code: def _conv_fx_gx(f_vals, g_vals, dt, min_f, min_g):
      """Numerical convolution given f(x), min(x) for two functions"""
      vals = np.convolve(f_vals, g_vals) * dt  # Full by default
      # f and g have been implicitly translated by -f_mn and -g_mn respectively,
      # because in terms of array indices, they both now start at 0.
      # Translate by f and g offsets
      time = np.arange(len(vals)) * dt + min_f + min_g
      return time, vals
  
imports: import itertools
  import numpy
  import sympy
  from scipy.interpolate import interp1d
  from sympy import DiracDelta, Symbol
  from sympy.utilities.lambdify import implemented_function, lambdify
  from nipy.algorithms.statistics.formula.formulae import Formula, Term
label_desc: Use image node_id 10 for calling a global function with example usage: _conv_fx_gx(f_vals, g_vals, dt, min_f, min_g) and returns: time,  vals