Update src/seismic_hazard_forecasting.py
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@ -33,11 +33,17 @@ def apply_beast(act_rate, **kwargs):
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ncp_median = int(result.trend.ncp_median) # No need for np.max if it's scalar
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ncp_median = int(result.trend.ncp_median) # No need for np.max if it's scalar
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cp_probs = result.trend.cpPr[:ncp_median]
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cp_probs = result.trend.cpPr[:ncp_median]
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cps = result.trend.cp[:ncp_median]
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cps = result.trend.cp[:ncp_median]
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sorted_indices = np.argsort(cps)
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sorted_cps = cps[sorted_indices]
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sorted_probs = cp_probs[sorted_indices]
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else:
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else:
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if not np.isnan(result.trend.ncp_pct90):
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if not np.isnan(result.trend.ncp_pct90):
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ncp_pct90 = int(result.trend.ncp_pct90)
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ncp_pct90 = int(result.trend.ncp_pct90)
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cp_probs = result.trend.cpPr[:ncp_pct90]
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cp_probs = result.trend.cpPr[:ncp_pct90]
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cps = result.trend.cp[:ncp_pct90]
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cps = result.trend.cp[:ncp_pct90]
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sorted_indices = np.argsort(cps)
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sorted_cps = cps[sorted_indices]
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sorted_probs = cp_probs[sorted_indices]
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else:
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else:
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# Optional fallback in case both are NaN
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# Optional fallback in case both are NaN
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cp_probs = []
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cp_probs = []
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@ -54,9 +60,9 @@ def apply_beast(act_rate, **kwargs):
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# cps = result.trend.cp[:ncp_pct90]
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# cps = result.trend.cp[:ncp_pct90]
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# Sort the change points and corresponding probabilities
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# Sort the change points and corresponding probabilities
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sorted_indices = np.argsort(cps)
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#sorted_indices = np.argsort(cps)
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sorted_cps = cps[sorted_indices]
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#sorted_cps = cps[sorted_indices]
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sorted_probs = cp_probs[sorted_indices]
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#sorted_probs = cp_probs[sorted_indices]
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# Store the sorted change points and probabilities
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# Store the sorted change points and probabilities
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prob.append(np.column_stack((sorted_probs, sorted_cps)))
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prob.append(np.column_stack((sorted_probs, sorted_cps)))
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