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@ -152,9 +152,11 @@ def foreshore_slope_from_profile(profile_x, profile_z, tide, runup_function, **k
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return None
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# Initalize estimates
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max_number_iterations = 20
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max_number_iterations = 30
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iteration_count = 0
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min_accuracy = 0.001
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averaged_accuracy = 0.03 # if slopes within this amount, average after max number of iterations
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acceptable_accuracy = 0.01 # if slopes within this amount, accept after max number of iterations
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preferred_accuracy = 0.001 # if slopes within this amount, accept
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beta = 0.05
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while True:
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@ -173,12 +175,17 @@ def foreshore_slope_from_profile(profile_x, profile_z, tide, runup_function, **k
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return None
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# If slopes do not change much between interactions, return the slope
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if abs(beta_new - beta) < min_accuracy:
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if abs(beta_new - beta) < preferred_accuracy:
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return beta
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# If we can't converge a solution, return None
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if iteration_count > max_number_iterations:
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return None
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if abs(beta_new - beta) < acceptable_accuracy:
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return beta
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elif abs(beta_new - beta) < averaged_accuracy:
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return (beta_new + beta) / 2
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else:
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return None
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beta = beta_new
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iteration_count += 1
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@ -230,7 +237,7 @@ def slope_from_profile(profile_x, profile_z, top_elevation, btm_elevation, metho
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return -(z_top - z_btm) / (x_top - x_btm)
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elif method == "least_squares":
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profile_mask = [True if end_points["top"]["x"] < pts < end_points["btm"]["x"] else False for pts in x]
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profile_mask = [True if end_points["top"]["x"] < pts < end_points["btm"]["x"] else False for pts in profile_x]
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slope_x = np.array(profile_x)[profile_mask].tolist()
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slope_z = np.array(profile_z)[profile_mask].tolist()
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slope, _, _, _, _ = stats.linregress(slope_x, slope_z)
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