Ryan Malloy
cf8394fa6f
Rename package from mcp-ltspice/mcp_ltspice to mcltspice throughout: source directory, imports, pyproject.toml, tests, and README. Remove startup banner prints from main() since FastMCP handles its own banner and stdout is the MCP JSON-RPC transport. Point repo URL at git.supported.systems/MCP/mcltspice.
185 lines
7.1 KiB
Python
185 lines
7.1 KiB
Python
"""Tests for waveform_math module: RMS, peak-to-peak, FFT, THD, bandwidth, settling, rise time."""
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import numpy as np
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import pytest
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from mcltspice.waveform_math import (
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compute_bandwidth,
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compute_fft,
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compute_peak_to_peak,
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compute_rise_time,
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compute_rms,
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compute_settling_time,
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compute_thd,
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)
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class TestComputeRms:
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def test_dc_signal_exact(self, dc_signal):
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"""RMS of a DC signal equals its DC value."""
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rms = compute_rms(dc_signal)
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assert rms == pytest.approx(3.3, abs=1e-10)
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def test_sine_vpk_over_sqrt2(self, sine_1khz):
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"""RMS of a pure sine (1 V peak) should be 1/sqrt(2)."""
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rms = compute_rms(sine_1khz)
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assert rms == pytest.approx(1.0 / np.sqrt(2), rel=0.01)
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def test_empty_signal(self):
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"""RMS of an empty array is 0."""
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assert compute_rms(np.array([])) == 0.0
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def test_single_sample(self):
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"""RMS of a single sample equals abs(sample)."""
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assert compute_rms(np.array([5.0])) == pytest.approx(5.0)
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def test_complex_signal(self):
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"""RMS uses only the real part of complex data."""
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sig = np.array([3.0 + 4j, 3.0 + 4j])
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rms = compute_rms(sig)
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assert rms == pytest.approx(3.0)
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class TestComputePeakToPeak:
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def test_sine_wave(self, sine_1khz):
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"""Peak-to-peak of a 1 V peak sine should be ~2 V."""
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result = compute_peak_to_peak(sine_1khz)
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assert result["peak_to_peak"] == pytest.approx(2.0, rel=0.01)
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assert result["max"] == pytest.approx(1.0, rel=0.01)
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assert result["min"] == pytest.approx(-1.0, rel=0.01)
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assert result["mean"] == pytest.approx(0.0, abs=0.01)
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def test_dc_signal(self, dc_signal):
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"""Peak-to-peak of a DC signal is 0."""
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result = compute_peak_to_peak(dc_signal)
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assert result["peak_to_peak"] == pytest.approx(0.0)
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def test_empty_signal(self):
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result = compute_peak_to_peak(np.array([]))
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assert result["peak_to_peak"] == 0.0
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class TestComputeFft:
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def test_known_sine_peak_at_correct_freq(self, time_array, sine_1khz):
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"""A 1 kHz sine should produce a dominant peak at 1 kHz."""
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result = compute_fft(time_array, sine_1khz)
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assert result["fundamental_freq"] == pytest.approx(1000, rel=0.05)
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assert result["dc_offset"] == pytest.approx(0.0, abs=0.01)
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def test_dc_offset_detection(self, time_array):
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"""A signal with DC offset should report correct dc_offset."""
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offset = 2.5
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sig = offset + np.sin(2 * np.pi * 1000 * time_array)
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result = compute_fft(time_array, sig)
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assert result["dc_offset"] == pytest.approx(offset, rel=0.05)
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def test_short_signal(self):
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"""Very short signals return empty results."""
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result = compute_fft(np.array([0.0]), np.array([1.0]))
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assert result["frequencies"] == []
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assert result["fundamental_freq"] == 0.0
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def test_zero_dt(self):
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"""Time array with zero duration returns gracefully."""
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result = compute_fft(np.array([1.0, 1.0]), np.array([1.0, 2.0]))
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assert result["frequencies"] == []
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class TestComputeThd:
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def test_pure_sine_low_thd(self, time_array, sine_1khz):
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"""A pure sine wave should have very low THD."""
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result = compute_thd(time_array, sine_1khz)
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assert result["thd_percent"] < 1.0
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assert result["fundamental_freq"] == pytest.approx(1000, rel=0.05)
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def test_clipped_sine_high_thd(self, time_array, sine_1khz):
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"""A hard-clipped sine should have significantly higher THD."""
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clipped = np.clip(sine_1khz, -0.5, 0.5)
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result = compute_thd(time_array, clipped)
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# Clipping at 50% introduces substantial harmonics
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assert result["thd_percent"] > 10.0
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def test_short_signal(self):
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result = compute_thd(np.array([0.0]), np.array([1.0]))
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assert result["thd_percent"] == 0.0
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class TestComputeBandwidth:
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def test_lowpass_cutoff(self, ac_frequency, lowpass_response):
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"""Lowpass with fc=1kHz should report bandwidth near 1 kHz."""
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result = compute_bandwidth(ac_frequency, lowpass_response)
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assert result["bandwidth_hz"] == pytest.approx(1000, rel=0.1)
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assert result["type"] == "lowpass"
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def test_all_above_cutoff(self, ac_frequency):
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"""If all magnitudes are above -3dB level, bandwidth spans entire range."""
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flat = np.zeros_like(ac_frequency)
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result = compute_bandwidth(ac_frequency, flat)
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assert result["bandwidth_hz"] > 0
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def test_short_input(self):
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result = compute_bandwidth(np.array([1.0]), np.array([0.0]))
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assert result["bandwidth_hz"] == 0.0
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def test_bandpass_shape(self):
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"""A peaked response should be detected as bandpass."""
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fc = 10_000.0
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Q = 5.0 # Q factor => BW = fc/Q = 2000 Hz
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bw_expected = fc / Q
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freq = np.logspace(2, 6, 2000)
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# Second-order bandpass: H(s) = (s/wn/Q) / (s^2/wn^2 + s/wn/Q + 1)
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wn = 2 * np.pi * fc
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s = 1j * 2 * np.pi * freq
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H = (s / wn / Q) / (s**2 / wn**2 + s / wn / Q + 1)
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mag_db = 20.0 * np.log10(np.abs(H))
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result = compute_bandwidth(freq, mag_db)
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assert result["type"] == "bandpass"
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assert result["bandwidth_hz"] == pytest.approx(bw_expected, rel=0.15)
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class TestComputeSettlingTime:
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def test_already_settled(self, time_array, dc_signal):
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"""A constant signal is already settled at t=0."""
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t = np.linspace(0, 0.01, len(dc_signal))
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result = compute_settling_time(t, dc_signal, final_value=3.3)
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assert result["settled"] is True
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assert result["settling_time"] == 0.0
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def test_step_response(self, time_array, step_signal):
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"""Step response should settle after the transient."""
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result = compute_settling_time(time_array, step_signal, final_value=1.0)
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assert result["settled"] is True
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assert result["settling_time"] > 0
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def test_never_settles(self, time_array, sine_1khz):
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"""An oscillating signal never settles to a DC value."""
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result = compute_settling_time(time_array, sine_1khz, final_value=0.5)
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assert result["settled"] is False
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def test_short_signal(self):
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result = compute_settling_time(np.array([0.0]), np.array([1.0]))
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assert result["settled"] is False
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class TestComputeRiseTime:
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def test_fast_step(self):
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"""A fast rising step should have a short rise time."""
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t = np.linspace(0, 1e-3, 10000)
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# Step with very fast exponential rise
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sig = np.where(t > 0.1e-3, 1.0 - np.exp(-(t - 0.1e-3) / 20e-6), 0.0)
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result = compute_rise_time(t, sig)
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assert result["rise_time"] > 0
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# 10-90% rise time of RC = ~2.2 * tau
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assert result["rise_time"] == pytest.approx(2.2 * 20e-6, rel=0.2)
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def test_no_swing(self):
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"""Flat signal has zero rise time."""
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t = np.linspace(0, 1, 100)
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sig = np.ones(100) * 5.0
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result = compute_rise_time(t, sig)
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assert result["rise_time"] == 0.0
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def test_short_signal(self):
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result = compute_rise_time(np.array([0.0]), np.array([0.0]))
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assert result["rise_time"] == 0.0
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