# put all result preprocessing here
# selection, aggregation

from io import BytesIO
import logging
from typing import Any

from .stage import Stage, StepOrder
from .test_run_class import TestRun
from .statistic import calc_norm_stat_props, calc_histo_stat_props
from .result_classes import StatProps, DataSource, TimeSeries
from .hlstorage import ResultStorage
from .suits.io.fio_hist import get_lat_vals, expected_lat_bins
from .suits.io.fio import FioTest
from .utils import StopTestError

import matplotlib
matplotlib.use('svg')
import matplotlib.pyplot as plt


logger = logging.getLogger("wally")


class CalcStatisticStage(Stage):
    priority = StepOrder.TEST + 1

    def run(self, ctx: TestRun) -> None:
        rstorage = ResultStorage(ctx.storage)

        for suite in rstorage.iter_suite(FioTest.name):
            for job in rstorage.iter_job(suite):
                results = {}
                for ts in rstorage.iter_ts(suite, job):
                    if ts.source.sensor == 'lat':
                        if ts.second_axis_size != expected_lat_bins:
                            logger.error("Sensor %s.%s on node %s has" +
                                         "second_axis_size=%s. Can only process sensors with second_axis_size=%s.",
                                         ts.source.dev, ts.source.sensor, ts.source.node_id,
                                         ts.second_axis_size, expected_lat_bins)
                            continue
                        ts.bins_edges = get_lat_vals(ts.second_axis_size)
                        stat_prop = calc_histo_stat_props(ts)  # type: StatProps

                    elif ts.second_axis_size != 1:
                        logger.warning("Sensor %s.%s on node %s provide 2D data with " +
                                       "ts.second_axis_size=%s. Can't process it.",
                                       ts.source.dev, ts.source.sensor, ts.source.node_id, ts.second_axis_size)
                        continue
                    else:
                        stat_prop = calc_norm_stat_props(ts)

        raise StopTestError()