Welcome to the PeakPerformance documentation!#
peak_performance is a Python toolbox for Bayesian inference of peak areas.
It defines PyMC models describing the intensity curves of chromatographic peaks.
Using Bayesian inference, this enables the fitting of peaks, yielding uncertainty estimates for retention times, peak height, area and much more.
Installation#
pip install peak-performance
You can also download the latest version from GitHub.
Tutorials#
The documentation features various notebooks that demonstrate the usage and investigate certain aspects of peak modeling.
- Installation
- Preparing raw data
- Composition and assumptions of peak models
- Validation of
PeakPerformance - PeakPerformance workflow
- Diagnostic plots
- How to adapt PeakPerformance to your methods / data
- Example 1: Build a pipeline using PeakPerformance’s convenience functions
- Example 2: Directly access PeakPerformance’s functions to create a custom pipeline
- Example 3: Example with a larger experimental data set
- Investigation of double-peak separation
- Investigating the effect of
noiseon the area uncertainty quantification
API Reference#
peak_performance.modelsModelTypebaseline_intercept_prior_params()baseline_slope_prior_params()compute_log_likelihood()define_model_double_normal()define_model_double_skew_normal()define_model_normal()define_model_skew()delta_calculation()double_model_mean_prior()double_normal_peak_shape()double_skew_normal_peak_shape()guess_noise()height_calculation()initial_guesses()mean_skew_calculation()mode_offset_calculation()mode_skew_calculation()model_comparison()mue_z_calculation()multi_peak_means_prior()normal_peak_shape()sigma_z_calculation()skew_normal_peak_shape()skewness_calculation()std_skew_calculation()
peak_performance.pipelineInputErrorParsingErrorUserInputdetect_raw_data()excel_template_prepare()initiate()model_selection()model_selection_check()parse_data()parse_files_for_model_selection()parse_unique_identifiers()pipeline()pipeline_loop()pipeline_read_template()pipeline_restart()posterior_predictive_sampling()postfiltering()prefiltering()prepare_model_selection()report_add_data_to_summary()report_add_nan_to_summary()report_area_sheet()report_save_idata()sampling()selected_models_to_template()selection_loop()
peak_performance.plots