"""
Copyright 2013 Steven Diamond

Licensed under the Apache License, Version 2.0 (the "License");
you may not use this file except in compliance with the License.
You may obtain a copy of the License at

    http://www.apache.org/licenses/LICENSE-2.0

Unless required by applicable law or agreed to in writing, software
distributed under the License is distributed on an "AS IS" BASIS,
WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
See the License for the specific language governing permissions and
limitations under the License.
"""

"""
THIS FILE IS DEPRECATED AND MAY BE REMOVED WITHOUT WARNING!
DO NOT CALL THESE FUNCTIONS IN YOUR CODE!
"""

import cvxpy as cp
import cvxpy.settings as s
import itertools
from cvxopt import spmatrix
import cvxopt
import numpy as np
import time

import cProfile
cProfile.run("""
import cvxpy as cp
n = 2000
A = cp.Variable(n*n, 1)
obj = cp.Minimize(cp.norm(A, 'fro'))
p = cp.Problem(obj, [A >= 2])
result = p.solve(verbose=True)
print result
""")

# m = 100
# n = 1000
# prob = 0.999

# a_arr = np.random.random((m, n))
# a_arr[a_arr < prob] = 0

# a_arr_sp = spmatrix(a_arr[a_arr.nonzero()[0],
#                           a_arr.nonzero()[1]],
#                     a_arr.nonzero()[0],
#                     a_arr.nonzero()[1],
#                     size=(m, n))

# W = cp.Variable(n, n)
# constraints = []

# constraints.extend( [W[i,i] == 0 for i in range(n)] )
# constraints.append(W >= 0)
# lam = 8
# beta = 0.5
# loss = cp.sum(a_arr_sp - a_arr_sp*W)
# l2_reg = 0.5*beta*cp.square(cp.norm(W))
# l1_reg = lam*cp.sum(W)
# obj = cp.Minimize(loss + l2_reg + l1_reg)
# # TODO No constraints, get error.
# p = cp.Problem(obj, constraints)

# import cProfile
# cProfile.run('p.solve()')


# objective, constr_map, dims = p.canonicalize()

# all_ineq = itertools.chain(constr_map[s.EQ], constr_map[s.INEQ])
# var_offsets, x_length = p._get_var_offsets(objective, all_ineq)

# c, obj_offset = p._constr_matrix([objective], var_offsets, x_length,
#                                  p._DENSE_INTF,
#                                  p._DENSE_INTF)
# A, b = p._constr_matrix(constr_map[s.EQ], var_offsets, x_length,
#                            p._SPARSE_INTF, p._DENSE_INTF)

# G, h = p._constr_matrix(constr_map[s.INEQ], var_offsets, x_length,
#                            p._SPARSE_INTF, p._DENSE_INTF)

# print len(constr_map[s.EQ])
# print len(constr_map[s.INEQ])

# cProfile.run("""
# G, h = p._constr_matrix(constr_map[s.INEQ], var_offsets, x_length,
#                            p._SPARSE_INTF, p._DENSE_INTF)
# """)
# import numbers

# aff_expressions = constr_map[s.INEQ]
# matrix_intf, vec_intf = p._SPARSE_INTF, p._DENSE_INTF

# expr_offsets = {}
# vert_offset = 0
# for aff_exp in aff_expressions:
#     expr_offsets[str(aff_exp)] = vert_offset
#     vert_offset += aff_exp.size[0]*aff_exp.size[1]

# #rows = sum([aff.size[0] * aff.size[1] for aff in aff_expressions])
# rows = vert_offset
# cols = x_length
# #const_vec = vec_intf.zeros(rows, 1)
# vert_offset = 0

# def carrier(expr_offsets, var_offsets):
#     def f(aff_exp):
#         V, I, J = [], [], []
#         vert_offset = expr_offsets[str(aff_exp)]
#         coefficients = aff_exp.coefficients()
#         for var, blocks in coefficients.items():
#             # Constant is not in var_offsets.
#             horiz_offset = var_offsets.get(var)
#             for col, block in enumerate(blocks):
#                 vert_start = vert_offset + col*aff_exp.size[0]
#                 vert_end = vert_start + aff_exp.size[0]
#                 if var is s.CONSTANT:
#                     pass
#                     #const_vec[vert_start:vert_end, :] = block
#                 else:
#                     if isinstance(block, numbers.Number):
#                         V.append(block)
#                         I.append(vert_start)
#                         J.append(horiz_offset)
#                     else: # Block is a matrix or spmatrix.
#                         if isinstance(block, cvxopt.matrix):
#                             block = cvxopt.sparse(block)
#                         V.extend(block.V)
#                         I.extend(block.I + vert_start)
#                         J.extend(block.J + horiz_offset)
#         return (V, I, J)
#     return f

# f = carrier(expr_offsets, var_offsets)

# from multiprocessing import Pool
# p = Pool(1)
# result = p.map(f, aff_expressions)
# V, I, J = [], [], []
# for v, i, j in result:
#     V.extend(v)
#     I.extend(i)
#     J.extend(j)

# #[item for sublist in l for item in sublist]
# # Create the constraints matrix.
# if len(V) > 0:
#     matrix = cvxopt.spmatrix(V, I, J, (rows, cols), tc='d')
#     # Convert the constraints matrix to the correct type.
#     matrix = matrix_intf.const_to_matrix(matrix, convert_scalars=True)
# else: # Empty matrix.
#     matrix = matrix_intf.zeros(rows, cols)
