Release v0.14.5 (#406)

* freeze sphinx-gallery version to 0.9.0 to fix current docs build (#371)

* Correct behavior of masking during fitting (#394)

* Deprecate distribution through Anaconda (#400)

- Remove all CI/CD files associated to the build and release process
- Adjust the installation instructions
- Adjust the CI/CD workflow to just run through PyPI

* examples: fix background plot error in 4-pulse DEER examples

* bootstrap_analysis: fix error when analyzing scalar variables (#402)

* fit: Expose `cores` option of bootstrap_analysis (#387)

* examples: minor fix

* update changelog

* bump version

* changelog: minor edit

.github/workflows/deploy_ghpages.yml

@@ -28,7 +28,7 @@ jobs:
            python -m pip install --upgrade pip
            python -m pip install pydata-sphinx-theme==0.7.1
            python -m pip install numpydoc
-           python -m pip install sphinx-gallery
+           python -m pip install sphinx-gallery==0.9.0
            python -m pip install sphinxcontrib-httpdomain
            python -m pip install sphinxcontrib-ghcontributors
            python -m pip install sphinx-issues

.github/workflows/docs_PR.yml

@@ -32,7 +32,7 @@ jobs:
            python -m pip install --upgrade pip
            python -m pip install pydata-sphinx-theme==0.7.1
            python -m pip install numpydoc
-           python -m pip install sphinx-gallery
+           python -m pip install sphinx-gallery==0.9.0
            python -m pip install sphinxcontrib-httpdomain
            python -m pip install sphinxcontrib-ghcontributors
            python -m pip install sphinx-copybutton  

.github/workflows/package_upload.yml

@@ -1,4 +1,4 @@
-name: Build & Upload Python Package
+name: Build & Upload DeerLab to PyPI
 
 on: 
   release:
@@ -26,28 +26,3 @@ jobs:
       with:
         user: __token__
         password: ${{ secrets.PYPI_API_TOKEN }}
-
-  conda-build-n-publish:
-    runs-on: ubuntu-latest
-    steps:
-    - name: Checkout
-      uses: actions/checkout@v1
-    - name: Set up Python 3.8
-      uses: actions/setup-python@v1
-      with:
-        python-version: 3.8
-    - name: Get DeerLab version
-      run: echo "DEERLAB_VERSION=$(cat VERSION)" >> $GITHUB_ENV
-    - name: Update version in Conda metadata 
-      uses: jacobtomlinson/gha-find-replace@master
-      with:
-        find: "VERSION"
-        replace: ${{env.DEERLAB_VERSION}}
-        include: "conda.recipe/meta.yaml" 
-    - name: Build & Publish to Anaconda
-      uses: ./.github/actions/conda_build_publish_package
-      with:
-        subdir: 'conda.recipe'
-        anacondatoken: ${{ secrets.ANACONDA_TOKEN }}
-        platforms: 'osx-64 linux-32 linux-64 win-32 win-64'
-        python: '3.8 3.9 3.10'

README.md

@@ -1,7 +1,6 @@
 # DeerLab
 
 [![https://jeschkelab.github.io/DeerLab/](https://img.shields.io/pypi/v/deerlab)](https://pypi.org/project/DeerLab/)
-[![https://img.shields.io/conda/v/JeschkeLab/deerlab](https://img.shields.io/conda/v/JeschkeLab/deerlab)](https://anaconda.org/jeschkelab/deerlab)
 [![Website](https://img.shields.io/website?down_message=offline&label=Documentation&up_message=online&url=https%3A%2F%2Fjeschkelab.github.io%2FDeerLab%2Findex.html)](https://jeschkelab.github.io/DeerLab/)
 [![PyPI - Python Version](https://img.shields.io/pypi/pyversions/deerlab)](https://www.python.org/downloads/)
 ![PyPI - Downloads](https://img.shields.io/pypi/dm/deerlab?color=brightgreen)
@@ -23,16 +22,12 @@ All additional dependencies are automatically downloaded and installed during th
 
 ## Setup
 
-A pre-built distribution can be installed from the PyPI repository using `pip` or from the Anaconda repository using `conda`.
+A pre-built distribution can be installed from the PyPI repository using `pip`.
 
 From a terminal (preferably with admin privileges) use the following command to install from PyPI:
 
     python -m pip install deerlab
 
-or the following command to install from Anaconda:
-
-    conda install deerlab -c JeschkeLab
-
 More details on the installation and updating of DeerLab can be found [here](https://jeschkelab.github.io/DeerLab/installation.html).
 
 ## Citing DeerLab

VERSION

@@ -1 +1 @@
-v0.14.4
+v0.14.5

deerlab/bootstrap_analysis.py

@@ -129,7 +129,7 @@ def sample():
 
     # Assert that all outputs are strictly numerical
     for var in varargout:
-        if not all(isnumeric(x) for x in var):
+        if not all(isnumeric(x) for x in np.atleast_1d(var)):
             raise ValueError('Non-numeric output arguments by the analyzed function are not accepted.')
 
     # Check that the full bootstrap analysis will not exceed the memory limits

deerlab/model.py

@@ -1018,7 +1018,7 @@ def decorator(func):
 #==============================================================================================
 @insert_snlls_optionals_docstrings()
 def fit(model_, y, *constants, par0=None, penalties=None, bootstrap=0, noiselvl=None, mask=None, weights=None,
-                regparam='aic',reg='auto',regparamrange=None,**kwargs):
+                regparam='aic',reg='auto',regparamrange=None, bootcores=1,**kwargs):
     r"""
     Fit the model(s) to the dataset(s)
 
@@ -1051,6 +1051,10 @@ def fit(model_, y, *constants, par0=None, penalties=None, bootstrap=0, noiselvl=
     bootstrap : scalar, optional,
         Bootstrap samples for uncertainty quantification. If ``bootstrap>0``, the uncertainty quantification will be 
         performed via the boostrapping method with based on the number of samples specified as the argument.
+    
+    bootcores : scalar, optional
+        Number of CPU cores/processes for parallelization of the bootstrap uncertainty quantification. If ``cores=1`` no parallel 
+        computing is used. If ``cores=-1`` all available CPUs are used. The default is one core (no parallelization).
 
     reg : boolean or string, optional
         Determines the use of regularization on the solution of the linear problem.
@@ -1127,6 +1131,8 @@ def fit(model_, y, *constants, par0=None, penalties=None, bootstrap=0, noiselvl=
         Uncertainty quantification of the fitted model response.        
     regparam : scalar
         Regularization parameter value used for the regularization of the linear parameters.
+    penweights : scalar or list thereof 
+        Penalty weight value(s) used for the penalties specified through ``penalties``.
     plot : callable
         Function to display the results. It will display the fitted data.
         The function returns the figure object (``matplotlib.figure.Figure``)
@@ -1143,13 +1149,13 @@ def fit(model_, y, *constants, par0=None, penalties=None, bootstrap=0, noiselvl=
         * ``stats['bic']`` - Bayesian information criterion
     cost : float
         Value of the cost function at the solution.
-
+    noiselvl : ndarray
+        Estimated or user-given noise standard deviations of the individual datasets.    
     evaluate(model, constants) : callable
         Function to evaluate a model at the fitted parameter values. Takes a model object or callable function ``model`` to be evaluated.
         All the parameters in the model or in the callable definition must match their corresponding parameter names in the ``FitResult`` object.   
         Any model constants present required by the model must be specified as a second argument ``constants``.  
         It returns the model's response at the fitted parameter values as an ndarray. 
-
     propagate(model,constants,lb,ub) : callable
         Function to propagate the uncertainty in the fit results to a model's response. Takes a model object or callable function ``model`` to be evaluated.
         All the parameters in the model or in the callable definition must match their corresponding parameter names in the ``FitResult`` object.  
@@ -1263,7 +1269,7 @@ def bootstrap_fcn(ysim):
             if not isinstance(fit.model,list): fit.model = [fit.model]
             return (fit.param,*fit.model)
         # Bootstrapped uncertainty quantification
-        param_uq = bootstrap_analysis(bootstrap_fcn,ysplit,fitresults.model,samples=bootstrap,noiselvl=noiselvl)
+        param_uq = bootstrap_analysis(bootstrap_fcn,ysplit,fitresults.model,samples=bootstrap,noiselvl=noiselvl,cores=bootcores)
         # Include information on the boundaries for better uncertainty estimates
         paramlb = model._vecsort(model._getvector('lb'))[np.concatenate(param_idx)] 
         paramub = model._vecsort(model._getvector('ub'))[np.concatenate(param_idx)]