%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= irene5871.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 irene5871.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/6419621irene5871.c-irene.mg1.tgcc.ccc.cea.fr_SEDNA_DELTA_MONITOR_04M_Sectionx/ CPU times: user 592 ms, sys: 131 ms, total: 723 ms Wall time: 23.2 s
Client-9da47652-13e4-11ed-a783-080038b936ab
Connection method: Cluster object | Cluster type: distributed.LocalCluster |
Dashboard: http://127.0.0.1:8787/status |
8093e162
Dashboard: http://127.0.0.1:8787/status | Workers: 16 |
Total threads: 128 | Total memory: 251.06 GiB |
Status: running | Using processes: True |
Scheduler-c44f5cf9-222c-43b5-8e8b-d95e018b069b
Comm: tcp://127.0.0.1:39411 | Workers: 16 |
Dashboard: http://127.0.0.1:8787/status | Total threads: 128 |
Started: Just now | Total memory: 251.06 GiB |
Comm: tcp://127.0.0.1:33847 | Total threads: 8 |
Dashboard: http://127.0.0.1:39809/status | Memory: 15.69 GiB |
Nanny: tcp://127.0.0.1:45776 | |
Local directory: /tmp/dask-worker-space/worker-zy4g9l7_ |
Comm: tcp://127.0.0.1:36565 | Total threads: 8 |
Dashboard: http://127.0.0.1:36161/status | Memory: 15.69 GiB |
Nanny: tcp://127.0.0.1:33492 | |
Local directory: /tmp/dask-worker-space/worker-yb8d5_jx |
Comm: tcp://127.0.0.1:35133 | Total threads: 8 |
Dashboard: http://127.0.0.1:40673/status | Memory: 15.69 GiB |
Nanny: tcp://127.0.0.1:34486 | |
Local directory: /tmp/dask-worker-space/worker-zkg6tynl |
Comm: tcp://127.0.0.1:34716 | Total threads: 8 |
Dashboard: http://127.0.0.1:40181/status | Memory: 15.69 GiB |
Nanny: tcp://127.0.0.1:42563 | |
Local directory: /tmp/dask-worker-space/worker-hfc_n6ya |
Comm: tcp://127.0.0.1:40086 | Total threads: 8 |
Dashboard: http://127.0.0.1:43805/status | Memory: 15.69 GiB |
Nanny: tcp://127.0.0.1:41767 | |
Local directory: /tmp/dask-worker-space/worker-o4ixnuoa |
Comm: tcp://127.0.0.1:40838 | Total threads: 8 |
Dashboard: http://127.0.0.1:38308/status | Memory: 15.69 GiB |
Nanny: tcp://127.0.0.1:44471 | |
Local directory: /tmp/dask-worker-space/worker-mpuot2mr |
Comm: tcp://127.0.0.1:35302 | Total threads: 8 |
Dashboard: http://127.0.0.1:44340/status | Memory: 15.69 GiB |
Nanny: tcp://127.0.0.1:37514 | |
Local directory: /tmp/dask-worker-space/worker-foan7exi |
Comm: tcp://127.0.0.1:38372 | Total threads: 8 |
Dashboard: http://127.0.0.1:43374/status | Memory: 15.69 GiB |
Nanny: tcp://127.0.0.1:34556 | |
Local directory: /tmp/dask-worker-space/worker-poufjf4s |
Comm: tcp://127.0.0.1:33728 | Total threads: 8 |
Dashboard: http://127.0.0.1:39902/status | Memory: 15.69 GiB |
Nanny: tcp://127.0.0.1:34178 | |
Local directory: /tmp/dask-worker-space/worker-ui52rymx |
Comm: tcp://127.0.0.1:35589 | Total threads: 8 |
Dashboard: http://127.0.0.1:41269/status | Memory: 15.69 GiB |
Nanny: tcp://127.0.0.1:46393 | |
Local directory: /tmp/dask-worker-space/worker-z5jan25j |
Comm: tcp://127.0.0.1:46564 | Total threads: 8 |
Dashboard: http://127.0.0.1:43665/status | Memory: 15.69 GiB |
Nanny: tcp://127.0.0.1:41006 | |
Local directory: /tmp/dask-worker-space/worker-qe1_nqju |
Comm: tcp://127.0.0.1:37467 | Total threads: 8 |
Dashboard: http://127.0.0.1:45268/status | Memory: 15.69 GiB |
Nanny: tcp://127.0.0.1:40309 | |
Local directory: /tmp/dask-worker-space/worker-f42fwl8u |
Comm: tcp://127.0.0.1:44567 | Total threads: 8 |
Dashboard: http://127.0.0.1:44704/status | Memory: 15.69 GiB |
Nanny: tcp://127.0.0.1:43199 | |
Local directory: /tmp/dask-worker-space/worker-riz9u0jf |
Comm: tcp://127.0.0.1:36072 | Total threads: 8 |
Dashboard: http://127.0.0.1:46752/status | Memory: 15.69 GiB |
Nanny: tcp://127.0.0.1:43229 | |
Local directory: /tmp/dask-worker-space/worker-52igpp0_ |
Comm: tcp://127.0.0.1:39893 | Total threads: 8 |
Dashboard: http://127.0.0.1:33153/status | Memory: 15.69 GiB |
Nanny: tcp://127.0.0.1:38963 | |
Local directory: /tmp/dask-worker-space/worker-0l2a1ks9 |
Comm: tcp://127.0.0.1:42101 | Total threads: 8 |
Dashboard: http://127.0.0.1:46221/status | Memory: 15.69 GiB |
Nanny: tcp://127.0.0.1:38363 | |
Local directory: /tmp/dask-worker-space/worker-klibehi3 |
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,gridU.vozocrtx,p... | data.chunk({'y':-1}).unify_chunks().persist() | BFGS | section | None | {'vosaline': (28,35), 'votemper': (-2,2), 'voz... | None | S-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= False ../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 gridS ['vosaline'] lazy= False using load_data_xios_kerchunk reading gridS using load_data_xios_kerchunk reading <bound method DataSourceBase.describe of sources: data_xios_kerchunk: args: consolidated: false storage_options: fo: file:////ccc/cont003/home/ra5563/ra5563/catalogue/DELTA/201204/gridS_0[0-5][0-9][0-9].json target_protocol: file urlpath: reference:// description: CREG025 NEMO outputs from different xios server in kerchunk format driver: intake_xarray.xzarr.ZarrSource metadata: catalog_dir: /ccc/work/cont003/gen7420/odakatin/monitor-sedna/notebook/../lib/ > took 39.97301435470581 seconds 0 merging gridS ['vosaline'] 1 read gridT ['votemper'] lazy= False using load_data_xios_kerchunk reading gridT using load_data_xios_kerchunk reading <bound method DataSourceBase.describe of sources: data_xios_kerchunk: args: consolidated: false storage_options: fo: file:////ccc/cont003/home/ra5563/ra5563/catalogue/DELTA/201204/gridT_0[0-5][0-9][0-9].json target_protocol: file urlpath: reference:// description: CREG025 NEMO outputs from different xios server in kerchunk format driver: intake_xarray.xzarr.ZarrSource metadata: catalog_dir: /ccc/work/cont003/gen7420/odakatin/monitor-sedna/notebook/../lib/ > took 20.300968170166016 seconds 1 merging gridT ['votemper'] took 0.7771613597869873 seconds 2 read gridU ['vozocrtx'] lazy= False using load_data_xios_kerchunk reading gridU using load_data_xios_kerchunk reading <bound method DataSourceBase.describe of sources: data_xios_kerchunk: args: consolidated: false storage_options: fo: file:////ccc/cont003/home/ra5563/ra5563/catalogue/DELTA/201204/gridU_0[0-5][0-9][0-9].json target_protocol: file urlpath: reference:// description: CREG025 NEMO outputs from different xios server in kerchunk format driver: intake_xarray.xzarr.ZarrSource metadata: catalog_dir: /ccc/work/cont003/gen7420/odakatin/monitor-sedna/notebook/../lib/ > took 48.510162353515625 seconds 2 merging gridU ['vozocrtx'] took 0.8074619770050049 seconds param mask will be included in data param nav_lat will be included in data param mask2d will be included in data param depth will be included in data param nav_lon will be included in data CPU times: user 54.3 s, sys: 8.11 s, total: 1min 2s Wall time: 2min 15s
<xarray.Dataset> Dimensions: (t: 30, z: 150, y: 6540, x: 6560) Coordinates: time_centered (t) object dask.array<chunksize=(1,), meta=np.ndarray> * t (t) object 2012-04-01 12:00:00 ... 2012-04-30 12:00:00 * y (y) int64 1 2 3 4 5 6 7 ... 6535 6536 6537 6538 6539 6540 * x (x) int64 1 2 3 4 5 6 7 ... 6555 6556 6557 6558 6559 6560 * z (z) int64 1 2 3 4 5 6 7 8 ... 143 144 145 146 147 148 149 150 mask (z, y, x) bool dask.array<chunksize=(150, 13, 6560), meta=np.ndarray> nav_lat (y, x) float32 dask.array<chunksize=(13, 6560), meta=np.ndarray> mask2d (y, x) bool dask.array<chunksize=(13, 6560), meta=np.ndarray> depth (z, y, x) float32 dask.array<chunksize=(150, 13, 6560), meta=np.ndarray> nav_lon (y, x) float32 dask.array<chunksize=(13, 6560), meta=np.ndarray> Data variables: vosaline (t, z, y, x) float32 dask.array<chunksize=(1, 150, 13, 6560), meta=np.ndarray> votemper (t, z, y, x) float32 dask.array<chunksize=(1, 150, 13, 6560), meta=np.ndarray> vozocrtx (t, z, y, x) float32 dask.array<chunksize=(1, 150, 13, 6560), 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-Jan-21 08:38:37 GMT title: ocean T grid variables uuid: d277f069-4681-4bdc-a897-fbf6d4f734e8
%%time
monitor.auto(df,data,savefig,daskreport,outputpath,file_exp='SEDNA'
)
#calc= True #save= True #plot= False Value='Section' Zone='BFGS' Plot='section' cmap='None' clabel='None' clim= {'vosaline': (28, 35), 'votemper': (-2, 2), 'vozocrtx': (-0.05, 0.05)} outputpath='../results/SEDNA_DELTA_MONITOR/' nc_outputpath='../nc_results/SEDNA_DELTA_MONITOR/' filename='SEDNA_section_BFGS_Section' data=monitor.optimize_dataset(data) #2 Zooming Data data= zoom.BFGS(data) data=monitor.optimize_dataset(data)
<xarray.Dataset> Dimensions: (t: 30, z: 95, y: 2384) Coordinates: time_centered (t) object dask.array<chunksize=(1,), meta=np.ndarray> * t (t) object 2012-04-01 12:00:00 ... 2012-04-30 12:00:00 * y (y) int64 3494 3495 3496 3497 3498 ... 5910 5911 5912 5913 x int64 2281 * z (z) int64 1 2 3 4 5 6 7 8 9 10 ... 87 88 89 90 91 92 93 94 95 mask (z, y) bool dask.array<chunksize=(95, 11), meta=np.ndarray> nav_lat (y) float32 dask.array<chunksize=(11,), meta=np.ndarray> mask2d (y) bool dask.array<chunksize=(11,), meta=np.ndarray> depth (z, y) float32 dask.array<chunksize=(95, 11), meta=np.ndarray> nav_lon (y) float32 dask.array<chunksize=(11,), meta=np.ndarray> Data variables: vosaline (t, z, y) float32 dask.array<chunksize=(1, 95, 11), meta=np.ndarray> votemper (t, z, y) float32 dask.array<chunksize=(1, 95, 11), meta=np.ndarray> vozocrtx (t, z, y) float32 dask.array<chunksize=(1, 95, 11), 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-Jan-21 08:38:37 GMT title: ocean T grid variables uuid: d277f069-4681-4bdc-a897-fbf6d4f734e8
#3 Start computing data= data.chunk({'y':-1}).unify_chunks().persist() monitor.optimize_dataset(data) add optimise here once otimise can recognise
<xarray.Dataset> Dimensions: (t: 30, z: 95, y: 2384) Coordinates: time_centered (t) object dask.array<chunksize=(1,), meta=np.ndarray> * t (t) object 2012-04-01 12:00:00 ... 2012-04-30 12:00:00 * y (y) int64 3494 3495 3496 3497 3498 ... 5910 5911 5912 5913 x int64 2281 * z (z) int64 1 2 3 4 5 6 7 8 9 10 ... 87 88 89 90 91 92 93 94 95 mask (z, y) bool dask.array<chunksize=(95, 2384), meta=np.ndarray> nav_lat (y) float32 dask.array<chunksize=(2384,), meta=np.ndarray> mask2d (y) bool dask.array<chunksize=(2384,), meta=np.ndarray> depth (z, y) float32 dask.array<chunksize=(95, 2384), meta=np.ndarray> nav_lon (y) float32 dask.array<chunksize=(2384,), meta=np.ndarray> Data variables: vosaline (t, z, y) float32 dask.array<chunksize=(1, 95, 2384), meta=np.ndarray> votemper (t, z, y) float32 dask.array<chunksize=(1, 95, 2384), meta=np.ndarray> vozocrtx (t, z, y) float32 dask.array<chunksize=(1, 95, 2384), 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-Jan-21 08:38:37 GMT title: ocean T grid variables uuid: d277f069-4681-4bdc-a897-fbf6d4f734e8
#4 Saving SEDNA_section_BFGS_Section data=save.datas(data,plot=Plot,path=nc_outputpath,filename=filename) start saving data saving data in a file t (1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1) 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 slice(0, 1, None) slice(1, 2, None) slice(2, 3, None) slice(3, 4, None) slice(4, 5, None) slice(5, 6, None) slice(6, 7, None) slice(7, 8, None) slice(8, 9, None) slice(9, 10, None) slice(10, 11, None) slice(11, 12, None) slice(12, 13, None) slice(13, 14, None) slice(14, 15, None) slice(15, 16, None) slice(16, 17, None) slice(17, 18, None) slice(18, 19, None) slice(19, 20, None) slice(20, 21, None) slice(21, 22, None) slice(22, 23, None) slice(23, 24, None) slice(24, 25, None) slice(25, 26, None) slice(26, 27, None) slice(27, 28, None) slice(28, 29, None) slice(29, 30, None) CPU times: user 2min 14s, sys: 12.3 s, total: 2min 26s Wall time: 6min 43s