code: def do_plot(is_h=True):
      from scipy.special._ufuncs import (
          _struve_power_series,
          _struve_asymp_large_z,
          _struve_bessel_series,
      )
  
      vs = np.linspace(-1000, 1000, 91)
      zs = np.sort(np.r_[1e-5, 1.0, np.linspace(0, 700, 91)[1:]])
  
      rp = _struve_power_series(vs[:, None], zs[None, :], is_h)
      ra = _struve_asymp_large_z(vs[:, None], zs[None, :], is_h)
      rb = _struve_bessel_series(vs[:, None], zs[None, :], is_h)
  
      mpmath.mp.dps = 50
      if is_h:
          sh = lambda v, z: float(
              mpmath.struveh(mpmath.mpf(v), mpmath.mpf(z))
          )
      else:
          sh = lambda v, z: float(
              mpmath.struvel(mpmath.mpf(v), mpmath.mpf(z))
          )
      ex = np.vectorize(sh, otypes="d")(vs[:, None], zs[None, :])
  
      err_a = err_metric(ra[0], ex) + 1e-300
      err_p = err_metric(rp[0], ex) + 1e-300
      err_b = err_metric(rb[0], ex) + 1e-300
  
      err_est_a = abs(ra[1] / ra[0])
      err_est_p = abs(rp[1] / rp[0])
      err_est_b = abs(rb[1] / rb[0])
  
      z_cutoff = 0.7 * abs(vs) + 12
  
      levels = [-1000, -12]
  
      plt.cla()
  
      plt.hold(1)
      plt.contourf(
          vs,
          zs,
          np.log10(err_p).T,
          levels=levels,
          colors=["r", "r"],
          alpha=0.1,
      )
      plt.contourf(
          vs,
          zs,
          np.log10(err_a).T,
          levels=levels,
          colors=["b", "b"],
          alpha=0.1,
      )
      plt.contourf(
          vs,
          zs,
          np.log10(err_b).T,
          levels=levels,
          colors=["g", "g"],
          alpha=0.1,
      )
  
      plt.contour(
          vs,
          zs,
          np.log10(err_p).T,
          levels=levels,
          colors=["r", "r"],
          linestyles=[":", ":"],
      )
      plt.contour(
          vs,
          zs,
          np.log10(err_a).T,
          levels=levels,
          colors=["b", "b"],
          linestyles=[":", ":"],
      )
      plt.contour(
          vs,
          zs,
          np.log10(err_b).T,
          levels=levels,
          colors=["g", "g"],
          linestyles=[":", ":"],
      )
  
      lp = plt.contour(
          vs,
          zs,
          np.log10(err_est_p).T,
          levels=levels,
          colors=["r", "r"],
          linestyles=["-", "-"],
      )
      la = plt.contour(
          vs,
          zs,
          np.log10(err_est_a).T,
          levels=levels,
          colors=["b", "b"],
          linestyles=["-", "-"],
      )
      lb = plt.contour(
          vs,
          zs,
          np.log10(err_est_b).T,
          levels=levels,
          colors=["g", "g"],
          linestyles=["-", "-"],
      )
  
      plt.clabel(lp, fmt={-1000: "P", -12: "P"})
      plt.clabel(la, fmt={-1000: "A", -12: "A"})
      plt.clabel(lb, fmt={-1000: "B", -12: "B"})
  
      plt.plot(vs, z_cutoff, "k--")
  
      plt.xlim(vs.min(), vs.max())
      plt.ylim(zs.min(), zs.max())
  
      plt.xlabel("v")
      plt.ylabel("z")
  
imports: from __future__ import absolute_import, division, print_function
  import numpy
  import matplotlib.pyplot
  import mpmath
label_desc: Use image node_id 2 for calling a global function with example usage: do_plot(is_h) without return types