Merge pull request #15 from VarIr/maint

General maintenance

README.rst

@@ -1,5 +1,5 @@
-.. image:: https://travis-ci.com/VarIr/copac.svg?token=Pv7ns6A7X34baaBVUTz8&branch=master
-    :target: https://travis-ci.com/VarIr/copac
+.. image:: https://travis-ci.org/VarIr/copac.svg?token=Pv7ns6A7X34baaBVUTz8&branch=master
+    :target: https://travis-ci.org/VarIr/copac
 .. image:: https://zenodo.org/badge/106809618.svg
    :target: https://zenodo.org/badge/latestdoi/106809618
 
@@ -13,20 +13,20 @@ We implement COPAC (Correlation Partition Clustering), which
 #. computes the local correlation dimensionality based on the largest eigenvalues
 #. partitions the data set based on this dimension
 #. calculates a Euclidean distance variant weighted with the correlation dimension, called correlation distance
-#. further clusters objects within each partition with Generalized DBSCAN, requiring a minimum number of objects to be within eps range for each core point.
+#. further clusters objects within each partition with Generalized DBSCAN,
+   requiring a minimum number of objects to be within eps range for each core point.
 
 
 Installation
 ------------
 
-Make sure you have a working Python3 environment (at least 3.6) with
-numpy, scipy and scikit-learn packages. Consider using 
-`Anaconda <https://www.anaconda.com/download/#linux>`_.
-You can install COPAC from within the cloned directory with
+Clone this repository and ``pip install`` like so:
 
 .. code-block:: bash
 
-  python3 setup.py install
+  git clone https://github.com/VarIr/copac.git
+  cd copac
+  python3 -m pip install .
 
 COPAC is then available through the `copac` package.
 

copac/__init__.py

@@ -1,9 +1,7 @@
-"""
-Created on 25.10.2017
-
-@author: roman
-"""
 from .copac import COPAC, copac
 
-__all__ = ['COPAC',
-           'copac']
+__all__ = [
+    'COPAC',
+    'copac',
+]
+__version__ = "0.2.1"

environment.yml

@@ -0,0 +1,50 @@
+name: copac
+channels:
+  - conda-forge
+  - bioconda
+  - defaults
+dependencies:
+  - _libgcc_mutex=0.1=conda_forge
+  - _openmp_mutex=4.5=2_gnu
+  - bzip2=1.0.8=h7f98852_4
+  - ca-certificates=2023.7.22=hbcca054_0
+  - colorama=0.4.6=pyhd8ed1ab_0
+  - exceptiongroup=1.1.3=pyhd8ed1ab_0
+  - iniconfig=2.0.0=pyhd8ed1ab_0
+  - joblib=1.3.2=pyhd8ed1ab_0
+  - ld_impl_linux-64=2.40=h41732ed_0
+  - libblas=3.9.0=18_linux64_openblas
+  - libcblas=3.9.0=18_linux64_openblas
+  - libexpat=2.5.0=hcb278e6_1
+  - libffi=3.4.2=h7f98852_5
+  - libgcc-ng=13.2.0=h807b86a_0
+  - libgfortran-ng=13.2.0=h69a702a_0
+  - libgfortran5=13.2.0=ha4646dd_0
+  - libgomp=13.2.0=h807b86a_0
+  - liblapack=3.9.0=18_linux64_openblas
+  - libnsl=2.0.0=h7f98852_0
+  - libopenblas=0.3.24=pthreads_h413a1c8_0
+  - libsqlite=3.43.0=h2797004_0
+  - libstdcxx-ng=13.2.0=h7e041cc_0
+  - libuuid=2.38.1=h0b41bf4_0
+  - libzlib=1.2.13=hd590300_5
+  - ncurses=6.4=hcb278e6_0
+  - openssl=3.1.2=hd590300_0
+  - packaging=23.1=pyhd8ed1ab_0
+  - pip=23.2.1=pyhd8ed1ab_0
+  - pluggy=1.3.0=pyhd8ed1ab_0
+  - pytest=7.4.2=pyhd8ed1ab_0
+  - python=3.11.5=hab00c5b_0_cpython
+  - python_abi=3.11=3_cp311
+  - readline=8.2=h8228510_1
+  - setuptools=68.2.2=pyhd8ed1ab_0
+  - threadpoolctl=3.2.0=pyha21a80b_0
+  - tk=8.6.12=h27826a3_0
+  - tomli=2.0.1=pyhd8ed1ab_0
+  - tzdata=2023c=h71feb2d_0
+  - wheel=0.41.2=pyhd8ed1ab_0
+  - xz=5.2.6=h166bdaf_0
+  - pip:
+    - numpy==1.25.2
+    - scikit-learn==1.3.0
+    - scipy==1.11.2

requirements.txt

@@ -1,49 +1,15 @@
-bleach>=2.1.1
-cycler>=0.10.0
-decorator>=4.1.2
-entrypoints>=0.2.3
-html5lib>=1.0b10
-ipykernel>=4.6.1
-ipython>=6.2.1
-ipython-genutils>=0.2.0
-ipywidgets>=7.0.3
-jedi>=0.11.0
-Jinja2>=2.9.6
-jsonschema>=2.6.0
-jupyter>=1.0.0
-jupyter-client>=5.1.0
-jupyter-console>=5.2.0
-jupyter-core>=4.3.0
-MarkupSafe>=1.0
-matplotlib>=2.1.0
-mistune>=0.7.4
-nbconvert>=5.3.1
-nbformat>=4.4.0
-nose>=1.3.7
-notebook>=5.7.2
-numpy>=1.13.3
-pandas>=0.20.3
-pandocfilters>=1.4.2
-parso>=0.1.0
-pexpect>=4.2.1
-pickleshare>=0.7.4
-prompt-toolkit>=1.0.15
-ptyprocess>=0.5.2
-Pygments>=2.2.0
-pyparsing>=2.2.0
-python-dateutil>=2.6.1
-pytz>=2017.2
-pyzmq>=16.0.2
-qtconsole>=4.3.1
-scikit-learn>=0.19.1
-scipy>=0.19.1
-seaborn>=0.8.1
-simplegeneric>=0.8.1
-six>=1.11.0
-terminado>=0.6
-testpath>=0.3.1
-tornado>=4.5.2
-traitlets>=4.3.2
-wcwidth>=0.1.7
-webencodings>=0.5.1
-widgetsnbextension>=3.0.6
+colorama==0.4.6
+exceptiongroup==1.1.3
+iniconfig==2.0.0
+joblib==1.3.2
+numpy==1.25.2
+packaging==23.1
+pip==23.2.1
+pluggy==1.3.0
+pytest==7.4.2
+scikit-learn==1.3.0
+scipy==1.11.2
+setuptools==68.2.2
+threadpoolctl==3.2.0
+tomli==2.0.1
+wheel==0.41.2

setup.cfg

@@ -0,0 +1,52 @@
+[bdist_wheel]
+universal = true
+
+[metadata]
+name = COPAC
+version = attr: copac.__version__
+author = Roman Feldbauer
+author_email = [email protected]
+url = https://github.com/VarIr/copac
+description = Correlation partition clustering
+long_description = file: README.rst
+long_description_content_type = text/restructured
+license = GPLv3
+keywords = "machine learning" "data science" "data mining"
+platform = any
+classifiers =
+    Development Status :: 4 - Beta
+    Environment :: Console
+    Intended Audience :: Science/Research
+    Programming Language :: Python
+    Programming Language :: Python :: 3",
+    Programming Language :: Python :: 3.6
+    Programming Language :: Python :: 3.7
+    Programming Language :: Python :: 3.8
+    Programming Language :: Python :: 3.9
+    Programming Language :: Python :: 3.10
+    Programming Language :: Python :: 3.11
+    Topic :: Scientific/Engineering
+project_urls =
+    Bug Tracker = https://github.com/VarIr/copac/issues
+
+[options]
+package_dir=
+    =copac
+python_requires = >= 3.6
+packages = find:
+# The following plus setuptools-scm include all version-controlled files in the package.
+include_package_data = True
+install_requires =
+    numpy
+    scikit-learn
+    scipy
+    pytest
+
+[options.packages.find]
+where = copac
+
+# Setup requirements also here for legacy packaging tools; pyproject.toml is the preferred place
+setup_requires =
+    setuptools
+    setuptools_scm
+    wheel