Instruction:
Explain what the following Python 3 code does
def rpad(self, ecc, k=None):
        '''Automatically right pad with null bytes an ecc to fill for missing bytes if too small, or leave unchanged if not necessary. This can be used as a workaround for field delimiter misdetection. Equivalent to puncturing (punctured reed-solomon code).'''
        if not k: k = self.k
        pad = None
        if len(ecc) < self.n-k:
            print("Warning: the ecc field may have been truncated (entrymarker or field_delim misdetection?).")
            #pad = "\x00" * (self.n-k-len(ecc))
            pad = bytearray(self.n-k-len(ecc))
            ecc = ecc + pad
        return [ecc, pad]

Response: Automatically right pad with null bytes an ecc to fill for missing bytes or leave unchanged if not necessary.


Instruction:
Here you have a function in Python 3, explain what it does
def check(self, message, ecc, k=None):
        '''Check if there's any error in a message+ecc. Can be used before decoding, in addition to hashes to detect if the message was tampered, or after decoding to check that the message was fully recovered.'''
        if not k: k = self.k
        message, _ = self.pad(message, k=k)
        ecc, _ = self.rpad(ecc, k=k)
        if self.algo == 1 or self.algo == 2:
            return self.ecc_manager.check_fast(message + ecc, k=k)
        elif self.algo == 3 or self.algo == 4:
            return reedsolo.rs_check(bytearray(message + ecc), self.n-k, fcr=self.fcr, generator=self.gen_nb)

Response: Check if there s any error in a message + ecc. Can be used before decoding and addition to hashes to detect if the message was fully recovered.


Instruction:
How would you implement a function in Python 3 that
provides a description for each algorithm available useful to print in ecc file.

Response:
def description(self):
        '''Provide a description for each algorithm available, useful to print in ecc file'''
        if 0 < self.algo <= 3:
            return "Reed-Solomon with polynomials in Galois field of characteristic %i (2^%i) with generator=%s, prime poly=%s and first consecutive root=%s." % (self.field_charac, self.c_exp, self.gen_nb, hex(self.prim), self.fcr)
        elif self.algo == 4:
            return "Reed-Solomon with polynomials in Galois field of characteristic %i (2^%i) under US FAA ADSB UAT RS FEC standard with generator=%s, prime poly=%s and first consecutive root=%s." % (self.field_charac, self.c_exp, self.gen_nb, hex(self.prim), self.fcr)
        else:
            return "No description for this ECC algorithm."


Instruction:
Can you generate a brief explanation for the following Python 3 code
def profile(fn=None, skip=0, filename=None, immediate=False, dirs=False,
            sort=None, entries=40,
            profiler=('cProfile', 'profile', 'hotshot')):
    """Mark `fn` for profiling.

    If `skip` is > 0, first `skip` calls to `fn` will not be profiled.

    If `immediate` is False, profiling results will be printed to
    sys.stdout on program termination.  Otherwise results will be printed
    after each call.

    If `dirs` is False only the name of the file will be printed.
    Otherwise the full path is used.

    `sort` can be a list of sort keys (defaulting to ['cumulative',
    'time', 'calls']).  The following ones are recognized::

        'calls'      -- call count
        'cumulative' -- cumulative time
        'file'       -- file name
        'line'       -- line number
        'module'     -- file name
        'name'       -- function name
        'nfl'        -- name/file/line
        'pcalls'     -- call count
        'stdname'    -- standard name
        'time'       -- internal time

    `entries` limits the output to the first N entries.

    `profiler` can be used to select the preferred profiler, or specify a
    sequence of them, in order of preference.  The default is ('cProfile'.
    'profile', 'hotshot').

    If `filename` is specified, the profile stats will be stored in the
    named file.  You can load them pstats.Stats(filename).

    Usage::

        def fn(...):
            ...
        fn = profile(fn, skip=1)

    If you are using Python 2.4, you should be able to use the decorator
    syntax::

        @profile(skip=3)
        def fn(...):
            ...

    or just ::

        @profile
        def fn(...):
            ...

    """
    if fn is None: # @profile() syntax -- we are a decorator maker
        def decorator(fn):
            return profile(fn, skip=skip, filename=filename,
                           immediate=immediate, dirs=dirs,
                           sort=sort, entries=entries,
                           profiler=profiler)
        return decorator
    # @profile syntax -- we are a decorator.
    if isinstance(profiler, str):
        profiler = [profiler]
    for p in profiler:
        if p in AVAILABLE_PROFILERS:
            profiler_class = AVAILABLE_PROFILERS[p]
            break
    else:
        raise ValueError('only these profilers are available: %s'
                             % ', '.join(AVAILABLE_PROFILERS))
    fp = profiler_class(fn, skip=skip, filename=filename,
                        immediate=immediate, dirs=dirs,
                        sort=sort, entries=entries)
    # fp = HotShotFuncProfile(fn, skip=skip, filename=filename, ...)
         # or HotShotFuncProfile
    # We cannot return fp or fp.__call__ directly as that would break method
    # definitions, instead we need to return a plain function.
    def new_fn(*args, **kw):
        return fp(*args, **kw)
    new_fn.__doc__ = fn.__doc__
    new_fn.__name__ = fn.__name__
    new_fn.__dict__ = fn.__dict__
    new_fn.__module__ = fn.__module__
    return new_fn

Response: Profile a file or file with the specified number of entries.


Instruction:
Can you generate the documentation for the following Python 3 function
def coverage(fn):
    fp = TraceFuncCoverage(fn) # or HotShotFuncCoverage
    # We cannot return fp or fp.__call__ directly as that would break method
    # definitions, instead we need to return a plain function.
    def new_fn(*args, **kw):
        return fp(*args, **kw)
    new_fn.__doc__ = fn.__doc__
    new_fn.__name__ = fn.__name__
    new_fn.__dict__ = fn.__dict__
    new_fn.__module__ = fn.__module__
    return new_fn

Response: Mark a function as line coverage analysis.


Instruction:
Can you implement a function in Python 3 that
marks fn for line coverage analysis.

Response:
def coverage_with_hotshot(fn):
    """Mark `fn` for line coverage analysis.

    Uses the 'hotshot' module for fast coverage analysis.

    BUG: Produces inaccurate results.

    See the docstring of `coverage` for usage examples.
    """
    fp = HotShotFuncCoverage(fn)
    # We cannot return fp or fp.__call__ directly as that would break method
    # definitions, instead we need to return a plain function.
    def new_fn(*args, **kw):
        return fp(*args, **kw)
    new_fn.__doc__ = fn.__doc__
    new_fn.__name__ = fn.__name__
    new_fn.__dict__ = fn.__dict__
    new_fn.__module__ = fn.__module__
    return new_fn


Instruction:
Explain what the following Python 3 code does
def timecall(fn=None, immediate=True, timer=time.time):
    if fn is None: # @timecall() syntax -- we are a decorator maker
        def decorator(fn):
            return timecall(fn, immediate=immediate, timer=timer)
        return decorator
    # @timecall syntax -- we are a decorator.
    fp = FuncTimer(fn, immediate=immediate, timer=timer)
    # We cannot return fp or fp.__call__ directly as that would break method
    # definitions, instead we need to return a plain function.
    def new_fn(*args, **kw):
        return fp(*args, **kw)
    new_fn.__doc__ = fn.__doc__
    new_fn.__name__ = fn.__name__
    new_fn.__dict__ = fn.__dict__
    new_fn.__module__ = fn.__module__
    return new_fn

Response: Wrap a function to print its execution time.


Instruction:
Can you generate a brief explanation for the following Python 3 code
def print_stats(self):
        funcname = self.fn.__name__
        filename = self.fn.__code__.co_filename
        lineno = self.fn.__code__.co_firstlineno
        print("")
        print("*** PROFILER RESULTS ***")
        print("%s (%s:%s)" % (funcname, filename, lineno))
        if self.skipped:
            skipped = "(%d calls not profiled)" % self.skipped
        else:
            skipped = ""
        print("function called %d times%s" % (self.ncalls, skipped))
        print("")
        stats = self.stats
        if self.filename:
            stats.dump_stats(self.filename)
        if not self.dirs:
            stats.strip_dirs()
        stats.sort_stats(*self.sort)
        stats.print_stats(self.entries)

Response: Print profile information to sys. stdout.


Instruction:
Write a Python 3 script to
reset accumulated profiler statistics.

Response:
def reset_stats(self):
        """Reset accumulated profiler statistics."""
        # Note: not using self.Profile, since pstats.Stats() fails then
        self.stats = pstats.Stats(Profile())
        self.ncalls = 0
        self.skipped = 0


Instruction:
Explain what the following Python 3 code does
def atexit(self):
        funcname = self.fn.__name__
        filename = self.fn.__code__.co_filename
        lineno = self.fn.__code__.co_firstlineno
        print("")
        print("*** COVERAGE RESULTS ***")
        print("%s (%s:%s)" % (funcname, filename, lineno))
        print("function called %d times" % self.ncalls)
        print("")
        fs = FuncSource(self.fn)
        for (filename, lineno), count in self.tracer.counts.items():
            if filename != fs.filename:
                continue
            fs.mark(lineno, count)
        print(fs)
        never_executed = fs.count_never_executed()
        if never_executed:
            print("%d lines were not executed." % never_executed)

Response: Stop profiling and print profile information to sys. stderr.


Instruction:
Can you generate a brief explanation for the following Python 3 code
def find_source_lines(self):
        strs = trace.find_strings(self.filename)
        lines = trace.find_lines_from_code(self.fn.__code__, strs)
        self.firstcodelineno = sys.maxint
        for lineno in lines:
            self.firstcodelineno = min(self.firstcodelineno, lineno)
            self.sourcelines.setdefault(lineno, 0)
        if self.firstcodelineno == sys.maxint:
            self.firstcodelineno = self.firstlineno

Response: Find all executable source lines in fn as executed 0 times.


Instruction:
Can you tell what is the following Python 3 function doing
def mark(self, lineno, count=1):
        self.sourcelines[lineno] = self.sourcelines.get(lineno, 0) + count

Response: Mark a given source line as executed count times.


Instruction:
How would you code a function in Python 3 to
count statements that were never executed.

Response:
def count_never_executed(self):
        """Count statements that were never executed."""
        lineno = self.firstlineno
        counter = 0
        for line in self.source:
            if self.sourcelines.get(lineno) == 0:
                if not self.blank_rx.match(line):
                    counter += 1
            lineno += 1
        return counter


Instruction:
Implement a function in Python 3 to
add the element val to the list.

Response:
def add(self, val):
        """Add the element *val* to the list."""
        _maxes, _lists = self._maxes, self._lists

        if _maxes:
            pos = bisect_right(_maxes, val)

            if pos == len(_maxes):
                pos -= 1
                _maxes[pos] = val
                _lists[pos].append(val)
            else:
                insort(_lists[pos], val)

            self._expand(pos)
        else:
            _maxes.append(val)
            _lists.append([val])

        self._len += 1


Instruction:
Write a Python 3 function for
removing first occurrence of val. Raises ValueError if val is not present.

Response:
def remove(self, val):
        """
        Remove first occurrence of *val*.

        Raises ValueError if *val* is not present.
        """
        _maxes = self._maxes

        if not _maxes:
            raise ValueError('{0} not in list'.format(repr(val)))

        pos = bisect_left(_maxes, val)

        if pos == len(_maxes):
            raise ValueError('{0} not in list'.format(repr(val)))

        _lists = self._lists
        idx = bisect_left(_lists[pos], val)
        if _lists[pos][idx] == val:
            self._delete(pos, idx)
        else:
            raise ValueError('{0} not in list'.format(repr(val)))


Instruction:
Make a summary of the following Python 3 code
def _delete(self, pos, idx):
        _maxes, _lists, _index = self._maxes, self._lists, self._index

        lists_pos = _lists[pos]

        del lists_pos[idx]
        self._len -= 1

        len_lists_pos = len(lists_pos)

        if len_lists_pos > self._half:

            _maxes[pos] = lists_pos[-1]

            if _index:
                child = self._offset + pos
                while child > 0:
                    _index[child] -= 1
                    child = (child - 1) >> 1
                _index[0] -= 1

        elif len(_lists) > 1:

            if not pos:
                pos += 1

            prev = pos - 1
            _lists[prev].extend(_lists[pos])
            _maxes[prev] = _lists[prev][-1]

            del _maxes[pos]
            del _lists[pos]
            del _index[:]

            self._expand(prev)

        elif len_lists_pos:

            _maxes[pos] = lists_pos[-1]

        else:

            del _maxes[pos]
            del _lists[pos]
            del _index[:]

Response: Delete the item at the given position.


Instruction:
Create a Python 3 function for
extending the list by appending all elements from the given list. Raises a ValueError if the sort order is violated.

Response:
def extend(self, values):
        """
        Extend the list by appending all elements from the *values*. Raises a
        ValueError if the sort order would be violated.
        """
        _maxes, _lists, _load = self._maxes, self._lists, self._load

        if not isinstance(values, list):
            values = list(values)

        if any(values[pos - 1] > values[pos]
               for pos in range(1, len(values))):
            raise ValueError('given sequence not in sort order')

        offset = 0

        if _maxes:
            if values[0] < _lists[-1][-1]:
                msg = '{0} not in sort order at index {1}'.format(repr(values[0]), self._len)
                raise ValueError(msg)

            if len(_lists[-1]) < self._half:
                _lists[-1].extend(values[:_load])
                _maxes[-1] = _lists[-1][-1]
                offset = _load

        len_lists = len(_lists)

        for idx in range(offset, len(values), _load):
            _lists.append(values[idx:(idx + _load)])
            _maxes.append(_lists[-1][-1])

        _index = self._index

        if len_lists == len(_lists):
            len_index = len(_index)
            if len_index > 0:
                len_values = len(values)
                child = len_index - 1
                while child:
                    _index[child] += len_values
                    child = (child - 1) >> 1
                _index[0] += len_values
        else:
            del _index[:]

        self._len += len(values)


Instruction:
Create a Python 3 function for
inserting the element val into the list at idx. Raises a ValueError if the element val is not in the sort order.

Response:
def insert(self, idx, val):
        """
        Insert the element *val* into the list at *idx*. Raises a ValueError if
        the *val* at *idx* would violate the sort order.
        """
        _maxes, _lists, _len = self._maxes, self._lists, self._len

        if idx < 0:
            idx += _len
        if idx < 0:
            idx = 0
        if idx > _len:
            idx = _len

        if not _maxes:
            # The idx must be zero by the inequalities above.
            _maxes.append(val)
            _lists.append([val])
            self._len = 1
            return

        if not idx:
            if val > _lists[0][0]:
                msg = '{0} not in sort order at index {1}'.format(repr(val), 0)
                raise ValueError(msg)
            else:
                _lists[0].insert(0, val)
                self._expand(0)
                self._len += 1
                return

        if idx == _len:
            pos = len(_lists) - 1
            if _lists[pos][-1] > val:
                msg = '{0} not in sort order at index {1}'.format(repr(val), _len)
                raise ValueError(msg)
            else:
                _lists[pos].append(val)
                _maxes[pos] = _lists[pos][-1]
                self._expand(pos)
                self._len += 1
                return

        pos, idx = self._pos(idx)
        idx_before = idx - 1
        if idx_before < 0:
            pos_before = pos - 1
            idx_before = len(_lists[pos_before]) - 1
        else:
            pos_before = pos

        before = _lists[pos_before][idx_before]
        if before <= val <= _lists[pos][idx]:
            _lists[pos].insert(idx, val)
            self._expand(pos)
            self._len += 1
        else:
            msg = '{0} not in sort order at index {1}'.format(repr(val), idx)
            raise ValueError(msg)


Instruction:
How would you implement a function in Python 3 that
updates the list by adding all elements from iterable.

Response:
def update(self, iterable):
        """Update the list by adding all elements from *iterable*."""
        _maxes, _lists, _keys = self._maxes, self._lists, self._keys
        values = sorted(iterable, key=self._key)

        if _maxes:
            if len(values) * 4 >= self._len:
                values.extend(chain.from_iterable(_lists))
                values.sort(key=self._key)
                self._clear()
            else:
                _add = self.add
                for val in values:
                    _add(val)
                return

        _load, _index = self._load, self._index
        _lists.extend(values[pos:(pos + _load)]
                      for pos in range(0, len(values), _load))
        _keys.extend(list(map(self._key, _list)) for _list in _lists)
        _maxes.extend(sublist[-1] for sublist in _keys)
        self._len = len(values)
        del _index[:]


Instruction:
Write a Python 3 function that can
set the username of the current object.

Response:
def set_username_ibm(self, username_ibm):
        """
        Parameters
        ----------
        username_ibm : str

        Raises
        ------
        Exception
            If mode is not `ibm`
        """
        if self.get_mode() == "ibm":
            self.__username_ibm = username_ibm
        else:
            raise Exception(
                "Mode is {}, whereas it must be `ibm`".format(
                    self.get_moder()))


Instruction:
Write a Python 3 script for
setting the password of the current locale.

Response:
def set_password_ibm(self, password_ibm):
        """
        Parameters
        ----------
        password_ibm : str

        Raises
        ------
        Exception
            If mode is not `ibm`
        """
        if self.get_mode() == "ibm":
            self.__password_ibm = password_ibm
        else:
            raise Exception(
                "Mode is {}, whereas it must be `ibm`".format(self.get_mode()))


Instruction:
How would you code a function in Python 3 to
return a list of all audio files in the source directory.

Response:
def _list_audio_files(self, sub_dir=""):
        """
        Parameters
        ----------
        sub_dir : one of `needed_directories`, optional
            Default is "", which means it'll look through all of subdirs.

        Returns
        -------
        audio_files : [str]
            A list whose elements are basenames of the present audiofiles whose
            formats are `wav`
        """
        audio_files = list()
        for possibly_audio_file in os.listdir("{}/{}".format(self.src_dir,
                                                             sub_dir)):
            file_format = ''.join(possibly_audio_file.split('.')[-1])
            if file_format.lower() == "wav":
                audio_files.append(possibly_audio_file)
        return audio_files


Instruction:
Can you generate the documentation for the following Python 3 function
def _get_audio_channels(self, audio_abs_path):
        channel_num = int(
            subprocess.check_output(
                ("""sox --i {} | grep "{}" | awk -F " : " '{{print $2}}'"""
                 ).format(audio_abs_path, "Channels"),
                shell=True, universal_newlines=True).rstrip())
        return channel_num

Response: Get the number of audio channels from the audio file.


Instruction:
Here you have a function in Python 3, explain what it does
def _get_audio_sample_rate(self, audio_abs_path):
        sample_rate = int(
           subprocess.check_output(
               ("""sox --i {} | grep "{}" | awk -F " : " '{{print $2}}'"""
                ).format(audio_abs_path, "Sample Rate"),
               shell=True, universal_newlines=True).rstrip())
        return sample_rate

Response: Get the audio sample rate from the audio file.


Instruction:
Implement a Python 3 function for
getting the audio sample bit from the cache file.

Response:
def _get_audio_sample_bit(self, audio_abs_path):
        """
        Parameters
        ----------
        audio_abs_path : str

        Returns
        -------
        sample_bit : int
        """
        sample_bit = int(
           subprocess.check_output(
               ("""sox --i {} | grep "{}" | awk -F " : " '{{print $2}}' | """
                """grep -oh "^[^-]*" """).format(audio_abs_path, "Precision"),
               shell=True, universal_newlines=True).rstrip())
        return sample_bit


Instruction:
How would you implement a function in Python 3 that
gets the total number of seconds until the audio file has been downloaded.

Response:
def _get_audio_duration_seconds(self, audio_abs_path):
        """
        Parameters
        ----------
        audio_abs_path : str

        Returns
        -------
        total_seconds : int
        """
        HHMMSS_duration = subprocess.check_output(
            ("""sox --i {} | grep "{}" | awk -F " : " '{{print $2}}' | """
             """grep -oh "^[^=]*" """).format(
                audio_abs_path, "Duration"),
            shell=True, universal_newlines=True).rstrip()
        total_seconds = sum(
            [float(x) * 60 ** (2 - i)
             for i, x in enumerate(HHMMSS_duration.split(":"))])
        return total_seconds


Instruction:
How would you code a function in Python 3 to
return the audio bit rate for the given audio file.

Response:
def _get_audio_bit_rate(self, audio_abs_path):
        """
        Parameters
        -----------
        audio_abs_path : str

        Returns
        -------
        bit_rate : int
        """
        bit_Rate_formatted = subprocess.check_output(
            """sox --i {} | grep "{}" | awk -F " : " '{{print $2}}'""".format(
                audio_abs_path, "Bit Rate"),
            shell=True, universal_newlines=True).rstrip()
        bit_rate = (lambda x:
                    int(x[:-1]) * 10 ** 3 if x[-1].lower() == "k" else
                    int(x[:-1]) * 10 ** 6 if x[-1].lower() == "m" else
                    int(x[:-1]) * 10 ** 9 if x[-1].lower() == "g" else
                    int(x))(bit_Rate_formatted)
        return bit_rate


Instruction:
Here you have a function in Python 3, explain what it does
def _seconds_to_HHMMSS(seconds):
        less_than_second = seconds - floor(seconds)
        minutes, seconds = divmod(floor(seconds), 60)
        hours, minutes = divmod(minutes, 60)
        return "{}H{}M{}S.{}".format(hours, minutes, seconds, less_than_second)

Response: Returns a string which is the hour minute second representation of the intput seconds.


Instruction:
Create a Python 3 function for
extracting the audio segments from the audio file.

Response:
def _audio_segment_extractor(self, audio_abs_path, segment_abs_path,
                                 starting_second, duration):
        """
        Parameters
        -----------
        audio_abs_path : str
        segment_abs_path : str
        starting_second : int
        duration : int
        """

        subprocess.Popen(["sox",  str(audio_abs_path), str(segment_abs_path),
                          "trim", str(starting_second), str(duration)],
                         universal_newlines=True).communicate()


Instruction:
Can you generate the documentation for the following Python 3 function
def _split_audio_by_duration(self, audio_abs_path,
                                 results_abs_path, duration_seconds):
        """
        Calculates the length of each segment and passes it to
        self._audio_segment_extractor

        Parameters
        ----------
        audio_abs_path : str
        results_abs_path : str
            A place for adding digits needs to be added prior the the format
            decleration i.e. name%03.wav. Here, we've added `*` at staging
            step, which we'll replace.
        duration_seconds : int
        """
        total_seconds = self._get_audio_duration_seconds(audio_abs_path)
        current_segment = 0
        while current_segment <= total_seconds // duration_seconds + 1:
            if current_segment + duration_seconds > total_seconds:
                ending_second = total_seconds
            else:
                ending_second = current_segment + duration_seconds
            self._audio_segment_extractor(
                audio_abs_path,
                results_abs_path.replace("*", "{:03d}".format(
                    current_segment)),
                starting_second=current_segment, duration=(ending_second -
                                                           current_segment))
            current_segment += 1

Response: Splits the audio file into individual segments.


Instruction:
Write a Python 3 function for
splitting the audio file into individual items and splits them into individual items.

Response:
def _split_audio_by_size(self, audio_abs_path, results_abs_path,
                             chunk_size):
        """
        Calculates the duration of the name.wav in order for all splits have
        the size of chunk_size except possibly the last split (which will be
        smaller) and then passes the duration to `split_audio_by_duration`

        Parameters
        ----------
        audio_abs_path : str
        results_abs_path : str
            A place for adding digits needs to be added prior the the format
            decleration i.e. name%03.wav
        chunk_size : int
            Should be in bytes
        """
        sample_rate = self._get_audio_sample_rate(audio_abs_path)
        sample_bit = self._get_audio_sample_bit(audio_abs_path)
        channel_num = self._get_audio_channels(audio_abs_path)
        duration = 8 * chunk_size / reduce(lambda x, y: int(x) * int(y),
                                           [sample_rate, sample_bit,
                                            channel_num])
        self._split_audio_by_duration(audio_abs_path, results_abs_path,
                                      duration)


Instruction:
Can you write a function in Python 3 where it
moves the audio file if the format is `wav` to `filtered` directory. Parameters ---------- basename : str A basename of `/home/random-guy/some-audio-file.wav` is `some-audio-file.wav`

Response:
def _filtering_step(self, basename):
        """
        Moves the audio file if the format is `wav` to `filtered` directory.

        Parameters
        ----------
        basename : str
            A basename of `/home/random-guy/some-audio-file.wav` is
            `some-audio-file.wav`
        """
        name = ''.join(basename.split('.')[:-1])
        # May cause problems if wav is not less than 9 channels.
        if basename.split('.')[-1] == "wav":
            if self.get_verbosity():
                print("Found wave! Copying to {}/filtered/{}".format(
                    self.src_dir, basename))
            subprocess.Popen(["cp", "{}/{}.wav".format(self.src_dir, name),
                              "{}/filtered/{}.wav".format(self.src_dir, name)],
                             universal_newlines=True).communicate()


Instruction:
Create a Python 3 function to
check the size of audio file, splits it if it's needed to manage api limit and then moves to `staged` directory while appending `*` to the end of the filename for self.split_audio_by_duration to replace it by a number. Parameters ---------- basename : str A basename of `/home/random-guy/some-audio-file.wav` is `some-audio-file.wav`

Response:
def _staging_step(self, basename):
        """
        Checks the size of audio file, splits it if it's needed to manage api
        limit and then moves to `staged` directory while appending `*` to
        the end of the filename for self.split_audio_by_duration to replace
        it by a number.

        Parameters
        ----------
        basename : str
            A basename of `/home/random-guy/some-audio-file.wav` is
            `some-audio-file.wav`
        """
        name = ''.join(basename.split('.')[:-1])

        if self.get_mode() == "ibm":
            # Checks the file size. It's better to use 95% of the allocated
            # size per file since the upper limit is not always respected.
            total_size = os.path.getsize("{}/filtered/{}.wav".format(
                self.src_dir, name))
            if total_size >= self.ibm_api_limit_bytes:
                if self.get_verbosity():
                    print(("{}'s size over API limit ({}). Splitting").format(
                        name, self.ibm_api_limit_bytes))
                self._split_audio_by_size(
                    "{}/filtered/{}.wav".format(self.src_dir, name),
                    "{}/staging/{}*.wav".format(self.src_dir, name),
                    self.ibm_api_limit_bytes * 95 / 100)
            else:
                if self.get_verbosity():
                    print("{}'s size is fine. Moving to staging dir'".format(
                        name))
                subprocess.Popen((
                    "mv {}/filtered/{}.wav {}/staging/{}000.wav").format(
                                    self.src_dir, name, self.src_dir, name),
                                 shell=True,
                                 universal_newlines=True).communicate()

        elif self.get_mode() == "cmu":
            if self.get_verbosity():
                print("Converting {} to a readable wav".format(basename))
            ffmpeg = os.path.basename(find_executable("ffmpeg") or
                                      find_executable("avconv"))
            if ffmpeg is None:
                raise Exception(("Either ffmpeg or avconv is needed. "
                                 "Neither is installed or accessible"))
            try:
                # ffmpeg log levels:
                # https://ffmpeg.org/ffmpeg.html#Generic-options
                ffmpeg_log_level = "8"  # fatal errors.
                if self.get_verbosity():
                    ffmpeg_log_level = "32"  # info `default for ffmpeg`
                subprocess.check_call([
                    str(ffmpeg), "-y", "-i", "{}/filtered/{}.wav".format(
                        self.src_dir, str(name)), "-acodec", "pcm_s16le",
                    "-ac", "1", "-ar", "16000", "{}/staging/{}000.wav".format(
                        self.src_dir, name),
                    "-v", ffmpeg_log_level], universal_newlines=True)
            except subprocess.CalledProcessError as e:
                print(e)
            if os.path.exists("{}/staging/{}000.wav".format(
                    self.src_dir, name)):
                if self.get_verbosity():
                    print(("{}/filtered/{} was converted to "
                           "{}/staging/{}000.wav Now removing the copy of "
                           "{} in filtered sub directory").format(
                                self.src_dir, basename,
                                self.src_dir, name, basename))
                subprocess.Popen([
                    "rm", "{}/filtered/{}".format(self.src_dir, basename)],
                                    universal_newlines=True).communicate()
            else:
                raise Exception("Something went wrong with ffmpeg conversion!")


Instruction:
How would you implement a function in Python 3 that
prepares and stages the audio files.

Response:
def _prepare_audio(self, basename, replace_already_indexed=False):
        """
        Prepares and stages the audio file to be indexed.

        Parameters
        ----------
        basename : str, None
            A basename of `/home/random-guy/some-audio-file.wav` is
            `some-audio-file.wav`
            If basename is `None`, it'll prepare all the audio files.
        """
        if basename is not None:
            if basename in self.get_timestamps():
                if self.get_verbosity():
                    print("File specified was already indexed. Reindexing...")
                del self.__timestamps[basename]
            self._filtering_step(basename)
            self._staging_step(basename)
        else:
            for audio_basename in self._list_audio_files():
                if audio_basename in self.__timestamps:
                    if replace_already_indexed:
                        if self.get_verbosity():
                            print("Already indexed {}. Reindexing...".format(
                                audio_basename))
                        del self.__timestamps[audio_basename]
                    else:
                        if self.get_verbosity():
                            print("Already indexed {}. Skipping...".format(
                                audio_basename))
                        continue
                self._filtering_step(audio_basename)
                self._staging_step(audio_basename)


Instruction:
Implement a function in Python 3 to
index audio with pocketsphinx. Beware that the output would not be sufficiently accurate. Use this only if you don't want to upload your files to IBM. Parameters ----------- basename : str, optional A specific basename to be indexed and is placed in src_dir E.g. `audio.wav`. If `None` is selected, all the valid audio files would be indexed. Default is `None`. Raises ------ OSError If the output of pocketsphinx command results in an error.

Response:
def _index_audio_cmu(self, basename=None, replace_already_indexed=False):
        """
        Indexes audio with pocketsphinx. Beware that the output would not be
        sufficiently accurate. Use this only if you don't want to upload your
        files to IBM.

        Parameters
        -----------
        basename : str, optional
            A specific basename to be indexed and is placed in src_dir
            E.g. `audio.wav`.

            If `None` is selected, all the valid audio files would be indexed.
            Default is `None`.

        Raises
        ------
        OSError
            If the output of pocketsphinx command results in an error.
        """
        self._prepare_audio(basename=basename,
                            replace_already_indexed=replace_already_indexed)

        for staging_audio_basename in self._list_audio_files(
                sub_dir="staging"):
            original_audio_name = ''.join(
                staging_audio_basename.split('.')[:-1])[:-3]
            pocketsphinx_command = ''.join([
                "pocketsphinx_continuous", "-infile",
                str("{}/staging/{}".format(
                    self.src_dir, staging_audio_basename)),
                "-time", "yes", "-logfn", "/dev/null"])
            try:
                if self.get_verbosity():
                    print("Now indexing {}".format(staging_audio_basename))
                output = subprocess.check_output([
                    "pocketsphinx_continuous", "-infile",
                    str("{}/staging/{}".format(
                        self.src_dir, staging_audio_basename)),
                    "-time", "yes", "-logfn", "/dev/null"
                ], universal_newlines=True).split('\n')
                str_timestamps_with_sil_conf = list(map(
                    lambda x: x.split(" "), filter(None, output[1:])))
                # Timestamps are putted in a list of a single element. To match
                # Watson's output.
                self.__timestamps_unregulated[
                    original_audio_name + ".wav"] = [(
                        self._timestamp_extractor_cmu(
                            staging_audio_basename,
                            str_timestamps_with_sil_conf))]
                if self.get_verbosity():
                    print("Done indexing {}".format(staging_audio_basename))
            except OSError as e:
                if self.get_verbosity():
                    print(e, "The command was: {}".format(
                        pocketsphinx_command))
                self.__errors[(time(), staging_audio_basename)] = e
        self._timestamp_regulator()

        if self.get_verbosity():
            print("Finished indexing procedure")


Instruction:
Can you generate the documentation for the following Python 3 function
def _timestamp_extractor_cmu(self, staging_audio_basename,
                                 str_timestamps_with_sil_conf):
        """
        Parameters
        ----------
        str_timestamps_with_sil_conf : [[str, str, str, str]]
            Of the form [[word, starting_sec, ending_sec, confidence]]

        Returns
        -------
        timestamps : [[str, float, float]]
        """
        filter_untimed = filter(lambda x: len(x) == 4,
                                str_timestamps_with_sil_conf)
        if filter_untimed != str_timestamps_with_sil_conf:
            self.__errors[
                (time(), staging_audio_basename)
            ] = str_timestamps_with_sil_conf
        str_timestamps = [
            str_timestamp[:-1]
            for str_timestamp in filter_untimed
            if not any([letter in {"<", ">", "/"}
                        for letter in ''.join(str_timestamp)])]
        timestamps = list([
            _WordBlock(
                word=re.findall("^[^\(]+", x[0])[0],
                start=round(float(x[1]), 2),
                end=round(float(x[2]), 2)
            ) for x in str_timestamps])
        return timestamps

Response: Extract the CMU timestamps from the given string.


Instruction:
How would you explain what the following Python 3 function does
def _index_audio_ibm(self, basename=None, replace_already_indexed=False,
                         continuous=True, model="en-US_BroadbandModel",
                         word_confidence=True, word_alternatives_threshold=0.9,
                         profanity_filter_for_US_results=False):
        """
        Implements a search-suitable interface for Watson speech API.

        Some explaination of the parameters here have been taken from [1]_

        Parameters
        ----------
        basename : str, optional
            A specific basename to be indexed and is placed in src_dir
            e.g `audio.wav`.

            If `None` is selected, all the valid audio files would be indexed.
            Default is `None`.

        replace_already_indexed : bool
            `True`, To reindex some audio file that's already in the
             timestamps.

             Default is `False`.

        continuous : bool
            Indicates whether multiple final results that represent consecutive
            phrases separated by long pauses are returned.
            If true, such phrases are returned; if false (the default),
            recognition ends after the first end-of-speech (EOS) incident is
            detected.

            Default is `True`.
        model :  {
                    'ar-AR_BroadbandModel',
                    'en-UK_BroadbandModel'
                    'en-UK_NarrowbandModel',
                    'en-US_BroadbandModel', (the default)
                    'en-US_NarrowbandModel',
                    'es-ES_BroadbandModel',
                    'es-ES_NarrowbandModel',
                    'fr-FR_BroadbandModel',
                    'ja-JP_BroadbandModel',
                    'ja-JP_NarrowbandModel',
                    'pt-BR_BroadbandModel',
                    'pt-BR_NarrowbandModel',
                    'zh-CN_BroadbandModel',
                    'zh-CN_NarrowbandModel'
                 }
            The identifier of the model to be used for the recognition

            Default is 'en-US_BroadbandModel'
        word_confidence : bool
            Indicates whether a confidence measure in the range of 0 to 1 is
            returned for each word.

            The default is True. (It's False in the original)
        word_alternatives_threshold : numeric
            A confidence value that is the lower bound for identifying a
            hypothesis as a possible word alternative (also known as
            "Confusion Networks"). An alternative word is considered if its
            confidence is greater than or equal to the threshold. Specify a
            probability between 0 and 1 inclusive.

            Default is `0.9`.
        profanity_filter_for_US_results : bool
            Indicates whether profanity filtering is performed on the
            transcript. If true, the service filters profanity from all output
            by replacing inappropriate words with a series of asterisks.

            If false, the service returns results with no censoring. Applies
            to US English transcription only.

            Default is `False`.

        References
        ----------
        .. [1] : https://ibm.com/watson/developercloud/speech-to-text/api/v1/
        """
        params = {'continuous': continuous,
                  'model': model,
                  'word_alternatives_threshold': word_alternatives_threshold,
                  'word_confidence': word_confidence,
                  'timestamps': True,
                  'inactivity_timeout': str(-1),
                  'profanity_filter': profanity_filter_for_US_results}

        self._prepare_audio(basename=basename,
                            replace_already_indexed=replace_already_indexed)

        for staging_audio_basename in self._list_audio_files(
                sub_dir="staging"):
            original_audio_name = ''.join(
                staging_audio_basename.split('.')[:-1])[:-3]
            with open("{}/staging/{}".format(
                    self.src_dir, staging_audio_basename), "rb") as f:
                if self.get_verbosity():
                    print("Uploading {}...".format(staging_audio_basename))
                response = requests.post(
                    url=("https://stream.watsonplatform.net/"
                         "speech-to-text/api/v1/recognize"),
                    auth=(self.get_username_ibm(), self.get_password_ibm()),
                    headers={'content-type': 'audio/wav'},
                    data=f.read(),
                    params=params)
                if self.get_verbosity():
                    print("Indexing {}...".format(staging_audio_basename))
                self.__timestamps_unregulated[
                    original_audio_name + ".wav"].append(
                        self._timestamp_extractor_ibm(
                            staging_audio_basename, json.loads(response.text)))
                if self.get_verbosity():
                    print("Done indexing {}".format(staging_audio_basename))
        self._timestamp_regulator()

        if self.get_verbosity():
            print("Indexing procedure finished")

Response: This method is used to index the audio files in src_dir_wav.


Instruction:
Create a Python 3 function to
extract the timestamps of the original audio file.

Response:
def _timestamp_extractor_ibm(self, staging_audio_basename, audio_json):
        """
        Parameters
        ----------
        audio_json : {str: [{str: [{str: str or nuneric}]}]}
            Refer to Watson Speech API refrence [1]_

        Returns
        -------
        [[str, float, float]]
            A list whose members are lists. Each member list has three
            elements. First one is a word. Second is the starting second and
            the third is the ending second of that word in the original
            audio file.
        """
        try:
            timestamps_of_sentences = [
                audio_json['results'][i]['alternatives'][0]['timestamps']
                for i in range(len(audio_json['results']))]
            return [
                _WordBlock(
                    word=word_block[0],
                    start=round(float(word_block[1]), 2),
                    end=round(float(word_block[2]), 2)
                ) for sentence_block in timestamps_of_sentences
                for word_block in sentence_block]
        except KeyError:
            self.__errors[(time(), staging_audio_basename)] = audio_json
            if self.get_verbosity():
                print(audio_json)
                print("The resulting request from Watson was unintelligible.")
            return False


Instruction:
How would you code a function in Python 3 to
call the correct indexer function based on the mode. If mode is `ibm`, _indexer_audio_ibm is called which is an interface for Watson. Note that some of the explaination of _indexer_audio_ibm's arguments is from [1]_ If mode is `cmu`, _indexer_audio_cmu is called which is an interface for PocketSphinx Beware that the output would not be sufficiently accurate. Use this only if you don't want to upload your files to IBM. Parameters ---------- mode : {"ibm", "cmu"} basename : str, optional A specific basename to be indexed and is placed in src_dir e.g `audio.wav`. If `None` is selected, all the valid audio files would be indexed. Default is `None`. replace_already_indexed : bool `True`, To reindex some audio file that's already in the timestamps. Default is `False`. continuous : bool Valid Only if mode is `ibm` Indicates whether multiple final results that represent consecutive phrases separated by long pauses are returned. If true, such phrases are returned; if false (the default), recognition ends after the first end-of-speech (EOS) incident is detected. Default is `True`. model : { 'ar-AR_BroadbandModel', 'en-UK_BroadbandModel' 'en-UK_NarrowbandModel', 'en-US_BroadbandModel', (the default) 'en-US_NarrowbandModel', 'es-ES_BroadbandModel', 'es-ES_NarrowbandModel', 'fr-FR_BroadbandModel', 'ja-JP_BroadbandModel', 'ja-JP_NarrowbandModel', 'pt-BR_BroadbandModel', 'pt-BR_NarrowbandModel', 'zh-CN_BroadbandModel', 'zh-CN_NarrowbandModel' } Valid Only if mode is `ibm` The identifier of the model to be used for the recognition Default is 'en-US_BroadbandModel' word_confidence : bool Valid Only if mode is `ibm` Indicates whether a confidence measure in the range of 0 to 1 is returned for each word. The default is True. (It's False in the original) word_alternatives_threshold : numeric Valid Only if mode is `ibm` A confidence value that is the lower bound for identifying a hypothesis as a possible word alternative (also known as "Confusion Networks"). An alternative word is considered if its confidence is greater than or equal to the threshold. Specify a probability between 0 and 1 inclusive. Default is `0.9`. profanity_filter_for_US_results : bool Valid Only if mode is `ibm` Indicates whether profanity filtering is performed on the transcript. If true, the service filters profanity from all output by replacing inappropriate words with a series of asterisks. If false, the service returns results with no censoring. Applies to US English transcription only. Default is `False`. Raises ------ OSError Valid only if mode is `cmu`. If the output of pocketsphinx command results in an error. References ---------- .. [1] : https://ibm.com/watson/developercloud/speech-to-text/api/v1/ Else if mode is `cmu`, then _index_audio_cmu would be called:

Response:
def index_audio(self, *args, **kwargs):
        """
        Calls the correct indexer function based on the mode.

        If mode is `ibm`, _indexer_audio_ibm is called which is an interface
        for Watson. Note that some of the explaination of _indexer_audio_ibm's
        arguments is from [1]_

        If mode is `cmu`, _indexer_audio_cmu is called which is an interface
        for PocketSphinx Beware that the output would not be sufficiently
        accurate. Use this only if you don't want to upload your files to IBM.

        Parameters
        ----------
        mode : {"ibm", "cmu"}

        basename : str, optional

            A specific basename to be indexed and is placed in src_dir
            e.g `audio.wav`.

            If `None` is selected, all the valid audio files would be indexed.
            Default is `None`.

        replace_already_indexed : bool

            `True`, To reindex some audio file that's already in the
            timestamps.

            Default is `False`.

        continuous : bool

            Valid Only if mode is `ibm`

            Indicates whether multiple final results that represent consecutive
            phrases separated by long pauses are returned.
            If true, such phrases are returned; if false (the default),
            recognition ends after the first end-of-speech (EOS) incident is
            detected.

            Default is `True`.

        model :  {
                    'ar-AR_BroadbandModel',
                    'en-UK_BroadbandModel'
                    'en-UK_NarrowbandModel',
                    'en-US_BroadbandModel', (the default)
                    'en-US_NarrowbandModel',
                    'es-ES_BroadbandModel',
                    'es-ES_NarrowbandModel',
                    'fr-FR_BroadbandModel',
                    'ja-JP_BroadbandModel',
                    'ja-JP_NarrowbandModel',
                    'pt-BR_BroadbandModel',
                    'pt-BR_NarrowbandModel',
                    'zh-CN_BroadbandModel',
                    'zh-CN_NarrowbandModel'
                 }

            Valid Only if mode is `ibm`

            The identifier of the model to be used for the recognition

            Default is 'en-US_BroadbandModel'

        word_confidence : bool

            Valid Only if mode is `ibm`

            Indicates whether a confidence measure in the range of 0 to 1 is
            returned for each word.

            The default is True. (It's False in the original)

        word_alternatives_threshold : numeric

            Valid Only if mode is `ibm`

            A confidence value that is the lower bound for identifying a
            hypothesis as a possible word alternative (also known as
            "Confusion Networks"). An alternative word is considered if its
            confidence is greater than or equal to the threshold. Specify a
            probability between 0 and 1 inclusive.

            Default is `0.9`.

        profanity_filter_for_US_results : bool

            Valid Only if mode is `ibm`

            Indicates whether profanity filtering is performed on the
            transcript. If true, the service filters profanity from all output
            by replacing inappropriate words with a series of asterisks.

            If false, the service returns results with no censoring. Applies
            to US English transcription only.

            Default is `False`.

        Raises
        ------
        OSError

            Valid only if mode is `cmu`.

            If the output of pocketsphinx command results in an error.

        References
        ----------
        .. [1] : https://ibm.com/watson/developercloud/speech-to-text/api/v1/

        Else if mode is `cmu`, then _index_audio_cmu would be called:
        """
        with _Subdirectory_Managing_Decorator(
                self.src_dir, self._needed_directories):
            if self.get_mode() == "ibm":
                self._index_audio_ibm(*args, **kwargs)
            elif self.get_mode() == "cmu":
                self._index_audio_cmu(*args, **kwargs)


Instruction:
How would you implement a function in Python 3 that
makes a dictionary whose keys are audio file basenames and whose values are a list of word blocks from unregulated timestamps and updates the main timestamp attribute. After all done, purges unregulated ones. In case the audio file was large enough to be splitted, it adds seconds to correct timing and in case the timestamp was manually loaded, it leaves it alone. Note that the difference between self.__timestamps and self.__timestamps_unregulated is that in the regulated version, right after the word, a list of word blocks must appear. However in the unregulated version, after a word, a list of individual splits containing word blocks would appear!

Response:
def _timestamp_regulator(self):
        """
        Makes a dictionary whose keys are audio file basenames and whose
        values are a list of word blocks from unregulated timestamps and
        updates the main timestamp attribute. After all done, purges
        unregulated ones.
        In case the audio file was large enough to be splitted, it adds seconds
        to correct timing and in case the timestamp was manually loaded, it
        leaves it alone.

        Note that the difference between self.__timestamps and
        self.__timestamps_unregulated is that in the regulated version,
        right after the word, a list of word blocks must appear. However in the
        unregulated version, after a word, a list of individual splits
        containing word blocks would appear!
        """
        unified_timestamps = _PrettyDefaultDict(list)
        staged_files = self._list_audio_files(sub_dir="staging")
        for timestamp_basename in self.__timestamps_unregulated:
            if len(self.__timestamps_unregulated[timestamp_basename]) > 1:
                # File has been splitted
                timestamp_name = ''.join(timestamp_basename.split('.')[:-1])
                staged_splitted_files_of_timestamp = list(
                    filter(lambda staged_file: (
                        timestamp_name == staged_file[:-3] and
                        all([(x in set(map(str, range(10))))
                             for x in staged_file[-3:]])), staged_files))
                if len(staged_splitted_files_of_timestamp) == 0:
                    self.__errors[(time(), timestamp_basename)] = {
                        "reason": "Missing staged file",
                        "current_staged_files": staged_files}
                    continue
                staged_splitted_files_of_timestamp.sort()
                unified_timestamp = list()
                for staging_digits, splitted_file in enumerate(
                        self.__timestamps_unregulated[timestamp_basename]):
                    prev_splits_sec = 0
                    if int(staging_digits) != 0:
                        prev_splits_sec = self._get_audio_duration_seconds(
                            "{}/staging/{}{:03d}".format(
                                self.src_dir, timestamp_name,
                                staging_digits - 1))
                    for word_block in splitted_file:
                        unified_timestamp.append(
                            _WordBlock(
                                word=word_block.word,
                                start=round(word_block.start +
                                            prev_splits_sec, 2),
                                end=round(word_block.end +
                                          prev_splits_sec, 2)))
                unified_timestamps[
                    str(timestamp_basename)] += unified_timestamp
            else:
                unified_timestamps[
                    timestamp_basename] += self.__timestamps_unregulated[
                        timestamp_basename][0]

        self.__timestamps.update(unified_timestamps)
        self.__timestamps_unregulated = _PrettyDefaultDict(list)


Instruction:
Can you implement a function in Python 3 that
writes the corrected timestamps to a file.

Response:
def save_indexed_audio(self, indexed_audio_file_abs_path):
        """
        Writes the corrected timestamps to a file. Timestamps are a python
        dictionary.

        Parameters
        ----------
        indexed_audio_file_abs_path : str
        """
        with open(indexed_audio_file_abs_path, "wb") as f:
            pickle.dump(self.get_timestamps(), f, pickle.HIGHEST_PROTOCOL)


Instruction:
Can you generate a brief explanation for the following Python 3 code
def load_indexed_audio(self, indexed_audio_file_abs_path):
        with open(indexed_audio_file_abs_path, "rb") as f:
            self.__timestamps = pickle.load(f)

Response: Loads the indexed audio file into the internal dictionary.


Instruction:
Can you generate a brief explanation for the following Python 3 code
def _is_subsequence_of(self, sub, sup):
        return bool(re.search(".*".join(sub), sup))

Response: Returns True if the subsequence of the supsequence of the current set of keys.


Instruction:
Can you generate the documentation for the following Python 3 function
def _partial_search_validator(self, sub, sup, anagram=False,
                                  subsequence=False, supersequence=False):
        """
        It's responsible for validating the partial results of `search` method.
        If it returns True, the search would return its result. Else, search
        method would discard what it found and look for others.

        First, checks to see if all elements of `sub` is in `sup` with at least
        the same frequency and then checks to see if every element `sub`
        appears in `sup` with the same order (index-wise).
        If advanced control sturctures are specified, the containment condition
        won't be checked.
        The code for index checking is from [1]_.

        Parameters
        ----------
        sub : list
        sup : list
        anagram : bool, optional
            Default is `False`
        subsequence : bool, optional
            Default is `False`
        supersequence : bool, optional
            Default is `False`

        Returns
        -------
        bool

        References
        ----------
        .. [1] : `
   https://stackoverflow.com/questions/35964155/checking-if-list-is-a-sublist`
        """
        def get_all_in(one, another):
            for element in one:
                if element in another:
                    yield element

        def containment_check(sub, sup):
            return (set(Counter(sub).keys()).issubset(
                set(Counter(sup).keys())))

        def containment_freq_check(sub, sup):
            return (all([Counter(sub)[element] <= Counter(sup)[element]
                         for element in Counter(sub)]))

        def extra_freq_check(sub, sup, list_of_tups):
            # Would be used for matching anagrams, subsequences etc.
            return (len(list_of_tups) > 0 and
                    all([Counter(sub)[tup[0]] <= Counter(sup)[tup[1]]
                         for tup in list_of_tups]))

        # Regarding containment checking while having extra conditions,
        # there's no good way to map each anagram or subseuqnece etc. that was
        # found to the query word, without making it more complicated than
        # it already is, because a query word can be anagram/subsequence etc.
        # to multiple words of the timestamps yet finding the one with the
        # right index would be the problem.
        # Therefore we just approximate the solution by just counting
        # the elements.
        if len(sub) > len(sup):
            return False

        for pred, func in set([(anagram, self._is_anagram_of),
                               (subsequence, self._is_subsequence_of),
                               (supersequence, self._is_supersequence_of)]):
            if pred:
                pred_seive = [(sub_key, sup_key)
                              for sub_key in set(Counter(sub).keys())
                              for sup_key in set(Counter(sup).keys())
                              if func(sub_key, sup_key)]
                if not extra_freq_check(sub, sup, pred_seive):
                    return False

        if (
                not any([anagram, subsequence, supersequence]) and
                (not containment_check(sub, sup) or
                 not containment_freq_check(sub, sup))
        ):
                return False

        for x1, x2 in zip(get_all_in(sup, sub), get_all_in(sub, sup)):
            if x1 != x2:
                return False

        return True

Response: This method is responsible for validating the partial results of a list of elements.


Instruction:
Write a Python 3 function for
generating a generator that searches for the given query within the source directory.

Response:
def search_gen(self, query, audio_basename=None, case_sensitive=False,
                   subsequence=False, supersequence=False, timing_error=0.0,
                   anagram=False, missing_word_tolerance=0):
        """
        A generator that searches for the `query` within the audiofiles of the
        src_dir.

        Parameters
        ----------
        query : str
            A string that'll be searched. It'll be splitted on spaces and then
            each word gets sequentially searched.
        audio_basename : str, optional
            Search only within the given audio_basename.

            Default is `None`
        case_sensitive : bool, optional
            Default is `False`
        subsequence : bool, optional
            `True` if it's not needed for the exact word be detected and larger
            strings that contain the given one are fine.

            If the query is a sentences with multiple words, it'll be
            considered for each word, not the whole sentence.

            Default is `False`.
        supersequence : bool, optional
            `True` if it's not needed for the exact word be detected and
            smaller strings that are contained within the given one are fine.

            If the query is a sentences with multiple words, it'll be
            considered for each word, not the whole sentence.

            Default is `False`.
        anagram : bool, optional
            `True` if it's acceptable for a complete permutation of the word to
            be found. e.g. "abcde" would be acceptable for "edbac".

            If the query is a sentences with multiple words, it'll be
            considered for each word, not the whole sentence.

            Default is `False`.
        timing_error : None or float, optional
            Sometimes other words (almost always very small) would be detected
            between the words of the `query`. This parameter defines the
            timing difference/tolerance of the search.

            Default is 0.0 i.e. No timing error is tolerated.
        missing_word_tolerance : int, optional
            The number of words that can be missed within the result.
            For example, if the query is "Some random text" and the tolerance
            value is `1`, then "Some text" would be a valid response.
            Note that the first and last words cannot be missed. Also,
            there'll be an error if the value is more than the number of
            available words. For the example above, any value more than 1
            would have given an error (since there's only one word i.e.
            "random" that can be missed)

            Default is 0.

        Yields
        ------
        {"File Name": str, "Query": `query`, "Result": (float, float)}
            The result of the search is returned as a tuple which is the value
            of the "Result" key. The first element of the tuple is the
            starting second of `query` and the last element is the ending
            second of `query`

        Raises
        ------
        AssertionError
            If `missing_word_tolerance` value is more than the total number of
            words in the query minus 2 (since the first and the last word
            cannot be removed)
        """
        def case_sensitivity_handler(case_sensitive=case_sensitive):

            def get_query_words(query, case_sensitive=case_sensitive):
                query_words = list(
                    filter(None, ''.join(
                        filter(lambda char: char in (ascii_letters + " "),
                               list(query))).split(" ")))
                if case_sensitive:
                    return query_words
                return [q.lower() for q in query_words]

            def get_timestamps(case_sensitive=case_sensitive):
                timestamps = self.get_timestamps().copy()
                if not case_sensitive:
                    return {
                        audio_basename: [
                            _WordBlock(word=word_block.word.lower(),
                                       start=word_block.start,
                                       end=word_block.end)
                            for word_block in timestamps[audio_basename]]
                        for audio_basename in timestamps}
                return timestamps

            return locals()

        query_words = case_sensitivity_handler()["get_query_words"](query)
        timestamps = case_sensitivity_handler()["get_timestamps"]()

        assert abs(missing_word_tolerance -
                   (len(query_words) - 2)) >= 0, (
            "The number of words that can be missing must be less than "
            "the total number of words within the query minus the first and "
            "the last word."
        )

        for audio_filename in (
                (lambda: (timestamps.keys() if audio_basename is None else
                          [audio_basename]))()):
            result = list()
            missed_words_so_far = 0
            query_cursor = 0
            try:
                for word_block in timestamps[audio_filename]:
                    if (
                            # When the query is identical
                            (word_block.word == query_words[query_cursor]) or
                            # When the query is a subsequence of what's
                            # available
                            (subsequence and
                             self._is_subsequence_of(query_words[query_cursor],
                                                     word_block.word)) or
                            # When the query is a supersequence of what's
                            # available
                            (supersequence and self._is_supersequence_of(
                                query_words[query_cursor], word_block.word)) or
                            # When query is a permutation of what's available.
                            (anagram and self._is_anagram_of(
                                query_words[query_cursor], word_block.word))
                    ):
                        result.append(word_block)

                        if timing_error is not None:
                            try:
                                if round(result[-1].start -
                                         result[-2].end, 4) > timing_error:
                                    result = list()
                                    query_cursor = 0
                            except IndexError:
                                pass

                        if self._partial_search_validator(
                                query_words, [x.word for x in result],
                                anagram=anagram,
                                subsequence=subsequence,
                                supersequence=supersequence):
                            yield {
                                "File Name": audio_filename,
                                "Query": query,
                                "Result": tuple([result[0].start,
                                                 result[-1].end])}
                            result = list()
                            query_cursor = 0

                        else:
                            query_cursor += 1

                    elif missed_words_so_far > missing_word_tolerance:
                        result = list()
                        query_cursor = 0

                    elif (missing_word_tolerance > 0) and (len(result) > 0):
                        result.append(word_block)
                        missed_words_so_far += 1

            except KeyError:
                # This is needed for the case where no timestamp is present.
                pass

            except IndexError:
                # This is needed when multiple timestamps are present, and
                # advanced control structures like `missed_word_tolerance` are
                # non-zero. In that case, it can search to the end of the first
                # timestamp looking to complete its partial result and since
                # there are no more `word_block`s left, it returns an error.
                # `continue` should be used to reset the partial result and
                # move to the next timestamp.
                continue


Instruction:
How would you explain what the following Python 3 function does
def search_all(self, queries, audio_basename=None, case_sensitive=False,
                   subsequence=False, supersequence=False, timing_error=0.0,
                   anagram=False, missing_word_tolerance=0):
        """
        Returns a dictionary of all results of all of the queries for all of
        the audio files.
        All the specified parameters work per query.

        Parameters
        ----------
        queries : [str] or str
            A list of the strings that'll be searched. If type of queries is
            `str`, it'll be insterted into a list within the body of the
            method.
        audio_basename : str, optional
            Search only within the given audio_basename.

            Default is `None`.
        case_sensitive : bool
            Default is `False`
        subsequence : bool, optional
            `True` if it's not needed for the exact word be detected and larger
            strings that contain the given one are fine.

            If the query is a sentences with multiple words, it'll be
            considered for each word, not the whole sentence.

            Default is `False`.
        supersequence : bool, optional
            `True` if it's not needed for the exact word be detected and
            smaller strings that are contained within the given one are fine.

            If the query is a sentences with multiple words, it'll be
            considered for each word, not the whole sentence.

            Default is `False`.
        anagram : bool, optional
            `True` if it's acceptable for a complete permutation of the word to
            be found. e.g. "abcde" would be acceptable for "edbac".

            If the query is a sentences with multiple words, it'll be
            considered for each word, not the whole sentence.

            Default is `False`.
        timing_error : None or float, optional
            Sometimes other words (almost always very small) would be detected
            between the words of the `query`. This parameter defines the
            timing difference/tolerance of the search.

            Default is 0.0 i.e. No timing error is tolerated.
        missing_word_tolerance : int, optional
            The number of words that can be missed within the result.
            For example, if the query is "Some random text" and the tolerance
            value is `1`, then "Some text" would be a valid response.
            Note that the first and last words cannot be missed. Also,
            there'll be an error if the value is more than the number of
            available words. For the example above, any value more than 1
            would have given an error (since there's only one word i.e.
            "random" that can be missed)

            Default is 0.

        Returns
        -------
        search_results : {str: {str: [(float, float)]}}
            A dictionary whose keys are queries and whose values are
            dictionaries whose keys are all the audiofiles in which the query
            is present and whose values are a list whose elements are 2-tuples
            whose first element is the starting second of the query and whose
            values are the ending second. e.g.
            {"apple": {"fruits.wav" : [(1.1, 1.12)]}}

        Raises
        ------
        TypeError
            if `queries` is neither a list nor a str
        """

        search_gen_rest_of_kwargs = {
            "audio_basename": audio_basename,
            "case_sensitive": case_sensitive,
            "subsequence": subsequence,
            "supersequence": supersequence,
            "timing_error": timing_error,
            "anagram": anagram,
            "missing_word_tolerance": missing_word_tolerance}

        if not isinstance(queries, (list, str)):
            raise TypeError("Invalid query type.")
        if type(queries) is not list:
            queries = [queries]
        search_results = _PrettyDefaultDict(lambda: _PrettyDefaultDict(list))
        for query in queries:
            search_gen = self.search_gen(query=query,
                                         **search_gen_rest_of_kwargs)
            for search_result in search_gen:
                search_results[query][
                    search_result["File Name"]].append(search_result["Result"])
        return search_results

Response: This method searches all of the audio files for all of the specified words.


Instruction:
Can you generate a brief explanation for the following Python 3 code
def search_regexp(self, pattern, audio_basename=None):

        def indexes_in_transcript_to_start_end_second(index_tup,
                                                      audio_basename):
            """
            Calculates the word block index by having the beginning and ending
            index of the matched result from the transcription

            Parameters
            ----------
            index_tup : (int, tup)
                index_tup is of the form tuple(index_start, index_end)
            audio_basename : str

            Retrun
            ------
            [float, float]
                The time of the output of the matched result. Derived from two
                separate word blocks belonging to the beginning and the end of
                the index_start and index_end.
            """
            space_indexes = [i for i, x in enumerate(
                transcription[audio_basename]) if x == " "]
            space_indexes.sort(reverse=True)
            index_start, index_end = index_tup
            # re.finditer returns the ending index by one more
            index_end -= 1
            while transcription[audio_basename][index_start] == " ":
                index_start += 1
            while transcription[audio_basename][index_end] == " ":
                index_end -= 1
            block_number_start = 0
            block_number_end = len(space_indexes)
            for block_cursor, space_index in enumerate(space_indexes):
                if index_start > space_index:
                    block_number_start = (len(space_indexes) - block_cursor)
                    break
            for block_cursor, space_index in enumerate(space_indexes):
                if index_end > space_index:
                    block_number_end = (len(space_indexes) - block_cursor)
                    break
            return (timestamps[audio_basename][block_number_start].start,
                    timestamps[audio_basename][block_number_end].end)

        timestamps = self.get_timestamps()
        if audio_basename is not None:
            timestamps = {audio_basename: timestamps[audio_basename]}
        transcription = {
            audio_basename: ' '.join(
                [word_block.word for word_block in timestamps[audio_basename]]
            ) for audio_basename in timestamps}
        match_map = map(
            lambda audio_basename: tuple((
                audio_basename,
                re.finditer(pattern, transcription[audio_basename]))),
            transcription.keys())
        search_results = _PrettyDefaultDict(lambda: _PrettyDefaultDict(list))
        for audio_basename, match_iter in match_map:
            for match in match_iter:
                search_results[match.group()][audio_basename].append(
                    tuple(indexes_in_transcript_to_start_end_second(
                        match.span(), audio_basename)))
        return search_results

Response: Search the word_blocks of the given pattern and returns a dictionary of the word_blocks that match the given pattern.


Instruction:
Here you have a function in Python 3, explain what it does
def rwh_primes1(n):
    # http://stackoverflow.com/questions/2068372/fastest-way-to-list-all-primes-below-n-in-python/3035188#3035188
    ''' Returns  a list of primes < n '''
    sieve = [True] * (n/2)
    for i in xrange(3,int(n**0.5)+1,2):
        if sieve[i/2]:
            sieve[i*i/2::i] = [False] * ((n-i*i-1)/(2*i)+1)
    return [2] + [2*i+1 for i in xrange(1,n/2) if sieve[i]]

Response: Returns a list of primes < n


Instruction:
Write a Python 3 function that can
compute the list of prime polynomials for the given generator and galois field characteristic exponent.

Response:
def find_prime_polys(generator=2, c_exp=8, fast_primes=False, single=False):
    '''Compute the list of prime polynomials for the given generator and galois field characteristic exponent.'''
    # fast_primes will output less results but will be significantly faster.
    # single will output the first prime polynomial found, so if all you want is to just find one prime polynomial to generate the LUT for Reed-Solomon to work, then just use that.

    # A prime polynomial (necessarily irreducible) is necessary to reduce the multiplications in the Galois Field, so as to avoid overflows.
    # Why do we need a "prime polynomial"? Can't we just reduce modulo 255 (for GF(2^8) for example)? Because we need the values to be unique.
    # For example: if the generator (alpha) = 2 and c_exp = 8 (GF(2^8) == GF(256)), then the generated Galois Field (0, 1, α, α^1, α^2, ..., α^(p-1)) will be galois field it becomes 0, 1, 2, 4, 8, 16, etc. However, upon reaching 128, the next value will be doubled (ie, next power of 2), which will give 256. Then we must reduce, because we have overflowed above the maximum value of 255. But, if we modulo 255, this will generate 256 == 1. Then 2, 4, 8, 16, etc. giving us a repeating pattern of numbers. This is very bad, as it's then not anymore a bijection (ie, a non-zero value doesn't have a unique index). That's why we can't just modulo 255, but we need another number above 255, which is called the prime polynomial.
    # Why so much hassle? Because we are using precomputed look-up tables for multiplication: instead of multiplying a*b, we precompute alpha^a, alpha^b and alpha^(a+b), so that we can just use our lookup table at alpha^(a+b) and get our result. But just like in our original field we had 0,1,2,...,p-1 distinct unique values, in our "LUT" field using alpha we must have unique distinct values (we don't care that they are different from the original field as long as they are unique and distinct). That's why we need to avoid duplicated values, and to avoid duplicated values we need to use a prime irreducible polynomial.

    # Here is implemented a bruteforce approach to find all these prime polynomials, by generating every possible prime polynomials (ie, every integers between field_charac+1 and field_charac*2), and then we build the whole Galois Field, and we reject the candidate prime polynomial if it duplicates even one value or if it generates a value above field_charac (ie, cause an overflow).
    # Note that this algorithm is slow if the field is too big (above 12), because it's an exhaustive search algorithm. There are probabilistic approaches, and almost surely prime approaches, but there is no determistic polynomial time algorithm to find irreducible monic polynomials. More info can be found at: http://people.mpi-inf.mpg.de/~csaha/lectures/lec9.pdf
    # Another faster algorithm may be found at Adleman, Leonard M., and Hendrik W. Lenstra. "Finding irreducible polynomials over finite fields." Proceedings of the eighteenth annual ACM symposium on Theory of computing. ACM, 1986.

    # Prepare the finite field characteristic (2^p - 1), this also represent the maximum possible value in this field
    root_charac = 2 # we're in GF(2)
    field_charac = int(root_charac**c_exp - 1)
    field_charac_next = int(root_charac**(c_exp+1) - 1)

    prim_candidates = []
    if fast_primes:
        prim_candidates = rwh_primes1(field_charac_next) # generate maybe prime polynomials and check later if they really are irreducible
        prim_candidates = [x for x in prim_candidates if x > field_charac] # filter out too small primes
    else:
        prim_candidates = xrange(field_charac+2, field_charac_next, root_charac) # try each possible prime polynomial, but skip even numbers (because divisible by 2 so necessarily not irreducible)

    # Start of the main loop
    correct_primes = []
    for prim in prim_candidates: # try potential candidates primitive irreducible polys
        seen = bytearray(field_charac+1) # memory variable to indicate if a value was already generated in the field (value at index x is set to 1) or not (set to 0 by default)
        conflict = False # flag to know if there was at least one conflict

        # Second loop, build the whole Galois Field
        x = 1
        for i in xrange(field_charac):
            # Compute the next value in the field (ie, the next power of alpha/generator)
            x = gf_mult_noLUT(x, generator, prim, field_charac+1)

            # Rejection criterion: if the value overflowed (above field_charac) or is a duplicate of a previously generated power of alpha, then we reject this polynomial (not prime)
            if x > field_charac or seen[x] == 1:
                conflict = True
                break
            # Else we flag this value as seen (to maybe detect future duplicates), and we continue onto the next power of alpha
            else:
                seen[x] = 1

        # End of the second loop: if there's no conflict (no overflow nor duplicated value), this is a prime polynomial!
        if not conflict:
            correct_primes.append(prim)
            if single: return prim

    # Return the list of all prime polynomials
    return correct_primes


Instruction:
Here you have a function in Python 3, explain what it does
def gf_poly_mul(p, q):
    '''Multiply two polynomials, inside Galois Field (but the procedure is generic). Optimized function by precomputation of log.'''
    # Pre-allocate the result array
    r = bytearray(len(p) + len(q) - 1)
    # Precompute the logarithm of p
    lp = [gf_log[p[i]] for i in xrange(len(p))]
    # Compute the polynomial multiplication (just like the outer product of two vectors, we multiply each coefficients of p with all coefficients of q)
    for j in xrange(len(q)):
        qj = q[j] # optimization: load the coefficient once
        if qj != 0: # log(0) is undefined, we need to check that
            lq = gf_log[qj] # Optimization: precache the logarithm of the current coefficient of q
            for i in xrange(len(p)):
                if p[i] != 0: # log(0) is undefined, need to check that...
                    r[i + j] ^= gf_exp[lp[i] + lq] # equivalent to: r[i + j] = gf_add(r[i+j], gf_mul(p[i], q[j]))
    return r

Response: Multiply two polynomials inside Galois Field. Optimized function by precomputation of log.


Instruction:
Create a Python 3 function to
multiply two polynomials inside Galois Field

Response:
def gf_poly_mul_simple(p, q): # simple equivalent way of multiplying two polynomials without precomputation, but thus it's slower
    '''Multiply two polynomials, inside Galois Field'''
    # Pre-allocate the result array
    r = bytearray(len(p) + len(q) - 1)
    # Compute the polynomial multiplication (just like the outer product of two vectors, we multiply each coefficients of p with all coefficients of q)
    for j in xrange(len(q)):
        for i in xrange(len(p)):
            r[i + j] ^= gf_mul(p[i], q[j]) # equivalent to: r[i + j] = gf_add(r[i+j], gf_mul(p[i], q[j])) -- you can see it's your usual polynomial multiplication
    return r


Instruction:
Implement a function in Python 3 to
fast polynomial division by using Extended Synthetic Division and optimized for GF ( 2^p computations.

Response:
def gf_poly_div(dividend, divisor):
    '''Fast polynomial division by using Extended Synthetic Division and optimized for GF(2^p) computations (doesn't work with standard polynomials outside of this galois field).'''
    # CAUTION: this function expects polynomials to follow the opposite convention at decoding: the terms must go from the biggest to lowest degree (while most other functions here expect a list from lowest to biggest degree). eg: 1 + 2x + 5x^2 = [5, 2, 1], NOT [1, 2, 5]

    msg_out = bytearray(dividend) # Copy the dividend list and pad with 0 where the ecc bytes will be computed
    #normalizer = divisor[0] # precomputing for performance
    for i in xrange(len(dividend) - (len(divisor)-1)):
        #msg_out[i] /= normalizer # for general polynomial division (when polynomials are non-monic), the usual way of using synthetic division is to divide the divisor g(x) with its leading coefficient (call it a). In this implementation, this means:we need to compute: coef = msg_out[i] / gen[0]. For more infos, see http://en.wikipedia.org/wiki/Synthetic_division
        coef = msg_out[i] # precaching
        if coef != 0: # log(0) is undefined, so we need to avoid that case explicitly (and it's also a good optimization). In fact if you remove it, it should still work because gf_mul() will take care of the condition. But it's still a good practice to put the condition here.
            for j in xrange(1, len(divisor)): # in synthetic division, we always skip the first coefficient of the divisior, because it's only used to normalize the dividend coefficient
                if divisor[j] != 0: # log(0) is undefined
                    msg_out[i + j] ^= gf_mul(divisor[j], coef) # equivalent to the more mathematically correct (but xoring directly is faster): msg_out[i + j] += -divisor[j] * coef

    # The resulting msg_out contains both the quotient and the remainder, the remainder being the size of the divisor (the remainder has necessarily the same degree as the divisor -- not length but degree == length-1 -- since it's what we couldn't divide from the dividend), so we compute the index where this separation is, and return the quotient and remainder.
    separator = -(len(divisor)-1)
    return msg_out[:separator], msg_out[separator:]


Instruction:
Here you have a function in Python 3, explain what it does
def gf_poly_square(poly):
    '''Linear time implementation of polynomial squaring. For details, see paper: "A fast software implementation for arithmetic operations in GF (2n)". De Win, E., Bosselaers, A., Vandenberghe, S., De Gersem, P., & Vandewalle, J. (1996, January). In Advances in Cryptology - Asiacrypt'96 (pp. 65-76). Springer Berlin Heidelberg.'''
    length = len(poly)
    out = bytearray(2*length - 1)
    for i in xrange(length-1):
        p = poly[i]
        k = 2*i
        if p != 0:
            #out[k] = gf_exp[(2*gf_log[p]) % field_charac] # not necessary to modulo (2^r)-1 since gf_exp is duplicated up to 510.
            out[k] = gf_exp[2*gf_log[p]]
        #else: # not necessary since the output is already initialized to an array of 0
            #out[k] = 0
    out[2*length-2] = gf_exp[2*gf_log[poly[length-1]]]
    if out[0] == 0: out[0] = 2*poly[1] - 1
    return out

Response: Linear time implementation of polynomial squaring.


Instruction:
Create a Python 3 function to
evaluate a polynomial in GF ( 2^p given the value for x. This is based on Horner s scheme for maximum efficiency.

Response:
def gf_poly_eval(poly, x):
    '''Evaluates a polynomial in GF(2^p) given the value for x. This is based on Horner's scheme for maximum efficiency.'''
    y = poly[0]
    for i in xrange(1, len(poly)):
        y = gf_mul(y, x) ^ poly[i]
    return y


Instruction:
Can you write a function in Python 3 where it
generates an irreducible generator polynomial

Response:
def rs_generator_poly(nsym, fcr=0, generator=2):
    '''Generate an irreducible generator polynomial (necessary to encode a message into Reed-Solomon)'''
    g = bytearray([1])
    for i in xrange(nsym):
        g = gf_poly_mul(g, [1, gf_pow(generator, i+fcr)])
    return g


Instruction:
Can you create a Python 3 function that
generates all irreducible generator polynomials up to max_nsym.

Response:
def rs_generator_poly_all(max_nsym, fcr=0, generator=2):
    '''Generate all irreducible generator polynomials up to max_nsym (usually you can use n, the length of the message+ecc). Very useful to reduce processing time if you want to encode using variable schemes and nsym rates.'''
    g_all = {}
    g_all[0] = g_all[1] = [1]
    for nsym in xrange(max_nsym):
        g_all[nsym] = rs_generator_poly(nsym, fcr, generator)
    return g_all


Instruction:
How would you implement a function in Python 3 that
calculates the syndromes polynomial for a given codeword msg.

Response:
def rs_calc_syndromes(msg, nsym, fcr=0, generator=2):
    '''Given the received codeword msg and the number of error correcting symbols (nsym), computes the syndromes polynomial.
    Mathematically, it's essentially equivalent to a Fourrier Transform (Chien search being the inverse).
    '''
    # Note the "[0] +" : we add a 0 coefficient for the lowest degree (the constant). This effectively shifts the syndrome, and will shift every computations depending on the syndromes (such as the errors locator polynomial, errors evaluator polynomial, etc. but not the errors positions).
    # This is not necessary as anyway syndromes are defined such as there are only non-zero coefficients (the only 0 is the shift of the constant here) and subsequent computations will/must account for the shift by skipping the first iteration (eg, the often seen range(1, n-k+1)), but you can also avoid prepending the 0 coeff and adapt every subsequent computations to start from 0 instead of 1.
    return [0] + [gf_poly_eval(msg, gf_pow(generator, i+fcr)) for i in xrange(nsym)]


Instruction:
Can you generate a brief explanation for the following Python 3 code
def rs_correct_errata(msg_in, synd, err_pos, fcr=0, generator=2): # err_pos is a list of the positions of the errors/erasures/errata
    '''Forney algorithm, computes the values (error magnitude) to correct the input message.'''
    global field_charac
    msg = bytearray(msg_in)
    # calculate errata locator polynomial to correct both errors and erasures (by combining the errors positions given by the error locator polynomial found by BM with the erasures positions given by caller)
    coef_pos = [len(msg) - 1 - p for p in err_pos] # need to convert the positions to coefficients degrees for the errata locator algo to work (eg: instead of [0, 1, 2] it will become [len(msg)-1, len(msg)-2, len(msg) -3])
    err_loc = rs_find_errata_locator(coef_pos, generator)
    # calculate errata evaluator polynomial (often called Omega or Gamma in academic papers)
    err_eval = rs_find_error_evaluator(synd[::-1], err_loc, len(err_loc)-1)[::-1]

    # Second part of Chien search to get the error location polynomial X from the error positions in err_pos (the roots of the error locator polynomial, ie, where it evaluates to 0)
    X = [] # will store the position of the errors
    for i in xrange(len(coef_pos)):
        l = field_charac - coef_pos[i]
        X.append( gf_pow(generator, -l) )

    # Forney algorithm: compute the magnitudes
    E = bytearray(len(msg)) # will store the values that need to be corrected (substracted) to the message containing errors. This is sometimes called the error magnitude polynomial.
    Xlength = len(X)
    for i, Xi in enumerate(X):

        Xi_inv = gf_inverse(Xi)

        # Compute the formal derivative of the error locator polynomial (see Blahut, Algebraic codes for data transmission, pp 196-197).
        # the formal derivative of the errata locator is used as the denominator of the Forney Algorithm, which simply says that the ith error value is given by error_evaluator(gf_inverse(Xi)) / error_locator_derivative(gf_inverse(Xi)). See Blahut, Algebraic codes for data transmission, pp 196-197.
        err_loc_prime_tmp = []
        for j in xrange(Xlength):
            if j != i:
                err_loc_prime_tmp.append( gf_sub(1, gf_mul(Xi_inv, X[j])) )
        # compute the product, which is the denominator of the Forney algorithm (errata locator derivative)
        err_loc_prime = 1
        for coef in err_loc_prime_tmp:
            err_loc_prime = gf_mul(err_loc_prime, coef)
        # equivalent to: err_loc_prime = functools.reduce(gf_mul, err_loc_prime_tmp, 1)

        # Compute y (evaluation of the errata evaluator polynomial)
        # This is a more faithful translation of the theoretical equation contrary to the old forney method. Here it is exactly copy/pasted from the included presentation decoding_rs.pdf: Yl = omega(Xl.inverse()) / prod(1 - Xj*Xl.inverse()) for j in len(X) (in the paper it's for j in s, but it's useless when len(X) < s because we compute neutral terms 1 for nothing, and wrong when correcting more than s erasures or erasures+errors since it prevents computing all required terms).
        # Thus here this method works with erasures too because firstly we fixed the equation to be like the theoretical one (don't know why it was modified in _old_forney(), if it's an optimization, it doesn't enhance anything), and secondly because we removed the product bound on s, which prevented computing errors and erasures above the s=(n-k)//2 bound.
        y = gf_poly_eval(err_eval[::-1], Xi_inv) # numerator of the Forney algorithm (errata evaluator evaluated)
        y = gf_mul(gf_pow(Xi, 1-fcr), y) # adjust to fcr parameter

        # Compute the magnitude
        magnitude = gf_div(y, err_loc_prime) # magnitude value of the error, calculated by the Forney algorithm (an equation in fact): dividing the errata evaluator with the errata locator derivative gives us the errata magnitude (ie, value to repair) the ith symbol
        E[err_pos[i]] = magnitude # store the magnitude for this error into the magnitude polynomial

    # Apply the correction of values to get our message corrected! (note that the ecc bytes also gets corrected!)
    # (this isn't the Forney algorithm, we just apply the result of decoding here)
    msg = gf_poly_add(msg, E) # equivalent to Ci = Ri - Ei where Ci is the correct message, Ri the received (senseword) message, and Ei the errata magnitudes (minus is replaced by XOR since it's equivalent in GF(2^p)). So in fact here we substract from the received message the errors magnitude, which logically corrects the value to what it should be.
    return msg

Response: Forney algorithm computes the values to correct the input message.