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this is not tested yet. Also compute the standard deviation at the end from the predicted amount which will be used as the extraction window for the non linear calibration.
fix runtime bugs. have a working implementation of the linear auto irt refinement!
jcharkow
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Aug 4, 2025
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| std::vector<double> predicted_values(pairs_corrected.size()); // these are the predicted values by applying the trafo on the values | ||
| std::vector<double> delta_true_predicted(pairs_corrected.size()); // these are the differences between the predicted and the true values | ||
| double sum_rt_differences = 0.0; | ||
| TransformationDescription trafo_inverse = trafo_out; | ||
| trafo_inverse.invert(); | ||
| for (size_t i = 0; i < pairs_corrected.size(); ++i) | ||
| { | ||
| predicted_values[i] = trafo_out.apply(pairs_corrected[i].first); | ||
| delta_true_predicted[i] = pairs_corrected[i].first - predicted_values[i]; | ||
| sum_rt_differences += delta_true_predicted[i]; // sum for computing the mean | ||
| OPENMS_LOG_DEBUG << "True - Predicted value: " << pairs_corrected[i].second << " " << predicted_values[i] << std::endl; | ||
| } | ||
| double mean_rt_difference = sum_rt_differences / delta_true_predicted.size(); | ||
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| // compute the variance | ||
| double rt_variance = 0.0; | ||
| for (double delta: delta_true_predicted) | ||
| { | ||
| rt_variance += (delta - mean_rt_difference) * (delta - mean_rt_difference); | ||
| } | ||
| rt_variance /= delta_true_predicted.size(); | ||
| double rt_stdev = sqrt(rt_variance); | ||
| double rt_extraction_window = rt_stdev * 3.0 * 2; // 3 times the standard deviation, doubled since the window is the full length (not half) | ||
| std::cout << "RT variance: " << rt_stdev << std::endl; | ||
| std::cout << "RT Extraction Window to Use: " << rt_extraction_window << std::endl; | ||
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| std::cout << "End of doDataNormalization_ method" << std::endl; | ||
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| trafo_out.printSummary(std::cout); | ||
| } | ||
| { |
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@singjc this is what I did for estimating RT calibration.
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