# Scratchpad

In this chapter, we do blah. Specifically

- foo

- bar.

- baz

For more details, see Inference in discrete SSMs and [Sar13].

## Python

We’re now ready to start coding.

    from matplotlib import rcParams, cycler
    import matplotlib.pyplot as plt
    import numpy as np
    plt.ion()

    # Fixing random state for reproducibility
    np.random.seed(19680801)

    N = 10
    data = [np.logspace(0, 1, 100) + np.random.randn(100) + ii for ii in range(N)]
    data = np.array(data).T
    cmap = plt.cm.coolwarm
    rcParams['axes.prop_cycle'] = cycler(color=cmap(np.linspace(0, 1, N)))

    from matplotlib.lines import Line2D
    custom_lines = [Line2D([0], [0], color=cmap(0.), lw=4),
                    Line2D([0], [0], color=cmap(.5), lw=4),
                    Line2D([0], [0], color=cmap(1.), lw=4)]

    fig, ax = plt.subplots(figsize=(10, 5))
    lines = ax.plot(data)
    ax.legend(custom_lines, ['Cold', 'Medium', 'Hot']);

    import matplotlib.pyplot as plt
    import numpy as np
    import jax
    import jax.numpy as jnp

    print(jax.devices())

    WARNING:absl:No GPU/TPU found, falling back to CPU. (Set TF_CPP_MIN_LOG_LEVEL=0 and rerun for more info.)

    [CpuDevice(id=0)]

## Images

Fig. 1 A photo of a cat and a dog.

Fig. 2 Another photo of a cat and a dog.

In Figure 1 we show catdog. In Figure 2 we show catdog2, its twin.

## Math

We have \(E= mc^2\), and also

(1)\[a x^2 + bx+ c = 0\]

From (1), it follows that

\[\begin{split} \begin{align} 0 &= a x^2 + bx+ c \\ 0 &= a x^2 + bx+ c \end{align} \end{split}\]

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State Space Models: A Modern Approach

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