"""
File parsers for exporting session data.

Each export function takes a `session_path` argument, a string pointing to the
location of a stored session JSON file, and returns a string reference to the
converted file stored in Visiomode's cache directory.
"""
#  This file is part of visiomode.
#  Copyright (c) 2023 Constantinos Eleftheriou <Constantinos.Eleftheriou@ed.ac.uk>
#  Distributed under the terms of the MIT Licence.
import os
import json
import pynwb
import pandas as pd

import visiomode.config as cfg

from datetime import datetime


config = cfg.Config()


def to_nwb(session_path):
    with open(session_path, "r") as f:
        session = json.load(f)

    session_start_time = datetime.fromisoformat(session["timestamp"])

    nwbfile = pynwb.NWBFile(
        session_description="Visiomode {} behaviour".format(session.get("protocol")),
        identifier=session_path.split("/")[-1].replace(".json", ""),
        session_start_time=session_start_time,
        experiment_description=session.get("experiment"),
    )

    nwbfile.subject = pynwb.file.Subject(subject_id=session["animal_id"])

    nwbfile.add_trial_column(
        name="stimulus", description="the visual stimuli during the trial"
    )
    nwbfile.add_trial_column(name="cue_onset", description="when the stimulus came on")
    nwbfile.add_trial_column(
        name="response", description="trial response type (left, right, lever)"
    )
    nwbfile.add_trial_column(name="response_time", description="response timestamp")
    nwbfile.add_trial_column(name="pos_x", description="response position in x-axis")
    nwbfile.add_trial_column(name="pos_y", description="response position in y-axis")
    nwbfile.add_trial_column(
        name="dist_x", description="response displacement in x-axis"
    )
    nwbfile.add_trial_column(
        name="dist_y", description="response displacement in y-axis"
    )
    nwbfile.add_trial_column(name="outcome", description="trial outcome")
    nwbfile.add_trial_column(
        name="correction", description="whether trial was a correction trial"
    )
    nwbfile.add_trial_column(
        name="sdt_type", description="signal detection theory classification"
    )

    for trial in _flatten_trials(session):
        nwbfile.add_trial(**trial)

    nwbfile.create_device(
        name=session["device"],
        description="Visiomode acquisition device version {}".format(
            session.get("version", "<0.5.0")
        ),
        manufacturer="Duguid Lab",
    )

    subject_id = session.get("animal_id")
    session_date = session.get("timestamp").split("T")[0].replace("-", "")
    fname = "sub-{}_ses-{}_behavior.nwb".format(subject_id, session_date)
    outpath = config.cache_dir + os.sep + fname
    with pynwb.NWBHDF5IO(outpath, "w") as io:
        io.write(nwbfile)

    return fname


def to_csv(session_path):
    with open(session_path, "r") as f:
        session = json.load(f)

    df = pd.DataFrame(_flatten_trials(session))

    fname = session_path.split("/")[-1].replace(".json", ".csv")
    outpath = config.cache_dir + os.sep + fname
    df.to_csv(outpath)

    return fname


def _flatten_trials(session):
    session_start_time = datetime.fromisoformat(session["timestamp"])

    for trial in session.get("trials"):
        start_time = (
            datetime.fromisoformat(trial["timestamp"]) - session_start_time
        ).total_seconds()

        stop_time = (
            start_time
            + trial["iti"]
            + float(session["spec"]["stimulus_duration"]) / 1000
        )
        if trial["response"].get("timestamp"):
            print(trial["response"])
            stop_time = (
                datetime.fromisoformat(trial["response"]["timestamp"])
                - session_start_time
            ).total_seconds()

        stimulus = (
            trial["stimulus"].get("common_name")
            if trial["stimulus"] != "None"
            else "None"
        )
        cue_onset = start_time + trial["iti"]

        response = trial["response"].get("name")
        response_time = trial["response_time"]

        pos_x = trial["response"].get("pos_x", 0)
        pos_y = trial["response"].get("pos_y", 0)
        dist_x = trial["response"].get("dist_x", 0)
        dist_y = trial["response"].get("dist_y", 0)

        sdt_type = trial.get("sdt_type", "unavailable")

        yield {
            "start_time": start_time,
            "stop_time": stop_time,
            "stimulus": stimulus,
            "cue_onset": cue_onset,
            "response": response,
            "response_time": response_time,
            "outcome": trial["outcome"],
            "correction": trial["correction"],
            "pos_x": pos_x,
            "pos_y": pos_y,
            "dist_x": dist_x,
            "dist_y": dist_y,
            "sdt_type": sdt_type,
        }
