Commit 504be2fc authored by valentin.emiya's avatar valentin.emiya
Browse files

renamed package

parent 996b0303
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......@@ -3,6 +3,6 @@ include *.rst
include VERSION
recursive-include doc *.rst *.py *.ipynb
include pomad/tests/*.py
include skpomade/tests/*.py
prune doc/build
pomad
skpomade
=====
pomad: PrObabilistic MAtrix Decompositions from Halko et al., 2011
skpomade: PrObabilistic MAtrix DEcompositions from Halko et al., 2011
Python implementation of algorithms from paper *Finding Structure
with Randomness: Probabilistic Algorithms for Constructing Approximate Matrix
......@@ -14,32 +14,32 @@ Install
Install the current release with ``pip``::
pip install pomad
pip install skpomade
For additional details, see doc/install.rst.
Usage
-----
See the `documentation <http://valentin.emiya.pages.lis-lab.fr/pomad/>`_.
See the `documentation <http://valentin.emiya.pages.lis-lab.fr/skpomade/>`_.
Bugs
----
Please report any bugs that you find through the `pomad GitLab project
<https://gitlab.lis-lab.fr/valentin.emiya/pomad/issues>`_.
Please report any bugs that you find through the `skpomade GitLab project
<https://gitlab.lis-lab.fr/valentin.emiya/skpomade/issues>`_.
You can also fork the repository and create a merge request.
Source code
-----------
The source code of pomad is available via its `GitLab project
<https://gitlab.lis-lab.fr/valentin.emiya/pomad>`_.
The source code of skpomade is available via its `GitLab project
<https://gitlab.lis-lab.fr/valentin.emiya/skpomade>`_.
You can clone the git repository of the project using the command::
git clone git@gitlab.lis-lab.fr:valentin.emiya/pomad.git
git clone git@gitlab.lis-lab.fr:valentin.emiya/skpomade.git
Copyright © 2019-2020
---------------------
......
pomad:0.1.3
skpomade:0.1.3
If you only want to get the documentation, note that a pre-built
version for the latest release is available
[online](http://valentin.emiya.pages.lis-lab.fr/pomad/).
[online](http://valentin.emiya.pages.lis-lab.fr/skpomade/).
Sphinx is used to generate the API and reference documentation.
## Instructions to build the documentation
In addition to installing ``pomad`` and its dependencies, install the
In addition to installing ``skpomade`` and its dependencies, install the
Python packages needed to build the documentation by entering
```
......
%% Cell type:markdown id: tags:
# Tutorial for `pomad`: PrObabilistic MAtrix Decompositions
# Tutorial for `skpomade`: PrObabilistic MAtrix DEcompositions
%% Cell type:code id: tags:
``` python
%load_ext autoreload
......@@ -25,12 +25,12 @@
```
%% Cell type:code id: tags:
``` python
from pomad.utils import build_test_matrix
from pomad.range_approximation import randomized_range_finder, adaptive_randomized_range_finder
from skpomade.utils import build_test_matrix
from skpomade.range_approximation import randomized_range_finder, adaptive_randomized_range_finder
```
%% Cell type:code id: tags:
``` python
......
......@@ -5,12 +5,12 @@ import os
from datetime import date
import pomad
import skpomade
# If extensions (or modules to document with autodoc) are in another directory,
# add these directories to sys.path here. If the directory is relative to the
# documentation root, use os.path.abspath to make it absolute, like shown here.
sys.path.insert(0, os.path.abspath('../pomad/'))
sys.path.insert(0, os.path.abspath('../skpomade/'))
# -- General configuration ------------------------------------------------
......@@ -46,7 +46,7 @@ source_encoding = 'utf-8-sig'
master_doc = 'index'
# General information about the project.
project = 'pomad'
project = 'skpomade'
author = 'V. Emiya'
copyright = '2019-{}, {}'.format(date.today().year, author)
......@@ -55,9 +55,9 @@ copyright = '2019-{}, {}'.format(date.today().year, author)
# built documents.
#
# The short X.Y version.
version = pomad.__version__
version = skpomade.__version__
# The full version, including alpha/beta/rc tags.
release = pomad.__version__.replace('_', '')
release = skpomade.__version__.replace('_', '')
# The language for content autogenerated by Sphinx. Refer to documentation
# for a list of supported languages.
......@@ -193,7 +193,7 @@ html_search_options = {'type': 'default'}
html_search_scorer = ''
# Output file base name for HTML help builder.
htmlhelp_basename = 'pomaddoc'
htmlhelp_basename = 'skpomadedoc'
# -- Options for LaTeX output ---------------------------------------------
......@@ -215,7 +215,7 @@ latex_elements = {
# (source start file, target name, title,
# author, documentclass [howto, manual, or own class]).
latex_documents = [
(master_doc, 'pomad.tex', 'pomad Documentation', author, 'manual'),
(master_doc, 'skpomade.tex', 'skpomade Documentation', author, 'manual'),
]
# The name of an image file (relative to this directory) to place at the top of
......@@ -242,7 +242,7 @@ latex_domain_indices = True
# One entry per manual page. List of tuples
# (source start file, name, description, authors, manual section).
man_pages = [(master_doc, 'pomad', 'pomad Documentation', [author], 1)]
man_pages = [(master_doc, 'skpomade', 'skpomade Documentation', [author], 1)]
# If true, show URL addresses after external links.
man_show_urls = False
......@@ -253,7 +253,7 @@ man_show_urls = False
# (source start file, target name, title, author,
# dir menu entry, description, category)
texinfo_documents = [
(master_doc, 'pomad.tex', 'pomad Documentation', author, 'manual'),
(master_doc, 'skpomade.tex', 'skpomade Documentation', author, 'manual'),
]
# Documents to append as an appendix to all manuals.
......
......@@ -20,7 +20,7 @@ and Ronan Hamon on other packages.
Description
-----------
`pomad` is a Python implementation of algorithms from
`skpomade` is a Python implementation of algorithms from
paper *Finding Structure with Randomness: Probabilistic Algorithms for
Constructing Approximate Matrix Decompositions*, by N. Halko, P. G.
Martinsson and J. A. Tropp, SIAM review, 53 (2), 2011, https://arxiv.org/abs/0909.4061.
......@@ -29,7 +29,7 @@ Martinsson and J. A. Tropp, SIAM review, 53 (2), 2011, https://arxiv.org/abs/090
Version
-------
* pomad version = 0.1.3
* skpomade version = 0.1.3
Licence
-------
......
##########################
:mod:`pomad` documentation
:mod:`skpomade` documentation
##########################
Overview
========
:mod:`pomad`: stands for PrObabilistic MAtrix Decompositions.
:mod:`skpomade`: stands for PrObabilistic MAtrix DEcompositions.
Package :mod:`pomad` offers a Python implementation of algorithms from
Package :mod:`skpomade` offers a Python implementation of algorithms from
paper *Finding Structure with Randomness: Probabilistic Algorithms for
Constructing Approximate Matrix Decompositions*, by N. Halko, P. G.
Martinsson and J. A. Tropp, SIAM review, 53 (2), 2011, https://arxiv.org/abs/0909.4061.
:mod:`pomad`: is mainly composed of two sub-modules:
:mod:`skpomade`: is mainly composed of two sub-modules:
* :mod:`pomad.range_approximation` contains algorithms for Stage A Randomized Schemes for Approximating the Range
* :mod:`pomad.factorization_construction` contains algorithms for Stage B Construction of Standard Factorizations
* :mod:`skpomade.range_approximation` contains algorithms for Stage A Randomized Schemes for Approximating the Range
* :mod:`skpomade.factorization_construction` contains algorithms for Stage B Construction of Standard Factorizations
Not all algorithms have been implemented yet.
......
Installation
############
``pomad`` requires the following packages:
``skpomade`` requires the following packages:
* `python >= 3.5 <https://wiki.python.org/moin/BeginnersGuide/Download>`_
* `numpy >= 1.13 <http://www.numpy.org>`_
Make sure your Python environment is properly configured. It is recommended to
install ``pomad`` in a virtual environment.
install ``skpomade`` in a virtual environment.
Release version
---------------
......@@ -18,20 +18,20 @@ manager) installed. If you do not, refer to the `Pip documentation
Install the current release with ``pip``::
pip install pomad
pip install skpomade
To upgrade to a newer release use the ``--upgrade`` flag::
pip install --upgrade pomad
pip install --upgrade skpomade
If you do not have permission to install software systemwide, you can install
into your user directory using the ``--user`` flag::
pip install --user pomad
pip install --user skpomade
Alternatively, you can manually download ``pomad`` from its `GitLab project
<https://gitlab.lis-lab.fr/valentin.emiya/pomad>`_ or `PyPI
<https://pypi.python.org/pypi/pomad>`_. To install one of these versions,
Alternatively, you can manually download ``skpomade`` from its `GitLab project
<https://gitlab.lis-lab.fr/valentin.emiya/skpomade>`_ or `PyPI
<https://pypi.python.org/pypi/skpomade>`_. To install one of these versions,
unpack it and run the following from the top-level source directory using the
Terminal::
......@@ -41,17 +41,17 @@ Development version
-------------------
If you have `Git <https://git-scm.com/>`_ installed on your system, it is also
possible to install the development version of ``pomad``.
possible to install the development version of ``skpomade``.
Before installing the development version, you may need to uninstall the
standard version of ``pomad`` using ``pip``::
standard version of ``skpomade`` using ``pip``::
pip uninstall pomad
pip uninstall skpomade
Clone the Git repository::
git clone git@gitlab.lis-lab.fr:valentin.emiya/pomad.git
cd pomad
git clone git@gitlab.lis-lab.fr:valentin.emiya/skpomade.git
cd skpomade
You may also need to install required packages::
......@@ -61,7 +61,7 @@ Then execute ``pip`` with flag ``-e`` to follow the development branch::
pip install -e .
To update ``pomad`` at any time, in the same directory do::
To update ``skpomade`` at any time, in the same directory do::
git pull
......
......@@ -4,26 +4,26 @@ References
:Release: |release|
:Date: |today|
pomad\.factorization_construction module
skpomade\.factorization_construction module
----------------------------------------
.. automodule:: pomad.factorization_construction
.. automodule:: skpomade.factorization_construction
:members:
:undoc-members:
:show-inheritance:
pomad\.range_approximation module
skpomade\.range_approximation module
---------------------------------
.. automodule:: pomad.range_approximation
.. automodule:: skpomade.range_approximation
:members:
:undoc-members:
:show-inheritance:
pomad\.utils module
skpomade\.utils module
-------------------
.. automodule:: pomad.utils
.. automodule:: skpomade.utils
:members:
:undoc-members:
:show-inheritance:
......@@ -4,4 +4,4 @@ Tutorials
.. toctree::
:maxdepth: 1
_notebooks/pomad.ipynb
_notebooks/skpomade.ipynb
[tool:pytest]
testpaths = pomad
testpaths = skpomade
addopts = --verbose
--cov-report=term-missing
--cov-report=html
--cov=pomad
--cov=skpomade
--doctest-modules
[coverage:run]
branch = True
source = pomad
include = */pomad/*
source = skpomade
include = */skpomade/*
omit = */tests/*
[coverage:report]
......
......@@ -23,7 +23,7 @@
# Description
# -----------
#
# `pomad` is a Python implementation of algorithms from
# `skpomade` is a Python implementation of algorithms from
# paper *Finding Structure with Randomness: Probabilistic Algorithms for
# Constructing Approximate Matrix Decompositions*, by N. Halko, P. G.
# Martinsson and J. A. Tropp, SIAM review, 53 (2), 2011, https://arxiv.org/abs/0909.4061.
......@@ -32,7 +32,7 @@
# Version
# -------
#
# * pomad version = 0.1.3
# * skpomade version = 0.1.3
#
# Licence
# -------
......@@ -56,8 +56,8 @@ import os
from setuptools import setup, find_packages
import sys
NAME = 'pomad'
DESCRIPTION = 'PrObabilistic MAtrix Decompositions from Halko et al., 2011'
NAME = 'skpomade'
DESCRIPTION = 'PrObabilistic MAtrix DEcompositions from Halko et al., 2011'
LICENSE = 'GNU General Public License v3 (GPLv3)'
URL = 'https://gitlab.lis-lab.fr/valentin.emiya/{}'.format(NAME)
AUTHOR = 'Valentin Emiya'
......
......@@ -22,7 +22,7 @@
# Description
# -----------
#
# `pomad` is a Python implementation of algorithms from
# `skpomade` is a Python implementation of algorithms from
# paper *Finding Structure with Randomness: Probabilistic Algorithms for
# Constructing Approximate Matrix Decompositions*, by N. Halko, P. G.
# Martinsson and J. A. Tropp, SIAM review, 53 (2), 2011, https://arxiv.org/abs/0909.4061.
......@@ -31,7 +31,7 @@
# Version
# -------
#
# * pomad version = 0.1.3
# * skpomade version = 0.1.3
#
# Licence
# -------
......@@ -303,7 +303,7 @@ def adaptive_randomized_range_finder_mem_alloc(a, tolerance, r=5,
# m, n, p_a = 100, 100, 2
# a_mat = build_test_matrix(m, n, p=p_a, rand_state=0)
# a_op = aslinearoperator(a_mat)
# from pomad.utils import FourierMultiplierOp
# from skpomade.utils import FourierMultiplierOp
# n = 123
# a_op = FourierMultiplierOp(n=n, p=30)
# a_mat = a_op @ np.eye(n)
......
......@@ -22,7 +22,7 @@
# Description
# -----------
#
# `pomad` is a Python implementation of algorithms from
# `skpomade` is a Python implementation of algorithms from
# paper *Finding Structure with Randomness: Probabilistic Algorithms for
# Constructing Approximate Matrix Decompositions*, by N. Halko, P. G.
# Martinsson and J. A. Tropp, SIAM review, 53 (2), 2011, https://arxiv.org/abs/0909.4061.
......@@ -31,7 +31,7 @@
# Version
# -------
#
# * pomad version = 0.1.3
# * skpomade version = 0.1.3
#
# Licence
# -------
......@@ -115,9 +115,9 @@ def evd_nystrom(a, q_mat):
# TODO refactor this code into nb
# if __name__ == '__main__':
# from pomad.range_approximation import randomized_range_finder
# from skpomade.range_approximation import randomized_range_finder
# import matplotlib.pyplot as plt
# from pomad.utils import FourierMultiplierOp
# from skpomade.utils import FourierMultiplierOp
# print('Test evd with operator')
#
# n = 53
......
......@@ -22,7 +22,7 @@
# Description
# -----------
#
# `pomad` is a Python implementation of algorithms from
# `skpomade` is a Python implementation of algorithms from
# paper *Finding Structure with Randomness: Probabilistic Algorithms for
# Constructing Approximate Matrix Decompositions*, by N. Halko, P. G.
# Martinsson and J. A. Tropp, SIAM review, 53 (2), 2011, https://arxiv.org/abs/0909.4061.
......@@ -31,7 +31,7 @@
# Version
# -------
#
# * pomad version = 0.1.3
# * skpomade version = 0.1.3
#
# Licence
# -------
......
......@@ -22,7 +22,7 @@
# Description
# -----------
#
# `pomad` is a Python implementation of algorithms from
# `skpomade` is a Python implementation of algorithms from
# paper *Finding Structure with Randomness: Probabilistic Algorithms for
# Constructing Approximate Matrix Decompositions*, by N. Halko, P. G.
# Martinsson and J. A. Tropp, SIAM review, 53 (2), 2011, https://arxiv.org/abs/0909.4061.
......@@ -31,7 +31,7 @@
# Version
# -------
#
# * pomad version = 0.1.3
# * skpomade version = 0.1.3
#
# Licence
# -------
......@@ -51,7 +51,7 @@
#
# ######### COPYRIGHT #########
"""Test of the module :module:`pomad._dev_range_approximation`
"""Test of the module :module:`skpomade._dev_range_approximation`
.. moduleauthor:: Valentin Emiya
"""
......@@ -60,10 +60,10 @@ import numpy as np
from scipy.sparse.linalg import aslinearoperator, svds
from pomad.range_approximation import adaptive_randomized_range_finder
from pomad.utils import \
from skpomade.range_approximation import adaptive_randomized_range_finder
from skpomade.utils import \
build_random_psd_matrix, build_test_matrix, FourierMultiplierOp
from pomad._dev_range_approximation import \
from skpomade._dev_range_approximation import \
adaptive_randomized_range_finder_naive, \
adaptive_randomized_range_finder_nolist, \
adaptive_randomized_range_finder_circy, \
......
......@@ -22,7 +22,7 @@
# Description
# -----------
#
# `pomad` is a Python implementation of algorithms from
# `skpomade` is a Python implementation of algorithms from
# paper *Finding Structure with Randomness: Probabilistic Algorithms for
# Constructing Approximate Matrix Decompositions*, by N. Halko, P. G.
# Martinsson and J. A. Tropp, SIAM review, 53 (2), 2011, https://arxiv.org/abs/0909.4061.
......@@ -31,7 +31,7 @@
# Version
# -------
#
# * pomad version = 0.1.3
# * skpomade version = 0.1.3
#
# Licence
# -------
......@@ -51,7 +51,7 @@
#
# ######### COPYRIGHT #########
"""Test of the module :module:`pomad.factorization_construction`
"""Test of the module :module:`skpomade.factorization_construction`
.. moduleauthor:: Valentin Emiya
"""
......@@ -59,9 +59,9 @@ import unittest
import numpy as np
from scipy.sparse.linalg import aslinearoperator, eigs, svds
from pomad.factorization_construction import direct_svd, evd_nystrom
from pomad.range_approximation import randomized_range_finder
from pomad.utils import \
from skpomade.factorization_construction import direct_svd, evd_nystrom
from skpomade.range_approximation import randomized_range_finder
from skpomade.utils import \
build_random_psd_matrix, build_test_matrix, FourierMultiplierOp
......
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