refractoring
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Daniel Bauer
4
.gitignore
vendored
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.gitignore
vendored
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venv
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.idea
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plot
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122
LICENSE.txt
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122
LICENSE.txt
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15
README.md
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15
README.md
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# Python module for adaptive umbrella sampling
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This module can be used to perform adaptive umbrella sampling of a multi-dimensional potential of mean force. The
|
||||
algorithm involves::
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1) calculate the free energy landscape
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2) Among existing windows, select windows with E < E_max
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||||
3) For each selected window, generate 3^N-1 neighbor windows
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||||
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)
|
||||
|
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5
adaptiveumbrella/__init__.py
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5
adaptiveumbrella/__init__.py
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from __future__ import absolute_import
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from .runner import UmbrellaRunner
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__all__ = ['AdaptiveUmbrella']
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__version__ = "0.1.0"
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@@ -216,99 +216,6 @@ class UmbrellaRunner():
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""" This can be implemented to hook into the simulation cycle before the simulation runs """
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pass
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class WHAM2DRunner(UmbrellaRunner):
|
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""" Umbrella runner implementation that uses wham-2d to perform
|
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the pmf calculation.
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|
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Attributes:
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WHAM_EXEC: path to wham executeable
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|
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"""
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def __init__(self):
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UmbrellaRunner.__init__(self)
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self.WHAM_EXEC = 'wham-2d'
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def calculate_new_pmf(self):
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import os
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from shutil import copyfile
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simulation_dir = "simulations"
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print("Collecting sampling data from simulations folder")
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# collect COLVARs
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wham_dir = "WHAM/"
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if not os.path.exists(wham_dir):
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os.makedirs(wham_dir)
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for folder in os.listdir(simulation_dir):
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src = os.path.join(simulation_dir, folder, "COLVAR")
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dst = os.path.join(wham_dir, folder + ".xvg")
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copyfile(src, dst)
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# create metadata file
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metadata_file = os.path.join(wham_dir, "{}_metadata.dat".format(self.num_iterations))
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fc_x = 100
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fc_y = 100
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with open(metadata_file, 'w') as out:
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for f in os.listdir(simulation_dir):
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prefix, x, y = f.split("_")
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out.write("WHAM/{}.xvg {} {} {} {}\n".format(f, x, y, fc_x, fc_y))
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|
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# run WHAM2d
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print("Running WHAM-2d")
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wham_output = os.path.join(wham_dir, "{}_freeenergy.dat".format(self.num_iterations))
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periodicity_x = "pi"
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periodicity_y = "pi"
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tolerance = 0.1
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frames_x, frames_y = 1002, 1002
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min_x = self.cvs[0][0]
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max_x = self.cvs[0][1]
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min_y = self.cvs[1][0]
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max_y = self.cvs[1][1]
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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(
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exec=self.WHAM_EXEC,
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px=periodicity_x,
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min_x=min_x,
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max_x=max_x,
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frames_x=frames_x,
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py=periodicity_y,
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min_y=min_y,
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max_y=max_y,
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frames_y=frames_y,
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tol=tolerance,
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metafile=metadata_file,
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outfile=wham_output
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)
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print(cmd)
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os.system(cmd)
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# read wham to new pmf
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return self.read_pmf(wham_output)
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def read_pmf(self, pmf_path):
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import pandas as pd
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print("Update PMF from WHAM")
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df = pd.read_csv(pmf_path, delim_whitespace=True, names=['x', 'y', 'e', 'pro'], skiprows=1,
|
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index_col=None)
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df = df.replace([np.inf, -np.inf], np.nan).dropna(subset=['e'], how='all')
|
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new_pmf = deepcopy(self.pmf)
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for x in range(new_pmf.shape[0]):
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for y in range(new_pmf.shape[1]):
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lambdax, lambday = self._get_lambdas_for_index((x, y))
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x_selection = (df.x - lambdax).abs() < 0.01
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y_selection = (df.y - lambday).abs() < 0.01
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selected_energies = df[(x_selection) & (y_selection)].e
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if len(selected_energies) == 0:
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new_pmf[x, y] = -1
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else:
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new_pmf[x, y] = selected_energies.iloc[0]
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return new_pmf
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|
||||
|
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|
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class UmbrellaRunnerTest(unittest.TestCase):
|
||||
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||||
def test_init_pmf_3d(self):
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59
example.py
59
example.py
@@ -1,59 +0,0 @@
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||||
from adaptive_umbrella import WHAM2DRunner
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from copy import deepcopy
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import os
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import numpy as np
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import matplotlib.pyplot as plt
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|
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|
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class MyUmbrellaRunner(WHAM2DRunner):
|
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def after_run_hook(self):
|
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filename = "pmf_{}.pdf".format(self.num_iterations)
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print("Writing new pmf to {}".format(filename))
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pmf_to_plot = deepcopy(self.pmf.T)
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pmf_to_plot[pmf_to_plot < 0] = None
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plt.figure()
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plt.imshow(pmf_to_plot, origin="bottom", cmap='jet')
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ticks = [(x,x) for x in [-3, -2, -1, 0, 1, 2, 3]]
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tick_positions = [ self._get_index_for_lambdas(x)[0] for x in ticks ]
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tick_labels = [ str(x[0]) for x in ticks ]
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plt.xticks(tick_positions, tick_labels)
|
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plt.yticks(tick_positions, tick_labels)
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|
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|
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cb = plt.colorbar(pad=0.1)
|
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cb.set_label("kJ/mol")
|
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plt.savefig(filename)
|
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os.system("cp {} {}".format(filename, "pmf_current.pdf"))
|
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|
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|
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def simulate_frames(self, lambdas, frames):
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print("{} new simulations:".format(len(lambdas)))
|
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counter = 0
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|
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threads = []
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for f in lambdas:
|
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counter += 1
|
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if os.path.exists("sim/sim_{}_{}/COLVAR".format(*f)):
|
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print("{}) Skipping lambdas={}/{}: COLVAR exists".format(counter, *f))
|
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continue
|
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|
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print("{}) Simulate lambda1={}, lambda2={}".format(counter, *f))
|
||||
command = "bash sim.sh {} {} 2>&1 > run.log".format(*f)
|
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# print("Running {}".format(command))
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os.system(command)
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runner = MyUmbrellaRunner()
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runner.WHAM_EXEC = "/opt/wham/wham-2d/wham-2d"
|
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runner.cvs = np.array([
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(-3, 3, 0.2),
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||||
(-3, 3, 0.2),
|
||||
])
|
||||
runner.cvs_init = (1.4, -1.4)
|
||||
runner.E_min = 10
|
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runner.E_max = 100
|
||||
runner.E_incr = 10
|
||||
runner.max_iterations = 100
|
||||
|
||||
runner.run()
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||||
152
examples/example.py
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152
examples/example.py
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@@ -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
32
setup.py
Normal 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'])
|
||||
Reference in New Issue
Block a user