I don't know how to generate a number of data points from this part of codeings

I have a package and this part is written to calculate the quantity called ‘action’ at a output temperature T. But now I need a set of ‘action’ at slightly different temperatures say T+1e-3,T+31e-3,T+41e-3… Etc.Since I am a beginner at python I can’t figure out where in. This definition I have to put a loop to print different values of action at slightly different temperatures. The part of code is attached as below

def _tunnelFromPhaseAtT(T, phases, start_phase, V, dV,
                    phitol, overlapAngle, nuclCriterion,
                    fullTunneling_params, verbose, outdict):
Find the lowest action tunneling solution.
Return ``nuclCriterion(S,T)``, and store a dictionary describing the
transition in outdict for key `T`.
    T = T[0]  # need this when the function is run from optimize.fmin
if T in outdict:
    return nuclCriterion(outdict[T]['action'], T)

def fmin(x):
    return optimize.fmin(V, x, args=(T,),
                         xtol=phitol, ftol=np.inf, disp=False)

# Loop through all the phases, adding acceptable minima
x0 = fmin(start_phase.valAt(T))
V0 = V(x0, T)
tunnel_list = []
for key in phases.keys():
    if key == start_phase.key:
    p = phases[key]
    if (p.T[0] > T or p.T[-1] < T):
    x1 = fmin(p.valAt(T))
    V1 = V(x1, T)
    if V1 >= V0:
    tdict = dict(low_vev=x1, high_vev=x0, Tnuc=T,
                 low_phase=key, high_phase=start_phase.key)
# Check for overlap
if overlapAngle > 0:
    excluded = []
    cos_overlap = np.cos(overlapAngle * np.pi/180)
    for i in xrange(1, len(tunnel_list)):
        for j in xrange(i):
            xi = tunnel_list[i]['low_vev']
            xj = tunnel_list[j]['low_vev']
            xi2 = np.sum((xi-x0)**2)
            xj2 = np.sum((xj-x0)**2)
            dotij = np.sum((xj-x0)*(xi-x0))
            if dotij >= np.sqrt(xi2*xj2) * cos_overlap:
                excluded.append(i if xi2 > xj2 else j)
    for i in sorted(excluded)[::-1]:
        del tunnel_list[i]
# Get rid of the T parameter for V and dV
def V_(x,T=T,V=V): return V(x,T)
def dV_(x,T=T,dV=dV): return dV(x,T)
# For each item in tunnel_list, try tunneling
lowest_action = np.inf
lowest_tdict = dict(action=np.inf)
for tdict in tunnel_list:
    x1 = tdict['low_vev']
        print("Tunneling from phase %s to phase %s at T=%0.4g"
              % (tdict['high_phase'], tdict['low_phase'], T))
        print("high_vev =", tdict['high_vev'])
        print("low_vev =", tdict['low_vev'])
        tobj = pathDeformation.fullTunneling(
            [x1,x0], V_, dV_, callback_data=T,
        tdict['instanton'] = tobj
        tdict['action'] = tobj.action
        tdict['trantype'] = 1
    except tunneling1D.PotentialError as err:
        if err.args[1] == "no barrier":
            tdict['trantype'] = 0
            tdict['action'] = 0.0
        elif err.args[1] == "stable, not metastable":
            tdict['trantype'] = 0
            tdict['action'] = np.inf
            print("Unexpected error message.")
    if tdict['action'] <= lowest_action:
        lowest_action = tdict['action']
        lowest_tdict = tdict
outdict[T] = lowest_tdict
return nuclCriterion(lowest_action, T)