nanny: tcp://127.0.0.1:43506
Local directory: /tmp/dask-worker-space/worker-pvbsp24_

Worker: 14

Comm: tcp://127.0.0.1:38895	Total threads: 8
Dashboard: http://127.0.0.1:33410/status	Memory: 15.69 GiB
Nanny: tcp://127.0.0.1:41304
Local directory: /tmp/dask-worker-space/worker-vzuaxmh5

Worker: 15

Comm: tcp://127.0.0.1:39768	Total threads: 8
Dashboard: http://127.0.0.1:33273/status	Memory: 15.69 GiB
Nanny: tcp://127.0.0.1:46698
Local directory: /tmp/dask-worker-space/worker-pdqwfwwz

read plotting information from a csv file

df=load.controlfile(control)
#Take out 'later' tagged computations
#df=df[~df['Value'].str.contains('later')]
df

	Value	Inputs	Equation	Zone	Plot	Colourmap	MinMax	Unit	Oldname	Unnamed: 10
	Section	gridT.votemper,gridS.vosaline,gridV.vomecrty,p...	data.unify_chunks().persist()	FramS	section	None	{'vosaline': (33,36.2), 'votemper': (-2,6), 'v...	None	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

%%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']
lazy= False
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/201203/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 21.180792570114136 seconds
0 merging gridS ['vosaline']
1 read gridT ['votemper']
lazy= False
using load_data_xios_kerchunk reading  gridT
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/201203/gridT_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 31.633633852005005 seconds
1 merging gridT ['votemper']
      took 0.7532386779785156 seconds
2 read gridV ['vomecrty']
lazy= False
using load_data_xios_kerchunk reading  gridV
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/201203/gridV_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 42.17132616043091 seconds
2 merging gridV ['vomecrty']
      took 0.8151638507843018 seconds
param nav_lon will be included in data
param depth will be included in data
param mask2d will be included in data
param mask will be included in data
param nav_lat will be included in data
ychunk= 10 calldatas_y_rechunk
sum_num (13, 13, 13, 13, 13, 13, 13, 13, 13, 13, 13, 13, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12)
start rechunking with (130, 122, 120, 120, 120, 120, 120, 120, 120, 120, 120, 120, 120, 120, 120, 120, 120, 120, 120, 120, 120, 120, 120, 120, 120, 120, 120, 120, 120, 120, 120, 120, 120, 120, 120, 120, 120, 120, 120, 120, 120, 120, 120, 120, 120, 120, 120, 120, 120, 120, 120, 120, 120, 120, 48)
end of y_rechunk
CPU times: user 53.8 s, sys: 7.4 s, total: 1min 1s
Wall time: 2min 2s

<xarray.Dataset>
Dimensions:   (t: 31, z: 150, y: 6540, x: 6560)
Coordinates:
  * t         (t) object 2012-03-01 12:00:00 ... 2012-03-31 12:00:00
  * y         (y) int64 1 2 3 4 5 6 7 8 ... 6534 6535 6536 6537 6538 6539 6540
  * x         (x) int64 1 2 3 4 5 6 7 8 ... 6554 6555 6556 6557 6558 6559 6560
  * z         (z) int64 1 2 3 4 5 6 7 8 9 ... 143 144 145 146 147 148 149 150
    nav_lon   (y, x) float32 dask.array<chunksize=(130, 6560), meta=np.ndarray>
    depth     (z, y, x) float32 dask.array<chunksize=(150, 130, 6560), meta=np.ndarray>
    mask2d    (y, x) bool dask.array<chunksize=(130, 6560), meta=np.ndarray>
    mask      (z, y, x) bool dask.array<chunksize=(150, 130, 6560), meta=np.ndarray>
    nav_lat   (y, x) float32 dask.array<chunksize=(130, 6560), meta=np.ndarray>
Data variables:
    vosaline  (t, z, y, x) float32 dask.array<chunksize=(1, 150, 130, 6560), meta=np.ndarray>
    votemper  (t, z, y, x) float32 dask.array<chunksize=(1, 150, 130, 6560), meta=np.ndarray>
    vomecrty  (t, z, y, x) float32 dask.array<chunksize=(1, 150, 130, 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-19 12:40:34 GMT
    title:                   ocean T grid variables
    uuid:                    b2000bef-0e1f-4f0d-ae2a-b9429429b7a2

xarray.Dataset

• Dimensions:
  • t: 31
  • z: 150
  • y: 6540
  • x: 6560
• Coordinates: (9)
  • t
    (t)
    object
    2012-03-01 12:00:00 ... 2012-03-...

    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, 3, 1, 12, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(2012, 3, 2, 12, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(2012, 3, 3, 12, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(2012, 3, 4, 12, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(2012, 3, 5, 12, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(2012, 3, 6, 12, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(2012, 3, 7, 12, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(2012, 3, 8, 12, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(2012, 3, 9, 12, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(2012, 3, 10, 12, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(2012, 3, 11, 12, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(2012, 3, 12, 12, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(2012, 3, 13, 12, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(2012, 3, 14, 12, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(2012, 3, 15, 12, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(2012, 3, 16, 12, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(2012, 3, 17, 12, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(2012, 3, 18, 12, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(2012, 3, 19, 12, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(2012, 3, 20, 12, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(2012, 3, 21, 12, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(2012, 3, 22, 12, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(2012, 3, 23, 12, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(2012, 3, 24, 12, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(2012, 3, 25, 12, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(2012, 3, 26, 12, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(2012, 3, 27, 12, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(2012, 3, 28, 12, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(2012, 3, 29, 12, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(2012, 3, 30, 12, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(2012, 3, 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=(130, 6560), meta=np.ndarray>

    Array Chunk Bytes 163.66 MiB 3.25 MiB Shape (6540, 6560) (130, 6560) Count 1687 Tasks 55 Chunks Type float32 numpy.ndarray

  • depth
    (z, y, x)
    float32
    dask.array<chunksize=(150, 130, 6560), meta=np.ndarray>

    Array Chunk Bytes 23.97 GiB 487.98 MiB Shape (150, 6540, 6560) (150, 130, 6560) Count 1687 Tasks 55 Chunks Type float32 numpy.ndarray

  • mask2d
    (y, x)
    bool
    dask.array<chunksize=(130, 6560), meta=np.ndarray>

    Array Chunk Bytes 40.91 MiB 832.81 kiB Shape (6540, 6560) (130, 6560) Count 1687 Tasks 55 Chunks Type bool numpy.ndarray

  • mask
    (z, y, x)
    bool
    dask.array<chunksize=(150, 130, 6560), meta=np.ndarray>

    Array Chunk Bytes 5.99 GiB 121.99 MiB Shape (150, 6540, 6560) (150, 130, 6560) Count 1687 Tasks 55 Chunks Type bool numpy.ndarray

  • nav_lat
    (y, x)
    float32
    dask.array<chunksize=(130, 6560), meta=np.ndarray>

    Array Chunk Bytes 163.66 MiB 3.25 MiB Shape (6540, 6560) (130, 6560) Count 1687 Tasks 55 Chunks Type float32 numpy.ndarray

• Data variables: (3)
  • vosaline
    (t, z, y, x)
    float32
    dask.array<chunksize=(1, 150, 130, 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 487.98 MiB Shape (31, 150, 6540, 6560) (1, 150, 130, 6560) Count 35977 Tasks 1705 Chunks Type float32 numpy.ndarray

  • votemper
    (t, z, y, x)
    float32
    dask.array<chunksize=(1, 150, 130, 6560), meta=np.ndarray>

    cell_methods :

    time: mean (interval: 40 s)

    interval_operation :

    40 s

    interval_write :

    1 d

    long_name :

    temperature

    online_operation :

    average

    standard_name :

    sea_water_potential_temperature

    units :

    degC

    Array Chunk Bytes 743.18 GiB 487.98 MiB Shape (31, 150, 6540, 6560) (1, 150, 130, 6560) Count 35977 Tasks 1705 Chunks Type float32 numpy.ndarray

  • vomecrty
    (t, z, y, x)
    float32
    dask.array<chunksize=(1, 150, 130, 6560), meta=np.ndarray>

    cell_methods :

    time: mean (interval: 40 s)

    interval_operation :

    40 s

    interval_write :

    1 d

    long_name :

    ocean current along j-axis

    online_operation :

    average

    standard_name :

    sea_water_y_velocity

    units :

    m/s

    Array Chunk Bytes 743.18 GiB 487.98 MiB Shape (31, 150, 6540, 6560) (1, 150, 130, 6560) Count 35977 Tasks 1705 Chunks Type float32 numpy.ndarray

• Attributes: (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]

  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 :

  Thu Jan 20 11:45:44 2022: ncks -4 -L 1 SEDNA-DELTA_1d_gridS_201203-201203_NOZIP_0000.nc /ccc/scratch/cont003/gen7420/talandel/SEDNA/SEDNA-DELTA-S/SPLIT/1d/2012/03/SEDNA-DELTA_1d_gridS_201203-201203_0000.nc Thu Jan 20 11:43:30 2022: ncrcat -n 31,2,1 SEDNA-DELTA_1d_gridS_0000_01.nc SEDNA-DELTA_1d_gridS_201203-201203_NOZIP_0000.nc

  ibegin :

  0

  jbegin :

  0

  name :

  /ccc/scratch/cont003/ra5563/talandel/ONGOING-RUNS/SEDNA-DELTA-XIOS.48/SEDNA-DELTA_1d_gridS

  ni :

  6560

  nj :

  13

  output_frequency :

  1d

  start_date :

  20090101

  timeStamp :

  2022-Jan-19 12:40:34 GMT

  title :

  ocean T grid variables

  uuid :

  b2000bef-0e1f-4f0d-ae2a-b9429429b7a2

%%time
monitor.auto(df,data,savefig,daskreport,outputpath,file_exp='SEDNA'
            )

#calc= True
#save= True
#plot= False
Value='Section'
Zone='FramS'
Plot='section'
cmap='None'
clabel='None'
clim= {'vosaline': (33, 36.2), 'votemper': (-2, 6), 'vomecrty': (-0.05, 0.05)}
outputpath='../results/SEDNA_DELTA_MONITOR/'
nc_outputpath='../nc_results/SEDNA_DELTA_MONITOR/'
filename='SEDNA_section_FramS_Section'
data=monitor.optimize_dataset(data)
#2 Zooming Data
data= zoom.FramS(data)
data=monitor.optimize_dataset(data)

<xarray.Dataset>
Dimensions:   (t: 31, z: 150, x: 601)
Coordinates:
  * t         (t) object 2012-03-01 12:00:00 ... 2012-03-31 12:00:00
    y         int64 2609
  * x         (x) int64 3734 3735 3736 3737 3738 ... 4330 4331 4332 4333 4334
  * z         (z) int64 1 2 3 4 5 6 7 8 9 ... 143 144 145 146 147 148 149 150
    nav_lon   (x) float32 dask.array<chunksize=(601,), meta=np.ndarray>
    depth     (z, x) float32 dask.array<chunksize=(150, 601), meta=np.ndarray>
    mask2d    (x) bool dask.array<chunksize=(601,), meta=np.ndarray>
    mask      (z, x) bool dask.array<chunksize=(150, 601), meta=np.ndarray>
    nav_lat   (x) float32 dask.array<chunksize=(601,), meta=np.ndarray>
Data variables:
    vosaline  (t, z, x) float32 dask.array<chunksize=(1, 150, 601), meta=np.ndarray>
    votemper  (t, z, x) float32 dask.array<chunksize=(1, 150, 601), meta=np.ndarray>
    vomecrty  (t, z, x) float32 dask.array<chunksize=(1, 150, 601), 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-19 12:40:34 GMT
    title:                   ocean T grid variables
    uuid:                    b2000bef-0e1f-4f0d-ae2a-b9429429b7a2

xarray.Dataset

• Dimensions:
  • t: 31
  • z: 150
  • x: 601
• Coordinates: (9)
  • t
    (t)
    object
    2012-03-01 12:00:00 ... 2012-03-...

    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, 3, 1, 12, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(2012, 3, 2, 12, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(2012, 3, 3, 12, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(2012, 3, 4, 12, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(2012, 3, 5, 12, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(2012, 3, 6, 12, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(2012, 3, 7, 12, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(2012, 3, 8, 12, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(2012, 3, 9, 12, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(2012, 3, 10, 12, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(2012, 3, 11, 12, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(2012, 3, 12, 12, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(2012, 3, 13, 12, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(2012, 3, 14, 12, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(2012, 3, 15, 12, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(2012, 3, 16, 12, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(2012, 3, 17, 12, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(2012, 3, 18, 12, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(2012, 3, 19, 12, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(2012, 3, 20, 12, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(2012, 3, 21, 12, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(2012, 3, 22, 12, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(2012, 3, 23, 12, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(2012, 3, 24, 12, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(2012, 3, 25, 12, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(2012, 3, 26, 12, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(2012, 3, 27, 12, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(2012, 3, 28, 12, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(2012, 3, 29, 12, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(2012, 3, 30, 12, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(2012, 3, 31, 12, 0, 0, 0, has_year_zero=True)],
          dtype=object)

  • y
    ()
    int64
    2609

    array(2609)

  • x
    (x)
    int64
    3734 3735 3736 ... 4332 4333 4334

    array([3734, 3735, 3736, ..., 4332, 4333, 4334])

  • 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
    (x)
    float32
    dask.array<chunksize=(601,), meta=np.ndarray>

    Array Chunk Bytes 2.35 kiB 2.35 kiB Shape (601,) (601,) Count 1688 Tasks 1 Chunks Type float32 numpy.ndarray

  • depth
    (z, x)
    float32
    dask.array<chunksize=(150, 601), meta=np.ndarray>

    Array Chunk Bytes 352.15 kiB 352.15 kiB Shape (150, 601) (150, 601) Count 1688 Tasks 1 Chunks Type float32 numpy.ndarray

  • mask2d
    (x)
    bool
    dask.array<chunksize=(601,), meta=np.ndarray>

    Array Chunk Bytes 601 B 601 B Shape (601,) (601,) Count 1688 Tasks 1 Chunks Type bool numpy.ndarray

  • mask
    (z, x)
    bool
    dask.array<chunksize=(150, 601), meta=np.ndarray>

    Array Chunk Bytes 88.04 kiB 88.04 kiB Shape (150, 601) (150, 601) Count 1688 Tasks 1 Chunks Type bool numpy.ndarray

  • nav_lat
    (x)
    float32
    dask.array<chunksize=(601,), meta=np.ndarray>

    Array Chunk Bytes 2.35 kiB 2.35 kiB Shape (601,) (601,) Count 1688 Tasks 1 Chunks Type float32 numpy.ndarray

• Data variables: (3)
  • vosaline
    (t, z, x)
    float32
    dask.array<chunksize=(1, 150, 601), 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 10.66 MiB 352.15 kiB Shape (31, 150, 601) (1, 150, 601) Count 787 Tasks 31 Chunks Type float32 numpy.ndarray

  • votemper
    (t, z, x)
    float32
    dask.array<chunksize=(1, 150, 601), meta=np.ndarray>

    cell_methods :

    time: mean (interval: 40 s)

    interval_operation :

    40 s

    interval_write :

    1 d

    long_name :

    temperature

    online_operation :

    average

    standard_name :

    sea_water_potential_temperature

    units :

    degC

    Array Chunk Bytes 10.66 MiB 352.15 kiB Shape (31, 150, 601) (1, 150, 601) Count 787 Tasks 31 Chunks Type float32 numpy.ndarray

  • vomecrty
    (t, z, x)
    float32
    dask.array<chunksize=(1, 150, 601), meta=np.ndarray>

    cell_methods :

    time: mean (interval: 40 s)

    interval_operation :

    40 s

    interval_write :

    1 d

    long_name :

    ocean current along j-axis

    online_operation :

    average

    standard_name :

    sea_water_y_velocity

    units :

    m/s

    Array Chunk Bytes 10.66 MiB 352.15 kiB Shape (31, 150, 601) (1, 150, 601) Count 787 Tasks 31 Chunks Type float32 numpy.ndarray

• Attributes: (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]

  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 :

  Thu Jan 20 11:45:44 2022: ncks -4 -L 1 SEDNA-DELTA_1d_gridS_201203-201203_NOZIP_0000.nc /ccc/scratch/cont003/gen7420/talandel/SEDNA/SEDNA-DELTA-S/SPLIT/1d/2012/03/SEDNA-DELTA_1d_gridS_201203-201203_0000.nc Thu Jan 20 11:43:30 2022: ncrcat -n 31,2,1 SEDNA-DELTA_1d_gridS_0000_01.nc SEDNA-DELTA_1d_gridS_201203-201203_NOZIP_0000.nc

  ibegin :

  0

  jbegin :

  0

  name :

  /ccc/scratch/cont003/ra5563/talandel/ONGOING-RUNS/SEDNA-DELTA-XIOS.48/SEDNA-DELTA_1d_gridS

  ni :

  6560

  nj :

  13

  output_frequency :

  1d

  start_date :

  20090101

  timeStamp :

  2022-Jan-19 12:40:34 GMT

  title :

  ocean T grid variables

  uuid :

  b2000bef-0e1f-4f0d-ae2a-b9429429b7a2

#3 Start computing 
data= data.unify_chunks().persist()
monitor.optimize_dataset(data)
add optimise here once otimise can recognise

<xarray.Dataset>
Dimensions:   (t: 31, z: 150, x: 601)
Coordinates:
  * t         (t) object 2012-03-01 12:00:00 ... 2012-03-31 12:00:00
    y         int64 2609
  * x         (x) int64 3734 3735 3736 3737 3738 ... 4330 4331 4332 4333 4334
  * z         (z) int64 1 2 3 4 5 6 7 8 9 ... 143 144 145 146 147 148 149 150
    nav_lon   (x) float32 dask.array<chunksize=(601,), meta=np.ndarray>
    depth     (z, x) float32 dask.array<chunksize=(150, 601), meta=np.ndarray>
    mask2d    (x) bool dask.array<chunksize=(601,), meta=np.ndarray>
    mask      (z, x) bool dask.array<chunksize=(150, 601), meta=np.ndarray>
    nav_lat   (x) float32 dask.array<chunksize=(601,), meta=np.ndarray>
Data variables:
    vosaline  (t, z, x) float32 dask.array<chunksize=(1, 150, 601), meta=np.ndarray>
    votemper  (t, z, x) float32 dask.array<chunksize=(1, 150, 601), meta=np.ndarray>
    vomecrty  (t, z, x) float32 dask.array<chunksize=(1, 150, 601), 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-19 12:40:34 GMT
    title:                   ocean T grid variables
    uuid:                    b2000bef-0e1f-4f0d-ae2a-b9429429b7a2

xarray.Dataset

• Dimensions:
  • t: 31
  • z: 150
  • x: 601
• Coordinates: (9)
  • t
    (t)
    object
    2012-03-01 12:00:00 ... 2012-03-...

    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, 3, 1, 12, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(2012, 3, 2, 12, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(2012, 3, 3, 12, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(2012, 3, 4, 12, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(2012, 3, 5, 12, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(2012, 3, 6, 12, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(2012, 3, 7, 12, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(2012, 3, 8, 12, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(2012, 3, 9, 12, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(2012, 3, 10, 12, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(2012, 3, 11, 12, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(2012, 3, 12, 12, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(2012, 3, 13, 12, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(2012, 3, 14, 12, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(2012, 3, 15, 12, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(2012, 3, 16, 12, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(2012, 3, 17, 12, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(2012, 3, 18, 12, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(2012, 3, 19, 12, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(2012, 3, 20, 12, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(2012, 3, 21, 12, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(2012, 3, 22, 12, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(2012, 3, 23, 12, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(2012, 3, 24, 12, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(2012, 3, 25, 12, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(2012, 3, 26, 12, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(2012, 3, 27, 12, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(2012, 3, 28, 12, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(2012, 3, 29, 12, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(2012, 3, 30, 12, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(2012, 3, 31, 12, 0, 0, 0, has_year_zero=True)],
          dtype=object)

  • y
    ()
    int64
    2609

    array(2609)

  • x
    (x)
    int64
    3734 3735 3736 ... 4332 4333 4334

    array([3734, 3735, 3736, ..., 4332, 4333, 4334])

  • 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
    (x)
    float32
    dask.array<chunksize=(601,), meta=np.ndarray>

    Array Chunk Bytes 2.35 kiB 2.35 kiB Shape (601,) (601,) Count 1 Tasks 1 Chunks Type float32 numpy.ndarray

  • depth
    (z, x)
    float32
    dask.array<chunksize=(150, 601), meta=np.ndarray>

    Array Chunk Bytes 352.15 kiB 352.15 kiB Shape (150, 601) (150, 601) Count 1 Tasks 1 Chunks Type float32 numpy.ndarray

  • mask2d
    (x)
    bool
    dask.array<chunksize=(601,), meta=np.ndarray>

    Array Chunk Bytes 601 B 601 B Shape (601,) (601,) Count 1 Tasks 1 Chunks Type bool numpy.ndarray

  • mask
    (z, x)
    bool
    dask.array<chunksize=(150, 601), meta=np.ndarray>

    Array Chunk Bytes 88.04 kiB 88.04 kiB Shape (150, 601) (150, 601) Count 1 Tasks 1 Chunks Type bool numpy.ndarray

  • nav_lat
    (x)
    float32
    dask.array<chunksize=(601,), meta=np.ndarray>

    Array Chunk Bytes 2.35 kiB 2.35 kiB Shape (601,) (601,) Count 1 Tasks 1 Chunks Type float32 numpy.ndarray

• Data variables: (3)
  • vosaline
    (t, z, x)
    float32
    dask.array<chunksize=(1, 150, 601), 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 10.66 MiB 352.15 kiB Shape (31, 150, 601) (1, 150, 601) Count 31 Tasks 31 Chunks Type float32 numpy.ndarray

  • votemper
    (t, z, x)
    float32
    dask.array<chunksize=(1, 150, 601), meta=np.ndarray>

    cell_methods :

    time: mean (interval: 40 s)

    interval_operation :

    40 s

    interval_write :

    1 d

    long_name :

    temperature

    online_operation :

    average

    standard_name :

    sea_water_potential_temperature

    units :

    degC

    Array Chunk Bytes 10.66 MiB 352.15 kiB Shape (31, 150, 601) (1, 150, 601) Count 31 Tasks 31 Chunks Type float32 numpy.ndarray

  • vomecrty
    (t, z, x)
    float32
    dask.array<chunksize=(1, 150, 601), meta=np.ndarray>

    cell_methods :

    time: mean (interval: 40 s)

    interval_operation :

    40 s

    interval_write :

    1 d

    long_name :

    ocean current along j-axis

    online_operation :

    average

    standard_name :

    sea_water_y_velocity

    units :

    m/s

    Array Chunk Bytes 10.66 MiB 352.15 kiB Shape (31, 150, 601) (1, 150, 601) Count 31 Tasks 31 Chunks Type float32 numpy.ndarray

• Attributes: (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]

  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 :

  Thu Jan 20 11:45:44 2022: ncks -4 -L 1 SEDNA-DELTA_1d_gridS_201203-201203_NOZIP_0000.nc /ccc/scratch/cont003/gen7420/talandel/SEDNA/SEDNA-DELTA-S/SPLIT/1d/2012/03/SEDNA-DELTA_1d_gridS_201203-201203_0000.nc Thu Jan 20 11:43:30 2022: ncrcat -n 31,2,1 SEDNA-DELTA_1d_gridS_0000_01.nc SEDNA-DELTA_1d_gridS_201203-201203_NOZIP_0000.nc

  ibegin :

  0

  jbegin :

  0

  name :

  /ccc/scratch/cont003/ra5563/talandel/ONGOING-RUNS/SEDNA-DELTA-XIOS.48/SEDNA-DELTA_1d_gridS

  ni :

  6560

  nj :

  13

  output_frequency :

  1d

  start_date :

  20090101

  timeStamp :

  2022-Jan-19 12:40:34 GMT

  title :

  ocean T grid variables

  uuid :

  b2000bef-0e1f-4f0d-ae2a-b9429429b7a2

#4 Saving  SEDNA_section_FramS_Section
data=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)
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 11.6 s, sys: 1.67 s, total: 13.3 s
Wall time: 1min 16s