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Given the following text description, write Python code to implement the functionality described below step by step
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Description:
 <a href="http
Step1:  Pick the initial and run conditions
Step2:  Elapsed time starts at 1 second. This prevents errors when setting our boundary conditions.
Step3:  Use Landlab methods to import an ARC ascii grid, and load the data into the field that the component needs to look at to get the data. This loads the elevation data, z, into a "field" in the grid itself, defined on the nodes.
Step4:  We can get at this data with this syntax
Step5:  Note that the boundary conditions for this grid mainly got handled with the final line of those three, but for the sake of completeness, we should probably manually "open" the outlet. We can find and set the outlet like this
Step6:  Now initialize a couple more grid fields that the component is going to need
Step7:  Let's look at our watershed topography
Step8:  Now instantiate the component itself
Step9:  Now we're going to run the loop that drives the component
Step10:  Now let's get clever, and run a set of time slices
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Python Code:
from landlab.components.overland_flow import OverlandFlow
from landlab.plot.imshow import imshow_grid
from landlab.plot.colors import water_colormap
from landlab import RasterModelGrid
from landlab.io.esri_ascii import read_esri_ascii
from matplotlib.pyplot import figure
import numpy as np
from time import time
%matplotlib inline
Explanation: <a href="http://landlab.github.io"><img style="float: left" src="../../landlab_header.png"></a>
The deAlmeida Overland Flow Component
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<small>For more Landlab tutorials, click here: <a href="https://landlab.readthedocs.io/en/latest/user_guide/tutorials.html">https://landlab.readthedocs.io/en/latest/user_guide/tutorials.html</a></small>
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This notebook illustrates running the deAlmeida overland flow component in an extremely simple-minded way on a real topography, then shows it creating a flood sequence along an inclined surface with an oscillating water surface at one end.
First, import what we'll need:
End of explanation
run_time = 100  # duration of run, (s)
h_init = 0.1  # initial thin layer of water (m)
n = 0.01  # roughness coefficient, (s/m^(1/3))
g = 9.8  # gravity (m/s^2)
alpha = 0.7  # time-step factor (nondimensional; from Bates et al., 2010)
u = 0.4  # constant velocity (m/s, de Almeida et al., 2012)
run_time_slices = (10, 50, 100)
Explanation: Pick the initial and run conditions
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elapsed_time = 1.0
Explanation: Elapsed time starts at 1 second. This prevents errors when setting our boundary conditions.
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rmg, z = read_esri_ascii("Square_TestBasin.asc")
rmg.add_field("topographic__elevation", z, at="node")
rmg.set_closed_boundaries_at_grid_edges(True, True, True, True)
Explanation: Use Landlab methods to import an ARC ascii grid, and load the data into the field that the component needs to look at to get the data. This loads the elevation data, z, into a "field" in the grid itself, defined on the nodes.
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np.all(rmg.at_node["topographic__elevation"] == z)
Explanation: We can get at this data with this syntax:
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my_outlet_node = (
    100  # This DEM was generated using Landlab and the outlet node ID was known
)
rmg.status_at_node[my_outlet_node] = 1  # 1 is the code for fixed value
Explanation: Note that the boundary conditions for this grid mainly got handled with the final line of those three, but for the sake of completeness, we should probably manually "open" the outlet. We can find and set the outlet like this:
End of explanation
rmg.add_zeros("surface_water__depth", at="node")  # water depth (m)
rmg.at_node["surface_water__depth"] += h_init
Explanation: Now initialize a couple more grid fields that the component is going to need:
End of explanation
imshow_grid(rmg, "topographic__elevation")
Explanation: Let's look at our watershed topography
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of = OverlandFlow(
    rmg, steep_slopes=True
)  # for stability in steeper environments, we set the steep_slopes flag to True
Explanation: Now instantiate the component itself
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while elapsed_time < run_time:
    # First, we calculate our time step.
    dt = of.calc_time_step()
    # Now, we can generate overland flow.
    of.overland_flow()
    # Increased elapsed time
    print("Elapsed time: ", elapsed_time)
    elapsed_time += dt
imshow_grid(rmg, "surface_water__depth", cmap="Blues")
Explanation: Now we're going to run the loop that drives the component:
End of explanation
elapsed_time = 1.0
for t in run_time_slices:
    while elapsed_time < t:
        # First, we calculate our time step.
        dt = of.calc_time_step()
        # Now, we can generate overland flow.
        of.overland_flow()
        # Increased elapsed time
        elapsed_time += dt
    figure(t)
    imshow_grid(rmg, "surface_water__depth", cmap="Blues")
Explanation: Now let's get clever, and run a set of time slices:
End of explanation
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