def run(self):
structure = self.search.run(self.steps)
self.ui.log.append("Lowest energy found: "+str(structure.energy))
if (isinstance(structure.lattice, SquareLattice)):
plot_2d(structure, self.ui.widget.canvas.fig)
else:
plot_3d(structure, self.ui.widget.canvas.fig)
self.ui.widget.canvas.draw()
plot_contact_map(structure, self.ui.widget_2.canvas.fig)
self.ui.widget_2.canvas.draw()
'''
This example demonstrates the setting up and running of a Monte Carlo
search on a two-dimensional square lattice.
@author: Mark Oakley
'''
from pylatt.model import HP
from pylatt.lattice import SquareLattice
from pylatt.plotter import plot_2d
from pylatt.search import MonteCarlo
import matplotlib.pyplot as plt
''' First choose a lattice. Here, we will use the two-dimensional
square lattice.'''
lattice = SquareLattice()
'''Select the protein sequrnce to search. Using the classic hydrophobic
polar model, we need to define a sequence of H and P residues.'''
model = HP("PHPPHPHHHPHHPHHHHH")
'''Now set up and run a Monte Carlo search. The temperature is an
optional parameter and defaults to 1.0 if not defined.'''
search = MonteCarlo(lattice, model, temperature = 2.0)
'''Run the search for 100 steps.The search returns the lowest energy
structure found. The energy of this structure is in structure.energy.'''
structure = search.run(100)
print("Lowest energy found: ", structure.energy)
'''To visualise this structure, use plot_2d.'''
plot_2d(structure)
plt.show()
def test_2d(self):
structure=random_avoid(100, SquareLattice())
self.assertIsNotNone(plot_2d(structure))
self.assertIsNotNone(plot_contact_map(structure))
