"""Tests for dashboard summary-stats helpers in utils/utils.py."""

import numpy as np
import pytest

from utils.utils import mean_rt_per_choice


def _make_sim_output(rts, choices):
    """Build a sim-output-shaped dict with rts/choices arrays."""
    rts = np.asarray(rts, dtype=np.float32).reshape(-1, 1, 1)
    choices = np.asarray(choices, dtype=np.float32).reshape(-1, 1, 1)
    return {"rts": rts, "choices": choices}


def test_mean_rt_per_choice_basic_two_choice():
    sim = _make_sim_output(
        rts=[0.4, 0.5, 0.6, 0.8, 1.0],
        choices=[1, 1, 1, -1, -1],
    )
    out = mean_rt_per_choice(sim)
    assert set(out.keys()) == {-1, 1}
    assert out[1] == pytest.approx(0.5)
    assert out[-1] == pytest.approx(0.9)


def test_mean_rt_per_choice_filters_deadline_timeouts():
    sim = _make_sim_output(
        rts=[0.4, -999, 0.6, -999, 1.0],
        choices=[1, -999, 1, -999, -1],
    )
    out = mean_rt_per_choice(sim)
    # Only valid samples count.
    assert out[1] == pytest.approx(0.5)
    assert out[-1] == pytest.approx(1.0)


def test_mean_rt_per_choice_one_sided():
    """All responses on one boundary — only that choice appears in the dict."""
    sim = _make_sim_output(
        rts=[0.3, 0.4, 0.5],
        choices=[1, 1, 1],
    )
    out = mean_rt_per_choice(sim)
    assert out == {1: pytest.approx(0.4)}


def test_mean_rt_per_choice_all_invalid():
    sim = _make_sim_output(
        rts=[-999, -999, -999],
        choices=[-999, -999, -999],
    )
    assert mean_rt_per_choice(sim) == {}


def test_mean_rt_per_choice_n_choice():
    sim = _make_sim_output(
        rts=[0.4, 0.5, 0.6, 0.7, 0.8],
        choices=[0, 1, 2, 0, 3],
    )
    out = mean_rt_per_choice(sim)
    assert set(out.keys()) == {0, 1, 2, 3}
    assert out[0] == pytest.approx(0.55)  # mean of 0.4 and 0.7
    assert out[1] == pytest.approx(0.5)
    assert out[2] == pytest.approx(0.6)
    assert out[3] == pytest.approx(0.8)


def test_mean_rt_per_choice_keys_are_int_when_integer_valued():
    """Choice keys should be int when integer-valued so DataFrame column names are clean."""
    sim = _make_sim_output(rts=[0.4, 0.5], choices=[-1, 1])
    out = mean_rt_per_choice(sim)
    for key in out:
        assert isinstance(key, int), f"expected int key, got {type(key)}"