Merge pull request #6 from mrava87/master

Prepare for v0.1.0

Author: mrava87

CHANGELOG.md

@@ -1,2 +1,16 @@
+# 0.1.0
+
+* Added ``pylops_distributed.Restriction`` operator
+* Added ``pylops_distributed.signalprocessing.Convolve1D``
+  and ``pylops_distributed.signalprocessing.FFT2D`` operators
+* Improved efficiency of
+  ``pylops_distributed.signalprocessing.Fredholm1`` when
+  ``saveGt=False``
+* Adapted ``pylops_distributed.optimization.cg.cg`` and
+  ``pylops_distributed.optimization.cg.cgls`` solvers for
+  complex numbers
+
+
+
 # 0.0.0
 * First official release.

docs/source/changelog.rst

@@ -3,6 +3,23 @@
 Changelog
 =========
 
+
+Version 0.1.0
+-------------
+
+*Released on: 09/02/2020*
+
+* Added :py:class:`pylops_distributed.Restriction` operator
+* Added :py:class:`pylops_distributed.signalprocessing.Convolve1D`
+  and :py:class:`pylops_distributed.signalprocessing.FFT2D` operators
+* Improved efficiency of
+  :py:class:`pylops_distributed.signalprocessing.Fredholm1` when
+  ``saveGt=False``
+* Adapted :py:func:`pylops_distributed.optimization.cg.cg` and
+  :py:func:`pylops_distributed.optimization.cg.cgls` solvers for
+  complex numbers
+
+
 Version 0.0.0
 -------------
 

pylops_distributed/basicoperators/FirstDerivative.py

@@ -0,0 +1,53 @@
+from pylops import Diagonal as pDiagonal
+from pylops_distributed import LinearOperator
+
+
+class Diagonal(LinearOperator):
+    r"""Diagonal operator.
+
+    Applies element-wise multiplication of the input vector with the vector
+    ``diag`` in forward and with its complex conjugate in adjoint mode.
+
+    This operator can also broadcast; in this case the input vector is
+    reshaped into its dimensions ``dims`` and the element-wise multiplication
+    with ``diag`` is perfomed on the direction ``dir``. Note that the
+    vector ``diag`` will need to have size equal to ``dims[dir]``.
+
+    Parameters
+    ----------
+    diag : :obj:`dask.array.ndarray`
+        Vector to be used for element-wise multiplication.
+    dims : :obj:`list`, optional
+        Number of samples for each dimension
+        (``None`` if only one dimension is available)
+    dir : :obj:`int`, optional
+        Direction along which multiplication is applied.
+    compute : :obj:`tuple`, optional
+        Compute the outcome of forward and adjoint or simply define the graph
+        and return a :obj:`dask.array.array`
+    todask : :obj:`tuple`, optional
+        Apply :func:`dask.array.from_array` to model and data before applying
+        forward and adjoint respectively
+    dtype : :obj:`str`, optional
+        Type of elements in input array.
+
+    Attributes
+    ----------
+    shape : :obj:`tuple`
+        Operator shape
+    explicit : :obj:`bool`
+        Operator contains a matrix that can be solved explicitly (``True``) or
+        not (``False``)
+
+    Notes
+    -----
+    Refer to :class:`pylops.basicoperators.Diagonal` for implementation
+    details.
+
+    """
+    def __init__(self, diag, dims=None, dir=0,
+                 compute=(False, False), todask=(False, False),
+                 dtype='float64'):
+        Op = pDiagonal(diag, dims=dims, dir=dir, dtype=dtype)
+        super().__init__(Op.shape, Op.dtype, Op, explicit=False,
+                         compute=compute, todask=todask)