%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.mg1.tgcc.ccc.cea.fr starting dask cluster on local= True workers 16 10000000000 rome local cluster starting This code is running on irene8000.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/7402615irene8000.c-irene.mg1.tgcc.ccc.cea.fr_SEDNA_DELTA_MONITOR_12M_AWTMD/ CPU times: user 572 ms, sys: 223 ms, total: 796 ms Wall time: 15.9 s
Client-705ff630-6df0-11ed-b2d7-080038bfd9c6
Connection method: Cluster object | Cluster type: distributed.LocalCluster |
Dashboard: http://127.0.0.1:8787/status |
65f9ce83
Dashboard: http://127.0.0.1:8787/status | Workers: 16 |
Total threads: 128 | Total memory: 2.86 TiB |
Status: running | Using processes: True |
Scheduler-5fc7d688-019d-43c9-9670-3cf572f7476b
Comm: tcp://127.0.0.1:42259 | Workers: 16 |
Dashboard: http://127.0.0.1:8787/status | Total threads: 128 |
Started: Just now | Total memory: 2.86 TiB |
Comm: tcp://127.0.0.1:43952 | Total threads: 8 |
Dashboard: http://127.0.0.1:45019/status | Memory: 183.20 GiB |
Nanny: tcp://127.0.0.1:46842 | |
Local directory: /tmp/dask-worker-space/worker-_z9rpue0 |
Comm: tcp://127.0.0.1:36640 | Total threads: 8 |
Dashboard: http://127.0.0.1:37686/status | Memory: 183.20 GiB |
Nanny: tcp://127.0.0.1:39108 | |
Local directory: /tmp/dask-worker-space/worker-4qnp79ht |
Comm: tcp://127.0.0.1:39868 | Total threads: 8 |
Dashboard: http://127.0.0.1:39088/status | Memory: 183.20 GiB |
Nanny: tcp://127.0.0.1:36903 | |
Local directory: /tmp/dask-worker-space/worker-yflytd_8 |
Comm: tcp://127.0.0.1:35719 | Total threads: 8 |
Dashboard: http://127.0.0.1:43155/status | Memory: 183.20 GiB |
Nanny: tcp://127.0.0.1:45185 | |
Local directory: /tmp/dask-worker-space/worker-8ma0yqtw |
Comm: tcp://127.0.0.1:34709 | Total threads: 8 |
Dashboard: http://127.0.0.1:46213/status | Memory: 183.20 GiB |
Nanny: tcp://127.0.0.1:45872 | |
Local directory: /tmp/dask-worker-space/worker-69v9_3zq |
Comm: tcp://127.0.0.1:43717 | Total threads: 8 |
Dashboard: http://127.0.0.1:43537/status | Memory: 183.20 GiB |
Nanny: tcp://127.0.0.1:34357 | |
Local directory: /tmp/dask-worker-space/worker-w5m5y4p6 |
Comm: tcp://127.0.0.1:39054 | Total threads: 8 |
Dashboard: http://127.0.0.1:46801/status | Memory: 183.20 GiB |
Nanny: tcp://127.0.0.1:34343 | |
Local directory: /tmp/dask-worker-space/worker-vesu2cr0 |
Comm: tcp://127.0.0.1:40745 | Total threads: 8 |
Dashboard: http://127.0.0.1:36919/status | Memory: 183.20 GiB |
Nanny: tcp://127.0.0.1:33400 | |
Local directory: /tmp/dask-worker-space/worker-ga9b45r_ |
Comm: tcp://127.0.0.1:33269 | Total threads: 8 |
Dashboard: http://127.0.0.1:44441/status | Memory: 183.20 GiB |
Nanny: tcp://127.0.0.1:39189 | |
Local directory: /tmp/dask-worker-space/worker-8b7qj6uw |
Comm: tcp://127.0.0.1:42726 | Total threads: 8 |
Dashboard: http://127.0.0.1:43990/status | Memory: 183.20 GiB |
Nanny: tcp://127.0.0.1:41719 | |
Local directory: /tmp/dask-worker-space/worker-u6i3znj7 |
Comm: tcp://127.0.0.1:43181 | Total threads: 8 |
Dashboard: http://127.0.0.1:37556/status | Memory: 183.20 GiB |
Nanny: tcp://127.0.0.1:42716 | |
Local directory: /tmp/dask-worker-space/worker-xmx05n78 |
Comm: tcp://127.0.0.1:39519 | Total threads: 8 |
Dashboard: http://127.0.0.1:33887/status | Memory: 183.20 GiB |
Nanny: tcp://127.0.0.1:44618 | |
Local directory: /tmp/dask-worker-space/worker-jli4my81 |
Comm: tcp://127.0.0.1:34242 | Total threads: 8 |
Dashboard: http://127.0.0.1:35010/status | Memory: 183.20 GiB |
Nanny: tcp://127.0.0.1:42526 | |
Local directory: /tmp/dask-worker-space/worker-m07xw6ey |
Comm: tcp://127.0.0.1:43251 | Total threads: 8 |
Dashboard: http://127.0.0.1:46503/status | Memory: 183.20 GiB |
Nanny: tcp://127.0.0.1:46491 | |
Local directory: /tmp/dask-worker-space/worker-56h8jbiy |
Comm: tcp://127.0.0.1:45192 | Total threads: 8 |
Dashboard: http://127.0.0.1:44219/status | Memory: 183.20 GiB |
Nanny: tcp://127.0.0.1:37123 | |
Local directory: /tmp/dask-worker-space/worker-zf59v24r |
Comm: tcp://127.0.0.1:40283 | Total threads: 8 |
Dashboard: http://127.0.0.1:37972/status | Memory: 183.20 GiB |
Nanny: tcp://127.0.0.1:41946 | |
Local directory: /tmp/dask-worker-space/worker-isgzx90a |
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 | |
---|---|---|---|---|---|---|---|---|---|---|
AW_maxtemp_depth | gridT.votemper,gridS.vosaline,param.mask,param... | calc.AWTD4(data) | ALL | AWTD_map | jet | (0,800) | m | M-5 |
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: 424 µs
0
%%time
monitor.auto(df,data,savefig,daskreport,outputpath,file_exp='SEDNA'
)
#calc= False #save= False #plot= True Value='AW_maxtemp_depth' Zone='ALL' Plot='AWTD_map' cmap='jet' clabel='m' clim= (0, 800) outputpath='../results/SEDNA_DELTA_MONITOR/' nc_outputpath='../nc_results/SEDNA_DELTA_MONITOR/' filename='SEDNA_AWTD_map_ALL_AW_maxtemp_depth' #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_AWTD_map_ALL_AW_maxtemp_depth/t_*/y_*/x_*.nc dim ('x', 'y', 't') load computed data completed
<xarray.Dataset> Dimensions: (t: 365, y: 6540, x: 6560) Coordinates: * t (t) object 2015-01-01 12:00:00 ... 2015-12-31 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 nav_lon (y, x) float32 dask.array<chunksize=(130, 6560), meta=np.ndarray> mask2d (y, x) bool dask.array<chunksize=(130, 6560), meta=np.ndarray> nav_lat (y, x) float32 dask.array<chunksize=(130, 6560), meta=np.ndarray> Data variables: AWT (t, y, x) float32 dask.array<chunksize=(1, 130, 6560), meta=np.ndarray> AWD (t, y, x) float32 dask.array<chunksize=(1, 130, 6560), meta=np.ndarray>
#5 Plotting filename= plots.AWTD_map(data,path=outputpath,filename=filename,save=savefig,cmap=cmap,clim=clim,clabel=clabel) ../results/SEDNA_DELTA_MONITOR/SEDNA_AWTD_map_ALL_AW_maxtemp_depth_20150101-20151231.html starts plotting
/ccc/cont003/home/ra5563/ra5563/monitor/lib/python3.10/site-packages/geoviews/operation/projection.py:99: ShapelyDeprecationWarning: __len__ for multi-part geometries is deprecated and will be removed in Shapely 2.0. Check the length of the `geoms` property instead to get the number of parts of a multi-part geometry. if proj_geom.geom_type == 'GeometryCollection' and len(proj_geom) == 0: /ccc/cont003/home/ra5563/ra5563/monitor/lib/python3.10/site-packages/geoviews/operation/projection.py:99: ShapelyDeprecationWarning: __len__ for multi-part geometries is deprecated and will be removed in Shapely 2.0. Check the length of the `geoms` property instead to get the number of parts of a multi-part geometry. if proj_geom.geom_type == 'GeometryCollection' and len(proj_geom) == 0:
../results/SEDNA_DELTA_MONITOR/SEDNA_AWTD_map_ALL_AW_maxtemp_depth_20150101-20151231.html created
CPU times: user 5h 31min 35s, sys: 1h 46min 16s, total: 7h 17min 51s Wall time: 6h 56min 58s