Coverage for biobb_mem/lipyphilic_biobb/lpp_zpositions.py: 75%
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« prev ^ index » next coverage.py v7.10.6, created at 2025-09-08 09:07 +0000
1#!/usr/bin/env python3
3"""Module containing the Lipyphilic ZPositions class and the command line interface."""
4import argparse
5from biobb_common.generic.biobb_object import BiobbObject
6from biobb_common.configuration import settings
7from biobb_common.tools.file_utils import launchlogger
8import MDAnalysis as mda
9from biobb_mem.lipyphilic_biobb.common import ignore_no_box
10from lipyphilic.lib.z_positions import ZPositions
11import pandas as pd
12import numpy as np
15class LPPZPositions(BiobbObject):
16 """
17 | biobb_mem LPPZPositions
18 | Wrapper of the LiPyphilic ZPositions module for calculating the z distance of lipids to the bilayer center.
19 | 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>`_.
21 Args:
22 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).
23 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).
24 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).
25 properties (dic - Python dictionary object containing the tool parameters, not input/output files):
26 * **start** (*int*) - (None) Starting frame for slicing.
27 * **stop** (*int*) - (None) Ending frame for slicing.
28 * **steps** (*int*) - (None) Step for slicing.
29 * **lipid_sel** (*str*) - ("all") Selection string for the lipids in a membrane. The selection should cover **all** residues in the membrane, including cholesterol.
30 * **height_sel** (*str*) - ("all") Atom selection for the molecules for which the z position will be calculated.
31 * **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.
32 * **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.
33 * **remove_tmp** (*bool*) - (True) [WF property] Remove temporal files.
34 * **restart** (*bool*) - (False) [WF property] Do not execute if output files exist.
35 * **sandbox_path** (*str*) - ("./") [WF property] Parent path to the sandbox directory.
37 Examples:
38 This is a use example of how to use the building block from Python::
40 from biobb_mem.lipyphilic_biobb.lpp_zpositions import lpp_zpositions
41 prop = {
42 'lipid_sel': 'name GL1 GL2 ROH',
43 }
44 lpp_zpositions(input_top_path='/path/to/myTopology.tpr',
45 input_traj_path='/path/to/myTrajectory.xtc',
46 output_positions_path='/path/to/zpositions.csv.csv',
47 properties=prop)
49 Info:
50 * wrapped_software:
51 * name: LiPyphilic
52 * version: 0.10.0
53 * license: GPL-2.0
54 * ontology:
55 * name: EDAM
56 * schema: http://edamontology.org/EDAM.owl
58 """
60 def __init__(self, input_top_path, input_traj_path, output_positions_path,
61 properties=None, **kwargs) -> None:
62 properties = properties or {}
64 # Call parent class constructor
65 super().__init__(properties)
66 self.locals_var_dict = locals().copy()
68 # Input/Output files
69 self.io_dict = {
70 "in": {"input_top_path": input_top_path, "input_traj_path": input_traj_path},
71 "out": {"output_positions_path": output_positions_path}
72 }
74 self.start = properties.get('start', None)
75 self.stop = properties.get('stop', None)
76 self.steps = properties.get('steps', None)
77 self.lipid_sel = properties.get('lipid_sel', 'all')
78 self.height_sel = properties.get('height_sel', 'all')
79 self.n_bins = properties.get('n_bins', 1)
80 self.ignore_no_box = properties.get('ignore_no_box', False)
81 # Properties specific for BB
82 self.remove_tmp = properties.get('remove_tmp', True)
83 self.restart = properties.get('restart', False)
84 self.sandbox_path = properties.get('sandbox_path', "./")
86 # Check the properties
87 self.check_properties(properties)
88 self.check_arguments()
90 @launchlogger
91 def launch(self) -> int:
92 """Execute the :class:`LPPZPositions <lipyphilic_biobb.lpp_zpositions.LPPZPositions>` object."""
94 # Setup Biobb
95 if self.check_restart():
96 return 0
97 self.stage_files()
99 # Load the trajectory
100 u = mda.Universe(self.stage_io_dict["in"]["input_top_path"], self.stage_io_dict["in"]["input_traj_path"])
101 ignore_no_box(u, self.ignore_no_box, self.out_log, self.global_log)
103 # Create ZPositions object
104 positions = ZPositions(
105 universe=u,
106 lipid_sel=self.lipid_sel,
107 height_sel=self.height_sel,
108 n_bins=self.n_bins
109 )
110 # Run the analysis
111 positions.run(start=self.start, stop=self.stop, step=self.steps)
112 # Save the results
113 frames = positions.z_positions.shape[1]
114 resnames = np.repeat(positions._height_atoms.resnames, frames)
115 resindices = np.tile(positions._height_atoms.resindices, frames)
116 frame_numbers = np.repeat(np.arange(frames), positions._height_atoms.n_residues)
118 df = pd.DataFrame({
119 'resname': resnames,
120 'resindex': resindices,
121 'frame': frame_numbers,
122 'zposition': positions.z_positions.T.flatten()
123 })
125 # Save the DataFrame to a CSV file
126 df.to_csv(self.stage_io_dict["out"]["output_positions_path"], index=False)
128 # Copy files to host
129 self.copy_to_host()
130 self.remove_tmp_files()
131 self.check_arguments(output_files_created=True, raise_exception=False)
133 return self.return_code
136def lpp_zpositions(input_top_path: str, input_traj_path: str, output_positions_path: str = None, properties: dict = None, **kwargs) -> int:
137 """Execute the :class:`LPPZPositions <lipyphilic_biobb.lpp_zpositions.LPPZPositions>` class and
138 execute the :meth:`launch() <lipyphilic_biobb.lpp_zpositions.LPPZPositions.launch>` method."""
139 return LPPZPositions(**dict(locals())).launch()
142def main():
143 """Command line execution of this building block. Please check the command line documentation."""
144 parser = argparse.ArgumentParser(description="Calculate the z distance in of lipids to the bilayer center.", formatter_class=lambda prog: argparse.RawTextHelpFormatter(prog, width=99999))
145 parser.add_argument('--config', required=False, help='Configuration file')
147 # Specific args of each building block
148 required_args = parser.add_argument_group('required arguments')
149 required_args.add_argument('--input_top_path', required=True, help='Path to the input structure or topology file. Accepted formats: crd, gro, mdcrd, mol2, pdb, pdbqt, prmtop, psf, top, tpr, xml, xyz.')
150 required_args.add_argument('--input_traj_path', required=True, help='Path to the input trajectory to be processed. Accepted formats: arc, crd, dcd, ent, gro, inpcrd, mdcrd, mol2, nc, pdb, pdbqt, restrt, tng, trr, xtc, xyz.')
151 required_args.add_argument('--output_positions_path', required=True, help=' Path to the output z positions.')
153 args = parser.parse_args()
154 args.config = args.config or "{}"
155 properties = settings.ConfReader(config=args.config).get_prop_dic()
157 # Specific call of each building block
158 lpp_zpositions(input_top_path=args.input_top_path,
159 input_traj_path=args.input_traj_path,
160 output_positions_path=args.output_positions_path,
161 properties=properties)
164def frame_df(output_positions_path):
165 """
166 Processes a CSV file containing z-position data and calculates the mean positive, mean negative,
167 thickness, and standard deviation of thickness for each frame.
169 Args:
170 output_positions_path (str): Path to the CSV file containing z-position data.
171 Returns:
172 pandas.DataFrame: A DataFrame with the following columns:
173 - mean_positive: Mean of positive z-positions for each frame.
174 - mean_negative: Mean of negative z-positions for each frame.
175 - thickness: Difference between mean_positive and mean_negative for each frame.
176 - std_thickness: Standard deviation of the absolute z-positions for each frame.
177 """
179 df = pd.read_csv(output_positions_path)
180 grouped = df.groupby('frame')['zposition'].agg(
181 mean_positive=lambda x: x[x > 0].mean(),
182 mean_negative=lambda x: x[x < 0].mean(),
183 std_positive=lambda x: x[x > 0].std(),
184 std_negative=lambda x: x[x < 0].std()
185 )
186 grouped['thickness'] = grouped['mean_positive'] - grouped['mean_negative']
187 grouped['std_thickness'] = df.groupby('frame')['zposition'].apply(lambda x: x.abs().std())
188 return grouped
191if __name__ == '__main__':
192 main()