# -*- coding: utf-8; py-indent-offset: 2 -*-
"""
Examines a structure for metal ions. Can iterate over all atoms, examining
their density and chemical environment to determine if they are correctly
identified, or if there are better candidate ions that they can be replaced
with.

See mmtbx.ions.build to actually modify the structure, the code in this module
handles ion identification, but only prints out messages to a log.
"""

from __future__ import absolute_import, division, print_function
from mmtbx.ions.geometry import find_coordination_geometry
from mmtbx.ions import environment
from mmtbx.ions import halides
from mmtbx.ions import utils
from mmtbx import validation
import mmtbx.ions
from iotbx.pdb import common_residue_names_water as WATER_RES_NAMES
from cctbx.eltbx import sasaki, henke
from cctbx import crystal, adptbx
from scitbx.array_family import flex
from scitbx.matrix import col
from libtbx.str_utils import make_sub_header, format_value, framed_output
from libtbx import group_args, adopt_init_args, Auto
from libtbx.utils import null_out, Sorry
from libtbx import easy_mp
import libtbx.load_env
import libtbx.phil
from math import sqrt
from six.moves import cStringIO as StringIO
import operator
import time
import sys
from libtbx.math_utils import cmp
from six.moves import zip
from six.moves import range
from six import string_types

ion_identification_phil_str = """
require_valence = False
  .type = bool
  .help = Toggles the use of valence calculations
  .short_caption = Require good bond valence
ambiguous_valence_cutoff = 0.5
  .type = float
  .help = "Cutoff to uniquely select one of many metals by comparing its observed and expected valence"
d_min_strict_valence = 1.5
  .type = float
  .short_caption = Resolution limit for strict valence rules
anom_map_type = *residual simple llg
  .type = choice
  .help = Type of anomalous difference map to use.  Default is a residual \
    map, showing only unmodeled scattering.
find_anomalous_substructure = Auto
  .type = bool
use_phaser = True
  .type = bool
  .help = Toggles the use of Phaser for calculation of f-prime values.
  .short_caption = Use Phaser to calculate f-double-prime
aggressive = False
  .type = bool
  .help = Toggles more permissive settings for flagging waters as heavier \
    elements.  Not recommended for automated use.
map_sampling_radius = 2.0
  .type = float
  .input_size = 80
  .short_caption = Sampling radius
water
  .short_caption = Water filtering
  .style = box auto_align
{
  min_2fofc_level = 1.8
    .type = float
    .input_size = 80
    .help = Minimum water 2mFo-DFc map value.  Waters below this cutoff will \
      not be analyzed further.
    .short_caption = Min. allowed 2mFo-DFc map value
  max_fofc_level = 3.0
    .type = float
    .input_size = 80
    .help = Maximum water mFo-DFc map value
    .short_caption = Max. expected mFo-DFc map value
  max_anom_level = 3.0
    .type = float
    .input_size = 80
    .help = Maximum water anomalous map value
    .short_caption = Max. expected anomalous map value
  max_occ = 1.0
    .type = float
    .input_size = 80
    .help = Maximum water occupancy
    .short_caption = Max. expected occupancy
  min_b_iso = 1.0
    .type = float
    .input_size = 80
    .help = Minimum water isotropic B-factor
    .short_caption = Min. expected B-factor
  fp_max = 1.0
    .type = float
  fpp_max = 0
    .type = float
  max_stddev_b_iso = 5
    .type = float
    .input_size = 80
    .short_caption = Max. standard deviations below mean B-iso
  min_frac_b_iso = 0.2
    .type = float
    .input_size = 80
    .short_caption = Min. fraction of mean B-iso
  min_frac_calpha_b_iso = 0.75
    .type = float
  max_frac_calpha_2fofc = 1.2
    .type = float
  min_2fofc_coordinating = 0.9
    .type = float
    .help = Minimum 2mFo-DFc for waters involved in coordination shells.
}
phaser
  .short_caption = Phaser options
  .caption = These parameters control the identification of anomalous \
    scatterers using Phaser substructure completion.
  .style = box auto_align
{
  llgc_ncycles = None
    .type = int
    .short_caption = Number of cycles
  distance_cutoff = 1.5
    .type = float
    .input_size = 80
    .short_caption = Min. required separation from other atoms
  distance_cutoff_same_site = 0.7
    .type = float
    .input_size = 80
    .short_caption = Max. separation from mapped atom
  fpp_ratio_min = 0.2
    .type = float
    .input_size = 80
    .help = Minimum ratio of refined/theoretical f-double-prime.
    .short_caption = Min. f-double-prime ratio
  fpp_ratio_max = 1.1
    .type = float
    .input_size = 80
    .help = Maximum ratio of refined/theoretical f-double-prime.
    .short_caption = Max. f-double-prime ratio
}
chloride
  .short_caption = Chloride ions
  .style = box auto_align
{
  %s
}
""" % halides.chloride_params_str

def ion_master_phil():
  return libtbx.phil.parse(ion_identification_phil_str, process_includes=True)

# various constants
WATER, WATER_POOR, LIGHT_ION, HEAVY_ION = range(4)
NUC_PHOSPHATE_BINDING = ["MG", "CA", "MN"]

class manager(object):
  """
  Wrapper object for extracting local environment and adding or modifying
  scatterers.
  """

  def __init__(self,
                fmodel,
                pdb_hierarchy,
                xray_structure,
                params = None,
                wavelength = None,
                connectivity = None,
                nproc = 1,
                verbose = False,
                log = None):
    if (log is None) : log = null_out()
    self.fmodel = fmodel
    self.atoms_to_props = {}
    self.params = params
    self.wavelength = wavelength
    self.nproc = nproc
    self.phaser_substructure = None
    self.flag_refine_substructure = False
    self.fpp_from_phaser_ax_sites = None
    self.site_fp = None
    self.site_fdp = None
    self.use_svm = getattr(params, "use_svm", None)
    self._map_values = {}
    self._map_gaussian_fits = {}
    self.anomalous_flag = None
    if (fmodel is not None):
      self.anomalous_flag = fmodel.f_obs().anomalous_flag()
    self.update_structure(
      pdb_hierarchy = pdb_hierarchy,
      xray_structure = xray_structure,
      connectivity = connectivity,
      log = log)
    self.update_maps()
    # The default behavior is to refine the anomalous structure when possible;
    # this can use either the CCTBX anomalous group refinement or Phaser's
    # substructure completion (but not both).  Some additional machinery is
    # required to handle this distinction.
    refine_substructure = False
    if (params is not None):
      refine_substructure = params.find_anomalous_substructure
    if (refine_substructure is Auto):
      refine_substructure = fmodel.f_obs().anomalous_flag() and \
                            (wavelength is not None)
    if (refine_substructure):
      if (wavelength is None):
        raise Sorry("Wavelength required when "+
                    "find_anomalous_substructure=True.")
      elif (not fmodel.f_obs().anomalous_flag()):
        raise Sorry("Anomalous data required when "+
                    "find_anomalous_substructure=True.")
      if ((params.use_phaser) and (libtbx.env.has_module("phaser"))):
        print("  Running Phaser substructure completion...", file=log)
        t1 = time.time()
        phaser_result = find_anomalous_scatterers(
          fmodel = fmodel,
          pdb_hierarchy = pdb_hierarchy,
          wavelength = wavelength,
          verbose = verbose,
          log = log,
          n_cycles = params.phaser.llgc_ncycles)
        t2 = time.time()
        print("    time: %.1fs" % (t2-t1), file=log)
        if (phaser_result is None):
          print("  ERROR: Phaser substructure completion failed!", file=log)
        else :
          self.phaser_substructure = phaser_result.atoms()
          if (len(self.phaser_substructure) == 0):
            print("  No anomalous scatterers found!", file=log)
          else :
            print("  %d anomalous scatterers found" % \
              len(self.phaser_substructure), file=log)
            self.analyze_substructure(log = log, verbose = True)
      else :
        self.flag_refine_substructure = True
        self.refine_anomalous_substructure(log = log)

  def water_selection(self):
    """
    Fetchs the selection for all waters in the model.

    Returns
    -------
    scitbx.array_family.flex.size_t
    """
    sel_cache = self.pdb_hierarchy.atom_selection_cache()
    sel_str = "({}) and element O and altloc ' '".format(
      " or ".join([ "resname " + i for i in WATER_RES_NAMES ]))
    return sel_cache.selection(sel_str).iselection()

  def refine_anomalous_substructure(self, log):
    """
    Run simple "substructure completion" implemented using CCTBX tools (the
    command-line equivalent is mmtbx.refine_anomalous_substructure).  A faster
    alternative to Phaser, but not clear how effective this is at the moment.

    Parameters
    ----------
    log : file
    """
    from mmtbx.refinement import anomalous_scatterer_groups
    fmodel_tmp = self.fmodel.deep_copy()
    # XXX should we only be refining f''?
    anom_groups = anomalous_scatterer_groups.refine_anomalous_substructure(
      fmodel = fmodel_tmp,
      pdb_hierarchy = self.pdb_hierarchy,
      wavelength = self.wavelength,
      reset_water_u_iso = True,
      verbose = True,
      use_all_anomalous = True,
      out = log)
    scatterers = fmodel_tmp.xray_structure.scatterers()
    self.use_fdp = flex.bool(scatterers.size(), False)
    self.site_fp = flex.double(scatterers.size(), 0)
    self.site_fdp = flex.double(scatterers.size(), 0)
    for group in anom_groups :
      for i_seq in group.iselection :
        sc = scatterers[i_seq]
        self.use_fdp[i_seq] = True
        self.site_fp[i_seq] = sc.fp
        self.site_fdp[i_seq] = sc.fdp

  def update_structure(self, pdb_hierarchy, xray_structure,
      connectivity = None, log = None, refine_if_necessary = True):
    """
    Set the current atomic data: PDB hierarchy, Xray structure, and simple
    connectivity list.

    Parameters
    ----------
    pdb_hierarchy : iotbx.pdb.hierarchy.root
    xray_structure : cctbx.xray.structure.structure
    connectivity : ...
    log : file, optional
    refine_if_necessary : bool, optional
    """
    self.pdb_hierarchy = pdb_hierarchy
    self.xray_structure = xray_structure
    if self.fmodel:
      self.fmodel.update_xray_structure(xray_structure, update_f_calc = True)
    self.sites_frac = self.xray_structure.sites_frac()
    self.sites_cart = self.xray_structure.sites_cart()
    self.connectivity = connectivity
    self.pdb_atoms = pdb_hierarchy.atoms()
    assert not self.pdb_atoms.extract_i_seq().all_eq(0)
    self.unit_cell = xray_structure.unit_cell()
    self.use_fdp = flex.bool(xray_structure.scatterers().size(), False)
    self.ax_chain = None
    self._pair_asu_cache = {}
    self.u_iso_all = xray_structure.extract_u_iso_or_u_equiv()
    # Extract some information about the structure, including B-factor
    # statistics for non-HD atoms and waters
    sctr_keys = xray_structure.scattering_type_registry().type_count_dict()
    self.hd_present = ("H" in sctr_keys) or ("D" in sctr_keys)
    sel_cache = pdb_hierarchy.atom_selection_cache()
    self.carbon_sel = sel_cache.selection("element C").iselection()
    self.calpha_sel = sel_cache.selection("name CA and element C").iselection()
    assert (len(self.carbon_sel) > 0)
    water_sel = self.water_selection()
    self.n_waters = len(water_sel)
    not_hd_sel = sel_cache.selection("not (element H or element D)").iselection()
    self.n_heavy = len(not_hd_sel)
    u_iso_all_tmp = self.u_iso_all.select(not_hd_sel)
    self.b_mean_all = adptbx.u_as_b(flex.mean(u_iso_all_tmp))
    self.b_stddev_all = adptbx.u_as_b(
       u_iso_all_tmp.standard_deviation_of_the_sample())
    self.b_mean_calpha = 0
    if (len(self.calpha_sel) > 0):
      self.b_mean_calpha = adptbx.u_as_b(flex.mean(self.u_iso_all.select(
        self.calpha_sel)))
    self.b_mean_hoh = self.b_stddev_hoh = None
    if (self.n_waters > 0):
      u_iso_hoh = self.u_iso_all.select(water_sel)
      self.b_mean_hoh = adptbx.u_as_b(flex.mean(u_iso_hoh))
      self.b_stddev_hoh = adptbx.u_as_b(
        u_iso_hoh.standard_deviation_of_the_sample())
    if (self.phaser_substructure is not None):
      self.analyze_substructure(log = log)
    elif (self.flag_refine_substructure) and (refine_if_necessary):
      self.refine_anomalous_substructure(log = log)

  def get_initial_b_iso(self):
    """
    Calculates the isotropic b-factor to assign to newly labeled ions during the
    building process. Uses either the mean b-factor of waters or of all atoms if
    the former is unavailable.

    Returns
    -------
    float
    """
    if (getattr(self, "b_mean_hoh", None) is not None):
      return self.b_mean_hoh
    else :
      return self.b_mean_all

  def get_map(self, map_type):
    """
    Creates the real-space version of a given map type.

    Parameters
    ----------
    map_type : str

    Returns
    -------
    scitbx.array_family.flex.double
    """
    map_coeffs = self.fmodel.map_coefficients(
      map_type = map_type,
      exclude_free_r_reflections = True,
      fill_missing = True,
      pdb_hierarchy = self.pdb_hierarchy)
    if (map_coeffs is None):
      return None
    return map_coeffs.fft_map(resolution_factor = 0.25,
      ).apply_sigma_scaling().real_map_unpadded()

  def update_maps(self):
    """
    Generate new maps for the current structure, including anomalous map if
    data are anomalous.

    If Phaser is installed and anomalous data are available, the anomalous
    log-likelihood gradient (LLG) map can be used instead of the conventional
    anomalous difference map. This is only really useful if the anomalous
    scattering of existing atoms is modeled (and ideally, refined).
    """
    if self.fmodel is None:
      return
    def fft_map(map_coeffs, resolution_factor = 0.25):
      return map_coeffs.fft_map(resolution_factor = resolution_factor,
        ).apply_sigma_scaling().real_map_unpadded()
    map_types = ["2mFo-DFc", "mFo-DFc"]
    map_keys = ["2mFo-DFc", "mFo-DFc"]
    if (self.fmodel.f_obs().anomalous_flag()):
      if (self.params.anom_map_type == "phaser"):
        map_types.append("llg")
      elif (self.params.anom_map_type == "residual"):
        map_types.append("anom_residual")
      else :
        map_types.append("anom")
      map_keys.append("anom")
    if (self.use_svm):
      map_types.append("mFo")
      map_keys.append("mFo")
    # To save memory, we sample atomic positions immediately and throw out
    # the actual maps (instead of keeping up to 3 in memory)
    sites_frac = self.xray_structure.sites_frac()
    sites_cart = self.xray_structure.sites_cart()
    self._principal_axes_of_inertia = [ None ] * len(sites_frac)
    self._map_variances = [ None ] * len(sites_frac)
    self._map_gaussian_fits = {}
    self.calpha_mean_two_fofc = 0
    for map_type, map_key in zip(map_types, map_keys):
      real_map = self.get_map(map_type)
      if (real_map is not None):
        # Gather values for map peaks at each site
        self._map_values[map_key] = flex.double(sites_frac.size(), 0)
        self._map_gaussian_fits[map_key] = [ None ] * len(sites_frac)
        for i_seq, site_frac in enumerate(sites_frac):
          atom = self.pdb_atoms[i_seq]
          resname = atom.fetch_labels().resname.strip().upper()
          if (resname in WATER_RES_NAMES + mmtbx.ions.SUPPORTED or
              atom.segid.strip().upper() in ["ION"]):
            value = real_map.eight_point_interpolation(site_frac)
            self._map_values[map_key][i_seq] = value
            if (self.use_svm):
              gaussian_fit = utils.fit_gaussian(
                unit_cell=self.unit_cell,
                site_cart=atom.xyz,
                real_map=real_map)
              self._map_gaussian_fits[map_key][i_seq] = gaussian_fit

        if map_type in ["2mFo-DFc"]:
          # Gather values on map variance and principal axes of interia
          from cctbx import maptbx
          for i_seq, site_cart in enumerate(sites_cart):
            resname = self.pdb_atoms[i_seq].fetch_labels().resname.strip()
            if resname in WATER_RES_NAMES + mmtbx.ions.SUPPORTED:
              # XXX not totally confident about how I'm weighting this...
              p_a_i = maptbx.principal_axes_of_inertia(
                real_map = real_map,
                site_cart = site_cart,
                unit_cell = self.unit_cell,
                radius = self.params.map_sampling_radius)
              self._principal_axes_of_inertia[i_seq] = p_a_i
              variance = maptbx.spherical_variance_around_point(
                real_map = real_map,
                unit_cell = self.unit_cell,
                site_cart = site_cart,
                radius = self.params.map_sampling_radius)
              self._map_variances[i_seq] = variance
            elif (i_seq in self.calpha_sel):
              # Also collect some info in average C_alpha 2FoFc peak heights
              self.calpha_mean_two_fofc += real_map.eight_point_interpolation(
                sites_frac[i_seq])
        del real_map

    if (self.calpha_mean_two_fofc > 0):
      n_calpha = len(self.calpha_sel)
      assert (n_calpha > 0)
      self.calpha_mean_two_fofc /= n_calpha

    # Gather info on carbons' average Fo peak height for use in estimating other
    # sites' atomic weight
    self.carbon_fo_values = None
    if (len(self.carbon_sel) > 0):
      self.carbon_fo_values = flex.double()
      self._map_values["mFo"] = flex.double(sites_frac.size(), 0)
      fo_map = fft_map(self.fmodel.map_coefficients(
        map_type = "mFo",
        exclude_free_r_reflections = True,
        fill_missing = True))

      for i_seq, site_frac in enumerate(sites_frac):
        resname = self.pdb_atoms[i_seq].fetch_labels().resname.strip()
        element = self.pdb_atoms[i_seq].element.strip()
        if (element == "C") or ((element == "O") and (resname in WATER_RES_NAMES)):
          map_value = fo_map.eight_point_interpolation(site_frac)
          self._map_values["mFo"][i_seq] = map_value
          if (element == "C"):
            self.carbon_fo_values.append(map_value)
      del fo_map

  def show_current_scattering_statistics(self, out=sys.stdout):
    """
    Prints out information about an entire model's scattering statistics, such
    as mean map heights and b-factors for carbons and waters.

    Parameters
    ----------
    out : file, optional
    """
    print("", file=out)
    print("Model and map statistics:", file=out)
    print("  mean mFo map height @ carbon: %s" % format_value("%.2f",
      flex.max(self.carbon_fo_values)), file=out)
    if (self.calpha_mean_two_fofc > 0):
      print("  mean 2mFo-DFc map height @ C-alpha: %s" % format_value(
        "%.2f", self.calpha_mean_two_fofc), file=out)
    print("  mean B-factor: %s" % format_value("%.2f", self.b_mean_all), file=out)
    if (self.b_mean_calpha > 0):
      print("  mean C-alpha B-factor: %s" % format_value("%.2f",
        self.b_mean_calpha), file=out)
    print("  mean water B-factor: %s" % format_value("%.2f",
      self.b_mean_hoh), file=out)
    n_water_fofc_peaks = 0
    n_water_anom_peaks = 0
    water_sel = self.water_selection()
    print("  %d water molecules" % len(water_sel), file=out)
    for i_seq in water_sel :
      map_stats = self.map_stats(i_seq)
      if (map_stats.fofc >= 3.0):
        n_water_fofc_peaks += 1
      if (map_stats.anom is not None) and (map_stats.anom >= 3.0):
        n_water_anom_peaks += 1
    print("  %d waters have mFo-DFc map >= 3.0 sigma" % \
      n_water_fofc_peaks, file=out)
    if (self.anomalous_flag):
      print("  %d waters have anomalous map >= 3.0 sigma" % \
        n_water_anom_peaks, file=out)
    print("", file=out)

  def get_strict_valence_flag(self):
    """
    Checks whether the resolution of a model is high enough to use more strict
    thresholds for ion valences.

    Returns
    -------
    bool
    """
    d_min = self.fmodel.f_obs().d_min()
    return (d_min < self.params.d_min_strict_valence)

  def find_nearby_atoms(self, i_seq, far_distance_cutoff = 3.0,
      near_distance_cutoff = 1.5, filter_by_bonding = True,
      filter_by_two_fofc = True):
    """
    Given site in the structure, return a list of contacts, optionally
    filtering by connectivity and 2mFo-DFc map level.

    Parameters
    ----------
    i_seq : int
    far_distance_cutoff : float, optional
    near_distance_cutoff : float, optional
    filter_by_bonding : bool, optional
    fitler_by_two_fofc : bool, optional

    Returns
    -------
    list of mmtbx.ions.environment.atom_contact
    """
    assert (i_seq < len(self.sites_frac))
    # Use pair_asu_table to find atoms within distance_cutoff of one another,
    # taking into account potential cell symmetry.
    # XXX should probably move this up, but it seems relatively fast
    pair_asu_table, asu_mappings, asu_table = self._pair_asu_cache.get(
      far_distance_cutoff, (None,None,None))
    if (pair_asu_table is None):
      pair_asu_table = self.xray_structure.pair_asu_table(
        distance_cutoff = far_distance_cutoff)
      asu_mappings = pair_asu_table.asu_mappings()
      asu_table = pair_asu_table.table()
      self._pair_asu_cache[far_distance_cutoff] = (pair_asu_table, asu_mappings,
        asu_table)
    contacts = environment.find_nearby_atoms(
      i_seq=i_seq,
      xray_structure=self.xray_structure,
      pdb_atoms=self.pdb_atoms,
      asu_mappings=asu_mappings,
      asu_table=asu_table,
      connectivity=self.connectivity,
      far_distance_cutoff=far_distance_cutoff,
      near_distance_cutoff=near_distance_cutoff,
      filter_by_bonding=filter_by_bonding)
    # Discard waters in poor density
    if (filter_by_two_fofc):
      filtered = []
      for contact in contacts :
        if (contact.resname() in WATER_RES_NAMES):
          two_fofc = self._map_values["2mFo-DFc"][contact.atom_i_seq()]
          if two_fofc < self.params.water.min_2fofc_coordinating:
            continue
        filtered.append(contact)
      contacts = filtered
    return contacts

  def guess_b_iso_real(self, i_seq):
    """
    Guess an approximate B_iso for an atom by averaging the values for
    coordinating atoms.  This should partially compensate for the deflation of
    the B-factor due to incorrect scattering type, and consequently make the
    occupancy refinement more accurate.

    Parameters
    ----------
    i_seq : int

    Returns
    -------
    float
    """
    contacts = self.find_nearby_atoms(i_seq, far_distance_cutoff = 3.0)
    if (len(contacts) == 0):
      return adptbx.u_as_b(self.u_iso_all[i_seq])
    u_iso_sum = 0
    for contact in contacts :
      u_iso_sum += self.u_iso_all[contact.atom_i_seq()]
    return adptbx.u_as_b(u_iso_sum / len(contacts))

  def principal_axes_of_inertia(self, i_seq):
    """
    Extracts the map grid points around a site, and calculates the axes of
    inertia (using the density values as weights). This is used to calculate
    the sphericity of the blob of density around a site.

    Parameters
    ----------
    i_seq : int

    Returns
    -------
    ...
    """
    return self._principal_axes_of_inertia[i_seq]

  def get_map_sphere_variance(self, i_seq):
    """
    Calculate the density levels for points on a sphere around a given point.
    This will give us some indication whether the density falls off in the
    expected manner around a single atom.

    Parameters
    ----------
    i_seq : int

    Returns
    -------
    float
    """
    return self._map_variances[i_seq]

  def get_b_iso(self, i_seq):
    """
    Calculates the isotropic b-factor of a site.

    Parameters
    ----------
    i_seq : int

    Returns
    -------
    float
    """
    sc = self.xray_structure.scatterers()[i_seq]
    return adptbx.u_as_b(sc.u_iso_or_equiv(self.unit_cell))

  def map_stats(self, i_seq):
    """
    Given a site in the structure, find the signal of the 2FoFc, FoFc, and
    anomalous map (When available).

    Parameters
    ----------
    i_seq : int

    Returns
    -------
    group_args
        Object with .two_fofc, .fofc, and .anom properties for the associated
        signals in each map.
    """
    value_2fofc = self._map_values["2mFo-DFc"][i_seq]
    value_fofc = self._map_values["mFo-DFc"][i_seq]
    value_anom = None
    if ("anom" in self._map_values):
      value_anom = self._map_values["anom"][i_seq]
    return group_args(
      two_fofc = value_2fofc,
      fofc = value_fofc,
      anom = value_anom)

  def get_map_gaussian_fit(self, map_type, i_seq):
    """
    Retrieves the two parameters of a gaussian function, fit to a given map at
    a site.

    Parameters
    ----------
    map_type : str
    i_seq : int

    Returns
    -------
    (tuple of float, float) or None
    """
    map_gaussians = self._map_gaussian_fits.get(map_type, None)
    if (map_gaussians is not None):
      return map_gaussians[i_seq]
    return None

  def guess_molecular_weight(self, i_seq):
    """
    Guesses the molecular weight of a site by scaling the signal from
    the mFo map by the average signal from carbon atoms.

    Parameters
    ----------
    i_seq : int

    Returns
    -------
    float
    """
    map_values = self._map_values.get("mFo", None)
    if (map_values is None) : return None
    height = map_values[i_seq]
    mean_carbon = flex.mean(self.carbon_fo_values)
    assert (mean_carbon > 0)
    return 6 * height / mean_carbon

  def _find_atoms_near_site(self,
      site_cart,
      distance_cutoff = 1.5,
      distance_cutoff_same_site = 0.5):
    """
    Given an XYZ coordinate, finds atoms near that site, separating out those
    which are close enough to be essentially equivalent.  Used to analyze the
    anomalously-scattering substructure calculated by Phaser.

    Parameters
    ----------
    site_cart : tuple of float, float, float
    distance_cutoff : float, optional
    distance_cutoff_same_site : float, optional

    Returns
    -------
    same_atoms : list of group_args
        List of atoms within distance_cutoff_same_site of site_cart, and and
        other_atoms, the rest of the atoms within distance_cutoff of site_cart.
    """
    site_frac = self.unit_cell.fractionalize(site_cart = site_cart)
    sites_frac = flex.vec3_double([site_frac])
    asu_mappings = self.xray_structure.asu_mappings(buffer_thickness =
      distance_cutoff + 0.1)
    asu_mappings.process_sites_frac(sites_frac,
      min_distance_sym_equiv = self.xray_structure.min_distance_sym_equiv())
    pair_generator = crystal.neighbors_fast_pair_generator(
      asu_mappings = asu_mappings,
      distance_cutoff = distance_cutoff)
    n_xray = self.xray_structure.scatterers().size()
    same_atoms = []
    other_atoms = []
    # Iterate through all interacting pairs of atoms in the structure
    # within distance_cutoff of each other
    for pair in pair_generator:
      # Find a pair where one's sequence ID is < n_xray and the other's
      # is >= n_xray and gather the site information about it
      if (pair.i_seq < n_xray and pair.j_seq < n_xray) or \
        (pair.i_seq >= n_xray and pair.j_seq >= n_xray):
        continue
      rt_mx_i = asu_mappings.get_rt_mx_i(pair)
      rt_mx_j = asu_mappings.get_rt_mx_j(pair)
      rt_mx_ji = rt_mx_i.inverse().multiply(rt_mx_j)
      new_site_frac = rt_mx_ji * site_frac
      new_site_cart = self.unit_cell.orthogonalize(site_frac = new_site_frac)
      atom_seq = pair.i_seq if pair.i_seq < n_xray else pair.j_seq
      site_info = group_args(
        i_seq = atom_seq,
        rt_mx = rt_mx_ji,
        new_site_cart = new_site_cart,
        distance = sqrt(pair.dist_sq))
      if site_info.distance < distance_cutoff_same_site:
        same_atoms.append(site_info)
      else:
        other_atoms.append(site_info)
    same_atoms.sort(key = lambda x: x.distance)
    other_atoms.sort(key = lambda x: x.distance)
    return same_atoms, other_atoms

  def analyze_substructure(self, log = None, verbose = True):
    """
    Given a list of AX pseudo-atoms placed by Phaser, finds the nearest real
    atoms, and if possible determines their equivalence.

    Parameters
    ----------
    log : file, optional
    verbose : bool, optional
    """
    def ax_atom_id(atom):
      """
      Parameters
      ----------
      atom : iotbx.pdb.hierarchy.atom

      Returns
      -------
      str
      """
      return "AX %s (fpp=%.3f)" % (atom.serial.strip(), atom.occ)

    assert (self.phaser_substructure is not None)
    self.fpp_from_phaser_ax_sites = flex.double(self.pdb_atoms.size(), -1)
    if (log is None) or (not verbose) : log = null_out()

    def _filter_site_infos(site_infos):
      """
      Parameters
      ----------
      site_infos : list of group_args

      Returns
      -------
      list of group_args
      """
      # Organize a dictionary, keyed with each site's atom i_seq
      from collections import OrderedDict
      i_seqs = OrderedDict()
      for site_info in site_infos:
        if site_info.i_seq in i_seqs:
          i_seqs[site_info.i_seq].append(site_info)
        else:
          i_seqs[site_info.i_seq] = [site_info]
      # If we are picking from multiple of the same i_seq, select the closest
      for val in i_seqs.values():
        # Prefer unit operators
        if any(i.rt_mx.is_unit_mx() for i in val):
          for site_info in val:
            if not site_info.rt_mx.is_unit_mx():
              val.remove(site_info)
        val.sort(key = lambda x: x.distance)
      return [i[0] for i in i_seqs.values()]

    make_sub_header("Analyzing Phaser anomalous substructure", out = log)
    for atom in self.phaser_substructure :
      print(ax_atom_id(atom), file=log)
      same_atoms, other_atoms = self._find_atoms_near_site(
        atom.xyz,
        distance_cutoff=self.params.phaser.distance_cutoff,
        distance_cutoff_same_site=self.params.phaser.distance_cutoff_same_site)

      same_atoms, other_atoms = \
        _filter_site_infos(same_atoms), _filter_site_infos(other_atoms)

      if len(same_atoms) == 0:
        print("  No match for %s" % ax_atom_id(atom), file=log)
        for other_atom in other_atoms:
          print("    %s (distance = %.3f)" % \
                (self.pdb_atoms[other_atom.i_seq].id_str(), other_atom.distance), file=log)
      elif len(same_atoms) == 1:
        print("  %s maps to %s" % \
          (ax_atom_id(atom), self.pdb_atoms[same_atoms[0].i_seq].id_str()), file=log)
        self.use_fdp[same_atoms[0].i_seq] = True
        self.fpp_from_phaser_ax_sites[same_atoms[0].i_seq] = atom.occ
      else :
        print("  ambiguous results for %s:" % ax_atom_id(atom), file=log)
        for same_atom in same_atoms:
          print("    %s" % self.pdb_atoms[same_atom.i_seq].id_str(), file=log)
    print("", file=log)

  def get_fpp(self, i_seq):
    """
    Retrieve the refined f'' for a site.  Because this can come from either the
    built-in anomalous refinement or Phaser, it is handled differently than
    the f' retrieval.

    Parameters
    ----------
    i_seq : int

    Returns
    -------
    float or None
    """
    if (not self.use_fdp[i_seq]):
      return None
    if (self.fpp_from_phaser_ax_sites is None):
      return self.site_fdp[i_seq]
    fpp = self.fpp_from_phaser_ax_sites[i_seq]
    if (fpp < 0) : fpp = None
    return fpp

  def get_fp(self, i_seq):
    """
    Retrieve the refined f' for a site.

    Parameters
    ----------
    i_seq : int

    Returns
    -------
    float or None
    """
    if (self.site_fp is None) or (not self.use_fdp[i_seq]):
      return None
    return self.site_fp[i_seq]

  def looks_like_halide_ion(self,
      i_seq,
      element = "CL",
      assume_hydrogens_all_missing = Auto):
    """
    Given a site, analyze the nearby atoms (taking geometry into account) to
    guess whether it might be a halide ion.  Among other things, halides will
    often be coordinated by amide hydrogens, and not in close contact with
    negatively charged atoms.  Note that this procedure has a very high
    false positive rate when used by itself, so additional information about
    the electron count (map, occ, b) is absolutely essential.

    Parameters
    ----------
    i_seq : int
    element : str
    assume_hydrogens_all_missing : bool, optional

    Returns
    -------
    bool
    """
    atom = self.pdb_atoms[i_seq]
    sites_frac = self.xray_structure.sites_frac()
    assert element.upper() in mmtbx.ions.HALIDES
    # discard atoms with B-factors greater than mean-1sigma for waters
    if ((self.b_mean_hoh is not None) and
        (atom.b > self.b_mean_hoh - self.b_stddev_hoh)):
      return False
    nearby_atoms = self.find_nearby_atoms(
      i_seq,
      far_distance_cutoff = 4.0,
      filter_by_bonding = False)
    favorable_environment = halides.is_favorable_halide_environment(
      i_seq=i_seq,
      contacts=nearby_atoms,
      pdb_atoms=self.pdb_atoms,
      sites_frac=self.xray_structure.sites_frac(),
      unit_cell=self.unit_cell,
      connectivity=self.connectivity,
      params = self.params.chloride,
      assume_hydrogens_all_missing=assume_hydrogens_all_missing)
    # TODO something smart...
    # the idea here is to determine whether the blob of density around the
    # site is approximately spherical and limited in extent.
    pai = self.principal_axes_of_inertia(i_seq)
    map_variance = self.get_map_sphere_variance(i_seq)
    good_map_falloff = False
    if ((map_variance is not None) and
        (map_variance.mean < 1.5) and
        (map_variance.standard_deviation < 0.4)) : # XXX very arbitrary
      good_map_falloff = True
    # XXX probably need something more sophisticated here too.  a CL which
    # coordinates a metal (e.g. in 4aqi) may not have a clear blob, but
    # will still be detectable by other criteria.
    return favorable_environment

  def analyze_water(self,
      i_seq,
      debug = True,
      candidates = Auto,
      no_final = False,
      out = sys.stdout):
    """
    Examines the environment around a single atom to determine if it is actually
    a misidentified metal ion.

    no_final is used internally, when candidates is not Auto, to try all of the
    Auto without selecting any as a final choice when none of the elements in
    candidates appear to probable ion identities.

    Parameters
    ----------
    i_seq : int
    debug : bool, optional
    candidates : list of str, optional
    no_file : bool, optional
    out : file, optional

    Returns
    -------
    mmtbx.ions.identify.water_result
    """

    atom = self.pdb_atoms[i_seq]
    # Keep track of this in case we find nothing from the user-specific candidates
    auto_candidates = candidates is Auto
    if auto_candidates:
      candidates = mmtbx.ions.DEFAULT_IONS
    elif isinstance(candidates, string_types):
      candidates = candidates.replace(",", " ").split()
    candidates = [i.strip().upper() for i in candidates]
    if (candidates == ['X']) : # XXX hack for testing - X is "dummy" element
      candidates = []

    resname = atom.fetch_labels().resname.strip().upper()
    assert resname in WATER_RES_NAMES

    atom_props = self.atoms_to_props[i_seq]
    final_choice = None

    # Gather some quick statistics on the atom and see if it looks like water
    atom_type = atom_props.get_atom_type(params=self.params.water,
      aggressive=self.params.aggressive)
    if (atom_type == WATER_POOR) : # not trustworthy, skip
      #_PRINT_DEBUG("SKIPPING %s" % atom.id_str())
      return None

    # XXX everything SVM-related happens in mmtbx.ions.svm, using a subclass of
    # this one
    assert (not self.use_svm)

    # Filter out metals based on whether they are more or less eletron-dense
    # in comparison with water
    filtered_candidates = []
    halide_candidates = False
    nuc_phosphate_site = atom_props.looks_like_nucleotide_phosphate_site()
    max_carbon_fo_map_value = sys.maxsize
    if (self.carbon_fo_values is not None):
      max_carbon_fo_map_value = flex.max(self.carbon_fo_values)

    for symbol in candidates :
      elem = mmtbx.ions.server.get_metal_parameters(symbol)
      if (elem is None):
        if (symbol in mmtbx.ions.HALIDES) : # halides are special!
          halide_candidates = True
          continue
        else :
          raise Sorry("Element '%s' not supported!" % symbol)

      # If we definitely look like water or a light ion, only look at the
      # metals isoelectronic with water.
      if ((atom_type in [WATER, LIGHT_ION]) and
          (symbol not in ["NA", "MG", "F", "NE"])):
        continue

      if (nuc_phosphate_site) and (not symbol in NUC_PHOSPHATE_BINDING):
        continue

      n_elec = sasaki.table(symbol.upper()).atomic_number() - elem.charge
      # lighter elements are not expected to have any anomalous signal
      if (n_elec <= 12) and (atom_props.fpp is not None and atom_props.fpp > 0.1):
        continue
      mass_ratio = atom_props.estimated_weight / max(n_elec, 1)
      # note that anomalous peaks are more important than the 2mFo-DFc level
      if (mass_ratio < 0.4) and (atom_type == WATER):
        continue

      filtered_candidates.append(elem)

    # if len(filtered_candidates) == 0 and not halide_candidates:
    #   return None

    # Try each different ion and see what is reasonable
    def try_candidates(require_valence = True):
      """
      Parameters
      ----------
      require_valence : bool, optional

      Returns
      -------
      list of tuple of mmtbx.ions.metal_parameters, float
      list of tuple of mmtbx.ions.metal_parameters, float
      """
      reasonable = []
      unreasonable = []
      for elem_params in filtered_candidates:
        # Try the ion with check_ion_environment()
        atom_props.check_ion_environment(
          ion_params = elem_params,
          wavelength = self.wavelength,
          require_valence = require_valence)
        atom_props.check_fpp_ratio(
          ion_params = elem_params,
          wavelength = self.wavelength,
          fpp_ratio_min = self.params.phaser.fpp_ratio_min,
          fpp_ratio_max = self.params.phaser.fpp_ratio_max)
        identity = str(elem_params)
        if atom_props.is_correctly_identified(identity = identity):
          reasonable.append((elem_params, atom_props.score[identity]))
        else :
          unreasonable.append((elem_params, atom_props.score[identity]))
      return reasonable, unreasonable

    # first try with bond valence included in criteria - then if that doesn't
    # yield any hits, try again without valences
    reasonable, unreasonable = try_candidates(require_valence = True)
    valence_used = True
    if (len(reasonable) == 0) and (not self.params.require_valence):
      reasonable, unreasonable = try_candidates(require_valence = False)
      if (len(reasonable) > 0):
        valence_used = False

    looks_like_halide = False
    if (not (reasonable or atom_type < HEAVY_ION or nuc_phosphate_site)):
      # try halides now
      candidate_halides = set(candidates).intersection(mmtbx.ions.HALIDES)
      filtered_halides = []
      for element in candidate_halides :
        fpp_ratio = atom_props.check_fpp_ratio(
          ion_params = mmtbx.ions.metal_parameters(element = element,
            charge = -1),
          wavelength = self.wavelength,
          fpp_ratio_min = self.params.phaser.fpp_ratio_min,
          fpp_ratio_max = self.params.phaser.fpp_ratio_max)
        # XXX chlorides are tricky, because they tend to be partial occupancy
        # anyway and the f'' is already small, so prone to error here
        if (fpp_ratio is not None) and (element != "CL"):
          if ((fpp_ratio < self.params.phaser.fpp_ratio_min) or
              (fpp_ratio > self.params.phaser.fpp_ratio_max)):
            #print "fpp_ratio:", fpp_ratio
            continue
        if (self.looks_like_halide_ion(i_seq = i_seq, element = element)):
          filtered_halides.append(element)

      looks_like_halide = (len(filtered_halides) > 0)
      reasonable += [(mmtbx.ions.metal_parameters(element = halide,
                      charge = -1), 0) for halide in filtered_halides]

    # If we can't find anything reasonable, relax our constraints...
    # Look for something that is a compatible ligand, an apparent fpp,
    # and compatible geometries
    if (len(reasonable) == 0) and (not self.params.require_valence):
      compatible = [params for params in filtered_candidates
                    if atom_props.has_compatible_ligands(str(params))]
      for ion_params in compatible :
        atomic_number = sasaki.table(ion_params.element).atomic_number()
        weight_ratio = 0
        if (atom_props.estimated_weight is not None):
          weight_ratio = atom_props.estimated_weight / (atomic_number - \
            ion_params.charge)
        # special handling for transition metals, but only if the user has
        # explicitly requested one (and there is no ambiguity)
        if ((ion_params.element in mmtbx.ions.TRANSITION_METALS) and
            (atom_type == HEAVY_ION) and
            #(atom_props.peak_2fofc > max_carbon_fo_map_value) and
            (not auto_candidates) and
            (atom_props.is_compatible_site(ion_params))):
          n_good_res = atom_props.number_of_favored_ligand_residues(ion_params,
            distance = 2.7)
          n_total_coord_atoms = atom_props.number_of_atoms_within_radius(2.8)
          # if we see at least one favorable residue coordinating the atom
          # and no more than six atoms total, accept the current guess
          if ((n_good_res >= 1) and (2 <= n_total_coord_atoms <= 6)):
            reasonable.append((ion_params, 0))
          else : pass
            #print "n_good_res = %d, n_total_coord_atoms = %d" % (n_good_res,
            #  n_total_coord_atoms)
        elif ((ion_params.element in ["K","CA"]) and
              (atom_type == HEAVY_ION) and
              (not auto_candidates) and
              (atom_props.is_compatible_site(ion_params))):
          n_good_res = atom_props.number_of_favored_ligand_residues(ion_params,
            distance = 2.9, exclude_atoms = ["O"])
          n_bb_oxygen = atom_props.number_of_backbone_oxygens(
            distance_cutoff = 2.9)
          n_total_coord_atoms = atom_props.number_of_atoms_within_radius(
            distance_cutoff = 2.9)
          if ((n_good_res + n_bb_oxygen) >= 2) and (n_total_coord_atoms >= 4):
            reasonable.append((ion_params, 0))
        # another special case: very heavy ions, which are probably not binding
        # physiologically
        elif ((atomic_number > 30) and (atom_type == HEAVY_ION) and
              (((weight_ratio > 0.5) and (weight_ratio < 1.05)) or
               (atom_props.fp > 10)) and
              (not auto_candidates) and
              (atom_props.is_compatible_site(ion_params,
                ignore_valence=not self.get_strict_valence_flag()))):
          n_total_coord_atoms = atom_props.number_of_atoms_within_radius(2.8)
          if (n_total_coord_atoms >= 3):
            reasonable.append((ion_params, 0))
        else : pass
          #print atom.id_str(), atomic_number, looks_like_water, weight_ratio, \
          #  atom_props.is_compatible_site(ion_params)
      if (len(compatible) == 1) and (not self.get_strict_valence_flag()):
        inaccuracies = atom_props.inaccuracies[str(ion_params)]
        if (compatible[0] in atom_props.fpp_ratios and
            atom_props.BAD_FPP not in inaccuracies and
            not inaccuracies.intersection([atom_props.BAD_GEOMETRY,
                                           atom_props.NO_GEOMETRY])):
          _PRINT_DEBUG(atom.id_str())
          _PRINT_DEBUG(filtered_candidates)
          _PRINT_DEBUG(compatible)

    if (len(reasonable) == 1):
      final_choice = reasonable[0][0]

    if (not reasonable) and (not auto_candidates):
      # Couldn't find anything from what the user suggested, try the other
      # default candidates and just let the user know about them
      result = self.analyze_water(
        i_seq = i_seq,
        debug = debug,
        no_final = True,
        out = out)
      if (result.final_choice is not None):
        return result
    return water_result(
      atom_props = atom_props,
      filtered_candidates = filtered_candidates,
      matching_candidates = reasonable,
      rejected_candidates = unreasonable,
      nuc_phosphate_site = nuc_phosphate_site,
      atom_type = atom_type,
      looks_like_halide = looks_like_halide,
      ambiguous_valence_cutoff = self.params.ambiguous_valence_cutoff,
      valence_used = valence_used,
      final_choice = final_choice,
      wavelength = self.wavelength,
      no_final = no_final)

  def _extract_waters(self):
    model = self.pdb_hierarchy.only_model()
    water_i_seqs = []
    for chain in model.chains():
      for residue_group in chain.residue_groups():
        atom_groups  = residue_group.atom_groups()
        if (len(atom_groups) > 1):
          continue
        for atom_group in atom_groups :
          resname = atom_group.resname.strip().upper()
          if (resname in WATER_RES_NAMES):
            atoms = atom_group.atoms()
            if (len(atoms) == 1) : # otherwise it probably has hydrogens, skip
              water_i_seqs.append(atoms[0].i_seq)
    return water_i_seqs

  def analyze_waters(self, out = sys.stdout, debug = True, candidates = Auto):
    """
    Iterates through all of the waters in a model, examining the maps and local
    environment to check their identity and suggest likely ions where
    appropriate.

    Parameters
    ----------
    out : file, otpional
    debug : bool, optional
    candidates : list of str, optional

    Returns
    -------
    list of tuple of int, mmtbx.ions.metal_parameters
        i_seq and metal parameters which indicate that waters of i_seq would be
        better changed to that new metal identity.
    """
    waters = self.water_selection()
    print("", file=out)
    print("  %d waters to analyze" % len(waters), file=out)
    if (len(waters) == 0) : return
    nproc = easy_mp.get_processes(self.nproc)
    ions = []
    self.atoms_to_props = dict((i_seq, AtomProperties(i_seq, self))
                               for i_seq in waters)
    if (nproc == 1):
      print("", file=out)
      for water_i_seq in waters :
        t1 = time.time()
        water_props = self.analyze_water(
          i_seq = water_i_seq,
          debug = debug,
          candidates = candidates,
          out = out)
        if (water_props is not None):
          water_props.show_summary(out = out, debug = debug)
          map_stats = self.map_stats(water_i_seq)
          if ((water_props.final_choice is not None) and
              (not water_props.no_final)):
            ions.append((water_i_seq, [water_props.final_choice],
              map_stats.two_fofc))
        t2 = time.time()
        #print "%s: %.3fs" % (self.pdb_atoms[water_i_seq].id_str(), t2-t1)
    else :
      print("  Parallelizing across %d processes" % nproc, file=out)
      print("", file=out)
      analyze_water = _analyze_water_wrapper(manager = self,
        debug = debug,
        candidates = candidates,
        out = out)
      results = easy_mp.pool_map(
        fixed_func = analyze_water,
        args = waters,
        processes = nproc)
      for result in results :
        if (result is None):
          continue
        water_i_seq, final_choice, result_str = result
        if (result_str is not None):
          print(result_str, file=out)
        if final_choice is not None :
          map_stats = self.map_stats(water_i_seq)
          ions.append((water_i_seq, [final_choice], map_stats.two_fofc))

    return sorted(ions, key=operator.itemgetter(2), reverse=True)

  def validate_ion(self, i_seq, out = sys.stdout, debug = True):
    """
    Examines one site in the model and determines if it was correctly modelled,
    returning a boolean indicating correctness.

    Parameters
    ----------
    i_seq : int
    out : file, optional
    debug : bool, optional
    """

    atom_props = self.atoms_to_props[i_seq]
    element = mmtbx.ions.server.get_element(atom_props.atom)
    elem_params = mmtbx.ions.server.get_metal_parameters(element)

    if elem_params is not None:
      atom_type = atom_props.get_atom_type(params=self.params.water)
      atom_props.check_ion_environment(
        ion_params = elem_params,
        wavelength = self.wavelength,
        require_valence = self.params.require_valence)
      atom_props.check_fpp_ratio(
        ion_params = elem_params,
        wavelength = self.wavelength,
        fpp_ratio_min = self.params.phaser.fpp_ratio_min,
        fpp_ratio_max = self.params.phaser.fpp_ratio_max)
    elif element in mmtbx.ions.HALIDES:
      identity = atom_props.identity()
      atom_props.inaccuracies[identity] = set()

      if not self.looks_like_halide_ion(i_seq = i_seq, element = element):
        atom_props.inaccuracies[identity].add(atom_props.BAD_HALIDE)
    else:
      raise Sorry("Element '%s' not supported:\n%s" %
                  (element, atom_props.atom.format_atom_record()))

    return atom_props

  def validate_ions(self, out = sys.stdout, debug = True, segid = None):
    """
    Iterate over all the ions built into the model by this module and double
    check their correctness. Looks for tell-tale signs such as negative peaks
    in the mFo-DFc and anomalous difference maps, abnormal b-factor, and
    disallowed chemical environments.

    Prints out a table of bad ions and their information and returns a list of
    their sites' i_seqs.

    Parameters
    ----------
    out : file, optional
    debug : bool, optional
    segid : str, optional
        If present, only validate ions with this segid.
    """

    if segid is None:
      ions = []
      for model in self.pdb_hierarchy.models():
        for chain in model.chains():
          for residue_group in chain.residue_groups():
            for atom_group in residue_group.atom_groups():
              if atom_group.resname.strip() in mmtbx.ions.SUPPORTED:
                atoms = atom_group.atoms()
                assert (len(atoms) == 1)
                for atom in atoms:
                  ions += atom.i_seq,
    else:
      sel_cache = self.pdb_hierarchy.atom_selection_cache()
      ions = sel_cache.selection("segid {0}".format(segid)).iselection()

    if len(ions) == 0:
      return
    ion_status = []

    self.atoms_to_props = dict((i_seq, AtomProperties(i_seq, self))
                               for i_seq in ions)
    for i_seq in ions:
      atom_props = self.validate_ion(
        i_seq = i_seq,
        debug = debug)
      ion_status.append((atom_props, atom_props.is_correctly_identified()))
    scatterers = self.xray_structure.scatterers()
    headers = ("atom", "occ", "b_iso", "2mFo-DFc", "mFo-DFc", "fp", "fdp",
               "ratio", "BVS", "VECSUM")
    fmt = "%-15s %-5s  %-5s  %-8s  %-7s  %-5s  %-5s  %-5s %-5s  %-6s"
    box = framed_output(out, title = "Validating new ions", width = 80)
    print(fmt % headers, file=box)
    print(" " + ("-" * 75), file=box)
    for props, okay_flag in ion_status :
      i_seq = props.atom.i_seq
      sc = scatterers[i_seq]
      fp = fdp = None
      if (sc.flags.use_fp_fdp):
        fp = sc.fp
        fdp = sc.fdp
      # XXX props.atom.b does not work here!
      b_iso = adptbx.u_as_b(sc.u_iso_or_equiv(unit_cell = self.unit_cell))
      identity = props.identity()
      def ff(fs, val) : return format_value(fs, val, replace_none_with = "---")
      print(fmt % (props.atom.id_str(suppress_segid = True)[5:-1],
        ff("%.2f", props.atom.occ), ff("%.2f", b_iso),
        ff("%.2f", props.peak_2fofc), ff("%.2f", props.peak_fofc),
        ff("%.2f", fp), ff("%.2f", fdp),
        ff("%.2f", None if self.wavelength is None else fdp / sasaki.table(
            mmtbx.ions.server.get_element(props.atom)).at_angstrom(self.wavelength).fdp()),
        ff("%.2f", props.valence_sum.get(identity)),
        ff("%.2f", props.vector_sum.get(identity))), file=box)
      # print >> box, props.geometries
      if not okay_flag:
        print("\n".join("!!! " + props.error_strs[i]
                                for i in props.inaccuracies[identity]), file=box)
      print("", file=box)
    box.close()

class _analyze_water_wrapper(object):
  """
  Simple wrapper for calling manager.analyze_water with keyword arguments
  in a parallelized loop.  Because the water_result object is not pickle-able,
  we only return the final ion choice and summary string.
  """
  def __init__(self, manager, **kwds):
    self.manager = manager
    self.kwds = dict(kwds)

  def __call__(self, i_seq):
    try:
      result = self.manager.analyze_water(i_seq, **(self.kwds))
      out = StringIO()
      if (result is not None):
        result.show_summary(out = out,
          debug = self.kwds.get("debug", False))
      result_str = out.getvalue()
      if (result_str == "") : result_str = None
      final_choice = None
      # only accept final choice if it's in the original list of elements
      if (not getattr(result, "no_final", False)):
        final_choice = getattr(result, "final_choice", None)
        #_PRINT_DEBUG("final_choice = %s" % final_choice)
      return i_seq, final_choice, result_str
    except KeyboardInterrupt :
      return (None, None, None)

class _validate_ion_wrapper(object):
  """
  Simple wrapper for calling manager.validate_ion with keyword arguments
  in a parallelized loop.  Because the water_result object is not pickle-able,
  we only return the final ion choice and summary string.
  """
  def __init__(self, manager, **kwds):
    self.manager = manager
    self.kwds = dict(kwds)

  def __call__(self, i_seq):
    try:
      correct = self.manager.validate_ion(i_seq, **(self.kwds))
      return i_seq, correct
    except KeyboardInterrupt :
      return (None, None)

class ion_result(validation.atom):
  """
  Class for validation results for an ion site. This includes information about
  both the atom modeled at that site as well as its chemical and scattering
  environment.
  """
  __slots__ = validation.atom.__slots__ + ["chem_env", "scatter_env"]

  def show(self, out = sys.stdout, prefix = ""):
    pass

  def show_brief(self, out = sys.stdout, prefix = ""):
    # Print brief statistics for lines in a table
    pass

class water_result(object):
  """
  Container for storing the results of manager.analyze_water for later display
  and retrieval.
  """
  def __init__(self,
      atom_props,
      filtered_candidates,
      matching_candidates,
      rejected_candidates,
      nuc_phosphate_site,
      atom_type,
      looks_like_halide,
      ambiguous_valence_cutoff,
      valence_used,
      final_choice,
      wavelength=None,
      no_final = False):
    adopt_init_args(self, locals())

  def show_summary(self, out = None, debug = False):
    """
    Prints out a summary of a site's chemical and scattering environment.

    Parameters
    ----------
    out : file, optional
    debug : bool, optional
    """
    if (out is None) : out = sys.stdout
    results = self.matching_candidates
    if (len(results) > 0):
      self.atom_props.show_properties(identity = "HOH", out = out)
      if (self.nuc_phosphate_site):
        print("  appears to be nucleotide coordination site", file=out)
      if (self.no_final):
        print("  Found potential ion%s outside of specified set:" % \
          ("s" if len(results) > 1 else ""), file=out)
      if (self.final_choice is not None):
        # We have one result that we are reasonably certain of
        elem_params, score = results[0]
        if elem_params.element not in mmtbx.ions.HALIDES:
          self.atom_props.show_ion_results(
            identity = str(self.final_choice),
            out = out,
            valence_used = self.valence_used,
            confirmed = True)
        else:
          print("  Probable anion:", str(elem_params), file=out)
        print("", file=out)
      elif (len(results) > 1):
        # We have a couple possible identities for the atom
        below_cutoff = [ elem_params for elem_params, score in results
                        if score < self.ambiguous_valence_cutoff]
        if len(below_cutoff) == 1:
          elem_params = below_cutoff[0]
          print("  ambigous results, best valence from %s" % \
            str(elem_params), file=out)
          self.atom_props.show_ion_results(
            identity = str(elem_params),
            out = out,
            valence_used = True)
          print("", file=out)
        else:
          ions = [str(i[0]) for i in sorted(results, key = lambda x: x[1])]
          print("  ambiguous results, could be %s" % ", ".join(ions), file=out)
          for elem_params, score in results :
            self.atom_props.show_ion_results(identity = str(elem_params),
              out = out)
          print("", file=out)
    else:
      if (self.atom_type != WATER) or (self.nuc_phosphate_site):
        self.atom_props.show_properties(identity = "HOH", out = out)
        if (self.nuc_phosphate_site):
          print("  appears to be nucleotide coordination site", file=out)
        # try anions now
        if (self.looks_like_halide):
          print("  Probable cation: %s" % str(self.final_choice), file=out)
          print("", file=out)
        else:
          # atom is definitely not water, but no reasonable candidates found
          # print out why all the metals we tried failed
          if (debug) and (len(self.filtered_candidates) > 0):
            print("  insufficient data to identify atom", file=out)
            possible = True
            for params in self.filtered_candidates:
              if (self.atom_props.has_compatible_ligands(str(params))):
                if possible:
                  print("  possible candidates:", file=out)
                  possible = False
                self.atom_props.show_ion_results(identity = str(params),
                  out = out)
              else :
                print("  incompatible ligands for %s" % str(params), file=out)
            #print >> out, "  rejected as unsuitable:"
            #for params in self.rejected_candidates:
            #  if (self.atom_props.has_compatible_ligands(str(params))):
            #    self.atom_props.show_ion_results(identity = str(params),
            #      out = out)
            #  else :
            #    print >> out, "  incompatible ligands for %s" % str(params)
          print("", file=out)

class AtomProperties(object):
  """
  Collect physical attributes of an atom, including B, occupancy, and map
  statistics, and track those which are at odds with its chemical identity.
  """

  LOW_B, HIGH_B, LOW_OCC, HIGH_OCC, NO_2FOFC_PEAK, FOFC_PEAK, FOFC_HOLE, \
    ANOM_PEAK, NO_ANOM_PEAK, BAD_GEOMETRY, NO_GEOMETRY, BAD_VECTORS, \
    BAD_VALENCES, TOO_FEW_NON_WATERS, TOO_FEW_COORD, TOO_MANY_COORD, \
    LIKE_COORD, BAD_COORD_ATOM, BAD_FPP, BAD_COORD_RESIDUE, VERY_BAD_VALENCES, \
    BAD_HALIDE, HIGH_2FOFC, COORDING_GEOMETRY, CLOSE_CONTACT \
    = range(25)

  error_strs = {
    LOW_B: "Abnormally low b-factor",
    HIGH_B: "Abnormally high b-factor",
    LOW_OCC: "Abnormally low occupancy",
    HIGH_OCC: "Abnormally high occupancy",
    NO_2FOFC_PEAK: "No 2mFo-DFc map peak",
    FOFC_PEAK: "Peak in mFo-DFc map",
    FOFC_HOLE: "Negative peak in dFo-mFc map",
    ANOM_PEAK: "Peak in the anomalous map",
    NO_ANOM_PEAK: "No peak in the anomalous map",
    BAD_GEOMETRY: "Unexpected geometry for coordinating atoms",
    NO_GEOMETRY: "No distinct geometry for coordinating atoms",
    BAD_VECTORS: "VECSUM above cutoff",
    BAD_VALENCES: "BVS above or below cutoff",
    TOO_FEW_NON_WATERS: "Too few non-water coordinating atoms",
    TOO_FEW_COORD: "Too few coordinating atoms",
    TOO_MANY_COORD: "Too many coordinating atoms",
    LIKE_COORD: "Like charge coordinating like",
    BAD_COORD_ATOM: "Disallowed coordinating atom",
    BAD_FPP: "Bad refined f'' value",
    BAD_COORD_RESIDUE: "Disallowed coordinating residue",
    VERY_BAD_VALENCES: "BVS far above or below cutoff",
    BAD_HALIDE: "Bad halide site",
    HIGH_2FOFC: "Unexpectedly high 2mFo-DFc value",
    COORDING_GEOMETRY: "No distinct geometry and coordinating another atom with distinct geometry",
    CLOSE_CONTACT : "Close contact to oxygen atom",
    }

  def __init__(self, i_seq, manager):
    self.i_seq = i_seq
    self.atom = manager.pdb_atoms[i_seq].fetch_labels()
    self.resname = self.atom.resname.strip().upper()
    self.d_min = manager.fmodel.f_obs().d_min()
    self.anomalous_flag = manager.fmodel.f_obs().anomalous_flag()
    self.strict_valence = manager.get_strict_valence_flag()

    # Grab all the atoms within 3.5 Angstroms
    nearby_atoms_unfiltered = manager.find_nearby_atoms(
      i_seq = i_seq,
      far_distance_cutoff = 3.5)
    self.nearby_atoms = []
    nearby_atoms_no_alts = []
    for contact in nearby_atoms_unfiltered :
      if (contact.element not in ["H", "D"]):
        self.nearby_atoms.append(contact)
        for other in nearby_atoms_no_alts :
          if (other == contact):
            break
        else :
          nearby_atoms_no_alts.append(contact)

    self.residue_counts = utils.count_coordinating_residues(
      self.nearby_atoms)
    self.geometries = find_coordination_geometry(nearby_atoms_no_alts)

    map_stats = manager.map_stats(i_seq)
    self.peak_2fofc = map_stats.two_fofc
    self.peak_fofc = map_stats.fofc
    self.peak_anom = map_stats.anom
    self.estimated_weight = manager.guess_molecular_weight(i_seq)
    self.b_iso = manager.get_b_iso(i_seq)
    # cache global properties
    self.b_mean_hoh = manager.b_mean_hoh
    self.b_stddev_hoh = manager.b_stddev_hoh
    self.b_mean_calpha = manager.b_mean_calpha
    self.calpha_mean_two_fofc = manager.calpha_mean_two_fofc

    self.inaccuracies = {}
    self.ignored = {}
    self.vectors = {}
    self.valence_sum = {}
    self.vector_sum = {}
    self.score = {}
    self.fpp_expected = {}
    self.expected_params = {}
    self.bad_coords = {}

    # Determine the f'' value if possible using phaser or anomalous refinement
    self.fpp = manager.get_fpp(i_seq)
    self.fpp_ratios = {}
    self.fp = manager.get_fp(i_seq)
    self.manager = manager

  def is_correctly_identified(self, identity = None):
    """
    Calculates whether factors indicate that the atom was correctly identified.

    Parameters
    ----------
    identity : mmtbx.ions.metal_parameters

    Returns
    -------
    bool
    """
    if identity is None:
      identity = self.identity()

    return len(self.inaccuracies[identity]) == 0

  def has_compatible_ligands(self, identity):
    """
    Indicates whether the coordinating atoms are of the allowed type (e.g.
    no N or S atoms coordinating CA, etc.) and residue (e.g. Ser is not an
    appropriate ligand for ZN).

    Parameters
    ----------
    identity : mmtbx.ions.metal_parameters

    Returns
    -------
    bool
    """
    return ((len(self.bad_coords[identity]) == 0) and
            (not self.BAD_COORD_RESIDUE in self.inaccuracies[identity]))

  def is_compatible_site(self, ion_params, require_anom = True,
      ignore_valence=False):
    """
    More minimal criteria for determining whether a site is chemically
    compatible, allowing for incomplete coordination shells.

    Parameters
    ----------
    ion_params : mmtbx.ions.metal_parameters
    require_anom : bool, optional
    ignore_valence : bool, optional

    Returns
    -------
    bool
    """
    inaccuracies = self.inaccuracies[str(ion_params)]
    anom_allowed = self.is_compatible_anomalous_scattering(ion_params) or \
                   (not require_anom)
    return (self.has_compatible_ligands(str(ion_params)) and
            (not self.TOO_MANY_COORD in inaccuracies) and
            (ignore_valence or (not self.VERY_BAD_VALENCES in inaccuracies))
            and (not self.BAD_COORD_RESIDUE in inaccuracies) and
            (anom_allowed))

  def number_of_favored_ligand_residues(self, ion_params, distance = 3.0,
      exclude_atoms = ()):
    """
    Counts the number of preferred residues coordinating the atom.  Used for
    approximate detection of transition-metal binding sites.

    Parameters
    ----------
    ion_params : mmtbx.ions.metal_parameters
    distance : float, optional
    exclude_atoms : tuple of ...

    Returns
    -------
    int
    """
    n_res = 0
    resids = []
    for contact in self.nearby_atoms:
      if (contact.atom_name() in exclude_atoms):
        continue
      if (contact.distance() < distance):
        labels = contact.atom.fetch_labels()
        other_resname = contact.resname()
        other_resid = labels.chain_id + labels.resid()
        if ((ion_params.allowed_coordinating_residues is not None) and
            (other_resname in ion_params.allowed_coordinating_residues) and
            (not other_resid in resids)):
          n_res += 1
          resids.append(other_resid)
    return n_res

  def number_of_atoms_within_radius(self, distance_cutoff):
    """
    Counts the number of coordinating atoms within a given radius.

    Parameters
    ----------
    float

    Returns
    -------
    int
    """
    n_atoms = 0
    atom_ids = []
    for contact in self.nearby_atoms:
      other_id = contact.atom_id_no_altloc()
      if (not other_id in atom_ids):
        if (contact.distance() < distance_cutoff):
          n_atoms += 1
        atom_ids.append(other_id) # check for alt confs.
    return n_atoms

  def number_of_backbone_oxygens(self, distance_cutoff=3.0):
    """
    Counts the number of backbone oxygens coordinating a site.

    Parameters
    ----------
    distance_cutoff : float, optional

    Returns
    -------
    int
    """
    n_bb_ox = 0
    for contact in self.nearby_atoms :
      if (contact.atom_name() == "O"):
        if (contact.distance() <= distance_cutoff):
          if (not contact.resname() in WATER_RES_NAMES):
            n_bb_ox += 1
    return n_bb_ox

  # FIXME needs to be refactored and combined with check_fpp_ratio
  def is_compatible_anomalous_scattering(self, ion_params):
    # lighter elements should have effectively no anomalous scattering
    if (ion_params.element.upper() in ["MG", "NA"]):
      return ((self.fpp is None) and (self.peak_anom is not None) and
              (self.peak_anom < 1.0))
    else :
      # XXX somewhat dangerous - we really need f'' for this to work reliably
      if (self.fpp is None):
        return (self.peak_anom is not None) and (self.peak_anom > 3.0)
      identity = self.identity(ion = ion_params)
      if (identity in self.fpp_ratios):
        return (not self.BAD_FPP in self.inaccuracies[identity])
    return False

  # XXX obsolete, delete?
  def atom_weight(self, manager):
    """
    Evaluates whether factors indicate that the atom is lighter, heavier, or
    isoelectric to what it is currently identified as.

    Parameters
    ----------
    manager : mmtbx.ions.identify.manager

    Returns
    -------
    int
        -1 if lighter, 0 if isoelectronic, and 1 if heavier.
    """
    identity = "HOH" if self.resname in WATER_RES_NAMES else self.identity()
    # Waters that don't have B-factors at least 1 stddev below the mean are
    # presumed to be correct
    if (identity == "HOH" and
        (self.atom.b > manager.b_mean_hoh - manager.b_stddev_hoh)):
      return 0
    if self.is_correctly_identified(identity = identity):
      return 0
    # B-factors/occupancies?
    if self.FOFC_PEAK in self.inaccuracies[identity] or self.atom.b < 1:
      return 1
    if self.FOFC_HOLE in self.inaccuracies[identity]:
      return -1
    return 0

  def check_ion_environment(self,
      ion_params,
      wavelength = None,
      require_valence = True):
    """
    Checks whether or not the specified ion satisfies the metal-coordination
    parameters, specified by ion_params, such as valence sum, geometry, etc.
    The criteria used here are quite strict, but many of the analyses are
    saved for later if we want to use looser critera.

    Parameters
    ----------
    ion_params : mmtbx.ions.metal_parameters
    wavelength : float, optional
    require_valence : bool, optional
    """
    from iotbx.pdb import common_residue_names_get_class as get_class

    identity = self.identity(ion_params)
    inaccuracies = self.inaccuracies[identity] = set()
    self.expected_params[identity] = ion_params
    ignored = self.ignored[identity] = set()

    # if the atom is clearly not a water, optionally relax some rules.  this
    # will be more sensitive for transition metals, without finding a lot of
    # spurious Mg/Na sites.
    strict_rules = require_valence or \
      self.is_correctly_identified(identity = "HOH") or \
      self.strict_valence or \
      ion_params.element in ["NA","MG"]

    # Check for all non-overlapping atoms within 3 A of the metal
    n_closest = 0
    coord_atoms = []
    for i_pair, contact1 in enumerate(self.nearby_atoms):
      distance = contact1.distance()
      if (distance < 3.0):
        for contact2 in self.nearby_atoms[(i_pair+1):] :
          if ((contact1 == contact2) or
              (contact1.distance_from(contact2) <= 0.3)):
            break
        else :
          coord_atoms.append(contact1)
          if (distance < 2.7):
            n_closest += 1

    if len(coord_atoms) < ion_params.coord_num_lower:
      inaccuracies.add(self.TOO_FEW_COORD)

    if n_closest > ion_params.coord_num_upper:
      inaccuracies.add(self.TOO_MANY_COORD)

    # Coordinating atoms closer than 3.0 A are not positively charged
    n_non_water = 0
    self.bad_coords[identity] = []

    for contact in self.nearby_atoms:
      other_name = contact.atom_name()
      other_resname = contact.resname()
      other_element = contact.element

      if (not other_resname in WATER_RES_NAMES):
        n_non_water += 1
      else:
        # Everything can potentially be coordinated by water
        continue

      if (contact.distance() < 3.0):
        # XXX: So, we have a a fair number of rules restricting nitrogens and
        # nitrogen-containing residues from coordinating a number of cations.
        #
        # However, this rule is dependent on the protonation of the nitrogen,
        # if the pKa is low at the site, it is possible for a metal to
        # coordinate the residue fine.
        #
        # We want a complex rule that takes into account coordinating geometry,
        # density signal, and the presence of other coordinating atoms that
        # might drop the site's pKa enough to lose the hydrogen.
        if ((ion_params.allowed_coordinating_atoms is not None) and
            (other_element not in ion_params.allowed_coordinating_atoms)):
          self.bad_coords[identity].append(contact)
          inaccuracies.add(self.BAD_COORD_ATOM)
        if (get_class(other_resname) == "common_amino_acid"):
          # limit elements allowed to bind to backbone atoms (mainly carbonyl
          # oxygen)
          if ((other_name in ["C","N","O","CA","H","HA"]) and
              ((ion_params.allowed_backbone_atoms is None) or
               (not other_name in ion_params.allowed_backbone_atoms))):
            if (other_name == "O") and (contact.is_carboxy_terminus):
              pass # C-terminal carboxyl group is allowed
            else :
              self.bad_coords[identity].append(contact)
              inaccuracies.add(self.BAD_COORD_ATOM)
          # Check if atom is of an allowed residue type, if part of a sidechain
          if (ion_params.allowed_coordinating_residues is not None):
            allowed = ion_params.allowed_coordinating_residues
            if ((not other_resname in allowed) and
                (other_name not in ["C", "O", "N", "CA", "OXT"])):
                # XXX probably just O
              self.bad_coords[identity].append(contact)
              inaccuracies.add(self.BAD_COORD_RESIDUE)
        elif (cmp(0, mmtbx.ions.server.get_charge(contact.atom)) ==
              cmp(0, ion_params.charge)):
          # Check if coordinating atom is of opposite charge
          self.bad_coords[identity].append(contact)
          inaccuracies.add(self.LIKE_COORD)
        elif (ion_params.charge > 0 and
            other_element in ["N"] and
            other_resname in ["LYS", "ARG", "ASN", "GLN"]):
          # Coordinating nitrogen most likely positive.
          #
          # Ignore nitrogens without a charge label that are on positively
          # charged amino acids.
          self.bad_coords[identity].append(contact)
          inaccuracies.add(self.LIKE_COORD)

    # Check the number of coordinating waters
    if (n_non_water < ion_params.min_coordinating_non_waters):
      inaccuracies.add(self.TOO_FEW_NON_WATERS)

    # Check the geometry of the coordinating atoms
    if ion_params.allowed_geometries and strict_rules:
      allowed = [i[0] in ion_params.allowed_geometries
                 for i in self.geometries]
      if "any" in ion_params.allowed_geometries:
        pass
      elif not self.geometries:
        if strict_rules:
          inaccuracies.add(self.NO_GEOMETRY)
      elif not any(allowed):
        inaccuracies.add(self.BAD_GEOMETRY)
      else:
        strict_rules = False

    # If no distinct geometry, check that none of the coordinating have distinct
    # geometry, either
    if self.geometries == []:
      for contact in self.nearby_atoms:
        o_atom = contact.atom
        if o_atom.i_seq in self.manager.atoms_to_props:
          o_geometry = self.manager.atoms_to_props[o_atom.i_seq].geometries
          if o_geometry != []:
            inaccuracies.add(self.COORDING_GEOMETRY)

    # Check for reasonable vector/valence values
    vectors = mmtbx.ions.server.calculate_valences(ion_params,
      self.nearby_atoms)
    self.vectors[identity] = vectors

    self.valence_sum[identity] = sum([abs(i) for i in vectors])
    self.vector_sum[identity] = abs(sum(vectors, col((0, 0, 0))))

    if self.vector_sum[identity] > ion_params.vec_sum_cutoff:
      if (strict_rules):
        inaccuracies.add(self.BAD_VECTORS)
      else :
        ignored.add(self.BAD_VECTORS)

    # XXX I am not sure how low a valence sum we want to allow, but many
    # structures with non-physiological cation binding have partial and/or
    # irregular coordination shells
    if (self.valence_sum[identity] < ion_params.cvbs_expected * 0.25 or
        self.valence_sum[identity] > ion_params.cvbs_expected * 1.25):
      inaccuracies.add(self.VERY_BAD_VALENCES)
    else:
      if (self.valence_sum[identity] < ion_params.cvbs_lower or
          self.valence_sum[identity] > ion_params.cvbs_upper):
        if strict_rules:
          inaccuracies.add(self.BAD_VALENCES)
        else :
          ignored.add(self.BAD_VALENCES)

    self.score[identity] = abs(self.valence_sum[identity] -
                               ion_params.cvbs_expected)

  # FIXME this really needs to be refactored and combined with the method
  # is_compatible_anomalous_scattering
  def check_fpp_ratio(self,
      ion_params,
      wavelength,
      fpp_ratio_min = 0.3,
      fpp_ratio_max = 1.05):
    """
    Compare the refined and theoretical f'' values if available.

    Parameters
    ----------
    ion_params : mmtbx.ions.metal_parameters
    wavelength : float
    fpp_ratio_min : float, optional
    fpp_ratio_max : float, optional

    Returns
    -------
    float
        f'' / f''_expected
    """
    identity = str(ion_params)
    inaccuracies = self.inaccuracies.get(identity, None)
    if (inaccuracies is None):
      inaccuracies = self.inaccuracies[identity] = set()
    if (ion_params.element.upper() in ["MG", "NA"]):
      if (self.fpp is not None) or (self.peak_anom is not None and self.peak_anom > 1):
        inaccuracies.add(self.BAD_FPP)
    else :
      # XXX in theory the fpp_ratio should be no more than 1.0 unless we are
      # right on the peak wavelength.  in practice Phaser can overshoot a little
      # bit, so we need to be more tolerant.  picking the maximum f'' from the
      # Sasaki and Henke tables will also limit the ratio.
      if (wavelength is not None) and (self.anomalous_flag):
        fpp_expected_sasaki = sasaki.table(ion_params.element).at_angstrom(
          wavelength).fdp()
        fpp_expected_henke = henke.table(ion_params.element).at_angstrom(
          wavelength).fdp()
        self.fpp_expected[identity] = max(fpp_expected_sasaki,
          fpp_expected_henke)
        if (self.fpp is not None) and (self.fpp_expected[identity] != 0):
          self.fpp_ratios[identity] = self.fpp / self.fpp_expected[identity]
          if ((self.fpp_ratios[identity] > fpp_ratio_max) or
              ((self.fpp >= 0.2) and
               (self.fpp_ratios[identity] < fpp_ratio_min))):
            inaccuracies.add(self.BAD_FPP)
        elif (self.fpp_expected[identity] > 0.75) and (self.peak_anom < 2):
          inaccuracies.add(self.BAD_FPP)
    return self.fpp_ratios.get(identity)

  def show_properties(self, identity, out = sys.stdout):
    """
    Show atomic properties that are independent of the suspected identity.

    Parameters
    ----------
    identity : mmtbx.ions.metal_parameters
    out : file, optional
    """
    print("%s:" % self.atom.id_str(), file=out)
    b_flag = ""
    if (self.LOW_B in self.inaccuracies[identity]):
      b_flag = " <<<"
    elif (self.HIGH_B in self.inaccuracies[identity]):
      b_flag = " !!!"
    print("  B-factor:      %6.2f%s" % (self.atom.b, b_flag), file=out)
    occ_flag = ""
    if (self.LOW_OCC in self.inaccuracies[identity]):
      occ_flag = " !!!"
    elif (self.HIGH_OCC in self.inaccuracies[identity]):
      occ_flag = " <<<"
    print("  Occupancy:     %6.2f%s" % (self.atom.occ, occ_flag), file=out)
    twofofc_flag = ""
    if (self.NO_2FOFC_PEAK in self.inaccuracies[identity]):
      twofofc_flag = " !!!"
    elif (self.HIGH_2FOFC in self.inaccuracies[identity]):
      twofofc_flag = " <<<"
    print("  2mFo-DFc map:  %6.2f%s" % (self.peak_2fofc, twofofc_flag), file=out)
    fofc_flag = ""
    if (self.FOFC_PEAK in self.inaccuracies[identity]):
      fofc_flag = " <<<"
    elif (self.FOFC_HOLE in self.inaccuracies[identity]):
      fofc_flag = " !!!"
    print("  mFo-DFc map:   %6.2f%s" % (self.peak_fofc, fofc_flag), file=out)
    if (self.peak_anom is not None):
      anom_flag = ""
      if (self.ANOM_PEAK in self.inaccuracies[identity]):
        anom_flag = " <<<"
      elif (self.NO_ANOM_PEAK in self.inaccuracies[identity]):
        anom_flag = " !!!"
      print("  Anomalous map: %6.2f%s" % (self.peak_anom, anom_flag), file=out)
    if (self.estimated_weight is not None):
      print("  Approx. mass:  %6d" % self.estimated_weight, file=out)
    if self.fpp is not None:
      fpp_flag = ""
      if (self.fpp >= 0.2):
        fpp_flag = " <<<"
      print("  f'':           %6.2f%s" % (self.fpp, fpp_flag), file=out)
      print("  f'' ratio:     %s" % format_value("%6.2f",
        self.fpp_ratios.get(identity)), file=out)
    if self.nearby_atoms is not None:
      angstrom = u"\N{ANGSTROM SIGN}".encode("utf-8", "strict")
      degree = u"\N{DEGREE SIGN}".encode("utf-8", "strict")

      print("  Nearby atoms: (%d within 3.0 %s)" % \
        (len([i for i in self.nearby_atoms if i.distance() < 3]), angstrom), file=out)

      for contact in self.nearby_atoms :
        print("    %s (%5.3f %s)" % \
        (contact.id_str(), contact.distance(), angstrom), file=out)

      if self.geometries:
        print("  Coordinating geometry:", file=out)
        for geometry, deviation in self.geometries:
          print("    %-15s (average deviation: %.3f%s)" % \
            (geometry, deviation, degree), file=out)

  def show_ion_results(self, identity = None, out = sys.stdout,
      confirmed = False, valence_used = True):
    """
    Show statistics for a proposed element identity.

    Parameters
    ----------
    identity : mmtbx.ions.metal_parameters
    out : file, optional
    confirmed : bool, optional
    valence_used : bool, optional
    """

    if not identity:
      identity = self.identity(self.atom)

    inaccuracies = self.inaccuracies.get(identity, set([]))
    ignored = self.ignored.get(identity, set([]))

    if identity != self.identity():
      if (confirmed):
        print("  Probable cation: %s" % identity, file=out)
      else :
        print("  Atom as %s:" % identity, file=out)
    else:
      print("    %s:" % self.atom.id_str(), file=out)

    if identity in self.vector_sum and self.vector_sum[identity] is not None:
      problem = ((self.BAD_VECTORS in inaccuracies) or
                 (self.BAD_VECTORS in ignored))

      print("    Vector sum:  %6.3f %s" % \
        (self.vector_sum[identity], " !!!" if problem else ""), file=out)

    if identity in self.valence_sum and self.valence_sum[identity] is not None:
      problem = inaccuracies.union(ignored).intersection(
        [self.BAD_VALENCES, self.VERY_BAD_VALENCES])

      print("    Valence sum: %6.3f" % self.valence_sum[identity], file=out)
      if valence_used:
        print("(expected: %6.3f) %s" % \
        (self.expected_params[identity].cvbs_expected, " !!!" if problem else ""), end=' ', file=out)

    if self.NO_GEOMETRY in inaccuracies:
      print("    No distinct geometry !!!", file=out)

    if self.BAD_GEOMETRY in inaccuracies:
      print("    Unexpected geometry  !!!", file=out)

    bad_coord = [self.LIKE_COORD, self.BAD_COORD_ATOM, self.BAD_COORD_RESIDUE]
    if inaccuracies.intersection(bad_coord):
      print("    Bad coordinating atom%s:" % \
         ("s" if len(self.bad_coords[identity]) != 1 else ""), file=out)
      angstrom = u"\u00C5".encode("utf-8", "strict").strip()
      for atom, vector in self.bad_coords[identity]:
        print("    %s (%5.3f %s) !!!" % \
          (atom.id_str(), abs(vector), angstrom), file=out)

    if self.TOO_FEW_NON_WATERS in inaccuracies:
      print("    Too few coordinating non-waters !!!", file=out)
    if self.TOO_FEW_COORD in inaccuracies:
      print("    Too few coordinating atoms !!!", file=out)
    if self.TOO_MANY_COORD in inaccuracies:
      print("    Too many coordinating atoms !!!", file=out)

    if (self.fpp is not None) and (identity in self.fpp_ratios):
      print("    f'' ratio:   %6.3f%s" % \
         (self.fpp_ratios[identity], " !!!" if self.BAD_FPP in inaccuracies else ""), file=out)

  # XXX can we get away with just one oxygen?
  def looks_like_nucleotide_phosphate_site(self,
      min_phosphate_oxygen_atoms = 2,
      distance_cutoff = 2.5) : # XXX wild guess
    """
    Decide whether the atom is coordinating phosphate oxygens from a
    nucleotide, based on common atom names.

    Parameters
    ----------
    min_phosphate_oxygen_atoms : int, optional
    distance_cutoff : float, optional

    Returns
    -------
    bool
    """
    n_phosphate_oxygens = 0
    for contact in self.nearby_atoms :
      atom_name = contact.atom_name()
      if (len(atom_name) < 3) or (contact.element not in ["O"]):
        continue
      if ((atom_name[0:2] in ["O1","O2","O3"]) and
          (atom_name[2] in ["A","B","G"])):
        if (contact.distance() <= distance_cutoff):
          n_phosphate_oxygens += 1
    return (n_phosphate_oxygens == min_phosphate_oxygen_atoms)

  def identity(self, ion=None):
    """
    Covers an atom into a string representing its element and charge.

    Parameters
    ----------
    ion : iotbx.pdb.hierarchy.atom, optional

    Returns
    -------
    str
    """
    if ion is None:
      ion = self.atom
    element = mmtbx.ions.server.get_element(ion)
    charge = mmtbx.ions.server.get_charge(ion)
    return "{}{:+}".format(element, charge)

  def get_atom_type(self, params, aggressive=False):
    """
    Checks the atom characteristics against what we would expect for a water.
    Updates self with any inaccuracies noticed (Surpringly low b-factor,
    high occupancy, etc).  Note that HEAVY_ION does not necessarily rule out
    NA/MG, as these often have mFo-DFc peaks at high resolution.

    Parameters
    ----------
    params : libtbx.phil.scope_extract
    aggressive : bool, optional

    Returns
    -------
    int
        One of WATER, WATER_POOR, HEAVY_ION, LIGHT_ION
    """
    inaccuracies = self.inaccuracies["HOH"] = set()
    atom_type = WATER
    # Skip over water if the 2mFo-DFc or mFo-DFc value is too low
    if ((self.peak_2fofc < params.min_2fofc_level) or
        (self.peak_fofc < -2.0)):
      return WATER_POOR
    if (self.fpp is not None) and (self.fpp > params.fpp_max):
      return HEAVY_ION
    if (self.fp is not None) and (self.fp > params.fp_max):
      return HEAVY_ION
    if self.peak_anom is not None and self.peak_anom > params.max_anom_level:
      inaccuracies.add(self.ANOM_PEAK)
      atom_type = HEAVY_ION
    if self.peak_fofc > params.max_fofc_level:
      inaccuracies.add(self.FOFC_PEAK)
      atom_type = HEAVY_ION
    if self.atom.occ > params.max_occ: # this will probably never happen...
      inaccuracies.add(self.HIGH_OCC)
      atom_type = HEAVY_ION
    # very low B-factors automatically trigger a check
    if (self.atom.b < params.min_b_iso):
      inaccuracies.add(self.LOW_B)
      atom_type = HEAVY_ION
    elif (self.b_stddev_hoh is not None) and (self.b_stddev_hoh > 0):
      # high B-factor relative to other waters
      z_value = (self.b_iso - self.b_mean_hoh) / self.b_stddev_hoh
      if z_value < -params.max_stddev_b_iso:
        inaccuracies.add(self.LOW_B)
      elif self.atom.b < self.b_mean_hoh * params.min_frac_b_iso:
        inaccuracies.add(self.LOW_B)
      if (atom_type == WATER) and (self.LOW_B in inaccuracies):
        atom_type = LIGHT_ION
    if (aggressive) and (atom_type == WATER):
      # high B-factor relative to C-alpha
      if (self.b_mean_calpha > 0):
        relative_b = self.b_iso / self.b_mean_calpha
        if (relative_b < params.min_frac_calpha_b_iso):
          inaccuracies.add(self.LOW_B)
          atom_type = LIGHT_ION
      # high 2Fo-Fc relative to C-alpha
      if (self.calpha_mean_two_fofc > 0):
        relative_2fofc = self.peak_2fofc / self.calpha_mean_two_fofc
        if (relative_2fofc > params.max_frac_calpha_2fofc):
          inaccuracies.add(self.HIGH_2FOFC)
          atom_type = LIGHT_ION
    # check for close contacts
    for i_pair, contact1 in enumerate(self.nearby_atoms):
      if (contact1.element.strip() == "O"):
        distance = contact1.distance()
        if (distance < 2.4):
          inaccuracies.add(self.CLOSE_CONTACT)
          if (atom_type < LIGHT_ION):
            atom_type = LIGHT_ION
    return atom_type

def find_anomalous_scatterers(*args, **kwds):
  """
  Wrapper for corresponding method in phaser.substructure, if phaser is
  available and configured.
  """
  if (not libtbx.env.has_module("phaser")):
    if "log" in kwds:
      print("Phaser not available", file=kwds["log"])
    return None
  from phaser import substructure
  return substructure.find_anomalous_scatterers(*args, **kwds)

def create_manager(
    pdb_hierarchy,
    geometry_restraints_manager,
    fmodel,
    wavelength,
    params,
    resolution_factor = 0.25,
    nproc = Auto,
    verbose = False,
    log = None,
    manager_class=None):
  """
  Wrapper around mmtbx.ions.identify.manager init method. Retrieves the
  connectivity and xray_structure from fmodel automatically.

  Parameters
  ----------
  pdb_hierarchy : iotbx.pdb.hierarchy.root
  geometry_restraints_manager : cctbx.geometry_restraints.manager.manager
  fmodel : mmtbx.f_model.manager
  wavelength : float
  params : libtbx.phil.scope_extract
  resolution_factor : float, optional
  nproc : int, optional
  verbose : bool, optional
  log : file, optional
  manager_class : class, optional

  Returns
  -------
  mmtbx.ions.identify.manager or mmtbx.ions.svm.manager
  """
  connectivity = \
    geometry_restraints_manager.shell_sym_tables[0].full_simple_connectivity()
  if (manager_class is None):
    manager_class = manager
  manager_obj = manager_class(
    fmodel = fmodel,
    pdb_hierarchy = pdb_hierarchy,
    xray_structure = fmodel.xray_structure,
    connectivity = connectivity,
    wavelength = wavelength,
    params = params,
    nproc = nproc,
    verbose = verbose,
    log = log)
  return manager_obj

def _PRINT_DEBUG(*args):
  """
  Prints a debugging message to stderr.
  """
  print(sys.stderr, args, file=sys.stderr)

031d668e9b7179bb975304808ea33753f197280c
<mask token>


class manager(object):
    <mask token>

    def __init__(self, fmodel, pdb_hierarchy, xray_structure, params=None,
        wavelength=None, connectivity=None, nproc=1, verbose=False, log=None):
        if log is None:
            log = null_out()
        self.fmodel = fmodel
        self.atoms_to_props = {}
        self.params = params
        self.wavelength = wavelength
        self.nproc = nproc
        self.phaser_substructure = None
        self.flag_refine_substructure = False
        self.fpp_from_phaser_ax_sites = None
        self.site_fp = None
        self.site_fdp = None
        self.use_svm = getattr(params, 'use_svm', None)
        self._map_values = {}
        self._map_gaussian_fits = {}
        self.anomalous_flag = None
        if fmodel is not None:
            self.anomalous_flag = fmodel.f_obs().anomalous_flag()
        self.update_structure(pdb_hierarchy=pdb_hierarchy, xray_structure=
            xray_structure, connectivity=connectivity, log=log)
        self.update_maps()
        refine_substructure = False
        if params is not None:
            refine_substructure = params.find_anomalous_substructure
        if refine_substructure is Auto:
            refine_substructure = fmodel.f_obs().anomalous_flag(
                ) and wavelength is not None
        if refine_substructure:
            if wavelength is None:
                raise Sorry('Wavelength required when ' +
                    'find_anomalous_substructure=True.')
            elif not fmodel.f_obs().anomalous_flag():
                raise Sorry('Anomalous data required when ' +
                    'find_anomalous_substructure=True.')
            if params.use_phaser and libtbx.env.has_module('phaser'):
                print('  Running Phaser substructure completion...', file=log)
                t1 = time.time()
                phaser_result = find_anomalous_scatterers(fmodel=fmodel,
                    pdb_hierarchy=pdb_hierarchy, wavelength=wavelength,
                    verbose=verbose, log=log, n_cycles=params.phaser.
                    llgc_ncycles)
                t2 = time.time()
                print('    time: %.1fs' % (t2 - t1), file=log)
                if phaser_result is None:
                    print('  ERROR: Phaser substructure completion failed!',
                        file=log)
                else:
                    self.phaser_substructure = phaser_result.atoms()
                    if len(self.phaser_substructure) == 0:
                        print('  No anomalous scatterers found!', file=log)
                    else:
                        print('  %d anomalous scatterers found' % len(self.
                            phaser_substructure), file=log)
                        self.analyze_substructure(log=log, verbose=True)
            else:
                self.flag_refine_substructure = True
                self.refine_anomalous_substructure(log=log)

    def water_selection(self):
        """
    Fetchs the selection for all waters in the model.

    Returns
    -------
    scitbx.array_family.flex.size_t
    """
        sel_cache = self.pdb_hierarchy.atom_selection_cache()
        sel_str = "({}) and element O and altloc ' '".format(' or '.join([(
            'resname ' + i) for i in WATER_RES_NAMES]))
        return sel_cache.selection(sel_str).iselection()

    def refine_anomalous_substructure(self, log):
        """
    Run simple "substructure completion" implemented using CCTBX tools (the
    command-line equivalent is mmtbx.refine_anomalous_substructure).  A faster
    alternative to Phaser, but not clear how effective this is at the moment.

    Parameters
    ----------
    log : file
    """
        from mmtbx.refinement import anomalous_scatterer_groups
        fmodel_tmp = self.fmodel.deep_copy()
        anom_groups = anomalous_scatterer_groups.refine_anomalous_substructure(
            fmodel=fmodel_tmp, pdb_hierarchy=self.pdb_hierarchy, wavelength
            =self.wavelength, reset_water_u_iso=True, verbose=True,
            use_all_anomalous=True, out=log)
        scatterers = fmodel_tmp.xray_structure.scatterers()
        self.use_fdp = flex.bool(scatterers.size(), False)
        self.site_fp = flex.double(scatterers.size(), 0)
        self.site_fdp = flex.double(scatterers.size(), 0)
        for group in anom_groups:
            for i_seq in group.iselection:
                sc = scatterers[i_seq]
                self.use_fdp[i_seq] = True
                self.site_fp[i_seq] = sc.fp
                self.site_fdp[i_seq] = sc.fdp

    def update_structure(self, pdb_hierarchy, xray_structure, connectivity=
        None, log=None, refine_if_necessary=True):
        """
    Set the current atomic data: PDB hierarchy, Xray structure, and simple
    connectivity list.

    Parameters
    ----------
    pdb_hierarchy : iotbx.pdb.hierarchy.root
    xray_structure : cctbx.xray.structure.structure
    connectivity : ...
    log : file, optional
    refine_if_necessary : bool, optional
    """
        self.pdb_hierarchy = pdb_hierarchy
        self.xray_structure = xray_structure
        if self.fmodel:
            self.fmodel.update_xray_structure(xray_structure, update_f_calc
                =True)
        self.sites_frac = self.xray_structure.sites_frac()
        self.sites_cart = self.xray_structure.sites_cart()
        self.connectivity = connectivity
        self.pdb_atoms = pdb_hierarchy.atoms()
        assert not self.pdb_atoms.extract_i_seq().all_eq(0)
        self.unit_cell = xray_structure.unit_cell()
        self.use_fdp = flex.bool(xray_structure.scatterers().size(), False)
        self.ax_chain = None
        self._pair_asu_cache = {}
        self.u_iso_all = xray_structure.extract_u_iso_or_u_equiv()
        sctr_keys = xray_structure.scattering_type_registry().type_count_dict()
        self.hd_present = 'H' in sctr_keys or 'D' in sctr_keys
        sel_cache = pdb_hierarchy.atom_selection_cache()
        self.carbon_sel = sel_cache.selection('element C').iselection()
        self.calpha_sel = sel_cache.selection('name CA and element C'
            ).iselection()
        assert len(self.carbon_sel) > 0
        water_sel = self.water_selection()
        self.n_waters = len(water_sel)
        not_hd_sel = sel_cache.selection('not (element H or element D)'
            ).iselection()
        self.n_heavy = len(not_hd_sel)
        u_iso_all_tmp = self.u_iso_all.select(not_hd_sel)
        self.b_mean_all = adptbx.u_as_b(flex.mean(u_iso_all_tmp))
        self.b_stddev_all = adptbx.u_as_b(u_iso_all_tmp.
            standard_deviation_of_the_sample())
        self.b_mean_calpha = 0
        if len(self.calpha_sel) > 0:
            self.b_mean_calpha = adptbx.u_as_b(flex.mean(self.u_iso_all.
                select(self.calpha_sel)))
        self.b_mean_hoh = self.b_stddev_hoh = None
        if self.n_waters > 0:
            u_iso_hoh = self.u_iso_all.select(water_sel)
            self.b_mean_hoh = adptbx.u_as_b(flex.mean(u_iso_hoh))
            self.b_stddev_hoh = adptbx.u_as_b(u_iso_hoh.
                standard_deviation_of_the_sample())
        if self.phaser_substructure is not None:
            self.analyze_substructure(log=log)
        elif self.flag_refine_substructure and refine_if_necessary:
            self.refine_anomalous_substructure(log=log)
    <mask token>
    <mask token>
    <mask token>

    def show_current_scattering_statistics(self, out=sys.stdout):
        """
    Prints out information about an entire model's scattering statistics, such
    as mean map heights and b-factors for carbons and waters.

    Parameters
    ----------
    out : file, optional
    """
        print('', file=out)
        print('Model and map statistics:', file=out)
        print('  mean mFo map height @ carbon: %s' % format_value('%.2f',
            flex.max(self.carbon_fo_values)), file=out)
        if self.calpha_mean_two_fofc > 0:
            print('  mean 2mFo-DFc map height @ C-alpha: %s' % format_value
                ('%.2f', self.calpha_mean_two_fofc), file=out)
        print('  mean B-factor: %s' % format_value('%.2f', self.b_mean_all),
            file=out)
        if self.b_mean_calpha > 0:
            print('  mean C-alpha B-factor: %s' % format_value('%.2f', self
                .b_mean_calpha), file=out)
        print('  mean water B-factor: %s' % format_value('%.2f', self.
            b_mean_hoh), file=out)
        n_water_fofc_peaks = 0
        n_water_anom_peaks = 0
        water_sel = self.water_selection()
        print('  %d water molecules' % len(water_sel), file=out)
        for i_seq in water_sel:
            map_stats = self.map_stats(i_seq)
            if map_stats.fofc >= 3.0:
                n_water_fofc_peaks += 1
            if map_stats.anom is not None and map_stats.anom >= 3.0:
                n_water_anom_peaks += 1
        print('  %d waters have mFo-DFc map >= 3.0 sigma' %
            n_water_fofc_peaks, file=out)
        if self.anomalous_flag:
            print('  %d waters have anomalous map >= 3.0 sigma' %
                n_water_anom_peaks, file=out)
        print('', file=out)
    <mask token>

    def find_nearby_atoms(self, i_seq, far_distance_cutoff=3.0,
        near_distance_cutoff=1.5, filter_by_bonding=True,
        filter_by_two_fofc=True):
        """
    Given site in the structure, return a list of contacts, optionally
    filtering by connectivity and 2mFo-DFc map level.

    Parameters
    ----------
    i_seq : int
    far_distance_cutoff : float, optional
    near_distance_cutoff : float, optional
    filter_by_bonding : bool, optional
    fitler_by_two_fofc : bool, optional

    Returns
    -------
    list of mmtbx.ions.environment.atom_contact
    """
        assert i_seq < len(self.sites_frac)
        pair_asu_table, asu_mappings, asu_table = self._pair_asu_cache.get(
            far_distance_cutoff, (None, None, None))
        if pair_asu_table is None:
            pair_asu_table = self.xray_structure.pair_asu_table(distance_cutoff
                =far_distance_cutoff)
            asu_mappings = pair_asu_table.asu_mappings()
            asu_table = pair_asu_table.table()
            self._pair_asu_cache[far_distance_cutoff
                ] = pair_asu_table, asu_mappings, asu_table
        contacts = environment.find_nearby_atoms(i_seq=i_seq,
            xray_structure=self.xray_structure, pdb_atoms=self.pdb_atoms,
            asu_mappings=asu_mappings, asu_table=asu_table, connectivity=
            self.connectivity, far_distance_cutoff=far_distance_cutoff,
            near_distance_cutoff=near_distance_cutoff, filter_by_bonding=
            filter_by_bonding)
        if filter_by_two_fofc:
            filtered = []
            for contact in contacts:
                if contact.resname() in WATER_RES_NAMES:
                    two_fofc = self._map_values['2mFo-DFc'][contact.
                        atom_i_seq()]
                    if two_fofc < self.params.water.min_2fofc_coordinating:
                        continue
                filtered.append(contact)
            contacts = filtered
        return contacts

    def guess_b_iso_real(self, i_seq):
        """
    Guess an approximate B_iso for an atom by averaging the values for
    coordinating atoms.  This should partially compensate for the deflation of
    the B-factor due to incorrect scattering type, and consequently make the
    occupancy refinement more accurate.

    Parameters
    ----------
    i_seq : int

    Returns
    -------
    float
    """
        contacts = self.find_nearby_atoms(i_seq, far_distance_cutoff=3.0)
        if len(contacts) == 0:
            return adptbx.u_as_b(self.u_iso_all[i_seq])
        u_iso_sum = 0
        for contact in contacts:
            u_iso_sum += self.u_iso_all[contact.atom_i_seq()]
        return adptbx.u_as_b(u_iso_sum / len(contacts))

    def principal_axes_of_inertia(self, i_seq):
        """
    Extracts the map grid points around a site, and calculates the axes of
    inertia (using the density values as weights). This is used to calculate
    the sphericity of the blob of density around a site.

    Parameters
    ----------
    i_seq : int

    Returns
    -------
    ...
    """
        return self._principal_axes_of_inertia[i_seq]
    <mask token>

    def get_b_iso(self, i_seq):
        """
    Calculates the isotropic b-factor of a site.

    Parameters
    ----------
    i_seq : int

    Returns
    -------
    float
    """
        sc = self.xray_structure.scatterers()[i_seq]
        return adptbx.u_as_b(sc.u_iso_or_equiv(self.unit_cell))
    <mask token>

    def get_map_gaussian_fit(self, map_type, i_seq):
        """
    Retrieves the two parameters of a gaussian function, fit to a given map at
    a site.

    Parameters
    ----------
    map_type : str
    i_seq : int

    Returns
    -------
    (tuple of float, float) or None
    """
        map_gaussians = self._map_gaussian_fits.get(map_type, None)
        if map_gaussians is not None:
            return map_gaussians[i_seq]
        return None
    <mask token>
    <mask token>
    <mask token>

    def get_fpp(self, i_seq):
        """
    Retrieve the refined f'' for a site.  Because this can come from either the
    built-in anomalous refinement or Phaser, it is handled differently than
    the f' retrieval.

    Parameters
    ----------
    i_seq : int

    Returns
    -------
    float or None
    """
        if not self.use_fdp[i_seq]:
            return None
        if self.fpp_from_phaser_ax_sites is None:
            return self.site_fdp[i_seq]
        fpp = self.fpp_from_phaser_ax_sites[i_seq]
        if fpp < 0:
            fpp = None
        return fpp

    def get_fp(self, i_seq):
        """
    Retrieve the refined f' for a site.

    Parameters
    ----------
    i_seq : int

    Returns
    -------
    float or None
    """
        if self.site_fp is None or not self.use_fdp[i_seq]:
            return None
        return self.site_fp[i_seq]
    <mask token>

    def analyze_water(self, i_seq, debug=True, candidates=Auto, no_final=
        False, out=sys.stdout):
        """
    Examines the environment around a single atom to determine if it is actually
    a misidentified metal ion.

    no_final is used internally, when candidates is not Auto, to try all of the
    Auto without selecting any as a final choice when none of the elements in
    candidates appear to probable ion identities.

    Parameters
    ----------
    i_seq : int
    debug : bool, optional
    candidates : list of str, optional
    no_file : bool, optional
    out : file, optional

    Returns
    -------
    mmtbx.ions.identify.water_result
    """
        atom = self.pdb_atoms[i_seq]
        auto_candidates = candidates is Auto
        if auto_candidates:
            candidates = mmtbx.ions.DEFAULT_IONS
        elif isinstance(candidates, string_types):
            candidates = candidates.replace(',', ' ').split()
        candidates = [i.strip().upper() for i in candidates]
        if candidates == ['X']:
            candidates = []
        resname = atom.fetch_labels().resname.strip().upper()
        assert resname in WATER_RES_NAMES
        atom_props = self.atoms_to_props[i_seq]
        final_choice = None
        atom_type = atom_props.get_atom_type(params=self.params.water,
            aggressive=self.params.aggressive)
        if atom_type == WATER_POOR:
            return None
        assert not self.use_svm
        filtered_candidates = []
        halide_candidates = False
        nuc_phosphate_site = atom_props.looks_like_nucleotide_phosphate_site()
        max_carbon_fo_map_value = sys.maxsize
        if self.carbon_fo_values is not None:
            max_carbon_fo_map_value = flex.max(self.carbon_fo_values)
        for symbol in candidates:
            elem = mmtbx.ions.server.get_metal_parameters(symbol)
            if elem is None:
                if symbol in mmtbx.ions.HALIDES:
                    halide_candidates = True
                    continue
                else:
                    raise Sorry("Element '%s' not supported!" % symbol)
            if atom_type in [WATER, LIGHT_ION] and symbol not in ['NA',
                'MG', 'F', 'NE']:
                continue
            if nuc_phosphate_site and not symbol in NUC_PHOSPHATE_BINDING:
                continue
            n_elec = sasaki.table(symbol.upper()).atomic_number() - elem.charge
            if n_elec <= 12 and (atom_props.fpp is not None and atom_props.
                fpp > 0.1):
                continue
            mass_ratio = atom_props.estimated_weight / max(n_elec, 1)
            if mass_ratio < 0.4 and atom_type == WATER:
                continue
            filtered_candidates.append(elem)

        def try_candidates(require_valence=True):
            """
      Parameters
      ----------
      require_valence : bool, optional

      Returns
      -------
      list of tuple of mmtbx.ions.metal_parameters, float
      list of tuple of mmtbx.ions.metal_parameters, float
      """
            reasonable = []
            unreasonable = []
            for elem_params in filtered_candidates:
                atom_props.check_ion_environment(ion_params=elem_params,
                    wavelength=self.wavelength, require_valence=require_valence
                    )
                atom_props.check_fpp_ratio(ion_params=elem_params,
                    wavelength=self.wavelength, fpp_ratio_min=self.params.
                    phaser.fpp_ratio_min, fpp_ratio_max=self.params.phaser.
                    fpp_ratio_max)
                identity = str(elem_params)
                if atom_props.is_correctly_identified(identity=identity):
                    reasonable.append((elem_params, atom_props.score[identity])
                        )
                else:
                    unreasonable.append((elem_params, atom_props.score[
                        identity]))
            return reasonable, unreasonable
        reasonable, unreasonable = try_candidates(require_valence=True)
        valence_used = True
        if len(reasonable) == 0 and not self.params.require_valence:
            reasonable, unreasonable = try_candidates(require_valence=False)
            if len(reasonable) > 0:
                valence_used = False
        looks_like_halide = False
        if not (reasonable or atom_type < HEAVY_ION or nuc_phosphate_site):
            candidate_halides = set(candidates).intersection(mmtbx.ions.HALIDES
                )
            filtered_halides = []
            for element in candidate_halides:
                fpp_ratio = atom_props.check_fpp_ratio(ion_params=mmtbx.
                    ions.metal_parameters(element=element, charge=-1),
                    wavelength=self.wavelength, fpp_ratio_min=self.params.
                    phaser.fpp_ratio_min, fpp_ratio_max=self.params.phaser.
                    fpp_ratio_max)
                if fpp_ratio is not None and element != 'CL':
                    if (fpp_ratio < self.params.phaser.fpp_ratio_min or 
                        fpp_ratio > self.params.phaser.fpp_ratio_max):
                        continue
                if self.looks_like_halide_ion(i_seq=i_seq, element=element):
                    filtered_halides.append(element)
            looks_like_halide = len(filtered_halides) > 0
            reasonable += [(mmtbx.ions.metal_parameters(element=halide,
                charge=-1), 0) for halide in filtered_halides]
        if len(reasonable) == 0 and not self.params.require_valence:
            compatible = [params for params in filtered_candidates if
                atom_props.has_compatible_ligands(str(params))]
            for ion_params in compatible:
                atomic_number = sasaki.table(ion_params.element).atomic_number(
                    )
                weight_ratio = 0
                if atom_props.estimated_weight is not None:
                    weight_ratio = atom_props.estimated_weight / (atomic_number
                         - ion_params.charge)
                if (ion_params.element in mmtbx.ions.TRANSITION_METALS and 
                    atom_type == HEAVY_ION and not auto_candidates and
                    atom_props.is_compatible_site(ion_params)):
                    n_good_res = atom_props.number_of_favored_ligand_residues(
                        ion_params, distance=2.7)
                    n_total_coord_atoms = (atom_props.
                        number_of_atoms_within_radius(2.8))
                    if n_good_res >= 1 and 2 <= n_total_coord_atoms <= 6:
                        reasonable.append((ion_params, 0))
                    else:
                        pass
                elif ion_params.element in ['K', 'CA'
                    ] and atom_type == HEAVY_ION and not auto_candidates and atom_props.is_compatible_site(
                    ion_params):
                    n_good_res = atom_props.number_of_favored_ligand_residues(
                        ion_params, distance=2.9, exclude_atoms=['O'])
                    n_bb_oxygen = atom_props.number_of_backbone_oxygens(
                        distance_cutoff=2.9)
                    n_total_coord_atoms = (atom_props.
                        number_of_atoms_within_radius(distance_cutoff=2.9))
                    if (n_good_res + n_bb_oxygen >= 2 and 
                        n_total_coord_atoms >= 4):
                        reasonable.append((ion_params, 0))
                elif atomic_number > 30 and atom_type == HEAVY_ION and (
                    weight_ratio > 0.5 and weight_ratio < 1.05 or 
                    atom_props.fp > 10
                    ) and not auto_candidates and atom_props.is_compatible_site(
                    ion_params, ignore_valence=not self.
                    get_strict_valence_flag()):
                    n_total_coord_atoms = (atom_props.
                        number_of_atoms_within_radius(2.8))
                    if n_total_coord_atoms >= 3:
                        reasonable.append((ion_params, 0))
                else:
                    pass
            if len(compatible) == 1 and not self.get_strict_valence_flag():
                inaccuracies = atom_props.inaccuracies[str(ion_params)]
                if (compatible[0] in atom_props.fpp_ratios and atom_props.
                    BAD_FPP not in inaccuracies and not inaccuracies.
                    intersection([atom_props.BAD_GEOMETRY, atom_props.
                    NO_GEOMETRY])):
                    _PRINT_DEBUG(atom.id_str())
                    _PRINT_DEBUG(filtered_candidates)
                    _PRINT_DEBUG(compatible)
        if len(reasonable) == 1:
            final_choice = reasonable[0][0]
        if not reasonable and not auto_candidates:
            result = self.analyze_water(i_seq=i_seq, debug=debug, no_final=
                True, out=out)
            if result.final_choice is not None:
                return result
        return water_result(atom_props=atom_props, filtered_candidates=
            filtered_candidates, matching_candidates=reasonable,
            rejected_candidates=unreasonable, nuc_phosphate_site=
            nuc_phosphate_site, atom_type=atom_type, looks_like_halide=
            looks_like_halide, ambiguous_valence_cutoff=self.params.
            ambiguous_valence_cutoff, valence_used=valence_used,
            final_choice=final_choice, wavelength=self.wavelength, no_final
            =no_final)

    def _extract_waters(self):
        model = self.pdb_hierarchy.only_model()
        water_i_seqs = []
        for chain in model.chains():
            for residue_group in chain.residue_groups():
                atom_groups = residue_group.atom_groups()
                if len(atom_groups) > 1:
                    continue
                for atom_group in atom_groups:
                    resname = atom_group.resname.strip().upper()
                    if resname in WATER_RES_NAMES:
                        atoms = atom_group.atoms()
                        if len(atoms) == 1:
                            water_i_seqs.append(atoms[0].i_seq)
        return water_i_seqs
    <mask token>
    <mask token>

    def validate_ions(self, out=sys.stdout, debug=True, segid=None):
        """
    Iterate over all the ions built into the model by this module and double
    check their correctness. Looks for tell-tale signs such as negative peaks
    in the mFo-DFc and anomalous difference maps, abnormal b-factor, and
    disallowed chemical environments.

    Prints out a table of bad ions and their information and returns a list of
    their sites' i_seqs.

    Parameters
    ----------
    out : file, optional
    debug : bool, optional
    segid : str, optional
        If present, only validate ions with this segid.
    """
        if segid is None:
            ions = []
            for model in self.pdb_hierarchy.models():
                for chain in model.chains():
                    for residue_group in chain.residue_groups():
                        for atom_group in residue_group.atom_groups():
                            if atom_group.resname.strip(
                                ) in mmtbx.ions.SUPPORTED:
                                atoms = atom_group.atoms()
                                assert len(atoms) == 1
                                for atom in atoms:
                                    ions += atom.i_seq,
        else:
            sel_cache = self.pdb_hierarchy.atom_selection_cache()
            ions = sel_cache.selection('segid {0}'.format(segid)).iselection()
        if len(ions) == 0:
            return
        ion_status = []
        self.atoms_to_props = dict((i_seq, AtomProperties(i_seq, self)) for
            i_seq in ions)
        for i_seq in ions:
            atom_props = self.validate_ion(i_seq=i_seq, debug=debug)
            ion_status.append((atom_props, atom_props.
                is_correctly_identified()))
        scatterers = self.xray_structure.scatterers()
        headers = ('atom', 'occ', 'b_iso', '2mFo-DFc', 'mFo-DFc', 'fp',
            'fdp', 'ratio', 'BVS', 'VECSUM')
        fmt = '%-15s %-5s  %-5s  %-8s  %-7s  %-5s  %-5s  %-5s %-5s  %-6s'
        box = framed_output(out, title='Validating new ions', width=80)
        print(fmt % headers, file=box)
        print(' ' + '-' * 75, file=box)
        for props, okay_flag in ion_status:
            i_seq = props.atom.i_seq
            sc = scatterers[i_seq]
            fp = fdp = None
            if sc.flags.use_fp_fdp:
                fp = sc.fp
                fdp = sc.fdp
            b_iso = adptbx.u_as_b(sc.u_iso_or_equiv(unit_cell=self.unit_cell))
            identity = props.identity()

            def ff(fs, val):
                return format_value(fs, val, replace_none_with='---')
            print(fmt % (props.atom.id_str(suppress_segid=True)[5:-1], ff(
                '%.2f', props.atom.occ), ff('%.2f', b_iso), ff('%.2f',
                props.peak_2fofc), ff('%.2f', props.peak_fofc), ff('%.2f',
                fp), ff('%.2f', fdp), ff('%.2f', None if self.wavelength is
                None else fdp / sasaki.table(mmtbx.ions.server.get_element(
                props.atom)).at_angstrom(self.wavelength).fdp()), ff('%.2f',
                props.valence_sum.get(identity)), ff('%.2f', props.
                vector_sum.get(identity))), file=box)
            if not okay_flag:
                print('\n'.join('!!! ' + props.error_strs[i] for i in props
                    .inaccuracies[identity]), file=box)
            print('', file=box)
        box.close()


class _analyze_water_wrapper(object):
    """
  Simple wrapper for calling manager.analyze_water with keyword arguments
  in a parallelized loop.  Because the water_result object is not pickle-able,
  we only return the final ion choice and summary string.
  """

    def __init__(self, manager, **kwds):
        self.manager = manager
        self.kwds = dict(kwds)

    def __call__(self, i_seq):
        try:
            result = self.manager.analyze_water(i_seq, **self.kwds)
            out = StringIO()
            if result is not None:
                result.show_summary(out=out, debug=self.kwds.get('debug', 
                    False))
            result_str = out.getvalue()
            if result_str == '':
                result_str = None
            final_choice = None
            if not getattr(result, 'no_final', False):
                final_choice = getattr(result, 'final_choice', None)
            return i_seq, final_choice, result_str
        except KeyboardInterrupt:
            return None, None, None


class _validate_ion_wrapper(object):
    """
  Simple wrapper for calling manager.validate_ion with keyword arguments
  in a parallelized loop.  Because the water_result object is not pickle-able,
  we only return the final ion choice and summary string.
  """

    def __init__(self, manager, **kwds):
        self.manager = manager
        self.kwds = dict(kwds)

    def __call__(self, i_seq):
        try:
            correct = self.manager.validate_ion(i_seq, **self.kwds)
            return i_seq, correct
        except KeyboardInterrupt:
            return None, None


class ion_result(validation.atom):
    """
  Class for validation results for an ion site. This includes information about
  both the atom modeled at that site as well as its chemical and scattering
  environment.
  """
    __slots__ = validation.atom.__slots__ + ['chem_env', 'scatter_env']

    def show(self, out=sys.stdout, prefix=''):
        pass

    def show_brief(self, out=sys.stdout, prefix=''):
        pass


class water_result(object):
    """
  Container for storing the results of manager.analyze_water for later display
  and retrieval.
  """

    def __init__(self, atom_props, filtered_candidates, matching_candidates,
        rejected_candidates, nuc_phosphate_site, atom_type,
        looks_like_halide, ambiguous_valence_cutoff, valence_used,
        final_choice, wavelength=None, no_final=False):
        adopt_init_args(self, locals())

    def show_summary(self, out=None, debug=False):
        """
    Prints out a summary of a site's chemical and scattering environment.

    Parameters
    ----------
    out : file, optional
    debug : bool, optional
    """
        if out is None:
            out = sys.stdout
        results = self.matching_candidates
        if len(results) > 0:
            self.atom_props.show_properties(identity='HOH', out=out)
            if self.nuc_phosphate_site:
                print('  appears to be nucleotide coordination site', file=out)
            if self.no_final:
                print('  Found potential ion%s outside of specified set:' %
                    ('s' if len(results) > 1 else ''), file=out)
            if self.final_choice is not None:
                elem_params, score = results[0]
                if elem_params.element not in mmtbx.ions.HALIDES:
                    self.atom_props.show_ion_results(identity=str(self.
                        final_choice), out=out, valence_used=self.
                        valence_used, confirmed=True)
                else:
                    print('  Probable anion:', str(elem_params), file=out)
                print('', file=out)
            elif len(results) > 1:
                below_cutoff = [elem_params for elem_params, score in
                    results if score < self.ambiguous_valence_cutoff]
                if len(below_cutoff) == 1:
                    elem_params = below_cutoff[0]
                    print('  ambigous results, best valence from %s' % str(
                        elem_params), file=out)
                    self.atom_props.show_ion_results(identity=str(
                        elem_params), out=out, valence_used=True)
                    print('', file=out)
                else:
                    ions = [str(i[0]) for i in sorted(results, key=lambda x:
                        x[1])]
                    print('  ambiguous results, could be %s' % ', '.join(
                        ions), file=out)
                    for elem_params, score in results:
                        self.atom_props.show_ion_results(identity=str(
                            elem_params), out=out)
                    print('', file=out)
        elif self.atom_type != WATER or self.nuc_phosphate_site:
            self.atom_props.show_properties(identity='HOH', out=out)
            if self.nuc_phosphate_site:
                print('  appears to be nucleotide coordination site', file=out)
            if self.looks_like_halide:
                print('  Probable cation: %s' % str(self.final_choice),
                    file=out)
                print('', file=out)
            else:
                if debug and len(self.filtered_candidates) > 0:
                    print('  insufficient data to identify atom', file=out)
                    possible = True
                    for params in self.filtered_candidates:
                        if self.atom_props.has_compatible_ligands(str(params)):
                            if possible:
                                print('  possible candidates:', file=out)
                                possible = False
                            self.atom_props.show_ion_results(identity=str(
                                params), out=out)
                        else:
                            print('  incompatible ligands for %s' % str(
                                params), file=out)
                print('', file=out)


class AtomProperties(object):
    """
  Collect physical attributes of an atom, including B, occupancy, and map
  statistics, and track those which are at odds with its chemical identity.
  """
    (LOW_B, HIGH_B, LOW_OCC, HIGH_OCC, NO_2FOFC_PEAK, FOFC_PEAK, FOFC_HOLE,
        ANOM_PEAK, NO_ANOM_PEAK, BAD_GEOMETRY, NO_GEOMETRY, BAD_VECTORS,
        BAD_VALENCES, TOO_FEW_NON_WATERS, TOO_FEW_COORD, TOO_MANY_COORD,
        LIKE_COORD, BAD_COORD_ATOM, BAD_FPP, BAD_COORD_RESIDUE,
        VERY_BAD_VALENCES, BAD_HALIDE, HIGH_2FOFC, COORDING_GEOMETRY,
        CLOSE_CONTACT) = range(25)
    error_strs = {LOW_B: 'Abnormally low b-factor', HIGH_B:
        'Abnormally high b-factor', LOW_OCC: 'Abnormally low occupancy',
        HIGH_OCC: 'Abnormally high occupancy', NO_2FOFC_PEAK:
        'No 2mFo-DFc map peak', FOFC_PEAK: 'Peak in mFo-DFc map', FOFC_HOLE:
        'Negative peak in dFo-mFc map', ANOM_PEAK:
        'Peak in the anomalous map', NO_ANOM_PEAK:
        'No peak in the anomalous map', BAD_GEOMETRY:
        'Unexpected geometry for coordinating atoms', NO_GEOMETRY:
        'No distinct geometry for coordinating atoms', BAD_VECTORS:
        'VECSUM above cutoff', BAD_VALENCES: 'BVS above or below cutoff',
        TOO_FEW_NON_WATERS: 'Too few non-water coordinating atoms',
        TOO_FEW_COORD: 'Too few coordinating atoms', TOO_MANY_COORD:
        'Too many coordinating atoms', LIKE_COORD:
        'Like charge coordinating like', BAD_COORD_ATOM:
        'Disallowed coordinating atom', BAD_FPP: "Bad refined f'' value",
        BAD_COORD_RESIDUE: 'Disallowed coordinating residue',
        VERY_BAD_VALENCES: 'BVS far above or below cutoff', BAD_HALIDE:
        'Bad halide site', HIGH_2FOFC: 'Unexpectedly high 2mFo-DFc value',
        COORDING_GEOMETRY:
        'No distinct geometry and coordinating another atom with distinct geometry'
        , CLOSE_CONTACT: 'Close contact to oxygen atom'}

    def __init__(self, i_seq, manager):
        self.i_seq = i_seq
        self.atom = manager.pdb_atoms[i_seq].fetch_labels()
        self.resname = self.atom.resname.strip().upper()
        self.d_min = manager.fmodel.f_obs().d_min()
        self.anomalous_flag = manager.fmodel.f_obs().anomalous_flag()
        self.strict_valence = manager.get_strict_valence_flag()
        nearby_atoms_unfiltered = manager.find_nearby_atoms(i_seq=i_seq,
            far_distance_cutoff=3.5)
        self.nearby_atoms = []
        nearby_atoms_no_alts = []
        for contact in nearby_atoms_unfiltered:
            if contact.element not in ['H', 'D']:
                self.nearby_atoms.append(contact)
                for other in nearby_atoms_no_alts:
                    if other == contact:
                        break
                else:
                    nearby_atoms_no_alts.append(contact)
        self.residue_counts = utils.count_coordinating_residues(self.
            nearby_atoms)
        self.geometries = find_coordination_geometry(nearby_atoms_no_alts)
        map_stats = manager.map_stats(i_seq)
        self.peak_2fofc = map_stats.two_fofc
        self.peak_fofc = map_stats.fofc
        self.peak_anom = map_stats.anom
        self.estimated_weight = manager.guess_molecular_weight(i_seq)
        self.b_iso = manager.get_b_iso(i_seq)
        self.b_mean_hoh = manager.b_mean_hoh
        self.b_stddev_hoh = manager.b_stddev_hoh
        self.b_mean_calpha = manager.b_mean_calpha
        self.calpha_mean_two_fofc = manager.calpha_mean_two_fofc
        self.inaccuracies = {}
        self.ignored = {}
        self.vectors = {}
        self.valence_sum = {}
        self.vector_sum = {}
        self.score = {}
        self.fpp_expected = {}
        self.expected_params = {}
        self.bad_coords = {}
        self.fpp = manager.get_fpp(i_seq)
        self.fpp_ratios = {}
        self.fp = manager.get_fp(i_seq)
        self.manager = manager

    def is_correctly_identified(self, identity=None):
        """
    Calculates whether factors indicate that the atom was correctly identified.

    Parameters
    ----------
    identity : mmtbx.ions.metal_parameters

    Returns
    -------
    bool
    """
        if identity is None:
            identity = self.identity()
        return len(self.inaccuracies[identity]) == 0

    def has_compatible_ligands(self, identity):
        """
    Indicates whether the coordinating atoms are of the allowed type (e.g.
    no N or S atoms coordinating CA, etc.) and residue (e.g. Ser is not an
    appropriate ligand for ZN).

    Parameters
    ----------
    identity : mmtbx.ions.metal_parameters

    Returns
    -------
    bool
    """
        return len(self.bad_coords[identity]
            ) == 0 and not self.BAD_COORD_RESIDUE in self.inaccuracies[identity
            ]

    def is_compatible_site(self, ion_params, require_anom=True,
        ignore_valence=False):
        """
    More minimal criteria for determining whether a site is chemically
    compatible, allowing for incomplete coordination shells.

    Parameters
    ----------
    ion_params : mmtbx.ions.metal_parameters
    require_anom : bool, optional
    ignore_valence : bool, optional

    Returns
    -------
    bool
    """
        inaccuracies = self.inaccuracies[str(ion_params)]
        anom_allowed = self.is_compatible_anomalous_scattering(ion_params
            ) or not require_anom
        return self.has_compatible_ligands(str(ion_params)
            ) and not self.TOO_MANY_COORD in inaccuracies and (ignore_valence
             or not self.VERY_BAD_VALENCES in inaccuracies
            ) and not self.BAD_COORD_RESIDUE in inaccuracies and anom_allowed

    def number_of_favored_ligand_residues(self, ion_params, distance=3.0,
        exclude_atoms=()):
        """
    Counts the number of preferred residues coordinating the atom.  Used for
    approximate detection of transition-metal binding sites.

    Parameters
    ----------
    ion_params : mmtbx.ions.metal_parameters
    distance : float, optional
    exclude_atoms : tuple of ...

    Returns
    -------
    int
    """
        n_res = 0
        resids = []
        for contact in self.nearby_atoms:
            if contact.atom_name() in exclude_atoms:
                continue
            if contact.distance() < distance:
                labels = contact.atom.fetch_labels()
                other_resname = contact.resname()
                other_resid = labels.chain_id + labels.resid()
                if (ion_params.allowed_coordinating_residues is not None and
                    other_resname in ion_params.
                    allowed_coordinating_residues and not other_resid in resids
                    ):
                    n_res += 1
                    resids.append(other_resid)
        return n_res

    def number_of_atoms_within_radius(self, distance_cutoff):
        """
    Counts the number of coordinating atoms within a given radius.

    Parameters
    ----------
    float

    Returns
    -------
    int
    """
        n_atoms = 0
        atom_ids = []
        for contact in self.nearby_atoms:
            other_id = contact.atom_id_no_altloc()
            if not other_id in atom_ids:
                if contact.distance() < distance_cutoff:
                    n_atoms += 1
                atom_ids.append(other_id)
        return n_atoms

    def number_of_backbone_oxygens(self, distance_cutoff=3.0):
        """
    Counts the number of backbone oxygens coordinating a site.

    Parameters
    ----------
    distance_cutoff : float, optional

    Returns
    -------
    int
    """
        n_bb_ox = 0
        for contact in self.nearby_atoms:
            if contact.atom_name() == 'O':
                if contact.distance() <= distance_cutoff:
                    if not contact.resname() in WATER_RES_NAMES:
                        n_bb_ox += 1
        return n_bb_ox

    def is_compatible_anomalous_scattering(self, ion_params):
        if ion_params.element.upper() in ['MG', 'NA']:
            return (self.fpp is None and self.peak_anom is not None and 
                self.peak_anom < 1.0)
        else:
            if self.fpp is None:
                return self.peak_anom is not None and self.peak_anom > 3.0
            identity = self.identity(ion=ion_params)
            if identity in self.fpp_ratios:
                return not self.BAD_FPP in self.inaccuracies[identity]
        return False

    def atom_weight(self, manager):
        """
    Evaluates whether factors indicate that the atom is lighter, heavier, or
    isoelectric to what it is currently identified as.

    Parameters
    ----------
    manager : mmtbx.ions.identify.manager

    Returns
    -------
    int
        -1 if lighter, 0 if isoelectronic, and 1 if heavier.
    """
        identity = 'HOH' if self.resname in WATER_RES_NAMES else self.identity(
            )
        if (identity == 'HOH' and self.atom.b > manager.b_mean_hoh -
            manager.b_stddev_hoh):
            return 0
        if self.is_correctly_identified(identity=identity):
            return 0
        if self.FOFC_PEAK in self.inaccuracies[identity] or self.atom.b < 1:
            return 1
        if self.FOFC_HOLE in self.inaccuracies[identity]:
            return -1
        return 0

    def check_ion_environment(self, ion_params, wavelength=None,
        require_valence=True):
        """
    Checks whether or not the specified ion satisfies the metal-coordination
    parameters, specified by ion_params, such as valence sum, geometry, etc.
    The criteria used here are quite strict, but many of the analyses are
    saved for later if we want to use looser critera.

    Parameters
    ----------
    ion_params : mmtbx.ions.metal_parameters
    wavelength : float, optional
    require_valence : bool, optional
    """
        from iotbx.pdb import common_residue_names_get_class as get_class
        identity = self.identity(ion_params)
        inaccuracies = self.inaccuracies[identity] = set()
        self.expected_params[identity] = ion_params
        ignored = self.ignored[identity] = set()
        strict_rules = require_valence or self.is_correctly_identified(identity
            ='HOH') or self.strict_valence or ion_params.element in ['NA', 'MG'
            ]
        n_closest = 0
        coord_atoms = []
        for i_pair, contact1 in enumerate(self.nearby_atoms):
            distance = contact1.distance()
            if distance < 3.0:
                for contact2 in self.nearby_atoms[i_pair + 1:]:
                    if contact1 == contact2 or contact1.distance_from(contact2
                        ) <= 0.3:
                        break
                else:
                    coord_atoms.append(contact1)
                    if distance < 2.7:
                        n_closest += 1
        if len(coord_atoms) < ion_params.coord_num_lower:
            inaccuracies.add(self.TOO_FEW_COORD)
        if n_closest > ion_params.coord_num_upper:
            inaccuracies.add(self.TOO_MANY_COORD)
        n_non_water = 0
        self.bad_coords[identity] = []
        for contact in self.nearby_atoms:
            other_name = contact.atom_name()
            other_resname = contact.resname()
            other_element = contact.element
            if not other_resname in WATER_RES_NAMES:
                n_non_water += 1
            else:
                continue
            if contact.distance() < 3.0:
                if (ion_params.allowed_coordinating_atoms is not None and 
                    other_element not in ion_params.allowed_coordinating_atoms
                    ):
                    self.bad_coords[identity].append(contact)
                    inaccuracies.add(self.BAD_COORD_ATOM)
                if get_class(other_resname) == 'common_amino_acid':
                    if other_name in ['C', 'N', 'O', 'CA', 'H', 'HA'] and (
                        ion_params.allowed_backbone_atoms is None or not 
                        other_name in ion_params.allowed_backbone_atoms):
                        if other_name == 'O' and contact.is_carboxy_terminus:
                            pass
                        else:
                            self.bad_coords[identity].append(contact)
                            inaccuracies.add(self.BAD_COORD_ATOM)
                    if ion_params.allowed_coordinating_residues is not None:
                        allowed = ion_params.allowed_coordinating_residues
                        if not other_resname in allowed and other_name not in [
                            'C', 'O', 'N', 'CA', 'OXT']:
                            self.bad_coords[identity].append(contact)
                            inaccuracies.add(self.BAD_COORD_RESIDUE)
                elif cmp(0, mmtbx.ions.server.get_charge(contact.atom)) == cmp(
                    0, ion_params.charge):
                    self.bad_coords[identity].append(contact)
                    inaccuracies.add(self.LIKE_COORD)
                elif ion_params.charge > 0 and other_element in ['N'
                    ] and other_resname in ['LYS', 'ARG', 'ASN', 'GLN']:
                    self.bad_coords[identity].append(contact)
                    inaccuracies.add(self.LIKE_COORD)
        if n_non_water < ion_params.min_coordinating_non_waters:
            inaccuracies.add(self.TOO_FEW_NON_WATERS)
        if ion_params.allowed_geometries and strict_rules:
            allowed = [(i[0] in ion_params.allowed_geometries) for i in
                self.geometries]
            if 'any' in ion_params.allowed_geometries:
                pass
            elif not self.geometries:
                if strict_rules:
                    inaccuracies.add(self.NO_GEOMETRY)
            elif not any(allowed):
                inaccuracies.add(self.BAD_GEOMETRY)
            else:
                strict_rules = False
        if self.geometries == []:
            for contact in self.nearby_atoms:
                o_atom = contact.atom
                if o_atom.i_seq in self.manager.atoms_to_props:
                    o_geometry = self.manager.atoms_to_props[o_atom.i_seq
                        ].geometries
                    if o_geometry != []:
                        inaccuracies.add(self.COORDING_GEOMETRY)
        vectors = mmtbx.ions.server.calculate_valences(ion_params, self.
            nearby_atoms)
        self.vectors[identity] = vectors
        self.valence_sum[identity] = sum([abs(i) for i in vectors])
        self.vector_sum[identity] = abs(sum(vectors, col((0, 0, 0))))
        if self.vector_sum[identity] > ion_params.vec_sum_cutoff:
            if strict_rules:
                inaccuracies.add(self.BAD_VECTORS)
            else:
                ignored.add(self.BAD_VECTORS)
        if self.valence_sum[identity
            ] < ion_params.cvbs_expected * 0.25 or self.valence_sum[identity
            ] > ion_params.cvbs_expected * 1.25:
            inaccuracies.add(self.VERY_BAD_VALENCES)
        elif self.valence_sum[identity
            ] < ion_params.cvbs_lower or self.valence_sum[identity
            ] > ion_params.cvbs_upper:
            if strict_rules:
                inaccuracies.add(self.BAD_VALENCES)
            else:
                ignored.add(self.BAD_VALENCES)
        self.score[identity] = abs(self.valence_sum[identity] - ion_params.
            cvbs_expected)

    def check_fpp_ratio(self, ion_params, wavelength, fpp_ratio_min=0.3,
        fpp_ratio_max=1.05):
        """
    Compare the refined and theoretical f'' values if available.

    Parameters
    ----------
    ion_params : mmtbx.ions.metal_parameters
    wavelength : float
    fpp_ratio_min : float, optional
    fpp_ratio_max : float, optional

    Returns
    -------
    float
        f'' / f''_expected
    """
        identity = str(ion_params)
        inaccuracies = self.inaccuracies.get(identity, None)
        if inaccuracies is None:
            inaccuracies = self.inaccuracies[identity] = set()
        if ion_params.element.upper() in ['MG', 'NA']:
            if (self.fpp is not None or self.peak_anom is not None and self
                .peak_anom > 1):
                inaccuracies.add(self.BAD_FPP)
        elif wavelength is not None and self.anomalous_flag:
            fpp_expected_sasaki = sasaki.table(ion_params.element).at_angstrom(
                wavelength).fdp()
            fpp_expected_henke = henke.table(ion_params.element).at_angstrom(
                wavelength).fdp()
            self.fpp_expected[identity] = max(fpp_expected_sasaki,
                fpp_expected_henke)
            if self.fpp is not None and self.fpp_expected[identity] != 0:
                self.fpp_ratios[identity] = self.fpp / self.fpp_expected[
                    identity]
                if self.fpp_ratios[identity
                    ] > fpp_ratio_max or self.fpp >= 0.2 and self.fpp_ratios[
                    identity] < fpp_ratio_min:
                    inaccuracies.add(self.BAD_FPP)
            elif self.fpp_expected[identity] > 0.75 and self.peak_anom < 2:
                inaccuracies.add(self.BAD_FPP)
        return self.fpp_ratios.get(identity)

    def show_properties(self, identity, out=sys.stdout):
        """
    Show atomic properties that are independent of the suspected identity.

    Parameters
    ----------
    identity : mmtbx.ions.metal_parameters
    out : file, optional
    """
        print('%s:' % self.atom.id_str(), file=out)
        b_flag = ''
        if self.LOW_B in self.inaccuracies[identity]:
            b_flag = ' <<<'
        elif self.HIGH_B in self.inaccuracies[identity]:
            b_flag = ' !!!'
        print('  B-factor:      %6.2f%s' % (self.atom.b, b_flag), file=out)
        occ_flag = ''
        if self.LOW_OCC in self.inaccuracies[identity]:
            occ_flag = ' !!!'
        elif self.HIGH_OCC in self.inaccuracies[identity]:
            occ_flag = ' <<<'
        print('  Occupancy:     %6.2f%s' % (self.atom.occ, occ_flag), file=out)
        twofofc_flag = ''
        if self.NO_2FOFC_PEAK in self.inaccuracies[identity]:
            twofofc_flag = ' !!!'
        elif self.HIGH_2FOFC in self.inaccuracies[identity]:
            twofofc_flag = ' <<<'
        print('  2mFo-DFc map:  %6.2f%s' % (self.peak_2fofc, twofofc_flag),
            file=out)
        fofc_flag = ''
        if self.FOFC_PEAK in self.inaccuracies[identity]:
            fofc_flag = ' <<<'
        elif self.FOFC_HOLE in self.inaccuracies[identity]:
            fofc_flag = ' !!!'
        print('  mFo-DFc map:   %6.2f%s' % (self.peak_fofc, fofc_flag),
            file=out)
        if self.peak_anom is not None:
            anom_flag = ''
            if self.ANOM_PEAK in self.inaccuracies[identity]:
                anom_flag = ' <<<'
            elif self.NO_ANOM_PEAK in self.inaccuracies[identity]:
                anom_flag = ' !!!'
            print('  Anomalous map: %6.2f%s' % (self.peak_anom, anom_flag),
                file=out)
        if self.estimated_weight is not None:
            print('  Approx. mass:  %6d' % self.estimated_weight, file=out)
        if self.fpp is not None:
            fpp_flag = ''
            if self.fpp >= 0.2:
                fpp_flag = ' <<<'
            print("  f'':           %6.2f%s" % (self.fpp, fpp_flag), file=out)
            print("  f'' ratio:     %s" % format_value('%6.2f', self.
                fpp_ratios.get(identity)), file=out)
        if self.nearby_atoms is not None:
            angstrom = u'Å'.encode('utf-8', 'strict')
            degree = u'°'.encode('utf-8', 'strict')
            print('  Nearby atoms: (%d within 3.0 %s)' % (len([i for i in
                self.nearby_atoms if i.distance() < 3]), angstrom), file=out)
            for contact in self.nearby_atoms:
                print('    %s (%5.3f %s)' % (contact.id_str(), contact.
                    distance(), angstrom), file=out)
            if self.geometries:
                print('  Coordinating geometry:', file=out)
                for geometry, deviation in self.geometries:
                    print('    %-15s (average deviation: %.3f%s)' % (
                        geometry, deviation, degree), file=out)

    def show_ion_results(self, identity=None, out=sys.stdout, confirmed=
        False, valence_used=True):
        """
    Show statistics for a proposed element identity.

    Parameters
    ----------
    identity : mmtbx.ions.metal_parameters
    out : file, optional
    confirmed : bool, optional
    valence_used : bool, optional
    """
        if not identity:
            identity = self.identity(self.atom)
        inaccuracies = self.inaccuracies.get(identity, set([]))
        ignored = self.ignored.get(identity, set([]))
        if identity != self.identity():
            if confirmed:
                print('  Probable cation: %s' % identity, file=out)
            else:
                print('  Atom as %s:' % identity, file=out)
        else:
            print('    %s:' % self.atom.id_str(), file=out)
        if identity in self.vector_sum and self.vector_sum[identity
            ] is not None:
            problem = (self.BAD_VECTORS in inaccuracies or self.BAD_VECTORS in
                ignored)
            print('    Vector sum:  %6.3f %s' % (self.vector_sum[identity],
                ' !!!' if problem else ''), file=out)
        if identity in self.valence_sum and self.valence_sum[identity
            ] is not None:
            problem = inaccuracies.union(ignored).intersection([self.
                BAD_VALENCES, self.VERY_BAD_VALENCES])
            print('    Valence sum: %6.3f' % self.valence_sum[identity],
                file=out)
            if valence_used:
                print('(expected: %6.3f) %s' % (self.expected_params[
                    identity].cvbs_expected, ' !!!' if problem else ''),
                    end=' ', file=out)
        if self.NO_GEOMETRY in inaccuracies:
            print('    No distinct geometry !!!', file=out)
        if self.BAD_GEOMETRY in inaccuracies:
            print('    Unexpected geometry  !!!', file=out)
        bad_coord = [self.LIKE_COORD, self.BAD_COORD_ATOM, self.
            BAD_COORD_RESIDUE]
        if inaccuracies.intersection(bad_coord):
            print('    Bad coordinating atom%s:' % ('s' if len(self.
                bad_coords[identity]) != 1 else ''), file=out)
            angstrom = u'Å'.encode('utf-8', 'strict').strip()
            for atom, vector in self.bad_coords[identity]:
                print('    %s (%5.3f %s) !!!' % (atom.id_str(), abs(vector),
                    angstrom), file=out)
        if self.TOO_FEW_NON_WATERS in inaccuracies:
            print('    Too few coordinating non-waters !!!', file=out)
        if self.TOO_FEW_COORD in inaccuracies:
            print('    Too few coordinating atoms !!!', file=out)
        if self.TOO_MANY_COORD in inaccuracies:
            print('    Too many coordinating atoms !!!', file=out)
        if self.fpp is not None and identity in self.fpp_ratios:
            print("    f'' ratio:   %6.3f%s" % (self.fpp_ratios[identity], 
                ' !!!' if self.BAD_FPP in inaccuracies else ''), file=out)

    def looks_like_nucleotide_phosphate_site(self,
        min_phosphate_oxygen_atoms=2, distance_cutoff=2.5):
        """
    Decide whether the atom is coordinating phosphate oxygens from a
    nucleotide, based on common atom names.

    Parameters
    ----------
    min_phosphate_oxygen_atoms : int, optional
    distance_cutoff : float, optional

    Returns
    -------
    bool
    """
        n_phosphate_oxygens = 0
        for contact in self.nearby_atoms:
            atom_name = contact.atom_name()
            if len(atom_name) < 3 or contact.element not in ['O']:
                continue
            if atom_name[0:2] in ['O1', 'O2', 'O3'] and atom_name[2] in ['A',
                'B', 'G']:
                if contact.distance() <= distance_cutoff:
                    n_phosphate_oxygens += 1
        return n_phosphate_oxygens == min_phosphate_oxygen_atoms

    def identity(self, ion=None):
        """
    Covers an atom into a string representing its element and charge.

    Parameters
    ----------
    ion : iotbx.pdb.hierarchy.atom, optional

    Returns
    -------
    str
    """
        if ion is None:
            ion = self.atom
        element = mmtbx.ions.server.get_element(ion)
        charge = mmtbx.ions.server.get_charge(ion)
        return '{}{:+}'.format(element, charge)

    def get_atom_type(self, params, aggressive=False):
        """
    Checks the atom characteristics against what we would expect for a water.
    Updates self with any inaccuracies noticed (Surpringly low b-factor,
    high occupancy, etc).  Note that HEAVY_ION does not necessarily rule out
    NA/MG, as these often have mFo-DFc peaks at high resolution.

    Parameters
    ----------
    params : libtbx.phil.scope_extract
    aggressive : bool, optional

    Returns
    -------
    int
        One of WATER, WATER_POOR, HEAVY_ION, LIGHT_ION
    """
        inaccuracies = self.inaccuracies['HOH'] = set()
        atom_type = WATER
        if self.peak_2fofc < params.min_2fofc_level or self.peak_fofc < -2.0:
            return WATER_POOR
        if self.fpp is not None and self.fpp > params.fpp_max:
            return HEAVY_ION
        if self.fp is not None and self.fp > params.fp_max:
            return HEAVY_ION
        if (self.peak_anom is not None and self.peak_anom > params.
            max_anom_level):
            inaccuracies.add(self.ANOM_PEAK)
            atom_type = HEAVY_ION
        if self.peak_fofc > params.max_fofc_level:
            inaccuracies.add(self.FOFC_PEAK)
            atom_type = HEAVY_ION
        if self.atom.occ > params.max_occ:
            inaccuracies.add(self.HIGH_OCC)
            atom_type = HEAVY_ION
        if self.atom.b < params.min_b_iso:
            inaccuracies.add(self.LOW_B)
            atom_type = HEAVY_ION
        elif self.b_stddev_hoh is not None and self.b_stddev_hoh > 0:
            z_value = (self.b_iso - self.b_mean_hoh) / self.b_stddev_hoh
            if z_value < -params.max_stddev_b_iso:
                inaccuracies.add(self.LOW_B)
            elif self.atom.b < self.b_mean_hoh * params.min_frac_b_iso:
                inaccuracies.add(self.LOW_B)
            if atom_type == WATER and self.LOW_B in inaccuracies:
                atom_type = LIGHT_ION
        if aggressive and atom_type == WATER:
            if self.b_mean_calpha > 0:
                relative_b = self.b_iso / self.b_mean_calpha
                if relative_b < params.min_frac_calpha_b_iso:
                    inaccuracies.add(self.LOW_B)
                    atom_type = LIGHT_ION
            if self.calpha_mean_two_fofc > 0:
                relative_2fofc = self.peak_2fofc / self.calpha_mean_two_fofc
                if relative_2fofc > params.max_frac_calpha_2fofc:
                    inaccuracies.add(self.HIGH_2FOFC)
                    atom_type = LIGHT_ION
        for i_pair, contact1 in enumerate(self.nearby_atoms):
            if contact1.element.strip() == 'O':
                distance = contact1.distance()
                if distance < 2.4:
                    inaccuracies.add(self.CLOSE_CONTACT)
                    if atom_type < LIGHT_ION:
                        atom_type = LIGHT_ION
        return atom_type


<mask token>

<mask token>


class manager(object):
    <mask token>

    def __init__(self, fmodel, pdb_hierarchy, xray_structure, params=None,
        wavelength=None, connectivity=None, nproc=1, verbose=False, log=None):
        if log is None:
            log = null_out()
        self.fmodel = fmodel
        self.atoms_to_props = {}
        self.params = params
        self.wavelength = wavelength
        self.nproc = nproc
        self.phaser_substructure = None
        self.flag_refine_substructure = False
        self.fpp_from_phaser_ax_sites = None
        self.site_fp = None
        self.site_fdp = None
        self.use_svm = getattr(params, 'use_svm', None)
        self._map_values = {}
        self._map_gaussian_fits = {}
        self.anomalous_flag = None
        if fmodel is not None:
            self.anomalous_flag = fmodel.f_obs().anomalous_flag()
        self.update_structure(pdb_hierarchy=pdb_hierarchy, xray_structure=
            xray_structure, connectivity=connectivity, log=log)
        self.update_maps()
        refine_substructure = False
        if params is not None:
            refine_substructure = params.find_anomalous_substructure
        if refine_substructure is Auto:
            refine_substructure = fmodel.f_obs().anomalous_flag(
                ) and wavelength is not None
        if refine_substructure:
            if wavelength is None:
                raise Sorry('Wavelength required when ' +
                    'find_anomalous_substructure=True.')
            elif not fmodel.f_obs().anomalous_flag():
                raise Sorry('Anomalous data required when ' +
                    'find_anomalous_substructure=True.')
            if params.use_phaser and libtbx.env.has_module('phaser'):
                print('  Running Phaser substructure completion...', file=log)
                t1 = time.time()
                phaser_result = find_anomalous_scatterers(fmodel=fmodel,
                    pdb_hierarchy=pdb_hierarchy, wavelength=wavelength,
                    verbose=verbose, log=log, n_cycles=params.phaser.
                    llgc_ncycles)
                t2 = time.time()
                print('    time: %.1fs' % (t2 - t1), file=log)
                if phaser_result is None:
                    print('  ERROR: Phaser substructure completion failed!',
                        file=log)
                else:
                    self.phaser_substructure = phaser_result.atoms()
                    if len(self.phaser_substructure) == 0:
                        print('  No anomalous scatterers found!', file=log)
                    else:
                        print('  %d anomalous scatterers found' % len(self.
                            phaser_substructure), file=log)
                        self.analyze_substructure(log=log, verbose=True)
            else:
                self.flag_refine_substructure = True
                self.refine_anomalous_substructure(log=log)

    def water_selection(self):
        """
    Fetchs the selection for all waters in the model.

    Returns
    -------
    scitbx.array_family.flex.size_t
    """
        sel_cache = self.pdb_hierarchy.atom_selection_cache()
        sel_str = "({}) and element O and altloc ' '".format(' or '.join([(
            'resname ' + i) for i in WATER_RES_NAMES]))
        return sel_cache.selection(sel_str).iselection()

    def refine_anomalous_substructure(self, log):
        """
    Run simple "substructure completion" implemented using CCTBX tools (the
    command-line equivalent is mmtbx.refine_anomalous_substructure).  A faster
    alternative to Phaser, but not clear how effective this is at the moment.

    Parameters
    ----------
    log : file
    """
        from mmtbx.refinement import anomalous_scatterer_groups
        fmodel_tmp = self.fmodel.deep_copy()
        anom_groups = anomalous_scatterer_groups.refine_anomalous_substructure(
            fmodel=fmodel_tmp, pdb_hierarchy=self.pdb_hierarchy, wavelength
            =self.wavelength, reset_water_u_iso=True, verbose=True,
            use_all_anomalous=True, out=log)
        scatterers = fmodel_tmp.xray_structure.scatterers()
        self.use_fdp = flex.bool(scatterers.size(), False)
        self.site_fp = flex.double(scatterers.size(), 0)
        self.site_fdp = flex.double(scatterers.size(), 0)
        for group in anom_groups:
            for i_seq in group.iselection:
                sc = scatterers[i_seq]
                self.use_fdp[i_seq] = True
                self.site_fp[i_seq] = sc.fp
                self.site_fdp[i_seq] = sc.fdp

    def update_structure(self, pdb_hierarchy, xray_structure, connectivity=
        None, log=None, refine_if_necessary=True):
        """
    Set the current atomic data: PDB hierarchy, Xray structure, and simple
    connectivity list.

    Parameters
    ----------
    pdb_hierarchy : iotbx.pdb.hierarchy.root
    xray_structure : cctbx.xray.structure.structure
    connectivity : ...
    log : file, optional
    refine_if_necessary : bool, optional
    """
        self.pdb_hierarchy = pdb_hierarchy
        self.xray_structure = xray_structure
        if self.fmodel:
            self.fmodel.update_xray_structure(xray_structure, update_f_calc
                =True)
        self.sites_frac = self.xray_structure.sites_frac()
        self.sites_cart = self.xray_structure.sites_cart()
        self.connectivity = connectivity
        self.pdb_atoms = pdb_hierarchy.atoms()
        assert not self.pdb_atoms.extract_i_seq().all_eq(0)
        self.unit_cell = xray_structure.unit_cell()
        self.use_fdp = flex.bool(xray_structure.scatterers().size(), False)
        self.ax_chain = None
        self._pair_asu_cache = {}
        self.u_iso_all = xray_structure.extract_u_iso_or_u_equiv()
        sctr_keys = xray_structure.scattering_type_registry().type_count_dict()
        self.hd_present = 'H' in sctr_keys or 'D' in sctr_keys
        sel_cache = pdb_hierarchy.atom_selection_cache()
        self.carbon_sel = sel_cache.selection('element C').iselection()
        self.calpha_sel = sel_cache.selection('name CA and element C'
            ).iselection()
        assert len(self.carbon_sel) > 0
        water_sel = self.water_selection()
        self.n_waters = len(water_sel)
        not_hd_sel = sel_cache.selection('not (element H or element D)'
            ).iselection()
        self.n_heavy = len(not_hd_sel)
        u_iso_all_tmp = self.u_iso_all.select(not_hd_sel)
        self.b_mean_all = adptbx.u_as_b(flex.mean(u_iso_all_tmp))
        self.b_stddev_all = adptbx.u_as_b(u_iso_all_tmp.
            standard_deviation_of_the_sample())
        self.b_mean_calpha = 0
        if len(self.calpha_sel) > 0:
            self.b_mean_calpha = adptbx.u_as_b(flex.mean(self.u_iso_all.
                select(self.calpha_sel)))
        self.b_mean_hoh = self.b_stddev_hoh = None
        if self.n_waters > 0:
            u_iso_hoh = self.u_iso_all.select(water_sel)
            self.b_mean_hoh = adptbx.u_as_b(flex.mean(u_iso_hoh))
            self.b_stddev_hoh = adptbx.u_as_b(u_iso_hoh.
                standard_deviation_of_the_sample())
        if self.phaser_substructure is not None:
            self.analyze_substructure(log=log)
        elif self.flag_refine_substructure and refine_if_necessary:
            self.refine_anomalous_substructure(log=log)
    <mask token>
    <mask token>

    def update_maps(self):
        """
    Generate new maps for the current structure, including anomalous map if
    data are anomalous.

    If Phaser is installed and anomalous data are available, the anomalous
    log-likelihood gradient (LLG) map can be used instead of the conventional
    anomalous difference map. This is only really useful if the anomalous
    scattering of existing atoms is modeled (and ideally, refined).
    """
        if self.fmodel is None:
            return

        def fft_map(map_coeffs, resolution_factor=0.25):
            return map_coeffs.fft_map(resolution_factor=resolution_factor
                ).apply_sigma_scaling().real_map_unpadded()
        map_types = ['2mFo-DFc', 'mFo-DFc']
        map_keys = ['2mFo-DFc', 'mFo-DFc']
        if self.fmodel.f_obs().anomalous_flag():
            if self.params.anom_map_type == 'phaser':
                map_types.append('llg')
            elif self.params.anom_map_type == 'residual':
                map_types.append('anom_residual')
            else:
                map_types.append('anom')
            map_keys.append('anom')
        if self.use_svm:
            map_types.append('mFo')
            map_keys.append('mFo')
        sites_frac = self.xray_structure.sites_frac()
        sites_cart = self.xray_structure.sites_cart()
        self._principal_axes_of_inertia = [None] * len(sites_frac)
        self._map_variances = [None] * len(sites_frac)
        self._map_gaussian_fits = {}
        self.calpha_mean_two_fofc = 0
        for map_type, map_key in zip(map_types, map_keys):
            real_map = self.get_map(map_type)
            if real_map is not None:
                self._map_values[map_key] = flex.double(sites_frac.size(), 0)
                self._map_gaussian_fits[map_key] = [None] * len(sites_frac)
                for i_seq, site_frac in enumerate(sites_frac):
                    atom = self.pdb_atoms[i_seq]
                    resname = atom.fetch_labels().resname.strip().upper()
                    if (resname in WATER_RES_NAMES + mmtbx.ions.SUPPORTED or
                        atom.segid.strip().upper() in ['ION']):
                        value = real_map.eight_point_interpolation(site_frac)
                        self._map_values[map_key][i_seq] = value
                        if self.use_svm:
                            gaussian_fit = utils.fit_gaussian(unit_cell=
                                self.unit_cell, site_cart=atom.xyz,
                                real_map=real_map)
                            self._map_gaussian_fits[map_key][i_seq
                                ] = gaussian_fit
                if map_type in ['2mFo-DFc']:
                    from cctbx import maptbx
                    for i_seq, site_cart in enumerate(sites_cart):
                        resname = self.pdb_atoms[i_seq].fetch_labels(
                            ).resname.strip()
                        if resname in WATER_RES_NAMES + mmtbx.ions.SUPPORTED:
                            p_a_i = maptbx.principal_axes_of_inertia(real_map
                                =real_map, site_cart=site_cart, unit_cell=
                                self.unit_cell, radius=self.params.
                                map_sampling_radius)
                            self._principal_axes_of_inertia[i_seq] = p_a_i
                            variance = maptbx.spherical_variance_around_point(
                                real_map=real_map, unit_cell=self.unit_cell,
                                site_cart=site_cart, radius=self.params.
                                map_sampling_radius)
                            self._map_variances[i_seq] = variance
                        elif i_seq in self.calpha_sel:
                            self.calpha_mean_two_fofc += (real_map.
                                eight_point_interpolation(sites_frac[i_seq]))
                del real_map
        if self.calpha_mean_two_fofc > 0:
            n_calpha = len(self.calpha_sel)
            assert n_calpha > 0
            self.calpha_mean_two_fofc /= n_calpha
        self.carbon_fo_values = None
        if len(self.carbon_sel) > 0:
            self.carbon_fo_values = flex.double()
            self._map_values['mFo'] = flex.double(sites_frac.size(), 0)
            fo_map = fft_map(self.fmodel.map_coefficients(map_type='mFo',
                exclude_free_r_reflections=True, fill_missing=True))
            for i_seq, site_frac in enumerate(sites_frac):
                resname = self.pdb_atoms[i_seq].fetch_labels().resname.strip()
                element = self.pdb_atoms[i_seq].element.strip()
                if (element == 'C' or element == 'O' and resname in
                    WATER_RES_NAMES):
                    map_value = fo_map.eight_point_interpolation(site_frac)
                    self._map_values['mFo'][i_seq] = map_value
                    if element == 'C':
                        self.carbon_fo_values.append(map_value)
            del fo_map

    def show_current_scattering_statistics(self, out=sys.stdout):
        """
    Prints out information about an entire model's scattering statistics, such
    as mean map heights and b-factors for carbons and waters.

    Parameters
    ----------
    out : file, optional
    """
        print('', file=out)
        print('Model and map statistics:', file=out)
        print('  mean mFo map height @ carbon: %s' % format_value('%.2f',
            flex.max(self.carbon_fo_values)), file=out)
        if self.calpha_mean_two_fofc > 0:
            print('  mean 2mFo-DFc map height @ C-alpha: %s' % format_value
                ('%.2f', self.calpha_mean_two_fofc), file=out)
        print('  mean B-factor: %s' % format_value('%.2f', self.b_mean_all),
            file=out)
        if self.b_mean_calpha > 0:
            print('  mean C-alpha B-factor: %s' % format_value('%.2f', self
                .b_mean_calpha), file=out)
        print('  mean water B-factor: %s' % format_value('%.2f', self.
            b_mean_hoh), file=out)
        n_water_fofc_peaks = 0
        n_water_anom_peaks = 0
        water_sel = self.water_selection()
        print('  %d water molecules' % len(water_sel), file=out)
        for i_seq in water_sel:
            map_stats = self.map_stats(i_seq)
            if map_stats.fofc >= 3.0:
                n_water_fofc_peaks += 1
            if map_stats.anom is not None and map_stats.anom >= 3.0:
                n_water_anom_peaks += 1
        print('  %d waters have mFo-DFc map >= 3.0 sigma' %
            n_water_fofc_peaks, file=out)
        if self.anomalous_flag:
            print('  %d waters have anomalous map >= 3.0 sigma' %
                n_water_anom_peaks, file=out)
        print('', file=out)
    <mask token>

    def find_nearby_atoms(self, i_seq, far_distance_cutoff=3.0,
        near_distance_cutoff=1.5, filter_by_bonding=True,
        filter_by_two_fofc=True):
        """
    Given site in the structure, return a list of contacts, optionally
    filtering by connectivity and 2mFo-DFc map level.

    Parameters
    ----------
    i_seq : int
    far_distance_cutoff : float, optional
    near_distance_cutoff : float, optional
    filter_by_bonding : bool, optional
    fitler_by_two_fofc : bool, optional

    Returns
    -------
    list of mmtbx.ions.environment.atom_contact
    """
        assert i_seq < len(self.sites_frac)
        pair_asu_table, asu_mappings, asu_table = self._pair_asu_cache.get(
            far_distance_cutoff, (None, None, None))
        if pair_asu_table is None:
            pair_asu_table = self.xray_structure.pair_asu_table(distance_cutoff
                =far_distance_cutoff)
            asu_mappings = pair_asu_table.asu_mappings()
            asu_table = pair_asu_table.table()
            self._pair_asu_cache[far_distance_cutoff
                ] = pair_asu_table, asu_mappings, asu_table
        contacts = environment.find_nearby_atoms(i_seq=i_seq,
            xray_structure=self.xray_structure, pdb_atoms=self.pdb_atoms,
            asu_mappings=asu_mappings, asu_table=asu_table, connectivity=
            self.connectivity, far_distance_cutoff=far_distance_cutoff,
            near_distance_cutoff=near_distance_cutoff, filter_by_bonding=
            filter_by_bonding)
        if filter_by_two_fofc:
            filtered = []
            for contact in contacts:
                if contact.resname() in WATER_RES_NAMES:
                    two_fofc = self._map_values['2mFo-DFc'][contact.
                        atom_i_seq()]
                    if two_fofc < self.params.water.min_2fofc_coordinating:
                        continue
                filtered.append(contact)
            contacts = filtered
        return contacts

    def guess_b_iso_real(self, i_seq):
        """
    Guess an approximate B_iso for an atom by averaging the values for
    coordinating atoms.  This should partially compensate for the deflation of
    the B-factor due to incorrect scattering type, and consequently make the
    occupancy refinement more accurate.

    Parameters
    ----------
    i_seq : int

    Returns
    -------
    float
    """
        contacts = self.find_nearby_atoms(i_seq, far_distance_cutoff=3.0)
        if len(contacts) == 0:
            return adptbx.u_as_b(self.u_iso_all[i_seq])
        u_iso_sum = 0
        for contact in contacts:
            u_iso_sum += self.u_iso_all[contact.atom_i_seq()]
        return adptbx.u_as_b(u_iso_sum / len(contacts))

    def principal_axes_of_inertia(self, i_seq):
        """
    Extracts the map grid points around a site, and calculates the axes of
    inertia (using the density values as weights). This is used to calculate
    the sphericity of the blob of density around a site.

    Parameters
    ----------
    i_seq : int

    Returns
    -------
    ...
    """
        return self._principal_axes_of_inertia[i_seq]

    def get_map_sphere_variance(self, i_seq):
        """
    Calculate the density levels for points on a sphere around a given point.
    This will give us some indication whether the density falls off in the
    expected manner around a single atom.

    Parameters
    ----------
    i_seq : int

    Returns
    -------
    float
    """
        return self._map_variances[i_seq]

    def get_b_iso(self, i_seq):
        """
    Calculates the isotropic b-factor of a site.

    Parameters
    ----------
    i_seq : int

    Returns
    -------
    float
    """
        sc = self.xray_structure.scatterers()[i_seq]
        return adptbx.u_as_b(sc.u_iso_or_equiv(self.unit_cell))

    def map_stats(self, i_seq):
        """
    Given a site in the structure, find the signal of the 2FoFc, FoFc, and
    anomalous map (When available).

    Parameters
    ----------
    i_seq : int

    Returns
    -------
    group_args
        Object with .two_fofc, .fofc, and .anom properties for the associated
        signals in each map.
    """
        value_2fofc = self._map_values['2mFo-DFc'][i_seq]
        value_fofc = self._map_values['mFo-DFc'][i_seq]
        value_anom = None
        if 'anom' in self._map_values:
            value_anom = self._map_values['anom'][i_seq]
        return group_args(two_fofc=value_2fofc, fofc=value_fofc, anom=
            value_anom)

    def get_map_gaussian_fit(self, map_type, i_seq):
        """
    Retrieves the two parameters of a gaussian function, fit to a given map at
    a site.

    Parameters
    ----------
    map_type : str
    i_seq : int

    Returns
    -------
    (tuple of float, float) or None
    """
        map_gaussians = self._map_gaussian_fits.get(map_type, None)
        if map_gaussians is not None:
            return map_gaussians[i_seq]
        return None
    <mask token>

    def _find_atoms_near_site(self, site_cart, distance_cutoff=1.5,
        distance_cutoff_same_site=0.5):
        """
    Given an XYZ coordinate, finds atoms near that site, separating out those
    which are close enough to be essentially equivalent.  Used to analyze the
    anomalously-scattering substructure calculated by Phaser.

    Parameters
    ----------
    site_cart : tuple of float, float, float
    distance_cutoff : float, optional
    distance_cutoff_same_site : float, optional

    Returns
    -------
    same_atoms : list of group_args
        List of atoms within distance_cutoff_same_site of site_cart, and and
        other_atoms, the rest of the atoms within distance_cutoff of site_cart.
    """
        site_frac = self.unit_cell.fractionalize(site_cart=site_cart)
        sites_frac = flex.vec3_double([site_frac])
        asu_mappings = self.xray_structure.asu_mappings(buffer_thickness=
            distance_cutoff + 0.1)
        asu_mappings.process_sites_frac(sites_frac, min_distance_sym_equiv=
            self.xray_structure.min_distance_sym_equiv())
        pair_generator = crystal.neighbors_fast_pair_generator(asu_mappings
            =asu_mappings, distance_cutoff=distance_cutoff)
        n_xray = self.xray_structure.scatterers().size()
        same_atoms = []
        other_atoms = []
        for pair in pair_generator:
            if (pair.i_seq < n_xray and pair.j_seq < n_xray or pair.i_seq >=
                n_xray and pair.j_seq >= n_xray):
                continue
            rt_mx_i = asu_mappings.get_rt_mx_i(pair)
            rt_mx_j = asu_mappings.get_rt_mx_j(pair)
            rt_mx_ji = rt_mx_i.inverse().multiply(rt_mx_j)
            new_site_frac = rt_mx_ji * site_frac
            new_site_cart = self.unit_cell.orthogonalize(site_frac=
                new_site_frac)
            atom_seq = pair.i_seq if pair.i_seq < n_xray else pair.j_seq
            site_info = group_args(i_seq=atom_seq, rt_mx=rt_mx_ji,
                new_site_cart=new_site_cart, distance=sqrt(pair.dist_sq))
            if site_info.distance < distance_cutoff_same_site:
                same_atoms.append(site_info)
            else:
                other_atoms.append(site_info)
        same_atoms.sort(key=lambda x: x.distance)
        other_atoms.sort(key=lambda x: x.distance)
        return same_atoms, other_atoms
    <mask token>

    def get_fpp(self, i_seq):
        """
    Retrieve the refined f'' for a site.  Because this can come from either the
    built-in anomalous refinement or Phaser, it is handled differently than
    the f' retrieval.

    Parameters
    ----------
    i_seq : int

    Returns
    -------
    float or None
    """
        if not self.use_fdp[i_seq]:
            return None
        if self.fpp_from_phaser_ax_sites is None:
            return self.site_fdp[i_seq]
        fpp = self.fpp_from_phaser_ax_sites[i_seq]
        if fpp < 0:
            fpp = None
        return fpp

    def get_fp(self, i_seq):
        """
    Retrieve the refined f' for a site.

    Parameters
    ----------
    i_seq : int

    Returns
    -------
    float or None
    """
        if self.site_fp is None or not self.use_fdp[i_seq]:
            return None
        return self.site_fp[i_seq]
    <mask token>

    def analyze_water(self, i_seq, debug=True, candidates=Auto, no_final=
        False, out=sys.stdout):
        """
    Examines the environment around a single atom to determine if it is actually
    a misidentified metal ion.

    no_final is used internally, when candidates is not Auto, to try all of the
    Auto without selecting any as a final choice when none of the elements in
    candidates appear to probable ion identities.

    Parameters
    ----------
    i_seq : int
    debug : bool, optional
    candidates : list of str, optional
    no_file : bool, optional
    out : file, optional

    Returns
    -------
    mmtbx.ions.identify.water_result
    """
        atom = self.pdb_atoms[i_seq]
        auto_candidates = candidates is Auto
        if auto_candidates:
            candidates = mmtbx.ions.DEFAULT_IONS
        elif isinstance(candidates, string_types):
            candidates = candidates.replace(',', ' ').split()
        candidates = [i.strip().upper() for i in candidates]
        if candidates == ['X']:
            candidates = []
        resname = atom.fetch_labels().resname.strip().upper()
        assert resname in WATER_RES_NAMES
        atom_props = self.atoms_to_props[i_seq]
        final_choice = None
        atom_type = atom_props.get_atom_type(params=self.params.water,
            aggressive=self.params.aggressive)
        if atom_type == WATER_POOR:
            return None
        assert not self.use_svm
        filtered_candidates = []
        halide_candidates = False
        nuc_phosphate_site = atom_props.looks_like_nucleotide_phosphate_site()
        max_carbon_fo_map_value = sys.maxsize
        if self.carbon_fo_values is not None:
            max_carbon_fo_map_value = flex.max(self.carbon_fo_values)
        for symbol in candidates:
            elem = mmtbx.ions.server.get_metal_parameters(symbol)
            if elem is None:
                if symbol in mmtbx.ions.HALIDES:
                    halide_candidates = True
                    continue
                else:
                    raise Sorry("Element '%s' not supported!" % symbol)
            if atom_type in [WATER, LIGHT_ION] and symbol not in ['NA',
                'MG', 'F', 'NE']:
                continue
            if nuc_phosphate_site and not symbol in NUC_PHOSPHATE_BINDING:
                continue
            n_elec = sasaki.table(symbol.upper()).atomic_number() - elem.charge
            if n_elec <= 12 and (atom_props.fpp is not None and atom_props.
                fpp > 0.1):
                continue
            mass_ratio = atom_props.estimated_weight / max(n_elec, 1)
            if mass_ratio < 0.4 and atom_type == WATER:
                continue
            filtered_candidates.append(elem)

        def try_candidates(require_valence=True):
            """
      Parameters
      ----------
      require_valence : bool, optional

      Returns
      -------
      list of tuple of mmtbx.ions.metal_parameters, float
      list of tuple of mmtbx.ions.metal_parameters, float
      """
            reasonable = []
            unreasonable = []
            for elem_params in filtered_candidates:
                atom_props.check_ion_environment(ion_params=elem_params,
                    wavelength=self.wavelength, require_valence=require_valence
                    )
                atom_props.check_fpp_ratio(ion_params=elem_params,
                    wavelength=self.wavelength, fpp_ratio_min=self.params.
                    phaser.fpp_ratio_min, fpp_ratio_max=self.params.phaser.
                    fpp_ratio_max)
                identity = str(elem_params)
                if atom_props.is_correctly_identified(identity=identity):
                    reasonable.append((elem_params, atom_props.score[identity])
                        )
                else:
                    unreasonable.append((elem_params, atom_props.score[
                        identity]))
            return reasonable, unreasonable
        reasonable, unreasonable = try_candidates(require_valence=True)
        valence_used = True
        if len(reasonable) == 0 and not self.params.require_valence:
            reasonable, unreasonable = try_candidates(require_valence=False)
            if len(reasonable) > 0:
                valence_used = False
        looks_like_halide = False
        if not (reasonable or atom_type < HEAVY_ION or nuc_phosphate_site):
            candidate_halides = set(candidates).intersection(mmtbx.ions.HALIDES
                )
            filtered_halides = []
            for element in candidate_halides:
                fpp_ratio = atom_props.check_fpp_ratio(ion_params=mmtbx.
                    ions.metal_parameters(element=element, charge=-1),
                    wavelength=self.wavelength, fpp_ratio_min=self.params.
                    phaser.fpp_ratio_min, fpp_ratio_max=self.params.phaser.
                    fpp_ratio_max)
                if fpp_ratio is not None and element != 'CL':
                    if (fpp_ratio < self.params.phaser.fpp_ratio_min or 
                        fpp_ratio > self.params.phaser.fpp_ratio_max):
                        continue
                if self.looks_like_halide_ion(i_seq=i_seq, element=element):
                    filtered_halides.append(element)
            looks_like_halide = len(filtered_halides) > 0
            reasonable += [(mmtbx.ions.metal_parameters(element=halide,
                charge=-1), 0) for halide in filtered_halides]
        if len(reasonable) == 0 and not self.params.require_valence:
            compatible = [params for params in filtered_candidates if
                atom_props.has_compatible_ligands(str(params))]
            for ion_params in compatible:
                atomic_number = sasaki.table(ion_params.element).atomic_number(
                    )
                weight_ratio = 0
                if atom_props.estimated_weight is not None:
                    weight_ratio = atom_props.estimated_weight / (atomic_number
                         - ion_params.charge)
                if (ion_params.element in mmtbx.ions.TRANSITION_METALS and 
                    atom_type == HEAVY_ION and not auto_candidates and
                    atom_props.is_compatible_site(ion_params)):
                    n_good_res = atom_props.number_of_favored_ligand_residues(
                        ion_params, distance=2.7)
                    n_total_coord_atoms = (atom_props.
                        number_of_atoms_within_radius(2.8))
                    if n_good_res >= 1 and 2 <= n_total_coord_atoms <= 6:
                        reasonable.append((ion_params, 0))
                    else:
                        pass
                elif ion_params.element in ['K', 'CA'
                    ] and atom_type == HEAVY_ION and not auto_candidates and atom_props.is_compatible_site(
                    ion_params):
                    n_good_res = atom_props.number_of_favored_ligand_residues(
                        ion_params, distance=2.9, exclude_atoms=['O'])
                    n_bb_oxygen = atom_props.number_of_backbone_oxygens(
                        distance_cutoff=2.9)
                    n_total_coord_atoms = (atom_props.
                        number_of_atoms_within_radius(distance_cutoff=2.9))
                    if (n_good_res + n_bb_oxygen >= 2 and 
                        n_total_coord_atoms >= 4):
                        reasonable.append((ion_params, 0))
                elif atomic_number > 30 and atom_type == HEAVY_ION and (
                    weight_ratio > 0.5 and weight_ratio < 1.05 or 
                    atom_props.fp > 10
                    ) and not auto_candidates and atom_props.is_compatible_site(
                    ion_params, ignore_valence=not self.
                    get_strict_valence_flag()):
                    n_total_coord_atoms = (atom_props.
                        number_of_atoms_within_radius(2.8))
                    if n_total_coord_atoms >= 3:
                        reasonable.append((ion_params, 0))
                else:
                    pass
            if len(compatible) == 1 and not self.get_strict_valence_flag():
                inaccuracies = atom_props.inaccuracies[str(ion_params)]
                if (compatible[0] in atom_props.fpp_ratios and atom_props.
                    BAD_FPP not in inaccuracies and not inaccuracies.
                    intersection([atom_props.BAD_GEOMETRY, atom_props.
                    NO_GEOMETRY])):
                    _PRINT_DEBUG(atom.id_str())
                    _PRINT_DEBUG(filtered_candidates)
                    _PRINT_DEBUG(compatible)
        if len(reasonable) == 1:
            final_choice = reasonable[0][0]
        if not reasonable and not auto_candidates:
            result = self.analyze_water(i_seq=i_seq, debug=debug, no_final=
                True, out=out)
            if result.final_choice is not None:
                return result
        return water_result(atom_props=atom_props, filtered_candidates=
            filtered_candidates, matching_candidates=reasonable,
            rejected_candidates=unreasonable, nuc_phosphate_site=
            nuc_phosphate_site, atom_type=atom_type, looks_like_halide=
            looks_like_halide, ambiguous_valence_cutoff=self.params.
            ambiguous_valence_cutoff, valence_used=valence_used,
            final_choice=final_choice, wavelength=self.wavelength, no_final
            =no_final)

    def _extract_waters(self):
        model = self.pdb_hierarchy.only_model()
        water_i_seqs = []
        for chain in model.chains():
            for residue_group in chain.residue_groups():
                atom_groups = residue_group.atom_groups()
                if len(atom_groups) > 1:
                    continue
                for atom_group in atom_groups:
                    resname = atom_group.resname.strip().upper()
                    if resname in WATER_RES_NAMES:
                        atoms = atom_group.atoms()
                        if len(atoms) == 1:
                            water_i_seqs.append(atoms[0].i_seq)
        return water_i_seqs

    def analyze_waters(self, out=sys.stdout, debug=True, candidates=Auto):
        """
    Iterates through all of the waters in a model, examining the maps and local
    environment to check their identity and suggest likely ions where
    appropriate.

    Parameters
    ----------
    out : file, otpional
    debug : bool, optional
    candidates : list of str, optional

    Returns
    -------
    list of tuple of int, mmtbx.ions.metal_parameters
        i_seq and metal parameters which indicate that waters of i_seq would be
        better changed to that new metal identity.
    """
        waters = self.water_selection()
        print('', file=out)
        print('  %d waters to analyze' % len(waters), file=out)
        if len(waters) == 0:
            return
        nproc = easy_mp.get_processes(self.nproc)
        ions = []
        self.atoms_to_props = dict((i_seq, AtomProperties(i_seq, self)) for
            i_seq in waters)
        if nproc == 1:
            print('', file=out)
            for water_i_seq in waters:
                t1 = time.time()
                water_props = self.analyze_water(i_seq=water_i_seq, debug=
                    debug, candidates=candidates, out=out)
                if water_props is not None:
                    water_props.show_summary(out=out, debug=debug)
                    map_stats = self.map_stats(water_i_seq)
                    if (water_props.final_choice is not None and not
                        water_props.no_final):
                        ions.append((water_i_seq, [water_props.final_choice
                            ], map_stats.two_fofc))
                t2 = time.time()
        else:
            print('  Parallelizing across %d processes' % nproc, file=out)
            print('', file=out)
            analyze_water = _analyze_water_wrapper(manager=self, debug=
                debug, candidates=candidates, out=out)
            results = easy_mp.pool_map(fixed_func=analyze_water, args=
                waters, processes=nproc)
            for result in results:
                if result is None:
                    continue
                water_i_seq, final_choice, result_str = result
                if result_str is not None:
                    print(result_str, file=out)
                if final_choice is not None:
                    map_stats = self.map_stats(water_i_seq)
                    ions.append((water_i_seq, [final_choice], map_stats.
                        two_fofc))
        return sorted(ions, key=operator.itemgetter(2), reverse=True)
    <mask token>

    def validate_ions(self, out=sys.stdout, debug=True, segid=None):
        """
    Iterate over all the ions built into the model by this module and double
    check their correctness. Looks for tell-tale signs such as negative peaks
    in the mFo-DFc and anomalous difference maps, abnormal b-factor, and
    disallowed chemical environments.

    Prints out a table of bad ions and their information and returns a list of
    their sites' i_seqs.

    Parameters
    ----------
    out : file, optional
    debug : bool, optional
    segid : str, optional
        If present, only validate ions with this segid.
    """
        if segid is None:
            ions = []
            for model in self.pdb_hierarchy.models():
                for chain in model.chains():
                    for residue_group in chain.residue_groups():
                        for atom_group in residue_group.atom_groups():
                            if atom_group.resname.strip(
                                ) in mmtbx.ions.SUPPORTED:
                                atoms = atom_group.atoms()
                                assert len(atoms) == 1
                                for atom in atoms:
                                    ions += atom.i_seq,
        else:
            sel_cache = self.pdb_hierarchy.atom_selection_cache()
            ions = sel_cache.selection('segid {0}'.format(segid)).iselection()
        if len(ions) == 0:
            return
        ion_status = []
        self.atoms_to_props = dict((i_seq, AtomProperties(i_seq, self)) for
            i_seq in ions)
        for i_seq in ions:
            atom_props = self.validate_ion(i_seq=i_seq, debug=debug)
            ion_status.append((atom_props, atom_props.
                is_correctly_identified()))
        scatterers = self.xray_structure.scatterers()
        headers = ('atom', 'occ', 'b_iso', '2mFo-DFc', 'mFo-DFc', 'fp',
            'fdp', 'ratio', 'BVS', 'VECSUM')
        fmt = '%-15s %-5s  %-5s  %-8s  %-7s  %-5s  %-5s  %-5s %-5s  %-6s'
        box = framed_output(out, title='Validating new ions', width=80)
        print(fmt % headers, file=box)
        print(' ' + '-' * 75, file=box)
        for props, okay_flag in ion_status:
            i_seq = props.atom.i_seq
            sc = scatterers[i_seq]
            fp = fdp = None
            if sc.flags.use_fp_fdp:
                fp = sc.fp
                fdp = sc.fdp
            b_iso = adptbx.u_as_b(sc.u_iso_or_equiv(unit_cell=self.unit_cell))
            identity = props.identity()

            def ff(fs, val):
                return format_value(fs, val, replace_none_with='---')
            print(fmt % (props.atom.id_str(suppress_segid=True)[5:-1], ff(
                '%.2f', props.atom.occ), ff('%.2f', b_iso), ff('%.2f',
                props.peak_2fofc), ff('%.2f', props.peak_fofc), ff('%.2f',
                fp), ff('%.2f', fdp), ff('%.2f', None if self.wavelength is
                None else fdp / sasaki.table(mmtbx.ions.server.get_element(
                props.atom)).at_angstrom(self.wavelength).fdp()), ff('%.2f',
                props.valence_sum.get(identity)), ff('%.2f', props.
                vector_sum.get(identity))), file=box)
            if not okay_flag:
                print('\n'.join('!!! ' + props.error_strs[i] for i in props
                    .inaccuracies[identity]), file=box)
            print('', file=box)
        box.close()


class _analyze_water_wrapper(object):
    """
  Simple wrapper for calling manager.analyze_water with keyword arguments
  in a parallelized loop.  Because the water_result object is not pickle-able,
  we only return the final ion choice and summary string.
  """

    def __init__(self, manager, **kwds):
        self.manager = manager
        self.kwds = dict(kwds)

    def __call__(self, i_seq):
        try:
            result = self.manager.analyze_water(i_seq, **self.kwds)
            out = StringIO()
            if result is not None:
                result.show_summary(out=out, debug=self.kwds.get('debug', 
                    False))
            result_str = out.getvalue()
            if result_str == '':
                result_str = None
            final_choice = None
            if not getattr(result, 'no_final', False):
                final_choice = getattr(result, 'final_choice', None)
            return i_seq, final_choice, result_str
        except KeyboardInterrupt:
            return None, None, None


class _validate_ion_wrapper(object):
    """
  Simple wrapper for calling manager.validate_ion with keyword arguments
  in a parallelized loop.  Because the water_result object is not pickle-able,
  we only return the final ion choice and summary string.
  """

    def __init__(self, manager, **kwds):
        self.manager = manager
        self.kwds = dict(kwds)

    def __call__(self, i_seq):
        try:
            correct = self.manager.validate_ion(i_seq, **self.kwds)
            return i_seq, correct
        except KeyboardInterrupt:
            return None, None


class ion_result(validation.atom):
    """
  Class for validation results for an ion site. This includes information about
  both the atom modeled at that site as well as its chemical and scattering
  environment.
  """
    __slots__ = validation.atom.__slots__ + ['chem_env', 'scatter_env']

    def show(self, out=sys.stdout, prefix=''):
        pass

    def show_brief(self, out=sys.stdout, prefix=''):
        pass


class water_result(object):
    """
  Container for storing the results of manager.analyze_water for later display
  and retrieval.
  """

    def __init__(self, atom_props, filtered_candidates, matching_candidates,
        rejected_candidates, nuc_phosphate_site, atom_type,
        looks_like_halide, ambiguous_valence_cutoff, valence_used,
        final_choice, wavelength=None, no_final=False):
        adopt_init_args(self, locals())

    def show_summary(self, out=None, debug=False):
        """
    Prints out a summary of a site's chemical and scattering environment.

    Parameters
    ----------
    out : file, optional
    debug : bool, optional
    """
        if out is None:
            out = sys.stdout
        results = self.matching_candidates
        if len(results) > 0:
            self.atom_props.show_properties(identity='HOH', out=out)
            if self.nuc_phosphate_site:
                print('  appears to be nucleotide coordination site', file=out)
            if self.no_final:
                print('  Found potential ion%s outside of specified set:' %
                    ('s' if len(results) > 1 else ''), file=out)
            if self.final_choice is not None:
                elem_params, score = results[0]
                if elem_params.element not in mmtbx.ions.HALIDES:
                    self.atom_props.show_ion_results(identity=str(self.
                        final_choice), out=out, valence_used=self.
                        valence_used, confirmed=True)
                else:
                    print('  Probable anion:', str(elem_params), file=out)
                print('', file=out)
            elif len(results) > 1:
                below_cutoff = [elem_params for elem_params, score in
                    results if score < self.ambiguous_valence_cutoff]
                if len(below_cutoff) == 1:
                    elem_params = below_cutoff[0]
                    print('  ambigous results, best valence from %s' % str(
                        elem_params), file=out)
                    self.atom_props.show_ion_results(identity=str(
                        elem_params), out=out, valence_used=True)
                    print('', file=out)
                else:
                    ions = [str(i[0]) for i in sorted(results, key=lambda x:
                        x[1])]
                    print('  ambiguous results, could be %s' % ', '.join(
                        ions), file=out)
                    for elem_params, score in results:
                        self.atom_props.show_ion_results(identity=str(
                            elem_params), out=out)
                    print('', file=out)
        elif self.atom_type != WATER or self.nuc_phosphate_site:
            self.atom_props.show_properties(identity='HOH', out=out)
            if self.nuc_phosphate_site:
                print('  appears to be nucleotide coordination site', file=out)
            if self.looks_like_halide:
                print('  Probable cation: %s' % str(self.final_choice),
                    file=out)
                print('', file=out)
            else:
                if debug and len(self.filtered_candidates) > 0:
                    print('  insufficient data to identify atom', file=out)
                    possible = True
                    for params in self.filtered_candidates:
                        if self.atom_props.has_compatible_ligands(str(params)):
                            if possible:
                                print('  possible candidates:', file=out)
                                possible = False
                            self.atom_props.show_ion_results(identity=str(
                                params), out=out)
                        else:
                            print('  incompatible ligands for %s' % str(
                                params), file=out)
                print('', file=out)


class AtomProperties(object):
    """
  Collect physical attributes of an atom, including B, occupancy, and map
  statistics, and track those which are at odds with its chemical identity.
  """
    (LOW_B, HIGH_B, LOW_OCC, HIGH_OCC, NO_2FOFC_PEAK, FOFC_PEAK, FOFC_HOLE,
        ANOM_PEAK, NO_ANOM_PEAK, BAD_GEOMETRY, NO_GEOMETRY, BAD_VECTORS,
        BAD_VALENCES, TOO_FEW_NON_WATERS, TOO_FEW_COORD, TOO_MANY_COORD,
        LIKE_COORD, BAD_COORD_ATOM, BAD_FPP, BAD_COORD_RESIDUE,
        VERY_BAD_VALENCES, BAD_HALIDE, HIGH_2FOFC, COORDING_GEOMETRY,
        CLOSE_CONTACT) = range(25)
    error_strs = {LOW_B: 'Abnormally low b-factor', HIGH_B:
        'Abnormally high b-factor', LOW_OCC: 'Abnormally low occupancy',
        HIGH_OCC: 'Abnormally high occupancy', NO_2FOFC_PEAK:
        'No 2mFo-DFc map peak', FOFC_PEAK: 'Peak in mFo-DFc map', FOFC_HOLE:
        'Negative peak in dFo-mFc map', ANOM_PEAK:
        'Peak in the anomalous map', NO_ANOM_PEAK:
        'No peak in the anomalous map', BAD_GEOMETRY:
        'Unexpected geometry for coordinating atoms', NO_GEOMETRY:
        'No distinct geometry for coordinating atoms', BAD_VECTORS:
        'VECSUM above cutoff', BAD_VALENCES: 'BVS above or below cutoff',
        TOO_FEW_NON_WATERS: 'Too few non-water coordinating atoms',
        TOO_FEW_COORD: 'Too few coordinating atoms', TOO_MANY_COORD:
        'Too many coordinating atoms', LIKE_COORD:
        'Like charge coordinating like', BAD_COORD_ATOM:
        'Disallowed coordinating atom', BAD_FPP: "Bad refined f'' value",
        BAD_COORD_RESIDUE: 'Disallowed coordinating residue',
        VERY_BAD_VALENCES: 'BVS far above or below cutoff', BAD_HALIDE:
        'Bad halide site', HIGH_2FOFC: 'Unexpectedly high 2mFo-DFc value',
        COORDING_GEOMETRY:
        'No distinct geometry and coordinating another atom with distinct geometry'
        , CLOSE_CONTACT: 'Close contact to oxygen atom'}

    def __init__(self, i_seq, manager):
        self.i_seq = i_seq
        self.atom = manager.pdb_atoms[i_seq].fetch_labels()
        self.resname = self.atom.resname.strip().upper()
        self.d_min = manager.fmodel.f_obs().d_min()
        self.anomalous_flag = manager.fmodel.f_obs().anomalous_flag()
        self.strict_valence = manager.get_strict_valence_flag()
        nearby_atoms_unfiltered = manager.find_nearby_atoms(i_seq=i_seq,
            far_distance_cutoff=3.5)
        self.nearby_atoms = []
        nearby_atoms_no_alts = []
        for contact in nearby_atoms_unfiltered:
            if contact.element not in ['H', 'D']:
                self.nearby_atoms.append(contact)
                for other in nearby_atoms_no_alts:
                    if other == contact:
                        break
                else:
                    nearby_atoms_no_alts.append(contact)
        self.residue_counts = utils.count_coordinating_residues(self.
            nearby_atoms)
        self.geometries = find_coordination_geometry(nearby_atoms_no_alts)
        map_stats = manager.map_stats(i_seq)
        self.peak_2fofc = map_stats.two_fofc
        self.peak_fofc = map_stats.fofc
        self.peak_anom = map_stats.anom
        self.estimated_weight = manager.guess_molecular_weight(i_seq)
        self.b_iso = manager.get_b_iso(i_seq)
        self.b_mean_hoh = manager.b_mean_hoh
        self.b_stddev_hoh = manager.b_stddev_hoh
        self.b_mean_calpha = manager.b_mean_calpha
        self.calpha_mean_two_fofc = manager.calpha_mean_two_fofc
        self.inaccuracies = {}
        self.ignored = {}
        self.vectors = {}
        self.valence_sum = {}
        self.vector_sum = {}
        self.score = {}
        self.fpp_expected = {}
        self.expected_params = {}
        self.bad_coords = {}
        self.fpp = manager.get_fpp(i_seq)
        self.fpp_ratios = {}
        self.fp = manager.get_fp(i_seq)
        self.manager = manager

    def is_correctly_identified(self, identity=None):
        """
    Calculates whether factors indicate that the atom was correctly identified.

    Parameters
    ----------
    identity : mmtbx.ions.metal_parameters

    Returns
    -------
    bool
    """
        if identity is None:
            identity = self.identity()
        return len(self.inaccuracies[identity]) == 0

    def has_compatible_ligands(self, identity):
        """
    Indicates whether the coordinating atoms are of the allowed type (e.g.
    no N or S atoms coordinating CA, etc.) and residue (e.g. Ser is not an
    appropriate ligand for ZN).

    Parameters
    ----------
    identity : mmtbx.ions.metal_parameters

    Returns
    -------
    bool
    """
        return len(self.bad_coords[identity]
            ) == 0 and not self.BAD_COORD_RESIDUE in self.inaccuracies[identity
            ]

    def is_compatible_site(self, ion_params, require_anom=True,
        ignore_valence=False):
        """
    More minimal criteria for determining whether a site is chemically
    compatible, allowing for incomplete coordination shells.

    Parameters
    ----------
    ion_params : mmtbx.ions.metal_parameters
    require_anom : bool, optional
    ignore_valence : bool, optional

    Returns
    -------
    bool
    """
        inaccuracies = self.inaccuracies[str(ion_params)]
        anom_allowed = self.is_compatible_anomalous_scattering(ion_params
            ) or not require_anom
        return self.has_compatible_ligands(str(ion_params)
            ) and not self.TOO_MANY_COORD in inaccuracies and (ignore_valence
             or not self.VERY_BAD_VALENCES in inaccuracies
            ) and not self.BAD_COORD_RESIDUE in inaccuracies and anom_allowed

    def number_of_favored_ligand_residues(self, ion_params, distance=3.0,
        exclude_atoms=()):
        """
    Counts the number of preferred residues coordinating the atom.  Used for
    approximate detection of transition-metal binding sites.

    Parameters
    ----------
    ion_params : mmtbx.ions.metal_parameters
    distance : float, optional
    exclude_atoms : tuple of ...

    Returns
    -------
    int
    """
        n_res = 0
        resids = []
        for contact in self.nearby_atoms:
            if contact.atom_name() in exclude_atoms:
                continue
            if contact.distance() < distance:
                labels = contact.atom.fetch_labels()
                other_resname = contact.resname()
                other_resid = labels.chain_id + labels.resid()
                if (ion_params.allowed_coordinating_residues is not None and
                    other_resname in ion_params.
                    allowed_coordinating_residues and not other_resid in resids
                    ):
                    n_res += 1
                    resids.append(other_resid)
        return n_res

    def number_of_atoms_within_radius(self, distance_cutoff):
        """
    Counts the number of coordinating atoms within a given radius.

    Parameters
    ----------
    float

    Returns
    -------
    int
    """
        n_atoms = 0
        atom_ids = []
        for contact in self.nearby_atoms:
            other_id = contact.atom_id_no_altloc()
            if not other_id in atom_ids:
                if contact.distance() < distance_cutoff:
                    n_atoms += 1
                atom_ids.append(other_id)
        return n_atoms

    def number_of_backbone_oxygens(self, distance_cutoff=3.0):
        """
    Counts the number of backbone oxygens coordinating a site.

    Parameters
    ----------
    distance_cutoff : float, optional

    Returns
    -------
    int
    """
        n_bb_ox = 0
        for contact in self.nearby_atoms:
            if contact.atom_name() == 'O':
                if contact.distance() <= distance_cutoff:
                    if not contact.resname() in WATER_RES_NAMES:
                        n_bb_ox += 1
        return n_bb_ox

    def is_compatible_anomalous_scattering(self, ion_params):
        if ion_params.element.upper() in ['MG', 'NA']:
            return (self.fpp is None and self.peak_anom is not None and 
                self.peak_anom < 1.0)
        else:
            if self.fpp is None:
                return self.peak_anom is not None and self.peak_anom > 3.0
            identity = self.identity(ion=ion_params)
            if identity in self.fpp_ratios:
                return not self.BAD_FPP in self.inaccuracies[identity]
        return False

    def atom_weight(self, manager):
        """
    Evaluates whether factors indicate that the atom is lighter, heavier, or
    isoelectric to what it is currently identified as.

    Parameters
    ----------
    manager : mmtbx.ions.identify.manager

    Returns
    -------
    int
        -1 if lighter, 0 if isoelectronic, and 1 if heavier.
    """
        identity = 'HOH' if self.resname in WATER_RES_NAMES else self.identity(
            )
        if (identity == 'HOH' and self.atom.b > manager.b_mean_hoh -
            manager.b_stddev_hoh):
            return 0
        if self.is_correctly_identified(identity=identity):
            return 0
        if self.FOFC_PEAK in self.inaccuracies[identity] or self.atom.b < 1:
            return 1
        if self.FOFC_HOLE in self.inaccuracies[identity]:
            return -1
        return 0

    def check_ion_environment(self, ion_params, wavelength=None,
        require_valence=True):
        """
    Checks whether or not the specified ion satisfies the metal-coordination
    parameters, specified by ion_params, such as valence sum, geometry, etc.
    The criteria used here are quite strict, but many of the analyses are
    saved for later if we want to use looser critera.

    Parameters
    ----------
    ion_params : mmtbx.ions.metal_parameters
    wavelength : float, optional
    require_valence : bool, optional
    """
        from iotbx.pdb import common_residue_names_get_class as get_class
        identity = self.identity(ion_params)
        inaccuracies = self.inaccuracies[identity] = set()
        self.expected_params[identity] = ion_params
        ignored = self.ignored[identity] = set()
        strict_rules = require_valence or self.is_correctly_identified(identity
            ='HOH') or self.strict_valence or ion_params.element in ['NA', 'MG'
            ]
        n_closest = 0
        coord_atoms = []
        for i_pair, contact1 in enumerate(self.nearby_atoms):
            distance = contact1.distance()
            if distance < 3.0:
                for contact2 in self.nearby_atoms[i_pair + 1:]:
                    if contact1 == contact2 or contact1.distance_from(contact2
                        ) <= 0.3:
                        break
                else:
                    coord_atoms.append(contact1)
                    if distance < 2.7:
                        n_closest += 1
        if len(coord_atoms) < ion_params.coord_num_lower:
            inaccuracies.add(self.TOO_FEW_COORD)
        if n_closest > ion_params.coord_num_upper:
            inaccuracies.add(self.TOO_MANY_COORD)
        n_non_water = 0
        self.bad_coords[identity] = []
        for contact in self.nearby_atoms:
            other_name = contact.atom_name()
            other_resname = contact.resname()
            other_element = contact.element
            if not other_resname in WATER_RES_NAMES:
                n_non_water += 1
            else:
                continue
            if contact.distance() < 3.0:
                if (ion_params.allowed_coordinating_atoms is not None and 
                    other_element not in ion_params.allowed_coordinating_atoms
                    ):
                    self.bad_coords[identity].append(contact)
                    inaccuracies.add(self.BAD_COORD_ATOM)
                if get_class(other_resname) == 'common_amino_acid':
                    if other_name in ['C', 'N', 'O', 'CA', 'H', 'HA'] and (
                        ion_params.allowed_backbone_atoms is None or not 
                        other_name in ion_params.allowed_backbone_atoms):
                        if other_name == 'O' and contact.is_carboxy_terminus:
                            pass
                        else:
                            self.bad_coords[identity].append(contact)
                            inaccuracies.add(self.BAD_COORD_ATOM)
                    if ion_params.allowed_coordinating_residues is not None:
                        allowed = ion_params.allowed_coordinating_residues
                        if not other_resname in allowed and other_name not in [
                            'C', 'O', 'N', 'CA', 'OXT']:
                            self.bad_coords[identity].append(contact)
                            inaccuracies.add(self.BAD_COORD_RESIDUE)
                elif cmp(0, mmtbx.ions.server.get_charge(contact.atom)) == cmp(
                    0, ion_params.charge):
                    self.bad_coords[identity].append(contact)
                    inaccuracies.add(self.LIKE_COORD)
                elif ion_params.charge > 0 and other_element in ['N'
                    ] and other_resname in ['LYS', 'ARG', 'ASN', 'GLN']:
                    self.bad_coords[identity].append(contact)
                    inaccuracies.add(self.LIKE_COORD)
        if n_non_water < ion_params.min_coordinating_non_waters:
            inaccuracies.add(self.TOO_FEW_NON_WATERS)
        if ion_params.allowed_geometries and strict_rules:
            allowed = [(i[0] in ion_params.allowed_geometries) for i in
                self.geometries]
            if 'any' in ion_params.allowed_geometries:
                pass
            elif not self.geometries:
                if strict_rules:
                    inaccuracies.add(self.NO_GEOMETRY)
            elif not any(allowed):
                inaccuracies.add(self.BAD_GEOMETRY)
            else:
                strict_rules = False
        if self.geometries == []:
            for contact in self.nearby_atoms:
                o_atom = contact.atom
                if o_atom.i_seq in self.manager.atoms_to_props:
                    o_geometry = self.manager.atoms_to_props[o_atom.i_seq
                        ].geometries
                    if o_geometry != []:
                        inaccuracies.add(self.COORDING_GEOMETRY)
        vectors = mmtbx.ions.server.calculate_valences(ion_params, self.
            nearby_atoms)
        self.vectors[identity] = vectors
        self.valence_sum[identity] = sum([abs(i) for i in vectors])
        self.vector_sum[identity] = abs(sum(vectors, col((0, 0, 0))))
        if self.vector_sum[identity] > ion_params.vec_sum_cutoff:
            if strict_rules:
                inaccuracies.add(self.BAD_VECTORS)
            else:
                ignored.add(self.BAD_VECTORS)
        if self.valence_sum[identity
            ] < ion_params.cvbs_expected * 0.25 or self.valence_sum[identity
            ] > ion_params.cvbs_expected * 1.25:
            inaccuracies.add(self.VERY_BAD_VALENCES)
        elif self.valence_sum[identity
            ] < ion_params.cvbs_lower or self.valence_sum[identity
            ] > ion_params.cvbs_upper:
            if strict_rules:
                inaccuracies.add(self.BAD_VALENCES)
            else:
                ignored.add(self.BAD_VALENCES)
        self.score[identity] = abs(self.valence_sum[identity] - ion_params.
            cvbs_expected)

    def check_fpp_ratio(self, ion_params, wavelength, fpp_ratio_min=0.3,
        fpp_ratio_max=1.05):
        """
    Compare the refined and theoretical f'' values if available.

    Parameters
    ----------
    ion_params : mmtbx.ions.metal_parameters
    wavelength : float
    fpp_ratio_min : float, optional
    fpp_ratio_max : float, optional

    Returns
    -------
    float
        f'' / f''_expected
    """
        identity = str(ion_params)
        inaccuracies = self.inaccuracies.get(identity, None)
        if inaccuracies is None:
            inaccuracies = self.inaccuracies[identity] = set()
        if ion_params.element.upper() in ['MG', 'NA']:
            if (self.fpp is not None or self.peak_anom is not None and self
                .peak_anom > 1):
                inaccuracies.add(self.BAD_FPP)
        elif wavelength is not None and self.anomalous_flag:
            fpp_expected_sasaki = sasaki.table(ion_params.element).at_angstrom(
                wavelength).fdp()
            fpp_expected_henke = henke.table(ion_params.element).at_angstrom(
                wavelength).fdp()
            self.fpp_expected[identity] = max(fpp_expected_sasaki,
                fpp_expected_henke)
            if self.fpp is not None and self.fpp_expected[identity] != 0:
                self.fpp_ratios[identity] = self.fpp / self.fpp_expected[
                    identity]
                if self.fpp_ratios[identity
                    ] > fpp_ratio_max or self.fpp >= 0.2 and self.fpp_ratios[
                    identity] < fpp_ratio_min:
                    inaccuracies.add(self.BAD_FPP)
            elif self.fpp_expected[identity] > 0.75 and self.peak_anom < 2:
                inaccuracies.add(self.BAD_FPP)
        return self.fpp_ratios.get(identity)

    def show_properties(self, identity, out=sys.stdout):
        """
    Show atomic properties that are independent of the suspected identity.

    Parameters
    ----------
    identity : mmtbx.ions.metal_parameters
    out : file, optional
    """
        print('%s:' % self.atom.id_str(), file=out)
        b_flag = ''
        if self.LOW_B in self.inaccuracies[identity]:
            b_flag = ' <<<'
        elif self.HIGH_B in self.inaccuracies[identity]:
            b_flag = ' !!!'
        print('  B-factor:      %6.2f%s' % (self.atom.b, b_flag), file=out)
        occ_flag = ''
        if self.LOW_OCC in self.inaccuracies[identity]:
            occ_flag = ' !!!'
        elif self.HIGH_OCC in self.inaccuracies[identity]:
            occ_flag = ' <<<'
        print('  Occupancy:     %6.2f%s' % (self.atom.occ, occ_flag), file=out)
        twofofc_flag = ''
        if self.NO_2FOFC_PEAK in self.inaccuracies[identity]:
            twofofc_flag = ' !!!'
        elif self.HIGH_2FOFC in self.inaccuracies[identity]:
            twofofc_flag = ' <<<'
        print('  2mFo-DFc map:  %6.2f%s' % (self.peak_2fofc, twofofc_flag),
            file=out)
        fofc_flag = ''
        if self.FOFC_PEAK in self.inaccuracies[identity]:
            fofc_flag = ' <<<'
        elif self.FOFC_HOLE in self.inaccuracies[identity]:
            fofc_flag = ' !!!'
        print('  mFo-DFc map:   %6.2f%s' % (self.peak_fofc, fofc_flag),
            file=out)
        if self.peak_anom is not None:
            anom_flag = ''
            if self.ANOM_PEAK in self.inaccuracies[identity]:
                anom_flag = ' <<<'
            elif self.NO_ANOM_PEAK in self.inaccuracies[identity]:
                anom_flag = ' !!!'
            print('  Anomalous map: %6.2f%s' % (self.peak_anom, anom_flag),
                file=out)
        if self.estimated_weight is not None:
            print('  Approx. mass:  %6d' % self.estimated_weight, file=out)
        if self.fpp is not None:
            fpp_flag = ''
            if self.fpp >= 0.2:
                fpp_flag = ' <<<'
            print("  f'':           %6.2f%s" % (self.fpp, fpp_flag), file=out)
            print("  f'' ratio:     %s" % format_value('%6.2f', self.
                fpp_ratios.get(identity)), file=out)
        if self.nearby_atoms is not None:
            angstrom = u'Å'.encode('utf-8', 'strict')
            degree = u'°'.encode('utf-8', 'strict')
            print('  Nearby atoms: (%d within 3.0 %s)' % (len([i for i in
                self.nearby_atoms if i.distance() < 3]), angstrom), file=out)
            for contact in self.nearby_atoms:
                print('    %s (%5.3f %s)' % (contact.id_str(), contact.
                    distance(), angstrom), file=out)
            if self.geometries:
                print('  Coordinating geometry:', file=out)
                for geometry, deviation in self.geometries:
                    print('    %-15s (average deviation: %.3f%s)' % (
                        geometry, deviation, degree), file=out)

    def show_ion_results(self, identity=None, out=sys.stdout, confirmed=
        False, valence_used=True):
        """
    Show statistics for a proposed element identity.

    Parameters
    ----------
    identity : mmtbx.ions.metal_parameters
    out : file, optional
    confirmed : bool, optional
    valence_used : bool, optional
    """
        if not identity:
            identity = self.identity(self.atom)
        inaccuracies = self.inaccuracies.get(identity, set([]))
        ignored = self.ignored.get(identity, set([]))
        if identity != self.identity():
            if confirmed:
                print('  Probable cation: %s' % identity, file=out)
            else:
                print('  Atom as %s:' % identity, file=out)
        else:
            print('    %s:' % self.atom.id_str(), file=out)
        if identity in self.vector_sum and self.vector_sum[identity
            ] is not None:
            problem = (self.BAD_VECTORS in inaccuracies or self.BAD_VECTORS in
                ignored)
            print('    Vector sum:  %6.3f %s' % (self.vector_sum[identity],
                ' !!!' if problem else ''), file=out)
        if identity in self.valence_sum and self.valence_sum[identity
            ] is not None:
            problem = inaccuracies.union(ignored).intersection([self.
                BAD_VALENCES, self.VERY_BAD_VALENCES])
            print('    Valence sum: %6.3f' % self.valence_sum[identity],
                file=out)
            if valence_used:
                print('(expected: %6.3f) %s' % (self.expected_params[
                    identity].cvbs_expected, ' !!!' if problem else ''),
                    end=' ', file=out)
        if self.NO_GEOMETRY in inaccuracies:
            print('    No distinct geometry !!!', file=out)
        if self.BAD_GEOMETRY in inaccuracies:
            print('    Unexpected geometry  !!!', file=out)
        bad_coord = [self.LIKE_COORD, self.BAD_COORD_ATOM, self.
            BAD_COORD_RESIDUE]
        if inaccuracies.intersection(bad_coord):
            print('    Bad coordinating atom%s:' % ('s' if len(self.
                bad_coords[identity]) != 1 else ''), file=out)
            angstrom = u'Å'.encode('utf-8', 'strict').strip()
            for atom, vector in self.bad_coords[identity]:
                print('    %s (%5.3f %s) !!!' % (atom.id_str(), abs(vector),
                    angstrom), file=out)
        if self.TOO_FEW_NON_WATERS in inaccuracies:
            print('    Too few coordinating non-waters !!!', file=out)
        if self.TOO_FEW_COORD in inaccuracies:
            print('    Too few coordinating atoms !!!', file=out)
        if self.TOO_MANY_COORD in inaccuracies:
            print('    Too many coordinating atoms !!!', file=out)
        if self.fpp is not None and identity in self.fpp_ratios:
            print("    f'' ratio:   %6.3f%s" % (self.fpp_ratios[identity], 
                ' !!!' if self.BAD_FPP in inaccuracies else ''), file=out)

    def looks_like_nucleotide_phosphate_site(self,
        min_phosphate_oxygen_atoms=2, distance_cutoff=2.5):
        """
    Decide whether the atom is coordinating phosphate oxygens from a
    nucleotide, based on common atom names.

    Parameters
    ----------
    min_phosphate_oxygen_atoms : int, optional
    distance_cutoff : float, optional

    Returns
    -------
    bool
    """
        n_phosphate_oxygens = 0
        for contact in self.nearby_atoms:
            atom_name = contact.atom_name()
            if len(atom_name) < 3 or contact.element not in ['O']:
                continue
            if atom_name[0:2] in ['O1', 'O2', 'O3'] and atom_name[2] in ['A',
                'B', 'G']:
                if contact.distance() <= distance_cutoff:
                    n_phosphate_oxygens += 1
        return n_phosphate_oxygens == min_phosphate_oxygen_atoms

    def identity(self, ion=None):
        """
    Covers an atom into a string representing its element and charge.

    Parameters
    ----------
    ion : iotbx.pdb.hierarchy.atom, optional

    Returns
    -------
    str
    """
        if ion is None:
            ion = self.atom
        element = mmtbx.ions.server.get_element(ion)
        charge = mmtbx.ions.server.get_charge(ion)
        return '{}{:+}'.format(element, charge)

    def get_atom_type(self, params, aggressive=False):
        """
    Checks the atom characteristics against what we would expect for a water.
    Updates self with any inaccuracies noticed (Surpringly low b-factor,
    high occupancy, etc).  Note that HEAVY_ION does not necessarily rule out
    NA/MG, as these often have mFo-DFc peaks at high resolution.

    Parameters
    ----------
    params : libtbx.phil.scope_extract
    aggressive : bool, optional

    Returns
    -------
    int
        One of WATER, WATER_POOR, HEAVY_ION, LIGHT_ION
    """
        inaccuracies = self.inaccuracies['HOH'] = set()
        atom_type = WATER
        if self.peak_2fofc < params.min_2fofc_level or self.peak_fofc < -2.0:
            return WATER_POOR
        if self.fpp is not None and self.fpp > params.fpp_max:
            return HEAVY_ION
        if self.fp is not None and self.fp > params.fp_max:
            return HEAVY_ION
        if (self.peak_anom is not None and self.peak_anom > params.
            max_anom_level):
            inaccuracies.add(self.ANOM_PEAK)
            atom_type = HEAVY_ION
        if self.peak_fofc > params.max_fofc_level:
            inaccuracies.add(self.FOFC_PEAK)
            atom_type = HEAVY_ION
        if self.atom.occ > params.max_occ:
            inaccuracies.add(self.HIGH_OCC)
            atom_type = HEAVY_ION
        if self.atom.b < params.min_b_iso:
            inaccuracies.add(self.LOW_B)
            atom_type = HEAVY_ION
        elif self.b_stddev_hoh is not None and self.b_stddev_hoh > 0:
            z_value = (self.b_iso - self.b_mean_hoh) / self.b_stddev_hoh
            if z_value < -params.max_stddev_b_iso:
                inaccuracies.add(self.LOW_B)
            elif self.atom.b < self.b_mean_hoh * params.min_frac_b_iso:
                inaccuracies.add(self.LOW_B)
            if atom_type == WATER and self.LOW_B in inaccuracies:
                atom_type = LIGHT_ION
        if aggressive and atom_type == WATER:
            if self.b_mean_calpha > 0:
                relative_b = self.b_iso / self.b_mean_calpha
                if relative_b < params.min_frac_calpha_b_iso:
                    inaccuracies.add(self.LOW_B)
                    atom_type = LIGHT_ION
            if self.calpha_mean_two_fofc > 0:
                relative_2fofc = self.peak_2fofc / self.calpha_mean_two_fofc
                if relative_2fofc > params.max_frac_calpha_2fofc:
                    inaccuracies.add(self.HIGH_2FOFC)
                    atom_type = LIGHT_ION
        for i_pair, contact1 in enumerate(self.nearby_atoms):
            if contact1.element.strip() == 'O':
                distance = contact1.distance()
                if distance < 2.4:
                    inaccuracies.add(self.CLOSE_CONTACT)
                    if atom_type < LIGHT_ION:
                        atom_type = LIGHT_ION
        return atom_type


<mask token>

<mask token>


def ion_master_phil():
    return libtbx.phil.parse(ion_identification_phil_str, process_includes=True
        )


<mask token>


class manager(object):
    """
  Wrapper object for extracting local environment and adding or modifying
  scatterers.
  """

    def __init__(self, fmodel, pdb_hierarchy, xray_structure, params=None,
        wavelength=None, connectivity=None, nproc=1, verbose=False, log=None):
        if log is None:
            log = null_out()
        self.fmodel = fmodel
        self.atoms_to_props = {}
        self.params = params
        self.wavelength = wavelength
        self.nproc = nproc
        self.phaser_substructure = None
        self.flag_refine_substructure = False
        self.fpp_from_phaser_ax_sites = None
        self.site_fp = None
        self.site_fdp = None
        self.use_svm = getattr(params, 'use_svm', None)
        self._map_values = {}
        self._map_gaussian_fits = {}
        self.anomalous_flag = None
        if fmodel is not None:
            self.anomalous_flag = fmodel.f_obs().anomalous_flag()
        self.update_structure(pdb_hierarchy=pdb_hierarchy, xray_structure=
            xray_structure, connectivity=connectivity, log=log)
        self.update_maps()
        refine_substructure = False
        if params is not None:
            refine_substructure = params.find_anomalous_substructure
        if refine_substructure is Auto:
            refine_substructure = fmodel.f_obs().anomalous_flag(
                ) and wavelength is not None
        if refine_substructure:
            if wavelength is None:
                raise Sorry('Wavelength required when ' +
                    'find_anomalous_substructure=True.')
            elif not fmodel.f_obs().anomalous_flag():
                raise Sorry('Anomalous data required when ' +
                    'find_anomalous_substructure=True.')
            if params.use_phaser and libtbx.env.has_module('phaser'):
                print('  Running Phaser substructure completion...', file=log)
                t1 = time.time()
                phaser_result = find_anomalous_scatterers(fmodel=fmodel,
                    pdb_hierarchy=pdb_hierarchy, wavelength=wavelength,
                    verbose=verbose, log=log, n_cycles=params.phaser.
                    llgc_ncycles)
                t2 = time.time()
                print('    time: %.1fs' % (t2 - t1), file=log)
                if phaser_result is None:
                    print('  ERROR: Phaser substructure completion failed!',
                        file=log)
                else:
                    self.phaser_substructure = phaser_result.atoms()
                    if len(self.phaser_substructure) == 0:
                        print('  No anomalous scatterers found!', file=log)
                    else:
                        print('  %d anomalous scatterers found' % len(self.
                            phaser_substructure), file=log)
                        self.analyze_substructure(log=log, verbose=True)
            else:
                self.flag_refine_substructure = True
                self.refine_anomalous_substructure(log=log)

    def water_selection(self):
        """
    Fetchs the selection for all waters in the model.

    Returns
    -------
    scitbx.array_family.flex.size_t
    """
        sel_cache = self.pdb_hierarchy.atom_selection_cache()
        sel_str = "({}) and element O and altloc ' '".format(' or '.join([(
            'resname ' + i) for i in WATER_RES_NAMES]))
        return sel_cache.selection(sel_str).iselection()

    def refine_anomalous_substructure(self, log):
        """
    Run simple "substructure completion" implemented using CCTBX tools (the
    command-line equivalent is mmtbx.refine_anomalous_substructure).  A faster
    alternative to Phaser, but not clear how effective this is at the moment.

    Parameters
    ----------
    log : file
    """
        from mmtbx.refinement import anomalous_scatterer_groups
        fmodel_tmp = self.fmodel.deep_copy()
        anom_groups = anomalous_scatterer_groups.refine_anomalous_substructure(
            fmodel=fmodel_tmp, pdb_hierarchy=self.pdb_hierarchy, wavelength
            =self.wavelength, reset_water_u_iso=True, verbose=True,
            use_all_anomalous=True, out=log)
        scatterers = fmodel_tmp.xray_structure.scatterers()
        self.use_fdp = flex.bool(scatterers.size(), False)
        self.site_fp = flex.double(scatterers.size(), 0)
        self.site_fdp = flex.double(scatterers.size(), 0)
        for group in anom_groups:
            for i_seq in group.iselection:
                sc = scatterers[i_seq]
                self.use_fdp[i_seq] = True
                self.site_fp[i_seq] = sc.fp
                self.site_fdp[i_seq] = sc.fdp

    def update_structure(self, pdb_hierarchy, xray_structure, connectivity=
        None, log=None, refine_if_necessary=True):
        """
    Set the current atomic data: PDB hierarchy, Xray structure, and simple
    connectivity list.

    Parameters
    ----------
    pdb_hierarchy : iotbx.pdb.hierarchy.root
    xray_structure : cctbx.xray.structure.structure
    connectivity : ...
    log : file, optional
    refine_if_necessary : bool, optional
    """
        self.pdb_hierarchy = pdb_hierarchy
        self.xray_structure = xray_structure
        if self.fmodel:
            self.fmodel.update_xray_structure(xray_structure, update_f_calc
                =True)
        self.sites_frac = self.xray_structure.sites_frac()
        self.sites_cart = self.xray_structure.sites_cart()
        self.connectivity = connectivity
        self.pdb_atoms = pdb_hierarchy.atoms()
        assert not self.pdb_atoms.extract_i_seq().all_eq(0)
        self.unit_cell = xray_structure.unit_cell()
        self.use_fdp = flex.bool(xray_structure.scatterers().size(), False)
        self.ax_chain = None
        self._pair_asu_cache = {}
        self.u_iso_all = xray_structure.extract_u_iso_or_u_equiv()
        sctr_keys = xray_structure.scattering_type_registry().type_count_dict()
        self.hd_present = 'H' in sctr_keys or 'D' in sctr_keys
        sel_cache = pdb_hierarchy.atom_selection_cache()
        self.carbon_sel = sel_cache.selection('element C').iselection()
        self.calpha_sel = sel_cache.selection('name CA and element C'
            ).iselection()
        assert len(self.carbon_sel) > 0
        water_sel = self.water_selection()
        self.n_waters = len(water_sel)
        not_hd_sel = sel_cache.selection('not (element H or element D)'
            ).iselection()
        self.n_heavy = len(not_hd_sel)
        u_iso_all_tmp = self.u_iso_all.select(not_hd_sel)
        self.b_mean_all = adptbx.u_as_b(flex.mean(u_iso_all_tmp))
        self.b_stddev_all = adptbx.u_as_b(u_iso_all_tmp.
            standard_deviation_of_the_sample())
        self.b_mean_calpha = 0
        if len(self.calpha_sel) > 0:
            self.b_mean_calpha = adptbx.u_as_b(flex.mean(self.u_iso_all.
                select(self.calpha_sel)))
        self.b_mean_hoh = self.b_stddev_hoh = None
        if self.n_waters > 0:
            u_iso_hoh = self.u_iso_all.select(water_sel)
            self.b_mean_hoh = adptbx.u_as_b(flex.mean(u_iso_hoh))
            self.b_stddev_hoh = adptbx.u_as_b(u_iso_hoh.
                standard_deviation_of_the_sample())
        if self.phaser_substructure is not None:
            self.analyze_substructure(log=log)
        elif self.flag_refine_substructure and refine_if_necessary:
            self.refine_anomalous_substructure(log=log)

    def get_initial_b_iso(self):
        """
    Calculates the isotropic b-factor to assign to newly labeled ions during the
    building process. Uses either the mean b-factor of waters or of all atoms if
    the former is unavailable.

    Returns
    -------
    float
    """
        if getattr(self, 'b_mean_hoh', None) is not None:
            return self.b_mean_hoh
        else:
            return self.b_mean_all

    def get_map(self, map_type):
        """
    Creates the real-space version of a given map type.

    Parameters
    ----------
    map_type : str

    Returns
    -------
    scitbx.array_family.flex.double
    """
        map_coeffs = self.fmodel.map_coefficients(map_type=map_type,
            exclude_free_r_reflections=True, fill_missing=True,
            pdb_hierarchy=self.pdb_hierarchy)
        if map_coeffs is None:
            return None
        return map_coeffs.fft_map(resolution_factor=0.25).apply_sigma_scaling(
            ).real_map_unpadded()

    def update_maps(self):
        """
    Generate new maps for the current structure, including anomalous map if
    data are anomalous.

    If Phaser is installed and anomalous data are available, the anomalous
    log-likelihood gradient (LLG) map can be used instead of the conventional
    anomalous difference map. This is only really useful if the anomalous
    scattering of existing atoms is modeled (and ideally, refined).
    """
        if self.fmodel is None:
            return

        def fft_map(map_coeffs, resolution_factor=0.25):
            return map_coeffs.fft_map(resolution_factor=resolution_factor
                ).apply_sigma_scaling().real_map_unpadded()
        map_types = ['2mFo-DFc', 'mFo-DFc']
        map_keys = ['2mFo-DFc', 'mFo-DFc']
        if self.fmodel.f_obs().anomalous_flag():
            if self.params.anom_map_type == 'phaser':
                map_types.append('llg')
            elif self.params.anom_map_type == 'residual':
                map_types.append('anom_residual')
            else:
                map_types.append('anom')
            map_keys.append('anom')
        if self.use_svm:
            map_types.append('mFo')
            map_keys.append('mFo')
        sites_frac = self.xray_structure.sites_frac()
        sites_cart = self.xray_structure.sites_cart()
        self._principal_axes_of_inertia = [None] * len(sites_frac)
        self._map_variances = [None] * len(sites_frac)
        self._map_gaussian_fits = {}
        self.calpha_mean_two_fofc = 0
        for map_type, map_key in zip(map_types, map_keys):
            real_map = self.get_map(map_type)
            if real_map is not None:
                self._map_values[map_key] = flex.double(sites_frac.size(), 0)
                self._map_gaussian_fits[map_key] = [None] * len(sites_frac)
                for i_seq, site_frac in enumerate(sites_frac):
                    atom = self.pdb_atoms[i_seq]
                    resname = atom.fetch_labels().resname.strip().upper()
                    if (resname in WATER_RES_NAMES + mmtbx.ions.SUPPORTED or
                        atom.segid.strip().upper() in ['ION']):
                        value = real_map.eight_point_interpolation(site_frac)
                        self._map_values[map_key][i_seq] = value
                        if self.use_svm:
                            gaussian_fit = utils.fit_gaussian(unit_cell=
                                self.unit_cell, site_cart=atom.xyz,
                                real_map=real_map)
                            self._map_gaussian_fits[map_key][i_seq
                                ] = gaussian_fit
                if map_type in ['2mFo-DFc']:
                    from cctbx import maptbx
                    for i_seq, site_cart in enumerate(sites_cart):
                        resname = self.pdb_atoms[i_seq].fetch_labels(
                            ).resname.strip()
                        if resname in WATER_RES_NAMES + mmtbx.ions.SUPPORTED:
                            p_a_i = maptbx.principal_axes_of_inertia(real_map
                                =real_map, site_cart=site_cart, unit_cell=
                                self.unit_cell, radius=self.params.
                                map_sampling_radius)
                            self._principal_axes_of_inertia[i_seq] = p_a_i
                            variance = maptbx.spherical_variance_around_point(
                                real_map=real_map, unit_cell=self.unit_cell,
                                site_cart=site_cart, radius=self.params.
                                map_sampling_radius)
                            self._map_variances[i_seq] = variance
                        elif i_seq in self.calpha_sel:
                            self.calpha_mean_two_fofc += (real_map.
                                eight_point_interpolation(sites_frac[i_seq]))
                del real_map
        if self.calpha_mean_two_fofc > 0:
            n_calpha = len(self.calpha_sel)
            assert n_calpha > 0
            self.calpha_mean_two_fofc /= n_calpha
        self.carbon_fo_values = None
        if len(self.carbon_sel) > 0:
            self.carbon_fo_values = flex.double()
            self._map_values['mFo'] = flex.double(sites_frac.size(), 0)
            fo_map = fft_map(self.fmodel.map_coefficients(map_type='mFo',
                exclude_free_r_reflections=True, fill_missing=True))
            for i_seq, site_frac in enumerate(sites_frac):
                resname = self.pdb_atoms[i_seq].fetch_labels().resname.strip()
                element = self.pdb_atoms[i_seq].element.strip()
                if (element == 'C' or element == 'O' and resname in
                    WATER_RES_NAMES):
                    map_value = fo_map.eight_point_interpolation(site_frac)
                    self._map_values['mFo'][i_seq] = map_value
                    if element == 'C':
                        self.carbon_fo_values.append(map_value)
            del fo_map

    def show_current_scattering_statistics(self, out=sys.stdout):
        """
    Prints out information about an entire model's scattering statistics, such
    as mean map heights and b-factors for carbons and waters.

    Parameters
    ----------
    out : file, optional
    """
        print('', file=out)
        print('Model and map statistics:', file=out)
        print('  mean mFo map height @ carbon: %s' % format_value('%.2f',
            flex.max(self.carbon_fo_values)), file=out)
        if self.calpha_mean_two_fofc > 0:
            print('  mean 2mFo-DFc map height @ C-alpha: %s' % format_value
                ('%.2f', self.calpha_mean_two_fofc), file=out)
        print('  mean B-factor: %s' % format_value('%.2f', self.b_mean_all),
            file=out)
        if self.b_mean_calpha > 0:
            print('  mean C-alpha B-factor: %s' % format_value('%.2f', self
                .b_mean_calpha), file=out)
        print('  mean water B-factor: %s' % format_value('%.2f', self.
            b_mean_hoh), file=out)
        n_water_fofc_peaks = 0
        n_water_anom_peaks = 0
        water_sel = self.water_selection()
        print('  %d water molecules' % len(water_sel), file=out)
        for i_seq in water_sel:
            map_stats = self.map_stats(i_seq)
            if map_stats.fofc >= 3.0:
                n_water_fofc_peaks += 1
            if map_stats.anom is not None and map_stats.anom >= 3.0:
                n_water_anom_peaks += 1
        print('  %d waters have mFo-DFc map >= 3.0 sigma' %
            n_water_fofc_peaks, file=out)
        if self.anomalous_flag:
            print('  %d waters have anomalous map >= 3.0 sigma' %
                n_water_anom_peaks, file=out)
        print('', file=out)

    def get_strict_valence_flag(self):
        """
    Checks whether the resolution of a model is high enough to use more strict
    thresholds for ion valences.

    Returns
    -------
    bool
    """
        d_min = self.fmodel.f_obs().d_min()
        return d_min < self.params.d_min_strict_valence

    def find_nearby_atoms(self, i_seq, far_distance_cutoff=3.0,
        near_distance_cutoff=1.5, filter_by_bonding=True,
        filter_by_two_fofc=True):
        """
    Given site in the structure, return a list of contacts, optionally
    filtering by connectivity and 2mFo-DFc map level.

    Parameters
    ----------
    i_seq : int
    far_distance_cutoff : float, optional
    near_distance_cutoff : float, optional
    filter_by_bonding : bool, optional
    fitler_by_two_fofc : bool, optional

    Returns
    -------
    list of mmtbx.ions.environment.atom_contact
    """
        assert i_seq < len(self.sites_frac)
        pair_asu_table, asu_mappings, asu_table = self._pair_asu_cache.get(
            far_distance_cutoff, (None, None, None))
        if pair_asu_table is None:
            pair_asu_table = self.xray_structure.pair_asu_table(distance_cutoff
                =far_distance_cutoff)
            asu_mappings = pair_asu_table.asu_mappings()
            asu_table = pair_asu_table.table()
            self._pair_asu_cache[far_distance_cutoff
                ] = pair_asu_table, asu_mappings, asu_table
        contacts = environment.find_nearby_atoms(i_seq=i_seq,
            xray_structure=self.xray_structure, pdb_atoms=self.pdb_atoms,
            asu_mappings=asu_mappings, asu_table=asu_table, connectivity=
            self.connectivity, far_distance_cutoff=far_distance_cutoff,
            near_distance_cutoff=near_distance_cutoff, filter_by_bonding=
            filter_by_bonding)
        if filter_by_two_fofc:
            filtered = []
            for contact in contacts:
                if contact.resname() in WATER_RES_NAMES:
                    two_fofc = self._map_values['2mFo-DFc'][contact.
                        atom_i_seq()]
                    if two_fofc < self.params.water.min_2fofc_coordinating:
                        continue
                filtered.append(contact)
            contacts = filtered
        return contacts

    def guess_b_iso_real(self, i_seq):
        """
    Guess an approximate B_iso for an atom by averaging the values for
    coordinating atoms.  This should partially compensate for the deflation of
    the B-factor due to incorrect scattering type, and consequently make the
    occupancy refinement more accurate.

    Parameters
    ----------
    i_seq : int

    Returns
    -------
    float
    """
        contacts = self.find_nearby_atoms(i_seq, far_distance_cutoff=3.0)
        if len(contacts) == 0:
            return adptbx.u_as_b(self.u_iso_all[i_seq])
        u_iso_sum = 0
        for contact in contacts:
            u_iso_sum += self.u_iso_all[contact.atom_i_seq()]
        return adptbx.u_as_b(u_iso_sum / len(contacts))

    def principal_axes_of_inertia(self, i_seq):
        """
    Extracts the map grid points around a site, and calculates the axes of
    inertia (using the density values as weights). This is used to calculate
    the sphericity of the blob of density around a site.

    Parameters
    ----------
    i_seq : int

    Returns
    -------
    ...
    """
        return self._principal_axes_of_inertia[i_seq]

    def get_map_sphere_variance(self, i_seq):
        """
    Calculate the density levels for points on a sphere around a given point.
    This will give us some indication whether the density falls off in the
    expected manner around a single atom.

    Parameters
    ----------
    i_seq : int

    Returns
    -------
    float
    """
        return self._map_variances[i_seq]

    def get_b_iso(self, i_seq):
        """
    Calculates the isotropic b-factor of a site.

    Parameters
    ----------
    i_seq : int

    Returns
    -------
    float
    """
        sc = self.xray_structure.scatterers()[i_seq]
        return adptbx.u_as_b(sc.u_iso_or_equiv(self.unit_cell))

    def map_stats(self, i_seq):
        """
    Given a site in the structure, find the signal of the 2FoFc, FoFc, and
    anomalous map (When available).

    Parameters
    ----------
    i_seq : int

    Returns
    -------
    group_args
        Object with .two_fofc, .fofc, and .anom properties for the associated
        signals in each map.
    """
        value_2fofc = self._map_values['2mFo-DFc'][i_seq]
        value_fofc = self._map_values['mFo-DFc'][i_seq]
        value_anom = None
        if 'anom' in self._map_values:
            value_anom = self._map_values['anom'][i_seq]
        return group_args(two_fofc=value_2fofc, fofc=value_fofc, anom=
            value_anom)

    def get_map_gaussian_fit(self, map_type, i_seq):
        """
    Retrieves the two parameters of a gaussian function, fit to a given map at
    a site.

    Parameters
    ----------
    map_type : str
    i_seq : int

    Returns
    -------
    (tuple of float, float) or None
    """
        map_gaussians = self._map_gaussian_fits.get(map_type, None)
        if map_gaussians is not None:
            return map_gaussians[i_seq]
        return None

    def guess_molecular_weight(self, i_seq):
        """
    Guesses the molecular weight of a site by scaling the signal from
    the mFo map by the average signal from carbon atoms.

    Parameters
    ----------
    i_seq : int

    Returns
    -------
    float
    """
        map_values = self._map_values.get('mFo', None)
        if map_values is None:
            return None
        height = map_values[i_seq]
        mean_carbon = flex.mean(self.carbon_fo_values)
        assert mean_carbon > 0
        return 6 * height / mean_carbon

    def _find_atoms_near_site(self, site_cart, distance_cutoff=1.5,
        distance_cutoff_same_site=0.5):
        """
    Given an XYZ coordinate, finds atoms near that site, separating out those
    which are close enough to be essentially equivalent.  Used to analyze the
    anomalously-scattering substructure calculated by Phaser.

    Parameters
    ----------
    site_cart : tuple of float, float, float
    distance_cutoff : float, optional
    distance_cutoff_same_site : float, optional

    Returns
    -------
    same_atoms : list of group_args
        List of atoms within distance_cutoff_same_site of site_cart, and and
        other_atoms, the rest of the atoms within distance_cutoff of site_cart.
    """
        site_frac = self.unit_cell.fractionalize(site_cart=site_cart)
        sites_frac = flex.vec3_double([site_frac])
        asu_mappings = self.xray_structure.asu_mappings(buffer_thickness=
            distance_cutoff + 0.1)
        asu_mappings.process_sites_frac(sites_frac, min_distance_sym_equiv=
            self.xray_structure.min_distance_sym_equiv())
        pair_generator = crystal.neighbors_fast_pair_generator(asu_mappings
            =asu_mappings, distance_cutoff=distance_cutoff)
        n_xray = self.xray_structure.scatterers().size()
        same_atoms = []
        other_atoms = []
        for pair in pair_generator:
            if (pair.i_seq < n_xray and pair.j_seq < n_xray or pair.i_seq >=
                n_xray and pair.j_seq >= n_xray):
                continue
            rt_mx_i = asu_mappings.get_rt_mx_i(pair)
            rt_mx_j = asu_mappings.get_rt_mx_j(pair)
            rt_mx_ji = rt_mx_i.inverse().multiply(rt_mx_j)
            new_site_frac = rt_mx_ji * site_frac
            new_site_cart = self.unit_cell.orthogonalize(site_frac=
                new_site_frac)
            atom_seq = pair.i_seq if pair.i_seq < n_xray else pair.j_seq
            site_info = group_args(i_seq=atom_seq, rt_mx=rt_mx_ji,
                new_site_cart=new_site_cart, distance=sqrt(pair.dist_sq))
            if site_info.distance < distance_cutoff_same_site:
                same_atoms.append(site_info)
            else:
                other_atoms.append(site_info)
        same_atoms.sort(key=lambda x: x.distance)
        other_atoms.sort(key=lambda x: x.distance)
        return same_atoms, other_atoms

    def analyze_substructure(self, log=None, verbose=True):
        """
    Given a list of AX pseudo-atoms placed by Phaser, finds the nearest real
    atoms, and if possible determines their equivalence.

    Parameters
    ----------
    log : file, optional
    verbose : bool, optional
    """

        def ax_atom_id(atom):
            """
      Parameters
      ----------
      atom : iotbx.pdb.hierarchy.atom

      Returns
      -------
      str
      """
            return 'AX %s (fpp=%.3f)' % (atom.serial.strip(), atom.occ)
        assert self.phaser_substructure is not None
        self.fpp_from_phaser_ax_sites = flex.double(self.pdb_atoms.size(), -1)
        if log is None or not verbose:
            log = null_out()

        def _filter_site_infos(site_infos):
            """
      Parameters
      ----------
      site_infos : list of group_args

      Returns
      -------
      list of group_args
      """
            from collections import OrderedDict
            i_seqs = OrderedDict()
            for site_info in site_infos:
                if site_info.i_seq in i_seqs:
                    i_seqs[site_info.i_seq].append(site_info)
                else:
                    i_seqs[site_info.i_seq] = [site_info]
            for val in i_seqs.values():
                if any(i.rt_mx.is_unit_mx() for i in val):
                    for site_info in val:
                        if not site_info.rt_mx.is_unit_mx():
                            val.remove(site_info)
                val.sort(key=lambda x: x.distance)
            return [i[0] for i in i_seqs.values()]
        make_sub_header('Analyzing Phaser anomalous substructure', out=log)
        for atom in self.phaser_substructure:
            print(ax_atom_id(atom), file=log)
            same_atoms, other_atoms = self._find_atoms_near_site(atom.xyz,
                distance_cutoff=self.params.phaser.distance_cutoff,
                distance_cutoff_same_site=self.params.phaser.
                distance_cutoff_same_site)
            same_atoms, other_atoms = _filter_site_infos(same_atoms
                ), _filter_site_infos(other_atoms)
            if len(same_atoms) == 0:
                print('  No match for %s' % ax_atom_id(atom), file=log)
                for other_atom in other_atoms:
                    print('    %s (distance = %.3f)' % (self.pdb_atoms[
                        other_atom.i_seq].id_str(), other_atom.distance),
                        file=log)
            elif len(same_atoms) == 1:
                print('  %s maps to %s' % (ax_atom_id(atom), self.pdb_atoms
                    [same_atoms[0].i_seq].id_str()), file=log)
                self.use_fdp[same_atoms[0].i_seq] = True
                self.fpp_from_phaser_ax_sites[same_atoms[0].i_seq] = atom.occ
            else:
                print('  ambiguous results for %s:' % ax_atom_id(atom),
                    file=log)
                for same_atom in same_atoms:
                    print('    %s' % self.pdb_atoms[same_atom.i_seq].id_str
                        (), file=log)
        print('', file=log)

    def get_fpp(self, i_seq):
        """
    Retrieve the refined f'' for a site.  Because this can come from either the
    built-in anomalous refinement or Phaser, it is handled differently than
    the f' retrieval.

    Parameters
    ----------
    i_seq : int

    Returns
    -------
    float or None
    """
        if not self.use_fdp[i_seq]:
            return None
        if self.fpp_from_phaser_ax_sites is None:
            return self.site_fdp[i_seq]
        fpp = self.fpp_from_phaser_ax_sites[i_seq]
        if fpp < 0:
            fpp = None
        return fpp

    def get_fp(self, i_seq):
        """
    Retrieve the refined f' for a site.

    Parameters
    ----------
    i_seq : int

    Returns
    -------
    float or None
    """
        if self.site_fp is None or not self.use_fdp[i_seq]:
            return None
        return self.site_fp[i_seq]

    def looks_like_halide_ion(self, i_seq, element='CL',
        assume_hydrogens_all_missing=Auto):
        """
    Given a site, analyze the nearby atoms (taking geometry into account) to
    guess whether it might be a halide ion.  Among other things, halides will
    often be coordinated by amide hydrogens, and not in close contact with
    negatively charged atoms.  Note that this procedure has a very high
    false positive rate when used by itself, so additional information about
    the electron count (map, occ, b) is absolutely essential.

    Parameters
    ----------
    i_seq : int
    element : str
    assume_hydrogens_all_missing : bool, optional

    Returns
    -------
    bool
    """
        atom = self.pdb_atoms[i_seq]
        sites_frac = self.xray_structure.sites_frac()
        assert element.upper() in mmtbx.ions.HALIDES
        if (self.b_mean_hoh is not None and atom.b > self.b_mean_hoh - self
            .b_stddev_hoh):
            return False
        nearby_atoms = self.find_nearby_atoms(i_seq, far_distance_cutoff=
            4.0, filter_by_bonding=False)
        favorable_environment = halides.is_favorable_halide_environment(i_seq
            =i_seq, contacts=nearby_atoms, pdb_atoms=self.pdb_atoms,
            sites_frac=self.xray_structure.sites_frac(), unit_cell=self.
            unit_cell, connectivity=self.connectivity, params=self.params.
            chloride, assume_hydrogens_all_missing=assume_hydrogens_all_missing
            )
        pai = self.principal_axes_of_inertia(i_seq)
        map_variance = self.get_map_sphere_variance(i_seq)
        good_map_falloff = False
        if (map_variance is not None and map_variance.mean < 1.5 and 
            map_variance.standard_deviation < 0.4):
            good_map_falloff = True
        return favorable_environment

    def analyze_water(self, i_seq, debug=True, candidates=Auto, no_final=
        False, out=sys.stdout):
        """
    Examines the environment around a single atom to determine if it is actually
    a misidentified metal ion.

    no_final is used internally, when candidates is not Auto, to try all of the
    Auto without selecting any as a final choice when none of the elements in
    candidates appear to probable ion identities.

    Parameters
    ----------
    i_seq : int
    debug : bool, optional
    candidates : list of str, optional
    no_file : bool, optional
    out : file, optional

    Returns
    -------
    mmtbx.ions.identify.water_result
    """
        atom = self.pdb_atoms[i_seq]
        auto_candidates = candidates is Auto
        if auto_candidates:
            candidates = mmtbx.ions.DEFAULT_IONS
        elif isinstance(candidates, string_types):
            candidates = candidates.replace(',', ' ').split()
        candidates = [i.strip().upper() for i in candidates]
        if candidates == ['X']:
            candidates = []
        resname = atom.fetch_labels().resname.strip().upper()
        assert resname in WATER_RES_NAMES
        atom_props = self.atoms_to_props[i_seq]
        final_choice = None
        atom_type = atom_props.get_atom_type(params=self.params.water,
            aggressive=self.params.aggressive)
        if atom_type == WATER_POOR:
            return None
        assert not self.use_svm
        filtered_candidates = []
        halide_candidates = False
        nuc_phosphate_site = atom_props.looks_like_nucleotide_phosphate_site()
        max_carbon_fo_map_value = sys.maxsize
        if self.carbon_fo_values is not None:
            max_carbon_fo_map_value = flex.max(self.carbon_fo_values)
        for symbol in candidates:
            elem = mmtbx.ions.server.get_metal_parameters(symbol)
            if elem is None:
                if symbol in mmtbx.ions.HALIDES:
                    halide_candidates = True
                    continue
                else:
                    raise Sorry("Element '%s' not supported!" % symbol)
            if atom_type in [WATER, LIGHT_ION] and symbol not in ['NA',
                'MG', 'F', 'NE']:
                continue
            if nuc_phosphate_site and not symbol in NUC_PHOSPHATE_BINDING:
                continue
            n_elec = sasaki.table(symbol.upper()).atomic_number() - elem.charge
            if n_elec <= 12 and (atom_props.fpp is not None and atom_props.
                fpp > 0.1):
                continue
            mass_ratio = atom_props.estimated_weight / max(n_elec, 1)
            if mass_ratio < 0.4 and atom_type == WATER:
                continue
            filtered_candidates.append(elem)

        def try_candidates(require_valence=True):
            """
      Parameters
      ----------
      require_valence : bool, optional

      Returns
      -------
      list of tuple of mmtbx.ions.metal_parameters, float
      list of tuple of mmtbx.ions.metal_parameters, float
      """
            reasonable = []
            unreasonable = []
            for elem_params in filtered_candidates:
                atom_props.check_ion_environment(ion_params=elem_params,
                    wavelength=self.wavelength, require_valence=require_valence
                    )
                atom_props.check_fpp_ratio(ion_params=elem_params,
                    wavelength=self.wavelength, fpp_ratio_min=self.params.
                    phaser.fpp_ratio_min, fpp_ratio_max=self.params.phaser.
                    fpp_ratio_max)
                identity = str(elem_params)
                if atom_props.is_correctly_identified(identity=identity):
                    reasonable.append((elem_params, atom_props.score[identity])
                        )
                else:
                    unreasonable.append((elem_params, atom_props.score[
                        identity]))
            return reasonable, unreasonable
        reasonable, unreasonable = try_candidates(require_valence=True)
        valence_used = True
        if len(reasonable) == 0 and not self.params.require_valence:
            reasonable, unreasonable = try_candidates(require_valence=False)
            if len(reasonable) > 0:
                valence_used = False
        looks_like_halide = False
        if not (reasonable or atom_type < HEAVY_ION or nuc_phosphate_site):
            candidate_halides = set(candidates).intersection(mmtbx.ions.HALIDES
                )
            filtered_halides = []
            for element in candidate_halides:
                fpp_ratio = atom_props.check_fpp_ratio(ion_params=mmtbx.
                    ions.metal_parameters(element=element, charge=-1),
                    wavelength=self.wavelength, fpp_ratio_min=self.params.
                    phaser.fpp_ratio_min, fpp_ratio_max=self.params.phaser.
                    fpp_ratio_max)
                if fpp_ratio is not None and element != 'CL':
                    if (fpp_ratio < self.params.phaser.fpp_ratio_min or 
                        fpp_ratio > self.params.phaser.fpp_ratio_max):
                        continue
                if self.looks_like_halide_ion(i_seq=i_seq, element=element):
                    filtered_halides.append(element)
            looks_like_halide = len(filtered_halides) > 0
            reasonable += [(mmtbx.ions.metal_parameters(element=halide,
                charge=-1), 0) for halide in filtered_halides]
        if len(reasonable) == 0 and not self.params.require_valence:
            compatible = [params for params in filtered_candidates if
                atom_props.has_compatible_ligands(str(params))]
            for ion_params in compatible:
                atomic_number = sasaki.table(ion_params.element).atomic_number(
                    )
                weight_ratio = 0
                if atom_props.estimated_weight is not None:
                    weight_ratio = atom_props.estimated_weight / (atomic_number
                         - ion_params.charge)
                if (ion_params.element in mmtbx.ions.TRANSITION_METALS and 
                    atom_type == HEAVY_ION and not auto_candidates and
                    atom_props.is_compatible_site(ion_params)):
                    n_good_res = atom_props.number_of_favored_ligand_residues(
                        ion_params, distance=2.7)
                    n_total_coord_atoms = (atom_props.
                        number_of_atoms_within_radius(2.8))
                    if n_good_res >= 1 and 2 <= n_total_coord_atoms <= 6:
                        reasonable.append((ion_params, 0))
                    else:
                        pass
                elif ion_params.element in ['K', 'CA'
                    ] and atom_type == HEAVY_ION and not auto_candidates and atom_props.is_compatible_site(
                    ion_params):
                    n_good_res = atom_props.number_of_favored_ligand_residues(
                        ion_params, distance=2.9, exclude_atoms=['O'])
                    n_bb_oxygen = atom_props.number_of_backbone_oxygens(
                        distance_cutoff=2.9)
                    n_total_coord_atoms = (atom_props.
                        number_of_atoms_within_radius(distance_cutoff=2.9))
                    if (n_good_res + n_bb_oxygen >= 2 and 
                        n_total_coord_atoms >= 4):
                        reasonable.append((ion_params, 0))
                elif atomic_number > 30 and atom_type == HEAVY_ION and (
                    weight_ratio > 0.5 and weight_ratio < 1.05 or 
                    atom_props.fp > 10
                    ) and not auto_candidates and atom_props.is_compatible_site(
                    ion_params, ignore_valence=not self.
                    get_strict_valence_flag()):
                    n_total_coord_atoms = (atom_props.
                        number_of_atoms_within_radius(2.8))
                    if n_total_coord_atoms >= 3:
                        reasonable.append((ion_params, 0))
                else:
                    pass
            if len(compatible) == 1 and not self.get_strict_valence_flag():
                inaccuracies = atom_props.inaccuracies[str(ion_params)]
                if (compatible[0] in atom_props.fpp_ratios and atom_props.
                    BAD_FPP not in inaccuracies and not inaccuracies.
                    intersection([atom_props.BAD_GEOMETRY, atom_props.
                    NO_GEOMETRY])):
                    _PRINT_DEBUG(atom.id_str())
                    _PRINT_DEBUG(filtered_candidates)
                    _PRINT_DEBUG(compatible)
        if len(reasonable) == 1:
            final_choice = reasonable[0][0]
        if not reasonable and not auto_candidates:
            result = self.analyze_water(i_seq=i_seq, debug=debug, no_final=
                True, out=out)
            if result.final_choice is not None:
                return result
        return water_result(atom_props=atom_props, filtered_candidates=
            filtered_candidates, matching_candidates=reasonable,
            rejected_candidates=unreasonable, nuc_phosphate_site=
            nuc_phosphate_site, atom_type=atom_type, looks_like_halide=
            looks_like_halide, ambiguous_valence_cutoff=self.params.
            ambiguous_valence_cutoff, valence_used=valence_used,
            final_choice=final_choice, wavelength=self.wavelength, no_final
            =no_final)

    def _extract_waters(self):
        model = self.pdb_hierarchy.only_model()
        water_i_seqs = []
        for chain in model.chains():
            for residue_group in chain.residue_groups():
                atom_groups = residue_group.atom_groups()
                if len(atom_groups) > 1:
                    continue
                for atom_group in atom_groups:
                    resname = atom_group.resname.strip().upper()
                    if resname in WATER_RES_NAMES:
                        atoms = atom_group.atoms()
                        if len(atoms) == 1:
                            water_i_seqs.append(atoms[0].i_seq)
        return water_i_seqs

    def analyze_waters(self, out=sys.stdout, debug=True, candidates=Auto):
        """
    Iterates through all of the waters in a model, examining the maps and local
    environment to check their identity and suggest likely ions where
    appropriate.

    Parameters
    ----------
    out : file, otpional
    debug : bool, optional
    candidates : list of str, optional

    Returns
    -------
    list of tuple of int, mmtbx.ions.metal_parameters
        i_seq and metal parameters which indicate that waters of i_seq would be
        better changed to that new metal identity.
    """
        waters = self.water_selection()
        print('', file=out)
        print('  %d waters to analyze' % len(waters), file=out)
        if len(waters) == 0:
            return
        nproc = easy_mp.get_processes(self.nproc)
        ions = []
        self.atoms_to_props = dict((i_seq, AtomProperties(i_seq, self)) for
            i_seq in waters)
        if nproc == 1:
            print('', file=out)
            for water_i_seq in waters:
                t1 = time.time()
                water_props = self.analyze_water(i_seq=water_i_seq, debug=
                    debug, candidates=candidates, out=out)
                if water_props is not None:
                    water_props.show_summary(out=out, debug=debug)
                    map_stats = self.map_stats(water_i_seq)
                    if (water_props.final_choice is not None and not
                        water_props.no_final):
                        ions.append((water_i_seq, [water_props.final_choice
                            ], map_stats.two_fofc))
                t2 = time.time()
        else:
            print('  Parallelizing across %d processes' % nproc, file=out)
            print('', file=out)
            analyze_water = _analyze_water_wrapper(manager=self, debug=
                debug, candidates=candidates, out=out)
            results = easy_mp.pool_map(fixed_func=analyze_water, args=
                waters, processes=nproc)
            for result in results:
                if result is None:
                    continue
                water_i_seq, final_choice, result_str = result
                if result_str is not None:
                    print(result_str, file=out)
                if final_choice is not None:
                    map_stats = self.map_stats(water_i_seq)
                    ions.append((water_i_seq, [final_choice], map_stats.
                        two_fofc))
        return sorted(ions, key=operator.itemgetter(2), reverse=True)

    def validate_ion(self, i_seq, out=sys.stdout, debug=True):
        """
    Examines one site in the model and determines if it was correctly modelled,
    returning a boolean indicating correctness.

    Parameters
    ----------
    i_seq : int
    out : file, optional
    debug : bool, optional
    """
        atom_props = self.atoms_to_props[i_seq]
        element = mmtbx.ions.server.get_element(atom_props.atom)
        elem_params = mmtbx.ions.server.get_metal_parameters(element)
        if elem_params is not None:
            atom_type = atom_props.get_atom_type(params=self.params.water)
            atom_props.check_ion_environment(ion_params=elem_params,
                wavelength=self.wavelength, require_valence=self.params.
                require_valence)
            atom_props.check_fpp_ratio(ion_params=elem_params, wavelength=
                self.wavelength, fpp_ratio_min=self.params.phaser.
                fpp_ratio_min, fpp_ratio_max=self.params.phaser.fpp_ratio_max)
        elif element in mmtbx.ions.HALIDES:
            identity = atom_props.identity()
            atom_props.inaccuracies[identity] = set()
            if not self.looks_like_halide_ion(i_seq=i_seq, element=element):
                atom_props.inaccuracies[identity].add(atom_props.BAD_HALIDE)
        else:
            raise Sorry("Element '%s' not supported:\n%s" % (element,
                atom_props.atom.format_atom_record()))
        return atom_props

    def validate_ions(self, out=sys.stdout, debug=True, segid=None):
        """
    Iterate over all the ions built into the model by this module and double
    check their correctness. Looks for tell-tale signs such as negative peaks
    in the mFo-DFc and anomalous difference maps, abnormal b-factor, and
    disallowed chemical environments.

    Prints out a table of bad ions and their information and returns a list of
    their sites' i_seqs.

    Parameters
    ----------
    out : file, optional
    debug : bool, optional
    segid : str, optional
        If present, only validate ions with this segid.
    """
        if segid is None:
            ions = []
            for model in self.pdb_hierarchy.models():
                for chain in model.chains():
                    for residue_group in chain.residue_groups():
                        for atom_group in residue_group.atom_groups():
                            if atom_group.resname.strip(
                                ) in mmtbx.ions.SUPPORTED:
                                atoms = atom_group.atoms()
                                assert len(atoms) == 1
                                for atom in atoms:
                                    ions += atom.i_seq,
        else:
            sel_cache = self.pdb_hierarchy.atom_selection_cache()
            ions = sel_cache.selection('segid {0}'.format(segid)).iselection()
        if len(ions) == 0:
            return
        ion_status = []
        self.atoms_to_props = dict((i_seq, AtomProperties(i_seq, self)) for
            i_seq in ions)
        for i_seq in ions:
            atom_props = self.validate_ion(i_seq=i_seq, debug=debug)
            ion_status.append((atom_props, atom_props.
                is_correctly_identified()))
        scatterers = self.xray_structure.scatterers()
        headers = ('atom', 'occ', 'b_iso', '2mFo-DFc', 'mFo-DFc', 'fp',
            'fdp', 'ratio', 'BVS', 'VECSUM')
        fmt = '%-15s %-5s  %-5s  %-8s  %-7s  %-5s  %-5s  %-5s %-5s  %-6s'
        box = framed_output(out, title='Validating new ions', width=80)
        print(fmt % headers, file=box)
        print(' ' + '-' * 75, file=box)
        for props, okay_flag in ion_status:
            i_seq = props.atom.i_seq
            sc = scatterers[i_seq]
            fp = fdp = None
            if sc.flags.use_fp_fdp:
                fp = sc.fp
                fdp = sc.fdp
            b_iso = adptbx.u_as_b(sc.u_iso_or_equiv(unit_cell=self.unit_cell))
            identity = props.identity()

            def ff(fs, val):
                return format_value(fs, val, replace_none_with='---')
            print(fmt % (props.atom.id_str(suppress_segid=True)[5:-1], ff(
                '%.2f', props.atom.occ), ff('%.2f', b_iso), ff('%.2f',
                props.peak_2fofc), ff('%.2f', props.peak_fofc), ff('%.2f',
                fp), ff('%.2f', fdp), ff('%.2f', None if self.wavelength is
                None else fdp / sasaki.table(mmtbx.ions.server.get_element(
                props.atom)).at_angstrom(self.wavelength).fdp()), ff('%.2f',
                props.valence_sum.get(identity)), ff('%.2f', props.
                vector_sum.get(identity))), file=box)
            if not okay_flag:
                print('\n'.join('!!! ' + props.error_strs[i] for i in props
                    .inaccuracies[identity]), file=box)
            print('', file=box)
        box.close()


class _analyze_water_wrapper(object):
    """
  Simple wrapper for calling manager.analyze_water with keyword arguments
  in a parallelized loop.  Because the water_result object is not pickle-able,
  we only return the final ion choice and summary string.
  """

    def __init__(self, manager, **kwds):
        self.manager = manager
        self.kwds = dict(kwds)

    def __call__(self, i_seq):
        try:
            result = self.manager.analyze_water(i_seq, **self.kwds)
            out = StringIO()
            if result is not None:
                result.show_summary(out=out, debug=self.kwds.get('debug', 
                    False))
            result_str = out.getvalue()
            if result_str == '':
                result_str = None
            final_choice = None
            if not getattr(result, 'no_final', False):
                final_choice = getattr(result, 'final_choice', None)
            return i_seq, final_choice, result_str
        except KeyboardInterrupt:
            return None, None, None


class _validate_ion_wrapper(object):
    """
  Simple wrapper for calling manager.validate_ion with keyword arguments
  in a parallelized loop.  Because the water_result object is not pickle-able,
  we only return the final ion choice and summary string.
  """

    def __init__(self, manager, **kwds):
        self.manager = manager
        self.kwds = dict(kwds)

    def __call__(self, i_seq):
        try:
            correct = self.manager.validate_ion(i_seq, **self.kwds)
            return i_seq, correct
        except KeyboardInterrupt:
            return None, None


class ion_result(validation.atom):
    """
  Class for validation results for an ion site. This includes information about
  both the atom modeled at that site as well as its chemical and scattering
  environment.
  """
    __slots__ = validation.atom.__slots__ + ['chem_env', 'scatter_env']

    def show(self, out=sys.stdout, prefix=''):
        pass

    def show_brief(self, out=sys.stdout, prefix=''):
        pass


class water_result(object):
    """
  Container for storing the results of manager.analyze_water for later display
  and retrieval.
  """

    def __init__(self, atom_props, filtered_candidates, matching_candidates,
        rejected_candidates, nuc_phosphate_site, atom_type,
        looks_like_halide, ambiguous_valence_cutoff, valence_used,
        final_choice, wavelength=None, no_final=False):
        adopt_init_args(self, locals())

    def show_summary(self, out=None, debug=False):
        """
    Prints out a summary of a site's chemical and scattering environment.

    Parameters
    ----------
    out : file, optional
    debug : bool, optional
    """
        if out is None:
            out = sys.stdout
        results = self.matching_candidates
        if len(results) > 0:
            self.atom_props.show_properties(identity='HOH', out=out)
            if self.nuc_phosphate_site:
                print('  appears to be nucleotide coordination site', file=out)
            if self.no_final:
                print('  Found potential ion%s outside of specified set:' %
                    ('s' if len(results) > 1 else ''), file=out)
            if self.final_choice is not None:
                elem_params, score = results[0]
                if elem_params.element not in mmtbx.ions.HALIDES:
                    self.atom_props.show_ion_results(identity=str(self.
                        final_choice), out=out, valence_used=self.
                        valence_used, confirmed=True)
                else:
                    print('  Probable anion:', str(elem_params), file=out)
                print('', file=out)
            elif len(results) > 1:
                below_cutoff = [elem_params for elem_params, score in
                    results if score < self.ambiguous_valence_cutoff]
                if len(below_cutoff) == 1:
                    elem_params = below_cutoff[0]
                    print('  ambigous results, best valence from %s' % str(
                        elem_params), file=out)
                    self.atom_props.show_ion_results(identity=str(
                        elem_params), out=out, valence_used=True)
                    print('', file=out)
                else:
                    ions = [str(i[0]) for i in sorted(results, key=lambda x:
                        x[1])]
                    print('  ambiguous results, could be %s' % ', '.join(
                        ions), file=out)
                    for elem_params, score in results:
                        self.atom_props.show_ion_results(identity=str(
                            elem_params), out=out)
                    print('', file=out)
        elif self.atom_type != WATER or self.nuc_phosphate_site:
            self.atom_props.show_properties(identity='HOH', out=out)
            if self.nuc_phosphate_site:
                print('  appears to be nucleotide coordination site', file=out)
            if self.looks_like_halide:
                print('  Probable cation: %s' % str(self.final_choice),
                    file=out)
                print('', file=out)
            else:
                if debug and len(self.filtered_candidates) > 0:
                    print('  insufficient data to identify atom', file=out)
                    possible = True
                    for params in self.filtered_candidates:
                        if self.atom_props.has_compatible_ligands(str(params)):
                            if possible:
                                print('  possible candidates:', file=out)
                                possible = False
                            self.atom_props.show_ion_results(identity=str(
                                params), out=out)
                        else:
                            print('  incompatible ligands for %s' % str(
                                params), file=out)
                print('', file=out)


class AtomProperties(object):
    """
  Collect physical attributes of an atom, including B, occupancy, and map
  statistics, and track those which are at odds with its chemical identity.
  """
    (LOW_B, HIGH_B, LOW_OCC, HIGH_OCC, NO_2FOFC_PEAK, FOFC_PEAK, FOFC_HOLE,
        ANOM_PEAK, NO_ANOM_PEAK, BAD_GEOMETRY, NO_GEOMETRY, BAD_VECTORS,
        BAD_VALENCES, TOO_FEW_NON_WATERS, TOO_FEW_COORD, TOO_MANY_COORD,
        LIKE_COORD, BAD_COORD_ATOM, BAD_FPP, BAD_COORD_RESIDUE,
        VERY_BAD_VALENCES, BAD_HALIDE, HIGH_2FOFC, COORDING_GEOMETRY,
        CLOSE_CONTACT) = range(25)
    error_strs = {LOW_B: 'Abnormally low b-factor', HIGH_B:
        'Abnormally high b-factor', LOW_OCC: 'Abnormally low occupancy',
        HIGH_OCC: 'Abnormally high occupancy', NO_2FOFC_PEAK:
        'No 2mFo-DFc map peak', FOFC_PEAK: 'Peak in mFo-DFc map', FOFC_HOLE:
        'Negative peak in dFo-mFc map', ANOM_PEAK:
        'Peak in the anomalous map', NO_ANOM_PEAK:
        'No peak in the anomalous map', BAD_GEOMETRY:
        'Unexpected geometry for coordinating atoms', NO_GEOMETRY:
        'No distinct geometry for coordinating atoms', BAD_VECTORS:
        'VECSUM above cutoff', BAD_VALENCES: 'BVS above or below cutoff',
        TOO_FEW_NON_WATERS: 'Too few non-water coordinating atoms',
        TOO_FEW_COORD: 'Too few coordinating atoms', TOO_MANY_COORD:
        'Too many coordinating atoms', LIKE_COORD:
        'Like charge coordinating like', BAD_COORD_ATOM:
        'Disallowed coordinating atom', BAD_FPP: "Bad refined f'' value",
        BAD_COORD_RESIDUE: 'Disallowed coordinating residue',
        VERY_BAD_VALENCES: 'BVS far above or below cutoff', BAD_HALIDE:
        'Bad halide site', HIGH_2FOFC: 'Unexpectedly high 2mFo-DFc value',
        COORDING_GEOMETRY:
        'No distinct geometry and coordinating another atom with distinct geometry'
        , CLOSE_CONTACT: 'Close contact to oxygen atom'}

    def __init__(self, i_seq, manager):
        self.i_seq = i_seq
        self.atom = manager.pdb_atoms[i_seq].fetch_labels()
        self.resname = self.atom.resname.strip().upper()
        self.d_min = manager.fmodel.f_obs().d_min()
        self.anomalous_flag = manager.fmodel.f_obs().anomalous_flag()
        self.strict_valence = manager.get_strict_valence_flag()
        nearby_atoms_unfiltered = manager.find_nearby_atoms(i_seq=i_seq,
            far_distance_cutoff=3.5)
        self.nearby_atoms = []
        nearby_atoms_no_alts = []
        for contact in nearby_atoms_unfiltered:
            if contact.element not in ['H', 'D']:
                self.nearby_atoms.append(contact)
                for other in nearby_atoms_no_alts:
                    if other == contact:
                        break
                else:
                    nearby_atoms_no_alts.append(contact)
        self.residue_counts = utils.count_coordinating_residues(self.
            nearby_atoms)
        self.geometries = find_coordination_geometry(nearby_atoms_no_alts)
        map_stats = manager.map_stats(i_seq)
        self.peak_2fofc = map_stats.two_fofc
        self.peak_fofc = map_stats.fofc
        self.peak_anom = map_stats.anom
        self.estimated_weight = manager.guess_molecular_weight(i_seq)
        self.b_iso = manager.get_b_iso(i_seq)
        self.b_mean_hoh = manager.b_mean_hoh
        self.b_stddev_hoh = manager.b_stddev_hoh
        self.b_mean_calpha = manager.b_mean_calpha
        self.calpha_mean_two_fofc = manager.calpha_mean_two_fofc
        self.inaccuracies = {}
        self.ignored = {}
        self.vectors = {}
        self.valence_sum = {}
        self.vector_sum = {}
        self.score = {}
        self.fpp_expected = {}
        self.expected_params = {}
        self.bad_coords = {}
        self.fpp = manager.get_fpp(i_seq)
        self.fpp_ratios = {}
        self.fp = manager.get_fp(i_seq)
        self.manager = manager

    def is_correctly_identified(self, identity=None):
        """
    Calculates whether factors indicate that the atom was correctly identified.

    Parameters
    ----------
    identity : mmtbx.ions.metal_parameters

    Returns
    -------
    bool
    """
        if identity is None:
            identity = self.identity()
        return len(self.inaccuracies[identity]) == 0

    def has_compatible_ligands(self, identity):
        """
    Indicates whether the coordinating atoms are of the allowed type (e.g.
    no N or S atoms coordinating CA, etc.) and residue (e.g. Ser is not an
    appropriate ligand for ZN).

    Parameters
    ----------
    identity : mmtbx.ions.metal_parameters

    Returns
    -------
    bool
    """
        return len(self.bad_coords[identity]
            ) == 0 and not self.BAD_COORD_RESIDUE in self.inaccuracies[identity
            ]

    def is_compatible_site(self, ion_params, require_anom=True,
        ignore_valence=False):
        """
    More minimal criteria for determining whether a site is chemically
    compatible, allowing for incomplete coordination shells.

    Parameters
    ----------
    ion_params : mmtbx.ions.metal_parameters
    require_anom : bool, optional
    ignore_valence : bool, optional

    Returns
    -------
    bool
    """
        inaccuracies = self.inaccuracies[str(ion_params)]
        anom_allowed = self.is_compatible_anomalous_scattering(ion_params
            ) or not require_anom
        return self.has_compatible_ligands(str(ion_params)
            ) and not self.TOO_MANY_COORD in inaccuracies and (ignore_valence
             or not self.VERY_BAD_VALENCES in inaccuracies
            ) and not self.BAD_COORD_RESIDUE in inaccuracies and anom_allowed

    def number_of_favored_ligand_residues(self, ion_params, distance=3.0,
        exclude_atoms=()):
        """
    Counts the number of preferred residues coordinating the atom.  Used for
    approximate detection of transition-metal binding sites.

    Parameters
    ----------
    ion_params : mmtbx.ions.metal_parameters
    distance : float, optional
    exclude_atoms : tuple of ...

    Returns
    -------
    int
    """
        n_res = 0
        resids = []
        for contact in self.nearby_atoms:
            if contact.atom_name() in exclude_atoms:
                continue
            if contact.distance() < distance:
                labels = contact.atom.fetch_labels()
                other_resname = contact.resname()
                other_resid = labels.chain_id + labels.resid()
                if (ion_params.allowed_coordinating_residues is not None and
                    other_resname in ion_params.
                    allowed_coordinating_residues and not other_resid in resids
                    ):
                    n_res += 1
                    resids.append(other_resid)
        return n_res

    def number_of_atoms_within_radius(self, distance_cutoff):
        """
    Counts the number of coordinating atoms within a given radius.

    Parameters
    ----------
    float

    Returns
    -------
    int
    """
        n_atoms = 0
        atom_ids = []
        for contact in self.nearby_atoms:
            other_id = contact.atom_id_no_altloc()
            if not other_id in atom_ids:
                if contact.distance() < distance_cutoff:
                    n_atoms += 1
                atom_ids.append(other_id)
        return n_atoms

    def number_of_backbone_oxygens(self, distance_cutoff=3.0):
        """
    Counts the number of backbone oxygens coordinating a site.

    Parameters
    ----------
    distance_cutoff : float, optional

    Returns
    -------
    int
    """
        n_bb_ox = 0
        for contact in self.nearby_atoms:
            if contact.atom_name() == 'O':
                if contact.distance() <= distance_cutoff:
                    if not contact.resname() in WATER_RES_NAMES:
                        n_bb_ox += 1
        return n_bb_ox

    def is_compatible_anomalous_scattering(self, ion_params):
        if ion_params.element.upper() in ['MG', 'NA']:
            return (self.fpp is None and self.peak_anom is not None and 
                self.peak_anom < 1.0)
        else:
            if self.fpp is None:
                return self.peak_anom is not None and self.peak_anom > 3.0
            identity = self.identity(ion=ion_params)
            if identity in self.fpp_ratios:
                return not self.BAD_FPP in self.inaccuracies[identity]
        return False

    def atom_weight(self, manager):
        """
    Evaluates whether factors indicate that the atom is lighter, heavier, or
    isoelectric to what it is currently identified as.

    Parameters
    ----------
    manager : mmtbx.ions.identify.manager

    Returns
    -------
    int
        -1 if lighter, 0 if isoelectronic, and 1 if heavier.
    """
        identity = 'HOH' if self.resname in WATER_RES_NAMES else self.identity(
            )
        if (identity == 'HOH' and self.atom.b > manager.b_mean_hoh -
            manager.b_stddev_hoh):
            return 0
        if self.is_correctly_identified(identity=identity):
            return 0
        if self.FOFC_PEAK in self.inaccuracies[identity] or self.atom.b < 1:
            return 1
        if self.FOFC_HOLE in self.inaccuracies[identity]:
            return -1
        return 0

    def check_ion_environment(self, ion_params, wavelength=None,
        require_valence=True):
        """
    Checks whether or not the specified ion satisfies the metal-coordination
    parameters, specified by ion_params, such as valence sum, geometry, etc.
    The criteria used here are quite strict, but many of the analyses are
    saved for later if we want to use looser critera.

    Parameters
    ----------
    ion_params : mmtbx.ions.metal_parameters
    wavelength : float, optional
    require_valence : bool, optional
    """
        from iotbx.pdb import common_residue_names_get_class as get_class
        identity = self.identity(ion_params)
        inaccuracies = self.inaccuracies[identity] = set()
        self.expected_params[identity] = ion_params
        ignored = self.ignored[identity] = set()
        strict_rules = require_valence or self.is_correctly_identified(identity
            ='HOH') or self.strict_valence or ion_params.element in ['NA', 'MG'
            ]
        n_closest = 0
        coord_atoms = []
        for i_pair, contact1 in enumerate(self.nearby_atoms):
            distance = contact1.distance()
            if distance < 3.0:
                for contact2 in self.nearby_atoms[i_pair + 1:]:
                    if contact1 == contact2 or contact1.distance_from(contact2
                        ) <= 0.3:
                        break
                else:
                    coord_atoms.append(contact1)
                    if distance < 2.7:
                        n_closest += 1
        if len(coord_atoms) < ion_params.coord_num_lower:
            inaccuracies.add(self.TOO_FEW_COORD)
        if n_closest > ion_params.coord_num_upper:
            inaccuracies.add(self.TOO_MANY_COORD)
        n_non_water = 0
        self.bad_coords[identity] = []
        for contact in self.nearby_atoms:
            other_name = contact.atom_name()
            other_resname = contact.resname()
            other_element = contact.element
            if not other_resname in WATER_RES_NAMES:
                n_non_water += 1
            else:
                continue
            if contact.distance() < 3.0:
                if (ion_params.allowed_coordinating_atoms is not None and 
                    other_element not in ion_params.allowed_coordinating_atoms
                    ):
                    self.bad_coords[identity].append(contact)
                    inaccuracies.add(self.BAD_COORD_ATOM)
                if get_class(other_resname) == 'common_amino_acid':
                    if other_name in ['C', 'N', 'O', 'CA', 'H', 'HA'] and (
                        ion_params.allowed_backbone_atoms is None or not 
                        other_name in ion_params.allowed_backbone_atoms):
                        if other_name == 'O' and contact.is_carboxy_terminus:
                            pass
                        else:
                            self.bad_coords[identity].append(contact)
                            inaccuracies.add(self.BAD_COORD_ATOM)
                    if ion_params.allowed_coordinating_residues is not None:
                        allowed = ion_params.allowed_coordinating_residues
                        if not other_resname in allowed and other_name not in [
                            'C', 'O', 'N', 'CA', 'OXT']:
                            self.bad_coords[identity].append(contact)
                            inaccuracies.add(self.BAD_COORD_RESIDUE)
                elif cmp(0, mmtbx.ions.server.get_charge(contact.atom)) == cmp(
                    0, ion_params.charge):
                    self.bad_coords[identity].append(contact)
                    inaccuracies.add(self.LIKE_COORD)
                elif ion_params.charge > 0 and other_element in ['N'
                    ] and other_resname in ['LYS', 'ARG', 'ASN', 'GLN']:
                    self.bad_coords[identity].append(contact)
                    inaccuracies.add(self.LIKE_COORD)
        if n_non_water < ion_params.min_coordinating_non_waters:
            inaccuracies.add(self.TOO_FEW_NON_WATERS)
        if ion_params.allowed_geometries and strict_rules:
            allowed = [(i[0] in ion_params.allowed_geometries) for i in
                self.geometries]
            if 'any' in ion_params.allowed_geometries:
                pass
            elif not self.geometries:
                if strict_rules:
                    inaccuracies.add(self.NO_GEOMETRY)
            elif not any(allowed):
                inaccuracies.add(self.BAD_GEOMETRY)
            else:
                strict_rules = False
        if self.geometries == []:
            for contact in self.nearby_atoms:
                o_atom = contact.atom
                if o_atom.i_seq in self.manager.atoms_to_props:
                    o_geometry = self.manager.atoms_to_props[o_atom.i_seq
                        ].geometries
                    if o_geometry != []:
                        inaccuracies.add(self.COORDING_GEOMETRY)
        vectors = mmtbx.ions.server.calculate_valences(ion_params, self.
            nearby_atoms)
        self.vectors[identity] = vectors
        self.valence_sum[identity] = sum([abs(i) for i in vectors])
        self.vector_sum[identity] = abs(sum(vectors, col((0, 0, 0))))
        if self.vector_sum[identity] > ion_params.vec_sum_cutoff:
            if strict_rules:
                inaccuracies.add(self.BAD_VECTORS)
            else:
                ignored.add(self.BAD_VECTORS)
        if self.valence_sum[identity
            ] < ion_params.cvbs_expected * 0.25 or self.valence_sum[identity
            ] > ion_params.cvbs_expected * 1.25:
            inaccuracies.add(self.VERY_BAD_VALENCES)
        elif self.valence_sum[identity
            ] < ion_params.cvbs_lower or self.valence_sum[identity
            ] > ion_params.cvbs_upper:
            if strict_rules:
                inaccuracies.add(self.BAD_VALENCES)
            else:
                ignored.add(self.BAD_VALENCES)
        self.score[identity] = abs(self.valence_sum[identity] - ion_params.
            cvbs_expected)

    def check_fpp_ratio(self, ion_params, wavelength, fpp_ratio_min=0.3,
        fpp_ratio_max=1.05):
        """
    Compare the refined and theoretical f'' values if available.

    Parameters
    ----------
    ion_params : mmtbx.ions.metal_parameters
    wavelength : float
    fpp_ratio_min : float, optional
    fpp_ratio_max : float, optional

    Returns
    -------
    float
        f'' / f''_expected
    """
        identity = str(ion_params)
        inaccuracies = self.inaccuracies.get(identity, None)
        if inaccuracies is None:
            inaccuracies = self.inaccuracies[identity] = set()
        if ion_params.element.upper() in ['MG', 'NA']:
            if (self.fpp is not None or self.peak_anom is not None and self
                .peak_anom > 1):
                inaccuracies.add(self.BAD_FPP)
        elif wavelength is not None and self.anomalous_flag:
            fpp_expected_sasaki = sasaki.table(ion_params.element).at_angstrom(
                wavelength).fdp()
            fpp_expected_henke = henke.table(ion_params.element).at_angstrom(
                wavelength).fdp()
            self.fpp_expected[identity] = max(fpp_expected_sasaki,
                fpp_expected_henke)
            if self.fpp is not None and self.fpp_expected[identity] != 0:
                self.fpp_ratios[identity] = self.fpp / self.fpp_expected[
                    identity]
                if self.fpp_ratios[identity
                    ] > fpp_ratio_max or self.fpp >= 0.2 and self.fpp_ratios[
                    identity] < fpp_ratio_min:
                    inaccuracies.add(self.BAD_FPP)
            elif self.fpp_expected[identity] > 0.75 and self.peak_anom < 2:
                inaccuracies.add(self.BAD_FPP)
        return self.fpp_ratios.get(identity)

    def show_properties(self, identity, out=sys.stdout):
        """
    Show atomic properties that are independent of the suspected identity.

    Parameters
    ----------
    identity : mmtbx.ions.metal_parameters
    out : file, optional
    """
        print('%s:' % self.atom.id_str(), file=out)
        b_flag = ''
        if self.LOW_B in self.inaccuracies[identity]:
            b_flag = ' <<<'
        elif self.HIGH_B in self.inaccuracies[identity]:
            b_flag = ' !!!'
        print('  B-factor:      %6.2f%s' % (self.atom.b, b_flag), file=out)
        occ_flag = ''
        if self.LOW_OCC in self.inaccuracies[identity]:
            occ_flag = ' !!!'
        elif self.HIGH_OCC in self.inaccuracies[identity]:
            occ_flag = ' <<<'
        print('  Occupancy:     %6.2f%s' % (self.atom.occ, occ_flag), file=out)
        twofofc_flag = ''
        if self.NO_2FOFC_PEAK in self.inaccuracies[identity]:
            twofofc_flag = ' !!!'
        elif self.HIGH_2FOFC in self.inaccuracies[identity]:
            twofofc_flag = ' <<<'
        print('  2mFo-DFc map:  %6.2f%s' % (self.peak_2fofc, twofofc_flag),
            file=out)
        fofc_flag = ''
        if self.FOFC_PEAK in self.inaccuracies[identity]:
            fofc_flag = ' <<<'
        elif self.FOFC_HOLE in self.inaccuracies[identity]:
            fofc_flag = ' !!!'
        print('  mFo-DFc map:   %6.2f%s' % (self.peak_fofc, fofc_flag),
            file=out)
        if self.peak_anom is not None:
            anom_flag = ''
            if self.ANOM_PEAK in self.inaccuracies[identity]:
                anom_flag = ' <<<'
            elif self.NO_ANOM_PEAK in self.inaccuracies[identity]:
                anom_flag = ' !!!'
            print('  Anomalous map: %6.2f%s' % (self.peak_anom, anom_flag),
                file=out)
        if self.estimated_weight is not None:
            print('  Approx. mass:  %6d' % self.estimated_weight, file=out)
        if self.fpp is not None:
            fpp_flag = ''
            if self.fpp >= 0.2:
                fpp_flag = ' <<<'
            print("  f'':           %6.2f%s" % (self.fpp, fpp_flag), file=out)
            print("  f'' ratio:     %s" % format_value('%6.2f', self.
                fpp_ratios.get(identity)), file=out)
        if self.nearby_atoms is not None:
            angstrom = u'Å'.encode('utf-8', 'strict')
            degree = u'°'.encode('utf-8', 'strict')
            print('  Nearby atoms: (%d within 3.0 %s)' % (len([i for i in
                self.nearby_atoms if i.distance() < 3]), angstrom), file=out)
            for contact in self.nearby_atoms:
                print('    %s (%5.3f %s)' % (contact.id_str(), contact.
                    distance(), angstrom), file=out)
            if self.geometries:
                print('  Coordinating geometry:', file=out)
                for geometry, deviation in self.geometries:
                    print('    %-15s (average deviation: %.3f%s)' % (
                        geometry, deviation, degree), file=out)

    def show_ion_results(self, identity=None, out=sys.stdout, confirmed=
        False, valence_used=True):
        """
    Show statistics for a proposed element identity.

    Parameters
    ----------
    identity : mmtbx.ions.metal_parameters
    out : file, optional
    confirmed : bool, optional
    valence_used : bool, optional
    """
        if not identity:
            identity = self.identity(self.atom)
        inaccuracies = self.inaccuracies.get(identity, set([]))
        ignored = self.ignored.get(identity, set([]))
        if identity != self.identity():
            if confirmed:
                print('  Probable cation: %s' % identity, file=out)
            else:
                print('  Atom as %s:' % identity, file=out)
        else:
            print('    %s:' % self.atom.id_str(), file=out)
        if identity in self.vector_sum and self.vector_sum[identity
            ] is not None:
            problem = (self.BAD_VECTORS in inaccuracies or self.BAD_VECTORS in
                ignored)
            print('    Vector sum:  %6.3f %s' % (self.vector_sum[identity],
                ' !!!' if problem else ''), file=out)
        if identity in self.valence_sum and self.valence_sum[identity
            ] is not None:
            problem = inaccuracies.union(ignored).intersection([self.
                BAD_VALENCES, self.VERY_BAD_VALENCES])
            print('    Valence sum: %6.3f' % self.valence_sum[identity],
                file=out)
            if valence_used:
                print('(expected: %6.3f) %s' % (self.expected_params[
                    identity].cvbs_expected, ' !!!' if problem else ''),
                    end=' ', file=out)
        if self.NO_GEOMETRY in inaccuracies:
            print('    No distinct geometry !!!', file=out)
        if self.BAD_GEOMETRY in inaccuracies:
            print('    Unexpected geometry  !!!', file=out)
        bad_coord = [self.LIKE_COORD, self.BAD_COORD_ATOM, self.
            BAD_COORD_RESIDUE]
        if inaccuracies.intersection(bad_coord):
            print('    Bad coordinating atom%s:' % ('s' if len(self.
                bad_coords[identity]) != 1 else ''), file=out)
            angstrom = u'Å'.encode('utf-8', 'strict').strip()
            for atom, vector in self.bad_coords[identity]:
                print('    %s (%5.3f %s) !!!' % (atom.id_str(), abs(vector),
                    angstrom), file=out)
        if self.TOO_FEW_NON_WATERS in inaccuracies:
            print('    Too few coordinating non-waters !!!', file=out)
        if self.TOO_FEW_COORD in inaccuracies:
            print('    Too few coordinating atoms !!!', file=out)
        if self.TOO_MANY_COORD in inaccuracies:
            print('    Too many coordinating atoms !!!', file=out)
        if self.fpp is not None and identity in self.fpp_ratios:
            print("    f'' ratio:   %6.3f%s" % (self.fpp_ratios[identity], 
                ' !!!' if self.BAD_FPP in inaccuracies else ''), file=out)

    def looks_like_nucleotide_phosphate_site(self,
        min_phosphate_oxygen_atoms=2, distance_cutoff=2.5):
        """
    Decide whether the atom is coordinating phosphate oxygens from a
    nucleotide, based on common atom names.

    Parameters
    ----------
    min_phosphate_oxygen_atoms : int, optional
    distance_cutoff : float, optional

    Returns
    -------
    bool
    """
        n_phosphate_oxygens = 0
        for contact in self.nearby_atoms:
            atom_name = contact.atom_name()
            if len(atom_name) < 3 or contact.element not in ['O']:
                continue
            if atom_name[0:2] in ['O1', 'O2', 'O3'] and atom_name[2] in ['A',
                'B', 'G']:
                if contact.distance() <= distance_cutoff:
                    n_phosphate_oxygens += 1
        return n_phosphate_oxygens == min_phosphate_oxygen_atoms

    def identity(self, ion=None):
        """
    Covers an atom into a string representing its element and charge.

    Parameters
    ----------
    ion : iotbx.pdb.hierarchy.atom, optional

    Returns
    -------
    str
    """
        if ion is None:
            ion = self.atom
        element = mmtbx.ions.server.get_element(ion)
        charge = mmtbx.ions.server.get_charge(ion)
        return '{}{:+}'.format(element, charge)

    def get_atom_type(self, params, aggressive=False):
        """
    Checks the atom characteristics against what we would expect for a water.
    Updates self with any inaccuracies noticed (Surpringly low b-factor,
    high occupancy, etc).  Note that HEAVY_ION does not necessarily rule out
    NA/MG, as these often have mFo-DFc peaks at high resolution.

    Parameters
    ----------
    params : libtbx.phil.scope_extract
    aggressive : bool, optional

    Returns
    -------
    int
        One of WATER, WATER_POOR, HEAVY_ION, LIGHT_ION
    """
        inaccuracies = self.inaccuracies['HOH'] = set()
        atom_type = WATER
        if self.peak_2fofc < params.min_2fofc_level or self.peak_fofc < -2.0:
            return WATER_POOR
        if self.fpp is not None and self.fpp > params.fpp_max:
            return HEAVY_ION
        if self.fp is not None and self.fp > params.fp_max:
            return HEAVY_ION
        if (self.peak_anom is not None and self.peak_anom > params.
            max_anom_level):
            inaccuracies.add(self.ANOM_PEAK)
            atom_type = HEAVY_ION
        if self.peak_fofc > params.max_fofc_level:
            inaccuracies.add(self.FOFC_PEAK)
            atom_type = HEAVY_ION
        if self.atom.occ > params.max_occ:
            inaccuracies.add(self.HIGH_OCC)
            atom_type = HEAVY_ION
        if self.atom.b < params.min_b_iso:
            inaccuracies.add(self.LOW_B)
            atom_type = HEAVY_ION
        elif self.b_stddev_hoh is not None and self.b_stddev_hoh > 0:
            z_value = (self.b_iso - self.b_mean_hoh) / self.b_stddev_hoh
            if z_value < -params.max_stddev_b_iso:
                inaccuracies.add(self.LOW_B)
            elif self.atom.b < self.b_mean_hoh * params.min_frac_b_iso:
                inaccuracies.add(self.LOW_B)
            if atom_type == WATER and self.LOW_B in inaccuracies:
                atom_type = LIGHT_ION
        if aggressive and atom_type == WATER:
            if self.b_mean_calpha > 0:
                relative_b = self.b_iso / self.b_mean_calpha
                if relative_b < params.min_frac_calpha_b_iso:
                    inaccuracies.add(self.LOW_B)
                    atom_type = LIGHT_ION
            if self.calpha_mean_two_fofc > 0:
                relative_2fofc = self.peak_2fofc / self.calpha_mean_two_fofc
                if relative_2fofc > params.max_frac_calpha_2fofc:
                    inaccuracies.add(self.HIGH_2FOFC)
                    atom_type = LIGHT_ION
        for i_pair, contact1 in enumerate(self.nearby_atoms):
            if contact1.element.strip() == 'O':
                distance = contact1.distance()
                if distance < 2.4:
                    inaccuracies.add(self.CLOSE_CONTACT)
                    if atom_type < LIGHT_ION:
                        atom_type = LIGHT_ION
        return atom_type


<mask token>


def create_manager(pdb_hierarchy, geometry_restraints_manager, fmodel,
    wavelength, params, resolution_factor=0.25, nproc=Auto, verbose=False,
    log=None, manager_class=None):
    """
  Wrapper around mmtbx.ions.identify.manager init method. Retrieves the
  connectivity and xray_structure from fmodel automatically.

  Parameters
  ----------
  pdb_hierarchy : iotbx.pdb.hierarchy.root
  geometry_restraints_manager : cctbx.geometry_restraints.manager.manager
  fmodel : mmtbx.f_model.manager
  wavelength : float
  params : libtbx.phil.scope_extract
  resolution_factor : float, optional
  nproc : int, optional
  verbose : bool, optional
  log : file, optional
  manager_class : class, optional

  Returns
  -------
  mmtbx.ions.identify.manager or mmtbx.ions.svm.manager
  """
    connectivity = geometry_restraints_manager.shell_sym_tables[0
        ].full_simple_connectivity()
    if manager_class is None:
        manager_class = manager
    manager_obj = manager_class(fmodel=fmodel, pdb_hierarchy=pdb_hierarchy,
        xray_structure=fmodel.xray_structure, connectivity=connectivity,
        wavelength=wavelength, params=params, nproc=nproc, verbose=verbose,
        log=log)
    return manager_obj


def _PRINT_DEBUG(*args):
    """
  Prints a debugging message to stderr.
  """
    print(sys.stderr, args, file=sys.stderr)

<mask token>
ion_identification_phil_str = (
    """
require_valence = False
  .type = bool
  .help = Toggles the use of valence calculations
  .short_caption = Require good bond valence
ambiguous_valence_cutoff = 0.5
  .type = float
  .help = "Cutoff to uniquely select one of many metals by comparing its observed and expected valence"
d_min_strict_valence = 1.5
  .type = float
  .short_caption = Resolution limit for strict valence rules
anom_map_type = *residual simple llg
  .type = choice
  .help = Type of anomalous difference map to use.  Default is a residual     map, showing only unmodeled scattering.
find_anomalous_substructure = Auto
  .type = bool
use_phaser = True
  .type = bool
  .help = Toggles the use of Phaser for calculation of f-prime values.
  .short_caption = Use Phaser to calculate f-double-prime
aggressive = False
  .type = bool
  .help = Toggles more permissive settings for flagging waters as heavier     elements.  Not recommended for automated use.
map_sampling_radius = 2.0
  .type = float
  .input_size = 80
  .short_caption = Sampling radius
water
  .short_caption = Water filtering
  .style = box auto_align
{
  min_2fofc_level = 1.8
    .type = float
    .input_size = 80
    .help = Minimum water 2mFo-DFc map value.  Waters below this cutoff will       not be analyzed further.
    .short_caption = Min. allowed 2mFo-DFc map value
  max_fofc_level = 3.0
    .type = float
    .input_size = 80
    .help = Maximum water mFo-DFc map value
    .short_caption = Max. expected mFo-DFc map value
  max_anom_level = 3.0
    .type = float
    .input_size = 80
    .help = Maximum water anomalous map value
    .short_caption = Max. expected anomalous map value
  max_occ = 1.0
    .type = float
    .input_size = 80
    .help = Maximum water occupancy
    .short_caption = Max. expected occupancy
  min_b_iso = 1.0
    .type = float
    .input_size = 80
    .help = Minimum water isotropic B-factor
    .short_caption = Min. expected B-factor
  fp_max = 1.0
    .type = float
  fpp_max = 0
    .type = float
  max_stddev_b_iso = 5
    .type = float
    .input_size = 80
    .short_caption = Max. standard deviations below mean B-iso
  min_frac_b_iso = 0.2
    .type = float
    .input_size = 80
    .short_caption = Min. fraction of mean B-iso
  min_frac_calpha_b_iso = 0.75
    .type = float
  max_frac_calpha_2fofc = 1.2
    .type = float
  min_2fofc_coordinating = 0.9
    .type = float
    .help = Minimum 2mFo-DFc for waters involved in coordination shells.
}
phaser
  .short_caption = Phaser options
  .caption = These parameters control the identification of anomalous     scatterers using Phaser substructure completion.
  .style = box auto_align
{
  llgc_ncycles = None
    .type = int
    .short_caption = Number of cycles
  distance_cutoff = 1.5
    .type = float
    .input_size = 80
    .short_caption = Min. required separation from other atoms
  distance_cutoff_same_site = 0.7
    .type = float
    .input_size = 80
    .short_caption = Max. separation from mapped atom
  fpp_ratio_min = 0.2
    .type = float
    .input_size = 80
    .help = Minimum ratio of refined/theoretical f-double-prime.
    .short_caption = Min. f-double-prime ratio
  fpp_ratio_max = 1.1
    .type = float
    .input_size = 80
    .help = Maximum ratio of refined/theoretical f-double-prime.
    .short_caption = Max. f-double-prime ratio
}
chloride
  .short_caption = Chloride ions
  .style = box auto_align
{
  %s
}
"""
     % halides.chloride_params_str)


def ion_master_phil():
    return libtbx.phil.parse(ion_identification_phil_str, process_includes=True
        )


WATER, WATER_POOR, LIGHT_ION, HEAVY_ION = range(4)
NUC_PHOSPHATE_BINDING = ['MG', 'CA', 'MN']


class manager(object):
    """
  Wrapper object for extracting local environment and adding or modifying
  scatterers.
  """

    def __init__(self, fmodel, pdb_hierarchy, xray_structure, params=None,
        wavelength=None, connectivity=None, nproc=1, verbose=False, log=None):
        if log is None:
            log = null_out()
        self.fmodel = fmodel
        self.atoms_to_props = {}
        self.params = params
        self.wavelength = wavelength
        self.nproc = nproc
        self.phaser_substructure = None
        self.flag_refine_substructure = False
        self.fpp_from_phaser_ax_sites = None
        self.site_fp = None
        self.site_fdp = None
        self.use_svm = getattr(params, 'use_svm', None)
        self._map_values = {}
        self._map_gaussian_fits = {}
        self.anomalous_flag = None
        if fmodel is not None:
            self.anomalous_flag = fmodel.f_obs().anomalous_flag()
        self.update_structure(pdb_hierarchy=pdb_hierarchy, xray_structure=
            xray_structure, connectivity=connectivity, log=log)
        self.update_maps()
        refine_substructure = False
        if params is not None:
            refine_substructure = params.find_anomalous_substructure
        if refine_substructure is Auto:
            refine_substructure = fmodel.f_obs().anomalous_flag(
                ) and wavelength is not None
        if refine_substructure:
            if wavelength is None:
                raise Sorry('Wavelength required when ' +
                    'find_anomalous_substructure=True.')
            elif not fmodel.f_obs().anomalous_flag():
                raise Sorry('Anomalous data required when ' +
                    'find_anomalous_substructure=True.')
            if params.use_phaser and libtbx.env.has_module('phaser'):
                print('  Running Phaser substructure completion...', file=log)
                t1 = time.time()
                phaser_result = find_anomalous_scatterers(fmodel=fmodel,
                    pdb_hierarchy=pdb_hierarchy, wavelength=wavelength,
                    verbose=verbose, log=log, n_cycles=params.phaser.
                    llgc_ncycles)
                t2 = time.time()
                print('    time: %.1fs' % (t2 - t1), file=log)
                if phaser_result is None:
                    print('  ERROR: Phaser substructure completion failed!',
                        file=log)
                else:
                    self.phaser_substructure = phaser_result.atoms()
                    if len(self.phaser_substructure) == 0:
                        print('  No anomalous scatterers found!', file=log)
                    else:
                        print('  %d anomalous scatterers found' % len(self.
                            phaser_substructure), file=log)
                        self.analyze_substructure(log=log, verbose=True)
            else:
                self.flag_refine_substructure = True
                self.refine_anomalous_substructure(log=log)

    def water_selection(self):
        """
    Fetchs the selection for all waters in the model.

    Returns
    -------
    scitbx.array_family.flex.size_t
    """
        sel_cache = self.pdb_hierarchy.atom_selection_cache()
        sel_str = "({}) and element O and altloc ' '".format(' or '.join([(
            'resname ' + i) for i in WATER_RES_NAMES]))
        return sel_cache.selection(sel_str).iselection()

    def refine_anomalous_substructure(self, log):
        """
    Run simple "substructure completion" implemented using CCTBX tools (the
    command-line equivalent is mmtbx.refine_anomalous_substructure).  A faster
    alternative to Phaser, but not clear how effective this is at the moment.

    Parameters
    ----------
    log : file
    """
        from mmtbx.refinement import anomalous_scatterer_groups
        fmodel_tmp = self.fmodel.deep_copy()
        anom_groups = anomalous_scatterer_groups.refine_anomalous_substructure(
            fmodel=fmodel_tmp, pdb_hierarchy=self.pdb_hierarchy, wavelength
            =self.wavelength, reset_water_u_iso=True, verbose=True,
            use_all_anomalous=True, out=log)
        scatterers = fmodel_tmp.xray_structure.scatterers()
        self.use_fdp = flex.bool(scatterers.size(), False)
        self.site_fp = flex.double(scatterers.size(), 0)
        self.site_fdp = flex.double(scatterers.size(), 0)
        for group in anom_groups:
            for i_seq in group.iselection:
                sc = scatterers[i_seq]
                self.use_fdp[i_seq] = True
                self.site_fp[i_seq] = sc.fp
                self.site_fdp[i_seq] = sc.fdp

    def update_structure(self, pdb_hierarchy, xray_structure, connectivity=
        None, log=None, refine_if_necessary=True):
        """
    Set the current atomic data: PDB hierarchy, Xray structure, and simple
    connectivity list.

    Parameters
    ----------
    pdb_hierarchy : iotbx.pdb.hierarchy.root
    xray_structure : cctbx.xray.structure.structure
    connectivity : ...
    log : file, optional
    refine_if_necessary : bool, optional
    """
        self.pdb_hierarchy = pdb_hierarchy
        self.xray_structure = xray_structure
        if self.fmodel:
            self.fmodel.update_xray_structure(xray_structure, update_f_calc
                =True)
        self.sites_frac = self.xray_structure.sites_frac()
        self.sites_cart = self.xray_structure.sites_cart()
        self.connectivity = connectivity
        self.pdb_atoms = pdb_hierarchy.atoms()
        assert not self.pdb_atoms.extract_i_seq().all_eq(0)
        self.unit_cell = xray_structure.unit_cell()
        self.use_fdp = flex.bool(xray_structure.scatterers().size(), False)
        self.ax_chain = None
        self._pair_asu_cache = {}
        self.u_iso_all = xray_structure.extract_u_iso_or_u_equiv()
        sctr_keys = xray_structure.scattering_type_registry().type_count_dict()
        self.hd_present = 'H' in sctr_keys or 'D' in sctr_keys
        sel_cache = pdb_hierarchy.atom_selection_cache()
        self.carbon_sel = sel_cache.selection('element C').iselection()
        self.calpha_sel = sel_cache.selection('name CA and element C'
            ).iselection()
        assert len(self.carbon_sel) > 0
        water_sel = self.water_selection()
        self.n_waters = len(water_sel)
        not_hd_sel = sel_cache.selection('not (element H or element D)'
            ).iselection()
        self.n_heavy = len(not_hd_sel)
        u_iso_all_tmp = self.u_iso_all.select(not_hd_sel)
        self.b_mean_all = adptbx.u_as_b(flex.mean(u_iso_all_tmp))
        self.b_stddev_all = adptbx.u_as_b(u_iso_all_tmp.
            standard_deviation_of_the_sample())
        self.b_mean_calpha = 0
        if len(self.calpha_sel) > 0:
            self.b_mean_calpha = adptbx.u_as_b(flex.mean(self.u_iso_all.
                select(self.calpha_sel)))
        self.b_mean_hoh = self.b_stddev_hoh = None
        if self.n_waters > 0:
            u_iso_hoh = self.u_iso_all.select(water_sel)
            self.b_mean_hoh = adptbx.u_as_b(flex.mean(u_iso_hoh))
            self.b_stddev_hoh = adptbx.u_as_b(u_iso_hoh.
                standard_deviation_of_the_sample())
        if self.phaser_substructure is not None:
            self.analyze_substructure(log=log)
        elif self.flag_refine_substructure and refine_if_necessary:
            self.refine_anomalous_substructure(log=log)

    def get_initial_b_iso(self):
        """
    Calculates the isotropic b-factor to assign to newly labeled ions during the
    building process. Uses either the mean b-factor of waters or of all atoms if
    the former is unavailable.

    Returns
    -------
    float
    """
        if getattr(self, 'b_mean_hoh', None) is not None:
            return self.b_mean_hoh
        else:
            return self.b_mean_all

    def get_map(self, map_type):
        """
    Creates the real-space version of a given map type.

    Parameters
    ----------
    map_type : str

    Returns
    -------
    scitbx.array_family.flex.double
    """
        map_coeffs = self.fmodel.map_coefficients(map_type=map_type,
            exclude_free_r_reflections=True, fill_missing=True,
            pdb_hierarchy=self.pdb_hierarchy)
        if map_coeffs is None:
            return None
        return map_coeffs.fft_map(resolution_factor=0.25).apply_sigma_scaling(
            ).real_map_unpadded()

    def update_maps(self):
        """
    Generate new maps for the current structure, including anomalous map if
    data are anomalous.

    If Phaser is installed and anomalous data are available, the anomalous
    log-likelihood gradient (LLG) map can be used instead of the conventional
    anomalous difference map. This is only really useful if the anomalous
    scattering of existing atoms is modeled (and ideally, refined).
    """
        if self.fmodel is None:
            return

        def fft_map(map_coeffs, resolution_factor=0.25):
            return map_coeffs.fft_map(resolution_factor=resolution_factor
                ).apply_sigma_scaling().real_map_unpadded()
        map_types = ['2mFo-DFc', 'mFo-DFc']
        map_keys = ['2mFo-DFc', 'mFo-DFc']
        if self.fmodel.f_obs().anomalous_flag():
            if self.params.anom_map_type == 'phaser':
                map_types.append('llg')
            elif self.params.anom_map_type == 'residual':
                map_types.append('anom_residual')
            else:
                map_types.append('anom')
            map_keys.append('anom')
        if self.use_svm:
            map_types.append('mFo')
            map_keys.append('mFo')
        sites_frac = self.xray_structure.sites_frac()
        sites_cart = self.xray_structure.sites_cart()
        self._principal_axes_of_inertia = [None] * len(sites_frac)
        self._map_variances = [None] * len(sites_frac)
        self._map_gaussian_fits = {}
        self.calpha_mean_two_fofc = 0
        for map_type, map_key in zip(map_types, map_keys):
            real_map = self.get_map(map_type)
            if real_map is not None:
                self._map_values[map_key] = flex.double(sites_frac.size(), 0)
                self._map_gaussian_fits[map_key] = [None] * len(sites_frac)
                for i_seq, site_frac in enumerate(sites_frac):
                    atom = self.pdb_atoms[i_seq]
                    resname = atom.fetch_labels().resname.strip().upper()
                    if (resname in WATER_RES_NAMES + mmtbx.ions.SUPPORTED or
                        atom.segid.strip().upper() in ['ION']):
                        value = real_map.eight_point_interpolation(site_frac)
                        self._map_values[map_key][i_seq] = value
                        if self.use_svm:
                            gaussian_fit = utils.fit_gaussian(unit_cell=
                                self.unit_cell, site_cart=atom.xyz,
                                real_map=real_map)
                            self._map_gaussian_fits[map_key][i_seq
                                ] = gaussian_fit
                if map_type in ['2mFo-DFc']:
                    from cctbx import maptbx
                    for i_seq, site_cart in enumerate(sites_cart):
                        resname = self.pdb_atoms[i_seq].fetch_labels(
                            ).resname.strip()
                        if resname in WATER_RES_NAMES + mmtbx.ions.SUPPORTED:
                            p_a_i = maptbx.principal_axes_of_inertia(real_map
                                =real_map, site_cart=site_cart, unit_cell=
                                self.unit_cell, radius=self.params.
                                map_sampling_radius)
                            self._principal_axes_of_inertia[i_seq] = p_a_i
                            variance = maptbx.spherical_variance_around_point(
                                real_map=real_map, unit_cell=self.unit_cell,
                                site_cart=site_cart, radius=self.params.
                                map_sampling_radius)
                            self._map_variances[i_seq] = variance
                        elif i_seq in self.calpha_sel:
                            self.calpha_mean_two_fofc += (real_map.
                                eight_point_interpolation(sites_frac[i_seq]))
                del real_map
        if self.calpha_mean_two_fofc > 0:
            n_calpha = len(self.calpha_sel)
            assert n_calpha > 0
            self.calpha_mean_two_fofc /= n_calpha
        self.carbon_fo_values = None
        if len(self.carbon_sel) > 0:
            self.carbon_fo_values = flex.double()
            self._map_values['mFo'] = flex.double(sites_frac.size(), 0)
            fo_map = fft_map(self.fmodel.map_coefficients(map_type='mFo',
                exclude_free_r_reflections=True, fill_missing=True))
            for i_seq, site_frac in enumerate(sites_frac):
                resname = self.pdb_atoms[i_seq].fetch_labels().resname.strip()
                element = self.pdb_atoms[i_seq].element.strip()
                if (element == 'C' or element == 'O' and resname in
                    WATER_RES_NAMES):
                    map_value = fo_map.eight_point_interpolation(site_frac)
                    self._map_values['mFo'][i_seq] = map_value
                    if element == 'C':
                        self.carbon_fo_values.append(map_value)
            del fo_map

    def show_current_scattering_statistics(self, out=sys.stdout):
        """
    Prints out information about an entire model's scattering statistics, such
    as mean map heights and b-factors for carbons and waters.

    Parameters
    ----------
    out : file, optional
    """
        print('', file=out)
        print('Model and map statistics:', file=out)
        print('  mean mFo map height @ carbon: %s' % format_value('%.2f',
            flex.max(self.carbon_fo_values)), file=out)
        if self.calpha_mean_two_fofc > 0:
            print('  mean 2mFo-DFc map height @ C-alpha: %s' % format_value
                ('%.2f', self.calpha_mean_two_fofc), file=out)
        print('  mean B-factor: %s' % format_value('%.2f', self.b_mean_all),
            file=out)
        if self.b_mean_calpha > 0:
            print('  mean C-alpha B-factor: %s' % format_value('%.2f', self
                .b_mean_calpha), file=out)
        print('  mean water B-factor: %s' % format_value('%.2f', self.
            b_mean_hoh), file=out)
        n_water_fofc_peaks = 0
        n_water_anom_peaks = 0
        water_sel = self.water_selection()
        print('  %d water molecules' % len(water_sel), file=out)
        for i_seq in water_sel:
            map_stats = self.map_stats(i_seq)
            if map_stats.fofc >= 3.0:
                n_water_fofc_peaks += 1
            if map_stats.anom is not None and map_stats.anom >= 3.0:
                n_water_anom_peaks += 1
        print('  %d waters have mFo-DFc map >= 3.0 sigma' %
            n_water_fofc_peaks, file=out)
        if self.anomalous_flag:
            print('  %d waters have anomalous map >= 3.0 sigma' %
                n_water_anom_peaks, file=out)
        print('', file=out)

    def get_strict_valence_flag(self):
        """
    Checks whether the resolution of a model is high enough to use more strict
    thresholds for ion valences.

    Returns
    -------
    bool
    """
        d_min = self.fmodel.f_obs().d_min()
        return d_min < self.params.d_min_strict_valence

    def find_nearby_atoms(self, i_seq, far_distance_cutoff=3.0,
        near_distance_cutoff=1.5, filter_by_bonding=True,
        filter_by_two_fofc=True):
        """
    Given site in the structure, return a list of contacts, optionally
    filtering by connectivity and 2mFo-DFc map level.

    Parameters
    ----------
    i_seq : int
    far_distance_cutoff : float, optional
    near_distance_cutoff : float, optional
    filter_by_bonding : bool, optional
    fitler_by_two_fofc : bool, optional

    Returns
    -------
    list of mmtbx.ions.environment.atom_contact
    """
        assert i_seq < len(self.sites_frac)
        pair_asu_table, asu_mappings, asu_table = self._pair_asu_cache.get(
            far_distance_cutoff, (None, None, None))
        if pair_asu_table is None:
            pair_asu_table = self.xray_structure.pair_asu_table(distance_cutoff
                =far_distance_cutoff)
            asu_mappings = pair_asu_table.asu_mappings()
            asu_table = pair_asu_table.table()
            self._pair_asu_cache[far_distance_cutoff
                ] = pair_asu_table, asu_mappings, asu_table
        contacts = environment.find_nearby_atoms(i_seq=i_seq,
            xray_structure=self.xray_structure, pdb_atoms=self.pdb_atoms,
            asu_mappings=asu_mappings, asu_table=asu_table, connectivity=
            self.connectivity, far_distance_cutoff=far_distance_cutoff,
            near_distance_cutoff=near_distance_cutoff, filter_by_bonding=
            filter_by_bonding)
        if filter_by_two_fofc:
            filtered = []
            for contact in contacts:
                if contact.resname() in WATER_RES_NAMES:
                    two_fofc = self._map_values['2mFo-DFc'][contact.
                        atom_i_seq()]
                    if two_fofc < self.params.water.min_2fofc_coordinating:
                        continue
                filtered.append(contact)
            contacts = filtered
        return contacts

    def guess_b_iso_real(self, i_seq):
        """
    Guess an approximate B_iso for an atom by averaging the values for
    coordinating atoms.  This should partially compensate for the deflation of
    the B-factor due to incorrect scattering type, and consequently make the
    occupancy refinement more accurate.

    Parameters
    ----------
    i_seq : int

    Returns
    -------
    float
    """
        contacts = self.find_nearby_atoms(i_seq, far_distance_cutoff=3.0)
        if len(contacts) == 0:
            return adptbx.u_as_b(self.u_iso_all[i_seq])
        u_iso_sum = 0
        for contact in contacts:
            u_iso_sum += self.u_iso_all[contact.atom_i_seq()]
        return adptbx.u_as_b(u_iso_sum / len(contacts))

    def principal_axes_of_inertia(self, i_seq):
        """
    Extracts the map grid points around a site, and calculates the axes of
    inertia (using the density values as weights). This is used to calculate
    the sphericity of the blob of density around a site.

    Parameters
    ----------
    i_seq : int

    Returns
    -------
    ...
    """
        return self._principal_axes_of_inertia[i_seq]

    def get_map_sphere_variance(self, i_seq):
        """
    Calculate the density levels for points on a sphere around a given point.
    This will give us some indication whether the density falls off in the
    expected manner around a single atom.

    Parameters
    ----------
    i_seq : int

    Returns
    -------
    float
    """
        return self._map_variances[i_seq]

    def get_b_iso(self, i_seq):
        """
    Calculates the isotropic b-factor of a site.

    Parameters
    ----------
    i_seq : int

    Returns
    -------
    float
    """
        sc = self.xray_structure.scatterers()[i_seq]
        return adptbx.u_as_b(sc.u_iso_or_equiv(self.unit_cell))

    def map_stats(self, i_seq):
        """
    Given a site in the structure, find the signal of the 2FoFc, FoFc, and
    anomalous map (When available).

    Parameters
    ----------
    i_seq : int

    Returns
    -------
    group_args
        Object with .two_fofc, .fofc, and .anom properties for the associated
        signals in each map.
    """
        value_2fofc = self._map_values['2mFo-DFc'][i_seq]
        value_fofc = self._map_values['mFo-DFc'][i_seq]
        value_anom = None
        if 'anom' in self._map_values:
            value_anom = self._map_values['anom'][i_seq]
        return group_args(two_fofc=value_2fofc, fofc=value_fofc, anom=
            value_anom)

    def get_map_gaussian_fit(self, map_type, i_seq):
        """
    Retrieves the two parameters of a gaussian function, fit to a given map at
    a site.

    Parameters
    ----------
    map_type : str
    i_seq : int

    Returns
    -------
    (tuple of float, float) or None
    """
        map_gaussians = self._map_gaussian_fits.get(map_type, None)
        if map_gaussians is not None:
            return map_gaussians[i_seq]
        return None

    def guess_molecular_weight(self, i_seq):
        """
    Guesses the molecular weight of a site by scaling the signal from
    the mFo map by the average signal from carbon atoms.

    Parameters
    ----------
    i_seq : int

    Returns
    -------
    float
    """
        map_values = self._map_values.get('mFo', None)
        if map_values is None:
            return None
        height = map_values[i_seq]
        mean_carbon = flex.mean(self.carbon_fo_values)
        assert mean_carbon > 0
        return 6 * height / mean_carbon

    def _find_atoms_near_site(self, site_cart, distance_cutoff=1.5,
        distance_cutoff_same_site=0.5):
        """
    Given an XYZ coordinate, finds atoms near that site, separating out those
    which are close enough to be essentially equivalent.  Used to analyze the
    anomalously-scattering substructure calculated by Phaser.

    Parameters
    ----------
    site_cart : tuple of float, float, float
    distance_cutoff : float, optional
    distance_cutoff_same_site : float, optional

    Returns
    -------
    same_atoms : list of group_args
        List of atoms within distance_cutoff_same_site of site_cart, and and
        other_atoms, the rest of the atoms within distance_cutoff of site_cart.
    """
        site_frac = self.unit_cell.fractionalize(site_cart=site_cart)
        sites_frac = flex.vec3_double([site_frac])
        asu_mappings = self.xray_structure.asu_mappings(buffer_thickness=
            distance_cutoff + 0.1)
        asu_mappings.process_sites_frac(sites_frac, min_distance_sym_equiv=
            self.xray_structure.min_distance_sym_equiv())
        pair_generator = crystal.neighbors_fast_pair_generator(asu_mappings
            =asu_mappings, distance_cutoff=distance_cutoff)
        n_xray = self.xray_structure.scatterers().size()
        same_atoms = []
        other_atoms = []
        for pair in pair_generator:
            if (pair.i_seq < n_xray and pair.j_seq < n_xray or pair.i_seq >=
                n_xray and pair.j_seq >= n_xray):
                continue
            rt_mx_i = asu_mappings.get_rt_mx_i(pair)
            rt_mx_j = asu_mappings.get_rt_mx_j(pair)
            rt_mx_ji = rt_mx_i.inverse().multiply(rt_mx_j)
            new_site_frac = rt_mx_ji * site_frac
            new_site_cart = self.unit_cell.orthogonalize(site_frac=
                new_site_frac)
            atom_seq = pair.i_seq if pair.i_seq < n_xray else pair.j_seq
            site_info = group_args(i_seq=atom_seq, rt_mx=rt_mx_ji,
                new_site_cart=new_site_cart, distance=sqrt(pair.dist_sq))
            if site_info.distance < distance_cutoff_same_site:
                same_atoms.append(site_info)
            else:
                other_atoms.append(site_info)
        same_atoms.sort(key=lambda x: x.distance)
        other_atoms.sort(key=lambda x: x.distance)
        return same_atoms, other_atoms

    def analyze_substructure(self, log=None, verbose=True):
        """
    Given a list of AX pseudo-atoms placed by Phaser, finds the nearest real
    atoms, and if possible determines their equivalence.

    Parameters
    ----------
    log : file, optional
    verbose : bool, optional
    """

        def ax_atom_id(atom):
            """
      Parameters
      ----------
      atom : iotbx.pdb.hierarchy.atom

      Returns
      -------
      str
      """
            return 'AX %s (fpp=%.3f)' % (atom.serial.strip(), atom.occ)
        assert self.phaser_substructure is not None
        self.fpp_from_phaser_ax_sites = flex.double(self.pdb_atoms.size(), -1)
        if log is None or not verbose:
            log = null_out()

        def _filter_site_infos(site_infos):
            """
      Parameters
      ----------
      site_infos : list of group_args

      Returns
      -------
      list of group_args
      """
            from collections import OrderedDict
            i_seqs = OrderedDict()
            for site_info in site_infos:
                if site_info.i_seq in i_seqs:
                    i_seqs[site_info.i_seq].append(site_info)
                else:
                    i_seqs[site_info.i_seq] = [site_info]
            for val in i_seqs.values():
                if any(i.rt_mx.is_unit_mx() for i in val):
                    for site_info in val:
                        if not site_info.rt_mx.is_unit_mx():
                            val.remove(site_info)
                val.sort(key=lambda x: x.distance)
            return [i[0] for i in i_seqs.values()]
        make_sub_header('Analyzing Phaser anomalous substructure', out=log)
        for atom in self.phaser_substructure:
            print(ax_atom_id(atom), file=log)
            same_atoms, other_atoms = self._find_atoms_near_site(atom.xyz,
                distance_cutoff=self.params.phaser.distance_cutoff,
                distance_cutoff_same_site=self.params.phaser.
                distance_cutoff_same_site)
            same_atoms, other_atoms = _filter_site_infos(same_atoms
                ), _filter_site_infos(other_atoms)
            if len(same_atoms) == 0:
                print('  No match for %s' % ax_atom_id(atom), file=log)
                for other_atom in other_atoms:
                    print('    %s (distance = %.3f)' % (self.pdb_atoms[
                        other_atom.i_seq].id_str(), other_atom.distance),
                        file=log)
            elif len(same_atoms) == 1:
                print('  %s maps to %s' % (ax_atom_id(atom), self.pdb_atoms
                    [same_atoms[0].i_seq].id_str()), file=log)
                self.use_fdp[same_atoms[0].i_seq] = True
                self.fpp_from_phaser_ax_sites[same_atoms[0].i_seq] = atom.occ
            else:
                print('  ambiguous results for %s:' % ax_atom_id(atom),
                    file=log)
                for same_atom in same_atoms:
                    print('    %s' % self.pdb_atoms[same_atom.i_seq].id_str
                        (), file=log)
        print('', file=log)

    def get_fpp(self, i_seq):
        """
    Retrieve the refined f'' for a site.  Because this can come from either the
    built-in anomalous refinement or Phaser, it is handled differently than
    the f' retrieval.

    Parameters
    ----------
    i_seq : int

    Returns
    -------
    float or None
    """
        if not self.use_fdp[i_seq]:
            return None
        if self.fpp_from_phaser_ax_sites is None:
            return self.site_fdp[i_seq]
        fpp = self.fpp_from_phaser_ax_sites[i_seq]
        if fpp < 0:
            fpp = None
        return fpp

    def get_fp(self, i_seq):
        """
    Retrieve the refined f' for a site.

    Parameters
    ----------
    i_seq : int

    Returns
    -------
    float or None
    """
        if self.site_fp is None or not self.use_fdp[i_seq]:
            return None
        return self.site_fp[i_seq]

    def looks_like_halide_ion(self, i_seq, element='CL',
        assume_hydrogens_all_missing=Auto):
        """
    Given a site, analyze the nearby atoms (taking geometry into account) to
    guess whether it might be a halide ion.  Among other things, halides will
    often be coordinated by amide hydrogens, and not in close contact with
    negatively charged atoms.  Note that this procedure has a very high
    false positive rate when used by itself, so additional information about
    the electron count (map, occ, b) is absolutely essential.

    Parameters
    ----------
    i_seq : int
    element : str
    assume_hydrogens_all_missing : bool, optional

    Returns
    -------
    bool
    """
        atom = self.pdb_atoms[i_seq]
        sites_frac = self.xray_structure.sites_frac()
        assert element.upper() in mmtbx.ions.HALIDES
        if (self.b_mean_hoh is not None and atom.b > self.b_mean_hoh - self
            .b_stddev_hoh):
            return False
        nearby_atoms = self.find_nearby_atoms(i_seq, far_distance_cutoff=
            4.0, filter_by_bonding=False)
        favorable_environment = halides.is_favorable_halide_environment(i_seq
            =i_seq, contacts=nearby_atoms, pdb_atoms=self.pdb_atoms,
            sites_frac=self.xray_structure.sites_frac(), unit_cell=self.
            unit_cell, connectivity=self.connectivity, params=self.params.
            chloride, assume_hydrogens_all_missing=assume_hydrogens_all_missing
            )
        pai = self.principal_axes_of_inertia(i_seq)
        map_variance = self.get_map_sphere_variance(i_seq)
        good_map_falloff = False
        if (map_variance is not None and map_variance.mean < 1.5 and 
            map_variance.standard_deviation < 0.4):
            good_map_falloff = True
        return favorable_environment

    def analyze_water(self, i_seq, debug=True, candidates=Auto, no_final=
        False, out=sys.stdout):
        """
    Examines the environment around a single atom to determine if it is actually
    a misidentified metal ion.

    no_final is used internally, when candidates is not Auto, to try all of the
    Auto without selecting any as a final choice when none of the elements in
    candidates appear to probable ion identities.

    Parameters
    ----------
    i_seq : int
    debug : bool, optional
    candidates : list of str, optional
    no_file : bool, optional
    out : file, optional

    Returns
    -------
    mmtbx.ions.identify.water_result
    """
        atom = self.pdb_atoms[i_seq]
        auto_candidates = candidates is Auto
        if auto_candidates:
            candidates = mmtbx.ions.DEFAULT_IONS
        elif isinstance(candidates, string_types):
            candidates = candidates.replace(',', ' ').split()
        candidates = [i.strip().upper() for i in candidates]
        if candidates == ['X']:
            candidates = []
        resname = atom.fetch_labels().resname.strip().upper()
        assert resname in WATER_RES_NAMES
        atom_props = self.atoms_to_props[i_seq]
        final_choice = None
        atom_type = atom_props.get_atom_type(params=self.params.water,
            aggressive=self.params.aggressive)
        if atom_type == WATER_POOR:
            return None
        assert not self.use_svm
        filtered_candidates = []
        halide_candidates = False
        nuc_phosphate_site = atom_props.looks_like_nucleotide_phosphate_site()
        max_carbon_fo_map_value = sys.maxsize
        if self.carbon_fo_values is not None:
            max_carbon_fo_map_value = flex.max(self.carbon_fo_values)
        for symbol in candidates:
            elem = mmtbx.ions.server.get_metal_parameters(symbol)
            if elem is None:
                if symbol in mmtbx.ions.HALIDES:
                    halide_candidates = True
                    continue
                else:
                    raise Sorry("Element '%s' not supported!" % symbol)
            if atom_type in [WATER, LIGHT_ION] and symbol not in ['NA',
                'MG', 'F', 'NE']:
                continue
            if nuc_phosphate_site and not symbol in NUC_PHOSPHATE_BINDING:
                continue
            n_elec = sasaki.table(symbol.upper()).atomic_number() - elem.charge
            if n_elec <= 12 and (atom_props.fpp is not None and atom_props.
                fpp > 0.1):
                continue
            mass_ratio = atom_props.estimated_weight / max(n_elec, 1)
            if mass_ratio < 0.4 and atom_type == WATER:
                continue
            filtered_candidates.append(elem)

        def try_candidates(require_valence=True):
            """
      Parameters
      ----------
      require_valence : bool, optional

      Returns
      -------
      list of tuple of mmtbx.ions.metal_parameters, float
      list of tuple of mmtbx.ions.metal_parameters, float
      """
            reasonable = []
            unreasonable = []
            for elem_params in filtered_candidates:
                atom_props.check_ion_environment(ion_params=elem_params,
                    wavelength=self.wavelength, require_valence=require_valence
                    )
                atom_props.check_fpp_ratio(ion_params=elem_params,
                    wavelength=self.wavelength, fpp_ratio_min=self.params.
                    phaser.fpp_ratio_min, fpp_ratio_max=self.params.phaser.
                    fpp_ratio_max)
                identity = str(elem_params)
                if atom_props.is_correctly_identified(identity=identity):
                    reasonable.append((elem_params, atom_props.score[identity])
                        )
                else:
                    unreasonable.append((elem_params, atom_props.score[
                        identity]))
            return reasonable, unreasonable
        reasonable, unreasonable = try_candidates(require_valence=True)
        valence_used = True
        if len(reasonable) == 0 and not self.params.require_valence:
            reasonable, unreasonable = try_candidates(require_valence=False)
            if len(reasonable) > 0:
                valence_used = False
        looks_like_halide = False
        if not (reasonable or atom_type < HEAVY_ION or nuc_phosphate_site):
            candidate_halides = set(candidates).intersection(mmtbx.ions.HALIDES
                )
            filtered_halides = []
            for element in candidate_halides:
                fpp_ratio = atom_props.check_fpp_ratio(ion_params=mmtbx.
                    ions.metal_parameters(element=element, charge=-1),
                    wavelength=self.wavelength, fpp_ratio_min=self.params.
                    phaser.fpp_ratio_min, fpp_ratio_max=self.params.phaser.
                    fpp_ratio_max)
                if fpp_ratio is not None and element != 'CL':
                    if (fpp_ratio < self.params.phaser.fpp_ratio_min or 
                        fpp_ratio > self.params.phaser.fpp_ratio_max):
                        continue
                if self.looks_like_halide_ion(i_seq=i_seq, element=element):
                    filtered_halides.append(element)
            looks_like_halide = len(filtered_halides) > 0
            reasonable += [(mmtbx.ions.metal_parameters(element=halide,
                charge=-1), 0) for halide in filtered_halides]
        if len(reasonable) == 0 and not self.params.require_valence:
            compatible = [params for params in filtered_candidates if
                atom_props.has_compatible_ligands(str(params))]
            for ion_params in compatible:
                atomic_number = sasaki.table(ion_params.element).atomic_number(
                    )
                weight_ratio = 0
                if atom_props.estimated_weight is not None:
                    weight_ratio = atom_props.estimated_weight / (atomic_number
                         - ion_params.charge)
                if (ion_params.element in mmtbx.ions.TRANSITION_METALS and 
                    atom_type == HEAVY_ION and not auto_candidates and
                    atom_props.is_compatible_site(ion_params)):
                    n_good_res = atom_props.number_of_favored_ligand_residues(
                        ion_params, distance=2.7)
                    n_total_coord_atoms = (atom_props.
                        number_of_atoms_within_radius(2.8))
                    if n_good_res >= 1 and 2 <= n_total_coord_atoms <= 6:
                        reasonable.append((ion_params, 0))
                    else:
                        pass
                elif ion_params.element in ['K', 'CA'
                    ] and atom_type == HEAVY_ION and not auto_candidates and atom_props.is_compatible_site(
                    ion_params):
                    n_good_res = atom_props.number_of_favored_ligand_residues(
                        ion_params, distance=2.9, exclude_atoms=['O'])
                    n_bb_oxygen = atom_props.number_of_backbone_oxygens(
                        distance_cutoff=2.9)
                    n_total_coord_atoms = (atom_props.
                        number_of_atoms_within_radius(distance_cutoff=2.9))
                    if (n_good_res + n_bb_oxygen >= 2 and 
                        n_total_coord_atoms >= 4):
                        reasonable.append((ion_params, 0))
                elif atomic_number > 30 and atom_type == HEAVY_ION and (
                    weight_ratio > 0.5 and weight_ratio < 1.05 or 
                    atom_props.fp > 10
                    ) and not auto_candidates and atom_props.is_compatible_site(
                    ion_params, ignore_valence=not self.
                    get_strict_valence_flag()):
                    n_total_coord_atoms = (atom_props.
                        number_of_atoms_within_radius(2.8))
                    if n_total_coord_atoms >= 3:
                        reasonable.append((ion_params, 0))
                else:
                    pass
            if len(compatible) == 1 and not self.get_strict_valence_flag():
                inaccuracies = atom_props.inaccuracies[str(ion_params)]
                if (compatible[0] in atom_props.fpp_ratios and atom_props.
                    BAD_FPP not in inaccuracies and not inaccuracies.
                    intersection([atom_props.BAD_GEOMETRY, atom_props.
                    NO_GEOMETRY])):
                    _PRINT_DEBUG(atom.id_str())
                    _PRINT_DEBUG(filtered_candidates)
                    _PRINT_DEBUG(compatible)
        if len(reasonable) == 1:
            final_choice = reasonable[0][0]
        if not reasonable and not auto_candidates:
            result = self.analyze_water(i_seq=i_seq, debug=debug, no_final=
                True, out=out)
            if result.final_choice is not None:
                return result
        return water_result(atom_props=atom_props, filtered_candidates=
            filtered_candidates, matching_candidates=reasonable,
            rejected_candidates=unreasonable, nuc_phosphate_site=
            nuc_phosphate_site, atom_type=atom_type, looks_like_halide=
            looks_like_halide, ambiguous_valence_cutoff=self.params.
            ambiguous_valence_cutoff, valence_used=valence_used,
            final_choice=final_choice, wavelength=self.wavelength, no_final
            =no_final)

    def _extract_waters(self):
        model = self.pdb_hierarchy.only_model()
        water_i_seqs = []
        for chain in model.chains():
            for residue_group in chain.residue_groups():
                atom_groups = residue_group.atom_groups()
                if len(atom_groups) > 1:
                    continue
                for atom_group in atom_groups:
                    resname = atom_group.resname.strip().upper()
                    if resname in WATER_RES_NAMES:
                        atoms = atom_group.atoms()
                        if len(atoms) == 1:
                            water_i_seqs.append(atoms[0].i_seq)
        return water_i_seqs

    def analyze_waters(self, out=sys.stdout, debug=True, candidates=Auto):
        """
    Iterates through all of the waters in a model, examining the maps and local
    environment to check their identity and suggest likely ions where
    appropriate.

    Parameters
    ----------
    out : file, otpional
    debug : bool, optional
    candidates : list of str, optional

    Returns
    -------
    list of tuple of int, mmtbx.ions.metal_parameters
        i_seq and metal parameters which indicate that waters of i_seq would be
        better changed to that new metal identity.
    """
        waters = self.water_selection()
        print('', file=out)
        print('  %d waters to analyze' % len(waters), file=out)
        if len(waters) == 0:
            return
        nproc = easy_mp.get_processes(self.nproc)
        ions = []
        self.atoms_to_props = dict((i_seq, AtomProperties(i_seq, self)) for
            i_seq in waters)
        if nproc == 1:
            print('', file=out)
            for water_i_seq in waters:
                t1 = time.time()
                water_props = self.analyze_water(i_seq=water_i_seq, debug=
                    debug, candidates=candidates, out=out)
                if water_props is not None:
                    water_props.show_summary(out=out, debug=debug)
                    map_stats = self.map_stats(water_i_seq)
                    if (water_props.final_choice is not None and not
                        water_props.no_final):
                        ions.append((water_i_seq, [water_props.final_choice
                            ], map_stats.two_fofc))
                t2 = time.time()
        else:
            print('  Parallelizing across %d processes' % nproc, file=out)
            print('', file=out)
            analyze_water = _analyze_water_wrapper(manager=self, debug=
                debug, candidates=candidates, out=out)
            results = easy_mp.pool_map(fixed_func=analyze_water, args=
                waters, processes=nproc)
            for result in results:
                if result is None:
                    continue
                water_i_seq, final_choice, result_str = result
                if result_str is not None:
                    print(result_str, file=out)
                if final_choice is not None:
                    map_stats = self.map_stats(water_i_seq)
                    ions.append((water_i_seq, [final_choice], map_stats.
                        two_fofc))
        return sorted(ions, key=operator.itemgetter(2), reverse=True)

    def validate_ion(self, i_seq, out=sys.stdout, debug=True):
        """
    Examines one site in the model and determines if it was correctly modelled,
    returning a boolean indicating correctness.

    Parameters
    ----------
    i_seq : int
    out : file, optional
    debug : bool, optional
    """
        atom_props = self.atoms_to_props[i_seq]
        element = mmtbx.ions.server.get_element(atom_props.atom)
        elem_params = mmtbx.ions.server.get_metal_parameters(element)
        if elem_params is not None:
            atom_type = atom_props.get_atom_type(params=self.params.water)
            atom_props.check_ion_environment(ion_params=elem_params,
                wavelength=self.wavelength, require_valence=self.params.
                require_valence)
            atom_props.check_fpp_ratio(ion_params=elem_params, wavelength=
                self.wavelength, fpp_ratio_min=self.params.phaser.
                fpp_ratio_min, fpp_ratio_max=self.params.phaser.fpp_ratio_max)
        elif element in mmtbx.ions.HALIDES:
            identity = atom_props.identity()
            atom_props.inaccuracies[identity] = set()
            if not self.looks_like_halide_ion(i_seq=i_seq, element=element):
                atom_props.inaccuracies[identity].add(atom_props.BAD_HALIDE)
        else:
            raise Sorry("Element '%s' not supported:\n%s" % (element,
                atom_props.atom.format_atom_record()))
        return atom_props

    def validate_ions(self, out=sys.stdout, debug=True, segid=None):
        """
    Iterate over all the ions built into the model by this module and double
    check their correctness. Looks for tell-tale signs such as negative peaks
    in the mFo-DFc and anomalous difference maps, abnormal b-factor, and
    disallowed chemical environments.

    Prints out a table of bad ions and their information and returns a list of
    their sites' i_seqs.

    Parameters
    ----------
    out : file, optional
    debug : bool, optional
    segid : str, optional
        If present, only validate ions with this segid.
    """
        if segid is None:
            ions = []
            for model in self.pdb_hierarchy.models():
                for chain in model.chains():
                    for residue_group in chain.residue_groups():
                        for atom_group in residue_group.atom_groups():
                            if atom_group.resname.strip(
                                ) in mmtbx.ions.SUPPORTED:
                                atoms = atom_group.atoms()
                                assert len(atoms) == 1
                                for atom in atoms:
                                    ions += atom.i_seq,
        else:
            sel_cache = self.pdb_hierarchy.atom_selection_cache()
            ions = sel_cache.selection('segid {0}'.format(segid)).iselection()
        if len(ions) == 0:
            return
        ion_status = []
        self.atoms_to_props = dict((i_seq, AtomProperties(i_seq, self)) for
            i_seq in ions)
        for i_seq in ions:
            atom_props = self.validate_ion(i_seq=i_seq, debug=debug)
            ion_status.append((atom_props, atom_props.
                is_correctly_identified()))
        scatterers = self.xray_structure.scatterers()
        headers = ('atom', 'occ', 'b_iso', '2mFo-DFc', 'mFo-DFc', 'fp',
            'fdp', 'ratio', 'BVS', 'VECSUM')
        fmt = '%-15s %-5s  %-5s  %-8s  %-7s  %-5s  %-5s  %-5s %-5s  %-6s'
        box = framed_output(out, title='Validating new ions', width=80)
        print(fmt % headers, file=box)
        print(' ' + '-' * 75, file=box)
        for props, okay_flag in ion_status:
            i_seq = props.atom.i_seq
            sc = scatterers[i_seq]
            fp = fdp = None
            if sc.flags.use_fp_fdp:
                fp = sc.fp
                fdp = sc.fdp
            b_iso = adptbx.u_as_b(sc.u_iso_or_equiv(unit_cell=self.unit_cell))
            identity = props.identity()

            def ff(fs, val):
                return format_value(fs, val, replace_none_with='---')
            print(fmt % (props.atom.id_str(suppress_segid=True)[5:-1], ff(
                '%.2f', props.atom.occ), ff('%.2f', b_iso), ff('%.2f',
                props.peak_2fofc), ff('%.2f', props.peak_fofc), ff('%.2f',
                fp), ff('%.2f', fdp), ff('%.2f', None if self.wavelength is
                None else fdp / sasaki.table(mmtbx.ions.server.get_element(
                props.atom)).at_angstrom(self.wavelength).fdp()), ff('%.2f',
                props.valence_sum.get(identity)), ff('%.2f', props.
                vector_sum.get(identity))), file=box)
            if not okay_flag:
                print('\n'.join('!!! ' + props.error_strs[i] for i in props
                    .inaccuracies[identity]), file=box)
            print('', file=box)
        box.close()


class _analyze_water_wrapper(object):
    """
  Simple wrapper for calling manager.analyze_water with keyword arguments
  in a parallelized loop.  Because the water_result object is not pickle-able,
  we only return the final ion choice and summary string.
  """

    def __init__(self, manager, **kwds):
        self.manager = manager
        self.kwds = dict(kwds)

    def __call__(self, i_seq):
        try:
            result = self.manager.analyze_water(i_seq, **self.kwds)
            out = StringIO()
            if result is not None:
                result.show_summary(out=out, debug=self.kwds.get('debug', 
                    False))
            result_str = out.getvalue()
            if result_str == '':
                result_str = None
            final_choice = None
            if not getattr(result, 'no_final', False):
                final_choice = getattr(result, 'final_choice', None)
            return i_seq, final_choice, result_str
        except KeyboardInterrupt:
            return None, None, None


class _validate_ion_wrapper(object):
    """
  Simple wrapper for calling manager.validate_ion with keyword arguments
  in a parallelized loop.  Because the water_result object is not pickle-able,
  we only return the final ion choice and summary string.
  """

    def __init__(self, manager, **kwds):
        self.manager = manager
        self.kwds = dict(kwds)

    def __call__(self, i_seq):
        try:
            correct = self.manager.validate_ion(i_seq, **self.kwds)
            return i_seq, correct
        except KeyboardInterrupt:
            return None, None


class ion_result(validation.atom):
    """
  Class for validation results for an ion site. This includes information about
  both the atom modeled at that site as well as its chemical and scattering
  environment.
  """
    __slots__ = validation.atom.__slots__ + ['chem_env', 'scatter_env']

    def show(self, out=sys.stdout, prefix=''):
        pass

    def show_brief(self, out=sys.stdout, prefix=''):
        pass


class water_result(object):
    """
  Container for storing the results of manager.analyze_water for later display
  and retrieval.
  """

    def __init__(self, atom_props, filtered_candidates, matching_candidates,
        rejected_candidates, nuc_phosphate_site, atom_type,
        looks_like_halide, ambiguous_valence_cutoff, valence_used,
        final_choice, wavelength=None, no_final=False):
        adopt_init_args(self, locals())

    def show_summary(self, out=None, debug=False):
        """
    Prints out a summary of a site's chemical and scattering environment.

    Parameters
    ----------
    out : file, optional
    debug : bool, optional
    """
        if out is None:
            out = sys.stdout
        results = self.matching_candidates
        if len(results) > 0:
            self.atom_props.show_properties(identity='HOH', out=out)
            if self.nuc_phosphate_site:
                print('  appears to be nucleotide coordination site', file=out)
            if self.no_final:
                print('  Found potential ion%s outside of specified set:' %
                    ('s' if len(results) > 1 else ''), file=out)
            if self.final_choice is not None:
                elem_params, score = results[0]
                if elem_params.element not in mmtbx.ions.HALIDES:
                    self.atom_props.show_ion_results(identity=str(self.
                        final_choice), out=out, valence_used=self.
                        valence_used, confirmed=True)
                else:
                    print('  Probable anion:', str(elem_params), file=out)
                print('', file=out)
            elif len(results) > 1:
                below_cutoff = [elem_params for elem_params, score in
                    results if score < self.ambiguous_valence_cutoff]
                if len(below_cutoff) == 1:
                    elem_params = below_cutoff[0]
                    print('  ambigous results, best valence from %s' % str(
                        elem_params), file=out)
                    self.atom_props.show_ion_results(identity=str(
                        elem_params), out=out, valence_used=True)
                    print('', file=out)
                else:
                    ions = [str(i[0]) for i in sorted(results, key=lambda x:
                        x[1])]
                    print('  ambiguous results, could be %s' % ', '.join(
                        ions), file=out)
                    for elem_params, score in results:
                        self.atom_props.show_ion_results(identity=str(
                            elem_params), out=out)
                    print('', file=out)
        elif self.atom_type != WATER or self.nuc_phosphate_site:
            self.atom_props.show_properties(identity='HOH', out=out)
            if self.nuc_phosphate_site:
                print('  appears to be nucleotide coordination site', file=out)
            if self.looks_like_halide:
                print('  Probable cation: %s' % str(self.final_choice),
                    file=out)
                print('', file=out)
            else:
                if debug and len(self.filtered_candidates) > 0:
                    print('  insufficient data to identify atom', file=out)
                    possible = True
                    for params in self.filtered_candidates:
                        if self.atom_props.has_compatible_ligands(str(params)):
                            if possible:
                                print('  possible candidates:', file=out)
                                possible = False
                            self.atom_props.show_ion_results(identity=str(
                                params), out=out)
                        else:
                            print('  incompatible ligands for %s' % str(
                                params), file=out)
                print('', file=out)


class AtomProperties(object):
    """
  Collect physical attributes of an atom, including B, occupancy, and map
  statistics, and track those which are at odds with its chemical identity.
  """
    (LOW_B, HIGH_B, LOW_OCC, HIGH_OCC, NO_2FOFC_PEAK, FOFC_PEAK, FOFC_HOLE,
        ANOM_PEAK, NO_ANOM_PEAK, BAD_GEOMETRY, NO_GEOMETRY, BAD_VECTORS,
        BAD_VALENCES, TOO_FEW_NON_WATERS, TOO_FEW_COORD, TOO_MANY_COORD,
        LIKE_COORD, BAD_COORD_ATOM, BAD_FPP, BAD_COORD_RESIDUE,
        VERY_BAD_VALENCES, BAD_HALIDE, HIGH_2FOFC, COORDING_GEOMETRY,
        CLOSE_CONTACT) = range(25)
    error_strs = {LOW_B: 'Abnormally low b-factor', HIGH_B:
        'Abnormally high b-factor', LOW_OCC: 'Abnormally low occupancy',
        HIGH_OCC: 'Abnormally high occupancy', NO_2FOFC_PEAK:
        'No 2mFo-DFc map peak', FOFC_PEAK: 'Peak in mFo-DFc map', FOFC_HOLE:
        'Negative peak in dFo-mFc map', ANOM_PEAK:
        'Peak in the anomalous map', NO_ANOM_PEAK:
        'No peak in the anomalous map', BAD_GEOMETRY:
        'Unexpected geometry for coordinating atoms', NO_GEOMETRY:
        'No distinct geometry for coordinating atoms', BAD_VECTORS:
        'VECSUM above cutoff', BAD_VALENCES: 'BVS above or below cutoff',
        TOO_FEW_NON_WATERS: 'Too few non-water coordinating atoms',
        TOO_FEW_COORD: 'Too few coordinating atoms', TOO_MANY_COORD:
        'Too many coordinating atoms', LIKE_COORD:
        'Like charge coordinating like', BAD_COORD_ATOM:
        'Disallowed coordinating atom', BAD_FPP: "Bad refined f'' value",
        BAD_COORD_RESIDUE: 'Disallowed coordinating residue',
        VERY_BAD_VALENCES: 'BVS far above or below cutoff', BAD_HALIDE:
        'Bad halide site', HIGH_2FOFC: 'Unexpectedly high 2mFo-DFc value',
        COORDING_GEOMETRY:
        'No distinct geometry and coordinating another atom with distinct geometry'
        , CLOSE_CONTACT: 'Close contact to oxygen atom'}

    def __init__(self, i_seq, manager):
        self.i_seq = i_seq
        self.atom = manager.pdb_atoms[i_seq].fetch_labels()
        self.resname = self.atom.resname.strip().upper()
        self.d_min = manager.fmodel.f_obs().d_min()
        self.anomalous_flag = manager.fmodel.f_obs().anomalous_flag()
        self.strict_valence = manager.get_strict_valence_flag()
        nearby_atoms_unfiltered = manager.find_nearby_atoms(i_seq=i_seq,
            far_distance_cutoff=3.5)
        self.nearby_atoms = []
        nearby_atoms_no_alts = []
        for contact in nearby_atoms_unfiltered:
            if contact.element not in ['H', 'D']:
                self.nearby_atoms.append(contact)
                for other in nearby_atoms_no_alts:
                    if other == contact:
                        break
                else:
                    nearby_atoms_no_alts.append(contact)
        self.residue_counts = utils.count_coordinating_residues(self.
            nearby_atoms)
        self.geometries = find_coordination_geometry(nearby_atoms_no_alts)
        map_stats = manager.map_stats(i_seq)
        self.peak_2fofc = map_stats.two_fofc
        self.peak_fofc = map_stats.fofc
        self.peak_anom = map_stats.anom
        self.estimated_weight = manager.guess_molecular_weight(i_seq)
        self.b_iso = manager.get_b_iso(i_seq)
        self.b_mean_hoh = manager.b_mean_hoh
        self.b_stddev_hoh = manager.b_stddev_hoh
        self.b_mean_calpha = manager.b_mean_calpha
        self.calpha_mean_two_fofc = manager.calpha_mean_two_fofc
        self.inaccuracies = {}
        self.ignored = {}
        self.vectors = {}
        self.valence_sum = {}
        self.vector_sum = {}
        self.score = {}
        self.fpp_expected = {}
        self.expected_params = {}
        self.bad_coords = {}
        self.fpp = manager.get_fpp(i_seq)
        self.fpp_ratios = {}
        self.fp = manager.get_fp(i_seq)
        self.manager = manager

    def is_correctly_identified(self, identity=None):
        """
    Calculates whether factors indicate that the atom was correctly identified.

    Parameters
    ----------
    identity : mmtbx.ions.metal_parameters

    Returns
    -------
    bool
    """
        if identity is None:
            identity = self.identity()
        return len(self.inaccuracies[identity]) == 0

    def has_compatible_ligands(self, identity):
        """
    Indicates whether the coordinating atoms are of the allowed type (e.g.
    no N or S atoms coordinating CA, etc.) and residue (e.g. Ser is not an
    appropriate ligand for ZN).

    Parameters
    ----------
    identity : mmtbx.ions.metal_parameters

    Returns
    -------
    bool
    """
        return len(self.bad_coords[identity]
            ) == 0 and not self.BAD_COORD_RESIDUE in self.inaccuracies[identity
            ]

    def is_compatible_site(self, ion_params, require_anom=True,
        ignore_valence=False):
        """
    More minimal criteria for determining whether a site is chemically
    compatible, allowing for incomplete coordination shells.

    Parameters
    ----------
    ion_params : mmtbx.ions.metal_parameters
    require_anom : bool, optional
    ignore_valence : bool, optional

    Returns
    -------
    bool
    """
        inaccuracies = self.inaccuracies[str(ion_params)]
        anom_allowed = self.is_compatible_anomalous_scattering(ion_params
            ) or not require_anom
        return self.has_compatible_ligands(str(ion_params)
            ) and not self.TOO_MANY_COORD in inaccuracies and (ignore_valence
             or not self.VERY_BAD_VALENCES in inaccuracies
            ) and not self.BAD_COORD_RESIDUE in inaccuracies and anom_allowed

    def number_of_favored_ligand_residues(self, ion_params, distance=3.0,
        exclude_atoms=()):
        """
    Counts the number of preferred residues coordinating the atom.  Used for
    approximate detection of transition-metal binding sites.

    Parameters
    ----------
    ion_params : mmtbx.ions.metal_parameters
    distance : float, optional
    exclude_atoms : tuple of ...

    Returns
    -------
    int
    """
        n_res = 0
        resids = []
        for contact in self.nearby_atoms:
            if contact.atom_name() in exclude_atoms:
                continue
            if contact.distance() < distance:
                labels = contact.atom.fetch_labels()
                other_resname = contact.resname()
                other_resid = labels.chain_id + labels.resid()
                if (ion_params.allowed_coordinating_residues is not None and
                    other_resname in ion_params.
                    allowed_coordinating_residues and not other_resid in resids
                    ):
                    n_res += 1
                    resids.append(other_resid)
        return n_res

    def number_of_atoms_within_radius(self, distance_cutoff):
        """
    Counts the number of coordinating atoms within a given radius.

    Parameters
    ----------
    float

    Returns
    -------
    int
    """
        n_atoms = 0
        atom_ids = []
        for contact in self.nearby_atoms:
            other_id = contact.atom_id_no_altloc()
            if not other_id in atom_ids:
                if contact.distance() < distance_cutoff:
                    n_atoms += 1
                atom_ids.append(other_id)
        return n_atoms

    def number_of_backbone_oxygens(self, distance_cutoff=3.0):
        """
    Counts the number of backbone oxygens coordinating a site.

    Parameters
    ----------
    distance_cutoff : float, optional

    Returns
    -------
    int
    """
        n_bb_ox = 0
        for contact in self.nearby_atoms:
            if contact.atom_name() == 'O':
                if contact.distance() <= distance_cutoff:
                    if not contact.resname() in WATER_RES_NAMES:
                        n_bb_ox += 1
        return n_bb_ox

    def is_compatible_anomalous_scattering(self, ion_params):
        if ion_params.element.upper() in ['MG', 'NA']:
            return (self.fpp is None and self.peak_anom is not None and 
                self.peak_anom < 1.0)
        else:
            if self.fpp is None:
                return self.peak_anom is not None and self.peak_anom > 3.0
            identity = self.identity(ion=ion_params)
            if identity in self.fpp_ratios:
                return not self.BAD_FPP in self.inaccuracies[identity]
        return False

    def atom_weight(self, manager):
        """
    Evaluates whether factors indicate that the atom is lighter, heavier, or
    isoelectric to what it is currently identified as.

    Parameters
    ----------
    manager : mmtbx.ions.identify.manager

    Returns
    -------
    int
        -1 if lighter, 0 if isoelectronic, and 1 if heavier.
    """
        identity = 'HOH' if self.resname in WATER_RES_NAMES else self.identity(
            )
        if (identity == 'HOH' and self.atom.b > manager.b_mean_hoh -
            manager.b_stddev_hoh):
            return 0
        if self.is_correctly_identified(identity=identity):
            return 0
        if self.FOFC_PEAK in self.inaccuracies[identity] or self.atom.b < 1:
            return 1
        if self.FOFC_HOLE in self.inaccuracies[identity]:
            return -1
        return 0

    def check_ion_environment(self, ion_params, wavelength=None,
        require_valence=True):
        """
    Checks whether or not the specified ion satisfies the metal-coordination
    parameters, specified by ion_params, such as valence sum, geometry, etc.
    The criteria used here are quite strict, but many of the analyses are
    saved for later if we want to use looser critera.

    Parameters
    ----------
    ion_params : mmtbx.ions.metal_parameters
    wavelength : float, optional
    require_valence : bool, optional
    """
        from iotbx.pdb import common_residue_names_get_class as get_class
        identity = self.identity(ion_params)
        inaccuracies = self.inaccuracies[identity] = set()
        self.expected_params[identity] = ion_params
        ignored = self.ignored[identity] = set()
        strict_rules = require_valence or self.is_correctly_identified(identity
            ='HOH') or self.strict_valence or ion_params.element in ['NA', 'MG'
            ]
        n_closest = 0
        coord_atoms = []
        for i_pair, contact1 in enumerate(self.nearby_atoms):
            distance = contact1.distance()
            if distance < 3.0:
                for contact2 in self.nearby_atoms[i_pair + 1:]:
                    if contact1 == contact2 or contact1.distance_from(contact2
                        ) <= 0.3:
                        break
                else:
                    coord_atoms.append(contact1)
                    if distance < 2.7:
                        n_closest += 1
        if len(coord_atoms) < ion_params.coord_num_lower:
            inaccuracies.add(self.TOO_FEW_COORD)
        if n_closest > ion_params.coord_num_upper:
            inaccuracies.add(self.TOO_MANY_COORD)
        n_non_water = 0
        self.bad_coords[identity] = []
        for contact in self.nearby_atoms:
            other_name = contact.atom_name()
            other_resname = contact.resname()
            other_element = contact.element
            if not other_resname in WATER_RES_NAMES:
                n_non_water += 1
            else:
                continue
            if contact.distance() < 3.0:
                if (ion_params.allowed_coordinating_atoms is not None and 
                    other_element not in ion_params.allowed_coordinating_atoms
                    ):
                    self.bad_coords[identity].append(contact)
                    inaccuracies.add(self.BAD_COORD_ATOM)
                if get_class(other_resname) == 'common_amino_acid':
                    if other_name in ['C', 'N', 'O', 'CA', 'H', 'HA'] and (
                        ion_params.allowed_backbone_atoms is None or not 
                        other_name in ion_params.allowed_backbone_atoms):
                        if other_name == 'O' and contact.is_carboxy_terminus:
                            pass
                        else:
                            self.bad_coords[identity].append(contact)
                            inaccuracies.add(self.BAD_COORD_ATOM)
                    if ion_params.allowed_coordinating_residues is not None:
                        allowed = ion_params.allowed_coordinating_residues
                        if not other_resname in allowed and other_name not in [
                            'C', 'O', 'N', 'CA', 'OXT']:
                            self.bad_coords[identity].append(contact)
                            inaccuracies.add(self.BAD_COORD_RESIDUE)
                elif cmp(0, mmtbx.ions.server.get_charge(contact.atom)) == cmp(
                    0, ion_params.charge):
                    self.bad_coords[identity].append(contact)
                    inaccuracies.add(self.LIKE_COORD)
                elif ion_params.charge > 0 and other_element in ['N'
                    ] and other_resname in ['LYS', 'ARG', 'ASN', 'GLN']:
                    self.bad_coords[identity].append(contact)
                    inaccuracies.add(self.LIKE_COORD)
        if n_non_water < ion_params.min_coordinating_non_waters:
            inaccuracies.add(self.TOO_FEW_NON_WATERS)
        if ion_params.allowed_geometries and strict_rules:
            allowed = [(i[0] in ion_params.allowed_geometries) for i in
                self.geometries]
            if 'any' in ion_params.allowed_geometries:
                pass
            elif not self.geometries:
                if strict_rules:
                    inaccuracies.add(self.NO_GEOMETRY)
            elif not any(allowed):
                inaccuracies.add(self.BAD_GEOMETRY)
            else:
                strict_rules = False
        if self.geometries == []:
            for contact in self.nearby_atoms:
                o_atom = contact.atom
                if o_atom.i_seq in self.manager.atoms_to_props:
                    o_geometry = self.manager.atoms_to_props[o_atom.i_seq
                        ].geometries
                    if o_geometry != []:
                        inaccuracies.add(self.COORDING_GEOMETRY)
        vectors = mmtbx.ions.server.calculate_valences(ion_params, self.
            nearby_atoms)
        self.vectors[identity] = vectors
        self.valence_sum[identity] = sum([abs(i) for i in vectors])
        self.vector_sum[identity] = abs(sum(vectors, col((0, 0, 0))))
        if self.vector_sum[identity] > ion_params.vec_sum_cutoff:
            if strict_rules:
                inaccuracies.add(self.BAD_VECTORS)
            else:
                ignored.add(self.BAD_VECTORS)
        if self.valence_sum[identity
            ] < ion_params.cvbs_expected * 0.25 or self.valence_sum[identity
            ] > ion_params.cvbs_expected * 1.25:
            inaccuracies.add(self.VERY_BAD_VALENCES)
        elif self.valence_sum[identity
            ] < ion_params.cvbs_lower or self.valence_sum[identity
            ] > ion_params.cvbs_upper:
            if strict_rules:
                inaccuracies.add(self.BAD_VALENCES)
            else:
                ignored.add(self.BAD_VALENCES)
        self.score[identity] = abs(self.valence_sum[identity] - ion_params.
            cvbs_expected)

    def check_fpp_ratio(self, ion_params, wavelength, fpp_ratio_min=0.3,
        fpp_ratio_max=1.05):
        """
    Compare the refined and theoretical f'' values if available.

    Parameters
    ----------
    ion_params : mmtbx.ions.metal_parameters
    wavelength : float
    fpp_ratio_min : float, optional
    fpp_ratio_max : float, optional

    Returns
    -------
    float
        f'' / f''_expected
    """
        identity = str(ion_params)
        inaccuracies = self.inaccuracies.get(identity, None)
        if inaccuracies is None:
            inaccuracies = self.inaccuracies[identity] = set()
        if ion_params.element.upper() in ['MG', 'NA']:
            if (self.fpp is not None or self.peak_anom is not None and self
                .peak_anom > 1):
                inaccuracies.add(self.BAD_FPP)
        elif wavelength is not None and self.anomalous_flag:
            fpp_expected_sasaki = sasaki.table(ion_params.element).at_angstrom(
                wavelength).fdp()
            fpp_expected_henke = henke.table(ion_params.element).at_angstrom(
                wavelength).fdp()
            self.fpp_expected[identity] = max(fpp_expected_sasaki,
                fpp_expected_henke)
            if self.fpp is not None and self.fpp_expected[identity] != 0:
                self.fpp_ratios[identity] = self.fpp / self.fpp_expected[
                    identity]
                if self.fpp_ratios[identity
                    ] > fpp_ratio_max or self.fpp >= 0.2 and self.fpp_ratios[
                    identity] < fpp_ratio_min:
                    inaccuracies.add(self.BAD_FPP)
            elif self.fpp_expected[identity] > 0.75 and self.peak_anom < 2:
                inaccuracies.add(self.BAD_FPP)
        return self.fpp_ratios.get(identity)

    def show_properties(self, identity, out=sys.stdout):
        """
    Show atomic properties that are independent of the suspected identity.

    Parameters
    ----------
    identity : mmtbx.ions.metal_parameters
    out : file, optional
    """
        print('%s:' % self.atom.id_str(), file=out)
        b_flag = ''
        if self.LOW_B in self.inaccuracies[identity]:
            b_flag = ' <<<'
        elif self.HIGH_B in self.inaccuracies[identity]:
            b_flag = ' !!!'
        print('  B-factor:      %6.2f%s' % (self.atom.b, b_flag), file=out)
        occ_flag = ''
        if self.LOW_OCC in self.inaccuracies[identity]:
            occ_flag = ' !!!'
        elif self.HIGH_OCC in self.inaccuracies[identity]:
            occ_flag = ' <<<'
        print('  Occupancy:     %6.2f%s' % (self.atom.occ, occ_flag), file=out)
        twofofc_flag = ''
        if self.NO_2FOFC_PEAK in self.inaccuracies[identity]:
            twofofc_flag = ' !!!'
        elif self.HIGH_2FOFC in self.inaccuracies[identity]:
            twofofc_flag = ' <<<'
        print('  2mFo-DFc map:  %6.2f%s' % (self.peak_2fofc, twofofc_flag),
            file=out)
        fofc_flag = ''
        if self.FOFC_PEAK in self.inaccuracies[identity]:
            fofc_flag = ' <<<'
        elif self.FOFC_HOLE in self.inaccuracies[identity]:
            fofc_flag = ' !!!'
        print('  mFo-DFc map:   %6.2f%s' % (self.peak_fofc, fofc_flag),
            file=out)
        if self.peak_anom is not None:
            anom_flag = ''
            if self.ANOM_PEAK in self.inaccuracies[identity]:
                anom_flag = ' <<<'
            elif self.NO_ANOM_PEAK in self.inaccuracies[identity]:
                anom_flag = ' !!!'
            print('  Anomalous map: %6.2f%s' % (self.peak_anom, anom_flag),
                file=out)
        if self.estimated_weight is not None:
            print('  Approx. mass:  %6d' % self.estimated_weight, file=out)
        if self.fpp is not None:
            fpp_flag = ''
            if self.fpp >= 0.2:
                fpp_flag = ' <<<'
            print("  f'':           %6.2f%s" % (self.fpp, fpp_flag), file=out)
            print("  f'' ratio:     %s" % format_value('%6.2f', self.
                fpp_ratios.get(identity)), file=out)
        if self.nearby_atoms is not None:
            angstrom = u'Å'.encode('utf-8', 'strict')
            degree = u'°'.encode('utf-8', 'strict')
            print('  Nearby atoms: (%d within 3.0 %s)' % (len([i for i in
                self.nearby_atoms if i.distance() < 3]), angstrom), file=out)
            for contact in self.nearby_atoms:
                print('    %s (%5.3f %s)' % (contact.id_str(), contact.
                    distance(), angstrom), file=out)
            if self.geometries:
                print('  Coordinating geometry:', file=out)
                for geometry, deviation in self.geometries:
                    print('    %-15s (average deviation: %.3f%s)' % (
                        geometry, deviation, degree), file=out)

    def show_ion_results(self, identity=None, out=sys.stdout, confirmed=
        False, valence_used=True):
        """
    Show statistics for a proposed element identity.

    Parameters
    ----------
    identity : mmtbx.ions.metal_parameters
    out : file, optional
    confirmed : bool, optional
    valence_used : bool, optional
    """
        if not identity:
            identity = self.identity(self.atom)
        inaccuracies = self.inaccuracies.get(identity, set([]))
        ignored = self.ignored.get(identity, set([]))
        if identity != self.identity():
            if confirmed:
                print('  Probable cation: %s' % identity, file=out)
            else:
                print('  Atom as %s:' % identity, file=out)
        else:
            print('    %s:' % self.atom.id_str(), file=out)
        if identity in self.vector_sum and self.vector_sum[identity
            ] is not None:
            problem = (self.BAD_VECTORS in inaccuracies or self.BAD_VECTORS in
                ignored)
            print('    Vector sum:  %6.3f %s' % (self.vector_sum[identity],
                ' !!!' if problem else ''), file=out)
        if identity in self.valence_sum and self.valence_sum[identity
            ] is not None:
            problem = inaccuracies.union(ignored).intersection([self.
                BAD_VALENCES, self.VERY_BAD_VALENCES])
            print('    Valence sum: %6.3f' % self.valence_sum[identity],
                file=out)
            if valence_used:
                print('(expected: %6.3f) %s' % (self.expected_params[
                    identity].cvbs_expected, ' !!!' if problem else ''),
                    end=' ', file=out)
        if self.NO_GEOMETRY in inaccuracies:
            print('    No distinct geometry !!!', file=out)
        if self.BAD_GEOMETRY in inaccuracies:
            print('    Unexpected geometry  !!!', file=out)
        bad_coord = [self.LIKE_COORD, self.BAD_COORD_ATOM, self.
            BAD_COORD_RESIDUE]
        if inaccuracies.intersection(bad_coord):
            print('    Bad coordinating atom%s:' % ('s' if len(self.
                bad_coords[identity]) != 1 else ''), file=out)
            angstrom = u'Å'.encode('utf-8', 'strict').strip()
            for atom, vector in self.bad_coords[identity]:
                print('    %s (%5.3f %s) !!!' % (atom.id_str(), abs(vector),
                    angstrom), file=out)
        if self.TOO_FEW_NON_WATERS in inaccuracies:
            print('    Too few coordinating non-waters !!!', file=out)
        if self.TOO_FEW_COORD in inaccuracies:
            print('    Too few coordinating atoms !!!', file=out)
        if self.TOO_MANY_COORD in inaccuracies:
            print('    Too many coordinating atoms !!!', file=out)
        if self.fpp is not None and identity in self.fpp_ratios:
            print("    f'' ratio:   %6.3f%s" % (self.fpp_ratios[identity], 
                ' !!!' if self.BAD_FPP in inaccuracies else ''), file=out)

    def looks_like_nucleotide_phosphate_site(self,
        min_phosphate_oxygen_atoms=2, distance_cutoff=2.5):
        """
    Decide whether the atom is coordinating phosphate oxygens from a
    nucleotide, based on common atom names.

    Parameters
    ----------
    min_phosphate_oxygen_atoms : int, optional
    distance_cutoff : float, optional

    Returns
    -------
    bool
    """
        n_phosphate_oxygens = 0
        for contact in self.nearby_atoms:
            atom_name = contact.atom_name()
            if len(atom_name) < 3 or contact.element not in ['O']:
                continue
            if atom_name[0:2] in ['O1', 'O2', 'O3'] and atom_name[2] in ['A',
                'B', 'G']:
                if contact.distance() <= distance_cutoff:
                    n_phosphate_oxygens += 1
        return n_phosphate_oxygens == min_phosphate_oxygen_atoms

    def identity(self, ion=None):
        """
    Covers an atom into a string representing its element and charge.

    Parameters
    ----------
    ion : iotbx.pdb.hierarchy.atom, optional

    Returns
    -------
    str
    """
        if ion is None:
            ion = self.atom
        element = mmtbx.ions.server.get_element(ion)
        charge = mmtbx.ions.server.get_charge(ion)
        return '{}{:+}'.format(element, charge)

    def get_atom_type(self, params, aggressive=False):
        """
    Checks the atom characteristics against what we would expect for a water.
    Updates self with any inaccuracies noticed (Surpringly low b-factor,
    high occupancy, etc).  Note that HEAVY_ION does not necessarily rule out
    NA/MG, as these often have mFo-DFc peaks at high resolution.

    Parameters
    ----------
    params : libtbx.phil.scope_extract
    aggressive : bool, optional

    Returns
    -------
    int
        One of WATER, WATER_POOR, HEAVY_ION, LIGHT_ION
    """
        inaccuracies = self.inaccuracies['HOH'] = set()
        atom_type = WATER
        if self.peak_2fofc < params.min_2fofc_level or self.peak_fofc < -2.0:
            return WATER_POOR
        if self.fpp is not None and self.fpp > params.fpp_max:
            return HEAVY_ION
        if self.fp is not None and self.fp > params.fp_max:
            return HEAVY_ION
        if (self.peak_anom is not None and self.peak_anom > params.
            max_anom_level):
            inaccuracies.add(self.ANOM_PEAK)
            atom_type = HEAVY_ION
        if self.peak_fofc > params.max_fofc_level:
            inaccuracies.add(self.FOFC_PEAK)
            atom_type = HEAVY_ION
        if self.atom.occ > params.max_occ:
            inaccuracies.add(self.HIGH_OCC)
            atom_type = HEAVY_ION
        if self.atom.b < params.min_b_iso:
            inaccuracies.add(self.LOW_B)
            atom_type = HEAVY_ION
        elif self.b_stddev_hoh is not None and self.b_stddev_hoh > 0:
            z_value = (self.b_iso - self.b_mean_hoh) / self.b_stddev_hoh
            if z_value < -params.max_stddev_b_iso:
                inaccuracies.add(self.LOW_B)
            elif self.atom.b < self.b_mean_hoh * params.min_frac_b_iso:
                inaccuracies.add(self.LOW_B)
            if atom_type == WATER and self.LOW_B in inaccuracies:
                atom_type = LIGHT_ION
        if aggressive and atom_type == WATER:
            if self.b_mean_calpha > 0:
                relative_b = self.b_iso / self.b_mean_calpha
                if relative_b < params.min_frac_calpha_b_iso:
                    inaccuracies.add(self.LOW_B)
                    atom_type = LIGHT_ION
            if self.calpha_mean_two_fofc > 0:
                relative_2fofc = self.peak_2fofc / self.calpha_mean_two_fofc
                if relative_2fofc > params.max_frac_calpha_2fofc:
                    inaccuracies.add(self.HIGH_2FOFC)
                    atom_type = LIGHT_ION
        for i_pair, contact1 in enumerate(self.nearby_atoms):
            if contact1.element.strip() == 'O':
                distance = contact1.distance()
                if distance < 2.4:
                    inaccuracies.add(self.CLOSE_CONTACT)
                    if atom_type < LIGHT_ION:
                        atom_type = LIGHT_ION
        return atom_type


def find_anomalous_scatterers(*args, **kwds):
    """
  Wrapper for corresponding method in phaser.substructure, if phaser is
  available and configured.
  """
    if not libtbx.env.has_module('phaser'):
        if 'log' in kwds:
            print('Phaser not available', file=kwds['log'])
        return None
    from phaser import substructure
    return substructure.find_anomalous_scatterers(*args, **kwds)


def create_manager(pdb_hierarchy, geometry_restraints_manager, fmodel,
    wavelength, params, resolution_factor=0.25, nproc=Auto, verbose=False,
    log=None, manager_class=None):
    """
  Wrapper around mmtbx.ions.identify.manager init method. Retrieves the
  connectivity and xray_structure from fmodel automatically.

  Parameters
  ----------
  pdb_hierarchy : iotbx.pdb.hierarchy.root
  geometry_restraints_manager : cctbx.geometry_restraints.manager.manager
  fmodel : mmtbx.f_model.manager
  wavelength : float
  params : libtbx.phil.scope_extract
  resolution_factor : float, optional
  nproc : int, optional
  verbose : bool, optional
  log : file, optional
  manager_class : class, optional

  Returns
  -------
  mmtbx.ions.identify.manager or mmtbx.ions.svm.manager
  """
    connectivity = geometry_restraints_manager.shell_sym_tables[0
        ].full_simple_connectivity()
    if manager_class is None:
        manager_class = manager
    manager_obj = manager_class(fmodel=fmodel, pdb_hierarchy=pdb_hierarchy,
        xray_structure=fmodel.xray_structure, connectivity=connectivity,
        wavelength=wavelength, params=params, nproc=nproc, verbose=verbose,
        log=log)
    return manager_obj


def _PRINT_DEBUG(*args):
    """
  Prints a debugging message to stderr.
  """
    print(sys.stderr, args, file=sys.stderr)

# -*- coding: utf-8; py-indent-offset: 2 -*-
"""
Examines a structure for metal ions. Can iterate over all atoms, examining
their density and chemical environment to determine if they are correctly
identified, or if there are better candidate ions that they can be replaced
with.

See mmtbx.ions.build to actually modify the structure, the code in this module
handles ion identification, but only prints out messages to a log.
"""

from __future__ import absolute_import, division, print_function
from mmtbx.ions.geometry import find_coordination_geometry
from mmtbx.ions import environment
from mmtbx.ions import halides
from mmtbx.ions import utils
from mmtbx import validation
import mmtbx.ions
from iotbx.pdb import common_residue_names_water as WATER_RES_NAMES
from cctbx.eltbx import sasaki, henke
from cctbx import crystal, adptbx
from scitbx.array_family import flex
from scitbx.matrix import col
from libtbx.str_utils import make_sub_header, format_value, framed_output
from libtbx import group_args, adopt_init_args, Auto
from libtbx.utils import null_out, Sorry
from libtbx import easy_mp
import libtbx.load_env
import libtbx.phil
from math import sqrt
from six.moves import cStringIO as StringIO
import operator
import time
import sys
from libtbx.math_utils import cmp
from six.moves import zip
from six.moves import range
from six import string_types

ion_identification_phil_str = """
require_valence = False
  .type = bool
  .help = Toggles the use of valence calculations
  .short_caption = Require good bond valence
ambiguous_valence_cutoff = 0.5
  .type = float
  .help = "Cutoff to uniquely select one of many metals by comparing its observed and expected valence"
d_min_strict_valence = 1.5
  .type = float
  .short_caption = Resolution limit for strict valence rules
anom_map_type = *residual simple llg
  .type = choice
  .help = Type of anomalous difference map to use.  Default is a residual \
    map, showing only unmodeled scattering.
find_anomalous_substructure = Auto
  .type = bool
use_phaser = True
  .type = bool
  .help = Toggles the use of Phaser for calculation of f-prime values.
  .short_caption = Use Phaser to calculate f-double-prime
aggressive = False
  .type = bool
  .help = Toggles more permissive settings for flagging waters as heavier \
    elements.  Not recommended for automated use.
map_sampling_radius = 2.0
  .type = float
  .input_size = 80
  .short_caption = Sampling radius
water
  .short_caption = Water filtering
  .style = box auto_align
{
  min_2fofc_level = 1.8
    .type = float
    .input_size = 80
    .help = Minimum water 2mFo-DFc map value.  Waters below this cutoff will \
      not be analyzed further.
    .short_caption = Min. allowed 2mFo-DFc map value
  max_fofc_level = 3.0
    .type = float
    .input_size = 80
    .help = Maximum water mFo-DFc map value
    .short_caption = Max. expected mFo-DFc map value
  max_anom_level = 3.0
    .type = float
    .input_size = 80
    .help = Maximum water anomalous map value
    .short_caption = Max. expected anomalous map value
  max_occ = 1.0
    .type = float
    .input_size = 80
    .help = Maximum water occupancy
    .short_caption = Max. expected occupancy
  min_b_iso = 1.0
    .type = float
    .input_size = 80
    .help = Minimum water isotropic B-factor
    .short_caption = Min. expected B-factor
  fp_max = 1.0
    .type = float
  fpp_max = 0
    .type = float
  max_stddev_b_iso = 5
    .type = float
    .input_size = 80
    .short_caption = Max. standard deviations below mean B-iso
  min_frac_b_iso = 0.2
    .type = float
    .input_size = 80
    .short_caption = Min. fraction of mean B-iso
  min_frac_calpha_b_iso = 0.75
    .type = float
  max_frac_calpha_2fofc = 1.2
    .type = float
  min_2fofc_coordinating = 0.9
    .type = float
    .help = Minimum 2mFo-DFc for waters involved in coordination shells.
}
phaser
  .short_caption = Phaser options
  .caption = These parameters control the identification of anomalous \
    scatterers using Phaser substructure completion.
  .style = box auto_align
{
  llgc_ncycles = None
    .type = int
    .short_caption = Number of cycles
  distance_cutoff = 1.5
    .type = float
    .input_size = 80
    .short_caption = Min. required separation from other atoms
  distance_cutoff_same_site = 0.7
    .type = float
    .input_size = 80
    .short_caption = Max. separation from mapped atom
  fpp_ratio_min = 0.2
    .type = float
    .input_size = 80
    .help = Minimum ratio of refined/theoretical f-double-prime.
    .short_caption = Min. f-double-prime ratio
  fpp_ratio_max = 1.1
    .type = float
    .input_size = 80
    .help = Maximum ratio of refined/theoretical f-double-prime.
    .short_caption = Max. f-double-prime ratio
}
chloride
  .short_caption = Chloride ions
  .style = box auto_align
{
  %s
}
""" % halides.chloride_params_str

def ion_master_phil():
  return libtbx.phil.parse(ion_identification_phil_str, process_includes=True)

# various constants
WATER, WATER_POOR, LIGHT_ION, HEAVY_ION = range(4)
NUC_PHOSPHATE_BINDING = ["MG", "CA", "MN"]

class manager(object):
  """
  Wrapper object for extracting local environment and adding or modifying
  scatterers.
  """

  def __init__(self,
                fmodel,
                pdb_hierarchy,
                xray_structure,
                params = None,
                wavelength = None,
                connectivity = None,
                nproc = 1,
                verbose = False,
                log = None):
    if (log is None) : log = null_out()
    self.fmodel = fmodel
    self.atoms_to_props = {}
    self.params = params
    self.wavelength = wavelength
    self.nproc = nproc
    self.phaser_substructure = None
    self.flag_refine_substructure = False
    self.fpp_from_phaser_ax_sites = None
    self.site_fp = None
    self.site_fdp = None
    self.use_svm = getattr(params, "use_svm", None)
    self._map_values = {}
    self._map_gaussian_fits = {}
    self.anomalous_flag = None
    if (fmodel is not None):
      self.anomalous_flag = fmodel.f_obs().anomalous_flag()
    self.update_structure(
      pdb_hierarchy = pdb_hierarchy,
      xray_structure = xray_structure,
      connectivity = connectivity,
      log = log)
    self.update_maps()
    # The default behavior is to refine the anomalous structure when possible;
    # this can use either the CCTBX anomalous group refinement or Phaser's
    # substructure completion (but not both).  Some additional machinery is
    # required to handle this distinction.
    refine_substructure = False
    if (params is not None):
      refine_substructure = params.find_anomalous_substructure
    if (refine_substructure is Auto):
      refine_substructure = fmodel.f_obs().anomalous_flag() and \
                            (wavelength is not None)
    if (refine_substructure):
      if (wavelength is None):
        raise Sorry("Wavelength required when "+
                    "find_anomalous_substructure=True.")
      elif (not fmodel.f_obs().anomalous_flag()):
        raise Sorry("Anomalous data required when "+
                    "find_anomalous_substructure=True.")
      if ((params.use_phaser) and (libtbx.env.has_module("phaser"))):
        print("  Running Phaser substructure completion...", file=log)
        t1 = time.time()
        phaser_result = find_anomalous_scatterers(
          fmodel = fmodel,
          pdb_hierarchy = pdb_hierarchy,
          wavelength = wavelength,
          verbose = verbose,
          log = log,
          n_cycles = params.phaser.llgc_ncycles)
        t2 = time.time()
        print("    time: %.1fs" % (t2-t1), file=log)
        if (phaser_result is None):
          print("  ERROR: Phaser substructure completion failed!", file=log)
        else :
          self.phaser_substructure = phaser_result.atoms()
          if (len(self.phaser_substructure) == 0):
            print("  No anomalous scatterers found!", file=log)
          else :
            print("  %d anomalous scatterers found" % \
              len(self.phaser_substructure), file=log)
            self.analyze_substructure(log = log, verbose = True)
      else :
        self.flag_refine_substructure = True
        self.refine_anomalous_substructure(log = log)

  def water_selection(self):
    """
    Fetchs the selection for all waters in the model.

    Returns
    -------
    scitbx.array_family.flex.size_t
    """
    sel_cache = self.pdb_hierarchy.atom_selection_cache()
    sel_str = "({}) and element O and altloc ' '".format(
      " or ".join([ "resname " + i for i in WATER_RES_NAMES ]))
    return sel_cache.selection(sel_str).iselection()

  def refine_anomalous_substructure(self, log):
    """
    Run simple "substructure completion" implemented using CCTBX tools (the
    command-line equivalent is mmtbx.refine_anomalous_substructure).  A faster
    alternative to Phaser, but not clear how effective this is at the moment.

    Parameters
    ----------
    log : file
    """
    from mmtbx.refinement import anomalous_scatterer_groups
    fmodel_tmp = self.fmodel.deep_copy()
    # XXX should we only be refining f''?
    anom_groups = anomalous_scatterer_groups.refine_anomalous_substructure(
      fmodel = fmodel_tmp,
      pdb_hierarchy = self.pdb_hierarchy,
      wavelength = self.wavelength,
      reset_water_u_iso = True,
      verbose = True,
      use_all_anomalous = True,
      out = log)
    scatterers = fmodel_tmp.xray_structure.scatterers()
    self.use_fdp = flex.bool(scatterers.size(), False)
    self.site_fp = flex.double(scatterers.size(), 0)
    self.site_fdp = flex.double(scatterers.size(), 0)
    for group in anom_groups :
      for i_seq in group.iselection :
        sc = scatterers[i_seq]
        self.use_fdp[i_seq] = True
        self.site_fp[i_seq] = sc.fp
        self.site_fdp[i_seq] = sc.fdp

  def update_structure(self, pdb_hierarchy, xray_structure,
      connectivity = None, log = None, refine_if_necessary = True):
    """
    Set the current atomic data: PDB hierarchy, Xray structure, and simple
    connectivity list.

    Parameters
    ----------
    pdb_hierarchy : iotbx.pdb.hierarchy.root
    xray_structure : cctbx.xray.structure.structure
    connectivity : ...
    log : file, optional
    refine_if_necessary : bool, optional
    """
    self.pdb_hierarchy = pdb_hierarchy
    self.xray_structure = xray_structure
    if self.fmodel:
      self.fmodel.update_xray_structure(xray_structure, update_f_calc = True)
    self.sites_frac = self.xray_structure.sites_frac()
    self.sites_cart = self.xray_structure.sites_cart()
    self.connectivity = connectivity
    self.pdb_atoms = pdb_hierarchy.atoms()
    assert not self.pdb_atoms.extract_i_seq().all_eq(0)
    self.unit_cell = xray_structure.unit_cell()
    self.use_fdp = flex.bool(xray_structure.scatterers().size(), False)
    self.ax_chain = None
    self._pair_asu_cache = {}
    self.u_iso_all = xray_structure.extract_u_iso_or_u_equiv()
    # Extract some information about the structure, including B-factor
    # statistics for non-HD atoms and waters
    sctr_keys = xray_structure.scattering_type_registry().type_count_dict()
    self.hd_present = ("H" in sctr_keys) or ("D" in sctr_keys)
    sel_cache = pdb_hierarchy.atom_selection_cache()
    self.carbon_sel = sel_cache.selection("element C").iselection()
    self.calpha_sel = sel_cache.selection("name CA and element C").iselection()
    assert (len(self.carbon_sel) > 0)
    water_sel = self.water_selection()
    self.n_waters = len(water_sel)
    not_hd_sel = sel_cache.selection("not (element H or element D)").iselection()
    self.n_heavy = len(not_hd_sel)
    u_iso_all_tmp = self.u_iso_all.select(not_hd_sel)
    self.b_mean_all = adptbx.u_as_b(flex.mean(u_iso_all_tmp))
    self.b_stddev_all = adptbx.u_as_b(
       u_iso_all_tmp.standard_deviation_of_the_sample())
    self.b_mean_calpha = 0
    if (len(self.calpha_sel) > 0):
      self.b_mean_calpha = adptbx.u_as_b(flex.mean(self.u_iso_all.select(
        self.calpha_sel)))
    self.b_mean_hoh = self.b_stddev_hoh = None
    if (self.n_waters > 0):
      u_iso_hoh = self.u_iso_all.select(water_sel)
      self.b_mean_hoh = adptbx.u_as_b(flex.mean(u_iso_hoh))
      self.b_stddev_hoh = adptbx.u_as_b(
        u_iso_hoh.standard_deviation_of_the_sample())
    if (self.phaser_substructure is not None):
      self.analyze_substructure(log = log)
    elif (self.flag_refine_substructure) and (refine_if_necessary):
      self.refine_anomalous_substructure(log = log)

  def get_initial_b_iso(self):
    """
    Calculates the isotropic b-factor to assign to newly labeled ions during the
    building process. Uses either the mean b-factor of waters or of all atoms if
    the former is unavailable.

    Returns
    -------
    float
    """
    if (getattr(self, "b_mean_hoh", None) is not None):
      return self.b_mean_hoh
    else :
      return self.b_mean_all

  def get_map(self, map_type):
    """
    Creates the real-space version of a given map type.

    Parameters
    ----------
    map_type : str

    Returns
    -------
    scitbx.array_family.flex.double
    """
    map_coeffs = self.fmodel.map_coefficients(
      map_type = map_type,
      exclude_free_r_reflections = True,
      fill_missing = True,
      pdb_hierarchy = self.pdb_hierarchy)
    if (map_coeffs is None):
      return None
    return map_coeffs.fft_map(resolution_factor = 0.25,
      ).apply_sigma_scaling().real_map_unpadded()

  def update_maps(self):
    """
    Generate new maps for the current structure, including anomalous map if
    data are anomalous.

    If Phaser is installed and anomalous data are available, the anomalous
    log-likelihood gradient (LLG) map can be used instead of the conventional
    anomalous difference map. This is only really useful if the anomalous
    scattering of existing atoms is modeled (and ideally, refined).
    """
    if self.fmodel is None:
      return
    def fft_map(map_coeffs, resolution_factor = 0.25):
      return map_coeffs.fft_map(resolution_factor = resolution_factor,
        ).apply_sigma_scaling().real_map_unpadded()
    map_types = ["2mFo-DFc", "mFo-DFc"]
    map_keys = ["2mFo-DFc", "mFo-DFc"]
    if (self.fmodel.f_obs().anomalous_flag()):
      if (self.params.anom_map_type == "phaser"):
        map_types.append("llg")
      elif (self.params.anom_map_type == "residual"):
        map_types.append("anom_residual")
      else :
        map_types.append("anom")
      map_keys.append("anom")
    if (self.use_svm):
      map_types.append("mFo")
      map_keys.append("mFo")
    # To save memory, we sample atomic positions immediately and throw out
    # the actual maps (instead of keeping up to 3 in memory)
    sites_frac = self.xray_structure.sites_frac()
    sites_cart = self.xray_structure.sites_cart()
    self._principal_axes_of_inertia = [ None ] * len(sites_frac)
    self._map_variances = [ None ] * len(sites_frac)
    self._map_gaussian_fits = {}
    self.calpha_mean_two_fofc = 0
    for map_type, map_key in zip(map_types, map_keys):
      real_map = self.get_map(map_type)
      if (real_map is not None):
        # Gather values for map peaks at each site
        self._map_values[map_key] = flex.double(sites_frac.size(), 0)
        self._map_gaussian_fits[map_key] = [ None ] * len(sites_frac)
        for i_seq, site_frac in enumerate(sites_frac):
          atom = self.pdb_atoms[i_seq]
          resname = atom.fetch_labels().resname.strip().upper()
          if (resname in WATER_RES_NAMES + mmtbx.ions.SUPPORTED or
              atom.segid.strip().upper() in ["ION"]):
            value = real_map.eight_point_interpolation(site_frac)
            self._map_values[map_key][i_seq] = value
            if (self.use_svm):
              gaussian_fit = utils.fit_gaussian(
                unit_cell=self.unit_cell,
                site_cart=atom.xyz,
                real_map=real_map)
              self._map_gaussian_fits[map_key][i_seq] = gaussian_fit

        if map_type in ["2mFo-DFc"]:
          # Gather values on map variance and principal axes of interia
          from cctbx import maptbx
          for i_seq, site_cart in enumerate(sites_cart):
            resname = self.pdb_atoms[i_seq].fetch_labels().resname.strip()
            if resname in WATER_RES_NAMES + mmtbx.ions.SUPPORTED:
              # XXX not totally confident about how I'm weighting this...
              p_a_i = maptbx.principal_axes_of_inertia(
                real_map = real_map,
                site_cart = site_cart,
                unit_cell = self.unit_cell,
                radius = self.params.map_sampling_radius)
              self._principal_axes_of_inertia[i_seq] = p_a_i
              variance = maptbx.spherical_variance_around_point(
                real_map = real_map,
                unit_cell = self.unit_cell,
                site_cart = site_cart,
                radius = self.params.map_sampling_radius)
              self._map_variances[i_seq] = variance
            elif (i_seq in self.calpha_sel):
              # Also collect some info in average C_alpha 2FoFc peak heights
              self.calpha_mean_two_fofc += real_map.eight_point_interpolation(
                sites_frac[i_seq])
        del real_map

    if (self.calpha_mean_two_fofc > 0):
      n_calpha = len(self.calpha_sel)
      assert (n_calpha > 0)
      self.calpha_mean_two_fofc /= n_calpha

    # Gather info on carbons' average Fo peak height for use in estimating other
    # sites' atomic weight
    self.carbon_fo_values = None
    if (len(self.carbon_sel) > 0):
      self.carbon_fo_values = flex.double()
      self._map_values["mFo"] = flex.double(sites_frac.size(), 0)
      fo_map = fft_map(self.fmodel.map_coefficients(
        map_type = "mFo",
        exclude_free_r_reflections = True,
        fill_missing = True))

      for i_seq, site_frac in enumerate(sites_frac):
        resname = self.pdb_atoms[i_seq].fetch_labels().resname.strip()
        element = self.pdb_atoms[i_seq].element.strip()
        if (element == "C") or ((element == "O") and (resname in WATER_RES_NAMES)):
          map_value = fo_map.eight_point_interpolation(site_frac)
          self._map_values["mFo"][i_seq] = map_value
          if (element == "C"):
            self.carbon_fo_values.append(map_value)
      del fo_map

  def show_current_scattering_statistics(self, out=sys.stdout):
    """
    Prints out information about an entire model's scattering statistics, such
    as mean map heights and b-factors for carbons and waters.

    Parameters
    ----------
    out : file, optional
    """
    print("", file=out)
    print("Model and map statistics:", file=out)
    print("  mean mFo map height @ carbon: %s" % format_value("%.2f",
      flex.max(self.carbon_fo_values)), file=out)
    if (self.calpha_mean_two_fofc > 0):
      print("  mean 2mFo-DFc map height @ C-alpha: %s" % format_value(
        "%.2f", self.calpha_mean_two_fofc), file=out)
    print("  mean B-factor: %s" % format_value("%.2f", self.b_mean_all), file=out)
    if (self.b_mean_calpha > 0):
      print("  mean C-alpha B-factor: %s" % format_value("%.2f",
        self.b_mean_calpha), file=out)
    print("  mean water B-factor: %s" % format_value("%.2f",
      self.b_mean_hoh), file=out)
    n_water_fofc_peaks = 0
    n_water_anom_peaks = 0
    water_sel = self.water_selection()
    print("  %d water molecules" % len(water_sel), file=out)
    for i_seq in water_sel :
      map_stats = self.map_stats(i_seq)
      if (map_stats.fofc >= 3.0):
        n_water_fofc_peaks += 1
      if (map_stats.anom is not None) and (map_stats.anom >= 3.0):
        n_water_anom_peaks += 1
    print("  %d waters have mFo-DFc map >= 3.0 sigma" % \
      n_water_fofc_peaks, file=out)
    if (self.anomalous_flag):
      print("  %d waters have anomalous map >= 3.0 sigma" % \
        n_water_anom_peaks, file=out)
    print("", file=out)

  def get_strict_valence_flag(self):
    """
    Checks whether the resolution of a model is high enough to use more strict
    thresholds for ion valences.

    Returns
    -------
    bool
    """
    d_min = self.fmodel.f_obs().d_min()
    return (d_min < self.params.d_min_strict_valence)

  def find_nearby_atoms(self, i_seq, far_distance_cutoff = 3.0,
      near_distance_cutoff = 1.5, filter_by_bonding = True,
      filter_by_two_fofc = True):
    """
    Given site in the structure, return a list of contacts, optionally
    filtering by connectivity and 2mFo-DFc map level.

    Parameters
    ----------
    i_seq : int
    far_distance_cutoff : float, optional
    near_distance_cutoff : float, optional
    filter_by_bonding : bool, optional
    fitler_by_two_fofc : bool, optional

    Returns
    -------
    list of mmtbx.ions.environment.atom_contact
    """
    assert (i_seq < len(self.sites_frac))
    # Use pair_asu_table to find atoms within distance_cutoff of one another,
    # taking into account potential cell symmetry.
    # XXX should probably move this up, but it seems relatively fast
    pair_asu_table, asu_mappings, asu_table = self._pair_asu_cache.get(
      far_distance_cutoff, (None,None,None))
    if (pair_asu_table is None):
      pair_asu_table = self.xray_structure.pair_asu_table(
        distance_cutoff = far_distance_cutoff)
      asu_mappings = pair_asu_table.asu_mappings()
      asu_table = pair_asu_table.table()
      self._pair_asu_cache[far_distance_cutoff] = (pair_asu_table, asu_mappings,
        asu_table)
    contacts = environment.find_nearby_atoms(
      i_seq=i_seq,
      xray_structure=self.xray_structure,
      pdb_atoms=self.pdb_atoms,
      asu_mappings=asu_mappings,
      asu_table=asu_table,
      connectivity=self.connectivity,
      far_distance_cutoff=far_distance_cutoff,
      near_distance_cutoff=near_distance_cutoff,
      filter_by_bonding=filter_by_bonding)
    # Discard waters in poor density
    if (filter_by_two_fofc):
      filtered = []
      for contact in contacts :
        if (contact.resname() in WATER_RES_NAMES):
          two_fofc = self._map_values["2mFo-DFc"][contact.atom_i_seq()]
          if two_fofc < self.params.water.min_2fofc_coordinating:
            continue
        filtered.append(contact)
      contacts = filtered
    return contacts

  def guess_b_iso_real(self, i_seq):
    """
    Guess an approximate B_iso for an atom by averaging the values for
    coordinating atoms.  This should partially compensate for the deflation of
    the B-factor due to incorrect scattering type, and consequently make the
    occupancy refinement more accurate.

    Parameters
    ----------
    i_seq : int

    Returns
    -------
    float
    """
    contacts = self.find_nearby_atoms(i_seq, far_distance_cutoff = 3.0)
    if (len(contacts) == 0):
      return adptbx.u_as_b(self.u_iso_all[i_seq])
    u_iso_sum = 0
    for contact in contacts :
      u_iso_sum += self.u_iso_all[contact.atom_i_seq()]
    return adptbx.u_as_b(u_iso_sum / len(contacts))

  def principal_axes_of_inertia(self, i_seq):
    """
    Extracts the map grid points around a site, and calculates the axes of
    inertia (using the density values as weights). This is used to calculate
    the sphericity of the blob of density around a site.

    Parameters
    ----------
    i_seq : int

    Returns
    -------
    ...
    """
    return self._principal_axes_of_inertia[i_seq]

  def get_map_sphere_variance(self, i_seq):
    """
    Calculate the density levels for points on a sphere around a given point.
    This will give us some indication whether the density falls off in the
    expected manner around a single atom.

    Parameters
    ----------
    i_seq : int

    Returns
    -------
    float
    """
    return self._map_variances[i_seq]

  def get_b_iso(self, i_seq):
    """
    Calculates the isotropic b-factor of a site.

    Parameters
    ----------
    i_seq : int

    Returns
    -------
    float
    """
    sc = self.xray_structure.scatterers()[i_seq]
    return adptbx.u_as_b(sc.u_iso_or_equiv(self.unit_cell))

  def map_stats(self, i_seq):
    """
    Given a site in the structure, find the signal of the 2FoFc, FoFc, and
    anomalous map (When available).

    Parameters
    ----------
    i_seq : int

    Returns
    -------
    group_args
        Object with .two_fofc, .fofc, and .anom properties for the associated
        signals in each map.
    """
    value_2fofc = self._map_values["2mFo-DFc"][i_seq]
    value_fofc = self._map_values["mFo-DFc"][i_seq]
    value_anom = None
    if ("anom" in self._map_values):
      value_anom = self._map_values["anom"][i_seq]
    return group_args(
      two_fofc = value_2fofc,
      fofc = value_fofc,
      anom = value_anom)

  def get_map_gaussian_fit(self, map_type, i_seq):
    """
    Retrieves the two parameters of a gaussian function, fit to a given map at
    a site.

    Parameters
    ----------
    map_type : str
    i_seq : int

    Returns
    -------
    (tuple of float, float) or None
    """
    map_gaussians = self._map_gaussian_fits.get(map_type, None)
    if (map_gaussians is not None):
      return map_gaussians[i_seq]
    return None

  def guess_molecular_weight(self, i_seq):
    """
    Guesses the molecular weight of a site by scaling the signal from
    the mFo map by the average signal from carbon atoms.

    Parameters
    ----------
    i_seq : int

    Returns
    -------
    float
    """
    map_values = self._map_values.get("mFo", None)
    if (map_values is None) : return None
    height = map_values[i_seq]
    mean_carbon = flex.mean(self.carbon_fo_values)
    assert (mean_carbon > 0)
    return 6 * height / mean_carbon

  def _find_atoms_near_site(self,
      site_cart,
      distance_cutoff = 1.5,
      distance_cutoff_same_site = 0.5):
    """
    Given an XYZ coordinate, finds atoms near that site, separating out those
    which are close enough to be essentially equivalent.  Used to analyze the
    anomalously-scattering substructure calculated by Phaser.

    Parameters
    ----------
    site_cart : tuple of float, float, float
    distance_cutoff : float, optional
    distance_cutoff_same_site : float, optional

    Returns
    -------
    same_atoms : list of group_args
        List of atoms within distance_cutoff_same_site of site_cart, and and
        other_atoms, the rest of the atoms within distance_cutoff of site_cart.
    """
    site_frac = self.unit_cell.fractionalize(site_cart = site_cart)
    sites_frac = flex.vec3_double([site_frac])
    asu_mappings = self.xray_structure.asu_mappings(buffer_thickness =
      distance_cutoff + 0.1)
    asu_mappings.process_sites_frac(sites_frac,
      min_distance_sym_equiv = self.xray_structure.min_distance_sym_equiv())
    pair_generator = crystal.neighbors_fast_pair_generator(
      asu_mappings = asu_mappings,
      distance_cutoff = distance_cutoff)
    n_xray = self.xray_structure.scatterers().size()
    same_atoms = []
    other_atoms = []
    # Iterate through all interacting pairs of atoms in the structure
    # within distance_cutoff of each other
    for pair in pair_generator:
      # Find a pair where one's sequence ID is < n_xray and the other's
      # is >= n_xray and gather the site information about it
      if (pair.i_seq < n_xray and pair.j_seq < n_xray) or \
        (pair.i_seq >= n_xray and pair.j_seq >= n_xray):
        continue
      rt_mx_i = asu_mappings.get_rt_mx_i(pair)
      rt_mx_j = asu_mappings.get_rt_mx_j(pair)
      rt_mx_ji = rt_mx_i.inverse().multiply(rt_mx_j)
      new_site_frac = rt_mx_ji * site_frac
      new_site_cart = self.unit_cell.orthogonalize(site_frac = new_site_frac)
      atom_seq = pair.i_seq if pair.i_seq < n_xray else pair.j_seq
      site_info = group_args(
        i_seq = atom_seq,
        rt_mx = rt_mx_ji,
        new_site_cart = new_site_cart,
        distance = sqrt(pair.dist_sq))
      if site_info.distance < distance_cutoff_same_site:
        same_atoms.append(site_info)
      else:
        other_atoms.append(site_info)
    same_atoms.sort(key = lambda x: x.distance)
    other_atoms.sort(key = lambda x: x.distance)
    return same_atoms, other_atoms

  def analyze_substructure(self, log = None, verbose = True):
    """
    Given a list of AX pseudo-atoms placed by Phaser, finds the nearest real
    atoms, and if possible determines their equivalence.

    Parameters
    ----------
    log : file, optional
    verbose : bool, optional
    """
    def ax_atom_id(atom):
      """
      Parameters
      ----------
      atom : iotbx.pdb.hierarchy.atom

      Returns
      -------
      str
      """
      return "AX %s (fpp=%.3f)" % (atom.serial.strip(), atom.occ)

    assert (self.phaser_substructure is not None)
    self.fpp_from_phaser_ax_sites = flex.double(self.pdb_atoms.size(), -1)
    if (log is None) or (not verbose) : log = null_out()

    def _filter_site_infos(site_infos):
      """
      Parameters
      ----------
      site_infos : list of group_args

      Returns
      -------
      list of group_args
      """
      # Organize a dictionary, keyed with each site's atom i_seq
      from collections import OrderedDict
      i_seqs = OrderedDict()
      for site_info in site_infos:
        if site_info.i_seq in i_seqs:
          i_seqs[site_info.i_seq].append(site_info)
        else:
          i_seqs[site_info.i_seq] = [site_info]
      # If we are picking from multiple of the same i_seq, select the closest
      for val in i_seqs.values():
        # Prefer unit operators
        if any(i.rt_mx.is_unit_mx() for i in val):
          for site_info in val:
            if not site_info.rt_mx.is_unit_mx():
              val.remove(site_info)
        val.sort(key = lambda x: x.distance)
      return [i[0] for i in i_seqs.values()]

    make_sub_header("Analyzing Phaser anomalous substructure", out = log)
    for atom in self.phaser_substructure :
      print(ax_atom_id(atom), file=log)
      same_atoms, other_atoms = self._find_atoms_near_site(
        atom.xyz,
        distance_cutoff=self.params.phaser.distance_cutoff,
        distance_cutoff_same_site=self.params.phaser.distance_cutoff_same_site)

      same_atoms, other_atoms = \
        _filter_site_infos(same_atoms), _filter_site_infos(other_atoms)

      if len(same_atoms) == 0:
        print("  No match for %s" % ax_atom_id(atom), file=log)
        for other_atom in other_atoms:
          print("    %s (distance = %.3f)" % \
                (self.pdb_atoms[other_atom.i_seq].id_str(), other_atom.distance), file=log)
      elif len(same_atoms) == 1:
        print("  %s maps to %s" % \
          (ax_atom_id(atom), self.pdb_atoms[same_atoms[0].i_seq].id_str()), file=log)
        self.use_fdp[same_atoms[0].i_seq] = True
        self.fpp_from_phaser_ax_sites[same_atoms[0].i_seq] = atom.occ
      else :
        print("  ambiguous results for %s:" % ax_atom_id(atom), file=log)
        for same_atom in same_atoms:
          print("    %s" % self.pdb_atoms[same_atom.i_seq].id_str(), file=log)
    print("", file=log)

  def get_fpp(self, i_seq):
    """
    Retrieve the refined f'' for a site.  Because this can come from either the
    built-in anomalous refinement or Phaser, it is handled differently than
    the f' retrieval.

    Parameters
    ----------
    i_seq : int

    Returns
    -------
    float or None
    """
    if (not self.use_fdp[i_seq]):
      return None
    if (self.fpp_from_phaser_ax_sites is None):
      return self.site_fdp[i_seq]
    fpp = self.fpp_from_phaser_ax_sites[i_seq]
    if (fpp < 0) : fpp = None
    return fpp

  def get_fp(self, i_seq):
    """
    Retrieve the refined f' for a site.

    Parameters
    ----------
    i_seq : int

    Returns
    -------
    float or None
    """
    if (self.site_fp is None) or (not self.use_fdp[i_seq]):
      return None
    return self.site_fp[i_seq]

  def looks_like_halide_ion(self,
      i_seq,
      element = "CL",
      assume_hydrogens_all_missing = Auto):
    """
    Given a site, analyze the nearby atoms (taking geometry into account) to
    guess whether it might be a halide ion.  Among other things, halides will
    often be coordinated by amide hydrogens, and not in close contact with
    negatively charged atoms.  Note that this procedure has a very high
    false positive rate when used by itself, so additional information about
    the electron count (map, occ, b) is absolutely essential.

    Parameters
    ----------
    i_seq : int
    element : str
    assume_hydrogens_all_missing : bool, optional

    Returns
    -------
    bool
    """
    atom = self.pdb_atoms[i_seq]
    sites_frac = self.xray_structure.sites_frac()
    assert element.upper() in mmtbx.ions.HALIDES
    # discard atoms with B-factors greater than mean-1sigma for waters
    if ((self.b_mean_hoh is not None) and
        (atom.b > self.b_mean_hoh - self.b_stddev_hoh)):
      return False
    nearby_atoms = self.find_nearby_atoms(
      i_seq,
      far_distance_cutoff = 4.0,
      filter_by_bonding = False)
    favorable_environment = halides.is_favorable_halide_environment(
      i_seq=i_seq,
      contacts=nearby_atoms,
      pdb_atoms=self.pdb_atoms,
      sites_frac=self.xray_structure.sites_frac(),
      unit_cell=self.unit_cell,
      connectivity=self.connectivity,
      params = self.params.chloride,
      assume_hydrogens_all_missing=assume_hydrogens_all_missing)
    # TODO something smart...
    # the idea here is to determine whether the blob of density around the
    # site is approximately spherical and limited in extent.
    pai = self.principal_axes_of_inertia(i_seq)
    map_variance = self.get_map_sphere_variance(i_seq)
    good_map_falloff = False
    if ((map_variance is not None) and
        (map_variance.mean < 1.5) and
        (map_variance.standard_deviation < 0.4)) : # XXX very arbitrary
      good_map_falloff = True
    # XXX probably need something more sophisticated here too.  a CL which
    # coordinates a metal (e.g. in 4aqi) may not have a clear blob, but
    # will still be detectable by other criteria.
    return favorable_environment

  def analyze_water(self,
      i_seq,
      debug = True,
      candidates = Auto,
      no_final = False,
      out = sys.stdout):
    """
    Examines the environment around a single atom to determine if it is actually
    a misidentified metal ion.

    no_final is used internally, when candidates is not Auto, to try all of the
    Auto without selecting any as a final choice when none of the elements in
    candidates appear to probable ion identities.

    Parameters
    ----------
    i_seq : int
    debug : bool, optional
    candidates : list of str, optional
    no_file : bool, optional
    out : file, optional

    Returns
    -------
    mmtbx.ions.identify.water_result
    """

    atom = self.pdb_atoms[i_seq]
    # Keep track of this in case we find nothing from the user-specific candidates
    auto_candidates = candidates is Auto
    if auto_candidates:
      candidates = mmtbx.ions.DEFAULT_IONS
    elif isinstance(candidates, string_types):
      candidates = candidates.replace(",", " ").split()
    candidates = [i.strip().upper() for i in candidates]
    if (candidates == ['X']) : # XXX hack for testing - X is "dummy" element
      candidates = []

    resname = atom.fetch_labels().resname.strip().upper()
    assert resname in WATER_RES_NAMES

    atom_props = self.atoms_to_props[i_seq]
    final_choice = None

    # Gather some quick statistics on the atom and see if it looks like water
    atom_type = atom_props.get_atom_type(params=self.params.water,
      aggressive=self.params.aggressive)
    if (atom_type == WATER_POOR) : # not trustworthy, skip
      #_PRINT_DEBUG("SKIPPING %s" % atom.id_str())
      return None

    # XXX everything SVM-related happens in mmtbx.ions.svm, using a subclass of
    # this one
    assert (not self.use_svm)

    # Filter out metals based on whether they are more or less eletron-dense
    # in comparison with water
    filtered_candidates = []
    halide_candidates = False
    nuc_phosphate_site = atom_props.looks_like_nucleotide_phosphate_site()
    max_carbon_fo_map_value = sys.maxsize
    if (self.carbon_fo_values is not None):
      max_carbon_fo_map_value = flex.max(self.carbon_fo_values)

    for symbol in candidates :
      elem = mmtbx.ions.server.get_metal_parameters(symbol)
      if (elem is None):
        if (symbol in mmtbx.ions.HALIDES) : # halides are special!
          halide_candidates = True
          continue
        else :
          raise Sorry("Element '%s' not supported!" % symbol)

      # If we definitely look like water or a light ion, only look at the
      # metals isoelectronic with water.
      if ((atom_type in [WATER, LIGHT_ION]) and
          (symbol not in ["NA", "MG", "F", "NE"])):
        continue

      if (nuc_phosphate_site) and (not symbol in NUC_PHOSPHATE_BINDING):
        continue

      n_elec = sasaki.table(symbol.upper()).atomic_number() - elem.charge
      # lighter elements are not expected to have any anomalous signal
      if (n_elec <= 12) and (atom_props.fpp is not None and atom_props.fpp > 0.1):
        continue
      mass_ratio = atom_props.estimated_weight / max(n_elec, 1)
      # note that anomalous peaks are more important than the 2mFo-DFc level
      if (mass_ratio < 0.4) and (atom_type == WATER):
        continue

      filtered_candidates.append(elem)

    # if len(filtered_candidates) == 0 and not halide_candidates:
    #   return None

    # Try each different ion and see what is reasonable
    def try_candidates(require_valence = True):
      """
      Parameters
      ----------
      require_valence : bool, optional

      Returns
      -------
      list of tuple of mmtbx.ions.metal_parameters, float
      list of tuple of mmtbx.ions.metal_parameters, float
      """
      reasonable = []
      unreasonable = []
      for elem_params in filtered_candidates:
        # Try the ion with check_ion_environment()
        atom_props.check_ion_environment(
          ion_params = elem_params,
          wavelength = self.wavelength,
          require_valence = require_valence)
        atom_props.check_fpp_ratio(
          ion_params = elem_params,
          wavelength = self.wavelength,
          fpp_ratio_min = self.params.phaser.fpp_ratio_min,
          fpp_ratio_max = self.params.phaser.fpp_ratio_max)
        identity = str(elem_params)
        if atom_props.is_correctly_identified(identity = identity):
          reasonable.append((elem_params, atom_props.score[identity]))
        else :
          unreasonable.append((elem_params, atom_props.score[identity]))
      return reasonable, unreasonable

    # first try with bond valence included in criteria - then if that doesn't
    # yield any hits, try again without valences
    reasonable, unreasonable = try_candidates(require_valence = True)
    valence_used = True
    if (len(reasonable) == 0) and (not self.params.require_valence):
      reasonable, unreasonable = try_candidates(require_valence = False)
      if (len(reasonable) > 0):
        valence_used = False

    looks_like_halide = False
    if (not (reasonable or atom_type < HEAVY_ION or nuc_phosphate_site)):
      # try halides now
      candidate_halides = set(candidates).intersection(mmtbx.ions.HALIDES)
      filtered_halides = []
      for element in candidate_halides :
        fpp_ratio = atom_props.check_fpp_ratio(
          ion_params = mmtbx.ions.metal_parameters(element = element,
            charge = -1),
          wavelength = self.wavelength,
          fpp_ratio_min = self.params.phaser.fpp_ratio_min,
          fpp_ratio_max = self.params.phaser.fpp_ratio_max)
        # XXX chlorides are tricky, because they tend to be partial occupancy
        # anyway and the f'' is already small, so prone to error here
        if (fpp_ratio is not None) and (element != "CL"):
          if ((fpp_ratio < self.params.phaser.fpp_ratio_min) or
              (fpp_ratio > self.params.phaser.fpp_ratio_max)):
            #print "fpp_ratio:", fpp_ratio
            continue
        if (self.looks_like_halide_ion(i_seq = i_seq, element = element)):
          filtered_halides.append(element)

      looks_like_halide = (len(filtered_halides) > 0)
      reasonable += [(mmtbx.ions.metal_parameters(element = halide,
                      charge = -1), 0) for halide in filtered_halides]

    # If we can't find anything reasonable, relax our constraints...
    # Look for something that is a compatible ligand, an apparent fpp,
    # and compatible geometries
    if (len(reasonable) == 0) and (not self.params.require_valence):
      compatible = [params for params in filtered_candidates
                    if atom_props.has_compatible_ligands(str(params))]
      for ion_params in compatible :
        atomic_number = sasaki.table(ion_params.element).atomic_number()
        weight_ratio = 0
        if (atom_props.estimated_weight is not None):
          weight_ratio = atom_props.estimated_weight / (atomic_number - \
            ion_params.charge)
        # special handling for transition metals, but only if the user has
        # explicitly requested one (and there is no ambiguity)
        if ((ion_params.element in mmtbx.ions.TRANSITION_METALS) and
            (atom_type == HEAVY_ION) and
            #(atom_props.peak_2fofc > max_carbon_fo_map_value) and
            (not auto_candidates) and
            (atom_props.is_compatible_site(ion_params))):
          n_good_res = atom_props.number_of_favored_ligand_residues(ion_params,
            distance = 2.7)
          n_total_coord_atoms = atom_props.number_of_atoms_within_radius(2.8)
          # if we see at least one favorable residue coordinating the atom
          # and no more than six atoms total, accept the current guess
          if ((n_good_res >= 1) and (2 <= n_total_coord_atoms <= 6)):
            reasonable.append((ion_params, 0))
          else : pass
            #print "n_good_res = %d, n_total_coord_atoms = %d" % (n_good_res,
            #  n_total_coord_atoms)
        elif ((ion_params.element in ["K","CA"]) and
              (atom_type == HEAVY_ION) and
              (not auto_candidates) and
              (atom_props.is_compatible_site(ion_params))):
          n_good_res = atom_props.number_of_favored_ligand_residues(ion_params,
            distance = 2.9, exclude_atoms = ["O"])
          n_bb_oxygen = atom_props.number_of_backbone_oxygens(
            distance_cutoff = 2.9)
          n_total_coord_atoms = atom_props.number_of_atoms_within_radius(
            distance_cutoff = 2.9)
          if ((n_good_res + n_bb_oxygen) >= 2) and (n_total_coord_atoms >= 4):
            reasonable.append((ion_params, 0))
        # another special case: very heavy ions, which are probably not binding
        # physiologically
        elif ((atomic_number > 30) and (atom_type == HEAVY_ION) and
              (((weight_ratio > 0.5) and (weight_ratio < 1.05)) or
               (atom_props.fp > 10)) and
              (not auto_candidates) and
              (atom_props.is_compatible_site(ion_params,
                ignore_valence=not self.get_strict_valence_flag()))):
          n_total_coord_atoms = atom_props.number_of_atoms_within_radius(2.8)
          if (n_total_coord_atoms >= 3):
            reasonable.append((ion_params, 0))
        else : pass
          #print atom.id_str(), atomic_number, looks_like_water, weight_ratio, \
          #  atom_props.is_compatible_site(ion_params)
      if (len(compatible) == 1) and (not self.get_strict_valence_flag()):
        inaccuracies = atom_props.inaccuracies[str(ion_params)]
        if (compatible[0] in atom_props.fpp_ratios and
            atom_props.BAD_FPP not in inaccuracies and
            not inaccuracies.intersection([atom_props.BAD_GEOMETRY,
                                           atom_props.NO_GEOMETRY])):
          _PRINT_DEBUG(atom.id_str())
          _PRINT_DEBUG(filtered_candidates)
          _PRINT_DEBUG(compatible)

    if (len(reasonable) == 1):
      final_choice = reasonable[0][0]

    if (not reasonable) and (not auto_candidates):
      # Couldn't find anything from what the user suggested, try the other
      # default candidates and just let the user know about them
      result = self.analyze_water(
        i_seq = i_seq,
        debug = debug,
        no_final = True,
        out = out)
      if (result.final_choice is not None):
        return result
    return water_result(
      atom_props = atom_props,
      filtered_candidates = filtered_candidates,
      matching_candidates = reasonable,
      rejected_candidates = unreasonable,
      nuc_phosphate_site = nuc_phosphate_site,
      atom_type = atom_type,
      looks_like_halide = looks_like_halide,
      ambiguous_valence_cutoff = self.params.ambiguous_valence_cutoff,
      valence_used = valence_used,
      final_choice = final_choice,
      wavelength = self.wavelength,
      no_final = no_final)

  def _extract_waters(self):
    model = self.pdb_hierarchy.only_model()
    water_i_seqs = []
    for chain in model.chains():
      for residue_group in chain.residue_groups():
        atom_groups  = residue_group.atom_groups()
        if (len(atom_groups) > 1):
          continue
        for atom_group in atom_groups :
          resname = atom_group.resname.strip().upper()
          if (resname in WATER_RES_NAMES):
            atoms = atom_group.atoms()
            if (len(atoms) == 1) : # otherwise it probably has hydrogens, skip
              water_i_seqs.append(atoms[0].i_seq)
    return water_i_seqs

  def analyze_waters(self, out = sys.stdout, debug = True, candidates = Auto):
    """
    Iterates through all of the waters in a model, examining the maps and local
    environment to check their identity and suggest likely ions where
    appropriate.

    Parameters
    ----------
    out : file, otpional
    debug : bool, optional
    candidates : list of str, optional

    Returns
    -------
    list of tuple of int, mmtbx.ions.metal_parameters
        i_seq and metal parameters which indicate that waters of i_seq would be
        better changed to that new metal identity.
    """
    waters = self.water_selection()
    print("", file=out)
    print("  %d waters to analyze" % len(waters), file=out)
    if (len(waters) == 0) : return
    nproc = easy_mp.get_processes(self.nproc)
    ions = []
    self.atoms_to_props = dict((i_seq, AtomProperties(i_seq, self))
                               for i_seq in waters)
    if (nproc == 1):
      print("", file=out)
      for water_i_seq in waters :
        t1 = time.time()
        water_props = self.analyze_water(
          i_seq = water_i_seq,
          debug = debug,
          candidates = candidates,
          out = out)
        if (water_props is not None):
          water_props.show_summary(out = out, debug = debug)
          map_stats = self.map_stats(water_i_seq)
          if ((water_props.final_choice is not None) and
              (not water_props.no_final)):
            ions.append((water_i_seq, [water_props.final_choice],
              map_stats.two_fofc))
        t2 = time.time()
        #print "%s: %.3fs" % (self.pdb_atoms[water_i_seq].id_str(), t2-t1)
    else :
      print("  Parallelizing across %d processes" % nproc, file=out)
      print("", file=out)
      analyze_water = _analyze_water_wrapper(manager = self,
        debug = debug,
        candidates = candidates,
        out = out)
      results = easy_mp.pool_map(
        fixed_func = analyze_water,
        args = waters,
        processes = nproc)
      for result in results :
        if (result is None):
          continue
        water_i_seq, final_choice, result_str = result
        if (result_str is not None):
          print(result_str, file=out)
        if final_choice is not None :
          map_stats = self.map_stats(water_i_seq)
          ions.append((water_i_seq, [final_choice], map_stats.two_fofc))

    return sorted(ions, key=operator.itemgetter(2), reverse=True)

  def validate_ion(self, i_seq, out = sys.stdout, debug = True):
    """
    Examines one site in the model and determines if it was correctly modelled,
    returning a boolean indicating correctness.

    Parameters
    ----------
    i_seq : int
    out : file, optional
    debug : bool, optional
    """

    atom_props = self.atoms_to_props[i_seq]
    element = mmtbx.ions.server.get_element(atom_props.atom)
    elem_params = mmtbx.ions.server.get_metal_parameters(element)

    if elem_params is not None:
      atom_type = atom_props.get_atom_type(params=self.params.water)
      atom_props.check_ion_environment(
        ion_params = elem_params,
        wavelength = self.wavelength,
        require_valence = self.params.require_valence)
      atom_props.check_fpp_ratio(
        ion_params = elem_params,
        wavelength = self.wavelength,
        fpp_ratio_min = self.params.phaser.fpp_ratio_min,
        fpp_ratio_max = self.params.phaser.fpp_ratio_max)
    elif element in mmtbx.ions.HALIDES:
      identity = atom_props.identity()
      atom_props.inaccuracies[identity] = set()

      if not self.looks_like_halide_ion(i_seq = i_seq, element = element):
        atom_props.inaccuracies[identity].add(atom_props.BAD_HALIDE)
    else:
      raise Sorry("Element '%s' not supported:\n%s" %
                  (element, atom_props.atom.format_atom_record()))

    return atom_props

  def validate_ions(self, out = sys.stdout, debug = True, segid = None):
    """
    Iterate over all the ions built into the model by this module and double
    check their correctness. Looks for tell-tale signs such as negative peaks
    in the mFo-DFc and anomalous difference maps, abnormal b-factor, and
    disallowed chemical environments.

    Prints out a table of bad ions and their information and returns a list of
    their sites' i_seqs.

    Parameters
    ----------
    out : file, optional
    debug : bool, optional
    segid : str, optional
        If present, only validate ions with this segid.
    """

    if segid is None:
      ions = []
      for model in self.pdb_hierarchy.models():
        for chain in model.chains():
          for residue_group in chain.residue_groups():
            for atom_group in residue_group.atom_groups():
              if atom_group.resname.strip() in mmtbx.ions.SUPPORTED:
                atoms = atom_group.atoms()
                assert (len(atoms) == 1)
                for atom in atoms:
                  ions += atom.i_seq,
    else:
      sel_cache = self.pdb_hierarchy.atom_selection_cache()
      ions = sel_cache.selection("segid {0}".format(segid)).iselection()

    if len(ions) == 0:
      return
    ion_status = []

    self.atoms_to_props = dict((i_seq, AtomProperties(i_seq, self))
                               for i_seq in ions)
    for i_seq in ions:
      atom_props = self.validate_ion(
        i_seq = i_seq,
        debug = debug)
      ion_status.append((atom_props, atom_props.is_correctly_identified()))
    scatterers = self.xray_structure.scatterers()
    headers = ("atom", "occ", "b_iso", "2mFo-DFc", "mFo-DFc", "fp", "fdp",
               "ratio", "BVS", "VECSUM")
    fmt = "%-15s %-5s  %-5s  %-8s  %-7s  %-5s  %-5s  %-5s %-5s  %-6s"
    box = framed_output(out, title = "Validating new ions", width = 80)
    print(fmt % headers, file=box)
    print(" " + ("-" * 75), file=box)
    for props, okay_flag in ion_status :
      i_seq = props.atom.i_seq
      sc = scatterers[i_seq]
      fp = fdp = None
      if (sc.flags.use_fp_fdp):
        fp = sc.fp
        fdp = sc.fdp
      # XXX props.atom.b does not work here!
      b_iso = adptbx.u_as_b(sc.u_iso_or_equiv(unit_cell = self.unit_cell))
      identity = props.identity()
      def ff(fs, val) : return format_value(fs, val, replace_none_with = "---")
      print(fmt % (props.atom.id_str(suppress_segid = True)[5:-1],
        ff("%.2f", props.atom.occ), ff("%.2f", b_iso),
        ff("%.2f", props.peak_2fofc), ff("%.2f", props.peak_fofc),
        ff("%.2f", fp), ff("%.2f", fdp),
        ff("%.2f", None if self.wavelength is None else fdp / sasaki.table(
            mmtbx.ions.server.get_element(props.atom)).at_angstrom(self.wavelength).fdp()),
        ff("%.2f", props.valence_sum.get(identity)),
        ff("%.2f", props.vector_sum.get(identity))), file=box)
      # print >> box, props.geometries
      if not okay_flag:
        print("\n".join("!!! " + props.error_strs[i]
                                for i in props.inaccuracies[identity]), file=box)
      print("", file=box)
    box.close()

class _analyze_water_wrapper(object):
  """
  Simple wrapper for calling manager.analyze_water with keyword arguments
  in a parallelized loop.  Because the water_result object is not pickle-able,
  we only return the final ion choice and summary string.
  """
  def __init__(self, manager, **kwds):
    self.manager = manager
    self.kwds = dict(kwds)

  def __call__(self, i_seq):
    try:
      result = self.manager.analyze_water(i_seq, **(self.kwds))
      out = StringIO()
      if (result is not None):
        result.show_summary(out = out,
          debug = self.kwds.get("debug", False))
      result_str = out.getvalue()
      if (result_str == "") : result_str = None
      final_choice = None
      # only accept final choice if it's in the original list of elements
      if (not getattr(result, "no_final", False)):
        final_choice = getattr(result, "final_choice", None)
        #_PRINT_DEBUG("final_choice = %s" % final_choice)
      return i_seq, final_choice, result_str
    except KeyboardInterrupt :
      return (None, None, None)

class _validate_ion_wrapper(object):
  """
  Simple wrapper for calling manager.validate_ion with keyword arguments
  in a parallelized loop.  Because the water_result object is not pickle-able,
  we only return the final ion choice and summary string.
  """
  def __init__(self, manager, **kwds):
    self.manager = manager
    self.kwds = dict(kwds)

  def __call__(self, i_seq):
    try:
      correct = self.manager.validate_ion(i_seq, **(self.kwds))
      return i_seq, correct
    except KeyboardInterrupt :
      return (None, None)

class ion_result(validation.atom):
  """
  Class for validation results for an ion site. This includes information about
  both the atom modeled at that site as well as its chemical and scattering
  environment.
  """
  __slots__ = validation.atom.__slots__ + ["chem_env", "scatter_env"]

  def show(self, out = sys.stdout, prefix = ""):
    pass

  def show_brief(self, out = sys.stdout, prefix = ""):
    # Print brief statistics for lines in a table
    pass

class water_result(object):
  """
  Container for storing the results of manager.analyze_water for later display
  and retrieval.
  """
  def __init__(self,
      atom_props,
      filtered_candidates,
      matching_candidates,
      rejected_candidates,
      nuc_phosphate_site,
      atom_type,
      looks_like_halide,
      ambiguous_valence_cutoff,
      valence_used,
      final_choice,
      wavelength=None,
      no_final = False):
    adopt_init_args(self, locals())

  def show_summary(self, out = None, debug = False):
    """
    Prints out a summary of a site's chemical and scattering environment.

    Parameters
    ----------
    out : file, optional
    debug : bool, optional
    """
    if (out is None) : out = sys.stdout
    results = self.matching_candidates
    if (len(results) > 0):
      self.atom_props.show_properties(identity = "HOH", out = out)
      if (self.nuc_phosphate_site):
        print("  appears to be nucleotide coordination site", file=out)
      if (self.no_final):
        print("  Found potential ion%s outside of specified set:" % \
          ("s" if len(results) > 1 else ""), file=out)
      if (self.final_choice is not None):
        # We have one result that we are reasonably certain of
        elem_params, score = results[0]
        if elem_params.element not in mmtbx.ions.HALIDES:
          self.atom_props.show_ion_results(
            identity = str(self.final_choice),
            out = out,
            valence_used = self.valence_used,
            confirmed = True)
        else:
          print("  Probable anion:", str(elem_params), file=out)
        print("", file=out)
      elif (len(results) > 1):
        # We have a couple possible identities for the atom
        below_cutoff = [ elem_params for elem_params, score in results
                        if score < self.ambiguous_valence_cutoff]
        if len(below_cutoff) == 1:
          elem_params = below_cutoff[0]
          print("  ambigous results, best valence from %s" % \
            str(elem_params), file=out)
          self.atom_props.show_ion_results(
            identity = str(elem_params),
            out = out,
            valence_used = True)
          print("", file=out)
        else:
          ions = [str(i[0]) for i in sorted(results, key = lambda x: x[1])]
          print("  ambiguous results, could be %s" % ", ".join(ions), file=out)
          for elem_params, score in results :
            self.atom_props.show_ion_results(identity = str(elem_params),
              out = out)
          print("", file=out)
    else:
      if (self.atom_type != WATER) or (self.nuc_phosphate_site):
        self.atom_props.show_properties(identity = "HOH", out = out)
        if (self.nuc_phosphate_site):
          print("  appears to be nucleotide coordination site", file=out)
        # try anions now
        if (self.looks_like_halide):
          print("  Probable cation: %s" % str(self.final_choice), file=out)
          print("", file=out)
        else:
          # atom is definitely not water, but no reasonable candidates found
          # print out why all the metals we tried failed
          if (debug) and (len(self.filtered_candidates) > 0):
            print("  insufficient data to identify atom", file=out)
            possible = True
            for params in self.filtered_candidates:
              if (self.atom_props.has_compatible_ligands(str(params))):
                if possible:
                  print("  possible candidates:", file=out)
                  possible = False
                self.atom_props.show_ion_results(identity = str(params),
                  out = out)
              else :
                print("  incompatible ligands for %s" % str(params), file=out)
            #print >> out, "  rejected as unsuitable:"
            #for params in self.rejected_candidates:
            #  if (self.atom_props.has_compatible_ligands(str(params))):
            #    self.atom_props.show_ion_results(identity = str(params),
            #      out = out)
            #  else :
            #    print >> out, "  incompatible ligands for %s" % str(params)
          print("", file=out)

class AtomProperties(object):
  """
  Collect physical attributes of an atom, including B, occupancy, and map
  statistics, and track those which are at odds with its chemical identity.
  """

  LOW_B, HIGH_B, LOW_OCC, HIGH_OCC, NO_2FOFC_PEAK, FOFC_PEAK, FOFC_HOLE, \
    ANOM_PEAK, NO_ANOM_PEAK, BAD_GEOMETRY, NO_GEOMETRY, BAD_VECTORS, \
    BAD_VALENCES, TOO_FEW_NON_WATERS, TOO_FEW_COORD, TOO_MANY_COORD, \
    LIKE_COORD, BAD_COORD_ATOM, BAD_FPP, BAD_COORD_RESIDUE, VERY_BAD_VALENCES, \
    BAD_HALIDE, HIGH_2FOFC, COORDING_GEOMETRY, CLOSE_CONTACT \
    = range(25)

  error_strs = {
    LOW_B: "Abnormally low b-factor",
    HIGH_B: "Abnormally high b-factor",
    LOW_OCC: "Abnormally low occupancy",
    HIGH_OCC: "Abnormally high occupancy",
    NO_2FOFC_PEAK: "No 2mFo-DFc map peak",
    FOFC_PEAK: "Peak in mFo-DFc map",
    FOFC_HOLE: "Negative peak in dFo-mFc map",
    ANOM_PEAK: "Peak in the anomalous map",
    NO_ANOM_PEAK: "No peak in the anomalous map",
    BAD_GEOMETRY: "Unexpected geometry for coordinating atoms",
    NO_GEOMETRY: "No distinct geometry for coordinating atoms",
    BAD_VECTORS: "VECSUM above cutoff",
    BAD_VALENCES: "BVS above or below cutoff",
    TOO_FEW_NON_WATERS: "Too few non-water coordinating atoms",
    TOO_FEW_COORD: "Too few coordinating atoms",
    TOO_MANY_COORD: "Too many coordinating atoms",
    LIKE_COORD: "Like charge coordinating like",
    BAD_COORD_ATOM: "Disallowed coordinating atom",
    BAD_FPP: "Bad refined f'' value",
    BAD_COORD_RESIDUE: "Disallowed coordinating residue",
    VERY_BAD_VALENCES: "BVS far above or below cutoff",
    BAD_HALIDE: "Bad halide site",
    HIGH_2FOFC: "Unexpectedly high 2mFo-DFc value",
    COORDING_GEOMETRY: "No distinct geometry and coordinating another atom with distinct geometry",
    CLOSE_CONTACT : "Close contact to oxygen atom",
    }

  def __init__(self, i_seq, manager):
    self.i_seq = i_seq
    self.atom = manager.pdb_atoms[i_seq].fetch_labels()
    self.resname = self.atom.resname.strip().upper()
    self.d_min = manager.fmodel.f_obs().d_min()
    self.anomalous_flag = manager.fmodel.f_obs().anomalous_flag()
    self.strict_valence = manager.get_strict_valence_flag()

    # Grab all the atoms within 3.5 Angstroms
    nearby_atoms_unfiltered = manager.find_nearby_atoms(
      i_seq = i_seq,
      far_distance_cutoff = 3.5)
    self.nearby_atoms = []
    nearby_atoms_no_alts = []
    for contact in nearby_atoms_unfiltered :
      if (contact.element not in ["H", "D"]):
        self.nearby_atoms.append(contact)
        for other in nearby_atoms_no_alts :
          if (other == contact):
            break
        else :
          nearby_atoms_no_alts.append(contact)

    self.residue_counts = utils.count_coordinating_residues(
      self.nearby_atoms)
    self.geometries = find_coordination_geometry(nearby_atoms_no_alts)

    map_stats = manager.map_stats(i_seq)
    self.peak_2fofc = map_stats.two_fofc
    self.peak_fofc = map_stats.fofc
    self.peak_anom = map_stats.anom
    self.estimated_weight = manager.guess_molecular_weight(i_seq)
    self.b_iso = manager.get_b_iso(i_seq)
    # cache global properties
    self.b_mean_hoh = manager.b_mean_hoh
    self.b_stddev_hoh = manager.b_stddev_hoh
    self.b_mean_calpha = manager.b_mean_calpha
    self.calpha_mean_two_fofc = manager.calpha_mean_two_fofc

    self.inaccuracies = {}
    self.ignored = {}
    self.vectors = {}
    self.valence_sum = {}
    self.vector_sum = {}
    self.score = {}
    self.fpp_expected = {}
    self.expected_params = {}
    self.bad_coords = {}

    # Determine the f'' value if possible using phaser or anomalous refinement
    self.fpp = manager.get_fpp(i_seq)
    self.fpp_ratios = {}
    self.fp = manager.get_fp(i_seq)
    self.manager = manager

  def is_correctly_identified(self, identity = None):
    """
    Calculates whether factors indicate that the atom was correctly identified.

    Parameters
    ----------
    identity : mmtbx.ions.metal_parameters

    Returns
    -------
    bool
    """
    if identity is None:
      identity = self.identity()

    return len(self.inaccuracies[identity]) == 0

  def has_compatible_ligands(self, identity):
    """
    Indicates whether the coordinating atoms are of the allowed type (e.g.
    no N or S atoms coordinating CA, etc.) and residue (e.g. Ser is not an
    appropriate ligand for ZN).

    Parameters
    ----------
    identity : mmtbx.ions.metal_parameters

    Returns
    -------
    bool
    """
    return ((len(self.bad_coords[identity]) == 0) and
            (not self.BAD_COORD_RESIDUE in self.inaccuracies[identity]))

  def is_compatible_site(self, ion_params, require_anom = True,
      ignore_valence=False):
    """
    More minimal criteria for determining whether a site is chemically
    compatible, allowing for incomplete coordination shells.

    Parameters
    ----------
    ion_params : mmtbx.ions.metal_parameters
    require_anom : bool, optional
    ignore_valence : bool, optional

    Returns
    -------
    bool
    """
    inaccuracies = self.inaccuracies[str(ion_params)]
    anom_allowed = self.is_compatible_anomalous_scattering(ion_params) or \
                   (not require_anom)
    return (self.has_compatible_ligands(str(ion_params)) and
            (not self.TOO_MANY_COORD in inaccuracies) and
            (ignore_valence or (not self.VERY_BAD_VALENCES in inaccuracies))
            and (not self.BAD_COORD_RESIDUE in inaccuracies) and
            (anom_allowed))

  def number_of_favored_ligand_residues(self, ion_params, distance = 3.0,
      exclude_atoms = ()):
    """
    Counts the number of preferred residues coordinating the atom.  Used for
    approximate detection of transition-metal binding sites.

    Parameters
    ----------
    ion_params : mmtbx.ions.metal_parameters
    distance : float, optional
    exclude_atoms : tuple of ...

    Returns
    -------
    int
    """
    n_res = 0
    resids = []
    for contact in self.nearby_atoms:
      if (contact.atom_name() in exclude_atoms):
        continue
      if (contact.distance() < distance):
        labels = contact.atom.fetch_labels()
        other_resname = contact.resname()
        other_resid = labels.chain_id + labels.resid()
        if ((ion_params.allowed_coordinating_residues is not None) and
            (other_resname in ion_params.allowed_coordinating_residues) and
            (not other_resid in resids)):
          n_res += 1
          resids.append(other_resid)
    return n_res

  def number_of_atoms_within_radius(self, distance_cutoff):
    """
    Counts the number of coordinating atoms within a given radius.

    Parameters
    ----------
    float

    Returns
    -------
    int
    """
    n_atoms = 0
    atom_ids = []
    for contact in self.nearby_atoms:
      other_id = contact.atom_id_no_altloc()
      if (not other_id in atom_ids):
        if (contact.distance() < distance_cutoff):
          n_atoms += 1
        atom_ids.append(other_id) # check for alt confs.
    return n_atoms

  def number_of_backbone_oxygens(self, distance_cutoff=3.0):
    """
    Counts the number of backbone oxygens coordinating a site.

    Parameters
    ----------
    distance_cutoff : float, optional

    Returns
    -------
    int
    """
    n_bb_ox = 0
    for contact in self.nearby_atoms :
      if (contact.atom_name() == "O"):
        if (contact.distance() <= distance_cutoff):
          if (not contact.resname() in WATER_RES_NAMES):
            n_bb_ox += 1
    return n_bb_ox

  # FIXME needs to be refactored and combined with check_fpp_ratio
  def is_compatible_anomalous_scattering(self, ion_params):
    # lighter elements should have effectively no anomalous scattering
    if (ion_params.element.upper() in ["MG", "NA"]):
      return ((self.fpp is None) and (self.peak_anom is not None) and
              (self.peak_anom < 1.0))
    else :
      # XXX somewhat dangerous - we really need f'' for this to work reliably
      if (self.fpp is None):
        return (self.peak_anom is not None) and (self.peak_anom > 3.0)
      identity = self.identity(ion = ion_params)
      if (identity in self.fpp_ratios):
        return (not self.BAD_FPP in self.inaccuracies[identity])
    return False

  # XXX obsolete, delete?
  def atom_weight(self, manager):
    """
    Evaluates whether factors indicate that the atom is lighter, heavier, or
    isoelectric to what it is currently identified as.

    Parameters
    ----------
    manager : mmtbx.ions.identify.manager

    Returns
    -------
    int
        -1 if lighter, 0 if isoelectronic, and 1 if heavier.
    """
    identity = "HOH" if self.resname in WATER_RES_NAMES else self.identity()
    # Waters that don't have B-factors at least 1 stddev below the mean are
    # presumed to be correct
    if (identity == "HOH" and
        (self.atom.b > manager.b_mean_hoh - manager.b_stddev_hoh)):
      return 0
    if self.is_correctly_identified(identity = identity):
      return 0
    # B-factors/occupancies?
    if self.FOFC_PEAK in self.inaccuracies[identity] or self.atom.b < 1:
      return 1
    if self.FOFC_HOLE in self.inaccuracies[identity]:
      return -1
    return 0

  def check_ion_environment(self,
      ion_params,
      wavelength = None,
      require_valence = True):
    """
    Checks whether or not the specified ion satisfies the metal-coordination
    parameters, specified by ion_params, such as valence sum, geometry, etc.
    The criteria used here are quite strict, but many of the analyses are
    saved for later if we want to use looser critera.

    Parameters
    ----------
    ion_params : mmtbx.ions.metal_parameters
    wavelength : float, optional
    require_valence : bool, optional
    """
    from iotbx.pdb import common_residue_names_get_class as get_class

    identity = self.identity(ion_params)
    inaccuracies = self.inaccuracies[identity] = set()
    self.expected_params[identity] = ion_params
    ignored = self.ignored[identity] = set()

    # if the atom is clearly not a water, optionally relax some rules.  this
    # will be more sensitive for transition metals, without finding a lot of
    # spurious Mg/Na sites.
    strict_rules = require_valence or \
      self.is_correctly_identified(identity = "HOH") or \
      self.strict_valence or \
      ion_params.element in ["NA","MG"]

    # Check for all non-overlapping atoms within 3 A of the metal
    n_closest = 0
    coord_atoms = []
    for i_pair, contact1 in enumerate(self.nearby_atoms):
      distance = contact1.distance()
      if (distance < 3.0):
        for contact2 in self.nearby_atoms[(i_pair+1):] :
          if ((contact1 == contact2) or
              (contact1.distance_from(contact2) <= 0.3)):
            break
        else :
          coord_atoms.append(contact1)
          if (distance < 2.7):
            n_closest += 1

    if len(coord_atoms) < ion_params.coord_num_lower:
      inaccuracies.add(self.TOO_FEW_COORD)

    if n_closest > ion_params.coord_num_upper:
      inaccuracies.add(self.TOO_MANY_COORD)

    # Coordinating atoms closer than 3.0 A are not positively charged
    n_non_water = 0
    self.bad_coords[identity] = []

    for contact in self.nearby_atoms:
      other_name = contact.atom_name()
      other_resname = contact.resname()
      other_element = contact.element

      if (not other_resname in WATER_RES_NAMES):
        n_non_water += 1
      else:
        # Everything can potentially be coordinated by water
        continue

      if (contact.distance() < 3.0):
        # XXX: So, we have a a fair number of rules restricting nitrogens and
        # nitrogen-containing residues from coordinating a number of cations.
        #
        # However, this rule is dependent on the protonation of the nitrogen,
        # if the pKa is low at the site, it is possible for a metal to
        # coordinate the residue fine.
        #
        # We want a complex rule that takes into account coordinating geometry,
        # density signal, and the presence of other coordinating atoms that
        # might drop the site's pKa enough to lose the hydrogen.
        if ((ion_params.allowed_coordinating_atoms is not None) and
            (other_element not in ion_params.allowed_coordinating_atoms)):
          self.bad_coords[identity].append(contact)
          inaccuracies.add(self.BAD_COORD_ATOM)
        if (get_class(other_resname) == "common_amino_acid"):
          # limit elements allowed to bind to backbone atoms (mainly carbonyl
          # oxygen)
          if ((other_name in ["C","N","O","CA","H","HA"]) and
              ((ion_params.allowed_backbone_atoms is None) or
               (not other_name in ion_params.allowed_backbone_atoms))):
            if (other_name == "O") and (contact.is_carboxy_terminus):
              pass # C-terminal carboxyl group is allowed
            else :
              self.bad_coords[identity].append(contact)
              inaccuracies.add(self.BAD_COORD_ATOM)
          # Check if atom is of an allowed residue type, if part of a sidechain
          if (ion_params.allowed_coordinating_residues is not None):
            allowed = ion_params.allowed_coordinating_residues
            if ((not other_resname in allowed) and
                (other_name not in ["C", "O", "N", "CA", "OXT"])):
                # XXX probably just O
              self.bad_coords[identity].append(contact)
              inaccuracies.add(self.BAD_COORD_RESIDUE)
        elif (cmp(0, mmtbx.ions.server.get_charge(contact.atom)) ==
              cmp(0, ion_params.charge)):
          # Check if coordinating atom is of opposite charge
          self.bad_coords[identity].append(contact)
          inaccuracies.add(self.LIKE_COORD)
        elif (ion_params.charge > 0 and
            other_element in ["N"] and
            other_resname in ["LYS", "ARG", "ASN", "GLN"]):
          # Coordinating nitrogen most likely positive.
          #
          # Ignore nitrogens without a charge label that are on positively
          # charged amino acids.
          self.bad_coords[identity].append(contact)
          inaccuracies.add(self.LIKE_COORD)

    # Check the number of coordinating waters
    if (n_non_water < ion_params.min_coordinating_non_waters):
      inaccuracies.add(self.TOO_FEW_NON_WATERS)

    # Check the geometry of the coordinating atoms
    if ion_params.allowed_geometries and strict_rules:
      allowed = [i[0] in ion_params.allowed_geometries
                 for i in self.geometries]
      if "any" in ion_params.allowed_geometries:
        pass
      elif not self.geometries:
        if strict_rules:
          inaccuracies.add(self.NO_GEOMETRY)
      elif not any(allowed):
        inaccuracies.add(self.BAD_GEOMETRY)
      else:
        strict_rules = False

    # If no distinct geometry, check that none of the coordinating have distinct
    # geometry, either
    if self.geometries == []:
      for contact in self.nearby_atoms:
        o_atom = contact.atom
        if o_atom.i_seq in self.manager.atoms_to_props:
          o_geometry = self.manager.atoms_to_props[o_atom.i_seq].geometries
          if o_geometry != []:
            inaccuracies.add(self.COORDING_GEOMETRY)

    # Check for reasonable vector/valence values
    vectors = mmtbx.ions.server.calculate_valences(ion_params,
      self.nearby_atoms)
    self.vectors[identity] = vectors

    self.valence_sum[identity] = sum([abs(i) for i in vectors])
    self.vector_sum[identity] = abs(sum(vectors, col((0, 0, 0))))

    if self.vector_sum[identity] > ion_params.vec_sum_cutoff:
      if (strict_rules):
        inaccuracies.add(self.BAD_VECTORS)
      else :
        ignored.add(self.BAD_VECTORS)

    # XXX I am not sure how low a valence sum we want to allow, but many
    # structures with non-physiological cation binding have partial and/or
    # irregular coordination shells
    if (self.valence_sum[identity] < ion_params.cvbs_expected * 0.25 or
        self.valence_sum[identity] > ion_params.cvbs_expected * 1.25):
      inaccuracies.add(self.VERY_BAD_VALENCES)
    else:
      if (self.valence_sum[identity] < ion_params.cvbs_lower or
          self.valence_sum[identity] > ion_params.cvbs_upper):
        if strict_rules:
          inaccuracies.add(self.BAD_VALENCES)
        else :
          ignored.add(self.BAD_VALENCES)

    self.score[identity] = abs(self.valence_sum[identity] -
                               ion_params.cvbs_expected)

  # FIXME this really needs to be refactored and combined with the method
  # is_compatible_anomalous_scattering
  def check_fpp_ratio(self,
      ion_params,
      wavelength,
      fpp_ratio_min = 0.3,
      fpp_ratio_max = 1.05):
    """
    Compare the refined and theoretical f'' values if available.

    Parameters
    ----------
    ion_params : mmtbx.ions.metal_parameters
    wavelength : float
    fpp_ratio_min : float, optional
    fpp_ratio_max : float, optional

    Returns
    -------
    float
        f'' / f''_expected
    """
    identity = str(ion_params)
    inaccuracies = self.inaccuracies.get(identity, None)
    if (inaccuracies is None):
      inaccuracies = self.inaccuracies[identity] = set()
    if (ion_params.element.upper() in ["MG", "NA"]):
      if (self.fpp is not None) or (self.peak_anom is not None and self.peak_anom > 1):
        inaccuracies.add(self.BAD_FPP)
    else :
      # XXX in theory the fpp_ratio should be no more than 1.0 unless we are
      # right on the peak wavelength.  in practice Phaser can overshoot a little
      # bit, so we need to be more tolerant.  picking the maximum f'' from the
      # Sasaki and Henke tables will also limit the ratio.
      if (wavelength is not None) and (self.anomalous_flag):
        fpp_expected_sasaki = sasaki.table(ion_params.element).at_angstrom(
          wavelength).fdp()
        fpp_expected_henke = henke.table(ion_params.element).at_angstrom(
          wavelength).fdp()
        self.fpp_expected[identity] = max(fpp_expected_sasaki,
          fpp_expected_henke)
        if (self.fpp is not None) and (self.fpp_expected[identity] != 0):
          self.fpp_ratios[identity] = self.fpp / self.fpp_expected[identity]
          if ((self.fpp_ratios[identity] > fpp_ratio_max) or
              ((self.fpp >= 0.2) and
               (self.fpp_ratios[identity] < fpp_ratio_min))):
            inaccuracies.add(self.BAD_FPP)
        elif (self.fpp_expected[identity] > 0.75) and (self.peak_anom < 2):
          inaccuracies.add(self.BAD_FPP)
    return self.fpp_ratios.get(identity)

  def show_properties(self, identity, out = sys.stdout):
    """
    Show atomic properties that are independent of the suspected identity.

    Parameters
    ----------
    identity : mmtbx.ions.metal_parameters
    out : file, optional
    """
    print("%s:" % self.atom.id_str(), file=out)
    b_flag = ""
    if (self.LOW_B in self.inaccuracies[identity]):
      b_flag = " <<<"
    elif (self.HIGH_B in self.inaccuracies[identity]):
      b_flag = " !!!"
    print("  B-factor:      %6.2f%s" % (self.atom.b, b_flag), file=out)
    occ_flag = ""
    if (self.LOW_OCC in self.inaccuracies[identity]):
      occ_flag = " !!!"
    elif (self.HIGH_OCC in self.inaccuracies[identity]):
      occ_flag = " <<<"
    print("  Occupancy:     %6.2f%s" % (self.atom.occ, occ_flag), file=out)
    twofofc_flag = ""
    if (self.NO_2FOFC_PEAK in self.inaccuracies[identity]):
      twofofc_flag = " !!!"
    elif (self.HIGH_2FOFC in self.inaccuracies[identity]):
      twofofc_flag = " <<<"
    print("  2mFo-DFc map:  %6.2f%s" % (self.peak_2fofc, twofofc_flag), file=out)
    fofc_flag = ""
    if (self.FOFC_PEAK in self.inaccuracies[identity]):
      fofc_flag = " <<<"
    elif (self.FOFC_HOLE in self.inaccuracies[identity]):
      fofc_flag = " !!!"
    print("  mFo-DFc map:   %6.2f%s" % (self.peak_fofc, fofc_flag), file=out)
    if (self.peak_anom is not None):
      anom_flag = ""
      if (self.ANOM_PEAK in self.inaccuracies[identity]):
        anom_flag = " <<<"
      elif (self.NO_ANOM_PEAK in self.inaccuracies[identity]):
        anom_flag = " !!!"
      print("  Anomalous map: %6.2f%s" % (self.peak_anom, anom_flag), file=out)
    if (self.estimated_weight is not None):
      print("  Approx. mass:  %6d" % self.estimated_weight, file=out)
    if self.fpp is not None:
      fpp_flag = ""
      if (self.fpp >= 0.2):
        fpp_flag = " <<<"
      print("  f'':           %6.2f%s" % (self.fpp, fpp_flag), file=out)
      print("  f'' ratio:     %s" % format_value("%6.2f",
        self.fpp_ratios.get(identity)), file=out)
    if self.nearby_atoms is not None:
      angstrom = u"\N{ANGSTROM SIGN}".encode("utf-8", "strict")
      degree = u"\N{DEGREE SIGN}".encode("utf-8", "strict")

      print("  Nearby atoms: (%d within 3.0 %s)" % \
        (len([i for i in self.nearby_atoms if i.distance() < 3]), angstrom), file=out)

      for contact in self.nearby_atoms :
        print("    %s (%5.3f %s)" % \
        (contact.id_str(), contact.distance(), angstrom), file=out)

      if self.geometries:
        print("  Coordinating geometry:", file=out)
        for geometry, deviation in self.geometries:
          print("    %-15s (average deviation: %.3f%s)" % \
            (geometry, deviation, degree), file=out)

  def show_ion_results(self, identity = None, out = sys.stdout,
      confirmed = False, valence_used = True):
    """
    Show statistics for a proposed element identity.

    Parameters
    ----------
    identity : mmtbx.ions.metal_parameters
    out : file, optional
    confirmed : bool, optional
    valence_used : bool, optional
    """

    if not identity:
      identity = self.identity(self.atom)

    inaccuracies = self.inaccuracies.get(identity, set([]))
    ignored = self.ignored.get(identity, set([]))

    if identity != self.identity():
      if (confirmed):
        print("  Probable cation: %s" % identity, file=out)
      else :
        print("  Atom as %s:" % identity, file=out)
    else:
      print("    %s:" % self.atom.id_str(), file=out)

    if identity in self.vector_sum and self.vector_sum[identity] is not None:
      problem = ((self.BAD_VECTORS in inaccuracies) or
                 (self.BAD_VECTORS in ignored))

      print("    Vector sum:  %6.3f %s" % \
        (self.vector_sum[identity], " !!!" if problem else ""), file=out)

    if identity in self.valence_sum and self.valence_sum[identity] is not None:
      problem = inaccuracies.union(ignored).intersection(
        [self.BAD_VALENCES, self.VERY_BAD_VALENCES])

      print("    Valence sum: %6.3f" % self.valence_sum[identity], file=out)
      if valence_used:
        print("(expected: %6.3f) %s" % \
        (self.expected_params[identity].cvbs_expected, " !!!" if problem else ""), end=' ', file=out)

    if self.NO_GEOMETRY in inaccuracies:
      print("    No distinct geometry !!!", file=out)

    if self.BAD_GEOMETRY in inaccuracies:
      print("    Unexpected geometry  !!!", file=out)

    bad_coord = [self.LIKE_COORD, self.BAD_COORD_ATOM, self.BAD_COORD_RESIDUE]
    if inaccuracies.intersection(bad_coord):
      print("    Bad coordinating atom%s:" % \
         ("s" if len(self.bad_coords[identity]) != 1 else ""), file=out)
      angstrom = u"\u00C5".encode("utf-8", "strict").strip()
      for atom, vector in self.bad_coords[identity]:
        print("    %s (%5.3f %s) !!!" % \
          (atom.id_str(), abs(vector), angstrom), file=out)

    if self.TOO_FEW_NON_WATERS in inaccuracies:
      print("    Too few coordinating non-waters !!!", file=out)
    if self.TOO_FEW_COORD in inaccuracies:
      print("    Too few coordinating atoms !!!", file=out)
    if self.TOO_MANY_COORD in inaccuracies:
      print("    Too many coordinating atoms !!!", file=out)

    if (self.fpp is not None) and (identity in self.fpp_ratios):
      print("    f'' ratio:   %6.3f%s" % \
         (self.fpp_ratios[identity], " !!!" if self.BAD_FPP in inaccuracies else ""), file=out)

  # XXX can we get away with just one oxygen?
  def looks_like_nucleotide_phosphate_site(self,
      min_phosphate_oxygen_atoms = 2,
      distance_cutoff = 2.5) : # XXX wild guess
    """
    Decide whether the atom is coordinating phosphate oxygens from a
    nucleotide, based on common atom names.

    Parameters
    ----------
    min_phosphate_oxygen_atoms : int, optional
    distance_cutoff : float, optional

    Returns
    -------
    bool
    """
    n_phosphate_oxygens = 0
    for contact in self.nearby_atoms :
      atom_name = contact.atom_name()
      if (len(atom_name) < 3) or (contact.element not in ["O"]):
        continue
      if ((atom_name[0:2] in ["O1","O2","O3"]) and
          (atom_name[2] in ["A","B","G"])):
        if (contact.distance() <= distance_cutoff):
          n_phosphate_oxygens += 1
    return (n_phosphate_oxygens == min_phosphate_oxygen_atoms)

  def identity(self, ion=None):
    """
    Covers an atom into a string representing its element and charge.

    Parameters
    ----------
    ion : iotbx.pdb.hierarchy.atom, optional

    Returns
    -------
    str
    """
    if ion is None:
      ion = self.atom
    element = mmtbx.ions.server.get_element(ion)
    charge = mmtbx.ions.server.get_charge(ion)
    return "{}{:+}".format(element, charge)

  def get_atom_type(self, params, aggressive=False):
    """
    Checks the atom characteristics against what we would expect for a water.
    Updates self with any inaccuracies noticed (Surpringly low b-factor,
    high occupancy, etc).  Note that HEAVY_ION does not necessarily rule out
    NA/MG, as these often have mFo-DFc peaks at high resolution.

    Parameters
    ----------
    params : libtbx.phil.scope_extract
    aggressive : bool, optional

    Returns
    -------
    int
        One of WATER, WATER_POOR, HEAVY_ION, LIGHT_ION
    """
    inaccuracies = self.inaccuracies["HOH"] = set()
    atom_type = WATER
    # Skip over water if the 2mFo-DFc or mFo-DFc value is too low
    if ((self.peak_2fofc < params.min_2fofc_level) or
        (self.peak_fofc < -2.0)):
      return WATER_POOR
    if (self.fpp is not None) and (self.fpp > params.fpp_max):
      return HEAVY_ION
    if (self.fp is not None) and (self.fp > params.fp_max):
      return HEAVY_ION
    if self.peak_anom is not None and self.peak_anom > params.max_anom_level:
      inaccuracies.add(self.ANOM_PEAK)
      atom_type = HEAVY_ION
    if self.peak_fofc > params.max_fofc_level:
      inaccuracies.add(self.FOFC_PEAK)
      atom_type = HEAVY_ION
    if self.atom.occ > params.max_occ: # this will probably never happen...
      inaccuracies.add(self.HIGH_OCC)
      atom_type = HEAVY_ION
    # very low B-factors automatically trigger a check
    if (self.atom.b < params.min_b_iso):
      inaccuracies.add(self.LOW_B)
      atom_type = HEAVY_ION
    elif (self.b_stddev_hoh is not None) and (self.b_stddev_hoh > 0):
      # high B-factor relative to other waters
      z_value = (self.b_iso - self.b_mean_hoh) / self.b_stddev_hoh
      if z_value < -params.max_stddev_b_iso:
        inaccuracies.add(self.LOW_B)
      elif self.atom.b < self.b_mean_hoh * params.min_frac_b_iso:
        inaccuracies.add(self.LOW_B)
      if (atom_type == WATER) and (self.LOW_B in inaccuracies):
        atom_type = LIGHT_ION
    if (aggressive) and (atom_type == WATER):
      # high B-factor relative to C-alpha
      if (self.b_mean_calpha > 0):
        relative_b = self.b_iso / self.b_mean_calpha
        if (relative_b < params.min_frac_calpha_b_iso):
          inaccuracies.add(self.LOW_B)
          atom_type = LIGHT_ION
      # high 2Fo-Fc relative to C-alpha
      if (self.calpha_mean_two_fofc > 0):
        relative_2fofc = self.peak_2fofc / self.calpha_mean_two_fofc
        if (relative_2fofc > params.max_frac_calpha_2fofc):
          inaccuracies.add(self.HIGH_2FOFC)
          atom_type = LIGHT_ION
    # check for close contacts
    for i_pair, contact1 in enumerate(self.nearby_atoms):
      if (contact1.element.strip() == "O"):
        distance = contact1.distance()
        if (distance < 2.4):
          inaccuracies.add(self.CLOSE_CONTACT)
          if (atom_type < LIGHT_ION):
            atom_type = LIGHT_ION
    return atom_type

def find_anomalous_scatterers(*args, **kwds):
  """
  Wrapper for corresponding method in phaser.substructure, if phaser is
  available and configured.
  """
  if (not libtbx.env.has_module("phaser")):
    if "log" in kwds:
      print("Phaser not available", file=kwds["log"])
    return None
  from phaser import substructure
  return substructure.find_anomalous_scatterers(*args, **kwds)

def create_manager(
    pdb_hierarchy,
    geometry_restraints_manager,
    fmodel,
    wavelength,
    params,
    resolution_factor = 0.25,
    nproc = Auto,
    verbose = False,
    log = None,
    manager_class=None):
  """
  Wrapper around mmtbx.ions.identify.manager init method. Retrieves the
  connectivity and xray_structure from fmodel automatically.

  Parameters
  ----------
  pdb_hierarchy : iotbx.pdb.hierarchy.root
  geometry_restraints_manager : cctbx.geometry_restraints.manager.manager
  fmodel : mmtbx.f_model.manager
  wavelength : float
  params : libtbx.phil.scope_extract
  resolution_factor : float, optional
  nproc : int, optional
  verbose : bool, optional
  log : file, optional
  manager_class : class, optional

  Returns
  -------
  mmtbx.ions.identify.manager or mmtbx.ions.svm.manager
  """
  connectivity = \
    geometry_restraints_manager.shell_sym_tables[0].full_simple_connectivity()
  if (manager_class is None):
    manager_class = manager
  manager_obj = manager_class(
    fmodel = fmodel,
    pdb_hierarchy = pdb_hierarchy,
    xray_structure = fmodel.xray_structure,
    connectivity = connectivity,
    wavelength = wavelength,
    params = params,
    nproc = nproc,
    verbose = verbose,
    log = log)
  return manager_obj

def _PRINT_DEBUG(*args):
  """
  Prints a debugging message to stderr.
  """
  print(sys.stderr, args, file=sys.stderr)
