chore: Configure Renovate

.github/renovate.json

@@ -0,0 +1,18 @@
+{
+  "$schema": "https://docs.renovatebot.com/renovate-schema.json",
+  "extends": ["config:recommended"],
+  "baseBranchPatterns":["main-with-renovate-comments"],
+  "packageRules": [
+    {
+      "matchManagers": ["pip_requirements", "conda"],
+      "groupName": "Python dependencies",
+      "separateMajorMinor": false
+    },
+    {
+      "matchManagers": ["github-actions"],
+      "groupName": "GitHub actions",
+      "separateMajorMinor": false
+    }
+  ],
+  "dependencyDashboard": true
+}

.gitignore

@@ -4,7 +4,6 @@ doc/build
 doc/source/*.rst
 doc/source/*.md
 
-doc/source/tutorials/1-monte_carlo_uq.rst
 doc/source/tutorials/*.ipynb
 
 # No results or logs

doc/README.md

@@ -6,7 +6,13 @@ We believe that documentation is essential and therefore welcome any improvement
 
 ## :woman_teacher: Build the documentation
 
-To build the documentation, you first need a development QUEENS installation as mentioned in the [CONTRIBUTING.md](../CONTRIBUTING.md). This adds additional packages needed for the html build. Once this is done, follow the next steps in the activated Python environment with the development installation.
+To build the documentation, you first need to set up a QUEENS environment as described in the [README.md](../README.md).
+In this Python environment, you also need to install packages for QUEENS development and tutorials and register the environment as a Jupyter kernel:
+
+```bash
+pip install -e .[safe_develop,tutorial]
+python -m ipykernel install --user --name queens --display-name "Python (queens)"
+```
 
 When building the documentation on your machine for the first time or after adding new modules or classes to QUEENS, one needs to first rebuild the `autodoc index` by running:
 

doc/source/_ext/create_documentation_files.py

@@ -157,10 +157,10 @@ def create_development():
     inject({"md_paths": md_paths}, development_template, development_path)
 
 
-def create_intro():
+def create_introduction():
     """Generate landing page."""
-    intro_template = get_template_path_by_name("intro.md.j2")
-    into_path = relative_to_doc_source("intro.md")
+    intro_template = get_template_path_by_name("introduction.md.j2")
+    into_path = relative_to_doc_source("introduction.md")
 
     def extract_from_markdown_by_marker(marker_name, md_path):
         return clean_markdown(extract_from_markdown_file_by_marker(marker_name, md_path))
@@ -176,10 +176,10 @@ def extract_from_markdown_by_marker(marker_name, md_path):
     )
 
 
-def create_overview():
+def create_packages():
     """Create overview of the QUEENS package."""
-    overview_template = get_template_path_by_name("overview.rst.j2")
-    overview_path = relative_to_doc_source("overview.rst")
+    overview_template = get_template_path_by_name("packages.rst.j2")
+    overview_path = relative_to_doc_source("packages.rst")
 
     def get_module_description(python_file):
         """Get module description.
@@ -298,7 +298,7 @@ def copy_tutorials():
 
 def main():
     """Create all the rst files."""
-    create_intro()
+    create_introduction()
     copy_tutorials()
     create_development()
-    create_overview()
+    create_packages()

doc/source/conf.py

@@ -52,6 +52,17 @@
     "myst_parser",
 ]
 
+# Control how typehints are displayed in the documentation.
+# 'description' shows typehints as content of the function (next to the parameters), not in the
+# function signature.
+# This mirrors the behavior of types provided in the docstrings.
+autodoc_typehints = "description"
+
+# Control how class signatures will be displayed.
+# 'separated' will display the signature as a method, meaning the __init__ method is explicitly
+# shown in the documentation.
+autodoc_class_signature = "separated"
+
 # Custom command to make the returns in the docstring behave like the parameter/argument section.
 # This will basically make the documentation a bit prettier as different returns are directly
 # rendered as bullet points.

doc/source/index.rst

@@ -15,11 +15,10 @@ Contents
 .. toctree::
    :maxdepth: 1
 
-   intro
-   overview
+   introduction
+   packages
    tutorials
    development
-   queens
    faq
 
 

doc/source/tutorials/0-onboarding.rst

@@ -12,7 +12,7 @@ Steps to get started with QUEENS
 
    .. code-block:: bash
 
-        git clone git@github.com:queens-py/queens.git
+      git clone git@github.com:queens-py/queens.git
 
 #. Read through our `introduction <https://queens-py.github.io/queens/intro.html>`_ and
    follow the instructions in the "Installation" section to set up your local environment.
@@ -34,11 +34,10 @@ Steps to get started with QUEENS
 
    .. code-block:: json
 
-            "python.pythonPath": "<path_to_your_QUEENS_environment>/bin/python",
-            "python.defaultInterpreterPath": "<path_to_your_QUEENS_environment>/bin/python",
-            "editor.formatOnSave": True,
-            "python.formatting.provider": "black",
-            "python.formatting.blackPath": "<path_to_your_QUEENS_environment>/bin/black",
+      "python.defaultInterpreterPath": "<path_to_your_QUEENS_environment>/bin/python",
+      "editor.formatOnSave": true,
+      "editor.defaultFormatter": "ms-python.black-formatter",
+      "black-formatter.args": ["--line-length", "100"],
 
 #. Browse through our `documentation <https://queens-py.github.io/queens/overview.html>`_ and our
    other tutorials to get a first impression.

src/queens/data_processors/csv_file.py

@@ -71,33 +71,44 @@ def __init__(
         """Instantiate data processor class for csv data.
 
         Args:
-            file_name_identifier (str): Identifier of file name
-                                             The file prefix can contain regex expression
-                                             and subdirectories.
+            file_name_identifier (str): Identifier of file name. The file prefix can contain regex
+                expression and subdirectories.
             file_options_dict (dict): Dictionary with read-in options for the file:
-                - header_row (int): Integer that determines which csv-row contains labels/headers of
-                                    the columns. Default is 'None', meaning no header used.
-                - use_cols_lst (lst): (optional) list with column numbers that should be read-in.
-                - skip_rows (int): Number of rows that should be skipped to be read-in in csv file.
-                - index_column (int, str): Column to use as the row labels of the DataFrame, either
-                                           given as string name or column index.
-                                           Note: index_column=False can be used to force pandas to
-                                           not use the first column as the index. Index_column is
-                                           used for filtering the remaining columns.
-                - returned_filter_format (str): Returned data format after filtering
-                - filter (dict): Dictionary with filter options:
-                    -- type (str): Filter type to use
-                    -- rows (lst): In case this options is used, the list contains the indices of
-                                  rows in the csv file that should be used as data
-                    -- range (lst): After data is selected by `use_cols_lst` and a filter column
-                                   is specified by `index_column`, this option selects which data
-                                   range shall be filtered by providing a minimum and maximum
-                                   value pair in list format
-                    -- target_values (list): target values to filter
-                    -- tolerance (float): Tolerance for the filter range
+
+                - header_row (int):
+                    Integer that determines which csv-row contains labels/headers of
+                    the columns. Default is 'None', meaning no header used.
+                - use_cols_lst (lst):
+                    (optional) list with column numbers that should be read-in.
+                - skip_rows (int):
+                    Number of rows that should be skipped to be read-in in csv file.
+                - index_column (int, str):
+                    Column to use as the row labels of the DataFrame, either
+                    given as string name or column index.
+                    Note: ``index_column=False`` can be used to force pandas to not use the first
+                    column as the index. ``index_column`` is used for filtering the remaining
+                    columns.
+                - returned_filter_format (str):
+                    Returned data format after filtering
+                - filter (dict):
+                    Dictionary with filter options:
+
+                    -- type (str):
+                        Filter type to use
+                    -- rows (lst):
+                        In case this options is used, the list contains the indices of
+                        rows in the csv file that should be used as data
+                    -- range (lst):
+                        After data is selected by `use_cols_lst` and a filter column is specified
+                        by `index_column`, this option selects which data range shall be filtered
+                        by providing a minimum and maximum value pair in list format
+                    -- target_values (list):
+                        target values to filter
+                    -- tolerance (float):
+                        Tolerance for the filter range
 
             files_to_be_deleted_regex_lst (lst): List with paths to files that should be deleted.
-                                                 The paths can contain regex expressions.
+                The paths can contain regex expressions.
 
         Returns:
             Instance of CsvFile class

src/queens/global_settings.py

@@ -21,7 +21,7 @@
 import logging
 from pathlib import Path
 
-from queens.schedulers._dask import SHUTDOWN_CLIENTS
+from queens.schedulers._scheduler import CLEANUP_SCHEDULERS
 from queens.utils.ascii_art import print_banner_and_description
 from queens.utils.logger_settings import reset_logging, setup_basic_logging
 from queens.utils.path import PATH_TO_ROOT, create_folder_if_not_existent
@@ -168,8 +168,8 @@ def __exit__(self, exception_type, exception_value, traceback):
             exception_value: indicates exception instance
             traceback: traceback object
         """
-        for shutdown_client in SHUTDOWN_CLIENTS.copy():
-            SHUTDOWN_CLIENTS.remove(shutdown_client)
-            shutdown_client()
+        for cleanup_scheduler in CLEANUP_SCHEDULERS.copy():
+            CLEANUP_SCHEDULERS.remove(cleanup_scheduler)
+            cleanup_scheduler()
 
         reset_logging()

src/queens/iterators/elementary_effects.py

@@ -174,13 +174,15 @@ def print_results(self, results):
 
         Args:
             results (dict): Dictionary with the results of the sensitivity analysis, including:
-                - 'parameter_names': List of parameter names.
-                - 'sensitivity_indices': Contains indices like:
-                    - 'names': Parameter names.
-                    - 'mu_star': Mean absolute effect.
-                    - 'mu': Mean effect.
-                    - 'mu_star_conf': Confidence interval for 'mu_star'.
-                    - 'sigma': Standard deviation of the effect.
+
+                - `parameter_names`: List of parameter names.
+                - `sensitivity_indices`: Contains indices like:
+
+                  - `names`: Parameter names.
+                  - `mu_star`: Mean absolute effect.
+                  - `mu`: Mean effect.
+                  - `mu_star_conf`: Confidence interval for `mu_star`.
+                  - `sigma`: Standard deviation of the effect.
         """
         _logger.info(
             "%-20s %10s %10s %15s %10s", "Parameter", "Mu_Star", "Mu", "Mu_Star_Conf", "Sigma"

src/queens/iterators/hamiltonian_monte_carlo.py

@@ -14,7 +14,7 @@
 #
 """HMC algorithm.
 
-"The Hamiltonian Monte Carlo sampler is a gradient based MCMC algortihm.
+The Hamiltonian Monte Carlo sampler is a gradient based MCMC algorithm.
 It is used to sample from arbitrary probability distributions.
 """
 
@@ -35,16 +35,13 @@ class HamiltonianMonteCarlo(PyMC):
 
     Attributes:
         max_steps (int): Maximum of leapfrog steps to take in one iteration
-        target_accept (float): Target accpetance rate which should be conistent after burn-in
+        target_accept (float): Target acceptance rate which should be consistent after burn-in
         path_length (float): Maximum length of particle trajectory
-        step_size (float): Step size, scaled by 1/(parameter dimension **0.25)
+        step_size (float): Step size, scaled by 1/(parameter dimension^0.25)
         scaling (np.array): The inverse mass, or precision matrix
         is_cov (boolean): Setting if the scaling is a mass or covariance matrix
         init_strategy (str): Strategy to tune mass damping matrix
         advi_iterations (int): Number of iteration steps of ADVI based init strategies
-
-    Returns:
-        hmc_iterator (obj): Instance of HMC Iterator
     """
 
     @log_init_args
@@ -94,10 +91,10 @@ def __init__(
                                              functions
             progressbar (boolean, opt): Setting for printing progress bar while sampling
             max_steps (int, opt): Maximum of leapfrog steps to take in one iteration
-            target_accept (float, opt): Target accpetance rate which should be conistent after
+            target_accept (float, opt): Target acceptance rate which should be consistent after
                                         burn-in
             path_length (float, opt): Maximum length of particle trajectory
-            step_size (float, opt): Step size, scaled by 1/(parameter dimension **0.25)
+            step_size (float, opt): Step size, scaled by 1/(parameter dimension^0.25)
             scaling (np.array, opt): The inverse mass, or precision matrix
             is_cov (boolean, opt): Setting if the scaling is a mass or covariance matrix
             init_strategy (str, opt): Strategy to tune mass damping matrix

src/queens/iterators/least_squares.py

@@ -36,15 +36,16 @@ class LeastSquares(Optimization):
     Attributes:
         algorithm (str): Algorithm to perform minimization:
 
-                         - trf : Trust Region Reflective algorithm, particularly suitable for large
-                                 sparse problems with bounds. Generally robust method.
-
-                         - dogbox : dogleg algorithm with rectangular trust regions, typical use
-                                    case is small problems with bounds. Not recommended for problems
-                                    with rank-deficient Jacobian.
-                         - lm : Levenberg-Marquardt algorithm as implemented in MINPACK. Doesn’t
-                                handle bounds and sparse Jacobians. Usually the most  efficient
-                                method for small unconstrained problems.
+            - trf:
+                Trust Region Reflective algorithm, particularly suitable for large sparse problems
+                with bounds. Generally robust method.
+            - dogbox:
+                dogleg algorithm with rectangular trust regions, typical use case is small problems
+                with bounds. Not recommended for problems with rank-deficient Jacobian.
+            - lm:
+                Levenberg-Marquardt algorithm as implemented in MINPACK. Doesn’t handle bounds and
+                sparse Jacobians. Usually the most efficient method for small unconstrained
+                problems.
     """
 
     @log_init_args
@@ -75,31 +76,31 @@ def __init__(
             verbose_output (int): Integer encoding which kind of verbose information should be
                                   printed by the optimizers.
             bounds (sequence, Bounds): Bounds on variables for Nelder-Mead, L-BFGS-B, TNC, SLSQP,
-                                       Powell, and trust-constr methods.
-                                       There are two ways to specify the bounds:
-
-                                       1. Instance of `Bounds` class.
-                                       2. A sequence with 2 elements. The first element corresponds
-                                       to a sequence of lower bounds and the second element to
-                                       sequence of upper bounds. The length of each of the two
-                                       subsequences must be equal to the number of variables.
+                Powell, and trust-constr methods. There are two ways to specify the bounds:
+
+                1. Instance of `Bounds` class.
+                2. A sequence with 2 elements. The first element corresponds to a sequence of lower
+                   bounds and the second element to a sequence of upper bounds. The length of each
+                   of the two subsequences must be equal to the number of variables.
+
             max_feval (int): Maximum number of function evaluations.
             algorithm (str): Algorithm to perform minimization:
 
-                             - trf : Trust Region Reflective algorithm, particularly suitable for
-                                       large sparse problems with bounds. Generally robust method.
+                - 'trf': Trust Region Reflective algorithm, particularly suitable for large sparse
+                  problems with bounds. Generally robust method.
+                - 'dogbox': dogleg algorithm with rectangular trust regions, typical use case is
+                  small problems with bounds. Not recommended for problems with rank-deficient
+                  Jacobian.
+                - 'lm': Levenberg-Marquardt algorithm as implemented in MINPACK. Doesn’t handle
+                  bounds and sparse Jacobians. Usually the most efficient method for small
+                  unconstrained problems.
 
-                             - dogbox : dogleg algorithm with rectangular trust regions, typical use
-                                        case is small problems with bounds. Not recommended for
-                                        problems with rank-deficient Jacobian.
-                             - lm : Levenberg-Marquardt algorithm as implemented in MINPACK.
-                                    Doesn’t handle bounds and sparse Jacobians. Usually the most
-                                    efficient method for small unconstrained problems.
             jac_method (str): Method to calculate a finite difference based approximation of the
                               Jacobian matrix:
 
                               - '2-point': a one-sided scheme by definition
                               - '3-point': more exact but needs twice as many function evaluations
+
             jac_rel_step (array_like): Relative step size to use for finite difference approximation
                                        of Jacobian matrix. If None (default) then it is selected
                                        automatically. (see SciPy documentation for details)

src/queens/iterators/metropolis_hastings.py

@@ -14,7 +14,7 @@
 #
 """Metropolis-Hastings algorithm.
 
-"The Metropolis-Hastings algorithm is a Markov Chain Monte Carlo (MCMC)
+The Metropolis-Hastings algorithm is a Markov Chain Monte Carlo (MCMC)
 method for obtaining a sequence of random samples from a probability
 distribution from which direct sampling is difficult." [1]