Source code for pylops.basicoperators.causalintegration

__all__ = ["CausalIntegration"]

from typing import Union

import numpy as np

from pylops import LinearOperator
from pylops.utils._internal import _value_or_sized_to_tuple
from pylops.utils.decorators import reshaped
from pylops.utils.typing import DTypeLike, InputDimsLike, NDArray

[docs]class CausalIntegration(LinearOperator):
r"""Causal integration.

Apply causal integration to a multi-dimensional array along axis.

Parameters
----------
dims : :obj:list or :obj:int
Number of samples for each dimension
axis : :obj:int, optional

Axis along which the model is integrated.
sampling : :obj:float, optional
Sampling step dx.
kind : :obj:str, optional
Integration kind (full, half, or trapezoidal).
removefirst : :obj:bool, optional
Remove first sample (True) or not (False).
dtype : :obj:str, optional
Type of elements in input array.
name : :obj:str, optional

Name of operator (to be used by :func:pylops.utils.describe.describe)

Attributes
----------
shape : :obj:tuple
Operator shape
explicit : :obj:bool
Operator contains a matrix that can be solved explicitly (True)
or not (False)

Notes
-----
The CausalIntegration operator applies a causal integration to any chosen
direction of a multi-dimensional array.

For simplicity, given a one dimensional array, the causal integration is:

.. math::
y(t) = \int\limits_{-\infty}^t x(\tau) \,\mathrm{d}\tau

which can be discretised as :

.. math::
y[i] = \sum_{j=0}^i x[j] \,\Delta t

or

.. math::
y[i] = \left(\sum_{j=0}^{i-1} x[j] + 0.5x[i]\right) \,\Delta t

or

.. math::
y[i] = \left(\sum_{j=1}^{i-1} x[j] + 0.5x[0] + 0.5x[i]\right) \,\Delta t

where :math:\Delta t is the sampling interval, and assuming the signal is zero
before sample :math:j=0. In our implementation, the
choice to add :math:x[i] or :math:0.5x[i] is made by selecting kind=full
or kind=half, respectively. The choice to add :math:0.5x[i] and
:math:0.5x[0] instead of made by selecting the kind=trapezoidal.

Note that the causal integral of a signal will depend, up to a constant,
on causal start of the signal. For example if :math:x(\tau) = t^2 the
resulting indefinite integration is:

.. math::
y(t) = \int \tau^2 \,\mathrm{d}\tau = \frac{t^3}{3} + C

However, if we apply a first derivative to :math:y always obtain:

.. math::
x(t) = \frac{\mathrm{d}y}{\mathrm{d}t} = t^2

no matter the choice of :math:C.

"""

def __init__(
self,
dims: Union[int, InputDimsLike],
axis: int = -1,
sampling: float = 1,
kind: str = "full",
removefirst: bool = False,
dtype: DTypeLike = "float64",
name: str = "C",
) -> None:
self.axis = axis
self.sampling = sampling
# backwards compatible
self.kind = kind
self.removefirst = removefirst
dims = _value_or_sized_to_tuple(dims)
dimsd = list(dims)
if self.removefirst:
dimsd[self.axis] -= 1
super().__init__(dtype=np.dtype(dtype), dims=dims, dimsd=dimsd, name=name)

@reshaped(swapaxis=True)
def _matvec(self, x: NDArray) -> NDArray:
y = self.sampling * np.cumsum(x, axis=-1)
if self.kind in ("half", "trapezoidal"):
y -= self.sampling * x / 2.0
if self.kind == "trapezoidal":
y[..., 1:] -= self.sampling * x[..., 0:1] / 2.0
if self.removefirst:
y = y[..., 1:]
return y

@reshaped(swapaxis=True)
def _rmatvec(self, x: NDArray) -> NDArray:
if self.removefirst:
x = np.insert(x, 0, 0, axis=-1)
xflip = np.flip(x, axis=-1)
if self.kind == "half":
y = self.sampling * (np.cumsum(xflip, axis=-1) - xflip / 2.0)
elif self.kind == "trapezoidal":
y = self.sampling * (np.cumsum(xflip, axis=-1) - xflip / 2.0)
y[..., -1] = self.sampling * np.sum(xflip, axis=-1) / 2.0
else:
y = self.sampling * np.cumsum(xflip, axis=-1)
y = np.flip(y, axis=-1)
return y