%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= irene4784.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 irene4784.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= 04 outputpath= ../results/SEDNA_DELTA_MONITOR/ daskreport= ../results/dask/6419604irene4784.c-irene.mg1.tgcc.ccc.cea.fr_SEDNA_DELTA_MONITOR_04M_Ice_quantities/ CPU times: user 544 ms, sys: 157 ms, total: 701 ms Wall time: 19.6 s
Client-8e67ab5e-13e5-11ed-95b1-080038b93f75
Connection method: Cluster object | Cluster type: distributed.LocalCluster |
Dashboard: http://127.0.0.1:8787/status |
097dd7da
Dashboard: http://127.0.0.1:8787/status | Workers: 16 |
Total threads: 128 | Total memory: 251.06 GiB |
Status: running | Using processes: True |
Scheduler-5d5b1910-6ce7-4ce3-9719-6ee18c1dea04
Comm: tcp://127.0.0.1:33487 | Workers: 16 |
Dashboard: http://127.0.0.1:8787/status | Total threads: 128 |
Started: Just now | Total memory: 251.06 GiB |
Comm: tcp://127.0.0.1:38811 | Total threads: 8 |
Dashboard: http://127.0.0.1:35825/status | Memory: 15.69 GiB |
Nanny: tcp://127.0.0.1:38171 | |
Local directory: /tmp/dask-worker-space/worker-1ezn30em |
Comm: tcp://127.0.0.1:44629 | Total threads: 8 |
Dashboard: http://127.0.0.1:37104/status | Memory: 15.69 GiB |
Nanny: tcp://127.0.0.1:36209 | |
Local directory: /tmp/dask-worker-space/worker-hluwtz95 |
Comm: tcp://127.0.0.1:34381 | Total threads: 8 |
Dashboard: http://127.0.0.1:44911/status | Memory: 15.69 GiB |
Nanny: tcp://127.0.0.1:40935 | |
Local directory: /tmp/dask-worker-space/worker-dj6ri3tv |
Comm: tcp://127.0.0.1:35648 | Total threads: 8 |
Dashboard: http://127.0.0.1:39745/status | Memory: 15.69 GiB |
Nanny: tcp://127.0.0.1:33118 | |
Local directory: /tmp/dask-worker-space/worker-sfa0iajs |
Comm: tcp://127.0.0.1:33934 | Total threads: 8 |
Dashboard: http://127.0.0.1:33436/status | Memory: 15.69 GiB |
Nanny: tcp://127.0.0.1:33108 | |
Local directory: /tmp/dask-worker-space/worker-vl76cswq |
Comm: tcp://127.0.0.1:34936 | Total threads: 8 |
Dashboard: http://127.0.0.1:33629/status | Memory: 15.69 GiB |
Nanny: tcp://127.0.0.1:38044 | |
Local directory: /tmp/dask-worker-space/worker-mvur7fn8 |
Comm: tcp://127.0.0.1:35403 | Total threads: 8 |
Dashboard: http://127.0.0.1:42426/status | Memory: 15.69 GiB |
Nanny: tcp://127.0.0.1:33353 | |
Local directory: /tmp/dask-worker-space/worker-4enz9e58 |
Comm: tcp://127.0.0.1:43391 | Total threads: 8 |
Dashboard: http://127.0.0.1:38217/status | Memory: 15.69 GiB |
Nanny: tcp://127.0.0.1:39377 | |
Local directory: /tmp/dask-worker-space/worker-k9hf5i5z |
Comm: tcp://127.0.0.1:41323 | Total threads: 8 |
Dashboard: http://127.0.0.1:40604/status | Memory: 15.69 GiB |
Nanny: tcp://127.0.0.1:34278 | |
Local directory: /tmp/dask-worker-space/worker-7vp5wbzi |
Comm: tcp://127.0.0.1:39868 | Total threads: 8 |
Dashboard: http://127.0.0.1:42658/status | Memory: 15.69 GiB |
Nanny: tcp://127.0.0.1:38437 | |
Local directory: /tmp/dask-worker-space/worker-cxpi4jl2 |
Comm: tcp://127.0.0.1:37734 | Total threads: 8 |
Dashboard: http://127.0.0.1:33802/status | Memory: 15.69 GiB |
Nanny: tcp://127.0.0.1:44548 | |
Local directory: /tmp/dask-worker-space/worker-iw9owih5 |
Comm: tcp://127.0.0.1:33301 | Total threads: 8 |
Dashboard: http://127.0.0.1:36151/status | Memory: 15.69 GiB |
Nanny: tcp://127.0.0.1:38225 | |
Local directory: /tmp/dask-worker-space/worker-gtpgpe8w |
Comm: tcp://127.0.0.1:41572 | Total threads: 8 |
Dashboard: http://127.0.0.1:36497/status | Memory: 15.69 GiB |
Nanny: tcp://127.0.0.1:34835 | |
Local directory: /tmp/dask-worker-space/worker-ckxik2j1 |
Comm: tcp://127.0.0.1:38084 | Total threads: 8 |
Dashboard: http://127.0.0.1:42040/status | Memory: 15.69 GiB |
Nanny: tcp://127.0.0.1:33603 | |
Local directory: /tmp/dask-worker-space/worker-79u4_nmr |
Comm: tcp://127.0.0.1:42785 | Total threads: 8 |
Dashboard: http://127.0.0.1:35138/status | Memory: 15.69 GiB |
Nanny: tcp://127.0.0.1:37465 | |
Local directory: /tmp/dask-worker-space/worker-n5y0dti3 |
Comm: tcp://127.0.0.1:45657 | Total threads: 8 |
Dashboard: http://127.0.0.1:37070/status | Memory: 15.69 GiB |
Nanny: tcp://127.0.0.1:34494 | |
Local directory: /tmp/dask-worker-space/worker-c46wbh_j |
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= False df.Inputs != nothing True lazy= True CPU times: user 602 µs, sys: 0 ns, total: 602 µs Wall time: 505 µs
0
%%time
monitor.auto(df,data,savefig,daskreport,outputpath,file_exp='SEDNA'
)
#calc= False #save= False #plot= True 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' #3 no computing , loading starts data=save.load_data(plot=Plot,path=nc_outputpath,filename=filename) start saving data load 1Dnc file from ../nc_results/SEDNA_DELTA_MONITOR/../*/SEDNA_Ice_intquant_ALL_Ice_quantities*.nc load computed data completed
<xarray.Dataset> Dimensions: (t: 151) Coordinates: * t (t) object 2012-01-01 12:00:00 ... 2012-01-31 12:00:00 time_centered (t) object dask.array<chunksize=(31,), meta=np.ndarray> Data variables: Ice volume (t) float64 dask.array<chunksize=(31,), meta=np.ndarray> Ice area (t) float64 dask.array<chunksize=(31,), meta=np.ndarray> Ice extent (t) float64 dask.array<chunksize=(31,), meta=np.ndarray> Ice drift (t) float64 dask.array<chunksize=(31,), meta=np.ndarray>
#5 Plotting filename= plots.Ice_intquant(data,path=outputpath,filename=filename,save=savefig,cmap=cmap,clim=clim,clabel=clabel)
WARNING:param.CurvePlot02009: 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.CurvePlot02018: 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.CurvePlot02025: 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.CurvePlot02032: 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_Ice_intquant_ALL_Ice_quantities_20120101-20120430.html starts plotting plotting ../results/SEDNA_DELTA_MONITOR/SEDNA_Ice_intquant_ALL_Ice_quantities_20120101-20120430.html ../results/SEDNA_DELTA_MONITOR/SEDNA_Ice_intquant_ALL_Ice_quantities_20120101-20120430.html created
CPU times: user 2.44 s, sys: 765 ms, total: 3.21 s Wall time: 16.8 s