#!/usr/bin/python
# -*- coding: utf-8 -*-

# ---------------------------------------------------------------------
# Copyright (c) 2012 Michael Hull.
# All rights reserved.
#
# Redistribution and use in source and binary forms, with or without
# modification, are permitted provided that the following conditions
# are met:
#
#  - Redistributions of source code must retain the above copyright
#    notice, this list of conditions and the following disclaimer.
#  - Redistributions in binary form must reproduce the above copyright
#    notice, this list of conditions and the following disclaimer in
#    the documentation and/or other materials provided with the
#    distribution.
#
# THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS
# "AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT
# LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR
# A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT
# HOLDER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL,
# SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT
# LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE,
# DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY
# THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT
# (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE
#  OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
# ----------------------------------------------------------------------

import json

import numpy as np


[docs]class NeuroCSVWriter(object):

    @classmethod
[docs]    def write_to_file(cls, fileName, **kwargs):
        with open(fileName, 'w') as f:
            return NeuroCSVWriter.write_to_buffer(fileObj=f, **kwargs)

    @classmethod
[docs]    def write_to_buffer(cls, fileObj, traces=None, event_sets=None, time_indices=None, csv_metadata=None ):
        traces = traces or []
        event_sets = event_sets or []

        # Column Headers:
        col_headers = [cls.generate_column_header(trace, i) for (i, trace) in enumerate(traces)]

        # Event Data:
        evt_headers = [cls.generate_eventset_header(evset) for evset in event_sets]

        # Build the Header:
        header_meta = ('' if csv_metadata is None else '!%s' % json.dumps(csv_metadata))
        header_cols = '\n'.join(col_headers)
        header_events = '\n'.join(evt_headers)
        header = '\n'.join([header_meta, header_cols, header_events])





        # Build the Column Data:
        #time_indices = traces[0]._time

        col_width = 10
        def missing_format(): 
            return "-".center(col_width)
        def data_format(d):    
            return ("%f" % d).ljust(col_width)

        col_data = []
        for trace in traces:

            data = [missing_format()] * len(time_indices)

            tr_valid_times_bool = trace.time_within_trace(time_indices)
            valid_time_indices = np.where(tr_valid_times_bool)[0]
            valid_time_data_vals = trace.get_values(time_indices[valid_time_indices])

            for tIndex, data_val in zip(valid_time_indices, valid_time_data_vals):
                data[tIndex] = data_format(float(data_val.magnitude))
            col_data.append(data)

        # Write the header
        fileObj.write(header)
        fileObj.write('\n')

        # Write the data:
        for (i, time) in enumerate(time_indices):
            tstr = data_format(time)
            cstrings = [cdata[i] for cdata in col_data]
            l = '\t'.join([tstr] + cstrings)
            fileObj.write(l + '\n')

        # print header

    @classmethod
[docs]    def generate_column_header(cls, tr, index):
        data_dct = {'label': tr.name, 
                    'unit': str(tr.data_unit),
                    'tags': ','.join(tr.tags)}
        return ('#! COLUMN%d: ' % index) + json.dumps(data_dct)

    @classmethod
[docs]    def generate_eventset_header(cls, eventset):
        data_dct = {'label': eventset.name, 
             'tags': ','.join(eventset.tags)}
        return ('#@ EVENT %s ' % json.dumps(data_dct)) + ' '.join(['%2.2f' % event.get_time() for event in eventset])