diff --git a/frc971/control_loops/python/BUILD b/frc971/control_loops/python/BUILD
index fb3e971..09945ba 100644
--- a/frc971/control_loops/python/BUILD
+++ b/frc971/control_loops/python/BUILD
@@ -96,3 +96,18 @@
     visibility = ["//visibility:public"],
     deps = ["//frc971/control_loops:python_init"],
 )
+
+py_binary(
+    name = "spline",
+    srcs = [
+        "spline.py",
+    ],
+    legacy_create_init = False,
+    restricted_to = ["//tools:k8"],
+    deps = [
+        "//external:python-gflags",
+        "//external:python-glog",
+        "//frc971/control_loops/python:controls",
+        "@matplotlib",
+    ],
+)
diff --git a/frc971/control_loops/python/spline.py b/frc971/control_loops/python/spline.py
new file mode 100644
index 0000000..2f8c836
--- /dev/null
+++ b/frc971/control_loops/python/spline.py
@@ -0,0 +1,320 @@
+#!/usr/bin/python
+
+from __future__ import print_function
+
+import numpy
+import sys
+from matplotlib import pylab
+import glog
+import gflags
+
+"""This file is my playground for implementing spline following."""
+
+FLAGS = gflags.FLAGS
+
+
+def spline(alpha, control_points):
+    """Computes a Bezier curve.
+
+    Args:
+        alpha: scalar or list of spline parameters to calculate the curve at.
+        control_points: n x 4 matrix of control points.  n[:, 0] is the
+            starting point, and n[:, 3] is the ending point.
+
+    Returns:
+        n x m matrix of spline points.  n is the dimension of the control
+        points, and m is the number of points in 'alpha'.
+    """
+    if numpy.isscalar(alpha):
+        alpha = [alpha]
+    alpha_matrix = [[(1.0 - a)**3.0, 3.0 * (1.0 - a)**2.0 * a,
+                     3.0 * (1.0 - a) * a**2.0, a**3.0] for a in alpha]
+
+    return control_points * numpy.matrix(alpha_matrix).T
+
+
+def dspline(alpha, control_points):
+    """Computes the derivitive of a Bezier curve wrt alpha.
+
+    Args:
+        alpha: scalar or list of spline parameters to calculate the curve at.
+        control_points: n x 4 matrix of control points.  n[:, 0] is the
+            starting point, and n[:, 3] is the ending point.
+
+    Returns:
+        n x m matrix of spline point derivatives.  n is the dimension of the
+        control points, and m is the number of points in 'alpha'.
+    """
+    if numpy.isscalar(alpha):
+        alpha = [alpha]
+    dalpha_matrix = [[
+        -3.0 * (1.0 - a)**2.0, 3.0 * (1.0 - a)**2.0 + -2.0 * 3.0 *
+        (1.0 - a) * a, -3.0 * a**2.0 + 2.0 * 3.0 * (1.0 - a) * a, 3.0 * a**2.0
+    ] for a in alpha]
+
+    return control_points * numpy.matrix(dalpha_matrix).T
+
+
+def ddspline(alpha, control_points):
+    """Computes the second derivitive of a Bezier curve wrt alpha.
+
+    Args:
+        alpha: scalar or list of spline parameters to calculate the curve at.
+        control_points: n x 4 matrix of control points.  n[:, 0] is the
+            starting point, and n[:, 3] is the ending point.
+
+    Returns:
+        n x m matrix of spline point second derivatives.  n is the dimension of
+        the control points, and m is the number of points in 'alpha'.
+    """
+    if numpy.isscalar(alpha):
+        alpha = [alpha]
+    ddalpha_matrix = [[
+        2.0 * 3.0 * (1.0 - a),
+        -2.0 * 3.0 * (1.0 - a) + -2.0 * 3.0 * (1.0 - a) + 2.0 * 3.0 * a,
+        -2.0 * 3.0 * a + 2.0 * 3.0 * (1.0 - a) - 2.0 * 3.0 * a,
+        2.0 * 3.0 * a
+    ] for a in alpha]
+
+    return control_points * numpy.matrix(ddalpha_matrix).T
+
+
+def dddspline(alpha, control_points):
+    """Computes the third derivitive of a Bezier curve wrt alpha.
+
+    Args:
+        alpha: scalar or list of spline parameters to calculate the curve at.
+        control_points: n x 4 matrix of control points.  n[:, 0] is the
+            starting point, and n[:, 3] is the ending point.
+
+    Returns:
+        n x m matrix of spline point second derivatives.  n is the dimension of
+        the control points, and m is the number of points in 'alpha'.
+    """
+    if numpy.isscalar(alpha):
+        alpha = [alpha]
+    ddalpha_matrix = [[
+        -2.0 * 3.0,
+        2.0 * 3.0 + 2.0 * 3.0 + 2.0 * 3.0,
+        -2.0 * 3.0 - 2.0 * 3.0 - 2.0 * 3.0,
+        2.0 * 3.0
+    ] for a in alpha]
+
+    return control_points * numpy.matrix(ddalpha_matrix).T
+
+
+def spline_theta(alpha, control_points, dspline_points=None):
+    """Computes the heading of a robot following a Bezier curve at alpha.
+
+    Args:
+        alpha: scalar or list of spline parameters to calculate the heading at.
+        control_points: n x 4 matrix of control points.  n[:, 0] is the
+            starting point, and n[:, 3] is the ending point.
+
+    Returns:
+        m array of spline point headings.  m is the number of points in 'alpha'.
+    """
+    if dspline_points is None:
+        dspline_points = dspline(alphas, control_points)
+
+    return numpy.arctan2(
+        numpy.array(dspline_points)[1, :], numpy.array(dspline_points)[0, :])
+
+
+def dspline_theta(alphas,
+                  control_points,
+                  dspline_points=None,
+                  ddspline_points=None):
+    """Computes the derivitive of the heading at alpha.
+
+    This is the derivitive of spline_theta wrt alpha.
+
+    Args:
+        alpha: scalar or list of spline parameters to calculate the derivative
+            of the heading at.
+        control_points: n x 4 matrix of control points.  n[:, 0] is the
+            starting point, and n[:, 3] is the ending point.
+
+    Returns:
+        m array of spline point heading derivatives.  m is the number of points
+        in 'alpha'.
+    """
+    if dspline_points is None:
+        dspline_points = dspline(alphas, control_points)
+
+    if ddspline_points is None:
+        ddspline_points = ddspline(alphas, control_points)
+
+    dx = numpy.array(dspline_points)[0, :]
+    dy = numpy.array(dspline_points)[1, :]
+
+    ddx = numpy.array(ddspline_points)[0, :]
+    ddy = numpy.array(ddspline_points)[1, :]
+
+    return 1.0 / (dx**2.0 + dy**2.0) * (dx * ddy - dy * ddx)
+
+
+def ddspline_theta(alphas,
+                   control_points,
+                   dspline_points=None,
+                   ddspline_points=None,
+                   dddspline_points=None):
+    """Computes the second derivitive of the heading at alpha.
+
+    This is the second derivitive of spline_theta wrt alpha.
+
+    Args:
+        alpha: scalar or list of spline parameters to calculate the second
+            derivative of the heading at.
+        control_points: n x 4 matrix of control points.  n[:, 0] is the
+            starting point, and n[:, 3] is the ending point.
+
+    Returns:
+        m array of spline point heading second derivatives.  m is the number of
+        points in 'alpha'.
+    """
+    if dspline_points is None:
+        dspline_points = dspline(alphas, control_points)
+
+    if ddspline_points is None:
+        ddspline_points = ddspline(alphas, control_points)
+
+    if dddspline_points is None:
+        dddspline_points = dddspline(alphas, control_points)
+
+    dddspline_points = dddspline(alphas, control_points)
+
+    dx = numpy.array(dspline_points)[0, :]
+    dy = numpy.array(dspline_points)[1, :]
+
+    ddx = numpy.array(ddspline_points)[0, :]
+    ddy = numpy.array(ddspline_points)[1, :]
+
+    dddx = numpy.array(dddspline_points)[0, :]
+    dddy = numpy.array(dddspline_points)[1, :]
+
+    return -1.0 / ((dx**2.0 + dy**2.0)**2.0) * (dx * ddy - dy * ddx) * 2.0 * (
+        dy * ddy + dx * ddx) + 1.0 / (dx**2.0 + dy**2.0) * (dx * dddy - dy *
+                                                            dddx)
+
+
+def main(argv):
+    # Build up the control point matrix
+    start = numpy.matrix([[0.0, 0.0]]).T
+    c1 = numpy.matrix([[0.5, 0.0]]).T
+    c2 = numpy.matrix([[0.5, 1.0]]).T
+    end = numpy.matrix([[1.0, 1.0]]).T
+    control_points = numpy.hstack((start, c1, c2, end))
+
+    # The alphas to plot
+    alphas = numpy.linspace(0.0, 1.0, 1000)
+
+    # Compute x, y and the 3 derivatives
+    spline_points = spline(alphas, control_points)
+    dspline_points = dspline(alphas, control_points)
+    ddspline_points = ddspline(alphas, control_points)
+    dddspline_points = dddspline(alphas, control_points)
+
+    # Compute theta and the two derivatives
+    theta = spline_theta(alphas, control_points, dspline_points=dspline_points)
+    dtheta = dspline_theta(alphas, control_points, dspline_points=dspline_points)
+    ddtheta = ddspline_theta(
+        alphas,
+        control_points,
+        dspline_points=dspline_points,
+        dddspline_points=dddspline_points)
+
+    # Plot the control points and the spline.
+    pylab.figure()
+    pylab.plot(
+        numpy.array(control_points)[0, :],
+        numpy.array(control_points)[1, :],
+        '-o',
+        label='control')
+    pylab.plot(
+        numpy.array(spline_points)[0, :],
+        numpy.array(spline_points)[1, :],
+        label='spline')
+    pylab.legend()
+
+    # For grins, confirm that the double integral of the acceleration (with
+    # respect to the spline parameter) matches the position.  This lets us
+    # confirm that the derivatives are consistent.
+    xint_plot = numpy.matrix(numpy.zeros((2, len(alphas))))
+    dxint_plot = xint_plot.copy()
+    xint = spline_points[:, 0].copy()
+    dxint = dspline_points[:, 0].copy()
+    xint_plot[:, 0] = xint
+    dxint_plot[:, 0] = dxint
+    for i in range(len(alphas) - 1):
+        xint += (alphas[i + 1] - alphas[i]) * dxint
+        dxint += (alphas[i + 1] - alphas[i]) * ddspline_points[:, i]
+        xint_plot[:, i + 1] = xint
+        dxint_plot[:, i + 1] = dxint
+
+    # Integrate up the spline velocity and heading to confirm that given a
+    # velocity (as a function of the spline parameter) and angle, we will move
+    # from the starting point to the ending point.
+    thetaint_plot = numpy.zeros((len(alphas),))
+    thetaint = theta[0]
+    dthetaint_plot = numpy.zeros((len(alphas),))
+    dthetaint = dtheta[0]
+    thetaint_plot[0] = thetaint
+    dthetaint_plot[0] = dthetaint
+
+    txint_plot = numpy.matrix(numpy.zeros((2, len(alphas))))
+    txint = spline_points[:, 0].copy()
+    txint_plot[:, 0] = txint
+    for i in range(len(alphas) - 1):
+        dalpha = alphas[i + 1] - alphas[i]
+        txint += dalpha * numpy.linalg.norm(
+            dspline_points[:, i]) * numpy.matrix(
+                [[numpy.cos(theta[i])], [numpy.sin(theta[i])]])
+        txint_plot[:, i + 1] = txint
+        thetaint += dalpha * dtheta[i]
+        dthetaint += dalpha * ddtheta[i]
+        thetaint_plot[i + 1] = thetaint
+        dthetaint_plot[i + 1] = dthetaint
+
+
+    # Now plot x, dx/dalpha, ddx/ddalpha, dddx/dddalpha, and integrals thereof
+    # to perform consistency checks.
+    pylab.figure()
+    pylab.plot(alphas, numpy.array(spline_points)[0, :], label='x')
+    pylab.plot(alphas, numpy.array(xint_plot)[0, :], label='ix')
+    pylab.plot(alphas, numpy.array(dspline_points)[0, :], label='dx')
+    pylab.plot(alphas, numpy.array(dxint_plot)[0, :], label='idx')
+    pylab.plot(alphas, numpy.array(txint_plot)[0, :], label='tix')
+    pylab.plot(alphas, numpy.array(ddspline_points)[0, :], label='ddx')
+    pylab.plot(alphas, numpy.array(dddspline_points)[0, :], label='dddx')
+    pylab.legend()
+
+    # Now do the same for y.
+    pylab.figure()
+    pylab.plot(alphas, numpy.array(spline_points)[1, :], label='y')
+    pylab.plot(alphas, numpy.array(xint_plot)[1, :], label='iy')
+    pylab.plot(alphas, numpy.array(dspline_points)[1, :], label='dy')
+    pylab.plot(alphas, numpy.array(dxint_plot)[1, :], label='idy')
+    pylab.plot(alphas, numpy.array(txint_plot)[1, :], label='tiy')
+    pylab.plot(alphas, numpy.array(ddspline_points)[1, :], label='ddy')
+    pylab.plot(alphas, numpy.array(dddspline_points)[1, :], label='dddy')
+    pylab.legend()
+
+    # And for theta.
+    pylab.figure()
+    pylab.plot(alphas, theta, label='theta')
+    pylab.plot(alphas, dtheta, label='dtheta')
+    pylab.plot(alphas, ddtheta, label='ddtheta')
+    pylab.plot(alphas, thetaint_plot, label='thetai')
+    pylab.plot(alphas, dthetaint_plot, label='dthetai')
+
+    # TODO(austin): Start creating a velocity plan now that we have all the
+    # derivitives of our spline.
+
+    pylab.legend()
+    pylab.show()
+
+
+if __name__ == '__main__':
+    argv = FLAGS(sys.argv)
+    sys.exit(main(argv))