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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:
 First example
Step1:  Note that we also chose a grid to solve the equation on. The $x$ and $y$ coordinates can be obtained by
Step2:  To integrate in time we need an initial state. Equations instances have a random_state method that generates a state. The distribution of these initial conditions, when sampled from different seeds, will define the training set for later. Let's sample one random initial state and plot it
Step3:  The state of an equation is a dict object that contains all relevant fields needed for integrating in time. For advection diffusion these are concentration, x_velocity, and  y_velocity
Step4:  To perform the actual integration we need to choose a method with which to estimate the spatial derivatives of the concentration $c$. The object which estimates the derivatives is called a model and there are various models defined in models.py. Here we will use a finite difference estimation. Lastly, we need to choose a timestep, which we can ask the equation instance to supply.
Step5:  The result is a dict object. The concentration member of the dict is a tensor whose first axis corresponds to the times at which the solution was evaluated. Here we save the result as an xarray.DataArray, which makes it easy to plot.
Step8:  Defining a new equation
In this section we learn how to define a new equation. We will look at coupled reaction diffusion equations, aka the Turing Equation. They decscribe the evolution of two fields, $A$ and $B$, according to
Step9:  Now we can generate a random state and evolve it in time
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Python Code:
equation = pde.advection.equations.FiniteVolumeAdvectionDiffusion(diffusion_coefficient=0.01)
grid = grids.Grid.from_period(size=256, length=2*np.pi)
Explanation: First example: Advection diffusion
In this example we'll see how to integrate in time a pre-defined equation. Here we deal with the Advection-Diffusion equation, which describes the time evolution of the concentration $c(x,y,t)$ when it is advected by the velocity field $\vec v(x,y)=(v_x(x,y), v_y(x,y)$ and also undergoes diffusion. The equation reads
$$\frac{\partial c}{\partial t}+\vec{v}\cdot\vec{\nabla}c= D \nabla^2 c$$
where $D$ is the diffusion coefficient. The equation is implemented in various forms in the folder advection/equations. Here we choose the Finite Volume formulation.
End of explanation
x, y = grid.get_mesh()
Explanation: Note that we also chose a grid to solve the equation on. The $x$ and $y$ coordinates can be obtained by
End of explanation
initial_state = equation.random_state(grid, seed=7109179)
fig, axs = plt.subplots(1,2, figsize=(8,4))
axs[0].pcolor(grid.get_mesh()[1],
              grid.get_mesh()[0],
              initial_state['concentration'])
axs[0].set_title('initial concentration')
axs[1].streamplot(grid.get_mesh()[1],
                  grid.get_mesh()[0],
                  initial_state['y_velocity'],initial_state['x_velocity'],
                  density=2)
axs[1].set_title('velocity field');
Explanation: To integrate in time we need an initial state. Equations instances have a random_state method that generates a state. The distribution of these initial conditions, when sampled from different seeds, will define the training set for later. Let's sample one random initial state and plot it:
End of explanation
print(initial_state.keys())
Explanation: The state of an equation is a dict object that contains all relevant fields needed for integrating in time. For advection diffusion these are concentration, x_velocity, and  y_velocity:
End of explanation
time_step = equation.get_time_step(grid)
times = time_step*np.arange(400)
results = pde.core.integrate.integrate_times(
    model=pde.core.models.FiniteDifferenceModel(equation,grid),
    state=initial_state,
    times=times, axis=0)
Explanation: To perform the actual integration we need to choose a method with which to estimate the spatial derivatives of the concentration $c$. The object which estimates the derivatives is called a model and there are various models defined in models.py. Here we will use a finite difference estimation. Lastly, we need to choose a timestep, which we can ask the equation instance to supply.
End of explanation
conc=xr.DataArray(results['concentration'].numpy(),
                  dims=['time', 'x','y'],
                  coords={'time':times, 'x': x[:,0], 'y': y[0]}
                 )
conc[::99].plot(col='time', robust=True, aspect=1)
Explanation: The result is a dict object. The concentration member of the dict is a tensor whose first axis corresponds to the times at which the solution was evaluated. Here we save the result as an xarray.DataArray, which makes it easy to plot.
End of explanation
from datadrivenpdes.core import equations
from datadrivenpdes.core import grids
from datadrivenpdes.core import polynomials
from datadrivenpdes.core import states
import scipy as sp
def smooth_random_field(N, amp=0.1, np_random_state=None):
    
    generates a random field of shape (N,1) and smoothes it a bit
    
    if np_random_state is None:
      np_random_state = np.random.RandomState()
    noise=np_random_state.randn(N)  
    kernel=np.exp(-np.linspace(-6,6,N)**2)  
    return amp*sp.ndimage.convolve(noise, kernel, mode='wrap')[:,np.newaxis]
class TuringEquation(equations.Equation):
    DISCRETIZATION_NAME = 'finite_difference'
    METHOD = polynomials.Method.FINITE_DIFFERENCE
    MONOTONIC = False
    CONTINUOUS_EQUATION_NAME = 'Turing'
    key_definitions = {
        'A': states.StateDefinition(name='A',
                      tensor_indices=(),
                      derivative_orders=(0,0,0),
                      offset=(0,0)),
        'A_xx': states.StateDefinition(name='A',
                         tensor_indices=(),
                         derivative_orders=(2, 0, 0),
                         offset=(0, 0)),
        'B': states.StateDefinition(name='B',
                      tensor_indices=(),
                      derivative_orders=(0, 0, 0),
                      offset=(0, 0)),
        'B_xx': states.StateDefinition(name='B',
                         tensor_indices=(),
                         derivative_orders=(2, 0, 0),
                         offset=(0, 0)),
        'Source' : states.StateDefinition(name='Source',
                         tensor_indices=(),
                         derivative_orders=(0, 0, 0),
                         offset=(0, 0)),
    }
    evolving_keys = {'A', 'B'}
    constant_keys = {'Source'}
    def __init__(self, alpha, beta, D_A, D_B, timestep=1e-4):
        self.alpha = alpha
        self.beta = beta
        self.D_A = D_A
        self.D_B = D_B
        self._timestep = timestep
        super().__init__()
    
    def time_derivative(
          self, grid, A, A_xx, B, B_xx, Source):
        See base class.
        rA = self.reaction_A(A, B)
        rB = self.reaction_B(A, B)
        diff_A = self.D_A * A_xx
        diff_B = self.D_B * B_xx
        return {'A': rA + diff_A + Source,
                'B': rB + diff_B,}
    def reaction_A(self, A, B):
        return A - (A ** 3) - B + self.alpha
    def reaction_B(self, A, B):
        return (A - B) * self.beta
    def get_time_step(self, grid):
        return self._timestep
    def random_state(self, grid, seed=None, dtype=tf.float32):
        if seed is None:
            R = np.random.RandomState()
        else:
            R = np.random.RandomState(seed=seed)
        state = {
          'A': smooth_random_field(N=grid.size_x, np_random_state=R),
          'B': smooth_random_field(N=grid.size_x, np_random_state=R),
          'Source': smooth_random_field(N=grid.size_x, np_random_state=R),
        }
        state = {k: tf.cast(v, dtype) for k, v in state.items()}
        return state
Explanation: Defining a new equation
In this section we learn how to define a new equation. We will look at coupled reaction diffusion equations, aka the Turing Equation. They decscribe the evolution of two fields, $A$ and $B$, according to:
$$\begin{align}
\frac{\partial A}{\partial t} &= D_A\nabla^2 A + R_A(A,B)+S\
\frac{\partial B}{\partial t} &= D_B\nabla^2 B + R_B(A,B)
\end{align}$$
$D_{A,B}$ are the diffusion constants of $A$ and $B$, $R_{A,B}$ are nonlinear reaction terms and $S$ is some constant source term. For example, we'll take
$$\begin{align}
R_A&=A(1-A^2)-\alpha B & 
R_B&=\beta(A-B)
\end{align}$$
where $\alpha$ and $\beta$ are model parameters. For simplicity, we'll implelment the equation in one spatial dimension. 
Equation Keys
Because the computational framework is fully differentiable, defining an equation requires specifiying in advance what are the quantities that are used in calcualting time derivatives. These are called keys and are stored in the equation attribute key_definitions. In our case, to calculate the time evolution we need $A, B, \partial_{xx}A, \partial_{xx}B $ and $S$.
The auxilliary function states.StateDefinition defines these keys. Its input arguments are:
* name - The base name of the field. For example, the field $\partial_{xx} A$ is derived from the base field A.
* tensor_indices - In 2D and above, specify whether a field is a component of a tensor (like $v_x$ and $v_y$ in the advection example).
* derivative_orders - Specifies whether a key is a spatial derivative of a different key.
* offset - Specifies whether a field is evaluated off the center point of a grid (useful for staggered grids, e.g. finite volume schemes)
For example, in our case the key_definitions for $A$ and $\partial_{xx}A$ are
```python
key_definitions = {
    'A': states.StateDefinition(name='A',   
                  tensor_indices=(),          # Not used in one dimenional equations
                  derivative_orders=(0,0,0),  # A is not a derivative of anything else
                  offset=(0,0)),              # A is evaluated on the centerpoints of the grid
'A_xx': states.StateDefinition(name='A',       # A_xx is is derived from A
                 tensor_indices=(),
                 derivative_orders=(2, 0, 0),  # Two derivatives on the x axis
                 offset=(0, 0)),
}
``
There are two types of keys: those that evolve in time, in our case $A$ and $B$, and constant ones, in our case $S$ (and in the Advection Diffusion example - the velocity field $v$). When defining the equation we need to set the attributesevolving_keysandconstant_keys, which are both pythonset`s. 
The different keys of an Equation instance can be inspected with
python
equation.all_keys        # in our case: {'A', 'A_xx', 'B', 'B_xx', 'Source'}
equation.base_keys       # in our case: {'A', 'B', 'Source'}
equation.evolving_keys   # in our case: {'A', 'B'}
equation.constant_keys   # in our case: {'Source'}
Defining the equation
Here is a full definition of the equation:
End of explanation
eq = TuringEquation(alpha=-0.0001, beta=10, D_A=1, D_B=30)
NX=100
NY=1 # 1D can be obtained by haveing a y dimension of size 1
LX=200
grid = grids.Grid(NX, NY, step=LX/NX)
x, y=grid.get_mesh()
initial_state = eq.random_state(grid=grid, seed=12345)
times = eq._timestep*np.arange(0, 1000, 20)
model = pde.core.models.FiniteDifferenceModel(eq,grid)
res = pde.core.integrate.integrate_times(
    model=model,
    state=initial_state,
    times=times, axis=0)
fig, axs=plt.subplots(1,2, figsize=(10,5), sharey=True)
for ax, k in zip(axs, ['A','B']):
    ax.pcolormesh(x.flat, times, res[k].numpy()[...,0], cmap='RdBu')
    ax.set_title(k)
    ax.set_xlabel('x')
axs[0].set_ylabel('time')
fig.tight_layout()
Explanation: Now we can generate a random state and evolve it in time
End of explanation
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