In [1]:

## check mean file ##

In [165]:

import cmocean.cm as cm
import copy
import matplotlib.pyplot as plt
from matplotlib.collections import PatchCollection
from matplotlib.patches import Rectangle
import numpy as np
import xarray as xr

In [179]:

plt.rcParams['font.size'] = 15
cm_balance = copy.copy(cm.balance)
cm_balance.set_bad('grey')

In [11]:

data = xr.open_dataset('/data/sallen/results/Reshapr/output/SalishSeaCast_hour_vvelocity_20150725_20150804.nc')

In [4]:

igrid = 386
j1, j2 = 210, 320

In [12]:

data.vomecrty

Out[12]:

<xarray.DataArray 'vomecrty' (time: 264, depth: 40, gridY: 1, gridX: 110)>
[1161600 values with dtype=float32]
Coordinates:
  * time     (time) datetime64[ns] 2015-07-25T00:30:00 ... 2015-08-04T23:30:00
  * depth    (depth) float32 0.5 1.5 2.5 3.5 4.5 ... 360.7 387.6 414.5 441.5
  * gridY    (gridY) int64 386
  * gridX    (gridX) int64 210 211 212 213 214 215 ... 314 315 316 317 318 319
Attributes:
    units:          m s-1
    standard_name:  sea_water_y_velocity
    long_name:      Ocean Current Along y-Axis

In [15]:

data.vomecrty[:].mean(axis=0).plot(yincrease=False);

In [16]:

data = xr.open_dataset('/data/sallen/results/Reshapr/output/SalishSeaCast_hour_vvelocity_20150601_20150901.nc')

In [17]:

data

In [29]:

fig, axs = plt.subplots(2, 2, figsize=(10,10))
data.vomecrty[0].plot(ax=axs[0,0], yincrease=False);
data.vomecrty[1].plot(ax=axs[0,1], yincrease=False);
data.vomecrty[2].plot(ax=axs[1,0], yincrease=False);
data.vomecrty[0:3].mean(axis=0).plot(ax=axs[1,1], yincrease=False);

In [10]:

mesh = xr.open_dataset('/home/sallen/MEOPAR/grid/mesh_mask202108.nc')
vmask = 1 - mesh.vmask[0]
e3v = mesh.e3v_0[0]
e1v = mesh.e1v[0]
mesh.close()

In [70]:

pos_values = np.array(data.vomecrty[0:3, :, 0].mean(axis=0))
neg_values = np.array(data.vomecrty[0:3, :, 0].mean(axis=0))
depth = np.array(data.depth)
pos_values[pos_values < 0] = 0
neg_values[neg_values > 0] = 0
xx, yy = np.meshgrid(depth, range(110))

fig, axs = plt.subplots(1, 2, figsize=(10, 4))
colours = axs[0].pcolormesh(range(110), depth, pos_values, cmap='bwr', vmax=0.4, vmin=-0.4, shading='nearest')
axs[0].invert_yaxis()
fig.colorbar(colours, ax=axs[0])
colours = axs[1].pcolormesh(range(110), depth, neg_values, cmap='bwr', vmax=0.4, vmin=-0.4, shading='nearest')
axs[1].invert_yaxis()
fig.colorbar(colours, ax=axs[1]);

In [59]:

print ((pos_values * e3v[:, igrid, j1:j2] * e1v[igrid, j1:j2]).sum())

<xarray.DataArray ()>
array(80727.6382259)

In [60]:

print ((neg_values * e3v[:, igrid, j1:j2] * e1v[igrid, j1:j2]).sum())

<xarray.DataArray ()>
array(-90961.69763315)

In [52]:

data_2015 = xr.open_dataset('/data/sallen/results/Reshapr/output/SalishSeaCast_hour_vvelocity_20150101_20151231.nc')

data_2016 = xr.open_dataset('/data/sallen/results/Reshapr/output/SalishSeaCast_hour_vvelocity_20160101_20161231.nc')

data_2017 = xr.open_dataset('/data/sallen/results/Reshapr/output/SalishSeaCast_hour_vvelocity_20170101_20171231.nc')

data_2018 = xr.open_dataset('/data/sallen/results/Reshapr/output/SalishSeaCast_hour_vvelocity_20180101_20181231.nc')

data = xr.concat([data_2015, data_2016, data_2017, data_2018], dim='time')
data

In [53]:

data_2015.vomecrty.mean(axis=0).plot(yincrease=False);

In [54]:

def process(data):
    pos_values = np.array(data.vomecrty[:, :, 0].mean(axis=0))
    neg_values = np.array(data.vomecrty[:, :, 0].mean(axis=0))
    depth = np.array(data.depth)
    pos_values[pos_values < 0] = 0
    neg_values[neg_values > 0] = 0

    fig, axs = plt.subplots(1, 2, figsize=(10, 4))
    colours = axs[0].pcolormesh(range(110), depth, pos_values, cmap='bwr', vmax=0.4, vmin=-0.4, shading='nearest')
    axs[0].invert_yaxis()
    fig.colorbar(colours, ax=axs[0])
    colours = axs[1].pcolormesh(range(110), depth, neg_values, cmap='bwr', vmax=0.4, vmin=-0.4, shading='nearest')
    axs[1].invert_yaxis()
    fig.colorbar(colours, ax=axs[1]);

    print ('Positive Flux', (pos_values * e3v[:, igrid, j1:j2] * e1v[igrid, j1:j2]).sum())
    print ('Negative Flux', (neg_values * e3v[:, igrid, j1:j2] * e1v[igrid, j1:j2]).sum())
    return pos_values, neg_values

In [55]:

pos_values, neg_values = process(data)

Positive Flux <xarray.DataArray ()>
array(62208.90137442)
Negative Flux <xarray.DataArray ()>
array(-70296.12312508)

In [68]:

def process_lateral_avg(data, cut):
    pos_values = np.array(data.vomecrty[:, :, 0, cut:].mean(axis=0))
    pos_values = (pos_values * e1v[igrid, j1+cut:j2].values 
                  * e3v[:, igrid, j1+cut:j2].values).sum(axis=1)
    print (pos_values.shape)
    neg_values = np.array(data.vomecrty[:, :, 0, cut:].mean(axis=0))
    neg_values = (neg_values * e1v[igrid, j1+cut:j2].values 
                  * e3v[:, igrid, j1+cut:j2].values).sum(axis=1)
    depth = np.array(data.depth)
    pos_values[pos_values < 0] = 0
    neg_values[neg_values > 0] = 0

    fig, ax = plt.subplots(1, 1, figsize=(5, 4))
    colours = ax.plot(depth, pos_values, c='tab:blue')
    colours = ax.plot(depth, neg_values, c='tab:red')

    print ('Positive Flux', pos_values.sum())
    print ('Negative Flux', neg_values.sum())

In [69]:

process_lateral_avg(data, cut=50)

(40,)
Positive Flux 36649.558997214444
Negative Flux -41839.67339921515

In [71]:

process_lateral_avg(data, cut=0)

(40,)
Positive Flux 37717.51127859701
Negative Flux -45804.73302926108

In [47]:

data

In [161]:

mymesh = xr.open_dataset('/home/sallen/MEOPAR/grid/mesh_mask202108.nc')


vmask = mymesh.vmask[0, :, figrid]
u_lons = mymesh.glamv[0, figrid]    # note switch to v with the switch from t to f
#w_depths = mymesh.gdepw_1d
w_depths = mymesh.gdepw_0[0, :, igrid]
w_depths.shape

Out[161]:

(40, 398)

In [181]:

fig, ax = plt.subplots(1, 1, figsize=(7, 4))
#data.vomecrty.mean(axis=0).plot(ax=ax, yincrease=False, cmap=cm.balance,);

plotfield = np.ma.array(data.vomecrty.mean(axis=0)[:,0], mask= 1-mymesh.vmask[0, :, igrid, j1:j2])

colours = ax.pcolormesh(np.array(u_lons[j1:j2]), np.array(w_depths[:, j1:j2]), plotfield, cmap=cm_balance, 
                            vmax = 0.2, vmin = -0.2)

cb = fig.colorbar(colours, ax=ax)
cb.set_label('Velocity [m s$^{-1}$]')

#pc = draw_patches(fmask, j1, j2, np.array(u_lons), np.array(w_depths))
#ax.add_collection(pc)

ax.set_ylim(250, 0)
ax.set_ylabel('Depth [m]')
ax.set_xlabel('Longitude [$^o$E]')
ax.set_title("Mean velocity across Point Roberts Transect");

plt.savefig('meanvelocity.pdf')
plt.savefig('meanvelocity.png')

/tmp/ipykernel_1372334/3745285282.py:6: UserWarning: The input coordinates to pcolormesh are interpreted as cell centers, but are not monotonically increasing or decreasing. This may lead to incorrectly calculated cell edges, in which case, please supply explicit cell edges to pcolormesh.
  colours = ax.pcolormesh(np.array(u_lons[j1:j2]), np.array(w_depths[:, j1:j2]), plotfield, cmap=cm_balance,

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