#!/usr/bin/env python
#
# Author: Mike McKerns (mmckerns @caltech and @uqfoundation)
# Author: Alta Fang (altafang @caltech and alta @princeton)
# Copyright (c) 1997-2016 California Institute of Technology.
# Copyright (c) 2016-2021 The Uncertainty Quantification Foundation.
# License: 3-clause BSD.  The full license text is available at:
#  - https://github.com/uqfoundation/mystic/blob/master/LICENSE

from mystic.symbolic import *
from mystic.math import almostEqual
from mystic.constraints import as_constraint

def test_generate_penalty():

  constraints = """
  x0**2 = 2.5*x3 - a
  exp(x2/x0) >= b"""

  ineq,eq = generate_conditions(constraints, nvars=4, locals={'a':5.0, 'b':7.0})
  assert ineq[0]([4,0,0,1,0]) == 6.0
  assert eq[0]([4,0,0,1,0]) == 18.5

  penalty = generate_penalty((ineq,eq))
  assert penalty([1,0,2,2.4]) == 0.0
  assert penalty([1,0,0,2.4]) == 7200.0
  assert penalty([1,0,2,2.8]) == 100.0

  constraint = as_constraint(penalty, nvars=4, solver='fmin')
  assert almostEqual(penalty(constraint([1,0,0,2.4])), 0.0, 1e-10)

def test_numpy_penalty():

  constraints = """
  mean([x0, x1, x2]) = 5.0
  x0 = x1 + x2"""

  ineq,eq = generate_conditions(constraints)
  assert eq[0]([7,5,3]) == 0.0
  assert eq[1]([7,4,3]) == 0.0

  penalty = generate_penalty((ineq,eq))
  assert penalty([9.0,5,4.0]) == 100.0
  assert penalty([7.5,4,3.5]) == 0.0

  constraint = as_constraint(penalty, solver='fmin')
  assert almostEqual(penalty(constraint([3,4,5])), 0.0, 1e-10)

def test_generate_constraint():

  constraints = """
  spread([x0, x1, x2]) = 10.0
  mean([x0, x1, x2]) = 5.0"""

  from mystic.math.measures import mean, spread
  solv = generate_solvers(constraints)
  assert almostEqual(mean(solv[0]([1,2,3])), 5.0)
  assert almostEqual(spread(solv[1]([1,2,3])), 10.0)

  constraint = generate_constraint(solv)
  assert almostEqual(constraint([1,2,3]), [0.0,5.0,10.0], 1e-10)

def test_solve_constraint():

  # sympy can no longer do "spread([x0,x1])"... so use "x1 - x0"
  constraints = """
  (x1 - x0) - 1.0 = mean([x0,x1])   
  mean([x0,x1,x2]) = x2"""

  from mystic.math.measures import mean
  _constraints = solve(constraints)
  solv = generate_solvers(_constraints)
  constraint = generate_constraint(solv)
  x = constraint([1.0, 2.0, 3.0])
  assert all(x) == all([1.0, 5.0, 3.0])
  assert mean(x) == x[2]
  assert (x[1] - x[0]) - 1.0 == mean(x[:-1])

def test_simplify():
  constraints = """
  mean([x0, x1, x2]) <= 5.0
  x0 <= x1 + x2"""

  from mystic.math.measures import mean
  _constraints = simplify(constraints)
  solv = generate_solvers(_constraints)
  constraint = generate_constraint(solv)
  x = constraint([1.0, -2.0, -3.0])
  assert all(x) == all([-5.0, -2.0, -3.0])
  assert mean(x) <= 5.0
  assert x[0] <= x[1] + x[2]

def test_simplify_ne():
  equations = '''
  A > 0
  B >= 0
  A != B
  C < A + B
  C < 100
  '''
  vars = list('ABC')
  p = generate_penalty(generate_conditions(equations, vars))
  assert p([-100, -100, -100]) > p([-10, -10, -10])
  assert p([-1, -1, 0]) > p([-1, -1, -1]) > p([-1, -1, -2])
  assert p([-1, -1, -2]) == p([-1, -1, -5])
  assert p([0, -1, -5]) > p([0, 0, -5])
  assert p([1, 1, -5]) > p([1, 0, -5])
  assert p([0, 1, -5]) > p([1e-5, 1, -5])
  assert p([0, 1, -5]) == p([1, -1e-15, -5])
  c = generate_constraint(generate_solvers(equations, vars))
  d = dict(zip(vars, c([0,0,0])))
  assert all(eval(i,d) for i in equations.strip().split('\n'))
  d = dict(zip(vars, c([3,-9,-6])))
  assert all(eval(i,d) for i in equations.strip().split('\n'))
  d = dict(zip(vars, c([100,100,100])))
  assert all(eval(i,d) for i in equations.strip().split('\n'))

def test_simplify_ne_more():
  equations = '''
  A != B
  B != A + C
  A >= C + 1
  C >= -1
  B <= 0
  '''
  vars = list('ABC')
  c = generate_constraint(generate_solvers(equations, vars))
  d = dict(zip(vars, c([0,0,0])))
  assert all(eval(i,d) for i in equations.strip().split('\n'))
  d = dict(zip(vars, c([4,-2,6])))
  assert all(eval(i,d) for i in equations.strip().split('\n'))
  equations = '''
  A != B
  B != C
  C != A + B
  C >= 4
  B <= 3
  A >= 5
  '''
  c = generate_constraint(generate_solvers(equations, vars))
  d = dict(zip(vars, c([0,0,0])))
  assert all(eval(i,d) for i in equations.strip().split('\n'))
  d = dict(zip(vars, c([5,0,5])))
  assert all(eval(i,d) for i in equations.strip().split('\n'))
  d = dict(zip(vars, c([-1,5,-1])))
  assert all(eval(i,d) for i in equations.strip().split('\n'))


if __name__ == '__main__':
  test_generate_penalty()
  test_numpy_penalty()
  test_generate_constraint()
  test_solve_constraint()
  test_simplify()
  test_simplify_ne()
  test_simplify_ne_more()

