%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
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= irene8000.c-irene.tgcc.ccc.cea.fr starting dask cluster on local= True workers 16 10000000000 False tgcc local cluster starting This code is running on irene8000.c-irene.tgcc.ccc.cea.fr using SEDNA_DELTA_MONITOR file experiment, read from ../lib/SEDNA_DELTA_MONITOR.yaml on year= 2015 on month= 12 outputpath= ../results/SEDNA_DELTA_MONITOR/ daskreport= ../results/dask/6610543irene8000.c-irene.tgcc.ccc.cea.fr_SEDNA_DELTA_MONITOR_12M_FWC_integrals/ CPU times: user 570 ms, sys: 189 ms, total: 759 ms Wall time: 18.2 s
Client-28efe2b3-2b23-11ed-91fa-080038bfd9c6
Connection method: Cluster object | Cluster type: distributed.LocalCluster |
Dashboard: http://127.0.0.1:8787/status |
8edf5144
Dashboard: http://127.0.0.1:8787/status | Workers: 12 |
Total threads: 48 | Total memory: 2.86 TiB |
Status: running | Using processes: True |
Scheduler-07c977ea-5bb2-448b-bc3d-fb02b4d3e66e
Comm: tcp://127.0.0.1:36292 | Workers: 12 |
Dashboard: http://127.0.0.1:8787/status | Total threads: 48 |
Started: Just now | Total memory: 2.86 TiB |
Comm: tcp://127.0.0.1:35074 | Total threads: 4 |
Dashboard: http://127.0.0.1:42434/status | Memory: 244.27 GiB |
Nanny: tcp://127.0.0.1:34246 | |
Local directory: /tmp/dask-worker-space/worker-l9c67wn6 |
Comm: tcp://127.0.0.1:45154 | Total threads: 4 |
Dashboard: http://127.0.0.1:38209/status | Memory: 244.27 GiB |
Nanny: tcp://127.0.0.1:33066 | |
Local directory: /tmp/dask-worker-space/worker-wl24rrly |
Comm: tcp://127.0.0.1:40004 | Total threads: 4 |
Dashboard: http://127.0.0.1:38416/status | Memory: 244.27 GiB |
Nanny: tcp://127.0.0.1:45051 | |
Local directory: /tmp/dask-worker-space/worker-otlxcd_m |
Comm: tcp://127.0.0.1:46789 | Total threads: 4 |
Dashboard: http://127.0.0.1:35644/status | Memory: 244.27 GiB |
Nanny: tcp://127.0.0.1:44558 | |
Local directory: /tmp/dask-worker-space/worker-7mafgyn9 |
Comm: tcp://127.0.0.1:42602 | Total threads: 4 |
Dashboard: http://127.0.0.1:39476/status | Memory: 244.27 GiB |
Nanny: tcp://127.0.0.1:37837 | |
Local directory: /tmp/dask-worker-space/worker-so2w3dg_ |
Comm: tcp://127.0.0.1:38299 | Total threads: 4 |
Dashboard: http://127.0.0.1:41335/status | Memory: 244.27 GiB |
Nanny: tcp://127.0.0.1:43887 | |
Local directory: /tmp/dask-worker-space/worker-3rzuw_e2 |
Comm: tcp://127.0.0.1:43910 | Total threads: 4 |
Dashboard: http://127.0.0.1:37642/status | Memory: 244.27 GiB |
Nanny: tcp://127.0.0.1:44378 | |
Local directory: /tmp/dask-worker-space/worker-_f899ycv |
Comm: tcp://127.0.0.1:42536 | Total threads: 4 |
Dashboard: http://127.0.0.1:42028/status | Memory: 244.27 GiB |
Nanny: tcp://127.0.0.1:40873 | |
Local directory: /tmp/dask-worker-space/worker-ifkevfmm |
Comm: tcp://127.0.0.1:33731 | Total threads: 4 |
Dashboard: http://127.0.0.1:41251/status | Memory: 244.27 GiB |
Nanny: tcp://127.0.0.1:37900 | |
Local directory: /tmp/dask-worker-space/worker-voxejvec |
Comm: tcp://127.0.0.1:37515 | Total threads: 4 |
Dashboard: http://127.0.0.1:41117/status | Memory: 244.27 GiB |
Nanny: tcp://127.0.0.1:33276 | |
Local directory: /tmp/dask-worker-space/worker-ext9jlt0 |
Comm: tcp://127.0.0.1:34198 | Total threads: 4 |
Dashboard: http://127.0.0.1:36632/status | Memory: 244.27 GiB |
Nanny: tcp://127.0.0.1:40127 | |
Local directory: /tmp/dask-worker-space/worker-v_l0nm8v |
Comm: tcp://127.0.0.1:43898 | Total threads: 4 |
Dashboard: http://127.0.0.1:42811/status | Memory: 244.27 GiB |
Nanny: tcp://127.0.0.1:34752 | |
Local directory: /tmp/dask-worker-space/worker-x770dl1c |
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_integrals | calc.FWC_load_integrals(data,nc_outputpath) | BBFG | FWC_integrals | (12000,24000) | Km^3 | I-1 |
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= False df.Inputs != nothing False lazy= False CPU times: user 404 µs, sys: 87 µs, total: 491 µs Wall time: 491 µs
0
%%time
monitor.auto(df,data,savefig,daskreport,outputpath,file_exp='SEDNA'
)
#calc= False #save= False #plot= True Value='FWC_integrals' Zone='BBFG' Plot='FWC_integrals' cmap='' clabel='Km^3' clim= (12000, 24000) outputpath='../results/SEDNA_DELTA_MONITOR/' nc_outputpath='../nc_results/SEDNA_DELTA_MONITOR/' filename='SEDNA_FWC_integrals_BBFG_FWC_integrals' #3 no computing , loading starts data=save.load_data(plot=Plot,path=nc_outputpath,filename=filename) start loading data load 1Dnc file from ../nc_results/SEDNA_DELTA_MONITOR/../*/SEDNA_FWC_integrals_BBFG_FWC_integrals*.nc load computed data completed
<xarray.Dataset> Dimensions: (t: 365) Coordinates: * t (t) object 2015-01-01 12:00:00 ... 2015-12-31 12:00:00 Data variables: FWC_Arctic (t) float64 dask.array<chunksize=(365,), meta=np.ndarray> FWC_CRF (t) float64 dask.array<chunksize=(365,), meta=np.ndarray>
#5 Plotting filename= plots.FWC_integrals(data,path=outputpath,filename=filename,save=savefig,cmap=cmap,clim=clim,clabel=clabel) title SEDNA_FWC_integrals_BBFG_FWC_integrals
WARNING:param.CurvePlot01742: Converting cftime.datetime from a non-standard calendar (noleap) to a standard calendar for plotting. This may lead to subtle errors in formatting dates, for accurate tick formatting switch to the matplotlib backend. WARNING:param.CurvePlot01743: Converting cftime.datetime from a non-standard calendar (noleap) to a standard calendar for plotting. This may lead to subtle errors in formatting dates, for accurate tick formatting switch to the matplotlib backend.
../results/SEDNA_DELTA_MONITOR/SEDNA_FWC_integrals_BBFG_FWC_integrals_20150101-20151231.html starts plotting plotting ../results/SEDNA_DELTA_MONITOR/SEDNA_FWC_integrals_BBFG_FWC_integrals_20150101-20151231.html ../results/SEDNA_DELTA_MONITOR/SEDNA_FWC_integrals_BBFG_FWC_integrals_20150101-20151231.html created
CPU times: user 1.75 s, sys: 564 ms, total: 2.32 s Wall time: 6.85 s