Reorganize documentation

CITATION.cff

@@ -10,5 +10,5 @@ authors:
     orcid: "https://orcid.org/0000-0002-1987-9268"
     affiliation: "McGill University"
 title: "decargroup/dkpy"
-version: v0.1.7
+version: v0.1.8
 url: "https://github.com/decargroup/dkpy"

README.rst

@@ -137,6 +137,6 @@ If you use this software in your research, please cite it as below or see
         url={https://github.com/decargroup/dkpy},
         publisher={Zenodo},
         author={Steven Dahdah and James Richard Forbes},
-        version = {{v0.1.7}},
+        version = {{v0.1.8}},
         year={2024},
     }

doc/conf.py

@@ -9,8 +9,8 @@
 project = "dkpy"
 copyright = "2024, Steven Dahdah and James Richard Forbes"
 author = "Steven Dahdah and James Richard Forbes"
-version = "0.1.7"
-release = "0.1.7"
+version = "0.1.8"
+release = "0.1.8"
 
 # -- General configuration ---------------------------------------------------
 # https://www.sphinx-doc.org/en/master/usage/configuration.html#general-configuration

doc/dkpy.rst

@@ -1,6 +1,12 @@
 D-K iteration methods
 =====================
 
+The D-K iteration methods provided by ``dkpy`` are presented below. Each one
+implements the interface specified in :class:`DkIteration`. The difference
+between these methods is the way the D-scale fit order is selected. It can
+either be fixed, specified via a list, selected automatically, or selected
+interactively.
+
 .. autosummary::
    :toctree: _autosummary/
 
@@ -9,19 +15,26 @@ D-K iteration methods
    dkpy.DkIterAutoOrder
    dkpy.DkIterInteractiveOrder
 
-D-K iteration extras
-====================
+Each :func:`DkIteration.synthesize` method returns (among other things) a list
+of :class:`IterResult` objects. These objects summarize the status of the D-K
+iteration process at each step. They can be plotted with :func:`plot_D` and
+:func:`plot_mu` to assess the accuracy of the D-scale fit and its impact on the
+structured singular value.
 
 .. autosummary::
    :toctree: _autosummary/
 
-   dkpy.DScaleFitInfo
-   dkpy.plot_D
+   dkpy.IterResult
    dkpy.plot_mu
+   dkpy.plot_D
 
 Controller synthesis
 ====================
 
+Supported continuous-time H-infinity controller synthesis methods are provided
+below. Each one implements the interface specified in
+:class:`ControllerSynthesis`.
+
 .. autosummary::
    :toctree: _autosummary/
 
@@ -33,6 +46,11 @@ Controller synthesis
 Structured singular value
 =========================
 
+Supported structured singular value computation methods are provided below.
+Only one approach is provided, which implements the interface in
+:class:`StructuredSingularValue`. The LMI solver settings may need to be
+adjusted depending on the problem.
+
 .. autosummary::
    :toctree: _autosummary/
 
@@ -41,13 +59,23 @@ Structured singular value
 D-scale fit
 ===========
 
+Supported D-scale fitting methods are provided below. Only one approach is
+provided currently, which implements the interface in :class:`DScaleFit`. There
+are currently no ways to customize the D-scale magnitude fitting process beyond
+selecting the order in :func:`DScaleFit.fit`.
+
 .. autosummary::
    :toctree: _autosummary/
 
    dkpy.DScaleFitSlicot
 
 Extending ``dkpy``
 ==================
+
+The abstract classes defining the structure of ``dkpy`` are presented below.
+Anyone aiming to extend or customize ``dkpy`` should familiarize themselves
+with them.
+
 .. autosummary::
    :toctree: _autosummary/
 

doc/examples.rst

@@ -2,10 +2,13 @@ Examples
 ========
 
 In all the examples on this page, three iterations with 4th order D-scale fits
-are used to reprouce the example from [SP06]_, Table 8.2 (p. 325). Each example
+are used to reproduce the example from [SP06]_, Table 8.2 (p. 325). Each example
 recovers the same result, but with a different way to specify the D-scale fit
 orders.
 
+This example is quite numerically challenging, so if you encounter a solver
+error, you may need to experiment with solver tolerances.
+
 D-K iteration with fixed fit order
 ----------------------------------
 
@@ -42,7 +45,7 @@ Output::
 D-K iteration with automatically selected fit orders
 ----------------------------------------------------
 
-In this example, multiple fit orders are atempted up to a maximum, and the one
+In this example, multiple fit orders are attempted up to a maximum, and the one
 with the lowest relative error is selected.
 
 .. literalinclude:: ../examples/3_example_dk_iter_auto_order.py

examples/1_example_dk_iter_fixed_order.py

@@ -20,7 +20,7 @@ def example_dk_iter_fixed_order():
 
     omega = np.logspace(-3, 3, 61)
     block_structure = np.array([[1, 1], [1, 1], [2, 2]])
-    K, N, mu, d_scale_fit_info, info = dk_iter.synthesize(
+    K, N, mu, iter_results, info = dk_iter.synthesize(
         eg["P"],
         eg["n_y"],
         eg["n_u"],
@@ -31,11 +31,11 @@ def example_dk_iter_fixed_order():
     print(f"mu={mu}")
 
     fig, ax = plt.subplots()
-    for i, ds in enumerate(d_scale_fit_info):
+    for i, ds in enumerate(iter_results):
         dkpy.plot_mu(ds, ax=ax, plot_kw=dict(label=f"iter{i}"))
 
     ax = None
-    for i, ds in enumerate(d_scale_fit_info):
+    for i, ds in enumerate(iter_results):
         _, ax = dkpy.plot_D(ds, ax=ax, plot_kw=dict(label=f"iter{i}"))
 
     plt.show()

examples/2_example_dk_iter_list_order.py

@@ -24,7 +24,7 @@ def example_dk_iter_list_order():
 
     omega = np.logspace(-3, 3, 61)
     block_structure = np.array([[1, 1], [1, 1], [2, 2]])
-    K, N, mu, d_scale_fit_info, info = dk_iter.synthesize(
+    K, N, mu, iter_results, info = dk_iter.synthesize(
         eg["P"],
         eg["n_y"],
         eg["n_u"],
@@ -35,11 +35,11 @@ def example_dk_iter_list_order():
     print(f"mu={mu}")
 
     fig, ax = plt.subplots()
-    for i, ds in enumerate(d_scale_fit_info):
+    for i, ds in enumerate(iter_results):
         dkpy.plot_mu(ds, ax=ax, plot_kw=dict(label=f"iter{i}"))
 
     ax = None
-    for i, ds in enumerate(d_scale_fit_info):
+    for i, ds in enumerate(iter_results):
         _, ax = dkpy.plot_D(ds, ax=ax, plot_kw=dict(label=f"iter{i}"))
 
     plt.show()

examples/3_example_dk_iter_auto_order.py

@@ -45,7 +45,7 @@ def example_dk_iter_auto_order():
 
     omega = np.logspace(-3, 3, 61)
     block_structure = np.array([[1, 1], [1, 1], [2, 2]])
-    K, N, mu, d_scale_fit_info, info = dk_iter.synthesize(
+    K, N, mu, iter_results, info = dk_iter.synthesize(
         eg["P"],
         eg["n_y"],
         eg["n_u"],
@@ -56,11 +56,11 @@ def example_dk_iter_auto_order():
     print(f"mu={mu}")
 
     fig, ax = plt.subplots()
-    for i, ds in enumerate(d_scale_fit_info):
+    for i, ds in enumerate(iter_results):
         dkpy.plot_mu(ds, ax=ax, plot_kw=dict(label=f"iter{i}"))
 
     ax = None
-    for i, ds in enumerate(d_scale_fit_info):
+    for i, ds in enumerate(iter_results):
         _, ax = dkpy.plot_D(ds, ax=ax, plot_kw=dict(label=f"iter{i}"))
 
     plt.show()

examples/4_example_dk_iter_interactive.py

@@ -38,7 +38,7 @@ def example_dk_iter_interactive():
 
     omega = np.logspace(-3, 3, 61)
     block_structure = np.array([[1, 1], [1, 1], [2, 2]])
-    K, N, mu, d_scale_fit_info, info = dk_iter.synthesize(
+    K, N, mu, iter_results, info = dk_iter.synthesize(
         eg["P"],
         eg["n_y"],
         eg["n_u"],
@@ -49,11 +49,11 @@ def example_dk_iter_interactive():
     print(f"mu={mu}")
 
     fig, ax = plt.subplots()
-    for i, ds in enumerate(d_scale_fit_info):
+    for i, ds in enumerate(iter_results):
         dkpy.plot_mu(ds, ax=ax, plot_kw=dict(label=f"iter{i}"))
 
     ax = None
-    for i, ds in enumerate(d_scale_fit_info):
+    for i, ds in enumerate(iter_results):
         _, ax = dkpy.plot_D(ds, ax=ax, plot_kw=dict(label=f"iter{i}"))
 
     plt.show()

examples/5_example_dk_iteration_custom.py

@@ -71,7 +71,7 @@ def example_dk_iter_custom():
 
     omega = np.logspace(-3, 3, 61)
     block_structure = np.array([[1, 1], [1, 1], [2, 2]])
-    K, N, mu, d_scale_fit_info, info = dk_iter.synthesize(
+    K, N, mu, iter_results, info = dk_iter.synthesize(
         eg["P"],
         eg["n_y"],
         eg["n_u"],
@@ -82,11 +82,11 @@ def example_dk_iter_custom():
     print(f"mu={mu}")
 
     fig, ax = plt.subplots()
-    for i, ds in enumerate(d_scale_fit_info):
+    for i, ds in enumerate(iter_results):
         dkpy.plot_mu(ds, ax=ax, plot_kw=dict(label=f"iter{i}"))
 
     ax = None
-    for i, ds in enumerate(d_scale_fit_info):
+    for i, ds in enumerate(iter_results):
         _, ax = dkpy.plot_D(ds, ax=ax, plot_kw=dict(label=f"iter{i}"))
 
     plt.show()

pyproject.toml

@@ -4,7 +4,7 @@ build-backend = "hatchling.build"
 
 [project]
 name = "dkpy"
-version = "0.1.7"
+version = "0.1.8"
 dependencies = [
     "numpy>=1.21.0",
     "scipy>=1.7.0",

src/dkpy/d_scale_fit.py

@@ -42,7 +42,7 @@ def fit(
         block_structure : np.ndarray
             2D array with 2 columns and as many rows as uncertainty blocks
             in Delta. The columns represent the number of rows and columns in
-            each uncertainty block.
+            each uncertainty block. See [#mussv]_.
 
         Returns
         -------
@@ -54,6 +54,10 @@ def fit(
         ValueError
             If ``order`` is an array but its dimensions are inconsistent with
             ``block_structure``.
+
+        References
+        ----------
+        .. [#mussv] https://www.mathworks.com/help/robust/ref/mussv.html
         """
         raise NotImplementedError()
 
@@ -162,13 +166,17 @@ def _mask_from_block_structure(block_structure: np.ndarray) -> np.ndarray:
     block_structure : np.ndarray
         2D array with 2 columns and as many rows as uncertainty blocks
         in Delta. The columns represent the number of rows and columns in
-        each uncertainty block.
+        each uncertainty block. See [#mussv]_.
 
     Returns
     -------
     np.ndarray
         Array of integers indicating zero, one, and unknown elements in the
         block structure.
+
+    References
+    ----------
+    .. [#mussv] https://www.mathworks.com/help/robust/ref/mussv.html
     """
     X_lst = []
     for i in range(block_structure.shape[0]):