Remove hard dependency of the DeerLab's installation routine and scripts on MKL

.github/workflows/ci_PR.yml

@@ -11,7 +11,7 @@ jobs:
       fail-fast: false
       matrix:
         os: [ubuntu-latest, macos-latest, windows-latest]
-        python-version: [3.8]
+        python-version: [3.9]
     steps:
       - uses: actions/checkout@v2
 
@@ -24,9 +24,8 @@ jobs:
         if: startsWith(runner.os, 'Windows')
         with:
           path: |
-            ~\pipwin
             ~\AppData\Local\pip\Cache
-          key: ${{ runner.os }}-${{ hashFiles('**/setup.py') }}
+          key: cache-${{ runner.os }}-${{ hashFiles('**/setup.py') }}
           restore-keys: |
             {{ runner.os }}-pip-
 
@@ -35,7 +34,7 @@ jobs:
         with:
           path: |
             ~/Library/Caches/pip
-          key: ${{ runner.os }}-${{ hashFiles('**/setup.py') }}
+          key: cache-${{ runner.os }}-${{ hashFiles('**/setup.py') }}
           restore-keys: |
             {{ runner.os }}-pip-
 
@@ -44,7 +43,7 @@ jobs:
         with:
           path: |
             ~/.cache/pip
-          key: ${{ runner.os }}-${{ hashFiles('**/setup.py') }}
+          key: cache-${{ runner.os }}-${{ hashFiles('**/setup.py') }}
           restore-keys: |
             {{ runner.os }}-pip-
 
@@ -53,7 +52,6 @@ jobs:
         run: |
           python -m pip install --upgrade pip
           python setup.py install
-          python -m pip install pytest
 
       - name: Test with pytest
         run: pytest

.github/workflows/ci_scheduled.yml

@@ -25,7 +25,6 @@ jobs:
         if: startsWith(runner.os, 'Windows')
         with:
           path: |
-            ~\pipwin
             ~\AppData\Local\pip\Cache
           key: ${{ runner.os }}-${{ hashFiles('**/setup.py') }}
           restore-keys: |
@@ -54,7 +53,6 @@ jobs:
         run: |
           python -m pip install --upgrade pip
           python setup.py install
-          python -m pip install pytest
 
       - name: Test with pytest
         run: pytest

.github/workflows/package_upload.yml

@@ -23,6 +23,7 @@ jobs:
     - name: Build distribution
       run: |
           python setup.py sdist
+          ./setup.py bdist_wheel
     - name: Publish distribution to PyPI
       uses: pypa/gh-action-pypi-publish@master
       with:

conda.recipe/meta.yaml

@@ -20,17 +20,17 @@ requirements:
     - numpy
     - scipy
     - cvxopt
+    - dill
+    - pytest
 
 about:
   home: https://jeschkelab.github.io/DeerLab/index.html
   license: MIT
   summary: 'Comprehensive package for data analysis of dipolar EPR spectroscopy'
   description: |
-    DeerLab is a Python package for the analysis of dipolar EPR (electron paramagnetic resonance)
-    spectroscopy data. Dipolar EPR spectroscopy techniques include DEER (double electron-electron resonance),
-    RIDME (relaxation-induced dipolar modulation enhancement), and others.
-    DeerLab consists of a collection of functions for modelling, data processing, and least-squares fitting.
-    They can be combined in scripts to build custom data analysis workflows. DeerLab supports both classes of 
-    distance distribution models: non-parametric (Tikhonov regularization and related) and parametric 
-    (multi-Gaussians etc). It also provides a selection of background and experiment models.
+    DeerLab is a comprehensive free scientific software package for Python focused on modelling,
+    penalized least-squares regression, and uncertainty quantification. It provides highly 
+    specialized on the analysis of dipolar EPR (electron paramagnetic resonance) spectroscopy data. 
+    Dipolar EPR spectroscopy techniques include DEER (double electron-electron resonance), 
+    RIDME (relaxation-induced dipolar modulation enhancement), and others. 
   dev_url: https://jeschkelab.github.io/DeerLab

deerlab/dd_models.py

@@ -140,7 +140,7 @@ def _nonparametric():
     dd_nonparametric = Model(_nonparametric,constants='r')
     dd_nonparametric.description = 'Non-parametric distribution model'
     # Parameters
-    dd_nonparametric.addlinear('P',vec=len(r),lb=0,par0=0,description='Non-parametric distance distribution',unit="nm⁻¹", normalization=lambda P: P/np.trapz(P,r))
+    dd_nonparametric.addlinear('P',vec=len(r),lb=0,par0=0,description='Non-parametric distance distribution',unit="nm⁻¹", normalization=lambda P: _normalize(r,P))
     return dd_nonparametric
 
 

docsrc/source/installation.rst

@@ -46,12 +46,6 @@ DeerLab installs the following packages:
 * `tqdm <https://github.com/tqdm/tqdm>`_ - A lightweight package for smart progress meters.
 * `dill <https://github.com/uqfoundation/dill>`_ - An extension of Python's pickle module for serializing and de-serializing python objects.
 
-The installed numerical computing packages (numpy, scipy, cvxopt) are linked against different BLAS libraries depending on the OS:
-
-* Windows: linked against the Intel Matrix Kernel Library (MKL)
-* Linux: linked against OpenBLAS
-* Mac: linked against BLAS/LAPACK from the Accelerate framework
-
 Installing from Anaconda
 *************************
 
@@ -125,6 +119,21 @@ In order to install DeerLab but be able to edit the code or update frequently wi
 
 Any changes made to the source code will then immediate effect.
 
+
+Linking against BLAS libraries
+*******************************
+
+The installed numerical computing packages (numpy, scipy, cvxopt) are automatically linked against different BLAS libraries depending on the OS:
+
+* Linux: OpenBLAS
+* Mac: BLAS/LAPACK from the Accelerate framework
+
+In Windows systems, these packages can be linked against the Intel MKL libraries. To do so, DeerLab provides a script ``mkl_link.py`` that can be used to link against the Intel MKL libraries automatically::
+
+        python mkl_link.py
+
+The script will download the binaries from the Gohlke repository and override any currently installed version of the mentioned packages. 
+
 Installation failed 
 --------------------
 

setup.py

@@ -1,70 +1,4 @@
 from setuptools import setup
-from setuptools.command.install import install
-from setuptools.command.develop import develop
-from sys import executable
-import platform
-import subprocess
-
-# Custom OS-specific installation script
-def install_dependencies(develop_mode=False):
-#-----------------------------------------------------------------------
-
-    # At the moment the dependencies must be handled manually this way due to 
-    # the dependency of Windows systems on the external Gohlke binaries. This
-    # also means that the package must be distributed as a source tarball instead
-    # of the commonly used wheels. 
-    # As soon as Numpy, Scipy and CVXOpt start distributing their own binaries for 
-    # Windows systems this will no longer be necessary and all dependencies will be 
-    # able to be handled by pip automatically. 
-
-    # DeerLab dependencies on the PyPI
-    dependencies = ["memoization","matplotlib","tqdm","joblib","dill"] 
-
-    if platform.system() == 'Windows':
-        # Use own forked pipwin repo for self-patched fixes 
-        dependencies += ["git+https://github.com/luisfabib/pipwin"]
-    else:
-        dependencies += ["numpy","scipy","cvxopt"]
-    if develop_mode:
-        dependencies += ["pytest"]
-
-    # Commands for pip installation of dependencies
-    pipinstall = [executable,'-m','pip','install']
-    userlevel = ['--user']
-
-    # Easier to Ask Forgiveness than Permission Principle: 
-    # try to install dependencies system-wide, if fails then at user-level
-    for dependency in dependencies:
-        try:
-            subprocess.run(pipinstall+[dependency],check=True)
-        except:
-            subprocess.run(pipinstall+userlevel+[dependency],check=False)
-
-    # Download and install pre-compiled binaries for Windows-systems
-    if platform.system() == 'Windows':  
-        # Refresh the pipwin cache to get the latest repo status
-        subprocess.run([executable,'-m','pipwin','refresh'],check=False)
-        # Install Numpy,SciPy, CVXopt linked to MKL from Gohlken's repository
-        subprocess.run([executable,'-m','pipwin','install','numpy','--filter=mkl'],check=False)
-        subprocess.run([executable,'-m','pipwin','install','scipy'],check=False)
-        subprocess.run([executable,'-m','pipwin','install','cvxopt'],check=False)
-#-----------------------------------------------------------------------    
-
-class install_routine(install):
-#-----------------------------------------------------------------------
-    """Customized setuptools install command"""
-    def run(self):
-        install.run(self)
-        install_dependencies()
-#-----------------------------------------------------------------------
-
-class develop_routine(develop):
-#-----------------------------------------------------------------------
-    """Customized setuptools install command"""
-    def run(self):
-        develop.run(self)
-        install_dependencies(develop_mode=True)
-#-----------------------------------------------------------------------
 
 setup(
     name='DeerLab',
@@ -85,10 +19,18 @@ def run(self):
     description='Comprehensive package for data analysis of dipolar EPR spectroscopy',
     long_description=open('README.md', encoding='utf-8').read(),
     long_description_content_type="text/markdown",
-    cmdclass={
-        'install': install_routine,
-        'develop': develop_routine
-    },
+    install_requires = [
+                        'numpy>=1.21.0',
+                        'cvxopt>=1.0.0',
+                        'scipy>=1.6.3',
+                        'joblib>-1.0.0',
+                        'dill>=0.3.0',
+                        'tqdm>=4.51.0',
+                        'matplotlib>=3.3.4',
+                        'memoization>=0.3.1',
+                        'pytest>=6.2.2',
+                        'setuptools>=53.0.0',
+                        ],
     classifiers=[
         'Development Status :: 4 - Beta',
         'Intended Audience :: Science/Research',

test/test_dipolarmodel.py

@@ -292,7 +292,7 @@ def test_ex_3pdeer_fit():
 
     assert np.allclose(V3pdeer,result.model,atol=1e-1) and ovl(result.P/1e5,Pr)>0.975
 # ======================================================================
-test_ex_3pdeer_fit()
+
 # ======================================================================
 def test_ex_4pdeer_type(): 
     "Check the 4-pulse DEER experimental model."

upgrade_mkl.py

@@ -0,0 +1,33 @@
+from sys import executable
+import platform
+import subprocess
+import os
+
+MKL_LINKED = os.environ.get('MKL_LINKED', False)
+
+# Custom OS-specific installation script
+def install_MKL_dependencies():
+#-----------------------------------------------------------------------
+
+    if platform.system() != 'Windows':
+        raise SystemError('MKL dependencies are only available on Windows systems.')
+
+    # At the moment the dependencies must be handled manually this way
+    subprocess.run([executable,'-m','pip','install','git+https://github.com/luisfabib/pipwin'],check=False)
+
+    try:
+        # Install Numpy,SciPy, CVXopt linked to MKL from Gohlken's repository
+        subprocess.run([executable,'-m','pipwin','install','numpy','--filter=mkl'],check=False)
+        subprocess.run([executable,'-m','pipwin','install','scipy'],check=False)
+        subprocess.run([executable,'-m','pipwin','install','cvxopt'],check=False)
+    except: 
+        subprocess.run([executable,'-m','pipwin','install','numpy','--filter=mkl','--user'],check=False)
+        subprocess.run([executable,'-m','pipwin','install','scipy','--user'],check=False)
+        subprocess.run([executable,'-m','pipwin','install','cvxopt','--user'],check=False)
+#-----------------------------------------------------------------------    
+
+
+if __name__ == "__main__":
+   print("Installing MKL-linked packages from the Gohlke repository...")
+   install_MKL_dependencies()
+   print("Finished installing MKL-linked packages from the Gohlke repository.")