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.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
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
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.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
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
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.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 iter_ts(self, i):
ts = self.first_frame.copy()
ts.positions = 2**i * self.first_frame.positions
ts.time = i
ts.frame = i
ts.aux.lowf = np.array(self.aux_lowf_data[i])
ts.aux.highf = np.array(self.aux_highf_data[i])
return ts
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.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 iter_ts(self, i):
ts = self.first_frame.copy()
ts.positions = 2 ** i * self.first_frame.positions
ts.time = i
ts.frame = i
ts.aux.lowf = np.array(self.aux_lowf_data[i])
ts.aux.highf = np.array(self.aux_highf_data[i])
return ts
