from __future__ import absolute_import, division, print_function
import scitbx.math
from scitbx import matrix
import libtbx.phil
from libtbx.math_utils import normalize_angle
from libtbx import slots_getstate_setstate

master_phil = libtbx.phil.parse("""\
  bond_min_distance = 1.2
    .type = float
  bond_max_distance = 1.8
    .type = float
  epsilon_range_min = 155.0
    .type = float
  epsilon_range_max = 310.0
    .type = float
  delta_range_2p_min = 129.0
    .type = float
  delta_range_2p_max = 162.0
    .type = float
  delta_range_3p_min = 65.0
    .type = float
  delta_range_3p_max = 104.0
    .type = float
  p_distance_c1p_outbound_line_2p_max = 2.9
    .type = float
  o3p_distance_c1p_outbound_line_2p_max = 2.4
    .type = float
  bond_detection_distance_tolerance = 0.5
    .type = float
  enable = True
    .type = bool
    .style = hidden
""")

class evaluate(slots_getstate_setstate):

  __slots__ = [
    "epsilon",
    "delta",
    "p_distance_c1p_outbound_line",
    "o3p_distance_c1p_outbound_line",
    "p_perp_xyz",
    "o3p_perp_xyz",
    "is_epsilon_outlier",
    "is_2p_delta",
    "is_3p_delta",
    "is_delta_outlier",
    "is_2p_p_distance_c1p_outbound_line",
    "is_2p_o3p_distance_c1p_outbound_line",
    "is_2p"]

  def assign(O, **keyword_args):
    for slot in evaluate.__slots__:
      setattr(O, slot, keyword_args.get(slot, None))

  def __init__(O,
        params,
        residue_1_deoxy_ribo_atom_dict,
        residue_1_c1p_outbound_atom,
        residue_2_p_atom):
    def v(key): return matrix.col(residue_1_deoxy_ribo_atom_dict[key].xyz)
    c1p = v("C1'")
    o3p = v("O3'")
    c3p = v("C3'")
    c4p = v("C4'")
    c5p = v("C5'")
    bonded_sites = [
      (o3p, c3p),
      (c3p, c4p),
      (c5p, c4p)]
    distances = [abs(s2-s1) for s1,s2 in bonded_sites]
    p = None
    if (residue_2_p_atom is not None):
      p_trial = matrix.col(residue_2_p_atom.xyz)
      d = abs(p_trial-o3p)
      if (d <= params.bond_max_distance):
        p = p_trial
        distances.append(d)
    if (residue_1_c1p_outbound_atom is None):
      c1p_outbound = None
    else:
      c1p_outbound = matrix.col(residue_1_c1p_outbound_atom.xyz)
      distances.append(abs(c1p-c1p_outbound))
    if (   min(distances) < params.bond_min_distance
        or max(distances) > params.bond_max_distance):
      O.assign()
      return
    #
    def dihe(sites):
      return normalize_angle(
        scitbx.math.dihedral_angle(sites=sites, deg=True),
        deg=True)
    if (p is None):
      epsilon = None
    else:
      epsilon = dihe([p, o3p, c3p, c4p])
    delta = dihe([c5p, c4p, c3p, o3p])
    if (c1p_outbound is None):
      p_distance_c1p_outbound_line = None
      o3p_distance_c1p_outbound_line = None
      p_perp_xyz = None
      o3p_perp_xyz = None
    else:
      if (p is None):
        p_distance_c1p_outbound_line = None
        p_perp_xyz = None
      else:
        p_distance_c1p_outbound_line = scitbx.math.line_given_points(
          points=(c1p, c1p_outbound)).distance_sq(point=p)**0.5
        p_perp_xyz = (p, scitbx.math.line_given_points(
          points=(c1p, c1p_outbound)).perp_xyz(point=p))
      o3p_distance_c1p_outbound_line = scitbx.math.line_given_points(
        points=(c1p, c1p_outbound)).distance_sq(point=o3p)**0.5
      o3p_perp_xyz = (o3p, scitbx.math.line_given_points(
        points=(c1p, c1p_outbound)).perp_xyz(point=o3p))
    n_decisions = 0
    if (   epsilon is None
        or params.epsilon_range_min is None
        or params.epsilon_range_max is None):
      is_epsilon_outlier = None
    else:
      is_epsilon_outlier = (   epsilon < params.epsilon_range_min
                               or epsilon > params.epsilon_range_max)
      n_decisions += 1
    if (   params.delta_range_2p_min is None
        or params.delta_range_2p_max is None):
      is_2p_delta = None
    else:
      is_2p_delta = (    delta >= params.delta_range_2p_min
                     and delta <= params.delta_range_2p_max)
      n_decisions += 1
    if (   params.delta_range_3p_min is None
        or params.delta_range_3p_max is None):
      is_3p_delta = None
    else:
      is_3p_delta = (    delta >= params.delta_range_3p_min
                     and delta <= params.delta_range_3p_max)
      n_decisions += 1
    if (   p_distance_c1p_outbound_line is None
        or params.p_distance_c1p_outbound_line_2p_max is None):
      is_2p_p_distance_c1p_outbound_line = None
    else:
      is_2p_p_distance_c1p_outbound_line = (
          p_distance_c1p_outbound_line
        < params.p_distance_c1p_outbound_line_2p_max)
      n_decisions += 1
    if (   o3p_distance_c1p_outbound_line is None
        or params.o3p_distance_c1p_outbound_line_2p_max is None):
      is_2p_o3p_distance_c1p_outbound_line = None
    else:
      is_2p_o3p_distance_c1p_outbound_line = (
          o3p_distance_c1p_outbound_line
        < params.o3p_distance_c1p_outbound_line_2p_max)
      n_decisions += 1
    if (n_decisions == 0):
      is_2p = None
    else:
      perp_distance = is_2p_p_distance_c1p_outbound_line
      if (perp_distance is None):
        perp_distance = is_2p_o3p_distance_c1p_outbound_line
      if (perp_distance):
        is_2p = True
      else:
        is_2p = False
    if (is_3p_delta is None or is_2p_delta is None or is_2p is None):
      is_delta_outlier = None
    else:
      if (is_2p and not is_2p_delta) or (not is_2p and not is_3p_delta):
        is_delta_outlier = True
      else:
        is_delta_outlier = False
    O.assign(
      epsilon=epsilon,
      delta=delta,
      p_distance_c1p_outbound_line=p_distance_c1p_outbound_line,
      o3p_distance_c1p_outbound_line=o3p_distance_c1p_outbound_line,
      p_perp_xyz=p_perp_xyz,
      o3p_perp_xyz=o3p_perp_xyz,
      is_epsilon_outlier=is_epsilon_outlier,
      is_2p_delta=is_2p_delta,
      is_3p_delta=is_3p_delta,
      is_delta_outlier=is_delta_outlier,
      is_2p_p_distance_c1p_outbound_line=is_2p_p_distance_c1p_outbound_line,
      is_2p_o3p_distance_c1p_outbound_line=is_2p_o3p_distance_c1p_outbound_line,
      is_2p=is_2p)
