# -*- coding: utf-8 -*-
"""
The :mod:`neurotic.datasets.data` module implements a function for loading a
dataset from selected metadata.

.. autofunction:: load_dataset
"""

from packaging import version
import numpy as np
import pandas as pd
import quantities as pq
import neo

from ..datasets.metadata import _abs_path
from ..elephant_tools import _butter, _peak_detection

def load_dataset(metadata, lazy=False, signal_group_mode='split-all', filter_events_from_epochs=False):
    """
    Load a dataset.

    ``metadata`` may be a :class:`MetadataSelector
    <neurotic.datasets.metadata.MetadataSelector>` or a simple dictionary
    containing the appropriate data.

    The ``data_file`` in ``metadata`` is read into a Neo :class:`Block
    <neo.core.Block>` using an automatically detected :mod:`neo.io` class
    if ``lazy=False`` or a :mod:`neo.rawio` class if ``lazy=True``.

    Epochs and events loaded from ``annotations_file`` and
    ``epoch_encoder_file`` and spike trains loaded from ``tridesclous_file``
    are added to the Neo Block.

    If ``lazy=False``, filters given in ``metadata`` are applied to the
    signals and amplitude discriminators are run to detect spikes.
    """

    # read in the electrophysiology data
    blk = _read_data_file(metadata, lazy, signal_group_mode)

    # apply filters to signals if not using lazy loading of signals
    if not lazy:
        blk = _apply_filters(metadata, blk)

    # copy events into epochs and vice versa
    epochs_from_events = [neo.Epoch(name=ev.name, times=ev.times, labels=ev.labels, durations=np.zeros_like(ev.times)) for ev in blk.segments[0].events]
    events_from_epochs = [neo.Event(name=ep.name, times=ep.times, labels=ep.labels) for ep in blk.segments[0].epochs]
    if not filter_events_from_epochs:
        blk.segments[0].epochs += epochs_from_events
    blk.segments[0].events += events_from_epochs

    # read in annotations
    annotations_dataframe = _read_annotations_file(metadata)
    blk.segments[0].epochs += _create_neo_epochs_from_dataframe(annotations_dataframe, metadata, _abs_path(metadata, 'annotations_file'), filter_events_from_epochs)
    blk.segments[0].events += _create_neo_events_from_dataframe(annotations_dataframe, metadata, _abs_path(metadata, 'annotations_file'))

    # read in epoch encoder file
    epoch_encoder_dataframe = _read_epoch_encoder_file(metadata)
    blk.segments[0].epochs += _create_neo_epochs_from_dataframe(epoch_encoder_dataframe, metadata, _abs_path(metadata, 'epoch_encoder_file'), filter_events_from_epochs)
    blk.segments[0].events += _create_neo_events_from_dataframe(epoch_encoder_dataframe, metadata, _abs_path(metadata, 'epoch_encoder_file'))

    # classify spikes by amplitude if not using lazy loading of signals
    if not lazy:
        blk.segments[0].spiketrains += _run_amplitude_discriminators(metadata, blk)

    # read in spikes identified by spike sorting using tridesclous
    t_start = blk.segments[0].analogsignals[0].t_start
    t_stop = blk.segments[0].analogsignals[0].t_stop
    sampling_period = blk.segments[0].analogsignals[0].sampling_period
    spikes_dataframe = _read_spikes_file(metadata, blk)
    blk.segments[0].spiketrains += _create_neo_spike_trains_from_dataframe(spikes_dataframe, metadata, t_start, t_stop, sampling_period)

    # alphabetize epoch and event channels by name
    blk.segments[0].epochs.sort(key=lambda ep: ep.name)
    blk.segments[0].events.sort(key=lambda ev: ev.name)

    return blk

def _read_data_file(metadata, lazy=False, signal_group_mode='split-all'):
    """
    Read in the ``data_file`` given in ``metadata`` using an automatically
    detected :mod:`neo.io` class if ``lazy=False`` or a :mod:`neo.rawio` class
    if ``lazy=True``. If ``lazy=True``, manually load epochs, events, and spike
    trains, but not signals. Return a Neo :class:`Block <neo.core.Block>`.
    """

    # read in the electrophysiology data
    # - signal_group_mode='split-all' ensures every channel gets its own
    #   AnalogSignal, which is important for indexing in EphyviewerConfigurator
    io = neo.io.get_io(_abs_path(metadata, 'data_file'))
    blk = io.read_block(lazy=lazy, signal_group_mode=signal_group_mode)

    # load all objects except analog signals
    if lazy:

        if version.parse(neo.__version__) >= version.parse('0.8.0'):  # Neo >= 0.8.0 has proxy objects with load method

            for i in range(len(blk.segments[0].epochs)):
                epoch = blk.segments[0].epochs[i]
                if hasattr(epoch, 'load'):
                    blk.segments[0].epochs[i] = epoch.load()

            for i in range(len(blk.segments[0].events)):
                event = blk.segments[0].events[i]
                if hasattr(event, 'load'):
                    blk.segments[0].events[i] = event.load()

            for i in range(len(blk.segments[0].spiketrains)):
                spiketrain = blk.segments[0].spiketrains[i]
                if hasattr(spiketrain, 'load'):
                    blk.segments[0].spiketrains[i] = spiketrain.load()

        else:  # Neo < 0.8.0 does not have proxy objects

            neorawioclass = neo.rawio.get_rawio_class(_abs_path(metadata, 'data_file'))
            if neorawioclass is not None:
                neorawio = neorawioclass(_abs_path(metadata, 'data_file'))
                neorawio.parse_header()

                for i in range(len(blk.segments[0].epochs)):
                    epoch = blk.segments[0].epochs[i]
                    channel_index = next((i for i, chan in enumerate(neorawio.header['event_channels']) if chan['name'] == epoch.name and chan['type'] == b'epoch'), None)
                    if channel_index is not None:
                        ep_raw_times, ep_raw_durations, ep_labels = neorawio.get_event_timestamps(event_channel_index=channel_index)
                        ep_times = neorawio.rescale_event_timestamp(ep_raw_times, dtype='float64')
                        ep_durations = neorawio.rescale_epoch_duration(ep_raw_durations, dtype='float64')
                        ep = neo.Epoch(times=ep_times*pq.s, durations=ep_durations*pq.s, labels=ep_labels, name=epoch.name)
                        blk.segments[0].epochs[i] = ep

                for i in range(len(blk.segments[0].events)):
                    event = blk.segments[0].events[i]
                    channel_index = next((i for i, chan in enumerate(neorawio.header['event_channels']) if chan['name'] == event.name and chan['type'] == b'event'), None)
                    if channel_index is not None:
                        ev_raw_times, _, ev_labels = neorawio.get_event_timestamps(event_channel_index=channel_index)
                        ev_times = neorawio.rescale_event_timestamp(ev_raw_times, dtype='float64')
                        ev = neo.Event(times=ev_times*pq.s, labels=ev_labels, name=event.name)
                        blk.segments[0].events[i] = ev

                for i in range(len(blk.segments[0].spiketrains)):
                    spiketrain = blk.segments[0].spiketrains[i]
                    channel_index = next((i for i, chan in enumerate(neorawio.header['unit_channels']) if chan['name'] == spiketrain.name), None)
                    if channel_index is not None:
                        st_raw_times = neorawio.get_spike_timestamps(unit_index=channel_index)
                        st_times = neorawio.rescale_spike_timestamp(st_raw_times, dtype='float64')
                        st = neo.SpikeTrain(times=st_times*pq.s, name=st.name)
                        blk.segments[0].spiketrains[i] = st

    # convert byte labels to Unicode strings
    for epoch in blk.segments[0].epochs:
        epoch.labels = epoch.labels.astype('U')

    for event in blk.segments[0].events:
        event.labels = event.labels.astype('U')

    return blk

def _read_annotations_file(metadata):
    """
    Read in epochs and events from the ``annotations_file`` in ``metadata`` and
    return a dataframe.
    """

    if metadata['annotations_file'] is None:

        return None

    else:

        # data types for each column in the file
        dtypes = {
            'Start (s)': float,
            'End (s)':   float,
            'Type':      str,
            'Label':     str,
        }

        # parse the file and create a dataframe
        df = pd.read_csv(_abs_path(metadata, 'annotations_file'), dtype = dtypes)

        # increment row labels by 2 so they match the source file
        # which is 1-indexed and has a header
        df.index += 2

        # discard entries with missing or negative start times
        bad_start = df['Start (s)'].isnull() | (df['Start (s)'] < 0)
        if bad_start.any():
            print('NOTE: These rows will be discarded because their Start times are missing or negative:')
            print(df[bad_start])
            df = df[~bad_start]

        # discard entries with end time preceding start time
        bad_end = df['End (s)'] < df['Start (s)']
        if bad_end.any():
            print('NOTE: These rows will be discarded because their End times precede their Start times:')
            print(df[bad_end])
            df = df[~bad_end]

        # compute durations
        df.insert(
            column = 'Duration (s)',
            value = df['End (s)'] - df['Start (s)'],
            loc = 2, # insert after 'End (s)'
        )

        # replace some NaNs
        df.fillna({
            'Duration (s)': 0,
            'Type': 'Other',
            'Label': '',
        }, inplace = True)

        # sort entries by time
        df.sort_values([
            'Start (s)',
            'Duration (s)',
        ], inplace = True)

        # return the dataframe
        return df

def _read_epoch_encoder_file(metadata):
    """
    Read in epochs from the ``epoch_encoder_file`` in ``metadata`` and return a
    dataframe.
    """

    if metadata['epoch_encoder_file'] is None:

        return None

    else:

        # data types for each column in the file
        dtypes = {
            'Start (s)': float,
            'End (s)':   float,
            'Type':      str,
        }

        # parse the file and create a dataframe
        df = pd.read_csv(_abs_path(metadata, 'epoch_encoder_file'), dtype = dtypes)

        # increment row labels by 2 so they match the source file
        # which is 1-indexed and has a header
        df.index += 2

        # discard entries with missing or negative start times
        bad_start = df['Start (s)'].isnull() | (df['Start (s)'] < 0)
        if bad_start.any():
            print('NOTE: These rows will be discarded because their Start times are missing or negative:')
            print(df[bad_start])
            df = df[~bad_start]

        # discard entries with end time preceding start time
        bad_end = df['End (s)'] < df['Start (s)']
        if bad_end.any():
            print('NOTE: These rows will be discarded because their End times precede their Start times:')
            print(df[bad_end])
            df = df[~bad_end]

        # compute durations
        df.insert(
            column = 'Duration (s)',
            value = df['End (s)'] - df['Start (s)'],
            loc = 2, # insert after 'End (s)'
        )

        # replace some NaNs
        df.fillna({
            'Duration (s)': 0,
            'Type': 'Other',
        }, inplace = True)

        # sort entries by time
        df.sort_values([
            'Start (s)',
            'Duration (s)',
        ], inplace = True)

        # add 'Label' column to indicate where these epochs came from
        df.insert(
            column = 'Label',
            value = '(from epoch encoder file)',
            loc = 4, # insert after 'Type'
        )

        # return the dataframe
        return df

def _read_spikes_file(metadata, blk):
    """
    Read in spikes identified by spike sorting with tridesclous and return a
    dataframe.
    """

    if metadata['tridesclous_file'] is None or metadata['tridesclous_channels'] is None:

        return None

    else:

        # parse the file and create a dataframe
        df = pd.read_csv(_abs_path(metadata, 'tridesclous_file'), names = ['index', 'label'])

        # drop clusters with negative labels
        df = df[df['label'] >= 0]

        if metadata['tridesclous_merge']:
            # merge some clusters and drop all others
            new_labels = []
            for clusters_to_merge in metadata['tridesclous_merge']:
                new_label = clusters_to_merge[0]
                new_labels.append(new_label)
                df.loc[df['label'].isin(clusters_to_merge), 'label'] = new_label
            df = df[df['label'].isin(new_labels)]

        # return the dataframe
        return df

def _create_neo_epochs_from_dataframe(dataframe, metadata, file_origin, filter_events_from_epochs=False):
    """
    Convert the contents of a dataframe into Neo :class:`Epochs
    <neo.core.Epoch>`.
    """

    epochs_list = []

    if dataframe is not None:

        if filter_events_from_epochs:
            # keep only rows with a positive duration
            dataframe = dataframe[dataframe['Duration (s)'] > 0]

        # group epochs by type
        for type_name, df in dataframe.groupby('Type'):

            # create a Neo Epoch for each type
            epoch = neo.Epoch(
                name = type_name,
                file_origin = file_origin,
                times = df['Start (s)'].values * pq.s,
                durations = df['Duration (s)'].values * pq.s,
                labels = df['Label'].values,
            )

            epochs_list.append(epoch)

    # return the list of Neo Epochs
    return epochs_list

def _create_neo_events_from_dataframe(dataframe, metadata, file_origin):
    """
    Convert the contents of a dataframe into Neo :class:`Events
    <neo.core.Event>`.
    """

    events_list = []

    if dataframe is not None:

        # group events by type
        for type_name, df in dataframe.groupby('Type'):

            # create a Neo Event for each type
            event = neo.Event(
                name = type_name,
                file_origin = file_origin,
                times = df['Start (s)'].values * pq.s,
                labels = df['Label'].values,
            )

            events_list.append(event)

    # return the list of Neo Events
    return events_list

def _create_neo_spike_trains_from_dataframe(dataframe, metadata, t_start, t_stop, sampling_period):
    """
    Convert the contents of a dataframe into Neo :class:`SpikeTrains
    <neo.core.SpikeTrain>`.
    """

    spiketrain_list = []

    if dataframe is not None:

        # group spikes by cluster label
        for spike_label, df in dataframe.groupby('label'):

            # look up the channels that this unit was found on
            channels = metadata['tridesclous_channels'][spike_label]

            # create a Neo SpikeTrain for each cluster label
            st = neo.SpikeTrain(
                name = str(spike_label),
                file_origin = _abs_path(metadata, 'tridesclous_file'),
                channels = channels, # custom annotation
                amplitude = None,    # custom annotation
                times = t_start + sampling_period * df['index'].values,
                t_start = t_start,
                t_stop = t_stop,
            )

            spiketrain_list.append(st)

    return spiketrain_list

def _apply_filters(metadata, blk):
    """
    Apply filters specified in ``metadata`` to the signals in ``blk``.
    """

    if metadata['filters'] is not None:

        signalNameToIndex = {sig.name:i for i, sig in enumerate(blk.segments[0].analogsignals)}

        for sig_filter in metadata['filters']:

            index = signalNameToIndex.get(sig_filter['channel'], None)
            if index is None:

                print('Warning: skipping filter with channel name {} because channel was not found!'.format(sig_filter['channel']))

            else:

                high = sig_filter.get('highpass', None)
                low  = sig_filter.get('lowpass',  None)
                if high:
                    high *= pq.Hz
                if low:
                    low  *= pq.Hz
                blk.segments[0].analogsignals[index] = _butter(
                    signal = blk.segments[0].analogsignals[index],
                    highpass_freq = high,
                    lowpass_freq  = low,
                )

    return blk

def _run_amplitude_discriminators(metadata, blk):
    """
    Run all amplitude discriminators for spike detection given in ``metadata``
    on the signals in ``blk``.
    """

    spiketrain_list = []

    if metadata['amplitude_discriminators'] is not None:

        signalNameToIndex = {sig.name:i for i, sig in enumerate(blk.segments[0].analogsignals)}
        epochs = blk.segments[0].epochs

        # classify spikes by amplitude
        for discriminator in metadata['amplitude_discriminators']:

            index = signalNameToIndex.get(discriminator['channel'], None)
            if index is None:

                print('Warning: skipping amplitude discriminator with channel name {} because channel was not found!'.format(discriminator['channel']))

            else:

                sig = blk.segments[0].analogsignals[index]
                st = _detect_spikes(sig, discriminator, epochs)
                spiketrain_list.append(st)

    return spiketrain_list


def _detect_spikes(sig, discriminator, epochs):
    """
    Detect spikes in the amplitude window given by ``discriminator`` and
    optionally filter them by coincidence with epochs of a given name.
    """

    assert sig.name == discriminator['channel'], 'sig name "{}" does not match amplitude discriminator channel "{}"'.format(sig.name, discriminator['channel'])

    min_threshold = min(discriminator['amplitude'])
    max_threshold = max(discriminator['amplitude'])
    if min_threshold >= 0 and max_threshold > 0:
        sign = 'above'
    elif min_threshold < 0 and max_threshold <= 0:
        sign = 'below'
    else:
        raise ValueError('amplitude discriminator must have two nonnegative thresholds or two nonpositive thresholds: {}'.format(discriminator))

    spikes_crossing_min = _peak_detection(sig, pq.Quantity(min_threshold, discriminator['units']), sign, 'raw')
    spikes_crossing_max = _peak_detection(sig, pq.Quantity(max_threshold, discriminator['units']), sign, 'raw')
    if sign == 'above':
        spikes_between_min_and_max = np.setdiff1d(spikes_crossing_min, spikes_crossing_max)
    elif sign == 'below':
        spikes_between_min_and_max = np.setdiff1d(spikes_crossing_max, spikes_crossing_min)
    else:
        raise ValueError('sign should be "above" or "below": {}'.format(sign))

    st = neo.SpikeTrain(
        name = discriminator['name'],
        channels = [discriminator['channel']],  # custom annotation
        amplitude = discriminator['amplitude'], # custom annotation
        times = spikes_between_min_and_max * pq.s,
        t_start = sig.t_start,
        t_stop  = sig.t_stop,
    )

    if 'epoch' in discriminator:

        time_masks = []
        if isinstance(discriminator['epoch'], str):
            # search for matching epochs
            ep = next((ep for ep in epochs if ep.name == discriminator['epoch']), None)
            if ep is not None:
                # select spike times that fall within each epoch
                for t_start, duration in zip(ep.times, ep.durations):
                    t_stop = t_start + duration
                    time_masks.append((t_start <= st) & (st < t_stop))
            else:
                # no matching epochs found
                time_masks.append([False] * len(st))
        else:
            # may eventually implement lists of ordered pairs, but
            # for now raise an error
            raise ValueError('amplitude discriminator epoch could not be handled: {}'.format(discriminator['epoch']))

        # select the subset of spikes that fall within the epoch
        # windows
        st = st[np.any(time_masks, axis=0)]

    return st
