Smooth bathymetry -grab from Susan's notebook¶
In [1]:
from __future__ import division, print_function
from salishsea_tools import (nc_tools,viz_tools,tidetools,bathy_tools)
from salishsea_tools.nowcast import figures
import scipy.io as sio
import matplotlib.pyplot as plt
import netCDF4 as nc
import numpy as np
%matplotlib inline
In [2]:
!cp /ocean/jieliu/research/meopar/river-treatment/bathy_meter_SalishSea10.nc \
bathy_meter_SalishSea11.nc
In [3]:
bathy_11 = nc.Dataset('bathy_meter_SalishSea11.nc','r' )
bathyy_11 = bathy_11.variables['Bathymetry'][:]
bathyy_11[418,365]
Out[3]:
13.125
In [4]:
bathy_10 = nc.Dataset('bathy_meter_SalishSea10.nc','r+' )
bathyy_10 = bathy_10.variables['Bathymetry']
bathyy_10[427,308]
Out[4]:
masked_array(data = --,
mask = True,
fill_value = 0.0)
In [5]:
bathyy_11[:] = bathy_tools.smooth(bathyy_11[:], max_norm_depth_diff=0.8, smooth_factor=0.2)
In [3]:
def find_max(bathy):
i,j = np.unravel_index(bathy.argmax(), bathy.shape)
return i,j
In [4]:
def find_slope(bathy,di,dj):
imax, jmax = bathy.shape
Da = 0.5*(bathy[di:,dj:]+bathy[0:imax-di,0:jmax-dj])
Dd = bathy[di:,dj:]-bathy[0:imax-di,0:jmax-dj]
return Dd/Da
In [5]:
def smooth(gamma,bathy,i,j,di,dj):
a = 0.5*(bathy[i,j]+bathy[i+di,j+dj])
if bathy[i,j] < bathy[i+di,j+dj]:
change = gamma
else:
change = -gamma
bathy[i,j] = bathy[i,j] + gamma*a
bathy[i+di,j+dj] = bathy[i+di,j+dj] - gamma*a
In [6]:
gamma = 0.2
maxslope = 0.8
slopei = find_slope(bathyy_8,1,0)
i,j = find_max(slopei)
slopej = find_slope(bathyy_8,0,1)
k,l = find_max(slopej)
while np.maximum(slopei[i,j],slopej[k,l]) > maxslope:
if slopei[i,j] > slopej[k,l]:
smooth(gamma,bathyy_8,i,j,1,0)
else:
smooth(gamma,bathyy_8,k,l,0,1)
slopei = find_slope(bathyy_8,1,0)
i,j = find_max(slopei)
slopej = find_slope(bathyy_8,0,1)
k,l = find_max(slopej)
print (slopei[i,j])
print (slopej[i,j])
0.8 --
In [6]:
bathyy_10[:] = bathyy_11
In [7]:
bathyy_10[428,306]
Out[7]:
masked_array(data = --,
mask = True,
fill_value = 0.0)
In [8]:
nc_tools.show_dataset_attrs(bathy_10)
file format: NETCDF4
Conventions: CF-1.6
title: Salish Sea NEMO Bathymetry
institution: Dept of Earth, Ocean & Atmospheric Sciences, University of British Columbia
source: https://bitbucket.org/salishsea/tools/src/tip/bathymetry/SalishSeaBathy.ipynb
references: https://bitbucket.org/salishsea/nemo-forcing/src/tip/grid/bathy_meter_SalishSea.nc
comment: Based on 1_bathymetry_seagrid_WestCoast.nc file from 2-Oct-2013 WCSD_PREP tarball provided by J-P Paquin.
history:
[2013-10-30 13:18] Created netCDF4 zlib=True dataset.
[2013-10-30 15:22] Set depths between 0 and 4m to 4m and those >428m to 428m.
[2013-10-31 17:10] Algorithmic smoothing.
[2013-11-21 19:53] Reverted to pre-smothing dataset (repo rev 3b301b5b9b6d).
[2013-11-21 20:14] Updated dataset and variable attributes to CF-1.6 conventions & project standards.
[2013-11-21 20:47] Removed east end of Jervis Inlet and Toba Inlet region due to deficient source bathymetry data in Cascadia dataset.
[2013-11-21 21:52] Algorithmic smoothing.
In [9]:
bathy_10.close()
Create TS for bathymetry6¶
In [1]:
from __future__ import division
from salishsea_tools import nc_tools
from salishsea_tools import tidetools
import matplotlib.pyplot as plt
import netCDF4 as nc
import numpy as np
%matplotlib inline
In [2]:
#old bathymetry inforamtion
old_path = '../nemo-forcing/grid/bathy_meter_SalishSea2.nc'
old_bathy = nc.Dataset(old_path, 'r')
old_depth = old_bathy.variables['Bathymetry']
In [16]:
#old initial file
initial_path = '/data/dlatorne/MEOPAR/SalishSea/nowcast/14jun15/SalishSea_02401920_restart.nc'
T_S = nc.Dataset(initial_path, 'r')
In [17]:
#nc_tools.show_variables(T_S)
old_T = T_S.variables['tb'][0] # omit the first dimension
old_S = T_S.variables['sb'][0]
depths = T_S.variables['nav_lev']
lon = T_S.variables['nav_lon']
lat = T_S.variables['nav_lat']
print (old_T[:, 427, 292])
print(depths[0:40])
[ 14.77021985 14.34337293 14.20415199 13.84946759 13.67585241
13.59131301 13.64740562 13.5873905 13.43705051 13.34423781
13.15459748 13.03363 12.96472808 12.91797194 12.89496075
12.87349044 12.84061662 12.78115335 12.70208798 12.59582154
12.51719442 12.35120913 12.09723483 0. 0. 0. 0.
0. 0. 0. 0. 0. 0. 0.
0. 0. 0. 0. 0. 0. ]
[ 0.5000003 1.5000031 2.50001144 3.50003052 4.50007057
5.50015068 6.50031042 7.50062323 8.50123596 9.50243282
10.50476551 11.50931168 12.51816654 13.53541183 14.56898212
15.63428783 16.76117325 18.00713539 19.48178482 21.38997841
24.10025597 28.22991562 34.68575668 44.51772308 58.48433304
76.58558655 98.06295776 121.86651611 147.08946228 173.11448669
199.57304382 226.26029968 253.06663513 279.93453979 306.834198
333.75018311 360.67453003 387.60321045 414.53408813 441.46609497]
In [18]:
#read in new bathymetry
new_path = '/ocean/jieliu/research/meopar/river-treatment/bathy_meter_SalishSea6.nc'
Fraser = nc.Dataset(new_path, 'r')
bathy = Fraser.variables['Bathymetry'][:]
print(bathy.shape)
plt.pcolormesh(bathy[350: 520, 280 : 398])
plt.colorbar()
(898, 398)
Out[18]:
<matplotlib.colorbar.Colorbar instance at 0x7fbaf2fa7200>
In [19]:
floor = np.empty_like(depths)
ceil = np.empty_like(depths)
ceil[0] = 0.
floor[0] = 2*depths[0]
for k in range(1,40):
ceil[k] = floor[k-1]
floor[k] = 2*depths[k] -floor[k-1]
In [20]:
S = np.empty_like(old_S)
T = np.empty_like(old_T)
#for every cell with top of cell depth < bathymetry, use old TS, if old TS=0, find closest point and use that.
for k in range(40):
for j in range(398):
for i in range(898):
if ceil[k] < bathy[i,j]:
if old_S[k,i,j] <> 0:
S[k,i,j] = old_S[k, i, j]
T[k,i,j] = old_T[k, i, j]
else:
# closest neighbour thing
masked_array = np.ma.array(old_S[k], mask = old_S[k] == 0)
X, Y = tidetools.find_closest_model_point(lon[i,j], lat[i,j], lon, lat, masked_array)
S[k, i, j] = old_S[k, X, Y]
T[k, i, j] = old_T[k, X, Y]
In [21]:
# build nc file
new_TS = nc.Dataset('TSnorless6.nc', 'w')
nc_tools.init_dataset_attrs(
new_TS,
title='Salinity Temperature Initial Conditions based on Nowcast June 14, 2015 for north-extended but not \
to Howe Sound, and widen at beginning Fraser river channel',
notebook_name='Smooth bathymetry & Create New TS file',
nc_filepath='/ocean/jieliu/research/meopar/nemo-forcing/initial_strat/TSnorless6.nc',
comment='Salinity and Temperature conditions from nowcast June 14, 2015 onto north extended Fraser bathymetry')
new_TS.createDimension('y', 898)
new_TS.createDimension('x', 398)
new_TS.createDimension('deptht',size = len(depths))
new_TS.createDimension('time_counter', None)
nc_tools.show_dimensions(new_TS)
# show variables
nav_lat = new_TS.createVariable('nav_lat', 'float32', ('y','x'))
nav_lat.long_name = 'Latitude'
nav_lat.units = 'degrees_north'
nav_lat = lat
nav_lon = new_TS.createVariable('nav_lon', 'float32', ('y','x'))
nav_lon.long_name = 'Longitude'
nav_lon.units = 'degrees_east'
nav_lon = lon
deptht = new_TS.createVariable('deptht', 'float32', ('deptht'))
deptht.long_name = 'Depth'
deptht.units = 'm'
deptht.positive = 'down'
deptht.valid_range = np.array((4., 428.))##minimum depth 4m
deptht = depths
time_counter = new_TS.createVariable('time_counter', 'float32', ('time_counter'))
time_counter.units = 'seconds since 2015-06-14 0:00:00'
time_counter.long_name = 'Time axis'
vosaline = new_TS.createVariable('vosaline', 'float32',
('time_counter','deptht','y','x'))
vosaline.units = 'none'
vosaline.long_name = 'Practical Salinity'
vosaline.coordinates = 'nav_lon nav_lat deptht time_counter'
vosaline.grid = 'SalishSea6, north extended Fraser'
vosaline[0] = S
votemper = new_TS.createVariable('votemper', 'float32',
('time_counter','deptht','y','x'))
votemper.units = 'degC'
votemper.long_name = 'Temperature'
votemper.coordinates = 'nav_lon nav_lat deptht time_counter'
votemper[0] = T
new_TS.history = """[2015-06-14] Created"""
file format: NETCDF4 Conventions: CF-1.6 title: Salinity Temperature Initial Conditions based on Nowcast June 14, 2015 for north-extended but not to Howe Sound, and widen at beginning Fraser river channel institution: Dept of Earth, Ocean & Atmospheric Sciences, University of British Columbia source: REQUIRED references: REQUIRED history: [2015-08-06 21:12:14] Created netCDF4 zlib=True dataset. comment: Salinity and Temperature conditions from nowcast June 14, 2015 onto north extended Fraser bathymetry <type 'netCDF4.Dimension'>: name = 'y', size = 898 <type 'netCDF4.Dimension'>: name = 'x', size = 398 <type 'netCDF4.Dimension'>: name = 'deptht', size = 40 <type 'netCDF4.Dimension'> (unlimited): name = 'time_counter', size = 0
In [22]:
## temperature before
plt.pcolormesh(votemper[0,0, 350: 520, 290 : 398])
plt.colorbar()
Out[22]:
<matplotlib.colorbar.Colorbar instance at 0x7fbaf2e93cf8>
In [23]:
## modify temperature for some grid cells
k = 0
i = 413
j = 351
votemper[0, : , i, j : j + 5] = 14. ## for(413, 351:355) from 0 to 4m
votemper[0, : , i + 4, j + 4: j +13] = 14. ## for (417, 355:363).. ..
votemper[0, : , i + 1, j + 4: j + 7] = 14. ## for (414, 355:357).. ..
votemper[0, : , i + 2, j + 6: j + 8] = 14. ## for (415, 357:358).. ..
votemper[0, : , i + 3, j + 7: j + 10] = 14. ## for (416, 358:360).. ..
votemper[0, : , i + 5, j + 9: j + 15] = 14. ## for (418, 360:365).. ..
## plus north ones(all depth):
votemper[0, k : , i + 5 : i + 88, j + 14] = 14. ## for (418-500, 365).. ..
votemper[0, k : , i + 87, j+ 14 : j + 45] = 14. ## for (500, 365-395)
In [24]:
## temperature after
plt.pcolormesh(votemper[0,0, 350: 520, 290 : 398])
plt.colorbar()
Out[24]:
<matplotlib.colorbar.Colorbar instance at 0x7fbaf2d085a8>
In [25]:
## salinity before
plt.pcolormesh(vosaline[0,0, 350: 520, 290 : 398])
plt.colorbar()
Out[25]:
<matplotlib.colorbar.Colorbar instance at 0x7fbaf2b6ea28>
In [26]:
## set salinity after New Westminster as 1,before as 0,river source cell as 0
k = 0
i = 418
j = 365
vosaline[0, :, i-2, j-30: j-13] = 0.#16,335:351)(416 ,358:360)
vosaline[0, :, i -2, j -7: j -4] = 0.
vosaline[0, :, i -1,j -14:j -1] = 0.#(417, 351:363).. ..
vosaline[0, :, i - 4, j - 18: j - 15] = 0.## for (414, 347:349)( 414, 355:357).. ..
vosaline[0, :, i - 4, j - 10: j - 7] = 0.
vosaline[0, :, i - 5,j - 16:j- 9]=0.## for (413, 349:355)
vosaline[0, :, i - 3, j - 19:j-17] =0.#(415, 346, 347, 357, 358).. ..
vosaline[0, :, i - 3, j - 8: j - 6] = 0.
vosaline[0, :, i , j -5 : j +1] = 0.#for(418,360:365) .. ..New Westminster
## plus north ones(all depth):
vosaline[0, : , i: i+ 83 , j] = 0. ## for (418-500, 365).. ..
vosaline[0, : , i + 82, j : j + 30] = 0. ## for (500, 365-394)
In [27]:
## make salinity(500, 395)==0(source) for all depth
vosaline[0, : , 500, 395] = 0.
In [28]:
## make original salinity of freshwater source point as 4
vosaline[0, 0:4 , 416, 334] = 1.
In [29]:
## modify damp salinity values but not for the widen part
k = 0; i = 425; j = 302; d = 1.
vosaline[0, k: k +4, i, j+1] = d
vosaline[0, k: k +4, i-1, j:j+3] = d
vosaline[0, k: k +4, i-2, j+1:j+5] = d
vosaline[0, k: k +4, i-3, j+3:j+7] = d
vosaline[0, k: k +4, i-4, j+5:j+9] = d
vosaline[0, k: k +4, i-5, j+7:j+11] = d
vosaline[0, k: k +4, i-6, j+9:j+13] = d
vosaline[0, k: k +4, i-7, j+11:j+14] = d
vosaline[0, k: k +4, i-8, j+12:j+16] = d
In [35]:
## modify salinity values of straight channel connect damp and further extended channel
k = 0; i = 416; j = 317; d= 1.
vosaline[0, k : k +4, i, j-1:j+18] = d
In [36]:
## salinity after
plt.pcolormesh(vosaline[0,0, 350: 520, 290 : 398])
plt.colorbar()
Out[36]:
<matplotlib.colorbar.Colorbar instance at 0x7fbaf29f0170>
In [37]:
plt.subplot(1, 2, 1)
plt.pcolormesh(votemper[0,0, 350: 520, 290 : 398])
plt.colorbar()
plt.subplot(1, 2, 2)
plt.pcolormesh(vosaline[0,0, 350: 520, 290 : 398])
plt.colorbar()
Out[37]:
<matplotlib.colorbar.Colorbar instance at 0x7fbaf277a680>
In [38]:
new_TS.close()
Create TS file for bathymetry 6 for sep25-oct7 spin up, then hindcast¶
In [3]:
#old initial file
initial_path = '/data/dlatorne/MEOPAR/SalishSea/nowcast/early-days/24-26sep14/SalishSea_00129600_restart.nc'
T_S = nc.Dataset(initial_path, 'r')
In [4]:
#nc_tools.show_variables(T_S)
old_T = T_S.variables['tb'][0] # omit the first dimension
old_S = T_S.variables['sb'][0]
depths = T_S.variables['nav_lev']
lon = T_S.variables['nav_lon']
lat = T_S.variables['nav_lat']
print (old_T[:, 427, 292])
print(depths[0:40])
[ 16.07758754 16.06995039 15.95650878 15.80249028 15.40679218
14.96464942 14.64882925 14.41677323 14.10704308 13.88312656
13.75059047 13.63027657 13.53098886 13.36371394 13.24643531
13.12736562 13.0202796 12.94171459 12.8280736 12.70510011
12.52621711 12.28949568 12.09501551 0. 0. 0. 0.
0. 0. 0. 0. 0. 0. 0.
0. 0. 0. 0. 0. 0. ]
[ 0.5000003 1.5000031 2.50001144 3.50003052 4.50007057
5.50015068 6.50031042 7.50062323 8.50123596 9.50243282
10.50476551 11.50931168 12.51816654 13.53541183 14.56898212
15.63428783 16.76117325 18.00713539 19.48178482 21.38997841
24.10025597 28.22991562 34.68575668 44.51772308 58.48433304
76.58558655 98.06295776 121.86651611 147.08946228 173.11448669
199.57304382 226.26029968 253.06663513 279.93453979 306.834198
333.75018311 360.67453003 387.60321045 414.53408813 441.46609497]
In [5]:
#read in new bathymetry
new_path = '/ocean/jieliu/research/meopar/river-treatment/bathy_meter_SalishSea6.nc'
Fraser = nc.Dataset(new_path, 'r')
bathy = Fraser.variables['Bathymetry'][:]
print(bathy.shape)
plt.pcolormesh(bathy[350: 520, 280 : 398])
plt.colorbar()
(898, 398)
Out[5]:
<matplotlib.colorbar.Colorbar instance at 0x7f744fb09e60>
In [6]:
floor = np.empty_like(depths)
ceil = np.empty_like(depths)
ceil[0] = 0.
floor[0] = 2*depths[0]
for k in range(1,40):
ceil[k] = floor[k-1]
floor[k] = 2*depths[k] -floor[k-1]
In [7]:
S = np.empty_like(old_S)
T = np.empty_like(old_T)
#for every cell with top of cell depth < bathymetry, use old TS, if old TS=0, find closest point and use that.
for k in range(40):
for j in range(398):
for i in range(898):
if ceil[k] < bathy[i,j]:
if old_S[k,i,j] <> 0:
S[k,i,j] = old_S[k, i, j]
T[k,i,j] = old_T[k, i, j]
else:
# closest neighbour thing
masked_array = np.ma.array(old_S[k], mask = old_S[k] == 0)
X, Y = tidetools.find_closest_model_point(lon[i,j], lat[i,j], lon, lat, masked_array)
S[k, i, j] = old_S[k, X, Y]
T[k, i, j] = old_T[k, X, Y]
In [8]:
# build nc file
new_TS = nc.Dataset('TSnorlessSep.nc', 'w')
nc_tools.init_dataset_attrs(
new_TS,
title='Salinity Temperature Initial Conditions based on Nowcast Sep 24, 2014 for north-extended but not \
to Howe Sound, and widen at beginning Fraser river channel',
notebook_name='Smooth bathymetry & Create New TS file',
nc_filepath='/ocean/jieliu/research/meopar/nemo-forcing/initial_strat/TSnorless6.nc',
comment='Salinity and Temperature conditions from nowcast Sep 24, 2014 onto north extended Fraser bathymetry')
new_TS.createDimension('y', 898)
new_TS.createDimension('x', 398)
new_TS.createDimension('deptht',size = len(depths))
new_TS.createDimension('time_counter', None)
nc_tools.show_dimensions(new_TS)
# show variables
nav_lat = new_TS.createVariable('nav_lat', 'float32', ('y','x'))
nav_lat.long_name = 'Latitude'
nav_lat.units = 'degrees_north'
nav_lat = lat
nav_lon = new_TS.createVariable('nav_lon', 'float32', ('y','x'))
nav_lon.long_name = 'Longitude'
nav_lon.units = 'degrees_east'
nav_lon = lon
deptht = new_TS.createVariable('deptht', 'float32', ('deptht'))
deptht.long_name = 'Depth'
deptht.units = 'm'
deptht.positive = 'down'
deptht.valid_range = np.array((4., 428.))##minimum depth 4m
deptht = depths
time_counter = new_TS.createVariable('time_counter', 'float32', ('time_counter'))
time_counter.units = 'seconds since 2015-06-14 0:00:00'
time_counter.long_name = 'Time axis'
vosaline = new_TS.createVariable('vosaline', 'float32',
('time_counter','deptht','y','x'))
vosaline.units = 'none'
vosaline.long_name = 'Practical Salinity'
vosaline.coordinates = 'nav_lon nav_lat deptht time_counter'
vosaline.grid = 'SalishSea6, north extended Fraser'
vosaline[0] = S
votemper = new_TS.createVariable('votemper', 'float32',
('time_counter','deptht','y','x'))
votemper.units = 'degC'
votemper.long_name = 'Temperature'
votemper.coordinates = 'nav_lon nav_lat deptht time_counter'
votemper[0] = T
new_TS.history = """[2014-09-24] Created"""
file format: NETCDF4 Conventions: CF-1.6 title: Salinity Temperature Initial Conditions based on Nowcast Sep 24, 2014 for north-extended but not to Howe Sound, and widen at beginning Fraser river channel institution: Dept of Earth, Ocean & Atmospheric Sciences, University of British Columbia source: REQUIRED references: REQUIRED history: [2015-10-05 14:34:52] Created netCDF4 zlib=True dataset. comment: Salinity and Temperature conditions from nowcast Sep 24, 2014 onto north extended Fraser bathymetry <type 'netCDF4._netCDF4.Dimension'>: name = 'y', size = 898 <type 'netCDF4._netCDF4.Dimension'>: name = 'x', size = 398 <type 'netCDF4._netCDF4.Dimension'>: name = 'deptht', size = 40 <type 'netCDF4._netCDF4.Dimension'> (unlimited): name = 'time_counter', size = 0
In [9]:
## temperature and I don't modify it further
plt.pcolormesh(votemper[0,0, 350: 520, 290 : 398])
plt.colorbar()
Out[9]:
<matplotlib.colorbar.Colorbar instance at 0x7f743d702c20>
In [10]:
## salinity before and I need to modify it further
plt.pcolormesh(vosaline[0,0, 350: 520, 290 : 398])
plt.colorbar()
Out[10]:
<matplotlib.colorbar.Colorbar instance at 0x7f743d564830>
In [11]:
## set salinity after New Westminster as 1,before as 0,river source cell as 0
k = 0
i = 418
j = 365
vosaline[0, :, i-2, j-30: j-13] = 0.#16,335:351)(416 ,358:360)
vosaline[0, :, i -2, j -7: j -4] = 0.
vosaline[0, :, i -1,j -14:j -1] = 0.#(417, 351:363).. ..
vosaline[0, :, i - 4, j - 18: j - 15] = 0.## for (414, 347:349)( 414, 355:357).. ..
vosaline[0, :, i - 4, j - 10: j - 7] = 0.
vosaline[0, :, i - 5,j - 16:j- 9]=0.## for (413, 349:355)
vosaline[0, :, i - 3, j - 19:j-17] =0.#(415, 346, 347, 357, 358).. ..
vosaline[0, :, i - 3, j - 8: j - 6] = 0.
vosaline[0, :, i , j -5 : j +1] = 0.#for(418,360:365) .. ..New Westminster
## plus north ones(all depth):
vosaline[0, : , i: i+ 83 , j] = 0. ## for (418-500, 365).. ..
vosaline[0, : , i + 82, j : j + 30] = 0. ## for (500, 365-394)
In [12]:
## make salinity(500, 395)==0(source) for all depth
vosaline[0, : , 500, 395] = 0.
In [13]:
## make original salinity of freshwater source point as 4
vosaline[0, : , 414:417, 334] = 1.
In [14]:
## modify damp salinity values but not for the widen part
k = 0; i = 425; j = 302; d = 1.
vosaline[0, k: k +4, i, j+1] = d
vosaline[0, k: k +4, i-1, j:j+3] = d
vosaline[0, k: k +4, i-2, j+1:j+5] = d
vosaline[0, k: k +4, i-3, j+3:j+7] = d
vosaline[0, k: k +4, i-4, j+5:j+9] = d
vosaline[0, k: k +4, i-5, j+7:j+11] = d
vosaline[0, k: k +4, i-6, j+9:j+13] = d
vosaline[0, k: k +4, i-7, j+11:j+14] = d
vosaline[0, k: k +4, i-8, j+12:j+16] = d
In [15]:
## modify salinity values of straight channel connect damp and further extended channel
k = 0; i = 416; j = 317; d= 1.
vosaline[0, k : k +4, i, j-1:j+18] = d
In [16]:
## salinity after
plt.pcolormesh(vosaline[0,0, 350: 520, 290 : 398])
plt.colorbar()
Out[16]:
<matplotlib.colorbar.Colorbar instance at 0x7f743d459560>
In [17]:
plt.subplot(1, 2, 1)
plt.pcolormesh(votemper[0,0, 350: 520, 290 : 398])
plt.colorbar()
plt.subplot(1, 2, 2)
plt.pcolormesh(vosaline[0,0, 350: 520, 290 : 398])
plt.colorbar()
Out[17]:
<matplotlib.colorbar.Colorbar instance at 0x7f743d1649e0>
In [18]:
new_TS.close()