In [1]:
import netCDF4 as nc
import numpy as np
import matplotlib.pyplot as plt
import glob
import xarray as xr
import grid
%matplotlib inline
In [2]:
base = '/ocean/nsoontie/MEOPAR/LiveOcean/*/*UBC.nc'
files = glob.glob(base)
files.sort()
In [3]:
d = xr.open_dataset(files[0])
for f in files[1:]:
with xr.open_dataset(f) as d1:
d =xr.concat([d,d1],dim='ocean_time',data_vars='minimal')
In [4]:
j=10
i=10
salt_ts = d.salt.sel(eta_rho=j,xi_rho=i)
fig, ax = plt.subplots(figsize=(10, 5))
salt_ts.plot(x='ocean_time', y='s_rho',ax=ax)
Out[4]:
<matplotlib.collections.QuadMesh at 0x7f702e1dffd0>
Surface salintiy¶
In [5]:
## Using sel in vertical instead of isel to select based on value
t = 0
s = 0
fig, ax = plt.subplots(figsize=(5, 5))
salt_surface = (
d.salt.isel(ocean_time=t)
.sel(s_rho=s,method='nearest')
)
salt_surface.plot(x='lon_rho', y='lat_rho',ax=ax)
ax.plot(d.lon_rho.values[j,i], d.lat_rho.values[j,i], 'ro')
Out[5]:
[<matplotlib.lines.Line2D at 0x7f702dd69588>]
Bottom Salinity¶
In [6]:
## Using isel in vertical instead of sel to select based on index
t = 0
s = 0
fig, ax = plt.subplots(figsize=(5, 5))
salt_bottom = (
d.salt.isel(ocean_time=t,s_rho=s)
)
salt_bottom.plot(x='lon_rho', y='lat_rho',ax=ax)
ax.plot(i,j, 'ro')
Out[6]:
[<matplotlib.lines.Line2D at 0x7f702dcdbda0>]
Be careful! There is a dimension and a variable named s_rho:
- s_rho the dimension takes values 0 to 39, ie., indices BUT it starts at the bottom. So s_rho (dimension)=0 means bottom and s_rho(dimension) = 39 means surface
- s_rho the variable contains 40 values between -1 and 0. s_rho (variable) =0 means surface and s_rho (variable) = -1 means bottom
In [7]:
d.s_rho
Out[7]:
<xarray.DataArray 's_rho' (s_rho: 40)>
array([-0.9875, -0.9625, -0.9375, -0.9125, -0.8875, -0.8625, -0.8375,
-0.8125, -0.7875, -0.7625, -0.7375, -0.7125, -0.6875, -0.6625,
-0.6375, -0.6125, -0.5875, -0.5625, -0.5375, -0.5125, -0.4875,
-0.4625, -0.4375, -0.4125, -0.3875, -0.3625, -0.3375, -0.3125,
-0.2875, -0.2625, -0.2375, -0.2125, -0.1875, -0.1625, -0.1375,
-0.1125, -0.0875, -0.0625, -0.0375, -0.0125])
Coordinates:
* s_rho (s_rho) float64 -0.9875 -0.9625 -0.9375 -0.9125 -0.8875 -0.8625 ...
Attributes:
standard_name: ocean_s_coordinate_g1
valid_min: -1.0
valid_max: 0.0
field: s_rho, scalar
long_name: S-coordinate at RHO-points
positive: up
formula_terms: s: s_rho C: Cs_r eta: zeta depth: h depth_c: hc
Daily average of surface salinity¶
In [8]:
salt_surf_daily_avg = (
d.salt.sel(s_rho=s,method='nearest')).resample('1D',dim='ocean_time')
g_simple = salt_surf_daily_avg.plot(x='lon_rho', y='lat_rho', col='ocean_time', col_wrap=6)
Because of the way I orgnanized the files, the first panel only has 23 hours of results and the last panel has one hour. It maybe be better to time_slice and then daily average.
Adding depth to xarray¶
Can I add a depth coordinate (z_rho) to the xarray dataset?
In [9]:
import grid
In [10]:
#build depth
fname = '/ocean/nsoontie/MEOPAR/LiveOcean/20160601/ocean_his_0002_UBC.nc'
G, S, T = grid.get_basic_info(fname)
z_rho = np.zeros(d.salt.shape)
for t in np.arange(z_rho.shape[0]):
zeta = d.zeta.values[t,:,:]
z_rho[t,:,:,:] = grid.get_z(G['h'],zeta,S)
In [11]:
#quick peak
times, _ = np.meshgrid(np.arange(d.ocean_time.shape[0]),np.arange(z_rho.shape[1]))
plt.pcolormesh(times.T/24,z_rho[:,:,j,i],d.salt.values[:,:,j,i],cmap='viridis')
cbar=plt.colorbar()
plt.ylabel('Depth [m]')
plt.xlabel('Time [day]')
cbar.set_label('salt')
plt.axis([0,33,-310,5])
plt.title('lon: {:.3f} lat: {:.3f}'.format(d.lon_rho.values[j,i],d.lat_rho.values[j,i]))
Out[11]:
<matplotlib.text.Text at 0x7f7029252978>
In [12]:
#build a z_rho data array
zrho_DA = xr.DataArray(z_rho, dims = ['ocean_time','s_rho', 'eta_rho', 'xi_rho'],
coords = {'ocean_time': d.ocean_time.values[:],
's_rho': d.s_rho.values[:],
'eta_rho': d.eta_rho.values[:],
'xi_rho': d.xi_rho.values[:]},
attrs = {'units': 'metres',
'positive': 'up',
'long_name': 'Depth at s-levels',
'field': 'z_rho ,scalar'})
In [13]:
#add z_rho to dataset
d = d.assign(z_rho = zrho_DA)
d = d.set_coords('z_rho') #make a coordinate
d.z_rho
Out[13]:
<xarray.DataArray 'z_rho' (ocean_time: 792, s_rho: 40, eta_rho: 31, xi_rho: 26)>
array([[[[ -2.02345870e+02, -2.35220118e+02, -2.44251343e+02, ...,
nan, nan, nan],
[ -2.07595682e+02, -2.23752651e+02, -1.99842358e+02, ...,
nan, nan, nan],
[ -2.38688055e+02, -2.41360546e+02, -1.97477155e+02, ...,
nan, nan, -6.73887899e+01],
...,
[ nan, nan, nan, ...,
nan, nan, nan],
[ nan, nan, nan, ...,
nan, nan, nan],
[ nan, nan, nan, ...,
nan, nan, nan]],
[[ -1.97913595e+02, -2.30067128e+02, -2.38900355e+02, ...,
nan, nan, nan],
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nan, nan, nan],
[ -2.33459144e+02, -2.36072998e+02, -1.93151641e+02, ...,
nan, nan, -6.59158709e+01],
...,
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nan, nan, -6.45093454e+01],
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nan, nan, -9.66787331e-01],
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nan, nan, nan],
[ -2.33462412e+02, -2.36076308e+02, -1.93154888e+02, ...,
nan, nan, -6.59115159e+01],
...,
[ nan, nan, nan, ...,
nan, nan, nan],
[ nan, nan, nan, ...,
nan, nan, nan],
[ nan, nan, nan, ...,
nan, nan, nan]],
[[ -1.93685768e+02, -2.25151354e+02, -2.33795613e+02, ...,
nan, nan, nan],
[ -1.98711123e+02, -2.14175773e+02, -1.91290144e+02, ...,
nan, nan, nan],
[ -2.28471251e+02, -2.31029181e+02, -1.89026389e+02, ...,
nan, nan, -6.45022569e+01],
...,
[ nan, nan, nan, ...,
nan, nan, nan],
[ nan, nan, nan, ...,
nan, nan, nan],
[ nan, nan, nan, ...,
nan, nan, nan]],
...,
[[ -2.55604723e+00, -2.93451922e+00, -3.03883973e+00, ...,
nan, nan, nan],
[ -2.62593181e+00, -2.81188227e+00, -2.53833669e+00, ...,
nan, nan, nan],
[ -2.98574908e+00, -3.01523115e+00, -2.51331224e+00, ...,
nan, nan, -8.36203116e-01],
...,
[ nan, nan, nan, ...,
nan, nan, nan],
[ nan, nan, nan, ...,
nan, nan, nan],
[ nan, nan, nan, ...,
nan, nan, nan]],
[[ -1.52365481e+00, -1.73420890e+00, -1.79240071e+00, ...,
nan, nan, nan],
[ -1.56677228e+00, -1.67019444e+00, -1.51878864e+00, ...,
nan, nan, nan],
[ -1.76778297e+00, -1.78360757e+00, -1.50585676e+00, ...,
nan, nan, -4.92309185e-01],
...,
[ nan, nan, nan, ...,
nan, nan, nan],
[ nan, nan, nan, ...,
nan, nan, nan],
[ nan, nan, nan, ...,
nan, nan, nan]],
[[ -6.45923696e-01, -7.13715436e-01, -7.32689005e-01, ...,
nan, nan, nan],
[ -6.66283996e-01, -6.99541291e-01, -6.51977691e-01, ...,
nan, nan, nan],
[ -7.32278698e-01, -7.36491827e-01, -6.49326801e-01, ...,
nan, nan, -1.99933533e-01],
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nan, nan, nan],
[ nan, nan, nan, ...,
nan, nan, nan],
[ nan, nan, nan, ...,
nan, nan, nan]]]])
Coordinates:
lon_rho (eta_rho, xi_rho) float64 -125.0 -125.0 -125.0 -124.9 -124.9 ...
lat_rho (eta_rho, xi_rho) float64 48.32 48.32 48.32 48.32 48.32 ...
* s_rho (s_rho) float64 -0.9875 -0.9625 -0.9375 -0.9125 -0.8875 ...
* eta_rho (eta_rho) int64 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 ...
* xi_rho (xi_rho) int64 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 ...
* ocean_time (ocean_time) datetime64[ns] 2016-06-01T01:00:00 ...
z_rho (ocean_time, s_rho, eta_rho, xi_rho) float64 -202.3 -235.2 ...
Attributes:
positive: up
long_name: Depth at s-levels
field: z_rho ,scalar
units: metres
In [14]:
# Select a depth
salt_100m = d.sel(z_rho=100, method='nearest')
salt_100m.isel(ocean_time=0).plot(x='lon_rho',y='lat_rho')
--------------------------------------------------------------------------- ValueError Traceback (most recent call last) <ipython-input-14-e2f140a35102> in <module>() 1 # Select a depth ----> 2 salt_100m = d.sel(z_rho=100, method='nearest') 3 salt_100m.isel(ocean_time=0).plot(x='lon_rho',y='lat_rho') /home/nsoontie/anaconda3/envs/analysis/lib/python3.5/site-packages/xarray/core/dataset.py in sel(self, method, tolerance, **indexers) 974 """ 975 return self.isel(**indexing.remap_label_indexers( --> 976 self, indexers, method=method, tolerance=tolerance)) 977 978 def isel_points(self, dim='points', **indexers): /home/nsoontie/anaconda3/envs/analysis/lib/python3.5/site-packages/xarray/core/indexing.py in remap_label_indexers(data_obj, indexers, method, tolerance) 189 return dict((dim, convert_label_indexer(data_obj[dim].to_index(), label, 190 dim, method, tolerance)) --> 191 for dim, label in iteritems(indexers)) 192 193 /home/nsoontie/anaconda3/envs/analysis/lib/python3.5/site-packages/xarray/core/indexing.py in <genexpr>(.0) 189 return dict((dim, convert_label_indexer(data_obj[dim].to_index(), label, 190 dim, method, tolerance)) --> 191 for dim, label in iteritems(indexers)) 192 193 /home/nsoontie/anaconda3/envs/analysis/lib/python3.5/site-packages/xarray/core/dataarray.py in to_index(self) 376 arrays. 377 """ --> 378 return self.variable.to_index() 379 380 @property /home/nsoontie/anaconda3/envs/analysis/lib/python3.5/site-packages/xarray/core/variable.py in to_index(self) 304 def to_index(self): 305 """Convert this variable to a pandas.Index""" --> 306 return self.to_coord().to_index() 307 308 @property /home/nsoontie/anaconda3/envs/analysis/lib/python3.5/site-packages/xarray/core/variable.py in to_coord(self) 300 """Return this variable as an xarray.Coordinate""" 301 return Coordinate(self.dims, self._data, self._attrs, --> 302 encoding=self._encoding, fastpath=True) 303 304 def to_index(self): /home/nsoontie/anaconda3/envs/analysis/lib/python3.5/site-packages/xarray/core/variable.py in __init__(self, name, data, attrs, encoding, fastpath) 1053 if self.ndim != 1: 1054 raise ValueError('%s objects must be 1-dimensional' % -> 1055 type(self).__name__) 1056 1057 def _data_cached(self): ValueError: Coordinate objects must be 1-dimensional
Ok, so xarray doens't easily extract to a coordinate value if the coordinate arrays are not 1D. What about plotting agains z_rho?
In [16]:
# check against s_rho first
salt_ts = d.salt.sel(eta_rho=10, xi_rho=10)
salt_ts.plot(x='ocean_time', y='s_rho')
Out[16]:
<matplotlib.collections.QuadMesh at 0x7f7028f6b780>
In [17]:
# try plotting with z_rho on y-axis
salt_ts.plot(x='ocean_time', y='z_rho')
--------------------------------------------------------------------------- TypeError Traceback (most recent call last) <ipython-input-17-59cdc6ff0a44> in <module>() 1 # try plotting with z_rho on y-axis ----> 2 salt_ts.plot(x='ocean_time', y='z_rho') /home/nsoontie/anaconda3/envs/analysis/lib/python3.5/site-packages/xarray/plot/plot.py in __call__(self, **kwargs) 244 245 def __call__(self, **kwargs): --> 246 return plot(self._da, **kwargs) 247 248 @functools.wraps(hist) /home/nsoontie/anaconda3/envs/analysis/lib/python3.5/site-packages/xarray/plot/plot.py in plot(darray, row, col, col_wrap, ax, rtol, subplot_kws, **kwargs) 122 kwargs['ax'] = ax 123 --> 124 return plotfunc(darray, **kwargs) 125 126 /home/nsoontie/anaconda3/envs/analysis/lib/python3.5/site-packages/xarray/plot/plot.py in newplotfunc(darray, x, y, ax, row, col, col_wrap, xincrease, yincrease, add_colorbar, add_labels, vmin, vmax, cmap, center, robust, extend, levels, colors, subplot_kws, **kwargs) 415 vmin=cmap_params['vmin'], 416 vmax=cmap_params['vmax'], --> 417 **kwargs) 418 419 # Label the plot with metadata /home/nsoontie/anaconda3/envs/analysis/lib/python3.5/site-packages/xarray/plot/plot.py in pcolormesh(x, y, z, ax, **kwargs) 547 y = _infer_interval_breaks(y) 548 --> 549 primitive = ax.pcolormesh(x, y, z, **kwargs) 550 551 # by default, pcolormesh picks "round" values for bounds /home/nsoontie/anaconda3/envs/analysis/lib/python3.5/site-packages/matplotlib/__init__.py in inner(ax, *args, **kwargs) 1810 warnings.warn(msg % (label_namer, func.__name__), 1811 RuntimeWarning, stacklevel=2) -> 1812 return func(ax, *args, **kwargs) 1813 pre_doc = inner.__doc__ 1814 if pre_doc is None: /home/nsoontie/anaconda3/envs/analysis/lib/python3.5/site-packages/matplotlib/axes/_axes.py in pcolormesh(self, *args, **kwargs) 5393 allmatch = (shading == 'gouraud') 5394 -> 5395 X, Y, C = self._pcolorargs('pcolormesh', *args, allmatch=allmatch) 5396 Ny, Nx = X.shape 5397 /home/nsoontie/anaconda3/envs/analysis/lib/python3.5/site-packages/matplotlib/axes/_axes.py in _pcolorargs(funcname, *args, **kw) 5009 raise TypeError( 5010 'Incompatible X, Y inputs to %s; see help(%s)' % ( -> 5011 funcname, funcname)) 5012 if allmatch: 5013 if not (Nx == numCols and Ny == numRows): TypeError: Incompatible X, Y inputs to pcolormesh; see help(pcolormesh)
I'm having troubles with this plot probably because ocean_time and z_rho are different shapes (z_rho has a time dimension too). Does the same thing happen if I try latitude on the y axis?
In [18]:
s=39
i=17
salt_surface_ts = d.salt.isel(s_rho=39, xi_rho=i)
salt_surface_ts.plot(x='ocean_time', y='lat_rho',vmin=28.5,vmax=32.5)
Out[18]:
<matplotlib.collections.QuadMesh at 0x7f7028d5dc50>
In [19]:
fig,ax=plt.subplots(1,1)
surf=d.salt.isel(ocean_time=81,s_rho=s)
surf.plot(x='lon_rho', y='lat_rho',ax=ax,vmin=28.5,vmax=32.5)
ax.plot(d.lon_rho.values[:,i], d.lat_rho.values[:,i], 'k-')
Out[19]:
[<matplotlib.lines.Line2D at 0x7f7028c666d8>]
This does work! Probably because the x-slice on lat_rho is now 1D and pcolormesh can deal with this.
For plotting with z_rho, I think that ocean_time is 1D and z_rho is 2D, so pcolormesh finds different shapes for X/Y and throws an error.
What if I try a 1D line plot with z_rho?
In [20]:
salt_ts.isel(ocean_time=0).plot(y='z_rho')
--------------------------------------------------------------------------- TypeError Traceback (most recent call last) <ipython-input-20-1486d6471f62> in <module>() ----> 1 salt_ts.isel(ocean_time=0).plot(y='z_rho') /home/nsoontie/anaconda3/envs/analysis/lib/python3.5/site-packages/xarray/plot/plot.py in __call__(self, **kwargs) 244 245 def __call__(self, **kwargs): --> 246 return plot(self._da, **kwargs) 247 248 @functools.wraps(hist) /home/nsoontie/anaconda3/envs/analysis/lib/python3.5/site-packages/xarray/plot/plot.py in plot(darray, row, col, col_wrap, ax, rtol, subplot_kws, **kwargs) 122 kwargs['ax'] = ax 123 --> 124 return plotfunc(darray, **kwargs) 125 126 /home/nsoontie/anaconda3/envs/analysis/lib/python3.5/site-packages/xarray/plot/plot.py in line(darray, *args, **kwargs) 161 _ensure_plottable([x]) 162 --> 163 primitive = ax.plot(x, darray, *args, **kwargs) 164 165 ax.set_xlabel(xlabel) /home/nsoontie/anaconda3/envs/analysis/lib/python3.5/site-packages/matplotlib/__init__.py in inner(ax, *args, **kwargs) 1810 warnings.warn(msg % (label_namer, func.__name__), 1811 RuntimeWarning, stacklevel=2) -> 1812 return func(ax, *args, **kwargs) 1813 pre_doc = inner.__doc__ 1814 if pre_doc is None: /home/nsoontie/anaconda3/envs/analysis/lib/python3.5/site-packages/matplotlib/axes/_axes.py in plot(self, *args, **kwargs) 1422 kwargs['color'] = c 1423 -> 1424 for line in self._get_lines(*args, **kwargs): 1425 self.add_line(line) 1426 lines.append(line) /home/nsoontie/anaconda3/envs/analysis/lib/python3.5/site-packages/matplotlib/axes/_base.py in _grab_next_args(self, *args, **kwargs) 384 return 385 if len(remaining) <= 3: --> 386 for seg in self._plot_args(remaining, kwargs): 387 yield seg 388 return /home/nsoontie/anaconda3/envs/analysis/lib/python3.5/site-packages/matplotlib/axes/_base.py in _plot_args(self, tup, kwargs) 372 ncx, ncy = x.shape[1], y.shape[1] 373 for j in xrange(max(ncx, ncy)): --> 374 seg = func(x[:, j % ncx], y[:, j % ncy], kw, kwargs) 375 ret.append(seg) 376 return ret /home/nsoontie/anaconda3/envs/analysis/lib/python3.5/site-packages/matplotlib/axes/_base.py in _makeline(self, x, y, kw, kwargs) 279 self._setdefaults(default_dict, kw, kwargs) 280 seg = mlines.Line2D(x, y, **kw) --> 281 self.set_lineprops(seg, **kwargs) 282 return seg 283 /home/nsoontie/anaconda3/envs/analysis/lib/python3.5/site-packages/matplotlib/axes/_base.py in set_lineprops(self, line, **kwargs) 187 def set_lineprops(self, line, **kwargs): 188 assert self.command == 'plot', 'set_lineprops only works with "plot"' --> 189 line.set(**kwargs) 190 191 def set_patchprops(self, fill_poly, **kwargs): /home/nsoontie/anaconda3/envs/analysis/lib/python3.5/site-packages/matplotlib/artist.py in set(self, **kwargs) 934 if func is None: 935 raise TypeError('There is no %s property "%s"' % --> 936 (self.__class__.__name__, k)) 937 ret.extend([func(v)]) 938 return ret TypeError: There is no Line2D property "y"
That won't work either.
Nonetheless, having z_rho in the xarray DataSet could still be useful, particularly for resampling. Let's see if that works.
In [21]:
daily = d.resample('1D', 'ocean_time')
In [22]:
daily.z_rho
--------------------------------------------------------------------------- AttributeError Traceback (most recent call last) <ipython-input-22-ee667854b461> in <module>() ----> 1 daily.z_rho /home/nsoontie/anaconda3/envs/analysis/lib/python3.5/site-packages/xarray/core/common.py in __getattr__(self, name) 192 return source[name] 193 raise AttributeError("%r object has no attribute %r" % --> 194 (type(self).__name__, name)) 195 196 def __setattr__(self, name, value): AttributeError: 'Dataset' object has no attribute 'z_rho'
Ok, so z_rho did not get resampled and is now not part of the Dataset. What if it was a variables instead of a coordinate?
In [23]:
d = d.reset_coords('z_rho')
In [24]:
daily = d.resample('1D', 'ocean_time')
daily.z_rho
Out[24]:
<xarray.DataArray 'z_rho' (ocean_time: 34, s_rho: 40, eta_rho: 31, xi_rho: 26)>
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nan, nan, nan],
[ nan, nan, nan, ...,
nan, nan, nan]],
[[ -6.45923696e-01, -7.13715436e-01, -7.32689005e-01, ...,
nan, nan, nan],
[ -6.66283996e-01, -6.99541291e-01, -6.51977691e-01, ...,
nan, nan, nan],
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[ nan, nan, nan, ...,
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[ nan, nan, nan, ...,
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Coordinates:
lon_rho (eta_rho, xi_rho) float64 -125.0 -125.0 -125.0 -124.9 -124.9 ...
lat_rho (eta_rho, xi_rho) float64 48.32 48.32 48.32 48.32 48.32 ...
* s_rho (s_rho) float64 -0.9875 -0.9625 -0.9375 -0.9125 -0.8875 ...
* eta_rho (eta_rho) int64 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 ...
* xi_rho (xi_rho) int64 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 ...
* ocean_time (ocean_time) datetime64[ns] 2016-06-01 2016-06-02 2016-06-03 ...
Excellent! So basically, I shouldn't set z_rho as a coordinate because it is multi-dimensional and time dependent. I don't get the same features as other 1D coordinates.
I think it is still worthwhile to include z_rho in the Dataset. Plotting vs z_rho will have to be done outside of xarray. And interpolating, etc too. But at least it is there.
In [ ]: