"""Tests for build_waveform_svg -- the plot_waveform core, sans file I/O.

Hand-built RawFiles drive every branch (auto plot-type detection, clipping,
multi-signal, errors) without LTspice or writing an SVG to disk.
"""

import numpy as np

from mcltspice.plotting import build_waveform_svg
from mcltspice.raw_parser import RawFile, Variable


def _ac_raw() -> RawFile:
    """Frequency axis + complex V(out), V(in) -> AC analysis."""
    freq = np.logspace(0, 6, 400)
    hout = 1.0 / (1.0 + 1j * (freq / 1000.0))
    data = np.array([freq.astype(complex), hout, np.ones_like(freq, dtype=complex)])
    return RawFile(
        title="ac", date="", plotname="AC Analysis", flags=["complex"],
        variables=[
            Variable(0, "frequency", "frequency"),
            Variable(1, "V(out)", "voltage"),
            Variable(2, "V(in)", "voltage"),
        ],
        points=len(freq), data=data,
    )


def _tran_raw() -> RawFile:
    """Time axis + two real signals -> transient."""
    t = np.linspace(0, 0.01, 500)
    data = np.array([t, np.sin(2 * np.pi * 1000 * t), np.cos(2 * np.pi * 1000 * t)])
    return RawFile(
        title="tran", date="", plotname="Transient Analysis", flags=["real"],
        variables=[
            Variable(0, "time", "time"),
            Variable(1, "V(out)", "voltage"),
            Variable(2, "V(ref)", "voltage"),
        ],
        points=len(t), data=data,
    )


class TestAutoPlotType:
    def test_complex_frequency_is_bode(self):
        r = build_waveform_svg(_ac_raw(), ["V(out)"])
        assert r["plot_type"] == "bode"
        assert r["svg"].lstrip().startswith("<")  # real SVG markup

    def test_real_frequency_is_spectrum(self):
        # frequency axis but real signal data -> spectrum
        freq = np.logspace(0, 6, 200)
        data = np.array([freq, np.abs(1.0 / (1.0 + 1j * freq / 1000.0))])
        raw = RawFile(
            title="", date="", plotname="AC Analysis", flags=["real"],
            variables=[Variable(0, "frequency", "frequency"), Variable(1, "V(out)", "voltage")],
            points=len(freq), data=data,
        )
        assert build_waveform_svg(raw, ["V(out)"])["plot_type"] == "spectrum"

    def test_time_axis_is_time(self):
        assert build_waveform_svg(_tran_raw(), ["V(out)"])["plot_type"] == "time"

    def test_explicit_overrides_auto(self):
        assert build_waveform_svg(_tran_raw(), ["V(out)"], plot_type="time")["plot_type"] == "time"


class TestErrorsAndMetadata:
    def test_missing_signal(self):
        r = build_waveform_svg(_ac_raw(), ["V(ghost)"])
        assert "error" in r and "V(ghost)" in r["error"]
        assert r["available_signals"] == ["frequency", "V(out)", "V(in)"]

    def test_multi_signal_non_time_is_error(self):
        r = build_waveform_svg(_ac_raw(), ["V(out)", "V(in)"], plot_type="bode")
        assert "error" in r and "time-domain" in r["error"]

    def test_dimensions_and_signal_metadata(self):
        r = build_waveform_svg(_tran_raw(), ["V(out)"], width=1000, height=600)
        assert r["dimensions"] == "1000x600"
        assert r["signal"] == "V(out)"

    def test_default_height_depends_on_plot_type(self):
        assert build_waveform_svg(_ac_raw(), ["V(out)"])["dimensions"] == "800x500"   # bode
        assert build_waveform_svg(_tran_raw(), ["V(out)"])["dimensions"] == "800x400"  # time


class TestSamplingAndMulti:
    def test_max_points_downsamples(self):
        r = build_waveform_svg(_tran_raw(), ["V(out)"], max_points=100)
        assert r["points"] <= 100

    def test_x_range_clip_reduces_points(self):
        full = build_waveform_svg(_tran_raw(), ["V(out)"], max_points=10000)["points"]
        clipped = build_waveform_svg(
            _tran_raw(), ["V(out)"], x_min=0.002, x_max=0.004, max_points=10000
        )["points"]
        assert clipped < full

    def test_multi_signal_time_overlay(self):
        r = build_waveform_svg(_tran_raw(), ["V(out)", "V(ref)"])
        assert r["plot_type"] == "time"
        assert r["signal"] == "V(out), V(ref)"