# Implementation of module {move_hp}
# Last edited on 2021-05-22 10:28:21 by jstolfi

import move_hp
import move
import move_parms
import path
import block
import hacks
import rn
from math import nan, inf, sqrt
import sys

# STATIC HOTPATH-RELATED ATTRIBUTES

def preprocess_input_data(BCS):
  assert type(BCS) is tuple or type(BCS) is list
  # Set fields {.hp_paths} for all moves that appear in {BCS}, as list.
  MVS = set() # Set of {Move} objects seen.
  for bc in BCS:
    assert isinstance(bc, block.Block)
    for ich in range(block.nchoices(bc)):
      oph = block.choice(bc, ich)
      for imv in range(path.nelems(oph)):
        omv = path.elem(oph, imv)
        mv, dr = move.unpack(omv) # Get the {Move} object.
        if not move.is_jump(mv):
          if mv.hp_paths == None: mv.hp_paths = []
          mv.hp_paths.append((oph, imv))
          MVS.add(mv)
  # Convert all lists to tuples:
  for mv in MVS:
    mv.hp_paths = tuple(mv.hp_paths)
  return
  # ----------------------------------------------------------------------

def input_paths(omv):
  # Assumes that the field {mv.hp_paths} has been set for every trace
  # object {mv} of the input dataset.
  mv, dr = move.unpack(omv)
  if move.is_jump(mv): 
    L = None
  else:
    L = mv.hp_paths
  return L
  # ----------------------------------------------------------------------
  
def clear_input_data(BCS):
  assert type(BCS) is tuple or type(BCS) is list
  # Set fields {.hp_paths} for all moves that appear in {BCS.
  for bc in BCS:
    assert isinstance(bc, block.Block)
    for ich in range(block.nchoices(bc)):
      oph = block.choice(bc, ich)
      for imv in range(path.nelems(oph)):
        omv = path.elem(oph, imv)
        mv, dr = move.unpack(omv) # Get the {Move} object.
        mv.hp_paths = None
  return
  # ----------------------------------------------------------------------

# DYNAMIC HOTPATH-RELATED ATTRIBUTES

def initialize_state(BCS):
  # Nothing to do:
  return
  # ----------------------------------------------------------------------