from __future__ import print_function
from __future__ import division

import math
from scitbx.array_family import flex
from scitbx.dtmin.minimizer import Minimizer
from scitbx.dtmin.refinebase import RefineBase
from scitbx.dtmin.reparams import Reparams
from scitbx.dtmin.bounds import Bounds
from cctbx import adptbx
from libtbx import group_args
from iotbx.map_model_manager import map_model_manager
from iotbx.data_manager import DataManager
import sys

class RefineCryoemSignal(RefineBase):
  # Set up refinement class for dtmin minimiser (based on Phaser minimiser)
  def __init__(self, sumfsqr_lm, f1f2cos_lm, r_star, ssqr_bins, target_spectrum, start_x):
    RefineBase.__init__(self)
    # Prepare data that will be used repeatedly for fgh evaluation
    # Sample local mean with spacing derived from averaging radius
    # Assume box is at least close to being a cube
    self.unit_cell = sumfsqr_lm.unit_cell()
    recip_params = self.unit_cell.reciprocal_parameters()
    (astar, bstar, cstar) = recip_params[:3]
    spacing = int(round(r_star/(2*max(astar,bstar,cstar))))
    self.subsample = spacing**3
    h,k,l = sumfsqr_lm.indices().as_vec3_double().parts()
    ih = h.iround()
    ik = k.iround()
    il = l.iround()
    # Modulus of negative element in flex array is negative or zero, but we need
    # it to be positive. Subtract 1 so that lowest resolution reflection is 1,1,1
    ihmod = (ih % spacing + spacing - 1) % spacing
    ikmod = (ik % spacing + spacing - 1) % spacing
    ilmod = (il % spacing + spacing - 1) % spacing
    modsum = ihmod + ikmod + ilmod
    sel = (modsum == 0)
    self.sumfsqr_miller = sumfsqr_lm.select(sel)
    self.sumfsqr_miller.use_binning_of(sumfsqr_lm) # Matches subset
    self.f1f2cos_miller = f1f2cos_lm.select(sel)
    self.f1f2cos_miller.use_binner_of(self.sumfsqr_miller)

    self.n_bins = self.sumfsqr_miller.binner().n_bins_used()  # Assume consistent binning
    self.ssqr_bins = ssqr_bins
    assert (self.n_bins == len(ssqr_bins))
    self.target_spectrum = target_spectrum
    self.start_x = start_x
    self.x = start_x[:]         # Full set of parameters
    d_min = math.sqrt(1./flex.max(sumfsqr_lm.d_star_sq().data()))
    cell_tensor = flex.double((astar*astar, bstar*bstar, cstar*cstar, astar*bstar, astar*cstar, bstar*cstar))
    # Maximum beta should give an exponential argument < 50 at resolution limit
    self.max_beta = list(cell_tensor*45*d_min**2)
    # Choose maximum shift in beta to change by less than factor of 2 at resolution limit
    self.large_shifts_beta = list(cell_tensor*math.log(2.)*d_min**2)
    # Apply a relatively weak restraint to sphericity of beta
    self.sigmaSphericityBeta = cell_tensor * 5*d_min**2

  def sphericity_restraint(self, anisoBeta, do_gradient=True, do_hessian=True,
      sigma_factor = 1.):
    sigmaSphericityBeta = self.sigmaSphericityBeta * sigma_factor
    f = 0.
    g = flex.double(6, 0)
    h = flex.double(6 * 6, 0)
    h.reshape(flex.grid(6, 6))
    unit_cell = self.unit_cell
    aniso_u_iso = adptbx.beta_as_u_iso(unit_cell, anisoBeta)
    aniso_delta_beta = flex.double(adptbx.u_iso_as_beta(unit_cell, aniso_u_iso))
    anisoRemoveIso = flex.double(anisoBeta) - aniso_delta_beta
    dBetaIso_by_dBIso = list(adptbx.u_iso_as_beta(unit_cell, adptbx.b_as_u(1.)))
    mm = unit_cell.metrical_matrix()
    dBIso_by_dBetaAno = list(4./3.*flex.double(mm))

    for ni in range(6):
      f += math.pow(anisoRemoveIso[ni]/sigmaSphericityBeta[ni],2)/2.
      if (do_gradient):
        for nj in range(6):
          dBetaIso_by_dBetaAnoI = dBetaIso_by_dBIso[nj]*dBIso_by_dBetaAno[ni]
          if ni == nj:
            dBetaAno_by_dBetaAnoI = 1.
          else:
            dBetaAno_by_dBetaAnoI = 0.
          g[ni] += (anisoRemoveIso[nj]*(dBetaAno_by_dBetaAnoI-dBetaIso_by_dBetaAnoI) /
                        math.pow(sigmaSphericityBeta[nj],2) )
          if (do_hessian):
            for nk in range(6):
              dBetaIso_by_dBetaAnoI = dBetaIso_by_dBIso[nk]*dBIso_by_dBetaAno[ni]
              if nk == ni:
                dBetaAno_by_dBetaAnoI = 1.
              else:
                dBetaAno_by_dBetaAnoI = 0.
              dBetaIso_by_dBetaAnoJ = dBetaIso_by_dBIso[nk]*dBIso_by_dBetaAno[nj]
              if nk == nj:
                dBetaAno_by_dBetaAnoJ = 1.
              else:
                dBetaAno_by_dBetaAnoJ = 0.
              h[ni,nj] += ( (dBetaAno_by_dBetaAnoJ - dBetaIso_by_dBetaAnoJ) *
                            (dBetaAno_by_dBetaAnoI - dBetaIso_by_dBetaAnoI) /
                            math.pow(sigmaSphericityBeta[nk],2) )
    return (f, g, h)

  def target_gradient_hessian(self, do_gradient=True, do_hessian=True):
    if do_hessian:
      assert (do_gradient)
    # Extract parameters into variables with sensible names
    subsample = self.subsample
    n_bins = self.n_bins
    i_par = 0
    asqr_scale = self.x[i_par]
    i_par += 1
    sigmaT_bins = self.x[i_par:i_par + n_bins]
    i_par += n_bins
    asqr_beta = tuple(self.x[i_par:i_par + 6])
    i_par += 6
    assert (i_par == len(self.x))

    # Initialise function, gradient and Hessian with zeros
    f = 0.
    g = flex.double(self.nmp, 0)
    h = flex.double(self.nmp * self.nmp, 0)
    h.reshape(flex.grid(self.nmp, self.nmp))

    # Loop over bins to accumulate target, gradient, Hessian
    sumfsqr_miller = self.sumfsqr_miller
    i_bin_used = 0 # Keep track in case full range of bins not used
    for i_bin in sumfsqr_miller.binner().range_used():
      sel = sumfsqr_miller.binner().selection(i_bin)
      sumfsqr_sel = sumfsqr_miller.select(sel)

      # Make Miller array as basis for computing aniso corrections in bin
      # Let u = A^2*sigmaT to simplify computation of derivatives
      ones_array = flex.double(sumfsqr_sel.size(), 1)
      all_ones = sumfsqr_sel.customized_copy(data=ones_array)
      beta_miller_Asqr = all_ones.apply_debye_waller_factors(
        u_star=adptbx.beta_as_u_star(asqr_beta))
      u_terms = (asqr_scale * sigmaT_bins[i_bin_used]
        * self.target_spectrum[i_bin_used]) * beta_miller_Asqr.data()

      # Noise is total power minus signal, but make sure it is positive
      f1f2cos = self.f1f2cos_miller.data().select(sel)
      sumfsqr = self.sumfsqr_miller.data().select(sel)
      sigmaE_terms = sumfsqr/2 - f1f2cos
      mf1000 = sumfsqr/2000
      sigmaE_terms = (sigmaE_terms+mf1000 + flex.abs(sigmaE_terms - mf1000))/2

      # Use local sphere fsc to compute relative weight of local vs global fitting
      # Steepness of sigmoid controlled by factor applied to fsc
      fsc = f1f2cos/(f1f2cos + sigmaE_terms)
      steep = 12.
      wt_terms = flex.exp(steep*fsc)
      wt_terms = 1. - (wt_terms/(math.exp(steep/2) + wt_terms))
      meanwt = flex.mean_default(wt_terms,0.)
      sigmaS_terms = wt_terms*u_terms + (1.-wt_terms)*f1f2cos
      s2sigE = 2*sigmaS_terms + sigmaE_terms
      var_terms = s2sigE*sigmaE_terms

      # Leave out constant twologpi * number of reflections involved
      assert (flex.min(var_terms) > 0.)
      minusLL_terms = (sumfsqr * (sigmaS_terms + sigmaE_terms)
        - 2 * sigmaS_terms * f1f2cos) / var_terms + flex.log(var_terms)
      f += subsample*flex.sum(minusLL_terms)

      fgh_a_restraint = self.sphericity_restraint(asqr_beta,
          do_gradient, do_hessian, sigma_factor = 2.) # Looser for amplitude-squared
      f += meanwt * fgh_a_restraint[0]

      if do_gradient:
        s2sigE2 = flex.pow2(s2sigE)
        sumsqrcos = sumfsqr + 2*f1f2cos
        if (self.refine_Asqr_scale or self.refine_sigmaT_bins
              or self.refine_Asqr_beta):
          dmLL_by_dsigmaS_terms = (2 * s2sigE - sumsqrcos) / s2sigE2
          dmLL_by_du_terms = wt_terms * dmLL_by_dsigmaS_terms
        if self.refine_Asqr_beta:
          h_as_double, k_as_double, l_as_double = (
            sumfsqr_sel.indices().as_vec3_double().parts())
          hh = flex.pow2(h_as_double)
          kk = flex.pow2(k_as_double)
          ll = flex.pow2(l_as_double)
          hk = h_as_double * k_as_double
          hl = h_as_double * l_as_double
          kl = k_as_double * l_as_double

        i_par = 0 # Keep track of index for unrefined parameters
        i_ref = 0 # Keep track of refined parameters
        if self.refine_Asqr_scale: # Only affects U
          du_by_dAsqr_scale = u_terms / asqr_scale
          i_Asqr_scale = i_ref # Save for mixed second derivatives
          g[i_Asqr_scale] += subsample*flex.sum(dmLL_by_du_terms*du_by_dAsqr_scale)
          i_ref += 1
        i_par += 1
        if self.refine_sigmaT_bins: # Only affects U, just current bin
          du_by_dsigmaT_bin = u_terms / sigmaT_bins[i_bin_used]
          i_sigmaT_bin = i_ref+i_bin_used # Save for restraint terms below
          g[i_sigmaT_bin] += subsample*flex.sum(dmLL_by_du_terms*du_by_dsigmaT_bin)
          i_ref += self.n_bins
        i_par += self.n_bins
        if self.refine_Asqr_beta:  # Only affects U
          hh_factors = [-hh, -kk, -ll, -2*hk, -2*hl, -2*kl]
          du_by_dbetaA = []
          for i_beta in range(6):
            du_by_dbetaA.append(hh_factors[i_beta]*u_terms)
          for i_beta in range(6):
            g[i_ref+i_beta] += subsample*flex.sum(dmLL_by_du_terms * du_by_dbetaA[i_beta])
            g[i_ref+i_beta] += meanwt * fgh_a_restraint[1][i_beta] # Restraint term
          i_ref += 6
        i_par += 6

        assert (i_par == len(self.x))
        assert (i_ref == self.nmp)

        if do_hessian:
          s2sigE3 = s2sigE * s2sigE2
          if (self.refine_Asqr_scale or self.refine_sigmaT_bins
                or self.refine_Asqr_beta):

            d2mLL_by_dsigmaS2_terms = 4 * (
              sumsqrcos - 2 * sigmaS_terms - sigmaE_terms) / s2sigE3
            d2mLL_by_du2_terms = flex.pow2(wt_terms) * d2mLL_by_dsigmaS2_terms

          i_par = 0 # Keep track of index for unrefined parameters
          i_ref = 0  # Keep track of refined parameters
          # Note that second derivatives u wrt Asqr_scale and sigmaT_bins are 0
          if self.refine_Asqr_scale: # Only affects U
            h[i_ref,i_ref] += subsample*(flex.sum(d2mLL_by_du2_terms
              * flex.pow2(du_by_dAsqr_scale)))
            i_ref += 1
          i_par += 1
          if self.refine_sigmaT_bins: # Only affects U, current bin
            h[i_sigmaT_bin,i_sigmaT_bin] += subsample*flex.sum(
              d2mLL_by_du2_terms * flex.pow2(du_by_dsigmaT_bin))
            if self.refine_Asqr_scale:
              d2u_by_dAsqr_scale_by_dsigmaT_bin = du_by_dsigmaT_bin / asqr_scale
              cross_term = subsample*flex.sum(
                d2mLL_by_du2_terms * du_by_dAsqr_scale * du_by_dsigmaT_bin +
                dmLL_by_du_terms * d2u_by_dAsqr_scale_by_dsigmaT_bin)
              h[i_Asqr_scale,i_sigmaT_bin] += cross_term
              h[i_sigmaT_bin,i_Asqr_scale] += cross_term
            i_ref += self.n_bins
          i_par += self.n_bins
          if self.refine_Asqr_beta:  # Only affects U
            for i_beta in range(6):
              if self.refine_Asqr_scale: # Add Asqr_scale mixed derivatives
                d2u_by_dAsqr_scale_by_dbetaA = du_by_dbetaA[i_beta] / asqr_scale
                cross_term = subsample*flex.sum(
                  d2mLL_by_du2_terms * du_by_dAsqr_scale * du_by_dbetaA[i_beta] +
                  dmLL_by_du_terms * d2u_by_dAsqr_scale_by_dbetaA)
                h[i_Asqr_scale,i_ref+i_beta] += cross_term
                h[i_ref+i_beta,i_Asqr_scale] += cross_term
              for j_beta in range(6):
                h[i_ref+i_beta, i_ref+j_beta] += subsample*(
                  flex.sum(d2mLL_by_du2_terms * du_by_dbetaA[i_beta]*du_by_dbetaA[j_beta])
                  + flex.sum(dmLL_by_du_terms * hh_factors[i_beta]*hh_factors[j_beta]*u_terms) )
                # Also add restraint term
                h[i_ref+i_beta,i_ref+j_beta] += meanwt * fgh_a_restraint[2][i_beta, j_beta]
            i_ref += 6
          i_par += 6

          assert (i_par == len(self.x))
          assert (i_ref == self.nmp)

      # Restrain log of sigmaT_bins to 0, but downweighting low resolution
      d_bin = 1./math.sqrt(self.ssqr_bins[i_bin_used])
      sigmascale = 0.15 + 0.0005 * d_bin**3
      stbin = sigmaT_bins[i_bin_used]
      logbin = math.log(stbin)
      f += meanwt * (logbin/sigmascale)**2 / 2
      if do_gradient and self.refine_sigmaT_bins:
        g[i_sigmaT_bin] += meanwt * logbin / (stbin * sigmascale**2)
        if do_hessian:
          h[i_sigmaT_bin,i_sigmaT_bin] += meanwt * (1.-logbin)/(stbin*sigmascale)**2

      i_bin_used += 1

    return (f, g, h, False)

  def target(self):
    f_g_h = self.target_gradient_hessian(do_gradient=False, do_hessian=False)
    return f_g_h[0]

  def target_gradient(self):
    f_g_h = self.target_gradient_hessian(do_hessian=False)
    f = f_g_h[0]
    g = f_g_h[1]
    return (f, g)

  def get_macrocycle_parameters(self):
    if len(self.refine_mask) == 0: # All parameters being refined
      return self.x

    mp = []  # Parameters for this macrocycle
    for i in range(len(self.x)):
      if self.refine_mask[i]:
        mp.append(self.x[i])
    assert (len(mp) == self.nmp)
    return mp

  def set_macrocycle_parameters(self, newx):
    if len(self.refine_mask) == 0:  # All parameters being refined
      self.x = newx
    else:
      npref = 0
      for i in range(len(self.x)):
        if self.refine_mask[i]:
          self.x[i] = newx[npref]
          npref += 1
      assert (npref == self.nmp)

  def macrocycle_large_shifts(self):
    i_par = 0 # Keep track of index for unrefined parameters
    large_shifts = []
    if self.refine_Asqr_scale:
      large_shifts.append(self.x[i_par]/4.)
    i_par += 1
    if self.refine_sigmaT_bins:
      for i_bin in range(self.n_bins):
        large_shifts.append(self.x[i_par+i_bin]/4.)
    i_par += self.n_bins
    if self.refine_Asqr_beta:
      large_shifts.extend(self.large_shifts_beta)
    i_par += 6
    assert (i_par == len(self.x))
    assert (len(large_shifts) == self.nmp)
    return large_shifts

  def set_macrocycle_protocol(self, macrocycle_protocol):
    # Possible parameters include overall scale of signal,
    # bin parameters for signal (BEST-like curve), anisotropy tensor for signal
    self.refine_mask = []  # Indicates "all" if left empty
    self.refine_Asqr_scale = True
    self.refine_sigmaT_bins = True
    self.refine_Asqr_beta = True

    # For each protocol, define variables that aren't refined
    # Currently only have default protocol to refine everything.
    if macrocycle_protocol == ["default"]:
      pass

    else:
      print("Macrocycle protocol", macrocycle_protocol, " not recognised")
      sys.stdout.flush()
      exit

    # Now accumulate mask
    self.nmp = 0

    if self.refine_Asqr_scale:
      self.refine_mask.append(True)
      self.nmp += 1
    else:
      self.refine_mask.append(False)

    if self.refine_sigmaT_bins:
      self.refine_mask.extend([True for i in range(self.n_bins)])
      self.nmp += self.n_bins
    else:
      self.refine_mask.extend([False for i in range(self.n_bins)])

    if self.refine_Asqr_beta:
      self.refine_mask.extend([True for i in range(6)])
      self.nmp += 6
    else:
      self.refine_mask.extend([False for i in range(6)])

    assert (len(self.refine_mask) == len(self.x))

  def macrocycle_parameter_names(self, full_list=False):
    parameter_names = []
    if full_list or self.refine_Asqr_scale:
      parameter_names.append("Asqr_scale")
    if full_list or self.refine_sigmaT_bins:
      for i in range(self.n_bins):
        parameter_names.append("SigmaT_bin#" + str(i + 1))
    if full_list or self.refine_Asqr_beta:
      parameter_names.append("Asqr_beta11")
      parameter_names.append("Asqr_beta22")
      parameter_names.append("Asqr_beta33")
      parameter_names.append("Asqr_beta12")
      parameter_names.append("Asqr_beta13")
      parameter_names.append("Asqr_beta23")

    if not full_list:
      assert (len(parameter_names) == self.nmp)
    else:
      assert (len(parameter_names) == len(self.x))
    return parameter_names

  def reparameterize(self):
    i_par = 0 # Keep track of index for unrefined parameters
    repar = []

    if self.refine_Asqr_scale:
      repar.append(Reparams(False))
    i_par += 1

    if self.refine_sigmaT_bins:
      repar.extend([Reparams(False) for i in range(self.n_bins)])
    i_par += self.n_bins

    if self.refine_Asqr_beta:
      repar.extend([Reparams(False) for i in range(6)])
    i_par += 6

    assert (i_par == len(self.x))
    assert (len(repar) == self.nmp)

    return repar

  def bounds(self):
    i_par = 0
    bounds_list = []

    if self.refine_Asqr_scale:
      this_bound = Bounds()
      this_bound.lower_on(0.0001*self.start_x[i_par])
      bounds_list.append(this_bound)
    i_par += 1

    if self.refine_sigmaT_bins:
      this_bound = Bounds()
      this_bound.lower_on(0.001)
      for i in range(self.n_bins):
        bounds_list.append(this_bound)
    i_par += self.n_bins

    if self.refine_Asqr_beta:
      this_bound = Bounds()
      # this_bound.off()
      for i in range(6):
        this_bound.on(-self.max_beta[i],self.max_beta[i])
        bounds_list.append(this_bound)
    i_par += 6

    assert (i_par == len(self.x))
    assert (len(bounds_list) == self.nmp)
    return bounds_list

  def current_statistics(self, level=3, full_list=False):
    self.log_tab_printf(1, level, "Log-likelihood: %10.6g\n", -self.target())
    self.log_blank(level)

    parameter_names = self.macrocycle_parameter_names(full_list=full_list)
    if full_list:
      self.log_tab(1, level, "All parameters")
    else:
      self.log_tab(1, level, "Refined parameters")
    list_all = (full_list or len(self.refine_mask) == 0)
    iref = 0
    for i in range(len(self.x)):
      if list_all or self.refine_mask[i]:
        self.log_tab_printf(2, level, "%-15s %10.5g\n", (parameter_names[iref], self.x[i]))
        iref += 1

  def initial_statistics(self):
    level=2
    self.log_blank(level)
    self.log_tab(1, level, "Initial statistics")
    self.current_statistics(level=level, full_list=True)

  def final_statistics(self):
    level=2
    self.log_blank(level)
    self.log_tab(1, level, "Final statistics")
    self.current_statistics(level=level, full_list=True)

  def cleanup(self):
    pass

def default_target_spectrum(ssqr):
  # Placeholder for something better based on analysis of cryoEM reconstructions
  # Scaled data from BEST curve. Original data obtained from Sasha Popov, then
  # rescaled to correspond at higher resolution to the average X-ray scattering
  # factor from proteins atoms (with average atomic composition)
  best_data = ((0.009, 3.40735),
              (0.013092, 2.9006),
              (0.0171839, 2.33083),
              (0.0212759, 1.80796),
              (0.0253679, 1.65133),
              (0.0294599, 1.75784),
              (0.0335518, 2.06865),
              (0.0376438, 2.57016),
              (0.0417358, 3.13121),
              (0.0458278, 3.62596),
              (0.0499197, 3.92071),
              (0.0540117, 3.98257),
              (0.0581037, 3.91846),
              (0.0621956, 3.80829),
              (0.0662876, 3.69517),
              (0.0703796, 3.59068),
              (0.0744716, 3.44971),
              (0.0785635, 3.30765),
              (0.0826555, 3.16069),
              (0.0867475, 2.98656),
              (0.0908395, 2.77615),
              (0.0949314, 2.56306),
              (0.0990234, 2.37314),
              (0.103115, 2.22874),
              (0.107207, 2.09477),
              (0.111299, 1.98107),
              (0.115391, 1.8652),
              (0.119483, 1.75908),
              (0.123575, 1.67093),
              (0.127667, 1.59257),
              (0.131759, 1.52962),
              (0.135851, 1.48468),
              (0.139943, 1.45848),
              (0.144035, 1.43042),
              (0.148127, 1.40953),
              (0.152219, 1.37291),
              (0.156311, 1.34217),
              (0.160403, 1.3308),
              (0.164495, 1.32782),
              (0.168587, 1.30862),
              (0.172679, 1.31319),
              (0.176771, 1.30907),
              (0.180863, 1.31456),
              (0.184955, 1.31055),
              (0.189047, 1.31484),
              (0.193139, 1.31828),
              (0.197231, 1.32321),
              (0.201323, 1.30853),
              (0.205415, 1.30257),
              (0.209507, 1.2851),
              (0.213599, 1.26912),
              (0.217691, 1.24259),
              (0.221783, 1.24119),
              (0.225875, 1.2382),
              (0.229967, 1.21605),
              (0.234059, 1.17269),
              (0.23815, 1.13909),
              (0.242242, 1.1165),
              (0.246334, 1.08484),
              (0.250426, 1.0495),
              (0.254518, 1.01289),
              (0.25861, 0.974819),
              (0.262702, 0.940975),
              (0.266794, 0.900938),
              (0.270886, 0.861657),
              (0.274978, 0.830192),
              (0.27907, 0.802167),
              (0.283162, 0.780746),
              (0.287254, 0.749194),
              (0.291346, 0.720884),
              (0.295438, 0.694409),
              (0.29953, 0.676239),
              (0.303622, 0.650672),
              (0.307714, 0.632438),
              (0.311806, 0.618569),
              (0.315898, 0.605762),
              (0.31999, 0.591398),
              (0.324082, 0.579308),
              (0.328174, 0.572076),
              (0.332266, 0.568138),
              (0.336358, 0.559537),
              (0.34045, 0.547927),
              (0.344542, 0.539319),
              (0.348634, 0.529009),
              (0.352726, 0.516954),
              (0.356818, 0.512218),
              (0.36091, 0.511836),
              (0.365002, 0.511873),
              (0.369094, 0.506957),
              (0.373186, 0.502738),
              (0.377278, 0.50191),
              (0.38137, 0.492422),
              (0.385462, 0.488461),
              (0.389553, 0.483436),
              (0.393645, 0.481468),
              (0.397737, 0.473786),
              (0.401829, 0.468684),
              (0.405921, 0.468291),
              (0.410013, 0.46645),
              (0.414105, 0.4643),
              (0.418197, 0.45641),
              (0.422289, 0.450462),
              (0.426381, 0.444678),
              (0.430473, 0.443807),
              (0.434565, 0.441158),
              (0.438657, 0.441303),
              (0.442749, 0.437144),
              (0.446841, 0.428504),
              (0.450933, 0.420459),
              (0.455025, 0.413754),
              (0.459117, 0.412064),
              (0.463209, 0.406677),
              (0.467301, 0.40253),
              (0.471393, 0.396454),
              (0.475485, 0.393192),
              (0.479577, 0.390452),
              (0.483669, 0.38408),
              (0.487761, 0.379456),
              (0.491853, 0.373123),
              (0.495945, 0.374026),
              (0.500037, 0.373344),
              (0.504129, 0.377639),
              (0.508221, 0.374029),
              (0.512313, 0.374691),
              (0.516405, 0.371632),
              (0.520497, 0.370724),
              (0.524589, 0.366095),
              (0.528681, 0.369447),
              (0.532773, 0.369043),
              (0.536865, 0.368967),
              (0.540956, 0.36583),
              (0.545048, 0.370593),
              (0.54914, 0.371047),
              (0.553232, 0.372723),
              (0.557324, 0.371915),
              (0.561416, 0.372882),
              (0.565508, 0.371052),
              (0.5696, 0.36775),
              (0.573692, 0.369884),
              (0.577784, 0.374098),
              (0.581876, 0.374169),
              (0.585968, 0.37261),
              (0.59006, 0.372356),
              (0.594152, 0.377055),
              (0.598244, 0.3817),
              (0.602336, 0.381867),
              (0.606428, 0.377746),
              (0.61052, 0.377157),
              (0.614612, 0.376604),
              (0.618704, 0.37532),
              (0.622796, 0.372488),
              (0.626888, 0.373312),
              (0.63098, 0.377505),
              (0.635072, 0.381011),
              (0.639164, 0.379326),
              (0.643256, 0.380193),
              (0.647348, 0.381122),
              (0.65144, 0.387213),
              (0.655532, 0.391928),
              (0.659624, 0.398986),
              (0.663716, 0.402951),
              (0.667808, 0.405893),
              (0.6719, 0.40217),
              (0.675992, 0.401806),
              (0.680084, 0.404238),
              (0.684176, 0.409404),
              (0.688268, 0.413486),
              (0.692359, 0.413167),
              (0.696451, 0.414008),
              (0.700543, 0.417128),
              (0.704635, 0.420275),
              (0.708727, 0.423617),
              (0.712819, 0.42441),
              (0.716911, 0.426445),
              (0.721003, 0.429012),
              (0.725095, 0.430132),
              (0.729187, 0.42992),
              (0.733279, 0.425202),
              (0.737371, 0.423159),
              (0.741463, 0.423913),
              (0.745555, 0.425542),
              (0.749647, 0.426682),
              (0.753739, 0.431186),
              (0.757831, 0.433959),
              (0.761923, 0.433839),
              (0.766015, 0.428679),
              (0.770107, 0.425968),
              (0.774199, 0.426528),
              (0.778291, 0.427093),
              (0.782383, 0.426848),
              (0.786475, 0.424549),
              (0.790567, 0.423785),
              (0.794659, 0.419892),
              (0.798751, 0.417391),
              (0.802843, 0.413128),
              (0.806935, 0.408498),
              (0.811027, 0.402764),
              (0.815119, 0.404852),
              (0.819211, 0.405915),
              (0.823303, 0.392919),
              (0.827395, 0.384632),
              (0.831487, 0.382626),
              (0.835579, 0.379891),
              (0.839671, 0.376414),
              (0.843762, 0.372915),
              (0.847854, 0.375089),
              (0.851946, 0.371918),
              (0.856038, 0.36652),
              (0.86013, 0.358529),
              (0.864222, 0.356496),
              (0.868314, 0.354707),
              (0.872406, 0.348802),
              (0.876498, 0.343693),
              (0.88059, 0.34059),
              (0.884682, 0.342432),
              (0.888774, 0.345099),
              (0.892866, 0.344524),
              (0.896958, 0.342489),
              (0.90105, 0.328009),
              (0.905142, 0.323685),
              (0.909234, 0.321378),
              (0.913326, 0.318832),
              (0.917418, 0.314999),
              (0.92151, 0.311775),
              (0.925602, 0.30844),
              (0.929694, 0.30678),
              (0.933786, 0.303484),
              (0.937878, 0.301197),
              (0.94197, 0.296788),
              (0.946062, 0.295353),
              (0.950154, 0.298028),
              (0.954246, 0.298098),
              (0.958338, 0.295081),
              (0.96243, 0.289337),
              (0.966522, 0.286116),
              (0.970614, 0.284319),
              (0.974706, 0.280972),
              (0.978798, 0.28015),
              (0.98289, 0.279016),
              (0.986982, 0.277532),
              (0.991074, 0.276013),
              (0.995165, 0.270923),
              (0.999257, 0.269446),
              (1.00335, 0.266567),
              (1.00744, 0.263561),
              (1.01153, 0.261002),
              (1.01563, 0.255349),
              (1.01972, 0.258644),
              (1.02381, 0.254974),
              (1.0279, 0.2523),
              (1.03199, 0.244489),
              (1.03609, 0.249418),
              (1.04018, 0.249519),
              (1.04427, 0.249316),
              (1.04836, 0.249197),
              (1.05245, 0.24415),
              (1.05655, 0.244556),
              (1.06064, 0.241169),
              (1.06473, 0.238484),
              (1.06882, 0.2392),
              (1.07291, 0.240651),
              (1.077, 0.243724),
              (1.0811, 0.243174),
              (1.08519, 0.239545),
              (1.08928, 0.239106),
              (1.09337, 0.238763),
              (1.09746, 0.238971),
              (1.10156, 0.229925),
              (1.10565, 0.225123),
              (1.10974, 0.226932),
              (1.11383, 0.23118),
              (1.11792, 0.228654),
              (1.12202, 0.225084),
              (1.12611, 0.225866),
              (1.1302, 0.227717),
              (1.13429, 0.229508),
              (1.13838, 0.227977),
              (1.14248, 0.226799),
              (1.14657, 0.228456),
              (1.15066, 0.22383),
              (1.15475, 0.22188),
              (1.15884, 0.219986),
              (1.16294, 0.217418),
              (1.16703, 0.214356),
              (1.17112, 0.211027),
              (1.17521, 0.210011),
              (1.1793, 0.210609),
              (1.1834, 0.210893),
              (1.18749, 0.212583),
              (1.19158, 0.208415),
              (1.19567, 0.204557),
              (1.19976, 0.198068),
              (1.20386, 0.197603),
              (1.20795, 0.196691),
              (1.21204, 0.200617),
              (1.21613, 0.199803),
              (1.22022, 0.199199),
              (1.22432, 0.196859),
              (1.22841, 0.197471),
              (1.2325, 0.19799))
  # 300 data points from 0.009 to 1.2325, so separated by 0.004091973
  s1 = (ssqr - 0.009) / 0.004091973
  is1 = int(math.floor(s1))
  if is1 < 0:
    return best_data[0][1] # Below low-res limit for BEST data
  elif is1 >= 299:
    return best_data[0][299]  # Above high-res limit, about 0.9A
  else:
    ds = s1 - is1
    is2 = is1 + 1
    best_val = (1.-ds)*best_data[is1][1] + ds*best_data[is2][1]
    return best_val

def sphere_enclosing_model(model):
  sites_cart = model.get_sites_cart()
  cart_min = flex.double(sites_cart.min())
  cart_max = flex.double(sites_cart.max())
  model_midpoint = (cart_max + cart_min) / 2
  dsqrmax = flex.max((sites_cart - tuple(model_midpoint)).norms()) ** 2
  model_radius = math.sqrt(dsqrmax)
  return model_midpoint, model_radius

def flatten_model_region(mmm, d_min):
  # Flatten the region covered by the model
  # For map_manager, replace it by the mask-weighted mean within this region
  # For half-maps, replace by the mean and put back the original half-map
  # map difference to preserve the error signal.
  mm = mmm.map_manager()
  mm1 = mmm.map_manager_1()
  mm2 = mmm.map_manager_2()
  delta_mm = mm1.customized_copy(map_data = mm1.map_data() - mm2.map_data())
  model = mmm.model()
  mmm.create_mask_around_atoms(model=model, soft_mask=True, soft_mask_radius=d_min/2)
  working_mmm = mmm.deep_copy() # Save a copy before masking to work on later

  working_mmm.add_map_manager_by_id(delta_mm, map_id = 'delta_map')

  # Invert original mask and apply to original maps to flatten density under model
  mask_mm_inverse = mmm.get_map_manager_by_id('mask')
  mask_mm_inverse.set_map_data(map_data = 1. - mask_mm_inverse.map_data())
  mmm.apply_mask_to_maps(set_outside_to_mean_inside = False)

  # Apply original mask to working_mmm to get density and difference density under model
  mask_mm = working_mmm.get_map_manager_by_id('mask')
  working_mmm.apply_mask_to_maps(set_outside_to_mean_inside = False)

  # Get mean density for part of each map under model, to add back to
  # flattened region of each map
  mask_info = working_mmm.mask_info()
  weighted_points = mask_info.size*mask_info.mean
  wmm = working_mmm.map_manager()
  mean_map = flex.sum(wmm.map_data()) / weighted_points
  mask_data = mask_mm.map_data()
  mm.set_map_data(map_data = mm.map_data() + mean_map * mask_data)
  wmm1 = working_mmm.map_manager_1()
  mean_map1 = flex.sum(wmm1.map_data()) / weighted_points
  mm1.set_map_data(map_data = mm1.map_data() +
      mean_map1 * mask_data + delta_mm.map_data()/2)
  wmm2 = working_mmm.map_manager_2()
  mean_map2 = flex.sum(wmm2.map_data()) / weighted_points
  mm2.set_map_data(map_data = mm2.map_data() +
      mean_map2 * mask_data - delta_mm.map_data()/2)
  mmm.remove_map_manager_by_id('mask')

def add_local_squared_deviation_map(
    mmm, coeffs_in, radius, d_min, map_id_out):
  """
  Add spherically-averaged squared map to map_model_manager

  Compulsory arguments:
  mmm:        map_model_manager to add new map
  coeffs_in:  Miller array with coefficients for input map
  radius:     radius of sphere over which squared deviation is averaged
  d_min:      resolution to use for calculation
  map_id_out: identifier of output map
  """

  map_out = coeffs_in.local_standard_deviation_map(radius=radius, d_min=d_min)
  # map_out is an fft_map object, which can't easily be added to
  # map_model_manager as a similar map_manager object, so cycle through FT
  mm_out = map_out.as_map_manager()
  mean_square_map_coeffs = mm_out.map_as_fourier_coefficients(d_min=d_min)
  mmm.add_map_from_fourier_coefficients(mean_square_map_coeffs,
      map_id=map_id_out)
  # All map values should be positive, but round trip through FT might change
  # this. Check and add an offset if required to make minimum non-negative.
  mm_out = mmm.get_map_manager_by_id(map_id=map_id_out)
  min_map_value = flex.min(mm_out.map_data())
  if min_map_value < 0:
    offset = -min_map_value
    mm_out.set_map_data(map_data = mm_out.map_data() + offset)

def auto_sharpen_isotropic(mc1, mc2):

  nref = mc1.size()
  num_per_bin = 1000
  max_bins = 50
  min_bins = 6
  n_bins = int(round(max(min(nref / num_per_bin, max_bins), min_bins)))
  mc1.setup_binner(n_bins=n_bins)
  mc2.use_binner_of(mc1)

  sumw = 0
  sumwx = 0.
  sumwy = 0.
  sumwx2 = 0.
  sumwxy = 0.
  for i_bin in mc1.binner().range_used():
    sel = mc1.binner().selection(i_bin)
    mc1sel = mc1.select(sel)
    mc2sel = mc2.select(sel)
    mapCC = mc1sel.map_correlation(other=mc2sel)
    assert (mapCC < 1.) # Ensure these are really independent half-maps
    mapCC = max(mapCC,0.001) # Avoid zero or negative values
    FSCref = math.sqrt(2./(1.+1./mapCC))
    ssqr = mc1sel.d_star_sq().data()
    x = flex.mean_default(ssqr, 0) # Mean 1/d^2 for bin
    fsq = flex.pow2(flex.abs(mc1sel.data()))
    meanfsq = flex.mean_default(fsq, 0)
    y = math.log(meanfsq/(FSCref*FSCref))
    w = fsq.size()
    sumw += w
    sumwx += w * x
    sumwy += w * y
    sumwx2 += w * x**2
    sumwxy += w * x * y

  assert (nref == sumw) # Check no Fourier terms lost outside bins
  slope = (sumw * sumwxy - (sumwx * sumwy)) / (sumw * sumwx2 - sumwx**2)
  b_sharpen = 2 * slope
  all_ones = mc1.customized_copy(data = flex.double(mc1.size(), 1))
  b_terms_miller = all_ones.apply_debye_waller_factors(b_iso = b_sharpen)
  mc1s = mc1.customized_copy(data = mc1.data()*b_terms_miller.data())
  mc2s = mc2.customized_copy(data = mc2.data()*b_terms_miller.data())

  return mc1s, mc2s

def add_ordered_volume_mask(
    mmm, d_min, rad_factor=2, protein_mw=None, nucleic_mw=None,
    map_id_out='ordered_volume_mask'):
  """
  Add map defining mask covering the volume of most ordered density required
  to contain the specified content of protein and nucleic acid, judged by ratio
  of local map variance and local noise variance.

  Compulsory arguments:
  mmm: map_model_manager containing input half-maps in default map_managers
  d_min: estimate of best resolution for map

  Optional arguments:
  rad_factor: factor by which d_min is multiplied to get radius for averaging
    sphere, defaults to 2
  protein_mw*: molecular weight of protein expected in map, if any
  nucleic_mw*: molecular weight of nucleic acid expected in map
  map_id_out: identifier of output map, defaults to ordered_volume_mask

  * Note that at least one of protein_mw and nucleic_mw must be specified,
    and that the map must be complete
  """

  assert (protein_mw is not None) or (nucleic_mw is not None)
  assert mmm.map_manager().unit_cell_grid == mmm.map_manager().map_data().all()

  # Compute local average of squared density, using a sphere that will cover a
  # sufficient number of independent points and extending at least 5 A to
  # sample non-bonded contact distances. A rad_factor of 2 should yield
  # 4*Pi/3 * (2*2)^3 or about 270 independent points for the average; fewer
  # if the higher resolution data barely contribute. Larger values give less
  # noise but lower resolution for producing a mask. A minimum radius of 5
  # is enforced to explore next-nearest-neighbour density.
  radius = max(d_min*rad_factor, 5.)

  d_work = (d_min + radius) / 2 # Save some time by lowering resolution
  mm1 = mmm.map_manager_1()
  mc1_in = mm1.map_as_fourier_coefficients(d_min=d_work)
  mm2 = mmm.map_manager_2()
  mc2_in = mm2.map_as_fourier_coefficients(d_min=d_work)
  mc1s, mc2s = auto_sharpen_isotropic(mc1_in, mc2_in)
  mcs_mean  = mc1s.customized_copy(data = (mc1s.data() + mc2s.data())/2)
  mcs_delta = mc1s.customized_copy(data = (mc1s.data() - mc2s.data()) )

  add_local_squared_deviation_map(mmm, mcs_mean, radius, d_work,
      map_id_out='map_variance')
  mvmm = mmm.get_map_manager_by_id('map_variance')
  add_local_squared_deviation_map(mmm, mcs_delta, radius, d_work,
      map_id_out='noise_variance')
  nvmm = mmm.get_map_manager_by_id('noise_variance')

  # When maps are masked near the corners and edges, both signal and noise can
  # approach zero. Avoid getting close to dividing zero by zero, by adding a
  # small offset to the noise variance.
  # At the same time, correct noise variance by factor of two for averaging
  nvmm_min = min(flex.min(nvmm.map_data()) , 0.)
  nvmm_max = flex.max(nvmm.map_data())
  nvmm.set_map_data(map_data = (nvmm.map_data() - nvmm_min + nvmm_max/100.) / 2.)

  # Compute square of Z-score where values much greater than 1 indicate signal
  mm_Zscore_sqr = mvmm.customized_copy(
      map_data = mvmm.map_data()/nvmm.map_data())

  # Choose enough points in averaged squared density map to covered expected
  # ordered structure. An alternative that could be implemented is to assign
  # any parts of map unlikely to arise from noise as ordered density, without
  # reference to expected content. This might require figuring out
  # the effective number of independent points in the averaging sphere to
  # calibrate the chi-square distribution.
  map_volume = mmm.map_manager().unit_cell().volume()
  Zscore_sqr_map_data = mm_Zscore_sqr.map_data()
  numpoints = Zscore_sqr_map_data.size()
  # Convert content into volume using partial specific volumes
  target_volume = 0.
  if protein_mw is not None:
    target_volume += protein_mw*1.229
  if nucleic_mw is not None:
    target_volume += nucleic_mw*0.945
  # Expand volume by amount needed for sphere expanded by d_min in radius
  equivalent_radius = math.pow(target_volume / (4*math.pi/3.),1./3)
  volume_factor = ((equivalent_radius+d_min)/equivalent_radius)**3
  expanded_target_volume = target_volume*volume_factor
  target_points = int(expanded_target_volume/map_volume * numpoints)

  # Find threshold for target number of masked points
  from cctbx.maptbx.segment_and_split_map import find_threshold_in_map
  threshold = find_threshold_in_map(target_points = target_points,
      map_data = Zscore_sqr_map_data)
  temp_bool_3D = (Zscore_sqr_map_data >=  threshold)
  # as_double method doesn't work for multidimensional flex.bool
  mask_shape = temp_bool_3D.all()
  overall_mask = temp_bool_3D.as_1d().as_double()
  overall_mask.reshape(flex.grid(mask_shape))
  new_mm = mmm.map_manager().customized_copy(map_data=overall_mask)

  # Clean up temporary maps, then add ordered volume mask
  mmm.remove_map_manager_by_id('map_variance')
  mmm.remove_map_manager_by_id('noise_variance')
  mmm.add_map_manager_by_id(new_mm,map_id=map_id_out)

def get_grid_spacings(unit_cell, unit_cell_grid):
  assert unit_cell.parameters()[3:] == (90,90,90) # Required for this method
  sp = []
  for a,n in zip(unit_cell.parameters()[:3], unit_cell_grid):
    sp.append(a/n)
  return sp

def get_distance_from_center(c, unit_cell, unit_cell_grid = None,
    center = None):
  """
  Return a 3D flex array containing the distance of each grid point from the
  center of the map.
  Code provided by Tom Terwilliger
  """
  acc = c.accessor()
  if not unit_cell_grid: # Assume c contains complete unit cell
    unit_cell_grid = acc.all()
  nu,nv,nw = unit_cell_grid
  dx,dy,dz = get_grid_spacings(unit_cell, unit_cell_grid)
  if not center:
    center = (nu//2,nv//2,nw//2)

  # d is initially going to be squared distance from center
  d = flex.double(nu*nv*nw, 0)
  d.reshape(acc)

  # sum over x,y,z in slices
  dx2 = dx**2
  for i in range(nu):
    dist_sqr = dx2 * (i - center[0])**2
    d[i:i+1,0:nv,0:nw] += dist_sqr
  dy2 = dy**2
  for j in range(nv):
    dist_sqr = dy2 * (j - center[1])**2
    d[0:nu,j:j+1,0:nw] += dist_sqr
  dz2 = dz**2
  for k in range(nw):
    dist_sqr = dz2 * (k - center[2])**2
    d[0:nu,0:nv,k:k+1] += dist_sqr

  # Take square root to get distances
  d = flex.sqrt(d)

  return d

def get_maximal_mask_radius(mm_ordered_mask):

  # Check assumption that this is a full map
  assert mm_ordered_mask.unit_cell_grid == mm_ordered_mask.map_data().all()

  unit_cell = mm_ordered_mask.unit_cell()
  om_data = mm_ordered_mask.map_data()
  d_from_c = get_distance_from_center(om_data, unit_cell = unit_cell)
  sel = om_data > 0
  selected_grid_indices = sel.iselection()
  mask_distances = d_from_c.select(selected_grid_indices)
  maximal_radius = mask_distances.min_max_mean().max
  return maximal_radius

def get_mask_radius(mm_ordered_mask,frac_coverage):
  """
  Get radius of sphere around map center enclosing desired fraction of
  ordered density
  """

  # Test assumption that this is a full map
  assert mm_ordered_mask.unit_cell_grid == mm_ordered_mask.map_data().all()

  unit_cell = mm_ordered_mask.unit_cell()
  om_data = mm_ordered_mask.map_data()
  d_from_c = get_distance_from_center(om_data, unit_cell = unit_cell)
  sel = om_data > 0
  selected_grid_indices = sel.iselection()
  mask_distances = d_from_c.select(selected_grid_indices)
  mask_distances = mask_distances.select(flex.sort_permutation(data=mask_distances))
  masked_points = mask_distances.size()
  mask_radius = mask_distances[math.floor(frac_coverage*masked_points)-1]
  return mask_radius

def get_d_star_sq_step(f_array, num_per_bin = 1000, max_bins = 50, min_bins = 6):
  d_star_sq = f_array.d_star_sq().data()
  num_tot = d_star_sq.size()
  n_bins = round(max(min(num_tot / num_per_bin, max_bins),min_bins))
  d_star_sq_min = flex.min(d_star_sq)
  d_star_sq_max = flex.max(d_star_sq) * 1.00001
  d_star_sq_step = (d_star_sq_max - d_star_sq_min) / n_bins
  return d_star_sq_step

def get_flex_max(a,b):
  c = a.deep_copy()
  sel = (b>a)
  c.set_selected(sel,b.select(sel))
  return c

def write_mtz(miller, file_name, root):
  mtz_dataset = miller.as_mtz_dataset(column_root_label=root)
  mtz_object=mtz_dataset.mtz_object()
  dm = DataManager()
  dm.set_overwrite(True)
  dm.write_miller_array_file(mtz_object, filename=file_name)

def largest_prime_factor(i):
  from libtbx.math_utils import prime_factors_of
  pf = prime_factors_of(i)
  if len(pf) == 0: # 0 or 1
    return 1
  else:
    return pf[-1]

def next_allowed_grid_size(i, largest_prime=5):
  if i<2:
    j = 2
  elif i%2 == 1:
    j = i+1
  else:
    j = i
  while (largest_prime_factor(j) > largest_prime):
    j += 2
  return j

def intensities_as_expanded_map(mm,marray):
  '''
  Take miller array (assumed here to be real-valued and in P1, but could be
  generalised), place values in a map, offset to the center.
  '''
  h_indices, k_indices, l_indices = marray.indices().as_vec3_double().parts()
  h_indices = h_indices.iround()
  k_indices = k_indices.iround()
  l_indices = l_indices.iround()
  hmin = flex.min(h_indices)
  hmax = flex.max(h_indices)
  kmin = flex.min(k_indices)
  kmax = flex.max(k_indices)
  lmin = flex.min(l_indices)
  lmax = flex.max(l_indices)
  assert ((hmin < 0) and (hmax >= -hmin))
  assert ((kmin < 0) and (kmax >= -kmin))
  assert (lmin == 0)
  data = marray.data()

  # Shift hkl to center, add buffer on edges to get to sensible grid for FFT
  # Apply Friedel symmetry to get full sphere of Fourier coefficients
  h_range = next_allowed_grid_size(2*hmax+2)
  h_offset = h_range//2
  h_map_indices = h_offset + h_indices
  h_inverse_indices = h_offset - h_indices

  k_range = next_allowed_grid_size(2*kmax+2)
  k_offset = k_range//2
  k_map_indices = k_offset + k_indices
  k_inverse_indices = k_offset - k_indices

  l_range = next_allowed_grid_size(2*lmax+2)
  l_offset = l_range//2
  l_map_indices = l_offset + l_indices
  l_inverse_indices = l_offset - l_indices

  miller_as_map_data = flex.double(h_range*k_range*l_range,0.)
  miller_as_map_data.reshape(flex.grid(h_range,k_range,l_range))
  for ih,ik,il,coeff in zip(h_map_indices,k_map_indices,l_map_indices,data):
    miller_as_map_data[ih,ik,il] = coeff
  for ih,ik,il,coeff in zip(h_inverse_indices,k_inverse_indices,l_inverse_indices,data):
    miller_as_map_data[ih,ik,il] = coeff

  # Unit cell for reciprocal space sphere of data is obtained from the reciprocal cell
  # parameters for the coefficients computed from the boxed map, times the
  # extents in h,k,l
  from cctbx import crystal
  abcstar = mm.crystal_symmetry().unit_cell().reciprocal_parameters()[:3]
  ucr = tuple(list(flex.double(abcstar)*flex.double((h_range,k_range,l_range)))+[90,90,90])
  crystal_symmetry=crystal.symmetry(ucr,1)
  from iotbx.map_manager import map_manager
  mm_data = map_manager(map_data=miller_as_map_data,unit_cell_grid=miller_as_map_data.all(),
      unit_cell_crystal_symmetry=crystal_symmetry,wrapping=False)
  return group_args(mm_data = mm_data,
                    h_map_indices = h_map_indices,
                    k_map_indices = k_map_indices,
                    l_map_indices = l_map_indices)

def expanded_map_as_intensities(rmm, marray, h_map_indices, k_map_indices, l_map_indices):
  '''
  Fetch miller array data back from map. Only take unique values, ignoring
  Friedel mates.
  '''
  rmap_data = rmm.map_data()
  miller_data = flex.double()
  for ih,ik,il in zip(h_map_indices,k_map_indices,l_map_indices):
    miller_data.append(rmap_data[ih,ik,il])
  miller_array = marray.customized_copy(data = miller_data)
  return miller_array

def local_mean_intensities(mm, d_min, intensities, r_star):
  """
  Compute local means of input intensities (or amplitudes)

  Compulsory argument:
  mm: map_manager corresponding to system from which intensities are obtained
  d_min: target resolution of locally-averaged intensities
  intensities: Terms to be locally averaged, extended by r_star from 1/d_min
  r_star: radius in reciprocal space for averaging
  """

  # Turn intensity values into a spherical map inside a cube
  results = intensities_as_expanded_map(mm,intensities)
  coeffs_as_map = results.mm_data # map_manager with intensities
  h_map_indices = results.h_map_indices # grid positions for original hkl in order
  k_map_indices = results.k_map_indices
  l_map_indices = results.l_map_indices

  # Put map into map_model_manager to ensure matching grid for spherically-averaged map
  rmmm = map_model_manager(map_manager = coeffs_as_map)

  # Cell for "map" of intensities is reciprocal of real-space cell
  # Compute reciprocal d_min as twice the grid spacing between intensities
  rcp = coeffs_as_map.unit_cell().parameters()
  nu, nv, nw = coeffs_as_map.map_data().all()
  rdmin = 2*flex.min(flex.double(rcp[:3])/flex.double((nu,nv,nw)))
  inverse_intensities = coeffs_as_map.map_as_fourier_coefficients(d_min=rdmin)

  # Compute G-function, avoiding divide by zero and numerical precision issues
  # for the origin term
  stol = flex.sqrt(inverse_intensities.sin_theta_over_lambda_sq().data())
  w = 4 * stol * math.pi * r_star
  sel = (w == 0.)
  w.set_selected(sel,0.0001)
  sphere_reciprocal = 3 * (flex.sin(w) - w * flex.cos(w))/flex.pow(w, 3)
  sphere_reciprocal.set_selected(sel,1.)

  # Spherically-averaged intensities from FT of product of FTs
  prod_coeffs = inverse_intensities.customized_copy(
      data = inverse_intensities.data()*sphere_reciprocal)
  rmmm.add_map_from_fourier_coefficients(prod_coeffs, map_id='local_mean_map')
  local_mean_as_map = rmmm.get_map_manager_by_id(map_id='local_mean_map')

  # Associate map values with hkl using saved indices, then restrict to dmin
  extended_mean = expanded_map_as_intensities(local_mean_as_map,intensities,
      h_map_indices,k_map_indices,l_map_indices)

  min_in = flex.min(intensities.data())
  if min_in >= 0: # Make sure strictly non-negative remains non-negative
    extended_mean = extended_mean.customized_copy( data =
      (extended_mean.data()+min_in + flex.abs(extended_mean.data()-min_in))/2 )

  local_mean = extended_mean.select(extended_mean.d_spacings().data() >= d_min)
  return local_mean

def assess_cryoem_errors(
    mmm, d_min,
    map_1_id="map_manager_1", map_2_id="map_manager_2",
    ordered_mask_id='ordered_volume_mask', sphere_points=500,
    sphere_cent=None, radius=None,
    verbosity=1, shift_map_origin=True, keep_full_map=False):
  """
  Refine error parameters from half-maps, make weighted map coeffs for region.

  Compulsory arguments:
  mmm: map_model_manager object containing two half-maps from reconstruction
  d_min: target resolution, either best resolution for map or resolution for
    target region

  Optional arguments:
  map_1_id: identifier of first half-map, if different from default of
    map_manager_1
  map_2_id: same for second half-map
  ordered_mask_id: identifier for mask defining ordered volume
  sphere_cent: center of sphere defining target region for analysis
    default is center of map
  radius: radius of sphere
    default (when sphere center not defined either) is 1/4 narrowest map width
  sphere_points: number of reflections to use for local spherical average
  shift_map_origin: should map coefficients be shifted to correspond to
    original origin, rather than the origin being the corner of the box,
    default True
  keep_full_map: don't mask or box the input map
    default False, use with caution
  verbosity: 0/1/2/3/4 for mute/log/verbose/debug/testing
  """

  from libtbx import group_args
  from iotbx.map_model_manager import map_model_manager

  if verbosity > 0:
    print("\nPrepare map for docking by analysing signal and errors")

  # Start from two half-maps and ordered volume mask in map_model_manager
  # Keep track of ordered volume in whole reconstruction
  ordered_mm = mmm.get_map_manager_by_id(map_id=ordered_mask_id)
  total_ordered_points = flex.mean(ordered_mm.map_data()) * ordered_mm.map_data().size()
  # Get map coefficients for maps after spherical masking
  ucpars = mmm.map_manager().unit_cell().parameters()
  if sphere_cent is None:
    # Default to sphere in center of cell extending 2/3 distance to nearest edge
    sphere_cent = flex.double((ucpars[0], ucpars[1], ucpars[2]))/2.
    if radius is None:
      radius = min(ucpars[0], ucpars[1], ucpars[2])/3.
  else:
    assert radius is not None
    sphere_cent = flex.double(sphere_cent)

  # Define box big enough to hold sphere plus soft masking
  boundary_to_smoothing_ratio = 2
  soft_mask_radius = d_min
  padding = soft_mask_radius * boundary_to_smoothing_ratio
  cushion = flex.double(3,radius+padding)
  cart_min = flex.double(sphere_cent) - cushion
  cart_max = flex.double(sphere_cent) + cushion
  for i in range(3): # Keep within input map
    cart_min[i] = max(cart_min[i],0)
    cart_max[i] = min(cart_max[i],ucpars[i])

  cs = mmm.crystal_symmetry()
  uc = cs.unit_cell()

  # Set some parameters that will be overwritten if masked and cut out
  over_sampling_factor = 1.
  box_volume = uc.volume()
  masked_volume = box_volume
  d_max = max(ucpars[0], ucpars[1], ucpars[2]) + d_min

  if keep_full_map: # Rearrange later so this choice comes first?
    working_mmm = mmm.deep_copy()
  else:
    # Box the map within xyz bounds, converted to map grid units
    lower_frac = uc.fractionalize(tuple(cart_min))
    upper_frac = uc.fractionalize(tuple(cart_max))
    map_data = mmm.map_data()
    all_orig = map_data.all()
    lower_bounds = [int(math.floor(f * n)) for f, n in zip(lower_frac, all_orig)]
    upper_bounds = [int(math.ceil( f * n)) for f, n in zip(upper_frac, all_orig)]
    working_mmm = mmm.extract_all_maps_with_bounds(
        lower_bounds=lower_bounds, upper_bounds=upper_bounds)
    # Make and apply spherical mask
    working_mmm.create_spherical_mask(
      soft_mask_radius=soft_mask_radius,
      boundary_to_smoothing_ratio=boundary_to_smoothing_ratio)
    working_mmm.apply_mask_to_maps()
    mask_info = working_mmm.mask_info()

    box_volume = uc.volume() * (
        working_mmm.map_data().size()/mmm.map_data().size())
    masked_volume = box_volume * mask_info.mean

    # Use weighted volume of ordered region of map to compute fraction of total
    # scattering contained in the working map
    cutout_ordered_mm = working_mmm.get_map_manager_by_id(map_id=ordered_mask_id)
    cutout_ordered_points = (flex.mean(cutout_ordered_mm.map_data()) *
          cutout_ordered_mm.map_data().size())
    fraction_scattering = cutout_ordered_points / total_ordered_points
    # Calculate an oversampling ratio defined as the ratio between the size of
    # the cut-out cube and the size of a cube that could contain a sphere
    # big enough to hold the volume of ordered density. Because the ratio of
    # the size of a cube to the sphere inscribed in it is about 1.9 (which is
    # close to the factor of two between typical protein volume and unit cell
    # volume in a crystal), this should yield likelihood scores on a similar
    # scale to crystallographic ones.
    over_sampling_factor = (cutout_ordered_mm.map_data().size() /
        (cutout_ordered_points * 6./math.pi) )

  # Compute local averages needed for initial covariance terms
  # Do these calculations at extended resolution to avoid edge effects up to
  # desired resolution
  wuc = working_mmm.crystal_symmetry().unit_cell()
  work_mm = working_mmm.map_manager()
  v_star = 1./wuc.volume()
  r_star = math.pow(3*sphere_points*v_star/(4*math.pi),1./3.)
  d_min_extended = 1./(1./d_min + r_star)
  map_sampling = flex.max(flex.double(wuc.parameters()[:3])/flex.double(work_mm.map_data().all()))
  d_min_extended = max(d_min_extended, 2*map_sampling)

  mc1 = working_mmm.map_as_fourier_coefficients(d_min=d_min_extended, d_max=d_max, map_id=map_1_id)
  mc2 = working_mmm.map_as_fourier_coefficients(d_min=d_min_extended, d_max=d_max, map_id=map_2_id)

  f1 = flex.abs(mc1.data())
  f2 = flex.abs(mc2.data())
  p1 = mc1.phases().data()
  p2 = mc2.phases().data()
  sumfsqr = mc1.customized_copy(data = flex.pow2(f1) + flex.pow2(f2))
  f1f2cos = mc1.customized_copy(data = f1 * f2 * flex.cos(p2 - p1))

  # Local mean calculations of covariance terms use data to extended resolution
  # but return results to desired resolution
  sumfsqr_local_mean = local_mean_intensities(work_mm, d_min, sumfsqr, r_star)
  f1f2cos_local_mean = local_mean_intensities(work_mm, d_min, f1f2cos, r_star)

  # Sum of f1^2 and f2^2 is always >= 2*f1*f2*cos(delta), but
  # this may be violated by numerical errors in calculation of local mean
  # Fix to avoid downstream numerical problems.
  sumfsqr_local_mean = sumfsqr_local_mean.customized_copy(
    data=get_flex_max(sumfsqr_local_mean.data(),2*f1f2cos_local_mean.data()))

  # Trim starting data back to desired resolution limit, setup binning
  mc1 = mc1.select(mc1.d_spacings().data() >= d_min)
  assert (mc1.size() == sumfsqr_local_mean.size())
  mc2 = mc2.select(mc2.d_spacings().data() >= d_min)
  sumfsqr = sumfsqr.select(sumfsqr.d_spacings().data() >= d_min)
  # f1f2cos = f1f2cos.select(f1f2cos.d_spacings().data() >= d_min)
  d_star_sq_step = get_d_star_sq_step(mc1)
  mc1.setup_binner_d_star_sq_step(d_star_sq_step=d_star_sq_step)
  mc2.use_binner_of(mc1)
  sumfsqr.use_binner_of(mc1)
  sumfsqr_local_mean.use_binner_of(mc1)
  f1f2cos_local_mean.use_binner_of(mc1)

  # Prepare starting parameters for signal refinement
  mapCC_bins = flex.double()
  ssqr_bins = flex.double()
  target_spectrum = flex.double()
  sumw = 0
  sumwx = 0.
  sumwy = 0.
  sumwx2 = 0.
  sumwxy = 0.
  for i_bin in sumfsqr.binner().range_used():
    sel = sumfsqr.binner().selection(i_bin)
    mc1sel = mc1.select(sel)
    mc2sel = mc2.select(sel)
    mapCC = mc1sel.map_correlation(other=mc2sel)
    assert (mapCC < 1.) # Ensure these are really independent half-maps
    mapCC = max(mapCC,0.001) # Avoid zero or negative values
    mapCC_bins.append(mapCC)
    # Adjust very low sumfsqr_local_mean values, then divide sum by 2 to get mean)
    sumfsqsel = sumfsqr.select(sel)
    mean_sumfsqr_bin = flex.mean_default(sumfsqsel.data(),0.)

    # Adjust very low sumfsqr_local_mean values
    sfsqlm_sel = sumfsqr_local_mean.select(sel)
    min_sfsqlm_bin = mean_sumfsqr_bin / 10000
    sfsqlm_sel = sfsqlm_sel.customized_copy(data =
      (sfsqlm_sel.data()+min_sfsqlm_bin + flex.abs(sfsqlm_sel.data()-min_sfsqlm_bin)))
    sumfsqr_local_mean.data().set_selected(sel, sfsqlm_sel.data())

    ssqr = sumfsqsel.d_star_sq().data()
    x = flex.mean_default(ssqr, 0) # Mean 1/d^2 for bin
    ssqr_bins.append(x)  # Save for later
    meanfsq = mean_sumfsqr_bin / 2
    target_power = default_target_spectrum(x) # Could have a different target
    target_spectrum.append(target_power)
    if mapCC < 0.143: # Only fit initial signal estimate where clear overall
      continue
    signal = meanfsq * mapCC / target_power
    y = math.log(signal)
    w = sumfsqsel.size()
    sumw += w
    sumwx += w * x
    sumwy += w * y
    sumwx2 += w * x**2
    sumwxy += w * x * y

  slope = (sumw * sumwxy - (sumwx * sumwy)) / (sumw * sumwx2 - sumwx**2)
  intercept = (sumwy - slope * sumwx) / sumw
  wilson_scale_signal = math.exp(intercept)
  wilson_b_signal = -4 * slope
  n_bins = ssqr_bins.size()

  sigmaT_bins = [1.]*n_bins
  start_params = []
  start_params.append(wilson_scale_signal/3.5) # Asqr_scale, factor out low-res BEST value
  start_params.extend(sigmaT_bins)
  wilson_u=adptbx.b_as_u(wilson_b_signal)
  asqr_beta=list(adptbx.u_iso_as_beta(mc1.unit_cell(), wilson_u))
  start_params.extend(asqr_beta)

  # create inputs for the minimizer's run method
  # Constrained refinement using prior parameters could be revisited later.
  # However, this would require all cryo-EM maps to obey the assumption
  # that half-maps are completely unmasked.
  macro1 = ["default"]
  macro2 = ["default"]
  protocol = [macro1, macro2] # overall minimization protocol
  ncyc = 100                  # maximum number of microcycles per macrocycle
  minimizer_type = "bfgs"     # minimizer, bfgs or newton
  study_params = False        # flag for calling studyparams procedure
  output_level=verbosity      # 0/1/2/3/4 for mute/log/verbose/debug/testing

  # create instances of refine and minimizer
  refine_cryoem_signal = RefineCryoemSignal(
    sumfsqr_lm = sumfsqr_local_mean, f1f2cos_lm = f1f2cos_local_mean,
    r_star = r_star, ssqr_bins = ssqr_bins, target_spectrum = target_spectrum,
    start_x = start_params)
  minimizer = Minimizer(output_level=output_level)

  # Run minimizer
  minimizer.run(refine_cryoem_signal, protocol, ncyc, minimizer_type, study_params)
  refined_params=refine_cryoem_signal.x

  # Extract and report refined parameters
  i_par = 0
  asqr_scale = refined_params[i_par] # Not used for correction: leave map on original scale
  i_par += 1
  sigmaT_bins = refined_params[i_par:i_par + n_bins]
  i_par += n_bins
  asqr_beta = tuple(refined_params[i_par:i_par + 6])
  i_par += 6
  assert (i_par == len(refined_params))

  # Convert asqr_beta to a_beta for reporting
  a_beta = tuple(flex.double(asqr_beta)/2)

  # Convert beta parameters to Baniso for (optional) use and output
  a_baniso = adptbx.u_as_b(adptbx.beta_as_u_cart(mc1.unit_cell(), a_beta))

  if verbosity > 0:
    print("\nRefinement of scales and error terms completed\n")
    print("\nParameters for A and BEST curve correction")
    print("  A overall scale: ",math.sqrt(asqr_scale))
    for i_bin in range(n_bins):
      print("  Bin #", i_bin + 1, "BEST curve correction: ", sigmaT_bins[i_bin])
    print("  A tensor as beta:", a_beta)
    print("  A tensor as Baniso: ", a_baniso)
    es = adptbx.eigensystem(a_baniso)
    print("  Eigenvalues and eigenvectors:")
    for iv in range(3):
      print("  ",es.values()[iv],es.vectors(iv))

    sys.stdout.flush()

  # Loop over bins to compute expectedE and Dobs for each Fourier term
  # Start with mean of half-map Fourier terms and make Miller array for Dobs
  expectE = mc1.customized_copy(data = (mc1.data() + mc2.data())/2)
  expectE.use_binner_of(mc1)
  dobs = expectE.customized_copy(data=flex.double(expectE.size(),0))
  i_bin_used = 0 # Keep track in case full range of bins not used
  weighted_map_noise = 0.
  if verbosity > 0:
    print("MapCC before and after rescaling as a function of resolution")
    print("Bin   <ssqr>   mapCC_before   mapCC_after")
  for i_bin in mc1.binner().range_used():
    sel = expectE.binner().selection(i_bin)
    eEsel = expectE.select(sel)

    # Make Miller array as basis for computing aniso corrections in this bin
    ones_array = flex.double(eEsel.size(), 1)
    all_ones = eEsel.customized_copy(data=ones_array)
    beta_miller_A = all_ones.apply_debye_waller_factors(
      u_star=adptbx.beta_as_u_star(a_beta))
    abeta_terms = beta_miller_A.data()
    beta_miller_Asqr = beta_miller_A.customized_copy(data = flex.pow2(abeta_terms))
    u_terms = (asqr_scale * sigmaT_bins[i_bin_used]
      * target_spectrum[i_bin_used]) * beta_miller_Asqr.data()

    # Noise is total power minus signal, but make sure it is positive
    f1f2cos = f1f2cos_local_mean.data().select(sel)
    sumfsqr = sumfsqr_local_mean.data().select(sel)
    sigmaE_terms = sumfsqr/2 - f1f2cos
    mf1000 = sumfsqr/2000
    sigmaE_terms = (sigmaE_terms+mf1000 + flex.abs(sigmaE_terms - mf1000))/2

    fsc = f1f2cos/(f1f2cos + sigmaE_terms)
    steep = 12.
    wt_terms = flex.exp(steep*fsc)
    wt_terms = 1. - (wt_terms/(math.exp(steep/2) + wt_terms))
    sigmaS_terms = wt_terms*u_terms + (1.-wt_terms)*f1f2cos
    sigmaS_terms = (sigmaS_terms+mf1000 + flex.abs(sigmaS_terms-mf1000))/2

    scale_terms = 1./flex.sqrt(sigmaS_terms + sigmaE_terms/2.)
    dobs_terms = 1./flex.sqrt(1. + sigmaE_terms/(2*sigmaS_terms))
    # expectedFSC_before = flex.mean_default(u_terms,0.)/flex.mean_default(u_terms + sigmaE_terms,0.)
    expectE.data().set_selected(sel, expectE.data().select(sel) * scale_terms)
    dobs.data().set_selected(sel, dobs_terms)
    weighted_map_noise += flex.sum(sigmaE_terms/(sigmaE_terms + 2*sigmaS_terms))

    # Apply corrections to mc1 and mc2 to compute mapCC after rescaling
    # SigmaE variance is twice as large for half-maps before averaging
    scale_terms_12 = 1./flex.sqrt(sigmaS_terms + sigmaE_terms)
    # scale_terms_12 = 1./(abeta_terms + sigmaE_terms / (asqr_scale * sigmaT_bins[i_bin_used] * abeta_terms))
    # Overwrite mc1 and mc2, but not needed later.
    mc1.data().set_selected(sel, mc1.data().select(sel) * scale_terms_12)
    mc2.data().set_selected(sel, mc2.data().select(sel) * scale_terms_12)
    mc1sel = mc1.select(sel)
    mc2sel = mc2.select(sel)
    mapCC = mc1sel.map_correlation(other=mc2sel)
    if verbosity > 0:
      print(i_bin_used+1, ssqr_bins[i_bin_used], mapCC_bins[i_bin_used], mapCC)
      sys.stdout.flush()
    mapCC_bins[i_bin_used] = mapCC # Update for returned output
    i_bin_used += 1

  # At this point, weighted_map_noise is the sum of the noise variance for a
  # weighted half-map. In the following, this sum could be multiplied by two
  # for Friedel symmetry, but then divided by two for effects of averaging.
  weighted_map_noise = math.sqrt(weighted_map_noise) / masked_volume

  if verbosity > 0:
    print("Fraction of full map scattering: ",fraction_scattering)
    print("Over-sampling factor: ",over_sampling_factor)
    print("Weighted map noise: ",weighted_map_noise)

  # Return a map_model_manager with the weighted map
  wEmean = dobs*expectE
  working_mmm.add_map_from_fourier_coefficients(map_coeffs=wEmean, map_id='map_manager_wtd')
  # working_mmm.write_map(map_id='map_manager_wtd',file_name='prepmap.map')
  working_mmm.remove_map_manager_by_id(map_1_id)
  working_mmm.remove_map_manager_by_id(map_2_id)

  shift_cart = working_mmm.shift_cart()
  if shift_map_origin:
    ucwork = expectE.crystal_symmetry().unit_cell()
    # shift_cart is position of original origin in boxed-map coordinate system
    # shift_frac should correspond to what has to be done to a model to put it
    # into the map, i.e. move it in the opposite direction
    shift_frac = ucwork.fractionalize(shift_cart)
    shift_frac = tuple(-flex.double(shift_frac))
    expectE = expectE.translational_shift(shift_frac)

  ssqmin = flex.min(mc1.d_star_sq().data())
  ssqmax = flex.max(mc1.d_star_sq().data())
  resultsdict = dict(
      n_bins = n_bins,
      ssqr_bins = ssqr_bins,
      ssqmin = ssqmin,
      ssqmax = ssqmax,
      asqr_scale = asqr_scale,
      sigmaT_bins = sigmaT_bins,
      asqr_beta = asqr_beta,
      a_baniso = a_baniso,
      mapCC_bins = mapCC_bins)
  return group_args(
    new_mmm = working_mmm,
    shift_cart = shift_cart,
    expectE = expectE, dobs = dobs,
    over_sampling_factor = over_sampling_factor,
    fraction_scattering = fraction_scattering,
    weighted_map_noise = weighted_map_noise,
    resultsdict = resultsdict)

# Command-line interface using argparse
def run():
  """
  Prepare cryo-EM map for docking by preparing weighted MTZ file.

  Obligatory command-line arguments (no keywords):
  half_map_1: name of file containing the first half-map from a reconstruction
  half_map_2: name of file containing the second half-map
  d_min: desired resolution, either best for whole map or for local region

  Optional command-line arguments (keyworded):
  --protein_mw*: molecular weight expected for protein component of ordered density
  --nucleic_mw*: same for nucleic acid component
  --model: PDB file for model that can either be used to flatten the map around
          the model, to search for the next component, or to cut out a sphere
          of density to refine and score the fit of the model
  --flatten_model: Use model to define region where map should be flattened
  --cutout_model: Use model to define sphere to process for refining and
          scoring the docking of this model
  --sphere_cent: Centre of sphere defining target map region (3 floats)
          defaults to centre of map
  --radius: radius of sphere (1 float)
          must be given if sphere_cent defined, otherwise
          defaults to narrowest extent of input map divided by 4
  --shift_map_origin: shift output mtz file to match input map on its origin?
          default True
  --file_root: root name for output files
  --mute (or -m): mute output
  --verbose (or -v): verbose output
  --testing: extra verbose output for debugging
  * NB: At least one of protein_mw or nucleic_mw must be given
        Either a model or a sphere can be used to specify a cutout region, but
        not both
        The flatten_model option cannot be combined with cutting out a sphere.
  """
  import argparse
  from iotbx.map_model_manager import map_model_manager
  from iotbx.data_manager import DataManager
  dm = DataManager()
  dm.set_overwrite(True)

  parser = argparse.ArgumentParser(
          description='Prepare cryo-EM map for docking')
  parser.add_argument('map1',help='Map file for half-map 1')
  parser.add_argument('map2', help='Map file for half-map 2')
  parser.add_argument('d_min', help='d_min for maps', type=float)
  parser.add_argument('--protein_mw',
                      help='Molecular weight of protein component of map',
                      type=float)
  parser.add_argument('--nucleic_mw',
                      help='Molecular weight of nucleic acid component of map',
                      type=float)
  parser.add_argument('--sphere_points',help='Target nrefs in averaging sphere', type=float)
  parser.add_argument('--model',help='Placed model')
  parser.add_argument('--flatten_model',help='Flatten map around model',
                      action='store_true')
  parser.add_argument('--cutout_model',help='Cut out sphere around model',
                      action='store_true')
  parser.add_argument('--sphere_cent',help='Centre of sphere for docking',
                      nargs=3, type=float)
  parser.add_argument('--radius',help='Radius of sphere for docking', type=float)
  parser.add_argument('--file_root',
                      help='Root of filenames for output')
  parser.add_argument('--shift_map_origin', dest='shift_map_origin', type=bool)
  parser.set_defaults(shift_map_origin=True)
  parser.add_argument('-m', '--mute', dest = 'mute',
                      help = 'Mute output', action = 'store_true')
  parser.add_argument('-v', '--verbose', dest = 'verbose',
                      help = 'Set output as verbose', action = 'store_true')
  parser.add_argument('--testing', dest = 'testing',
                      help='Set output as testing', action='store_true')
  args = parser.parse_args()
  d_min = args.d_min
  verbosity = 1
  if args.mute: verbosity = 0
  if args.verbose: verbosity = 2
  if args.testing: verbosity = 4
  shift_map_origin = args.shift_map_origin

  cutout_specified = False
  sphere_cent = None
  radius = None
  model = None
  sphere_points = None

  protein_mw = None
  nucleic_mw = None
  if (args.protein_mw is None) and (args.nucleic_mw is None):
    print("At least one of protein_mw or nucleic_mw must be given")
    sys.stdout.flush()
    exit
  if args.protein_mw is not None:
    protein_mw = args.protein_mw
  if args.nucleic_mw is not None:
    nucleic_mw = args.nucleic_mw

  if args.sphere_points is not None:
    sphere_points = args.sphere_points

  if args.model is not None:
    if not (args.cutout_model or args.flatten_model):
      print('Use for model must be specified (flatten or cut out map')
      sys.stdout.flush()
      exit
    model_file = args.model
    model = dm.get_model(model_file)

  if (args.sphere_cent is not None) and args.cutout_model:
    print("Only one method to define region to cut out (sphere or model) can be given")
    sys.stdout.flush()
    exit
  if args.sphere_cent is not None:
    assert args.radius is not None
    sphere_cent = tuple(args.sphere_cent)
    radius = args.radius
    cutout_specified = True
  if args.cutout_model:
    assert args.model is not None
    sphere_cent, radius = sphere_enclosing_model(model)
    radius = radius + d_min # Expand to allow width for density
    cutout_specified = True

  # Create map_model_manager containing half-maps
  map1_filename = args.map1
  mm1 = dm.get_real_map(map1_filename)
  map2_filename = args.map2
  mm2 = dm.get_real_map(map2_filename)
  mmm = map_model_manager(model=model, map_manager_1=mm1, map_manager_2=mm2)

  # Prepare maps by flattening model volume if desired
  if args.flatten_model:
    assert args.model is not None
    flatten_model_region(mmm, d_min)

  mask_id = 'ordered_volume_mask'
  add_ordered_volume_mask(mmm, d_min,
      protein_mw=protein_mw, nucleic_mw=nucleic_mw,
      map_id_out=mask_id)
  if verbosity>1:
    ordered_mm = mmm.get_map_manager_by_id(map_id=mask_id)
    if args.file_root is not None:
      map_file_name = args.file_root + "_ordered_volume_mask.map"
    else:
      map_file_name = "ordered_volume_mask.map"
    ordered_mm.write_map(map_file_name)

  # The following could loop over different regions
  if cutout_specified:
    # Refine to get scale and error parameters for docking region
    results = assess_cryoem_errors(mmm, d_min, sphere_points=sphere_points,
      verbosity=verbosity, sphere_cent=sphere_cent, radius=radius,
      shift_map_origin=shift_map_origin)

  expectE = results.expectE
  mtz_dataset = expectE.as_mtz_dataset(column_root_label='Emean')
  dobs = results.dobs
  mtz_dataset.add_miller_array(
      dobs,column_root_label='Dobs',column_types='W')
  mtz_object=mtz_dataset.mtz_object()

  if args.file_root is not None:
    mtzout_file_name = args.file_root + ".mtz"
  else:
    mtzout_file_name = "weighted_map_data.mtz"
  print ("Writing mtz for docking as",mtzout_file_name)
  if not shift_map_origin:
    shift_cart = results.shift_cart
    print ("Origin of full map relative to mtz:", shift_cart)
  dm.write_miller_array_file(mtz_object, filename=mtzout_file_name)
  over_sampling_factor = results.over_sampling_factor
  fraction_scattering = results.fraction_scattering
  print ("Over-sampling factor for Fourier terms:",over_sampling_factor)
  print ("Fraction of total scattering:",fraction_scattering)
  sys.stdout.flush()

if __name__ == "__main__":
  run()
