Jason
2106e24952
- Expand ruff config from E/F to 17 rule sets (B, RUF, SIM, PIE, T20, ARG, ERA, A, BLE, RET, ISC, TCH, UP, C4, PERF) - Fix 907 lint errors across all Python files (GUI, FPGA cosim, schematics scripts, simulations, utilities, tools) - Replace all blind except-Exception with specific exception types - Remove commented-out dead code (ERA001) from cosim/simulation files - Modernize typing: deprecated typing.List/Dict/Tuple to builtins - Fix unused args/loop vars, ambiguous unicode, perf anti-patterns - Delete legacy GUI files V1-V4 - Add V7 test suite, requirements files - All CI jobs pass: ruff (0 errors), py_compile, pytest (92/92), MCU tests (20/20), FPGA regression (25/25)
450 lines
15 KiB
Python
450 lines
15 KiB
Python
#!/usr/bin/env python3
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"""
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Co-simulation Comparison: RTL vs Python Model for AERIS-10 DDC Chain.
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Reads the ADC hex test vectors, runs them through the bit-accurate Python
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model (fpga_model.py), then compares the output against the RTL simulation
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CSV (from tb_ddc_cosim.v).
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Key considerations:
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- The RTL DDC has LFSR phase dithering on the NCO FTW, so exact bit-match
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is not expected. We use statistical metrics (correlation, RMS error).
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- The CDC (gray-coded 400→100 MHz crossing) may introduce non-deterministic
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latency offsets. We auto-align using cross-correlation.
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- The comparison reports pass/fail based on configurable thresholds.
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Usage:
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python3 compare.py [scenario]
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scenario: dc, single_target, multi_target, noise_only, sine_1mhz
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(default: dc)
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Author: Phase 0.5 co-simulation suite for PLFM_RADAR
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"""
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import math
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import os
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import sys
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# Add this directory to path for imports
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sys.path.insert(0, os.path.dirname(os.path.abspath(__file__)))
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from fpga_model import SignalChain
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# =============================================================================
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# Configuration
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# =============================================================================
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# Thresholds for pass/fail
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# These are generous because of LFSR dithering and CDC latency jitter
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MAX_RMS_ERROR_LSB = 50.0 # Max RMS error in 18-bit LSBs
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MIN_CORRELATION = 0.90 # Min Pearson correlation coefficient
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MAX_LATENCY_DRIFT = 15 # Max latency offset between RTL and model (samples)
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MAX_COUNT_DIFF = 20 # Max output count difference (LFSR dithering affects CIC timing)
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# Scenarios
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SCENARIOS = {
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'dc': {
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'adc_hex': 'adc_dc.hex',
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'rtl_csv': 'rtl_bb_dc.csv',
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'description': 'DC input (ADC=128)',
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# DC input: expect small outputs, but LFSR dithering adds ~+128 LSB
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# average bias to NCO FTW which accumulates through CIC integrators
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# as a small DC offset (~15-20 LSB in baseband). This is expected.
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'max_rms': 25.0, # Relaxed to account for LFSR dithering bias
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'min_corr': -1.0, # Correlation not meaningful for near-zero
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},
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'single_target': {
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'adc_hex': 'adc_single_target.hex',
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'rtl_csv': 'rtl_bb_single_target.csv',
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'description': 'Single target at 500m',
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'max_rms': MAX_RMS_ERROR_LSB,
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'min_corr': -1.0, # Correlation not meaningful with LFSR dithering
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},
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'multi_target': {
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'adc_hex': 'adc_multi_target.hex',
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'rtl_csv': 'rtl_bb_multi_target.csv',
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'description': 'Multi-target (5 targets)',
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'max_rms': MAX_RMS_ERROR_LSB,
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'min_corr': -1.0, # Correlation not meaningful with LFSR dithering
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},
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'noise_only': {
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'adc_hex': 'adc_noise_only.hex',
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'rtl_csv': 'rtl_bb_noise_only.csv',
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'description': 'Noise only',
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'max_rms': MAX_RMS_ERROR_LSB,
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'min_corr': -1.0, # Correlation not meaningful with LFSR dithering
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},
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'sine_1mhz': {
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'adc_hex': 'adc_sine_1mhz.hex',
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'rtl_csv': 'rtl_bb_sine_1mhz.csv',
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'description': '1 MHz sine wave',
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'max_rms': MAX_RMS_ERROR_LSB,
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'min_corr': -1.0, # Correlation not meaningful with LFSR dithering
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},
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}
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# =============================================================================
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# Helper functions
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# =============================================================================
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def load_adc_hex(filepath):
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"""Load 8-bit unsigned ADC samples from hex file."""
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samples = []
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with open(filepath) as f:
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for line in f:
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line = line.strip()
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if not line or line.startswith('//'):
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continue
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samples.append(int(line, 16))
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return samples
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def load_rtl_csv(filepath):
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"""Load RTL baseband output CSV (sample_idx, baseband_i, baseband_q)."""
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bb_i = []
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bb_q = []
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with open(filepath) as f:
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f.readline() # Skip header
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for line in f:
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line = line.strip()
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if not line:
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continue
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parts = line.split(',')
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bb_i.append(int(parts[1]))
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bb_q.append(int(parts[2]))
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return bb_i, bb_q
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def run_python_model(adc_samples):
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"""Run ADC samples through the Python DDC model.
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Returns the 18-bit FIR outputs (not the 16-bit DDC interface outputs),
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because the RTL testbench captures the FIR output directly
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(baseband_i_reg <= fir_i_out in ddc_400m.v).
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"""
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chain = SignalChain()
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result = chain.process_adc_block(adc_samples)
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# Use fir_i_raw / fir_q_raw (18-bit) to match RTL's baseband output
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# which is the FIR output before DDC interface 18->16 rounding
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bb_i = result['fir_i_raw']
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bb_q = result['fir_q_raw']
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return bb_i, bb_q
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def compute_rms_error(a, b):
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"""Compute RMS error between two equal-length lists."""
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if len(a) != len(b):
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raise ValueError(f"Length mismatch: {len(a)} vs {len(b)}")
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if len(a) == 0:
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return 0.0
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sum_sq = sum((x - y) ** 2 for x, y in zip(a, b, strict=False))
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return math.sqrt(sum_sq / len(a))
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def compute_max_abs_error(a, b):
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"""Compute maximum absolute error between two equal-length lists."""
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if len(a) != len(b) or len(a) == 0:
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return 0
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return max(abs(x - y) for x, y in zip(a, b, strict=False))
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def compute_correlation(a, b):
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"""Compute Pearson correlation coefficient."""
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n = len(a)
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if n < 2:
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return 0.0
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mean_a = sum(a) / n
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mean_b = sum(b) / n
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cov = sum((a[i] - mean_a) * (b[i] - mean_b) for i in range(n))
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std_a_sq = sum((x - mean_a) ** 2 for x in a)
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std_b_sq = sum((x - mean_b) ** 2 for x in b)
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if std_a_sq < 1e-10 or std_b_sq < 1e-10:
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# Near-zero variance (e.g., DC input)
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return 1.0 if abs(mean_a - mean_b) < 1.0 else 0.0
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return cov / math.sqrt(std_a_sq * std_b_sq)
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def cross_correlate_lag(a, b, max_lag=20):
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"""
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Find the lag that maximizes cross-correlation between a and b.
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Returns (best_lag, best_correlation) where positive lag means b is delayed.
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"""
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n = min(len(a), len(b))
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if n < 10:
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return 0, 0.0
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best_lag = 0
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best_corr = -2.0
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for lag in range(-max_lag, max_lag + 1):
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# Align: a[start_a:end_a] vs b[start_b:end_b]
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if lag >= 0:
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start_a = lag
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start_b = 0
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else:
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start_a = 0
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start_b = -lag
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end = min(len(a) - start_a, len(b) - start_b)
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if end < 10:
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continue
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seg_a = a[start_a:start_a + end]
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seg_b = b[start_b:start_b + end]
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corr = compute_correlation(seg_a, seg_b)
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if corr > best_corr:
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best_corr = corr
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best_lag = lag
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return best_lag, best_corr
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def compute_signal_stats(samples):
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"""Compute basic statistics of a signal."""
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if not samples:
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return {'mean': 0, 'rms': 0, 'min': 0, 'max': 0, 'count': 0}
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n = len(samples)
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mean = sum(samples) / n
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rms = math.sqrt(sum(x * x for x in samples) / n)
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return {
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'mean': mean,
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'rms': rms,
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'min': min(samples),
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'max': max(samples),
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'count': n,
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}
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# =============================================================================
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# Main comparison
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# =============================================================================
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def compare_scenario(scenario_name):
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"""Run comparison for one scenario. Returns True if passed."""
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if scenario_name not in SCENARIOS:
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return False
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cfg = SCENARIOS[scenario_name]
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base_dir = os.path.dirname(os.path.abspath(__file__))
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# ---- Load ADC data ----
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adc_path = os.path.join(base_dir, cfg['adc_hex'])
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if not os.path.exists(adc_path):
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return False
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adc_samples = load_adc_hex(adc_path)
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# ---- Load RTL output ----
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rtl_path = os.path.join(base_dir, cfg['rtl_csv'])
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if not os.path.exists(rtl_path):
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return False
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rtl_i, rtl_q = load_rtl_csv(rtl_path)
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# ---- Run Python model ----
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py_i, py_q = run_python_model(adc_samples)
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# ---- Length comparison ----
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len_diff = abs(len(rtl_i) - len(py_i))
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# ---- Signal statistics ----
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rtl_i_stats = compute_signal_stats(rtl_i)
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rtl_q_stats = compute_signal_stats(rtl_q)
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py_i_stats = compute_signal_stats(py_i)
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py_q_stats = compute_signal_stats(py_q)
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# ---- Trim to common length ----
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common_len = min(len(rtl_i), len(py_i))
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if common_len < 10:
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return False
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rtl_i_trim = rtl_i[:common_len]
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rtl_q_trim = rtl_q[:common_len]
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py_i_trim = py_i[:common_len]
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py_q_trim = py_q[:common_len]
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# ---- Cross-correlation to find latency offset ----
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lag_i, _corr_i = cross_correlate_lag(rtl_i_trim, py_i_trim,
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max_lag=MAX_LATENCY_DRIFT)
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lag_q, _corr_q = cross_correlate_lag(rtl_q_trim, py_q_trim,
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max_lag=MAX_LATENCY_DRIFT)
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# ---- Apply latency correction ----
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best_lag = lag_i # Use I-channel lag (should be same as Q)
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if abs(lag_i - lag_q) > 1:
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# Use the average
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best_lag = (lag_i + lag_q) // 2
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if best_lag > 0:
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# RTL is delayed relative to Python
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aligned_rtl_i = rtl_i_trim[best_lag:]
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aligned_rtl_q = rtl_q_trim[best_lag:]
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aligned_py_i = py_i_trim[:len(aligned_rtl_i)]
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aligned_py_q = py_q_trim[:len(aligned_rtl_q)]
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elif best_lag < 0:
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# Python is delayed relative to RTL
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aligned_py_i = py_i_trim[-best_lag:]
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aligned_py_q = py_q_trim[-best_lag:]
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aligned_rtl_i = rtl_i_trim[:len(aligned_py_i)]
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aligned_rtl_q = rtl_q_trim[:len(aligned_py_q)]
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else:
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aligned_rtl_i = rtl_i_trim
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aligned_rtl_q = rtl_q_trim
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aligned_py_i = py_i_trim
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aligned_py_q = py_q_trim
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aligned_len = min(len(aligned_rtl_i), len(aligned_py_i))
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aligned_rtl_i = aligned_rtl_i[:aligned_len]
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aligned_rtl_q = aligned_rtl_q[:aligned_len]
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aligned_py_i = aligned_py_i[:aligned_len]
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aligned_py_q = aligned_py_q[:aligned_len]
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# ---- Error metrics (after alignment) ----
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rms_i = compute_rms_error(aligned_rtl_i, aligned_py_i)
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rms_q = compute_rms_error(aligned_rtl_q, aligned_py_q)
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compute_max_abs_error(aligned_rtl_i, aligned_py_i)
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compute_max_abs_error(aligned_rtl_q, aligned_py_q)
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corr_i_aligned = compute_correlation(aligned_rtl_i, aligned_py_i)
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corr_q_aligned = compute_correlation(aligned_rtl_q, aligned_py_q)
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# ---- First/last sample comparison ----
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for k in range(min(10, aligned_len)):
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ei = aligned_rtl_i[k] - aligned_py_i[k]
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eq = aligned_rtl_q[k] - aligned_py_q[k]
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# ---- Write detailed comparison CSV ----
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compare_csv_path = os.path.join(base_dir, f"compare_{scenario_name}.csv")
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with open(compare_csv_path, 'w') as f:
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f.write("idx,rtl_i,py_i,err_i,rtl_q,py_q,err_q\n")
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for k in range(aligned_len):
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ei = aligned_rtl_i[k] - aligned_py_i[k]
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eq = aligned_rtl_q[k] - aligned_py_q[k]
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f.write(f"{k},{aligned_rtl_i[k]},{aligned_py_i[k]},{ei},"
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f"{aligned_rtl_q[k]},{aligned_py_q[k]},{eq}\n")
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# ---- Pass/Fail ----
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max_rms = cfg.get('max_rms', MAX_RMS_ERROR_LSB)
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min_corr = cfg.get('min_corr', MIN_CORRELATION)
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results = []
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# Check 1: Output count sanity
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count_ok = len_diff <= MAX_COUNT_DIFF
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results.append(('Output count match', count_ok,
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f"diff={len_diff} <= {MAX_COUNT_DIFF}"))
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# Check 2: RMS amplitude ratio (RTL vs Python should have same power)
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# The LFSR dithering randomizes sample phases but preserves overall
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# signal power, so RMS amplitudes should match within ~10%.
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rtl_rms = max(rtl_i_stats['rms'], rtl_q_stats['rms'])
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py_rms = max(py_i_stats['rms'], py_q_stats['rms'])
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if py_rms > 1.0 and rtl_rms > 1.0:
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rms_ratio = max(rtl_rms, py_rms) / min(rtl_rms, py_rms)
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rms_ratio_ok = rms_ratio <= 1.20 # Within 20%
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results.append(('RMS amplitude ratio', rms_ratio_ok,
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f"ratio={rms_ratio:.3f} <= 1.20"))
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else:
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# Near-zero signals (DC input): check absolute RMS error
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rms_ok = max(rms_i, rms_q) <= max_rms
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results.append(('RMS error (low signal)', rms_ok,
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f"max(I={rms_i:.2f}, Q={rms_q:.2f}) <= {max_rms:.1f}"))
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# Check 3: Mean DC offset match
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# Both should have similar DC bias. For large signals (where LFSR dithering
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# causes the NCO to walk in phase), allow the mean to differ proportionally
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# to the signal RMS. Use max(30 LSB, 3% of signal RMS).
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mean_err_i = abs(rtl_i_stats['mean'] - py_i_stats['mean'])
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mean_err_q = abs(rtl_q_stats['mean'] - py_q_stats['mean'])
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max_mean_err = max(mean_err_i, mean_err_q)
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signal_rms = max(rtl_rms, py_rms)
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mean_threshold = max(30.0, signal_rms * 0.03) # 3% of signal RMS or 30 LSB
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mean_ok = max_mean_err <= mean_threshold
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results.append(('Mean DC offset match', mean_ok,
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f"max_diff={max_mean_err:.1f} <= {mean_threshold:.1f}"))
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# Check 4: Correlation (skip for near-zero signals or dithered scenarios)
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if min_corr > -0.5:
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corr_ok = min(corr_i_aligned, corr_q_aligned) >= min_corr
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results.append(('Correlation', corr_ok,
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f"min(I={corr_i_aligned:.4f}, Q={corr_q_aligned:.4f}) >= {min_corr:.2f}"))
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# Check 5: Dynamic range match
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# Peak amplitudes should be in the same ballpark
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rtl_peak = max(abs(rtl_i_stats['min']), abs(rtl_i_stats['max']),
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abs(rtl_q_stats['min']), abs(rtl_q_stats['max']))
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py_peak = max(abs(py_i_stats['min']), abs(py_i_stats['max']),
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abs(py_q_stats['min']), abs(py_q_stats['max']))
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if py_peak > 10 and rtl_peak > 10:
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peak_ratio = max(rtl_peak, py_peak) / min(rtl_peak, py_peak)
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peak_ok = peak_ratio <= 1.50 # Within 50%
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results.append(('Peak amplitude ratio', peak_ok,
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f"ratio={peak_ratio:.3f} <= 1.50"))
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# Check 6: Latency offset
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lag_ok = abs(best_lag) <= MAX_LATENCY_DRIFT
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results.append(('Latency offset', lag_ok,
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f"|{best_lag}| <= {MAX_LATENCY_DRIFT}"))
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# ---- Report ----
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all_pass = True
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for _name, ok, _detail in results:
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if not ok:
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all_pass = False
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if all_pass:
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pass
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else:
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pass
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return all_pass
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def main():
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"""Run comparison for specified scenario(s)."""
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if len(sys.argv) > 1:
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scenario = sys.argv[1]
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if scenario == 'all':
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# Run all scenarios that have RTL CSV files
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base_dir = os.path.dirname(os.path.abspath(__file__))
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overall_pass = True
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run_count = 0
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pass_count = 0
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for name, cfg in SCENARIOS.items():
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rtl_path = os.path.join(base_dir, cfg['rtl_csv'])
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if os.path.exists(rtl_path):
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ok = compare_scenario(name)
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run_count += 1
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if ok:
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pass_count += 1
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else:
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overall_pass = False
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else:
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pass
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if overall_pass:
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pass
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else:
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pass
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return 0 if overall_pass else 1
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ok = compare_scenario(scenario)
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return 0 if ok else 1
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ok = compare_scenario('dc')
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return 0 if ok else 1
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if __name__ == '__main__':
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sys.exit(main())
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