def write(self, filename, start=0, end=None):
'''
Writes the cofasu out to the specified trajectory file. The
neccessary topology data is taken from the first fasu - note
that since not every fasu in a cofasu has to have the same
topology, this needs some caution from the user. The optional
start and end arguments specify the range of frames to write
(from start to end-1 inclusive).
>>> topfile = '../../../test/2ozq.pdb'
>>> trjfile1 = '../../../test/2ozq.dcd'
>>> trjfile2 = '../../../test/2ozq.xtc'
>>> f1 = Fasu(topfile, trjfile1)
>>> f2 = Fasu(topfile, trjfile2)
>>> c = Cofasu([f1, f2])
>>> c.write('/tmp/cofasuwritetest.dcd', end=40)
>>> f3 = Fasu(topfile, '/tmp/cofasuwritetest.dcd')
>>> c3 = Cofasu(f3)
>>> print c3.numframes()
40
'''
if end is None:
end = self.numframes()
else:
end = min(self.numframes(),end)
if rank == self.fasulist[0].owner:
w = mda.Writer(filename, self.natoms)
kw = {'dt':1.0}
ts = Timestep(self.natoms, **kw)
for ts_index in xrange(start,end):
ts._pos=self.coords(ts_index)
if rank == self.fasulist[0].owner:
w.write(ts)
if rank == self.fasulist[0].owner:
w.close()
def test_different_precision(self, ref, tempdir):
out = self.tmp_file('precision-test', ref, tempdir)
# store more then 9 atoms to enable compression
n_atoms = 40
with ref.writer(out, n_atoms, precision=5) as w:
ts = Timestep(n_atoms=n_atoms)
ts.positions = np.random.random(size=(n_atoms, 3))
w.write(ts)
xtc = mda.lib.formats.libmdaxdr.XTCFile(out)
frame = xtc.read()
assert_equal(len(xtc), 1)
assert_equal(xtc.n_atoms, n_atoms)
assert_equal(frame.prec, 10.0**5)
def __init__(self):
self.trajectory = None
self.n_atoms = 5
self.n_frames = 5
# default for the numpy test functions
self.prec = 6
self.container_format = False
self.changing_dimensions = False
self.first_frame = Timestep(self.n_atoms)
self.first_frame.positions = np.arange(
3 * self.n_atoms).reshape(self.n_atoms, 3)
self.first_frame.frame = 0
self.second_frame = self.first_frame.copy()
self.second_frame.positions = 2 ** 1 * self.first_frame.positions
self.second_frame.frame = 1
self.last_frame = self.first_frame.copy()
self.last_frame.positions = 2 ** 4 * self.first_frame.positions
self.last_frame.frame = self.n_frames - 1
# remember frames are 0 indexed
self.jump_to_frame = self.first_frame.copy()
self.jump_to_frame.positions = 2 ** 3 * self.first_frame.positions
self.jump_to_frame.frame = 3
self.dimensions = np.array([81.1, 82.2, 83.3, 75, 80, 85],
dtype=np.float32)
self.dimensions_second_frame = np.array([82.1, 83.2, 84.3, 75.1, 80.1,
85.1], dtype=np.float32)
self.volume = mda.lib.mdamath.box_volume(self.dimensions)
self.time = 0
self.dt = 1
self.totaltime = 5
class BaseReference(object):
def __init__(self):
self.trajectory = None
self.n_atoms = 5
self.n_frames = 5
# default for the numpy test functions
self.prec = 6
self.container_format = False
self.first_frame = Timestep(self.n_atoms)
self.first_frame.positions = np.arange(
3 * self.n_atoms).reshape(self.n_atoms, 3)
self.first_frame.frame = 0
self.second_frame = self.first_frame.copy()
self.second_frame.positions = 2 ** 1 * self.first_frame.positions
self.second_frame.frame = 1
self.last_frame = self.first_frame.copy()
self.last_frame.positions = 2 ** 4 * self.first_frame.positions
self.last_frame.frame = self.n_frames - 1
# remember frames are 0 indexed
self.jump_to_frame = self.first_frame.copy()
self.jump_to_frame.positions = 2 ** 3 * self.first_frame.positions
self.jump_to_frame.frame = 3
self.dimensions = np.array([80, 80, 80, 60, 60, 90], dtype=np.float32)
self.volume = mda.lib.mdamath.box_volume(self.dimensions)
self.time = 0
self.dt = 1
self.totaltime = 5
def iter_ts(self, i):
ts = self.first_frame.copy()
ts.positions = 2**i * self.first_frame.positions
ts.time = i
ts.frame = i
return ts
def _read_first_frame(self):
self.n_atoms = 10
self.ts = Timestep(self.n_atoms)
self.ts.frame = 0
def __init__(self):
self.trajectory = None
self.n_atoms = 5
self.n_frames = 5
# default for the numpy test functions
self.prec = 6
self.container_format = False
self.changing_dimensions = False
# for testing auxiliary addition
self.aux_lowf = AUX_XVG_LOWF # test auxiliary with lower frequency
self.aux_lowf_dt = 2 # has steps at 0ps, 2ps, 4ps
# representative data for each trajectory frame, assuming 'closest' option
self.aux_lowf_data = [
[2**0], # frame 0 = 0ps = step 0
[np.nan], # frame 1 = 1ps = no step
[2**1], # frame 2 = 2ps = step 1
[np.nan], # frame 3 = 3ps = no step
[2**2], # frame 4 = 4ps = step 2
]
self.aux_lowf_frames_with_steps = [0, 2, 4] # trajectory frames with
# corresponding auxiliary steps
self.aux_highf = AUX_XVG_HIGHF # test auxiliary with higher frequency
self.aux_highf_dt = 0.5 # has steps at 0, 0.5, 1, ... 3.5, 4ps
self.aux_highf_data = [
[2**0], # frame 0 = 0ps = step 0
[2**2], # frame 1 = 1ps = step 2
[2**4], # frame 2 = 2ps = step 4
[2**6], # frame 3 = 3ps = step 6
[2**8], # frame 4 = 4ps = step 8
]
self.aux_highf_n_steps = 10
self.aux_highf_all_data = [[2**i]
for i in range(self.aux_highf_n_steps)]
self.aux_offset_by = 0.25
self.first_frame = Timestep(self.n_atoms)
self.first_frame.positions = np.arange(3 * self.n_atoms).reshape(
self.n_atoms, 3)
self.first_frame.frame = 0
self.first_frame.aux.lowf = self.aux_lowf_data[0]
self.first_frame.aux.highf = self.aux_highf_data[0]
self.second_frame = self.first_frame.copy()
self.second_frame.positions = 2**1 * self.first_frame.positions
self.second_frame.frame = 1
self.second_frame.aux.lowf = self.aux_lowf_data[1]
self.second_frame.aux.highf = self.aux_highf_data[1]
self.last_frame = self.first_frame.copy()
self.last_frame.positions = 2**4 * self.first_frame.positions
self.last_frame.frame = self.n_frames - 1
self.last_frame.aux.lowf = self.aux_lowf_data[-1]
self.last_frame.aux.highf = self.aux_highf_data[-1]
# remember frames are 0 indexed
self.jump_to_frame = self.first_frame.copy()
self.jump_to_frame.positions = 2**3 * self.first_frame.positions
self.jump_to_frame.frame = 3
self.jump_to_frame.aux.lowf = self.aux_lowf_data[3]
self.jump_to_frame.aux.highf = self.aux_highf_data[3]
self.dimensions = np.array([81.1, 82.2, 83.3, 75, 80, 85],
dtype=np.float32)
self.dimensions_second_frame = np.array(
[82.1, 83.2, 84.3, 75.1, 80.1, 85.1], dtype=np.float32)
self.volume = mda.lib.mdamath.box_volume(self.dimensions)
self.time = 0
self.dt = 1
self.totaltime = 4
def __init__(self):
self.trajectory = None
self.n_atoms = 5
self.n_frames = 5
# default for the numpy test functions
self.prec = 6
self.container_format = False
self.changing_dimensions = False
# for testing auxiliary addition
self.aux_lowf = AUX_XVG_LOWF # test auxiliary with lower frequency
self.aux_lowf_dt = 2 # has steps at 0ps, 2ps, 4ps
# representative data for each trajectory frame, assuming 'closest' option
self.aux_lowf_data = [[2 ** 0], # frame 0 = 0ps = step 0
[np.nan], # frame 1 = 1ps = no step
[2 ** 1], # frame 2 = 2ps = step 1
[np.nan], # frame 3 = 3ps = no step
[2 ** 2], # frame 4 = 4ps = step 2
]
self.aux_lowf_frames_with_steps = [0, 2, 4] # trajectory frames with
# corresponding auxiliary steps
self.aux_highf = AUX_XVG_HIGHF # test auxiliary with higher frequency
self.aux_highf_dt = 0.5 # has steps at 0, 0.5, 1, ... 3.5, 4ps
self.aux_highf_data = [[2 ** 0], # frame 0 = 0ps = step 0
[2 ** 2], # frame 1 = 1ps = step 2
[2 ** 4], # frame 2 = 2ps = step 4
[2 ** 6], # frame 3 = 3ps = step 6
[2 ** 8], # frame 4 = 4ps = step 8
]
self.aux_highf_n_steps = 10
self.aux_highf_all_data = [[2 ** i] for i in range(self.aux_highf_n_steps)]
self.aux_offset_by = 0.25
self.first_frame = Timestep(self.n_atoms)
self.first_frame.positions = np.arange(
3 * self.n_atoms).reshape(self.n_atoms, 3)
self.first_frame.frame = 0
self.first_frame.aux.lowf = self.aux_lowf_data[0]
self.first_frame.aux.highf = self.aux_highf_data[0]
self.second_frame = self.first_frame.copy()
self.second_frame.positions = 2 ** 1 * self.first_frame.positions
self.second_frame.frame = 1
self.second_frame.aux.lowf = self.aux_lowf_data[1]
self.second_frame.aux.highf = self.aux_highf_data[1]
self.last_frame = self.first_frame.copy()
self.last_frame.positions = 2 ** 4 * self.first_frame.positions
self.last_frame.frame = self.n_frames - 1
self.last_frame.aux.lowf = self.aux_lowf_data[-1]
self.last_frame.aux.highf = self.aux_highf_data[-1]
# remember frames are 0 indexed
self.jump_to_frame = self.first_frame.copy()
self.jump_to_frame.positions = 2 ** 3 * self.first_frame.positions
self.jump_to_frame.frame = 3
self.jump_to_frame.aux.lowf = self.aux_lowf_data[3]
self.jump_to_frame.aux.highf = self.aux_highf_data[3]
self.dimensions = np.array([81.1, 82.2, 83.3, 75, 80, 85],
dtype=np.float32)
self.dimensions_second_frame = np.array([82.1, 83.2, 84.3, 75.1, 80.1,
85.1], dtype=np.float32)
self.volume = mda.lib.mdamath.box_volume(self.dimensions)
self.time = 0
self.dt = 1
self.totaltime = 4
