bauer/adaptiveumbrella
1
0
Fork 0
Code Issues Pull Requests Actions Packages Projects Releases Wiki Activity

refractoring

Browse Source
This commit is contained in:
Daniel Bauer
2018-06-17 10:08:55 +02:00
parent 65fc1f6c35
commit 778e2d08df
8 changed files with 330 additions and 152 deletions
Download Patch File Download Diff File Expand all files Collapse all files

4
.gitignore vendored Normal file
View File

@@ -0,0 +1,4 @@
venv
.idea
plot

122
LICENSE.txt Normal file
View File

@@ -0,0 +1,122 @@
Creative Commons Legal Code
CC0 1.0 Universal
CREATIVE COMMONS CORPORATION IS NOT A LAW FIRM AND DOES NOT PROVIDE
LEGAL SERVICES. DISTRIBUTION OF THIS DOCUMENT DOES NOT CREATE AN
ATTORNEY-CLIENT RELATIONSHIP. CREATIVE COMMONS PROVIDES THIS
INFORMATION ON AN "AS-IS" BASIS. CREATIVE COMMONS MAKES NO WARRANTIES
REGARDING THE USE OF THIS DOCUMENT OR THE INFORMATION OR WORKS
PROVIDED HEREUNDER, AND DISCLAIMS LIABILITY FOR DAMAGES RESULTING FROM
THE USE OF THIS DOCUMENT OR THE INFORMATION OR WORKS PROVIDED
HEREUNDER.
Statement of Purpose
The laws of most jurisdictions throughout the world automatically confer
exclusive Copyright and Related Rights (defined below) upon the creator
and subsequent owner(s) (each and all, an "owner") of an original work of
authorship and/or a database (each, a "Work").
Certain owners wish to permanently relinquish those rights to a Work for
the purpose of contributing to a commons of creative, cultural and
scientific works ("Commons") that the public can reliably and without fear
of later claims of infringement build upon, modify, incorporate in other
works, reuse and redistribute as freely as possible in any form whatsoever
and for any purposes, including without limitation commercial purposes.
These owners may contribute to the Commons to promote the ideal of a free
culture and the further production of creative, cultural and scientific
works, or to gain reputation or greater distribution for their Work in
part through the use and efforts of others.
For these and/or other purposes and motivations, and without any
expectation of additional consideration or compensation, the person
associating CC0 with a Work (the "Affirmer"), to the extent that he or she
is an owner of Copyright and Related Rights in the Work, voluntarily
elects to apply CC0 to the Work and publicly distribute the Work under its
terms, with knowledge of his or her Copyright and Related Rights in the
Work and the meaning and intended legal effect of CC0 on those rights.
1. Copyright and Related Rights. A Work made available under CC0 may be
protected by copyright and related or neighboring rights ("Copyright and
Related Rights"). Copyright and Related Rights include, but are not
limited to, the following:
i. the right to reproduce, adapt, distribute, perform, display,
communicate, and translate a Work;
ii. moral rights retained by the original author(s) and/or performer(s);
iii. publicity and privacy rights pertaining to a person's image or
likeness depicted in a Work;
iv. rights protecting against unfair competition in regards to a Work,
subject to the limitations in paragraph 4(a), below;
v. rights protecting the extraction, dissemination, use and reuse of data
in a Work;
vi. database rights (such as those arising under Directive 96/9/EC of the
European Parliament and of the Council of 11 March 1996 on the legal
protection of databases, and under any national implementation
thereof, including any amended or successor version of such
directive); and
vii. other similar, equivalent or corresponding rights throughout the
world based on applicable law or treaty, and any national
implementations thereof.
2. Waiver. To the greatest extent permitted by, but not in contravention
of, applicable law, Affirmer hereby overtly, fully, permanently,
irrevocably and unconditionally waives, abandons, and surrenders all of
Affirmer's Copyright and Related Rights and associated claims and causes
of action, whether now known or unknown (including existing as well as
future claims and causes of action), in the Work (i) in all territories
worldwide, (ii) for the maximum duration provided by applicable law or
treaty (including future time extensions), (iii) in any current or future
medium and for any number of copies, and (iv) for any purpose whatsoever,
including without limitation commercial, advertising or promotional
purposes (the "Waiver"). Affirmer makes the Waiver for the benefit of each
member of the public at large and to the detriment of Affirmer's heirs and
successors, fully intending that such Waiver shall not be subject to
revocation, rescission, cancellation, termination, or any other legal or
equitable action to disrupt the quiet enjoyment of the Work by the public
as contemplated by Affirmer's express Statement of Purpose.
3. Public License Fallback. Should any part of the Waiver for any reason
be judged legally invalid or ineffective under applicable law, then the
Waiver shall be preserved to the maximum extent permitted taking into
account Affirmer's express Statement of Purpose. In addition, to the
extent the Waiver is so judged Affirmer hereby grants to each affected
person a royalty-free, non transferable, non sublicensable, non exclusive,
irrevocable and unconditional license to exercise Affirmer's Copyright and
Related Rights in the Work (i) in all territories worldwide, (ii) for the
maximum duration provided by applicable law or treaty (including future
time extensions), (iii) in any current or future medium and for any number
of copies, and (iv) for any purpose whatsoever, including without
limitation commercial, advertising or promotional purposes (the
"License"). The License shall be deemed effective as of the date CC0 was
applied by Affirmer to the Work. Should any part of the License for any
reason be judged legally invalid or ineffective under applicable law, such
partial invalidity or ineffectiveness shall not invalidate the remainder
of the License, and in such case Affirmer hereby affirms that he or she
will not (i) exercise any of his or her remaining Copyright and Related
Rights in the Work or (ii) assert any associated claims and causes of
action with respect to the Work, in either case contrary to Affirmer's
express Statement of Purpose.
4. Limitations and Disclaimers.
a. No trademark or patent rights held by Affirmer are waived, abandoned,
surrendered, licensed or otherwise affected by this document.
b. Affirmer offers the Work as-is and makes no representations or
warranties of any kind concerning the Work, express, implied,
statutory or otherwise, including without limitation warranties of
title, merchantability, fitness for a particular purpose, non
infringement, or the absence of latent or other defects, accuracy, or
the present or absence of errors, whether or not discoverable, all to
the greatest extent permissible under applicable law.
c. Affirmer disclaims responsibility for clearing rights of other persons
that may apply to the Work or any use thereof, including without
limitation any person's Copyright and Related Rights in the Work.
Further, Affirmer disclaims responsibility for obtaining any necessary
consents, permissions or other rights required for any use of the
Work.
d. Affirmer understands and acknowledges that Creative Commons is not a
party to this document and has no duty or obligation with respect to
this CC0 or use of the Work.

15
README.md Normal file
View File

@@ -0,0 +1,15 @@
# Python module for adaptive umbrella sampling
This module can be used to perform adaptive umbrella sampling of a multi-dimensional potential of mean force. The
algorithm involves::
1) calculate the free energy landscape
2) Among existing windows, select windows with E < E_max
3) For each selected window, generate 3^N-1 neighbor windows
4) Sample new windows, then go to 1) or stop if no new windows can be found
For more details about the algorithm: See
Self-Learning Adaptive Umbrella Sampling Method for the Determination of Free Energy Landscapes in Multiple Dimensions (Wojtas-Niziurski†, Meng, Roux, Bernèche, 2013)
[(https://doi.org/10.1021/ct300978b)](https://doi.org/10.1021/ct300978b)

5
adaptiveumbrella/__init__.py Normal file
View File

@@ -0,0 +1,5 @@
from __future__ import absolute_import
from .runner import UmbrellaRunner
__all__ = ['AdaptiveUmbrella']
__version__ = "0.1.0"

93
adaptive_umbrella.py → adaptiveumbrella/runner.py
View File

@@ -216,99 +216,6 @@ class UmbrellaRunner():
""" This can be implemented to hook into the simulation cycle before the simulation runs """
pass
class WHAM2DRunner(UmbrellaRunner):
""" Umbrella runner implementation that uses wham-2d to perform
the pmf calculation.
Attributes:
WHAM_EXEC: path to wham executeable
"""
def __init__(self):
UmbrellaRunner.__init__(self)
self.WHAM_EXEC = 'wham-2d'
def calculate_new_pmf(self):
import os
from shutil import copyfile
simulation_dir = "simulations"
print("Collecting sampling data from simulations folder")
# collect COLVARs
wham_dir = "WHAM/"
if not os.path.exists(wham_dir):
os.makedirs(wham_dir)
for folder in os.listdir(simulation_dir):
src = os.path.join(simulation_dir, folder, "COLVAR")
dst = os.path.join(wham_dir, folder + ".xvg")
copyfile(src, dst)
# create metadata file
metadata_file = os.path.join(wham_dir, "{}_metadata.dat".format(self.num_iterations))
fc_x = 100
fc_y = 100
with open(metadata_file, 'w') as out:
for f in os.listdir(simulation_dir):
prefix, x, y = f.split("_")
out.write("WHAM/{}.xvg {} {} {} {}\n".format(f, x, y, fc_x, fc_y))
# run WHAM2d
print("Running WHAM-2d")
wham_output = os.path.join(wham_dir, "{}_freeenergy.dat".format(self.num_iterations))
periodicity_x = "pi"
periodicity_y = "pi"
tolerance = 0.1
frames_x, frames_y = 1002, 1002
min_x = self.cvs[0][0]
max_x = self.cvs[0][1]
min_y = self.cvs[1][0]
max_y = self.cvs[1][1]
cmd = "{exec} Px={px} {min_x} {max_x} {frames_x} Py={py} {min_y} {max_y} {frames_y} {tol} 298 0 {metafile} {outfile} 0".format(
exec=self.WHAM_EXEC,
px=periodicity_x,
min_x=min_x,
max_x=max_x,
frames_x=frames_x,
py=periodicity_y,
min_y=min_y,
max_y=max_y,
frames_y=frames_y,
tol=tolerance,
metafile=metadata_file,
outfile=wham_output
)
print(cmd)
os.system(cmd)
# read wham to new pmf
return self.read_pmf(wham_output)
def read_pmf(self, pmf_path):
import pandas as pd
print("Update PMF from WHAM")
df = pd.read_csv(pmf_path, delim_whitespace=True, names=['x', 'y', 'e', 'pro'], skiprows=1,
index_col=None)
df = df.replace([np.inf, -np.inf], np.nan).dropna(subset=['e'], how='all')
new_pmf = deepcopy(self.pmf)
for x in range(new_pmf.shape[0]):
for y in range(new_pmf.shape[1]):
lambdax, lambday = self._get_lambdas_for_index((x, y))
x_selection = (df.x - lambdax).abs() < 0.01
y_selection = (df.y - lambday).abs() < 0.01
selected_energies = df[(x_selection) & (y_selection)].e
if len(selected_energies) == 0:
new_pmf[x, y] = -1
else:
new_pmf[x, y] = selected_energies.iloc[0]
return new_pmf
class UmbrellaRunnerTest(unittest.TestCase):
def test_init_pmf_3d(self):

59
example.py
View File

@@ -1,59 +0,0 @@
from adaptive_umbrella import WHAM2DRunner
from copy import deepcopy
import os
import numpy as np
import matplotlib.pyplot as plt
class MyUmbrellaRunner(WHAM2DRunner):
def after_run_hook(self):
filename = "pmf_{}.pdf".format(self.num_iterations)
print("Writing new pmf to {}".format(filename))
pmf_to_plot = deepcopy(self.pmf.T)
pmf_to_plot[pmf_to_plot < 0] = None
plt.figure()
plt.imshow(pmf_to_plot, origin="bottom", cmap='jet')
ticks = [(x,x) for x in [-3, -2, -1, 0, 1, 2, 3]]
tick_positions = [ self._get_index_for_lambdas(x)[0] for x in ticks ]
tick_labels = [ str(x[0]) for x in ticks ]
plt.xticks(tick_positions, tick_labels)
plt.yticks(tick_positions, tick_labels)
cb = plt.colorbar(pad=0.1)
cb.set_label("kJ/mol")
plt.savefig(filename)
os.system("cp {} {}".format(filename, "pmf_current.pdf"))
def simulate_frames(self, lambdas, frames):
print("{} new simulations:".format(len(lambdas)))
counter = 0
threads = []
for f in lambdas:
counter += 1
if os.path.exists("sim/sim_{}_{}/COLVAR".format(*f)):
print("{}) Skipping lambdas={}/{}: COLVAR exists".format(counter, *f))
continue
print("{}) Simulate lambda1={}, lambda2={}".format(counter, *f))
command = "bash sim.sh {} {} 2>&1 > run.log".format(*f)
# print("Running {}".format(command))
os.system(command)
runner = MyUmbrellaRunner()
runner.WHAM_EXEC = "/opt/wham/wham-2d/wham-2d"
runner.cvs = np.array([
(-3, 3, 0.2),
(-3, 3, 0.2),
])
runner.cvs_init = (1.4, -1.4)
runner.E_min = 10
runner.E_max = 100
runner.E_incr = 10
runner.max_iterations = 100
runner.run()

152
examples/example.py Normal file
View File

@@ -0,0 +1,152 @@
import os
from copy import deepcopy
import matplotlib.pyplot as plt
import numpy as np
from adaptiveumbrella.runner import UmbrellaRunner
class WHAM2DRunner(UmbrellaRunner):
""" Umbrella runner implementation that uses wham-2d to perform
the pmf calculation.
Attributes:
WHAM_EXEC: path to wham executeable
"""
def __init__(self):
UmbrellaRunner.__init__(self)
self.WHAM_EXEC = 'wham-2d'
def calculate_new_pmf(self):
import os
from shutil import copyfile
simulation_dir = "simulations"
print("Collecting sampling data from simulations folder")
# collect COLVARs
wham_dir = "WHAM/"
if not os.path.exists(wham_dir):
os.makedirs(wham_dir)
for folder in os.listdir(simulation_dir):
src = os.path.join(simulation_dir, folder, "COLVAR")
dst = os.path.join(wham_dir, folder + ".xvg")
copyfile(src, dst)
# create metadata file
metadata_file = os.path.join(wham_dir, "{}_metadata.dat".format(self.num_iterations))
fc_x = 100
fc_y = 100
with open(metadata_file, 'w') as out:
for f in os.listdir(simulation_dir):
prefix, x, y = f.split("_")
out.write("WHAM/{}.xvg {} {} {} {}\n".format(f, x, y, fc_x, fc_y))
# run WHAM2d
print("Running WHAM-2d")
wham_output = os.path.join(wham_dir, "{}_freeenergy.dat".format(self.num_iterations))
periodicity_x = "pi"
periodicity_y = "pi"
tolerance = 0.1
frames_x, frames_y = 1002, 1002
min_x = self.cvs[0][0]
max_x = self.cvs[0][1]
min_y = self.cvs[1][0]
max_y = self.cvs[1][1]
cmd = "{exec} Px={px} {min_x} {max_x} {frames_x} Py={py} {min_y} {max_y} {frames_y} {tol} 298 0 {metafile} {outfile} 0".format(
exec=self.WHAM_EXEC,
px=periodicity_x,
min_x=min_x,
max_x=max_x,
frames_x=frames_x,
py=periodicity_y,
min_y=min_y,
max_y=max_y,
frames_y=frames_y,
tol=tolerance,
metafile=metadata_file,
outfile=wham_output
)
print(cmd)
os.system(cmd)
# read wham to new pmf
return self.read_pmf(wham_output)
def read_pmf(self, pmf_path):
import pandas as pd
print("Update PMF from WHAM")
df = pd.read_csv(pmf_path, delim_whitespace=True, names=['x', 'y', 'e', 'pro'], skiprows=1,
index_col=None)
df = df.replace([np.inf, -np.inf], np.nan).dropna(subset=['e'], how='all')
new_pmf = deepcopy(self.pmf)
for x in range(new_pmf.shape[0]):
for y in range(new_pmf.shape[1]):
lambdax, lambday = self._get_lambdas_for_index((x, y))
x_selection = (df.x - lambdax).abs() < 0.01
y_selection = (df.y - lambday).abs() < 0.01
selected_energies = df[(x_selection) & (y_selection)].e
if len(selected_energies) == 0:
new_pmf[x, y] = -1
else:
new_pmf[x, y] = selected_energies.iloc[0]
return new_pmf
class MyUmbrellaRunner(WHAM2DRunner):
def after_run_hook(self):
filename = "pmf_{}.pdf".format(self.num_iterations)
print("Writing new pmf to {}".format(filename))
pmf_to_plot = deepcopy(self.pmf.T)
pmf_to_plot[pmf_to_plot < 0] = None
plt.figure()
plt.imshow(pmf_to_plot, origin="bottom", cmap='jet')
ticks = [(x,x) for x in [-3, -2, -1, 0, 1, 2, 3]]
tick_positions = [ self._get_index_for_lambdas(x)[0] for x in ticks ]
tick_labels = [ str(x[0]) for x in ticks ]
plt.xticks(tick_positions, tick_labels)
plt.yticks(tick_positions, tick_labels)
cb = plt.colorbar(pad=0.1)
cb.set_label("kJ/mol")
plt.savefig(filename)
os.system("cp {} {}".format(filename, "pmf_current.pdf"))
def simulate_frames(self, lambdas, frames):
print("{} new simulations:".format(len(lambdas)))
counter = 0
threads = []
for f in lambdas:
counter += 1
if os.path.exists("sim/sim_{}_{}/COLVAR".format(*f)):
print("{}) Skipping lambdas={}/{}: COLVAR exists".format(counter, *f))
continue
print("{}) Simulate lambda1={}, lambda2={}".format(counter, *f))
command = "bash sim.sh {} {} 2>&1 > run.log".format(*f)
# print("Running {}".format(command))
os.system(command)
runner = MyUmbrellaRunner()
runner.WHAM_EXEC = "/opt/wham/wham-2d/wham-2d"
runner.cvs = np.array([
(-3, 3, 0.2),
(-3, 3, 0.2),
])
runner.cvs_init = (1.4, -1.4)
runner.E_min = 10
runner.E_max = 100
runner.E_incr = 10
runner.max_iterations = 100
runner.run()

32
setup.py Normal file
View File

@@ -0,0 +1,32 @@
#!/usr/bin/env python3
try:
from setuptools import setup
except ImportError:
from distutils.core import setup
LONG_DESCRIPTION = """`adaptiveumbrella` is a python implementation of the
[self-learning adaptive umbrella sampling algorithm (Wojtas-Niziurski†, Meng, Roux, Bernèche, 2013)]
(https://pubs.acs.org/doi/abs/10.1021/ct300978b) technique. It allows the calculation of a multidimensional potential
of mean force while automatically exploring the phase space.
"""
# Parse the version from the fiona module.
with open('adaptiveumbrella/__init__.py') as f:
for line in f:
if line.find("__version__") >= 0:
version = line.split("=")[1].strip()
version = version.strip('"')
version = version.strip("'")
break
setup(
name='adaptiveumbrella',
version=version,
description='Adaptive umbrella sampling in Python',
license='CC0',
author='Daniel Bauer',
author_email='bauer@cbs.tu-darmstadt.de',
url='https://github.com/danijoo/adaptiveumbrella',
long_description=LONG_DESCRIPTION,
packages=['adaptiveumbrella'],
install_requires=['numpy'])