use crate::numops::NumOps;
use crate::streams::{Collectable, Collector, ToJsonBytes, ToJsonResult};
use crate::{
    ts_offs_from_abs, Appendable, FilterFittingInside, Fits, FitsInside, IsoDateTime, RangeOverlapInfo, ReadPbv,
    ReadableFromFile, Sitemty, SitemtyFrameType, SubFrId, TimeBinnableType, TimeBinnableTypeAggregator, TimeBins,
    WithLen,
};
use chrono::{TimeZone, Utc};
use err::Error;
use netpod::log::*;
use netpod::timeunits::SEC;
use netpod::NanoRange;
use num_traits::Zero;
use serde::{Deserialize, Serialize};
use std::fmt;
use std::marker::PhantomData;
use tokio::fs::File;

#[derive(Clone, Serialize, Deserialize)]
pub struct MinMaxAvgDim0Bins<NTY> {
    pub ts1s: Vec<u64>,
    pub ts2s: Vec<u64>,
    pub counts: Vec<u64>,
    // TODO get rid of Option:
    pub mins: Vec<Option<NTY>>,
    pub maxs: Vec<Option<NTY>>,
    pub avgs: Vec<Option<f32>>,
}

impl<NTY> SitemtyFrameType for MinMaxAvgDim0Bins<NTY>
where
    NTY: SubFrId,
{
    const FRAME_TYPE_ID: u32 = crate::MIN_MAX_AVG_BINS + NTY::SUB;
}

impl<NTY> fmt::Debug for MinMaxAvgDim0Bins<NTY>
where
    NTY: fmt::Debug,
{
    fn fmt(&self, fmt: &mut fmt::Formatter) -> fmt::Result {
        write!(
            fmt,
            "MinMaxAvgBins  count {}  ts1s {:?}  ts2s {:?}  counts {:?}  mins {:?}  maxs {:?}  avgs {:?}",
            self.ts1s.len(),
            self.ts1s.iter().map(|k| k / SEC).collect::<Vec<_>>(),
            self.ts2s.iter().map(|k| k / SEC).collect::<Vec<_>>(),
            self.counts,
            self.mins,
            self.maxs,
            self.avgs,
        )
    }
}

impl<NTY> MinMaxAvgDim0Bins<NTY> {
    pub fn empty() -> Self {
        Self {
            ts1s: vec![],
            ts2s: vec![],
            counts: vec![],
            mins: vec![],
            maxs: vec![],
            avgs: vec![],
        }
    }
}

impl<NTY> FitsInside for MinMaxAvgDim0Bins<NTY> {
    fn fits_inside(&self, range: NanoRange) -> Fits {
        if self.ts1s.is_empty() {
            Fits::Empty
        } else {
            let t1 = *self.ts1s.first().unwrap();
            let t2 = *self.ts2s.last().unwrap();
            if t2 <= range.beg {
                Fits::Lower
            } else if t1 >= range.end {
                Fits::Greater
            } else if t1 < range.beg && t2 > range.end {
                Fits::PartlyLowerAndGreater
            } else if t1 < range.beg {
                Fits::PartlyLower
            } else if t2 > range.end {
                Fits::PartlyGreater
            } else {
                Fits::Inside
            }
        }
    }
}

impl<NTY> FilterFittingInside for MinMaxAvgDim0Bins<NTY> {
    fn filter_fitting_inside(self, fit_range: NanoRange) -> Option<Self> {
        match self.fits_inside(fit_range) {
            Fits::Inside | Fits::PartlyGreater | Fits::PartlyLower | Fits::PartlyLowerAndGreater => Some(self),
            _ => None,
        }
    }
}

impl<NTY> RangeOverlapInfo for MinMaxAvgDim0Bins<NTY> {
    fn ends_before(&self, range: NanoRange) -> bool {
        match self.ts2s.last() {
            Some(&ts) => ts <= range.beg,
            None => true,
        }
    }

    fn ends_after(&self, range: NanoRange) -> bool {
        match self.ts2s.last() {
            Some(&ts) => ts > range.end,
            None => panic!(),
        }
    }

    fn starts_after(&self, range: NanoRange) -> bool {
        match self.ts1s.first() {
            Some(&ts) => ts >= range.end,
            None => panic!(),
        }
    }
}

impl<NTY> TimeBins for MinMaxAvgDim0Bins<NTY>
where
    NTY: NumOps,
{
    fn ts1s(&self) -> &Vec<u64> {
        &self.ts1s
    }

    fn ts2s(&self) -> &Vec<u64> {
        &self.ts2s
    }
}

impl<NTY> WithLen for MinMaxAvgDim0Bins<NTY> {
    fn len(&self) -> usize {
        self.ts1s.len()
    }
}

impl<NTY> Appendable for MinMaxAvgDim0Bins<NTY>
where
    NTY: NumOps,
{
    fn empty_like_self(&self) -> Self {
        Self::empty()
    }

    fn append(&mut self, src: &Self) {
        self.ts1s.extend_from_slice(&src.ts1s);
        self.ts2s.extend_from_slice(&src.ts2s);
        self.counts.extend_from_slice(&src.counts);
        self.mins.extend_from_slice(&src.mins);
        self.maxs.extend_from_slice(&src.maxs);
        self.avgs.extend_from_slice(&src.avgs);
    }
}

impl<NTY> ReadableFromFile for MinMaxAvgDim0Bins<NTY>
where
    NTY: NumOps,
{
    // TODO this function is not needed in the trait:
    fn read_from_file(file: File) -> Result<ReadPbv<Self>, Error> {
        Ok(ReadPbv::new(file))
    }

    fn from_buf(buf: &[u8]) -> Result<Self, Error> {
        let dec = serde_cbor::from_slice(&buf)?;
        Ok(dec)
    }
}

impl<NTY> TimeBinnableType for MinMaxAvgDim0Bins<NTY>
where
    NTY: NumOps,
{
    type Output = MinMaxAvgDim0Bins<NTY>;
    type Aggregator = MinMaxAvgBinsAggregator<NTY>;

    fn aggregator(range: NanoRange, x_bin_count: usize, do_time_weight: bool) -> Self::Aggregator {
        debug!(
            "TimeBinnableType for XBinnedScalarEvents  aggregator()  range {:?}  x_bin_count {}  do_time_weight {}",
            range, x_bin_count, do_time_weight
        );
        Self::Aggregator::new(range, do_time_weight)
    }
}

impl<NTY> ToJsonResult for Sitemty<MinMaxAvgDim0Bins<NTY>>
where
    NTY: NumOps,
{
    fn to_json_result(&self) -> Result<Box<dyn ToJsonBytes>, Error> {
        Ok(Box::new(serde_json::Value::String(format!(
            "MinMaxAvgBins/non-json-item"
        ))))
    }
}

pub struct MinMaxAvgBinsCollected<NTY> {
    _m1: PhantomData<NTY>,
}

impl<NTY> MinMaxAvgBinsCollected<NTY> {
    pub fn new() -> Self {
        Self { _m1: PhantomData }
    }
}

#[derive(Serialize)]
pub struct MinMaxAvgBinsCollectedResult<NTY> {
    #[serde(rename = "tsAnchor")]
    ts_anchor_sec: u64,
    #[serde(rename = "tsMs")]
    ts_off_ms: Vec<u64>,
    #[serde(rename = "tsNs")]
    ts_off_ns: Vec<u64>,
    //ts_bin_edges: Vec<IsoDateTime>,
    counts: Vec<u64>,
    mins: Vec<Option<NTY>>,
    maxs: Vec<Option<NTY>>,
    avgs: Vec<Option<f32>>,
    #[serde(skip_serializing_if = "crate::bool_is_false", rename = "finalisedRange")]
    finalised_range: bool,
    #[serde(skip_serializing_if = "Zero::is_zero", rename = "missingBins")]
    missing_bins: u32,
    #[serde(skip_serializing_if = "Option::is_none", rename = "continueAt")]
    continue_at: Option<IsoDateTime>,
}

pub struct MinMaxAvgBinsCollector<NTY> {
    bin_count_exp: u32,
    timed_out: bool,
    range_complete: bool,
    vals: MinMaxAvgDim0Bins<NTY>,
    _m1: PhantomData<NTY>,
}

impl<NTY> MinMaxAvgBinsCollector<NTY> {
    pub fn new(bin_count_exp: u32) -> Self {
        Self {
            bin_count_exp,
            timed_out: false,
            range_complete: false,
            vals: MinMaxAvgDim0Bins::<NTY>::empty(),
            _m1: PhantomData,
        }
    }
}

impl<NTY> WithLen for MinMaxAvgBinsCollector<NTY>
where
    NTY: NumOps + Serialize,
{
    fn len(&self) -> usize {
        self.vals.ts1s.len()
    }
}

impl<NTY> Collector for MinMaxAvgBinsCollector<NTY>
where
    NTY: NumOps + Serialize,
{
    type Input = MinMaxAvgDim0Bins<NTY>;
    type Output = MinMaxAvgBinsCollectedResult<NTY>;

    fn ingest(&mut self, src: &Self::Input) {
        Appendable::append(&mut self.vals, src);
    }

    fn set_range_complete(&mut self) {
        self.range_complete = true;
    }

    fn set_timed_out(&mut self) {
        self.timed_out = true;
    }

    fn result(self) -> Result<Self::Output, Error> {
        let bin_count = self.vals.ts1s.len() as u32;
        // TODO could save the copy:
        let mut ts_all = self.vals.ts1s.clone();
        if self.vals.ts2s.len() > 0 {
            ts_all.push(*self.vals.ts2s.last().unwrap());
        }
        let continue_at = if self.vals.ts1s.len() < self.bin_count_exp as usize {
            match ts_all.last() {
                Some(&k) => {
                    let iso = IsoDateTime(Utc.timestamp_nanos(k as i64));
                    Some(iso)
                }
                None => Err(Error::with_msg("partial_content but no bin in result"))?,
            }
        } else {
            None
        };
        let tst = ts_offs_from_abs(&ts_all);
        let ret = MinMaxAvgBinsCollectedResult::<NTY> {
            ts_anchor_sec: tst.0,
            ts_off_ms: tst.1,
            ts_off_ns: tst.2,
            counts: self.vals.counts,
            mins: self.vals.mins,
            maxs: self.vals.maxs,
            avgs: self.vals.avgs,
            finalised_range: self.range_complete,
            missing_bins: self.bin_count_exp - bin_count,
            continue_at,
        };
        Ok(ret)
    }
}

impl<NTY> Collectable for MinMaxAvgDim0Bins<NTY>
where
    NTY: NumOps + Serialize,
{
    type Collector = MinMaxAvgBinsCollector<NTY>;

    fn new_collector(bin_count_exp: u32) -> Self::Collector {
        Self::Collector::new(bin_count_exp)
    }
}

pub struct MinMaxAvgBinsAggregator<NTY> {
    range: NanoRange,
    count: u64,
    min: Option<NTY>,
    max: Option<NTY>,
    sumc: u64,
    sum: f32,
}

impl<NTY> MinMaxAvgBinsAggregator<NTY> {
    pub fn new(range: NanoRange, _do_time_weight: bool) -> Self {
        Self {
            range,
            count: 0,
            min: None,
            max: None,
            sumc: 0,
            sum: 0f32,
        }
    }
}

impl<NTY> TimeBinnableTypeAggregator for MinMaxAvgBinsAggregator<NTY>
where
    NTY: NumOps,
{
    type Input = MinMaxAvgDim0Bins<NTY>;
    type Output = MinMaxAvgDim0Bins<NTY>;

    fn range(&self) -> &NanoRange {
        &self.range
    }

    fn ingest(&mut self, item: &Self::Input) {
        for i1 in 0..item.ts1s.len() {
            if item.ts2s[i1] <= self.range.beg {
            } else if item.ts1s[i1] >= self.range.end {
            } else {
                self.min = match &self.min {
                    None => item.mins[i1].clone(),
                    Some(min) => match &item.mins[i1] {
                        None => Some(min.clone()),
                        Some(v) => {
                            if v < &min {
                                Some(v.clone())
                            } else {
                                Some(min.clone())
                            }
                        }
                    },
                };
                self.max = match &self.max {
                    None => item.maxs[i1].clone(),
                    Some(max) => match &item.maxs[i1] {
                        None => Some(max.clone()),
                        Some(v) => {
                            if v > &max {
                                Some(v.clone())
                            } else {
                                Some(max.clone())
                            }
                        }
                    },
                };
                match item.avgs[i1] {
                    None => {}
                    Some(v) => {
                        if v.is_nan() {
                        } else {
                            self.sum += v;
                            self.sumc += 1;
                        }
                    }
                }
                self.count += item.counts[i1];
            }
        }
    }

    fn result_reset(&mut self, range: NanoRange, _expand: bool) -> Self::Output {
        let avg = if self.sumc == 0 {
            None
        } else {
            Some(self.sum / self.sumc as f32)
        };
        let ret = Self::Output {
            ts1s: vec![self.range.beg],
            ts2s: vec![self.range.end],
            counts: vec![self.count],
            mins: vec![self.min.clone()],
            maxs: vec![self.max.clone()],
            avgs: vec![avg],
        };
        self.count = 0;
        self.min = None;
        self.max = None;
        self.range = range;
        self.sum = 0f32;
        self.sumc = 0;
        ret
    }
}