def test_read_one(self):
reader = Reader().readThisFile('data/amber_mini.pdb')
coordinates = reader.read()
self.assertEqual(148, reader.numberOfAtoms)
self.assertEqual(3, reader.numberOfFrames)
numpy.testing.assert_almost_equal(testPdbReader.mini_all_coords,
coordinates, 12)
def test_read_multiple_only_CA(self):
reader = Reader().readThisFile('data/amber_mini.pdb').andThisOtherFile('data/amber_mini.pdb').gettingOnlyCAs()
coordinates = reader.read()
self.assertEqual(9, reader.numberOfAtoms)
self.assertEqual(6, reader.numberOfFrames)
coord_shape = coordinates.shape
self.assertEqual(coord_shape[0]*coord_shape[1]*coord_shape[2],9*6*3)
numpy.testing.assert_almost_equal(numpy.reshape(self.mini_CA_coords,3*9*3), numpy.reshape(coordinates,6*9*3)[0:9*3*3], 10)
numpy.testing.assert_almost_equal(numpy.reshape(self.mini_CA_coords,3*9*3), numpy.reshape(coordinates,6*9*3)[9*3*3:], 10)
def test_read_multiple(self):
reader = Reader().readThisFile('data/amber_mini.pdb').andThisOtherFile('data/amber_mini.pdb')
coordinates = reader.read()
self.assertEqual(148, reader.numberOfAtoms)
self.assertEqual(6, reader.numberOfFrames)
coord_shape = coordinates.shape
self.assertEqual(coord_shape[0]*coord_shape[1]*coord_shape[2],148*6*3)
numpy.testing.assert_almost_equal(numpy.reshape(self.mini_all_coords,3*148*3), numpy.reshape(coordinates,148*6*3)[0:148*3*3], 10)
numpy.testing.assert_almost_equal(numpy.reshape(self.mini_all_coords,3*148*3), numpy.reshape(coordinates,148*6*3)[148*3*3:], 10)
def test_read_multiple_only_CA(self):
reader = Reader().readThisFile('data/amber_mini.pdb').andThisOtherFile(
'data/amber_mini.pdb').gettingOnlyCAs()
coordinates = reader.read()
self.assertEqual(9, reader.numberOfAtoms)
self.assertEqual(6, reader.numberOfFrames)
coord_shape = coordinates.shape
self.assertEqual(coord_shape[0] * coord_shape[1] * coord_shape[2],
9 * 6 * 3)
numpy.testing.assert_almost_equal(
numpy.reshape(self.mini_CA_coords, 3 * 9 * 3),
numpy.reshape(coordinates, 6 * 9 * 3)[0:9 * 3 * 3], 10)
numpy.testing.assert_almost_equal(
numpy.reshape(self.mini_CA_coords, 3 * 9 * 3),
numpy.reshape(coordinates, 6 * 9 * 3)[9 * 3 * 3:], 10)
def test_read_multiple(self):
reader = Reader().readThisFile('data/amber_mini.pdb').andThisOtherFile(
'data/amber_mini.pdb')
coordinates = reader.read()
self.assertEqual(148, reader.numberOfAtoms)
self.assertEqual(6, reader.numberOfFrames)
coord_shape = coordinates.shape
self.assertEqual(coord_shape[0] * coord_shape[1] * coord_shape[2],
148 * 6 * 3)
numpy.testing.assert_almost_equal(
numpy.reshape(self.mini_all_coords, 3 * 148 * 3),
numpy.reshape(coordinates, 148 * 6 * 3)[0:148 * 3 * 3], 10)
numpy.testing.assert_almost_equal(
numpy.reshape(self.mini_all_coords, 3 * 148 * 3),
numpy.reshape(coordinates, 148 * 6 * 3)[148 * 3 * 3:], 10)
def test_read_one_only_CA(self):
reader = Reader().readThisFile('data/amber_mini.pdb').gettingOnlyCAs()
coordinates = reader.read()
self.assertEqual(9, reader.numberOfAtoms)
self.assertEqual(3, reader.numberOfFrames)
numpy.testing.assert_almost_equal(self.mini_CA_coords, coordinates, 10)
def test_read_one(self):
reader = Reader().readThisFile('data/amber_mini.pdb')
coordinates = reader.read()
self.assertEqual(148, reader.numberOfAtoms)
self.assertEqual(3, reader.numberOfFrames)
numpy.testing.assert_almost_equal(testPdbReader.mini_all_coords, coordinates, 12)
def test_read_one_only_CA(self):
reader = Reader().readThisFile('data/amber_mini.pdb').gettingOnlyCAs()
coordinates = reader.read()
self.assertEqual(9, reader.numberOfAtoms)
self.assertEqual(3, reader.numberOfFrames)
numpy.testing.assert_almost_equal(self.mini_CA_coords, coordinates, 10)
import time
import os
import bz2
from pyRMSD.utils.proteinReading import Reader
if __name__ == '__main__':
using_cuda = "QCP_CUDA_CALCULATOR" in pyRMSD.RMSDCalculator.availableCalculators()
######################
# BENCHMARKING
######################
print "Loading file..."
t1 = time.time()
print "\tUncompressing..."
open("tmp_amber_long.pdb","w").write(bz2.BZ2File("data/amber_long.pdb.tar.bz2").read())
print "\tLoading..."
reader = Reader().readThisFile('tmp_amber_long.pdb').gettingOnlyCAs()
coordsets = reader.read()
number_of_atoms = reader.numberOfAtoms
number_of_conformations = reader.numberOfFrames
os.system("rm tmp_amber_long.pdb")
print "\tDeleting temporary file"
t2 = time.time()
print 'Loading took %0.3f s' % (t2-t1)
#######################
# CALCULATION
#######################
types = pyRMSD.RMSDCalculator.availableCalculators()
DEFAULT_PRECISSION = 1e-8
precissions = collections.defaultdict(lambda:DEFAULT_PRECISSION)
expect_script_str = ("".join(
open("data/expect_script",
"r").readlines())) % (TRAJECTORY_FILE, TRAJECTORY_FILE)
open("data/expect_script_tmp", "w").write(expect_script_str)
# Use 'expect' to spawn the program
start_t = time.time()
subprocess.call(["expect", "data/expect_script_tmp"])
# Load the matrix
g_rmsd_matrix = XPMConverter().convert(open("data/matrix.xpm", "r"))
stop_t = time.time()
print "g_rms's computation time:", stop_t - start_t
os.system("rm data/matrix.xpm data/rmsd.xvg data/expect_script_tmp")
# Computation with pyRMSD, g_rms uses KABSCH's
start_t = time.time()
coordinates = Reader().readThisFile(TRAJECTORY_FILE).read()
RMSDCalculator(coordinates, "KABSCH_SERIAL_CALCULATOR").oneVsFollowing(
0) # This is to mimic g_rmsd pipeline
pyrmsd_rmsd_values = RMSDCalculator(
coordinates, "QTRFIT_SERIAL_CALCULATOR").pairwiseRMSDMatrix()
stop_t = time.time()
print "pyRMSD's computation time:", stop_t - start_t
# Convert g_rmsd matrix to 'condensed'
dim = len(g_rmsd_matrix)
c_m_values = []
for i in range(dim - 1):
for j in range(i + 1, dim):
c_m_values.append(g_rmsd_matrix[i][j])
rmsd = numpy.sqrt((
from nma_algo_char.mode_application_analysis import process_energy_differences
if __name__ == '__main__':
parser = OptionParser()
parser.add_option("--type", dest="sim_type")
parser.add_option("-f", dest="work_folder")
parser.add_option("-o", dest="open_pdb_file")
parser.add_option("-c", dest="closed_pdb_file")
parser.add_option("-t", type="int", dest="temperature")
(options, args) = parser.parse_args()
open_coords = Reader().readThisFile(options.open_pdb_file).gettingOnlyCAs().read()
closed_coords = Reader().readThisFile(options.closed_pdb_file).gettingOnlyCAs().read()
# Get eigenvectors, used modes and coordinates
eigenfile = {"CC": "original_modes.1.nmd", "IC":"original_modes_cc.1.nmd"}
eigenvalues, eigenvectors, _ = ProdyNMDParser.read(os.path.join(options.work_folder,"info", eigenfile[options.sim_type]))
raw_data, min_len = load_single_proc_data(options.sim_type, os.path.join(options.work_folder,"info"))
mode_parser = LineParser("Picked", 2, int)
for line in open(os.path.join(options.work_folder, "log.txt")):
mode_parser.parse_line(line)
raw_data["picked_mode"] = numpy.array(mode_parser.data)[:min_len]
frequencies = Counter(raw_data["picked_mode"])
energy_increments = process_energy_differences(raw_data)[1:]
mc = MetropolisMCSimulator(energy_increments)
who_is_accepted = mc.who_is_accepted(options.temperature)
acc_frequencies = Counter(raw_data["picked_mode"][who_is_accepted])
def ref_rmsf_calculation(reftraj):
coords = Reader().readThisFile(reftraj).gettingOnlyCAs().read()
return coords_rmsf(coords)
def get_coordinates(trajectories, skip, max_frames):
all_coordsets = []
for traj_path in trajectories:
coords = Reader().readThisFile(traj_path).gettingOnlyCAs().read()
all_coordsets.append(coords[skip:min(len(coords), max_frames + skip)])
return all_coordsets
