trans_per_state

mdtools.dtrj.trans_per_state(dtrj, **kwargs)

Count the number of transitions leading into or out of a state.

Parameters:

• dtrj (array_like) – Array containing the discrete trajectory.

• kwargs (dict, optional) – Additional keyword arguments to parse to mdtools.dtrj.locate_trans(). See there for possible choices. This function sets the following default values:

  • pin = "end"

  • trans_type = None

  • min_state = None

  • max_state = None

Returns:

• hist_start (numpy.ndarray or tuple) – 1d array or tuple of 1d arrays (if trans_type is 'both' or an iterable of ints). The histogram counts how many state transitions started from a given state, i.e. how many transitions led out of a given state. Is not returned if pin is "end".

• hist_end (numpy.ndarray or tuple) – 1d array or tuple of 1d arrays (if trans_type is 'both' or an iterable of ints). The histogram counts how many state transitions ended in a given state, i.e. how many transitions led into a given state. Is not returned if pin is "start".

See also

mdtools.dtrj.trans_per_state_vs_time()

Count the number of transitions leading into or out of a state for each frame in a discrete trajectory

mdtools.dtrj.locate_trans()

Locate the frames of state transitions inside a discrete trajectory

Notes

The histograms contain the counts for all states ranging from the minimum to the maximum state in dtrj in whole numbers. This means, the states corresponding to the counts in hist_start and hist_end are given by numpy.arange(np.min(dtrj), np.max(dtrj)+1).

Examples

>>> dtrj = np.array([[1, 2, 2, 3, 3, 3],
...                  [2, 2, 3, 3, 3, 1],
...                  [3, 3, 3, 1, 2, 2],
...                  [1, 3, 3, 3, 2, 2]])
>>> hist_start, hist_end = mdt.dtrj.trans_per_state(
...     dtrj, pin="both"
... )
>>> # Number of transitions starting from the 1st, 2nd or 3rd state
>>> hist_start
array([3, 2, 3])
>>> # Number of transitions ending in the 1st, 2nd or 3rd state
>>> hist_end
array([2, 3, 3])
>>> hist_start_type, hist_end_type = mdt.dtrj.trans_per_state(
...     dtrj, pin="both", trans_type="both"
... )
>>> # Number of transitions starting from the 1st, 2nd or 3rd state and ending in an
>>> #   * higher state (hist_start_type[0])
>>> #   * lower state (hist_start_type[1])
>>> hist_start_type
(array([3, 2, 0]), array([0, 0, 3]))
>>> np.array_equal(np.sum(hist_start_type, axis=0), hist_start)
True
>>> # Number of transitions ending in the 1st, 2nd or 3rd state and starting from an
>>> #   * lower state (hist_end_type[0])
>>> #   * higher state (hist_end_type[1])
>>> hist_end_type
(array([0, 2, 3]), array([2, 1, 0]))
>>> np.array_equal(np.sum(hist_end_type, axis=0), hist_end)
True
>>> # Number of transitions starting from the 1st, 2nd or 3rd state where the difference
>>> # between the final and initial state is plus or minus one
>>> hist_plus_one, hist_minus_one = mdt.dtrj.trans_per_state(
...     dtrj, pin="start", trans_type=(1, -1)
... )
>>> hist_plus_one
array([2, 2, 0])
>>> hist_minus_one
array([0, 0, 1])
>>> hist_start_wrap, hist_end_wrap = mdt.dtrj.trans_per_state(
...     dtrj, pin="both", wrap=True
... )
>>> hist_start_wrap
array([4, 4, 4])
>>> hist_end_wrap
array([4, 4, 4])
>>> hist_start_type_wrap, hist_end_type_wrap = mdt.dtrj.trans_per_state(
...     dtrj, pin="both", trans_type="both", wrap=True
... )
>>> hist_start_type_wrap
(array([4, 3, 0]), array([0, 1, 4]))
>>> np.array_equal(
...     np.sum(hist_start_type_wrap, axis=0), hist_start_wrap
... )
True
>>> hist_end_type_wrap
(array([0, 3, 4]), array([4, 1, 0]))
>>> np.array_equal(
...     np.sum(hist_end_type_wrap, axis=0), hist_end_wrap
...  )
True
>>> hist_start_tfft, hist_end_tfft = mdt.dtrj.trans_per_state(
...     dtrj, pin="both", tfft=True
... )
>>> hist_start_tfft
array([3, 2, 3])
>>> hist_end_tfft
array([4, 4, 4])
>>> hist_start_tlft, hist_end_tlft = mdt.dtrj.trans_per_state(
...     dtrj, pin="both", tlft=True
... )
>>> hist_start_tlft
array([4, 4, 4])
>>> hist_end_tlft
array([2, 3, 3])
>>> hist_start_tfft_tlft, hist_end_tfft_tlft = mdt.dtrj.trans_per_state(
...     dtrj, pin="both", tfft=True, tlft=True
... )
>>> hist_start_tfft_tlft
array([4, 4, 4])
>>> hist_end_tfft_tlft
array([4, 4, 4])

Interpret first dimension as frames and second dimension as compounds:

>>> hist_start, hist_end = mdt.dtrj.trans_per_state(
...     dtrj, axis=0, pin="both"
... )
>>> hist_start
array([3, 3, 4])
>>> hist_end
array([3, 3, 4])
>>> hist_start_type, hist_end_type = mdt.dtrj.trans_per_state(
...     dtrj, axis=0, pin="both", trans_type="both"
... )
>>> hist_start_type
(array([3, 3, 0]), array([0, 0, 4]))
>>> np.array_equal(np.sum(hist_start_type, axis=0), hist_start)
True
>>> hist_end_type
(array([0, 2, 4]), array([3, 1, 0]))
>>> np.array_equal(np.sum(hist_end_type, axis=0), hist_end)
True
>>> hist_plus_one, hist_minus_one = mdt.dtrj.trans_per_state(
...     dtrj, axis=0, pin="start", trans_type=(1, -1)
... )
>>> hist_plus_one
array([2, 3, 0])
>>> hist_minus_one
array([0, 0, 1])
>>> hist_start_wrap, hist_end_wrap = mdt.dtrj.trans_per_state(
...     dtrj, axis=0, pin="both", wrap=True
... )
>>> hist_start_wrap
array([3, 5, 6])
>>> hist_end_wrap
array([3, 5, 6])
>>> hist_start_type_wrap, hist_end_type_wrap = mdt.dtrj.trans_per_state(
...     dtrj, axis=0, pin="both", trans_type="both", wrap=True
... )
>>> hist_start_type_wrap
(array([3, 5, 0]), array([0, 0, 6]))
>>> np.array_equal(
...     np.sum(hist_start_type_wrap, axis=0), hist_start_wrap
... )
True
>>> hist_end_type_wrap
(array([0, 2, 6]), array([3, 3, 0]))
>>> np.array_equal(
...     np.sum(hist_end_type_wrap, axis=0), hist_end_wrap
...  )
True
>>> hist_start_tfft, hist_end_tfft = mdt.dtrj.trans_per_state(
...     dtrj, axis=0, pin="both", tfft=True
... )
>>> hist_start_tfft
array([3, 3, 4])
>>> hist_end_tfft
array([4, 5, 7])
>>> hist_start_tlft, hist_end_tlft = mdt.dtrj.trans_per_state(
...     dtrj, axis=0, pin="both", tlft=True
... )
>>> hist_start_tlft
array([4, 5, 7])
>>> hist_end_tlft
array([3, 3, 4])
>>> hist_start_tfft_tlft, hist_end_tfft_tlft = mdt.dtrj.trans_per_state(
...     dtrj, axis=0, pin="both", tfft=True, tlft=True
... )
>>> hist_start_tfft_tlft
array([4, 5, 7])
>>> hist_end_tfft_tlft
array([4, 5, 7])