v0.1.0 (#9)

* Set version to 0.0.1

* Integrate class docstrings for BDM variants

* Add some defensing programming and tests for BDM initialization

* Start documentation process

* Add tests for probability normalization in D1 and commented in D2 (D2 does not pass probably since the 2D ruleset is much bigger and is not entirely covered with small dataset parts)

* Add docstrings to theory tests

* Minor fix

* Remove nbdm and nent methods; use normalize flag in the main bdm and ent methods instead

* Add better data checks to bdm and ent methods (controlled by 'normalize' flag)

* Implement functions for iterating over slices and datasets parts in an easy manner

* Reimplement normalized bdm/ent so it has lower memory footprint and is slightly faster (no need for calling partition method on artifical datasets)

* Raise NotImplementedError in normalized bdm/ent for BDMBase with positive shift

* Add preliminary version of documentation

* Use version 0.1.0

* Implement partition algorithm classes + simplify slicing utils

* BDM class reimplemented

* Rename boundary attribute in the BDM class to partition and rename partition method to decompose (both in BDM and Partition classes)

* Start rewriting the docs

* Add global package options

* Rename the package to pybdm

* Fix docs

* Add docs + minor changes in perturbation experiment

* Main docs finish / module docs are yet to be done

* Fix BDM docs + add slight API changes

* Finish docs

* Change history and minor fixes in the docs

* Add separate requirements for sphinx builds

* Add docs status badges

* Minor changes to the docs

* Add tests for multiplt OS on Travis CI

* Run Travis tests only agains linux and osx

* Remove tox-conda from requirements.txt

* Simplify travis config

* Minor fixes

* Remove tests for OSX

.travis.yml

@@ -1,20 +1,40 @@
 # Config file for automatic testing at travis-ci.org
-dist: bionic
 language: python
-
+dist: bionic
 python:
-  - "3.7"
-  - "3.6"
-#  - "3.5"
+  - 3.5
+  - 3.6
+  - 3.7
 
 # command to install dependencies, e.g. pip install -r requirements.txt --use-mirrors
 install:
   - pip install -r requirements.txt
   - pip install .
 
+# matrix:
+#   include:
+#   - os: osx
+#     language: minimal
+#     python: 3.7
+#     install:
+#       - pip3 install -r requirements.txt
+#       - pip3 install -e .[tests]
+#     script:
+#       - pytest --slow
+#   - os: windows
+#     language: minimal
+#     python:
+#       - "3.5"
+#       - "3.6"
+#       - "3.7"
+#     before_install:
+#     - choco install python3
+#     - export PATH="/c/Python37:/c/Python37/Scripts:$PATH"
+
 # command to run tests, e.g. python setup.py test
 script:
-  - pytest --slow --cov-report term --cov=bdm/ --cov=test/
+  - pytest --slow --cov-report term --cov=pybdm/ --cov=tests/
+  - pytest --pylint -m pylint
 
 after_success:
   - codecov

AUTHORS.rst

@@ -2,12 +2,17 @@
 Authors
 =======
 
-Core Team
----------
+Core Team (AlgoDyn Development Team)
+------------------------------------
 
 * Szymon Talaga <[email protected]>
 * Kostas Tsampourakis <[email protected]>
 
+Maintainer
+----------
+
+* Szymon Talaga <[email protected]>
+
 Contributors
 ------------
 

HISTORY.rst

@@ -3,7 +3,7 @@
 History
 -------
 
-0.0.0 (2019-01-01)
+0.1.0 (2019-09-22)
 ++++++++++++++++++
 
 * First release on PyPI.

LICENSE

@@ -1,6 +1,6 @@
 The MIT License (MIT)
 
-Copyright (c) 2018 Szymon Talaga
+Copyright (c) 2019 Szymon Talaga
 
 Permission is hereby granted, free of charge, to any person obtaining a copy of
 this software and associated documentation files (the "Software"), to deal in

MANIFEST.in

@@ -3,4 +3,4 @@ include CONTRIBUTING.rst
 include HISTORY.rst
 include LICENSE
 include README.rst
-recursive-include bdm/ctmdata *
+recursive-include pybdm/ctmdata *

Makefile

@@ -26,7 +26,7 @@ clean-pyc:
 clean-misc:
 	find . -name '.benchmarks' -exec rm -rf {} +
 	find . -name '.pytest-cache' -exec rm -rf {} +
-	rm .coverage*
+	rm -f .coverage*
 
 lint:
 	py.test --pylint -m pylint
@@ -38,16 +38,16 @@ test-all:
 	tox
 
 coverage:
-	coverage run --source bdm setup.py test
+	coverage run --source pybdm setup.py test
 	coverage report -m
 	coverage html
 	xdg-open htmlcov/index.html
 	# open htmlcov/index.html
 
 docs:
-	rm -f docs/bdm.rst
+	rm -f docs/pybdm.rst
 	rm -f docs/modules.rst
-	sphinx-apidoc -o docs/ bdm
+	sphinx-apidoc -o docs/ pybdm
 	$(MAKE) -C docs clean
 	$(MAKE) -C docs html
 	xdg-open docs/_build/html/index.html

README.rst

@@ -1,5 +1,5 @@
 =============================================================
-PyBDM: Python interface to the *Block Deincomposition Method*
+PyBDM: Python interface to the *Block Decomposition Method*
 =============================================================
 
 .. image:: https://badge.fury.io/py/pybdm.png
@@ -11,6 +11,10 @@ PyBDM: Python interface to the *Block Deincomposition Method*
 .. image:: https://codecov.io/gh/sztal/pybdm/branch/master/graph/badge.svg?branch=master
     :target: https://codecov.io/gh/sztal/pybdm
 
+.. image:: https://readthedocs.org/projects/pybdm-docs/badge/?version=latest
+    :target: https://pybdm-docs.readthedocs.io/en/latest/?badge=latest
+    :alt: Documentation Status
+
 The Block Decomposition Method (BDM) approximates algorithmic complexity
 of a dataset of arbitrary size, that is, the length of the shortest computer
 program that generates it. This is not trivial as algorithmic complexity
@@ -20,28 +24,38 @@ mechanistic connections between elements of a system, even such that
 do not yield any regular statistical patterns that can be captured with
 more traditional tools based on probability theory and information theory.
 
-Currently 1D and 2D binary arrays are supported, but this may be extended to higher dimensionalities and more complex alphabets in the future.
+Currently 1D and 2D binary arrays are supported, as well as 1D arrays
+with 4, 5, 6 and 9 discrete symbols.
 
 BDM and the necessary parts of the algorithmic information theory
-it is based on are described in `this paper <https://www.mdpi.com/1099-4300/20/8/605>`__.
+it is based on are described in `this article <https://www.mdpi.com/1099-4300/20/8/605>`_.
+
+See the official documentation_ for more information.
 
 
 Installation
 ============
 
+Standard installation (stable)::
+
+    pip install pybdm
+
+Development version installation::
+
+    pip install git+https://github.com/sztal/pybdm.git
+
 Local development::
 
     git clone https://github.com/sztal/pybdm
     cd pybdm
     pip install --editable .
 
-Development version installation::
-
-    pip install git+https://github.com/sztal/pybdm.git
 
-Standard installation (not yet on *PyPI*)::
+Supported versions
+------------------
 
-    pip install bdm
+Python3.5+ is supported. Tests are run against Linux, but
+Windows and OSX should work as well.
 
 
 Usage
@@ -56,14 +70,16 @@ input represented as `Numpy <http://www.numpy.org/>`__ arrays of integer type.
    Hence, any alphabet must be first mapped to a set of integers ranging
    from ``0`` to ``k``.
 
+Detailed usage examples can be found in the official documentation_.
+
 
 Binary sequences (1D)
 ---------------------
 
 .. code-block:: python
 
     import numpy as np
-    from bdm import BDM
+    from pybdm import BDM
 
     # Create a dataset (must be of integer type)
     X = np.ones((100,), dtype=int)
@@ -85,7 +101,7 @@ Binary matrices (2D)
 .. code-block:: python
 
     import numpy as np
-    from bdm import BDM
+    from pybdm import BDM
 
     # Create a dataset (must be of integer type)
     X = np.ones((100, 100), dtype=int)
@@ -99,6 +115,25 @@ Binary matrices (2D)
     # BDM objects may also compute standard Shannon entropy in base 2
     bdm.ent(X)
 
+Non-binary sequences (1D)
+-------------------------
+
+.. code-block:: python
+
+    import numpy as np
+    from pybdm import BDM
+
+    # Create a dataset (4 discrete symbols)
+    np.random.seed(303)
+    X = np.random.randint(0, 4, (100,))
+
+    # Initialize BDM object with 4-symbols alphabet
+    bdm = BDM(ndim=1, nsymbols=4)
+
+    # Compute BDM
+    bdm.bdm(X)
+
+
 
 Parallel processing
 -------------------
@@ -113,22 +148,29 @@ compute so-called counter objects (final BDM computation operates on such object
 in parallel in 4 independent processes, and aggregate the results
 into a single BDM approximation of the algorithmic complexity of the dataset.
 
+.. highlights::
+
+    Remember that data has to be sliced correctly during parallelization
+    in order to ensure fully correct BDM computations. That is, all slices
+    except lower and right boundaries have to be decomposable without
+    any boundary leftovers by the selected decomposition algorithm.
+
 .. code-block:: python
 
     import numpy as np
     from joblib import Parallel, delayed
-    from bdm import BDM
-    from bdm.utils import slice_dataset
+    from pybdm import BDM
+    from pybdm.utils import decompose_dataset
 
     # Create a dataset (must be of integer type)
     X = np.ones((1000, 1000), dtype=int)
 
     # Initialize BDM object
-    BDM = bdm(ndim=2)
+    bdm = BDM(ndim=2)
 
     # Compute counter objects in parallel
     counters = Parallel(n_jobs=4) \
-        (delayed(bdm.lookup_and_count)(d) for d in slice_dataset(X, (200, 200)))
+        (delayed(bdm.decompose_and_count)(d) for d in decompose_dataset(X, (200, 200)))
 
     # Compute BDM
     bdm.compute_bdm(*counters)
@@ -155,14 +197,14 @@ to be noise since they extend the system's description length.
 .. code-block:: python
 
     import numpy as np
-    from bdm import BDM
-    from bdm.algorithms import PerturbationExperiment
+    from pybdm import BDM
+    from pybdm.algorithms import PerturbationExperiment
 
     # Create a dataset (must be of integer type)
     X = np.ones((100, 100), dtype=int)
 
     # Initialize BDM object
-    BDM = bdm(ndim=2)
+    bdm = BDM(ndim=2)
 
     # Initialize perturbation experiment object
     # (may be run for both bdm or entropy)
@@ -186,3 +228,6 @@ Authors & Contact
 
 * Szymon Talaga <[email protected]>
 * Kostas Tsampourakis <[email protected]>
+
+
+.. _documentation: http://pybdm-docs.rtfd.org