24 lines
774 B
Python
24 lines
774 B
Python
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import numpy as np
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import math
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stochastic_noise_std = 0.05
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rng = np.random.default_rng(42)
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# Euler-Maruyama test
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similarity = complex(1.0, 0.0)
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dt_true = 0.05 # 20 Hz
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dt_hardcoded = 1.0 # What the codebase uses
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for i in range(100):
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dW = (rng.normal(0, 1.0) + 1j * rng.normal(0, 1.0)) * math.sqrt(dt_hardcoded)
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similarity += similarity * (0.0 * dt_hardcoded + stochastic_noise_std * dW)
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print(f"Similarity after 100 steps (dt=1.0): {abs(similarity)}")
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similarity2 = complex(1.0, 0.0)
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rng2 = np.random.default_rng(42)
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for i in range(100):
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dW = (rng2.normal(0, 1.0) + 1j * rng2.normal(0, 1.0)) * math.sqrt(dt_true)
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similarity2 += similarity2 * (0.0 * dt_true + stochastic_noise_std * dW)
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print(f"Similarity after 100 steps (dt=0.05): {abs(similarity2)}")
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