$ conda install notebook pymt_cemDownload local copy of notebook:
$ curl -O https://raw.githubusercontent.com/csdms/pymt/master/docs/demos/cem.ipynb
This example explores how to use a BMI implementation using the CEM model as an example.
Some magic that allows us to view images within the notebook.
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%matplotlib inline
Import the Cem class, and instantiate it. In Python, a model with a BMI will have no arguments for its constructor. Note that although the class has been instantiated, it's not yet ready to be run. We'll get to that later!
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import pymt.models
cem = pymt.models.Cem()
Even though we can't run our waves model yet, we can still get some information about it. Just don't try to run it. Some things we can do with our model are get the names of the input variables.
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cem.output_var_names
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cem.input_var_names
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We can also get information about specific variables. Here we'll look at some info about wave direction. This is the main input of the Cem model. Notice that BMI components always use CSDMS standard names. The CSDMS Standard Name for wave angle is,
"sea_surface_water_wave__azimuth_angle_of_opposite_of_phase_velocity"
Quite a mouthful, I know. With that name we can get information about that variable and the grid that it is on (it's actually not a one).
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angle_name = 'sea_surface_water_wave__azimuth_angle_of_opposite_of_phase_velocity'
print("Data type: %s" % cem.get_var_type(angle_name))
print("Units: %s" % cem.get_var_units(angle_name))
print("Grid id: %d" % cem.get_var_grid(angle_name))
print("Number of elements in grid: %d" % cem.get_grid_number_of_nodes(0))
print("Type of grid: %s" % cem.get_grid_type(0))
OK. We're finally ready to run the model. Well not quite. First we initialize the model with the BMI initialize method. Normally we would pass it a string that represents the name of an input file. For this example we'll pass None, which tells Cem to use some defaults.
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args = cem.setup(number_of_rows=100, number_of_cols=200, grid_spacing=200.)
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cem.initialize(*args)
Before running the model, let's set a couple input parameters. These two parameters represent the wave height and wave period of the incoming waves to the coastline.
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import numpy as np
cem.set_value("sea_surface_water_wave__height", 2.)
cem.set_value("sea_surface_water_wave__period", 7.)
cem.set_value("sea_surface_water_wave__azimuth_angle_of_opposite_of_phase_velocity", 0. * np.pi / 180.)
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The main output variable for this model is water depth. In this case, the CSDMS Standard Name is much shorter:
"sea_water__depth"
First we find out which of Cem's grids contains water depth.
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grid_id = cem.get_var_grid('sea_water__depth')
With the grid_id, we can now get information about the grid. For instance, the number of dimension and the type of grid (structured, unstructured, etc.). This grid happens to be uniform rectilinear. If you were to look at the "grid" types for wave height and period, you would see that they aren't on grids at all but instead are scalars.
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grid_type = cem.get_grid_type(grid_id)
grid_rank = cem.get_grid_ndim(grid_id)
print('Type of grid: %s (%dD)' % (grid_type, grid_rank))
Because this grid is uniform rectilinear, it is described by a set of BMI methods that are only available for grids of this type. These methods include:
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spacing = np.empty((grid_rank, ), dtype=float)
shape = cem.get_grid_shape(grid_id)
cem.get_grid_spacing(grid_id, out=spacing)
print('The grid has %d rows and %d columns' % (shape[0], shape[1]))
print('The spacing between rows is %f and between columns is %f' % (spacing[0], spacing[1]))
Allocate memory for the water depth grid and get the current values from cem.
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z = np.empty(shape, dtype=float)
cem.get_value('sea_water__depth', out=z)
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Here I define a convenience function for plotting the water depth and making it look pretty. You don't need to worry too much about it's internals for this tutorial. It just saves us some typing later on.
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def plot_coast(spacing, z):
import matplotlib.pyplot as plt
xmin, xmax = 0., z.shape[1] * spacing[0] * 1e-3
ymin, ymax = 0., z.shape[0] * spacing[1] * 1e-3
plt.imshow(z, extent=[xmin, xmax, ymin, ymax], origin='lower', cmap='ocean')
plt.colorbar().ax.set_ylabel('Water Depth (m)')
plt.xlabel('Along shore (km)')
plt.ylabel('Cross shore (km)')
It generates plots that look like this. We begin with a flat delta (green) and a linear coastline (y = 3 km). The bathymetry drops off linearly to the top of the domain.
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plot_coast(spacing, z)
Right now we have waves coming in but no sediment entering the ocean. To add some discharge, we need to figure out where to put it. For now we'll put it on a cell that's next to the ocean.
Allocate memory for the sediment discharge array and set the discharge at the coastal cell to some value.
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qs = np.zeros_like(z)
qs[0, 100] = 1250
The CSDMS Standard Name for this variable is:
"land_surface_water_sediment~bedload__mass_flow_rate"
You can get an idea of the units based on the quantity part of the name. "mass_flow_rate" indicates mass per time. You can double-check this with the BMI method function get_var_units.
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cem.get_var_units('land_surface_water_sediment~bedload__mass_flow_rate')
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cem.time_step, cem.time_units, cem.time
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Set the bedload flux and run the model.
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for time in range(3000):
cem.set_value('land_surface_water_sediment~bedload__mass_flow_rate', qs)
cem.update_until(time)
cem.get_value('sea_water__depth', out=z)
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cem.time
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cem.get_value('sea_water__depth', out=z)
plot_coast(spacing, z)
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val = np.empty((5, ), dtype=float)
cem.get_value("basin_outlet~coastal_center__x_coordinate", val)
val / 100.
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Let's add another sediment source with a different flux and update the model.
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qs[0, 150] = 1500
for time in range(3750):
cem.set_value('land_surface_water_sediment~bedload__mass_flow_rate', qs)
cem.update_until(time)
cem.get_value('sea_water__depth', out=z)
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In [23]:
plot_coast(spacing, z)
Here we shut off the sediment supply completely.
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qs.fill(0.)
for time in range(4000):
cem.set_value('land_surface_water_sediment~bedload__mass_flow_rate', qs)
cem.update_until(time)
cem.get_value('sea_water__depth', out=z)
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plot_coast(spacing, z)
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