%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
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'
proceed saving? True or False , Default is setted as True
proceed plotting? True or False , Default is setted as True
proceed computation? or just load computed result? True or False , Default is setted as True
save output file used for plotting
using kerchunked file -> False, not using kerhcunk -> True
name of control file to be used for computation/plots/save/ We have number of M_xxx.csv
Monitor.sh calls M_MLD_2D
and AWTD.sh, Fluxnet.sh, Siconc.sh, IceClim.sh, FWC_SSH.sh, Integrals.sh , Sections.sh
M_AWTMD
M_Fluxnet
M_Ice_quantities
M_IceClim M_IceConce M_IceThick
M_FWC_2D M_FWC_integrals M_FWC_SSH M_SSH_anomaly
M_Mean_temp_velo M_Mooring
M_Sectionx M_Sectiony
%%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= irene5311.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 irene5311.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= 06 outputpath= ../results/SEDNA_DELTA_MONITOR/ daskreport= ../results/dask/6476235irene5311.c-irene.mg1.tgcc.ccc.cea.fr_SEDNA_DELTA_MONITOR_06M_Ice_quantities/ CPU times: user 620 ms, sys: 129 ms, total: 748 ms Wall time: 22.2 s
Client-fd8eb34f-196f-11ed-9989-080038b948c1
Connection method: Cluster object | Cluster type: distributed.LocalCluster |
Dashboard: http://127.0.0.1:8787/status |
1de00996
Dashboard: http://127.0.0.1:8787/status | Workers: 16 |
Total threads: 128 | Total memory: 251.06 GiB |
Status: running | Using processes: True |
Scheduler-f7994892-4291-48bc-ae1d-d6008a56331b
Comm: tcp://127.0.0.1:35539 | Workers: 16 |
Dashboard: http://127.0.0.1:8787/status | Total threads: 128 |
Started: Just now | Total memory: 251.06 GiB |
Comm: tcp://127.0.0.1:35863 | Total threads: 8 |
Dashboard: http://127.0.0.1:40252/status | Memory: 15.69 GiB |
Nanny: tcp://127.0.0.1:42333 | |
Local directory: /tmp/dask-worker-space/worker-f4mlj4u5 |
Comm: tcp://127.0.0.1:44058 | Total threads: 8 |
Dashboard: http://127.0.0.1:39079/status | Memory: 15.69 GiB |
Nanny: tcp://127.0.0.1:43117 | |
Local directory: /tmp/dask-worker-space/worker-0ma4onuo |
Comm: tcp://127.0.0.1:42655 | Total threads: 8 |
Dashboard: http://127.0.0.1:42767/status | Memory: 15.69 GiB |
Nanny: tcp://127.0.0.1:37509 | |
Local directory: /tmp/dask-worker-space/worker-imws6qxd |
Comm: tcp://127.0.0.1:43443 | Total threads: 8 |
Dashboard: http://127.0.0.1:35082/status | Memory: 15.69 GiB |
Nanny: tcp://127.0.0.1:45989 | |
Local directory: /tmp/dask-worker-space/worker-cejwx9fb |
Comm: tcp://127.0.0.1:41402 | Total threads: 8 |
Dashboard: http://127.0.0.1:38929/status | Memory: 15.69 GiB |
Nanny: tcp://127.0.0.1:33165 | |
Local directory: /tmp/dask-worker-space/worker-hmjf7abx |
Comm: tcp://127.0.0.1:37689 | Total threads: 8 |
Dashboard: http://127.0.0.1:35379/status | Memory: 15.69 GiB |
Nanny: tcp://127.0.0.1:33597 | |
Local directory: /tmp/dask-worker-space/worker-tw7vkvsi |
Comm: tcp://127.0.0.1:33569 | Total threads: 8 |
Dashboard: http://127.0.0.1:43808/status | Memory: 15.69 GiB |
Nanny: tcp://127.0.0.1:44910 | |
Local directory: /tmp/dask-worker-space/worker-876q9ak0 |
Comm: tcp://127.0.0.1:36796 | Total threads: 8 |
Dashboard: http://127.0.0.1:40398/status | Memory: 15.69 GiB |
Nanny: tcp://127.0.0.1:46197 | |
Local directory: /tmp/dask-worker-space/worker-hr_rmw6x |
Comm: tcp://127.0.0.1:41052 | Total threads: 8 |
Dashboard: http://127.0.0.1:40626/status | Memory: 15.69 GiB |
Nanny: tcp://127.0.0.1:37116 | |
Local directory: /tmp/dask-worker-space/worker-uj9gvci9 |
Comm: tcp://127.0.0.1:45923 | Total threads: 8 |
Dashboard: http://127.0.0.1:36454/status | Memory: 15.69 GiB |
Nanny: tcp://127.0.0.1:39835 | |
Local directory: /tmp/dask-worker-space/worker-guuj_tzd |
Comm: tcp://127.0.0.1:35469 | Total threads: 8 |
Dashboard: http://127.0.0.1:36113/status | Memory: 15.69 GiB |
Nanny: tcp://127.0.0.1:36803 | |
Local directory: /tmp/dask-worker-space/worker-bqwxdtgv |
Comm: tcp://127.0.0.1:34949 | Total threads: 8 |
Dashboard: http://127.0.0.1:33492/status | Memory: 15.69 GiB |
Nanny: tcp://127.0.0.1:43213 | |
Local directory: /tmp/dask-worker-space/worker-llo51hgf |
Comm: tcp://127.0.0.1:43000 | Total threads: 8 |
Dashboard: http://127.0.0.1:36200/status | Memory: 15.69 GiB |
Nanny: tcp://127.0.0.1:42463 | |
Local directory: /tmp/dask-worker-space/worker-7w6ov2a3 |
Comm: tcp://127.0.0.1:39366 | Total threads: 8 |
Dashboard: http://127.0.0.1:32938/status | Memory: 15.69 GiB |
Nanny: tcp://127.0.0.1:34665 | |
Local directory: /tmp/dask-worker-space/worker-eauu3hgg |
Comm: tcp://127.0.0.1:34013 | Total threads: 8 |
Dashboard: http://127.0.0.1:45179/status | Memory: 15.69 GiB |
Nanny: tcp://127.0.0.1:36233 | |
Local directory: /tmp/dask-worker-space/worker-dc8spmhs |
Comm: tcp://127.0.0.1:33237 | Total threads: 8 |
Dashboard: http://127.0.0.1:35635/status | Memory: 15.69 GiB |
Nanny: tcp://127.0.0.1:45491 | |
Local directory: /tmp/dask-worker-space/worker-hkxqmn2_ |
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 | |
---|---|---|---|---|---|---|---|---|---|---|
Ice_quantities | param.e1te2t,icemod.sivelo,icemod.sivolu,icemo... | calc.Ice_quant(data) | ALL | Ice_intquant | None | (0,20) | cm s^(-1) | I-2 |
Each computation consists of
%%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= True ../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 icemod ['siconc', 'sivelo', 'sivolu'] lazy= True using load_data_xios reading icemod using load_data_xios reading {'name': 'data_xios', 'container': 'xarray', 'plugin': ['netcdf'], 'driver': ['netcdf'], 'description': 'SEDNA NEMO outputs from different xios server', 'direct_access': 'forbid', 'user_parameters': [{'name': 'path', 'description': 'name of config', 'type': 'str', 'default': '/ccc/scratch/cont003/gen7420/talandel/SEDNA/SEDNA-DELTA-S/SPLIT/1d'}, {'name': 'fileexp', 'description': 'name of config', 'type': 'str', 'default': 'SEDNA-DELTA'}, {'name': 'month', 'description': 'running number 2 digit', 'type': 'str', 'default': '02'}, {'name': 'freq', 'description': '1d or 1m', 'type': 'str', 'default': '1d'}, {'name': 'year', 'description': 'last digits of yearmonthdate.', 'type': 'str', 'default': '2012'}, {'name': 'file', 'description': 'file name', 'type': 'str', 'default': 'icemod'}, {'name': 'eio', 'description': 'xios mpi number', 'type': 'str', 'default': '0[0-5][0-9][0-9]'}], 'metadata': {}, 'args': {'urlpath': '{{path}}/{{year}}/{{month}}/*{{file}}_*_{{eio}}.nc', 'combine': 'nested', 'concat_dim': 'y'}} took 61.22362542152405 seconds 0 merging icemod ['siconc', 'sivelo', 'sivolu'] param e1te2t will be included in data param nav_lat will be included in data param nav_lon will be included in data param mask2d will be included in data CPU times: user 14.3 s, sys: 2.86 s, total: 17.1 s Wall time: 1min 27s
<xarray.Dataset> Dimensions: (t: 30, y: 6540, x: 6560) Coordinates: nav_lat (y, x) float32 dask.array<chunksize=(13, 6560), meta=np.ndarray> nav_lon (y, x) float32 dask.array<chunksize=(13, 6560), meta=np.ndarray> * t (t) object 2012-06-01 12:00:00 ... 2012-06-30 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 e1te2t (y, x) float64 dask.array<chunksize=(13, 6560), meta=np.ndarray> mask2d (y, x) bool dask.array<chunksize=(13, 6560), meta=np.ndarray> Data variables: siconc (t, y, x) float32 dask.array<chunksize=(30, 13, 6560), meta=np.ndarray> sivelo (t, y, x) float32 dask.array<chunksize=(30, 13, 6560), meta=np.ndarray> sivolu (t, y, x) float32 dask.array<chunksize=(30, 13, 6560), meta=np.ndarray> Attributes: (12/26) name: /ccc/scratch/cont003/ra5563/talandel/ONGOING-RUN... description: ice variables title: ice variables Conventions: CF-1.6 timeStamp: 2022-Jan-23 07:59:16 GMT uuid: dba3c2fa-169e-4a20-a987-bc38ce6af96e ... ... start_date: 20090101 output_frequency: 1d CONFIG: SEDNA CASE: DELTA history: Mon Jan 24 12:33:11 2022: ncks -4 -L 1 SEDNA-DEL... NCO: netCDF Operators version 4.9.1 (Homepage = http:...
%%time
monitor.auto(df,data,savefig,daskreport,outputpath,file_exp='SEDNA'
)
#calc= True #save= True #plot= False Value='Ice_quantities' Zone='ALL' Plot='Ice_intquant' cmap='None' clabel='cm s^(-1)' clim= (0, 20) outputpath='../results/SEDNA_DELTA_MONITOR/' nc_outputpath='../nc_results/SEDNA_DELTA_MONITOR/' filename='SEDNA_Ice_intquant_ALL_Ice_quantities' data=monitor.optimize_dataset(data) #3 Start computing data= calc.Ice_quant(data) monitor.optimize_dataset(data) add optimise here once otimise can recognise
<xarray.Dataset> Dimensions: (t: 30) Coordinates: * t (t) object 2012-06-01 12:00:00 ... 2012-06-30 12:00:00 Data variables: Ice volume (t) float64 dask.array<chunksize=(30,), meta=np.ndarray> Ice area (t) float64 dask.array<chunksize=(30,), meta=np.ndarray> Ice extent (t) float64 dask.array<chunksize=(30,), meta=np.ndarray> Ice drift (t) float64 dask.array<chunksize=(30,), meta=np.ndarray>
#4 Saving SEDNA_Ice_intquant_ALL_Ice_quantities data=save.datas(data,plot=Plot,path=nc_outputpath,filename=filename) start saving data saving data in a csv file ../nc_results/SEDNA_DELTA_MONITOR/SEDNA_Ice_intquant_ALL_Ice_quantities2012-06-01_2012-06-30.nc save computed data at ../nc_results/SEDNA_DELTA_MONITOR/SEDNA_Ice_intquant_ALL_Ice_quantities2012-06-01_2012-06-30.nc completed CPU times: user 45.3 s, sys: 2.53 s, total: 47.8 s Wall time: 54.3 s