mytec
defa3ad440
Major refactoring of RFCP backend: - Modular propagation models (8 models) - SharedMemoryManager for terrain data - ProcessPoolExecutor parallel processing - WebSocket progress streaming - Building filtering pipeline (351k → 15k) - 82 unit tests Performance: Standard preset 38s → 5s (7.6x speedup) Known issue: Detailed preset timeout (fix in 3.1.0)
88 lines
2.2 KiB
Python
88 lines
2.2 KiB
Python
"""
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Knife-edge diffraction model based on ITU-R P.526.
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Used for calculating additional loss when terrain or obstacles
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block the line of sight between TX and RX.
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Reference: ITU-R P.526-15
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"""
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import math
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class KnifeEdgeDiffractionModel:
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"""
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Single knife-edge diffraction model.
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Stateless utility — not a full PropagationModel since it calculates
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additional loss, not total path loss.
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"""
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@staticmethod
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def calculate_loss(
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d1_m: float,
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d2_m: float,
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h_m: float,
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wavelength_m: float,
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) -> float:
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"""
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Calculate diffraction loss over single knife edge.
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Args:
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d1_m: Distance from TX to obstacle
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d2_m: Distance from obstacle to RX
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h_m: Obstacle height above LOS line (positive = above)
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wavelength_m: Signal wavelength
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Returns:
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Loss in dB (always >= 0)
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"""
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if d1_m <= 0 or d2_m <= 0 or wavelength_m <= 0:
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return 0.0
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# Fresnel-Kirchhoff parameter
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v = h_m * math.sqrt(2 * (d1_m + d2_m) / (wavelength_m * d1_m * d2_m))
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# Diffraction loss (Lee approximation)
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if v < -0.78:
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L = 0.0
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elif v < 0:
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L = 6.02 + 9.11 * v - 1.27 * v ** 2
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elif v < 2.4:
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L = 6.02 + 9.11 * v + 1.65 * v ** 2
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else:
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L = 12.95 + 20 * math.log10(v)
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return max(0.0, L)
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@staticmethod
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def calculate_clearance_loss(
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clearance_m: float,
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frequency_mhz: float,
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) -> float:
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"""
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Simplified diffraction loss from terrain clearance.
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Matches the existing coverage_service._diffraction_loss logic.
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Args:
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clearance_m: Minimum LOS clearance (negative = blocked)
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frequency_mhz: Signal frequency
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Returns:
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Loss in dB (0 if positive clearance)
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"""
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if clearance_m >= 0:
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return 0.0
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v = abs(clearance_m) / 10
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if v <= 0:
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loss = 0.0
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elif v < 2.4:
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loss = 6.02 + 9.11 * v - 1.27 * v ** 2
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else:
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loss = 13.0 + 20 * math.log10(v)
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return min(loss, 40.0)
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