%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
%%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= irene4067.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 irene4067.c-irene.mg1.tgcc.ccc.cea.fr using SEDNA_ALPHA_MONITOR file experiment, read from ../lib/SEDNA_ALPHA_MONITOR.yaml on year= * on month= 22 outputpath= ../results/rome_SEDNA_ALPHA_MONITOR/22/ daskreport= ../results/dask/2474296irene4067.c-irene.mg1.tgcc.ccc.cea.fr_SEDNA_ALPHA_MONITOR_22vozocrtx_BFGS_moni/ CPU times: user 389 ms, sys: 246 ms, total: 634 ms Wall time: 12.7 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 | |
---|---|---|---|---|---|---|---|---|---|---|
vozocrtx_BFGS | gridU.vozocrtx,param.depth,param.mask | data.vozocrtx | BFGS | section | bwr | (-0.05,0.05) | m/s | S-1 |
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 dates here')
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 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.22/SEDNA-ALPHA_1d_gridU_*_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 183.8663957118988 seconds 0 merging gridU ['vozocrtx'] param nav_lat will be included in data param nav_lon will be included in data param mask will be included in data param mask2d will be included in data param depth 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 56.3 s, sys: 11.5 s, total: 1min 7s Wall time: 3min 20s
<xarray.Dataset> Dimensions: (t: 15, x: 6560, y: 6540, z: 150) Coordinates: * t (t) object 2004-06-16 12:00:00 ... 2004-06-30 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 nav_lat (y, x) float32 dask.array<chunksize=(130, 6560), meta=np.ndarray> nav_lon (y, x) float32 dask.array<chunksize=(130, 6560), meta=np.ndarray> mask (z, y, x) bool dask.array<chunksize=(150, 130, 6560), meta=np.ndarray> mask2d (y, x) bool dask.array<chunksize=(130, 6560), meta=np.ndarray> depth (z, y, x) float64 dask.array<chunksize=(150, 130, 6560), meta=np.ndarray> Data variables: vozocrtx (t, z, y, x) float32 dask.array<chunksize=(1, 150, 130, 6560), meta=np.ndarray> Attributes: name: /ccc/scratch/cont003/gen7420/talandel/ONGOING-RU... description: ocean U grid variables title: ocean U grid variables Conventions: CF-1.6 timeStamp: 2021-Jul-14 09:45:00 GMT uuid: 753d5408-c4b5-4905-a796-f3e553a23036 ibegin: 0 ni: 6560 jbegin: 0 nj: 13 DOMAIN_number_total: 544 DOMAIN_number: 0 DOMAIN_dimensions_ids: [2 3] DOMAIN_size_global: [6560 6540] DOMAIN_size_local: [6560 13] DOMAIN_position_first: [1 1] DOMAIN_position_last: [6560 13] DOMAIN_halo_size_start: [0 0] DOMAIN_halo_size_end: [0 0] DOMAIN_type: box start_date: 20030101 output_frequency: 1d CONFIG: SEDNA CASE: ALPHA
array([cftime.DatetimeNoLeap(2004, 6, 16, 12, 0, 0, 0), cftime.DatetimeNoLeap(2004, 6, 17, 12, 0, 0, 0), cftime.DatetimeNoLeap(2004, 6, 18, 12, 0, 0, 0), cftime.DatetimeNoLeap(2004, 6, 19, 12, 0, 0, 0), cftime.DatetimeNoLeap(2004, 6, 20, 12, 0, 0, 0), cftime.DatetimeNoLeap(2004, 6, 21, 12, 0, 0, 0), cftime.DatetimeNoLeap(2004, 6, 22, 12, 0, 0, 0), cftime.DatetimeNoLeap(2004, 6, 23, 12, 0, 0, 0), cftime.DatetimeNoLeap(2004, 6, 24, 12, 0, 0, 0), cftime.DatetimeNoLeap(2004, 6, 25, 12, 0, 0, 0), cftime.DatetimeNoLeap(2004, 6, 26, 12, 0, 0, 0), cftime.DatetimeNoLeap(2004, 6, 27, 12, 0, 0, 0), cftime.DatetimeNoLeap(2004, 6, 28, 12, 0, 0, 0), cftime.DatetimeNoLeap(2004, 6, 29, 12, 0, 0, 0), cftime.DatetimeNoLeap(2004, 6, 30, 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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%%time
monitor.auto(df,data,savefig,daskreport,outputpath,file_exp='SEDNA'
)
switch:calcswitch,saveswitch,plotswitch True False True data= zoom.BFGS(data) #2 Zooming Data
<xarray.Dataset> Dimensions: (t: 15, y: 2420, z: 95) Coordinates: * t (t) object 2004-06-16 12:00:00 ... 2004-06-30 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 nav_lat (y) float32 dask.array<chunksize=(119,), meta=np.ndarray> nav_lon (y) float32 dask.array<chunksize=(119,), meta=np.ndarray> mask (z, y) bool dask.array<chunksize=(95, 119), meta=np.ndarray> mask2d (y) bool dask.array<chunksize=(119,), meta=np.ndarray> depth (z, y) float64 dask.array<chunksize=(95, 119), meta=np.ndarray> Data variables: vozocrtx (t, z, y) float32 dask.array<chunksize=(1, 95, 119), meta=np.ndarray> Attributes: name: /ccc/scratch/cont003/gen7420/talandel/ONGOING-RU... description: ocean U grid variables title: ocean U grid variables Conventions: CF-1.6 timeStamp: 2021-Jul-14 09:45:00 GMT uuid: 753d5408-c4b5-4905-a796-f3e553a23036 ibegin: 0 ni: 6560 jbegin: 0 nj: 13 DOMAIN_number_total: 544 DOMAIN_number: 0 DOMAIN_dimensions_ids: [2 3] DOMAIN_size_global: [6560 6540] DOMAIN_size_local: [6560 13] DOMAIN_position_first: [1 1] DOMAIN_position_last: [6560 13] DOMAIN_halo_size_start: [0 0] DOMAIN_halo_size_end: [0 0] DOMAIN_type: box start_date: 20030101 output_frequency: 1d CONFIG: SEDNA CASE: ALPHA
array([cftime.DatetimeNoLeap(2004, 6, 16, 12, 0, 0, 0), cftime.DatetimeNoLeap(2004, 6, 17, 12, 0, 0, 0), cftime.DatetimeNoLeap(2004, 6, 18, 12, 0, 0, 0), cftime.DatetimeNoLeap(2004, 6, 19, 12, 0, 0, 0), cftime.DatetimeNoLeap(2004, 6, 20, 12, 0, 0, 0), cftime.DatetimeNoLeap(2004, 6, 21, 12, 0, 0, 0), cftime.DatetimeNoLeap(2004, 6, 22, 12, 0, 0, 0), cftime.DatetimeNoLeap(2004, 6, 23, 12, 0, 0, 0), cftime.DatetimeNoLeap(2004, 6, 24, 12, 0, 0, 0), cftime.DatetimeNoLeap(2004, 6, 25, 12, 0, 0, 0), cftime.DatetimeNoLeap(2004, 6, 26, 12, 0, 0, 0), cftime.DatetimeNoLeap(2004, 6, 27, 12, 0, 0, 0), cftime.DatetimeNoLeap(2004, 6, 28, 12, 0, 0, 0), cftime.DatetimeNoLeap(2004, 6, 29, 12, 0, 0, 0), cftime.DatetimeNoLeap(2004, 6, 30, 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.vozocrtx #3 Start computing count: <xarray.Dataset> Dimensions: () Coordinates: x int64 2281 Data variables: vozocrtx int64 dask.array<chunksize=(), meta=np.ndarray>
<xarray.Dataset> Dimensions: () Coordinates: x int64 2281 Data variables: vozocrtx int64 dask.array<chunksize=(), meta=np.ndarray>
array(2281)
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nbytes: 13794000 count: <xarray.DataArray 'vozocrtx' ()> dask.array<sum-aggregate, shape=(), dtype=int64, chunksize=(), chunktype=numpy.ndarray> Coordinates: x int64 2281
<xarray.DataArray 'vozocrtx' (t: 15, z: 95, y: 2420)> dask.array<where, shape=(15, 95, 2420), dtype=float32, chunksize=(1, 95, 120), chunktype=numpy.ndarray> Coordinates: * t (t) object 2004-06-16 12:00:00 ... 2004-06-30 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 11 ... 86 87 88 89 90 91 92 93 94 95 nav_lat (y) float32 dask.array<chunksize=(119,), meta=np.ndarray> nav_lon (y) float32 dask.array<chunksize=(119,), meta=np.ndarray> mask (z, y) bool dask.array<chunksize=(95, 119), meta=np.ndarray> mask2d (y) bool dask.array<chunksize=(119,), meta=np.ndarray> depth (z, y) float64 dask.array<chunksize=(95, 119), meta=np.ndarray> Attributes: standard_name: sea_water_x_velocity long_name: ocean current along i-axis units: m/s online_operation: average interval_operation: 36 s interval_write: 1 d cell_methods: time: mean (interval: 36 s)
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array([cftime.DatetimeNoLeap(2004, 6, 16, 12, 0, 0, 0), cftime.DatetimeNoLeap(2004, 6, 17, 12, 0, 0, 0), cftime.DatetimeNoLeap(2004, 6, 18, 12, 0, 0, 0), cftime.DatetimeNoLeap(2004, 6, 19, 12, 0, 0, 0), cftime.DatetimeNoLeap(2004, 6, 20, 12, 0, 0, 0), cftime.DatetimeNoLeap(2004, 6, 21, 12, 0, 0, 0), cftime.DatetimeNoLeap(2004, 6, 22, 12, 0, 0, 0), cftime.DatetimeNoLeap(2004, 6, 23, 12, 0, 0, 0), cftime.DatetimeNoLeap(2004, 6, 24, 12, 0, 0, 0), cftime.DatetimeNoLeap(2004, 6, 25, 12, 0, 0, 0), cftime.DatetimeNoLeap(2004, 6, 26, 12, 0, 0, 0), cftime.DatetimeNoLeap(2004, 6, 27, 12, 0, 0, 0), cftime.DatetimeNoLeap(2004, 6, 28, 12, 0, 0, 0), cftime.DatetimeNoLeap(2004, 6, 29, 12, 0, 0, 0), cftime.DatetimeNoLeap(2004, 6, 30, 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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plots.section(data,path=outputpath,filename=filename,save=savefig,cmap='bwr',clim=(-0.05,0.05),clabel='m/s') #5 Plotting SEDNA_section_BFGS_vozocrtx_BFGS
<xarray.DataArray 'vozocrtx' (t: 15, z: 95, y: 2420)> array([[[-0.01630619, -0.01551258, -0.01195839, ..., -0.15004514, -0.14445505, -0.09003767], [-0.01594029, -0.01568485, -0.01158105, ..., -0.14153035, -0.14019535, -0.08983918], [-0.00645296, -0.00771357, -0.00485468, ..., -0.12924156, -0.13028961, -0.08599033], ..., [ nan, nan, nan, ..., nan, nan, nan], [ nan, nan, nan, ..., nan, nan, nan], [ nan, nan, nan, ..., nan, nan, nan]], [[-0.01092502, -0.00806537, -0.0045767 , ..., -0.16792104, -0.1693952 , -0.10510457], [-0.00993355, -0.00760169, -0.00369572, ..., -0.14795268, -0.158997 , -0.10260876], [ 0.00229896, 0.00552724, 0.00665693, ..., -0.12739739, -0.13810907, -0.09289903], ... [ nan, nan, nan, ..., nan, nan, nan], [ nan, nan, nan, ..., nan, nan, nan], [ nan, nan, nan, ..., nan, nan, nan]], [[-0.01875936, -0.02292397, -0.02237452, ..., -0.09669027, -0.09723404, -0.06176817], [-0.02037049, -0.02510868, -0.02436421, ..., -0.09559572, -0.09733476, -0.06313202], [-0.01813791, -0.02554294, -0.02616478, ..., -0.09231869, -0.09402745, -0.06214776], ..., [ nan, nan, nan, ..., nan, nan, nan], [ nan, nan, nan, ..., nan, nan, nan], [ nan, nan, nan, ..., nan, nan, nan]]], dtype=float32) Coordinates: * t (t) object 2004-06-16 12:00:00 ... 2004-06-30 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 11 ... 86 87 88 89 90 91 92 93 94 95 nav_lat (y) float32 80.35 80.35 80.35 80.35 ... 70.73 70.72 70.71 70.7 nav_lon (y) float32 -96.63 -96.67 -96.71 -96.74 ... -159.9 -159.9 -159.9 mask (z, y) bool True True True True True ... False False False False mask2d (y) bool True True True True True True ... True True True True True depth (z, y) float64 0.4915 0.4915 0.4915 0.4915 ... 608.2 608.2 608.2 new_lon (z, y) float64 -96.63 -96.67 -96.71 -96.74 ... -159.9 -159.9 -159.9 Attributes: standard_name: sea_water_x_velocity long_name: ocean current along i-axis units: m/s online_operation: average interval_operation: 36 s interval_write: 1 d cell_methods: time: mean (interval: 36 s)
array([[[-0.01630619, -0.01551258, -0.01195839, ..., -0.15004514, -0.14445505, -0.09003767], [-0.01594029, -0.01568485, -0.01158105, ..., -0.14153035, -0.14019535, -0.08983918], [-0.00645296, -0.00771357, -0.00485468, ..., -0.12924156, -0.13028961, -0.08599033], ..., [ nan, nan, nan, ..., nan, nan, nan], [ nan, nan, nan, ..., nan, nan, nan], [ nan, nan, nan, ..., nan, nan, nan]], [[-0.01092502, -0.00806537, -0.0045767 , ..., -0.16792104, -0.1693952 , -0.10510457], [-0.00993355, -0.00760169, -0.00369572, ..., -0.14795268, -0.158997 , -0.10260876], [ 0.00229896, 0.00552724, 0.00665693, ..., -0.12739739, -0.13810907, -0.09289903], ... [ nan, nan, nan, ..., nan, nan, nan], [ nan, nan, nan, ..., nan, nan, nan], [ nan, nan, nan, ..., nan, nan, nan]], [[-0.01875936, -0.02292397, -0.02237452, ..., -0.09669027, -0.09723404, -0.06176817], [-0.02037049, -0.02510868, -0.02436421, ..., -0.09559572, -0.09733476, -0.06313202], [-0.01813791, -0.02554294, -0.02616478, ..., -0.09231869, -0.09402745, -0.06214776], ..., [ nan, nan, nan, ..., nan, nan, nan], [ nan, nan, nan, ..., nan, nan, nan], [ nan, nan, nan, ..., nan, nan, nan]]], dtype=float32)
array([cftime.DatetimeNoLeap(2004, 6, 16, 12, 0, 0, 0), cftime.DatetimeNoLeap(2004, 6, 17, 12, 0, 0, 0), cftime.DatetimeNoLeap(2004, 6, 18, 12, 0, 0, 0), cftime.DatetimeNoLeap(2004, 6, 19, 12, 0, 0, 0), cftime.DatetimeNoLeap(2004, 6, 20, 12, 0, 0, 0), cftime.DatetimeNoLeap(2004, 6, 21, 12, 0, 0, 0), cftime.DatetimeNoLeap(2004, 6, 22, 12, 0, 0, 0), cftime.DatetimeNoLeap(2004, 6, 23, 12, 0, 0, 0), cftime.DatetimeNoLeap(2004, 6, 24, 12, 0, 0, 0), cftime.DatetimeNoLeap(2004, 6, 25, 12, 0, 0, 0), cftime.DatetimeNoLeap(2004, 6, 26, 12, 0, 0, 0), cftime.DatetimeNoLeap(2004, 6, 27, 12, 0, 0, 0), cftime.DatetimeNoLeap(2004, 6, 28, 12, 0, 0, 0), cftime.DatetimeNoLeap(2004, 6, 29, 12, 0, 0, 0), cftime.DatetimeNoLeap(2004, 6, 30, 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])
array([80.34586 , 80.34723 , 80.348595, ..., 70.72043 , 70.71271 , 70.70499 ], dtype=float32)
array([ -96.63371 , -96.67019 , -96.706665, ..., -159.87817 , -159.88771 , -159.89725 ], dtype=float32)
array([[ True, True, True, ..., True, True, True], [ True, True, True, ..., True, True, True], [ True, True, True, ..., True, True, True], ..., [False, False, False, ..., False, False, False], [False, False, False, ..., False, False, False], [False, False, False, ..., False, False, False]])
array([ True, True, True, ..., True, True, True])
array([[4.91469981e-01, 4.91469981e-01, 4.91469981e-01, ..., 4.91469981e-01, 4.91469981e-01, 4.91469981e-01], [1.52587202e+00, 1.52587202e+00, 1.52587202e+00, ..., 1.52587202e+00, 1.52587202e+00, 1.52587202e+00], [2.62983144e+00, 2.62983144e+00, 2.62983144e+00, ..., 2.62983144e+00, 2.62983144e+00, 2.62983144e+00], ..., [5.53127859e+02, 5.52784659e+02, 5.53120732e+02, ..., 5.53134865e+02, 5.53134865e+02, 5.53134865e+02], [5.78901267e+02, 5.78558068e+02, 5.78894140e+02, ..., 5.78908273e+02, 5.78908273e+02, 5.78908273e+02], [6.08241386e+02, 6.07898186e+02, 6.08234259e+02, ..., 6.08248391e+02, 6.08248391e+02, 6.08248391e+02]])
array([[ -96.63371277, -96.6701889 , -96.70666504, ..., -159.87817383, -159.88771057, -159.89724731], [ -96.63371277, -96.6701889 , -96.70666504, ..., -159.87817383, -159.88771057, -159.89724731], [ -96.63371277, -96.6701889 , -96.70666504, ..., -159.87817383, -159.88771057, -159.89724731], ..., [ -96.63371277, -96.6701889 , -96.70666504, ..., -159.87817383, -159.88771057, -159.89724731], [ -96.63371277, -96.6701889 , -96.70666504, ..., -159.87817383, -159.88771057, -159.89724731], [ -96.63371277, -96.6701889 , -96.70666504, ..., -159.87817383, -159.88771057, -159.89724731]])
start saving data
--------------------------------------------------------------------------- NameError Traceback (most recent call last) <timed eval> in <module> /ccc/work/cont003/gen7420/odakatin/monitor-sedna/notebook/core/monitor.py in auto(df, val, savefig, daskreport, outputpath, file_exp) 64 print(command, '#5 Plotting',filename ) 65 with performance_report(filename=daskreport+"_plot_"+step.Value+".html"): ---> 66 filename=eval(command ) 67 #if savefig: 68 print(filename,'created') /ccc/work/cont003/gen7420/odakatin/monitor-sedna/notebook/core/monitor.py in <module> /ccc/work/cont003/gen7420/odakatin/monitor-sedna/notebook/core/plots.py in section(data, path, filename, save, cmap, clim, clabel) 119 display(data) 120 print('start saving data') --> 121 savedfile=twoD_save(data,path,filename) 122 print('before plotting save computed data at',savedfile) 123 plot=data.where(data.mask).hvplot.quadmesh(x='new_lon',y='depth' NameError: name 'twoD_save' is not defined