apollon.tools module

Common tool library. Licensed under the terms of the BSD-3-Clause license.

Copyright (C) 2019 Michael Blaß

apollon.tools.L1_Norm(arr: numpy.ndarray) → float

Compute the L_1 norm of input vector x.

This implementation is generally faster than np.norm(arr, ord=1).

apollon.tools.assert_and_pass(func: Callable, arg: Any)

Call func` with arg and return arg. Additionally allow arg to be None.

Parameters

  • func – Test function.

  • arg – Function argument.

Returns

Result of func(arg).

apollon.tools.assert_array(arr: numpy.ndarray, ndim: int, size: int, lower_bound: float = - inf, upper_bound: float = inf, name: str = 'arr')

Raise an error if shape of arr does not match given arguments.

Parameters

  • arr – Array to test.

  • ndim – Expected number of dimensions.

  • size – Expected total number of elements.

  • lower_bound – Lower bound for array elements.

  • upper_bound – Upper bound for array elements.

Raises

ValueError –

apollon.tools.fsum(arr: numpy.ndarray, axis: Optional[int] = None, keepdims: bool = False, dtype: str = 'float64') → numpy.ndarray

Return math.fsum along the specifyed axis.

This function supports at most two-dimensional arrays.

Parameters

  • arr – Input array.

  • axis – Reduction axis.

  • keepdims – If True, the output will have the same dimensionality as the input.

  • dtype – Numpy data type.

Returns

Sums along axis.

apollon.tools.jsonify(inp: Any)

Returns a representation of inp that can be serialized to JSON.

This method passes through Python objects of type dict, list, str, int float, True, False, and None. Tuples will be converted to list by the JSON encoder. Numpy arrays will be converted to list using thier .to_list() method. On all other types, the method will try to call str() and raises on error.

Parameters

inp – Input to be jsonified.

Returns

Jsonified input.

apollon.tools.normalize(arr: numpy.ndarray, mode: str = 'array')

Normalize an arbitrary array_like.

Parameters

  • arr – Input signal.

  • mode – Normalization mode: ‘array’ -> (default) Normalize whole array. ‘rows’ -> Normalize each row separately. ‘cols’ -> Normalize each col separately.

Returns

Normalized input.

apollon.tools.pca(data: numpy.ndarray, n_comps: int = 2) → Tuple[numpy.ndarray, numpy.ndarray, numpy.ndarray]

Compute a PCA based on numpy.linalg.svd.

Interanlly, data will be centered but not scaled.

Parameters

  • data – Data set.

  • n_comps – Number of principal components.

Returns

n_comps largest singular values, n_comps largest eigen vectors, transformed input data.

apollon.tools.rowdiag(arr: numpy.ndarray, k: int = 0) → numpy.ndarray

Get or set k th diagonal of square matrix.

Get the k th diagonal of a square matrix sorted by rows or construct a sqare matrix with the elements of v as the main diagonal of the second and third dimension.

Parameters

  • arr – Square array.

  • k – Number of diagonal.

Returns

Flattened diagonal.

apollon.tools.scale(arr: numpy.ndarray, new_min: int = 0, new_max: int = 1, axis: int = - 1) → numpy.ndarray

Scale arr between new_min and new_max.

Parameters

  • arr – Array to be scaled.

  • new_min – Lower bound.

  • new_max – Upper bound.

Returns

One-dimensional array of transformed values.

apollon.tools.smooth_stat(arr: numpy.ndarray) → numpy.ndarray

Smooth the signal based on its mean and standard deviation.

Parameters

arr – Input signal.

Returns

smoothed input signal.

apollon.tools.standardize(arr: numpy.ndarray) → numpy.ndarray

Retrun z-transformed values of arr.

Parameters

arr – Input array.

Returns

z-transformed values

apollon.tools.time_stamp(fmt: Optional[str] = None) → str

Report call time as UTC time stamp.

If fmt is not given, this function returns time stampes in ISO 8601 format.

Parameters

fmt – Format specification.

Returns

Time stamp according to fmt.

apollon.tools.within(val: float, bounds: Tuple[float, float]) → bool

Return True if x is in window.

Parameters

val – Value to test.

Returns

True, if val is within bounds.

apollon.tools.within_any(val: float, windows: numpy.ndarray) → bool

Return True if x is in any of the given windows.

Parameters

  • val – Value to test.

  • windows – Array of bounds.

Returns: