"""This file includes functionality to convert between Bob's four column or five column score files and the Matrix files used in OpenBR."""

import numpy
import sys
import logging
logger = logging.getLogger("bob.measure")

from .load import open_file, four_column, five_column

def write_matrix(
    score_file,
    matrix_file,
    mask_file,
    model_names = None,
    probe_names = None,
    score_file_format = '4column',
    gallery_file_name = 'unknown-gallery.lst',
    probe_file_name = 'unknown-probe.lst',
    search = None
):
  """Writes the OpenBR matrix and mask files (version 2), given the score file.
  If gallery and probe names are provided, the matrices in both files will be sorted by gallery and probe names.
  Otherwise, the order will be the same as given in the score file.

  If ``search`` is given (as an integer), the resulting matrix files will be in the *search* format, keeping the given number of gallery scores with the highest values for each probe.

  .. warning::
     When provided with a 4-column score file, this function will work only, if there is only a single model id for each client.

  **Parameters:**

  ``score_file`` : str
    The 4 or 5 column style score file written by bob.

  ``matrix_file`` : str
    The OpenBR matrix file that should be written.
    Usually, the file name extension is ``.mtx``

  ``mask_file`` : str
    The OpenBR mask file that should be written.
    The mask file defines, which values are positives, negatives or to be ignored.
    Usually, the file name extension is ``.mask``

  ``model_names`` : [str] or ``None``
    If given, the matrix will be written in the same order as the given model names.
    The model names must be identical with the second column in the 5-column ``score_file``.

    .. note::
       If the score file is in four column format, the model_names must be the client ids stored in the first column.
       In this case, there might be only a single model per client

    Only the scores of the given models will be considered.

  ``probe_names`` : [str] or ``None``
    If given, the matrix will be written in the same order as the given probe names (the ``path`` of the probe).
    The probe names are identical to the third column of the 4-column (or the fourth column of the 5-column) ``score_file``.
    Only the scores of the given probe names will be considered in this case.

  ``score_file_format`` : one of ``('4column', '5column')``
    The format, in which the ``score_file`` is; defaults to ``'4column'``

  ``gallery_file_name`` : str
    The name of the gallery file that will be written in the header of the OpenBR files.

  ``probe_file_name`` : str
    The name of the probe file that will be written in the header of the OpenBR files.

  ``search`` : int or ``None``
    If given, the scores will be sorted per probe, keeping the specified number of highest scores.
    If the given number is higher than the models, ``NaN`` values will be added, and the mask will contain ``0x00`` values.
  """

  def _write_matrix(filename, matrix):
    ## Helper function to write a matrix file as required by OpenBR
    with open(filename, 'wb') as f:
      # write the first four lines
      header = "S2\n%s\n%s\nM%s %d %d " % (gallery_file_name, probe_file_name, 'B' if matrix.dtype == numpy.uint8 else 'F', matrix.shape[0], matrix.shape[1])
      footer = "\n"
      if sys.version_info[0] > 2: header, footer = header.encode('utf-8'), footer.encode('utf-8')
      f.write(header)
      # write magic number
      numpy.array(0x12345678, numpy.int32).tofile(f)
      f.write(footer)
      # write the matrix
      matrix.tofile(f)


  # define read functions, and which information should be read
  read_function = {'4column' : four_column, '5column' : five_column}[score_file_format]
  offset = {'4column' : 0, '5column' : 1}[score_file_format]

  # first, read the score file and estimate model and probe names, if not given
  if model_names is None or probe_names is None:
    model_names, probe_names = [], []
    model_set, probe_set = set(), set()

    # read the score file
    for line in read_function(score_file):
      model, probe = line[offset], line[2+offset]
      if model not in model_set:
        model_names.append(model)
        model_set.add(model)
      if probe not in probe_set:
        probe_names.append(probe)
        probe_set.add(probe)

  if search is None:
    # create a shortcut to get indices for client and probe subset (to increase speed)
    model_dict, probe_dict = {}, {}
    for i,m in enumerate(model_names): model_dict[m]=i
    for i,p in enumerate(probe_names): probe_dict[p]=i

    # create the matrices in the desired size
    matrix = numpy.ndarray((len(probe_names), len(model_names)), numpy.float32)
    matrix[:] = numpy.nan
    mask = numpy.zeros(matrix.shape, numpy.uint8)

    # now, iterate through the score file and fill in the matrix
    for line in read_function(score_file):
      client, model, id, probe, score = line[0], line[offset], line[1+offset], line[2+offset], line[3+offset]

      assert model in model_dict, "model " + model + " unknown"
      assert probe in probe_dict, "probe " + probe + " unknown"

      model_index = model_dict[model]
      probe_index = probe_dict[probe]

      # check, if we have already written something into that matrix element
      if mask[probe_index, model_index]:
        logger.warn("Overwriting existing matrix '%f' element of client '%s' and probe '%s' with '%f'", matrix[probe_index, model_index], client, probe, score)

      matrix[probe_index, model_index] = score
      mask[probe_index, model_index] = 0xff if client == id else 0x7f

  else:
    # get the correct search parameter, if negative
    if search < 0:
      search = len(model_names)

    # create the matrices in the desired size
    matrix = numpy.ndarray((len(probe_names), search), numpy.float32)
    matrix[:] = numpy.nan
    mask = numpy.zeros(matrix.shape, numpy.uint8)

    # get the scores, sorted by probe
    scores = {}
    for line in read_function(score_file):
      client, model, id, probe, score = line[0], line[offset], line[1+offset], line[2+offset], line[3+offset]

      if probe not in scores:
        scores[probe] = []
      scores[probe].append((score, 0xff if client == id else 0x7f))

    # go ahead and sort the scores per probe
    sorted_scores = {}
    for k,v in scores.items(): sorted_scores[k] = sorted(v, key=lambda x: x[0], reverse=True)

    # now, write matrix
    for p, probe in enumerate(probe_names):
      if probe in scores:
        for m in range(min(search, len(sorted_scores[probe]))):
          matrix[p,m], mask[p,m] = sorted_scores[probe][m]


  # OK, now finally write the file in the desired format
  _write_matrix(mask_file, mask)
  _write_matrix(matrix_file, matrix)


def write_score_file(
    matrix_file,
    mask_file,
    score_file,
    models_ids = None,
    probes_ids = None,
    model_names = None,
    probe_names = None,
    score_file_format = '4column',
    replace_nan = None
):
  """Writes the Bob score file in the desired ``score_file_format`` (four or five column), given the OpenBR matrix and mask files.

  In principle, the score file can be written based on the matrix and mask files, and the format suffice the requirements to compute CMC curves.
  However, the contents of the score files can be adapted.
  If given, the ``models_ids`` and ``probes_ids`` define the **client ids** of model and probe, and they have to be in the same order as used to compute the OpenBR matrix.
  The ``model_names`` and ``probe_names`` define the **paths** of model and probe, and they should be in the same order as the ids.

  In rare cases, the OpenBR matrix contains NaN values, which Bob's score files cannot handle.
  You can use the ``replace_nan`` parameter to decide, what to do with these values.
  By default (``None``), these values are ignored, i.e., not written into the score file.
  This is, what OpenBR is doing as well.
  However, you can also set ``replace_nan`` to any value, which will be written instead of the NaN values.

  **Parameters:**

  ``matrix_file`` : str
    The OpenBR matrix file that should be read.
    Usually, the file name extension is ``.mtx``

  ``mask_file`` : str
    The OpenBR mask file that should be read.
    Usually, the file name extension is ``.mask``

  ``score_file`` : str
    The 4 or 5 column style score file that should be written.

  ``models_ids`` : [str] or ``None``
    The client ids of the models that will be written in the first column of the score file.
    If given, the size must be identical to the number of models (gallery templates) in the OpenBR files.
    If not given, client ids of the model will be identical to the **gallery index** in the matrix file.

  ``probes_ids`` : [str] or ``None``:
    The client ids of the probes that will be written in the second/third column of the four/five column score file.
    If given, the size must be identical to the number of probe templates in the OpenBR files.
    It will be checked that the OpenBR mask fits to the model/probe client ids.
    If not given, the probe ids will be estimated automatically, i.e., to fit the OpenBR matrix.

  ``model_names`` : [str] or ``None``
    A list of model path written in the second column of the five column score file.
    If not given, the model index in the OpenBR file will be used.

    .. note::
       This entry is ignored in the four column score file format.

  ``probe_names`` : [str] or ``None``
    A list of probe path to be written in the third/fourth column in the four/five column score file.
    If given, the size must be identical to the number of probe templates in the OpenBR files.
    If not given, the probe index in the OpenBR file will be used.

  ``score_file_format`` : one of ``('4column', '5column')``
    The format, in which the ``score_file`` should be written; defaults to ``'4column'``.

  ``replace_nan`` : float or ``None``:
    If NaN values are encountered in the OpenBR matrix (which are not ignored due to the mask being non-NULL), this value will be written instead.
    If ``None``, the values will not be written in the score file at all.
  """
  def _read_matrix(filename):
    py3 = sys.version_info[0] >=3
    ## Helper function to read a matrix file as written by OpenBR
    with open(filename, 'rb') as f:
      # get version
      header = f.readline()
      if py3: header = header.decode("utf-8")
      assert header[:2] == "S2"
      # skip gallery and probe files
      f.readline()
      f.readline()
      # read size and type of matrix
      size = f.readline()
      if py3: size = size.decode("utf-8")
      splits = size.rstrip().split()
      # TODO: check the endianess of the magic number stored in split[3]
      assert splits[0][0] == 'M'
      w,h = int(splits[1]), int(splits[2])
      # read matrix data
      data = numpy.fromfile(f, dtype={'B':numpy.uint8, 'F': numpy.float32}[splits[0][1]])
      assert data.shape[0] == w*h
      data.shape = (w,h)
    return data

  # check parameters
  if score_file_format not in ("4column", "5column"):
    raise ValueError("The given score file format %s is not known; choose one of ('4column', '5column')" % score_file_format)
  # get type of score file
  four_col = score_file_format == "4column"

  # read the two matrices
  scores = _read_matrix(matrix_file)
  mask = _read_matrix(mask_file)

  # generate the id lists, if not given
  if models_ids is None:
    models_ids = [str(g+1) for g in range(mask.shape[1])]
  assert len(models_ids) == mask.shape[1]

  if probes_ids is None:
    probes_ids = []
    # iterate over all probes
    for p in range(mask.shape[0]):
      # get indices, where model and probe id should be identical
      equal_indices = numpy.where(mask[p] == 0xff)
      if len(equal_indices):
        # model id found, use the first one
        probes_ids.append(models_ids[equal_indices[0][0]])
      else:
        # no model found; add non-existing id
        probes_ids.append("unknown")
  else:
    assert len(probes_ids) == mask.shape[0]
    # check that the probes client ids are in the correct order
    for p in range(mask.shape[0]):
      for g in range(mask.shape[1]):
        if mask[p,g] == 0x7f:
          if models_ids[g] == probes_ids[p]: raise ValueError("The probe id %s with index %d should not be identical to model id %s with index %d" % (probes_ids[p], p, models_ids[g], g))
        elif mask[p,g] == 0xff:
          if models_ids[g] != probes_ids[p]: raise ValueError("The probe id %s with index %d should be identical to model id %s with index %d" % (probes_ids[p], p, models_ids[g], g))

  # generate model and probe names, if not given
  if not four_col and model_names is None:
    model_names = [str(g+1) for g in range(mask.shape[1])]
  if probe_names is None:
    probe_names = [str(p+1) for p in range(mask.shape[0])]

  # iterate through the files and write scores
  with open(score_file, 'w') as f:
    for g in range(mask.shape[1]):
      for p in range(mask.shape[0]):
        if mask[p,g]:
          score = scores[p,g]
          # handle NaN values
          if numpy.isnan(score):
            if replace_nan is None: continue
            score = replace_nan
          # write score file
          if four_col:
            f.write("%s %s %s %3.8f\n" % (models_ids[g], probes_ids[p], probe_names[p], score))
          else:
            f.write("%s %s %s %s %3.8f\n" % (models_ids[g], model_names[g], probes_ids[p], probe_names[p], score))
