Average TS profiles over my tidal analysis period.

In [1]:

import pandas as pd
import netCDF4 as nc
import numpy as np

import matplotlib.pyplot as plt

from nowcast import analyze 
from salishsea_tools import places

import datetime

%matplotlib inline

In [2]:

grid = nc.Dataset('/data/nsoontie/MEOPAR/NEMO-forcing/grid/bathy_meter_SalishSea2.nc')
mesh_mask = nc.Dataset('/data/nsoontie/MEOPAR/NEMO-forcing/grid/mesh_mask_SalishSea2.nc')

tmask = mesh_mask.variables['tmask'][:]
gdept = mesh_mask.variables['gdept'][:]

In [3]:

PLACES = places.PLACES

d1=datetime.datetime(2014,11,26)
d2=datetime.datetime(2015,4,26)
files = analyze.get_filenames(d1,d2,'1d','grid_T','/results/SalishSea/nowcast/')

sals ={}
temps = {}
depths={}
masks={}
for name in ['Central node', 'East node']:
    j = PLACES[name]['NEMO grid ji'][0]
    i = PLACES[name]['NEMO grid ji'][1]
    sals[name], time = analyze.combine_files(files, 'vosaline', np.arange(40), j, i)
    temps[name], time = analyze.combine_files(files, 'votemper', np.arange(40), j, i)
    depths[name] = gdept[0,:,j,i]
    masks[name] = tmask[0,:,j,i]
    
    sals[name] = np.mean(sals[name], axis=0)
    temps[name] = np.mean(temps[name], axis=0)
    
    inds = np.where(masks[name]==1)
    sals[name] = sals[name][inds]
    temps[name] = temps[name][inds]
    depths[name] = depths[name][inds]

/data/nsoontie/MEOPAR/tools/SalishSeaNowcast/nowcast/analyze.py:171: FutureWarning: elementwise comparison failed; returning scalar instead, but in the future will perform elementwise comparison
  if kss == 'None':

In [4]:

fig, axs = plt.subplots(1,2,figsize=(10,5))
for name in ['Central node', 'East node']:
    ax=axs[0]
    ax.plot(sals[name],depths[name],'o-', label=name)
    ax.set_ylim([300,0])
    ax.set_xlabel('Practical Salinity [psu]')
    ax.set_ylabel('Depth [m]')
    
    ax=axs[1]
    ax.plot(temps[name],depths[name],'o-', label=name)
    ax.set_ylim([300,0])
    ax.set_xlabel('Temperature [deg C]')
    ax.set_ylabel('Depth [m]')
for ax in axs:
    ax.grid()
    ax.legend(loc=0)

Save using pandas

In [5]:

for name in ['Central node', 'East node']:
    data = pd.DataFrame({'Depth [m]': depths[name],
                         'Practical Salinity [psu]': sals[name],
                         'Temperature [deg C]': temps[name]})
    
    fname = '{}_meanTS_{}_{}.csv'.format(name[:-5], d1.strftime('%Y%m%d'), d2.strftime('%Y%m%d'))
    data.to_csv(fname)

In [6]:

!ls *.csv

Central_meanTS_20141126_20150426.csv  East_meanTS_20141126_20150426.csv

In [7]:

data

Out[7]:

	Depth [m]	Practical Salinity [psu]	Temperature [deg C]
0	0.500000	19.681507	7.649859
1	1.500003	21.742039	7.993243
2	2.500011	23.447821	8.262228
3	3.500031	24.506578	8.452448
4	4.500071	25.250780	8.612563
5	5.500151	25.851595	8.760666
6	6.500310	26.347780	8.891456
7	7.500623	26.754166	9.002347
8	8.501236	27.090258	9.097125
9	9.502433	27.373615	9.179514
10	10.504766	27.609207	9.251032
11	11.509312	27.807636	9.312590
12	12.518167	27.976418	9.365047
13	13.535412	28.123075	9.410112
14	14.568982	28.252239	9.448424
15	15.634288	28.367905	9.480542
16	16.761173	28.473934	9.507227
17	18.007135	28.574501	9.529068
18	19.481785	28.675165	9.545954
19	21.389978	28.781658	9.555981
20	24.100256	28.900185	9.552986
21	28.229916	29.031275	9.525632
22	34.685757	29.166651	9.460915
23	44.517723	29.289066	9.372986
24	58.484333	29.398809	9.314327
25	76.585587	29.524263	9.334321
26	98.062958	29.738846	9.455692
27	121.866516	29.990423	9.615647
28	147.089462	30.195532	9.720541
29	164.994690	30.296865	9.763904