# Path: sdopt/ordering/block_sparsity_pattern.py
# def set_permutation_with_block_boundaries(bsp):
#     blockid = 'blockid'
#     if (blockid not in bsp.row_suffixes) or (blockid not in bsp.col_suffixes):
#         make_one_big_fake_block(bsp)
#         return
#     row_partition = bsp.row_suffixes[blockid] 
#     col_partition = bsp.col_suffixes[blockid]
#     bsp.row_permutation, bsp.rblx = reconstruct(row_partition) 
#     bsp.col_permutation, bsp.cblx = reconstruct(col_partition)
#     if DEBUG:
#         assertEqLength(bsp.rblx, bsp.cblx) # it is not necessary in the general case
#         print('ROWS')
#         dbg_show(row_partition, bsp.row_permutation, bsp.rblx)
#         print('COLS')
#         dbg_show(col_partition, bsp.col_permutation, bsp.cblx)
#     set_min_degree_order_and_coloring(bsp)
# 
# class BlockSparsityPattern(object):
#     
#     @staticmethod
#     def createFrom(nl_file_name, show_sparsity_pattern=True):
#         from ..parsers.ampl_parser import read_flattened_ampl
#         return read_flattened_ampl(nl_file_name, show_sparsity_pattern)
#     
#     def __init__(self, name, nrows, ncols, nzeros):
#         self.name = name
#         self.nrows = nrows
#         self.ncols = ncols
#         self.nzeros = nzeros
#         self.row_names = None
#         self.col_names = None
#         self.row_suffixes = { } # suffix name -> np.array of (index, value)
#         self.col_suffixes = { } # suffix name -> np.array of (index, value)
#         # Sparsity pattern of the Jacobian in the CSR format
#         self.csr_data    = np.zeros(nzeros,  dtype=np.float64)
#         self.csr_indices = np.zeros(nzeros,  dtype=np.int32)
#         self.csr_indptr  = np.zeros(nrows+1, dtype=np.int32)
#         self.csr_mat     = None   # View of (data, indices, indptr) as a csr_mat
#         self.col_len = None # redundant info, can be computed from Jacobian too
#         # These below are only needed to build the Jacobian, factor them out?
#         self.csr_pos     = int(0) # Counter needed to build the csr_mat
#         self.dbg_prev_row= int(-1)# Check whether the J segments are ordered 
#         # The data below comes from block reconstruction
#         self.row_permutation  = None # AMPL row indices in permuted order
#         self.col_permutation  = None # AMPL col indices in permuted order
#         self.inverse_row_perm = None # inverse of row_permutation
#         self.inverse_col_perm = None # inverse of col_permutation
#         # Block boundaries
#         self.rblx = None # adjancent elements of blx give the block slices 
#         self.cblx = None # block beg end indices: zip(blx[:-1],blx[1:])
#         # indices in the ith block = permutation[slice(beg, end)] 
#         # block count: len(blx)-1
#         self.coloring = None # Graph coloring for good seeds in forward mode AD
#         self.color_count = 0
#         
#     @property
#     def n_blocks(self):
#         return self.n_rblx, self.n_cblx
#     
#     @property
#     def n_rblx(self):
#         return len(self.rblx)-1
#     
#     @property
#     def n_cblx(self):
#         return len(self.cblx)-1
# 
# Path: sdopt/util/file_reader.py
# @contextmanager
# def lines_of(filename):
#     print('Trying to read file \"%s\"' % filename)
#     try:
#         f = fileinput.input(filename, mode='r')
#         yield _rstripped_lines(f)
#     finally:
#         print('Read ',f.lineno(),' lines from file \"',filename,'\"', sep='')
#         f.close()
# 
# Path: sdopt/util/assert_helpers.py
# def assertEqual(a, b, msg=None):
#     assert a==b, _message(a, b, '!=', msg)
# 
# Path: sdopt/util/misc.py
# def nth(iterable, n, default=None):
#     'Returns the nth item or a default value.'
#     return next(islice(iterable, n, None), default)
# 
# Path: sdopt/ordering/csr_utils.py
# def itr_col_indices(m):
#     assert isinstance(m, sp.csr_matrix)
#     for row in irange(m.shape[0]):
#         yield m.indices[m.indptr[row]:m.indptr[row+1]]
# 
# def itr_col_indices_with_row_index(m):
#     assert isinstance(m, sp.csr_matrix)
#     for row in irange(m.shape[0]):
#         yield row, m.indices[m.indptr[row]:m.indptr[row+1]]


from itertools import islice
from ..ordering.block_sparsity_pattern \
    import  set_permutation_with_block_boundaries, BlockSparsityPattern
from ..util.file_reader import lines_of
from ..util.assert_helpers import assertEqual
from ..util.misc import nth
from ..ordering.csr_utils import itr_col_indices, itr_col_indices_with_row_index
        from ..ordering.sparse_plot import plot
import numpy as np
import scipy.sparse as sp
from __future__ import print_function

def read_flattened_ampl(filename, show_sparsity_pattern=True):
    bsp = read_nl(filename)
    bsp.csr_mat = sp.csr_matrix((bsp.csr_data, bsp.csr_indices, bsp.csr_indptr), 
                                shape=(bsp.nrows,bsp.ncols) ) 
    check_J_segment(bsp)
    dbg_info(bsp)
    set_permutation_with_block_boundaries(bsp)
    bsp.row_names = read_names(filename, 'row', bsp.nrows)
    bsp.col_names = read_names(filename, 'col', bsp.ncols)
    plot_sparsity(bsp, show_sparsity_pattern)
    return bsp

def read_nl(filename):
    with lines_of(filename) as lines:
        return parse(lines)

def read_names(filename, kind, count):

# Path: sdopt/ordering/block_sparsity_pattern.py
# def set_permutation_with_block_boundaries(bsp):
#     blockid = 'blockid'
#     if (blockid not in bsp.row_suffixes) or (blockid not in bsp.col_suffixes):
#         make_one_big_fake_block(bsp)
#         return
#     row_partition = bsp.row_suffixes[blockid] 
#     col_partition = bsp.col_suffixes[blockid]
#     bsp.row_permutation, bsp.rblx = reconstruct(row_partition) 
#     bsp.col_permutation, bsp.cblx = reconstruct(col_partition)
#     if DEBUG:
#         assertEqLength(bsp.rblx, bsp.cblx) # it is not necessary in the general case
#         print('ROWS')
#         dbg_show(row_partition, bsp.row_permutation, bsp.rblx)
#         print('COLS')
#         dbg_show(col_partition, bsp.col_permutation, bsp.cblx)
#     set_min_degree_order_and_coloring(bsp)
# 
# class BlockSparsityPattern(object):
#     
#     @staticmethod
#     def createFrom(nl_file_name, show_sparsity_pattern=True):
#         from ..parsers.ampl_parser import read_flattened_ampl
#         return read_flattened_ampl(nl_file_name, show_sparsity_pattern)
#     
#     def __init__(self, name, nrows, ncols, nzeros):
#         self.name = name
#         self.nrows = nrows
#         self.ncols = ncols
#         self.nzeros = nzeros
#         self.row_names = None
#         self.col_names = None
#         self.row_suffixes = { } # suffix name -> np.array of (index, value)
#         self.col_suffixes = { } # suffix name -> np.array of (index, value)
#         # Sparsity pattern of the Jacobian in the CSR format
#         self.csr_data    = np.zeros(nzeros,  dtype=np.float64)
#         self.csr_indices = np.zeros(nzeros,  dtype=np.int32)
#         self.csr_indptr  = np.zeros(nrows+1, dtype=np.int32)
#         self.csr_mat     = None   # View of (data, indices, indptr) as a csr_mat
#         self.col_len = None # redundant info, can be computed from Jacobian too
#         # These below are only needed to build the Jacobian, factor them out?
#         self.csr_pos     = int(0) # Counter needed to build the csr_mat
#         self.dbg_prev_row= int(-1)# Check whether the J segments are ordered 
#         # The data below comes from block reconstruction
#         self.row_permutation  = None # AMPL row indices in permuted order
#         self.col_permutation  = None # AMPL col indices in permuted order
#         self.inverse_row_perm = None # inverse of row_permutation
#         self.inverse_col_perm = None # inverse of col_permutation
#         # Block boundaries
#         self.rblx = None # adjancent elements of blx give the block slices 
#         self.cblx = None # block beg end indices: zip(blx[:-1],blx[1:])
#         # indices in the ith block = permutation[slice(beg, end)] 
#         # block count: len(blx)-1
#         self.coloring = None # Graph coloring for good seeds in forward mode AD
#         self.color_count = 0
#         
#     @property
#     def n_blocks(self):
#         return self.n_rblx, self.n_cblx
#     
#     @property
#     def n_rblx(self):
#         return len(self.rblx)-1
#     
#     @property
#     def n_cblx(self):
#         return len(self.cblx)-1
# 
# Path: sdopt/util/file_reader.py
# @contextmanager
# def lines_of(filename):
#     print('Trying to read file \"%s\"' % filename)
#     try:
#         f = fileinput.input(filename, mode='r')
#         yield _rstripped_lines(f)
#     finally:
#         print('Read ',f.lineno(),' lines from file \"',filename,'\"', sep='')
#         f.close()
# 
# Path: sdopt/util/assert_helpers.py
# def assertEqual(a, b, msg=None):
#     assert a==b, _message(a, b, '!=', msg)
# 
# Path: sdopt/util/misc.py
# def nth(iterable, n, default=None):
#     'Returns the nth item or a default value.'
#     return next(islice(iterable, n, None), default)
# 
# Path: sdopt/ordering/csr_utils.py
# def itr_col_indices(m):
#     assert isinstance(m, sp.csr_matrix)
#     for row in irange(m.shape[0]):
#         yield m.indices[m.indptr[row]:m.indptr[row+1]]
# 
# def itr_col_indices_with_row_index(m):
#     assert isinstance(m, sp.csr_matrix)
#     for row in irange(m.shape[0]):
#         yield row, m.indices[m.indptr[row]:m.indptr[row+1]]


# Path: sdopt/parsers/ampl_parser.py
from itertools import islice
from ..ordering.block_sparsity_pattern \
    import  set_permutation_with_block_boundaries, BlockSparsityPattern
from ..util.file_reader import lines_of
from ..util.assert_helpers import assertEqual
from ..util.misc import nth
from ..ordering.csr_utils import itr_col_indices, itr_col_indices_with_row_index
        from ..ordering.sparse_plot import plot
import numpy as np
import scipy.sparse as sp

from __future__ import print_function

def read_flattened_ampl(filename, show_sparsity_pattern=True):
    bsp = read_nl(filename)
    bsp.csr_mat = sp.csr_matrix((bsp.csr_data, bsp.csr_indices, bsp.csr_indptr), 
                                shape=(bsp.nrows,bsp.ncols) ) 
    check_J_segment(bsp)
    dbg_info(bsp)
    set_permutation_with_block_boundaries(bsp)
    bsp.row_names = read_names(filename, 'row', bsp.nrows)
    bsp.col_names = read_names(filename, 'col', bsp.ncols)
    plot_sparsity(bsp, show_sparsity_pattern)
    return bsp

def read_nl(filename):
    with lines_of(filename) as lines:
        return parse(lines)

def read_names(filename, kind, count):

    with lines_of(filename[:-2] + kind) as lines: