%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= irene8003.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 irene8003.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= 12 outputpath= ../results/SEDNA_DELTA_MONITOR/ daskreport= ../results/dask/7402451irene8003.c-irene.mg1.tgcc.ccc.cea.fr_SEDNA_DELTA_MONITOR_12M_Sectiony/ CPU times: user 463 ms, sys: 167 ms, total: 630 ms Wall time: 10.6 s
Client-5f8dfbb0-6dab-11ed-b08e-080038b5aba1
Connection method: Cluster object | Cluster type: distributed.LocalCluster |
Dashboard: http://127.0.0.1:8787/status |
716dc630
Dashboard: http://127.0.0.1:8787/status | Workers: 16 |
Total threads: 128 | Total memory: 2.86 TiB |
Status: running | Using processes: True |
Scheduler-890c7047-9810-4743-9ab4-11d442efe560
Comm: tcp://127.0.0.1:34119 | Workers: 16 |
Dashboard: http://127.0.0.1:8787/status | Total threads: 128 |
Started: Just now | Total memory: 2.86 TiB |
Comm: tcp://127.0.0.1:44639 | Total threads: 8 |
Dashboard: http://127.0.0.1:34398/status | Memory: 183.20 GiB |
Nanny: tcp://127.0.0.1:36168 | |
Local directory: /tmp/dask-worker-space/worker-_fee43ai |
Comm: tcp://127.0.0.1:33948 | Total threads: 8 |
Dashboard: http://127.0.0.1:36039/status | Memory: 183.20 GiB |
Nanny: tcp://127.0.0.1:37037 | |
Local directory: /tmp/dask-worker-space/worker-u7ebwhs3 |
Comm: tcp://127.0.0.1:39988 | Total threads: 8 |
Dashboard: http://127.0.0.1:42048/status | Memory: 183.20 GiB |
Nanny: tcp://127.0.0.1:33786 | |
Local directory: /tmp/dask-worker-space/worker-1lqxs913 |
Comm: tcp://127.0.0.1:45444 | Total threads: 8 |
Dashboard: http://127.0.0.1:42538/status | Memory: 183.20 GiB |
Nanny: tcp://127.0.0.1:46289 | |
Local directory: /tmp/dask-worker-space/worker-2u02gbne |
Comm: tcp://127.0.0.1:35938 | Total threads: 8 |
Dashboard: http://127.0.0.1:42969/status | Memory: 183.20 GiB |
Nanny: tcp://127.0.0.1:46255 | |
Local directory: /tmp/dask-worker-space/worker-quofvwho |
Comm: tcp://127.0.0.1:38931 | Total threads: 8 |
Dashboard: http://127.0.0.1:35666/status | Memory: 183.20 GiB |
Nanny: tcp://127.0.0.1:34390 | |
Local directory: /tmp/dask-worker-space/worker-fzk87rl9 |
Comm: tcp://127.0.0.1:41923 | Total threads: 8 |
Dashboard: http://127.0.0.1:39257/status | Memory: 183.20 GiB |
Nanny: tcp://127.0.0.1:45041 | |
Local directory: /tmp/dask-worker-space/worker-jejni83g |
Comm: tcp://127.0.0.1:46566 | Total threads: 8 |
Dashboard: http://127.0.0.1:33551/status | Memory: 183.20 GiB |
Nanny: tcp://127.0.0.1:33762 | |
Local directory: /tmp/dask-worker-space/worker-rkm71rv6 |
Comm: tcp://127.0.0.1:39431 | Total threads: 8 |
Dashboard: http://127.0.0.1:36068/status | Memory: 183.20 GiB |
Nanny: tcp://127.0.0.1:35843 | |
Local directory: /tmp/dask-worker-space/worker-l_tu8yxs |
Comm: tcp://127.0.0.1:37755 | Total threads: 8 |
Dashboard: http://127.0.0.1:38704/status | Memory: 183.20 GiB |
Nanny: tcp://127.0.0.1:42410 | |
Local directory: /tmp/dask-worker-space/worker-zcyl3bpf |
Comm: tcp://127.0.0.1:46723 | Total threads: 8 |
Dashboard: http://127.0.0.1:39598/status | Memory: 183.20 GiB |
Nanny: tcp://127.0.0.1:43261 | |
Local directory: /tmp/dask-worker-space/worker-d551swyd |
Comm: tcp://127.0.0.1:38152 | Total threads: 8 |
Dashboard: http://127.0.0.1:32857/status | Memory: 183.20 GiB |
Nanny: tcp://127.0.0.1:36162 | |
Local directory: /tmp/dask-worker-space/worker-rs3epdgp |
Comm: tcp://127.0.0.1:35483 | Total threads: 8 |
Dashboard: http://127.0.0.1:40698/status | Memory: 183.20 GiB |
Nanny: tcp://127.0.0.1:39706 | |
Local directory: /tmp/dask-worker-space/worker-s_1jf3xi |
Comm: tcp://127.0.0.1:46634 | Total threads: 8 |
Dashboard: http://127.0.0.1:35413/status | Memory: 183.20 GiB |
Nanny: tcp://127.0.0.1:44749 | |
Local directory: /tmp/dask-worker-space/worker-vy4bar_9 |
Comm: tcp://127.0.0.1:46103 | Total threads: 8 |
Dashboard: http://127.0.0.1:36354/status | Memory: 183.20 GiB |
Nanny: tcp://127.0.0.1:44156 | |
Local directory: /tmp/dask-worker-space/worker-xxwsvoae |
Comm: tcp://127.0.0.1:38927 | Total threads: 8 |
Dashboard: http://127.0.0.1:38745/status | Memory: 183.20 GiB |
Nanny: tcp://127.0.0.1:45794 | |
Local directory: /tmp/dask-worker-space/worker-9gxcb6t4 |
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 | |
---|---|---|---|---|---|---|---|---|---|---|
Section | gridT.votemper,gridS.vosaline,gridV.vomecrty,p... | data.unify_chunks().persist() | FramS | section | None | {'vosaline': (33,36.2), 'votemper': (-2,6), 'v... | None | S-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 True lazy= False CPU times: user 0 ns, sys: 449 µs, total: 449 µs Wall time: 451 µs
0
%%time
monitor.auto(df,data,savefig,daskreport,outputpath,file_exp='SEDNA'
)
#calc= False #save= False #plot= True Value='Section' Zone='FramS' Plot='section' cmap='None' clabel='None' clim= {'vosaline': (33, 36.2), 'votemper': (-2, 6), 'vomecrty': (-0.05, 0.05)} outputpath='../results/SEDNA_DELTA_MONITOR/' nc_outputpath='../nc_results/SEDNA_DELTA_MONITOR/' filename='SEDNA_section_FramS_Section' #3 no computing , loading starts data=save.load_data(plot=Plot,path=nc_outputpath,filename=filename) start loading data filename= ../nc_results/SEDNA_DELTA_MONITOR/SEDNA_section_FramS_Section/t_*.nc dim t load computed data completed
<xarray.Dataset> Dimensions: (t: 365, z: 150, x: 601) Coordinates: * t (t) object 2015-01-01 12:00:00 ... 2015-12-31 12:00:00 y int64 ... * x (x) int64 3734 3735 3736 3737 3738 ... 4330 4331 4332 4333 4334 * z (z) int64 1 2 3 4 5 6 7 8 9 ... 143 144 145 146 147 148 149 150 nav_lon (x) float32 dask.array<chunksize=(601,), meta=np.ndarray> nav_lat (x) float32 dask.array<chunksize=(601,), meta=np.ndarray> mask2d (x) bool dask.array<chunksize=(601,), meta=np.ndarray> mask (z, x) bool dask.array<chunksize=(150, 601), meta=np.ndarray> depth (z, x) float32 dask.array<chunksize=(150, 601), meta=np.ndarray> Data variables: vosaline (t, z, x) float32 dask.array<chunksize=(1, 150, 601), meta=np.ndarray> votemper (t, z, x) float32 dask.array<chunksize=(1, 150, 601), meta=np.ndarray> vomecrty (t, z, x) float32 dask.array<chunksize=(1, 150, 601), 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
#5 Plotting filename= plots.section(data,path=outputpath,filename=filename,save=savefig,cmap=cmap,clim=clim,clabel=clabel) ../results/SEDNA_DELTA_MONITOR/SEDNA_section_FramS_Section_20150101-20151231.html starts plotting ../results/SEDNA_DELTA_MONITOR/SEDNA_section_FramS_Section_20150101-20151231.html created
CPU times: user 4min 39s, sys: 49.9 s, total: 5min 29s Wall time: 8min 56s