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--- 2d_rdfs [2026/08/26 15:21] – [2) Run Scripts] edit
+++ 2d_rdfs [2026/09/24 00:24] (current) – [1) Edit Necessary Files] edit
@@ Line 119: / Line 119: @@
 ====1) Edit Necessary Files====
-a) Open ''config.py'' and read the comments I left throughout. Then, adjust the fields to work for your membrane system. I would not recommend you change num_blocks (which is set at 1 by default) because it is already highly optimized and can process 20 dcds in under a minute. There's no point in trying to parallelize the workflow further.\\
+a) Open ''config.py'' and read the comments I left throughout. Then, adjust the fields to work for your membrane system. I would not recommend you change num_blocks (which is set at 1 by default) because it is already highly optimized and can process 20 dcds in under a minute. There's no point in trying to parallelize the workflow further. Also, custom_grouping_dict should only be modified if you have non segid MEMB lipids (check psf).
-Example config_dict: a complex sphingolipid membrane with some of my own custom patched NAAA lipids
 <code>
+# Example config_dict: a complex sphingolipid membrane with some of my own custom patched NAAA lipids
 config_dict = {
     'MEMB':['AIPE','AMPG','DPPC','CER160','CER221','LSM','BSM'],
     'NAAA':['LLL',],
 }
-# because most of these are MEMB lipids in the psf, you don't need to specify their groupings in custom_grouping_dict
-# FYI, the default MEMB groupings are for CHL, PE, PG, PC, PI, PS, PA, CER, SM lipids; other native MEMB lipids need manual code adjustment
+# corresponding custom_grouping_dict which handles non-memb lipids like LLL, where LLL is grouped under an arbitrary label called OL
+custom_grouping_dict = {
+    'OL':['LLL'],
+}
+# Example of a simple POPE/POPG membrane which have segid MEMB because they are native lipids
+config_dict = {
+    'MEMB':['POPE','POPG',],
+}
+# corresponding custom_grouping_dict which is empty since you only have normal memb lipids
+custom_grouping_dict = {
+}
 </code>
@@ Line 135: / Line 147: @@
 b) Run ''./1_rdf.scr''. The first time you run it, it will take a minute to set up the Python virtual environment and install MDAnalysis. Then, it will execute the RDF Slurm job. The entire run should take less than a minute, or longer for really big systems.
-c) Once the slurm job finishes, run ''./2_analysis.py'' and ''./3_plot_rdf.py''. You should now see .dat files and a combined figure stored inside the results directory. Feel free to adjust the plot settings if you're familiar with matplotlib, or take the .dat files into Excel or any other software and plot them differently. It also averages the top and bottom leaflets and plots the averages automatically if the lipids in each leaflet are the same.
+c) Once the slurm job finishes, run ''./2_analysis.py'' and ''./3_plot_rdf.py''. You should now see .dat files and a combined figure stored inside the results directory. Feel free to adjust the plot settings if you're familiar with matplotlib, or take the .dat files into Excel or any other software and plot them differently. It also averages the top and bottom leaflets and plots the averages automatically if you specified symmetric = True in config.py.