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--- lipid_wobble [2026/08/13 19:43] – Python Workflow Addition edit
+++ lipid_wobble [2026/08/13 22:06] (current) – [Step 2: Run Scripts] edit
@@ Line 15: / Line 15: @@
 If on ZT1, use the files in the following directory: /afs/shell.umd.edu/project/energybio/shared/jbklauda/scripts/lip_wobble/
+Below, is the instructions to use the legacy scripts. Scroll down further to get instructions on the modern python workflow.
 =====Calculate the correlation function=====
@@ Line 146: / Line 147: @@
 Next, run ''2_analysis.py.'' This creates a results folder which holds all of the generated plots and wobble_results.txt, which contains your wobble parameters. I suggest playing around with the analysis fields in config.py to see how your results change.
-If these fits fail or give unphysical results, ask Professor Klauda for advice. But in general, boundary-pinning errors indicate you don't have enough data (the graph looks like it decays to zero, but a proper correlation function should plateau). You can try adjusting the truncated window to capture more or less of the data. For more rigid systems, 3rd-order exponential fitting may be preferred, and/or you can raise the upper bounds manually by editing the "tau1_ub" variable inside src/utils/analysis_utils.py (the lower bound is 0, which should not be touched). Using weighted least squares can stabilize results if you have noisy tail data, but it carries its own statistical limitations. In most cases, unweighted least squares is what is reported in the literature.
+If these fits fail or give unphysical results, ask Professor Klauda for advice. But in general, boundary-pinning errors indicate you don't have enough data (the graph looks like it decays to zero, but a proper correlation function should plateau). You can try adjusting the truncated window to capture more or less of the data. For more rigid systems, 3rd-order exponential fitting may be preferred, and/or you can raise the upper bounds manually by editing the "tau1_ub" variable inside src/utils/analysis_utils.py (the lower bound is 0, which should not be touched). Using weighted least squares is a fancy alternative that enables you to use most of the data with minimal, if any, truncation in scenarios where data is limited. Unweighted least squares seems to be what is reported in the literature in most cases.