%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'>
# 'month': = 'JOBID' almost month but not really,
# If you submit the job with job scheduler, above
#below are list of enviroment variable one can pass
#%env local='2"
# 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 control=Ints_monitor
# name of control file to be used for computation/plots/save/
#%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.
#%env month=
# for monitoring this corresponds to file path path-XIOS.{month}/
#
#%env save= proceed saving? True or False , Default is setted as True
#%env plot= proceed plotting? True or False , Default is setted as True
#%env calc= proceed computation? or just load computed result? True or False , Default is setted as True
#%env calc=True
%%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= irene4612.c-irene.mg1.tgcc.ccc.cea.fr starting dask cluster on local= True workers 16 10000000000 False not local in tgcc rome local cluster starting This code is running on irene4612.c-irene.mg1.tgcc.ccc.cea.fr using SEDNA_ALPHA_MONITOR file experiment, read from ../lib/SEDNA_ALPHA_MONITOR.yaml on year= *[0,3][0-9] on month= 23 outputpath= ../results/rome_SEDNA_ALPHA_MONITOR/23/ daskreport= ../results/dask/2514383irene4612.c-irene.mg1.tgcc.ccc.cea.fr_SEDNA_ALPHA_MONITOR_23Section_test/ CPU times: user 327 ms, sys: 222 ms, total: 549 ms Wall time: 11 s
Client
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Cluster
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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 | |
---|---|---|---|---|---|---|---|---|---|---|
Sectiontest | gridS.vosaline,gridT.votemper,gridV.vomecrty,p... | data.drop_vars('vozocrtx').unify_chunks() | FramS | section | None | {'vosaline': (33,36.2), 'votemper': (-2,6), 'v... | None | S-1 | ||
Sectiontest | gridS.vosaline,gridT.votemper,gridU.vozocrtx,p... | data.drop_vars('vomecrty').chunk({'y':-1}).uni... | BFGS | section | None | {'vosaline': (28,35), 'votemper': (-2,2), 'voz... | None | S-2 |
Each computation consists of
%%time
#todo add 'year' here.
import os
calcswitch=os.environ.get('calc', 'True')
print('calcswitch=',calcswitch)
if calcswitch=='True':
data=load.datas(catalog_url,df.Inputs,month,year,daskreport)
data
#print('#1 Data: created:')
#print('# if we raed too much file, we can do sel to take out some dates here')
#data
calcswitch= True ../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_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': '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.23/SEDNA-ALPHA_1d_gridS_*[0,3][0-9]_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 174.54447650909424 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.23/SEDNA-ALPHA_1d_gridT_*[0,3][0-9]_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 54.53135657310486 seconds 1 merging gridT ['votemper'] took 0.3247373104095459 seconds 2 read gridU ['vozocrtx'] using load_data_xios reading gridU 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.23/SEDNA-ALPHA_1d_gridU_*[0,3][0-9]_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 52.342119216918945 seconds 2 merging gridU ['vozocrtx'] took 0.28951168060302734 seconds 3 read gridV ['vomecrty'] using load_data_xios reading gridV 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.23/SEDNA-ALPHA_1d_gridV_*[0,3][0-9]_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 54.820205211639404 seconds 3 merging gridV ['vomecrty'] took 0.30220890045166016 seconds param mask2d will be included in data param mask will be included in data param depth will be included in data param nav_lat will be included in data param nav_lon will be included in data 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 (130, 122, 120, 120, 120, 120, 120, 120, 120, 120, 120, 120, 120, 120, 120, 120, 120, 120, 120, 120, 120, 120, 120, 120, 120, 120, 120, 120, 120, 120, 120, 120, 120, 120, 120, 120, 120, 120, 120, 120, 120, 120, 120, 120, 120, 120, 120, 120, 120, 120, 120, 120, 120, 120, 48) end of y_rechunk CPU times: user 2min 28s, sys: 37.2 s, total: 3min 5s Wall time: 5min 53s
%%time
monitor.auto(df,data,savefig,daskreport,outputpath,file_exp='SEDNA'
)
True True ../nc_results/rome_SEDNA_ALPHA_MONITOR/23/ switch:calcswitch,saveswitch,plotswitch True True False data= zoom.FramS(data) #2 Zooming Data
<xarray.Dataset> Dimensions: (t: 11, x: 554, z: 103) Coordinates: * t (t) object 2004-07-01 12:00:00 ... 2004-07-31 12:00:00 y int64 2609 * x (x) int64 3749 3750 3751 3752 3753 ... 4298 4299 4300 4301 4302 * z (z) int64 1 2 3 4 5 6 7 8 9 10 ... 95 96 97 98 99 100 101 102 103 mask2d (x) bool dask.array<chunksize=(554,), meta=np.ndarray> mask (z, x) bool dask.array<chunksize=(103, 554), meta=np.ndarray> depth (z, x) float32 dask.array<chunksize=(103, 554), meta=np.ndarray> nav_lat (x) float32 dask.array<chunksize=(554,), meta=np.ndarray> nav_lon (x) float32 dask.array<chunksize=(554,), meta=np.ndarray> Data variables: vosaline (t, z, x) float32 dask.array<chunksize=(1, 103, 554), meta=np.ndarray> votemper (t, z, x) float32 dask.array<chunksize=(1, 103, 554), meta=np.ndarray> vozocrtx (t, z, x) float32 dask.array<chunksize=(1, 103, 554), meta=np.ndarray> vomecrty (t, z, x) float32 dask.array<chunksize=(1, 103, 554), meta=np.ndarray>
array([cftime.DatetimeNoLeap(2004, 7, 1, 12, 0, 0, 0), cftime.DatetimeNoLeap(2004, 7, 2, 12, 0, 0, 0), cftime.DatetimeNoLeap(2004, 7, 3, 12, 0, 0, 0), cftime.DatetimeNoLeap(2004, 7, 4, 12, 0, 0, 0), cftime.DatetimeNoLeap(2004, 7, 5, 12, 0, 0, 0), cftime.DatetimeNoLeap(2004, 7, 6, 12, 0, 0, 0), cftime.DatetimeNoLeap(2004, 7, 7, 12, 0, 0, 0), cftime.DatetimeNoLeap(2004, 7, 8, 12, 0, 0, 0), cftime.DatetimeNoLeap(2004, 7, 9, 12, 0, 0, 0), cftime.DatetimeNoLeap(2004, 7, 30, 12, 0, 0, 0), cftime.DatetimeNoLeap(2004, 7, 31, 12, 0, 0, 0)], dtype=object)
array(2609)
array([3749, 3750, 3751, ..., 4300, 4301, 4302])
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])
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dtaa= data.drop_vars('vozocrtx').unify_chunks() #3 Start computing count: <xarray.Dataset> Dimensions: () Coordinates: y int64 2609 Data variables: vosaline int64 dask.array<chunksize=(), meta=np.ndarray> votemper int64 dask.array<chunksize=(), meta=np.ndarray> vozocrtx int64 dask.array<chunksize=(), meta=np.ndarray> vomecrty int64 dask.array<chunksize=(), meta=np.ndarray>
<xarray.Dataset> Dimensions: () Coordinates: y int64 2609 Data variables: vosaline int64 dask.array<chunksize=(), meta=np.ndarray> votemper int64 dask.array<chunksize=(), meta=np.ndarray> vozocrtx int64 dask.array<chunksize=(), meta=np.ndarray> vomecrty int64 dask.array<chunksize=(), meta=np.ndarray>
array(2609)
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nbytes: 7827832 count: <xarray.Dataset> Dimensions: () Coordinates: y int64 2609 Data variables: vosaline int64 dask.array<chunksize=(), meta=np.ndarray> votemper int64 dask.array<chunksize=(), meta=np.ndarray> vomecrty int64 dask.array<chunksize=(), meta=np.ndarray>
<xarray.Dataset> Dimensions: (t: 11, x: 554, z: 103) Coordinates: * t (t) object 2004-07-01 12:00:00 ... 2004-07-31 12:00:00 y int64 2609 * x (x) int64 3749 3750 3751 3752 3753 ... 4298 4299 4300 4301 4302 * z (z) int64 1 2 3 4 5 6 7 8 9 10 ... 95 96 97 98 99 100 101 102 103 mask2d (x) bool dask.array<chunksize=(554,), meta=np.ndarray> mask (z, x) bool dask.array<chunksize=(103, 554), meta=np.ndarray> depth (z, x) float32 dask.array<chunksize=(103, 554), meta=np.ndarray> nav_lat (x) float32 dask.array<chunksize=(554,), meta=np.ndarray> nav_lon (x) float32 dask.array<chunksize=(554,), meta=np.ndarray> Data variables: vosaline (t, z, x) float32 dask.array<chunksize=(1, 103, 554), meta=np.ndarray> votemper (t, z, x) float32 dask.array<chunksize=(1, 103, 554), meta=np.ndarray> vomecrty (t, z, x) float32 dask.array<chunksize=(1, 103, 554), meta=np.ndarray>
array([cftime.DatetimeNoLeap(2004, 7, 1, 12, 0, 0, 0), cftime.DatetimeNoLeap(2004, 7, 2, 12, 0, 0, 0), cftime.DatetimeNoLeap(2004, 7, 3, 12, 0, 0, 0), cftime.DatetimeNoLeap(2004, 7, 4, 12, 0, 0, 0), cftime.DatetimeNoLeap(2004, 7, 5, 12, 0, 0, 0), cftime.DatetimeNoLeap(2004, 7, 6, 12, 0, 0, 0), cftime.DatetimeNoLeap(2004, 7, 7, 12, 0, 0, 0), cftime.DatetimeNoLeap(2004, 7, 8, 12, 0, 0, 0), cftime.DatetimeNoLeap(2004, 7, 9, 12, 0, 0, 0), cftime.DatetimeNoLeap(2004, 7, 30, 12, 0, 0, 0), cftime.DatetimeNoLeap(2004, 7, 31, 12, 0, 0, 0)], dtype=object)
array(2609)
array([3749, 3750, 3751, ..., 4300, 4301, 4302])
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])
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filename='SEDNA_section_FramS_Sectiontest' outputpath='../nc_results/rome_SEDNA_ALPHA_MONITOR/23/' saving starting start saving data saving data in a file t (1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1) 0 1 2 3 4 5 6 7 8 9 10 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) data= zoom.BFGS(data) #2 Zooming Data
<xarray.Dataset> Dimensions: (t: 11, y: 2360, z: 95) Coordinates: * t (t) object 2004-07-01 12:00:00 ... 2004-07-31 12:00:00 * y (y) int64 3494 3495 3496 3497 3498 ... 5909 5910 5911 5912 5913 x int64 2281 * z (z) int64 1 2 3 4 5 6 7 8 9 10 ... 86 87 88 89 90 91 92 93 94 95 mask2d (y) bool dask.array<chunksize=(119,), meta=np.ndarray> mask (z, y) bool dask.array<chunksize=(95, 119), meta=np.ndarray> depth (z, y) float32 dask.array<chunksize=(95, 119), meta=np.ndarray> nav_lat (y) float32 dask.array<chunksize=(119,), meta=np.ndarray> nav_lon (y) float32 dask.array<chunksize=(119,), meta=np.ndarray> Data variables: vosaline (t, z, y) float32 dask.array<chunksize=(1, 95, 119), meta=np.ndarray> votemper (t, z, y) float32 dask.array<chunksize=(1, 95, 119), meta=np.ndarray> vozocrtx (t, z, y) float32 dask.array<chunksize=(1, 95, 119), meta=np.ndarray> vomecrty (t, z, y) float32 dask.array<chunksize=(1, 95, 119), meta=np.ndarray>
array([cftime.DatetimeNoLeap(2004, 7, 1, 12, 0, 0, 0), cftime.DatetimeNoLeap(2004, 7, 2, 12, 0, 0, 0), cftime.DatetimeNoLeap(2004, 7, 3, 12, 0, 0, 0), cftime.DatetimeNoLeap(2004, 7, 4, 12, 0, 0, 0), cftime.DatetimeNoLeap(2004, 7, 5, 12, 0, 0, 0), cftime.DatetimeNoLeap(2004, 7, 6, 12, 0, 0, 0), cftime.DatetimeNoLeap(2004, 7, 7, 12, 0, 0, 0), cftime.DatetimeNoLeap(2004, 7, 8, 12, 0, 0, 0), cftime.DatetimeNoLeap(2004, 7, 9, 12, 0, 0, 0), cftime.DatetimeNoLeap(2004, 7, 30, 12, 0, 0, 0), cftime.DatetimeNoLeap(2004, 7, 31, 12, 0, 0, 0)], dtype=object)
array([3494, 3495, 3496, ..., 5911, 5912, 5913])
array(2281)
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])
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dtaa= data.drop_vars('vomecrty').chunk({'y':-1}).unify_chunks() #3 Start computing count: <xarray.Dataset> Dimensions: () Coordinates: x int64 2281 Data variables: vosaline int64 dask.array<chunksize=(), meta=np.ndarray> votemper int64 dask.array<chunksize=(), meta=np.ndarray> vozocrtx int64 dask.array<chunksize=(), meta=np.ndarray> vomecrty int64 dask.array<chunksize=(), meta=np.ndarray>
<xarray.Dataset> Dimensions: () Coordinates: x int64 2281 Data variables: vosaline int64 dask.array<chunksize=(), meta=np.ndarray> votemper int64 dask.array<chunksize=(), meta=np.ndarray> vozocrtx int64 dask.array<chunksize=(), meta=np.ndarray> vomecrty int64 dask.array<chunksize=(), meta=np.ndarray>
array(2281)
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nbytes: 30756376 count: <xarray.Dataset> Dimensions: () Coordinates: x int64 2281 Data variables: vosaline int64 dask.array<chunksize=(), meta=np.ndarray> votemper int64 dask.array<chunksize=(), meta=np.ndarray> vozocrtx int64 dask.array<chunksize=(), meta=np.ndarray>
<xarray.Dataset> Dimensions: (t: 11, y: 2360, z: 95) Coordinates: * t (t) object 2004-07-01 12:00:00 ... 2004-07-31 12:00:00 * y (y) int64 3494 3495 3496 3497 3498 ... 5909 5910 5911 5912 5913 x int64 2281 * z (z) int64 1 2 3 4 5 6 7 8 9 10 ... 86 87 88 89 90 91 92 93 94 95 mask2d (y) bool dask.array<chunksize=(2360,), meta=np.ndarray> mask (z, y) bool dask.array<chunksize=(95, 2360), meta=np.ndarray> depth (z, y) float32 dask.array<chunksize=(95, 2360), meta=np.ndarray> nav_lat (y) float32 dask.array<chunksize=(2360,), meta=np.ndarray> nav_lon (y) float32 dask.array<chunksize=(2360,), meta=np.ndarray> Data variables: vosaline (t, z, y) float32 dask.array<chunksize=(1, 95, 2360), meta=np.ndarray> votemper (t, z, y) float32 dask.array<chunksize=(1, 95, 2360), meta=np.ndarray> vozocrtx (t, z, y) float32 dask.array<chunksize=(1, 95, 2360), meta=np.ndarray>
array([cftime.DatetimeNoLeap(2004, 7, 1, 12, 0, 0, 0), cftime.DatetimeNoLeap(2004, 7, 2, 12, 0, 0, 0), cftime.DatetimeNoLeap(2004, 7, 3, 12, 0, 0, 0), cftime.DatetimeNoLeap(2004, 7, 4, 12, 0, 0, 0), cftime.DatetimeNoLeap(2004, 7, 5, 12, 0, 0, 0), cftime.DatetimeNoLeap(2004, 7, 6, 12, 0, 0, 0), cftime.DatetimeNoLeap(2004, 7, 7, 12, 0, 0, 0), cftime.DatetimeNoLeap(2004, 7, 8, 12, 0, 0, 0), cftime.DatetimeNoLeap(2004, 7, 9, 12, 0, 0, 0), cftime.DatetimeNoLeap(2004, 7, 30, 12, 0, 0, 0), cftime.DatetimeNoLeap(2004, 7, 31, 12, 0, 0, 0)], dtype=object)
array([3494, 3495, 3496, ..., 5911, 5912, 5913])
array(2281)
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filename='SEDNA_section_BFGS_Sectiontest' outputpath='../nc_results/rome_SEDNA_ALPHA_MONITOR/23/' saving starting start saving data saving data in a file t (1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1) 0 1 2 3 4 5 6 7 8 9 10 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) CPU times: user 1h 26min 49s, sys: 1min 23s, total: 1h 28min 13s Wall time: 1h 29min 9s