#Source : https://github.com/akaraspt/deepsleepnet

'''

Reader for EDF+ files.

TODO:

 - add support for log-transformed channels:

   http://www.edfplus.info/specs/edffloat.html and test with

   data generated with

   http://www.edfplus.info/downloads/software/NeuroLoopGain.zip.

 - check annotations with Schalk's Physiobank data.

Copyright (c) 2012 Boris Reuderink.

'''

import re, datetime, operator, logging

import numpy as np

from collections import namedtuple

EVENT_CHANNEL = 'EDF Annotations'

log = logging.getLogger(__name__)

class EDFEndOfData: pass

def tal(tal_str):

  '''Return a list with (onset, duration, annotation) tuples for an EDF+ TAL

  stream.

  '''

  exp = '(?P<onset>[+\-]\d+(?:\.\d*)?)' + \

    '(?:\x15(?P<duration>\d+(?:\.\d*)?))?' + \

    '(\x14(?P<annotation>[^\x00]*))?' + \

    '(?:\x14\x00)'

  def annotation_to_list(annotation):

    return unicode(annotation, 'utf-8').split('\x14') if annotation else []

  def parse(dic):

    return (

      float(dic['onset']),

      float(dic['duration']) if dic['duration'] else 0.,

      annotation_to_list(dic['annotation']))

  return [parse(m.groupdict()) for m in re.finditer(exp, tal_str)]

def edf_header(f):

  h = {}

  assert f.tell() == 0  # check file position

  assert f.read(8) == '0       '

  # recording info)

  h['local_subject_id'] = f.read(80).strip()

  h['local_recording_id'] = f.read(80).strip()

  # parse timestamp

  (day, month, year) = [int(x) for x in re.findall('(\d+)', f.read(8))]

  (hour, minute, sec)= [int(x) for x in re.findall('(\d+)', f.read(8))]

  h['date_time'] = str(datetime.datetime(year + 2000, month, day,

    hour, minute, sec))

  # misc

  header_nbytes = int(f.read(8))

  subtype = f.read(44)[:5]

  h['EDF+'] = subtype in ['EDF+C', 'EDF+D']

  h['contiguous'] = subtype != 'EDF+D'

  h['n_records'] = int(f.read(8))

  h['record_length'] = float(f.read(8))  # in seconds

  nchannels = h['n_channels'] = int(f.read(4))

  # read channel info

  channels = range(h['n_channels'])

  h['label'] = [f.read(16).strip() for n in channels]

  h['transducer_type'] = [f.read(80).strip() for n in channels]

  h['units'] = [f.read(8).strip() for n in channels]

  h['physical_min'] = np.asarray([float(f.read(8)) for n in channels])

  h['physical_max'] = np.asarray([float(f.read(8)) for n in channels])

  h['digital_min'] = np.asarray([float(f.read(8)) for n in channels])

  h['digital_max'] = np.asarray([float(f.read(8)) for n in channels])

  h['prefiltering'] = [f.read(80).strip() for n in channels]

  h['n_samples_per_record'] = [int(f.read(8)) for n in channels]

  f.read(32 * nchannels)  # reserved

  assert f.tell() == header_nbytes

  return h

class BaseEDFReader:

  def __init__(self, file):

    self.file = file

  def read_header(self):

    self.header = h = edf_header(self.file)

    # calculate ranges for rescaling

    self.dig_min = h['digital_min']

    self.phys_min = h['physical_min']

    phys_range = h['physical_max'] - h['physical_min']

    dig_range = h['digital_max'] - h['digital_min']

    assert np.all(phys_range > 0)

    assert np.all(dig_range > 0)

    self.gain = phys_range / dig_range

  def read_raw_record(self):

    '''Read a record with data and return a list containing arrays with raw

    bytes.

    '''

    result = []

    for nsamp in self.header['n_samples_per_record']:

      samples = self.file.read(nsamp * 2)

      if len(samples) != nsamp * 2:

        raise EDFEndOfData

      result.append(samples)

    return result

  def convert_record(self, raw_record):

    '''Convert a raw record to a (time, signals, events) tuple based on

    information in the header.

    '''

    h = self.header

    dig_min, phys_min, gain = self.dig_min, self.phys_min, self.gain

    time = float('nan')

    signals = []

    events = []

    for (i, samples) in enumerate(raw_record):

      if h['label'][i] == EVENT_CHANNEL:

        ann = tal(samples)

        time = ann[0][0]

        events.extend(ann[1:])

        # print(i, samples)

        # exit()

      else:

        # 2-byte little-endian integers

        dig = np.fromstring(samples, '<i2').astype(np.float32)

        phys = (dig - dig_min[i]) * gain[i] + phys_min[i]

        signals.append(phys)

    return time, signals, events

  def read_record(self):

    return self.convert_record(self.read_raw_record())

  def records(self):

    '''

    Record generator.

    '''

    try:

      while True:

        yield self.read_record()

    except EDFEndOfData:

      pass

def load_edf(edffile):

  '''Load an EDF+ file.

  Very basic reader for EDF and EDF+ files. While BaseEDFReader does support

  exotic features like non-homogeneous sample rates and loading only parts of

  the stream, load_edf expects a single fixed sample rate for all channels and

  tries to load the whole file.

  Parameters

  ----------

  edffile : file-like object or string

  Returns

  -------

  Named tuple with the fields:

    X : NumPy array with shape p by n.

      Raw recording of n samples in p dimensions.

    sample_rate : float

      The sample rate of the recording. Note that mixed sample-rates are not

      supported.

    sens_lab : list of length p with strings

      The labels of the sensors used to record X.

    time : NumPy array with length n

      The time offset in the recording for each sample.

    annotations : a list with tuples

      EDF+ annotations are stored in (start, duration, description) tuples.

      start : float

        Indicates the start of the event in seconds.

      duration : float

        Indicates the duration of the event in seconds.

      description : list with strings

        Contains (multiple?) descriptions of the annotation event.

  '''

  if isinstance(edffile, basestring):

    with open(edffile, 'rb') as f:

      return load_edf(f)  # convert filename to file

  reader = BaseEDFReader(edffile)

  reader.read_header()

  h = reader.header

  log.debug('EDF header: %s' % h)

  # get sample rate info

  nsamp = np.unique(

    [n for (l, n) in zip(h['label'], h['n_samples_per_record'])

    if l != EVENT_CHANNEL])

  assert nsamp.size == 1, 'Multiple sample rates not supported!'

  sample_rate = float(nsamp[0]) / h['record_length']

  rectime, X, annotations = zip(*reader.records())

  X = np.hstack(X)

  annotations = reduce(operator.add, annotations)

  chan_lab = [lab for lab in reader.header['label'] if lab != EVENT_CHANNEL]

  # create timestamps

  if reader.header['contiguous']:

    time = np.arange(X.shape[1]) / sample_rate

  else:

    reclen = reader.header['record_length']

    within_rec_time = np.linspace(0, reclen, nsamp, endpoint=False)

    time = np.hstack([t + within_rec_time for t in rectime])

  tup = namedtuple('EDF', 'X sample_rate chan_lab time annotations')

  return tup(X, sample_rate, chan_lab, time, annotations)