In [1]:
import matplotlib.pyplot as plt
import os
import glob
import netCDF4 as nc
import numpy as np
from salishsea_tools import places, geo_tools
from nowcast.figures import shared
%matplotlib inline
In [2]:
grid = nc.Dataset('/data/nsoontie/MEOPAR/NEMO-forcing/grid/bathy_meter_SalishSea2.nc')
X = grid.variables['nav_lon'][:]
Y = grid.variables['nav_lat'][:]
bathy = grid.variables['Bathymetry'][:]
mesh_mask = nc.Dataset('/data/nsoontie/MEOPAR/NEMO-forcing/grid/mesh_mask_SalishSea2.nc')
Base¶
In [3]:
path='/ocean/nsoontie/MEOPAR/SalishSea/results/mixing_paper/base_spinup/'
base_subdirs = ['13sep2014_13dec2014', '14dec2014_22jan2015', '23jan2015_03mar2015',
'04mar2015_05jun2015', '06jun2015_07jul2015', '08jul2015', '09jul2015_17aug2015']
stations = ['Central node', 'East node']
In [4]:
sal_base = {}
time_base = {}
for station in stations:
lon = places.PLACES[station]['lon lat'][0]
lat = places.PLACES[station]['lon lat'][1]
j, i = geo_tools.find_closest_model_point(lon,lat,X,Y, land_mask=bathy.mask)
depth = places.PLACES[station]['depth']
model_depths = mesh_mask.variables['gdept'][0,:,j,i]
tmask = mesh_mask.variables['tmask'][0,:,j,i]
wdeps = mesh_mask.variables['gdepw'][0,:,j,i]
sal_array = np.array([])
time_array = np.array([])
for subdir in base_subdirs:
fname = glob.glob(os.path.join(path, subdir, 'SalishSea_*_grid_T.nc'))[0]
f = nc.Dataset(fname)
sal = f.variables['vosaline'][:,:,j,i]
sal_interp = np.array([shared.interpolate_tracer_to_depths(sal[t,:],model_depths,depth,tmask,wdeps)
for t in range(sal.shape[0])])
tc = f.variables['time_counter']
time = nc.num2date(tc[:], tc.units)
sal_array = np.concatenate((sal_array, sal_interp))
time_array = np.concatenate((time_array, time))
sal_base[station] = sal_array
time_base[station] = time_array
In [5]:
station='Central node'
plt.plot(time_base[station], sal_base[station])
Out[5]:
[<matplotlib.lines.Line2D at 0x7f0916b8a278>]
In [6]:
station='East node'
plt.plot(time_base[station], sal_base[station])
Out[6]:
[<matplotlib.lines.Line2D at 0x7f09169c84e0>]
Holl Spinup¶
In [7]:
path='/ocean/nsoontie/MEOPAR/SalishSea/results/mixing_paper/holl_spinup/'
holl_subdirs = ['13sep2014_13dec2014', '14dec2014_22jan2015', '23jan2015_26apr2015',
'27apr2015_07jul2015', '08jul2015', '09jul2015_17aug2015']
In [8]:
sal_holl = {}
time_holl = {}
for station in stations:
lon = places.PLACES[station]['lon lat'][0]
lat = places.PLACES[station]['lon lat'][1]
j, i = geo_tools.find_closest_model_point(lon,lat,X,Y, land_mask=bathy.mask)
depth = places.PLACES[station]['depth']
model_depths = mesh_mask.variables['gdept'][0,:,j,i]
tmask = mesh_mask.variables['tmask'][0,:,j,i]
wdeps = mesh_mask.variables['gdepw'][0,:,j,i]
sal_array = np.array([])
time_array = np.array([])
for subdir in holl_subdirs:
fname = glob.glob(os.path.join(path, subdir, 'SalishSea_*_grid_T.nc'))[0]
f = nc.Dataset(fname)
sal = f.variables['vosaline'][:,:,j,i]
sal_interp = np.array([shared.interpolate_tracer_to_depths(sal[t,:],model_depths,depth,tmask,wdeps)
for t in range(sal.shape[0])])
tc = f.variables['time_counter']
time = nc.num2date(tc[:], tc.units)
sal_array = np.concatenate((sal_array, sal_interp))
time_array = np.concatenate((time_array, time))
sal_holl[station] = sal_array
time_holl[station] = time_array
In [9]:
station='Central node'
plt.plot(time_holl[station], sal_holl[station])
plt.plot(time_base[station], sal_base[station])
Out[9]:
[<matplotlib.lines.Line2D at 0x7f0916b9ef98>]
In [10]:
station='East node'
plt.plot(time_holl[station], sal_holl[station])
plt.plot(time_base[station], sal_base[station])
Out[10]:
[<matplotlib.lines.Line2D at 0x7f09163aad68>]
In [11]:
def resample_daily_obs(obs):
obs_new=obs
obs_new[['sal','temp']]=obs_new[['sal','temp']].astype(float)
obs_new['date_index'] = pd.DatetimeIndex(obs_new['date'])
obs_new.set_index('date_index', inplace=True)
obs_new = obs_new.resample('D', how='mean')
obs_new = obs_new.reset_index()
obs_new = obs_new.rename(columns=({'date_index':'date'}))
return obs_new
In [19]:
from nowcast.figures import research_VENUS
import pandas as pd
from salishsea_tools import teos_tools
import datetime
In [17]:
East = research_VENUS.load_VENUS('East')
Central = research_VENUS.load_VENUS('Central')
East_daily = resample_daily_obs(East)
Central_daily = resample_daily_obs(Central)
/home/nsoontie/anaconda3/envs/mixing_paper/lib/python3.5/site-packages/ipykernel/__main__.py:7: FutureWarning: how in .resample() is deprecated the new syntax is .resample(...).mean()
In [18]:
import seaborn as sns
sns.set_context('talk')
sns.set_style('darkgrid')
sns.set_color_codes()
In [25]:
station='Central node'
fig,ax=plt.subplots(1,1,figsize= (15,5))
ax.plot(time_holl[station], teos_tools.psu_teos(sal_holl[station]), color='r', label='EEN-c',lw=2)
ax.plot(time_base[station], teos_tools.psu_teos(sal_base[station]), color='b', label='EEN', lw=2)
ax.plot(Central_daily.date[:], teos_tools.psu_teos(Central_daily['sal'][:]), color='k', label='Obervations', lw=2)
ax.set_xlim(datetime.datetime(2014,9,13),datetime.datetime(2015,8,17))
ax.set_ylabel('Salinity [g/kg]')
plt.setp( ax.xaxis.get_majorticklabels(), rotation=20)
ax.legend(loc=0)
ax.set_title('Salinity comparison at Central node')
Out[25]:
<matplotlib.text.Text at 0x7f0912f52ac8>
In [26]:
fig.savefig('/home/nsoontie/Desktop/Central_hollbasespin.png', dpi=300, bbox_inches='tight')
In [27]:
station='East node'
fig,ax=plt.subplots(1,1,figsize= (15,5))
ax.plot(time_holl[station], teos_tools.psu_teos(sal_holl[station]), color='r', label='EEN-c',lw=2)
ax.plot(time_base[station], teos_tools.psu_teos(sal_base[station]), color='b', label='EEN', lw=2)
ax.plot(East_daily.date[:], teos_tools.psu_teos(East_daily['sal'][:]), color='k', label='Obervations', lw=2)
ax.set_xlim(datetime.datetime(2014,9,13),datetime.datetime(2015,8,17))
ax.set_ylabel('Salinity [g/kg]')
plt.setp( ax.xaxis.get_majorticklabels(), rotation=20)
ax.legend(loc=0)
ax.set_title('Salinity comparison at East node')
Out[27]:
<matplotlib.text.Text at 0x7f09130c0fd0>
In [28]:
fig.savefig('/home/nsoontie/Desktop/East_hollbasespin.png', dpi=300, bbox_inches='tight')
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