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)
96 lines
3.3 KiB
Python
96 lines
3.3 KiB
Python
import numpy as np
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import pandas as pd
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import math
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def generate_small_radar_csv(filename="small_test_radar_data.csv"):
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"""
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Generate a smaller, faster-to-process radar CSV
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"""
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# Reduced parameters for faster processing
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num_long_chirps = 8 # Reduced from 16
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num_short_chirps = 8 # Reduced from 16
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samples_per_chirp = 128 # Reduced from 512
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fs_adc = 400e6
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targets = [
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{'range': 3000, 'velocity': 25, 'snr': 40},
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{'range': 5000, 'velocity': -15, 'snr': 35},
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]
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data = []
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chirp_number = 0
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timestamp_ns = 0
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# Generate Long Chirps
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for chirp in range(num_long_chirps):
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for sample in range(samples_per_chirp):
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i_val = np.random.normal(0, 3)
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q_val = np.random.normal(0, 3)
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# Add targets
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for target in targets:
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range_bin = int(target['range'] / 40)
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doppler_phase = 2 * math.pi * target['velocity'] * chirp / 50
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if abs(sample - range_bin) < 5:
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amplitude = target['snr'] * (8000 / target['range'])
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phase = 2 * math.pi * sample / 30 + doppler_phase
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i_val += amplitude * math.cos(phase)
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q_val += amplitude * math.sin(phase)
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magnitude_squared = i_val**2 + q_val**2
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data.append({
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'timestamp_ns': timestamp_ns,
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'chirp_number': chirp_number,
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'chirp_type': 'LONG',
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'sample_index': sample,
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'I_value': int(i_val),
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'Q_value': int(q_val),
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'magnitude_squared': int(magnitude_squared)
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})
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timestamp_ns += int(1e9 / fs_adc)
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chirp_number += 1
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timestamp_ns += 137000
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# Generate Short Chirps
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for chirp in range(num_short_chirps):
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for sample in range(samples_per_chirp):
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i_val = np.random.normal(0, 3)
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q_val = np.random.normal(0, 3)
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for target in targets:
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range_bin = int(target['range'] / 60)
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doppler_phase = 2 * math.pi * target['velocity'] * (chirp + 2) / 40
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if abs(sample - range_bin) < 4:
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amplitude = target['snr'] * 0.6 * (6000 / target['range'])
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phase = 2 * math.pi * sample / 25 + doppler_phase
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i_val += amplitude * math.cos(phase)
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q_val += amplitude * math.sin(phase)
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magnitude_squared = i_val**2 + q_val**2
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data.append({
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'timestamp_ns': timestamp_ns,
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'chirp_number': chirp_number,
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'chirp_type': 'SHORT',
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'sample_index': sample,
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'I_value': int(i_val),
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'Q_value': int(q_val),
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'magnitude_squared': int(magnitude_squared)
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})
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timestamp_ns += int(1e9 / fs_adc)
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chirp_number += 1
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timestamp_ns += 174500
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df = pd.DataFrame(data)
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df.to_csv(filename, index=False)
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return df
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generate_small_radar_csv()
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