%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=FWC_SSH
# 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 save=False
%%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= irene4660.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 irene4660.c-irene.mg1.tgcc.ccc.cea.fr using SEDNA_ALPHA_MONITOR file experiment, read from ../lib/SEDNA_ALPHA_MONITOR.yaml on year= *0[0-9] on month= 25 outputpath= ../results/SEDNA_ALPHA_MONITOR/25/ daskreport= ../results/dask/2585206irene4660.c-irene.mg1.tgcc.ccc.cea.fr_SEDNA_ALPHA_MONITOR_25Fluxnet/ CPU times: user 324 ms, sys: 248 ms, total: 572 ms Wall time: 8.91 s
Client
|
Cluster
|
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 | |
---|---|---|---|---|---|---|---|---|---|---|
Fluxnet | gridV.vomecrty,param.e3v_0,param.e1v,param.mas... | calc.Fluxnet(data) | FramS_All | Fluxnet_integrals | None | ((-10,10),(-10,50) ,(-150,50),(-25,5) ) | (Sv,TW, mSv,10^-2 Sv) | I-6 | ||
Fluxnet | gridV.vomecrty,param.e3v_0,param.e1v,param.mas... | calc.Fluxnet(data) | Davis | Fluxnet_integrals | None | ((-5.0,5.0),(-25,27) ,(-200,50),(-9,5) ) | (Sv,TW, mSv,10^-2 Sv) | I-6 | ||
Fluxnet | gridV.vomecrty,param.e3v_0,param.e1v,param.mas... | calc.Fluxnet(data) | Bering | Fluxnet_integrals | None | ((-2,2),(-10,50) ,(-150,50),(-2,4) ) | (Sv,TW, mSv,10^-2 Sv) | I-6 |
Each computation consists of
%%time
import os
calcswitch=os.environ.get('calc', 'True')
loaddata=((df.Inputs != '').any())
print('calcswitch=',calcswitch,'df.Inputs != nothing',loaddata)
data = load.datas(catalog_url,df.Inputs,month,year,daskreport) if ((calcswitch=='True' )*loaddata) else 0
data
calcswitch= True df.Inputs != nothing 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.25/SEDNA-ALPHA_1d_gridS_*0[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 168.77219772338867 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.25/SEDNA-ALPHA_1d_gridT_*0[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 57.535046339035034 seconds 1 merging gridT ['votemper'] took 1.0119051933288574 seconds 2 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.25/SEDNA-ALPHA_1d_gridV_*0[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 54.94194197654724 seconds 2 merging gridV ['vomecrty'] took 1.110196590423584 seconds 3 read icemod ['sivelv', 'sivolu'] using load_data_xios reading icemod 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.25/SEDNA-ALPHA_1d_icemod_*0[0-9]_0[0-5][0-9][0-9].nc xarray_kwargs: compat: override coords: minimal data_vars: minimal drop_variables: !!set botpres: null deptht_bounds: null depthu_bounds: null iicestru: null iicestrv: null intstrx: null intstry: null mldkz5: null rhop_sig0: null siages: null sidive: null sisali: null sishea: null sistre: null sitemp: null snthic: null snvolu: null sometauy: null sozotaux: null time_centered_bounds: null time_counter_bounds: null utau_atmoce: null utau_iceoce: null uwspd10: null vtau_atmoce: null vtau_iceoce: null vwspd10: 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 106.6852514743805 seconds 3 merging icemod ['sivelv', 'sivolu'] took 0.4404487609863281 seconds param nav_lat will be included in data param e3v_0 will be included in data param mask will be included in data param e1v will be included in data param mask2d 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 37s, sys: 37.1 s, total: 3min 15s Wall time: 6min 44s
<xarray.Dataset> Dimensions: (t: 9, x: 6560, y: 6540, z: 150) Coordinates: * t (t) object 2004-09-01 12:00:00 ... 2004-09-09 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_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> * z (z) int64 1 2 3 4 5 6 7 8 9 ... 143 144 145 146 147 148 149 150 e3v_0 (z, y, x) float64 dask.array<chunksize=(150, 130, 6560), meta=np.ndarray> mask (z, y, x) bool dask.array<chunksize=(150, 130, 6560), meta=np.ndarray> e1v (y, x) float64 dask.array<chunksize=(130, 6560), meta=np.ndarray> mask2d (y, x) bool dask.array<chunksize=(130, 6560), meta=np.ndarray> Data variables: vosaline (t, z, y, x) float32 dask.array<chunksize=(1, 150, 130, 6560), meta=np.ndarray> votemper (t, z, y, x) float32 dask.array<chunksize=(1, 150, 130, 6560), meta=np.ndarray> vomecrty (t, z, y, x) float32 dask.array<chunksize=(1, 150, 130, 6560), meta=np.ndarray> sivelv (t, y, x) float32 dask.array<chunksize=(1, 130, 6560), meta=np.ndarray> sivolu (t, y, x) float32 dask.array<chunksize=(1, 130, 6560), meta=np.ndarray>
array([cftime.DatetimeNoLeap(2004, 9, 1, 12, 0, 0, 0), cftime.DatetimeNoLeap(2004, 9, 2, 12, 0, 0, 0), cftime.DatetimeNoLeap(2004, 9, 3, 12, 0, 0, 0), cftime.DatetimeNoLeap(2004, 9, 4, 12, 0, 0, 0), cftime.DatetimeNoLeap(2004, 9, 5, 12, 0, 0, 0), cftime.DatetimeNoLeap(2004, 9, 6, 12, 0, 0, 0), cftime.DatetimeNoLeap(2004, 9, 7, 12, 0, 0, 0), cftime.DatetimeNoLeap(2004, 9, 8, 12, 0, 0, 0), cftime.DatetimeNoLeap(2004, 9, 9, 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])
|
|
|
|
|
|
|
|
|
%%time
monitor.auto(df,data,savefig,daskreport,outputpath,file_exp='SEDNA'
)
#calc= True #save= True #plot= False Zone='FramS_All' Value='Fluxnet' cmap='None' clabel='(Sv,TW, mSv,10^-2 Sv)' clim= ((-10, 10), (-10, 50), (-150, 50), (-25, 5)) outputpath='../results/SEDNA_ALPHA_MONITOR/25/' nc_outputpath='../nc_results/SEDNA_ALPHA_MONITOR/25/' filename='SEDNA_Fluxnet_integrals_FramS_All_Fluxnet' #2 Zooming Data dataa= zoom.FramS_All(data)
<xarray.Dataset> Dimensions: (t: 9, x: 556, y: 2, z: 137) Coordinates: * t (t) object 2004-09-01 12:00:00 ... 2004-09-09 12:00:00 * y (y) int64 2608 2609 * x (x) int64 3748 3749 3750 3751 3752 ... 4299 4300 4301 4302 4303 nav_lat (y, x) float32 dask.array<chunksize=(2, 556), meta=np.ndarray> nav_lon (y, x) float32 dask.array<chunksize=(2, 556), meta=np.ndarray> * z (z) int64 1 2 3 4 5 6 7 8 9 ... 130 131 132 133 134 135 136 137 e3v_0 (z, y, x) float64 dask.array<chunksize=(137, 2, 556), meta=np.ndarray> mask (z, y, x) bool dask.array<chunksize=(137, 2, 556), meta=np.ndarray> e1v (y, x) float64 dask.array<chunksize=(2, 556), meta=np.ndarray> mask2d (y, x) bool dask.array<chunksize=(2, 556), meta=np.ndarray> Data variables: vosaline (t, z, y, x) float32 dask.array<chunksize=(1, 137, 2, 556), meta=np.ndarray> votemper (t, z, y, x) float32 dask.array<chunksize=(1, 137, 2, 556), meta=np.ndarray> vomecrty (t, z, y, x) float32 dask.array<chunksize=(1, 137, 2, 556), meta=np.ndarray> sivelv (t, y, x, z) float32 dask.array<chunksize=(1, 2, 556, 137), meta=np.ndarray> sivolu (t, y, x, z) float32 dask.array<chunksize=(1, 2, 556, 137), meta=np.ndarray>
array([cftime.DatetimeNoLeap(2004, 9, 1, 12, 0, 0, 0), cftime.DatetimeNoLeap(2004, 9, 2, 12, 0, 0, 0), cftime.DatetimeNoLeap(2004, 9, 3, 12, 0, 0, 0), cftime.DatetimeNoLeap(2004, 9, 4, 12, 0, 0, 0), cftime.DatetimeNoLeap(2004, 9, 5, 12, 0, 0, 0), cftime.DatetimeNoLeap(2004, 9, 6, 12, 0, 0, 0), cftime.DatetimeNoLeap(2004, 9, 7, 12, 0, 0, 0), cftime.DatetimeNoLeap(2004, 9, 8, 12, 0, 0, 0), cftime.DatetimeNoLeap(2004, 9, 9, 12, 0, 0, 0)], dtype=object)
array([2608, 2609])
array([3748, 3749, 3750, ..., 4301, 4302, 4303])
|
|
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])
|
|
|
|
|
|
|
|
|
#3 Start computing dtaa= calc.Fluxnet(data)
<xarray.Dataset> Dimensions: (t: 9) Coordinates: * t (t) object 2004-09-01 12:00:00 ... 2004-09-09 12:0... y int64 2608 Data variables: Volume flux Net (t) float64 dask.array<chunksize=(1,), meta=np.ndarray> Volume flux Northward (t) float64 dask.array<chunksize=(1,), meta=np.ndarray> Heat flux Net (t) float64 dask.array<chunksize=(1,), meta=np.ndarray> Heat flux Northward (t) float64 dask.array<chunksize=(1,), meta=np.ndarray> Freshwater Net (t) float64 dask.array<chunksize=(1,), meta=np.ndarray> Freshwater Northward (t) float64 dask.array<chunksize=(1,), meta=np.ndarray> Ice export (t) float64 dask.array<chunksize=(1,), meta=np.ndarray> Volume flux South (t) float64 dask.array<chunksize=(1,), meta=np.ndarray> Heat flux South (t) float64 dask.array<chunksize=(1,), meta=np.ndarray> Freshwater South (t) float64 dask.array<chunksize=(1,), meta=np.ndarray>
array([cftime.DatetimeNoLeap(2004, 9, 1, 12, 0, 0, 0), cftime.DatetimeNoLeap(2004, 9, 2, 12, 0, 0, 0), cftime.DatetimeNoLeap(2004, 9, 3, 12, 0, 0, 0), cftime.DatetimeNoLeap(2004, 9, 4, 12, 0, 0, 0), cftime.DatetimeNoLeap(2004, 9, 5, 12, 0, 0, 0), cftime.DatetimeNoLeap(2004, 9, 6, 12, 0, 0, 0), cftime.DatetimeNoLeap(2004, 9, 7, 12, 0, 0, 0), cftime.DatetimeNoLeap(2004, 9, 8, 12, 0, 0, 0), cftime.DatetimeNoLeap(2004, 9, 9, 12, 0, 0, 0)], dtype=object)
array(2608)
|
|
|
|
|
|
|
|
|
|
#4 Saving SEDNA_Fluxnet_integrals_FramS_All_Fluxnet dtaa=save.datas(data,plot=Plot,path=nc_outputpath,filename=filename) start saving data saving data in a csv file ../nc_results/SEDNA_ALPHA_MONITOR/25/SEDNA_Fluxnet_integrals_FramS_All_Fluxnet2004-09-01_2004-09-09.nc save computed data at ../nc_results/SEDNA_ALPHA_MONITOR/25/SEDNA_Fluxnet_integrals_FramS_All_Fluxnet2004-09-01_2004-09-09.nc completed Zone='Davis' Value='Fluxnet' cmap='None' clabel='(Sv,TW, mSv,10^-2 Sv)' clim= ((-5.0, 5.0), (-25, 27), (-200, 50), (-9, 5)) outputpath='../results/SEDNA_ALPHA_MONITOR/25/' nc_outputpath='../nc_results/SEDNA_ALPHA_MONITOR/25/' filename='SEDNA_Fluxnet_integrals_Davis_Fluxnet' #2 Zooming Data dataa= zoom.Davis(data)
<xarray.Dataset> Dimensions: (t: 9, x: 415, y: 2, z: 104) Coordinates: * t (t) object 2004-09-01 12:00:00 ... 2004-09-09 12:00:00 * y (y) int64 1308 1309 * x (x) int64 1749 1750 1751 1752 1753 ... 2159 2160 2161 2162 2163 nav_lat (y, x) float32 dask.array<chunksize=(2, 415), meta=np.ndarray> nav_lon (y, x) float32 dask.array<chunksize=(2, 415), meta=np.ndarray> * z (z) int64 1 2 3 4 5 6 7 8 9 10 ... 96 97 98 99 100 101 102 103 104 e3v_0 (z, y, x) float64 dask.array<chunksize=(104, 2, 415), meta=np.ndarray> mask (z, y, x) bool dask.array<chunksize=(104, 2, 415), meta=np.ndarray> e1v (y, x) float64 dask.array<chunksize=(2, 415), meta=np.ndarray> mask2d (y, x) bool dask.array<chunksize=(2, 415), meta=np.ndarray> Data variables: vosaline (t, z, y, x) float32 dask.array<chunksize=(1, 104, 2, 415), meta=np.ndarray> votemper (t, z, y, x) float32 dask.array<chunksize=(1, 104, 2, 415), meta=np.ndarray> vomecrty (t, z, y, x) float32 dask.array<chunksize=(1, 104, 2, 415), meta=np.ndarray> sivelv (t, y, x, z) float32 dask.array<chunksize=(1, 2, 415, 104), meta=np.ndarray> sivolu (t, y, x, z) float32 dask.array<chunksize=(1, 2, 415, 104), meta=np.ndarray>
array([cftime.DatetimeNoLeap(2004, 9, 1, 12, 0, 0, 0), cftime.DatetimeNoLeap(2004, 9, 2, 12, 0, 0, 0), cftime.DatetimeNoLeap(2004, 9, 3, 12, 0, 0, 0), cftime.DatetimeNoLeap(2004, 9, 4, 12, 0, 0, 0), cftime.DatetimeNoLeap(2004, 9, 5, 12, 0, 0, 0), cftime.DatetimeNoLeap(2004, 9, 6, 12, 0, 0, 0), cftime.DatetimeNoLeap(2004, 9, 7, 12, 0, 0, 0), cftime.DatetimeNoLeap(2004, 9, 8, 12, 0, 0, 0), cftime.DatetimeNoLeap(2004, 9, 9, 12, 0, 0, 0)], dtype=object)
array([1308, 1309])
array([1749, 1750, 1751, ..., 2161, 2162, 2163])
|
|
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])
|
|
|
|
|
|
|
|
|
#3 Start computing dtaa= calc.Fluxnet(data)
<xarray.Dataset> Dimensions: (t: 9) Coordinates: * t (t) object 2004-09-01 12:00:00 ... 2004-09-09 12:0... y int64 1308 Data variables: Volume flux Net (t) float64 dask.array<chunksize=(1,), meta=np.ndarray> Volume flux Northward (t) float64 dask.array<chunksize=(1,), meta=np.ndarray> Heat flux Net (t) float64 dask.array<chunksize=(1,), meta=np.ndarray> Heat flux Northward (t) float64 dask.array<chunksize=(1,), meta=np.ndarray> Freshwater Net (t) float64 dask.array<chunksize=(1,), meta=np.ndarray> Freshwater Northward (t) float64 dask.array<chunksize=(1,), meta=np.ndarray> Ice export (t) float64 dask.array<chunksize=(1,), meta=np.ndarray> Volume flux South (t) float64 dask.array<chunksize=(1,), meta=np.ndarray> Heat flux South (t) float64 dask.array<chunksize=(1,), meta=np.ndarray> Freshwater South (t) float64 dask.array<chunksize=(1,), meta=np.ndarray>
array([cftime.DatetimeNoLeap(2004, 9, 1, 12, 0, 0, 0), cftime.DatetimeNoLeap(2004, 9, 2, 12, 0, 0, 0), cftime.DatetimeNoLeap(2004, 9, 3, 12, 0, 0, 0), cftime.DatetimeNoLeap(2004, 9, 4, 12, 0, 0, 0), cftime.DatetimeNoLeap(2004, 9, 5, 12, 0, 0, 0), cftime.DatetimeNoLeap(2004, 9, 6, 12, 0, 0, 0), cftime.DatetimeNoLeap(2004, 9, 7, 12, 0, 0, 0), cftime.DatetimeNoLeap(2004, 9, 8, 12, 0, 0, 0), cftime.DatetimeNoLeap(2004, 9, 9, 12, 0, 0, 0)], dtype=object)
array(1308)
|
|
|
|
|
|
|
|
|
|
#4 Saving SEDNA_Fluxnet_integrals_Davis_Fluxnet dtaa=save.datas(data,plot=Plot,path=nc_outputpath,filename=filename) start saving data saving data in a csv file ../nc_results/SEDNA_ALPHA_MONITOR/25/SEDNA_Fluxnet_integrals_Davis_Fluxnet2004-09-01_2004-09-09.nc save computed data at ../nc_results/SEDNA_ALPHA_MONITOR/25/SEDNA_Fluxnet_integrals_Davis_Fluxnet2004-09-01_2004-09-09.nc completed Zone='Bering' Value='Fluxnet' cmap='None' clabel='(Sv,TW, mSv,10^-2 Sv)' clim= ((-2, 2), (-10, 50), (-150, 50), (-2, 4)) outputpath='../results/SEDNA_ALPHA_MONITOR/25/' nc_outputpath='../nc_results/SEDNA_ALPHA_MONITOR/25/' filename='SEDNA_Fluxnet_integrals_Bering_Fluxnet' #2 Zooming Data dataa= zoom.Bering(data)
<xarray.Dataset> Dimensions: (t: 9, x: 113, y: 2, z: 29) Coordinates: * t (t) object 2004-09-01 12:00:00 ... 2004-09-09 12:00:00 * y (y) int64 6538 6539 * x (x) int64 2430 2431 2432 2433 2434 ... 2538 2539 2540 2541 2542 nav_lat (y, x) float32 dask.array<chunksize=(2, 113), meta=np.ndarray> nav_lon (y, x) float32 dask.array<chunksize=(2, 113), meta=np.ndarray> * z (z) int64 1 2 3 4 5 6 7 8 9 10 ... 20 21 22 23 24 25 26 27 28 29 e3v_0 (z, y, x) float64 dask.array<chunksize=(29, 2, 113), meta=np.ndarray> mask (z, y, x) bool dask.array<chunksize=(29, 2, 113), meta=np.ndarray> e1v (y, x) float64 dask.array<chunksize=(2, 113), meta=np.ndarray> mask2d (y, x) bool dask.array<chunksize=(2, 113), meta=np.ndarray> Data variables: vosaline (t, z, y, x) float32 dask.array<chunksize=(1, 29, 2, 113), meta=np.ndarray> votemper (t, z, y, x) float32 dask.array<chunksize=(1, 29, 2, 113), meta=np.ndarray> vomecrty (t, z, y, x) float32 dask.array<chunksize=(1, 29, 2, 113), meta=np.ndarray> sivelv (t, y, x, z) float32 dask.array<chunksize=(1, 2, 113, 29), meta=np.ndarray> sivolu (t, y, x, z) float32 dask.array<chunksize=(1, 2, 113, 29), meta=np.ndarray>
array([cftime.DatetimeNoLeap(2004, 9, 1, 12, 0, 0, 0), cftime.DatetimeNoLeap(2004, 9, 2, 12, 0, 0, 0), cftime.DatetimeNoLeap(2004, 9, 3, 12, 0, 0, 0), cftime.DatetimeNoLeap(2004, 9, 4, 12, 0, 0, 0), cftime.DatetimeNoLeap(2004, 9, 5, 12, 0, 0, 0), cftime.DatetimeNoLeap(2004, 9, 6, 12, 0, 0, 0), cftime.DatetimeNoLeap(2004, 9, 7, 12, 0, 0, 0), cftime.DatetimeNoLeap(2004, 9, 8, 12, 0, 0, 0), cftime.DatetimeNoLeap(2004, 9, 9, 12, 0, 0, 0)], dtype=object)
array([6538, 6539])
array([2430, 2431, 2432, 2433, 2434, 2435, 2436, 2437, 2438, 2439, 2440, 2441, 2442, 2443, 2444, 2445, 2446, 2447, 2448, 2449, 2450, 2451, 2452, 2453, 2454, 2455, 2456, 2457, 2458, 2459, 2460, 2461, 2462, 2463, 2464, 2465, 2466, 2467, 2468, 2469, 2470, 2471, 2472, 2473, 2474, 2475, 2476, 2477, 2478, 2479, 2480, 2481, 2482, 2483, 2484, 2485, 2486, 2487, 2488, 2489, 2490, 2491, 2492, 2493, 2494, 2495, 2496, 2497, 2498, 2499, 2500, 2501, 2502, 2503, 2504, 2505, 2506, 2507, 2508, 2509, 2510, 2511, 2512, 2513, 2514, 2515, 2516, 2517, 2518, 2519, 2520, 2521, 2522, 2523, 2524, 2525, 2526, 2527, 2528, 2529, 2530, 2531, 2532, 2533, 2534, 2535, 2536, 2537, 2538, 2539, 2540, 2541, 2542])
|
|
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])
|
|
|
|
|
|
|
|
|
#3 Start computing dtaa= calc.Fluxnet(data)
<xarray.Dataset> Dimensions: (t: 9) Coordinates: * t (t) object 2004-09-01 12:00:00 ... 2004-09-09 12:0... y int64 6538 Data variables: Volume flux Net (t) float64 dask.array<chunksize=(1,), meta=np.ndarray> Volume flux Northward (t) float64 dask.array<chunksize=(1,), meta=np.ndarray> Heat flux Net (t) float64 dask.array<chunksize=(1,), meta=np.ndarray> Heat flux Northward (t) float64 dask.array<chunksize=(1,), meta=np.ndarray> Freshwater Net (t) float64 dask.array<chunksize=(1,), meta=np.ndarray> Freshwater Northward (t) float64 dask.array<chunksize=(1,), meta=np.ndarray> Ice export (t) float64 dask.array<chunksize=(1,), meta=np.ndarray> Volume flux South (t) float64 dask.array<chunksize=(1,), meta=np.ndarray> Heat flux South (t) float64 dask.array<chunksize=(1,), meta=np.ndarray> Freshwater South (t) float64 dask.array<chunksize=(1,), meta=np.ndarray>
array([cftime.DatetimeNoLeap(2004, 9, 1, 12, 0, 0, 0), cftime.DatetimeNoLeap(2004, 9, 2, 12, 0, 0, 0), cftime.DatetimeNoLeap(2004, 9, 3, 12, 0, 0, 0), cftime.DatetimeNoLeap(2004, 9, 4, 12, 0, 0, 0), cftime.DatetimeNoLeap(2004, 9, 5, 12, 0, 0, 0), cftime.DatetimeNoLeap(2004, 9, 6, 12, 0, 0, 0), cftime.DatetimeNoLeap(2004, 9, 7, 12, 0, 0, 0), cftime.DatetimeNoLeap(2004, 9, 8, 12, 0, 0, 0), cftime.DatetimeNoLeap(2004, 9, 9, 12, 0, 0, 0)], dtype=object)
array(6538)
|
|
|
|
|
|
|
|
|
|
#4 Saving SEDNA_Fluxnet_integrals_Bering_Fluxnet dtaa=save.datas(data,plot=Plot,path=nc_outputpath,filename=filename) start saving data saving data in a csv file ../nc_results/SEDNA_ALPHA_MONITOR/25/SEDNA_Fluxnet_integrals_Bering_Fluxnet2004-09-01_2004-09-09.nc save computed data at ../nc_results/SEDNA_ALPHA_MONITOR/25/SEDNA_Fluxnet_integrals_Bering_Fluxnet2004-09-01_2004-09-09.nc completed CPU times: user 12min 49s, sys: 13.4 s, total: 13min 3s Wall time: 13min 11s