"""Obsidian Import.

Sub-module for importing time-based data into Obsidian.
"""

import datetime
from logging import getLogger
from pathlib import Path
from typing import Any
from collections.abc import Iterator

from personal_data.csv_import import start_end, determine_possible_keys, load_csv_file
from personal_data.activity import ActivitySample, Label, RealizedActivitySample, heuristically_realize_samples

from .obsidian import Event, ObsidianVault

logger = getLogger(__name__)

Row = dict[str, Any]
Rows = list[Row]

def iterate_samples_from_rows(rows: Rows) -> Iterator[ActivitySample]:
    assert len(rows) > 0

    if True:
        event_data = rows[len(rows) // 2]  # Hopefully select a useful representative.
        possible_keys = determine_possible_keys(event_data)
        logger.info('Found possible keys: %s', possible_keys)
        del event_data

    assert len(possible_keys.time_start) + len(possible_keys.time_end) >= 1
    assert len(possible_keys.image) >= 0

    for event_data in rows:
        (start_at, end_at) = start_end(event_data, possible_keys)
        labels = [Label(k, event_data[k]) for k in possible_keys.misc]

        # Create event
        yield ActivitySample(
            labels=tuple(labels),
            start_at=start_at,
            end_at=end_at,
        )

        del event_data



def import_workout_csv(vault: ObsidianVault, rows: Rows) -> int:
    num_updated = 0
    for row in rows:
        date = row['Date']
        was_updated = False
        mapping = {
            'Cycling (mins)': ('Cycling (Duration)', 'minutes'),
            'Cycling (kcals)': ('Cycling (kcals)', ''),
            'Weight (Kg)': ('Weight (Kg)', ''),
        }

        for input_key, (output_key, unit) in mapping.items():
            v = row.get(input_key)
            if v is not None:
                if unit:
                    v = str(v) + ' ' + unit
            was_updated |= vault.add_statistic(date, output_key, v)
            if input_key != output_key:
                was_updated |= vault.add_statistic(date, input_key, None)
            del input_key, output_key, unit, v

        if was_updated:
            num_updated += 1
        del row, date
    return num_updated


def import_step_counts_csv(vault: ObsidianVault, rows: Rows) -> int:
    MINIMUM = 300

    num_updated = 0

    rows_per_date = {}
    for row in rows:
        date = row['Start'].date()
        rows_per_date.setdefault(date, [])
        rows_per_date[date].append(row)
        del date, row

    steps_per_date = {
        date: sum(row['Steps'] for row in rows) for date, rows in rows_per_date.items()
    }

    for date, steps in steps_per_date.items():
        if steps < MINIMUM:
            continue
        was_updated = vault.add_statistic(date, 'Steps', steps)
        if was_updated:
            num_updated += 1
        del date, steps, was_updated

    return num_updated


def import_watched_series_csv(vault: ObsidianVault, rows: Rows) -> int:
    verb = 'Watched'

    samples = heuristically_realize_samples(list(iterate_samples_from_rows(rows)))

    samples_per_date: dict[datetime.date, list[RealizedActivitySample]] = {}
    for sample in samples:
        date: datetime.date = sample.start_at.date()
        samples_per_date.setdefault(date, [])
        samples_per_date[date].append(sample)
        del date, sample
    del rows

    def map_to_event(sample: RealizedActivitySample) -> Event:
        comment = '{} Episode {}: *{}*'.format(
            sample.single_label_with_category('season.name'),
            sample.single_label_with_category('episode.index'),
            sample.single_label_with_category('episode.name'),
        )
        expected_tz = datetime.timezone(datetime.timedelta(hours=2)) # TODO: Determine this in a more intelligent manner
        return Event(sample.start_at.astimezone(expected_tz).replace(second=0,microsecond=0).time(),
                     sample.end_at.astimezone(expected_tz).replace(second=0,microsecond=0).time(),
                     verb,
                     sample.single_label_with_category('series.name'),
                     comment,
        )

    num_updated = 0

    for date, samples in samples_per_date.items():
        events = [map_to_event(sample) for sample in samples]
        was_updated = vault.add_events(date, events)

        if was_updated:
            num_updated += 1
        del date, was_updated

    return num_updated


def import_data(obsidian_path: Path, dry_run=True):
    vault = ObsidianVault(obsidian_path, read_only=dry_run and 'silent' or None)

    if False:
        data_path = Path('/home/jmaa/Notes/workout.csv')
        rows = load_csv_file(data_path)
        logger.info('Loaded CSV with %d lines', len(rows))
        num_updated = import_workout_csv(vault, rows)
        logger.info('Updated %d files', num_updated)

    if False:
        data_path = Path(
            '/home/jmaa/personal-archive/misc-data/step_counts_2023-07-26_to_2024-09-21.csv',
        )
        rows = load_csv_file(data_path)
        logger.info('Loaded CSV with %d lines', len(rows))
        num_updated = import_step_counts_csv(vault, rows)
        logger.info('Updated %d files', num_updated)

    if True:
        data_path = Path('output/show_episodes_watched.csv')
        rows = load_csv_file(data_path)
        logger.info('Loaded CSV with %d lines', len(rows))
        num_updated = import_watched_series_csv(vault, rows)
        logger.info('Updated %d files', num_updated)