sammosummo · January 2, 2023 07:44
diff --git a/ddm_in_aesara.py b/ddm_in_aesara.py
 """Aesara/Theano functions for calculating the probability density of the Wiener
 diffusion first-passage time (WFPT) distribution used in drift diffusion models (DDMs).

 """
 import arviz as az
 import matplotlib.pyplot as plt
 import numpy as np
 import pymc3 as pm
 from pymc3.distributions import draw_values, generate_samples

 from in_jax import jax_wfpt_rvs

 try:

    import aesara.tensor as T

 except ImportError:

    import theano.tensor as T


 def aesara_use_fast(x):
    """For each element in `x`, return `True` if the fast-RT expansion is more efficient
    than the slow-RT expansion.

    Args:
        x: RTs. (0, inf).

    """
    err = 1e-7  # I don't think this value matters much so long as it's small

    # determine number of terms needed for small-t expansion
    _a = 2 * T.sqrt(2 * np.pi * x) * err < 1
    _b = 2 + T.sqrt(-2 * x * T.log(2 * T.sqrt(2 * np.pi * x) * err))
    _c = T.sqrt(x) + 1
    _d = T.max(T.stack([_b, _c]), axis=0)
    ks = _a * _d + (1 - _a) * 2

    # determine number of terms needed for large-t expansion
    _a = np.pi * x * err < 1
    _b = 1.0 / (np.pi * T.sqrt(x))
    _c = T.sqrt(-2 * T.log(np.pi * x * err) / (np.pi ** 2 * x))
    _d = T.max(T.stack([_b, _c]), axis=0)
    kl = _a * _d + (1 - _a) * _b

    return ks < kl  # select the most accurate expansion for a fixed number of terms


 def aesara_fnorm_fast(x, w):
    """Density function for lower-bound first-passage times with drift rate set to 0 and
    upper bound set to 1, calculated using the fast-RT expansion.

    Args:
        x: RTs. (0, inf).
        w: Normalized decision starting point. (0, 1).

    """
    bigk = 7  # number of terms to evaluate; TODO: this needs tuning eventually

    # calculated the dumb way
    # p1 = (w + 2 * -3) * T.exp(-T.power(w + 2 * -3, 2) / 2 / x)
    # p2 = (w + 2 * -2) * T.exp(-T.power(w + 2 * -2, 2) / 2 / x)
    # p3 = (w + 2 * -1) * T.exp(-T.power(w + 2 * -1, 2) / 2 / x)
    # p4 = (w + 2 * 0) * T.exp(-T.power(w + 2 * 0, 2) / 2 / x)
    # p5 = (w + 2 * 1) * T.exp(-T.power(w + 2 * 1, 2) / 2 / x)
    # p6 = (w + 2 * 2) * T.exp(-T.power(w + 2 * 2, 2) / 2 / x)
    # p7 = (w + 2 * 3) * T.exp(-T.power(w + 2 * 3, 2) / 2 / x)
    # p = (p1 + p2 + p3 + p4 + p5 + p6 + p7) / T.sqrt(2 * np.pi * T.power(x, 3))

    # calculated the better way
    # k = (T.arange(bigk) - T.floor(bigk / 2)).reshape((-1, 1))
    # y = w + 2 * k
    # r = -T.power(y, 2) / 2 / x
    # p = T.sum(y * T.exp(r), axis=0) / T.sqrt(2 * np.pi * T.power(x, 3))

    # calculated using the "log-sum-exp trick" to reduce under/overflows
    k = T.arange(bigk) - T.floor(bigk / 2)
    y = w + 2 * k.reshape((-1, 1))
    r = -T.power(y, 2) / 2 / x
    c = T.max(r, axis=0)
    p = T.exp(c + T.log(T.sum(y * T.exp(r - c), axis=0)))
    p = p / T.sqrt(2 * np.pi * T.power(x, 3))

    return p


 def aesara_fnorm_slow(x, w):
    """Density function for lower-bound first-passage times with drift rate set to 0 and
    upper bound set to 1, calculated using the slow-RT expansion.

    Args:
        x: RTs. (0, inf).
        w: Normalized decision starting point. (0, 1).

    """
    bigk = 7  # number of terms to evaluate; TODO: this needs tuning eventually

    # calculated the dumb way
    # b = T.power(np.pi, 2) * x / 2
    # p1 = T.exp(-T.power(1, 2) * b) * T.sin(np.pi * w)
    # p2 = 2 * T.exp(-T.power(2, 2) * b) * T.sin(2 * np.pi * w)
    # p3 = 3 * T.exp(-T.power(3, 2) * b) * T.sin(3 * np.pi * w)
    # p4 = 4 * T.exp(-T.power(4, 2) * b) * T.sin(4 * np.pi * w)
    # p5 = 5 * T.exp(-T.power(5, 2) * b) * T.sin(5 * np.pi * w)
    # p6 = 6 * T.exp(-T.power(6, 2) * b) * T.sin(6 * np.pi * w)
    # p7 = 7 * T.exp(-T.power(7, 2) * b) * T.sin(7 * np.pi * w)
    # p = (p1 + p2 + p3 + p4 + p5 + p6 + p7) * np.pi
    # print(p)

    # calculated the better way
    k = T.arange(1, bigk + 1).reshape((-1, 1))
    y = k * T.sin(k * np.pi * w)
    r = -T.power(k, 2) * T.power(np.pi, 2) * x / 2
    p = T.sum(y * T.exp(r), axis=0) * np.pi
    # print(p)

    # calculated using the "log-sum-exp trick" to reduce under/overflows
    # k = T.arange(1, bigk + 1).reshape((-1, 1))
    # y = k * T.sin(k * np.pi * w)
    # r = -T.power(k, 2) * T.power(np.pi, 2) * x / 2
    # c = T.max(r, axis=0)
    # p = T.exp(c + T.log(T.sum(y * T.exp(r - c), axis=0))) * np.pi

    return p


 def aesara_fnorm(x, w):
    """Density function for lower-bound first-passage times with drift rate set to 0 and
    upper bound set to 1, selecting the most efficient expansion per element in `x`.

    Args:
        x: RTs. (0, inf).
        w: Normalized decision starting point. (0, 1).

    """
    y = T.abs_(x)
    f = aesara_fnorm_fast(y, w)
    s = aesara_fnorm_slow(y, w)
    u = aesara_use_fast(y)
    positive = x > 0
    return (f * u + s * (1 - u)) * positive


 def aesara_pdf_sv(x, v, sv, a, z, t):
    """Probability density function with drift rates normally distributed over trials.

    Args:
        x: RTs. (-inf, inf) except 0. Negative values correspond to the lower bound.
        v: Mean drift rate. (-inf, inf).
        sv: Standard deviation of drift rate. [0, inf), 0 if fixed.
        a: Value of decision upper bound. (0, inf).
        z: Normalized decision starting point. (0, 1).
        t: Non-decision time. [0, inf)

    """
    flip = x > 0
    v = flip * -v + (1 - flip) * v  # transform v if x is upper-bound response
    z = flip * (1 - z) + (1 - flip) * z  # transform z if x is upper-bound response
    x = T.abs_(x)  # abs_olute rts
    x = x - t  # remove nondecision time

    tt = x / T.power(a, 2)  # "normalize" RTs
    p = aesara_fnorm(tt, z)  # normalized densities

    return (
        T.exp(
            T.log(p)
            + ((a * z * sv) ** 2 - 2 * a * v * z - (v ** 2) * x)
            / (2 * (sv ** 2) * x + 2)
        )
        / T.sqrt((sv ** 2) * x + 1)
        / (a ** 2)
    )


 def aesara_pdf_sv_sz(x, v, sv, a, z, sz, t):
    """Probability density function with normally distributed drift rate and uniformly
    distributed starting point.

    Args:
        x: RTs. (-inf, inf) except 0. Negative values correspond to the lower bound.
        v: Mean drift rate. (-inf, inf).
        sv: Standard deviation of drift rate. [0, inf), 0 if fixed.
        a: Value of decision upper bound. (0, inf).
        z: Normalized decision starting point. (0, 1).
        sz: Range of starting points [0, 1), 0 if fixed.
        t: Non-decision time. [0, inf)

    """
    # uses eq. 4.1.14 from Press et al., numerical recipes: the art of scientific
    # computing, 2007 for a more efficient 10-step approximation to the integral based
    # on simpson's rule
    n = 10
    h = sz / n
    z0 = z - sz / 2

    f = 17 * aesara_pdf_sv(x, v, sv, a, z0, t)
    f = f + 59 * aesara_pdf_sv(x, v, sv, a, z0 + h, t)
    f = f + 43 * aesara_pdf_sv(x, v, sv, a, z0 + 2 * h, t)
    f = f + 49 * aesara_pdf_sv(x, v, sv, a, z0 + 3 * h, t)

    f = f + 48 * aesara_pdf_sv(x, v, sv, a, z0 + 4 * h, t)
    f = f + 48 * aesara_pdf_sv(x, v, sv, a, z0 + 5 * h, t)
    f = f + 48 * aesara_pdf_sv(x, v, sv, a, z0 + 6 * h, t)

    f = f + 49 * aesara_pdf_sv(x, v, sv, a, z0 + h * (n - 3), t)
    f = f + 43 * aesara_pdf_sv(x, v, sv, a, z0 + h * (n - 2), t)
    f = f + 59 * aesara_pdf_sv(x, v, sv, a, z0 + h * (n - 1), t)
    f = f + 17 * aesara_pdf_sv(x, v, sv, a, z0 + h * n, t)

    return f / 48 / n


 def aesara_pdf_sv_sz_st(x, v, sv, a, z, sz, t, st):
    """ "Probability density function with normally distributed drift rate, uniformly
    distributed starting point, and uniformly distributed nondecision time.

    Args:
        x: RTs. (-inf, inf) except 0. Negative values correspond to the lower bound.
        v: Mean drift rate. (-inf, inf).
        sv: Standard deviation of drift rate. [0, inf), 0 if fixed.
        a: Value of decision upper bound. (0, inf).
        z: Normalized decision starting point. (0, 1).
        sz: Range of starting points [0, 1), 0 if fixed.
        t: Nondecision time. [0, inf)
        st: Range of Nondecision points [0, 1), 0 if fixed.

    """
    # uses eq. 4.1.14 from Press et al., Numerical recipes: the art of scientific
    # computing, 2007 for a more efficient n-step approximation to the integral based
    # on simpson's rule when n is even and > 4.
    n = 10
    h = st / n
    t0 = t - st / 2

    f = 17 * aesara_pdf_sv_sz(x, v, sv, a, z, sz, t0)
    f = f + 59 * aesara_pdf_sv_sz(x, v, sv, a, z, sz, t0 + h)
    f = f + 43 * aesara_pdf_sv_sz(x, v, sv, a, z, sz, t0 + 2 * h)
    f = f + 49 * aesara_pdf_sv_sz(x, v, sv, a, z, sz, t0 + 3 * h)

    f = f + 48 * aesara_pdf_sv_sz(x, v, sv, a, z, sz, t0 + 4 * h)
    f = f + 48 * aesara_pdf_sv_sz(x, v, sv, a, z, sz, t0 + 5 * h)
    f = f + 48 * aesara_pdf_sv_sz(x, v, sv, a, z, sz, t0 + 6 * h)

    f = f + 49 * aesara_pdf_sv_sz(x, v, sv, a, z, sz, t0 + h * (n - 3))
    f = f + 43 * aesara_pdf_sv_sz(x, v, sv, a, z, sz, t0 + h * (n - 2))
    f = f + 59 * aesara_pdf_sv_sz(x, v, sv, a, z, sz, t0 + h * (n - 1))
    f = f + 17 * aesara_pdf_sv_sz(x, v, sv, a, z, sz, t0 + h * n)

    return f / 48 / n


 def aesara_pdf_contaminant(x, l, r):
    """Probability density function of exponentially distributed RTs.

    Args:
        x: RTs. (-inf, inf) except 0. Negative values correspond to the lower bound.
        l: Shape parameter of the exponential distribution. (0, inf).
        r: Proportion of upper-bound contaminants. [0, 1].

    """
    p = l * T.exp(-l * T.abs_(x))
    return r * p * (x > 0) + (1 - r) * p * (x < 0)


 def aesara_pdf_sv_sz_st_con(x, v, sv, a, z, sz, t, st, q, l, r):
    """ "Probability density function with normally distributed drift rate, uniformly
    distributed starting point, uniformly distributed nondecision time, and
    exponentially distributed contaminants.

    Args:
        x: RTs. (-inf, inf) except 0. Negative values correspond to the lower bound.
        v: Mean drift rate. (-inf, inf).
        sv: Standard deviation of drift rate. [0, inf), 0 if fixed.
        a: Value of decision upper bound. (0, inf).
        z: Normalized decision starting point. (0, 1).
        sz: Range of starting points [0, 1), 0 if fixed.
        t: Nondecision time. [0, inf)
        st: Range of Nondecision points [0, 1), 0 if fixed.
        q: Proportion of contaminants. [0, 1].
        l: Shape parameter of the exponential distribution. (0, inf).
        r: Proportion of upper-bound contaminants. [0, 1].

    """
    p_rts = aesara_pdf_sv_sz_st(x, v, sv, a, z, sz, t, st)
    p_con = aesara_pdf_contaminant(x, l, r)
    return p_rts * (1 - q) + p_con * q


 def aesara_wfpt_log_like(x, v, sv, a, z, sz, t, st, q, l, r):
    """Returns the log likelihood of the WFPT distribution with normally distributed
    drift rate, uniformly distributed starting point, uniformly distributed nondecision
    time, and exponentially distributed contaminants.

    Args:
        x: RTs. (-inf, inf) except 0. Negative values correspond to the lower bound.
        v: Mean drift rate. (-inf, inf).
        sv: Standard deviation of drift rate. [0, inf), 0 if fixed.
        a: Value of decision upper bound. (0, inf).
        z: Normalized decision starting point. (0, 1).
        sz: Range of starting points [0, 1), 0 if fixed.
        t: Nondecision time. [0, inf)
        st: Range of Nondecision points [0, 1), 0 if fixed.
        q: Proportion of contaminants. [0, 1].
        l: Shape parameter of the exponential distribution. (0, inf).
        r: Proportion of upper-bound contaminants. [0, 1].

    """
    return T.sum(T.log(aesara_pdf_sv_sz_st_con(x, v, sv, a, z, sz, t, st, q, l, r)))


 def aesara_wfpt_rvs(v, sv, a, z, sz, t, st, q, l, r, size):
    """Generate random samples from the WFPT distribution with normally distributed
    drift rate, uniformly distributed starting point, uniformly distributed nondecision
    time, and exponentially distributed contaminants.

    Args:
        v: Mean drift rate. (-inf, inf).
        sv: Standard deviation of drift rate. [0, inf), 0 if fixed.
        a: Value of decision upper bound. (0, inf).
        z: Normalized decision starting point. (0, 1).
        sz: Range of starting points [0, 1), 0 if fixed.
        t: Nondecision time. [0, inf)
        st: Range of Nondecision points [0, 1), 0 if fixed.
        q: Proportion of contaminants. [0, 1].
        l: Shape parameter of the exponential distribution. (0, inf).
        r: Proportion of upper-bound contaminants. [0, 1].
        size: Number of samples to generate.

    """

    x = np.linspace(-12, 12, 48000)
    dx = x[1] - x[0]

    pdf = aesara_pdf_sv_sz_st_con(x, v, sv + 1, a, z, sz, t, st, q, l, r)
    cdf = np.cumsum(pdf) * dx
    cdf = cdf / cdf.max()
    u = np.random.random_sample(size)
    ix = np.searchsorted(cdf, u)

    return x[ix]





 class WFPT(pm.Continuous):
    """Wiener first-passage time (WFPT) log-likelihood."""

    def __init__(self, v, sv, a, z, sz, t, st, q, l, r, **kwargs):
        self.v = v = T.as_tensor_variable(pm.floatX(v))
        self.sv = sv = T.as_tensor_variable(pm.floatX(sv))
        self.a = a = T.as_tensor_variable(pm.floatX(a))
        self.z = z = T.as_tensor_variable(pm.floatX(z))
        self.sz = sz = T.as_tensor_variable(pm.floatX(sz))
        self.t = t = T.as_tensor_variable(pm.floatX(t))
        self.st = st = T.as_tensor_variable(pm.floatX(st))
        self.q = q = T.as_tensor_variable(pm.floatX(q))
        self.l = l = T.as_tensor_variable(pm.floatX(l))
        self.r = r = T.as_tensor_variable(pm.floatX(r))
        super().__init__(**kwargs)

    def logp(self, value):
        return aesara_wfpt_log_like(
            value,
            self.v,
            self.sv,
            self.a,
            self.z,
            self.sz,
            self.t,
            self.st,
            self.q,
            self.l,
            self.r,
        )

    def random(self, point=None, size=None):
        v, sv, a, z, sz, t, st, q, l, r, _ = draw_values([self.v,
            self.sv,
            self.a,
            self.z,
            self.sz,
            self.t,
            self.st,
            self.q,
            self.l,
            self.r,], point=point, size=size)
        return generate_samples(
            jax_wfpt_rvs, v, sv, a, z, sz, t, st, q, l, r, size
        )


 def test():

    v = 1
    sv = 0
    a = 0.8
    z = 0.5
    sz = 0.0
    t = 0.0
    st = 0.
    q = .0
    l = 0.5
    r = 0.8

    size = 3000

    x = jax_wfpt_rvs(v, sv, a, z, sz, t, st, q, l, r, size)


    with pm.Model() as model:

        v = pm.Normal(name="v")
        WFPT(name="x", v=v, sv=sv, a=a, z=z, sz=sz, t=t, st=st, q=q, l=l, r=r, observed=x)
        results = pm.sample(10000, return_inferencedata=True)
        az.plot_trace(results)
        plt.show()


 if __name__ == '__main__':
    test()
	"""Aesara/Theano functions for calculating the probability density of the Wiener
	diffusion first-passage time (WFPT) distribution used in drift diffusion models (DDMs).

	"""
	import arviz as az
	import matplotlib.pyplot as plt
	import numpy as np
	import pymc3 as pm
	from pymc3.distributions import draw_values, generate_samples

	from in_jax import jax_wfpt_rvs

	try:

	import aesara.tensor as T

	except ImportError:

	import theano.tensor as T


	def aesara_use_fast(x):
	"""For each element in `x`, return `True` if the fast-RT expansion is more efficient
	than the slow-RT expansion.

	Args:
	x: RTs. (0, inf).

	"""
	err = 1e-7 # I don't think this value matters much so long as it's small

	# determine number of terms needed for small-t expansion
	_a = 2 * T.sqrt(2 * np.pi * x) * err < 1
	_b = 2 + T.sqrt(-2 * x * T.log(2 * T.sqrt(2 * np.pi * x) * err))
	_c = T.sqrt(x) + 1
	_d = T.max(T.stack([_b, _c]), axis=0)
	ks = _a * _d + (1 - _a) * 2

	# determine number of terms needed for large-t expansion
	_a = np.pi * x * err < 1
	_b = 1.0 / (np.pi * T.sqrt(x))
	_c = T.sqrt(-2 * T.log(np.pi * x * err) / (np.pi ** 2 * x))
	_d = T.max(T.stack([_b, _c]), axis=0)
	kl = _a * _d + (1 - _a) * _b

	return ks < kl # select the most accurate expansion for a fixed number of terms


	def aesara_fnorm_fast(x, w):
	"""Density function for lower-bound first-passage times with drift rate set to 0 and
	upper bound set to 1, calculated using the fast-RT expansion.

	Args:
	x: RTs. (0, inf).
	w: Normalized decision starting point. (0, 1).

	"""
	bigk = 7 # number of terms to evaluate; TODO: this needs tuning eventually

	# calculated the dumb way
	# p1 = (w + 2 * -3) * T.exp(-T.power(w + 2 * -3, 2) / 2 / x)
	# p2 = (w + 2 * -2) * T.exp(-T.power(w + 2 * -2, 2) / 2 / x)
	# p3 = (w + 2 * -1) * T.exp(-T.power(w + 2 * -1, 2) / 2 / x)
	# p4 = (w + 2 * 0) * T.exp(-T.power(w + 2 * 0, 2) / 2 / x)
	# p5 = (w + 2 * 1) * T.exp(-T.power(w + 2 * 1, 2) / 2 / x)
	# p6 = (w + 2 * 2) * T.exp(-T.power(w + 2 * 2, 2) / 2 / x)
	# p7 = (w + 2 * 3) * T.exp(-T.power(w + 2 * 3, 2) / 2 / x)
	# p = (p1 + p2 + p3 + p4 + p5 + p6 + p7) / T.sqrt(2 * np.pi * T.power(x, 3))

	# calculated the better way
	# k = (T.arange(bigk) - T.floor(bigk / 2)).reshape((-1, 1))
	# y = w + 2 * k
	# r = -T.power(y, 2) / 2 / x
	# p = T.sum(y * T.exp(r), axis=0) / T.sqrt(2 * np.pi * T.power(x, 3))

	# calculated using the "log-sum-exp trick" to reduce under/overflows
	k = T.arange(bigk) - T.floor(bigk / 2)
	y = w + 2 * k.reshape((-1, 1))
	r = -T.power(y, 2) / 2 / x
	c = T.max(r, axis=0)
	p = T.exp(c + T.log(T.sum(y * T.exp(r - c), axis=0)))
	p = p / T.sqrt(2 * np.pi * T.power(x, 3))

	return p


	def aesara_fnorm_slow(x, w):
	"""Density function for lower-bound first-passage times with drift rate set to 0 and
	upper bound set to 1, calculated using the slow-RT expansion.

	Args:
	x: RTs. (0, inf).
	w: Normalized decision starting point. (0, 1).

	"""
	bigk = 7 # number of terms to evaluate; TODO: this needs tuning eventually

	# calculated the dumb way
	# b = T.power(np.pi, 2) * x / 2
	# p1 = T.exp(-T.power(1, 2) * b) * T.sin(np.pi * w)
	# p2 = 2 * T.exp(-T.power(2, 2) * b) * T.sin(2 * np.pi * w)
	# p3 = 3 * T.exp(-T.power(3, 2) * b) * T.sin(3 * np.pi * w)
	# p4 = 4 * T.exp(-T.power(4, 2) * b) * T.sin(4 * np.pi * w)
	# p5 = 5 * T.exp(-T.power(5, 2) * b) * T.sin(5 * np.pi * w)
	# p6 = 6 * T.exp(-T.power(6, 2) * b) * T.sin(6 * np.pi * w)
	# p7 = 7 * T.exp(-T.power(7, 2) * b) * T.sin(7 * np.pi * w)
	# p = (p1 + p2 + p3 + p4 + p5 + p6 + p7) * np.pi
	# print(p)

	# calculated the better way
	k = T.arange(1, bigk + 1).reshape((-1, 1))
	y = k * T.sin(k * np.pi * w)
	r = -T.power(k, 2) * T.power(np.pi, 2) * x / 2
	p = T.sum(y * T.exp(r), axis=0) * np.pi
	# print(p)

	# calculated using the "log-sum-exp trick" to reduce under/overflows
	# k = T.arange(1, bigk + 1).reshape((-1, 1))
	# y = k * T.sin(k * np.pi * w)
	# r = -T.power(k, 2) * T.power(np.pi, 2) * x / 2
	# c = T.max(r, axis=0)
	# p = T.exp(c + T.log(T.sum(y * T.exp(r - c), axis=0))) * np.pi

	return p


	def aesara_fnorm(x, w):
	"""Density function for lower-bound first-passage times with drift rate set to 0 and
	upper bound set to 1, selecting the most efficient expansion per element in `x`.

	Args:
	x: RTs. (0, inf).
	w: Normalized decision starting point. (0, 1).

	"""
	y = T.abs_(x)
	f = aesara_fnorm_fast(y, w)
	s = aesara_fnorm_slow(y, w)
	u = aesara_use_fast(y)
	positive = x > 0
	return (f * u + s * (1 - u)) * positive


	def aesara_pdf_sv(x, v, sv, a, z, t):
	"""Probability density function with drift rates normally distributed over trials.

	Args:
	x: RTs. (-inf, inf) except 0. Negative values correspond to the lower bound.
	v: Mean drift rate. (-inf, inf).
	sv: Standard deviation of drift rate. [0, inf), 0 if fixed.
	a: Value of decision upper bound. (0, inf).
	z: Normalized decision starting point. (0, 1).
	t: Non-decision time. [0, inf)

	"""
	flip = x > 0
	v = flip * -v + (1 - flip) * v # transform v if x is upper-bound response
	z = flip * (1 - z) + (1 - flip) * z # transform z if x is upper-bound response
	x = T.abs_(x) # abs_olute rts
	x = x - t # remove nondecision time

	tt = x / T.power(a, 2) # "normalize" RTs
	p = aesara_fnorm(tt, z) # normalized densities

	return (
	T.exp(
	T.log(p)
	+ ((a * z * sv) ** 2 - 2 * a * v * z - (v ** 2) * x)
	/ (2 * (sv ** 2) * x + 2)
	)
	/ T.sqrt((sv ** 2) * x + 1)
	/ (a ** 2)
	)


	def aesara_pdf_sv_sz(x, v, sv, a, z, sz, t):
	"""Probability density function with normally distributed drift rate and uniformly
	distributed starting point.

	Args:
	x: RTs. (-inf, inf) except 0. Negative values correspond to the lower bound.
	v: Mean drift rate. (-inf, inf).
	sv: Standard deviation of drift rate. [0, inf), 0 if fixed.
	a: Value of decision upper bound. (0, inf).
	z: Normalized decision starting point. (0, 1).
	sz: Range of starting points [0, 1), 0 if fixed.
	t: Non-decision time. [0, inf)

	"""
	# uses eq. 4.1.14 from Press et al., numerical recipes: the art of scientific
	# computing, 2007 for a more efficient 10-step approximation to the integral based
	# on simpson's rule
	n = 10
	h = sz / n
	z0 = z - sz / 2

	f = 17 * aesara_pdf_sv(x, v, sv, a, z0, t)
	f = f + 59 * aesara_pdf_sv(x, v, sv, a, z0 + h, t)
	f = f + 43 * aesara_pdf_sv(x, v, sv, a, z0 + 2 * h, t)
	f = f + 49 * aesara_pdf_sv(x, v, sv, a, z0 + 3 * h, t)

	f = f + 48 * aesara_pdf_sv(x, v, sv, a, z0 + 4 * h, t)
	f = f + 48 * aesara_pdf_sv(x, v, sv, a, z0 + 5 * h, t)
	f = f + 48 * aesara_pdf_sv(x, v, sv, a, z0 + 6 * h, t)

	f = f + 49 * aesara_pdf_sv(x, v, sv, a, z0 + h * (n - 3), t)
	f = f + 43 * aesara_pdf_sv(x, v, sv, a, z0 + h * (n - 2), t)
	f = f + 59 * aesara_pdf_sv(x, v, sv, a, z0 + h * (n - 1), t)
	f = f + 17 * aesara_pdf_sv(x, v, sv, a, z0 + h * n, t)

	return f / 48 / n


	def aesara_pdf_sv_sz_st(x, v, sv, a, z, sz, t, st):
	""" "Probability density function with normally distributed drift rate, uniformly
	distributed starting point, and uniformly distributed nondecision time.

	Args:
	x: RTs. (-inf, inf) except 0. Negative values correspond to the lower bound.
	v: Mean drift rate. (-inf, inf).
	sv: Standard deviation of drift rate. [0, inf), 0 if fixed.
	a: Value of decision upper bound. (0, inf).
	z: Normalized decision starting point. (0, 1).
	sz: Range of starting points [0, 1), 0 if fixed.
	t: Nondecision time. [0, inf)
	st: Range of Nondecision points [0, 1), 0 if fixed.

	"""
	# uses eq. 4.1.14 from Press et al., Numerical recipes: the art of scientific
	# computing, 2007 for a more efficient n-step approximation to the integral based
	# on simpson's rule when n is even and > 4.
	n = 10
	h = st / n
	t0 = t - st / 2

	f = 17 * aesara_pdf_sv_sz(x, v, sv, a, z, sz, t0)
	f = f + 59 * aesara_pdf_sv_sz(x, v, sv, a, z, sz, t0 + h)
	f = f + 43 * aesara_pdf_sv_sz(x, v, sv, a, z, sz, t0 + 2 * h)
	f = f + 49 * aesara_pdf_sv_sz(x, v, sv, a, z, sz, t0 + 3 * h)

	f = f + 48 * aesara_pdf_sv_sz(x, v, sv, a, z, sz, t0 + 4 * h)
	f = f + 48 * aesara_pdf_sv_sz(x, v, sv, a, z, sz, t0 + 5 * h)
	f = f + 48 * aesara_pdf_sv_sz(x, v, sv, a, z, sz, t0 + 6 * h)

	f = f + 49 * aesara_pdf_sv_sz(x, v, sv, a, z, sz, t0 + h * (n - 3))
	f = f + 43 * aesara_pdf_sv_sz(x, v, sv, a, z, sz, t0 + h * (n - 2))
	f = f + 59 * aesara_pdf_sv_sz(x, v, sv, a, z, sz, t0 + h * (n - 1))
	f = f + 17 * aesara_pdf_sv_sz(x, v, sv, a, z, sz, t0 + h * n)

	return f / 48 / n


	def aesara_pdf_contaminant(x, l, r):
	"""Probability density function of exponentially distributed RTs.

	Args:
	x: RTs. (-inf, inf) except 0. Negative values correspond to the lower bound.
	l: Shape parameter of the exponential distribution. (0, inf).
	r: Proportion of upper-bound contaminants. [0, 1].

	"""
	p = l * T.exp(-l * T.abs_(x))
	return r * p * (x > 0) + (1 - r) * p * (x < 0)


	def aesara_pdf_sv_sz_st_con(x, v, sv, a, z, sz, t, st, q, l, r):
	""" "Probability density function with normally distributed drift rate, uniformly
	distributed starting point, uniformly distributed nondecision time, and
	exponentially distributed contaminants.

	Args:
	x: RTs. (-inf, inf) except 0. Negative values correspond to the lower bound.
	v: Mean drift rate. (-inf, inf).
	sv: Standard deviation of drift rate. [0, inf), 0 if fixed.
	a: Value of decision upper bound. (0, inf).
	z: Normalized decision starting point. (0, 1).
	sz: Range of starting points [0, 1), 0 if fixed.
	t: Nondecision time. [0, inf)
	st: Range of Nondecision points [0, 1), 0 if fixed.
	q: Proportion of contaminants. [0, 1].
	l: Shape parameter of the exponential distribution. (0, inf).
	r: Proportion of upper-bound contaminants. [0, 1].

	"""
	p_rts = aesara_pdf_sv_sz_st(x, v, sv, a, z, sz, t, st)
	p_con = aesara_pdf_contaminant(x, l, r)
	return p_rts * (1 - q) + p_con * q


	def aesara_wfpt_log_like(x, v, sv, a, z, sz, t, st, q, l, r):
	"""Returns the log likelihood of the WFPT distribution with normally distributed
	drift rate, uniformly distributed starting point, uniformly distributed nondecision
	time, and exponentially distributed contaminants.

	Args:
	x: RTs. (-inf, inf) except 0. Negative values correspond to the lower bound.
	v: Mean drift rate. (-inf, inf).
	sv: Standard deviation of drift rate. [0, inf), 0 if fixed.
	a: Value of decision upper bound. (0, inf).
	z: Normalized decision starting point. (0, 1).
	sz: Range of starting points [0, 1), 0 if fixed.
	t: Nondecision time. [0, inf)
	st: Range of Nondecision points [0, 1), 0 if fixed.
	q: Proportion of contaminants. [0, 1].
	l: Shape parameter of the exponential distribution. (0, inf).
	r: Proportion of upper-bound contaminants. [0, 1].

	"""
	return T.sum(T.log(aesara_pdf_sv_sz_st_con(x, v, sv, a, z, sz, t, st, q, l, r)))


	def aesara_wfpt_rvs(v, sv, a, z, sz, t, st, q, l, r, size):
	"""Generate random samples from the WFPT distribution with normally distributed
	drift rate, uniformly distributed starting point, uniformly distributed nondecision
	time, and exponentially distributed contaminants.

	Args:
	v: Mean drift rate. (-inf, inf).
	sv: Standard deviation of drift rate. [0, inf), 0 if fixed.
	a: Value of decision upper bound. (0, inf).
	z: Normalized decision starting point. (0, 1).
	sz: Range of starting points [0, 1), 0 if fixed.
	t: Nondecision time. [0, inf)
	st: Range of Nondecision points [0, 1), 0 if fixed.
	q: Proportion of contaminants. [0, 1].
	l: Shape parameter of the exponential distribution. (0, inf).
	r: Proportion of upper-bound contaminants. [0, 1].
	size: Number of samples to generate.

	"""

	x = np.linspace(-12, 12, 48000)
	dx = x[1] - x[0]

	pdf = aesara_pdf_sv_sz_st_con(x, v, sv + 1, a, z, sz, t, st, q, l, r)
	cdf = np.cumsum(pdf) * dx
	cdf = cdf / cdf.max()
	u = np.random.random_sample(size)
	ix = np.searchsorted(cdf, u)

	return x[ix]





	class WFPT(pm.Continuous):
	"""Wiener first-passage time (WFPT) log-likelihood."""

	def __init__(self, v, sv, a, z, sz, t, st, q, l, r, **kwargs):
	self.v = v = T.as_tensor_variable(pm.floatX(v))
	self.sv = sv = T.as_tensor_variable(pm.floatX(sv))
	self.a = a = T.as_tensor_variable(pm.floatX(a))
	self.z = z = T.as_tensor_variable(pm.floatX(z))
	self.sz = sz = T.as_tensor_variable(pm.floatX(sz))
	self.t = t = T.as_tensor_variable(pm.floatX(t))
	self.st = st = T.as_tensor_variable(pm.floatX(st))
	self.q = q = T.as_tensor_variable(pm.floatX(q))
	self.l = l = T.as_tensor_variable(pm.floatX(l))
	self.r = r = T.as_tensor_variable(pm.floatX(r))
	super().__init__(**kwargs)

	def logp(self, value):
	return aesara_wfpt_log_like(
	value,
	self.v,
	self.sv,
	self.a,
	self.z,
	self.sz,
	self.t,
	self.st,
	self.q,
	self.l,
	self.r,
	)

	def random(self, point=None, size=None):
	v, sv, a, z, sz, t, st, q, l, r, _ = draw_values([self.v,
	self.sv,
	self.a,
	self.z,
	self.sz,
	self.t,
	self.st,
	self.q,
	self.l,
	self.r,], point=point, size=size)
	return generate_samples(
	jax_wfpt_rvs, v, sv, a, z, sz, t, st, q, l, r, size
	)


	def test():

	v = 1
	sv = 0
	a = 0.8
	z = 0.5
	sz = 0.0
	t = 0.0
	st = 0.
	q = .0
	l = 0.5
	r = 0.8

	size = 3000

	x = jax_wfpt_rvs(v, sv, a, z, sz, t, st, q, l, r, size)


	with pm.Model() as model:

	v = pm.Normal(name="v")
	WFPT(name="x", v=v, sv=sv, a=a, z=z, sz=sz, t=t, st=st, q=q, l=l, r=r, observed=x)
	results = pm.sample(10000, return_inferencedata=True)
	az.plot_trace(results)
	plt.show()


	if __name__ == '__main__':
	test()