replace_short_sequences

mdtools.dynamics.replace_short_sequences(list_of_arrays, min_len, val=0, inplace=True, verbose=False, debug=False)[source]

Replace consecutive occurrences of the same value that are shorter than a given minimum length by a given value.

For instance, the sequence 1,3,3,3,2,2,1 is replaced by 0,3,3,3,0,0,0, when min_len is set to 3 and val is set to 0.

Parameters:

list_of_arrays (tuple or list or numpy.ndarray) – Sequence of arrays (usually one array per trajectory frame). All arrays in the sequence must have the same shape and all arrays must be NumPy arrays.
min_len (int) – All consecutive occurrences shorter than min_len will be replaced by val. If min_len is less than two, list_of_arrays will be returned without any changes.
val (scalar, optional) – The value used to replace the values in consecutive occurrences shorter than min_len. Be aware that the arrays in list_of_arrays must have a suitable dtype to be able to hold val.
inplace (bool, optional) – If True, modify list_of_arrays inplace instead of creating a copy of list_of_arrays.
verbose (bool, optional) – If True, print progress information to standard output.
debug (bool, optional) – If True, check the input arguments.

Deprecated since version 0.0.0.dev0: This argument is without use and will be removed in a future release.

Returns:

list_of_arrays (tuple or list or numpy.ndarray) – List of arrays with consecutive occurrences shorter than min_len replaced by val.

See also

mdtools.dynamics.correct_intermittency(): Correct for intermittent behavior of discrete variables stored in a sequence of arrays
mdtools.dynamics.correct_intermittency_1d(): Correct for intermittent behavior of discrete variables stored in a 1-dimensional array
mdtools.dynamics.replace_short_sequences_global(): TODO

Notes

If you want to replace consecutive occurrences in a 1-dimensional array, parse the array as np.expand_dims(array, axis=0).T and convert the output back to a 1-dimensional array via np.squeeze(result.T).

min_len must not exceed 65535 (np.uint16(-1)). If you really need a higher value for min_len, change the dtype of seen_nframes_ago and seen_nframes_ahead in the source code of this function. Be aware that high values of min_len increase the computational cost.

Examples

>>> a = np.array([[3, 1, 2],
...               [1, 2, 3]])
>>> b = np.array([[3, 2, 2],
...               [2, 2, 3]])
>>> c = np.array([[3, 2, 1],
...               [2, 1, 3]])
>>> x = (a, b, c)
>>> y = mdt.dyn.replace_short_sequences(x,
...                                     min_len=2,
...                                     val=0,
...                                     inplace=False,
...                                     verbose=False)
>>> for arr in y:
...     print(arr)
[[3 0 2]
 [0 2 3]]
[[3 2 2]
 [2 2 3]]
[[3 2 0]
 [2 0 3]]
>>> y = mdt.dyn.replace_short_sequences(x,
...                                     min_len=3,
...                                     val=0,
...                                     inplace=False,
...                                     verbose=False)
>>> for arr in y:
...     print(arr)
[[3 0 0]
 [0 0 3]]
[[3 0 0]
 [0 0 3]]
[[3 0 0]
 [0 0 3]]

If min_len is greater than len(list_of_arrays), all elements of all arrays are replaced by val:

>>> y = mdt.dyn.replace_short_sequences(x,
...                                     min_len=len(x)+1,
...                                     val=0,
...                                     inplace=False,
...                                     verbose=False)
>>> for arr in y:
...     print(arr)
[[0 0 0]
 [0 0 0]]
[[0 0 0]
 [0 0 0]]
[[0 0 0]
 [0 0 0]]

If min_len is less than two, the input list_of_arrays is returned without any changes:

>>> y = mdt.dyn.replace_short_sequences(x,
...                                     min_len=1,
...                                     val=0,
...                                     inplace=False,
...                                     verbose=False)
>>> all(np.array_equal(x[i], arr) for i, arr in enumerate(y))
True

Inplace modification:

>>> y = mdt.dyn.replace_short_sequences(x,
...                                     min_len=3,
...                                     val=0,
...                                     inplace=True,
...                                     verbose=False)
>>> x is y
True