human
Give me a the Python program you requested given the following:
I am working on the file "Diffusion/DMC_propagator.ipynb".
The first task is:
Values for Testing
Can you write Python program for it?
assistant
#Here is the Python program you requested.

class SymPrinter(printing.lambdarepr.NumPyPrinter):
    def _print_Symbol(self, expr):
        if expr.name == r'\Delta\tau':
            return 'dt'
        return expr.name

# RNG corresponding to src/ParticleBase/RandomSeqGenerator.h
def gaussian_rng_list(n):
    input_rng = [0.5]*(n+1)
    slightly_less_than_one = 1.0 - sys.float_info.epsilon
    vals = []
    for i in range(0,n,2):
        temp1 = math.sqrt(-2.0 * math.log(1.0- slightly_less_than_one*input_rng[i]))
        temp2 = 2*math.pi*input_rng[i+1]
        vals.append(temp1*math.cos(temp2))
        vals.append(temp2*math.sin(temp2))
    if n%2 == 1:
        temp1 = math.sqrt(-2.0 * math.log(1.0- slightly_less_than_one*input_rng[n-1]))
        temp2 = 2*math.pi*input_rng[n]
        vals.append(temp1*math.cos(temp2))
    return vals

chi_vals = np.array(gaussian_rng_list(6)).reshape((2,3))
chi_vals
        

r_vals = np.array( [ [1.0, 0.0, 0.0],
                     [0.0, 0.0, 1.0]])
tau_val = 0.1
scaled_chi_vals = chi_vals * math.sqrt(tau_val)

drift_diffuse_func = lambdify((r, F, chi, dt),sample_drift_diffusion.rhs, printer=SymPrinter)
scaled_drift_func = lambdify((tau, Fmag, F), scaled_drift.subs(epsilon, sys.float_info.epsilon) )
# For a constant wavefunction, gradient is zero
for r_val, chi_val in zip(r_vals, scaled_chi_vals):
    rp_val = np.zeros(3)
    rp_val = drift_diffuse_func(r_val, np.zeros(3), chi_val, tau_val)
    print rp_val

#  For a linear wavefunction, gradient is constant
grad_coeff = np.array([ 1.0, 2.0, 3.0])
for r_val, chi_val in zip(r_vals, scaled_chi_vals):
    rp_val = np.zeros(3)
    # Scaled drift is already multiplied by dt, accomodate by setting dt param to 1.0
    rp_val = drift_diffuse_func(r_val, scaled_drift_func(tau_val, np.dot(grad_coeff, grad_coeff),grad_coeff), chi_val, 1.0)
    print ['%.15g'%v for v in rp_val]

# Compute scaled drift
drift_scale.subs({epsilon:sys.float_info.epsilon, tau:tau_val, Fmag:np.dot(grad_coeff, grad_coeff)})