Coverage for biobb_mem/lipyphilic_biobb/lpp_zpositions.py: 88%
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« prev ^ index » next coverage.py v7.10.7, created at 2025-10-03 16:03 +0000
1#!/usr/bin/env python3
3"""Module containing the Lipyphilic ZPositions class and the command line interface."""
5from biobb_common.generic.biobb_object import BiobbObject
6from biobb_common.tools.file_utils import launchlogger
7import MDAnalysis as mda
8from biobb_mem.lipyphilic_biobb.common import ignore_no_box
9from lipyphilic.lib.z_positions import ZPositions
10import pandas as pd
11import numpy as np
14class LPPZPositions(BiobbObject):
15 """
16 | biobb_mem LPPZPositions
17 | Wrapper of the LiPyphilic ZPositions module for calculating the z distance of lipids to the bilayer center.
18 | LiPyphilic is a Python package for analyzing MD simulations of lipid bilayers. The parameter names and defaults are the same as the ones in the official `Lipyphilic documentation <https://lipyphilic.readthedocs.io/en/latest/reference/analysis/z_pos.html>`_.
20 Args:
21 input_top_path (str): Path to the input structure or topology file. File type: input. `Sample file <https://github.com/bioexcel/biobb_mem/raw/main/biobb_mem/test/data/A01JD/A01JD.pdb>`_. Accepted formats: crd (edam:3878), gro (edam:2033), mdcrd (edam:3878), mol2 (edam:3816), pdb (edam:1476), pdbqt (edam:1476), prmtop (edam:3881), psf (edam:3882), top (edam:3881), tpr (edam:2333), xml (edam:2332), xyz (edam:3887).
22 input_traj_path (str): Path to the input trajectory to be processed. File type: input. `Sample file <https://github.com/bioexcel/biobb_mem/raw/main/biobb_mem/test/data/A01JD/A01JD.xtc>`_. Accepted formats: arc (edam:2333), crd (edam:3878), dcd (edam:3878), ent (edam:1476), gro (edam:2033), inpcrd (edam:3878), mdcrd (edam:3878), mol2 (edam:3816), nc (edam:3650), pdb (edam:1476), pdbqt (edam:1476), restrt (edam:3886), tng (edam:3876), trr (edam:3910), xtc (edam:3875), xyz (edam:3887).
23 output_positions_path (str): Path to the output z positions. File type: output. `Sample file <https://github.com/bioexcel/biobb_mem/raw/main/biobb_mem/test/reference/lipyphilic_biobb/zpositions.csv>`_. Accepted formats: csv (edam:format_3752).
24 properties (dic - Python dictionary object containing the tool parameters, not input/output files):
25 * **start** (*int*) - (None) Starting frame for slicing.
26 * **stop** (*int*) - (None) Ending frame for slicing.
27 * **steps** (*int*) - (None) Step for slicing.
28 * **lipid_sel** (*str*) - ("all") Selection string for the lipids in a membrane. The selection should cover **all** residues in the membrane, including cholesterol.
29 * **height_sel** (*str*) - ("all") Atom selection for the molecules for which the z position will be calculated.
30 * **n_bins** (*int*) - (1) Number of bins in *x* and *y* to use to create a grid of membrane patches. Local membrane midpoints are computed for each patch, and lipids assigned a leaflet based on the distance to their local membrane midpoint. The default is `1`, which is equivalent to computing a single global midpoint.
31 * **ignore_no_box** (*bool*) - (False) Ignore the absence of box information in the trajectory. If the trajectory does not contain box information, the box will be set to the minimum and maximum positions of the atoms in the trajectory.
32 * **remove_tmp** (*bool*) - (True) [WF property] Remove temporal files.
33 * **restart** (*bool*) - (False) [WF property] Do not execute if output files exist.
34 * **sandbox_path** (*str*) - ("./") [WF property] Parent path to the sandbox directory.
36 Examples:
37 This is a use example of how to use the building block from Python::
39 from biobb_mem.lipyphilic_biobb.lpp_zpositions import lpp_zpositions
40 prop = {
41 'lipid_sel': 'name GL1 GL2 ROH',
42 }
43 lpp_zpositions(input_top_path='/path/to/myTopology.tpr',
44 input_traj_path='/path/to/myTrajectory.xtc',
45 output_positions_path='/path/to/zpositions.csv.csv',
46 properties=prop)
48 Info:
49 * wrapped_software:
50 * name: LiPyphilic
51 * version: 0.10.0
52 * license: GPL-2.0
53 * ontology:
54 * name: EDAM
55 * schema: http://edamontology.org/EDAM.owl
57 """
59 def __init__(self, input_top_path, input_traj_path, output_positions_path,
60 properties=None, **kwargs) -> None:
61 properties = properties or {}
63 # Call parent class constructor
64 super().__init__(properties)
65 self.locals_var_dict = locals().copy()
67 # Input/Output files
68 self.io_dict = {
69 "in": {"input_top_path": input_top_path, "input_traj_path": input_traj_path},
70 "out": {"output_positions_path": output_positions_path}
71 }
73 self.start = properties.get('start', None)
74 self.stop = properties.get('stop', None)
75 self.steps = properties.get('steps', None)
76 self.lipid_sel = properties.get('lipid_sel', 'all')
77 self.height_sel = properties.get('height_sel', 'all')
78 self.n_bins = properties.get('n_bins', 1)
79 self.ignore_no_box = properties.get('ignore_no_box', False)
80 # Properties specific for BB
81 self.remove_tmp = properties.get('remove_tmp', True)
82 self.restart = properties.get('restart', False)
83 self.sandbox_path = properties.get('sandbox_path', "./")
85 # Check the properties
86 self.check_properties(properties)
87 self.check_arguments()
89 @launchlogger
90 def launch(self) -> int:
91 """Execute the :class:`LPPZPositions <lipyphilic_biobb.lpp_zpositions.LPPZPositions>` object."""
93 # Setup Biobb
94 if self.check_restart():
95 return 0
96 self.stage_files()
98 # Load the trajectory
99 u = mda.Universe(self.stage_io_dict["in"]["input_top_path"], self.stage_io_dict["in"]["input_traj_path"])
100 ignore_no_box(u, self.ignore_no_box, self.out_log, self.global_log)
102 # Create ZPositions object
103 positions = ZPositions(
104 universe=u,
105 lipid_sel=self.lipid_sel,
106 height_sel=self.height_sel,
107 n_bins=self.n_bins
108 )
109 # Run the analysis
110 positions.run(start=self.start, stop=self.stop, step=self.steps)
111 # Save the results
112 frames = positions.z_positions.shape[1]
113 resnames = np.repeat(positions._height_atoms.resnames, frames)
114 resindices = np.tile(positions._height_atoms.resindices, frames)
115 frame_numbers = np.repeat(np.arange(frames), positions._height_atoms.n_residues)
117 df = pd.DataFrame({
118 'resname': resnames,
119 'resindex': resindices,
120 'frame': frame_numbers,
121 'zposition': positions.z_positions.T.flatten()
122 })
124 # Save the DataFrame to a CSV file
125 df.to_csv(self.stage_io_dict["out"]["output_positions_path"], index=False)
127 # Copy files to host
128 self.copy_to_host()
129 self.remove_tmp_files()
130 self.check_arguments(output_files_created=True, raise_exception=False)
132 return self.return_code
135def lpp_zpositions(input_top_path: str, input_traj_path: str, output_positions_path: str = None, properties: dict = None, **kwargs) -> int:
136 """Execute the :class:`LPPZPositions <lipyphilic_biobb.lpp_zpositions.LPPZPositions>` class and
137 execute the :meth:`launch() <lipyphilic_biobb.lpp_zpositions.LPPZPositions.launch>` method."""
138 return LPPZPositions(**dict(locals())).launch()
141lpp_zpositions.__doc__ = LPPZPositions.__doc__
142main = LPPZPositions.get_main(lpp_zpositions, "Calculate the z distance in of lipids to the bilayer center.")
145def frame_df(output_positions_path):
146 """
147 Processes a CSV file containing z-position data and calculates the mean positive, mean negative,
148 thickness, and standard deviation of thickness for each frame.
150 Args:
151 output_positions_path (str): Path to the CSV file containing z-position data.
152 Returns:
153 pandas.DataFrame: A DataFrame with the following columns:
154 - mean_positive: Mean of positive z-positions for each frame.
155 - mean_negative: Mean of negative z-positions for each frame.
156 - thickness: Difference between mean_positive and mean_negative for each frame.
157 - std_thickness: Standard deviation of the absolute z-positions for each frame.
158 """
160 df = pd.read_csv(output_positions_path)
161 grouped = df.groupby('frame')['zposition'].agg(
162 mean_positive=lambda x: x[x > 0].mean(),
163 mean_negative=lambda x: x[x < 0].mean(),
164 std_positive=lambda x: x[x > 0].std(),
165 std_negative=lambda x: x[x < 0].std()
166 )
167 grouped['thickness'] = grouped['mean_positive'] - grouped['mean_negative']
168 grouped['std_thickness'] = df.groupby('frame')['zposition'].apply(lambda x: x.abs().std())
169 return grouped
172if __name__ == '__main__':
173 main()