Repackage xpore & add Yuk Kei's CI

.github/workflows/test.yaml

@@ -14,7 +14,7 @@ jobs:
     runs-on: ubuntu-latest
     strategy:
       matrix:
-        python-version: ["3.8"]
+        python-version: ["3.11"]
 
     steps:
       - uses: actions/checkout@v3
@@ -28,7 +28,7 @@ jobs:
           pip install .
       - name: Install xpore-package
         run: |
-          python -m pip install --upgrade pip
+          #python -m pip install --upgrade pip
           pip install pytest pytest-dependency
       - name: Test with pytest
         run: pytest --junitxml=junit/test-results-${{ matrix.python-version }}.xml

README.md

@@ -1,4 +1,4 @@
-![alt text](https://github.com/GoekeLab/xpore/blob/master/figures/xpore_textlogo.png "xPore")
+![alt text](./docs/xpore_textlogo.png "xPore")
 
 
 

pyproject.toml

@@ -0,0 +1,50 @@
+[build-system]
+requires = ["setuptools>=42"]
+build-backend = "setuptools.build_meta"
+
+
+[project]
+name = "xpore"
+authors = [{name = "Ploy N. Pratanwanich", email= "[email protected]"}]
+maintainers = [
+	{name = "Ploy N. Pratanwanich", email= "[email protected]"},
+	{name = "Leon Rauschning"},
+]
+requires-python = ">=3.8"
+description="xpore is a python package for Nanopore data analysis of differential RNA modifications."
+dependencies = [
+	"numpy>=2.0.0",
+	"pandas>=2.0.0",
+	"scipy>=1.10.1",
+	"PyYAML",
+	"h5py>=3.0.0",
+	"pyensembl>=2.2.0",
+	"ujson>=4.0.1"
+]
+readme = "README.md"
+license = {text = "MIT"}
+classifiers = [
+	# Trove classifiers
+	# (https://pypi.python.org/pypi?%3Aaction=list_classifiers)
+	"Development Status :: 1 - Planning",
+	"License :: OSI Approved :: MIT License",
+	"Programming Language :: Python",
+	"Programming Language :: Python :: 3.8",
+	"Topic :: Software Development :: Libraries",
+	"Topic :: Scientific/Engineering :: Bio-Informatics",
+	"Intended Audience :: Science/Research",
+]
+dynamic = ["version"]
+
+[project.urls]
+homepage = "https://github.com/GoekeLab/xpore"
+
+[project.scripts]
+xpore = "xpore.scripts.xpore:main"
+
+[tool.setuptools]
+include-package-data = true # set explicitly in case it changes in the future
+
+[tool.setuptools.dynamic]
+version = {attr = "xpore.__version__"}
+

xpore/diffmod/configurator.py

@@ -7,7 +7,7 @@
 def get_condition_run_name(condition_name,run_name):
     return '-'.join([condition_name,run_name])
 
-class Configurator(object):
+class Configurator:
     def __init__(self, config_filepath):
         self.filepath = os.path.abspath(config_filepath)
         self.filename = self.filepath.split('/')[-1]

xpore/diffmod/gmm.py

@@ -4,7 +4,7 @@
 import scipy.stats
 
 
-class GMM(object):  
+class GMM:  
     """
     1D multi-sample 2-Gaussian mixture model.
     """
@@ -121,6 +121,7 @@ def fit(self):
                 if (diff < self.method['stopping_criteria']):
                     converged = True
                     break
+
         self.info['n_iterations'] = iteration
         self.info['converged'] = converged
         self.info['convergence_ratio'] = diff
@@ -131,7 +132,7 @@ def fit(self):
 ################################
 
 
-class Constant(object):
+class Constant:
     """
     Constant node.
     """
@@ -140,7 +141,7 @@ def __init__(self, data=None, inits=None):
         self.data = data
 
 
-class UnivariateNormalMixture(object):
+class UnivariateNormalMixture:
     def __init__(self, parents=None, data=None, inits=None):
 
         self.parents = parents
@@ -189,7 +190,7 @@ def _update(self):
         self.params['variance'] = N_inverse * np.sum(self.parents['z'].expected()*(residuals**2), axis=-2)  # sum across reads => K
 
 
-class Bernoulli(object):
+class Bernoulli:
     def __init__(self, dim, parents=None, data=None, inits=None):
         self.params = dict()
         self.params['prob'] = np.full(dim, np.nan)
@@ -244,7 +245,7 @@ def _update(self, children):
 
 
 
-class Dirichlet(object):
+class Dirichlet:
     def __init__(self, dim, parents=None, data=None, inits=None, priors=None):  # dim - [,n_categories]
 
         self.priors = dict()
@@ -297,7 +298,7 @@ def __log_C(alpha):
         return scipy.special.gammaln(np.sum(alpha, axis=-1)) - np.sum(scipy.special.gammaln(alpha), axis=-1)
 
 
-class UnivariateNormalGamma(object):
+class UnivariateNormalGamma:
     def __init__(self, dim, parents=None, data=None, inits=None, priors=None):
 
         self.priors = dict.fromkeys(['location', 'lambda', 'alpha', 'beta'], np.full(dim, np.nan))  # alpha = shape, beta=rate

xpore/diffmod/statstest.py

@@ -1,7 +1,7 @@
 import scipy.stats
 import numpy as np
 
-class StatsTest(object):
+class StatsTest:
     def __init__(self,data):
         if self.__isok(data):
             self.data = [data['y'][data['x'][:,0]==1],data['y'][data['x'][:,1]==1]]