tools#

Various tools.

Functions

center_avalanche(arr)

Shift to center avalanche. Example usage.

center_avalanche_per_row(arr)

Shift to center avalanche, per row. Example usage::.

check_docstring(string, variable[, key])

Make sure that all variables in a dictionary are documented in a docstring. The function assumes a docstring as follows::.

fill_avalanche(broken)

Fill avalanche such that the largest spatial extension can be selected.

h5py_read_unique(file, path[, asstr])

Return original array.

h5py_save_unique(data, file, path[, asstr, ...])

Save a list of strings (or other data, but mostly relevant for strings) with many duplicates as two datasets:

inboth(a, b[, name_a, name_b])

Check if a dictionary/list a has all fields as b and vice-versa.

indep_roll(arr, shifts[, axis])

Apply an independent roll for each dimensions of a single axis.

Details

depinning_inertia_2024.tools.center_avalanche(arr)#
Shift to center avalanche. Example usage::

R = center_avalanche(S) C = np.roll(S, R)

Parameters

arr – If the block yielded (or the number of times it yielded).

Returns

Shift.

depinning_inertia_2024.tools.center_avalanche_per_row(arr)#

Shift to center avalanche, per row. Example usage:

R = center_avalanche_per_row(S)
C = indep_roll(S, R, axis=1)

Note that the input array is interpreted as follows: - any positive value == 1 - any zero or negative value == 0

Parameters

arr – Per row: if the block yielded.

Returns

Shift per row.

depinning_inertia_2024.tools.check_docstring(string: str, variable: dict, key: str = ':return:')#

Make sure that all variables in a dictionary are documented in a docstring. The function assumes a docstring as follows:

:param a: ...
:param b: ...
:return: ...::
    name: description

Thereby the docstring is split: 1. At a parameter (e.g. “:return:”) 2. At .. code-block:: or ::

The indented code after is assumed to be formatted as YAML and is the code we search.

depinning_inertia_2024.tools.fill_avalanche(broken)#

Fill avalanche such that the largest spatial extension can be selected.

Parameters

broken – Per block if it is broken.

Returns

broken for filled avalanche.

depinning_inertia_2024.tools.h5py_read_unique(file: File, path: str, asstr: bool = False) → ndarray#

Return original array. The array is stored by depinning_inertia_2024.tools.h5py_save_unique().

Parameters

  • file – HDF5 archive.

  • path – Group containing index and value.

  • asstr – Return as list of strings.

Returns

Data.

depinning_inertia_2024.tools.h5py_save_unique(data: ArrayLike, file: File, path: str, asstr: bool = False, split: str = None)#

Save a list of strings (or other data, but mostly relevant for strings) with many duplicates as two datasets:

  • path/value: list of unique strings.

  • path/index: per item which index from path/value to take.

Use depinning_inertia_2024.tools.h5py_read_unique() to read data.

Parameters

  • data – Data to store.

  • file – HDF5 archive.

  • path – Group containing index and value.

  • asstr – Convert to list of strings before storing.

  • split – Split every item for a list of strings before storing.

depinning_inertia_2024.tools.inboth(a: dict | list, b: dict | list, name_a: str = 'a', name_b: str = 'b')#

Check if a dictionary/list a has all fields as b and vice-versa.

Parameters

  • a – List or dictionary.

  • b – List or dictionary.

depinning_inertia_2024.tools.indep_roll(arr, shifts, axis=1)#

Apply an independent roll for each dimensions of a single axis. See: https://stackoverflow.com/a/56175538/2646505

Parameters

  • arr – Array of any shape.

  • shifts – Shifting to use for each dimension. Shape: (arr.shape[axis],).

  • axis – Axis along which elements are shifted.

Returns

Rolled array.