%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/talandel/TOOLS/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= irene8002.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  irene8002.c-irene.mg1.tgcc.ccc.cea.fr using  SEDNA_DELTA_MONITOR file experiment, read from  ../lib/SEDNA_DELTA_MONITOR.yaml  on year= 2015  on month= 05  outputpath= ../results/SEDNA_DELTA_MONITOR/ daskreport= ../results/dask/7402596irene8002.c-irene.mg1.tgcc.ccc.cea.fr_SEDNA_DELTA_MONITOR_05M_AWTMD/
CPU times: user 462 ms, sys: 210 ms, total: 672 ms
Wall time: 11.1 s

Client

Client-7dc48d6c-6d8b-11ed-bba1-080038bfdcae

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

Cluster Info

LocalCluster

553c8174

Dashboard: http://127.0.0.1:8787/status	Workers: 16
Total threads: 128	Total memory: 2.86 TiB
Status: running	Using processes: True

Scheduler Info

Scheduler

Scheduler-32521f54-1a64-4d39-8a82-dae374d57b27

Comm: tcp://127.0.0.1:46610	Workers: 16
Dashboard: http://127.0.0.1:8787/status	Total threads: 128
Started: Just now	Total memory: 2.86 TiB

Workers

Worker: 0

Comm: tcp://127.0.0.1:38881	Total threads: 8
Dashboard: http://127.0.0.1:46370/status	Memory: 183.20 GiB
Nanny: tcp://127.0.0.1:44875
Local directory: /tmp/dask-worker-space/worker-_r4i94uy

Worker: 1

Comm: tcp://127.0.0.1:35619	Total threads: 8
Dashboard: http://127.0.0.1:38349/status	Memory: 183.20 GiB
Nanny: tcp://127.0.0.1:33796
Local directory: /tmp/dask-worker-space/worker-ic9a07ak

Worker: 2

Comm: tcp://127.0.0.1:42873	Total threads: 8
Dashboard: http://127.0.0.1:34704/status	Memory: 183.20 GiB
Nanny: tcp://127.0.0.1:41883
Local directory: /tmp/dask-worker-space/worker-rg9nf4i4

Worker: 3

Comm: tcp://127.0.0.1:45887	Total threads: 8
Dashboard: http://127.0.0.1:39576/status	Memory: 183.20 GiB
Nanny: tcp://127.0.0.1:44796
Local directory: /tmp/dask-worker-space/worker-bnaswydj

Worker: 4

Comm: tcp://127.0.0.1:39406	Total threads: 8
Dashboard: http://127.0.0.1:43267/status	Memory: 183.20 GiB
Nanny: tcp://127.0.0.1:34408
Local directory: /tmp/dask-worker-space/worker-xlbrpp9q

Worker: 5

Comm: tcp://127.0.0.1:39435	Total threads: 8
Dashboard: http://127.0.0.1:35620/status	Memory: 183.20 GiB
Nanny: tcp://127.0.0.1:45988
Local directory: /tmp/dask-worker-space/worker-knnqdyus

Worker: 6

Comm: tcp://127.0.0.1:40305	Total threads: 8
Dashboard: http://127.0.0.1:44659/status	Memory: 183.20 GiB
Nanny: tcp://127.0.0.1:33044
Local directory: /tmp/dask-worker-space/worker-0q_sw5j8

Worker: 7

Comm: tcp://127.0.0.1:38715	Total threads: 8
Dashboard: http://127.0.0.1:40373/status	Memory: 183.20 GiB
Nanny: tcp://127.0.0.1:46432
Local directory: /tmp/dask-worker-space/worker-s1cinie6

Worker: 8

Comm: tcp://127.0.0.1:37807	Total threads: 8
Dashboard: http://127.0.0.1:33395/status	Memory: 183.20 GiB
Nanny: tcp://127.0.0.1:46059
Local directory: /tmp/dask-worker-space/worker-trh7ao9w

Worker: 9

Comm: tcp://127.0.0.1:32938	Total threads: 8
Dashboard: http://127.0.0.1:35978/status	Memory: 183.20 GiB
Nanny: tcp://127.0.0.1:43128
Local directory: /tmp/dask-worker-space/worker-jae7z31v

Worker: 10

Comm: tcp://127.0.0.1:41218	Total threads: 8
Dashboard: http://127.0.0.1:40674/status	Memory: 183.20 GiB
Nanny: tcp://127.0.0.1:35123
Local directory: /tmp/dask-worker-space/worker-0sjxs96u

Worker: 11

Comm: tcp://127.0.0.1:42563	Total threads: 8
Dashboard: http://127.0.0.1:43881/status	Memory: 183.20 GiB
Nanny: tcp://127.0.0.1:39034
Local directory: /tmp/dask-worker-space/worker-lf13hj6g

Worker: 12

Comm: tcp://127.0.0.1:38683	Total threads: 8
Dashboard: http://127.0.0.1:43223/status	Memory: 183.20 GiB
Nanny: tcp://127.0.0.1:45384
Local directory: /tmp/dask-worker-space/worker-pr1cvg7h

Worker: 13

Comm: tcp://127.0.0.1:43391	Total threads: 8
Dashboard: http://127.0.0.1:43959/status	Memory: 183.20 GiB
Nanny: tcp://127.0.0.1:45165
Local directory: /tmp/dask-worker-space/worker-vsfnekxg

Worker: 14

Comm: tcp://127.0.0.1:37404	Total threads: 8
Dashboard: http://127.0.0.1:33833/status	Memory: 183.20 GiB
Nanny: tcp://127.0.0.1:41627
Local directory: /tmp/dask-worker-space/worker-nplxnm1u

Worker: 15

Comm: tcp://127.0.0.1:46508	Total threads: 8
Dashboard: http://127.0.0.1:42922/status	Memory: 183.20 GiB
Nanny: tcp://127.0.0.1:40207
Local directory: /tmp/dask-worker-space/worker-efb4k4kc

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
	AW_maxtemp_depth	gridT.votemper,gridS.vosaline,param.mask,param...	calc.AWTD4(data)	ALL	AWTD_map	jet	(0,800)	m	M-5

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/talandel/TOOLS/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/201505/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/talandel/TOOLS/monitor-sedna/notebook/../lib/
>
      took 81.64056324958801 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/201505/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/talandel/TOOLS/monitor-sedna/notebook/../lib/
>
      took 85.77381372451782 seconds
1 merging gridT ['votemper']
      took 0.7089996337890625 seconds
param depth will be included in data
param mask will be included in data
param nav_lat will be included in data
param mask2d will be included in data
param nav_lon 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 45.8 s, sys: 11.7 s, total: 57.5 s
Wall time: 3min 3s

<xarray.Dataset>
Dimensions:   (t: 31, z: 150, y: 6540, x: 6560)
Coordinates:
  * t         (t) object 2015-05-01 12:00:00 ... 2015-05-31 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
    depth     (z, y, x) float32 dask.array<chunksize=(150, 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>
    mask2d    (y, x) bool dask.array<chunksize=(130, 6560), meta=np.ndarray>
    nav_lon   (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>
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-Aug-13 17:48:19 GMT
    title:                   ocean T grid variables
    uuid:                    22b22da9-9f65-4677-98f9-c4f9017c28bc

xarray.Dataset

• Dimensions:
  • t: 31
  • z: 150
  • y: 6540
  • x: 6560
• Coordinates: (9)
  • t
    (t)
    object
    2015-05-01 12:00:00 ... 2015-05-...

    axis :

    T

    bounds :

    time_counter_bounds

    long_name :

    Time axis

    standard_name :

    time

    time_origin :

    1900-01-01 00:00:00

    array([cftime.DatetimeNoLeap(2015, 5, 1, 12, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(2015, 5, 2, 12, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(2015, 5, 3, 12, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(2015, 5, 4, 12, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(2015, 5, 5, 12, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(2015, 5, 6, 12, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(2015, 5, 7, 12, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(2015, 5, 8, 12, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(2015, 5, 9, 12, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(2015, 5, 10, 12, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(2015, 5, 11, 12, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(2015, 5, 12, 12, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(2015, 5, 13, 12, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(2015, 5, 14, 12, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(2015, 5, 15, 12, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(2015, 5, 16, 12, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(2015, 5, 17, 12, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(2015, 5, 18, 12, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(2015, 5, 19, 12, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(2015, 5, 20, 12, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(2015, 5, 21, 12, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(2015, 5, 22, 12, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(2015, 5, 23, 12, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(2015, 5, 24, 12, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(2015, 5, 25, 12, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(2015, 5, 26, 12, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(2015, 5, 27, 12, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(2015, 5, 28, 12, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(2015, 5, 29, 12, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(2015, 5, 30, 12, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(2015, 5, 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])

  • 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

  • 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

  • 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

  • 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

• Data variables: (2)
  • 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

• 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 :

  Sun Aug 14 18:22:32 2022: ncks -4 -L 1 SEDNA-DELTA_1d_gridS_201505-201505_NOZIP_0000.nc /ccc/scratch/cont003/gen7420/talandel/SEDNA/SEDNA-DELTA-S/SPLIT/1d/2015/05/SEDNA-DELTA_1d_gridS_201505-201505_0000.nc Sun Aug 14 18:22:11 2022: ncrcat -n 31,2,1 SEDNA-DELTA_1d_gridS_0000_01.nc SEDNA-DELTA_1d_gridS_201505-201505_NOZIP_0000.nc

  ibegin :

  0

  jbegin :

  0

  name :

  /ccc/scratch/cont003/ra5563/talandel/ONGOING-RUNS/SEDNA-DELTA-XIOS.87/SEDNA-DELTA_1d_gridS

  ni :

  6560

  nj :

  13

  output_frequency :

  1d

  start_date :

  20090101

  timeStamp :

  2022-Aug-13 17:48:19 GMT

  title :

  ocean T grid variables

  uuid :

  22b22da9-9f65-4677-98f9-c4f9017c28bc

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

#calc= True
#save= True
#plot= False
Value='AW_maxtemp_depth'
Zone='ALL'
Plot='AWTD_map'
cmap='jet'
clabel='m'
clim= (0, 800)
outputpath='../results/SEDNA_DELTA_MONITOR/'
nc_outputpath='../nc_results/SEDNA_DELTA_MONITOR/'
filename='SEDNA_AWTD_map_ALL_AW_maxtemp_depth'
data=monitor.optimize_dataset(data)
#3 Start computing 
data= calc.AWTD4(data)
monitor.optimize_dataset(data)
add optimise here once otimise can recognise

<xarray.Dataset>
Dimensions:  (t: 31, y: 6540, x: 6560)
Coordinates:
  * t        (t) object 2015-05-01 12:00:00 ... 2015-05-31 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
    nav_lat  (y, x) float32 dask.array<chunksize=(130, 6560), meta=np.ndarray>
    mask2d   (y, x) bool dask.array<chunksize=(130, 6560), meta=np.ndarray>
    nav_lon  (y, x) float32 dask.array<chunksize=(130, 6560), meta=np.ndarray>
Data variables:
    AWT      (t, y, x) float32 dask.array<chunksize=(1, 130, 6560), meta=np.ndarray>
    AWD      (t, y, x) float32 dask.array<chunksize=(1, 130, 6560), meta=np.ndarray>

xarray.Dataset

• Dimensions:
  • t: 31
  • y: 6540
  • x: 6560
• Coordinates: (6)
  • t
    (t)
    object
    2015-05-01 12:00:00 ... 2015-05-...

    array([cftime.DatetimeNoLeap(2015, 5, 1, 12, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(2015, 5, 2, 12, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(2015, 5, 3, 12, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(2015, 5, 4, 12, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(2015, 5, 5, 12, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(2015, 5, 6, 12, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(2015, 5, 7, 12, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(2015, 5, 8, 12, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(2015, 5, 9, 12, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(2015, 5, 10, 12, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(2015, 5, 11, 12, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(2015, 5, 12, 12, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(2015, 5, 13, 12, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(2015, 5, 14, 12, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(2015, 5, 15, 12, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(2015, 5, 16, 12, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(2015, 5, 17, 12, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(2015, 5, 18, 12, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(2015, 5, 19, 12, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(2015, 5, 20, 12, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(2015, 5, 21, 12, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(2015, 5, 22, 12, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(2015, 5, 23, 12, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(2015, 5, 24, 12, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(2015, 5, 25, 12, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(2015, 5, 26, 12, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(2015, 5, 27, 12, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(2015, 5, 28, 12, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(2015, 5, 29, 12, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(2015, 5, 30, 12, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(2015, 5, 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])

  • 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

  • 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

  • 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

• Data variables: (2)
  • AWT
    (t, y, x)
    float32
    dask.array<chunksize=(1, 130, 6560), meta=np.ndarray>

    long_name :

    AWT

    Array Chunk Bytes 4.95 GiB 3.25 MiB Shape (31, 6540, 6560) (1, 130, 6560) Count 83926 Tasks 1705 Chunks Type float32 numpy.ndarray

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

    Array Chunk Bytes 4.95 GiB 3.25 MiB Shape (31, 6540, 6560) (1, 130, 6560) Count 102663 Tasks 1705 Chunks Type float32 numpy.ndarray

• Attributes: (0)

#4 Saving  SEDNA_AWTD_map_ALL_AW_maxtemp_depth
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)

/ccc/cont003/home/ra5563/ra5563/monitor/lib/python3.10/site-packages/dask/array/reductions.py:608: RuntimeWarning: All-NaN slice encountered
  return np.nanmax(x_chunk, axis=axis, keepdims=keepdims)
/ccc/cont003/home/ra5563/ra5563/monitor/lib/python3.10/site-packages/dask/array/reductions.py:608: RuntimeWarning: All-NaN slice encountered
  return np.nanmax(x_chunk, axis=axis, keepdims=keepdims)
/ccc/cont003/home/ra5563/ra5563/monitor/lib/python3.10/site-packages/dask/array/reductions.py:608: RuntimeWarning: All-NaN slice encountered
  return np.nanmax(x_chunk, axis=axis, keepdims=keepdims)
/ccc/cont003/home/ra5563/ra5563/monitor/lib/python3.10/site-packages/dask/array/reductions.py:608: RuntimeWarning: All-NaN slice encountered
  return np.nanmax(x_chunk, axis=axis, keepdims=keepdims)
/ccc/cont003/home/ra5563/ra5563/monitor/lib/python3.10/site-packages/dask/array/reductions.py:608: RuntimeWarning: All-NaN slice encountered
  return np.nanmax(x_chunk, axis=axis, keepdims=keepdims)
/ccc/cont003/home/ra5563/ra5563/monitor/lib/python3.10/site-packages/dask/array/reductions.py:608: RuntimeWarning: All-NaN slice encountered
  return np.nanmax(x_chunk, axis=axis, keepdims=keepdims)
/ccc/cont003/home/ra5563/ra5563/monitor/lib/python3.10/site-packages/dask/array/reductions.py:608: RuntimeWarning: All-NaN slice encountered
  return np.nanmax(x_chunk, axis=axis, keepdims=keepdims)
/ccc/cont003/home/ra5563/ra5563/monitor/lib/python3.10/site-packages/dask/array/reductions.py:608: RuntimeWarning: All-NaN slice encountered
  return np.nanmax(x_chunk, axis=axis, keepdims=keepdims)
/ccc/cont003/home/ra5563/ra5563/monitor/lib/python3.10/site-packages/dask/array/reductions.py:608: RuntimeWarning: All-NaN slice encountered
  return np.nanmax(x_chunk, axis=axis, keepdims=keepdims)
/ccc/cont003/home/ra5563/ra5563/monitor/lib/python3.10/site-packages/dask/array/reductions.py:608: RuntimeWarning: All-NaN slice encountered
  return np.nanmax(x_chunk, axis=axis, keepdims=keepdims)
/ccc/cont003/home/ra5563/ra5563/monitor/lib/python3.10/site-packages/dask/array/reductions.py:608: RuntimeWarning: All-NaN slice encountered
  return np.nanmax(x_chunk, axis=axis, keepdims=keepdims)
/ccc/cont003/home/ra5563/ra5563/monitor/lib/python3.10/site-packages/dask/array/reductions.py:608: RuntimeWarning: All-NaN slice encountered
  return np.nanmax(x_chunk, axis=axis, keepdims=keepdims)
/ccc/cont003/home/ra5563/ra5563/monitor/lib/python3.10/site-packages/dask/array/reductions.py:608: RuntimeWarning: All-NaN slice encountered
  return np.nanmax(x_chunk, axis=axis, keepdims=keepdims)
/ccc/cont003/home/ra5563/ra5563/monitor/lib/python3.10/site-packages/dask/array/reductions.py:608: RuntimeWarning: All-NaN slice encountered
  return np.nanmax(x_chunk, axis=axis, keepdims=keepdims)
/ccc/cont003/home/ra5563/ra5563/monitor/lib/python3.10/site-packages/dask/array/reductions.py:608: RuntimeWarning: All-NaN slice encountered
  return np.nanmax(x_chunk, axis=axis, keepdims=keepdims)
/ccc/cont003/home/ra5563/ra5563/monitor/lib/python3.10/site-packages/dask/array/reductions.py:608: RuntimeWarning: All-NaN slice encountered
  return np.nanmax(x_chunk, axis=axis, keepdims=keepdims)
/ccc/cont003/home/ra5563/ra5563/monitor/lib/python3.10/site-packages/dask/array/reductions.py:608: RuntimeWarning: All-NaN slice encountered
  return np.nanmax(x_chunk, axis=axis, keepdims=keepdims)

slice(1, 2, None)

/ccc/cont003/home/ra5563/ra5563/monitor/lib/python3.10/site-packages/dask/array/reductions.py:608: RuntimeWarning: All-NaN slice encountered
  return np.nanmax(x_chunk, axis=axis, keepdims=keepdims)
/ccc/cont003/home/ra5563/ra5563/monitor/lib/python3.10/site-packages/dask/array/reductions.py:608: RuntimeWarning: All-NaN slice encountered
  return np.nanmax(x_chunk, axis=axis, keepdims=keepdims)
/ccc/cont003/home/ra5563/ra5563/monitor/lib/python3.10/site-packages/dask/array/reductions.py:608: RuntimeWarning: All-NaN slice encountered
  return np.nanmax(x_chunk, axis=axis, keepdims=keepdims)
/ccc/cont003/home/ra5563/ra5563/monitor/lib/python3.10/site-packages/dask/array/reductions.py:608: RuntimeWarning: All-NaN slice encountered
  return np.nanmax(x_chunk, axis=axis, keepdims=keepdims)
/ccc/cont003/home/ra5563/ra5563/monitor/lib/python3.10/site-packages/dask/array/reductions.py:608: RuntimeWarning: All-NaN slice encountered
  return np.nanmax(x_chunk, axis=axis, keepdims=keepdims)
/ccc/cont003/home/ra5563/ra5563/monitor/lib/python3.10/site-packages/dask/array/reductions.py:608: RuntimeWarning: All-NaN slice encountered
  return np.nanmax(x_chunk, axis=axis, keepdims=keepdims)
/ccc/cont003/home/ra5563/ra5563/monitor/lib/python3.10/site-packages/dask/array/reductions.py:608: RuntimeWarning: All-NaN slice encountered
  return np.nanmax(x_chunk, axis=axis, keepdims=keepdims)
/ccc/cont003/home/ra5563/ra5563/monitor/lib/python3.10/site-packages/dask/array/reductions.py:608: RuntimeWarning: All-NaN slice encountered
  return np.nanmax(x_chunk, axis=axis, keepdims=keepdims)
/ccc/cont003/home/ra5563/ra5563/monitor/lib/python3.10/site-packages/dask/array/reductions.py:608: RuntimeWarning: All-NaN slice encountered
  return np.nanmax(x_chunk, axis=axis, keepdims=keepdims)
/ccc/cont003/home/ra5563/ra5563/monitor/lib/python3.10/site-packages/dask/array/reductions.py:608: RuntimeWarning: All-NaN slice encountered
  return np.nanmax(x_chunk, axis=axis, keepdims=keepdims)
/ccc/cont003/home/ra5563/ra5563/monitor/lib/python3.10/site-packages/dask/array/reductions.py:608: RuntimeWarning: All-NaN slice encountered
  return np.nanmax(x_chunk, axis=axis, keepdims=keepdims)
/ccc/cont003/home/ra5563/ra5563/monitor/lib/python3.10/site-packages/dask/array/reductions.py:608: RuntimeWarning: All-NaN slice encountered
  return np.nanmax(x_chunk, axis=axis, keepdims=keepdims)

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)

2022-11-26 14:27:42,162 - distributed.utils_perf - WARNING - full garbage collections took 11% CPU time recently (threshold: 10%)

slice(24, 25, None)

2022-11-26 14:28:19,255 - distributed.utils_perf - WARNING - full garbage collections took 11% CPU time recently (threshold: 10%)
2022-11-26 14:28:21,831 - distributed.utils_perf - WARNING - full garbage collections took 11% CPU time recently (threshold: 10%)

slice(25, 26, None)

2022-11-26 14:28:57,209 - distributed.utils_perf - WARNING - full garbage collections took 11% CPU time recently (threshold: 10%)

slice(26, 27, None)

2022-11-26 14:29:31,859 - distributed.utils_perf - WARNING - full garbage collections took 11% CPU time recently (threshold: 10%)
2022-11-26 14:29:34,461 - distributed.utils_perf - WARNING - full garbage collections took 11% CPU time recently (threshold: 10%)

slice(27, 28, None)

2022-11-26 14:30:20,828 - distributed.utils_perf - WARNING - full garbage collections took 11% CPU time recently (threshold: 10%)

slice(28, 29, None)

2022-11-26 14:30:51,838 - distributed.utils_perf - WARNING - full garbage collections took 11% CPU time recently (threshold: 10%)
2022-11-26 14:30:58,187 - distributed.utils_perf - WARNING - full garbage collections took 11% CPU time recently (threshold: 10%)

slice(29, 30, None)

2022-11-26 14:31:33,394 - distributed.utils_perf - WARNING - full garbage collections took 11% CPU time recently (threshold: 10%)
2022-11-26 14:31:42,105 - distributed.utils_perf - WARNING - full garbage collections took 11% CPU time recently (threshold: 10%)

slice(30, 31, None)

2022-11-26 14:32:12,095 - distributed.utils_perf - WARNING - full garbage collections took 11% CPU time recently (threshold: 10%)

CPU times: user 8min 9s, sys: 2min 4s, total: 10min 13s
Wall time: 20min 42s