Merge pull request #9 from tudo-astroparticlephysics/pypi_upload

Pypi upload

.github/workflows/pypi.yml

@@ -0,0 +1,32 @@
+name: Deploy to PyPi
+
+on:
+  push:
+    tags:
+      - 'v*'
+
+jobs:
+  pypi:
+    name: Publish packages to PyPI
+    runs-on: ubuntu-latest
+    environment:
+      name: pypi
+      url: https://pypi.org/p/funfolding
+    permissions:
+      id-token: write
+
+    steps:
+      - name: Set up Python
+        uses: actions/setup-python@v5
+        with:
+          python-version: "3.9"
+
+      - name: Install dependencies
+        run: |
+          pip install -U build
+
+      - name: Build package
+        run: python -m build
+
+      - name: Publish distribution 📦 to PyPI
+        uses: pypa/gh-action-pypi-publish@release/v1

README.md

@@ -45,9 +45,8 @@ want to install the available packages via `conda` first.
 matplotlib
 numpy
 pymc3
-scikit-learn>=0.18.1
+scikit-learn>=0.19.0
 scipy
-six
 ```
 
 ### Installing

docs/source/conf.py

@@ -68,7 +68,7 @@
 #
 # This is also used if you do content translation via gettext catalogs.
 # Usually you set "language" from the command line for these cases.
-language = None
+language = "en"
 
 # List of patterns, relative to source directory, that match files and
 # directories to ignore when looking for source files.
@@ -83,18 +83,7 @@
 
 
 # -- Options for HTML output ----------------------------------------------
-try:
-    import sphinx_rtd_theme
-except ImportError:
-    print('No sphinx_rtd_theme found! Default theme is used!')
-else:
-    html_theme = "sphinx_rtd_theme"
-
-    html_theme_path = [sphinx_rtd_theme.get_html_theme_path()]
-# The theme to use for HTML and HTML Help pages.  See the documentation for
-# a list of builtin themes.
-#
-html_theme = 'alabaster'
+html_theme = "sphinx_rtd_theme"
 
 # Theme options are theme-specific and customize the look and feel of a theme
 # further.  For a list of options available for each theme, see the
@@ -105,7 +94,7 @@
 # Add any paths that contain custom static files (such as style sheets) here,
 # relative to this directory. They are copied after the builtin static files,
 # so a file named "default.css" will overwrite the builtin "default.css".
-html_static_path = ['_static']
+# html_static_path = ['_static']
 
 
 # -- Options for HTMLHelp output ------------------------------------------

funfolding/binning/tree_sklearn_based.py

@@ -1,18 +1,13 @@
 import numpy as np
 import copy
-from distutils.version import StrictVersion
 
-import sklearn
 from sklearn.tree import DecisionTreeClassifier, DecisionTreeRegressor
 from sklearn.ensemble import AdaBoostClassifier, AdaBoostRegressor
 
 from ._binning import Binning
 import warnings
 
 
-old_sklean = StrictVersion("0.19.0") >= StrictVersion(sklearn.__version__)
-
-
 def __sample_uniform__(y, sample_weight=None, random_state=None):
     """Function used to sample a uniform distribution from a binned y.
 
@@ -298,28 +293,16 @@ def __init__(self,
             self.uniform = uniform
 
         if regression:
-            if old_sklean:
-                if min_impurity_decrease != 0.:
-                    warnings.warn('min_impurity_decrease is supported '
-                                  ' only in sklearn version >= 0.19.0')
-                self.tree = DecisionTreeRegressor(
-                    max_depth=max_depth,
-                    min_samples_split=min_samples_split,
-                    min_samples_leaf=min_samples_leaf,
-                    max_leaf_nodes=max_leaf_nodes,
-                    max_features=max_features,
-                    min_weight_fraction_leaf=min_weight_fraction_leaf,
-                    random_state=random_state)
-            else:
-                self.tree = DecisionTreeRegressor(
-                    max_depth=max_depth,
-                    min_samples_split=min_samples_split,
-                    min_samples_leaf=min_samples_leaf,
-                    max_leaf_nodes=max_leaf_nodes,
-                    max_features=max_features,
-                    min_impurity_decrease=min_impurity_decrease,
-                    min_weight_fraction_leaf=min_weight_fraction_leaf,
-                    random_state=random_state)
+            self.tree = DecisionTreeRegressor(
+                max_depth=max_depth,
+                min_samples_split=min_samples_split,
+                min_samples_leaf=min_samples_leaf,
+                max_leaf_nodes=max_leaf_nodes,
+                max_features=max_features,
+                min_impurity_decrease=min_impurity_decrease,
+                min_weight_fraction_leaf=min_weight_fraction_leaf,
+                random_state=random_state,
+            )
             if boosted in ['linear', 'square', 'exponential']:
                 self.boosted = AdaBoostRegressor(
                     base_estimator=self.tree,
@@ -336,35 +319,24 @@ def __init__(self,
             else:
                 self.boosted = None
         else:
-            if old_sklean:
-                if min_impurity_decrease != 0.:
-                    warnings.warn('min_impurity_decrease is supported '
-                                  ' only in sklearn version >= 0.19.0')
-                self.tree = DecisionTreeClassifier(
-                    max_depth=max_depth,
-                    min_samples_split=min_samples_split,
-                    min_samples_leaf=min_samples_leaf,
-                    max_leaf_nodes=max_leaf_nodes,
-                    max_features=max_features,
-                    min_weight_fraction_leaf=min_weight_fraction_leaf,
-                    random_state=random_state)
-            else:
-                self.tree = DecisionTreeClassifier(
-                    max_depth=max_depth,
-                    min_samples_split=min_samples_split,
-                    min_samples_leaf=min_samples_leaf,
-                    max_leaf_nodes=max_leaf_nodes,
-                    max_features=max_features,
-                    min_weight_fraction_leaf=min_weight_fraction_leaf,
-                    min_impurity_decrease=min_impurity_decrease,
-                    random_state=random_state)
+            self.tree = DecisionTreeClassifier(
+                max_depth=max_depth,
+                min_samples_split=min_samples_split,
+                min_samples_leaf=min_samples_leaf,
+                max_leaf_nodes=max_leaf_nodes,
+                max_features=max_features,
+                min_weight_fraction_leaf=min_weight_fraction_leaf,
+                min_impurity_decrease=min_impurity_decrease,
+                random_state=random_state,
+            )
             if boosted in ['SAMME', 'SAMME.R']:
                 self.boosted = AdaBoostClassifier(
                     base_estimator=self.tree,
                     n_estimators=n_estimators,
                     learning_rate=learning_rate,
                     algorithm=boosted,
-                    random_state=random_state)
+                    random_state=random_state,
+                )
             elif boosted is not None:
                 raise ValueError(
                     '\'boosted\' should be None for no boosting '

funfolding/model/_model.py

@@ -3,7 +3,6 @@
 from scipy import linalg
 from scipy import stats
 from numpy.linalg import svd
-import six
 
 
 class Model(object):
@@ -1174,7 +1173,7 @@ def generate_fit_x0(self, vec_g, vec_f_0=None, size=None):
             x0_i = vec_x_0_def[x0_slice]
             if sample_x0 is None:
                 pos_x0_i = x0_i
-            elif isinstance(sample_x0, six.string_types):
+            elif isinstance(sample_x0, str):
                 if sample_x0 == 'poisson':
                     pos_x0_i = self.random_state.poisson(x0_i,
                                                          size=size)

funfolding/solution/_solution.py

@@ -3,7 +3,6 @@
 import numpy as np
 from scipy import linalg
 from scipy.optimize import minimize
-import six
 
 import emcee
 
@@ -323,7 +322,7 @@ def fit(self,
                     sigma_limits=error_interval_sigma_limits,
                     n_nuissance=self.model.n_nuissance_parameters)
         if thin is not None:
-            if isinstance(thin, six.string_types):
+            if isinstance(thin, str):
                 if thin.lower() == 'autocorr':
                     thin = int(np.max(autocorr_time[0]) + 0.5)
             if isinstance(thin, int):

funfolding/solution/likelihood.py

@@ -1,5 +1,4 @@
 import numpy as np
-import six
 
 from ..model import LinearModel, Model
 
@@ -108,7 +107,7 @@ def initialize(self,
 
         if self.tau is None:
             self._tau = None
-        elif isinstance(self.tau, six.string_types):
+        elif isinstance(self.tau, str):
             if self._tau.lower() == 'None':
                 self._tau = None
         else:
@@ -142,7 +141,7 @@ def initialize(self,
                                  "callable!")
             if self._tau is not None:
                 m_C = None
-                if isinstance(self.C, six.string_types):
+                if isinstance(self.C, str):
                     if self.C.lower() == 'thikonov' or self.C.lower() == '2':
                         m_C = create_C_thikonov(
                             eff_f_length)

pyproject.toml

@@ -1,3 +1,3 @@
 [build-system]
-requires = ["setuptools"]
-build-backend = "setuptools.build_meta"
+requires = ["setuptools>=68"]
+build-backend = "setuptools.build_meta"

setup.py

@@ -8,9 +8,16 @@
 with open(path.join(here, 'README.md'), encoding='utf-8') as f:
     long_description = f.read()
 
+
+extras_require = {
+    'tests': ['pytest'],
+    'docs': ['sphinx', 'sphinx-rtd-theme'],
+}
+extras_require['all'] = extras_require['tests'] + extras_require['docs']
+
 setup(
     name='funfolding',
-    version='0.2.2',
+    version='0.3.0',
 
     description='Having fun with unfolding.',
     long_description=long_description,
@@ -30,10 +37,12 @@
 
         'License :: OSI Approved :: MIT License',
 
-        'Programming Language :: Python :: 2.7',
-        'Programming Language :: Python :: 3.5',
-        'Programming Language :: Python :: 3.6',
+        'Programming Language :: Python :: 3',
+        'Programming Language :: Python :: 3.9',
+        'Programming Language :: Python :: 3.10',
+        'Programming Language :: Python :: 3.11',
     ],
+    python_requires=">= 3.9, <3.12a0",
     keywords='unfolding',
     packages=find_packages(exclude=['contrib', 'docs', 'tests']),
     install_requires=[
@@ -42,12 +51,8 @@
         'matplotlib',
         'numpy',
         'pymc3',
-        'scikit-learn>=0.18.1',
+        'scikit-learn>=0.19.0',
         'scipy',
-        'six>=1.1',
     ],
-    extras_require={
-        ':python_version == "2.7"': ['futures'],
-        'tests': ['pytest'],
-    },
+    extras_require=extras_require,
 )