%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'>
# 'catalog_url': we chose intake catalog for reading.
# 'month': = 'JOBID' almost month but not really,
# 'savefig': Do we save output in html? or not.
# 'file_exp': Which 'experiment' name is it?
#. this corresopnds to catalog name without path and .yaml
# If you submit the job with job scheduler, above
# 4 valus can be passed as enviroment variable.
#
%%time
savefig=True
client,cluster,control,catalog_url,month,year,daskreport,outputpath = load.set_control(host)
!mkdir -p $outputpath
!mkdir -p $daskreport
client
using host= irene5430.c-irene.mg1.tgcc.ccc.cea.fr starting dask cluster on local= True 10000000000 False not local in tgcc rome local cluster starting This code is running on irene5430.c-irene.mg1.tgcc.ccc.cea.fr using SEDNA_ALPHA_MONITOR file experiment, read from ../lib/SEDNA_ALPHA_MONITOR.yaml on year= *[8-9] on month= 22 outputpath= ../results/rome_SEDNA_ALPHA_MONITOR/22/ daskreport= ../results/dask/2453062irene5430.c-irene.mg1.tgcc.ccc.cea.fr_SEDNA_ALPHA_MONITOR_22compute/ CPU times: user 360 ms, sys: 233 ms, total: 594 ms Wall time: 12.2 s
Client
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Cluster
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df=load.controlfile(control)
#
#df=df[~df.duplicated(subset='Plot' )]
df=df[df['Plot'] == 'AWTD_map']
#df=df[3:4]
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
#todo add 'year' here.
data=load.datas(catalog_url,df.Inputs,month,year,daskreport)
print('#1 Data: created:')
print('# if we raed too much file, we can do sel to take out some datess..')
data
../lib/SEDNA_ALPHA_MONITOR.yaml using param_xios reading ../lib/SEDNA_ALPHA_MONITOR.yaml using param_xios reading <bound method DataSourceBase.describe of sources: param_xios: args: combine: by_coords concat_dim: y urlpath: /ccc/work/cont003/gen7420/odakatin/CONFIGS/SEDNA/SEDNA-I/SEDNA_Domain_cfg_Tgt_20210423_tsh10m_L1/param/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/x_*.nc', 'combine': 'by_coords', 'concat_dim': 'y'}} 0 read gridS ['vosaline'] using load_data_xios reading gridS using load_data_xios reading <bound method DataSourceBase.describe of sources: data_xios: args: combine: by_coords concat_dim: time_counter,x,y urlpath: /ccc/scratch/cont003/gen7420/talandel/ONGOING-RUNS/SEDNA-ALPHA-XIOS.22/SEDNA-ALPHA_1d_gridS_*[8-9]_0[0-5][0-9][0-9].nc xarray_kwargs: compat: override coords: minimal data_vars: minimal drop_variables: !!set deptht_bounds: null depthu_bounds: null nav_lat: null nav_lon: null time_centerd: null time_centered_bounds: null time_counter_bounds: null parallel: true preprocess: !!python/name:core.load.prep '' description: SEDNA NEMO outputs from different xios server driver: intake_xarray.netcdf.NetCDFSource metadata: catalog_dir: /ccc/work/cont003/gen7420/odakatin/monitor-sedna/notebook/../lib/ > took 100.22520065307617 seconds 0 merging gridS ['vosaline'] 1 read gridT ['votemper'] using load_data_xios reading gridT using load_data_xios reading <bound method DataSourceBase.describe of sources: data_xios: args: combine: by_coords concat_dim: time_counter,x,y urlpath: /ccc/scratch/cont003/gen7420/talandel/ONGOING-RUNS/SEDNA-ALPHA-XIOS.22/SEDNA-ALPHA_1d_gridT_*[8-9]_0[0-5][0-9][0-9].nc xarray_kwargs: compat: override coords: minimal data_vars: minimal drop_variables: !!set deptht_bounds: null depthu_bounds: null nav_lat: null nav_lon: null time_centerd: null time_centered_bounds: null time_counter_bounds: null parallel: true preprocess: !!python/name:core.load.prep '' description: SEDNA NEMO outputs from different xios server driver: intake_xarray.netcdf.NetCDFSource metadata: catalog_dir: /ccc/work/cont003/gen7420/odakatin/monitor-sedna/notebook/../lib/ > took 16.329047679901123 seconds 1 merging gridT ['votemper'] took 0.2588372230529785 seconds param mask2d will be included in data param nav_lon will be included in data param nav_lat will be included in data param depth will be included in data param mask will be included in data #1 Data: created: # if we raed too much file, we can do sel to take out some datess.. CPU times: user 30.4 s, sys: 10 s, total: 40.4 s Wall time: 2min 12s
<xarray.Dataset> Dimensions: (t: 4, x: 6560, y: 6540, z: 150) Coordinates: * t (t) object 2004-06-18 12:00:00 ... 2004-06-29 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 * z (z) int64 1 2 3 4 5 6 7 8 9 ... 143 144 145 146 147 148 149 150 mask2d (y, x) bool dask.array<chunksize=(13, 6560), meta=np.ndarray> nav_lon (y, x) float32 dask.array<chunksize=(13, 6560), meta=np.ndarray> nav_lat (y, x) float32 dask.array<chunksize=(13, 6560), meta=np.ndarray> depth (z, y, x) float64 dask.array<chunksize=(150, 13, 6560), meta=np.ndarray> mask (z, y, x) bool dask.array<chunksize=(150, 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>
array([cftime.DatetimeNoLeap(2004, 6, 18, 12, 0, 0, 0), cftime.DatetimeNoLeap(2004, 6, 19, 12, 0, 0, 0), cftime.DatetimeNoLeap(2004, 6, 28, 12, 0, 0, 0), cftime.DatetimeNoLeap(2004, 6, 29, 12, 0, 0, 0)], dtype=object)
array([ 1, 2, 3, ..., 6538, 6539, 6540])
array([ 1, 2, 3, ..., 6558, 6559, 6560])
array([ 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, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71, 72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 84, 85, 86, 87, 88, 89, 90, 91, 92, 93, 94, 95, 96, 97, 98, 99, 100, 101, 102, 103, 104, 105, 106, 107, 108, 109, 110, 111, 112, 113, 114, 115, 116, 117, 118, 119, 120, 121, 122, 123, 124, 125, 126, 127, 128, 129, 130, 131, 132, 133, 134, 135, 136, 137, 138, 139, 140, 141, 142, 143, 144, 145, 146, 147, 148, 149, 150])
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ds=load.datas_rechunk(data,daskreport=daskreport,ysize=20)
sum_num (13, 13, 13, 13, 13, 13, 13, 13, 13, 13, 13, 13, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12) start rechunking with (252, 240, 240, 240, 240, 240, 240, 240, 240, 240, 240, 240, 240, 240, 240, 240, 240, 240, 240, 240, 240, 240, 240, 240, 240, 240, 240, 48) end of rechunk
%%time
monitor.auto(df,ds,savefig,daskreport,outputpath,file_exp='SEDNA'
)
switch:calcswitch,saveswitch,plotswitch True False True dtaa= calc.AWTD4(data) #3 Start computing count: <xarray.Dataset> Dimensions: () Data variables: vosaline int64 dask.array<chunksize=(), meta=np.ndarray> votemper int64 dask.array<chunksize=(), meta=np.ndarray>
<xarray.Dataset> Dimensions: () Data variables: vosaline int64 dask.array<chunksize=(), meta=np.ndarray> votemper int64 dask.array<chunksize=(), meta=np.ndarray>
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nbytes: 2445541632 count: <xarray.Dataset> Dimensions: () Data variables: AWT int64 dask.array<chunksize=(), meta=np.ndarray> AWD int64 dask.array<chunksize=(), meta=np.ndarray>
<xarray.Dataset> Dimensions: (t: 4, x: 6560, y: 6540) Coordinates: * t (t) object 2004-06-18 12:00:00 ... 2004-06-29 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 mask2d (y, x) bool dask.array<chunksize=(252, 6560), meta=np.ndarray> nav_lon (y, x) float32 dask.array<chunksize=(252, 6560), meta=np.ndarray> nav_lat (y, x) float32 dask.array<chunksize=(252, 6560), meta=np.ndarray> Data variables: AWT (t, y, x) float32 dask.array<chunksize=(1, 252, 6560), meta=np.ndarray> AWD (t, y, x) float64 dask.array<chunksize=(1, 252, 6560), meta=np.ndarray>
array([cftime.DatetimeNoLeap(2004, 6, 18, 12, 0, 0, 0), cftime.DatetimeNoLeap(2004, 6, 19, 12, 0, 0, 0), cftime.DatetimeNoLeap(2004, 6, 28, 12, 0, 0, 0), cftime.DatetimeNoLeap(2004, 6, 29, 12, 0, 0, 0)], dtype=object)
array([ 1, 2, 3, ..., 6538, 6539, 6540])
array([ 1, 2, 3, ..., 6558, 6559, 6560])
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plots.AWTD_map(data,path=outputpath,filename=filename,save=savefig,cmap='jet',clim=(0,800),clabel='m') #5 Plotting SEDNA_AWTD_map_ALL_AW_maxtemp_depth ../results/rome_SEDNA_ALPHA_MONITOR/22/SEDNA_AWTD_map_ALL_AW_maxtemp_depth_20040618-20040629.html starts plotting
distributed.nanny - WARNING - Worker exceeded 99% memory budget. Restarting distributed.nanny - WARNING - Restarting worker distributed.nanny - WARNING - Worker exceeded 99% memory budget. Restarting distributed.nanny - WARNING - Restarting worker
0%| | 0/4 [00:00<?, ?it/s]
distributed.nanny - WARNING - Worker exceeded 99% memory budget. Restarting distributed.nanny - WARNING - Restarting worker distributed.nanny - WARNING - Worker exceeded 99% memory budget. Restarting distributed.nanny - WARNING - Restarting worker distributed.nanny - WARNING - Worker exceeded 99% memory budget. Restarting distributed.nanny - WARNING - Worker exceeded 99% memory budget. Restarting distributed.nanny - WARNING - Restarting worker distributed.nanny - WARNING - Restarting worker distributed.nanny - WARNING - Worker exceeded 99% memory budget. Restarting distributed.nanny - WARNING - Restarting worker
25%|██▌ | 1/4 [03:09<09:29, 189.69s/it]
distributed.nanny - WARNING - Worker exceeded 99% memory budget. Restarting distributed.nanny - WARNING - Restarting worker distributed.nanny - WARNING - Worker exceeded 99% memory budget. Restarting distributed.nanny - WARNING - Restarting worker
50%|█████ | 2/4 [06:03<06:01, 180.52s/it]
distributed.nanny - WARNING - Worker exceeded 99% memory budget. Restarting distributed.nanny - WARNING - Restarting worker distributed.nanny - WARNING - Worker exceeded 99% memory budget. Restarting distributed.nanny - WARNING - Restarting worker
../results/rome_SEDNA_ALPHA_MONITOR/22/SEDNA_AWTD_map_ALL_AW_maxtemp_depth_20040618-20040629.html created
CPU times: user 8min 52s, sys: 1min, total: 9min 52s Wall time: 14min 3s