def test_autocorrelation_windowBigJump():
#The empty sets are ignored (no intermittency)
input_ids = [{1}, {1}, {1},
set(),
set(), {1}, {1}, {1},
set(),
set(), {1}, {1}, {1}]
tau_timeseries, sp_timeseries, sp_timeseries_data = autocorrelation(
input_ids, tau_max=2, window_step=5)
assert_almost_equal(sp_timeseries, [1, 1, 1])
def test_autocorrelation_windowBigJump_absence():
# In the last frame the molecules are absent
input_ids = [{1}, {1}, {1},
set(),
set(), {1}, {1}, {1},
set(),
set(), {1},
set(),
set()]
tau_timeseries, sp_timeseries, sp_timeseries_data = autocorrelation(
input_ids, tau_max=2, window_step=5)
assert_almost_equal(sp_timeseries, [1, 2 / 3., 2 / 3.])
def test_autocorrelation_intermittency2_windowBigJump():
# The intermittency corrects the last frame
input_ids = [{1}, {1}, {1},
set(),
set(), {1}, {1}, {1},
set(),
set(), {1},
set(),
set(), {1}]
corrected = correct_intermittency(input_ids, intermittency=2)
tau_timeseries, sp_timeseries, sp_timeseries_data = autocorrelation(
corrected, tau_max=2, window_step=5)
assert_almost_equal(sp_timeseries, [1, 1, 1])
def test_autocorrelation_intermittency1_many():
input_ids = [{1},
set(), {1},
set(), {1},
set(), {1},
set(), {1},
set(), {1},
set(), {1},
set(), {1}]
corrected = correct_intermittency(input_ids, intermittency=1)
tau_timeseries, sp_timeseries, sp_timeseries_data = autocorrelation(
corrected, tau_max=14, window_step=5)
assert_almost_equal(sp_timeseries, [1] * 15)
def test_autocorrelation_intermittency1_windowJump_intermittencyAll():
"""
Step leads to skipping frames if (tau_max + 1) + (intermittency * 2) < step.
No frames should be skipped so intermittency should be applied to all.
"""
input_ids = [{2, 3}, {
3,
}, {2, 3}, {
3,
}, {
2,
}, {
3,
}, {2, 3}, {
3,
}, {2, 3}, {2, 3}]
corrected = correct_intermittency(input_ids, intermittency=1)
tau_timeseries, sp_timeseries, sp_timeseries_data = autocorrelation(
corrected, tau_max=2, window_step=5)
assert all((x == {2, 3} for x in corrected))
assert_almost_equal(sp_timeseries, [1, 1, 1])
def test_autocorrelation_definedTaus():
input_ids = [{9, 8, 7}, {8, 7, 6}, {7, 6, 5}, {6, 5, 4}, {5, 4, 3},
{4, 3, 2}, {3, 2, 1}]
tau_timeseries, sp_timeseries, sp_timeseries_data = autocorrelation(
input_ids, tau_max=3)
assert_almost_equal(sp_timeseries, [1, 2 / 3., 1 / 3., 0])
def test_autocorrelation_alwaysPresent():
input = [{1, 2}, {1, 2}, {1, 2}, {1, 2}, {1, 2}, {1, 2}, {1, 2}]
tau_timeseries, sp_timeseries, sp_timeseries_data = autocorrelation(
input, tau_max=3)
assert all(np.equal(sp_timeseries, 1))
def run(self,
tau_max=20,
start=None,
stop=None,
step=None,
residues=False,
intermittency=0,
verbose=False):
"""
Computes and returns the Survival Probability (SP) timeseries
Parameters
----------
start : int, optional
Zero-based index of the first frame to be analysed, Default: None
(first frame).
stop : int, optional
Zero-based index of the last frame to be analysed (exclusive),
Default: None (last frame).
step : int, optional
Jump every `step`-th frame. This is compatible but independant of
the taus used, and it is good to consider using the `step` equal
to `tau_max` to remove the overlap. Note that `step` and `tau_max`
work consistently with intermittency. Default: None
(use every frame).
tau_max : int, optional
Survival probability is calculated for the range
1 <= `tau` <= `tau_max`.
residues : Boolean, optional
If true, the analysis will be carried out on the residues
(.resids) rather than on atom (.ids). A single atom is sufficient
to classify the residue as within the distance.
intermittency : int, optional
The maximum number of consecutive frames for which an atom can
leave but be counted as present if it returns at the next frame.
An intermittency of `0` is equivalent to a continuous survival
probability, which does not allow for the leaving and returning of
atoms. For example, for `intermittency=2`, any given atom may leave
a region of interest for up to two consecutive frames yet be
treated as being present at all frames. The default is continuous
(0).
verbose : Boolean, optional
Print the progress to the console.
Returns
-------
tau_timeseries : list
tau from 1 to `tau_max`. Saved in the field tau_timeseries.
sp_timeseries : list
survival probability for each value of `tau`. Saved in the field
sp_timeseries.
sp_timeseries_data: list
raw datapoints from which the average is taken (sp_timeseries).
Time dependancy and distribution can be extracted.
.. versionchanged:: 1.0.0
To math other analysis methods, the `stop` keyword is now exclusive
rather than inclusive.
"""
start, stop, step = self.universe.trajectory.check_slice_indices(
start, stop, step)
if tau_max > (stop - start):
raise ValueError("Too few frames selected for given tau_max.")
# preload the frames (atom IDs) to a list of sets
self._selected_ids = []
# fixme - to parallise: the section should be rewritten so that this loop only creates a list of indices,
# on which the parallel _single_frame can be applied.
# skip frames that will not be used in order to improve performance
# because AtomGroup.select_atoms is the most expensive part of this calculation
# Example: step 5 and tau 2: LLLSS LLLSS, ... where L = Load, and S = Skip
# Intermittency means that we have to load the extra frames to know if the atom is actually missing.
# Say step=5 and tau=1, intermittency=0: LLSSS LLSSS
# Say step=5 and tau=1, intermittency=1: LLLSL LLLSL
frame_loaded_counter = 0
# only for the first window (frames before t are not used)
frames_per_window = tau_max + 1 + intermittency
# This number will apply after the first windows was loaded
frames_per_window_subsequent = (tau_max + 1) + (2 * intermittency)
num_frames_to_skip = max(step - frames_per_window_subsequent, 0)
frame_no = start
while frame_no < stop: # we have already added 1 to stop, therefore <
if num_frames_to_skip != 0 and frame_loaded_counter == frames_per_window:
logger.info("Skipping the next %d frames:", num_frames_to_skip)
frame_no += num_frames_to_skip
frame_loaded_counter = 0
# Correct the number of frames to be loaded after the first window (which starts at t=0, and
# intermittency does not apply to the frames before)
frames_per_window = frames_per_window_subsequent
continue
# update the frame number
self.universe.trajectory[frame_no]
logger.info("Loading frame: %d", self.universe.trajectory.frame)
atoms = self.universe.select_atoms(self.selection)
# SP of residues or of atoms
ids = atoms.residues.resids if residues else atoms.ids
self._selected_ids.append(set(ids))
frame_no += 1
frame_loaded_counter += 1
# adjust for the frames that were not loaded (step>tau_max + 1),
# and for extra frames that were loaded (intermittency)
window_jump = step - num_frames_to_skip
self._intermittent_selected_ids = correct_intermittency(
self._selected_ids, intermittency=intermittency)
tau_timeseries, sp_timeseries, sp_timeseries_data = autocorrelation(
self._intermittent_selected_ids, tau_max, window_jump)
# warn the user if the NaN are found
if all(np.isnan(sp_timeseries[1:])):
logger.warning(
'NaN Error: Most likely data was not found. Check your atom selections. '
)
# user can investigate the distribution and sample size
self.sp_timeseries_data = sp_timeseries_data
self.tau_timeseries = tau_timeseries
self.sp_timeseries = sp_timeseries
return self