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#!f2py --f90exec=mpif90 -I. -c -m ocean ocean.f90



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import time
import numpy as np

import wave as ocean

import cmocean as cmo
import matplotlib.pyplot as plt
from mpl_toolkits.axes_grid1 import make_axes_locatable

%matplotlib inline

Definition of model variables

Model domain / grid parameters



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file1='../data/gbr_south.csv'
file2='../data/topoGBR1000.csv'

# Bathymetric filename
bfile = file1

# Resolution factor
rfac = 4

Definition of wave parameters



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# Wave heights (m)
H0 = [2,3,2]

# Define wave source direction at boundary 
# (angle in degrees counterclock wise from horizontal axis)
dir = [300,0,90]

# Percentage of each wave scenario activity (in %)
perc = [3,3,4]

# Maximum depth for wave influence (m)
wbase = 20

# Sea level position (m)
slvl = 0.

Definition of sediment parameters



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# Mean grain size diameter in m
d50 = 0.0001

# Steps used to perform sediment transport
tsteps = 1000

# Steps used to perform sediment diffusion
dsteps = 1000

Running wavesed

Here we use a unique function that computes wave and sediment evolution for a set of multiple input forcing conditions



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#help(ocean.runWaveSed)



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avewH,avewS,aveDZ,sim = ocean.runWaveSed(bfile,rfac,H0,dir,perc,
                                         wbase,slvl,d50,tsteps,
                                         dsteps,size = (10,40))

Plotting combined evolution



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size = (20,40)
# i1 = 0 
# i2 = -1
# j1 = 0
# j2 = -1

# Zooming to a specific region
i1 = 600 
i2 = 1200 
j1 = 0
j2 = 500 

fig = plt.figure(figsize=size)
ax = plt.gca()
    
ax.set_title('Erosion/deposition (m)', fontsize=10)
im = ax.imshow(np.flipud(aveDZ[i1:i2,j1:j2].T),interpolation='nearest',
                         cmap=cmo.cm.balance,vmin=-0.25, vmax=0.25)


ax.contour(np.flipud(sim.regZ[i1:i2,j1:j2].T-sim.sealvl), 0, 
            colors='k',  linewidths=2)
divider1 = make_axes_locatable(ax)
cax1 = divider1.append_axes("right", size="5%", pad=0.05)
cbar1 = plt.colorbar(im,cax=cax1)

plt.tight_layout()
plt.show()



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