pyBrown

pyBrown is a bundle of tools useful for Brownian and Stokesian dynamics simulations and subsequent analysis of the results.

Features

computing Mean Square Displacement
computing Mean Square Angular Displacement and Mean Orientation Autocorrelation
computing Radial Distribution Function
structure (.str) files generation
Monte Carlo Excluded Volume computation
diffusion/mobility/resistance matrix analysis

First steps

Type following commands in a terminal:

make

make test

General remarks

Units

Physical property	Units
Temperature	kelvin (K)
Viscosity	poise (P)
Time	picosecond (ps)
Distance	Angstrom (Å)

Generating structure files

Keywords

Trajectory analysis

`MSD.py`

Keywords

Required keywords:

"labels": [string, ...] — bead labels in input XYZ file,
"sizes": [integer, ...] — numbers of beads representing individual entities,
"box_size": float — size of simulation (cubic) box (Å),
"temperature": float — temperature (K),
"viscosity": float — dynamic viscosity (P),
"input_xyz_template": string — template of input xyz filenames,
"input_xyz_range": [integer, integer] — the number range defining input xyz filenames.

pyBrown expects input xyz files to follow a specific naming scheme: ..., (TEMPLATE)(NUMBER).xyz, (TEMPLATE)(NUMBER).xyz, ... (where TEMPLATE is a string variable defined with the keyword "input_xyz_template" and NUMBER is an integer from range defined with the keyword "input_xyz_range")

(Have in mind, that ranges in python are defined in such a way that the upper limit is not contained in a range. For example, range(1,4) returns [1, 2, 3] (without 4!).)

Optional keywords:

"debug": boolean — print extra information useful for debugging purposes (default: false)
"verbose": boolean — print extra information (default: false)
"fit_MSD": boolean — fit linear functions to the computed MSDs and plot them in the output figure (default: false)
"probing_frequency": integer — read every N-th geometry (default: 1)
"min_time:" float — not include snapshots with time smaller than "min_time" (default: 0.0)
"mode": option — learn more here (options: "direct"/"window" , default: window)
"float_type": option — number of bits per float number (options: 32/64, default: 32)

Example input file

{
  "labels": ["FIC", "DNA"],
  "sizes": [1, 8],
  "box_size": 750.0,
  "temperature": 293.15,
  "viscosity": 0.01005,
  "input_xyz_template": "ficoll_41_DNA_14_",
  "input_xyz_range": [1, 94],
  "fit_MSD": true,
  "verbose": true,
  "debug": true,
  "probing_frequency": 10,
  "mode": "window"
}

Usage

If you have already prepared an input JSON file (using keywords introduced above), you can run the MSD.py program using following command:

python MSD.py input.json

Output files

Successful computations should produce:

(TEMPLATE)msd.txt data file with MSD as a function of time,
(TEMPLATE)msd.pdf image file with MSD as a function of time (and optionally, linear fit),

`Energy.py`

Keywords

Required keywords:

"input_enr_template": string — template of input enr filenames,
"input_enr_range": [integer, integer] — the number range defining input enr filenames.

pyBrown expects input enr files to follow a specific naming scheme: ..., (TEMPLATE)(NUMBER).enr, (TEMPLATE)(NUMBER).enr, ... (where TEMPLATE is a string variable defined with the keyword "input_enr_template" and NUMBER is an integer from range defined with the keyword "input_enr_range")

(Have in mind, that ranges in python are defined in such a way that the upper limit is not contained in a range. For example, range(1,4) returns [1, 2, 3] (without 4!).)

Optional keywords:

"debug": boolean — print extra information useful for debugging purposes (default: false)
"verbose": boolean — print extra information (default: false)
"float_type": option — number of bits per float number (options: 32/64, default: 32)

Example input file

{
  "input_enr_template": "enzymes_double_1_",
  "input_enr_range": [1, 11],
  "verbose": false,
  "debug": false
}

Usage

If you have already prepared an input JSON file (using keywords introduced above), you can run the Energy.py program using following command:

python Energy.py input.json

Output files

Successful computations should produce:

(TEMPLATE)enr.txt data file with mean energy as a function of time,
(TEMPLATE)enr.pdf image file with mean energy as a function of time (and optionally, linear fit)

`Lengths.py`

Keywords

Required keywords:

"labels": [string, ...] — bead labels in input XYZ file,
"sizes": [integer, ...] — numbers of beads representing individual entities,
"box_size": float — size of simulation (cubic) box (Å),
"bin_range": [float, float] — range of length values (Å) used to construct histogram bins;
"number_of_bins": integer — number of bins
"input_xyz_template": string — template of input xyz filenames,
"input_xyz_range": [integer, integer] — the number range defining input xyz filenames.

pyBrown expects input xyz files to follow a specific naming scheme: ..., (TEMPLATE)(NUMBER).xyz, (TEMPLATE)(NUMBER).xyz, ... (where TEMPLATE is a string variable defined with the keyword "input_xyz_template" and NUMBER is an integer from range defined with the keyword "input_xyz_range")

(Have in mind, that ranges in python are defined in such a way that the upper limit is not contained in a range. For example, range(1,4) returns [1, 2, 3] (without 4!).)

Optional keywords:

"debug": boolean — print extra information useful for debugging purposes (default: false)
"verbose": boolean — print extra information (default: false)
"probing_frequency": integer — read every N-th geometry (default: 1)
"min_time:" float — not include snapshots with time smaller than "min_time" (default: 0.0)
"float_type": option — number of bits per float number (options: 32/64, default: 32)

Example input file

{
  "labels": ["ALD", "TRC"],
  "sizes": [2, 1],
  "box_size": 250.0,
  "input_xyz_template": "enzymes_double_100_tracers_492_",
  "input_xyz_range": [1, 11],
  "min_time": 0.0,
  "number_of_bins": 100,
  "bin_range": [0, 30]
}

Usage

If you have already prepared an input JSON file (using keywords introduced above), you can run the Lengths.py program using following command:

python Lengths.py input.json

Output files

Successful computations should produce:

(TEMPLATE)len.txt data file with bond lengths histogram,

Monte Carlo Excluded Volume

`ExVol.py`

Keywords

Required keywords:

"box_size": float — size of simulation (cubic) box (Å),
"tracer_radii": [float, ...] — radii of tracers randomly inserted into the box: if list contains one value, spherical particle is being inserted; else, linear particle composed of beads of given radii is being inserted (Å),
"number_of_trials": integer — number of Monte Carlo insertions per snapshot,
"input_str_filename": string — input str file, from which crowder sizes are loaded,
"radii_mode": option — bead radius definition (options: "hydrodynamic"/"lennard-jones")
"times": [float, ...] — times for which configurations are loaded from .xyz files,
"input_xyz_template": string — template of input xyz filenames,
"input_xyz_range": [integer, integer] — the number range defining input xyz filenames.

pyBrown expects input xyz files to follow a specific naming scheme: ..., (TEMPLATE)(NUMBER).xyz, (TEMPLATE)(NUMBER).xyz, ... (where TEMPLATE is a string variable defined with the keyword "input_xyz_template" and NUMBER is an integer from range defined with the keyword "input_xyz_range")

Optional keywords:

"debug": boolean — print extra information useful for debugging purposes (default: false)
"verbose": boolean — print extra information (default: false)
"bond_lengths": option — way of defining separations between beads in case of nonspherical tracer (default (for now only option): "hydrodynamic_radii")
"withdraw": [string, ...] — list of labels representing particles that are randomly withdrawn before every insertion (default: []),
"scan_mode": boolean — if false: excluded volume is computed for tracer defined by "tracer_radii"; if true: excluded volume is computed for tracers of size from 0.0 to "tracer_radii" (default: false)
"scan_density": integer — number of tracer radii scanned in "scan_mode" (default: 2)
"float_type": option — number of bits per float number (options: 32/64, default: 32)

Example input files

{
  "box_size": 750.0,
  "tracer_radii": [0.0],
  "number_of_trials": 1000,
  "input_xyz_template": "ficoll_42_",
  "input_xyz_range": [1, 4],
  "input_str_filename": "ficoll_42_1.str",
  "radii_mode": "hydrodynamic",
  "times": [0.0, 1000000.0, 2000000.0, 3000000.0, 4000000.0, 4500000.0],
  "withdraw": ["FIC", "FIC"]
}

{
  "box_size": 750.0,
  "tracer_radii": [51],
  "number_of_trials": 1000,
  "input_xyz_template": "ficoll_42_",
  "input_xyz_range": [1, 4],
  "input_str_filename": "ficoll_42_1.str",
  "radii_mode": "hydrodynamic",
  "times": [0.0, 1000000.0, 2000000.0, 3000000.0, 4000000.0, 4500000.0],
  "withdraw": [],
  "scan_mode": true,
  "scan_density": 3
}

Usage

If you have already prepared an input JSON file (using keywords introduced above), you can run the ExVol.py program using following command:

python ExVol.py input.json

Output files

Successful computations should produce:

*fex.txt data file with excluded volume fractions as a function of tracer radii,

Pore Size Distribution

`Pores.py`

Keywords

Required keywords:

"box_size": float — size of simulation (cubic) box (Å),
"max_tracer_radius": float — (Å),
"dr_tracer": float — tracer size increment (Å),
"input_xyz_template": string — template of input xyz filenames,
"input_xyz_range": [integer, integer] — the number range defining input xyz filenames,
"input_str_filename": string — input str filename,
"times": [float, ...] — times for which configurations are loaded from .xyz files (ps),
"radii_mode": option — bead radius definition (options: "hydrodynamic"/"lennard-jones"),
"number_of_bins": integer — number of histogram bins.

Optional keywords:

"debug": boolean — print extra information useful for debugging purposes (default: false),
"verbose": boolean — print extra information (default: false),
"float_type": option — number of bits per float number (options: 32/64, default: 32),
"omp_cores": integer — number of CPU units (default: 0 meaning autopick via multiprocessing.cpu_count()).

Example input file

{
  "box_size": 750.0,
  "input_xyz_template": "cytoplasm_250_",
  "input_xyz_range": [1,2],
  "times": [1000000.0],
  "input_str_filename": "cytoplasm_250_1.str",
  "radii_mode": "lennard-jones",
  "max_tracer_radius": 200.0,
  "dr_tracer": 1.0,
  "number_of_trials": 100,
  "number_of_bins": 40,
  "verbose": true,
  "omp_cores": 4
}

Usage

If you have already prepared an input JSON file (using keywords introduced above), you can run the Pores.py program using following command:

python Pores.py input.json

Output files

Successful computations should produce:

*psd.txt data file with pore size distribution,
*psd.pdf image file with pore size distribution

Snapshot voxelization

`Voxels.py`

Keywords

Required keywords:

"box_size": float — size of simulation (cubic) box (Å),
"input_xyz_filename": string — input xyz filename,
"input_str_filename": string — input str filename,
"snapshot_time": float — time defining single snapshot from .xyz file (ps),
"grid_density": integer — number of voxels per box side,
"radii_mode": option — bead radius definition (options: "hydrodynamic"/"lennard-jones")

Optional keywords:

"debug": boolean — print extra information useful for debugging purposes (default: false),
"verbose": boolean — print extra information (default: false),
"float_type": option — number of bits per float number (options: 32/64, default: 32),
"omp_cores": integer — number of CPU units (default: 0 meaning autopick via multiprocessing.cpu_count()).

Example input file

{
  "box_size": 750.0,
  "input_xyz_filename": "cytoplasm_250_1.xyz",
  "input_str_filename": "cytoplasm_250_1.str",
  "verbose": true,
  "debug": true,
  "grid_density": 10,
  "snapshot_time": 1629699.184723,
  "radii_mode": "lennard-jones"
}

Usage

If you have already prepared an input JSON file (using keywords introduced above), you can run the Voxels.py program using following command:

python Voxels.py input.json

Output files

Successful computations should produce:

*voxels.txt data file with digitized snapshot,

`PlotVoxels.py`

If you have already voxelized snapshot you should have *voxels.txt file. You can run the PlotVoxels.py program using following command:

python PlotVoxels.py *voxels.txt

Name		Name	Last commit message	Last commit date
Latest commit History 242 Commits
pyBrown_tools		pyBrown_tools
tests		tests
.travis.yml		.travis.yml
Angles.py		Angles.py
Concentration.py		Concentration.py
DispCorr.py		DispCorr.py
Energy.py		Energy.py
ExVol.py		ExVol.py
Fluxes.py		Fluxes.py
H_cell_sim.py		H_cell_sim.py
LICENSE		LICENSE
Lengths.py		Lengths.py
MSD.py		MSD.py
Makefile		Makefile
Order.py		Order.py
Pack.py		Pack.py
PlotVoxels.py		PlotVoxels.py
Pores.py		Pores.py
RDF.py		RDF.py
README.md		README.md
RotDiff.py		RotDiff.py
Voxels.py		Voxels.py
Xyz_to_str.py		Xyz_to_str.py
Zmat.py		Zmat.py
requirements.txt		requirements.txt

License

tskora/pyBrown-tools

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pyBrown

Features

First steps

Table of Contents

General remarks

Units

Generating structure files

Keywords

Trajectory analysis

MSD.py

Keywords

Example input file

Usage

Output files

Energy.py

Keywords

Example input file

Usage

Output files

Lengths.py

Keywords

Example input file

Usage

Output files

Monte Carlo Excluded Volume

ExVol.py

Keywords

Example input files

Usage

Output files

Pore Size Distribution

Pores.py

Keywords

Example input file

Usage

Output files

Snapshot voxelization

Voxels.py

Keywords

Example input file

Usage

Output files

PlotVoxels.py

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`MSD.py`

`Energy.py`

`Lengths.py`

`ExVol.py`

`Pores.py`

`Voxels.py`

`PlotVoxels.py`

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