Using Gaussian priors in a blackbox likelihood function

I am trying to fit a transit model to some data with a blackbox likelihood function with gaussian priors on the period. But when I run pymc with the priors the mcmc fails. When debugging it looks like the mcmc is exploring values way outside the standard deviation of the normal distribution I am using. I suspect it has something to do with the gradient function I am defining? I don’t get any errors just a final corner plot that doesn’t look like it explored the space properly. I am very new to using mcmc so any help would be appreciated!!
This is how I am setting up the model:


    logl = LogLikeWithGrad(log_likelihood, t, data, sigma, constants)
    with pm.Model():
        RP_RS = pm.Uniform("RP_RS", lower=0.16, upper=0.18)

        period = pm.Normal("period", mu=period, sigma=err_period)

        theta = pt.as_tensor_variable([RP_RS, period])
        pm.Potential("likelihood", logl(theta))
        idata_grad = pm.sample(1000, tune=2000)

this is my grad function:

def normal_gradients(theta, t, data, sigma, constants):
    def lnlike(values, i):
        theta[i] = values
        return log_likelihood(theta, t, data, sigma, constants, prior)
    eps = np.sqrt(np.finfo(float).eps)
    grads = np.empty(len(theta))
    for i in range(-1, len(theta)-1):
        grads[i] = optimize.approx_fprime(theta[i], lnlike, eps, i)
    return grads

and this is my likelihood function:

def log_likelihood(fit_variables, t, data, sigma, constants, prior):
    lnp_data = 0.0
    fits['RP_RS'] = fit_variables[0]
    fits['period'] = fit_variables[1]
    model = transit_model(t, fits, constants)
    lnp_data += -0.5 * np.sum(((data - model)**2 / sigma**2) + np.log(sigma*2*np.pi))
   #the gaussian prior (informative prior) on the period
    lnp_params += -0.5*((fit_variables[1] - prior['period'])**2 / prior['period']**2) + np.log(prior['period']*2*np.pi)

    return lnp_data + lnp_params


Can we see the plots that look suspect?