104 lines
2.7 KiB
Python
Executable File
104 lines
2.7 KiB
Python
Executable File
#!/usr/bin/env python3
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import os
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from copy import deepcopy
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import matplotlib.pyplot as plt
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import numpy as np
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import sys
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from adaptiveumbrella.wham2d import WHAM2DRunner
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sys.path.append('..')
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class MyUmbrellaRunner(WHAM2DRunner):
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def __init__(self):
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WHAM2DRunner.__init__(self)
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cum_frames = [0]
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def after_run_hook(self):
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filename = "tmp/pmf_{}.pdf".format("%02d" % self.num_iterations)
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print("Writing new pmf to {}".format(filename))
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pmf_to_plot = deepcopy(self.pmf)
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pmf_to_plot[pmf_to_plot < 0] = None
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pmf_to_plot[self.sample_list == 0] = None
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pmf_to_plot = pmf_to_plot.T
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self.cum_frames.append(len(self.sample_list[self.sample_list > 0]))
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fig, (ax0, ax1) = plt.subplots(ncols=2)
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im = ax0.imshow(pmf_to_plot, origin='lower', cmap='jet')
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cb = fig.colorbar(im, ax=ax0, orientation='horizontal', pad=0.15)
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cb.set_label("kJ/mol")
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ax1.plot(self.cum_frames, linewidth=0.5, marker="o", color='black')
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ax1.set_xlabel("Cycles")
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ax1.set_ylabel("Number of umbrella Windows")
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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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# ax0.set_yticks(tick_positions)
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# ax0.set_yticklabels(tick_labels)
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# ax0.set_xticks(tick_positions)
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# ax0.set_xticklabels(tick_labels)
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ax0.set_ylabel("$\phi$")
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ax0.set_xlabel("$\psi$")
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fig.subplots_adjust(wspace=.5)
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plt.savefig(filename, bbox_inches='tight', dpi=200)
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os.system("cp {} {}".format(filename, "tmp/pmf_current.pdf"))
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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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if not os.path.exists("tmp"):
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os.mkdir('tmp')
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threads = []
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for f in lambdas:
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counter += 1
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print("{}) Simulate lambda1={}, lambda2={}".format(counter, *f))
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command = "bash data/sim.sh {} {} 2>&1 > tmp/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.whamconfig = {
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'Px': 'pi',
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'hist_min_x': -3,
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'hist_max_x': 3,
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'num_bins_x': 100,
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'Py': 'pi',
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'hist_min_y': -3,
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'hist_max_y': 3,
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'num_bins_y': 100,
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'tolerance': 0.1,
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'fc_x': 100,
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'fc_y': 100
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}
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runner.cvs = np.array([
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(-3, 3, 0.2),
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(-3, 3, 0.2),
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])
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runner.cvs_init = (0, 0)
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runner.E_min = 5
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runner.E_max = 100
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runner.E_incr = 10
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runner.max_iterations = 5
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runner.run()
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