%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= irene5028.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  irene5028.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= 01  outputpath= ../results/SEDNA_DELTA_MONITOR/ daskreport= ../results/dask/7449016irene5028.c-irene.mg1.tgcc.ccc.cea.fr_SEDNA_DELTA_MONITOR_01M_FWC_2D/
CPU times: user 484 ms, sys: 116 ms, total: 600 ms
Wall time: 17.5 s

Client

Client-95bacd1d-6f42-11ed-a02f-080038b93e55

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

Cluster Info

LocalCluster

3e1e72b1

Dashboard: http://127.0.0.1:8787/status	Workers: 16
Total threads: 128	Total memory: 221.88 GiB
Status: running	Using processes: True

Scheduler Info

Scheduler

Scheduler-2a1ac358-36d9-4d90-a064-b1b7a0830425

Comm: tcp://127.0.0.1:36670	Workers: 16
Dashboard: http://127.0.0.1:8787/status	Total threads: 128
Started: Just now	Total memory: 221.88 GiB

Workers

Worker: 0

Comm: tcp://127.0.0.1:32999	Total threads: 8
Dashboard: http://127.0.0.1:46632/status	Memory: 13.87 GiB
Nanny: tcp://127.0.0.1:46350
Local directory: /tmp/dask-worker-space/worker-4c7g8hnd

Worker: 1

Comm: tcp://127.0.0.1:38166	Total threads: 8
Dashboard: http://127.0.0.1:42617/status	Memory: 13.87 GiB
Nanny: tcp://127.0.0.1:35846
Local directory: /tmp/dask-worker-space/worker-0njtl1mg

Worker: 2

Comm: tcp://127.0.0.1:36038	Total threads: 8
Dashboard: http://127.0.0.1:38965/status	Memory: 13.87 GiB
Nanny: tcp://127.0.0.1:42085
Local directory: /tmp/dask-worker-space/worker-mffzj088

Worker: 3

Comm: tcp://127.0.0.1:40104	Total threads: 8
Dashboard: http://127.0.0.1:39615/status	Memory: 13.87 GiB
Nanny: tcp://127.0.0.1:44883
Local directory: /tmp/dask-worker-space/worker-_3tg4ex8

Worker: 4

Comm: tcp://127.0.0.1:46448	Total threads: 8
Dashboard: http://127.0.0.1:39143/status	Memory: 13.87 GiB
Nanny: tcp://127.0.0.1:38242
Local directory: /tmp/dask-worker-space/worker-359dabp7

Worker: 5

Comm: tcp://127.0.0.1:38480	Total threads: 8
Dashboard: http://127.0.0.1:38252/status	Memory: 13.87 GiB
Nanny: tcp://127.0.0.1:44534
Local directory: /tmp/dask-worker-space/worker-ud0e_bfi

Worker: 6

Comm: tcp://127.0.0.1:45000	Total threads: 8
Dashboard: http://127.0.0.1:43214/status	Memory: 13.87 GiB
Nanny: tcp://127.0.0.1:44166
Local directory: /tmp/dask-worker-space/worker-iwk46lm4

Worker: 7

Comm: tcp://127.0.0.1:42914	Total threads: 8
Dashboard: http://127.0.0.1:37290/status	Memory: 13.87 GiB
Nanny: tcp://127.0.0.1:37565
Local directory: /tmp/dask-worker-space/worker-h34n_973

Worker: 8

Comm: tcp://127.0.0.1:35069	Total threads: 8
Dashboard: http://127.0.0.1:43802/status	Memory: 13.87 GiB
Nanny: tcp://127.0.0.1:43982
Local directory: /tmp/dask-worker-space/worker-bvq12a0e

Worker: 9

Comm: tcp://127.0.0.1:43455	Total threads: 8
Dashboard: http://127.0.0.1:44097/status	Memory: 13.87 GiB
Nanny: tcp://127.0.0.1:41934
Local directory: /tmp/dask-worker-space/worker-lf44agl9

Worker: 10

Comm: tcp://127.0.0.1:37613	Total threads: 8
Dashboard: http://127.0.0.1:45725/status	Memory: 13.87 GiB
Nanny: tcp://127.0.0.1:37747
Local directory: /tmp/dask-worker-space/worker-cjrrb3j6

Worker: 11

Comm: tcp://127.0.0.1:39847	Total threads: 8
Dashboard: http://127.0.0.1:45552/status	Memory: 13.87 GiB
Nanny: tcp://127.0.0.1:41483
Local directory: /tmp/dask-worker-space/worker-sgq70c_x

Worker: 12

Comm: tcp://127.0.0.1:37485	Total threads: 8
Dashboard: http://127.0.0.1:46741/status	Memory: 13.87 GiB
Nanny: tcp://127.0.0.1:36916
Local directory: /tmp/dask-worker-space/worker-_cccayng

Worker: 13

Comm: tcp://127.0.0.1:42862	Total threads: 8
Dashboard: http://127.0.0.1:34686/status	Memory: 13.87 GiB
Nanny: tcp://127.0.0.1:37503
Local directory: /tmp/dask-worker-space/worker-bpgegl0p

Worker: 14

Comm: tcp://127.0.0.1:41743	Total threads: 8
Dashboard: http://127.0.0.1:41195/status	Memory: 13.87 GiB
Nanny: tcp://127.0.0.1:45814
Local directory: /tmp/dask-worker-space/worker-w_p_vyjn

Worker: 15

Comm: tcp://127.0.0.1:33999	Total threads: 8
Dashboard: http://127.0.0.1:33208/status	Memory: 13.87 GiB
Nanny: tcp://127.0.0.1:46530
Local directory: /tmp/dask-worker-space/worker-bvy7ftdf

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
	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

%%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/201501/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 66.23906087875366 seconds
0 merging gridS ['vosaline']
param nav_lat will be included in data
param mask2d will be included in data
param nav_lon will be included in data
param mask will be included in data
param e3t will be included in data
param e1te2t 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 22.4 s, sys: 3.98 s, total: 26.4 s
Wall time: 1min 26s

<xarray.Dataset>
Dimensions:   (t: 31, z: 150, y: 6540, x: 6560)
Coordinates:
  * t         (t) object 2015-01-01 12:00:00 ... 2015-01-31 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_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>
    mask      (z, y, x) bool dask.array<chunksize=(150, 130, 6560), meta=np.ndarray>
    e3t       (z, y, x) float64 dask.array<chunksize=(150, 130, 6560), meta=np.ndarray>
    e1te2t    (y, x) float64 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>
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-Jul-21 16:35:22 GMT
    title:                   ocean T grid variables
    uuid:                    9aef3543-35d6-4da0-a58a-f2c75b69d3a7

xarray.Dataset

• Dimensions:
  • t: 31
  • z: 150
  • y: 6540
  • x: 6560
• Coordinates: (10)
  • t
    (t)
    object
    2015-01-01 12:00:00 ... 2015-01-...

    axis :

    T

    bounds :

    time_counter_bounds

    long_name :

    Time axis

    standard_name :

    time

    time_origin :

    1900-01-01 00:00:00

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

  • 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

  • e3t
    (z, y, x)
    float64
    dask.array<chunksize=(150, 130, 6560), meta=np.ndarray>

    Array Chunk Bytes 47.95 GiB 0.95 GiB Shape (150, 6540, 6560) (150, 130, 6560) Count 1687 Tasks 55 Chunks Type float64 numpy.ndarray

  • e1te2t
    (y, x)
    float64
    dask.array<chunksize=(130, 6560), meta=np.ndarray>

    Array Chunk Bytes 327.32 MiB 6.51 MiB Shape (6540, 6560) (130, 6560) Count 1687 Tasks 55 Chunks Type float64 numpy.ndarray

• Data variables: (1)
  • 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

• 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 :

  Fri Jul 22 15:59:58 2022: ncks -4 -L 1 SEDNA-DELTA_1d_gridS_201501-201501_NOZIP_0000.nc /ccc/scratch/cont003/gen7420/talandel/SEDNA/SEDNA-DELTA-S/SPLIT/1d/2015/01/SEDNA-DELTA_1d_gridS_201501-201501_0000.nc Fri Jul 22 15:59:29 2022: ncrcat -n 31,2,1 SEDNA-DELTA_1d_gridS_0000_01.nc SEDNA-DELTA_1d_gridS_201501-201501_NOZIP_0000.nc

  ibegin :

  0

  jbegin :

  0

  name :

  /ccc/scratch/cont003/ra5563/talandel/ONGOING-RUNS/SEDNA-DELTA-XIOS.83/SEDNA-DELTA_1d_gridS

  ni :

  6560

  nj :

  13

  output_frequency :

  1d

  start_date :

  20090101

  timeStamp :

  2022-Jul-21 16:35:22 GMT

  title :

  ocean T grid variables

  uuid :

  9aef3543-35d6-4da0-a58a-f2c75b69d3a7

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

#calc= True
#save= True
#plot= False
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'
data=monitor.optimize_dataset(data)
#2 Zooming Data
data= zoom.BBFG(data)
data=monitor.optimize_dataset(data)

<xarray.Dataset>
Dimensions:   (t: 31, z: 150, y: 5264, x: 6560)
Coordinates:
  * t         (t) object 2015-01-01 12:00:00 ... 2015-01-31 12:00:00
  * y         (y) int64 1277 1278 1279 1280 1281 ... 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_lat   (y, x) float32 dask.array<chunksize=(56, 6560), meta=np.ndarray>
    mask2d    (y, x) bool dask.array<chunksize=(56, 6560), meta=np.ndarray>
    nav_lon   (y, x) float32 dask.array<chunksize=(56, 6560), meta=np.ndarray>
    mask      (z, y, x) bool dask.array<chunksize=(150, 56, 6560), meta=np.ndarray>
    e3t       (z, y, x) float64 dask.array<chunksize=(150, 56, 6560), meta=np.ndarray>
    e1te2t    (y, x) float64 dask.array<chunksize=(56, 6560), meta=np.ndarray>
Data variables:
    vosaline  (t, z, y, x) float32 dask.array<chunksize=(1, 150, 56, 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-Jul-21 16:35:22 GMT
    title:                   ocean T grid variables
    uuid:                    9aef3543-35d6-4da0-a58a-f2c75b69d3a7

xarray.Dataset

• Dimensions:
  • t: 31
  • z: 150
  • y: 5264
  • x: 6560
• Coordinates: (10)
  • t
    (t)
    object
    2015-01-01 12:00:00 ... 2015-01-...

    axis :

    T

    bounds :

    time_counter_bounds

    long_name :

    Time axis

    standard_name :

    time

    time_origin :

    1900-01-01 00:00:00

    array([cftime.DatetimeNoLeap(2015, 1, 1, 12, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(2015, 1, 2, 12, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(2015, 1, 3, 12, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(2015, 1, 4, 12, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(2015, 1, 5, 12, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(2015, 1, 6, 12, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(2015, 1, 7, 12, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(2015, 1, 8, 12, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(2015, 1, 9, 12, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(2015, 1, 10, 12, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(2015, 1, 11, 12, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(2015, 1, 12, 12, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(2015, 1, 13, 12, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(2015, 1, 14, 12, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(2015, 1, 15, 12, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(2015, 1, 16, 12, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(2015, 1, 17, 12, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(2015, 1, 18, 12, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(2015, 1, 19, 12, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(2015, 1, 20, 12, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(2015, 1, 21, 12, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(2015, 1, 22, 12, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(2015, 1, 23, 12, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(2015, 1, 24, 12, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(2015, 1, 25, 12, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(2015, 1, 26, 12, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(2015, 1, 27, 12, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(2015, 1, 28, 12, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(2015, 1, 29, 12, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(2015, 1, 30, 12, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(2015, 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_lat
    (y, x)
    float32
    dask.array<chunksize=(56, 6560), meta=np.ndarray>

    Array Chunk Bytes 131.73 MiB 3.00 MiB Shape (5264, 6560) (120, 6560) Count 1732 Tasks 45 Chunks Type float32 numpy.ndarray

  • mask2d
    (y, x)
    bool
    dask.array<chunksize=(56, 6560), meta=np.ndarray>

    Array Chunk Bytes 32.93 MiB 768.75 kiB Shape (5264, 6560) (120, 6560) Count 1732 Tasks 45 Chunks Type bool numpy.ndarray

  • nav_lon
    (y, x)
    float32
    dask.array<chunksize=(56, 6560), meta=np.ndarray>

    Array Chunk Bytes 131.73 MiB 3.00 MiB Shape (5264, 6560) (120, 6560) Count 1732 Tasks 45 Chunks Type float32 numpy.ndarray

  • mask
    (z, y, x)
    bool
    dask.array<chunksize=(150, 56, 6560), meta=np.ndarray>

    Array Chunk Bytes 4.82 GiB 112.61 MiB Shape (150, 5264, 6560) (150, 120, 6560) Count 1732 Tasks 45 Chunks Type bool numpy.ndarray

  • e3t
    (z, y, x)
    float64
    dask.array<chunksize=(150, 56, 6560), meta=np.ndarray>

    Array Chunk Bytes 38.59 GiB 900.88 MiB Shape (150, 5264, 6560) (150, 120, 6560) Count 1732 Tasks 45 Chunks Type float64 numpy.ndarray

  • e1te2t
    (y, x)
    float64
    dask.array<chunksize=(56, 6560), meta=np.ndarray>

    Array Chunk Bytes 263.46 MiB 6.01 MiB Shape (5264, 6560) (120, 6560) Count 1732 Tasks 45 Chunks Type float64 numpy.ndarray

• Data variables: (1)
  • vosaline
    (t, z, y, x)
    float32
    dask.array<chunksize=(1, 150, 56, 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 450.44 MiB Shape (31, 150, 5264, 6560) (1, 150, 120, 6560) Count 32248 Tasks 1395 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 :

  Fri Jul 22 15:59:58 2022: ncks -4 -L 1 SEDNA-DELTA_1d_gridS_201501-201501_NOZIP_0000.nc /ccc/scratch/cont003/gen7420/talandel/SEDNA/SEDNA-DELTA-S/SPLIT/1d/2015/01/SEDNA-DELTA_1d_gridS_201501-201501_0000.nc Fri Jul 22 15:59:29 2022: ncrcat -n 31,2,1 SEDNA-DELTA_1d_gridS_0000_01.nc SEDNA-DELTA_1d_gridS_201501-201501_NOZIP_0000.nc

  ibegin :

  0

  jbegin :

  0

  name :

  /ccc/scratch/cont003/ra5563/talandel/ONGOING-RUNS/SEDNA-DELTA-XIOS.83/SEDNA-DELTA_1d_gridS

  ni :

  6560

  nj :

  13

  output_frequency :

  1d

  start_date :

  20090101

  timeStamp :

  2022-Jul-21 16:35:22 GMT

  title :

  ocean T grid variables

  uuid :

  9aef3543-35d6-4da0-a58a-f2c75b69d3a7

#3 Start computing 
data= calc.FWC2D_UFUNC(data)
monitor.optimize_dataset(data)
add optimise here once otimise can recognise

<xarray.Dataset>
Dimensions:  (t: 31, y: 5264, x: 6560)
Coordinates:
  * t        (t) object 2015-01-01 12:00:00 ... 2015-01-31 12:00:00
  * y        (y) int64 1277 1278 1279 1280 1281 ... 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=(56, 6560), meta=np.ndarray>
    mask2d   (y, x) bool dask.array<chunksize=(56, 6560), meta=np.ndarray>
    nav_lon  (y, x) float32 dask.array<chunksize=(56, 6560), meta=np.ndarray>
    e1te2t   (y, x) float64 dask.array<chunksize=(56, 6560), meta=np.ndarray>
Data variables:
    FWC2D    (t, y, x) float32 dask.array<chunksize=(1, 56, 6560), meta=np.ndarray>

xarray.Dataset

• Dimensions:
  • t: 31
  • y: 5264
  • x: 6560
• Coordinates: (7)
  • t
    (t)
    object
    2015-01-01 12:00:00 ... 2015-01-...

    array([cftime.DatetimeNoLeap(2015, 1, 1, 12, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(2015, 1, 2, 12, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(2015, 1, 3, 12, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(2015, 1, 4, 12, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(2015, 1, 5, 12, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(2015, 1, 6, 12, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(2015, 1, 7, 12, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(2015, 1, 8, 12, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(2015, 1, 9, 12, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(2015, 1, 10, 12, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(2015, 1, 11, 12, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(2015, 1, 12, 12, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(2015, 1, 13, 12, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(2015, 1, 14, 12, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(2015, 1, 15, 12, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(2015, 1, 16, 12, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(2015, 1, 17, 12, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(2015, 1, 18, 12, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(2015, 1, 19, 12, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(2015, 1, 20, 12, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(2015, 1, 21, 12, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(2015, 1, 22, 12, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(2015, 1, 23, 12, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(2015, 1, 24, 12, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(2015, 1, 25, 12, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(2015, 1, 26, 12, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(2015, 1, 27, 12, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(2015, 1, 28, 12, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(2015, 1, 29, 12, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(2015, 1, 30, 12, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(2015, 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_lat
    (y, x)
    float32
    dask.array<chunksize=(56, 6560), meta=np.ndarray>

    Array Chunk Bytes 131.73 MiB 3.00 MiB Shape (5264, 6560) (120, 6560) Count 1732 Tasks 45 Chunks Type float32 numpy.ndarray

  • mask2d
    (y, x)
    bool
    dask.array<chunksize=(56, 6560), meta=np.ndarray>

    Array Chunk Bytes 32.93 MiB 768.75 kiB Shape (5264, 6560) (120, 6560) Count 1732 Tasks 45 Chunks Type bool numpy.ndarray

  • nav_lon
    (y, x)
    float32
    dask.array<chunksize=(56, 6560), meta=np.ndarray>

    Array Chunk Bytes 131.73 MiB 3.00 MiB Shape (5264, 6560) (120, 6560) Count 1732 Tasks 45 Chunks Type float32 numpy.ndarray

  • e1te2t
    (y, x)
    float64
    dask.array<chunksize=(56, 6560), meta=np.ndarray>

    Array Chunk Bytes 263.46 MiB 6.01 MiB Shape (5264, 6560) (120, 6560) Count 1732 Tasks 45 Chunks Type float64 numpy.ndarray

• Data variables: (1)
  • FWC2D
    (t, y, x)
    float32
    dask.array<chunksize=(1, 56, 6560), meta=np.ndarray>

    long_name :

    Fresh Water Content 2D

    Array Chunk Bytes 3.99 GiB 3.00 MiB Shape (31, 5264, 6560) (1, 120, 6560) Count 44172 Tasks 1395 Chunks Type float32 numpy.ndarray

• Attributes: (0)

#4 Saving  SEDNA_maps_BBFG_FWC_2D
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)

2022-11-28 18:38:18,623 - distributed.worker_memory - WARNING - Worker is at 81% memory usage. Pausing worker.  Process memory: 11.36 GiB -- Worker memory limit: 13.87 GiB
2022-11-28 18:38:24,593 - distributed.worker_memory - WARNING - Worker tcp://127.0.0.1:37485 (pid=41115) exceeded 95% memory budget. Restarting...
2022-11-28 18:38:25,435 - distributed.worker - ERROR - failed during get data with tcp://127.0.0.1:35069 -> tcp://127.0.0.1:37485
Traceback (most recent call last):
  File "/ccc/cont003/home/ra5563/ra5563/monitor/lib/python3.10/site-packages/distributed/comm/tcp.py", line 223, in read
    frames_nbytes = await stream.read_bytes(fmt_size)
tornado.iostream.StreamClosedError: Stream is closed

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 1692, in get_data
    response = await comm.read(deserializers=serializers)
  File "/ccc/cont003/home/ra5563/ra5563/monitor/lib/python3.10/site-packages/distributed/comm/tcp.py", line 239, in read
    convert_stream_closed_error(self, e)
  File "/ccc/cont003/home/ra5563/ra5563/monitor/lib/python3.10/site-packages/distributed/comm/tcp.py", line 144, in convert_stream_closed_error
    raise CommClosedError(f"in {obj}: {exc}") from exc
distributed.comm.core.CommClosedError: in <TCP (closed)  local=tcp://127.0.0.1:35069 remote=tcp://127.0.0.1:53384>: Stream is closed
2022-11-28 18:38:25,435 - distributed.worker - ERROR - Worker stream died during communication: tcp://127.0.0.1:37485
Traceback (most recent call last):
  File "/ccc/cont003/home/ra5563/ra5563/monitor/lib/python3.10/site-packages/distributed/comm/tcp.py", line 223, in read
    frames_nbytes = await stream.read_bytes(fmt_size)
tornado.iostream.StreamClosedError: Stream is closed

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 2705, in _get_data
    response = await send_recv(
  File "/ccc/cont003/home/ra5563/ra5563/monitor/lib/python3.10/site-packages/distributed/core.py", line 918, in send_recv
    response = await comm.read(deserializers=deserializers)
  File "/ccc/cont003/home/ra5563/ra5563/monitor/lib/python3.10/site-packages/distributed/comm/tcp.py", line 239, in read
    convert_stream_closed_error(self, e)
  File "/ccc/cont003/home/ra5563/ra5563/monitor/lib/python3.10/site-packages/distributed/comm/tcp.py", line 144, in convert_stream_closed_error
    raise CommClosedError(f"in {obj}: {exc}") from exc
distributed.comm.core.CommClosedError: in <TCP (closed) Ephemeral Worker->Worker for gather local=tcp://127.0.0.1:42528 remote=tcp://127.0.0.1:37485>: Stream is closed
2022-11-28 18:38:25,435 - distributed.worker - ERROR - failed during get data with tcp://127.0.0.1:37613 -> tcp://127.0.0.1:37485
Traceback (most recent call last):
  File "/ccc/cont003/home/ra5563/ra5563/monitor/lib/python3.10/site-packages/distributed/comm/tcp.py", line 223, in read
    frames_nbytes = await stream.read_bytes(fmt_size)
tornado.iostream.StreamClosedError: Stream is closed

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 1692, in get_data
    response = await comm.read(deserializers=serializers)
  File "/ccc/cont003/home/ra5563/ra5563/monitor/lib/python3.10/site-packages/distributed/comm/tcp.py", line 239, in read
    convert_stream_closed_error(self, e)
  File "/ccc/cont003/home/ra5563/ra5563/monitor/lib/python3.10/site-packages/distributed/comm/tcp.py", line 144, in convert_stream_closed_error
    raise CommClosedError(f"in {obj}: {exc}") from exc
distributed.comm.core.CommClosedError: in <TCP (closed)  local=tcp://127.0.0.1:37613 remote=tcp://127.0.0.1:54158>: Stream is closed
2022-11-28 18:38:25,435 - distributed.worker - ERROR - Worker stream died during communication: tcp://127.0.0.1:37485
Traceback (most recent call last):
  File "/ccc/cont003/home/ra5563/ra5563/monitor/lib/python3.10/site-packages/tornado/iostream.py", line 867, in _read_to_buffer
    bytes_read = self.read_from_fd(buf)
  File "/ccc/cont003/home/ra5563/ra5563/monitor/lib/python3.10/site-packages/tornado/iostream.py", line 1140, in read_from_fd
    return self.socket.recv_into(buf, len(buf))
ConnectionResetError: [Errno 104] Connection reset by peer

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 2705, in _get_data
    response = await send_recv(
  File "/ccc/cont003/home/ra5563/ra5563/monitor/lib/python3.10/site-packages/distributed/core.py", line 918, in send_recv
    response = await comm.read(deserializers=deserializers)
  File "/ccc/cont003/home/ra5563/ra5563/monitor/lib/python3.10/site-packages/distributed/comm/tcp.py", line 239, in read
    convert_stream_closed_error(self, e)
  File "/ccc/cont003/home/ra5563/ra5563/monitor/lib/python3.10/site-packages/distributed/comm/tcp.py", line 142, in convert_stream_closed_error
    raise CommClosedError(f"in {obj}: {exc.__class__.__name__}: {exc}") from exc
distributed.comm.core.CommClosedError: in <TCP (closed) Ephemeral Worker->Worker for gather local=tcp://127.0.0.1:42504 remote=tcp://127.0.0.1:37485>: ConnectionResetError: [Errno 104] Connection reset by peer
2022-11-28 18:38:25,438 - distributed.worker - ERROR - Worker stream died during communication: tcp://127.0.0.1:37485
Traceback (most recent call last):
  File "/ccc/cont003/home/ra5563/ra5563/monitor/lib/python3.10/site-packages/tornado/iostream.py", line 867, in _read_to_buffer
    bytes_read = self.read_from_fd(buf)
  File "/ccc/cont003/home/ra5563/ra5563/monitor/lib/python3.10/site-packages/tornado/iostream.py", line 1140, in read_from_fd
    return self.socket.recv_into(buf, len(buf))
ConnectionResetError: [Errno 104] Connection reset by peer

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 2705, in _get_data
    response = await send_recv(
  File "/ccc/cont003/home/ra5563/ra5563/monitor/lib/python3.10/site-packages/distributed/core.py", line 918, in send_recv
    response = await comm.read(deserializers=deserializers)
  File "/ccc/cont003/home/ra5563/ra5563/monitor/lib/python3.10/site-packages/distributed/comm/tcp.py", line 239, in read
    convert_stream_closed_error(self, e)
  File "/ccc/cont003/home/ra5563/ra5563/monitor/lib/python3.10/site-packages/distributed/comm/tcp.py", line 142, in convert_stream_closed_error
    raise CommClosedError(f"in {obj}: {exc.__class__.__name__}: {exc}") from exc
distributed.comm.core.CommClosedError: in <TCP (closed) Ephemeral Worker->Worker for gather local=tcp://127.0.0.1:42518 remote=tcp://127.0.0.1:37485>: ConnectionResetError: [Errno 104] Connection reset by peer
2022-11-28 18:38:25,446 - distributed.nanny - WARNING - Restarting worker
2022-11-28 18:38:26,995 - distributed.worker - ERROR - failed during get data with tcp://127.0.0.1:42914 -> tcp://127.0.0.1:37485
Traceback (most recent call last):
  File "/ccc/cont003/home/ra5563/ra5563/monitor/lib/python3.10/site-packages/distributed/comm/tcp.py", line 223, in read
    frames_nbytes = await stream.read_bytes(fmt_size)
tornado.iostream.StreamClosedError: Stream is closed

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 1692, in get_data
    response = await comm.read(deserializers=serializers)
  File "/ccc/cont003/home/ra5563/ra5563/monitor/lib/python3.10/site-packages/distributed/comm/tcp.py", line 239, in read
    convert_stream_closed_error(self, e)
  File "/ccc/cont003/home/ra5563/ra5563/monitor/lib/python3.10/site-packages/distributed/comm/tcp.py", line 144, in convert_stream_closed_error
    raise CommClosedError(f"in {obj}: {exc}") from exc
distributed.comm.core.CommClosedError: in <TCP (closed)  local=tcp://127.0.0.1:42914 remote=tcp://127.0.0.1:59372>: Stream is closed
2022-11-28 18:38:27,818 - distributed.worker - ERROR - Worker stream died during communication: tcp://127.0.0.1:37485
Traceback (most recent call last):
  File "/ccc/cont003/home/ra5563/ra5563/monitor/lib/python3.10/site-packages/distributed/comm/tcp.py", line 223, in read
    frames_nbytes = await stream.read_bytes(fmt_size)
tornado.iostream.StreamClosedError: Stream is closed

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 2705, in _get_data
    response = await send_recv(
  File "/ccc/cont003/home/ra5563/ra5563/monitor/lib/python3.10/site-packages/distributed/core.py", line 918, in send_recv
    response = await comm.read(deserializers=deserializers)
  File "/ccc/cont003/home/ra5563/ra5563/monitor/lib/python3.10/site-packages/distributed/comm/tcp.py", line 239, in read
    convert_stream_closed_error(self, e)
  File "/ccc/cont003/home/ra5563/ra5563/monitor/lib/python3.10/site-packages/distributed/comm/tcp.py", line 144, in convert_stream_closed_error
    raise CommClosedError(f"in {obj}: {exc}") from exc
distributed.comm.core.CommClosedError: in <TCP (closed) Ephemeral Worker->Worker for gather local=tcp://127.0.0.1:42840 remote=tcp://127.0.0.1:37485>: Stream is closed
2022-11-28 18:38:27,821 - distributed.worker - ERROR - failed during get data with tcp://127.0.0.1:43455 -> tcp://127.0.0.1:37485
Traceback (most recent call last):
  File "/ccc/cont003/home/ra5563/ra5563/monitor/lib/python3.10/site-packages/distributed/comm/tcp.py", line 223, in read
    frames_nbytes = await stream.read_bytes(fmt_size)
tornado.iostream.StreamClosedError: Stream is closed

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 1692, in get_data
    response = await comm.read(deserializers=serializers)
  File "/ccc/cont003/home/ra5563/ra5563/monitor/lib/python3.10/site-packages/distributed/comm/tcp.py", line 239, in read
    convert_stream_closed_error(self, e)
  File "/ccc/cont003/home/ra5563/ra5563/monitor/lib/python3.10/site-packages/distributed/comm/tcp.py", line 144, in convert_stream_closed_error
    raise CommClosedError(f"in {obj}: {exc}") from exc
distributed.comm.core.CommClosedError: in <TCP (closed)  local=tcp://127.0.0.1:43455 remote=tcp://127.0.0.1:52632>: Stream is closed

slice(2, 3, None)
slice(3, 4, None)
slice(4, 5, None)
slice(5, 6, None)
slice(6, 7, None)

2022-11-28 18:46:52,358 - distributed.worker_memory - WARNING - Worker is at 80% memory usage. Pausing worker.  Process memory: 11.14 GiB -- Worker memory limit: 13.87 GiB
2022-11-28 18:47:07,091 - distributed.worker_memory - WARNING - Worker is at 59% memory usage. Resuming worker. Process memory: 8.28 GiB -- Worker memory limit: 13.87 GiB

slice(7, 8, None)
slice(8, 9, None)
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slice(10, 11, None)
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CPU times: user 7min 57s, sys: 1min 32s, total: 9min 30s
Wall time: 46min 5s