Package Reference
+This section contains auto-generated documentation of all modules and +functions.
+ +
+
+
+
+
+
+
+
+
diff --git a/docs/0.6.4/docs/_sources/tutorials/MPC.md.txt b/docs/0.6.4/docs/_sources/tutorials/MPC.md.txt
new file mode 100644
index 0000000..4f6e721
--- /dev/null
+++ b/docs/0.6.4/docs/_sources/tutorials/MPC.md.txt
@@ -0,0 +1,352 @@
+Model Predictive Control
+------------------------
+
+### Creating an MPC in agentlib with CasADi
+To run a model predictive controller, a system model for use in optimization
+is required. What model types are available depends on the chosen
+optimization backend. In this section, creating an MPC with a CasADi backend
+is explained. \
+Open the 'examples/one_room_mpc/physical/simple_mpc.py' example.
+#### Imports
+As usual, let's look at the imports first.
+```python
+import logging
+from typing import List
+import matplotlib.pyplot as plt
+from agentlib.models.casadi_model import CasadiModel, CasadiInput, CasadiState, \
+ CasadiParameter, CasadiOutput
+from agentlib.utils.multi_agent_system import LocalMASAgency
+```
+We import logging as usual and
+``typing`` is used to annotate the optimzation model we will be creating,
+and matplotlib is used to plot the results. Next, we import the CasadiModel and
+some CasadiVariables. We will use these to specify an agentlib-style
+CasadiModel. Finally, we import the LocalMASAgency utility. This can be used
+to conveniently create and run your local MAS, without creating the agents
+and their environment by hand.
+
+#### Model creation
+
+Now let's see how we can create an optimization model. The model contains
+the physical system dynamics, as well as the cost function and additional
+constraints on the system.
+
+
+In this example, we will create a model of a room, which is under a constant
+heat load and can be controlled by changing the mass flow of cool air from
+an air handling unit.
+
+Creating a custom CasadiModel is similar to creating a module.
+1. Creating a class that inherits from ``CasadiModelConfig``
+ - Declare the model variables in the config class
+ - inputs
+ - outputs
+ - states
+ - parameters
+2. Creating a class that inherits from ``CasadiModel``
+ 1. Assign the config with ``config: <+ Why do I have to declare two variables, if they mean the same thing? +
++Because the models follow the FMU standard, where variables are divided +between inputs, outputs, locals/states and parameters. In this case, our +cooler model takes a mass flow as an input ("mDot" in this case) and +produces the same mass flow as an output to other systems ("mDot_out" in +this case). In a more complex setting, the cooler might have an internal PID +controller to set the mass flow to its correct value. In that case, "mDot" +would be setpoint of the mass flow, and "mDot_out" would be the actual mass +flow. ++
+
+
+.. note::
+ Python intuition tells us ``self.+ What's the difference between an algebraic equation and setting an +equality constraint? +
++Algebraic equations are explicit assignments to a CasadiOutput. They are considered when simulating the model or when doing MPC with it. +Constraints specified as tuples can be of implicit nature, however they are +ignored for simulation. The only limitation on constraints is, that +variables that make up the upper or lower bound cannot be used as +optimization variables in the MPC. ++
+
+
+With all of the setup done, we can now see our MAS run.
+Last time, we manually created the agents and the environment. This time, we
+use the ``LocalMASAgency`` utility to setup the system and save results. By
+setting the ``variable_logging`` option to True, time series of all agent
+variables present in the system will be saved. After running the MAS, we can
+retrieve and plot the results of our simulation.
+````python
+def run_example(with_plots=True):
+ mas = LocalMASAgency(agent_configs=[AGENT_MPC, AGENT_SIM],
+ env=ENV_CONFIG,
+ variable_logging=True)
+ mas.run(until=10000)
+ results = mas.get_results()
+````
\ No newline at end of file
diff --git a/docs/0.6.4/docs/_sources/tutorials/Trajectories.md.txt b/docs/0.6.4/docs/_sources/tutorials/Trajectories.md.txt
new file mode 100644
index 0000000..47ec41f
--- /dev/null
+++ b/docs/0.6.4/docs/_sources/tutorials/Trajectories.md.txt
@@ -0,0 +1,5 @@
+Working with time series
+------------------------
+Explanation of 'examples/multi-agent-systems/casadi/mpc_trajectories.py'
+
+tbd
\ No newline at end of file
diff --git a/docs/0.6.4/docs/_sources/tutorials/tutorial.md.txt b/docs/0.6.4/docs/_sources/tutorials/tutorial.md.txt
new file mode 100644
index 0000000..3247db1
--- /dev/null
+++ b/docs/0.6.4/docs/_sources/tutorials/tutorial.md.txt
@@ -0,0 +1,14 @@
+Tutorial
+========
+
+This section collects tutorials on how to get started with agentlib.
+
+.. note::
+This tutorial is periodically updated, but for the most current and accurate information, please refer to the actual example files in the repository. The example files should be considered the authoritative source if there are any discrepancies.
+
+.. toctree::
+ :maxdepth: 2
+
+ MPC
+ Trajectories
+ ADMM
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diff --git a/docs/0.6.4/docs/_static/doctools.js b/docs/0.6.4/docs/_static/doctools.js
new file mode 100644
index 0000000..d06a71d
--- /dev/null
+++ b/docs/0.6.4/docs/_static/doctools.js
@@ -0,0 +1,156 @@
+/*
+ * doctools.js
+ * ~~~~~~~~~~~
+ *
+ * Base JavaScript utilities for all Sphinx HTML documentation.
+ *
+ * :copyright: Copyright 2007-2023 by the Sphinx team, see AUTHORS.
+ * :license: BSD, see LICENSE for details.
+ *
+ */
+"use strict";
+
+const BLACKLISTED_KEY_CONTROL_ELEMENTS = new Set([
+ "TEXTAREA",
+ "INPUT",
+ "SELECT",
+ "BUTTON",
+]);
+
+const _ready = (callback) => {
+ if (document.readyState !== "loading") {
+ callback();
+ } else {
+ document.addEventListener("DOMContentLoaded", callback);
+ }
+};
+
+/**
+ * Small JavaScript module for the documentation.
+ */
+const Documentation = {
+ init: () => {
+ Documentation.initDomainIndexTable();
+ Documentation.initOnKeyListeners();
+ },
+
+ /**
+ * i18n support
+ */
+ TRANSLATIONS: {},
+ PLURAL_EXPR: (n) => (n === 1 ? 0 : 1),
+ LOCALE: "unknown",
+
+ // gettext and ngettext don't access this so that the functions
+ // can safely bound to a different name (_ = Documentation.gettext)
+ gettext: (string) => {
+ const translated = Documentation.TRANSLATIONS[string];
+ switch (typeof translated) {
+ case "undefined":
+ return string; // no translation
+ case "string":
+ return translated; // translation exists
+ default:
+ return translated[0]; // (singular, plural) translation tuple exists
+ }
+ },
+
+ ngettext: (singular, plural, n) => {
+ const translated = Documentation.TRANSLATIONS[singular];
+ if (typeof translated !== "undefined")
+ return translated[Documentation.PLURAL_EXPR(n)];
+ return n === 1 ? singular : plural;
+ },
+
+ addTranslations: (catalog) => {
+ Object.assign(Documentation.TRANSLATIONS, catalog.messages);
+ Documentation.PLURAL_EXPR = new Function(
+ "n",
+ `return (${catalog.plural_expr})`
+ );
+ Documentation.LOCALE = catalog.locale;
+ },
+
+ /**
+ * helper function to focus on search bar
+ */
+ focusSearchBar: () => {
+ document.querySelectorAll("input[name=q]")[0]?.focus();
+ },
+
+ /**
+ * Initialise the domain index toggle buttons
+ */
+ initDomainIndexTable: () => {
+ const toggler = (el) => {
+ const idNumber = el.id.substr(7);
+ const toggledRows = document.querySelectorAll(`tr.cg-${idNumber}`);
+ if (el.src.substr(-9) === "minus.png") {
+ el.src = `${el.src.substr(0, el.src.length - 9)}plus.png`;
+ toggledRows.forEach((el) => (el.style.display = "none"));
+ } else {
+ el.src = `${el.src.substr(0, el.src.length - 8)}minus.png`;
+ toggledRows.forEach((el) => (el.style.display = ""));
+ }
+ };
+
+ const togglerElements = document.querySelectorAll("img.toggler");
+ togglerElements.forEach((el) =>
+ el.addEventListener("click", (event) => toggler(event.currentTarget))
+ );
+ togglerElements.forEach((el) => (el.style.display = ""));
+ if (DOCUMENTATION_OPTIONS.COLLAPSE_INDEX) togglerElements.forEach(toggler);
+ },
+
+ initOnKeyListeners: () => {
+ // only install a listener if it is really needed
+ if (
+ !DOCUMENTATION_OPTIONS.NAVIGATION_WITH_KEYS &&
+ !DOCUMENTATION_OPTIONS.ENABLE_SEARCH_SHORTCUTS
+ )
+ return;
+
+ document.addEventListener("keydown", (event) => {
+ // bail for input elements
+ if (BLACKLISTED_KEY_CONTROL_ELEMENTS.has(document.activeElement.tagName)) return;
+ // bail with special keys
+ if (event.altKey || event.ctrlKey || event.metaKey) return;
+
+ if (!event.shiftKey) {
+ switch (event.key) {
+ case "ArrowLeft":
+ if (!DOCUMENTATION_OPTIONS.NAVIGATION_WITH_KEYS) break;
+
+ const prevLink = document.querySelector('link[rel="prev"]');
+ if (prevLink && prevLink.href) {
+ window.location.href = prevLink.href;
+ event.preventDefault();
+ }
+ break;
+ case "ArrowRight":
+ if (!DOCUMENTATION_OPTIONS.NAVIGATION_WITH_KEYS) break;
+
+ const nextLink = document.querySelector('link[rel="next"]');
+ if (nextLink && nextLink.href) {
+ window.location.href = nextLink.href;
+ event.preventDefault();
+ }
+ break;
+ }
+ }
+
+ // some keyboard layouts may need Shift to get /
+ switch (event.key) {
+ case "/":
+ if (!DOCUMENTATION_OPTIONS.ENABLE_SEARCH_SHORTCUTS) break;
+ Documentation.focusSearchBar();
+ event.preventDefault();
+ }
+ });
+ },
+};
+
+// quick alias for translations
+const _ = Documentation.gettext;
+
+_ready(Documentation.init);
diff --git a/docs/0.6.4/docs/_static/documentation_options.js b/docs/0.6.4/docs/_static/documentation_options.js
new file mode 100644
index 0000000..9bf5561
--- /dev/null
+++ b/docs/0.6.4/docs/_static/documentation_options.js
@@ -0,0 +1,14 @@
+var DOCUMENTATION_OPTIONS = {
+ URL_ROOT: document.getElementById("documentation_options").getAttribute('data-url_root'),
+ VERSION: '0.6.4',
+ LANGUAGE: 'en',
+ COLLAPSE_INDEX: false,
+ BUILDER: 'html',
+ FILE_SUFFIX: '.html',
+ LINK_SUFFIX: '.html',
+ HAS_SOURCE: true,
+ SOURCELINK_SUFFIX: '.txt',
+ NAVIGATION_WITH_KEYS: false,
+ SHOW_SEARCH_SUMMARY: true,
+ ENABLE_SEARCH_SHORTCUTS: true,
+};
\ No newline at end of file
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diff --git a/docs/0.6.4/docs/_static/jquery.js b/docs/0.6.4/docs/_static/jquery.js
new file mode 100644
index 0000000..c4c6022
--- /dev/null
+++ b/docs/0.6.4/docs/_static/jquery.js
@@ -0,0 +1,2 @@
+/*! jQuery v3.6.0 | (c) OpenJS Foundation and other contributors | jquery.org/license */
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++Before every step, the simulator gets the current input values from the +agent and sets them to the model. After performing the step, the outputs +from the model are written to the agent. Since states per definition are +internal to the model, they are not set by the agent and their initial +values have to be changed in the model itself. The same goes for parameters. ++
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+ agentlib_mpc.modules.dmpc.admm package — agentlib_mpc 0.6.4 documentation
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+ agentlib_mpc.modules.dmpc package — agentlib_mpc 0.6.4 documentation
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+ agentlib_mpc.optimization_backends.casadi_.core package — agentlib_mpc 0.6.4 documentation
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+ agentlib_mpc.optimization_backends.casadi_ package — agentlib_mpc 0.6.4 documentation
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+ agentlib_mpc.utils package — agentlib_mpc 0.6.4 documentation
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+ Index — agentlib_mpc 0.6.4 documentation
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+ Welcome to agentlib_mpc’s documentation! — agentlib_mpc 0.6.4 documentation
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+ Python Module Index — agentlib_mpc 0.6.4 documentation
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+ Alternating Direction Method of Multipliers — agentlib_mpc 0.6.4 documentation
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+ Model Predictive Control — agentlib_mpc 0.6.4 documentation
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+ Working with time series — agentlib_mpc 0.6.4 documentation
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+ Tutorial — agentlib_mpc 0.6.4 documentation
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diff --git a/docs/0.6.4/pylint/pylint.json b/docs/0.6.4/pylint/pylint.json
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+
+
diff --git a/docs/0.6.4/pylint/pylint.txt b/docs/0.6.4/pylint/pylint.txt
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--- /dev/null
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@@ -0,0 +1,181 @@
+************* Module /builds/EBC/EBC_all/github_ci/AgentLib-MPC/pylintrc
+pylintrc:1:0: R0022: Useless option value for '--disable', 'print-statement' was removed from pylint, see https://github.com/pylint-dev/pylint/pull/4942. (useless-option-value)
+pylintrc:1:0: R0022: Useless option value for '--disable', 'parameter-unpacking' was removed from pylint, see https://github.com/pylint-dev/pylint/pull/4942. (useless-option-value)
+pylintrc:1:0: R0022: Useless option value for '--disable', 'unpacking-in-except' was removed from pylint, see https://github.com/pylint-dev/pylint/pull/4942. (useless-option-value)
+pylintrc:1:0: R0022: Useless option value for '--disable', 'old-raise-syntax' was removed from pylint, see https://github.com/pylint-dev/pylint/pull/4942. (useless-option-value)
+pylintrc:1:0: R0022: Useless option value for '--disable', 'backtick' was removed from pylint, see https://github.com/pylint-dev/pylint/pull/4942. (useless-option-value)
+pylintrc:1:0: R0022: Useless option value for '--disable', 'import-star-module-level' was removed from pylint, see https://github.com/pylint-dev/pylint/pull/4942. (useless-option-value)
+pylintrc:1:0: R0022: Useless option value for '--disable', 'apply-builtin' was removed from pylint, see https://github.com/pylint-dev/pylint/pull/4942. (useless-option-value)
+pylintrc:1:0: R0022: Useless option value for '--disable', 'basestring-builtin' was removed from pylint, see https://github.com/pylint-dev/pylint/pull/4942. (useless-option-value)
+pylintrc:1:0: R0022: Useless option value for '--disable', 'buffer-builtin' was removed from pylint, see https://github.com/pylint-dev/pylint/pull/4942. (useless-option-value)
+pylintrc:1:0: R0022: Useless option value for '--disable', 'cmp-builtin' was removed from pylint, see https://github.com/pylint-dev/pylint/pull/4942. (useless-option-value)
+pylintrc:1:0: R0022: Useless option value for '--disable', 'coerce-builtin' was removed from pylint, see https://github.com/pylint-dev/pylint/pull/4942. (useless-option-value)
+pylintrc:1:0: R0022: Useless option value for '--disable', 'execfile-builtin' was removed from pylint, see https://github.com/pylint-dev/pylint/pull/4942. (useless-option-value)
+pylintrc:1:0: R0022: Useless option value for '--disable', 'file-builtin' was removed from pylint, see https://github.com/pylint-dev/pylint/pull/4942. (useless-option-value)
+pylintrc:1:0: R0022: Useless option value for '--disable', 'long-builtin' was removed from pylint, see https://github.com/pylint-dev/pylint/pull/4942. (useless-option-value)
+pylintrc:1:0: R0022: Useless option value for '--disable', 'raw_input-builtin' was removed from pylint, see https://github.com/pylint-dev/pylint/pull/4942. (useless-option-value)
+pylintrc:1:0: R0022: Useless option value for '--disable', 'reduce-builtin' was removed from pylint, see https://github.com/pylint-dev/pylint/pull/4942. (useless-option-value)
+pylintrc:1:0: R0022: Useless option value for '--disable', 'standarderror-builtin' was removed from pylint, see https://github.com/pylint-dev/pylint/pull/4942. (useless-option-value)
+pylintrc:1:0: R0022: Useless option value for '--disable', 'unicode-builtin' was removed from pylint, see https://github.com/pylint-dev/pylint/pull/4942. (useless-option-value)
+pylintrc:1:0: R0022: Useless option value for '--disable', 'xrange-builtin' was removed from pylint, see https://github.com/pylint-dev/pylint/pull/4942. (useless-option-value)
+pylintrc:1:0: R0022: Useless option value for '--disable', 'coerce-method' was removed from pylint, see https://github.com/pylint-dev/pylint/pull/4942. (useless-option-value)
+pylintrc:1:0: R0022: Useless option value for '--disable', 'delslice-method' was removed from pylint, see https://github.com/pylint-dev/pylint/pull/4942. (useless-option-value)
+pylintrc:1:0: R0022: Useless option value for '--disable', 'getslice-method' was removed from pylint, see https://github.com/pylint-dev/pylint/pull/4942. (useless-option-value)
+pylintrc:1:0: R0022: Useless option value for '--disable', 'setslice-method' was removed from pylint, see https://github.com/pylint-dev/pylint/pull/4942. (useless-option-value)
+pylintrc:1:0: R0022: Useless option value for '--disable', 'no-absolute-import' was removed from pylint, see https://github.com/pylint-dev/pylint/pull/4942. (useless-option-value)
+pylintrc:1:0: R0022: Useless option value for '--disable', 'old-division' was removed from pylint, see https://github.com/pylint-dev/pylint/pull/4942. (useless-option-value)
+pylintrc:1:0: R0022: Useless option value for '--disable', 'dict-iter-method' was removed from pylint, see https://github.com/pylint-dev/pylint/pull/4942. (useless-option-value)
+pylintrc:1:0: R0022: Useless option value for '--disable', 'dict-view-method' was removed from pylint, see https://github.com/pylint-dev/pylint/pull/4942. (useless-option-value)
+pylintrc:1:0: R0022: Useless option value for '--disable', 'next-method-called' was removed from pylint, see https://github.com/pylint-dev/pylint/pull/4942. (useless-option-value)
+pylintrc:1:0: R0022: Useless option value for '--disable', 'metaclass-assignment' was removed from pylint, see https://github.com/pylint-dev/pylint/pull/4942. (useless-option-value)
+pylintrc:1:0: R0022: Useless option value for '--disable', 'indexing-exception' was removed from pylint, see https://github.com/pylint-dev/pylint/pull/4942. (useless-option-value)
+pylintrc:1:0: R0022: Useless option value for '--disable', 'raising-string' was removed from pylint, see https://github.com/pylint-dev/pylint/pull/4942. (useless-option-value)
+pylintrc:1:0: R0022: Useless option value for '--disable', 'reload-builtin' was removed from pylint, see https://github.com/pylint-dev/pylint/pull/4942. (useless-option-value)
+pylintrc:1:0: R0022: Useless option value for '--disable', 'oct-method' was removed from pylint, see https://github.com/pylint-dev/pylint/pull/4942. (useless-option-value)
+pylintrc:1:0: R0022: Useless option value for '--disable', 'hex-method' was removed from pylint, see https://github.com/pylint-dev/pylint/pull/4942. (useless-option-value)
+pylintrc:1:0: R0022: Useless option value for '--disable', 'nonzero-method' was removed from pylint, see https://github.com/pylint-dev/pylint/pull/4942. (useless-option-value)
+pylintrc:1:0: R0022: Useless option value for '--disable', 'cmp-method' was removed from pylint, see https://github.com/pylint-dev/pylint/pull/4942. (useless-option-value)
+pylintrc:1:0: R0022: Useless option value for '--disable', 'input-builtin' was removed from pylint, see https://github.com/pylint-dev/pylint/pull/4942. (useless-option-value)
+pylintrc:1:0: R0022: Useless option value for '--disable', 'round-builtin' was removed from pylint, see https://github.com/pylint-dev/pylint/pull/4942. (useless-option-value)
+pylintrc:1:0: R0022: Useless option value for '--disable', 'intern-builtin' was removed from pylint, see https://github.com/pylint-dev/pylint/pull/4942. (useless-option-value)
+pylintrc:1:0: R0022: Useless option value for '--disable', 'unichr-builtin' was removed from pylint, see https://github.com/pylint-dev/pylint/pull/4942. (useless-option-value)
+pylintrc:1:0: R0022: Useless option value for '--disable', 'map-builtin-not-iterating' was removed from pylint, see https://github.com/pylint-dev/pylint/pull/4942. (useless-option-value)
+pylintrc:1:0: R0022: Useless option value for '--disable', 'zip-builtin-not-iterating' was removed from pylint, see https://github.com/pylint-dev/pylint/pull/4942. (useless-option-value)
+pylintrc:1:0: R0022: Useless option value for '--disable', 'range-builtin-not-iterating' was removed from pylint, see https://github.com/pylint-dev/pylint/pull/4942. (useless-option-value)
+pylintrc:1:0: R0022: Useless option value for '--disable', 'filter-builtin-not-iterating' was removed from pylint, see https://github.com/pylint-dev/pylint/pull/4942. (useless-option-value)
+pylintrc:1:0: R0022: Useless option value for '--disable', 'using-cmp-argument' was removed from pylint, see https://github.com/pylint-dev/pylint/pull/4942. (useless-option-value)
+pylintrc:1:0: R0022: Useless option value for '--disable', 'div-method' was removed from pylint, see https://github.com/pylint-dev/pylint/pull/4942. (useless-option-value)
+pylintrc:1:0: R0022: Useless option value for '--disable', 'idiv-method' was removed from pylint, see https://github.com/pylint-dev/pylint/pull/4942. (useless-option-value)
+pylintrc:1:0: R0022: Useless option value for '--disable', 'rdiv-method' was removed from pylint, see https://github.com/pylint-dev/pylint/pull/4942. (useless-option-value)
+pylintrc:1:0: R0022: Useless option value for '--disable', 'exception-message-attribute' was removed from pylint, see https://github.com/pylint-dev/pylint/pull/4942. (useless-option-value)
+pylintrc:1:0: R0022: Useless option value for '--disable', 'invalid-str-codec' was removed from pylint, see https://github.com/pylint-dev/pylint/pull/4942. (useless-option-value)
+pylintrc:1:0: R0022: Useless option value for '--disable', 'sys-max-int' was removed from pylint, see https://github.com/pylint-dev/pylint/pull/4942. (useless-option-value)
+pylintrc:1:0: R0022: Useless option value for '--disable', 'bad-python3-import' was removed from pylint, see https://github.com/pylint-dev/pylint/pull/4942. (useless-option-value)
+pylintrc:1:0: R0022: Useless option value for '--disable', 'deprecated-string-function' was removed from pylint, see https://github.com/pylint-dev/pylint/pull/4942. (useless-option-value)
+pylintrc:1:0: R0022: Useless option value for '--disable', 'deprecated-str-translate-call' was removed from pylint, see https://github.com/pylint-dev/pylint/pull/4942. (useless-option-value)
+pylintrc:1:0: R0022: Useless option value for '--disable', 'deprecated-itertools-function' was removed from pylint, see https://github.com/pylint-dev/pylint/pull/4942. (useless-option-value)
+pylintrc:1:0: R0022: Useless option value for '--disable', 'deprecated-types-field' was removed from pylint, see https://github.com/pylint-dev/pylint/pull/4942. (useless-option-value)
+pylintrc:1:0: R0022: Useless option value for '--disable', 'next-method-defined' was removed from pylint, see https://github.com/pylint-dev/pylint/pull/4942. (useless-option-value)
+pylintrc:1:0: R0022: Useless option value for '--disable', 'dict-items-not-iterating' was removed from pylint, see https://github.com/pylint-dev/pylint/pull/4942. (useless-option-value)
+pylintrc:1:0: R0022: Useless option value for '--disable', 'dict-keys-not-iterating' was removed from pylint, see https://github.com/pylint-dev/pylint/pull/4942. (useless-option-value)
+pylintrc:1:0: R0022: Useless option value for '--disable', 'dict-values-not-iterating' was removed from pylint, see https://github.com/pylint-dev/pylint/pull/4942. (useless-option-value)
+pylintrc:1:0: R0022: Useless option value for '--disable', 'deprecated-operator-function' was removed from pylint, see https://github.com/pylint-dev/pylint/pull/4942. (useless-option-value)
+pylintrc:1:0: R0022: Useless option value for '--disable', 'deprecated-urllib-function' was removed from pylint, see https://github.com/pylint-dev/pylint/pull/4942. (useless-option-value)
+pylintrc:1:0: R0022: Useless option value for '--disable', 'xreadlines-attribute' was removed from pylint, see https://github.com/pylint-dev/pylint/pull/4942. (useless-option-value)
+pylintrc:1:0: R0022: Useless option value for '--disable', 'deprecated-sys-function' was removed from pylint, see https://github.com/pylint-dev/pylint/pull/4942. (useless-option-value)
+pylintrc:1:0: R0022: Useless option value for '--disable', 'exception-escape' was removed from pylint, see https://github.com/pylint-dev/pylint/pull/4942. (useless-option-value)
+pylintrc:1:0: R0022: Useless option value for '--disable', 'comprehension-escape' was removed from pylint, see https://github.com/pylint-dev/pylint/pull/4942. (useless-option-value)
+pylintrc:1:0: R0022: Useless option value for '--disable', 'no-self-use' was moved to an optional extension, see https://pylint.readthedocs.io/en/latest/whatsnew/2/2.14/summary.html#removed-checkers. (useless-option-value)
+pylintrc:1:0: W0012: Unknown option value for '--disable', expected a valid pylint message and got 'long-suffix' (unknown-option-value)
+pylintrc:1:0: W0012: Unknown option value for '--disable', expected a valid pylint message and got 'old-ne-operator' (unknown-option-value)
+pylintrc:1:0: W0012: Unknown option value for '--disable', expected a valid pylint message and got 'old-octal-literal' (unknown-option-value)
+pylintrc:1:0: W0012: Unknown option value for '--disable', expected a valid pylint message and got 'non-ascii-bytes-literal' (unknown-option-value)
+pylintrc:1:0: W0012: Unknown option value for '--disable', expected a valid pylint message and got 'eq-without-hash' (unknown-option-value)
+************* Module ci.test_examples
+ci/test_examples.py:9:0: E0401: Unable to import 'pytest' (import-error)
+ci/test_examples.py:65:8: W0105: String statement has no effect (pointless-string-statement)
+ci/test_examples.py:73:4: C0116: Missing function or method docstring (missing-function-docstring)
+ci/test_examples.py:83:4: C0116: Missing function or method docstring (missing-function-docstring)
+ci/test_examples.py:93:4: C0116: Missing function or method docstring (missing-function-docstring)
+ci/test_examples.py:109:4: C0116: Missing function or method docstring (missing-function-docstring)
+ci/test_examples.py:5:0: W0611: Unused import subprocess (unused-import)
+************* Module conftest
+tests/conftest.py:1:0: C0114: Missing module docstring (missing-module-docstring)
+tests/conftest.py:2:0: E0401: Unable to import 'pytest' (import-error)
+tests/conftest.py:7:0: C0116: Missing function or method docstring (missing-function-docstring)
+tests/conftest.py:13:0: C0116: Missing function or method docstring (missing-function-docstring)
+tests/conftest.py:13:25: W0621: Redefining name 'model_type' from outer scope (line 7) (redefined-outer-name)
+************* Module test_admm
+tests/test_admm.py:1:0: C0114: Missing module docstring (missing-module-docstring)
+tests/test_admm.py:22:0: C0103: Constant name "a" doesn't conform to UPPER_CASE naming style (invalid-name)
+tests/test_admm.py:63:0: C0115: Missing class docstring (missing-class-docstring)
+tests/test_admm.py:74:4: C0116: Missing function or method docstring (missing-function-docstring)
+tests/test_admm.py:76:8: W0612: Unused variable 'admm_module' (unused-variable)
+tests/test_admm.py:80:4: C0116: Missing function or method docstring (missing-function-docstring)
+tests/test_admm.py:85:13: W0212: Access to a protected member _start_executing_callbacks of a client class (protected-access)
+tests/test_admm.py:86:13: W0212: Access to a protected member _start_executing_callbacks of a client class (protected-access)
+tests/test_admm.py:109:8: W0212: Access to a protected member _solve_local_optimization of a client class (protected-access)
+tests/test_admm.py:110:12: W0212: Access to a protected member _solve_local_optimization_debug of a client class (protected-access)
+tests/test_admm.py:112:8: W0212: Access to a protected member _solve_local_optimization of a client class (protected-access)
+tests/test_admm.py:113:12: W0212: Access to a protected member _solve_local_optimization_debug of a client class (protected-access)
+tests/test_admm.py:118:19: W0212: Access to a protected member _admm_loop of a client class (protected-access)
+tests/test_admm.py:123:19: W0212: Access to a protected member _admm_loop of a client class (protected-access)
+tests/test_admm.py:127:13: W0212: Access to a protected member _process_realtime of a client class (protected-access)
+tests/test_admm.py:128:13: W0212: Access to a protected member _process_realtime of a client class (protected-access)
+tests/test_admm.py:149:24: W0212: Access to a protected member _admm_variables of a client class (protected-access)
+tests/test_admm.py:150:24: W0212: Access to a protected member _admm_variables of a client class (protected-access)
+tests/test_admm.py:163:4: C0116: Missing function or method docstring (missing-function-docstring)
+tests/test_admm.py:167:4: C0116: Missing function or method docstring (missing-function-docstring)
+tests/test_admm.py:88:8: W0201: Attribute 'admm_module1' defined outside __init__ (attribute-defined-outside-init)
+tests/test_admm.py:89:8: W0201: Attribute 'admm_module2' defined outside __init__ (attribute-defined-outside-init)
+tests/test_admm.py:92:8: W0201: Attribute 'counter' defined outside __init__ (attribute-defined-outside-init)
+tests/test_admm.py:93:8: W0201: Attribute 'counter2' defined outside __init__ (attribute-defined-outside-init)
+************* Module test_casadi_backend
+tests/test_casadi_backend.py:1:0: C0114: Missing module docstring (missing-module-docstring)
+tests/test_casadi_backend.py:4:0: E0401: Unable to import 'pytest' (import-error)
+tests/test_casadi_backend.py:26:0: C0116: Missing function or method docstring (missing-function-docstring)
+tests/test_casadi_backend.py:37:0: C0116: Missing function or method docstring (missing-function-docstring)
+tests/test_casadi_backend.py:37:48: W0621: Redefining name 'var_ref' from outer scope (line 26) (redefined-outer-name)
+tests/test_casadi_backend.py:44:0: C0116: Missing function or method docstring (missing-function-docstring)
+tests/test_casadi_backend.py:44:31: W0621: Redefining name 'example_casadi_system' from outer scope (line 37) (redefined-outer-name)
+tests/test_casadi_backend.py:55:0: C0116: Missing function or method docstring (missing-function-docstring)
+tests/test_casadi_backend.py:55:32: W0621: Redefining name 'var_ref' from outer scope (line 26) (redefined-outer-name)
+tests/test_casadi_backend.py:56:4: C0103: Variable name "be" doesn't conform to snake_case naming style (invalid-name)
+tests/test_casadi_backend.py:61:0: C0116: Missing function or method docstring (missing-function-docstring)
+tests/test_casadi_backend.py:68:8: W0612: Unused variable 'optimization_variable' (unused-variable)
+tests/test_casadi_backend.py:75:4: W0612: Unused variable 'optimization_variable_1' (unused-variable)
+tests/test_casadi_backend.py:90:8: W0612: Unused variable 'optimization_variable_3' (unused-variable)
+tests/test_casadi_backend.py:97:0: C0116: Missing function or method docstring (missing-function-docstring)
+tests/test_casadi_backend.py:97:16: W0621: Redefining name 'example_casadi_system' from outer scope (line 37) (redefined-outer-name)
+tests/test_casadi_backend.py:113:0: C0116: Missing function or method docstring (missing-function-docstring)
+tests/test_casadi_backend.py:114:4: W0621: Redefining name 'collocation_discretization' from outer scope (line 44) (redefined-outer-name)
+tests/test_casadi_backend.py:114:51: W0621: Redefining name 'example_casadi_system' from outer scope (line 37) (redefined-outer-name)
+tests/test_casadi_backend.py:135:0: C0116: Missing function or method docstring (missing-function-docstring)
+tests/test_casadi_backend.py:135:17: W0621: Redefining name 'example_casadi_system' from outer scope (line 37) (redefined-outer-name)
+tests/test_casadi_backend.py:154:0: C0116: Missing function or method docstring (missing-function-docstring)
+tests/test_casadi_backend.py:154:24: W0621: Redefining name 'example_backend' from outer scope (line 55) (redefined-outer-name)
+tests/test_casadi_backend.py:155:4: C0103: Variable name "be" doesn't conform to snake_case naming style (invalid-name)
+tests/test_casadi_backend.py:159:13: W0212: Access to a protected member _mpc_inputs_to_nlp_inputs of a client class (protected-access)
+************* Module test_casadi_ml_model
+tests/test_casadi_ml_model.py:3:0: E0401: Unable to import 'pytest' (import-error)
+tests/test_casadi_ml_model.py:9:0: C0115: Missing class docstring (missing-class-docstring)
+tests/test_casadi_ml_model.py:14:0: C0115: Missing class docstring (missing-class-docstring)
+tests/test_casadi_ml_model.py:23:0: C0116: Missing function or method docstring (missing-function-docstring)
+************* Module test_mpc
+tests/test_mpc.py:1:0: C0114: Missing module docstring (missing-module-docstring)
+tests/test_mpc.py:18:0: C0103: Constant name "a" doesn't conform to UPPER_CASE naming style (invalid-name)
+tests/test_mpc.py:24:4: C0116: Missing function or method docstring (missing-function-docstring)
+tests/test_mpc.py:75:4: C0116: Missing function or method docstring (missing-function-docstring)
+tests/test_mpc.py:81:4: C0116: Missing function or method docstring (missing-function-docstring)
+tests/test_mpc.py:82:8: C0103: Variable name "sr" doesn't conform to snake_case naming style (invalid-name)
+tests/test_mpc.py:94:4: C0116: Missing function or method docstring (missing-function-docstring)
+tests/test_mpc.py:105:4: C0116: Missing function or method docstring (missing-function-docstring)
+tests/test_mpc.py:152:4: C0103: Method name "test_CasadiMPC" doesn't conform to snake_case naming style (invalid-name)
+tests/test_mpc.py:158:14: W0212: Access to a protected member _modules of a client class (protected-access)
+tests/test_mpc.py:166:4: C0116: Missing function or method docstring (missing-function-docstring)
+tests/test_mpc.py:6:0: C0411: standard import "import pathlib" should be placed before "import numpy as np" (wrong-import-order)
+tests/test_mpc.py:13:0: W0611: Unused OptimizationBackend imported from agentlib_mpc.optimization_backends.backend (unused-import)
+************* Module test_serialized_ann
+tests/test_serialized_ann.py:5:0: C0411: standard import "import unittest" should be placed before "import numpy as np" (wrong-import-order)
+tests/test_serialized_ann.py:8:0: C0411: standard import "from pathlib import Path" should be placed before "import numpy as np" (wrong-import-order)
+tests/test_serialized_ann.py:13:0: C0411: third party import "from fixtures.ann import ANNTrainer" should be placed before "from agentlib_mpc.data_structures import ml_model_datatypes" (wrong-import-order)
+tests/test_serialized_ann.py:14:0: C0411: third party import "from fixtures.data_generator import DataGenerator" should be placed before "from agentlib_mpc.data_structures import ml_model_datatypes" (wrong-import-order)
+************* Module test_serialized_gpr
+tests/test_serialized_gpr.py:1:0: C0114: Missing module docstring (missing-module-docstring)
+tests/test_serialized_gpr.py:2:0: C0411: standard import "import os" should be placed before "import numpy as np" (wrong-import-order)
+tests/test_serialized_gpr.py:3:0: C0411: standard import "import unittest" should be placed before "import numpy as np" (wrong-import-order)
+tests/test_serialized_gpr.py:5:0: C0411: standard import "from pathlib import Path" should be placed before "import numpy as np" (wrong-import-order)
+tests/test_serialized_gpr.py:11:0: C0411: third party import "from fixtures.gpr import GPRTrainer" should be placed before "from agentlib_mpc.data_structures import ml_model_datatypes" (wrong-import-order)
+tests/test_serialized_gpr.py:12:0: C0411: third party import "from fixtures.data_generator import DataGenerator" should be placed before "from agentlib_mpc.data_structures import ml_model_datatypes" (wrong-import-order)
+tests/test_serialized_gpr.py:10:0: W0611: Unused MLModels imported from agentlib_mpc.models.serialized_ml_model (unused-import)
+************* Module test_serialized_linreg
+tests/test_serialized_linreg.py:1:0: C0114: Missing module docstring (missing-module-docstring)
+tests/test_serialized_linreg.py:2:0: C0411: standard import "import os" should be placed before "import numpy as np" (wrong-import-order)
+tests/test_serialized_linreg.py:3:0: C0411: standard import "import unittest" should be placed before "import numpy as np" (wrong-import-order)
+tests/test_serialized_linreg.py:5:0: C0411: standard import "from pathlib import Path" should be placed before "import numpy as np" (wrong-import-order)
+tests/test_serialized_linreg.py:11:0: C0411: third party import "from fixtures.linreg import LinRegTrainer" should be placed before "from agentlib_mpc.data_structures import ml_model_datatypes" (wrong-import-order)
+tests/test_serialized_linreg.py:12:0: C0411: third party import "from fixtures.data_generator import DataGenerator" should be placed before "from agentlib_mpc.data_structures import ml_model_datatypes" (wrong-import-order)
+
+-----------------------------------
+Your code has been rated at 7.35/10
+
+
+ `;
+
+ // Inject the generated flyout into the body HTML element.
+ document.body.insertAdjacentHTML("beforeend", flyout);
+
+ // Trigger the Read the Docs Addons Search modal when clicking on the "Search docs" input from inside the flyout.
+ document
+ .querySelector("#flyout-search-form")
+ .addEventListener("focusin", () => {
+ const event = new CustomEvent("readthedocs-search-show");
+ document.dispatchEvent(event);
+ });
+ })
+}
+
+if (themeLanguageSelector || themeVersionSelector) {
+ function onSelectorSwitch(event) {
+ const option = event.target.selectedIndex;
+ const item = event.target.options[option];
+ window.location.href = item.dataset.url;
+ }
+
+ document.addEventListener("readthedocs-addons-data-ready", function (event) {
+ const config = event.detail.data();
+
+ const versionSwitch = document.querySelector(
+ "div.switch-menus > div.version-switch",
+ );
+ if (themeVersionSelector) {
+ let versions = config.versions.active;
+ if (config.versions.current.hidden || config.versions.current.type === "external") {
+ versions.unshift(config.versions.current);
+ }
+ const versionSelect = `
+
+ `;
+
+ versionSwitch.innerHTML = versionSelect;
+ versionSwitch.firstElementChild.addEventListener("change", onSelectorSwitch);
+ }
+
+ const languageSwitch = document.querySelector(
+ "div.switch-menus > div.language-switch",
+ );
+
+ if (themeLanguageSelector) {
+ if (config.projects.translations.length) {
+ // Add the current language to the options on the selector
+ let languages = config.projects.translations.concat(
+ config.projects.current,
+ );
+ languages = languages.sort((a, b) =>
+ a.language.name.localeCompare(b.language.name),
+ );
+
+ const languageSelect = `
+
+ `;
+
+ languageSwitch.innerHTML = languageSelect;
+ languageSwitch.firstElementChild.addEventListener("change", onSelectorSwitch);
+ }
+ else {
+ languageSwitch.remove();
+ }
+ }
+ });
+}
+
+document.addEventListener("readthedocs-addons-data-ready", function (event) {
+ // Trigger the Read the Docs Addons Search modal when clicking on "Search docs" input from the topnav.
+ document
+ .querySelector("[role='search'] input")
+ .addEventListener("focusin", () => {
+ const event = new CustomEvent("readthedocs-search-show");
+ document.dispatchEvent(event);
+ });
+});
\ No newline at end of file
diff --git a/docs/0.6.4/docs/_static/language_data.js b/docs/0.6.4/docs/_static/language_data.js
new file mode 100644
index 0000000..250f566
--- /dev/null
+++ b/docs/0.6.4/docs/_static/language_data.js
@@ -0,0 +1,199 @@
+/*
+ * language_data.js
+ * ~~~~~~~~~~~~~~~~
+ *
+ * This script contains the language-specific data used by searchtools.js,
+ * namely the list of stopwords, stemmer, scorer and splitter.
+ *
+ * :copyright: Copyright 2007-2023 by the Sphinx team, see AUTHORS.
+ * :license: BSD, see LICENSE for details.
+ *
+ */
+
+var stopwords = ["a", "and", "are", "as", "at", "be", "but", "by", "for", "if", "in", "into", "is", "it", "near", "no", "not", "of", "on", "or", "such", "that", "the", "their", "then", "there", "these", "they", "this", "to", "was", "will", "with"];
+
+
+/* Non-minified version is copied as a separate JS file, is available */
+
+/**
+ * Porter Stemmer
+ */
+var Stemmer = function() {
+
+ var step2list = {
+ ational: 'ate',
+ tional: 'tion',
+ enci: 'ence',
+ anci: 'ance',
+ izer: 'ize',
+ bli: 'ble',
+ alli: 'al',
+ entli: 'ent',
+ eli: 'e',
+ ousli: 'ous',
+ ization: 'ize',
+ ation: 'ate',
+ ator: 'ate',
+ alism: 'al',
+ iveness: 'ive',
+ fulness: 'ful',
+ ousness: 'ous',
+ aliti: 'al',
+ iviti: 'ive',
+ biliti: 'ble',
+ logi: 'log'
+ };
+
+ var step3list = {
+ icate: 'ic',
+ ative: '',
+ alize: 'al',
+ iciti: 'ic',
+ ical: 'ic',
+ ful: '',
+ ness: ''
+ };
+
+ var c = "[^aeiou]"; // consonant
+ var v = "[aeiouy]"; // vowel
+ var C = c + "[^aeiouy]*"; // consonant sequence
+ var V = v + "[aeiou]*"; // vowel sequence
+
+ var mgr0 = "^(" + C + ")?" + V + C; // [C]VC... is m>0
+ var meq1 = "^(" + C + ")?" + V + C + "(" + V + ")?$"; // [C]VC[V] is m=1
+ var mgr1 = "^(" + C + ")?" + V + C + V + C; // [C]VCVC... is m>1
+ var s_v = "^(" + C + ")?" + v; // vowel in stem
+
+ this.stemWord = function (w) {
+ var stem;
+ var suffix;
+ var firstch;
+ var origword = w;
+
+ if (w.length < 3)
+ return w;
+
+ var re;
+ var re2;
+ var re3;
+ var re4;
+
+ firstch = w.substr(0,1);
+ if (firstch == "y")
+ w = firstch.toUpperCase() + w.substr(1);
+
+ // Step 1a
+ re = /^(.+?)(ss|i)es$/;
+ re2 = /^(.+?)([^s])s$/;
+
+ if (re.test(w))
+ w = w.replace(re,"$1$2");
+ else if (re2.test(w))
+ w = w.replace(re2,"$1$2");
+
+ // Step 1b
+ re = /^(.+?)eed$/;
+ re2 = /^(.+?)(ed|ing)$/;
+ if (re.test(w)) {
+ var fp = re.exec(w);
+ re = new RegExp(mgr0);
+ if (re.test(fp[1])) {
+ re = /.$/;
+ w = w.replace(re,"");
+ }
+ }
+ else if (re2.test(w)) {
+ var fp = re2.exec(w);
+ stem = fp[1];
+ re2 = new RegExp(s_v);
+ if (re2.test(stem)) {
+ w = stem;
+ re2 = /(at|bl|iz)$/;
+ re3 = new RegExp("([^aeiouylsz])\\1$");
+ re4 = new RegExp("^" + C + v + "[^aeiouwxy]$");
+ if (re2.test(w))
+ w = w + "e";
+ else if (re3.test(w)) {
+ re = /.$/;
+ w = w.replace(re,"");
+ }
+ else if (re4.test(w))
+ w = w + "e";
+ }
+ }
+
+ // Step 1c
+ re = /^(.+?)y$/;
+ if (re.test(w)) {
+ var fp = re.exec(w);
+ stem = fp[1];
+ re = new RegExp(s_v);
+ if (re.test(stem))
+ w = stem + "i";
+ }
+
+ // Step 2
+ re = /^(.+?)(ational|tional|enci|anci|izer|bli|alli|entli|eli|ousli|ization|ation|ator|alism|iveness|fulness|ousness|aliti|iviti|biliti|logi)$/;
+ if (re.test(w)) {
+ var fp = re.exec(w);
+ stem = fp[1];
+ suffix = fp[2];
+ re = new RegExp(mgr0);
+ if (re.test(stem))
+ w = stem + step2list[suffix];
+ }
+
+ // Step 3
+ re = /^(.+?)(icate|ative|alize|iciti|ical|ful|ness)$/;
+ if (re.test(w)) {
+ var fp = re.exec(w);
+ stem = fp[1];
+ suffix = fp[2];
+ re = new RegExp(mgr0);
+ if (re.test(stem))
+ w = stem + step3list[suffix];
+ }
+
+ // Step 4
+ re = /^(.+?)(al|ance|ence|er|ic|able|ible|ant|ement|ment|ent|ou|ism|ate|iti|ous|ive|ize)$/;
+ re2 = /^(.+?)(s|t)(ion)$/;
+ if (re.test(w)) {
+ var fp = re.exec(w);
+ stem = fp[1];
+ re = new RegExp(mgr1);
+ if (re.test(stem))
+ w = stem;
+ }
+ else if (re2.test(w)) {
+ var fp = re2.exec(w);
+ stem = fp[1] + fp[2];
+ re2 = new RegExp(mgr1);
+ if (re2.test(stem))
+ w = stem;
+ }
+
+ // Step 5
+ re = /^(.+?)e$/;
+ if (re.test(w)) {
+ var fp = re.exec(w);
+ stem = fp[1];
+ re = new RegExp(mgr1);
+ re2 = new RegExp(meq1);
+ re3 = new RegExp("^" + C + v + "[^aeiouwxy]$");
+ if (re.test(stem) || (re2.test(stem) && !(re3.test(stem))))
+ w = stem;
+ }
+ re = /ll$/;
+ re2 = new RegExp(mgr1);
+ if (re.test(w) && re2.test(w)) {
+ re = /.$/;
+ w = w.replace(re,"");
+ }
+
+ // and turn initial Y back to y
+ if (firstch == "y")
+ w = firstch.toLowerCase() + w.substr(1);
+ return w;
+ }
+}
+
diff --git a/docs/0.6.4/docs/_static/minus.png b/docs/0.6.4/docs/_static/minus.png
new file mode 100644
index 0000000..d96755f
Binary files /dev/null and b/docs/0.6.4/docs/_static/minus.png differ
diff --git a/docs/0.6.4/docs/_static/plus.png b/docs/0.6.4/docs/_static/plus.png
new file mode 100644
index 0000000..7107cec
Binary files /dev/null and b/docs/0.6.4/docs/_static/plus.png differ
diff --git a/docs/0.6.4/docs/_static/pygments.css b/docs/0.6.4/docs/_static/pygments.css
new file mode 100644
index 0000000..84ab303
--- /dev/null
+++ b/docs/0.6.4/docs/_static/pygments.css
@@ -0,0 +1,75 @@
+pre { line-height: 125%; }
+td.linenos .normal { color: inherit; background-color: transparent; padding-left: 5px; padding-right: 5px; }
+span.linenos { color: inherit; background-color: transparent; padding-left: 5px; padding-right: 5px; }
+td.linenos .special { color: #000000; background-color: #ffffc0; padding-left: 5px; padding-right: 5px; }
+span.linenos.special { color: #000000; background-color: #ffffc0; padding-left: 5px; padding-right: 5px; }
+.highlight .hll { background-color: #ffffcc }
+.highlight { background: #f8f8f8; }
+.highlight .c { color: #3D7B7B; font-style: italic } /* Comment */
+.highlight .err { border: 1px solid #FF0000 } /* Error */
+.highlight .k { color: #008000; font-weight: bold } /* Keyword */
+.highlight .o { color: #666666 } /* Operator */
+.highlight .ch { color: #3D7B7B; font-style: italic } /* Comment.Hashbang */
+.highlight .cm { color: #3D7B7B; font-style: italic } /* Comment.Multiline */
+.highlight .cp { color: #9C6500 } /* Comment.Preproc */
+.highlight .cpf { color: #3D7B7B; font-style: italic } /* Comment.PreprocFile */
+.highlight .c1 { color: #3D7B7B; font-style: italic } /* Comment.Single */
+.highlight .cs { color: #3D7B7B; font-style: italic } /* Comment.Special */
+.highlight .gd { color: #A00000 } /* Generic.Deleted */
+.highlight .ge { font-style: italic } /* Generic.Emph */
+.highlight .ges { font-weight: bold; font-style: italic } /* Generic.EmphStrong */
+.highlight .gr { color: #E40000 } /* Generic.Error */
+.highlight .gh { color: #000080; font-weight: bold } /* Generic.Heading */
+.highlight .gi { color: #008400 } /* Generic.Inserted */
+.highlight .go { color: #717171 } /* Generic.Output */
+.highlight .gp { color: #000080; font-weight: bold } /* Generic.Prompt */
+.highlight .gs { font-weight: bold } /* Generic.Strong */
+.highlight .gu { color: #800080; font-weight: bold } /* Generic.Subheading */
+.highlight .gt { color: #0044DD } /* Generic.Traceback */
+.highlight .kc { color: #008000; font-weight: bold } /* Keyword.Constant */
+.highlight .kd { color: #008000; font-weight: bold } /* Keyword.Declaration */
+.highlight .kn { color: #008000; font-weight: bold } /* Keyword.Namespace */
+.highlight .kp { color: #008000 } /* Keyword.Pseudo */
+.highlight .kr { color: #008000; font-weight: bold } /* Keyword.Reserved */
+.highlight .kt { color: #B00040 } /* Keyword.Type */
+.highlight .m { color: #666666 } /* Literal.Number */
+.highlight .s { color: #BA2121 } /* Literal.String */
+.highlight .na { color: #687822 } /* Name.Attribute */
+.highlight .nb { color: #008000 } /* Name.Builtin */
+.highlight .nc { color: #0000FF; font-weight: bold } /* Name.Class */
+.highlight .no { color: #880000 } /* Name.Constant */
+.highlight .nd { color: #AA22FF } /* Name.Decorator */
+.highlight .ni { color: #717171; font-weight: bold } /* Name.Entity */
+.highlight .ne { color: #CB3F38; font-weight: bold } /* Name.Exception */
+.highlight .nf { color: #0000FF } /* Name.Function */
+.highlight .nl { color: #767600 } /* Name.Label */
+.highlight .nn { color: #0000FF; font-weight: bold } /* Name.Namespace */
+.highlight .nt { color: #008000; font-weight: bold } /* Name.Tag */
+.highlight .nv { color: #19177C } /* Name.Variable */
+.highlight .ow { color: #AA22FF; font-weight: bold } /* Operator.Word */
+.highlight .w { color: #bbbbbb } /* Text.Whitespace */
+.highlight .mb { color: #666666 } /* Literal.Number.Bin */
+.highlight .mf { color: #666666 } /* Literal.Number.Float */
+.highlight .mh { color: #666666 } /* Literal.Number.Hex */
+.highlight .mi { color: #666666 } /* Literal.Number.Integer */
+.highlight .mo { color: #666666 } /* Literal.Number.Oct */
+.highlight .sa { color: #BA2121 } /* Literal.String.Affix */
+.highlight .sb { color: #BA2121 } /* Literal.String.Backtick */
+.highlight .sc { color: #BA2121 } /* Literal.String.Char */
+.highlight .dl { color: #BA2121 } /* Literal.String.Delimiter */
+.highlight .sd { color: #BA2121; font-style: italic } /* Literal.String.Doc */
+.highlight .s2 { color: #BA2121 } /* Literal.String.Double */
+.highlight .se { color: #AA5D1F; font-weight: bold } /* Literal.String.Escape */
+.highlight .sh { color: #BA2121 } /* Literal.String.Heredoc */
+.highlight .si { color: #A45A77; font-weight: bold } /* Literal.String.Interpol */
+.highlight .sx { color: #008000 } /* Literal.String.Other */
+.highlight .sr { color: #A45A77 } /* Literal.String.Regex */
+.highlight .s1 { color: #BA2121 } /* Literal.String.Single */
+.highlight .ss { color: #19177C } /* Literal.String.Symbol */
+.highlight .bp { color: #008000 } /* Name.Builtin.Pseudo */
+.highlight .fm { color: #0000FF } /* Name.Function.Magic */
+.highlight .vc { color: #19177C } /* Name.Variable.Class */
+.highlight .vg { color: #19177C } /* Name.Variable.Global */
+.highlight .vi { color: #19177C } /* Name.Variable.Instance */
+.highlight .vm { color: #19177C } /* Name.Variable.Magic */
+.highlight .il { color: #666666 } /* Literal.Number.Integer.Long */
\ No newline at end of file
diff --git a/docs/0.6.4/docs/_static/searchtools.js b/docs/0.6.4/docs/_static/searchtools.js
new file mode 100644
index 0000000..97d56a7
--- /dev/null
+++ b/docs/0.6.4/docs/_static/searchtools.js
@@ -0,0 +1,566 @@
+/*
+ * searchtools.js
+ * ~~~~~~~~~~~~~~~~
+ *
+ * Sphinx JavaScript utilities for the full-text search.
+ *
+ * :copyright: Copyright 2007-2023 by the Sphinx team, see AUTHORS.
+ * :license: BSD, see LICENSE for details.
+ *
+ */
+"use strict";
+
+/**
+ * Simple result scoring code.
+ */
+if (typeof Scorer === "undefined") {
+ var Scorer = {
+ // Implement the following function to further tweak the score for each result
+ // The function takes a result array [docname, title, anchor, descr, score, filename]
+ // and returns the new score.
+ /*
+ score: result => {
+ const [docname, title, anchor, descr, score, filename] = result
+ return score
+ },
+ */
+
+ // query matches the full name of an object
+ objNameMatch: 11,
+ // or matches in the last dotted part of the object name
+ objPartialMatch: 6,
+ // Additive scores depending on the priority of the object
+ objPrio: {
+ 0: 15, // used to be importantResults
+ 1: 5, // used to be objectResults
+ 2: -5, // used to be unimportantResults
+ },
+ // Used when the priority is not in the mapping.
+ objPrioDefault: 0,
+
+ // query found in title
+ title: 15,
+ partialTitle: 7,
+ // query found in terms
+ term: 5,
+ partialTerm: 2,
+ };
+}
+
+const _removeChildren = (element) => {
+ while (element && element.lastChild) element.removeChild(element.lastChild);
+};
+
+/**
+ * See https://developer.mozilla.org/en-US/docs/Web/JavaScript/Guide/Regular_Expressions#escaping
+ */
+const _escapeRegExp = (string) =>
+ string.replace(/[.*+\-?^${}()|[\]\\]/g, "\\$&"); // $& means the whole matched string
+
+const _displayItem = (item, searchTerms) => {
+ const docBuilder = DOCUMENTATION_OPTIONS.BUILDER;
+ const docUrlRoot = DOCUMENTATION_OPTIONS.URL_ROOT;
+ const docFileSuffix = DOCUMENTATION_OPTIONS.FILE_SUFFIX;
+ const docLinkSuffix = DOCUMENTATION_OPTIONS.LINK_SUFFIX;
+ const showSearchSummary = DOCUMENTATION_OPTIONS.SHOW_SEARCH_SUMMARY;
+
+ const [docName, title, anchor, descr, score, _filename] = item;
+
+ let listItem = document.createElement("li");
+ let requestUrl;
+ let linkUrl;
+ if (docBuilder === "dirhtml") {
+ // dirhtml builder
+ let dirname = docName + "/";
+ if (dirname.match(/\/index\/$/))
+ dirname = dirname.substring(0, dirname.length - 6);
+ else if (dirname === "index/") dirname = "";
+ requestUrl = docUrlRoot + dirname;
+ linkUrl = requestUrl;
+ } else {
+ // normal html builders
+ requestUrl = docUrlRoot + docName + docFileSuffix;
+ linkUrl = docName + docLinkSuffix;
+ }
+ let linkEl = listItem.appendChild(document.createElement("a"));
+ linkEl.href = linkUrl + anchor;
+ linkEl.dataset.score = score;
+ linkEl.innerHTML = title;
+ if (descr)
+ listItem.appendChild(document.createElement("span")).innerHTML =
+ " (" + descr + ")";
+ else if (showSearchSummary)
+ fetch(requestUrl)
+ .then((responseData) => responseData.text())
+ .then((data) => {
+ if (data)
+ listItem.appendChild(
+ Search.makeSearchSummary(data, searchTerms)
+ );
+ });
+ Search.output.appendChild(listItem);
+};
+const _finishSearch = (resultCount) => {
+ Search.stopPulse();
+ Search.title.innerText = _("Search Results");
+ if (!resultCount)
+ Search.status.innerText = Documentation.gettext(
+ "Your search did not match any documents. Please make sure that all words are spelled correctly and that you've selected enough categories."
+ );
+ else
+ Search.status.innerText = _(
+ `Search finished, found ${resultCount} page(s) matching the search query.`
+ );
+};
+const _displayNextItem = (
+ results,
+ resultCount,
+ searchTerms
+) => {
+ // results left, load the summary and display it
+ // this is intended to be dynamic (don't sub resultsCount)
+ if (results.length) {
+ _displayItem(results.pop(), searchTerms);
+ setTimeout(
+ () => _displayNextItem(results, resultCount, searchTerms),
+ 5
+ );
+ }
+ // search finished, update title and status message
+ else _finishSearch(resultCount);
+};
+
+/**
+ * Default splitQuery function. Can be overridden in ``sphinx.search`` with a
+ * custom function per language.
+ *
+ * The regular expression works by splitting the string on consecutive characters
+ * that are not Unicode letters, numbers, underscores, or emoji characters.
+ * This is the same as ``\W+`` in Python, preserving the surrogate pair area.
+ */
+if (typeof splitQuery === "undefined") {
+ var splitQuery = (query) => query
+ .split(/[^\p{Letter}\p{Number}_\p{Emoji_Presentation}]+/gu)
+ .filter(term => term) // remove remaining empty strings
+}
+
+/**
+ * Search Module
+ */
+const Search = {
+ _index: null,
+ _queued_query: null,
+ _pulse_status: -1,
+
+ htmlToText: (htmlString) => {
+ const htmlElement = new DOMParser().parseFromString(htmlString, 'text/html');
+ htmlElement.querySelectorAll(".headerlink").forEach((el) => { el.remove() });
+ const docContent = htmlElement.querySelector('[role="main"]');
+ if (docContent !== undefined) return docContent.textContent;
+ console.warn(
+ "Content block not found. Sphinx search tries to obtain it via '[role=main]'. Could you check your theme or template."
+ );
+ return "";
+ },
+
+ init: () => {
+ const query = new URLSearchParams(window.location.search).get("q");
+ document
+ .querySelectorAll('input[name="q"]')
+ .forEach((el) => (el.value = query));
+ if (query) Search.performSearch(query);
+ },
+
+ loadIndex: (url) =>
+ (document.body.appendChild(document.createElement("script")).src = url),
+
+ setIndex: (index) => {
+ Search._index = index;
+ if (Search._queued_query !== null) {
+ const query = Search._queued_query;
+ Search._queued_query = null;
+ Search.query(query);
+ }
+ },
+
+ hasIndex: () => Search._index !== null,
+
+ deferQuery: (query) => (Search._queued_query = query),
+
+ stopPulse: () => (Search._pulse_status = -1),
+
+ startPulse: () => {
+ if (Search._pulse_status >= 0) return;
+
+ const pulse = () => {
+ Search._pulse_status = (Search._pulse_status + 1) % 4;
+ Search.dots.innerText = ".".repeat(Search._pulse_status);
+ if (Search._pulse_status >= 0) window.setTimeout(pulse, 500);
+ };
+ pulse();
+ },
+
+ /**
+ * perform a search for something (or wait until index is loaded)
+ */
+ performSearch: (query) => {
+ // create the required interface elements
+ const searchText = document.createElement("h2");
+ searchText.textContent = _("Searching");
+ const searchSummary = document.createElement("p");
+ searchSummary.classList.add("search-summary");
+ searchSummary.innerText = "";
+ const searchList = document.createElement("ul");
+ searchList.classList.add("search");
+
+ const out = document.getElementById("search-results");
+ Search.title = out.appendChild(searchText);
+ Search.dots = Search.title.appendChild(document.createElement("span"));
+ Search.status = out.appendChild(searchSummary);
+ Search.output = out.appendChild(searchList);
+
+ const searchProgress = document.getElementById("search-progress");
+ // Some themes don't use the search progress node
+ if (searchProgress) {
+ searchProgress.innerText = _("Preparing search...");
+ }
+ Search.startPulse();
+
+ // index already loaded, the browser was quick!
+ if (Search.hasIndex()) Search.query(query);
+ else Search.deferQuery(query);
+ },
+
+ /**
+ * execute search (requires search index to be loaded)
+ */
+ query: (query) => {
+ const filenames = Search._index.filenames;
+ const docNames = Search._index.docnames;
+ const titles = Search._index.titles;
+ const allTitles = Search._index.alltitles;
+ const indexEntries = Search._index.indexentries;
+
+ // stem the search terms and add them to the correct list
+ const stemmer = new Stemmer();
+ const searchTerms = new Set();
+ const excludedTerms = new Set();
+ const highlightTerms = new Set();
+ const objectTerms = new Set(splitQuery(query.toLowerCase().trim()));
+ splitQuery(query.trim()).forEach((queryTerm) => {
+ const queryTermLower = queryTerm.toLowerCase();
+
+ // maybe skip this "word"
+ // stopwords array is from language_data.js
+ if (
+ stopwords.indexOf(queryTermLower) !== -1 ||
+ queryTerm.match(/^\d+$/)
+ )
+ return;
+
+ // stem the word
+ let word = stemmer.stemWord(queryTermLower);
+ // select the correct list
+ if (word[0] === "-") excludedTerms.add(word.substr(1));
+ else {
+ searchTerms.add(word);
+ highlightTerms.add(queryTermLower);
+ }
+ });
+
+ if (SPHINX_HIGHLIGHT_ENABLED) { // set in sphinx_highlight.js
+ localStorage.setItem("sphinx_highlight_terms", [...highlightTerms].join(" "))
+ }
+
+ // console.debug("SEARCH: searching for:");
+ // console.info("required: ", [...searchTerms]);
+ // console.info("excluded: ", [...excludedTerms]);
+
+ // array of [docname, title, anchor, descr, score, filename]
+ let results = [];
+ _removeChildren(document.getElementById("search-progress"));
+
+ const queryLower = query.toLowerCase();
+ for (const [title, foundTitles] of Object.entries(allTitles)) {
+ if (title.toLowerCase().includes(queryLower) && (queryLower.length >= title.length/2)) {
+ for (const [file, id] of foundTitles) {
+ let score = Math.round(100 * queryLower.length / title.length)
+ results.push([
+ docNames[file],
+ titles[file] !== title ? `${titles[file]} > ${title}` : title,
+ id !== null ? "#" + id : "",
+ null,
+ score,
+ filenames[file],
+ ]);
+ }
+ }
+ }
+
+ // search for explicit entries in index directives
+ for (const [entry, foundEntries] of Object.entries(indexEntries)) {
+ if (entry.includes(queryLower) && (queryLower.length >= entry.length/2)) {
+ for (const [file, id] of foundEntries) {
+ let score = Math.round(100 * queryLower.length / entry.length)
+ results.push([
+ docNames[file],
+ titles[file],
+ id ? "#" + id : "",
+ null,
+ score,
+ filenames[file],
+ ]);
+ }
+ }
+ }
+
+ // lookup as object
+ objectTerms.forEach((term) =>
+ results.push(...Search.performObjectSearch(term, objectTerms))
+ );
+
+ // lookup as search terms in fulltext
+ results.push(...Search.performTermsSearch(searchTerms, excludedTerms));
+
+ // let the scorer override scores with a custom scoring function
+ if (Scorer.score) results.forEach((item) => (item[4] = Scorer.score(item)));
+
+ // now sort the results by score (in opposite order of appearance, since the
+ // display function below uses pop() to retrieve items) and then
+ // alphabetically
+ results.sort((a, b) => {
+ const leftScore = a[4];
+ const rightScore = b[4];
+ if (leftScore === rightScore) {
+ // same score: sort alphabetically
+ const leftTitle = a[1].toLowerCase();
+ const rightTitle = b[1].toLowerCase();
+ if (leftTitle === rightTitle) return 0;
+ return leftTitle > rightTitle ? -1 : 1; // inverted is intentional
+ }
+ return leftScore > rightScore ? 1 : -1;
+ });
+
+ // remove duplicate search results
+ // note the reversing of results, so that in the case of duplicates, the highest-scoring entry is kept
+ let seen = new Set();
+ results = results.reverse().reduce((acc, result) => {
+ let resultStr = result.slice(0, 4).concat([result[5]]).map(v => String(v)).join(',');
+ if (!seen.has(resultStr)) {
+ acc.push(result);
+ seen.add(resultStr);
+ }
+ return acc;
+ }, []);
+
+ results = results.reverse();
+
+ // for debugging
+ //Search.lastresults = results.slice(); // a copy
+ // console.info("search results:", Search.lastresults);
+
+ // print the results
+ _displayNextItem(results, results.length, searchTerms);
+ },
+
+ /**
+ * search for object names
+ */
+ performObjectSearch: (object, objectTerms) => {
+ const filenames = Search._index.filenames;
+ const docNames = Search._index.docnames;
+ const objects = Search._index.objects;
+ const objNames = Search._index.objnames;
+ const titles = Search._index.titles;
+
+ const results = [];
+
+ const objectSearchCallback = (prefix, match) => {
+ const name = match[4]
+ const fullname = (prefix ? prefix + "." : "") + name;
+ const fullnameLower = fullname.toLowerCase();
+ if (fullnameLower.indexOf(object) < 0) return;
+
+ let score = 0;
+ const parts = fullnameLower.split(".");
+
+ // check for different match types: exact matches of full name or
+ // "last name" (i.e. last dotted part)
+ if (fullnameLower === object || parts.slice(-1)[0] === object)
+ score += Scorer.objNameMatch;
+ else if (parts.slice(-1)[0].indexOf(object) > -1)
+ score += Scorer.objPartialMatch; // matches in last name
+
+ const objName = objNames[match[1]][2];
+ const title = titles[match[0]];
+
+ // If more than one term searched for, we require other words to be
+ // found in the name/title/description
+ const otherTerms = new Set(objectTerms);
+ otherTerms.delete(object);
+ if (otherTerms.size > 0) {
+ const haystack = `${prefix} ${name} ${objName} ${title}`.toLowerCase();
+ if (
+ [...otherTerms].some((otherTerm) => haystack.indexOf(otherTerm) < 0)
+ )
+ return;
+ }
+
+ let anchor = match[3];
+ if (anchor === "") anchor = fullname;
+ else if (anchor === "-") anchor = objNames[match[1]][1] + "-" + fullname;
+
+ const descr = objName + _(", in ") + title;
+
+ // add custom score for some objects according to scorer
+ if (Scorer.objPrio.hasOwnProperty(match[2]))
+ score += Scorer.objPrio[match[2]];
+ else score += Scorer.objPrioDefault;
+
+ results.push([
+ docNames[match[0]],
+ fullname,
+ "#" + anchor,
+ descr,
+ score,
+ filenames[match[0]],
+ ]);
+ };
+ Object.keys(objects).forEach((prefix) =>
+ objects[prefix].forEach((array) =>
+ objectSearchCallback(prefix, array)
+ )
+ );
+ return results;
+ },
+
+ /**
+ * search for full-text terms in the index
+ */
+ performTermsSearch: (searchTerms, excludedTerms) => {
+ // prepare search
+ const terms = Search._index.terms;
+ const titleTerms = Search._index.titleterms;
+ const filenames = Search._index.filenames;
+ const docNames = Search._index.docnames;
+ const titles = Search._index.titles;
+
+ const scoreMap = new Map();
+ const fileMap = new Map();
+
+ // perform the search on the required terms
+ searchTerms.forEach((word) => {
+ const files = [];
+ const arr = [
+ { files: terms[word], score: Scorer.term },
+ { files: titleTerms[word], score: Scorer.title },
+ ];
+ // add support for partial matches
+ if (word.length > 2) {
+ const escapedWord = _escapeRegExp(word);
+ Object.keys(terms).forEach((term) => {
+ if (term.match(escapedWord) && !terms[word])
+ arr.push({ files: terms[term], score: Scorer.partialTerm });
+ });
+ Object.keys(titleTerms).forEach((term) => {
+ if (term.match(escapedWord) && !titleTerms[word])
+ arr.push({ files: titleTerms[word], score: Scorer.partialTitle });
+ });
+ }
+
+ // no match but word was a required one
+ if (arr.every((record) => record.files === undefined)) return;
+
+ // found search word in contents
+ arr.forEach((record) => {
+ if (record.files === undefined) return;
+
+ let recordFiles = record.files;
+ if (recordFiles.length === undefined) recordFiles = [recordFiles];
+ files.push(...recordFiles);
+
+ // set score for the word in each file
+ recordFiles.forEach((file) => {
+ if (!scoreMap.has(file)) scoreMap.set(file, {});
+ scoreMap.get(file)[word] = record.score;
+ });
+ });
+
+ // create the mapping
+ files.forEach((file) => {
+ if (fileMap.has(file) && fileMap.get(file).indexOf(word) === -1)
+ fileMap.get(file).push(word);
+ else fileMap.set(file, [word]);
+ });
+ });
+
+ // now check if the files don't contain excluded terms
+ const results = [];
+ for (const [file, wordList] of fileMap) {
+ // check if all requirements are matched
+
+ // as search terms with length < 3 are discarded
+ const filteredTermCount = [...searchTerms].filter(
+ (term) => term.length > 2
+ ).length;
+ if (
+ wordList.length !== searchTerms.size &&
+ wordList.length !== filteredTermCount
+ )
+ continue;
+
+ // ensure that none of the excluded terms is in the search result
+ if (
+ [...excludedTerms].some(
+ (term) =>
+ terms[term] === file ||
+ titleTerms[term] === file ||
+ (terms[term] || []).includes(file) ||
+ (titleTerms[term] || []).includes(file)
+ )
+ )
+ break;
+
+ // select one (max) score for the file.
+ const score = Math.max(...wordList.map((w) => scoreMap.get(file)[w]));
+ // add result to the result list
+ results.push([
+ docNames[file],
+ titles[file],
+ "",
+ null,
+ score,
+ filenames[file],
+ ]);
+ }
+ return results;
+ },
+
+ /**
+ * helper function to return a node containing the
+ * search summary for a given text. keywords is a list
+ * of stemmed words.
+ */
+ makeSearchSummary: (htmlText, keywords) => {
+ const text = Search.htmlToText(htmlText);
+ if (text === "") return null;
+
+ const textLower = text.toLowerCase();
+ const actualStartPosition = [...keywords]
+ .map((k) => textLower.indexOf(k.toLowerCase()))
+ .filter((i) => i > -1)
+ .slice(-1)[0];
+ const startWithContext = Math.max(actualStartPosition - 120, 0);
+
+ const top = startWithContext === 0 ? "" : "...";
+ const tail = startWithContext + 240 < text.length ? "..." : "";
+
+ let summary = document.createElement("p");
+ summary.classList.add("context");
+ summary.textContent = top + text.substr(startWithContext, 240).trim() + tail;
+
+ return summary;
+ },
+};
+
+_ready(Search.init);
diff --git a/docs/0.6.4/docs/_static/sphinx_highlight.js b/docs/0.6.4/docs/_static/sphinx_highlight.js
new file mode 100644
index 0000000..aae669d
--- /dev/null
+++ b/docs/0.6.4/docs/_static/sphinx_highlight.js
@@ -0,0 +1,144 @@
+/* Highlighting utilities for Sphinx HTML documentation. */
+"use strict";
+
+const SPHINX_HIGHLIGHT_ENABLED = true
+
+/**
+ * highlight a given string on a node by wrapping it in
+ * span elements with the given class name.
+ */
+const _highlight = (node, addItems, text, className) => {
+ if (node.nodeType === Node.TEXT_NODE) {
+ const val = node.nodeValue;
+ const parent = node.parentNode;
+ const pos = val.toLowerCase().indexOf(text);
+ if (
+ pos >= 0 &&
+ !parent.classList.contains(className) &&
+ !parent.classList.contains("nohighlight")
+ ) {
+ let span;
+
+ const closestNode = parent.closest("body, svg, foreignObject");
+ const isInSVG = closestNode && closestNode.matches("svg");
+ if (isInSVG) {
+ span = document.createElementNS("http://www.w3.org/2000/svg", "tspan");
+ } else {
+ span = document.createElement("span");
+ span.classList.add(className);
+ }
+
+ span.appendChild(document.createTextNode(val.substr(pos, text.length)));
+ parent.insertBefore(
+ span,
+ parent.insertBefore(
+ document.createTextNode(val.substr(pos + text.length)),
+ node.nextSibling
+ )
+ );
+ node.nodeValue = val.substr(0, pos);
+
+ if (isInSVG) {
+ const rect = document.createElementNS(
+ "http://www.w3.org/2000/svg",
+ "rect"
+ );
+ const bbox = parent.getBBox();
+ rect.x.baseVal.value = bbox.x;
+ rect.y.baseVal.value = bbox.y;
+ rect.width.baseVal.value = bbox.width;
+ rect.height.baseVal.value = bbox.height;
+ rect.setAttribute("class", className);
+ addItems.push({ parent: parent, target: rect });
+ }
+ }
+ } else if (node.matches && !node.matches("button, select, textarea")) {
+ node.childNodes.forEach((el) => _highlight(el, addItems, text, className));
+ }
+};
+const _highlightText = (thisNode, text, className) => {
+ let addItems = [];
+ _highlight(thisNode, addItems, text, className);
+ addItems.forEach((obj) =>
+ obj.parent.insertAdjacentElement("beforebegin", obj.target)
+ );
+};
+
+/**
+ * Small JavaScript module for the documentation.
+ */
+const SphinxHighlight = {
+
+ /**
+ * highlight the search words provided in localstorage in the text
+ */
+ highlightSearchWords: () => {
+ if (!SPHINX_HIGHLIGHT_ENABLED) return; // bail if no highlight
+
+ // get and clear terms from localstorage
+ const url = new URL(window.location);
+ const highlight =
+ localStorage.getItem("sphinx_highlight_terms")
+ || url.searchParams.get("highlight")
+ || "";
+ localStorage.removeItem("sphinx_highlight_terms")
+ url.searchParams.delete("highlight");
+ window.history.replaceState({}, "", url);
+
+ // get individual terms from highlight string
+ const terms = highlight.toLowerCase().split(/\s+/).filter(x => x);
+ if (terms.length === 0) return; // nothing to do
+
+ // There should never be more than one element matching "div.body"
+ const divBody = document.querySelectorAll("div.body");
+ const body = divBody.length ? divBody[0] : document.querySelector("body");
+ window.setTimeout(() => {
+ terms.forEach((term) => _highlightText(body, term, "highlighted"));
+ }, 10);
+
+ const searchBox = document.getElementById("searchbox");
+ if (searchBox === null) return;
+ searchBox.appendChild(
+ document
+ .createRange()
+ .createContextualFragment(
+ '
+ ${renderLanguages(config)}
+ ${renderVersions(config)}
+ ${renderDownloads(config)}
+
+ + Hosted by Read the Docs + +
+ -
+
- On Read the Docs +
- + Project Home + +
- + Builds + +
- + Downloads + +
-
+
- Search +
- + + +
+ + Hosted by Read the Docs + +
' + + '' + + _("Hide Search Matches") + + "
" + ) + ); + }, + + /** + * helper function to hide the search marks again + */ + hideSearchWords: () => { + document + .querySelectorAll("#searchbox .highlight-link") + .forEach((el) => el.remove()); + document + .querySelectorAll("span.highlighted") + .forEach((el) => el.classList.remove("highlighted")); + localStorage.removeItem("sphinx_highlight_terms") + }, + + initEscapeListener: () => { + // only install a listener if it is really needed + if (!DOCUMENTATION_OPTIONS.ENABLE_SEARCH_SHORTCUTS) return; + + document.addEventListener("keydown", (event) => { + // bail for input elements + if (BLACKLISTED_KEY_CONTROL_ELEMENTS.has(document.activeElement.tagName)) return; + // bail with special keys + if (event.shiftKey || event.altKey || event.ctrlKey || event.metaKey) return; + if (DOCUMENTATION_OPTIONS.ENABLE_SEARCH_SHORTCUTS && (event.key === "Escape")) { + SphinxHighlight.hideSearchWords(); + event.preventDefault(); + } + }); + }, +}; + +_ready(SphinxHighlight.highlightSearchWords); +_ready(SphinxHighlight.initEscapeListener); diff --git a/docs/0.6.4/docs/agentlib_mpc.data_structures.html b/docs/0.6.4/docs/agentlib_mpc.data_structures.html new file mode 100644 index 0000000..9e39349 --- /dev/null +++ b/docs/0.6.4/docs/agentlib_mpc.data_structures.html @@ -0,0 +1,1241 @@ + + + + + + + + +
+
+
+
+
+
+
+
+
+
+
\ No newline at end of file
diff --git a/docs/0.6.4/docs/agentlib_mpc.html b/docs/0.6.4/docs/agentlib_mpc.html
new file mode 100644
index 0000000..27891a0
--- /dev/null
+++ b/docs/0.6.4/docs/agentlib_mpc.html
@@ -0,0 +1,543 @@
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+ agentlib_mpc.data_structures package
+Submodules
+agentlib_mpc.data_structures.admm_datatypes module
+-
+
- +class agentlib_mpc.data_structures.admm_datatypes.ADMMParameters(penalty_factor: float, prediction_horizon: int, time_step: float)[source] +
Bases:
+objectCollection of parameters which have to be shared across all agents in ADMM.
+-
+
- +penalty_factor: float +
-
+
- +prediction_horizon: int +
-
+
- +time_step: float +
-
+
- +class agentlib_mpc.data_structures.admm_datatypes.ADMMParticipation(source: Source, ready: bool = False, participating: bool = False)[source] +
Bases:
+objectHelper class to organize ADMM participants.
+-
+
- +participating: bool = False +
-
+
- +ready: bool = False +
-
+
- +source: Source +
-
+
- +class agentlib_mpc.data_structures.admm_datatypes.AgentDictEntry(name: str, status: ~agentlib_mpc.data_structures.coordinator_datatypes.AgentStatus = AgentStatus.pending, coup_vars: ~typing.List[str] = <factory>, exchange_vars: ~typing.List[str] = <factory>)[source] +
Bases:
+AgentDictEntryHolds participating coupling variables (consensus and exchange) of a single +agent in ADMM. Used in the coordinator.
+-
+
- +coup_vars: List[str] +
-
+
- +exchange_vars: List[str] +
-
+
- +class agentlib_mpc.data_structures.admm_datatypes.AgentToCoordinator(local_trajectory: Dict[str, numpy.ndarray], local_exchange_trajectory: Dict[str, numpy.ndarray])[source] +
Bases:
+StructuredValue-
+
- +local_exchange_trajectory: Dict[str, ndarray] +
-
+
- +local_trajectory: Dict[str, ndarray] +
-
+
- +class agentlib_mpc.data_structures.admm_datatypes.ConsensusVariable(local_trajectories: Dict[agentlib.core.datamodels.Source, list] = <factory>, mean_trajectory: list = <factory>, delta_mean: numpy.ndarray = <factory>, primal_residual: numpy.ndarray = <factory>, multipliers: Dict[agentlib.core.datamodels.Source, list] = <factory>)[source] +
Bases:
+ + +CouplingVariable-
+
- +multipliers: Dict[Source, list] +
-
+
- +class agentlib_mpc.data_structures.admm_datatypes.CoordinatorToAgent(target: str, mean_trajectory: Dict[str, list], multiplier: Dict[str, list], mean_diff_trajectory: Dict[str, list], exchange_multiplier: Dict[str, list], penalty_parameter: float)[source] +
Bases:
+StructuredValue-
+
- +exchange_multiplier: Dict[str, list] +
-
+
- +mean_diff_trajectory: Dict[str, list] +
-
+
- +mean_trajectory: Dict[str, list] +
-
+
- +multiplier: Dict[str, list] +
-
+
- +penalty_parameter: float +
-
+
- +target: str +
-
+
- +class agentlib_mpc.data_structures.admm_datatypes.CouplingEntry(name: str)[source] +
Bases:
+objectHolds naming conventions for different optimizatin variables / parameters +associated with a coupling variable in consensus ADMM.
+ + +-
+
- +property lagged +
-
+
- +property local +
-
+
- +property mean +
-
+
- +property multiplier +
-
+
- +name: str +
-
+
- +class agentlib_mpc.data_structures.admm_datatypes.CouplingVariable(local_trajectories: ~typing.Dict[~agentlib.core.datamodels.Source, list] = <factory>, mean_trajectory: list = <factory>, delta_mean: ~numpy.ndarray = <factory>, primal_residual: ~numpy.ndarray = <factory>)[source] +
Bases:
+objectHolds information about a phy
+-
+
- +delta_mean: ndarray +
-
+
- +flat_locals(sources: Iterable[Source] = None) list[float][source] +
Returns the flattened array of all local variables and their multipliers.
+-
+
- Parameters: +
sources – list of sources that should be included in the update. +By default, all are included.
+
+- Returns: +
flat lists of local variables and multipliers (locals, multipliers)
+
+
-
+
- +get_residual(rho: float) Tuple[ndarray, ndarray][source] +
Returns the primal and dual residual of the last iteration as a tuple +of flattened Arrays. +:param rho:
+-
+
- Returns: +
(primal residual, dual residual)
+
+
-
+
- +local_trajectories: Dict[Source, list] +
-
+
- +mean_trajectory: list +
-
+
- +property participants +
Returns all agent sources that are registered to this coupling.
+
-
+
- +primal_residual: ndarray +
-
+
- +class agentlib_mpc.data_structures.admm_datatypes.ExchangeEntry(name: str)[source] +
Bases:
+objectHolds naming conventions for different optimizatin variables / parameters +associated with a coupling variable in exchange ADMM.
+ + +-
+
- +property lagged +
-
+
- +property local +
-
+
- +property mean_diff +
-
+
- +property multiplier +
-
+
- +name: str +
-
+
- +class agentlib_mpc.data_structures.admm_datatypes.ExchangeVariable(local_trajectories: Dict[agentlib.core.datamodels.Source, list] = <factory>, mean_trajectory: list = <factory>, delta_mean: numpy.ndarray = <factory>, primal_residual: numpy.ndarray = <factory>, diff_trajectories: Dict[agentlib.core.datamodels.Source, list[float]] = <factory>, multiplier: list[float] = <factory>)[source] +
Bases:
+CouplingVariable-
+
- +diff_trajectories: Dict[Source, list[float]] +
-
+
- +multiplier: list[float] +
-
+
- +class agentlib_mpc.data_structures.admm_datatypes.StructuredValue[source] +
Bases:
+objectBase Class to specify the structure of an AgentVariable Value. It will +be efficiently sent and deserialized.
+ + + + +
-
+
- +class agentlib_mpc.data_structures.admm_datatypes.VariableReference(states: ~typing.List[str] = <factory>, controls: ~typing.List[str] = <factory>, inputs: ~typing.List[str] = <factory>, parameters: ~typing.List[str] = <factory>, outputs: ~typing.List[str] = <factory>, couplings: list[agentlib_mpc.data_structures.admm_datatypes.CouplingEntry] = <factory>, exchange: list[agentlib_mpc.data_structures.admm_datatypes.ExchangeEntry] = <factory>)[source] +
Bases:
+FullVariableReferenceHolds info about all variables of an MPC and their role in the optimization +problem.
+ + +-
+
- +couplings: list[agentlib_mpc.data_structures.admm_datatypes.CouplingEntry] +
-
+
- +exchange: list[agentlib_mpc.data_structures.admm_datatypes.ExchangeEntry] +
agentlib_mpc.data_structures.casadi_utils module
+Stores all sorts of Dataclasses, Enums or Factories to help with the +CasadiBackend.
+-
+
- +pydantic model agentlib_mpc.data_structures.casadi_utils.CasadiDiscretizationOptions[source] +
Bases:
+DiscretizationOptions-
+
- Config: +
-
+
extra: str = forbid
+
+- Fields: +
- + +
-
+
- +field collocation_method: CollocationMethod = CollocationMethod.legendre +
-
+
- +field collocation_order: int = 3 +
-
+
- Constraints: +
-
+
ge = 1
+le = 9
+
+
-
+
- +field integrator: Integrators = Integrators.cvodes +
-
+
- +field method: DiscretizationMethod = DiscretizationMethod.collocation +
-
+
- +class agentlib_mpc.data_structures.casadi_utils.CollocationMethod(value, names=None, *, module=None, qualname=None, type=None, start=1, boundary=None)[source] +
Bases:
+str,Enum-
+
- +legendre = 'legendre' +
-
+
- +radau = 'radau' +
-
+
- +class agentlib_mpc.data_structures.casadi_utils.Constraint(function: casadi.casadi.MX, lb: casadi.casadi.MX, ub: casadi.casadi.MX)[source] +
Bases:
+object-
+
- +function: MX +
-
+
- +lb: MX +
-
+
- +ub: MX +
-
+
- +class agentlib_mpc.data_structures.casadi_utils.DiscretizationMethod(value, names=None, *, module=None, qualname=None, type=None, start=1, boundary=None)[source] +
Bases:
+str,Enum-
+
- +collocation = 'collocation' +
-
+
- +multiple_shooting = 'multiple_shooting' +
-
+
- +class agentlib_mpc.data_structures.casadi_utils.Integrators(value, names=None, *, module=None, qualname=None, type=None, start=1, boundary=None)[source] +
Bases:
+str,Enum-
+
- +cvodes = 'cvodes' +
-
+
- +euler = 'euler' +
-
+
- +rk = 'rk' +
-
+
- +class agentlib_mpc.data_structures.casadi_utils.ModelConstraint(lb, function, ub)[source] +
Bases:
+NamedTuple-
+
- +function: MX +
Alias for field number 1
+
-
+
- +lb: MX | SX | Sparsity | DM | float | int +
Alias for field number 0
+
-
+
- +ub: MX | SX | Sparsity | DM | float | int +
Alias for field number 2
+
-
+
- +class agentlib_mpc.data_structures.casadi_utils.OptParMXContainer(var: ~typing.List[~casadi.casadi.MX] = <factory>, grid: ~typing.List[float] = <factory>)[source] +
Bases:
+objectStores the necessary MX variables created during discretization for +OptimizationParameters.
+-
+
- +grid: List[float] +
-
+
- +var: List[MX] +
-
+
- +class agentlib_mpc.data_structures.casadi_utils.OptVarMXContainer(var: ~typing.List[~casadi.casadi.MX] = <factory>, grid: ~typing.List[float] = <factory>, lb: ~typing.List[~casadi.casadi.MX] = <factory>, ub: ~typing.List[~casadi.casadi.MX] = <factory>, guess: ~typing.List[~casadi.casadi.MX] = <factory>, opt: ~casadi.casadi.DM = None)[source] +
Bases:
+OptParMXContainerStores the necessary MX variables created during discretization for +OptimizationVariables.
+-
+
- +guess: List[MX] +
-
+
- +lb: List[MX] +
-
+
- +opt: DM = None +
-
+
- +ub: List[MX] +
-
+
- +class agentlib_mpc.data_structures.casadi_utils.SolverFactory(do_jit: bool, bat_file: ~pathlib.Path = None, name: str = None, options: ~agentlib_mpc.data_structures.casadi_utils.SolverOptions = <factory>, logger: ~logging.Logger = None)[source] +
Bases:
+objectCreates a solver given an NLP and an options construct.
+-
+
- +bat_file: Path = None +
-
+
- +create_solver(nlp: dict | str, discrete: List[bool] = None, equalities: list[bool] = None) Function[source] +
-
+
- +do_jit: bool +
-
+
- +logger: Logger = None +
-
+
- +make_casadi_nlp(problem: dict | str, solver: str, opts: dict, problem_type: Literal['nlp', 'qp'] = 'nlp')[source] +
-
+
- +name: str = None +
-
+
- +options: SolverOptions +
-
+
- +pydantic model agentlib_mpc.data_structures.casadi_utils.SolverOptions[source] +
Bases:
+BaseModel-
+
- Config: +
-
+
extra: str = forbid
+
+- Fields: +
- + +
-
+
- +field options: dict = {} +
-
+
- +class agentlib_mpc.data_structures.casadi_utils.Solvers(value, names=None, *, module=None, qualname=None, type=None, start=1, boundary=None)[source] +
Bases:
+str,Enum-
+
- +bonmin = 'bonmin' +
-
+
- +fatrop = 'fatrop' +
-
+
- +gurobi = 'gurobi' +
-
+
- +ipopt = 'ipopt' +
-
+
- +osqp = 'osqp' +
-
+
- +proxqp = 'proxqp' +
-
+
- +qpoases = 'qpoases' +
-
+
- +sqpmethod = 'sqpmethod' +
-
+
- +agentlib_mpc.data_structures.casadi_utils.compile_solver(bat_file: Path, name: str, optimizer: Function) str[source] +
Code-generates an ipopt solver and compiles it. +Currently, only works on Windows! Requires a batch file that knows +how to setup Visual Studio command line and compiles the source code.
+-
+
- Returns: +
The Path to the .dll file for the compiled solver.
+
+- Raises: +
-
+
TypeError –
+FileNotFoundError –
+RuntimeError –
+
+
agentlib_mpc.data_structures.coordinator_datatypes module
+-
+
- +class agentlib_mpc.data_structures.coordinator_datatypes.AgentDictEntry(name: str, status: AgentStatus = AgentStatus.pending)[source] +
Bases:
+objectDataclass holding the status of a participating agent in DMPC.
+-
+
- +name: str +
-
+
- +optimization_data = OptimizationData(x=array([], dtype=float64), u=array([], dtype=float64)) +
-
+
- +status: AgentStatus = 'pending' +
-
+
- +class agentlib_mpc.data_structures.coordinator_datatypes.AgentStatus(value, names=None, *, module=None, qualname=None, type=None, start=1, boundary=None)[source] +
Bases:
+str,EnumEnum used within a DMPC-Coordinator to keep track of the statuses of its +participating agents.
+-
+
- +busy = 'busy' +
-
+
- +pending = 'pending' +
-
+
- +ready = 'ready' +
-
+
- +standby = 'standby' +
-
+
- +class agentlib_mpc.data_structures.coordinator_datatypes.CoordinatorStatus(value, names=None, *, module=None, qualname=None, type=None, start=1, boundary=None)[source] +
Bases:
+str,EnumEnum used to keep track of the status of a DMPC-Coordinator.
+-
+
- +init_iterations = 'init_iterations' +
-
+
- +optimization = 'optimization' +
-
+
- +sleeping = 'sleeping' +
-
+
- +updating = 'updating' +
-
+
- +class agentlib_mpc.data_structures.coordinator_datatypes.OptimizationData(x: ~numpy.array = <factory>, u: ~numpy.array = <factory>)[source] +
Bases:
+objectContains specific variables (or time series) of the agent
+ + + + +-
+
- +u: array +
-
+
- +x: array +
-
+
- +class agentlib_mpc.data_structures.coordinator_datatypes.RegistrationMessage(status: AgentStatus = None, opts: dict = None, agent_id: str = None, coupling: list = None)[source] +
Bases:
+objectDataclass structuring the communication during registration between a +participating agent and the coordinator in DMPC.
+-
+
- +agent_id: str = None +
-
+
- +coupling: list = None +
-
+
- +opts: dict = None +
-
+
- +status: AgentStatus = None +
agentlib_mpc.data_structures.mpc_datamodels module
+-
+
- +class agentlib_mpc.data_structures.mpc_datamodels.BaseVariableReference[source] +
Bases:
+ + + + +object
-
+
- +pydantic model agentlib_mpc.data_structures.mpc_datamodels.DiscretizationOptions[source] +
Bases:
+BaseModelClass defining the options to discretize an MPC. Can be extended for different +optimization implementations.
+-
+
- Config: +
-
+
extra: str = allow
+
+- Fields: +
- + +
-
+
- +field prediction_horizon: int = 5 +
Prediction horizon of the MPC.
+-
+
- Constraints: +
-
+
ge = 0
+
+
-
+
- +field time_step: float = 60 +
Time step of the MPC.
+-
+
- Constraints: +
-
+
ge = 0
+
+
-
+
- +class agentlib_mpc.data_structures.mpc_datamodels.FullVariableReference(states: List[str] = <factory>, controls: List[str] = <factory>, inputs: List[str] = <factory>, parameters: List[str] = <factory>, outputs: List[str] = <factory>)[source] +
Bases:
+VariableReference-
+
- +property r_del_u: List[str] +
-
+
- +class agentlib_mpc.data_structures.mpc_datamodels.InitStatus(value, names=None, *, module=None, qualname=None, type=None, start=1, boundary=None)[source] +
Bases:
+str,EnumKeep track of the readyness status of the MPC.
+-
+
- +during_update = '2' +
-
+
- +pre_module_init = '1' +
-
+
- +ready = '3' +
-
+
- +class agentlib_mpc.data_structures.mpc_datamodels.MHEVariableReference(states: List[str] = <factory>, measured_states: List[str] = <factory>, weights_states: List[str] = <factory>, estimated_inputs: List[str] = <factory>, estimated_parameters: List[str] = <factory>, known_inputs: List[str] = <factory>, known_parameters: List[str] = <factory>, outputs: List[str] = <factory>)[source] +
Bases:
+BaseVariableReference-
+
- +all_variables() Iterable[str][source] +
Returns a list of all variables registered in the var_ref which the MHE can +get from the config with get()
+
-
+
- +estimated_inputs: List[str] +
-
+
- +estimated_parameters: List[str] +
-
+
- +known_inputs: List[str] +
-
+
- +known_parameters: List[str] +
-
+
- +measured_states: List[str] +
-
+
- +outputs: List[str] +
-
+
- +states: List[str] +
-
+
- +weights_states: List[str] +
-
+
- +class agentlib_mpc.data_structures.mpc_datamodels.MINLPVariableReference(states: List[str] = <factory>, controls: List[str] = <factory>, inputs: List[str] = <factory>, parameters: List[str] = <factory>, outputs: List[str] = <factory>, binary_controls: List[str] = <factory>)[source] +
Bases:
+VariableReference-
+
- +binary_controls: List[str] +
-
+
- +class agentlib_mpc.data_structures.mpc_datamodels.MPCVariable(*, name: str, type: str | None = None, timestamp: float | None = None, unit: str = 'Not defined', description: str = 'Not defined', ub=inf, lb=-inf, clip: bool = False, allowed_values: List[Any] = [], value: Any = None, alias: str = None, source: dict | Source | str = Source(agent_id=None, module_id=None), shared: bool | None = None, rdf_class: str | None = None, interpolation_method: InterpolationMethods = InterpolationMethods.linear)[source] +
Bases:
+AgentVariableAgentVariable used to define input variables of MPC.
+-
+
- +interpolation_method: InterpolationMethods +
-
+
- +class agentlib_mpc.data_structures.mpc_datamodels.Results(*args, **kwargs)[source] +
Bases:
+Protocol-
+
- +df: DataFrame +
-
+
- +class agentlib_mpc.data_structures.mpc_datamodels.VariableReference(states: List[str] = <factory>, controls: List[str] = <factory>, inputs: List[str] = <factory>, parameters: List[str] = <factory>, outputs: List[str] = <factory>)[source] +
Bases:
+BaseVariableReference-
+
- +controls: List[str] +
-
+
- +inputs: List[str] +
-
+
- +outputs: List[str] +
-
+
- +parameters: List[str] +
-
+
- +states: List[str] +
-
+
- +agentlib_mpc.data_structures.mpc_datamodels.cia_relaxed_results_path(path: Path | str) Path[source] +
Module contents
+
+
+
+
+
+
+
+
+
+
+
\ No newline at end of file
diff --git a/docs/0.6.4/docs/agentlib_mpc.models.html b/docs/0.6.4/docs/agentlib_mpc.models.html
new file mode 100644
index 0000000..683ac6a
--- /dev/null
+++ b/docs/0.6.4/docs/agentlib_mpc.models.html
@@ -0,0 +1,453 @@
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+ agentlib_mpc package
+Subpackages
+
+
+-
+
- agentlib_mpc.data_structures package
-
+
- Submodules +
- agentlib_mpc.data_structures.admm_datatypes module + +
- agentlib_mpc.data_structures.casadi_utils module + +
- agentlib_mpc.data_structures.coordinator_datatypes module
-
+
AgentDictEntry+
+AgentStatus+
+CoordinatorStatus+
+OptimizationData+
+RegistrationMessage+
+
+ - agentlib_mpc.data_structures.mpc_datamodels module
-
+
BaseVariableReference+
+DiscretizationOptions+
+FullVariableReference+
+InitStatus+
+MHEVariableReference-
+
MHEVariableReference.all_variables()
+MHEVariableReference.estimated_inputs
+MHEVariableReference.estimated_parameters
+MHEVariableReference.known_inputs
+MHEVariableReference.known_parameters
+MHEVariableReference.measured_states
+MHEVariableReference.outputs
+MHEVariableReference.states
+MHEVariableReference.weights_states
+
+MINLPVariableReference+
+MPCVariable+
+Results-
+
Results.df
+
+VariableReference+
+cia_relaxed_results_path()
+r_del_u_convention()
+stats_path()
+
+ - Module contents +
+ - agentlib_mpc.models package
-
+
- Submodules +
- agentlib_mpc.models.ann_model module +
- agentlib_mpc.models.casadi_model module
-
+
CasadiInput+
+CasadiModel-
+
CasadiModel.auxiliaries
+CasadiModel.differentials
+CasadiModel.do_step()
+CasadiModel.get()
+CasadiModel.get_constraints()
+CasadiModel.get_differential_values()
+CasadiModel.get_input_values()
+CasadiModel.initialize()
+CasadiModel.inputs
+CasadiModel.output_equations
+CasadiModel.outputs
+CasadiModel.parameters
+CasadiModel.set_differential_values()
+CasadiModel.set_output_values()
+CasadiModel.setup_system()
+CasadiModel.states
+
+CasadiModelConfig+
+CasadiOutput+
+CasadiParameter
+CasadiState+
+CasadiVariable+
+get_symbolic()
+
+ - agentlib_mpc.models.casadi_model_ann module +
- agentlib_mpc.models.casadi_neural_network module +
- agentlib_mpc.models.serialized_ann module +
- Module contents +
+ - agentlib_mpc.optimization_backends package
-
+
- Subpackages
-
+
- agentlib_mpc.optimization_backends.casadi_ package
-
+
- Subpackages +
- Submodules +
- agentlib_mpc.optimization_backends.casadi_.admm module +
- agentlib_mpc.optimization_backends.casadi_.basic module +
- agentlib_mpc.optimization_backends.casadi_.casadi_admm_nn module +
- agentlib_mpc.optimization_backends.casadi_.casadi_nn module +
- agentlib_mpc.optimization_backends.casadi_.full module +
- agentlib_mpc.optimization_backends.casadi_.minlp module +
- Module contents +
+
+ - agentlib_mpc.optimization_backends.casadi_ package
- Submodules +
- agentlib_mpc.optimization_backends.backend module
-
+
ADMMBackend+
+BackendConfig+
+OptimizationBackend-
+
OptimizationBackend.config_type
+OptimizationBackend.get_lags_per_variable()
+OptimizationBackend.model_from_config()
+OptimizationBackend.mpc_backend_parameters
+OptimizationBackend.register_logger()
+OptimizationBackend.results_file_exists()
+OptimizationBackend.setup_optimization()
+OptimizationBackend.solve()
+OptimizationBackend.update_discretization_options()
+OptimizationBackend.update_model_variables()
+
+
+ - Module contents + +
+ - Subpackages
- agentlib_mpc.utils package + +
Module contents
+Plugin for the AgentLib which contains modules for MPC, distributed MPC with ADMM +and data-driven modeling.
+
+
+
+
+
+
+
+
+
+
+
\ No newline at end of file
diff --git a/docs/0.6.4/docs/agentlib_mpc.modules.dmpc.admm.html b/docs/0.6.4/docs/agentlib_mpc.modules.dmpc.admm.html
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+++ b/docs/0.6.4/docs/agentlib_mpc.modules.dmpc.admm.html
@@ -0,0 +1,887 @@
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+ agentlib_mpc.models package
+Submodules
+agentlib_mpc.models.ann_model module
+agentlib_mpc.models.casadi_model module
+Holds the classes for CasADi variables and the CasADi model.
+-
+
- +class agentlib_mpc.models.casadi_model.CasadiInput(*, name: str, timestamp: float | None = None, unit: str = 'Not defined', description: str = 'Not defined', ub=inf, lb=-inf, clip: bool = False, allowed_values: List[Any] = [], value: Any = None, causality: Causality = None, variability: Variability = None, type: str | None = 'float', sim_time: float = 0.0, _sym: MX | SX | DM | Sparsity = None)[source] +
Bases:
+CasadiVariableClass that stores various attributes of control variables.
+-
+
- +property alg: MX | SX | DM | Sparsity +
-
+
- +class agentlib_mpc.models.casadi_model.CasadiModel(**kwargs)[source] +
Bases:
+ModelBase Class for CasADi models. To implement your own model, inherit +from this class, specify the variables (inputs, outputs, states, +parameters and override the setup_system() method.
+-
+
- +property auxiliaries: List[CasadiState] +
List of all CasadiStates without an associated equation. Common +uses for this are slack variables that appear in cost functions and +constraints of optimization models.
+
-
+
- +property differentials: List[CasadiState] +
List of all CasadiStates with an associated differential equation.
+
-
+
- +do_step(*, t_start, t_sample=None)[source] +
Performing one simulation step +:param t_start: start time for integration +:param t_sample: increment of solver integration
+Returns:
+
-
+
- +get(name: str) CasadiVariable[source] +
Get any variable from using name:
+-
+
- Parameters: +
name (str) – The item to get from config by name of Variable. +Hence, item=ModelVariable.name
+
+- Returns: +
The matching variable
+
+- Return type: +
var (ModelVariable)
+
+- Raises: +
AttributeError – If the item was not found in the variables of the + module.
+
+
-
+
- +get_constraints() List[ModelConstraint][source] +
List of constraints of the form (lower, function, upper).
+
-
+
- +initialize(**ignored)[source] +
Initializes Casadi model. Creates the integrator to be used in +do_step(). The integrator takes the current state and input values as +input and returns the state values at the end of the interval and the +value of the cost function integrated over the interval.
+
-
+
- +property inputs: list[agentlib_mpc.models.casadi_model.CasadiInput] +
Get all model inputs as a list
+
-
+
- +property output_equations: List[MX | SX | DM | Sparsity] +
List of algebraic equations RHS in the form +0 = z - g(x, z, p, … )
+
-
+
- +property outputs: list[agentlib_mpc.models.casadi_model.CasadiOutput] +
Get all model outputs as a list
+
-
+
- +property parameters: list[agentlib_mpc.models.casadi_model.CasadiParameter] +
Get all model parameters as a list
+
-
+
- +set_differential_values(values: List | ndarray)[source] +
Sets the values for all differential variables. Provided values list MUST +match the order in which differentials are saved, there is no check.
+
-
+
- +set_output_values(values: List | ndarray)[source] +
Sets the values for all outputs. Provided values list MUST match the order +in which outputs are saved, there is no check.
+
-
+
- +property states: list[agentlib_mpc.models.casadi_model.CasadiState] +
Get all model states as a list
+
-
+
- +pydantic model agentlib_mpc.models.casadi_model.CasadiModelConfig[source] +
Bases:
+ModelConfig-
+
- Config: +
-
+
validate_assignment: bool = True
+arbitrary_types_allowed: bool = True
+extra: str = forbid
+
+- Fields: +
-
+
cost_function (casadi.casadi.MX | casadi.casadi.SX | casadi.casadi.DM | casadi.casadi.Sparsity)
+- +
outputs (List[agentlib_mpc.models.casadi_model.CasadiOutput])
+parameters (List[agentlib_mpc.models.casadi_model.CasadiParameter])
+- +
system (casadi.casadi.MX | casadi.casadi.SX | casadi.casadi.DM | casadi.casadi.Sparsity)
+
+- Validators: +
-
+
include_default_model_variables»inputs
+include_default_model_variables»outputs
+include_default_model_variables»parameters
+include_default_model_variables»states
+
+
-
+
- +field cost_function: MX | SX | DM | Sparsity = None +
-
+
- +field inputs: List[CasadiInput] = [] +
-
+
- Validated by: +
-
+
include_default_model_variables
+
+
-
+
- +field outputs: List[CasadiOutput] = [] +
-
+
- Validated by: +
-
+
include_default_model_variables
+
+
-
+
- +field parameters: List[CasadiParameter] = [] +
-
+
- Validated by: +
-
+
include_default_model_variables
+
+
-
+
- +field states: List[CasadiState] = [] +
-
+
- Validated by: +
-
+
include_default_model_variables
+
+
-
+
- +field system: MX | SX | DM | Sparsity = None +
-
+
- +model_post_init(context: Any, /) None +
We need to both initialize private attributes and call the user-defined model_post_init +method.
+
-
+
- +class agentlib_mpc.models.casadi_model.CasadiOutput(*, name: str, timestamp: float | None = None, unit: str = 'Not defined', description: str = 'Not defined', ub=inf, lb=-inf, clip: bool = False, allowed_values: List[Any] = [], value: Any = None, causality: Causality = None, variability: Variability = None, type: str | None = 'float', sim_time: float = 0.0, _sym: MX | SX | DM | Sparsity = None, _alg: MX | SX | DM | Sparsity = None)[source] +
Bases:
+CasadiVariableClass that stores various attributes of control variables.
+-
+
- +property alg: MX | SX | DM | Sparsity +
-
+
- +class agentlib_mpc.models.casadi_model.CasadiParameter(*, name: str, timestamp: float | None = None, unit: str = 'Not defined', description: str = 'Not defined', ub=inf, lb=-inf, clip: bool = False, allowed_values: List[Any] = [], value: Any = None, causality: Causality = None, variability: Variability = None, type: str | None = 'float', sim_time: float = 0.0, _sym: MX | SX | DM | Sparsity = None)[source] +
Bases:
+CasadiVariableClass that stores various attributes of parameters.
+
-
+
- +class agentlib_mpc.models.casadi_model.CasadiState(*, name: str, timestamp: float | None = None, unit: str = 'Not defined', description: str = 'Not defined', ub=inf, lb=-inf, clip: bool = False, allowed_values: List[Any] = [], value: Any = None, causality: Causality = None, variability: Variability = None, type: str | None = 'float', sim_time: float = 0.0, _sym: MX | SX | DM | Sparsity = None, _ode: MX | SX | DM | Sparsity | None = None)[source] +
Bases:
+CasadiVariableClass that stores various attributes of CasADi differential variables.
+-
+
- +property alg: MX | SX | DM | Sparsity +
-
+
- +json(indent: int = 2, **kwargs)[source] +
Serializes the Variable in json format and returns a string
+
-
+
- +property ode: MX | SX | DM | Sparsity +
-
+
- +class agentlib_mpc.models.casadi_model.CasadiVariable(*, name: str, timestamp: float | None = None, unit: str = 'Not defined', description: str = 'Not defined', ub=inf, lb=-inf, clip: bool = False, allowed_values: List[Any] = [], value: Any = None, causality: Causality = None, variability: Variability = None, type: str | None = 'float', sim_time: float = 0.0, _sym: MX | SX | DM | Sparsity = None)[source] +
Bases:
+ModelVariableBase Class for variables used in Casadi Models for simulation and +optimization. Implements the standard arithmetic operations, +so CasadiVariables can be used in equations. +.. attribute:: sym
++
+The symbolic CasADi variable used to define ode’s and +optimization problems.
+-
+
- +create_sym() MX[source] +
Ensures a symbolic MX variable is created with each CasadiVariable +instance, and that its dimensions are consistent.
+
-
+
- +property sym: MX +
agentlib_mpc.models.casadi_model_ann module
+agentlib_mpc.models.casadi_neural_network module
+agentlib_mpc.models.serialized_ann module
+Module contents
+Package containing models for agentlib_mpc.
+
+
+
+
+
+
+
+
+
+
+
\ No newline at end of file
diff --git a/docs/0.6.4/docs/agentlib_mpc.modules.dmpc.html b/docs/0.6.4/docs/agentlib_mpc.modules.dmpc.html
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+ agentlib_mpc.modules.dmpc.admm package
+Submodules
+agentlib_mpc.modules.dmpc.admm.admm module
+Holds functionality for ADMM modules.
+-
+
- +class agentlib_mpc.modules.dmpc.admm.admm.ADMM(config: dict, agent)[source] +
Bases:
+DistributedMPCThis class represents a module participating in a fully decentralized +Consensus-ADMM optimization for distributed MPC. +Agents autonomously send the values of their coupling variables, register +other participants and perform update steps.
+ + +-
+
- +all_coupling_statuses() Iterable[ADMMParticipation][source] +
Gives and iterator of all ADMMParticipation that are registered.
+
-
+
- +assert_mpc_variables_are_in_model()[source] +
Checks whether all variables of var_ref are contained in the model. +Returns names of model variables not contained in the var_ref, +sorted by keys: ‘states’, ‘inputs’, ‘outputs’, ‘parameters’.
+
-
+
- +collect_couplings_for_optimization()[source] +
Collects updated AgentVariables only of the coupling variables.
+
-
+
- +collect_variables_for_optimization(var_ref: VariableReference = None) dict[str, agentlib.core.datamodels.AgentVariable][source] +
Gets all variables noted in the var ref and puts them in a flat +dictionary.
+
-
+
- +property cons_and_exchange: List[ExchangeEntry | CouplingEntry] +
-
+
- +deregister_all_participants()[source] +
Sets the participating status of all participating agents to +False.
+
-
+
- +get_participants_values(coupling_alias: str) List[Series][source] +
Get the values of all agents for a coupling variable.
+
-
+
- +get_results() DataFrame | None[source] +
Read the results that were saved from the optimization backend and +returns them as Dataframe.
+-
+
- Returns: +
(results, stats) tuple of Dataframes.
+
+
-
+
- +participant_callback(variable: AgentVariable)[source] +
Puts received variables in the correct queue, depending on +registration status of this agent.
+
-
+
- +property penalty_factor_var: MPCVariable +
-
+
- +process()[source] +
This abstract method must be implemented in order to sync the module +with the other processes of the agent and the whole MAS.
+
-
+
- +receive_participant(variable: AgentVariable)[source] +
Set the participation to true for the given coupling input.
+
-
+
- +property registered_participants: Dict[str, Dict[str, ADMMParticipation]] +
Dictionary containing all other agents this agent shares variables with. +Ordered in a two-layer form, with variables at the first layer and +agents at the second layer. Contains ADMMParticipation objects at +the base layer.
+Examples
+self.registered_participants = +{‘coupling_var_1’: {‘src_of_agent1’: status_1,
++
++
+‘src_of_agent2’: status_2, +‘src_of_agent3’: status_3}
+-
+
- ‘coupling_var_1’: {‘src_of_agent3’: status_a,
‘src_of_agent2’: status_b, +‘src_of_agent4’: status_c}
+
+
} +here, <status> refers to an ADMMParticipation object.
+
-
+
- +reset_participants_ready()[source] +
Sets the ready status of all participating agents to False.
+
-
+
- +send_coupling_values(solution: Results)[source] +
Sets the coupling outputs to the data_broker, which automatically sends them.
+-
+
- Parameters: +
solution – Output dictionary from optimization_backend.solve().
+
+
-
+
- +send_coupling_variable(name: str, value: int | float | list[Union[int, float]] | Series | ndarray)[source] +
Sends an admm coupling variable through the data_broker and sets its +value locally
+
-
+
- +update_lambda()[source] +
Performs the update of the lagrange multipliers. +lambda^k+1 := lambda^k - rho*(z-x_i)
+
-
+
- +var_ref: VariableReference +
-
+
- +pydantic model agentlib_mpc.modules.dmpc.admm.admm.ADMMConfig[source] +
Bases:
+DistributedMPCConfig-
+
- Config: +
-
+
arbitrary_types_allowed: bool = True
+validate_assignment: bool = True
+extra: str = forbid
+frozen: bool = True
+
+- Fields: +
- + +
- Validators: +
- + +
-
+
- +field couplings: List[MPCVariable] = [] +
-
+
- Validated by: +
- + +
-
+
- +field exchange: List[MPCVariable] = [] +
-
+
- Validated by: +
- + +
-
+
- +field iteration_timeout: float = 20 +
Maximum computation + waiting time for one iteration.
+-
+
- Constraints: +
-
+
ge = 0
+
+
-
+
- +field max_iterations: float = 20 +
Maximum number of ADMM iterations before termination of control step.
+-
+
- Constraints: +
-
+
ge = 0
+
+
-
+
- +field penalty_factor: float = 10 +
Penalty factor of the ADMM algorithm. Should be equal for all agents.
+-
+
- Constraints: +
-
+
ge = 0
+
+
-
+
- +field registration_period: float = 2 +
Time spent on registration before each optimization
+-
+
- Constraints: +
-
+
ge = 0
+
+
-
+
- +validator check_prefixes_of_variables » exchange, controls, states, couplings, outputs, inputs, parameters[source] +
Ensures no user provided variable is named with the reserved ADMM prefix.
+
-
+
- +model_post_init(context: Any, /) None +
We need to both initialize private attributes and call the user-defined model_post_init +method.
+
-
+
- +class agentlib_mpc.modules.dmpc.admm.admm.ADMMParticipation(variable)[source] +
Bases:
+objectHolds data for the status of a shared variable of another system.
+ + + + +
-
+
- +class agentlib_mpc.modules.dmpc.admm.admm.LocalADMM(config: dict, agent)[source] +
Bases:
+ADMM-
+
- +process()[source] +
This abstract method must be implemented in order to sync the module +with the other processes of the agent and the whole MAS.
+
-
+
- +property registration_delay: float +
Timeout value used to wait one on registration. Waits in real time +(time.sleep)
+
-
+
- +property sync_delay: float +
Timeout value used to sync local admm processes. Should be very +small.
+
-
+
- +pydantic model agentlib_mpc.modules.dmpc.admm.admm.LocalADMMConfig[source] +
Bases:
+ADMMConfig-
+
- Config: +
-
+
arbitrary_types_allowed: bool = True
+validate_assignment: bool = True
+extra: str = forbid
+frozen: bool = True
+
+- Fields: +
- + +
- Validators: +
- +
-
+
- +field registration_delay: float = 0.1 +
-
+
- +field sync_delay: float = 0.001 +
-
+
- +model_post_init(context: Any, /) None +
We need to both initialize private attributes and call the user-defined model_post_init +method.
+
-
+
- +class agentlib_mpc.modules.dmpc.admm.admm.ModuleStatus(value, names=None, *, module=None, qualname=None, type=None, start=1, boundary=None)[source] +
Bases:
+Enum-
+
- +at_registration = 3 +
-
+
- +not_started = 1 +
-
+
- +optimizing = 4 +
-
+
- +sleeping = 7 +
-
+
- +syncing = 2 +
-
+
- +updating = 5 +
-
+
- +waiting_for_other_agents = 6 +
agentlib_mpc.modules.dmpc.admm.admm_coordinated module
+Module implementing the coordinated ADMM module, which works together +with a coordinator.
+-
+
- +class agentlib_mpc.modules.dmpc.admm.admm_coordinated.CoordinatedADMM(*, config: dict, agent: Agent)[source] +
Bases:
+MiniEmployee,ADMMModule to implement an ADMM agent, which is guided by a coordinator. +Only optimizes based on callbacks.
+ + +-
+
- +init_iteration_callback(variable: AgentVariable)[source] +
Callback that answers the coordinators init_iteration flag.
+
-
+
- +optimize(variable: AgentVariable)[source] +
Performs the optimization given the mean trajectories and multipliers from the +coordinator. +Replies with the local optimal trajectories. +Returns:
+
-
+
- +pydantic model agentlib_mpc.modules.dmpc.admm.admm_coordinated.CoordinatedADMMConfig[source] +
Bases:
+MiniEmployeeConfig,ADMMConfig-
+
- Config: +
-
+
arbitrary_types_allowed: bool = True
+validate_assignment: bool = True
+extra: str = forbid
+frozen: bool = True
+
+- Fields: +
- + +
- Validators: +
-
+
check_valid_fields»shared_variable_fields
+- +
- +
+
-
+
+
-
+
- Validated by: +
-
+
check_valid_fields
+
+
-
+
- +validator couplings_should_have_values » exchange, couplings[source] +
Asserts that couplings and exchange have values, as they are needed for +initial guess.
+
-
+
- +model_post_init(context: Any, /) None +
We need to both initialize private attributes and call the user-defined model_post_init +method.
+
-
+
- +agentlib_mpc.modules.dmpc.admm.admm_coordinated.coupInput +
alias of
+coup_input
agentlib_mpc.modules.dmpc.admm.admm_coordinator module
+Defines classes that coordinate an ADMM process.
+-
+
- +class agentlib_mpc.modules.dmpc.admm.admm_coordinator.ADMMCoordinator(*, config: dict, agent: Agent)[source] +
Bases:
+Coordinator-
+
- +cleanup_results()[source] +
Deletes all files this module created.
+Override this method, if your module creates e.g. results files etc.
+
-
+
- +optim_results_callback(variable: AgentVariable)[source] +
Saves the results of a local optimization. +:param variable:
+Returns:
+
-
+
- +pydantic model agentlib_mpc.modules.dmpc.admm.admm_coordinator.ADMMCoordinatorConfig[source] +
Bases:
+CoordinatorConfigHold the config for ADMMCoordinator
+-
+
- Config: +
-
+
arbitrary_types_allowed: bool = True
+validate_assignment: bool = True
+extra: str = forbid
+frozen: bool = True
+
+- Fields: +
-
+
- +
- +
- +
- +
- +
- +
- +
- +
- +
- +
- +
- +
- +
- +
- +
- +
- +
+- Validators: +
- + +
-
+
- +field abs_tol: float = 0.001 +
Absolute stopping criterion.
+
-
+
- +field admm_iter_max: int = 20 +
Maximum number of ADMM iterations before termination of control step.
+
-
+
- +field dual_tol: float = 0.001 +
Absolute dual stopping criterion.
+
-
+
- +field penalty_change_factor: float = 2 +
Factor to vary the penalty parameter with.
+
-
+
- +field penalty_change_threshold: float = -1 +
When the primal residual is x times higher, vary the penalty parameter and vice versa.
+
-
+
- +field penalty_factor: float = 10 +
Penalty factor of the ADMM algorithm. Should be equal for all agents.
+
-
+
- +field prediction_horizon: int = 10 +
Prediction horizon of participating agents.
+
-
+
- +field primal_tol: float = 0.001 +
Absolute primal stopping criterion.
+
-
+
- +field registration_period: float = 5 +
Time spent on registration before each optimization
+
-
+
- +field rel_tol: float = 0.001 +
Relative stopping criterion.
+
-
+
- +field sampling_time: float | None = None +
Sampling interval for control steps. If None, will be the same as time step. Does not affect the discretization of the MPC, only the interval with which there will be optimization steps.
+-
+
- Validated by: +
- + +
-
+
- +field save_iter_interval: int = 1000 +
-
+
- +field save_solve_stats: bool = False +
When True, saves the solve stats to a file.
+
-
+
- +field solve_stats_file: str = 'admm_stats.csv' +
File name for the solve stats.
+-
+
- Validated by: +
- + +
-
+
- +field time_step: float = 600 +
Sampling interval of between two control steps. Will be used in the discretization for MPC.
+
-
+
- +field use_relative_tolerances: bool = True +
If True, use abs_tol and rel_tol, if False us prim_tol and dual_tol.
+
-
+
- +field wait_time_on_start_iters: float = 0.1 +
wait_on_start_iterations
+
-
+
- +validator default_sampling_time » sampling_time[source] +
-
+
- +model_post_init(context: Any, /) None +
We need to both initialize private attributes and call the user-defined model_post_init +method.
+
-
+
- +validator solve_stats_file_is_csv » solve_stats_file[source] +
Module contents
+
+
+
+
+
+
+
+
+
+
+
\ No newline at end of file
diff --git a/docs/0.6.4/docs/agentlib_mpc.modules.html b/docs/0.6.4/docs/agentlib_mpc.modules.html
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+ agentlib_mpc.modules.dmpc package
+Subpackages
+
+
+-
+
- agentlib_mpc.modules.dmpc.admm package
-
+
- Submodules +
- agentlib_mpc.modules.dmpc.admm.admm module
-
+
ADMM-
+
ADMM.admm_step()
+ADMM.all_coupling_statuses()
+ADMM.assert_mpc_variables_are_in_model()
+ADMM.collect_couplings_for_optimization()
+ADMM.collect_variables_for_optimization()
+ADMM.cons_and_exchange
+ADMM.deregister_all_participants()
+ADMM.get_participants_values()
+ADMM.get_results()
+ADMM.participant_callback()
+ADMM.penalty_factor_var
+ADMM.process()
+ADMM.receive_participant()
+ADMM.registered_participants
+ADMM.reset_participants_ready()
+ADMM.send_coupling_values()
+ADMM.send_coupling_variable()
+ADMM.update_lambda()
+ADMM.var_ref
+
+ADMMConfig+
+ADMMParticipation+
+LocalADMM+
+LocalADMMConfig+
+ModuleStatus+
+ParticipantStatus+
+
+ - agentlib_mpc.modules.dmpc.admm.admm_coordinated module + +
- agentlib_mpc.modules.dmpc.admm.admm_coordinator module
-
+
ADMMCoordinator+
+ADMMCoordinatorConfig-
+
ADMMCoordinatorConfig.abs_tol
+ADMMCoordinatorConfig.admm_iter_max
+ADMMCoordinatorConfig.dual_tol
+ADMMCoordinatorConfig.penalty_change_factor
+ADMMCoordinatorConfig.penalty_change_threshold
+ADMMCoordinatorConfig.penalty_factor
+ADMMCoordinatorConfig.prediction_horizon
+ADMMCoordinatorConfig.primal_tol
+ADMMCoordinatorConfig.registration_period
+ADMMCoordinatorConfig.rel_tol
+ADMMCoordinatorConfig.sampling_time
+ADMMCoordinatorConfig.save_iter_interval
+ADMMCoordinatorConfig.save_solve_stats
+ADMMCoordinatorConfig.solve_stats_file
+ADMMCoordinatorConfig.time_step
+ADMMCoordinatorConfig.use_relative_tolerances
+ADMMCoordinatorConfig.wait_time_on_start_iters
+ADMMCoordinatorConfig.default_sampling_time
+ADMMCoordinatorConfig.model_post_init()
+ADMMCoordinatorConfig.solve_stats_file_is_csv
+
+
+ - Module contents +
+
Submodules
+agentlib_mpc.modules.dmpc.coordinator module
+-
+
- +class agentlib_mpc.modules.dmpc.coordinator.Coordinator(*, config: dict, agent: Agent)[source] +
Bases:
+BaseModuleClass implementing the base coordination for distributed MPC
+-
+
- +property all_finished +
Returns: +True, if there are no busy agents, else False
+
-
+
- +init_iteration_callback(variable: AgentVariable)[source] +
Processes and Agents InitIteration confirmation. +:param variable:
+Returns:
+
-
+
- +optim_results_callback(variable: AgentVariable)[source] +
Saves the results of a local optimization. +:param variable:
+Returns:
+
-
+
- +pydantic model agentlib_mpc.modules.dmpc.coordinator.CoordinatorConfig[source] +
Bases:
+BaseModuleConfig-
+
- Config: +
-
+
arbitrary_types_allowed: bool = True
+validate_assignment: bool = True
+extra: str = forbid
+frozen: bool = True
+
+- Fields: +
- + +
- Validators: +
-
+
check_valid_fields»shared_variable_fields
+
+
-
+
- +field maxIter: int = 10 +
Maximum number of iterations
+
-
+
- +field messages_in: List[AgentVariable] = [AgentVariable(name='registration_agent_to_coordinator', type=None, timestamp=None, unit='Not defined', description='Not defined', ub=inf, lb=-inf, clip=False, allowed_values=[], value=None, alias='registration_agent_to_coordinator', source=Source(agent_id=None, module_id=None), shared=None, rdf_class=None), AgentVariable(name='startIteration_agent_to_coordinator', type=None, timestamp=None, unit='Not defined', description='Not defined', ub=inf, lb=-inf, clip=False, allowed_values=[], value=None, alias='startIteration_agent_to_coordinator', source=Source(agent_id=None, module_id=None), shared=None, rdf_class=None), AgentVariable(name='optimization_agent_to_coordinator', type=None, timestamp=None, unit='Not defined', description='Not defined', ub=inf, lb=-inf, clip=False, allowed_values=[], value=None, alias='optimization_agent_to_coordinator', source=Source(agent_id=None, module_id=None), shared=None, rdf_class=None)] +
-
+
- +field messages_out: List[AgentVariable] = [AgentVariable(name='registration_coordinator_to_agent', type=None, timestamp=None, unit='Not defined', description='Not defined', ub=inf, lb=-inf, clip=False, allowed_values=[], value=None, alias='registration_coordinator_to_agent', source=Source(agent_id=None, module_id=None), shared=None, rdf_class=None), AgentVariable(name='startIteration_coordinator_to_agent', type=None, timestamp=None, unit='Not defined', description='Not defined', ub=inf, lb=-inf, clip=False, allowed_values=[], value=None, alias='startIteration_coordinator_to_agent', source=Source(agent_id=None, module_id=None), shared=None, rdf_class=None), AgentVariable(name='optimization_coordinator_to_agent', type=None, timestamp=None, unit='Not defined', description='Not defined', ub=inf, lb=-inf, clip=False, allowed_values=[], value=None, alias='optimization_coordinator_to_agent', source=Source(agent_id=None, module_id=None), shared=None, rdf_class=None)] +
-
+
+
-
+
- Validated by: +
-
+
check_valid_fields
+
+
-
+
- +field time_out_non_responders: float = 1 +
Maximum wait time for subsystems in seconds
+
-
+
- +model_post_init(context: Any, /) None +
We need to both initialize private attributes and call the user-defined model_post_init +method.
+
agentlib_mpc.modules.dmpc.employee module
+-
+
- +class agentlib_mpc.modules.dmpc.employee.MiniEmployee(*, config: dict, agent: Agent)[source] +
Bases:
+ + +BaseModule-
+
- +init_iteration_callback(variable: AgentVariable)[source] +
Callback that processes the coordinators ‘startIteration’ flag. +:param variable:
+
-
+
- +abstract optimize(variable: AgentVariable)[source] +
Performs the optimization given the information from the coordinator. +Replies with local information. +Returns:
+
-
+
- +pre_computation_hook()[source] +
This method is called in every computation step before the optimization starts. +Overwrite this method in a derived subclass if you want to take some actions each time before the optimal control problem is solved.
+
-
+
- +pydantic model agentlib_mpc.modules.dmpc.employee.MiniEmployeeConfig[source] +
Bases:
+BaseModuleConfig-
+
- Config: +
-
+
arbitrary_types_allowed: bool = True
+validate_assignment: bool = True
+extra: str = forbid
+frozen: bool = True
+
+- Fields: +
- + +
- Validators: +
-
+
check_valid_fields»shared_variable_fields
+
+
-
+
- +field coordinator: Source [Required] +
Define the agents coordinator
+
-
+
- +field messages_in: List[AgentVariable] = [AgentVariable(name='registration_coordinator_to_agent', type=None, timestamp=None, unit='Not defined', description='Not defined', ub=inf, lb=-inf, clip=False, allowed_values=[], value=None, alias='registration_coordinator_to_agent', source=Source(agent_id=None, module_id=None), shared=None, rdf_class=None), AgentVariable(name='startIteration_coordinator_to_agent', type=None, timestamp=None, unit='Not defined', description='Not defined', ub=inf, lb=-inf, clip=False, allowed_values=[], value=None, alias='startIteration_coordinator_to_agent', source=Source(agent_id=None, module_id=None), shared=None, rdf_class=None), AgentVariable(name='optimization_coordinator_to_agent', type=None, timestamp=None, unit='Not defined', description='Not defined', ub=inf, lb=-inf, clip=False, allowed_values=[], value=None, alias='optimization_coordinator_to_agent', source=Source(agent_id=None, module_id=None), shared=None, rdf_class=None)] +
-
+
- +field messages_out: List[AgentVariable] = [AgentVariable(name='registration_agent_to_coordinator', type=None, timestamp=None, unit='Not defined', description='Not defined', ub=inf, lb=-inf, clip=False, allowed_values=[], value=None, alias='registration_agent_to_coordinator', source=Source(agent_id=None, module_id=None), shared=None, rdf_class=None), AgentVariable(name='startIteration_agent_to_coordinator', type=None, timestamp=None, unit='Not defined', description='Not defined', ub=inf, lb=-inf, clip=False, allowed_values=[], value=None, alias='startIteration_agent_to_coordinator', source=Source(agent_id=None, module_id=None), shared=None, rdf_class=None), AgentVariable(name='optimization_agent_to_coordinator', type=None, timestamp=None, unit='Not defined', description='Not defined', ub=inf, lb=-inf, clip=False, allowed_values=[], value=None, alias='optimization_agent_to_coordinator', source=Source(agent_id=None, module_id=None), shared=None, rdf_class=None)] +
-
+
- +field registration_interval: float = 10 +
Interval in seconds after which a registration attempt is made.
+-
+
- Constraints: +
-
+
ge = 0
+
+
-
+
- +field request_frequency: float = 1 +
Wait time between signup_requests
+
-
+
+
-
+
- Validated by: +
-
+
check_valid_fields
+
+
-
+
- +model_post_init(context: Any, /) None +
We need to both initialize private attributes and call the user-defined model_post_init +method.
+
Module contents
+-
+
- +class agentlib_mpc.modules.dmpc.DistributedMPC(config: dict, agent: Agent)[source] +
Bases:
+MPCBase class which defines common interfaces among all +distributed mpc approaches (either optimization based, +game theory based or some other).
+
-
+
- +pydantic model agentlib_mpc.modules.dmpc.DistributedMPCConfig[source] +
Bases:
+MPCConfigBase config class with common configurations
+-
+
- Config: +
-
+
arbitrary_types_allowed: bool = True
+validate_assignment: bool = True
+extra: str = forbid
+frozen: bool = True
+
+- Fields: +
- +
- Validators: +
- +
-
+
- +model_post_init(context: Any, /) None +
We need to both initialize private attributes and call the user-defined model_post_init +method.
+
+
+
+
+
+
+
+
+
+
+
\ No newline at end of file
diff --git a/docs/0.6.4/docs/agentlib_mpc.optimization_backends.casadi_.core.html b/docs/0.6.4/docs/agentlib_mpc.optimization_backends.casadi_.core.html
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+
+ agentlib_mpc.modules package
+Submodules
+agentlib_mpc.modules.mpc module
+Holds the base class for MPCs.
+-
+
- +class agentlib_mpc.modules.mpc.BaseMPC(config: dict, agent: Agent)[source] +
Bases:
+BaseModuleA model predictive controller. +More info to follow.
+-
+
- +assert_mpc_variables_are_in_model() dict[str, set[str]][source] +
Checks whether all variables of var_ref are contained in the model. +Returns names of model variables not contained in the var_ref, +sorted by keys: ‘states’, ‘inputs’, ‘outputs’, ‘parameters’.
+
-
+
- +assert_subset(mpc_names, model_names, message_head)[source] +
Helper function for assert assert_mpc_variables_are_in_model. Asserts +the variables of the var_ref corresponding to ref_key are a subset of +a list of names provided (usually obtained from the model) and prints +out an error if false. Returns the portion of model_names that are +not in the given var_ref.
+
-
+
- +cleanup_results()[source] +
Deletes all files this module created.
+Override this method, if your module creates e.g. results files etc.
+
-
+
- +collect_variables_for_optimization(var_ref: VariableReference = None) Dict[str, AgentVariable][source] +
Gets all variables noted in the var ref and puts them in a flat +dictionary.
+
-
+
- +get_results() DataFrame | None[source] +
Read the results that were saved from the optimization backend and +returns them as Dataframe.
+-
+
- Returns: +
(results, stats) tuple of Dataframes.
+
+
-
+
- +property model: Model +
Getter for current simulation model
+-
+
- Returns: +
Current simulation model
+
+- Return type: +
agentlib.model
+
+
-
+
- +pre_computation_hook()[source] +
This method is called in every computation step before the optimization starts. +Overwrite this method in a derived subclass if you want to take some actions +each time before the optimal control problem is solved.
+
-
+
- +process()[source] +
This abstract method must be implemented in order to sync the module +with the other processes of the agent and the whole MAS.
+
-
+
- +re_init_optimization(parameter: AgentVariable)[source] +
Re-initializes the optimization backend with new parameters.
+
-
+
- +static read_results_file(results_file: str) Tuple[DataFrame, DataFrame][source] +
Read the provided csv-file as an MPC results file. +:param results_file: File path
+-
+
- Returns: +
results, stats +results is the Dataframe with all inputs and outputs of the MPC +optimizations. +stats is the Dataframe with matching solver stats
+
+
-
+
- +register_callbacks()[source] +
Registers the init_optimization callback to all parameters which +cannot be changed without recreating the optimization problem.
+
-
+
- +set_actuation(solution: Results)[source] +
Takes the solution from optimization backend and sends the first +step to AgentVariables.
+
-
+
- +pydantic model agentlib_mpc.modules.mpc.BaseMPCConfig[source] +
Bases:
+BaseModuleConfigPydantic data model for MPC configuration parser
+-
+
- Config: +
-
+
arbitrary_types_allowed: bool = True
+validate_assignment: bool = True
+extra: str = forbid
+frozen: bool = True
+
+- Fields: +
-
+
controls (List[agentlib_mpc.data_structures.mpc_datamodels.MPCVariable])
+inputs (List[agentlib_mpc.data_structures.mpc_datamodels.MPCVariable])
+- +
outputs (List[agentlib_mpc.data_structures.mpc_datamodels.MPCVariable])
+parameters (List[agentlib_mpc.data_structures.mpc_datamodels.MPCVariable])
+- +
- +
- +
- +
states (List[agentlib_mpc.data_structures.mpc_datamodels.MPCVariable])
+- +
+- Validators: +
-
+
check_valid_fields»shared_variable_fields
+- +
+
-
+
- +field controls: List[MPCVariable] = [] +
List of all control variables of the MPC.
+
-
+
- +field inputs: List[MPCVariable] = [] +
List of all input variables of the MPC. Includes predictions for disturbances, set_points, dynamic constraint boundaries etc.
+
-
+
- +field optimization_backend: dict [Required] +
-
+
- +field outputs: List[MPCVariable] = [] +
List of all shared outputs of the MPC.
+
-
+
- +field parameters: List[MPCVariable] = [] +
List of model parameters of the MPC. They are constant over the horizon. Parameters not listed here will have their default from the model file.
+
-
+
- +field prediction_horizon: int = 5 +
Prediction horizon of the MPC.
+-
+
- Constraints: +
-
+
ge = 0
+
+
-
+
- +field sampling_time: float | None = None +
Sampling interval for control steps. If None, will be the same as time step. Does not affect the discretization of the MPC, only the interval with which there will be optimization steps.
+-
+
- Validated by: +
- + +
-
+
- +field set_outputs: bool = False +
Sets the full output time series to the data broker.
+
-
+
+
-
+
- Validated by: +
-
+
check_valid_fields
+
+
-
+
- +field states: List[MPCVariable] = [] +
List of all differential states of the MPC. The entries can define the boundaries and the source for the measurements
+
-
+
- +field time_step: float = 60 +
Time step of the MPC.
+-
+
- Constraints: +
-
+
ge = 0
+
+
-
+
- +validator default_sampling_time » sampling_time[source] +
-
+
- +model_post_init(context: Any, /) None +
We need to both initialize private attributes and call the user-defined model_post_init +method.
+
Module contents
+This package contains all modules for the +distributed model predictive control using multi agent systems.
+It contains classes for local optimization and global coordination.
+ + +
+
+
+
+
+
+
+
+
+
+
\ No newline at end of file
diff --git a/docs/0.6.4/docs/agentlib_mpc.optimization_backends.casadi_.html b/docs/0.6.4/docs/agentlib_mpc.optimization_backends.casadi_.html
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+ agentlib_mpc.optimization_backends.casadi_.core package
+Submodules
+agentlib_mpc.optimization_backends.casadi_.core.VariableGroup module
+-
+
- +class agentlib_mpc.optimization_backends.casadi_.core.VariableGroup.OptimizationParameter(name: 'str', full_symbolic: 'ca.MX', dim: 'int', ref_names: 'tuple[str]', full_names: 'tuple[str]', use_in_stage_function: 'bool', full_with_defaults: 'ca.MX', add_default_values: 'ca.Function')[source] +
Bases:
+OptimizationQuantity-
+
- +add_default_values: Function +
-
+
- +classmethod declare(denotation: str, variables: list[agentlib_mpc.models.casadi_model.CasadiVariable], ref_list: list[str], use_in_stage_function=True, assert_complete: bool = False)[source] +
Declares a group of optimization parameters that serve a purpose in +the optimization problem. Typical groups are uncontrollable inputs or +physical parameters.
+-
+
- Parameters: +
-
+
denotation – The key of the variable, e.g. ‘p’, ‘d’, etc. Use this +key in the discretization function to add the parameter at +different stages of the optimization problem.
+variables – A list of CasadiVariables including all parameters +within this category.
+ref_list – A list of names indicating which parameters in full_list +are AgentVariables and need to be updated before each +optimization.
+use_in_stage_function – If False, the parameter is not added to the +stage function. If True, the variable needs to be provided to +the stage function at every point in the discretization function.
+assert_complete – If True, throws an error if the ref_list does +not contain all variables.
+
+
-
+
- +full_with_defaults: MX +
-
+
- +class agentlib_mpc.optimization_backends.casadi_.core.VariableGroup.OptimizationQuantity(name: 'str', full_symbolic: 'ca.MX', dim: 'int', ref_names: 'tuple[str]', full_names: 'tuple[str]', use_in_stage_function: 'bool')[source] +
Bases:
+object-
+
- +dim: int +
-
+
- +full_names: tuple[str] +
-
+
- +full_symbolic: MX +
-
+
- +name: str +
-
+
- +ref_names: tuple[str] +
-
+
- +use_in_stage_function: bool +
-
+
- +class agentlib_mpc.optimization_backends.casadi_.core.VariableGroup.OptimizationVariable(name: 'str', full_symbolic: 'ca.MX', dim: 'int', ref_names: 'tuple[str]', full_names: 'tuple[str]', use_in_stage_function: 'bool', input_map: 'ca.Function', output_map: 'ca.Function', binary: 'bool')[source] +
Bases:
+OptimizationQuantity-
+
- +binary: bool +
-
+
- +classmethod declare(denotation: str, variables: list[agentlib_mpc.models.casadi_model.CasadiVariable], ref_list: list[str], use_in_stage_function: bool = True, assert_complete: bool = False, binary: bool = False) OptimizationVariable[source] +
Declares a group of optimization variables that serve a purpose in +the optimization problem. Typical groups are states, the control +inputs or slack variables.
+-
+
- Parameters: +
-
+
binary – Flag, whether these variables are binary
+denotation – The key of the variable, e.g. ‘X’, ‘U’, etc. Use +this key in the discretization function to add the variable at +different stages of the optimization problem. The optimal value +of these variables will also be mapped to this key.
+variables – A list of +CasadiVariables or an MX/SX vector including all variables +within this category.
+ref_list – A list of names indicating which variables +in full_list are AgentVariables and need to be updated before +each optimization.
+use_in_stage_function – If False, the variable is not +added to the stage function. If True, the variable needs to be +provided to the stage function at every point in the +discretization function.
+assert_complete – If True, throws an error if the ref_list does +not contain all variables.
+
+
-
+
- +input_map: Function +
-
+
- +output_map: Function +
-
+
- +use_in_stage_function: bool +
agentlib_mpc.optimization_backends.casadi_.core.casadi_backend module
+-
+
- +class agentlib_mpc.optimization_backends.casadi_.core.casadi_backend.CasADiBackend(config: dict)[source] +
Bases:
+OptimizationBackendOptimizationBackend for solving the optimization problem with CasADi. +Requires the model to be a CasADi model.
+-
+
- +config_type +
alias of
+CasadiBackendConfig
-
+
- +discretization: DiscretizationT +
-
+
- +discretization_types: dict[agentlib_mpc.data_structures.casadi_utils.DiscretizationMethod, Type[DiscretizationT]] +
-
+
- +save_result_df(results: Results, now: float = 0)[source] +
Save the results of solve into a dataframe at each time step.
+Example results dataframe:
+value_type variable … lower +variable T_0 T_0_slack … T_0_slack mDot_0 +time_step … +2 0.000000 298.160000 NaN … NaN NaN
++
+101.431499 297.540944 -149.465942 … -inf 0.0 +450.000000 295.779780 -147.704779 … -inf 0.0 +798.568501 294.720770 -146.645769 … -inf 0.0
+-
+
- Parameters: +
-
+
results –
+now –
+
+
Returns:
+
-
+
- +setup_optimization(var_ref: VariableReference)[source] +
Performs all necessary steps to make the
+solvemethod usable. +To do this, it calls several auxiliary functions. These functions can +be overloaded to change the resulting optimization problem.-
+
- Parameters: +
var_ref – class with variable name lists sorted by function in the mpc.
+
+
-
+
- +solve(now: float, current_vars: dict[str, agentlib_mpc.data_structures.mpc_datamodels.MPCVariable]) Results[source] +
Solves the optimization problem given the current values of the +corresponding AgentVariables and system time. The standardization of +return values is a work in progress.
+-
+
- Parameters: +
-
+
now – Current time used for interpolation of input trajectories.
+current_vars – Dict of AgentVariables holding the values relevant to +the optimization problem. Keys are the names
+
+- Returns: +
A dataframe with all optimization variables over their respective +grids. Depending on discretization, can include many nan’s, so care +should be taken when using this, e.g. always use dropna() after +accessing a column.
++
+ +-
+
- Example:
+
+variables mDot | T_0 | slack_T
+time +0 0.1 | 298 | nan +230 nan | 297 | 3 +470 nan | 296 | 2 +588 nan | 295 | 1 +700 0.05 | 294 | nan +930 nan | 294 | 0.1
+
+
+
-
+
- +system: SystemT +
-
+
- +system_type: Type[SystemT] +
-
+
- +pydantic model agentlib_mpc.optimization_backends.casadi_.core.casadi_backend.CasadiBackendConfig[source] +
Bases:
+BackendConfig-
+
- Config: +
-
+
extra: str = forbid
+
+- Fields: +
- + +
- Validators: +
-
+
- +
+
-
+
- +field build_batch_bat: Path | None = None +
Path to a batch file, which can compile C code on windows.
+
-
+
- +field discretization_options: CasadiDiscretizationOptions [Optional] +
-
+
- +field do_jit: bool | None = None +
Boolean to turn JIT of the optimization problems on or off.
+-
+
- Validated by: +
- + +
-
+
- +field solver: SolverOptions [Optional] +
agentlib_mpc.optimization_backends.casadi_.core.discretization module
+Holds classes that implement different transcriptions of the OCP
+-
+
- +class agentlib_mpc.optimization_backends.casadi_.core.discretization.Discretization(options: CasadiDiscretizationOptions)[source] +
Bases:
+ABCopt_vars: holds symbolic variables during problem creation +opt_vars_lb: holds symbolic variables during problem creation +opt_vars_ub: holds symbolic variables during problem creation +initial_guess: holds symbolic variables during problem creation +opt_pars: holds symbolic variables during problem creation +constraints: holds symbolic variables during problem creation +constraints_lb: holds symbolic variables during problem creation +constraints_ub: holds symbolic variables during problem creation +objective_function: cost function during problem creation +mpc_opt_vars (dict): holds the symbolic variables and grids during
++
+problem creation sorted by type as in system_variables
+-
+
- mpc_opt_pars (dict): holds the symbolic variables and grids during
problem creation sorted by type as in system_parameters
+
+
-
+
- +add_constraint(constraint_function: MX | SX | Sparsity | DM | float | int, lb: MX | SX | Sparsity | DM | float | int = None, ub: MX | SX | Sparsity | DM | float | int = None, *, gap_closing: bool = False)[source] +
Add a constraint to the optimization problem. If no bounds are given, +adds an equality constraint.
+
-
+
- +add_opt_par(quantity: OptimizationParameter, post_den: str = '')[source] +
Create an optimization parameter and append to all the associated lists.
+denotation[str]: the key of the parameter, e.g. ‘P’, ‘Q’, … +dimension[int]: the dimension of the parameter +post_den[str]: string to add to casadi MX after denotation (for debugging)
+
-
+
- +add_opt_var(quantity: OptimizationVariable, lb: MX = None, ub: MX = None, guess: float = None, post_den: str = '')[source] +
Create an optimization variable and append to all the associated +lists. If lb or ub are given, they override the values provided at +runtime! The usual application of this is, to fix the initial value +of a state to a parameter.
+-
+
- Parameters: +
-
+
quantity – corresponding system variable
+lb – lower bound of the variable
+ub – upper bound of the variable
+guess – default for the initial guess
+post_den – string to add to casadi MX after denotation (for debugging)
+
+
-
+
- +property binary_vars: list[bool] +
List specifying for every optimization variable, whether it is binary.
+
-
+
- +create_nlp_in_out_mapping(system: System)[source] +
Function creating mapping functions between the MPC variables ordered +by type (as defined in declare_quantities and the raw input/output +vector of the CasADi NLP.
+
-
+
- +grid(var: OptimizationQuantity) list[float][source] +
-
+
- +initialize(system: System, solver_factory: SolverFactory)[source] +
Initializes the trajectory optimization problem, creating all symbolic +variables of the OCP, the mapping function and the numerical solver.
+
-
+
- +property nlp: dict[str, casadi.casadi.MX] +
The nlp dict that casadi solvers need for instantiation
+
-
+
- +only_positive_times_in_results = True +
-
+
- +solve(mpc_inputs: dict[str, casadi.casadi.DM]) Results[source] +
Solves the discretized trajectory optimization problem.
+-
+
- Parameters: +
mpc_inputs – Casadi Matrices specifying the input of all different types +of optimization parameters. Matrices consist of different variable rows +and have a column for each time step in the discretization. +There are separate matrices for each input type (as defined in the +System), and also for the upper and lower boundaries of variables +respectively.
+
+- Returns: +
-
+
- The complete evolution of the states, inputs and boundaries of each
variable and parameter over the prediction horizon, as well as solve +statistics.
+
+
+- Return type: +
- + +
-
+
- +class agentlib_mpc.optimization_backends.casadi_.core.discretization.Results(matrix: casadi.casadi.MX, grid: list[float], columns: pandas.core.indexes.multi.MultiIndex, stats: dict, variable_grid_indices: dict[str, list[int]], _variable_name_to_index: dict[str, int] = None)[source] +
Bases:
+object-
+
- +columns: MultiIndex +
-
+
- +property df: DataFrame +
-
+
- +grid: list[float] +
-
+
- +matrix: MX +
-
+
- +stats: dict +
-
+
- +variable_grid_indices: dict[str, list[int]] +
agentlib_mpc.optimization_backends.casadi_.core.system module
+Holds the System class, which knows the model
+-
+
- +class agentlib_mpc.optimization_backends.casadi_.core.system.System[source] +
Bases:
+ABCExamples
+class MySystem(System):
++
+# variables +states: OptimizationVariable +controls: OptimizationVariable +algebraics: OptimizationVariable +outputs: OptimizationVariable
+# parameters +non_controlled_inputs: OptimizationParameter +model_parameters: OptimizationParameter +initial_state: OptimizationParameter
+# dynamics +model_constraints: Constraint +cost_function: ca.MX +ode: ca.MX
+def initialize(self, model: CasadiModel, var_ref: VariableReference):
++
+-
+
- self.states = OptimizationVariable.declare(
denotation=”state”, +variables=model.get_states(var_ref.states), +ref_list=var_ref.states, +assert_complete=True,
+
+
)
+.
+)
+-
+
- +abstract initialize(model: CasadiModel, var_ref: VariableReference)[source] +
-
+
- +property parameters: list[agentlib_mpc.optimization_backends.casadi_.core.VariableGroup.OptimizationParameter] +
-
+
- +property quantities: list[Union[agentlib_mpc.optimization_backends.casadi_.core.VariableGroup.OptimizationParameter, agentlib_mpc.optimization_backends.casadi_.core.VariableGroup.OptimizationVariable]] +
-
+
- +property variables: list[agentlib_mpc.optimization_backends.casadi_.core.VariableGroup.OptimizationVariable] +
Module contents
+
+
+
+
+
+
+
+
+
+
+
\ No newline at end of file
diff --git a/docs/0.6.4/docs/agentlib_mpc.optimization_backends.html b/docs/0.6.4/docs/agentlib_mpc.optimization_backends.html
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+ agentlib_mpc.optimization_backends.casadi_ package
+Subpackages
+
+
+-
+
- agentlib_mpc.optimization_backends.casadi_.core package
-
+
- Submodules +
- agentlib_mpc.optimization_backends.casadi_.core.VariableGroup module + +
- agentlib_mpc.optimization_backends.casadi_.core.casadi_backend module
-
+
CasADiBackend+
+CasadiBackendConfig+
+
+ - agentlib_mpc.optimization_backends.casadi_.core.discretization module
-
+
Discretization-
+
Discretization.add_constraint()
+Discretization.add_opt_par()
+Discretization.add_opt_var()
+Discretization.binary_vars
+Discretization.create_nlp_in_out_mapping()
+Discretization.grid()
+Discretization.initialize()
+Discretization.nlp
+Discretization.only_positive_times_in_results
+Discretization.solve()
+
+Results+
+
+ - agentlib_mpc.optimization_backends.casadi_.core.system module + +
- Module contents +
+
Submodules
+agentlib_mpc.optimization_backends.casadi_.admm module
+-
+
- +class agentlib_mpc.optimization_backends.casadi_.admm.ADMMCollocation(options: CasadiDiscretizationOptions)[source] +
Bases:
+DirectCollocation
-
+
- +class agentlib_mpc.optimization_backends.casadi_.admm.ADMMMultipleShooting(options: CasadiDiscretizationOptions)[source] +
Bases:
+MultipleShooting
-
+
- +class agentlib_mpc.optimization_backends.casadi_.admm.CasADiADMMBackend(config: dict)[source] +
Bases:
+CasADiBaseBackend,ADMMBackendClass doing optimization of ADMM subproblems with CasADi.
+-
+
- +property coupling_grid +
Returns the grid on which the coupling variables are discretized.
+
-
+
- +discretization_types: dict[DiscretizationMethod, Type[DiscretizationT]] = {DiscretizationMethod.collocation: <class 'agentlib_mpc.optimization_backends.casadi_.admm.ADMMCollocation'>, DiscretizationMethod.multiple_shooting: <class 'agentlib_mpc.optimization_backends.casadi_.admm.ADMMMultipleShooting'>} +
-
+
- +save_result_df(results: Results, now: float = 0)[source] +
Save the results of solve into a dataframe at each time step.
+Example results dataframe:
+value_type variable … lower +variable T_0 T_0_slack … T_0_slack mDot_0 +time_step … +2 0.000000 298.160000 NaN … NaN NaN
++
+101.431499 297.540944 -149.465942 … -inf 0.0 +450.000000 295.779780 -147.704779 … -inf 0.0 +798.568501 294.720770 -146.645769 … -inf 0.0
+-
+
- Parameters: +
-
+
results –
+now –
+
+
Returns:
+
-
+
- +system: CasadiADMMSystem +
-
+
- +system_type +
alias of
+CasadiADMMSystem
-
+
- +class agentlib_mpc.optimization_backends.casadi_.admm.CasadiADMMSystem[source] +
Bases:
+FullSystem-
+
- +exchange_diff: OptimizationParameter +
-
+
- +exchange_multipliers: OptimizationParameter +
-
+
- +global_couplings: OptimizationParameter +
-
+
- +initialize(model: CasadiModel, var_ref: VariableReference)[source] +
-
+
- +local_couplings: OptimizationVariable +
-
+
- +local_exchange: OptimizationVariable +
-
+
- +multipliers: OptimizationParameter +
-
+
- +penalty_factor: OptimizationParameter +
agentlib_mpc.optimization_backends.casadi_.basic module
+-
+
- +class agentlib_mpc.optimization_backends.casadi_.basic.BaseSystem[source] +
Bases:
+System-
+
- +algebraics: OptimizationVariable +
-
+
- +controls: OptimizationVariable +
-
+
- +cost_function: MX +
-
+
- +initial_state: OptimizationParameter +
-
+
- +initialize(model: CasadiModel, var_ref: VariableReference)[source] +
-
+
- +model_constraints: Constraint +
-
+
- +model_parameters: OptimizationParameter +
-
+
- +non_controlled_inputs: OptimizationParameter +
-
+
- +ode: MX +
-
+
- +outputs: OptimizationVariable +
-
+
- +states: OptimizationVariable +
-
+
- +class agentlib_mpc.optimization_backends.casadi_.basic.CasADiBaseBackend(config: dict)[source] +
Bases:
+CasADiBackendClass doing optimization of ADMM subproblems with CasADi.
+-
+
- +discretization_types: dict[DiscretizationMethod, Type[DiscretizationT]] = {DiscretizationMethod.collocation: <class 'agentlib_mpc.optimization_backends.casadi_.basic.DirectCollocation'>, DiscretizationMethod.multiple_shooting: <class 'agentlib_mpc.optimization_backends.casadi_.basic.MultipleShooting'>} +
-
+
- +system: BaseSystem +
-
+
- +system_type +
alias of
+BaseSystem
-
+
- +class agentlib_mpc.optimization_backends.casadi_.basic.CollocationMatrices(order: int, root: numpy.ndarray, B: numpy.ndarray, C: numpy.ndarray, D: numpy.ndarray)[source] +
Bases:
+object-
+
- +B: ndarray +
-
+
- +C: ndarray +
-
+
- +D: ndarray +
-
+
- +order: int +
-
+
- +root: ndarray +
-
+
- +class agentlib_mpc.optimization_backends.casadi_.basic.DirectCollocation(options: CasadiDiscretizationOptions)[source] +
Bases:
+Discretization-
+
- +initialize(system: BaseSystem, solver_factory: SolverFactory)[source] +
Initializes the trajectory optimization problem, creating all symbolic +variables of the OCP, the mapping function and the numerical solver.
+
-
+
- +class agentlib_mpc.optimization_backends.casadi_.basic.MultipleShooting(options: CasadiDiscretizationOptions)[source] +
Bases:
+Discretization-
+
- +initialize(system: BaseSystem, solver_factory: SolverFactory)[source] +
Initializes the trajectory optimization problem, creating all symbolic +variables of the OCP, the mapping function and the numerical solver.
+
agentlib_mpc.optimization_backends.casadi_.casadi_admm_nn module
+agentlib_mpc.optimization_backends.casadi_.casadi_nn module
+agentlib_mpc.optimization_backends.casadi_.full module
+-
+
- +class agentlib_mpc.optimization_backends.casadi_.full.CasADiFullBackend(config: dict)[source] +
Bases:
+CasADiBackendClass doing optimization of ADMM subproblems with CasADi.
+-
+
- +discretization_types: dict[DiscretizationMethod, Type[DiscretizationT]] = {DiscretizationMethod.collocation: <class 'agentlib_mpc.optimization_backends.casadi_.full.DirectCollocation'>, DiscretizationMethod.multiple_shooting: <class 'agentlib_mpc.optimization_backends.casadi_.full.MultipleShooting'>} +
-
+
- +system: FullSystem +
-
+
- +system_type +
alias of
+FullSystem
-
+
- +class agentlib_mpc.optimization_backends.casadi_.full.DirectCollocation(options: CasadiDiscretizationOptions)[source] +
Bases:
+DirectCollocation
-
+
- +class agentlib_mpc.optimization_backends.casadi_.full.FullSystem[source] +
Bases:
+BaseSystem-
+
- +initialize(model: CasadiModel, var_ref: FullVariableReference)[source] +
-
+
- +last_control: OptimizationParameter +
-
+
- +r_del_u: OptimizationParameter +
-
+
- +class agentlib_mpc.optimization_backends.casadi_.full.MultipleShooting(options: CasadiDiscretizationOptions)[source] +
Bases:
+MultipleShooting
agentlib_mpc.optimization_backends.casadi_.minlp module
+-
+
- +class agentlib_mpc.optimization_backends.casadi_.minlp.CasADiMINLPBackend(config: dict)[source] +
Bases:
+CasADiBackendClass doing optimization of ADMM subproblems with CasADi.
+-
+
- +discretization_types: dict[DiscretizationMethod, Type[DiscretizationT]] = {DiscretizationMethod.collocation: <class 'agentlib_mpc.optimization_backends.casadi_.minlp.DirectCollocation'>} +
-
+
- +system: CasadiMINLPSystem +
-
+
- +system_type +
alias of
+CasadiMINLPSystem
-
+
- +class agentlib_mpc.optimization_backends.casadi_.minlp.CasadiMINLPSystem[source] +
Bases:
+BaseSystem-
+
- +binary_controls: OptimizationVariable +
-
+
- +initialize(model: CasadiModel, var_ref: MINLPVariableReference)[source] +
-
+
- +class agentlib_mpc.optimization_backends.casadi_.minlp.DirectCollocation(options: CasadiDiscretizationOptions)[source] +
Bases:
+DirectCollocation
Module contents
+
+
+
+
+
+
+
+
+
+
+
\ No newline at end of file
diff --git a/docs/0.6.4/docs/agentlib_mpc.utils.html b/docs/0.6.4/docs/agentlib_mpc.utils.html
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+
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+
+
+
+
+
+
+
+
+
+
+
+
+
+ agentlib_mpc.optimization_backends package
+Subpackages
+
+
+-
+
- agentlib_mpc.optimization_backends.casadi_ package
-
+
- Subpackages
-
+
- agentlib_mpc.optimization_backends.casadi_.core package
-
+
- Submodules +
- agentlib_mpc.optimization_backends.casadi_.core.VariableGroup module +
- agentlib_mpc.optimization_backends.casadi_.core.casadi_backend module +
- agentlib_mpc.optimization_backends.casadi_.core.discretization module +
- agentlib_mpc.optimization_backends.casadi_.core.system module +
- Module contents +
+
+ - agentlib_mpc.optimization_backends.casadi_.core package
- Submodules +
- agentlib_mpc.optimization_backends.casadi_.admm module + +
- agentlib_mpc.optimization_backends.casadi_.basic module
-
+
BaseSystem+
+CasADiBaseBackend+
+CollocationMatrices+
+DirectCollocation+
+MultipleShooting+
+
+ - agentlib_mpc.optimization_backends.casadi_.casadi_admm_nn module +
- agentlib_mpc.optimization_backends.casadi_.casadi_nn module +
- agentlib_mpc.optimization_backends.casadi_.full module + +
- agentlib_mpc.optimization_backends.casadi_.minlp module + +
- Module contents +
+ - Subpackages
Submodules
+agentlib_mpc.optimization_backends.backend module
+-
+
- +class agentlib_mpc.optimization_backends.backend.ADMMBackend(config: dict)[source] +
Bases:
+OptimizationBackendBase class for implementations of optimization backends for ADMM +algorithms.
+-
+
- +abstract property coupling_grid: list[float] +
Returns the grid on which the coupling variables are discretized.
+
-
+
- +pydantic model agentlib_mpc.optimization_backends.backend.BackendConfig[source] +
Bases:
+BaseModel-
+
- Config: +
-
+
extra: str = forbid
+
+- Fields: +
- + +
- Validators: +
- + +
-
+
- +field discretization_options: DiscretizationOptions [Required] +
-
+
- +field model: dict [Required] +
-
+
- +field name: str | None = None +
-
+
- +field overwrite_result_file: bool | None = False +
-
+
- Validated by: +
- + +
-
+
- +field save_results: bool | None = None +
-
+
- Validated by: +
- + +
-
+
- +validator check_csv » results_file[source] +
-
+
- +validator check_overwrite » overwrite_result_file[source] +
Checks, whether the overwrite results sttings are valid, and deletes +existing result files if applicable.
+
-
+
- +validator disable_results_if_no_file » save_results[source] +
-
+
- +class agentlib_mpc.optimization_backends.backend.OptimizationBackend(config: dict)[source] +
Bases:
+ABCBase class for all optimization backends. OptimizationBackends are a +plugin for the ‘mpc’ module. They provide means to setup and solve the +underlying optimization problem of the MPC. They also can save data of +the solutions.
+-
+
- +config_type +
alias of
+BackendConfig
-
+
- +get_lags_per_variable() dict[str, float][source] +
Returns the name of variables which include lags and their lag in seconds. +The MPC module can use this information to save relevant past data of lagged +variables
+
-
+
- +mpc_backend_parameters = ('time_step', 'prediction_horizon') +
-
+
- +register_logger(logger: Logger)[source] +
Registers a logger, can be used to use the module logger
+
-
+
- +results_file_exists() bool[source] +
Checks if the results file already exists, and if not, creates it with +headers.
+
-
+
- +abstract setup_optimization(var_ref: VariableReference)[source] +
Performs all necessary steps to make the
+solvemethod usable.-
+
- Parameters: +
var_ref – Variable Reference that specifies the role of each model variable +in the mpc
+
+
-
+
- +abstract solve(now: float | datetime, current_vars: Dict[str, AgentVariable]) Results[source] +
Solves the optimization problem given the current values of the +corresponding AgentVariables and system time. The standardization of +return values is a work in progress.
+-
+
- Parameters: +
-
+
now – Current time used for interpolation of input trajectories.
+current_vars – Dict of AgentVariables holding the values relevant to +the optimization problem. Keys are the names
+
+- Returns: +
A dataframe with all optimization variables over their respective +grids. Depending on discretization, can include many nan’s, so care +should be taken when using this, e.g. always use dropna() after +accessing a column.
++
+ +-
+
- Example:
+
+variables mDot | T_0 | slack_T
+time +0 0.1 | 298 | nan +230 nan | 297 | 3 +470 nan | 296 | 2 +588 nan | 295 | 1 +700 0.05 | 294 | nan +930 nan | 294 | 0.1
+
+
+
Module contents
+ + +
+
+
+
+
+
+
+
+
+
+
\ No newline at end of file
diff --git a/docs/0.6.4/docs/agentlib_mpc.utils.plotting.html b/docs/0.6.4/docs/agentlib_mpc.utils.plotting.html
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+
+
+
+
+ agentlib_mpc.utils package
+Subpackages
+
+
+-
+
- agentlib_mpc.utils.plotting package + +
Submodules
+agentlib_mpc.utils.analysis module
+-
+
- +agentlib_mpc.utils.analysis.admm_at_time_step(data: DataFrame | Series, time_step: float = None, variable=None, iteration: float = -1, index_offset: float | Literal['auto'] | bool = True, convert_to: Literal['seconds', 'minutes', 'hours', 'days'] = 'seconds') DataFrame[source] +
Gets the results of an optimization at a time step. +:param index_offset: Determines how the index will be updated when loading the data. +:param The offset will be subtracted from the time-index. This is useful for results: +:param of realtime systems: to cut the number down to something understandable. For example, if the time
++
+index (level 0 of the input Dataframe) is [105, 115, 125] and we give an +index_offset of 100, the data will be handled as if the index was [5, 15, 25].
++
+-
+
- If “auto” or True is provided as an argument, the index will be modified to
start at 0. If 0 or False are provided, no modifications will be made.
+
+
-
+
- Parameters: +
-
+
want (where the time value with be a unix time stamp and we) –
to cut the number down to something understandable. For example, if the time +index (level 0 of the input Dataframe) is [105, 115, 125] and we give an +index_offset of 100, the data will be handled as if the index was [5, 15, 25].
++
+-
+
- If “auto” or True is provided as an argument, the index will be modified to
start at 0. If 0 or False are provided, no modifications will be made.
+
+
+data – The multi-indexed results data frame from the mpc
+time_step – The time step from which results should be shown. +If no exact match, shows closest.
+variable – If specified, only returns results +with regard to a certain variable.
+iteration – Specifies, from which inner ADMM iteration data should be +from. If negative, counts from last iteration. Default -1.
+convert_to – Whether the data should be converted to datetime, minutes etc.
+
+- Returns: +
A single-indexed Dataframe of the optimization results +at the specified time step. If variable is not specified, +returns all variables with a double column index, if it +is specified returns only values and/or bounds with +single indexed columns.
+
+
-
+
- +agentlib_mpc.utils.analysis.convert_index(convert_to: Literal['seconds', 'minutes', 'hours', 'days'] | Literal['datetime'], index: Index)[source] +
Converts an index from seconds to datetime or another unit +:param convert_to: unit, e.g. minutes, hours, datetime +:param index: pandas index object
+Returns:
+
-
+
- +agentlib_mpc.utils.analysis.convert_multi_index(data: DataFrame, convert_to: Literal['seconds', 'minutes', 'hours', 'days'] | Literal['datetime'])[source] +
Converts an index of an MPC or ADMM results Dataframe to a different unit, +assuming it is passed in seconds.
+
-
+
- +agentlib_mpc.utils.analysis.first_vals_at_trajectory_index(data: DataFrame | Series)[source] +
Gets the first values at each time step of a results trajectory.
+
-
+
- +agentlib_mpc.utils.analysis.get_number_of_iterations(data: DataFrame) dict[agentlib_mpc.utils.analysis.SimulationTime, int][source] +
Returns the number of iterations at each time instance of the ADMM simulation.
+
-
+
- +agentlib_mpc.utils.analysis.get_time_steps(data: DataFrame) Iterable[float][source] +
Returns the time steps at which an MPC step was performed.
+
-
+
- +agentlib_mpc.utils.analysis.last_vals_at_trajectory_index(data: DataFrame | Series)[source] +
Gets the last values at each time step of a results trajectory.
+
-
+
- +agentlib_mpc.utils.analysis.mpc_at_time_step(data: DataFrame, time_step: float, variable=None, variable_type='variable', index_offset: float | Literal['auto'] | bool = True) DataFrame[source] +
Gets the results of an optimization at a time step.
+-
+
- Parameters: +
-
+
data – The multi-indexed results data frame from the mpc
+time_step – The time step from which results should be shown. +If no exact match, shows closest.
+variable – If specified, only returns results +with regard to a certain variable.
+variable_type – The type of the variable provided (parameter, variable, lower, …)
+index_offset – Determines how the index will be updated when loading the data.
+results (The offset will be subtracted from the time-index. This is useful for) –
+systems (of realtime) –
to cut the number down to something understandable. For example, if the time +index (level 0 of the input Dataframe) is [105, 115, 125] and we give an +index_offset of 100, the data will be handled as if the index was [5, 15, 25].
++
+-
+
- If “auto” or True is provided as an argument, the index will be modified to
start at 0. If 0 or False are provided, no modifications will be made.
+
+
+want (where the time value with be a unix time stamp and we) –
to cut the number down to something understandable. For example, if the time +index (level 0 of the input Dataframe) is [105, 115, 125] and we give an +index_offset of 100, the data will be handled as if the index was [5, 15, 25].
++
+-
+
- If “auto” or True is provided as an argument, the index will be modified to
start at 0. If 0 or False are provided, no modifications will be made.
+
+
+
+- Returns: +
-
+
- A single-indexed Dataframe of the optimization results
at the specified time step. If variable is not specified, +returns all variables with a double column index, if it +is specified returns only values and/or bounds with +single indexed columns.
+
+
+- Return type: +
pd.DataFrame
+
+
agentlib_mpc.utils.debug module
+Module contents
+Package containing utils for agentlib_mpc.
+
+
+
+
+
+
+
+
+
+
+
\ No newline at end of file
diff --git a/docs/0.6.4/docs/code/agentlib_mpc.data_structures.html b/docs/0.6.4/docs/code/agentlib_mpc.data_structures.html
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+
+
+ agentlib_mpc.utils.plotting package
+Submodules
+agentlib_mpc.utils.plotting.admm_animation module
+Modules that defines functions to be used for automatically creating animations of +ADMM convergence
+-
+
- +agentlib_mpc.utils.plotting.admm_animation.animate_full(i: int, lines: dict[str, matplotlib.lines.Line2D], annotation: Annotation, data: dict[str, pandas.core.frame.DataFrame], time_step: float)[source] +
-
+
- +agentlib_mpc.utils.plotting.admm_animation.init_full(lines: dict[str, matplotlib.lines.Line2D], annotation: Annotation, ax: Axes)[source] +
-
+
- +agentlib_mpc.utils.plotting.admm_animation.make_animation(data: dict[str, pandas.core.frame.DataFrame], time_step: float = 0, file_name: str = '', customize: Callable[[Figure, Axes, Style], Tuple[Figure, Axes]] = None, iteration=-1, interval: int = 300)[source] +
agentlib_mpc.utils.plotting.admm_residuals module
+-
+
- +agentlib_mpc.utils.plotting.admm_residuals.load_residuals(file: str | Path) DataFrame[source] +
Loads a residuals csv file in the correct format.
+
-
+
- +agentlib_mpc.utils.plotting.admm_residuals.plot_over_time(residuals: ~pandas.core.frame.DataFrame, primal_tol: float = None, dual_tol: float = None, max_iters: int = None) -> (<class 'matplotlib.figure.Figure'>, (<class 'matplotlib.axes._axes.Axes'>, <class 'matplotlib.axes._axes.Axes'>))[source] +
Plots the final residuals over time.
+
-
+
- +agentlib_mpc.utils.plotting.admm_residuals.plot_single_time_step(residuals: ~pandas.core.frame.DataFrame, time_step: float = None, primal_tol: float = None, dual_tol: float = None) -> (<class 'matplotlib.figure.Figure'>, <class 'matplotlib.axes._axes.Axes'>)[source] +
Plots the decrease of the residuals over iterations for a time step
+
-
+
- +agentlib_mpc.utils.plotting.admm_residuals.residuals_over_time(residuals: DataFrame) DataFrame[source] +
Evaluates the residuals over time. Takes a raw residuals DataFrame and returns a +Dataframe, which has for each time step the number of iterations and the final primal and dual residuals.
+-
+
- Returns: +
DataFrame with float index (time in seconds) and the columns +(“primal_residual”, “dual_residual”, “iters”)
+
+
agentlib_mpc.utils.plotting.basic module
+Some basic plotting utilities
+-
+
- +class agentlib_mpc.utils.plotting.basic.EBCColors[source] +
Bases:
+object-
+
- +blue: tuple[float, float, float] = (0.0, 0.32941176470588235, 0.6235294117647059) +
-
+
- +dark_grey: tuple[float, float, float] = (0.3058823529411765, 0.30980392156862746, 0.3137254901960784) +
-
+
- +dark_red: tuple[float, float, float] = (0.6745098039215687, 0.16862745098039217, 0.10980392156862745) +
-
+
- +ebc_palette_sort_1: list[tuple[float, float, float]] = [(0.6745098039215687, 0.16862745098039217, 0.10980392156862745), (0.8666666666666667, 0.25098039215686274, 0.17647058823529413), (0.9215686274509803, 0.5490196078431373, 0.5058823529411764), (0.3058823529411765, 0.30980392156862746, 0.3137254901960784), (0.615686274509804, 0.6196078431372549, 0.6274509803921569), (0.8509803921568627, 0.8509803921568627, 0.8509803921568627), (0.0, 0.32941176470588235, 0.6235294117647059), (0.615686274509804, 0.7647058823529411, 0.9019607843137255), (0.4392156862745098, 0.6784313725490196, 0.2784313725490196)] +
-
+
- +ebc_palette_sort_2: list[tuple[float, float, float]] = [(0.8666666666666667, 0.25098039215686274, 0.17647058823529413), (0.0, 0.32941176470588235, 0.6235294117647059), (0.615686274509804, 0.6196078431372549, 0.6274509803921569), (0.4392156862745098, 0.6784313725490196, 0.2784313725490196), (0.6745098039215687, 0.16862745098039217, 0.10980392156862745), (0.3058823529411765, 0.30980392156862746, 0.3137254901960784), (0.9215686274509803, 0.5490196078431373, 0.5058823529411764), (0.615686274509804, 0.7647058823529411, 0.9019607843137255), (0.8509803921568627, 0.8509803921568627, 0.8509803921568627)] +
-
+
- +green: tuple[float, float, float] = (0.4392156862745098, 0.6784313725490196, 0.2784313725490196) +
-
+
- +grey: tuple[float, float, float] = (0.615686274509804, 0.6196078431372549, 0.6274509803921569) +
-
+
- +light_blue: tuple[float, float, float] = (0.615686274509804, 0.7647058823529411, 0.9019607843137255) +
-
+
- +light_grey: tuple[float, float, float] = (0.8509803921568627, 0.8509803921568627, 0.8509803921568627) +
-
+
- +light_red: tuple[float, float, float] = (0.9215686274509803, 0.5490196078431373, 0.5058823529411764) +
-
+
- +red: tuple[float, float, float] = (0.8666666666666667, 0.25098039215686274, 0.17647058823529413) +
-
+
- +class agentlib_mpc.utils.plotting.basic.FontDict[source] +
Bases:
+TypedDict-
+
- +fontsize: float +
-
+
- +class agentlib_mpc.utils.plotting.basic.ValueRange(min: float, max: float)[source] +
Bases:
+object-
+
- +max: float +
-
+
- +min: float +
-
+
- +agentlib_mpc.utils.plotting.basic.make_fig(style: Style, customizer: Callable[[Figure, Axes, Style], Tuple[Figure, Axes]] = None, rows: int = 1) tuple[matplotlib.figure.Figure, tuple[matplotlib.axes._axes.Axes, ...]][source] +
- +agentlib_mpc.utils.plotting.basic.make_fig(style: Style, customizer: Callable[[Figure, tuple[matplotlib.axes._axes.Axes], Style], Tuple[Figure, tuple[matplotlib.axes._axes.Axes]]] = None) tuple[matplotlib.figure.Figure, matplotlib.axes._axes.Axes] +
- +agentlib_mpc.utils.plotting.basic.make_fig(style: Style) tuple[matplotlib.figure.Figure, matplotlib.axes._axes.Axes] +
Creates a figure and axes with an amount of rows. If rows is specified, return +a tuple of axes, else only an ax
+
Module contents
+
+
+
+
+
+
+
+
+
+
+
\ No newline at end of file
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+
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+
+
+ agentlib_mpc.data_structures package
+Submodules
+agentlib_mpc.data_structures.admm_datatypes module
+-
+
- +class agentlib_mpc.data_structures.admm_datatypes.ADMMParameters(penalty_factor: float, prediction_horizon: int, time_step: float)[source] +
Bases:
+objectCollection of parameters which have to be shared across all agents in ADMM.
+-
+
- +penalty_factor: float +
-
+
- +prediction_horizon: int +
-
+
- +time_step: float +
-
+
- +class agentlib_mpc.data_structures.admm_datatypes.ADMMParticipation(source: Source, ready: bool = False, participating: bool = False)[source] +
Bases:
+objectHelper class to organize ADMM participants.
+-
+
- +participating: bool = False +
-
+
- +ready: bool = False +
-
+
- +source: Source +
-
+
- +class agentlib_mpc.data_structures.admm_datatypes.AgentDictEntry(name: str, status: ~agentlib_mpc.data_structures.coordinator_datatypes.AgentStatus = AgentStatus.pending, coup_vars: ~typing.List[str] = <factory>, exchange_vars: ~typing.List[str] = <factory>)[source] +
Bases:
+AgentDictEntryHolds participating coupling variables (consensus and exchange) of a single +agent in ADMM. Used in the coordinator.
+-
+
- +coup_vars: List[str] +
-
+
- +exchange_vars: List[str] +
-
+
- +name: str +
-
+
- +class agentlib_mpc.data_structures.admm_datatypes.AgentToCoordinator(local_trajectory: Dict[str, numpy.ndarray], local_exchange_trajectory: Dict[str, numpy.ndarray])[source] +
Bases:
+StructuredValue-
+
- +local_exchange_trajectory: Dict[str, ndarray] +
-
+
- +local_trajectory: Dict[str, ndarray] +
-
+
- +class agentlib_mpc.data_structures.admm_datatypes.ConsensusVariable(local_trajectories: Dict[agentlib.core.datamodels.Source, list] = <factory>, mean_trajectory: list = <factory>, delta_mean: numpy.ndarray = <factory>, primal_residual: numpy.ndarray = <factory>, multipliers: Dict[agentlib.core.datamodels.Source, list] = <factory>)[source] +
Bases:
+CouplingVariable-
+
- +delta_mean: ndarray +
-
+
- +local_trajectories: Dict[Source, list] +
-
+
- +mean_trajectory: list +
-
+
- +multipliers: Dict[Source, list] +
-
+
- +primal_residual: ndarray +
-
+
- +class agentlib_mpc.data_structures.admm_datatypes.CoordinatorToAgent(target: str, mean_trajectory: Dict[str, list], multiplier: Dict[str, list], mean_diff_trajectory: Dict[str, list], exchange_multiplier: Dict[str, list], penalty_parameter: float)[source] +
Bases:
+StructuredValue-
+
- +exchange_multiplier: Dict[str, list] +
-
+
- +mean_diff_trajectory: Dict[str, list] +
-
+
- +mean_trajectory: Dict[str, list] +
-
+
- +multiplier: Dict[str, list] +
-
+
- +penalty_parameter: float +
-
+
- +target: str +
-
+
- +class agentlib_mpc.data_structures.admm_datatypes.CouplingEntry(name: str)[source] +
Bases:
+objectHolds naming conventions for different optimizatin variables / parameters +associated with a coupling variable in consensus ADMM.
+ + +-
+
- +property lagged +
-
+
- +property local +
-
+
- +property mean +
-
+
- +property multiplier +
-
+
- +name: str +
-
+
- +class agentlib_mpc.data_structures.admm_datatypes.CouplingVariable(local_trajectories: ~typing.Dict[~agentlib.core.datamodels.Source, list] = <factory>, mean_trajectory: list = <factory>, delta_mean: ~numpy.ndarray = <factory>, primal_residual: ~numpy.ndarray = <factory>)[source] +
Bases:
+objectHolds information about a phy
+-
+
- +delta_mean: ndarray +
-
+
- +flat_locals(sources: Iterable[Source] = None) list[float][source] +
Returns the flattened array of all local variables and their multipliers.
+-
+
- Parameters: +
sources – list of sources that should be included in the update. +By default, all are included.
+
+- Returns: +
flat lists of local variables and multipliers (locals, multipliers)
+
+
-
+
- +get_residual(rho: float) Tuple[ndarray, ndarray][source] +
Returns the primal and dual residual of the last iteration as a tuple +of flattened Arrays. +:param rho:
+-
+
- Returns: +
(primal residual, dual residual)
+
+
-
+
- +local_trajectories: Dict[Source, list] +
-
+
- +mean_trajectory: list +
-
+
- +property participants +
Returns all agent sources that are registered to this coupling.
+
-
+
- +primal_residual: ndarray +
-
+
- +class agentlib_mpc.data_structures.admm_datatypes.ExchangeEntry(name: str)[source] +
Bases:
+objectHolds naming conventions for different optimizatin variables / parameters +associated with a coupling variable in exchange ADMM.
+ + +-
+
- +property lagged +
-
+
- +property local +
-
+
- +property mean_diff +
-
+
- +property multiplier +
-
+
- +name: str +
-
+
- +class agentlib_mpc.data_structures.admm_datatypes.ExchangeVariable(local_trajectories: Dict[agentlib.core.datamodels.Source, list] = <factory>, mean_trajectory: list = <factory>, delta_mean: numpy.ndarray = <factory>, primal_residual: numpy.ndarray = <factory>, diff_trajectories: Dict[agentlib.core.datamodels.Source, list[float]] = <factory>, multiplier: list[float] = <factory>)[source] +
Bases:
+CouplingVariable-
+
- +delta_mean: ndarray +
-
+
- +diff_trajectories: Dict[Source, list[float]] +
-
+
- +local_trajectories: Dict[Source, list] +
-
+
- +mean_trajectory: list +
-
+
- +multiplier: list[float] +
-
+
- +primal_residual: ndarray +
-
+
- +class agentlib_mpc.data_structures.admm_datatypes.StructuredValue[source] +
Bases:
+objectBase Class to specify the structure of an AgentVariable Value. It will +be efficiently sent and deserialized.
+ + + + +
-
+
- +class agentlib_mpc.data_structures.admm_datatypes.VariableReference(states: ~typing.List[str] = <factory>, controls: ~typing.List[str] = <factory>, inputs: ~typing.List[str] = <factory>, parameters: ~typing.List[str] = <factory>, outputs: ~typing.List[str] = <factory>, couplings: list[agentlib_mpc.data_structures.admm_datatypes.CouplingEntry] = <factory>, exchange: list[agentlib_mpc.data_structures.admm_datatypes.ExchangeEntry] = <factory>)[source] +
Bases:
+FullVariableReferenceHolds info about all variables of an MPC and their role in the optimization +problem.
+ + +-
+
- +controls: List[str] +
-
+
- +couplings: list[agentlib_mpc.data_structures.admm_datatypes.CouplingEntry] +
-
+
- +exchange: list[agentlib_mpc.data_structures.admm_datatypes.ExchangeEntry] +
-
+
- +classmethod from_config(config: BaseModuleConfigClass)[source] +
Creates an instance from a pydantic values dict which includes lists of +AgentVariables with the keys corresponding to ‘states’, ‘inputs’, etc..
+
-
+
- +inputs: List[str] +
-
+
- +outputs: List[str] +
-
+
- +parameters: List[str] +
-
+
- +states: List[str] +
agentlib_mpc.data_structures.casadi_utils module
+Stores all sorts of Dataclasses, Enums or Factories to help with the +CasadiBackend.
+-
+
- +pydantic model agentlib_mpc.data_structures.casadi_utils.CasadiDiscretizationOptions[source] +
Bases:
+DiscretizationOptions-
+
- Config: +
-
+
extra: str = forbid
+
+- Fields: +
- + +
-
+
- +field collocation_method: CollocationMethod = CollocationMethod.legendre +
-
+
- +field collocation_order: int = 3 +
-
+
- Constraints: +
-
+
ge = 1
+le = 9
+
+
-
+
- +field integrator: Integrators = Integrators.cvodes +
-
+
- +field method: DiscretizationMethod = DiscretizationMethod.collocation +
-
+
- +field prediction_horizon: int = 5 +
Prediction horizon of the MPC.
+-
+
- Constraints: +
-
+
ge = 0
+
+
-
+
- +field time_step: float = 60 +
Time step of the MPC.
+-
+
- Constraints: +
-
+
ge = 0
+
+
-
+
- +class agentlib_mpc.data_structures.casadi_utils.CollocationMethod(value, names=None, *, module=None, qualname=None, type=None, start=1, boundary=None)[source] +
Bases:
+str,Enum-
+
- +legendre = 'legendre' +
-
+
- +radau = 'radau' +
-
+
- +class agentlib_mpc.data_structures.casadi_utils.Constraint(function: casadi.casadi.MX, lb: casadi.casadi.MX, ub: casadi.casadi.MX)[source] +
Bases:
+object-
+
- +function: MX +
-
+
- +lb: MX +
-
+
- +ub: MX +
-
+
- +class agentlib_mpc.data_structures.casadi_utils.DiscretizationMethod(value, names=None, *, module=None, qualname=None, type=None, start=1, boundary=None)[source] +
Bases:
+str,Enum-
+
- +collocation = 'collocation' +
-
+
- +multiple_shooting = 'multiple_shooting' +
-
+
- +class agentlib_mpc.data_structures.casadi_utils.Integrators(value, names=None, *, module=None, qualname=None, type=None, start=1, boundary=None)[source] +
Bases:
+str,Enum-
+
- +cvodes = 'cvodes' +
-
+
- +euler = 'euler' +
-
+
- +rk = 'rk' +
-
+
- +class agentlib_mpc.data_structures.casadi_utils.ModelConstraint(lb, function, ub)[source] +
Bases:
+NamedTuple-
+
- +function: MX +
Alias for field number 1
+
-
+
- +lb: MX | SX | Sparsity | DM | float | int +
Alias for field number 0
+
-
+
- +ub: MX | SX | Sparsity | DM | float | int +
Alias for field number 2
+
-
+
- +class agentlib_mpc.data_structures.casadi_utils.OptParMXContainer(var: ~typing.List[~casadi.casadi.MX] = <factory>, grid: ~typing.List[float] = <factory>)[source] +
Bases:
+objectStores the necessary MX variables created during discretization for +OptimizationParameters.
+-
+
- +grid: List[float] +
-
+
- +var: List[MX] +
-
+
- +class agentlib_mpc.data_structures.casadi_utils.OptVarMXContainer(var: ~typing.List[~casadi.casadi.MX] = <factory>, grid: ~typing.List[float] = <factory>, lb: ~typing.List[~casadi.casadi.MX] = <factory>, ub: ~typing.List[~casadi.casadi.MX] = <factory>, guess: ~typing.List[~casadi.casadi.MX] = <factory>, opt: ~casadi.casadi.DM = None)[source] +
Bases:
+OptParMXContainerStores the necessary MX variables created during discretization for +OptimizationVariables.
+-
+
- +grid: List[float] +
-
+
- +guess: List[MX] +
-
+
- +lb: List[MX] +
-
+
- +opt: DM = None +
-
+
- +ub: List[MX] +
-
+
- +var: List[MX] +
-
+
- +class agentlib_mpc.data_structures.casadi_utils.SolverFactory(do_jit: bool, bat_file: ~pathlib.Path = None, name: str = None, options: ~agentlib_mpc.data_structures.casadi_utils.SolverOptions = <factory>, logger: ~logging.Logger = None)[source] +
Bases:
+objectCreates a solver given an NLP and an options construct.
+-
+
- +bat_file: Path = None +
-
+
- +create_solver(nlp: dict | str, discrete: List[bool] = None, equalities: list[bool] = None) Function[source] +
-
+
- +do_jit: bool +
-
+
- +logger: Logger = None +
-
+
- +make_casadi_nlp(problem: dict | str, solver: str, opts: dict, problem_type: Literal['nlp', 'qp'] = 'nlp')[source] +
-
+
- +name: str = None +
-
+
- +options: SolverOptions +
-
+
- +pydantic model agentlib_mpc.data_structures.casadi_utils.SolverOptions[source] +
Bases:
+BaseModel-
+
- Config: +
-
+
extra: str = forbid
+
+- Fields: +
- + +
-
+
- +field options: dict = {} +
-
+
- +class agentlib_mpc.data_structures.casadi_utils.Solvers(value, names=None, *, module=None, qualname=None, type=None, start=1, boundary=None)[source] +
Bases:
+str,Enum-
+
- +bonmin = 'bonmin' +
-
+
- +fatrop = 'fatrop' +
-
+
- +gurobi = 'gurobi' +
-
+
- +ipopt = 'ipopt' +
-
+
- +osqp = 'osqp' +
-
+
- +proxqp = 'proxqp' +
-
+
- +qpoases = 'qpoases' +
-
+
- +sqpmethod = 'sqpmethod' +
-
+
- +agentlib_mpc.data_structures.casadi_utils.compile_solver(bat_file: Path, name: str, optimizer: Function) str[source] +
Code-generates an ipopt solver and compiles it. +Currently, only works on Windows! Requires a batch file that knows +how to setup Visual Studio command line and compiles the source code.
+-
+
- Returns: +
The Path to the .dll file for the compiled solver.
+
+- Raises: +
-
+
TypeError –
+FileNotFoundError –
+RuntimeError –
+
+
agentlib_mpc.data_structures.coordinator_datatypes module
+-
+
- +class agentlib_mpc.data_structures.coordinator_datatypes.AgentDictEntry(name: str, status: AgentStatus = AgentStatus.pending)[source] +
Bases:
+objectDataclass holding the status of a participating agent in DMPC.
+-
+
- +name: str +
-
+
- +optimization_data = OptimizationData(x=array([], dtype=float64), u=array([], dtype=float64)) +
-
+
- +status: AgentStatus = 'pending' +
-
+
- +class agentlib_mpc.data_structures.coordinator_datatypes.AgentStatus(value, names=None, *, module=None, qualname=None, type=None, start=1, boundary=None)[source] +
Bases:
+str,EnumEnum used within a DMPC-Coordinator to keep track of the statuses of its +participating agents.
+-
+
- +busy = 'busy' +
-
+
- +pending = 'pending' +
-
+
- +ready = 'ready' +
-
+
- +standby = 'standby' +
-
+
- +class agentlib_mpc.data_structures.coordinator_datatypes.CoordinatorStatus(value, names=None, *, module=None, qualname=None, type=None, start=1, boundary=None)[source] +
Bases:
+str,EnumEnum used to keep track of the status of a DMPC-Coordinator.
+-
+
- +init_iterations = 'init_iterations' +
-
+
- +optimization = 'optimization' +
-
+
- +sleeping = 'sleeping' +
-
+
- +updating = 'updating' +
-
+
- +class agentlib_mpc.data_structures.coordinator_datatypes.OptimizationData(x: ~numpy.array = <factory>, u: ~numpy.array = <factory>)[source] +
Bases:
+objectContains specific variables (or time series) of the agent
+ + + + +-
+
- +u: array +
-
+
- +x: array +
-
+
- +class agentlib_mpc.data_structures.coordinator_datatypes.RegistrationMessage(status: AgentStatus = None, opts: dict = None, agent_id: str = None, coupling: list = None)[source] +
Bases:
+objectDataclass structuring the communication during registration between a +participating agent and the coordinator in DMPC.
+-
+
- +agent_id: str = None +
-
+
- +coupling: list = None +
-
+
- +opts: dict = None +
-
+
- +status: AgentStatus = None +
agentlib_mpc.data_structures.interpolation module
+-
+
- +class agentlib_mpc.data_structures.interpolation.InterpolationMethods(value, names=None, *, module=None, qualname=None, type=None, start=1, boundary=None)[source] +
Bases:
+str,Enum-
+
- +linear = 'linear' +
-
+
- +mean_over_interval = 'mean_over_interval' +
-
+
- +no_interpolation = 'no_interpolation' +
-
+
- +previous = 'previous' +
-
+
- +spline3 = 'spline3' +
agentlib_mpc.data_structures.ml_model_datatypes module
+-
+
- +pydantic model agentlib_mpc.data_structures.ml_model_datatypes.EarlyStoppingCallback[source] +
Bases:
+BaseModel-
+
- Fields: +
- + +
-
+
- +field activate: bool = False +
-
+
- +field patience: int = (1000,) +
-
+
- +field restore_best_weights: bool = True +
-
+
- +field verbose: Literal[0, 1] = 0 +
-
+
- +pydantic model agentlib_mpc.data_structures.ml_model_datatypes.Feature[source] +
Bases:
+BaseModel-
+
- Fields: +
-
+
- +
- +
+
-
+
- +field lag: int = 1 +
-
+
- +field name: str [Required] +
-
+
- +pydantic model agentlib_mpc.data_structures.ml_model_datatypes.OutputFeature[source] +
Bases:
+Feature-
+
- Fields: +
- + +
- Validators: +
- + +
-
+
- +field output_type: OutputType [Required] +
What kind of output this is. If ‘absolute’ an forward pass of the MLModel will yield the absolute value of the featuer at the next time step. If it is ‘difference’, the difference to the last time step will be generated, so it has to be added again.
+
-
+
- +field recursive: bool = True +
If the output feature is recursive, it will also be used as an input for the model. This is useful for mpc, where we want to model the evolution of states based on their previous value. If false, can be used to model algebraic relationships. Default is true.
+-
+
- Validated by: +
- + +
-
+
- +class agentlib_mpc.data_structures.ml_model_datatypes.OutputType(value, names=None, *, module=None, qualname=None, type=None, start=1, boundary=None)[source] +
Bases:
+str,Enum-
+
- +absolute = 'absolute' +
-
+
- +difference = 'difference' +
-
+
- +class agentlib_mpc.data_structures.ml_model_datatypes.TrainingData(training_inputs: DataFrame, training_outputs: DataFrame, validation_inputs: DataFrame, validation_outputs: DataFrame, test_inputs: DataFrame, test_outputs: DataFrame)[source] +
Bases:
+objectStores the data which is used to train a model.
+ + +-
+
- +save(path: Path)[source] +
Saves three csv files in the path location. The csv files contain the test, +training and validation data
+
-
+
- +test_inputs: DataFrame +
-
+
- +test_outputs: DataFrame +
-
+
- +training_inputs: DataFrame +
-
+
- +training_outputs: DataFrame +
-
+
- +validation_inputs: DataFrame +
-
+
- +validation_outputs: DataFrame +
-
+
- +agentlib_mpc.data_structures.ml_model_datatypes.column_order(inputs: dict[str, agentlib_mpc.data_structures.ml_model_datatypes.Feature], outputs: dict[str, agentlib_mpc.data_structures.ml_model_datatypes.OutputFeature]) list[str][source] +
Defines the order of the columns in which Training data should be passed to +keras, and saved for the Serialization.
+
agentlib_mpc.data_structures.mpc_datamodels module
+-
+
- +class agentlib_mpc.data_structures.mpc_datamodels.BaseVariableReference[source] +
Bases:
+ + + + +object
-
+
- +pydantic model agentlib_mpc.data_structures.mpc_datamodels.DiscretizationOptions[source] +
Bases:
+BaseModelClass defining the options to discretize an MPC. Can be extended for different +optimization implementations.
+-
+
- Config: +
-
+
extra: str = allow
+
+- Fields: +
- + +
-
+
- +field prediction_horizon: int = 5 +
Prediction horizon of the MPC.
+-
+
- Constraints: +
-
+
ge = 0
+
+
-
+
- +field time_step: float = 60 +
Time step of the MPC.
+-
+
- Constraints: +
-
+
ge = 0
+
+
-
+
- +class agentlib_mpc.data_structures.mpc_datamodels.FullVariableReference(states: List[str] = <factory>, controls: List[str] = <factory>, inputs: List[str] = <factory>, parameters: List[str] = <factory>, outputs: List[str] = <factory>)[source] +
Bases:
+VariableReference-
+
- +property r_del_u: List[str] +
-
+
- +class agentlib_mpc.data_structures.mpc_datamodels.InitStatus(value, names=None, *, module=None, qualname=None, type=None, start=1, boundary=None)[source] +
Bases:
+str,EnumKeep track of the readyness status of the MPC.
+-
+
- +during_update = '2' +
-
+
- +pre_module_init = '1' +
-
+
- +ready = '3' +
-
+
- +class agentlib_mpc.data_structures.mpc_datamodels.MHEVariableReference(states: List[str] = <factory>, measured_states: List[str] = <factory>, weights_states: List[str] = <factory>, estimated_inputs: List[str] = <factory>, estimated_parameters: List[str] = <factory>, known_inputs: List[str] = <factory>, known_parameters: List[str] = <factory>, outputs: List[str] = <factory>)[source] +
Bases:
+BaseVariableReference-
+
- +all_variables() Iterable[str][source] +
Returns a list of all variables registered in the var_ref which the MHE can +get from the config with get()
+
-
+
- +estimated_inputs: List[str] +
-
+
- +estimated_parameters: List[str] +
-
+
- +known_inputs: List[str] +
-
+
- +known_parameters: List[str] +
-
+
- +measured_states: List[str] +
-
+
- +outputs: List[str] +
-
+
- +states: List[str] +
-
+
- +weights_states: List[str] +
-
+
- +class agentlib_mpc.data_structures.mpc_datamodels.MINLPVariableReference(states: List[str] = <factory>, controls: List[str] = <factory>, inputs: List[str] = <factory>, parameters: List[str] = <factory>, outputs: List[str] = <factory>, binary_controls: List[str] = <factory>)[source] +
Bases:
+VariableReference-
+
- +binary_controls: List[str] +
-
+
- +controls: List[str] +
-
+
- +inputs: List[str] +
-
+
- +outputs: List[str] +
-
+
- +parameters: List[str] +
-
+
- +states: List[str] +
-
+
- +class agentlib_mpc.data_structures.mpc_datamodels.MPCVariable(*, name: str, type: str | None = None, timestamp: float | None = None, unit: str = 'Not defined', description: str = 'Not defined', ub=inf, lb=-inf, clip: bool = False, allowed_values: List[Any] = [], value: Any = None, alias: str = None, source: dict | Source | str = Source(agent_id=None, module_id=None), shared: bool | None = None, rdf_class: str | None = None, interpolation_method: InterpolationMethods = InterpolationMethods.linear)[source] +
Bases:
+AgentVariableAgentVariable used to define input variables of MPC.
+-
+
- +interpolation_method: InterpolationMethods +
-
+
- +class agentlib_mpc.data_structures.mpc_datamodels.Results(*args, **kwargs)[source] +
Bases:
+Protocol-
+
- +df: DataFrame +
-
+
- +class agentlib_mpc.data_structures.mpc_datamodels.VariableReference(states: List[str] = <factory>, controls: List[str] = <factory>, inputs: List[str] = <factory>, parameters: List[str] = <factory>, outputs: List[str] = <factory>)[source] +
Bases:
+BaseVariableReference-
+
- +controls: List[str] +
-
+
- +inputs: List[str] +
-
+
- +outputs: List[str] +
-
+
- +parameters: List[str] +
-
+
- +states: List[str] +
-
+
- +agentlib_mpc.data_structures.mpc_datamodels.cia_relaxed_results_path(path: Path | str) Path[source] +
+
+
+
+
+
+
+
+
+
+
\ No newline at end of file
diff --git a/docs/0.6.4/docs/code/agentlib_mpc.models.html b/docs/0.6.4/docs/code/agentlib_mpc.models.html
new file mode 100644
index 0000000..f19b9e9
--- /dev/null
+++ b/docs/0.6.4/docs/code/agentlib_mpc.models.html
@@ -0,0 +1,1579 @@
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+ agentlib_mpc package
+Plugin for the AgentLib which contains modules for MPC, distributed MPC with ADMM +and data-driven modeling.
+Subpackages
+
+
+-
+
- agentlib_mpc.data_structures package
-
+
- Submodules +
- agentlib_mpc.data_structures.admm_datatypes module
-
+
ADMMParameters+
+ADMMParticipation+
+AgentDictEntry+
+AgentToCoordinator+
+ConsensusVariable-
+
ConsensusVariable.delta_mean
+ConsensusVariable.flat_multipliers()
+ConsensusVariable.local_trajectories
+ConsensusVariable.mean_trajectory
+ConsensusVariable.multipliers
+ConsensusVariable.primal_residual
+ConsensusVariable.shift_values_by_one()
+ConsensusVariable.update_mean_trajectory()
+ConsensusVariable.update_multipliers()
+
+CoordinatorToAgent+
+CouplingEntry+
+CouplingVariable+
+ExchangeEntry+
+ExchangeVariable-
+
ExchangeVariable.delta_mean
+ExchangeVariable.diff_trajectories
+ExchangeVariable.local_trajectories
+ExchangeVariable.mean_trajectory
+ExchangeVariable.multiplier
+ExchangeVariable.primal_residual
+ExchangeVariable.shift_values_by_one()
+ExchangeVariable.update_diff_trajectories()
+ExchangeVariable.update_multiplier()
+
+StructuredValue+
+VariableReference+
+coupling_alias()
+exchange_alias()
+
+ - agentlib_mpc.data_structures.casadi_utils module + +
- agentlib_mpc.data_structures.coordinator_datatypes module
-
+
AgentDictEntry+
+AgentStatus+
+CoordinatorStatus+
+OptimizationData+
+RegistrationMessage+
+
+ - agentlib_mpc.data_structures.interpolation module + +
- agentlib_mpc.data_structures.ml_model_datatypes module
-
+
EarlyStoppingCallback+
+Feature+
+OutputFeature+
+OutputType+
+TrainingData+
+column_order()
+name_with_lag()
+
+ - agentlib_mpc.data_structures.mpc_datamodels module
-
+
BaseVariableReference+
+DiscretizationOptions+
+FullVariableReference+
+InitStatus+
+MHEVariableReference-
+
MHEVariableReference.all_variables()
+MHEVariableReference.estimated_inputs
+MHEVariableReference.estimated_parameters
+MHEVariableReference.known_inputs
+MHEVariableReference.known_parameters
+MHEVariableReference.measured_states
+MHEVariableReference.outputs
+MHEVariableReference.states
+MHEVariableReference.weights_states
+
+MINLPVariableReference+
+MPCVariable+
+Results-
+
Results.df
+
+VariableReference+
+cia_relaxed_results_path()
+r_del_u_convention()
+stats_path()
+
+
+ - agentlib_mpc.models package
-
+
- Submodules +
- agentlib_mpc.models.casadi_ml_model module
-
+
CasadiMLModel-
+
CasadiMLModel.auxiliaries
+CasadiMLModel.bb_outputs
+CasadiMLModel.bb_states
+CasadiMLModel.config_type
+CasadiMLModel.do_step()
+CasadiMLModel.get_ml_model_values()
+CasadiMLModel.initialize()
+CasadiMLModel.make_predict_function_for_mpc()
+CasadiMLModel.register_ml_models()
+CasadiMLModel.set_with_timestamp()
+CasadiMLModel.setup_system()
+CasadiMLModel.update_ml_models()
+
+CasadiMLModelConfig+
+assert_recursive_outputs_are_states()
+compute_dupes()
+
+ - agentlib_mpc.models.casadi_model module
-
+
CasadiInput+
+CasadiModel-
+
CasadiModel.auxiliaries
+CasadiModel.differentials
+CasadiModel.do_step()
+CasadiModel.get()
+CasadiModel.get_constraints()
+CasadiModel.get_differential_values()
+CasadiModel.get_input_values()
+CasadiModel.initialize()
+CasadiModel.inputs
+CasadiModel.output_equations
+CasadiModel.outputs
+CasadiModel.parameters
+CasadiModel.set_differential_values()
+CasadiModel.set_output_values()
+CasadiModel.setup_system()
+CasadiModel.states
+
+CasadiModelConfig-
+
CasadiModelConfig.cost_function
+CasadiModelConfig.description
+CasadiModelConfig.dt
+CasadiModelConfig.inputs
+CasadiModelConfig.name
+CasadiModelConfig.outputs
+CasadiModelConfig.parameters
+CasadiModelConfig.sim_time
+CasadiModelConfig.states
+CasadiModelConfig.system
+CasadiModelConfig.user_config
+CasadiModelConfig.validate_variables
+CasadiModelConfig.model_post_init()
+
+CasadiOutput+
+CasadiParameter
+CasadiState+
+CasadiVariable+
+get_symbolic()
+
+ - agentlib_mpc.models.casadi_predictor module
-
+
ANNLayerTypes+
+BatchNormalization+
+CasadiANN+
+CasadiGPR+
+CasadiLinReg+
+CasadiPredictor+
+Dense+
+Flatten+
+LSTM+
+Layer+
+
+ - agentlib_mpc.models.serialized_ml_model module
-
+
CustomGPR+
+GPRDataHandlingParameters+
+GPRKernelParameters+
+GPRParameters+
+LinRegParameters+
+MLModels+
+SerializedANN+
+SerializedGPR+
+SerializedLinReg+
+SerializedMLModel-
+
SerializedMLModel.agentlib_mpc_hash
+SerializedMLModel.dt
+SerializedMLModel.input
+SerializedMLModel.model_type
+SerializedMLModel.output
+SerializedMLModel.training_info
+SerializedMLModel.deserialize()
+SerializedMLModel.load_serialized_model()
+SerializedMLModel.load_serialized_model_from_dict()
+SerializedMLModel.load_serialized_model_from_file()
+SerializedMLModel.load_serialized_model_from_string()
+SerializedMLModel.save_serialized_model()
+SerializedMLModel.serialize()
+
+get_git_revision_short_hash()
+
+
+ - agentlib_mpc.modules package
-
+
ModuleImport+
+- Subpackages + +
- Submodules +
- agentlib_mpc.modules.data_source module
-
+
DataSource+
+DataSourceConfig+
+
+ - agentlib_mpc.modules.minlp_mpc module + +
- agentlib_mpc.modules.ml_model_simulator module +
- agentlib_mpc.modules.mpc module
-
+
BaseMPC-
+
BaseMPC.assert_mpc_variables_are_in_model()
+BaseMPC.assert_subset()
+BaseMPC.cleanup_results()
+BaseMPC.collect_variables_for_optimization()
+BaseMPC.do_step()
+BaseMPC.get_results()
+BaseMPC.model
+BaseMPC.pre_computation_hook()
+BaseMPC.process()
+BaseMPC.re_init_optimization()
+BaseMPC.read_results_file()
+BaseMPC.register_callbacks()
+BaseMPC.set_actuation()
+BaseMPC.set_output()
+BaseMPC.warn_for_missed_solves()
+
+BaseMPCConfig-
+
BaseMPCConfig.controls
+BaseMPCConfig.inputs
+BaseMPCConfig.log_level
+BaseMPCConfig.module_id
+BaseMPCConfig.optimization_backend
+BaseMPCConfig.outputs
+BaseMPCConfig.parameters
+BaseMPCConfig.prediction_horizon
+BaseMPCConfig.sampling_time
+BaseMPCConfig.set_outputs
+BaseMPCConfig.shared_variable_fields
+BaseMPCConfig.states
+BaseMPCConfig.time_step
+BaseMPCConfig.type
+BaseMPCConfig.validate_incoming_values
+BaseMPCConfig.default_sampling_time
+BaseMPCConfig.model_post_init()
+
+create_optimization_backend()
+
+ - agentlib_mpc.modules.mpc_full module + +
+ - agentlib_mpc.optimization_backends package
-
+
BackendImport+
+- Subpackages
-
+
- agentlib_mpc.optimization_backends.casadi_ package
-
+
- Subpackages +
- Submodules +
- agentlib_mpc.optimization_backends.casadi_.admm module +
- agentlib_mpc.optimization_backends.casadi_.basic module +
- agentlib_mpc.optimization_backends.casadi_.casadi_admm_ml module +
- agentlib_mpc.optimization_backends.casadi_.casadi_ml module +
- agentlib_mpc.optimization_backends.casadi_.full module +
- agentlib_mpc.optimization_backends.casadi_.mhe module +
- agentlib_mpc.optimization_backends.casadi_.minlp module +
- agentlib_mpc.optimization_backends.casadi_.minlp_cia module +
+
+ - agentlib_mpc.optimization_backends.casadi_ package
- Submodules +
- agentlib_mpc.optimization_backends.backend module
-
+
ADMMBackend+
+BackendConfig+
+OptimizationBackend-
+
OptimizationBackend.config_type
+OptimizationBackend.cost_function
+OptimizationBackend.get_lags_per_variable()
+OptimizationBackend.model
+OptimizationBackend.model_from_config()
+OptimizationBackend.mpc_backend_parameters
+OptimizationBackend.register_logger()
+OptimizationBackend.results_file_exists()
+OptimizationBackend.setup_optimization()
+OptimizationBackend.solve()
+OptimizationBackend.update_discretization_options()
+OptimizationBackend.update_model_variables()
+OptimizationBackend.var_ref
+
+
+
+ - agentlib_mpc.utils package
-
+
- Subpackages
-
+
- agentlib_mpc.utils.plotting package
-
+
- Submodules +
- agentlib_mpc.utils.plotting.admm_animation module +
- agentlib_mpc.utils.plotting.admm_consensus_shades module +
- agentlib_mpc.utils.plotting.admm_dashboard module +
- agentlib_mpc.utils.plotting.admm_residuals module +
- agentlib_mpc.utils.plotting.basic module +
- agentlib_mpc.utils.plotting.discretization_structure module +
- agentlib_mpc.utils.plotting.interactive module +
- agentlib_mpc.utils.plotting.ml_model_test module +
- agentlib_mpc.utils.plotting.mpc module +
+
+ - agentlib_mpc.utils.plotting package
- Submodules +
- agentlib_mpc.utils.analysis module + +
- agentlib_mpc.utils.sampling module + +
+ - Subpackages
+
+
+
+
+
+
+
+
+
+
\ No newline at end of file
diff --git a/docs/0.6.4/docs/code/agentlib_mpc.modules.InputPrediction.html b/docs/0.6.4/docs/code/agentlib_mpc.modules.InputPrediction.html
new file mode 100644
index 0000000..eafc272
--- /dev/null
+++ b/docs/0.6.4/docs/code/agentlib_mpc.modules.InputPrediction.html
@@ -0,0 +1,176 @@
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+ agentlib_mpc.models package
+Package containing models for agentlib_mpc.
+Submodules
+agentlib_mpc.models.casadi_ml_model module
+Holds the classes for CasADi variables and the CasADi model.
+-
+
- +class agentlib_mpc.models.casadi_ml_model.CasadiMLModel(**kwargs)[source] +
Bases:
+CasadiModelThis class is created to handle one or multiple ML models +used to predict the states. Compared to previous version, it is now +only dependent on the trained models which provides information about the lags +with the serialized_ML_Models. This way, there is no need to define the lags +again in the model class
+-
+
- +property auxiliaries: List[CasadiState] +
List of all CasadiStates without an associated equation. Common +uses for this are slack variables that appear in cost functions and +constraints of optimization models.
+
-
+
- +property bb_outputs: List[CasadiOutput] +
List of all CasadiStates with an associated black box equation.
+
-
+
- +property bb_states: List[CasadiState] +
List of all CasadiStates with an associated black box equation.
+
-
+
- +config_type: CasadiMLModelConfig +
-
+
- +do_step(*, t_start, t_sample=None)[source] +
Simulates a time step of the simulation model. In CasADi MLModel model, both black- +and white-box models can be used in the simulation to be combined into a grey-box
+
-
+
- +get_ml_model_values(time: float)[source] +
gets the inputs values with the correct lags or all MLModels
+
-
+
- +initialize(**ignored)[source] +
Prepare the black- and white-box models for CasADi backend optimization and +simulation
+
-
+
- +make_predict_function_for_mpc() Function[source] +
Creates a prediction step function which is suitable for MPC with multiple +shooting.
+
-
+
- +register_ml_models() tuple[dict[str, agentlib_mpc.models.serialized_ml_model.SerializedMLModel], dict[str, agentlib_mpc.models.casadi_predictor.CasadiPredictor]][source] +
Loads a serialized MLModel and find the output states of the MLModel +Divides the differential states of the model into states determined by white-box +model (self._differentials) and by black-box model (self._differentials_network)
+
-
+
- +update_ml_models(*ml_models: SerializedMLModel, time: float)[source] +
Updates the internal MLModels with the passed MLModels.
+Warning: This function does not fully check, if the result makes sense! +Consider the following case: +You have two ml_models with outputs out1 in ml_model1, and out2 and out3 in ml_model2. +You call this function with an ml_model3 that defines out2. +This function would replace ml_model2 with ml_model3, leaving the out3 undefined, causing +an error in subequent functions. Try to make sure you specify all outputs when +supplying ml_models, that make parts of other ml_models obsolete.
+
-
+
- +pydantic model agentlib_mpc.models.casadi_ml_model.CasadiMLModelConfig[source] +
Bases:
+CasadiModelConfig-
+
- Config: +
-
+
validate_assignment: bool = True
+arbitrary_types_allowed: bool = True
+extra: str = forbid
+
+- Fields: +
- + +
- Validators: +
-
+
check_dt»all fields
+- +
+
-
+
- +field ml_model_sources: list[Union[agentlib_mpc.models.serialized_ml_model.SerializedMLModel, pathlib.Path]] = [] +
-
+
- Validated by: +
- + +
-
+
- +validator check_or_load_models » ml_model_sources[source] +
-
+
- +model_post_init(context: Any, /) None +
We need to both initialize private attributes and call the user-defined model_post_init +method.
+
-
+
- +agentlib_mpc.models.casadi_ml_model.assert_recursive_outputs_are_states(serialized: SerializedMLModel, outputs: dict[str, agentlib.core.datamodels.AgentVariable])[source] +
Raises a ConfigurationError if there are recursive ML-models for outputs.
+
agentlib_mpc.models.casadi_model module
+Holds the classes for CasADi variables and the CasADi model.
+-
+
- +class agentlib_mpc.models.casadi_model.CasadiInput(*, name: str, timestamp: float | None = None, unit: str = 'Not defined', description: str = 'Not defined', ub=inf, lb=-inf, clip: bool = False, allowed_values: List[Any] = [], value: Any = None, causality: Causality = None, variability: Variability = None, type: str | None = 'float', sim_time: float = 0.0, _sym: MX | SX | DM | Sparsity = None)[source] +
Bases:
+CasadiVariableClass that stores various attributes of control variables.
+-
+
- +property alg: MX | SX | DM | Sparsity +
-
+
- +class agentlib_mpc.models.casadi_model.CasadiModel(**kwargs)[source] +
Bases:
+ModelBase Class for CasADi models. To implement your own model, inherit +from this class, specify the variables (inputs, outputs, states, +parameters and override the setup_system() method.
+-
+
- +property auxiliaries: List[CasadiState] +
List of all CasadiStates without an associated equation. Common +uses for this are slack variables that appear in cost functions and +constraints of optimization models.
+
-
+
- +property differentials: List[CasadiState] +
List of all CasadiStates with an associated differential equation.
+
-
+
- +do_step(*, t_start, t_sample=None)[source] +
Performing one simulation step +:param t_start: start time for integration +:param t_sample: increment of solver integration
+Returns:
+
-
+
- +get(name: str) CasadiVariable[source] +
Get any variable from using name:
+-
+
- Parameters: +
name (str) – The item to get from config by name of Variable. +Hence, item=ModelVariable.name
+
+- Returns: +
The matching variable
+
+- Return type: +
var (ModelVariable)
+
+- Raises: +
AttributeError – If the item was not found in the variables of the + module.
+
+
-
+
- +get_constraints() List[ModelConstraint][source] +
List of constraints of the form (lower, function, upper).
+
-
+
- +initialize(**ignored)[source] +
Initializes Casadi model. Creates the integrator to be used in +do_step(). The integrator takes the current state and input values as +input and returns the state values at the end of the interval and the +value of the cost function integrated over the interval.
+
-
+
- +property inputs: list[agentlib_mpc.models.casadi_model.CasadiInput] +
Get all model inputs as a list
+
-
+
- +property output_equations: List[MX | SX | DM | Sparsity] +
List of algebraic equations RHS in the form +0 = z - g(x, z, p, … )
+
-
+
- +property outputs: list[agentlib_mpc.models.casadi_model.CasadiOutput] +
Get all model outputs as a list
+
-
+
- +property parameters: list[agentlib_mpc.models.casadi_model.CasadiParameter] +
Get all model parameters as a list
+
-
+
- +set_differential_values(values: List | ndarray)[source] +
Sets the values for all differential variables. Provided values list MUST +match the order in which differentials are saved, there is no check.
+
-
+
- +set_output_values(values: List | ndarray)[source] +
Sets the values for all outputs. Provided values list MUST match the order +in which outputs are saved, there is no check.
+
-
+
- +property states: list[agentlib_mpc.models.casadi_model.CasadiState] +
Get all model states as a list
+
-
+
- +pydantic model agentlib_mpc.models.casadi_model.CasadiModelConfig[source] +
Bases:
+ModelConfig-
+
- Config: +
-
+
validate_assignment: bool = True
+arbitrary_types_allowed: bool = True
+extra: str = forbid
+
+- Fields: +
-
+
cost_function (casadi.casadi.MX | casadi.casadi.SX | casadi.casadi.DM | casadi.casadi.Sparsity)
+- +
- +
- +
- +
outputs (List[agentlib_mpc.models.casadi_model.CasadiOutput])
+parameters (List[agentlib_mpc.models.casadi_model.CasadiParameter])
+- +
- +
system (casadi.casadi.MX | casadi.casadi.SX | casadi.casadi.DM | casadi.casadi.Sparsity)
+- +
- +
+- Validators: +
-
+
check_name»name
+include_default_model_variables»inputs
+include_default_model_variables»outputs
+include_default_model_variables»parameters
+include_default_model_variables»states
+
+
-
+
- +field cost_function: MX | SX | DM | Sparsity = None +
-
+
- +field description: str = 'You forgot to document your model!' +
-
+
- +field dt: float | int = 1 +
-
+
- +field inputs: List[CasadiInput] = [] +
-
+
- Validated by: +
-
+
include_default_model_variables
+
+
-
+
- +field name: str | None = None +
-
+
- Validated by: +
-
+
check_name
+
+
-
+
- +field outputs: List[CasadiOutput] = [] +
-
+
- Validated by: +
-
+
include_default_model_variables
+
+
-
+
- +field parameters: List[CasadiParameter] = [] +
-
+
- Validated by: +
-
+
include_default_model_variables
+
+
-
+
- +field sim_time: float = 0 +
-
+
- +field states: List[CasadiState] = [] +
-
+
- Validated by: +
-
+
include_default_model_variables
+
+
-
+
- +field system: MX | SX | DM | Sparsity = None +
-
+
- +field user_config: dict = None +
The config given by the user to instantiate this class.Will be stored to enable a valid overwriting of the default config and to better restart modules.Is also useful to debug validators and the general BaseModuleConfig.
+
-
+
- +field validate_variables: bool = True +
If true, the validator of a variables value is called whenever a new value is set. Disabled by default for performance reasons.
+
-
+
- +model_post_init(context: Any, /) None +
We need to both initialize private attributes and call the user-defined model_post_init +method.
+
-
+
- +class agentlib_mpc.models.casadi_model.CasadiOutput(*, name: str, timestamp: float | None = None, unit: str = 'Not defined', description: str = 'Not defined', ub=inf, lb=-inf, clip: bool = False, allowed_values: List[Any] = [], value: Any = None, causality: Causality = None, variability: Variability = None, type: str | None = 'float', sim_time: float = 0.0, _sym: MX | SX | DM | Sparsity = None, _alg: MX | SX | DM | Sparsity = None)[source] +
Bases:
+CasadiVariableClass that stores various attributes of control variables.
+-
+
- +property alg: MX | SX | DM | Sparsity +
-
+
- +class agentlib_mpc.models.casadi_model.CasadiParameter(*, name: str, timestamp: float | None = None, unit: str = 'Not defined', description: str = 'Not defined', ub=inf, lb=-inf, clip: bool = False, allowed_values: List[Any] = [], value: Any = None, causality: Causality = None, variability: Variability = None, type: str | None = 'float', sim_time: float = 0.0, _sym: MX | SX | DM | Sparsity = None)[source] +
Bases:
+CasadiVariableClass that stores various attributes of parameters.
+
-
+
- +class agentlib_mpc.models.casadi_model.CasadiState(*, name: str, timestamp: float | None = None, unit: str = 'Not defined', description: str = 'Not defined', ub=inf, lb=-inf, clip: bool = False, allowed_values: List[Any] = [], value: Any = None, causality: Causality = None, variability: Variability = None, type: str | None = 'float', sim_time: float = 0.0, _sym: MX | SX | DM | Sparsity = None, _ode: MX | SX | DM | Sparsity | None = None)[source] +
Bases:
+CasadiVariableClass that stores various attributes of CasADi differential variables.
+-
+
- +property alg: MX | SX | DM | Sparsity +
-
+
- +json(indent: int = 2, **kwargs)[source] +
Serializes the Variable in json format and returns a string
+
-
+
- +property ode: MX | SX | DM | Sparsity +
-
+
- +class agentlib_mpc.models.casadi_model.CasadiVariable(*, name: str, timestamp: float | None = None, unit: str = 'Not defined', description: str = 'Not defined', ub=inf, lb=-inf, clip: bool = False, allowed_values: List[Any] = [], value: Any = None, causality: Causality = None, variability: Variability = None, type: str | None = 'float', sim_time: float = 0.0, _sym: MX | SX | DM | Sparsity = None)[source] +
Bases:
+ModelVariableBase Class for variables used in Casadi Models for simulation and +optimization. Implements the standard arithmetic operations, +so CasadiVariables can be used in equations. +.. attribute:: sym
++
+The symbolic CasADi variable used to define ode’s and +optimization problems.
+-
+
- +create_sym() MX[source] +
Ensures a symbolic MX variable is created with each CasadiVariable +instance, and that its dimensions are consistent.
+
-
+
- +property sym: MX +
agentlib_mpc.models.casadi_predictor module
+-
+
- +class agentlib_mpc.models.casadi_predictor.ANNLayerTypes(value, names=None, *, module=None, qualname=None, type=None, start=1, boundary=None)[source] +
Bases:
+str,Enum-
+
- +BATCHNORMALIZATION = 'batch_normalization' +
-
+
- +DENSE = 'dense' +
-
+
- +FLATTEN = 'flatten' +
-
+
- +LSTM = 'lstm' +
-
+
- +RESCALING = 'rescaling' +
-
+
- +class agentlib_mpc.models.casadi_predictor.BatchNormalization(layer: BatchNormalization)[source] +
Bases:
+LayerBatch Normalizing layer. Make sure the axis setting is set to two.
+ + +
-
+
- +class agentlib_mpc.models.casadi_predictor.CasadiANN(serialized_model: SerializedANN)[source] +
Bases:
+CasadiPredictorGeneric implementations of sequential Keras models in CasADi.
+-
+
- +property input_shape: tuple[int, int] +
Input shape of Predictor.
+
-
+
- +class agentlib_mpc.models.casadi_predictor.CasadiGPR(serialized_model: SerializedGPR)[source] +
Bases:
+CasadiPredictorGeneric implementation of scikit-learn Gaussian Process Regressor.
+-
+
- +property input_shape: tuple[int, int] +
Input shape of Predictor.
+
-
+
- +class agentlib_mpc.models.casadi_predictor.CasadiLinReg(serialized_model: SerializedLinReg)[source] +
Bases:
+CasadiPredictorGeneric Casadi implementation of scikit-learn LinerRegression.
+-
+
- +property input_shape: tuple[int, int] +
Input shape of Predictor.
+
-
+
- +class agentlib_mpc.models.casadi_predictor.CasadiPredictor(serialized_model: SerializedMLModel)[source] +
Bases:
+ABCProtocol for generic Casadi implementation of various ML-Model-based predictors.
+-
+
- +serialized_model +
Serialized model which will be translated to a casadi model.
+
-
+
- +predictor_model +
Predictor model from other libraries, which are translated to
+
-
+
- +casadi syntax. +
-
+
- +sym_input +
Symbolical input of predictor. Has the necessary shape of the input.
+
-
+
- +prediction_function +
Symbolical casadi prediction function of the given model.
+
-
+
- +classmethod from_serialized_model(serialized_model: SerializedMLModel)[source] +
Initialize sub predictor class.
+
-
+
- +abstract property input_shape: tuple[int, int] +
Input shape of Predictor.
+
-
+
- +property output_shape: tuple[int, int] +
Output shape of Predictor.
+
-
+
- +class agentlib_mpc.models.casadi_predictor.Dense(layer: Dense)[source] +
Bases:
+LayerFully connected layer.
+ + +
-
+
- +class agentlib_mpc.models.casadi_predictor.LSTM(layer: LSTM)[source] +
Bases:
+LayerLong Short Term Memory cell.
+ + + + +
agentlib_mpc.models.serialized_ml_model module
+-
+
- +class agentlib_mpc.models.serialized_ml_model.CustomGPR(kernel=None, *, alpha=1e-10, optimizer='fmin_l_bfgs_b', n_restarts_optimizer=0, normalize_y=False, copy_X_train=True, random_state=None, data_handling=GPRDataHandlingParameters(normalize=False, scale=1.0, mean=None, std=None))[source] +
Bases:
+GaussianProcessRegressorExtends scikit-learn GaussianProcessRegressor with normalizing and scaling option +by adding the attribute data_handling, customizing the predict function accordingly +and adding a normalize function.
+-
+
- +predict(X, return_std=False, return_cov=False)[source] +
Overwrite predict method of GaussianProcessRegressor to include normalization.
+
-
+
- +set_predict_request(*, return_cov: bool | None | str = '$UNCHANGED$', return_std: bool | None | str = '$UNCHANGED$') CustomGPR +
Request metadata passed to the
+predictmethod.Note that this method is only relevant if +
+enable_metadata_routing=True(seesklearn.set_config()). +Please see User Guide on how the routing +mechanism works.The options for each parameter are:
+-
+
True: metadata is requested, and passed topredictif provided. The request is ignored if metadata is not provided.
+False: metadata is not requested and the meta-estimator will not pass it topredict.
+None: metadata is not requested, and the meta-estimator will raise an error if the user provides it.
+str: metadata should be passed to the meta-estimator with this given alias instead of the original name.
+
The default (
+sklearn.utils.metadata_routing.UNCHANGED) retains the +existing request. This allows you to change the request for some +parameters and not others.++New in version 1.3.
+++Note
+This method is only relevant if this estimator is used as a +sub-estimator of a meta-estimator, e.g. used inside a +
+Pipeline. Otherwise it has no effect.-
+
- Parameters: +
-
+
return_cov (str, True, False, or None, default=sklearn.utils.metadata_routing.UNCHANGED) – Metadata routing for
return_covparameter inpredict.
+return_std (str, True, False, or None, default=sklearn.utils.metadata_routing.UNCHANGED) – Metadata routing for
return_stdparameter inpredict.
+
+- Returns: +
self – The updated object.
+
+- Return type: +
object
+
+
-
+
- +set_score_request(*, sample_weight: bool | None | str = '$UNCHANGED$') CustomGPR +
Request metadata passed to the
+scoremethod.Note that this method is only relevant if +
+enable_metadata_routing=True(seesklearn.set_config()). +Please see User Guide on how the routing +mechanism works.The options for each parameter are:
+-
+
True: metadata is requested, and passed toscoreif provided. The request is ignored if metadata is not provided.
+False: metadata is not requested and the meta-estimator will not pass it toscore.
+None: metadata is not requested, and the meta-estimator will raise an error if the user provides it.
+str: metadata should be passed to the meta-estimator with this given alias instead of the original name.
+
The default (
+sklearn.utils.metadata_routing.UNCHANGED) retains the +existing request. This allows you to change the request for some +parameters and not others.++New in version 1.3.
+++Note
+This method is only relevant if this estimator is used as a +sub-estimator of a meta-estimator, e.g. used inside a +
+Pipeline. Otherwise it has no effect.-
+
- Parameters: +
sample_weight (str, True, False, or None, default=sklearn.utils.metadata_routing.UNCHANGED) – Metadata routing for
+sample_weightparameter inscore.
+- Returns: +
self – The updated object.
+
+- Return type: +
object
+
+
-
+
- +pydantic model agentlib_mpc.models.serialized_ml_model.GPRDataHandlingParameters[source] +
Bases:
+BaseModel-
+
- Fields: +
- + +
-
+
- +field mean: list | None = None +
Mean values of input data for normalization. None if normalize equals to False.
+
-
+
- +field normalize: bool = False +
Boolean which defines whether the input data will be normalized or not.
+
-
+
- +field scale: float = 1.0 +
Number by which the y vector is divided before training and multiplied after evaluation.
+
-
+
- +field std: list | None = None +
Standard deviation of input data for normalization. None if normalize equals to False.
+
-
+
- +pydantic model agentlib_mpc.models.serialized_ml_model.GPRKernelParameters[source] +
Bases:
+BaseModel-
+
- Config: +
-
+
arbitrary_types_allowed: bool = True
+
+- Fields: +
- + +
-
+
- +field constant_value: float = 1.0 +
The constant value which defines the covariance: k(x_1, x_2) = constant_value.
+
-
+
- +field constant_value_bounds: tuple | str = (1e-05, 100000.0) +
The lower and upper bound on constant_value. If set to “fixed”, constant_value cannot be changed during hyperparameter tuning.
+
-
+
- +field length_scale: float | list = 1.0 +
The length scale of the kernel. If a float, an isotropic kernel is used. If an array, an anisotropic kernel is used where each dimension of l defines the length-scale of the respective feature dimension.
+
-
+
- +field length_scale_bounds: tuple | str = (1e-05, 100000.0) +
The lower and upper bound on ‘length_scale’. If set to “fixed”, ‘length_scale’ cannot be changed during hyperparameter tuning.
+
-
+
- +field noise_level: float = 1.0 +
Parameter controlling the noise level (variance).
+
-
+
- +field noise_level_bounds: tuple | str = (1e-05, 100000.0) +
The lower and upper bound on ‘noise_level’. If set to “fixed”, ‘noise_level’ cannot be changed during hyperparameter tuning.
+
-
+
- +field theta: list [Required] +
Returns the (flattened, log-transformed) non-fixed gpr_parameters.
+
-
+
- +classmethod from_model(model: CustomGPR) GPRKernelParameters[source] +
-
+
- +pydantic model agentlib_mpc.models.serialized_ml_model.GPRParameters[source] +
Bases:
+BaseModel-
+
- Config: +
-
+
arbitrary_types_allowed: bool = True
+
+- Fields: +
- + +
-
+
- +field L: list [Required] +
Lower-triangular Cholesky decomposition of the kernel in X_train.
+
-
+
- +field X_train: list [Required] +
Feature vectors or other representations of training data (also required for prediction).
+
-
+
- +field alpha: float | list = 1e-10 +
Value added to the diagonal of the kernel matrix during fitting. This can prevent a potential numerical issue during fitting, by ensuring that the calculated values form a positive definite matrix. It can also be interpreted as the variance of additional Gaussian measurement noise on the training observations. Note that this is different from using a WhiteKernel. If an array is passed, it must have the same number of entries as the data used for fitting and is used as datapoint-dependent noise level. Allowing to specify the noise level directly as a parameter is mainly for convenience and for consistency with Ridge.
+
-
+
- +field log_marginal_likelihood_value: float [Required] +
The log-marginal-likelihood of self.kernel_.theta.
+
-
+
- +field n_features_in: int [Required] +
Number of features seen during fit.
+
-
+
- +field y_train: list [Required] +
Target values in training data (also required for prediction).
+
-
+
- +classmethod from_model(model: CustomGPR) GPRParameters[source] +
-
+
- +pydantic model agentlib_mpc.models.serialized_ml_model.LinRegParameters[source] +
Bases:
+BaseModel-
+
- Fields: +
- + +
-
+
- +field coef: list [Required] +
Estimated coefficients for the linear regression problem. If multiple targets are passed during the fit (y 2D), this is a 2D array of shape (n_targets, n_features), while if only one target is passed, this is a 1D array of length n_features.
+
-
+
- +field intercept: float | list [Required] +
Independent term in the linear model. Set to 0.0 if fit_intercept = False.
+
-
+
- +field n_features_in: int [Required] +
Number of features seen during fit.
+
-
+
- +field rank: int [Required] +
Rank of matrix X. Only available when X is dense.
+
-
+
- +field singular: list [Required] +
Singular values of X. Only available when X is dense.
+
-
+
- +class agentlib_mpc.models.serialized_ml_model.MLModels(value, names=None, *, module=None, qualname=None, type=None, start=1, boundary=None)[source] +
Bases:
+str,Enum-
+
- +ANN = 'ANN' +
-
+
- +GPR = 'GPR' +
-
+
- +LINREG = 'LinReg' +
-
+
- +pydantic model agentlib_mpc.models.serialized_ml_model.SerializedANN[source] +
Bases:
+SerializedMLModelContains Keras ANN in serialized form and offers functions to transform +Keras Sequential ANNs to SerializedANN objects (from_ANN) and vice versa (deserialize).
+-
+
- +structure +
architecture/structure of ANN saved as json string.
+
-
+
- +weights +
weights and biases of all layers saved as lists of np.ndarrays.
+
-
+
- Config: +
-
+
protected_namespaces: tuple = ()
+arbitrary_types_allowed: bool = True
+
+- Fields: +
- + +
-
+
- +field structure: str = None +
The structure of the ANN as json string.
+
-
+
- +field weights: list = None +
The weights of the ANN.
+
-
+
- +deserialize() Sequential[source] +
Deserializes SerializedANN object and returns a Keras Sequential ANN.
+
-
+
- +classmethod serialize(model: Sequential, dt: float | int, input: dict[str, agentlib_mpc.data_structures.ml_model_datatypes.Feature], output: dict[str, agentlib_mpc.data_structures.ml_model_datatypes.OutputFeature], training_info: dict | None = None)[source] +
Serializes Keras Sequential ANN and returns SerializedANN object
+
-
+
- +pydantic model agentlib_mpc.models.serialized_ml_model.SerializedGPR[source] +
Bases:
+SerializedMLModelContains scikit-learn GaussianProcessRegressor and its Kernel and provides functions to transform +these to SerializedGPR objects and vice versa.
+Attributes:
+-
+
- Config: +
-
+
protected_namespaces: tuple = ()
+arbitrary_types_allowed: bool = True
+
+- Fields: +
-
+
data_handling (agentlib_mpc.models.serialized_ml_model.GPRDataHandlingParameters)
+gpr_parameters (agentlib_mpc.models.serialized_ml_model.GPRParameters)
+kernel_parameters (agentlib_mpc.models.serialized_ml_model.GPRKernelParameters)
+model_type (agentlib_mpc.models.serialized_ml_model.MLModels)
+
+
-
+
- +field data_handling: GPRDataHandlingParameters = None +
Information about data handling for GPR.
+
-
+
- +field gpr_parameters: GPRParameters = None +
GPR parameters of GPR and its Kernel and Data of fitted GPR.
+
-
+
- +field kernel_parameters: GPRKernelParameters = None +
Parameters of kernel of the fitted GPR.
+
-
+
- +deserialize() CustomGPR[source] +
Deserializes SerializedGPR object and returns a scikit learn GaussionProcessRegressor. +:returns: GPR version of the SerializedGPR +:rtype: gpr_fitted
+
-
+
- +classmethod serialize(model: CustomGPR, dt: float | int, input: dict[str, agentlib_mpc.data_structures.ml_model_datatypes.Feature], output: dict[str, agentlib_mpc.data_structures.ml_model_datatypes.OutputFeature], training_info: dict | None = None)[source] +
-
+
- Parameters: +
-
+
model – GaussianProcessRegressor from ScikitLearn.
+dt – The length of time step of one prediction of GPR in seconds.
+input – GPR input variables with their lag order.
+output – GPR output variables (which are automatically also inputs, as +we need them recursively in MPC.) with their lag order.
+training_info – Config of Trainer Class, which trained the Model.
+
+- Returns: +
SerializedGPR version of the passed GPR.
+
+
-
+
- +pydantic model agentlib_mpc.models.serialized_ml_model.SerializedLinReg[source] +
Bases:
+SerializedMLModelContains scikit-learn LinearRegression and provides functions to transform +these to SerializedLinReg objects and vice versa.
+Attributes:
+-
+
- Config: +
-
+
protected_namespaces: tuple = ()
+arbitrary_types_allowed: bool = True
+
+- Fields: +
- + +
-
+
- +field parameters: LinRegParameters [Required] +
Parameters of kernel of the fitted linear model.
+
-
+
- +deserialize() LinearRegression[source] +
Deserializes SerializedLinReg object and returns a LinearRegression object of scikit-learn. +:returns: LinearRegression version of the SerializedLinReg +:rtype: linear_model_fitted
+
-
+
- +classmethod serialize(model: LinearRegression, dt: float | int, input: dict[str, agentlib_mpc.data_structures.ml_model_datatypes.Feature], output: dict[str, agentlib_mpc.data_structures.ml_model_datatypes.OutputFeature], training_info: dict | None = None)[source] +
-
+
- Parameters: +
-
+
model – LinearRegression from ScikitLearn.
+dt – The length of time step of one prediction of LinReg in seconds.
+input – LinReg input variables with their lag order.
+output – LinReg output variables (which are automatically also inputs, as ” +“we need them recursively in MPC.) with their lag order.
+training_info – Config of Trainer Class, which trained the Model.
+
+- Returns: +
SerializedLinReg version of the passed linear model.
+
+
-
+
- +pydantic model agentlib_mpc.models.serialized_ml_model.SerializedMLModel[source] +
Bases:
+BaseModel,ABC-
+
- Config: +
-
+
protected_namespaces: tuple = ()
+
+- Fields: +
- + +
-
+
- +field agentlib_mpc_hash: str [Optional] +
The commit hash of the agentlib_mpc version this was created with.
+
-
+
- +field dt: float | int [Required] +
The length of time step of one prediction of Model in seconds.
+
-
+
- +field input: dict[str, agentlib_mpc.data_structures.ml_model_datatypes.Feature] = None +
Model input variables with their lag order.
+
-
+
- +field output: dict[str, agentlib_mpc.data_structures.ml_model_datatypes.OutputFeature] = None +
Model output variables (which are automatically also inputs, as we need them recursively in MPC.) with their lag order.
+
-
+
- +field training_info: dict | None = None +
Config of Trainer class with all the meta data used for training of the Model.
+
-
+
- +abstract deserialize()[source] +
Deserializes SerializedMLModel object and returns a specific Machine Learning Model object. +:returns: Machine Learning Model. +:rtype: MLModel
+
-
+
- +classmethod load_serialized_model(model_data: dict | str | Path)[source] +
Loads the ML model from a source
+
-
+
- +classmethod load_serialized_model_from_dict(model_data: dict)[source] +
Loads SerializedMLModel object from a dict and creates a new specific Machine Learning Model object +which is returned.
+-
+
- Parameters: +
json_string – json string which will be loaded.
+
+- Returns: +
SerializedMLModel object with data from json file.
+
+
-
+
- +classmethod load_serialized_model_from_file(path: Path)[source] +
Loads SerializedMLModel object from a json file and creates a new specific Machine Learning Model object +which is returned.
+-
+
- Parameters: +
path – relative/absolute path which determines which json file will be loaded.
+
+- Returns: +
SerializedMLModel object with data from json file.
+
+
-
+
- +classmethod load_serialized_model_from_string(json_string: str)[source] +
Loads SerializedMLModel object from a json string and creates a new specific Machine Learning Model object +which is returned.
+-
+
- Parameters: +
json_string – json string which will be loaded.
+
+- Returns: +
SerializedMLModel object with data from json file.
+
+
-
+
- +save_serialized_model(path: Path)[source] +
Saves MLModel object as json string. +:param path: relative/absolute path which determines where the json will be saved.
+
-
+
- +abstract classmethod serialize(model: Sequential | GaussianProcessRegressor | LinearRegression, dt: float | int, input: dict[str, agentlib_mpc.data_structures.ml_model_datatypes.Feature], output: dict[str, agentlib_mpc.data_structures.ml_model_datatypes.OutputFeature], training_info: dict | None = None)[source] +
-
+
- Parameters: +
-
+
model – Machine Learning Model.
+dt – The length of time step of one prediction of Model in seconds.
+input – Model input variables with their lag order.
+output – Model output variables (which are automatically also inputs, as +we need them recursively in MPC.) with their lag order.
+training_info – Config of Trainer Class, which trained the Model.
+
+- Returns: +
SerializedMLModel version of the passed ML Model.
+
+
+
+
+
+
+
+
+
+
+
+
\ No newline at end of file
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+
+
+
+
+
+
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+
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+
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+
+
+
+
+
+
+
+ agentlib_mpc.modules.InputPrediction package
+Submodules
+agentlib_mpc.modules.InputPrediction.try_predictor module
+-
+
- +class agentlib_mpc.modules.InputPrediction.try_predictor.TRYPredictor(*, config: dict, agent: Agent)[source] +
Bases:
+ + + + + + +TRYSensor
-
+
- +pydantic model agentlib_mpc.modules.InputPrediction.try_predictor.TRYPredictorConfig[source] +
Bases:
+TRYSensorConfig-
+
- Config: +
-
+
arbitrary_types_allowed: bool = True
+validate_assignment: bool = True
+extra: str = forbid
+frozen: bool = True
+
+- Fields: +
- + +
- Validators: +
- +
-
+
- +field prediction_length: int = 24 +
prediction length in hours
+
-
+
- +field predictions: List[AgentVariable] = [AgentVariable(name='T_oda_prediction', type=None, timestamp=None, unit='K', description='Air temperature 2m over ground [K]', ub=inf, lb=-inf, clip=False, allowed_values=[], value=None, alias='T_oda_prediction', source=Source(agent_id=None, module_id=None), shared=None, rdf_class=None), AgentVariable(name='pressure_prediction', type=None, timestamp=None, unit='hPa', description='Air pressure in standard height [hPa]', ub=inf, lb=-inf, clip=False, allowed_values=[], value=None, alias='pressure_prediction', source=Source(agent_id=None, module_id=None), shared=None, rdf_class=None), AgentVariable(name='wind_direction_prediction', type=None, timestamp=None, unit='°', description='Wind direction 10 m above gorund [Grad] {0..360;999}', ub=inf, lb=-inf, clip=False, allowed_values=[], value=None, alias='wind_direction_prediction', source=Source(agent_id=None, module_id=None), shared=None, rdf_class=None), AgentVariable(name='wind_speed_prediction', type=None, timestamp=None, unit='m/s', description='Wind speed 10 m above ground [m/s]', ub=inf, lb=-inf, clip=False, allowed_values=[], value=None, alias='wind_speed_prediction', source=Source(agent_id=None, module_id=None), shared=None, rdf_class=None), AgentVariable(name='coverage_prediction', type=None, timestamp=None, unit='eighth', description='[eighth] {0..8;9}', ub=inf, lb=-inf, clip=False, allowed_values=[], value=None, alias='coverage_prediction', source=Source(agent_id=None, module_id=None), shared=None, rdf_class=None), AgentVariable(name='absolute_humidity_prediction', type=None, timestamp=None, unit='g/kg', description='[g/kg]', ub=inf, lb=-inf, clip=False, allowed_values=[], value=None, alias='absolute_humidity_prediction', source=Source(agent_id=None, module_id=None), shared=None, rdf_class=None), AgentVariable(name='relative_humidity_prediction', type=None, timestamp=None, unit='%', description='Relative humidity 2 m above ground [%] {1..100}', ub=inf, lb=-inf, clip=False, allowed_values=[], value=None, alias='relative_humidity_prediction', source=Source(agent_id=None, module_id=None), shared=None, rdf_class=None), AgentVariable(name='beam_direct_prediction', type=None, timestamp=None, unit='W/m^2', description='Direct beam of sun (hor. plane) [W/m^2] downwards: positive', ub=inf, lb=-inf, clip=False, allowed_values=[], value=None, alias='beam_direct_prediction', source=Source(agent_id=None, module_id=None), shared=None, rdf_class=None), AgentVariable(name='beam_diffuse_prediction', type=None, timestamp=None, unit='/m^2', description='Diffuse beam of sun (hor. plane) [W/m^2] downwards: positive', ub=inf, lb=-inf, clip=False, allowed_values=[], value=None, alias='beam_diffuse_prediction', source=Source(agent_id=None, module_id=None), shared=None, rdf_class=None), AgentVariable(name='beam_atm_prediction', type=None, timestamp=None, unit='/m^2', description='Beam of atmospheric heat (hor. plane) [W/m^2] downwards: positive', ub=inf, lb=-inf, clip=False, allowed_values=[], value=None, alias='beam_atm_prediction', source=Source(agent_id=None, module_id=None), shared=None, rdf_class=None), AgentVariable(name='beam_terr_prediction', type=None, timestamp=None, unit='/m^2', description='Beam of terrestrial heat [W/m^2] upwards: negative', ub=inf, lb=-inf, clip=False, allowed_values=[], value=None, alias='beam_terr_prediction', source=Source(agent_id=None, module_id=None), shared=None, rdf_class=None)] +
-
+
- +model_post_init(context: Any, /) None +
We need to both initialize private attributes and call the user-defined model_post_init +method.
+
+
+
+
+
+
+
+
+
+
+
\ No newline at end of file
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+ agentlib_mpc.modules.dmpc.admm package
+Submodules
+agentlib_mpc.modules.dmpc.admm.admm module
+Holds functionality for ADMM modules.
+-
+
- +class agentlib_mpc.modules.dmpc.admm.admm.ADMM(config: dict, agent)[source] +
Bases:
+DistributedMPCThis class represents a module participating in a fully decentralized +Consensus-ADMM optimization for distributed MPC. +Agents autonomously send the values of their coupling variables, register +other participants and perform update steps.
+ + +-
+
- +all_coupling_statuses() Iterable[ADMMParticipation][source] +
Gives and iterator of all ADMMParticipation that are registered.
+
-
+
- +assert_mpc_variables_are_in_model()[source] +
Checks whether all variables of var_ref are contained in the model. +Returns names of model variables not contained in the var_ref, +sorted by keys: ‘states’, ‘inputs’, ‘outputs’, ‘parameters’.
+
-
+
- +collect_couplings_for_optimization()[source] +
Collects updated AgentVariables only of the coupling variables.
+
-
+
- +collect_variables_for_optimization(var_ref: VariableReference = None) dict[str, agentlib.core.datamodels.AgentVariable][source] +
Gets all variables noted in the var ref and puts them in a flat +dictionary.
+
-
+
- +property cons_and_exchange: List[ExchangeEntry | CouplingEntry] +
-
+
- +deregister_all_participants()[source] +
Sets the participating status of all participating agents to +False.
+
-
+
- +get_participants_values(coupling_alias: str) List[Series][source] +
Get the values of all agents for a coupling variable.
+
-
+
- +get_results() DataFrame | None[source] +
Read the results that were saved from the optimization backend and +returns them as Dataframe.
+-
+
- Returns: +
(results, stats) tuple of Dataframes.
+
+
-
+
- +participant_callback(variable: AgentVariable)[source] +
Puts received variables in the correct queue, depending on +registration status of this agent.
+
-
+
- +property penalty_factor_var: MPCVariable +
-
+
- +process()[source] +
This abstract method must be implemented in order to sync the module +with the other processes of the agent and the whole MAS.
+
-
+
- +receive_participant(variable: AgentVariable)[source] +
Set the participation to true for the given coupling input.
+
-
+
- +property registered_participants: Dict[str, Dict[str, ADMMParticipation]] +
Dictionary containing all other agents this agent shares variables with. +Ordered in a two-layer form, with variables at the first layer and +agents at the second layer. Contains ADMMParticipation objects at +the base layer.
+Examples
+self.registered_participants = +{‘coupling_var_1’: {‘src_of_agent1’: status_1,
++
++
+‘src_of_agent2’: status_2, +‘src_of_agent3’: status_3}
+-
+
- ‘coupling_var_1’: {‘src_of_agent3’: status_a,
‘src_of_agent2’: status_b, +‘src_of_agent4’: status_c}
+
+
} +here, <status> refers to an ADMMParticipation object.
+
-
+
- +reset_participants_ready()[source] +
Sets the ready status of all participating agents to False.
+
-
+
- +send_coupling_values(solution: Results)[source] +
Sets the coupling outputs to the data_broker, which automatically sends them.
+-
+
- Parameters: +
solution – Output dictionary from optimization_backend.solve().
+
+
-
+
- +send_coupling_variable(name: str, value: int | float | list[Union[int, float]] | Series | ndarray)[source] +
Sends an admm coupling variable through the data_broker and sets its +value locally
+
-
+
- +update_lambda()[source] +
Performs the update of the lagrange multipliers. +lambda^k+1 := lambda^k - rho*(z-x_i)
+
-
+
- +var_ref: VariableReference +
-
+
- +pydantic model agentlib_mpc.modules.dmpc.admm.admm.ADMMConfig[source] +
Bases:
+DistributedMPCConfig-
+
- Config: +
-
+
arbitrary_types_allowed: bool = True
+validate_assignment: bool = True
+extra: str = forbid
+frozen: bool = True
+
+- Fields: +
-
+
- +
couplings (List[agentlib_mpc.data_structures.mpc_datamodels.MPCVariable])
+exchange (List[agentlib_mpc.data_structures.mpc_datamodels.MPCVariable])
+- +
- +
- +
- +
- +
- +
- +
- +
- +
- +
- +
- +
- +
- +
- +
- +
- +
- +
- +
+- Validators: +
-
+
- +
- +
- +
- +
- +
- +
- +
- +
check_valid_fields»shared_variable_fields
+check_valid_level»log_level
+- +
+
-
+
- +field controls: mpc_datamodels.MPCVariables = [] +
List of all control variables of the MPC.
+-
+
- Validated by: +
- + +
-
+
- +field couplings: List[MPCVariable] = [] +
-
+
- Validated by: +
- + +
-
+
- +field exchange: List[MPCVariable] = [] +
-
+
- Validated by: +
- + +
-
+
- +field inputs: mpc_datamodels.MPCVariables = [] +
List of all input variables of the MPC. Includes predictions for disturbances, set_points, dynamic constraint boundaries etc.
+-
+
- Validated by: +
- + +
-
+
- +field iteration_timeout: float = 20 +
Maximum computation + waiting time for one iteration.
+-
+
- Constraints: +
-
+
ge = 0
+
+
-
+
- +field log_level: str | None = None +
The log level for this Module. Default uses the root-loggers level.Options: DEBUG; INFO; WARNING; ERROR; CRITICAL
+-
+
- Validated by: +
-
+
check_valid_level
+
+
-
+
- +field max_iterations: float = 20 +
Maximum number of ADMM iterations before termination of control step.
+-
+
- Constraints: +
-
+
ge = 0
+
+
-
+
- +field module_id: str [Required] +
The unqiue id of the module within an agent, used only to communicate withing the agent.
+
-
+
- +field optimization_backend: dict [Required] +
-
+
- +field outputs: mpc_datamodels.MPCVariables = [] +
List of all shared outputs of the MPC.
+-
+
- Validated by: +
- + +
-
+
- +field parameters: mpc_datamodels.MPCVariables = [] +
List of model parameters of the MPC. They are constant over the horizon. Parameters not listed here will have their default from the model file.
+-
+
- Validated by: +
- + +
-
+
- +field penalty_factor: float = 10 +
Penalty factor of the ADMM algorithm. Should be equal for all agents.
+-
+
- Constraints: +
-
+
ge = 0
+
+
-
+
- +field prediction_horizon: int = 5 +
Prediction horizon of the MPC.
+-
+
- Constraints: +
-
+
ge = 0
+
+
-
+
- +field r_del_u: dict[str, float] = {} +
Weights that are applied to the change in control variables.
+-
+
- Validated by: +
- + +
-
+
- +field registration_period: float = 2 +
Time spent on registration before each optimization
+-
+
- Constraints: +
-
+
ge = 0
+
+
-
+
- +field sampling_time: float | None = None +
Sampling interval for control steps. If None, will be the same as time step. Does not affect the discretization of the MPC, only the interval with which there will be optimization steps.
+-
+
- Validated by: +
- + +
-
+
- +field set_outputs: bool = False +
Sets the full output time series to the data broker.
+
-
+
+
-
+
- Validated by: +
-
+
check_valid_fields
+
+
-
+
- +field states: mpc_datamodels.MPCVariables = [] +
List of all differential states of the MPC. The entries can define the boundaries and the source for the measurements
+-
+
- Validated by: +
- + +
-
+
- +field time_step: float = 60 +
Time step of the MPC.
+-
+
- Constraints: +
-
+
ge = 0
+
+
-
+
- +field type: str | Dict[str, str] [Required] +
The type of the Module. Used to find the Python-Object from all agentlib-core and plugin Module options. If a dict is given,it must contain the keys ‘file’ and ‘class_name’. ‘file’ is the filepath of a python file containing the Module.’class_name’ is the name of the Module class within this file.
+
-
+
- +field validate_incoming_values: bool | None = True +
If true, the validator of the AgentVariable value is called when receiving a new value from the DataBroker.
+
-
+
- +validator check_prefixes_of_variables » exchange, controls, states, couplings, outputs, inputs, parameters[source] +
Ensures no user provided variable is named with the reserved ADMM prefix.
+
-
+
- +model_post_init(context: Any, /) None +
We need to both initialize private attributes and call the user-defined model_post_init +method.
+
-
+
- +class agentlib_mpc.modules.dmpc.admm.admm.ADMMParticipation(variable)[source] +
Bases:
+objectHolds data for the status of a shared variable of another system.
+ + + + +
-
+
- +class agentlib_mpc.modules.dmpc.admm.admm.LocalADMM(config: dict, agent)[source] +
Bases:
+ADMM-
+
- +process()[source] +
This abstract method must be implemented in order to sync the module +with the other processes of the agent and the whole MAS.
+
-
+
- +property registration_delay: float +
Timeout value used to wait one on registration. Waits in real time +(time.sleep)
+
-
+
- +property sync_delay: float +
Timeout value used to sync local admm processes. Should be very +small.
+
-
+
- +pydantic model agentlib_mpc.modules.dmpc.admm.admm.LocalADMMConfig[source] +
Bases:
+ADMMConfig-
+
- Config: +
-
+
arbitrary_types_allowed: bool = True
+validate_assignment: bool = True
+extra: str = forbid
+frozen: bool = True
+
+- Fields: +
-
+
- +
- +
- +
- +
- +
- +
- +
- +
- +
- +
- +
- +
- +
- +
- +
- +
- +
- +
- +
- +
- +
- +
- +
- +
+- Validators: +
-
+
- +
- +
- +
- +
- +
- +
- +
- +
check_valid_fields»shared_variable_fields
+check_valid_level»log_level
+- +
+
-
+
- +field controls: mpc_datamodels.MPCVariables = [] +
List of all control variables of the MPC.
+-
+
- Validated by: +
- + +
-
+
- +field couplings: List[mpc_datamodels.MPCVariable] = [] +
-
+
- Validated by: +
- + +
-
+
- +field exchange: List[mpc_datamodels.MPCVariable] = [] +
-
+
- Validated by: +
- + +
-
+
- +field inputs: mpc_datamodels.MPCVariables = [] +
List of all input variables of the MPC. Includes predictions for disturbances, set_points, dynamic constraint boundaries etc.
+-
+
- Validated by: +
- + +
-
+
- +field iteration_timeout: float = 20 +
Maximum computation + waiting time for one iteration.
+-
+
- Constraints: +
-
+
ge = 0
+
+
-
+
- +field log_level: str | None = None +
The log level for this Module. Default uses the root-loggers level.Options: DEBUG; INFO; WARNING; ERROR; CRITICAL
+-
+
- Validated by: +
-
+
check_valid_level
+
+
-
+
- +field max_iterations: float = 20 +
Maximum number of ADMM iterations before termination of control step.
+-
+
- Constraints: +
-
+
ge = 0
+
+
-
+
- +field module_id: str [Required] +
The unqiue id of the module within an agent, used only to communicate withing the agent.
+
-
+
- +field optimization_backend: dict [Required] +
-
+
- +field outputs: mpc_datamodels.MPCVariables = [] +
List of all shared outputs of the MPC.
+-
+
- Validated by: +
- + +
-
+
- +field parameters: mpc_datamodels.MPCVariables = [] +
List of model parameters of the MPC. They are constant over the horizon. Parameters not listed here will have their default from the model file.
+-
+
- Validated by: +
- + +
-
+
- +field penalty_factor: float = 10 +
Penalty factor of the ADMM algorithm. Should be equal for all agents.
+-
+
- Constraints: +
-
+
ge = 0
+
+
-
+
- +field prediction_horizon: int = 5 +
Prediction horizon of the MPC.
+-
+
- Constraints: +
-
+
ge = 0
+
+
-
+
- +field r_del_u: dict[str, float] = {} +
Weights that are applied to the change in control variables.
+-
+
- Validated by: +
- + +
-
+
- +field registration_delay: float = 0.1 +
-
+
- +field registration_period: float = 2 +
Time spent on registration before each optimization
+-
+
- Constraints: +
-
+
ge = 0
+
+
-
+
- +field sampling_time: float | None = None +
Sampling interval for control steps. If None, will be the same as time step. Does not affect the discretization of the MPC, only the interval with which there will be optimization steps.
+-
+
- Validated by: +
- + +
-
+
- +field set_outputs: bool = False +
Sets the full output time series to the data broker.
+
-
+
+
-
+
- Validated by: +
-
+
check_valid_fields
+
+
-
+
- +field states: mpc_datamodels.MPCVariables = [] +
List of all differential states of the MPC. The entries can define the boundaries and the source for the measurements
+-
+
- Validated by: +
- + +
-
+
- +field sync_delay: float = 0.001 +
-
+
- +field time_step: float = 60 +
Time step of the MPC.
+-
+
- Constraints: +
-
+
ge = 0
+
+
-
+
- +field type: str | Dict[str, str] [Required] +
The type of the Module. Used to find the Python-Object from all agentlib-core and plugin Module options. If a dict is given,it must contain the keys ‘file’ and ‘class_name’. ‘file’ is the filepath of a python file containing the Module.’class_name’ is the name of the Module class within this file.
+
-
+
- +field validate_incoming_values: bool | None = True +
If true, the validator of the AgentVariable value is called when receiving a new value from the DataBroker.
+
-
+
- +model_post_init(context: Any, /) None +
We need to both initialize private attributes and call the user-defined model_post_init +method.
+
-
+
- +class agentlib_mpc.modules.dmpc.admm.admm.ModuleStatus(value, names=None, *, module=None, qualname=None, type=None, start=1, boundary=None)[source] +
Bases:
+Enum-
+
- +at_registration = 3 +
-
+
- +not_started = 1 +
-
+
- +optimizing = 4 +
-
+
- +sleeping = 7 +
-
+
- +syncing = 2 +
-
+
- +updating = 5 +
-
+
- +waiting_for_other_agents = 6 +
agentlib_mpc.modules.dmpc.admm.admm_coordinated module
+Module implementing the coordinated ADMM module, which works together +with a coordinator.
+-
+
- +class agentlib_mpc.modules.dmpc.admm.admm_coordinated.CoordinatedADMM(*, config: dict, agent: Agent)[source] +
Bases:
+MiniEmployee,ADMMModule to implement an ADMM agent, which is guided by a coordinator. +Only optimizes based on callbacks.
+ + +-
+
- +init_iteration_callback(variable: AgentVariable)[source] +
Callback that answers the coordinators init_iteration flag.
+
-
+
- +optimize(variable: AgentVariable)[source] +
Performs the optimization given the mean trajectories and multipliers from the +coordinator. +Replies with the local optimal trajectories. +Returns:
+
-
+
- +pydantic model agentlib_mpc.modules.dmpc.admm.admm_coordinated.CoordinatedADMMConfig[source] +
Bases:
+MiniEmployeeConfig,ADMMConfig-
+
- Config: +
-
+
arbitrary_types_allowed: bool = True
+validate_assignment: bool = True
+extra: str = forbid
+frozen: bool = True
+
+- Fields: +
-
+
- +
- +
- +
- +
- +
- +
- +
- +
- +
- +
- +
- +
- +
- +
- +
- +
- +
- +
- +
- +
- +
- +
- +
- +
- +
- +
- +
+- Validators: +
-
+
- +
- +
- +
- +
- +
- +
- +
- +
check_valid_fields»shared_variable_fields
+check_valid_level»log_level
+- +
- +
- +
+
-
+
- +field controls: mpc_datamodels.MPCVariables = [] +
List of all control variables of the MPC.
+-
+
- Validated by: +
- + +
-
+
- +field coordinator: Source [Required] +
Define the agents coordinator
+
-
+
- +field couplings: List[mpc_datamodels.MPCVariable] = [] +
-
+
- Validated by: +
- + +
-
+
- +field exchange: List[mpc_datamodels.MPCVariable] = [] +
-
+
- Validated by: +
- + +
-
+
- +field inputs: mpc_datamodels.MPCVariables = [] +
List of all input variables of the MPC. Includes predictions for disturbances, set_points, dynamic constraint boundaries etc.
+-
+
- Validated by: +
- + +
-
+
- +field iteration_timeout: float = 20 +
Maximum computation + waiting time for one iteration.
+-
+
- Constraints: +
-
+
ge = 0
+
+
-
+
- +field log_level: str | None = None +
The log level for this Module. Default uses the root-loggers level.Options: DEBUG; INFO; WARNING; ERROR; CRITICAL
+-
+
- Validated by: +
-
+
check_valid_level
+
+
-
+
- +field max_iterations: float = 20 +
Maximum number of ADMM iterations before termination of control step.
+-
+
- Constraints: +
-
+
ge = 0
+
+
-
+
- +field messages_in: AgentVariables = [AgentVariable(name='registration_coordinator_to_agent', type=None, timestamp=None, unit='Not defined', description='Not defined', ub=inf, lb=-inf, clip=False, allowed_values=[], value=None, alias='registration_coordinator_to_agent', source=Source(agent_id=None, module_id=None), shared=None, rdf_class=None), AgentVariable(name='startIteration_coordinator_to_agent', type=None, timestamp=None, unit='Not defined', description='Not defined', ub=inf, lb=-inf, clip=False, allowed_values=[], value=None, alias='startIteration_coordinator_to_agent', source=Source(agent_id=None, module_id=None), shared=None, rdf_class=None), AgentVariable(name='optimization_coordinator_to_agent', type=None, timestamp=None, unit='Not defined', description='Not defined', ub=inf, lb=-inf, clip=False, allowed_values=[], value=None, alias='optimization_coordinator_to_agent', source=Source(agent_id=None, module_id=None), shared=None, rdf_class=None)] +
-
+
- +field messages_out: AgentVariables = [AgentVariable(name='registration_agent_to_coordinator', type=None, timestamp=None, unit='Not defined', description='Not defined', ub=inf, lb=-inf, clip=False, allowed_values=[], value=None, alias='registration_agent_to_coordinator', source=Source(agent_id=None, module_id=None), shared=None, rdf_class=None), AgentVariable(name='startIteration_agent_to_coordinator', type=None, timestamp=None, unit='Not defined', description='Not defined', ub=inf, lb=-inf, clip=False, allowed_values=[], value=None, alias='startIteration_agent_to_coordinator', source=Source(agent_id=None, module_id=None), shared=None, rdf_class=None), AgentVariable(name='optimization_agent_to_coordinator', type=None, timestamp=None, unit='Not defined', description='Not defined', ub=inf, lb=-inf, clip=False, allowed_values=[], value=None, alias='optimization_agent_to_coordinator', source=Source(agent_id=None, module_id=None), shared=None, rdf_class=None)] +
-
+
- +field module_id: str [Required] +
The unqiue id of the module within an agent, used only to communicate withing the agent.
+
-
+
- +field optimization_backend: dict [Required] +
-
+
- +field outputs: mpc_datamodels.MPCVariables = [] +
List of all shared outputs of the MPC.
+-
+
- Validated by: +
- + +
-
+
- +field parameters: mpc_datamodels.MPCVariables = [] +
List of model parameters of the MPC. They are constant over the horizon. Parameters not listed here will have their default from the model file.
+-
+
- Validated by: +
- + +
-
+
- +field penalty_factor: float = 10 +
Penalty factor of the ADMM algorithm. Should be equal for all agents.
+-
+
- Constraints: +
-
+
ge = 0
+
+
-
+
- +field prediction_horizon: int = 5 +
Prediction horizon of the MPC.
+-
+
- Constraints: +
-
+
ge = 0
+
+
-
+
- +field r_del_u: dict[str, float] = {} +
Weights that are applied to the change in control variables.
+-
+
- Validated by: +
- + +
-
+
- +field registration_interval: float = 10 +
Interval in seconds after which a registration attempt is made.
+-
+
- Constraints: +
-
+
ge = 0
+
+
-
+
- +field registration_period: float = 2 +
Time spent on registration before each optimization
+-
+
- Constraints: +
-
+
ge = 0
+
+
-
+
- +field request_frequency: float = 1 +
Wait time between signup_requests
+
-
+
- +field sampling_time: float | None = None +
Sampling interval for control steps. If None, will be the same as time step. Does not affect the discretization of the MPC, only the interval with which there will be optimization steps.
+-
+
- Validated by: +
- + +
-
+
- +field set_outputs: bool = False +
Sets the full output time series to the data broker.
+
-
+
+
-
+
- Validated by: +
-
+
check_valid_fields
+
+
-
+
- +field states: mpc_datamodels.MPCVariables = [] +
List of all differential states of the MPC. The entries can define the boundaries and the source for the measurements
+-
+
- Validated by: +
- + +
-
+
- +field time_step: float = 60 +
Time step of the MPC.
+-
+
- Constraints: +
-
+
ge = 0
+
+
-
+
- +field type: str | Dict[str, str] [Required] +
The type of the Module. Used to find the Python-Object from all agentlib-core and plugin Module options. If a dict is given,it must contain the keys ‘file’ and ‘class_name’. ‘file’ is the filepath of a python file containing the Module.’class_name’ is the name of the Module class within this file.
+
-
+
- +field validate_incoming_values: bool | None = True +
If true, the validator of the AgentVariable value is called when receiving a new value from the DataBroker.
+
-
+
- +validator couplings_should_have_values » exchange, couplings[source] +
Asserts that couplings and exchange have values, as they are needed for +initial guess.
+
-
+
- +model_post_init(context: Any, /) None +
We need to both initialize private attributes and call the user-defined model_post_init +method.
+
-
+
- +agentlib_mpc.modules.dmpc.admm.admm_coordinated.coupInput +
alias of
+coup_input
agentlib_mpc.modules.dmpc.admm.admm_coordinator module
+Defines classes that coordinate an ADMM process.
+-
+
- +class agentlib_mpc.modules.dmpc.admm.admm_coordinator.ADMMCoordinator(*, config: dict, agent: Agent)[source] +
Bases:
+Coordinator-
+
- +cleanup_results()[source] +
Deletes all files this module created.
+Override this method, if your module creates e.g. results files etc.
+
-
+
- +optim_results_callback(variable: AgentVariable)[source] +
Saves the results of a local optimization. +:param variable:
+Returns:
+
-
+
- +pydantic model agentlib_mpc.modules.dmpc.admm.admm_coordinator.ADMMCoordinatorConfig[source] +
Bases:
+CoordinatorConfigHold the config for ADMMCoordinator
+-
+
- Config: +
-
+
arbitrary_types_allowed: bool = True
+validate_assignment: bool = True
+extra: str = forbid
+frozen: bool = True
+
+- Fields: +
-
+
- +
- +
- +
- +
- +
- +
- +
- +
- +
- +
- +
- +
- +
- +
- +
- +
- +
- +
- +
- +
- +
- +
- +
- +
- +
- +
+- Validators: +
-
+
check_valid_fields»shared_variable_fields
+check_valid_level»log_level
+- +
- +
+
-
+
- +field abs_tol: float = 0.001 +
Absolute stopping criterion.
+
-
+
- +field admm_iter_max: int = 20 +
Maximum number of ADMM iterations before termination of control step.
+
-
+
- +field dual_tol: float = 0.001 +
Absolute dual stopping criterion.
+
-
+
- +field log_level: str | None = None +
The log level for this Module. Default uses the root-loggers level.Options: DEBUG; INFO; WARNING; ERROR; CRITICAL
+-
+
- Validated by: +
-
+
check_valid_level
+
+
-
+
- +field maxIter: int = 10 +
Maximum number of iterations
+
-
+
- +field messages_in: AgentVariables = [AgentVariable(name='registration_agent_to_coordinator', type=None, timestamp=None, unit='Not defined', description='Not defined', ub=inf, lb=-inf, clip=False, allowed_values=[], value=None, alias='registration_agent_to_coordinator', source=Source(agent_id=None, module_id=None), shared=None, rdf_class=None), AgentVariable(name='startIteration_agent_to_coordinator', type=None, timestamp=None, unit='Not defined', description='Not defined', ub=inf, lb=-inf, clip=False, allowed_values=[], value=None, alias='startIteration_agent_to_coordinator', source=Source(agent_id=None, module_id=None), shared=None, rdf_class=None), AgentVariable(name='optimization_agent_to_coordinator', type=None, timestamp=None, unit='Not defined', description='Not defined', ub=inf, lb=-inf, clip=False, allowed_values=[], value=None, alias='optimization_agent_to_coordinator', source=Source(agent_id=None, module_id=None), shared=None, rdf_class=None)] +
-
+
- +field messages_out: AgentVariables = [AgentVariable(name='registration_coordinator_to_agent', type=None, timestamp=None, unit='Not defined', description='Not defined', ub=inf, lb=-inf, clip=False, allowed_values=[], value=None, alias='registration_coordinator_to_agent', source=Source(agent_id=None, module_id=None), shared=None, rdf_class=None), AgentVariable(name='startIteration_coordinator_to_agent', type=None, timestamp=None, unit='Not defined', description='Not defined', ub=inf, lb=-inf, clip=False, allowed_values=[], value=None, alias='startIteration_coordinator_to_agent', source=Source(agent_id=None, module_id=None), shared=None, rdf_class=None), AgentVariable(name='optimization_coordinator_to_agent', type=None, timestamp=None, unit='Not defined', description='Not defined', ub=inf, lb=-inf, clip=False, allowed_values=[], value=None, alias='optimization_coordinator_to_agent', source=Source(agent_id=None, module_id=None), shared=None, rdf_class=None)] +
-
+
- +field module_id: str [Required] +
The unqiue id of the module within an agent, used only to communicate withing the agent.
+
-
+
- +field penalty_change_factor: float = 2 +
Factor to vary the penalty parameter with.
+
-
+
- +field penalty_change_threshold: float = -1 +
When the primal residual is x times higher, vary the penalty parameter and vice versa.
+
-
+
- +field penalty_factor: float = 10 +
Penalty factor of the ADMM algorithm. Should be equal for all agents.
+
-
+
- +field prediction_horizon: int = 10 +
Prediction horizon of participating agents.
+
-
+
- +field primal_tol: float = 0.001 +
Absolute primal stopping criterion.
+
-
+
- +field registration_period: float = 5 +
Time spent on registration before each optimization
+
-
+
- +field rel_tol: float = 0.001 +
Relative stopping criterion.
+
-
+
- +field sampling_time: float | None = None +
Sampling interval for control steps. If None, will be the same as time step. Does not affect the discretization of the MPC, only the interval with which there will be optimization steps.
+-
+
- Validated by: +
- + +
-
+
- +field save_iter_interval: int = 1000 +
-
+
- +field save_solve_stats: bool = False +
When True, saves the solve stats to a file.
+
-
+
+
-
+
- Validated by: +
-
+
check_valid_fields
+
+
-
+
- +field solve_stats_file: str = 'admm_stats.csv' +
File name for the solve stats.
+-
+
- Validated by: +
- + +
-
+
- +field time_out_non_responders: float = 1 +
Maximum wait time for subsystems in seconds
+
-
+
- +field time_step: float = 600 +
Sampling interval of between two control steps. Will be used in the discretization for MPC.
+
-
+
- +field type: str | Dict[str, str] [Required] +
The type of the Module. Used to find the Python-Object from all agentlib-core and plugin Module options. If a dict is given,it must contain the keys ‘file’ and ‘class_name’. ‘file’ is the filepath of a python file containing the Module.’class_name’ is the name of the Module class within this file.
+
-
+
- +field use_relative_tolerances: bool = True +
If True, use abs_tol and rel_tol, if False us prim_tol and dual_tol.
+
-
+
- +field validate_incoming_values: bool | None = True +
If true, the validator of the AgentVariable value is called when receiving a new value from the DataBroker.
+
-
+
- +field wait_time_on_start_iters: float = 0.1 +
wait_on_start_iterations
+
-
+
- +validator default_sampling_time » sampling_time[source] +
-
+
- +model_post_init(context: Any, /) None +
We need to both initialize private attributes and call the user-defined model_post_init +method.
+
-
+
- +validator solve_stats_file_is_csv » solve_stats_file[source] +
+
+
+
+
+
+
+
+
+
+
\ No newline at end of file
diff --git a/docs/0.6.4/docs/code/agentlib_mpc.modules.estimation.html b/docs/0.6.4/docs/code/agentlib_mpc.modules.estimation.html
new file mode 100644
index 0000000..71970bc
--- /dev/null
+++ b/docs/0.6.4/docs/code/agentlib_mpc.modules.estimation.html
@@ -0,0 +1,338 @@
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+ agentlib_mpc.modules.dmpc package
+-
+
- +class agentlib_mpc.modules.dmpc.DistributedMPC(config: dict, agent: Agent)[source] +
Bases:
+MPCBase class which defines common interfaces among all +distributed mpc approaches (either optimization based, +game theory based or some other).
+
-
+
- +pydantic model agentlib_mpc.modules.dmpc.DistributedMPCConfig[source] +
Bases:
+MPCConfigBase config class with common configurations
+-
+
- Config: +
-
+
arbitrary_types_allowed: bool = True
+validate_assignment: bool = True
+extra: str = forbid
+frozen: bool = True
+
+- Fields: +
- +
- Validators: +
- +
-
+
- +model_post_init(context: Any, /) None +
We need to both initialize private attributes and call the user-defined model_post_init +method.
+
Subpackages
+
+
+-
+
- agentlib_mpc.modules.dmpc.admm package
-
+
- Submodules +
- agentlib_mpc.modules.dmpc.admm.admm module
-
+
ADMM-
+
ADMM.admm_step()
+ADMM.all_coupling_statuses()
+ADMM.assert_mpc_variables_are_in_model()
+ADMM.collect_couplings_for_optimization()
+ADMM.collect_variables_for_optimization()
+ADMM.cons_and_exchange
+ADMM.deregister_all_participants()
+ADMM.get_participants_values()
+ADMM.get_results()
+ADMM.participant_callback()
+ADMM.penalty_factor_var
+ADMM.process()
+ADMM.receive_participant()
+ADMM.registered_participants
+ADMM.reset_participants_ready()
+ADMM.send_coupling_values()
+ADMM.send_coupling_variable()
+ADMM.update_lambda()
+ADMM.var_ref
+
+ADMMConfig-
+
ADMMConfig.controls
+ADMMConfig.couplings
+ADMMConfig.exchange
+ADMMConfig.inputs
+ADMMConfig.iteration_timeout
+ADMMConfig.log_level
+ADMMConfig.max_iterations
+ADMMConfig.module_id
+ADMMConfig.optimization_backend
+ADMMConfig.outputs
+ADMMConfig.parameters
+ADMMConfig.penalty_factor
+ADMMConfig.prediction_horizon
+ADMMConfig.r_del_u
+ADMMConfig.registration_period
+ADMMConfig.sampling_time
+ADMMConfig.set_outputs
+ADMMConfig.shared_variable_fields
+ADMMConfig.states
+ADMMConfig.time_step
+ADMMConfig.type
+ADMMConfig.validate_incoming_values
+ADMMConfig.check_prefixes_of_variables
+ADMMConfig.model_post_init()
+
+ADMMParticipation+
+LocalADMM+
+LocalADMMConfig-
+
LocalADMMConfig.controls
+LocalADMMConfig.couplings
+LocalADMMConfig.exchange
+LocalADMMConfig.inputs
+LocalADMMConfig.iteration_timeout
+LocalADMMConfig.log_level
+LocalADMMConfig.max_iterations
+LocalADMMConfig.module_id
+LocalADMMConfig.optimization_backend
+LocalADMMConfig.outputs
+LocalADMMConfig.parameters
+LocalADMMConfig.penalty_factor
+LocalADMMConfig.prediction_horizon
+LocalADMMConfig.r_del_u
+LocalADMMConfig.registration_delay
+LocalADMMConfig.registration_period
+LocalADMMConfig.sampling_time
+LocalADMMConfig.set_outputs
+LocalADMMConfig.shared_variable_fields
+LocalADMMConfig.states
+LocalADMMConfig.sync_delay
+LocalADMMConfig.time_step
+LocalADMMConfig.type
+LocalADMMConfig.validate_incoming_values
+LocalADMMConfig.model_post_init()
+
+ModuleStatus+
+ParticipantStatus+
+
+ - agentlib_mpc.modules.dmpc.admm.admm_coordinated module
-
+
CoordinatedADMM+
+CoordinatedADMMConfig-
+
CoordinatedADMMConfig.controls
+CoordinatedADMMConfig.coordinator
+CoordinatedADMMConfig.couplings
+CoordinatedADMMConfig.exchange
+CoordinatedADMMConfig.inputs
+CoordinatedADMMConfig.iteration_timeout
+CoordinatedADMMConfig.log_level
+CoordinatedADMMConfig.max_iterations
+CoordinatedADMMConfig.messages_in
+CoordinatedADMMConfig.messages_out
+CoordinatedADMMConfig.module_id
+CoordinatedADMMConfig.optimization_backend
+CoordinatedADMMConfig.outputs
+CoordinatedADMMConfig.parameters
+CoordinatedADMMConfig.penalty_factor
+CoordinatedADMMConfig.prediction_horizon
+CoordinatedADMMConfig.r_del_u
+CoordinatedADMMConfig.registration_interval
+CoordinatedADMMConfig.registration_period
+CoordinatedADMMConfig.request_frequency
+CoordinatedADMMConfig.sampling_time
+CoordinatedADMMConfig.set_outputs
+CoordinatedADMMConfig.shared_variable_fields
+CoordinatedADMMConfig.states
+CoordinatedADMMConfig.time_step
+CoordinatedADMMConfig.type
+CoordinatedADMMConfig.validate_incoming_values
+CoordinatedADMMConfig.couplings_should_have_values
+CoordinatedADMMConfig.model_post_init()
+
+coupInput
+
+ - agentlib_mpc.modules.dmpc.admm.admm_coordinator module
-
+
ADMMCoordinator+
+ADMMCoordinatorConfig-
+
ADMMCoordinatorConfig.abs_tol
+ADMMCoordinatorConfig.admm_iter_max
+ADMMCoordinatorConfig.dual_tol
+ADMMCoordinatorConfig.log_level
+ADMMCoordinatorConfig.maxIter
+ADMMCoordinatorConfig.messages_in
+ADMMCoordinatorConfig.messages_out
+ADMMCoordinatorConfig.module_id
+ADMMCoordinatorConfig.penalty_change_factor
+ADMMCoordinatorConfig.penalty_change_threshold
+ADMMCoordinatorConfig.penalty_factor
+ADMMCoordinatorConfig.prediction_horizon
+ADMMCoordinatorConfig.primal_tol
+ADMMCoordinatorConfig.registration_period
+ADMMCoordinatorConfig.rel_tol
+ADMMCoordinatorConfig.sampling_time
+ADMMCoordinatorConfig.save_iter_interval
+ADMMCoordinatorConfig.save_solve_stats
+ADMMCoordinatorConfig.shared_variable_fields
+ADMMCoordinatorConfig.solve_stats_file
+ADMMCoordinatorConfig.time_out_non_responders
+ADMMCoordinatorConfig.time_step
+ADMMCoordinatorConfig.type
+ADMMCoordinatorConfig.use_relative_tolerances
+ADMMCoordinatorConfig.validate_incoming_values
+ADMMCoordinatorConfig.wait_time_on_start_iters
+ADMMCoordinatorConfig.default_sampling_time
+ADMMCoordinatorConfig.model_post_init()
+ADMMCoordinatorConfig.solve_stats_file_is_csv
+
+
+
+
Submodules
+agentlib_mpc.modules.dmpc.coordinator module
+-
+
- +class agentlib_mpc.modules.dmpc.coordinator.Coordinator(*, config: dict, agent: Agent)[source] +
Bases:
+BaseModuleClass implementing the base coordination for distributed MPC
+-
+
- +property all_finished +
Returns: +True, if there are no busy agents, else False
+
-
+
- +init_iteration_callback(variable: AgentVariable)[source] +
Processes and Agents InitIteration confirmation. +:param variable:
+Returns:
+
-
+
- +optim_results_callback(variable: AgentVariable)[source] +
Saves the results of a local optimization. +:param variable:
+Returns:
+
-
+
- +pydantic model agentlib_mpc.modules.dmpc.coordinator.CoordinatorConfig[source] +
Bases:
+BaseModuleConfig-
+
- Config: +
-
+
arbitrary_types_allowed: bool = True
+validate_assignment: bool = True
+extra: str = forbid
+frozen: bool = True
+
+- Fields: +
-
+
- +
- +
- +
- +
- +
- +
- +
- +
- +
+- Validators: +
-
+
check_valid_fields»shared_variable_fields
+check_valid_level»log_level
+
+
-
+
- +field log_level: str | None = None +
The log level for this Module. Default uses the root-loggers level.Options: DEBUG; INFO; WARNING; ERROR; CRITICAL
+-
+
- Validated by: +
-
+
check_valid_level
+
+
-
+
- +field maxIter: int = 10 +
Maximum number of iterations
+
-
+
- +field messages_in: List[AgentVariable] = [AgentVariable(name='registration_agent_to_coordinator', type=None, timestamp=None, unit='Not defined', description='Not defined', ub=inf, lb=-inf, clip=False, allowed_values=[], value=None, alias='registration_agent_to_coordinator', source=Source(agent_id=None, module_id=None), shared=None, rdf_class=None), AgentVariable(name='startIteration_agent_to_coordinator', type=None, timestamp=None, unit='Not defined', description='Not defined', ub=inf, lb=-inf, clip=False, allowed_values=[], value=None, alias='startIteration_agent_to_coordinator', source=Source(agent_id=None, module_id=None), shared=None, rdf_class=None), AgentVariable(name='optimization_agent_to_coordinator', type=None, timestamp=None, unit='Not defined', description='Not defined', ub=inf, lb=-inf, clip=False, allowed_values=[], value=None, alias='optimization_agent_to_coordinator', source=Source(agent_id=None, module_id=None), shared=None, rdf_class=None)] +
-
+
- +field messages_out: List[AgentVariable] = [AgentVariable(name='registration_coordinator_to_agent', type=None, timestamp=None, unit='Not defined', description='Not defined', ub=inf, lb=-inf, clip=False, allowed_values=[], value=None, alias='registration_coordinator_to_agent', source=Source(agent_id=None, module_id=None), shared=None, rdf_class=None), AgentVariable(name='startIteration_coordinator_to_agent', type=None, timestamp=None, unit='Not defined', description='Not defined', ub=inf, lb=-inf, clip=False, allowed_values=[], value=None, alias='startIteration_coordinator_to_agent', source=Source(agent_id=None, module_id=None), shared=None, rdf_class=None), AgentVariable(name='optimization_coordinator_to_agent', type=None, timestamp=None, unit='Not defined', description='Not defined', ub=inf, lb=-inf, clip=False, allowed_values=[], value=None, alias='optimization_coordinator_to_agent', source=Source(agent_id=None, module_id=None), shared=None, rdf_class=None)] +
-
+
- +field module_id: str [Required] +
The unqiue id of the module within an agent, used only to communicate withing the agent.
+
-
+
+
-
+
- Validated by: +
-
+
check_valid_fields
+
+
-
+
- +field time_out_non_responders: float = 1 +
Maximum wait time for subsystems in seconds
+
-
+
- +field type: str | Dict[str, str] [Required] +
The type of the Module. Used to find the Python-Object from all agentlib-core and plugin Module options. If a dict is given,it must contain the keys ‘file’ and ‘class_name’. ‘file’ is the filepath of a python file containing the Module.’class_name’ is the name of the Module class within this file.
+
-
+
- +field validate_incoming_values: bool | None = True +
If true, the validator of the AgentVariable value is called when receiving a new value from the DataBroker.
+
-
+
- +model_post_init(context: Any, /) None +
We need to both initialize private attributes and call the user-defined model_post_init +method.
+
agentlib_mpc.modules.dmpc.employee module
+-
+
- +class agentlib_mpc.modules.dmpc.employee.MiniEmployee(*, config: dict, agent: Agent)[source] +
Bases:
+ + +BaseModule-
+
- +init_iteration_callback(variable: AgentVariable)[source] +
Callback that processes the coordinators ‘startIteration’ flag. +:param variable:
+
-
+
- +abstract optimize(variable: AgentVariable)[source] +
Performs the optimization given the information from the coordinator. +Replies with local information. +Returns:
+
-
+
- +pre_computation_hook()[source] +
This method is called in every computation step before the optimization starts. +Overwrite this method in a derived subclass if you want to take some actions each time before the optimal control problem is solved.
+
-
+
- +pydantic model agentlib_mpc.modules.dmpc.employee.MiniEmployeeConfig[source] +
Bases:
+BaseModuleConfig-
+
- Config: +
-
+
arbitrary_types_allowed: bool = True
+validate_assignment: bool = True
+extra: str = forbid
+frozen: bool = True
+
+- Fields: +
-
+
- +
- +
- +
- +
- +
- +
- +
- +
- +
- +
+- Validators: +
-
+
check_valid_fields»shared_variable_fields
+check_valid_level»log_level
+
+
-
+
- +field coordinator: Source [Required] +
Define the agents coordinator
+
-
+
- +field log_level: str | None = None +
The log level for this Module. Default uses the root-loggers level.Options: DEBUG; INFO; WARNING; ERROR; CRITICAL
+-
+
- Validated by: +
-
+
check_valid_level
+
+
-
+
- +field messages_in: List[AgentVariable] = [AgentVariable(name='registration_coordinator_to_agent', type=None, timestamp=None, unit='Not defined', description='Not defined', ub=inf, lb=-inf, clip=False, allowed_values=[], value=None, alias='registration_coordinator_to_agent', source=Source(agent_id=None, module_id=None), shared=None, rdf_class=None), AgentVariable(name='startIteration_coordinator_to_agent', type=None, timestamp=None, unit='Not defined', description='Not defined', ub=inf, lb=-inf, clip=False, allowed_values=[], value=None, alias='startIteration_coordinator_to_agent', source=Source(agent_id=None, module_id=None), shared=None, rdf_class=None), AgentVariable(name='optimization_coordinator_to_agent', type=None, timestamp=None, unit='Not defined', description='Not defined', ub=inf, lb=-inf, clip=False, allowed_values=[], value=None, alias='optimization_coordinator_to_agent', source=Source(agent_id=None, module_id=None), shared=None, rdf_class=None)] +
-
+
- +field messages_out: List[AgentVariable] = [AgentVariable(name='registration_agent_to_coordinator', type=None, timestamp=None, unit='Not defined', description='Not defined', ub=inf, lb=-inf, clip=False, allowed_values=[], value=None, alias='registration_agent_to_coordinator', source=Source(agent_id=None, module_id=None), shared=None, rdf_class=None), AgentVariable(name='startIteration_agent_to_coordinator', type=None, timestamp=None, unit='Not defined', description='Not defined', ub=inf, lb=-inf, clip=False, allowed_values=[], value=None, alias='startIteration_agent_to_coordinator', source=Source(agent_id=None, module_id=None), shared=None, rdf_class=None), AgentVariable(name='optimization_agent_to_coordinator', type=None, timestamp=None, unit='Not defined', description='Not defined', ub=inf, lb=-inf, clip=False, allowed_values=[], value=None, alias='optimization_agent_to_coordinator', source=Source(agent_id=None, module_id=None), shared=None, rdf_class=None)] +
-
+
- +field module_id: str [Required] +
The unqiue id of the module within an agent, used only to communicate withing the agent.
+
-
+
- +field registration_interval: float = 10 +
Interval in seconds after which a registration attempt is made.
+-
+
- Constraints: +
-
+
ge = 0
+
+
-
+
- +field request_frequency: float = 1 +
Wait time between signup_requests
+
-
+
+
-
+
- Validated by: +
-
+
check_valid_fields
+
+
-
+
- +field type: str | Dict[str, str] [Required] +
The type of the Module. Used to find the Python-Object from all agentlib-core and plugin Module options. If a dict is given,it must contain the keys ‘file’ and ‘class_name’. ‘file’ is the filepath of a python file containing the Module.’class_name’ is the name of the Module class within this file.
+
-
+
- +field validate_incoming_values: bool | None = True +
If true, the validator of the AgentVariable value is called when receiving a new value from the DataBroker.
+
-
+
- +model_post_init(context: Any, /) None +
We need to both initialize private attributes and call the user-defined model_post_init +method.
+
+
+
+
+
+
+
+
+
+
+
\ No newline at end of file
diff --git a/docs/0.6.4/docs/code/agentlib_mpc.modules.html b/docs/0.6.4/docs/code/agentlib_mpc.modules.html
new file mode 100644
index 0000000..0f138ca
--- /dev/null
+++ b/docs/0.6.4/docs/code/agentlib_mpc.modules.html
@@ -0,0 +1,999 @@
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+ agentlib_mpc.modules.estimation package
+Submodules
+agentlib_mpc.modules.estimation.mhe module
+-
+
- +class agentlib_mpc.modules.estimation.mhe.MHE(config: dict, agent: Agent)[source] +
Bases:
+BaseModuleA moving horizon estimator.
+-
+
- +cleanup_results()[source] +
Deletes all files this module created.
+Override this method, if your module creates e.g. results files etc.
+
-
+
- +collect_variables_for_optimization(var_ref: MHEVariableReference = None) Dict[str, AgentVariable][source] +
Gets all variables noted in the var ref and puts them in a flat +dictionary. The MHE Version of this function has to perform some checks and +lookups extra, since variables come from different sources, and some need to +incorporate trajectories of past values.
+
-
+
- +get_results() DataFrame | None[source] +
Read the results that were saved from the optimization backend and +returns them as Dataframe.
+-
+
- Returns: +
(results, stats) tuple of Dataframes.
+
+
-
+
- +process()[source] +
This abstract method must be implemented in order to sync the module +with the other processes of the agent and the whole MAS.
+
-
+
- +static read_results_file(results_file: str) Tuple[DataFrame, DataFrame][source] +
Read the provided csv-file as an MPC results file. +:param results_file: File path
+-
+
- Returns: +
results, stats +results is the Dataframe with all inputs and outputs of the MPC +optimizations. +stats is the Dataframe with matching solver stats
+
+
-
+
- +register_callbacks()[source] +
Registers callbacks which listen to the variables which have to be saved as +time series. These callbacks save the values in the history for use in the +optimization.
+
-
+
- +var_ref: MHEVariableReference +
-
+
- +pydantic model agentlib_mpc.modules.estimation.mhe.MHEConfig[source] +
Bases:
+BaseModuleConfigPydantic data model for MPC configuration parser
+-
+
- Config: +
-
+
arbitrary_types_allowed: bool = True
+validate_assignment: bool = True
+extra: str = forbid
+frozen: bool = True
+
+- Fields: +
-
+
estimated_inputs (List[agentlib_mpc.data_structures.mpc_datamodels.MPCVariable])
+estimated_parameters (List[agentlib_mpc.data_structures.mpc_datamodels.MPCVariable])
+- +
known_inputs (List[agentlib_mpc.data_structures.mpc_datamodels.MPCVariable])
+known_parameters (List[agentlib_mpc.data_structures.mpc_datamodels.MPCVariable])
+- +
- +
- +
states (List[agentlib_mpc.data_structures.mpc_datamodels.MPCVariable])
+- +
+- Validators: +
-
+
check_valid_fields»shared_variable_fields
+
+
-
+
- +field estimated_inputs: List[MPCVariable] = [] +
List of unknown input variables of the MHE. Includes mainly disturbances.
+
-
+
- +field estimated_parameters: List[MPCVariable] = [] +
List of unknown parameters of the MHE. They are constant over the horizon and will be estimated.
+
-
+
- +field horizon: int = 5 +
Estimation horizon of the MHE.
+-
+
- Constraints: +
-
+
ge = 0
+
+
-
+
- +field known_inputs: List[MPCVariable] = [] +
List of known input variables of the MHE. Includes controls, disturbances, setpoints, dynamic constraint boundaries etc.
+
-
+
- +field known_parameters: List[MPCVariable] = [] +
List of known parameters of the MHE. They are constant over the horizon. Parameters not listed here will have their default from the model file.
+
-
+
- +field optimization_backend: dict [Required] +
-
+
+
-
+
- Validated by: +
-
+
check_valid_fields
+
+
-
+
- +field state_weights: dict[str, float] = {} +
Mapping of state names to their weight in the MHE problem. If you are certain with your measurement, chose a high value. If you dont have a measurement / do not trust it, choose 0. Default is 0.
+
-
+
- +field states: List[MPCVariable] = [] +
List of all differential states of the MHE.
+
-
+
- +field time_step: float = 60 +
Time step of the MHE.
+-
+
- Constraints: +
-
+
ge = 0
+
+
-
+
- +model_post_init(context: Any, /) None +
We need to both initialize private attributes and call the user-defined model_post_init +method.
+
-
+
- +classmethod state_weights_are_in_states(state_weights: dict, info: ValidationInfo)[source] +
Wrap a classmethod, staticmethod, property or unbound function +and act as a descriptor that allows us to detect decorated items +from the class’ attributes.
+This class’ __get__ returns the wrapped item’s __get__ result, +which makes it transparent for classmethods and staticmethods.
+-
+
- +wrapped +
The decorator that has to be wrapped.
+
-
+
- +decorator_info +
The decorator info.
+
-
+
- +shim +
A wrapper function to wrap V1 style function.
+
+
+
+
+
+
+
+
+
+
+
\ No newline at end of file
diff --git a/docs/0.6.4/docs/code/agentlib_mpc.modules.ml_model_training.html b/docs/0.6.4/docs/code/agentlib_mpc.modules.ml_model_training.html
new file mode 100644
index 0000000..0c6b317
--- /dev/null
+++ b/docs/0.6.4/docs/code/agentlib_mpc.modules.ml_model_training.html
@@ -0,0 +1,910 @@
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+ agentlib_mpc.modules package
+This package contains all modules for the +distributed model predictive control using multi agent systems.
+It contains classes for local optimization and global coordination.
+-
+
- +class agentlib_mpc.modules.ModuleImport(module_path: str, class_name: str)[source] +
Bases:
+ + +object
Subpackages
+
+
+-
+
- agentlib_mpc.modules.InputPrediction package + +
- agentlib_mpc.modules.dmpc package
-
+
DistributedMPC
+DistributedMPCConfig+
+- Subpackages + +
- Submodules +
- agentlib_mpc.modules.dmpc.coordinator module
-
+
Coordinator+
+CoordinatorConfig-
+
CoordinatorConfig.log_level
+CoordinatorConfig.maxIter
+CoordinatorConfig.messages_in
+CoordinatorConfig.messages_out
+CoordinatorConfig.module_id
+CoordinatorConfig.shared_variable_fields
+CoordinatorConfig.time_out_non_responders
+CoordinatorConfig.type
+CoordinatorConfig.validate_incoming_values
+CoordinatorConfig.model_post_init()
+
+
+ - agentlib_mpc.modules.dmpc.employee module
-
+
MiniEmployee+
+MiniEmployeeConfig-
+
MiniEmployeeConfig.coordinator
+MiniEmployeeConfig.log_level
+MiniEmployeeConfig.messages_in
+MiniEmployeeConfig.messages_out
+MiniEmployeeConfig.module_id
+MiniEmployeeConfig.registration_interval
+MiniEmployeeConfig.request_frequency
+MiniEmployeeConfig.shared_variable_fields
+MiniEmployeeConfig.type
+MiniEmployeeConfig.validate_incoming_values
+MiniEmployeeConfig.model_post_init()
+
+
+
+ - agentlib_mpc.modules.estimation package
-
+
- Submodules +
- agentlib_mpc.modules.estimation.mhe module
-
+
MHE+
+MHEConfig-
+
MHEConfig.estimated_inputs
+MHEConfig.estimated_parameters
+MHEConfig.horizon
+MHEConfig.known_inputs
+MHEConfig.known_parameters
+MHEConfig.optimization_backend
+MHEConfig.shared_variable_fields
+MHEConfig.state_weights
+MHEConfig.states
+MHEConfig.time_step
+MHEConfig.model_post_init()
+MHEConfig.state_weights_are_in_states()
+MHEConfig.wrapped
+MHEConfig.decorator_info
+MHEConfig.shim
+
+
+
+ - agentlib_mpc.modules.ml_model_training package
-
+
- Submodules +
- agentlib_mpc.modules.ml_model_training.data_reduction module + +
- agentlib_mpc.modules.ml_model_training.ml_model_trainer module
-
+
ANNTrainer+
+ANNTrainerConfig+
+GPRTrainer+
+GPRTrainerConfig+
+LinRegTrainer+
+LinRegTrainerConfig+
+MLModelTrainer-
+
MLModelTrainer.agent_and_time
+MLModelTrainer.build_ml_model()
+MLModelTrainer.create_inputs_and_outputs()
+MLModelTrainer.divide_in_tvt()
+MLModelTrainer.fit_ml_model()
+MLModelTrainer.input_names
+MLModelTrainer.model_type
+MLModelTrainer.output_names
+MLModelTrainer.process()
+MLModelTrainer.register_callbacks()
+MLModelTrainer.resample()
+MLModelTrainer.retrain_model()
+MLModelTrainer.save_all()
+MLModelTrainer.save_ml_model()
+MLModelTrainer.serialize_ml_model()
+MLModelTrainer.training_info
+
+MLModelTrainerConfig-
+
MLModelTrainerConfig.MLModel
+MLModelTrainerConfig.data_sources
+MLModelTrainerConfig.inputs
+MLModelTrainerConfig.interpolations
+MLModelTrainerConfig.lags
+MLModelTrainerConfig.output_types
+MLModelTrainerConfig.outputs
+MLModelTrainerConfig.recursive_outputs
+MLModelTrainerConfig.retrain_delay
+MLModelTrainerConfig.save_data
+MLModelTrainerConfig.save_directory
+MLModelTrainerConfig.save_ml_model
+MLModelTrainerConfig.save_plots
+MLModelTrainerConfig.shared_variable_fields
+MLModelTrainerConfig.step_size
+MLModelTrainerConfig.test_share
+MLModelTrainerConfig.time_series_length
+MLModelTrainerConfig.time_series_memory_size
+MLModelTrainerConfig.train_share
+MLModelTrainerConfig.use_values_for_incomplete_data
+MLModelTrainerConfig.validation_share
+MLModelTrainerConfig.check_data_sources_exist
+MLModelTrainerConfig.check_if_save_path_is_there
+MLModelTrainerConfig.check_shares_amount_to_one
+MLModelTrainerConfig.fill_interpolations
+MLModelTrainerConfig.fill_lags
+MLModelTrainerConfig.fill_output_types
+MLModelTrainerConfig.fill_recursive_outputs
+MLModelTrainerConfig.model_post_init()
+
+
+ - agentlib_mpc.modules.ml_model_training.setpoint_generator module
-
+
SetPointGenerator+
+SetPointGeneratorConfig-
+
SetPointGeneratorConfig.day_end
+SetPointGeneratorConfig.day_lb
+SetPointGeneratorConfig.day_start
+SetPointGeneratorConfig.day_ub
+SetPointGeneratorConfig.interval
+SetPointGeneratorConfig.night_lb
+SetPointGeneratorConfig.night_ub
+SetPointGeneratorConfig.shared_variable_fields
+SetPointGeneratorConfig.target_variable
+SetPointGeneratorConfig.model_post_init()
+
+
+
+
Submodules
+agentlib_mpc.modules.data_source module
+-
+
- +class agentlib_mpc.modules.data_source.DataSource(config: dict, agent: Agent)[source] +
Bases:
+ + + + + + +BaseModule
-
+
- +pydantic model agentlib_mpc.modules.data_source.DataSourceConfig[source] +
Bases:
+BaseModuleConfig-
+
- Config: +
-
+
arbitrary_types_allowed: bool = True
+validate_assignment: bool = True
+extra: str = forbid
+frozen: bool = True
+
+- Fields: +
- + +
- Validators: +
- + +
-
+
- +field columns: List[str] | None = None +
Optional list of columns of data frame that should be sent.If ommited, all datapoint in frame are sent.
+
-
+
- +field data: DataFrame | Path = Empty DataFrame Columns: [] Index: [] +
Data that should be communicated during execution.Index should be either numeric or Datetime, numeric values are interpreted as seconds.
+-
+
- Validated by: +
-
+
- +
+
-
+
- +field data_offset: Timedelta | float | None = 0 +
Offset will be subtracted from index.
+
-
+
- +field interpolation_method: InterpolationMethods | None = InterpolationMethods.previous +
Interpolation method used for resampling of data.Only ‘linear’ and ‘previous’ are allowed.
+-
+
- Validated by: +
- + +
-
+
- +field t_sample: float | int = 1 +
Sample time of data source. Default is 1 s.
+
-
+
- +validator check_data » data[source] +
Makes sure data is a data frame, and loads it if required.
+
-
+
- +validator check_interpolation_method » interpolation_method[source] +
-
+
- +model_post_init(context: Any, /) None +
We need to both initialize private attributes and call the user-defined model_post_init +method.
+
agentlib_mpc.modules.minlp_mpc module
+-
+
- +class agentlib_mpc.modules.minlp_mpc.MINLPMPC(config: dict, agent: Agent)[source] +
Bases:
+ + + + +BaseMPC
-
+
- +pydantic model agentlib_mpc.modules.minlp_mpc.MINLPMPCConfig[source] +
Bases:
+BaseMPCConfigPydantic data model for MPC configuration parser
+-
+
- Config: +
-
+
arbitrary_types_allowed: bool = True
+validate_assignment: bool = True
+extra: str = forbid
+frozen: bool = True
+
+- Fields: +
- + +
- Validators: +
- + +
-
+
- +field binary_controls: List[MPCVariable] = [] +
List of all binary control variables of the MPC.
+-
+
- Validated by: +
- + +
-
+
- +model_post_init(context: Any, /) None +
We need to both initialize private attributes and call the user-defined model_post_init +method.
+
-
+
- +validator validate_binary_bounds » binary_controls[source] +
Assures all binary variables have 0 and 1 as boundaries.
+
agentlib_mpc.modules.ml_model_simulator module
+agentlib_mpc.modules.mpc module
+Holds the base class for MPCs.
+-
+
- +class agentlib_mpc.modules.mpc.BaseMPC(config: dict, agent: Agent)[source] +
Bases:
+BaseModuleA model predictive controller. +More info to follow.
+-
+
- +assert_mpc_variables_are_in_model() dict[str, set[str]][source] +
Checks whether all variables of var_ref are contained in the model. +Returns names of model variables not contained in the var_ref, +sorted by keys: ‘states’, ‘inputs’, ‘outputs’, ‘parameters’.
+
-
+
- +assert_subset(mpc_names, model_names, message_head)[source] +
Helper function for assert assert_mpc_variables_are_in_model. Asserts +the variables of the var_ref corresponding to ref_key are a subset of +a list of names provided (usually obtained from the model) and prints +out an error if false. Returns the portion of model_names that are +not in the given var_ref.
+
-
+
- +cleanup_results()[source] +
Deletes all files this module created.
+Override this method, if your module creates e.g. results files etc.
+
-
+
- +collect_variables_for_optimization(var_ref: VariableReference = None) Dict[str, AgentVariable][source] +
Gets all variables noted in the var ref and puts them in a flat +dictionary.
+
-
+
- +get_results() DataFrame | None[source] +
Read the results that were saved from the optimization backend and +returns them as Dataframe.
+-
+
- Returns: +
(results, stats) tuple of Dataframes.
+
+
-
+
- +property model: Model +
Getter for current simulation model
+-
+
- Returns: +
Current simulation model
+
+- Return type: +
agentlib.model
+
+
-
+
- +pre_computation_hook()[source] +
This method is called in every computation step before the optimization starts. +Overwrite this method in a derived subclass if you want to take some actions +each time before the optimal control problem is solved.
+
-
+
- +process()[source] +
This abstract method must be implemented in order to sync the module +with the other processes of the agent and the whole MAS.
+
-
+
- +re_init_optimization(parameter: AgentVariable)[source] +
Re-initializes the optimization backend with new parameters.
+
-
+
- +static read_results_file(results_file: str) Tuple[DataFrame, DataFrame][source] +
Read the provided csv-file as an MPC results file. +:param results_file: File path
+-
+
- Returns: +
results, stats +results is the Dataframe with all inputs and outputs of the MPC +optimizations. +stats is the Dataframe with matching solver stats
+
+
-
+
- +register_callbacks()[source] +
Registers the init_optimization callback to all parameters which +cannot be changed without recreating the optimization problem.
+
-
+
- +set_actuation(solution: Results)[source] +
Takes the solution from optimization backend and sends the first +step to AgentVariables.
+
-
+
- +pydantic model agentlib_mpc.modules.mpc.BaseMPCConfig[source] +
Bases:
+BaseModuleConfigPydantic data model for MPC configuration parser
+-
+
- Config: +
-
+
arbitrary_types_allowed: bool = True
+validate_assignment: bool = True
+extra: str = forbid
+frozen: bool = True
+
+- Fields: +
-
+
controls (List[agentlib_mpc.data_structures.mpc_datamodels.MPCVariable])
+inputs (List[agentlib_mpc.data_structures.mpc_datamodels.MPCVariable])
+- +
- +
- +
outputs (List[agentlib_mpc.data_structures.mpc_datamodels.MPCVariable])
+parameters (List[agentlib_mpc.data_structures.mpc_datamodels.MPCVariable])
+- +
- +
- +
- +
states (List[agentlib_mpc.data_structures.mpc_datamodels.MPCVariable])
+- +
- +
- +
+- Validators: +
-
+
check_valid_fields»shared_variable_fields
+check_valid_level»log_level
+- +
+
-
+
- +field controls: List[MPCVariable] = [] +
List of all control variables of the MPC.
+
-
+
- +field inputs: List[MPCVariable] = [] +
List of all input variables of the MPC. Includes predictions for disturbances, set_points, dynamic constraint boundaries etc.
+
-
+
- +field log_level: str | None = None +
The log level for this Module. Default uses the root-loggers level.Options: DEBUG; INFO; WARNING; ERROR; CRITICAL
+-
+
- Validated by: +
-
+
check_valid_level
+
+
-
+
- +field module_id: str [Required] +
The unqiue id of the module within an agent, used only to communicate withing the agent.
+
-
+
- +field optimization_backend: dict [Required] +
-
+
- +field outputs: List[MPCVariable] = [] +
List of all shared outputs of the MPC.
+
-
+
- +field parameters: List[MPCVariable] = [] +
List of model parameters of the MPC. They are constant over the horizon. Parameters not listed here will have their default from the model file.
+
-
+
- +field prediction_horizon: int = 5 +
Prediction horizon of the MPC.
+-
+
- Constraints: +
-
+
ge = 0
+
+
-
+
- +field sampling_time: float | None = None +
Sampling interval for control steps. If None, will be the same as time step. Does not affect the discretization of the MPC, only the interval with which there will be optimization steps.
+-
+
- Validated by: +
- + +
-
+
- +field set_outputs: bool = False +
Sets the full output time series to the data broker.
+
-
+
+
-
+
- Validated by: +
-
+
check_valid_fields
+
+
-
+
- +field states: List[MPCVariable] = [] +
List of all differential states of the MPC. The entries can define the boundaries and the source for the measurements
+
-
+
- +field time_step: float = 60 +
Time step of the MPC.
+-
+
- Constraints: +
-
+
ge = 0
+
+
-
+
- +field type: str | Dict[str, str] [Required] +
The type of the Module. Used to find the Python-Object from all agentlib-core and plugin Module options. If a dict is given,it must contain the keys ‘file’ and ‘class_name’. ‘file’ is the filepath of a python file containing the Module.’class_name’ is the name of the Module class within this file.
+
-
+
- +field validate_incoming_values: bool | None = True +
If true, the validator of the AgentVariable value is called when receiving a new value from the DataBroker.
+
-
+
- +validator default_sampling_time » sampling_time[source] +
-
+
- +model_post_init(context: Any, /) None +
We need to both initialize private attributes and call the user-defined model_post_init +method.
+
agentlib_mpc.modules.mpc_full module
+Holds the class for full featured MPCs.
+-
+
- +class agentlib_mpc.modules.mpc_full.MPC(config: dict, agent: Agent)[source] +
Bases:
+BaseMPCA model predictive controller. +More info to follow.
+-
+
- +collect_variables_for_optimization(var_ref: VariableReference = None) dict[str, agentlib.core.datamodels.AgentVariable][source] +
Gets all variables noted in the var ref and puts them in a flat +dictionary.
+
-
+
- +pydantic model agentlib_mpc.modules.mpc_full.MPCConfig[source] +
Bases:
+BaseMPCConfigPydantic data model for MPC configuration parser
+-
+
- Config: +
-
+
arbitrary_types_allowed: bool = True
+validate_assignment: bool = True
+extra: str = forbid
+frozen: bool = True
+
+- Fields: +
- + +
- Validators: +
- + +
-
+
- +field r_del_u: dict[str, float] = {} +
Weights that are applied to the change in control variables.
+-
+
- Validated by: +
- + +
-
+
- +validator check_r_del_u_in_controls » r_del_u[source] +
Ensures r_del_u is only set for control variables.
+
-
+
- +model_post_init(context: Any, /) None +
We need to both initialize private attributes and call the user-defined model_post_init +method.
+
+
+
+
+
+
+
+
+
+
+
\ No newline at end of file
diff --git a/docs/0.6.4/docs/code/agentlib_mpc.optimization_backends.casadi_.core.html b/docs/0.6.4/docs/code/agentlib_mpc.optimization_backends.casadi_.core.html
new file mode 100644
index 0000000..bf6f486
--- /dev/null
+++ b/docs/0.6.4/docs/code/agentlib_mpc.optimization_backends.casadi_.core.html
@@ -0,0 +1,867 @@
+
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+
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+
+
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+
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+
+
+
+
+
+
+
+ agentlib_mpc.modules.ml_model_training package
+Submodules
+agentlib_mpc.modules.ml_model_training.data_reduction module
+Code stolen from Max Berktold
+-
+
- +class agentlib_mpc.modules.ml_model_training.data_reduction.InducingPoints[source] +
Bases:
+ABCThe idea is to reduce the effective number of input data points x to the GP +from n to m, with m<n, where the set of m points are called inducing points.
++
+ + +Since this makes the effective covariance matrix K smaller, +many inducing point approaches reduce the computational complexity from O(n3) to O(nm2). +The smaller m is, the bigger the speed up.
+Source: https://bwengals.github.io/inducing-point-methods-to-speed-up-gps.html
+
-
+
- +class agentlib_mpc.modules.ml_model_training.data_reduction.NystroemReducer(n_components: int, kernel: Kernel = None)[source] +
Bases:
+ + +InducingPoints
agentlib_mpc.modules.ml_model_training.ml_model_trainer module
+-
+
- +class agentlib_mpc.modules.ml_model_training.ml_model_trainer.ANNTrainer(config: dict, agent: Agent)[source] +
Bases:
+MLModelTrainerModule that generates ANNs based on received data.
+-
+
- +build_ml_model() Sequential[source] +
Build an ANN with a one layer structure, can only create one ANN
+
-
+
- +fit_ml_model(training_data: TrainingData)[source] +
Fits the ML Model with the training data.
+
-
+
- +model_type +
alias of
+SerializedANN
-
+
- +pydantic model agentlib_mpc.modules.ml_model_training.ml_model_trainer.ANNTrainerConfig[source] +
Bases:
+MLModelTrainerConfigPydantic data model for ANNTrainer configuration parser
+-
+
- Config: +
-
+
arbitrary_types_allowed: bool = True
+validate_assignment: bool = True
+extra: str = forbid
+frozen: bool = True
+
+- Fields: +
- + +
- Validators: +
- +
-
+
- +field batch_size: int = 100 +
-
+
- +field early_stopping: EarlyStoppingCallback = EarlyStoppingCallback(patience=(1000,), verbose=0, restore_best_weights=True, activate=False) +
Specification of the EarlyStopping Callback for training
+
-
+
- +field epochs: int = 100 +
-
+
- +field layers: list[tuple[int, Literal['relu', 'sigmoid', 'softmax', 'softplus', 'softsign', 'tanh', 'selu', 'elu', 'exponential']]] = [(16, 'sigmoid')] +
Hidden layers which should be created for the ANN. An ANN always has a BatchNormalization Layer, and an Output Layer the size of the output dimensions. Additional hidden layers can be specified here as a list of tuples: (#neurons of layer, activation function).
+
-
+
- +model_post_init(context: Any, /) None +
We need to both initialize private attributes and call the user-defined model_post_init +method.
+
-
+
- +class agentlib_mpc.modules.ml_model_training.ml_model_trainer.GPRTrainer(config: dict, agent: Agent)[source] +
Bases:
+MLModelTrainerModule that generates ANNs based on received data.
+-
+
- +build_ml_model()[source] +
Build a GPR with a constant Kernel in combination with a white kernel.
+
-
+
- +fit_ml_model(training_data: TrainingData)[source] +
Fits GPR to training data
+
-
+
- +model_type +
alias of
+SerializedGPR
-
+
- +pydantic model agentlib_mpc.modules.ml_model_training.ml_model_trainer.GPRTrainerConfig[source] +
Bases:
+MLModelTrainerConfigPydantic data model for GPRTrainer configuration parser
+-
+
- Config: +
-
+
arbitrary_types_allowed: bool = True
+validate_assignment: bool = True
+extra: str = forbid
+frozen: bool = True
+
+- Fields: +
- + +
- Validators: +
- +
-
+
- +field constant_value_bounds: tuple = (0.001, 100000.0) +
-
+
- +field length_scale_bounds: tuple = (0.001, 100000.0) +
-
+
- +field n_restarts_optimizer: int = 0 +
Defines the number of restarts of the Optimizer for the gpr_parameters of the kernel.
+
-
+
- +field noise_level: float = 1.5 +
-
+
- +field noise_level_bounds: tuple = (0.001, 100000.0) +
-
+
- +field normalize: bool = False +
Defines whether the training data and the inputs are for predictionare normalized before given to GPR.
+
-
+
- +field scale: float = 1.0 +
Defines by which value the output data is divided for training and multiplied after prediction.
+
-
+
- +model_post_init(context: Any, /) None +
We need to both initialize private attributes and call the user-defined model_post_init +method.
+
-
+
- +class agentlib_mpc.modules.ml_model_training.ml_model_trainer.LinRegTrainer(config: dict, agent: Agent)[source] +
Bases:
+MLModelTrainerModule that generates ANNs based on received data.
+ + +-
+
- +fit_ml_model(training_data: TrainingData)[source] +
Fits linear model to training data
+
-
+
- +model_type +
alias of
+SerializedLinReg
-
+
- +pydantic model agentlib_mpc.modules.ml_model_training.ml_model_trainer.LinRegTrainerConfig[source] +
Bases:
+MLModelTrainerConfigPydantic data model for GPRTrainer configuration parser
+-
+
- Config: +
-
+
arbitrary_types_allowed: bool = True
+validate_assignment: bool = True
+extra: str = forbid
+frozen: bool = True
+
+- Fields: +
- +
- Validators: +
- +
-
+
- +model_post_init(context: Any, /) None +
We need to both initialize private attributes and call the user-defined model_post_init +method.
+
-
+
- +class agentlib_mpc.modules.ml_model_training.ml_model_trainer.MLModelTrainer(config: dict, agent: Agent)[source] +
Bases:
+BaseModule,ABCAbstract Base Class for all Trainer classes.
+-
+
- +property agent_and_time: str +
A string that specifies id and time. Used to create save paths
+
-
+
- +create_inputs_and_outputs(full_data_sampled: DataFrame) tuple[pandas.core.frame.DataFrame, pandas.core.frame.DataFrame][source] +
Creates extra columns in the data which contain the shifted time-series data +which is lagged accordingly. Returns a tuple (input_data, output_data)
+
-
+
- +divide_in_tvt(inputs: DataFrame, outputs: DataFrame)[source] +
splits the samples into mpc, validating and testing sets
+
-
+
- +abstract fit_ml_model(training_data: TrainingData)[source] +
Fits the ML Model with the training data.
+
-
+
- +property input_names +
-
+
- +model_type: Type[SerializedMLModel] +
-
+
- +property output_names +
-
+
- +process()[source] +
This abstract method must be implemented in order to sync the module +with the other processes of the agent and the whole MAS.
+
-
+
- +resample() DataFrame[source] +
Samples the available time_series data to the required step size.
+
-
+
- +save_all(serialized_ml_model: SerializedMLModel, training_data: TrainingData)[source] +
Saves all relevant data and results of the training process if desired.
+
-
+
- +save_ml_model(serialized_ml_model: SerializedMLModel, path: Path)[source] +
Saves the ML Model in serialized format.
+
-
+
- +serialize_ml_model() SerializedMLModel[source] +
Serializes the ML Model, sa that it can be saved +as json file. +:returns: SerializedMLModel version of the passed ML Model.
+
-
+
- +property training_info: dict +
Returns a dict with relevant config parameters regarding the training.
+
-
+
- +pydantic model agentlib_mpc.modules.ml_model_training.ml_model_trainer.MLModelTrainerConfig[source] +
Bases:
+BaseModuleConfig,ABCAbstract Base Class for all Trainer Configs.
+-
+
- Config: +
-
+
arbitrary_types_allowed: bool = True
+validate_assignment: bool = True
+extra: str = forbid
+frozen: bool = True
+
+- Fields: +
- + +
- Validators: +
-
+
- +
- +
- +
- +
- +
- +
check_valid_fields»shared_variable_fields
+- +
- +
- +
- +
+
-
+
- +field MLModel: AgentVariable = AgentVariable(name='MLModel', type=None, timestamp=None, unit='Not defined', description='Not defined', ub=inf, lb=-inf, clip=False, allowed_values=[], value=None, alias='MLModel', source=Source(agent_id=None, module_id=None), shared=None, rdf_class=None) +
Serialized ML Model which can be sent to other Agents.
+
-
+
- +field data_sources: list[pathlib.Path] = [] +
List of paths to time series data, which can be loaded on initialization of the agent.
+-
+
- Validated by: +
- + +
-
+
- +field inputs: List[AgentVariable] = [] +
Variables which are inputs of the ML Model that should be trained.
+
-
+
- +field interpolations: dict[str, agentlib_mpc.data_structures.interpolation.InterpolationMethods] = {} +
Dictionary specifying the interpolation types of output variables. If not specified, will be set to ‘linear’.
+-
+
- Validated by: +
- + +
-
+
- +field lags: dict[str, int] = {} +
Dictionary specifying the lags of each input and output variable. If not specified, will be set to one.
+-
+
- Validated by: +
-
+
- +
+
-
+
- +field output_types: dict[str, agentlib_mpc.data_structures.ml_model_datatypes.OutputType] = {} +
Dictionary specifying the output types of output variables. If not specified, will be set to ‘difference’.
+-
+
- Validated by: +
- + +
-
+
- +field outputs: List[AgentVariable] = [] +
Variables which are outputs of the ML Model that should be trained.
+
-
+
- +field recursive_outputs: dict[str, bool] = {} +
Dictionary specifying whether output variables are recursive, i.e. automatically appear as an input as well. If not specified, will be set to ‘recursive’.
+-
+
- Validated by: +
- + +
-
+
- +field retrain_delay: float = 10000000000 +
Time in seconds, after which retraining is triggered in regular intervals
+
-
+
- +field save_data: bool = False +
Whether the training data should be saved.
+-
+
- Validated by: +
- + +
-
+
- +field save_directory: Path = None +
Path, where created ML Models should be saved.
+
-
+
- +field save_ml_model: bool = False +
Whether the created ML Models should be saved.
+-
+
- Validated by: +
- + +
-
+
- +field save_plots: bool = False +
Whether a plot of the created ML Models performance should be saved.
+
-
+
+
-
+
- Validated by: +
-
+
check_valid_fields
+
+
-
+
- +field step_size: float [Required] +
-
+
+
-
+
- Validated by: +
- + +
-
+
- +field time_series_length: float = 315360000 +
Maximum time window of data which is kept for the ML Model training. If saved data is older than current time minus time_series_length, it will be deleted.
+
-
+
- +field time_series_memory_size: int = 1000000000 +
Maximum size of the data which is kept in memory for the ML Model training. If saved data exceeds this value, the oldest data is deleted.
+
-
+
+
-
+
- Validated by: +
- + +
-
+
- +field use_values_for_incomplete_data: bool = False +
Default False. If True, the values of inputs and outputs which are defined in the config will be used for training, in case historic data has not reached the trainer. If False, an Error will be raised when the data is not sufficient.
+
-
+
+
-
+
- Validated by: +
- + +
-
+
- +validator check_data_sources_exist » data_sources[source] +
Checks if all given data sources exist
+
-
+
- +validator check_if_save_path_is_there » save_data, save_ml_model[source] +
-
+
+
Makes sure, the shares amount to one.
+
-
+
- +validator fill_interpolations » interpolations[source] +
Adds interpolation method to all unspecified methods.
+
-
+
- +validator fill_output_types » output_types[source] +
Adds output type one to all unspecified output types.
+
-
+
- +validator fill_recursive_outputs » recursive_outputs[source] +
Adds recursive flag to all unspecified outputs.
+
-
+
- +model_post_init(context: Any, /) None +
We need to both initialize private attributes and call the user-defined model_post_init +method.
+
agentlib_mpc.modules.ml_model_training.setpoint_generator module
+Module which generates random set points within a comfort zone. Code heavily stolen +from Max Berktold
+-
+
- +class agentlib_mpc.modules.ml_model_training.setpoint_generator.SetPointGenerator(config: dict, agent: Agent)[source] +
Bases:
+BaseModuleModule that generates and sends random set points based on daytime and values.
+ + + + + + +
-
+
- +pydantic model agentlib_mpc.modules.ml_model_training.setpoint_generator.SetPointGeneratorConfig[source] +
Bases:
+BaseModuleConfigPydantic data model for ANNTrainer configuration parser
+-
+
- Config: +
-
+
arbitrary_types_allowed: bool = True
+validate_assignment: bool = True
+extra: str = forbid
+frozen: bool = True
+
+- Fields: +
- + +
- Validators: +
-
+
check_valid_fields»shared_variable_fields
+
+
-
+
- +field day_end: int = 16 +
-
+
- +field day_lb: float = 292.15 +
-
+
- +field day_start: int = 8 +
-
+
- +field day_ub: float = 294.15 +
-
+
- +field interval: int = 14400 +
-
+
- +field night_lb: float = 289.15 +
-
+
- +field night_ub: float = 297.15 +
-
+
+
-
+
- Validated by: +
-
+
check_valid_fields
+
+
-
+
- +field target_variable: AgentVariable = AgentVariable(name='target', type=None, timestamp=None, unit='Not defined', description='Not defined', ub=inf, lb=-inf, clip=False, allowed_values=[], value=None, alias='target', source=Source(agent_id=None, module_id=None), shared=None, rdf_class=None) +
-
+
- +model_post_init(context: Any, /) None +
We need to both initialize private attributes and call the user-defined model_post_init +method.
+
+
+
+
+
+
+
+
+
+
+
\ No newline at end of file
diff --git a/docs/0.6.4/docs/code/agentlib_mpc.optimization_backends.casadi_.html b/docs/0.6.4/docs/code/agentlib_mpc.optimization_backends.casadi_.html
new file mode 100644
index 0000000..43233e1
--- /dev/null
+++ b/docs/0.6.4/docs/code/agentlib_mpc.optimization_backends.casadi_.html
@@ -0,0 +1,1227 @@
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+ agentlib_mpc.optimization_backends.casadi_.core package
+Submodules
+agentlib_mpc.optimization_backends.casadi_.core.VariableGroup module
+-
+
- +class agentlib_mpc.optimization_backends.casadi_.core.VariableGroup.OptimizationParameter(name: 'str', full_symbolic: 'ca.MX', dim: 'int', ref_names: 'tuple[str]', full_names: 'tuple[str]', use_in_stage_function: 'bool', full_with_defaults: 'ca.MX', add_default_values: 'ca.Function')[source] +
Bases:
+OptimizationQuantity-
+
- +add_default_values: Function +
-
+
- +classmethod declare(denotation: str, variables: list[agentlib_mpc.models.casadi_model.CasadiVariable], ref_list: list[str], use_in_stage_function=True, assert_complete: bool = False)[source] +
Declares a group of optimization parameters that serve a purpose in +the optimization problem. Typical groups are uncontrollable inputs or +physical parameters.
+-
+
- Parameters: +
-
+
denotation – The key of the variable, e.g. ‘p’, ‘d’, etc. Use this +key in the discretization function to add the parameter at +different stages of the optimization problem.
+variables – A list of CasadiVariables including all parameters +within this category.
+ref_list – A list of names indicating which parameters in full_list +are AgentVariables and need to be updated before each +optimization.
+use_in_stage_function – If False, the parameter is not added to the +stage function. If True, the variable needs to be provided to +the stage function at every point in the discretization function.
+assert_complete – If True, throws an error if the ref_list does +not contain all variables.
+
+
-
+
- +dim: int +
-
+
- +full_names: tuple[str] +
-
+
- +full_symbolic: MX +
-
+
- +full_with_defaults: MX +
-
+
- +name: str +
-
+
- +ref_names: tuple[str] +
-
+
- +use_in_stage_function: bool +
-
+
- +class agentlib_mpc.optimization_backends.casadi_.core.VariableGroup.OptimizationQuantity(name: 'str', full_symbolic: 'ca.MX', dim: 'int', ref_names: 'tuple[str]', full_names: 'tuple[str]', use_in_stage_function: 'bool')[source] +
Bases:
+object-
+
- +dim: int +
-
+
- +full_names: tuple[str] +
-
+
- +full_symbolic: MX +
-
+
- +name: str +
-
+
- +ref_names: tuple[str] +
-
+
- +use_in_stage_function: bool +
-
+
- +class agentlib_mpc.optimization_backends.casadi_.core.VariableGroup.OptimizationVariable(name: 'str', full_symbolic: 'ca.MX', dim: 'int', ref_names: 'tuple[str]', full_names: 'tuple[str]', use_in_stage_function: 'bool', input_map: 'ca.Function', output_map: 'ca.Function', binary: 'bool')[source] +
Bases:
+OptimizationQuantity-
+
- +binary: bool +
-
+
- +classmethod declare(denotation: str, variables: list[agentlib_mpc.models.casadi_model.CasadiVariable], ref_list: list[str], use_in_stage_function: bool = True, assert_complete: bool = False, binary: bool = False) OptimizationVariable[source] +
Declares a group of optimization variables that serve a purpose in +the optimization problem. Typical groups are states, the control +inputs or slack variables.
+-
+
- Parameters: +
-
+
binary – Flag, whether these variables are binary
+denotation – The key of the variable, e.g. ‘X’, ‘U’, etc. Use +this key in the discretization function to add the variable at +different stages of the optimization problem. The optimal value +of these variables will also be mapped to this key.
+variables – A list of +CasadiVariables or an MX/SX vector including all variables +within this category.
+ref_list – A list of names indicating which variables +in full_list are AgentVariables and need to be updated before +each optimization.
+use_in_stage_function – If False, the variable is not +added to the stage function. If True, the variable needs to be +provided to the stage function at every point in the +discretization function.
+assert_complete – If True, throws an error if the ref_list does +not contain all variables.
+
+
-
+
- +dim: int +
-
+
- +full_names: tuple[str] +
-
+
- +full_symbolic: MX +
-
+
- +input_map: Function +
-
+
- +name: str +
-
+
- +output_map: Function +
-
+
- +ref_names: tuple[str] +
-
+
- +use_in_stage_function: bool +
agentlib_mpc.optimization_backends.casadi_.core.casadi_backend module
+-
+
- +class agentlib_mpc.optimization_backends.casadi_.core.casadi_backend.CasADiBackend(config: dict)[source] +
Bases:
+OptimizationBackendOptimizationBackend for solving the optimization problem with CasADi. +Requires the model to be a CasADi model.
+-
+
- +config_type +
alias of
+CasadiBackendConfig
-
+
- +cost_function: Callable | None +
-
+
- +discretization: DiscretizationT +
-
+
- +discretization_types: dict[agentlib_mpc.data_structures.casadi_utils.DiscretizationMethod, Type[DiscretizationT]] +
-
+
- +model: ModelT +
-
+
- +save_result_df(results: Results, now: float = 0)[source] +
Save the results of solve into a dataframe at each time step.
+Example results dataframe:
+value_type variable … lower +variable T_0 T_0_slack … T_0_slack mDot_0 +time_step … +2 0.000000 298.160000 NaN … NaN NaN
++
+101.431499 297.540944 -149.465942 … -inf 0.0 +450.000000 295.779780 -147.704779 … -inf 0.0 +798.568501 294.720770 -146.645769 … -inf 0.0
+-
+
- Parameters: +
-
+
results –
+now –
+
+
Returns:
+
-
+
- +setup_optimization(var_ref: VariableReference)[source] +
Performs all necessary steps to make the
+solvemethod usable. +To do this, it calls several auxiliary functions. These functions can +be overloaded to change the resulting optimization problem.-
+
- Parameters: +
var_ref – class with variable name lists sorted by function in the mpc.
+
+
-
+
- +solve(now: float, current_vars: dict[str, agentlib_mpc.data_structures.mpc_datamodels.MPCVariable]) Results[source] +
Solves the optimization problem given the current values of the +corresponding AgentVariables and system time. The standardization of +return values is a work in progress.
+-
+
- Parameters: +
-
+
now – Current time used for interpolation of input trajectories.
+current_vars – Dict of AgentVariables holding the values relevant to +the optimization problem. Keys are the names
+
+- Returns: +
A dataframe with all optimization variables over their respective +grids. Depending on discretization, can include many nan’s, so care +should be taken when using this, e.g. always use dropna() after +accessing a column.
++
+ +-
+
- Example:
+
+variables mDot | T_0 | slack_T
+time +0 0.1 | 298 | nan +230 nan | 297 | 3 +470 nan | 296 | 2 +588 nan | 295 | 1 +700 0.05 | 294 | nan +930 nan | 294 | 0.1
+
+
+
-
+
- +system: SystemT +
-
+
- +system_type: Type[SystemT] +
-
+
- +var_ref: mpc_datamodels.VariableReference | None +
-
+
- +pydantic model agentlib_mpc.optimization_backends.casadi_.core.casadi_backend.CasadiBackendConfig[source] +
Bases:
+BackendConfig-
+
- Config: +
-
+
extra: str = forbid
+
+- Fields: +
- + +
- Validators: +
- + +
-
+
- +field build_batch_bat: Path | None = None +
Path to a batch file, which can compile C code on windows.
+
-
+
- +field discretization_options: CasadiDiscretizationOptions [Optional] +
-
+
- +field do_jit: bool | None = None +
Boolean to turn JIT of the optimization problems on or off.
+-
+
- Validated by: +
- + +
-
+
- +field model: dict [Required] +
-
+
- +field name: str | None = None +
-
+
- +field overwrite_result_file: bool | None = False +
-
+
- Validated by: +
- + +
-
+
- +field save_results: bool | None = None +
-
+
- Validated by: +
- + +
-
+
- +field solver: SolverOptions [Optional] +
agentlib_mpc.optimization_backends.casadi_.core.discretization module
+Holds classes that implement different transcriptions of the OCP
+-
+
- +class agentlib_mpc.optimization_backends.casadi_.core.discretization.Discretization(options: CasadiDiscretizationOptions)[source] +
Bases:
+ABCopt_vars: holds symbolic variables during problem creation +opt_vars_lb: holds symbolic variables during problem creation +opt_vars_ub: holds symbolic variables during problem creation +initial_guess: holds symbolic variables during problem creation +opt_pars: holds symbolic variables during problem creation +constraints: holds symbolic variables during problem creation +constraints_lb: holds symbolic variables during problem creation +constraints_ub: holds symbolic variables during problem creation +objective_function: cost function during problem creation +mpc_opt_vars (dict): holds the symbolic variables and grids during
++
+problem creation sorted by type as in system_variables
+-
+
- mpc_opt_pars (dict): holds the symbolic variables and grids during
problem creation sorted by type as in system_parameters
+
+
-
+
- +add_constraint(constraint_function: MX | SX | Sparsity | DM | float | int, lb: MX | SX | Sparsity | DM | float | int = None, ub: MX | SX | Sparsity | DM | float | int = None, *, gap_closing: bool = False)[source] +
Add a constraint to the optimization problem. If no bounds are given, +adds an equality constraint.
+
-
+
- +add_opt_par(quantity: OptimizationParameter, post_den: str = '')[source] +
Create an optimization parameter and append to all the associated lists.
+denotation[str]: the key of the parameter, e.g. ‘P’, ‘Q’, … +dimension[int]: the dimension of the parameter +post_den[str]: string to add to casadi MX after denotation (for debugging)
+
-
+
- +add_opt_var(quantity: OptimizationVariable, lb: MX = None, ub: MX = None, guess: float = None, post_den: str = '')[source] +
Create an optimization variable and append to all the associated +lists. If lb or ub are given, they override the values provided at +runtime! The usual application of this is, to fix the initial value +of a state to a parameter.
+-
+
- Parameters: +
-
+
quantity – corresponding system variable
+lb – lower bound of the variable
+ub – upper bound of the variable
+guess – default for the initial guess
+post_den – string to add to casadi MX after denotation (for debugging)
+
+
-
+
- +property binary_vars: list[bool] +
List specifying for every optimization variable, whether it is binary.
+
-
+
- +constraints: list[casadi.casadi.MX] | MX +
-
+
- +constraints_lb: list[casadi.casadi.MX] +
-
+
- +constraints_ub: list[casadi.casadi.MX] +
-
+
- +create_nlp_in_out_mapping(system: System)[source] +
Function creating mapping functions between the MPC variables ordered +by type (as defined in declare_quantities and the raw input/output +vector of the CasADi NLP.
+
-
+
- +equalities: list[bool] +
-
+
- +grid(var: OptimizationQuantity) list[float][source] +
-
+
- +initial_guess: list[casadi.casadi.MX] +
-
+
- +initialize(system: System, solver_factory: SolverFactory)[source] +
Initializes the trajectory optimization problem, creating all symbolic +variables of the OCP, the mapping function and the numerical solver.
+
-
+
- +k: int +
-
+
- +mpc_opt_pars: dict[str, agentlib_mpc.data_structures.casadi_utils.OptParMXContainer] +
-
+
- +mpc_opt_vars: dict[str, agentlib_mpc.data_structures.casadi_utils.OptVarMXContainer] +
-
+
- +property nlp: dict[str, casadi.casadi.MX] +
The nlp dict that casadi solvers need for instantiation
+
-
+
- +objective_function: MX | SX | Sparsity | DM | float | int +
-
+
- +only_positive_times_in_results = True +
-
+
- +opt_pars: list[casadi.casadi.MX] | MX +
-
+
- +opt_vars: list[casadi.casadi.MX] | MX +
-
+
- +opt_vars_lb: list[casadi.casadi.MX] +
-
+
- +opt_vars_ub: list[casadi.casadi.MX] +
-
+
- +pred_time: float +
-
+
- +solve(mpc_inputs: dict[str, casadi.casadi.DM]) Results[source] +
Solves the discretized trajectory optimization problem.
+-
+
- Parameters: +
mpc_inputs – Casadi Matrices specifying the input of all different types +of optimization parameters. Matrices consist of different variable rows +and have a column for each time step in the discretization. +There are separate matrices for each input type (as defined in the +System), and also for the upper and lower boundaries of variables +respectively.
+
+- Returns: +
-
+
- The complete evolution of the states, inputs and boundaries of each
variable and parameter over the prediction horizon, as well as solve +statistics.
+
+
+- Return type: +
- + +
-
+
- +class agentlib_mpc.optimization_backends.casadi_.core.discretization.Results(matrix: casadi.casadi.MX, grid: list[float], columns: pandas.core.indexes.multi.MultiIndex, stats: dict, variable_grid_indices: dict[str, list[int]], _variable_name_to_index: dict[str, int] = None)[source] +
Bases:
+object-
+
- +columns: MultiIndex +
-
+
- +property df: DataFrame +
-
+
- +grid: list[float] +
-
+
- +matrix: MX +
-
+
- +stats: dict +
-
+
- +variable_grid_indices: dict[str, list[int]] +
agentlib_mpc.optimization_backends.casadi_.core.system module
+Holds the System class, which knows the model
+-
+
- +class agentlib_mpc.optimization_backends.casadi_.core.system.System[source] +
Bases:
+ABCExamples
+class MySystem(System):
++
+# variables +states: OptimizationVariable +controls: OptimizationVariable +algebraics: OptimizationVariable +outputs: OptimizationVariable
+# parameters +non_controlled_inputs: OptimizationParameter +model_parameters: OptimizationParameter +initial_state: OptimizationParameter
+# dynamics +model_constraints: Constraint +cost_function: ca.MX +ode: ca.MX
+def initialize(self, model: CasadiModel, var_ref: VariableReference):
++
+-
+
- self.states = OptimizationVariable.declare(
denotation=”state”, +variables=model.get_states(var_ref.states), +ref_list=var_ref.states, +assert_complete=True,
+
+
)
+.
+)
+-
+
- +abstract initialize(model: CasadiModel, var_ref: VariableReference)[source] +
-
+
- +property parameters: list[agentlib_mpc.optimization_backends.casadi_.core.VariableGroup.OptimizationParameter] +
-
+
- +property quantities: list[Union[agentlib_mpc.optimization_backends.casadi_.core.VariableGroup.OptimizationParameter, agentlib_mpc.optimization_backends.casadi_.core.VariableGroup.OptimizationVariable]] +
-
+
- +property variables: list[agentlib_mpc.optimization_backends.casadi_.core.VariableGroup.OptimizationVariable] +
+
+
+
+
+
+
+
+
+
+
\ No newline at end of file
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+
+
+
+
+
+
+
+
+
+
+
+
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+
+
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+
+
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+
+
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+
+
+
+ agentlib_mpc.optimization_backends.casadi_ package
+Subpackages
+
+
+-
+
- agentlib_mpc.optimization_backends.casadi_.core package
-
+
- Submodules +
- agentlib_mpc.optimization_backends.casadi_.core.VariableGroup module
-
+
OptimizationParameter-
+
OptimizationParameter.add_default_values
+OptimizationParameter.declare()
+OptimizationParameter.dim
+OptimizationParameter.full_names
+OptimizationParameter.full_symbolic
+OptimizationParameter.full_with_defaults
+OptimizationParameter.name
+OptimizationParameter.ref_names
+OptimizationParameter.use_in_stage_function
+
+OptimizationQuantity+
+OptimizationVariable-
+
OptimizationVariable.binary
+OptimizationVariable.declare()
+OptimizationVariable.dim
+OptimizationVariable.full_names
+OptimizationVariable.full_symbolic
+OptimizationVariable.input_map
+OptimizationVariable.name
+OptimizationVariable.output_map
+OptimizationVariable.ref_names
+OptimizationVariable.use_in_stage_function
+
+
+ - agentlib_mpc.optimization_backends.casadi_.core.casadi_backend module
-
+
CasADiBackend-
+
CasADiBackend.config_type
+CasADiBackend.cost_function
+CasADiBackend.discretization
+CasADiBackend.discretization_types
+CasADiBackend.model
+CasADiBackend.reset_setup_attributes()
+CasADiBackend.save_result_df()
+CasADiBackend.setup_optimization()
+CasADiBackend.solve()
+CasADiBackend.system
+CasADiBackend.system_type
+CasADiBackend.var_ref
+
+CasadiBackendConfig-
+
CasadiBackendConfig.build_batch_bat
+CasadiBackendConfig.discretization_options
+CasadiBackendConfig.do_jit
+CasadiBackendConfig.model
+CasadiBackendConfig.name
+CasadiBackendConfig.overwrite_result_file
+CasadiBackendConfig.results_file
+CasadiBackendConfig.save_results
+CasadiBackendConfig.solver
+CasadiBackendConfig.validate_compile
+
+
+ - agentlib_mpc.optimization_backends.casadi_.core.discretization module
-
+
Discretization-
+
Discretization.add_constraint()
+Discretization.add_opt_par()
+Discretization.add_opt_var()
+Discretization.binary_vars
+Discretization.constraints
+Discretization.constraints_lb
+Discretization.constraints_ub
+Discretization.create_nlp_in_out_mapping()
+Discretization.equalities
+Discretization.grid()
+Discretization.initial_guess
+Discretization.initialize()
+Discretization.k
+Discretization.mpc_opt_pars
+Discretization.mpc_opt_vars
+Discretization.nlp
+Discretization.objective_function
+Discretization.only_positive_times_in_results
+Discretization.opt_pars
+Discretization.opt_vars
+Discretization.opt_vars_lb
+Discretization.opt_vars_ub
+Discretization.pred_time
+Discretization.solve()
+
+Results+
+
+ - agentlib_mpc.optimization_backends.casadi_.core.system module + +
+
Submodules
+agentlib_mpc.optimization_backends.casadi_.admm module
+-
+
- +class agentlib_mpc.optimization_backends.casadi_.admm.ADMMCollocation(options: CasadiDiscretizationOptions)[source] +
Bases:
+DirectCollocation
-
+
- +class agentlib_mpc.optimization_backends.casadi_.admm.ADMMMultipleShooting(options: CasadiDiscretizationOptions)[source] +
Bases:
+MultipleShooting
-
+
- +class agentlib_mpc.optimization_backends.casadi_.admm.CasADiADMMBackend(config: dict)[source] +
Bases:
+CasADiBaseBackend,ADMMBackendClass doing optimization of ADMM subproblems with CasADi.
+-
+
- +cost_function: Callable | None +
-
+
- +property coupling_grid +
Returns the grid on which the coupling variables are discretized.
+
-
+
- +discretization: DiscretizationT +
-
+
- +discretization_types: dict[DiscretizationMethod, Type[DiscretizationT]] = {DiscretizationMethod.collocation: <class 'agentlib_mpc.optimization_backends.casadi_.admm.ADMMCollocation'>, DiscretizationMethod.multiple_shooting: <class 'agentlib_mpc.optimization_backends.casadi_.admm.ADMMMultipleShooting'>} +
-
+
- +it: int +
-
+
- +model: ModelT +
-
+
- +now: float +
-
+
- +result_stats: list[str] +
-
+
- +results: list[pd.DataFrame] +
-
+
- +save_result_df(results: Results, now: float = 0)[source] +
Save the results of solve into a dataframe at each time step.
+Example results dataframe:
+value_type variable … lower +variable T_0 T_0_slack … T_0_slack mDot_0 +time_step … +2 0.000000 298.160000 NaN … NaN NaN
++
+101.431499 297.540944 -149.465942 … -inf 0.0 +450.000000 295.779780 -147.704779 … -inf 0.0 +798.568501 294.720770 -146.645769 … -inf 0.0
+-
+
- Parameters: +
-
+
results –
+now –
+
+
Returns:
+
-
+
- +system: CasadiADMMSystem +
-
+
- +system_type +
alias of
+CasadiADMMSystem
-
+
- +var_ref: mpc_datamodels.VariableReference | None +
-
+
- +class agentlib_mpc.optimization_backends.casadi_.admm.CasadiADMMSystem[source] +
Bases:
+FullSystem-
+
- +algebraics: OptimizationVariable +
-
+
- +controls: OptimizationVariable +
-
+
- +cost_function: ca.MX +
-
+
- +exchange_diff: OptimizationParameter +
-
+
- +exchange_multipliers: OptimizationParameter +
-
+
- +global_couplings: OptimizationParameter +
-
+
- +initial_state: OptimizationParameter +
-
+
- +initialize(model: CasadiModel, var_ref: VariableReference)[source] +
-
+
- +last_control: OptimizationParameter +
-
+
- +local_couplings: OptimizationVariable +
-
+
- +local_exchange: OptimizationVariable +
-
+
- +model_constraints: Constraint +
-
+
- +model_parameters: OptimizationParameter +
-
+
- +multipliers: OptimizationParameter +
-
+
- +non_controlled_inputs: OptimizationParameter +
-
+
- +ode: ca.MX +
-
+
- +outputs: OptimizationVariable +
-
+
- +penalty_factor: OptimizationParameter +
-
+
- +r_del_u: OptimizationParameter +
-
+
- +states: OptimizationVariable +
agentlib_mpc.optimization_backends.casadi_.basic module
+-
+
- +class agentlib_mpc.optimization_backends.casadi_.basic.BaseSystem[source] +
Bases:
+System-
+
- +algebraics: OptimizationVariable +
-
+
- +controls: OptimizationVariable +
-
+
- +cost_function: MX +
-
+
- +initial_state: OptimizationParameter +
-
+
- +initialize(model: CasadiModel, var_ref: VariableReference)[source] +
-
+
- +model_constraints: Constraint +
-
+
- +model_parameters: OptimizationParameter +
-
+
- +non_controlled_inputs: OptimizationParameter +
-
+
- +ode: MX +
-
+
- +outputs: OptimizationVariable +
-
+
- +states: OptimizationVariable +
-
+
- +class agentlib_mpc.optimization_backends.casadi_.basic.CasADiBaseBackend(config: dict)[source] +
Bases:
+CasADiBackendClass doing optimization of ADMM subproblems with CasADi.
+-
+
- +cost_function: Callable | None +
-
+
- +discretization: DiscretizationT +
-
+
- +discretization_types: dict[DiscretizationMethod, Type[DiscretizationT]] = {DiscretizationMethod.collocation: <class 'agentlib_mpc.optimization_backends.casadi_.basic.DirectCollocation'>, DiscretizationMethod.multiple_shooting: <class 'agentlib_mpc.optimization_backends.casadi_.basic.MultipleShooting'>} +
-
+
- +model: ModelT +
-
+
- +system: BaseSystem +
-
+
- +system_type +
alias of
+BaseSystem
-
+
- +var_ref: mpc_datamodels.VariableReference | None +
-
+
- +class agentlib_mpc.optimization_backends.casadi_.basic.CollocationMatrices(order: int, root: numpy.ndarray, B: numpy.ndarray, C: numpy.ndarray, D: numpy.ndarray)[source] +
Bases:
+object-
+
- +B: ndarray +
-
+
- +C: ndarray +
-
+
- +D: ndarray +
-
+
- +order: int +
-
+
- +root: ndarray +
-
+
- +class agentlib_mpc.optimization_backends.casadi_.basic.DirectCollocation(options: CasadiDiscretizationOptions)[source] +
Bases:
+Discretization-
+
- +initialize(system: BaseSystem, solver_factory: SolverFactory)[source] +
Initializes the trajectory optimization problem, creating all symbolic +variables of the OCP, the mapping function and the numerical solver.
+
-
+
- +class agentlib_mpc.optimization_backends.casadi_.basic.MultipleShooting(options: CasadiDiscretizationOptions)[source] +
Bases:
+Discretization-
+
- +initialize(system: BaseSystem, solver_factory: SolverFactory)[source] +
Initializes the trajectory optimization problem, creating all symbolic +variables of the OCP, the mapping function and the numerical solver.
+
agentlib_mpc.optimization_backends.casadi_.casadi_admm_ml module
+-
+
- +class agentlib_mpc.optimization_backends.casadi_.casadi_admm_ml.CasADiADMMBackend_NN(config: dict)[source] +
Bases:
+CasADiADMMBackend,CasADiBBBackendClass doing optimization with an MLModel.
+-
+
- +discretization_types: dict[DiscretizationMethod, Type[DiscretizationT]] = {DiscretizationMethod.multiple_shooting: <class 'agentlib_mpc.optimization_backends.casadi_.casadi_admm_ml.MultipleShootingADMMNN'>} +
-
+
- +system: CasadiADMMNNSystem +
-
+
- +system_type +
alias of
+CasadiADMMNNSystem
-
+
- +class agentlib_mpc.optimization_backends.casadi_.casadi_admm_ml.CasadiADMMNNSystem[source] +
Bases:
+CasadiADMMSystem,CasadiMLSystemIn this class, the lags are determined by the trainer alone and the lags are +saved in the serialized MLModel so that it doesn’t have to be defined in the +model again
+-
+
- +initialize(model: CasadiMLModel, var_ref: VariableReference)[source] +
-
+
- +property parameters: list[agentlib_mpc.optimization_backends.casadi_.core.VariableGroup.OptimizationParameter] +
-
+
- +past_couplings: OptimizationParameter +
-
+
- +past_exchange: OptimizationParameter +
-
+
- +property quantities: list[Union[agentlib_mpc.optimization_backends.casadi_.core.VariableGroup.OptimizationParameter, agentlib_mpc.optimization_backends.casadi_.core.VariableGroup.OptimizationVariable]] +
-
+
- +property sim_step_quantities: dict[str, Union[agentlib_mpc.optimization_backends.casadi_.core.VariableGroup.OptimizationParameter, agentlib_mpc.optimization_backends.casadi_.core.VariableGroup.OptimizationVariable]] +
-
+
- +property variables: list[agentlib_mpc.optimization_backends.casadi_.core.VariableGroup.OptimizationVariable] +
-
+
- +class agentlib_mpc.optimization_backends.casadi_.casadi_admm_ml.MultipleShootingADMMNN(options: CasadiDiscretizationOptions)[source] +
Bases:
+ADMMMultipleShooting,MultipleShooting_ML-
+
- +max_lag: int +
agentlib_mpc.optimization_backends.casadi_.casadi_ml module
+-
+
- +class agentlib_mpc.optimization_backends.casadi_.casadi_ml.CasADiBBBackend(config: dict)[source] +
Bases:
+CasADiBaseBackendClass doing optimization with a MLModel.
+-
+
- +discretization_types: dict[DiscretizationMethod, Type[DiscretizationT]] = {DiscretizationMethod.multiple_shooting: <class 'agentlib_mpc.optimization_backends.casadi_.casadi_ml.MultipleShooting_ML'>} +
-
+
- +get_lags_per_variable() dict[str, float][source] +
Returns the name of variables which include lags and their lag. The MPC +module can use this information to save relevant past data of lagged +variables
+
-
+
- +lag_collection: Dict[str, deque] = {} +
-
+
- +max_lag: int +
-
+
- +system: CasadiMLSystem +
-
+
- +system_type +
alias of
+CasadiMLSystem
-
+
- +class agentlib_mpc.optimization_backends.casadi_.casadi_ml.CasadiMLSystem[source] +
Bases:
+FullSystem-
+
- +all_system_quantities() dict[str, agentlib_mpc.optimization_backends.casadi_.core.VariableGroup.OptimizationQuantity][source] +
-
+
- +initialize(model: CasadiMLModel, var_ref: FullVariableReference)[source] +
-
+
- +lags_dict: dict[str, int] +
-
+
- +property max_lag: int +
-
+
- +model: CasadiMLModel +
-
+
- +sim_step: Function +
-
+
- +class agentlib_mpc.optimization_backends.casadi_.casadi_ml.MultipleShooting_ML(options: CasadiDiscretizationOptions)[source] +
Bases:
+MultipleShooting-
+
- +initialize(system: CasadiMLSystem, solver_factory: SolverFactory)[source] +
Initializes the trajectory optimization problem, creating all symbolic +variables of the OCP, the mapping function and the numerical solver.
+
-
+
- +max_lag: int +
agentlib_mpc.optimization_backends.casadi_.full module
+-
+
- +class agentlib_mpc.optimization_backends.casadi_.full.CasADiFullBackend(config: dict)[source] +
Bases:
+CasADiBackendClass doing optimization of ADMM subproblems with CasADi.
+-
+
- +cost_function: Callable | None +
-
+
- +discretization: DiscretizationT +
-
+
- +discretization_types: dict[DiscretizationMethod, Type[DiscretizationT]] = {DiscretizationMethod.collocation: <class 'agentlib_mpc.optimization_backends.casadi_.full.DirectCollocation'>, DiscretizationMethod.multiple_shooting: <class 'agentlib_mpc.optimization_backends.casadi_.full.MultipleShooting'>} +
-
+
- +model: ModelT +
-
+
- +system: FullSystem +
-
+
- +system_type +
alias of
+FullSystem
-
+
- +var_ref: mpc_datamodels.VariableReference | None +
-
+
- +class agentlib_mpc.optimization_backends.casadi_.full.DirectCollocation(options: CasadiDiscretizationOptions)[source] +
Bases:
+DirectCollocation
-
+
- +class agentlib_mpc.optimization_backends.casadi_.full.FullSystem[source] +
Bases:
+BaseSystem-
+
- +algebraics: OptimizationVariable +
-
+
- +controls: OptimizationVariable +
-
+
- +cost_function: ca.MX +
-
+
- +initial_state: OptimizationParameter +
-
+
- +initialize(model: CasadiModel, var_ref: FullVariableReference)[source] +
-
+
- +last_control: OptimizationParameter +
-
+
- +model_constraints: Constraint +
-
+
- +model_parameters: OptimizationParameter +
-
+
- +non_controlled_inputs: OptimizationParameter +
-
+
- +ode: ca.MX +
-
+
- +outputs: OptimizationVariable +
-
+
- +r_del_u: OptimizationParameter +
-
+
- +states: OptimizationVariable +
-
+
- +class agentlib_mpc.optimization_backends.casadi_.full.MultipleShooting(options: CasadiDiscretizationOptions)[source] +
Bases:
+MultipleShooting
agentlib_mpc.optimization_backends.casadi_.mhe module
+-
+
- +class agentlib_mpc.optimization_backends.casadi_.mhe.CollocationMatrices(order: int, root: numpy.ndarray, B: numpy.ndarray, C: numpy.ndarray, D: numpy.ndarray)[source] +
Bases:
+object-
+
- +B: ndarray +
-
+
- +C: ndarray +
-
+
- +D: ndarray +
-
+
- +order: int +
-
+
- +root: ndarray +
-
+
- +class agentlib_mpc.optimization_backends.casadi_.mhe.DirectCollocation(options: CasadiDiscretizationOptions)[source] +
Bases:
+Discretization-
+
- +initialize(system: MHESystem, solver_factory: SolverFactory)[source] +
Initializes the trajectory optimization problem, creating all symbolic +variables of the OCP, the mapping function and the numerical solver.
+
-
+
- +only_positive_times_in_results: bool = False +
-
+
- +class agentlib_mpc.optimization_backends.casadi_.mhe.MHEBackend(config: dict)[source] +
Bases:
+CasADiBackendClass doing optimization of ADMM subproblems with CasADi.
+-
+
- +discretization_types: dict[DiscretizationMethod, Type[DiscretizationT]] = {DiscretizationMethod.collocation: <class 'agentlib_mpc.optimization_backends.casadi_.mhe.DirectCollocation'>} +
-
+
- +static sample(trajectory: float | int | Series | list[Union[float, int]], grid: list | ndarray, current: float = 0, method: str = 'linear') list[source] +
Obtain the specified portion of the trajectory.
+-
+
- Parameters: +
-
+
trajectory – The trajectory to be sampled. Scalars will be +expanded onto the grid. Lists need to exactly match the provided +grid. Otherwise, a list of tuples is accepted with the form ( +timestamp, value). A dict with the keys ‘grid’ and ‘value’ is also +accepted.
+current – start time of requested trajectory
+grid – target interpolation grid in seconds in relative terms (i.e. +starting from 0 usually)
+method – interpolation method, currently accepted: ‘linear’, +‘spline’, ‘previous’
+
+- Returns: +
Sampled list of values.
+
+
Takes a slice of the trajectory from the current time step with the +specified length and interpolates it to match the requested sampling. +If the requested horizon is longer than the available data, the last +available value will be used for the remainder.
+-
+
- Raises: +
-
+
ValueError –
+TypeError –
+
+
-
+
- +class agentlib_mpc.optimization_backends.casadi_.mhe.MHESystem[source] +
Bases:
+System-
+
- +algebraics: OptimizationVariable +
-
+
- +cost_function: MX +
-
+
- +estimated_inputs: OptimizationVariable +
-
+
- +estimated_parameters: OptimizationVariable +
-
+
- +estimated_states: OptimizationVariable +
-
+
- +initialize(model: CasadiModel, var_ref: MHEVariableReference)[source] +
-
+
- +known_inputs: OptimizationParameter +
-
+
- +known_parameters: OptimizationParameter +
-
+
- +measured_states: OptimizationParameter +
-
+
- +model_constraints: Constraint +
-
+
- +ode: MX +
-
+
- +outputs: OptimizationVariable +
agentlib_mpc.optimization_backends.casadi_.minlp module
+-
+
- +class agentlib_mpc.optimization_backends.casadi_.minlp.CasADiMINLPBackend(config: dict)[source] +
Bases:
+CasADiBackendClass doing optimization of ADMM subproblems with CasADi.
+-
+
- +cost_function: Callable | None +
-
+
- +discretization: DiscretizationT +
-
+
- +discretization_types: dict[DiscretizationMethod, Type[DiscretizationT]] = {DiscretizationMethod.collocation: <class 'agentlib_mpc.optimization_backends.casadi_.minlp.DirectCollocation'>} +
-
+
- +model: ModelT +
-
+
- +system: CasadiMINLPSystem +
-
+
- +system_type +
alias of
+CasadiMINLPSystem
-
+
- +var_ref: mpc_datamodels.VariableReference | None +
-
+
- +class agentlib_mpc.optimization_backends.casadi_.minlp.CasadiMINLPSystem[source] +
Bases:
+BaseSystem-
+
- +algebraics: OptimizationVariable +
-
+
- +binary_controls: OptimizationVariable +
-
+
- +controls: OptimizationVariable +
-
+
- +cost_function: ca.MX +
-
+
- +initial_state: OptimizationParameter +
-
+
- +initialize(model: CasadiModel, var_ref: MINLPVariableReference)[source] +
-
+
- +model_constraints: Constraint +
-
+
- +model_parameters: OptimizationParameter +
-
+
- +non_controlled_inputs: OptimizationParameter +
-
+
- +ode: ca.MX +
-
+
- +outputs: OptimizationVariable +
-
+
- +states: OptimizationVariable +
-
+
- +class agentlib_mpc.optimization_backends.casadi_.minlp.DirectCollocation(options: CasadiDiscretizationOptions)[source] +
Bases:
+DirectCollocation
agentlib_mpc.optimization_backends.casadi_.minlp_cia module
+
+
+
+
+
+
+
+
+
+
+
\ No newline at end of file
diff --git a/docs/0.6.4/docs/code/agentlib_mpc.utils.html b/docs/0.6.4/docs/code/agentlib_mpc.utils.html
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+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+ agentlib_mpc.optimization_backends package
+-
+
- +pydantic model agentlib_mpc.optimization_backends.BackendImport[source] +
Bases:
+BaseModelData-Class to import a given python file +from
+import_pathand load the given +class_name-
+
- Fields: +
- + +
-
+
- +field class_name: str [Required] +
-
+
- +field import_path: str [Required] +
Subpackages
+
+
+-
+
- agentlib_mpc.optimization_backends.casadi_ package
-
+
- Subpackages + +
- Submodules +
- agentlib_mpc.optimization_backends.casadi_.admm module
-
+
ADMMCollocation
+ADMMMultipleShooting
+CasADiADMMBackend-
+
CasADiADMMBackend.cost_function
+CasADiADMMBackend.coupling_grid
+CasADiADMMBackend.discretization
+CasADiADMMBackend.discretization_types
+CasADiADMMBackend.it
+CasADiADMMBackend.model
+CasADiADMMBackend.now
+CasADiADMMBackend.result_stats
+CasADiADMMBackend.results
+CasADiADMMBackend.save_result_df()
+CasADiADMMBackend.system
+CasADiADMMBackend.system_type
+CasADiADMMBackend.var_ref
+
+CasadiADMMSystem-
+
CasadiADMMSystem.algebraics
+CasadiADMMSystem.controls
+CasadiADMMSystem.cost_function
+CasadiADMMSystem.exchange_diff
+CasadiADMMSystem.exchange_multipliers
+CasadiADMMSystem.global_couplings
+CasadiADMMSystem.initial_state
+CasadiADMMSystem.initialize()
+CasadiADMMSystem.last_control
+CasadiADMMSystem.local_couplings
+CasadiADMMSystem.local_exchange
+CasadiADMMSystem.model_constraints
+CasadiADMMSystem.model_parameters
+CasadiADMMSystem.multipliers
+CasadiADMMSystem.non_controlled_inputs
+CasadiADMMSystem.ode
+CasadiADMMSystem.outputs
+CasadiADMMSystem.penalty_factor
+CasadiADMMSystem.r_del_u
+CasadiADMMSystem.states
+
+
+ - agentlib_mpc.optimization_backends.casadi_.basic module
-
+
BaseSystem+
+CasADiBaseBackend+
+CollocationMatrices+
+DirectCollocation+
+MultipleShooting+
+
+ - agentlib_mpc.optimization_backends.casadi_.casadi_admm_ml module + +
- agentlib_mpc.optimization_backends.casadi_.casadi_ml module
-
+
CasADiBBBackend+
+CasadiMLSystem+
+MultipleShooting_ML+
+
+ - agentlib_mpc.optimization_backends.casadi_.full module
-
+
CasADiFullBackend+
+DirectCollocation
+FullSystem-
+
FullSystem.algebraics
+FullSystem.controls
+FullSystem.cost_function
+FullSystem.initial_state
+FullSystem.initialize()
+FullSystem.last_control
+FullSystem.model_constraints
+FullSystem.model_parameters
+FullSystem.non_controlled_inputs
+FullSystem.ode
+FullSystem.outputs
+FullSystem.r_del_u
+FullSystem.states
+
+MultipleShooting
+
+ - agentlib_mpc.optimization_backends.casadi_.mhe module
-
+
CollocationMatrices+
+DirectCollocation+
+MHEBackend+
+MHESystem-
+
MHESystem.algebraics
+MHESystem.cost_function
+MHESystem.estimated_inputs
+MHESystem.estimated_parameters
+MHESystem.estimated_states
+MHESystem.initialize()
+MHESystem.known_inputs
+MHESystem.known_parameters
+MHESystem.measured_states
+MHESystem.model_constraints
+MHESystem.ode
+MHESystem.outputs
+
+
+ - agentlib_mpc.optimization_backends.casadi_.minlp module
-
+
CasADiMINLPBackend+
+CasadiMINLPSystem-
+
CasadiMINLPSystem.algebraics
+CasadiMINLPSystem.binary_controls
+CasadiMINLPSystem.controls
+CasadiMINLPSystem.cost_function
+CasadiMINLPSystem.initial_state
+CasadiMINLPSystem.initialize()
+CasadiMINLPSystem.model_constraints
+CasadiMINLPSystem.model_parameters
+CasadiMINLPSystem.non_controlled_inputs
+CasadiMINLPSystem.ode
+CasadiMINLPSystem.outputs
+CasadiMINLPSystem.states
+
+DirectCollocation
+
+ - agentlib_mpc.optimization_backends.casadi_.minlp_cia module +
+
Submodules
+agentlib_mpc.optimization_backends.backend module
+-
+
- +class agentlib_mpc.optimization_backends.backend.ADMMBackend(config: dict)[source] +
Bases:
+OptimizationBackendBase class for implementations of optimization backends for ADMM +algorithms.
+-
+
- +abstract property coupling_grid: list[float] +
Returns the grid on which the coupling variables are discretized.
+
-
+
- +pydantic model agentlib_mpc.optimization_backends.backend.BackendConfig[source] +
Bases:
+BaseModel-
+
- Config: +
-
+
extra: str = forbid
+
+- Fields: +
- + +
- Validators: +
- + +
-
+
- +field discretization_options: DiscretizationOptions [Required] +
-
+
- +field model: dict [Required] +
-
+
- +field name: str | None = None +
-
+
- +field overwrite_result_file: bool | None = False +
-
+
- Validated by: +
- + +
-
+
- +field save_results: bool | None = None +
-
+
- Validated by: +
- + +
-
+
- +validator check_csv » results_file[source] +
-
+
- +validator check_overwrite » overwrite_result_file[source] +
Checks, whether the overwrite results sttings are valid, and deletes +existing result files if applicable.
+
-
+
- +validator disable_results_if_no_file » save_results[source] +
-
+
- +class agentlib_mpc.optimization_backends.backend.OptimizationBackend(config: dict)[source] +
Bases:
+ABCBase class for all optimization backends. OptimizationBackends are a +plugin for the ‘mpc’ module. They provide means to setup and solve the +underlying optimization problem of the MPC. They also can save data of +the solutions.
+-
+
- +config_type +
alias of
+BackendConfig
-
+
- +cost_function: Callable | None +
-
+
- +get_lags_per_variable() dict[str, float][source] +
Returns the name of variables which include lags and their lag in seconds. +The MPC module can use this information to save relevant past data of lagged +variables
+
-
+
- +model: ModelT +
-
+
- +mpc_backend_parameters = ('time_step', 'prediction_horizon') +
-
+
- +register_logger(logger: Logger)[source] +
Registers a logger, can be used to use the module logger
+
-
+
- +results_file_exists() bool[source] +
Checks if the results file already exists, and if not, creates it with +headers.
+
-
+
- +abstract setup_optimization(var_ref: VariableReference)[source] +
Performs all necessary steps to make the
+solvemethod usable.-
+
- Parameters: +
var_ref – Variable Reference that specifies the role of each model variable +in the mpc
+
+
-
+
- +abstract solve(now: float | datetime, current_vars: Dict[str, AgentVariable]) Results[source] +
Solves the optimization problem given the current values of the +corresponding AgentVariables and system time. The standardization of +return values is a work in progress.
+-
+
- Parameters: +
-
+
now – Current time used for interpolation of input trajectories.
+current_vars – Dict of AgentVariables holding the values relevant to +the optimization problem. Keys are the names
+
+- Returns: +
A dataframe with all optimization variables over their respective +grids. Depending on discretization, can include many nan’s, so care +should be taken when using this, e.g. always use dropna() after +accessing a column.
++
+ +-
+
- Example:
+
+variables mDot | T_0 | slack_T
+time +0 0.1 | 298 | nan +230 nan | 297 | 3 +470 nan | 296 | 2 +588 nan | 295 | 1 +700 0.05 | 294 | nan +930 nan | 294 | 0.1
+
+
+
-
+
- +update_discretization_options(opts: dict)[source] +
Updates the discretization options with the new dict.
+
-
+
- +update_model_variables(current_vars: Dict[str, AgentVariable])[source] +
Internal method to write current data_broker to model variables. +Only update values, not other module_types.
+
-
+
- +var_ref: VariableReference | None +
+
+
+
+
+
+
+
+
+
+
\ No newline at end of file
diff --git a/docs/0.6.4/docs/code/agentlib_mpc.utils.plotting.html b/docs/0.6.4/docs/code/agentlib_mpc.utils.plotting.html
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+
+
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+
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+
+
+
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+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+ agentlib_mpc.utils package
+Package containing utils for agentlib_mpc.
+Subpackages
+
+
+-
+
- agentlib_mpc.utils.plotting package
-
+
- Submodules +
- agentlib_mpc.utils.plotting.admm_animation module + +
- agentlib_mpc.utils.plotting.admm_consensus_shades module + +
- agentlib_mpc.utils.plotting.admm_dashboard module + +
- agentlib_mpc.utils.plotting.admm_residuals module + +
- agentlib_mpc.utils.plotting.basic module
-
+
EBCColors+
+FontDict+
+Style
+ValueRange+
+make_fig()
+make_grid()
+make_side_legend()
+
+ - agentlib_mpc.utils.plotting.discretization_structure module + +
- agentlib_mpc.utils.plotting.interactive module + +
- agentlib_mpc.utils.plotting.ml_model_test module + +
- agentlib_mpc.utils.plotting.mpc module + +
+
Submodules
+agentlib_mpc.utils.analysis module
+-
+
- +agentlib_mpc.utils.analysis.admm_at_time_step(data: DataFrame | Series, time_step: float = None, variable=None, iteration: float = -1, index_offset: float | Literal['auto'] | bool = True, convert_to: Literal['seconds', 'minutes', 'hours', 'days'] = 'seconds') DataFrame[source] +
Gets the results of an optimization at a time step. +:param index_offset: Determines how the index will be updated when loading the data. +:param The offset will be subtracted from the time-index. This is useful for results: +:param of realtime systems: to cut the number down to something understandable. For example, if the time
++
+index (level 0 of the input Dataframe) is [105, 115, 125] and we give an +index_offset of 100, the data will be handled as if the index was [5, 15, 25].
++
+-
+
- If “auto” or True is provided as an argument, the index will be modified to
start at 0. If 0 or False are provided, no modifications will be made.
+
+
-
+
- Parameters: +
-
+
want (where the time value with be a unix time stamp and we) –
to cut the number down to something understandable. For example, if the time +index (level 0 of the input Dataframe) is [105, 115, 125] and we give an +index_offset of 100, the data will be handled as if the index was [5, 15, 25].
++
+-
+
- If “auto” or True is provided as an argument, the index will be modified to
start at 0. If 0 or False are provided, no modifications will be made.
+
+
+data – The multi-indexed results data frame from the mpc
+time_step – The time step from which results should be shown. +If no exact match, shows closest.
+variable – If specified, only returns results +with regard to a certain variable.
+iteration – Specifies, from which inner ADMM iteration data should be +from. If negative, counts from last iteration. Default -1.
+convert_to – Whether the data should be converted to datetime, minutes etc.
+
+- Returns: +
A single-indexed Dataframe of the optimization results +at the specified time step. If variable is not specified, +returns all variables with a double column index, if it +is specified returns only values and/or bounds with +single indexed columns.
+
+
-
+
- +agentlib_mpc.utils.analysis.convert_index(convert_to: Literal['seconds', 'minutes', 'hours', 'days'] | Literal['datetime'], index: Index)[source] +
Converts an index from seconds to datetime or another unit +:param convert_to: unit, e.g. minutes, hours, datetime +:param index: pandas index object
+Returns:
+
-
+
- +agentlib_mpc.utils.analysis.convert_multi_index(data: DataFrame, convert_to: Literal['seconds', 'minutes', 'hours', 'days'] | Literal['datetime'])[source] +
Converts an index of an MPC or ADMM results Dataframe to a different unit, +assuming it is passed in seconds.
+
-
+
- +agentlib_mpc.utils.analysis.first_vals_at_trajectory_index(data: DataFrame | Series)[source] +
Gets the first values at each time step of a results trajectory.
+
-
+
- +agentlib_mpc.utils.analysis.get_number_of_iterations(data: DataFrame) dict[agentlib_mpc.utils.analysis.SimulationTime, int][source] +
Returns the number of iterations at each time instance of the ADMM simulation.
+
-
+
- +agentlib_mpc.utils.analysis.get_time_steps(data: DataFrame) Iterable[float][source] +
Returns the time steps at which an MPC step was performed.
+
-
+
- +agentlib_mpc.utils.analysis.last_vals_at_trajectory_index(data: DataFrame | Series)[source] +
Gets the last values at each time step of a results trajectory.
+
-
+
- +agentlib_mpc.utils.analysis.mpc_at_time_step(data: DataFrame, time_step: float, variable=None, variable_type='variable', index_offset: float | Literal['auto'] | bool = True) DataFrame[source] +
Gets the results of an optimization at a time step.
+-
+
- Parameters: +
-
+
data – The multi-indexed results data frame from the mpc
+time_step – The time step from which results should be shown. +If no exact match, shows closest.
+variable – If specified, only returns results +with regard to a certain variable.
+variable_type – The type of the variable provided (parameter, variable, lower, …)
+index_offset – Determines how the index will be updated when loading the data.
+results (The offset will be subtracted from the time-index. This is useful for) –
+systems (of realtime) –
to cut the number down to something understandable. For example, if the time +index (level 0 of the input Dataframe) is [105, 115, 125] and we give an +index_offset of 100, the data will be handled as if the index was [5, 15, 25].
++
+-
+
- If “auto” or True is provided as an argument, the index will be modified to
start at 0. If 0 or False are provided, no modifications will be made.
+
+
+want (where the time value with be a unix time stamp and we) –
to cut the number down to something understandable. For example, if the time +index (level 0 of the input Dataframe) is [105, 115, 125] and we give an +index_offset of 100, the data will be handled as if the index was [5, 15, 25].
++
+-
+
- If “auto” or True is provided as an argument, the index will be modified to
start at 0. If 0 or False are provided, no modifications will be made.
+
+
+
+- Returns: +
-
+
- A single-indexed Dataframe of the optimization results
at the specified time step. If variable is not specified, +returns all variables with a double column index, if it +is specified returns only values and/or bounds with +single indexed columns.
+
+
+- Return type: +
pd.DataFrame
+
+
agentlib_mpc.utils.sampling module
+-
+
- +agentlib_mpc.utils.sampling.earliest_index(time, arr, stop, start=0)[source] +
Helper function for interpolate_to_previous. +Finds the current index to which we should forwardfill.
+
-
+
- +agentlib_mpc.utils.sampling.interpolate_to_previous(target_grid: Iterable[float], original_grid: Iterable[float], values: Sequence[float]) List[float][source] +
Interpolates to previous value of original grid, i.e. a forward fill.
+Stand-in for the following scipy code: +tck = interpolate.interp1d(list(original_grid), values, kind=”previous”) +result = list(tck(target_grid))
+
-
+
- +agentlib_mpc.utils.sampling.pairwise(iterable: Iterable)[source] +
s -> (s0,s1), (s1,s2), (s2, s3), …
+
-
+
- +agentlib_mpc.utils.sampling.sample(trajectory: Real | Series | list[numbers.Real] | dict[numbers.Real, numbers.Real], grid: list | ndarray, current: float = 0, method: str = 'linear') list[source] +
Obtain the specified portion of the trajectory.
+-
+
- Parameters: +
-
+
trajectory –
The trajectory to be sampled. Scalars will be +expanded onto the grid. Lists need to exactly match the provided +grid. Otherwise, a pandas Series is accepted with the timestamp as index. A
++
+dict with the keys as time stamps is also accepted.
+
+current – start time of requested trajectory
+grid – target interpolation grid in seconds in relative terms (i.e. +starting from 0 usually)
+method – interpolation method, currently accepted: ‘linear’, +‘spline’, ‘previous’
+
+- Returns: +
Sampled list of values.
+
+
Takes a slice of the trajectory from the current time step with the +specified length and interpolates it to match the requested sampling. +If the requested horizon is longer than the available data, the last +available value will be used for the remainder.
+-
+
- Raises: +
-
+
ValueError –
+TypeError –
+
+
-
+
- +agentlib_mpc.utils.sampling.sample_values_to_target_grid(values: Iterable[float], original_grid: Iterable[float], target_grid: Iterable[float], method: str | InterpolationMethods) list[float][source] +
+
+
+
+
+
+
+
+
+
+
\ No newline at end of file
diff --git a/docs/0.6.4/docs/code/modules.html b/docs/0.6.4/docs/code/modules.html
new file mode 100644
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+
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+
+
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+
+
+
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+
+
+
+
+
+
+
+
+
+
+ agentlib_mpc.utils.plotting package
+Submodules
+agentlib_mpc.utils.plotting.admm_animation module
+Modules that defines functions to be used for automatically creating animations of +ADMM convergence
+-
+
- +agentlib_mpc.utils.plotting.admm_animation.animate_full(i: int, lines: dict[str, matplotlib.lines.Line2D], annotation: Annotation, data: dict[str, pandas.core.frame.DataFrame], time_step: float)[source] +
-
+
- +agentlib_mpc.utils.plotting.admm_animation.init_full(lines: dict[str, matplotlib.lines.Line2D], annotation: Annotation, ax: Axes)[source] +
-
+
- +agentlib_mpc.utils.plotting.admm_animation.make_animation(data: dict[str, pandas.core.frame.DataFrame], time_step: float = 0, file_name: str = '', customize: Callable[[Figure, Axes, Style], Tuple[Figure, Axes]] = None, iteration=-1, interval: int = 300)[source] +
agentlib_mpc.utils.plotting.admm_consensus_shades module
+ + +agentlib_mpc.utils.plotting.admm_dashboard module
+-
+
- +agentlib_mpc.utils.plotting.admm_dashboard.create_app(agent_data: Dict[str, DataFrame], residuals_df: DataFrame)[source] +
Create and configure the Dash app.
+Args: +agent_data (Dict[str, pd.DataFrame]): Dictionary containing data for each agent. +residuals_df (pd.DataFrame): DataFrame containing residuals data.
+Returns: +dash.Dash: Configured Dash app.
+
-
+
- +agentlib_mpc.utils.plotting.admm_dashboard.create_coupling_var_plot(plot_data: Dict[str, List[float]], prediction_grid: List[float], coupling_var: str) Figure[source] +
Create a plotly figure for the coupling variable plot.
+Args: +plot_data (Dict[str, List[float]]): Dictionary with agent names as keys and their values as lists. +prediction_grid (List[float]): List of prediction grid values. +coupling_var (str): Name of the coupling variable.
+Returns: +go.Figure: Plotly figure object.
+
-
+
- +agentlib_mpc.utils.plotting.admm_dashboard.create_residuals_plot(residuals_df: DataFrame, time_step: float) Figure[source] +
Create a plotly figure for the residuals plot.
+Args: +residuals_df (pd.DataFrame): DataFrame containing residuals data. +time_step (float): Selected time step.
+Returns: +go.Figure: Plotly figure object.
+
-
+
- +agentlib_mpc.utils.plotting.admm_dashboard.get_coupling_variables(df: DataFrame) List[str][source] +
Identify coupling variables in the dataframe.
+Args: +df (pd.DataFrame): The MPC data for an agent.
+Returns: +List[str]: List of coupling variable names.
+
-
+
- +agentlib_mpc.utils.plotting.admm_dashboard.get_data_for_plot(agent_data: Dict[str, DataFrame], time_step: float, iteration: int, coupling_var: str) Dict[str, List[float]][source] +
Extract data for the coupling variable plot.
+Args: +agent_data (Dict[str, pd.DataFrame]): Dictionary containing data for each agent. +time_step (float): Selected time step. +iteration (int): Selected iteration number. +coupling_var (str): Name of the selected coupling variable.
+Returns: +Dict[str, List[float]]: Dictionary with agent names as keys and their values as lists.
+
-
+
- +agentlib_mpc.utils.plotting.admm_dashboard.get_max_iterations_per_timestep(agent_data: Dict[str, DataFrame]) Dict[float, int][source] +
-
+
- +agentlib_mpc.utils.plotting.admm_dashboard.load_agent_data(directory: str) Dict[str, DataFrame][source] +
Load MPC data for multiple agents from files containing ‘admm’ in their name.
+Args: +directory (str): Directory path containing the data files.
+Returns: +Dict[str, pd.DataFrame]: Dictionary with agent names as keys and their data as values.
+
agentlib_mpc.utils.plotting.admm_residuals module
+-
+
- +agentlib_mpc.utils.plotting.admm_residuals.load_residuals(file: str | Path) DataFrame[source] +
Loads a residuals csv file in the correct format.
+
-
+
- +agentlib_mpc.utils.plotting.admm_residuals.plot_over_time(residuals: ~pandas.core.frame.DataFrame, primal_tol: float = None, dual_tol: float = None, max_iters: int = None) -> (<class 'matplotlib.figure.Figure'>, (<class 'matplotlib.axes._axes.Axes'>, <class 'matplotlib.axes._axes.Axes'>))[source] +
Plots the final residuals over time.
+
-
+
- +agentlib_mpc.utils.plotting.admm_residuals.plot_single_time_step(residuals: ~pandas.core.frame.DataFrame, time_step: float = None, primal_tol: float = None, dual_tol: float = None) -> (<class 'matplotlib.figure.Figure'>, <class 'matplotlib.axes._axes.Axes'>)[source] +
Plots the decrease of the residuals over iterations for a time step
+
-
+
- +agentlib_mpc.utils.plotting.admm_residuals.residuals_over_time(residuals: DataFrame) DataFrame[source] +
Evaluates the residuals over time. Takes a raw residuals DataFrame and returns a +Dataframe, which has for each time step the number of iterations and the final primal and dual residuals.
+-
+
- Returns: +
DataFrame with float index (time in seconds) and the columns +(“primal_residual”, “dual_residual”, “iters”)
+
+
agentlib_mpc.utils.plotting.basic module
+Some basic plotting utilities
+-
+
- +class agentlib_mpc.utils.plotting.basic.EBCColors[source] +
Bases:
+object-
+
- +blue: tuple[float, float, float] = (0.0, 0.32941176470588235, 0.6235294117647059) +
-
+
- +dark_grey: tuple[float, float, float] = (0.3058823529411765, 0.30980392156862746, 0.3137254901960784) +
-
+
- +dark_red: tuple[float, float, float] = (0.6745098039215687, 0.16862745098039217, 0.10980392156862745) +
-
+
- +ebc_palette_sort_1: list[tuple[float, float, float]] = [(0.6745098039215687, 0.16862745098039217, 0.10980392156862745), (0.8666666666666667, 0.25098039215686274, 0.17647058823529413), (0.9215686274509803, 0.5490196078431373, 0.5058823529411764), (0.3058823529411765, 0.30980392156862746, 0.3137254901960784), (0.615686274509804, 0.6196078431372549, 0.6274509803921569), (0.8509803921568627, 0.8509803921568627, 0.8509803921568627), (0.0, 0.32941176470588235, 0.6235294117647059), (0.615686274509804, 0.7647058823529411, 0.9019607843137255), (0.4392156862745098, 0.6784313725490196, 0.2784313725490196)] +
-
+
- +ebc_palette_sort_2: list[tuple[float, float, float]] = [(0.8666666666666667, 0.25098039215686274, 0.17647058823529413), (0.0, 0.32941176470588235, 0.6235294117647059), (0.615686274509804, 0.6196078431372549, 0.6274509803921569), (0.4392156862745098, 0.6784313725490196, 0.2784313725490196), (0.6745098039215687, 0.16862745098039217, 0.10980392156862745), (0.3058823529411765, 0.30980392156862746, 0.3137254901960784), (0.9215686274509803, 0.5490196078431373, 0.5058823529411764), (0.615686274509804, 0.7647058823529411, 0.9019607843137255), (0.8509803921568627, 0.8509803921568627, 0.8509803921568627)] +
-
+
- +green: tuple[float, float, float] = (0.4392156862745098, 0.6784313725490196, 0.2784313725490196) +
-
+
- +grey: tuple[float, float, float] = (0.615686274509804, 0.6196078431372549, 0.6274509803921569) +
-
+
- +light_blue: tuple[float, float, float] = (0.615686274509804, 0.7647058823529411, 0.9019607843137255) +
-
+
- +light_grey: tuple[float, float, float] = (0.8509803921568627, 0.8509803921568627, 0.8509803921568627) +
-
+
- +light_red: tuple[float, float, float] = (0.9215686274509803, 0.5490196078431373, 0.5058823529411764) +
-
+
- +red: tuple[float, float, float] = (0.8666666666666667, 0.25098039215686274, 0.17647058823529413) +
-
+
- +class agentlib_mpc.utils.plotting.basic.FontDict[source] +
Bases:
+TypedDict-
+
- +fontsize: float +
-
+
- +class agentlib_mpc.utils.plotting.basic.ValueRange(min: float, max: float)[source] +
Bases:
+object-
+
- +max: float +
-
+
- +min: float +
-
+
- +agentlib_mpc.utils.plotting.basic.make_fig(style: Style, customizer: Callable[[Figure, Axes, Style], Tuple[Figure, Axes]] = None, rows: int = 1) tuple[matplotlib.figure.Figure, tuple[matplotlib.axes._axes.Axes, ...]][source] +
- +agentlib_mpc.utils.plotting.basic.make_fig(style: Style, customizer: Callable[[Figure, tuple[matplotlib.axes._axes.Axes], Style], Tuple[Figure, tuple[matplotlib.axes._axes.Axes]]] = None) tuple[matplotlib.figure.Figure, matplotlib.axes._axes.Axes] +
- +agentlib_mpc.utils.plotting.basic.make_fig(style: Style) tuple[matplotlib.figure.Figure, matplotlib.axes._axes.Axes] +
Creates a figure and axes with an amount of rows. If rows is specified, return +a tuple of axes, else only an ax
+
agentlib_mpc.utils.plotting.discretization_structure module
+ + +agentlib_mpc.utils.plotting.interactive module
+ + + + +-
+
- +agentlib_mpc.utils.plotting.interactive.make_components(columns, data, convert_to, stats: DataFrame | None = None) [<class 'dash.html.Div.Div'>][source] +
-
+
- +agentlib_mpc.utils.plotting.interactive.obj_plot(data, convert_to: Literal['seconds', 'minutes', 'hours', 'days'] = 'seconds') Graph[source] +
-
+
- +agentlib_mpc.utils.plotting.interactive.plot_admm_plotly(series: Series, plot_actual_values: bool = True, plot_predictions: bool = False, step: bool = False, convert_to: Literal['seconds', 'minutes', 'hours', 'days'] = 'seconds')[source] +
-
+
- Parameters: +
-
+
series – A column of the MPC results Dataframe
+fig – Plotly figure to plot on
+plot_actual_values – whether the closed loop actual values at the start of each +optimization should be plotted (default True)
+plot_predictions – whether all predicted trajectories should be plotted
+step – whether to use a step plot or a line plot
+convert_to – Will convert the index of the returned series to the specified unit +(seconds, minutes, hours, days)
+
+- Returns: +
None
+
+
-
+
- +agentlib_mpc.utils.plotting.interactive.plot_mpc_plotly(series: Series, step: bool = False, convert_to: Literal['seconds', 'minutes', 'hours', 'days'] = 'seconds', y_axis_label: str = '') Figure[source] +
-
+
- Parameters: +
-
+
title –
+y_axis_label –
+series – A column of the MPC results Dataframe
+plot_actual_values – whether the closed loop actual values at the start of each +optimization should be plotted (default True)
+plot_predictions – whether all predicted trajectories should be plotted
+step – whether to use a step plot or a line plot
+convert_to – Will convert the index of the returned series to the specified unit +(seconds, minutes, hours, days)
+
+- Returns: +
Figure
+
+
agentlib_mpc.utils.plotting.ml_model_test module
+-
+
- +agentlib_mpc.utils.plotting.ml_model_test.calc_scores(errors: ndarray, metric: Callable) float[source] +
-
+
- +agentlib_mpc.utils.plotting.ml_model_test.evaluate_model(training_data: TrainingData, model: CasadiPredictor | SerializedMLModel, metric: Callable = None, show_plot: bool = True, save_path: Path | None = None)[source] +
Tests the Model on test data
+
-
+
- +agentlib_mpc.utils.plotting.ml_model_test.pairwise_sort(*arrays: tuple[numpy.ndarray, numpy.ndarray])[source] +
-
+
- +agentlib_mpc.utils.plotting.ml_model_test.predict_array(df: DataFrame, ml_model: CasadiPredictor, outputs: Index) DataFrame[source] +
agentlib_mpc.utils.plotting.mpc module
+-
+
- +agentlib_mpc.utils.plotting.mpc.interpolate_colors(progress: float, colors: list[tuple[float, float, float]]) tuple[float, float, float][source] +
Interpolates colors based on a sample number. To be used when plotting many mpc +predictions in one plot, so a fade from old to new predictions can be seen.
+Original credit to Max Berktold.
+-
+
- Parameters: +
-
+
progress –
+colors –
+
+
Returns:
+
-
+
- +agentlib_mpc.utils.plotting.mpc.plot_admm(series: Series, ax: Axes, plot_actual_values: bool = True, plot_predictions: bool = False, step: bool = False, convert_to: Literal['seconds', 'minutes', 'hours', 'days'] = 'seconds')[source] +
-
+
- Parameters: +
-
+
series – A column of the MPC results Dataframe
+ax – which Axes to plot on
+plot_actual_values – whether the closed loop actual values at the start of each +optimization should be plotted (default True)
+plot_predictions – whether all predicted trajectories should be plotted
+step –
+convert_to – Will convert the index of the returned series to the specified unit +(seconds, minutes, hours, days)
+
+
Returns:
+
-
+
- +agentlib_mpc.utils.plotting.mpc.plot_mpc(series: Series, ax: Axes, plot_actual_values: bool = True, plot_predictions: bool = False, step: bool = False, convert_to: Literal['seconds', 'minutes', 'hours', 'days'] = 'seconds')[source] +
-
+
- Parameters: +
-
+
series – A column of the MPC results Dataframe
+ax – which Axes to plot on
+plot_actual_values – whether the closed loop actual values at the start of each +optimization should be plotted (default True)
+plot_predictions – whether all predicted trajectories should be plotted
+step –
+convert_to – Will convert the index of the returned series to the specified unit +(seconds, minutes, hours, days)
+
+
Returns:
+
+
+
+
+
+
+
+
+
+
+
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+-
+
- agentlib_mpc package
-
+
- Subpackages
-
+
- agentlib_mpc.data_structures package
-
+
- Submodules +
- agentlib_mpc.data_structures.admm_datatypes module +
- agentlib_mpc.data_structures.casadi_utils module +
- agentlib_mpc.data_structures.coordinator_datatypes module +
- agentlib_mpc.data_structures.interpolation module +
- agentlib_mpc.data_structures.ml_model_datatypes module +
- agentlib_mpc.data_structures.mpc_datamodels module +
+ - agentlib_mpc.models package + +
- agentlib_mpc.modules package + +
- agentlib_mpc.optimization_backends package + +
- agentlib_mpc.utils package + +
+ - agentlib_mpc.data_structures package
+ - Subpackages
+
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+ Index
+ +
+ A
+ | B
+ | C
+ | D
+ | E
+ | F
+ | G
+ | H
+ | I
+ | J
+ | K
+ | L
+ | M
+ | N
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+ Welcome to agentlib_mpc’s documentation!
+Content of the documentation
+
+
+-
+
- Installation +
- Tutorial + +
- Package Reference + +
Readme of the agentlib
+agentlib_mpc
+
+
+
+This is a plugin for AgentLib. +Includes functions for modeling with CasADi, and using those models in nonlinear MPC, central and distributed (based on ADMM).
+See examples and the tutorial in the docs. +Best example to start is an MPC for a single air conditioned room.
+Installation
+Install with:
+pip install agentlib_mpc
+To install with full dependencies (recommended), run:
+pip install agentlib_mpc[full]
+Optional Dependencies
+AgentLib_MPC has a number of optional dependencies:
+-
+
fmu: Support simulation of FMU models (https://fmi-standard.org/).
+ml: Use machine learning based NARX models for MPC. Currently supports neural networks, gaussian process regression and linear regression. Installs tensorflow, keras and scikit-learn.
+interactive: Utility functions for displaying mpc results in an interactive dashboard. Installs plotly and dash.
+
Install these like
+pip install agentlib_mpc[ml]
+Citing AgentLib_MPC
+For now, please cite the base framework under https://github.com/RWTH-EBC/AgentLib.
+A preprint is available under http://dx.doi.org/10.2139/ssrn.4884846 and can be cited as:
+++Eser, Steffen and Storek, Thomas and Wüllhorst, Fabian and Dähling, Stefan and Gall, Jan and Stoffel, Phillip and Müller, Dirk, A Modular Python Framework for Rapid Development of Advanced Control Algorithms for Energy Systems. Available at SSRN: https://ssrn.com/abstract=4884846 or http://dx.doi.org/10.2139/ssrn.4884846
+
When using AgentLib-MPC, please remember to cite other tools that you are using, for example CasADi or IPOPT.
+Acknowledgments
+We gratefully acknowledge the financial support by Federal Ministry for Economic Affairs and Climate Action (BMWK), promotional reference 03ET1495A.
+
Indices and tables
+-
+
- +
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+ Installation
+Clone agentlib_mpc into a local directory. +Then, run:
+pip install .\
+If you want to contribute code and help develop this repository, first clone the repo and then install it via an .egg:
+pip install -e .
+
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+Search.setIndex({"docnames": ["PackageReference", "agentlib_mpc", "agentlib_mpc.data_structures", "agentlib_mpc.models", "agentlib_mpc.modules", "agentlib_mpc.modules.dmpc", "agentlib_mpc.modules.dmpc.admm", "agentlib_mpc.optimization_backends", "agentlib_mpc.optimization_backends.casadi_", "agentlib_mpc.optimization_backends.casadi_.core", "agentlib_mpc.utils", "agentlib_mpc.utils.plotting", "code/agentlib_mpc", "code/agentlib_mpc.data_structures", "code/agentlib_mpc.models", "code/agentlib_mpc.modules", "code/agentlib_mpc.modules.InputPrediction", "code/agentlib_mpc.modules.dmpc", "code/agentlib_mpc.modules.dmpc.admm", "code/agentlib_mpc.modules.estimation", "code/agentlib_mpc.modules.ml_model_training", "code/agentlib_mpc.optimization_backends", "code/agentlib_mpc.optimization_backends.casadi_", "code/agentlib_mpc.optimization_backends.casadi_.core", "code/agentlib_mpc.utils", "code/agentlib_mpc.utils.plotting", "code/modules", "index", "installation", "modules", "tutorials/ADMM", 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14, 15, 17, 18, 19, 20, 21, 22, 23], "inform": [2, 4, 5, 7, 13, 14, 15, 17, 21, 22, 30, 33], "about": [2, 13, 14, 30], "phy": [2, 13], "flatten": [2, 12, 13, 14], "arrai": [2, 13, 14, 25], "flat": [2, 4, 6, 13, 15, 18, 19], "tupl": [2, 4, 6, 9, 11, 13, 14, 15, 18, 19, 20, 22, 23, 25, 31], "primal": [2, 6, 11, 13, 18, 25], "dual": [2, 6, 11, 13, 18, 25], "residu": [2, 6, 11, 13, 18, 25], "last": [2, 10, 13, 22, 24, 30, 31], "param": [2, 3, 4, 5, 6, 10, 13, 14, 15, 17, 18, 19, 24], "regist": [2, 4, 6, 7, 13, 15, 18, 19, 21], "specifi": [2, 3, 7, 9, 10, 11, 13, 14, 20, 21, 22, 23, 24, 25, 30, 31], "structur": [2, 12, 13, 14, 20, 30], "an": [2, 3, 4, 6, 9, 10, 11, 13, 14, 15, 17, 18, 19, 20, 22, 23, 24, 25, 27, 28, 30, 33], "agentvari": [2, 4, 5, 6, 7, 9, 13, 14, 15, 16, 17, 18, 19, 20, 21, 23], "valu": [2, 3, 5, 6, 7, 9, 10, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 30, 31], "It": [2, 4, 13, 14, 15, 31], "effici": [2, 13], "sent": [2, 6, 13, 15, 18, 20, 30], "deseri": [2, 12, 13, 14], "classmethod": [2, 9, 13, 14, 19, 23], "serial": [2, 3, 12, 13, 14, 20, 22], "self": [2, 6, 9, 13, 14, 18, 23, 31], "byte": [2, 13], "can": [2, 3, 4, 7, 9, 13, 14, 15, 18, 20, 21, 22, 23, 25, 27, 30, 31], "commun": [2, 13, 15, 17, 18, 31, 33], "info": [2, 4, 13, 15, 17, 18, 19, 30], "role": [2, 7, 13, 21], "problem": [2, 3, 4, 5, 7, 8, 9, 13, 14, 15, 17, 19, 21, 22, 23, 30, 31], "var_ref": [2, 4, 5, 6, 7, 8, 9, 12, 13, 15, 17, 18, 19, 21, 22, 23], "config": [2, 3, 4, 5, 6, 7, 8, 9, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 31, 33], "basemoduleconfigclass": [2, 13], "creat": [2, 3, 4, 6, 7, 8, 9, 11, 13, 14, 15, 18, 19, 20, 21, 22, 23, 25, 30, 33], "instanc": [2, 3, 10, 13, 14, 24], "from": [2, 3, 4, 5, 6, 7, 9, 10, 13, 14, 15, 17, 18, 19, 20, 21, 22, 23, 24, 25, 30, 31], "pydant": [2, 3, 4, 5, 6, 7, 9, 13, 14, 15, 16, 17, 18, 19, 20, 21, 23], "kei": [2, 4, 6, 7, 9, 13, 15, 17, 18, 21, 22, 23, 24, 25, 31], "correspond": [2, 4, 7, 9, 13, 15, 21, 23], "etc": [2, 4, 6, 9, 10, 13, 15, 18, 19, 23, 24], "alia": [2, 5, 6, 7, 8, 9, 13, 14, 16, 17, 18, 19, 20, 21, 22, 23, 30, 31], "send": [2, 4, 6, 13, 15, 18, 20, 30, 31], "receiv": [2, 6, 13, 15, 17, 18, 20, 30, 31], "store": [2, 3, 13, 14, 30], "sort": [2, 4, 6, 9, 13, 15, 18, 23], "dataclass": [2, 13], "enum": [2, 6, 13, 14, 18], "help": [2, 13, 28, 30, 31], "casadibackend": [2, 8, 9, 13, 22, 23], "extra": [2, 3, 4, 5, 6, 7, 9, 13, 14, 15, 16, 17, 18, 19, 20, 21, 23], "forbid": [2, 3, 4, 5, 6, 7, 9, 13, 14, 15, 16, 17, 18, 19, 20, 21, 23], "field": [2, 3, 4, 5, 6, 7, 9, 13, 14, 15, 16, 17, 18, 19, 20, 21, 23], "3": [2, 6, 7, 9, 13, 14, 18, 21, 23, 31], "ge": [2, 4, 5, 6, 13, 15, 17, 18, 19], "1": [2, 5, 6, 7, 9, 10, 11, 13, 14, 15, 16, 17, 18, 20, 21, 23, 24, 25, 30, 31], "le": [2, 13], "9": [2, 13, 16], "qualnam": [2, 6, 13, 14, 18], "start": [2, 3, 4, 5, 6, 10, 13, 14, 15, 17, 18, 22, 24, 25, 27, 31, 33], "boundari": [2, 4, 6, 9, 13, 14, 15, 18, 19, 23, 30, 31], "casadi": [2, 3, 8, 9, 13, 14, 22, 23, 27, 32, 33], "mx": [2, 3, 8, 9, 13, 14, 22, 23], "namedtupl": [2, 13], "number": [2, 5, 6, 10, 11, 13, 14, 17, 18, 20, 24, 25, 27], "sx": [2, 3, 9, 13, 14, 23], "sparsiti": [2, 3, 9, 13, 14, 23], "dm": [2, 3, 9, 13, 14, 23], "0": [2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 30, 31], "2": [2, 3, 6, 7, 8, 9, 13, 14, 16, 18, 21, 22, 23, 31], "necessari": [2, 7, 9, 13, 14, 21, 23], "dure": [2, 9, 13, 14, 15, 23], "discret": [2, 4, 6, 7, 8, 13, 15, 18, 21, 22], "optimizationparamet": [2, 8, 9, 13, 22, 23], "optimizationvari": [2, 8, 9, 13, 22, 23], "pathlib": [2, 7, 9, 13, 14, 15, 20, 21, 23], "path": [2, 4, 7, 9, 10, 11, 13, 14, 15, 19, 20, 21, 23, 24, 25, 30], "log": [2, 13, 14, 15, 17, 18, 30, 31], "nlp": [2, 8, 9, 13, 22, 23, 30], "construct": [2, 13], "equal": [2, 6, 9, 13, 14, 18, 22, 23, 30, 31], "problem_typ": [2, 13], "liter": [2, 10, 13, 20, 24, 25], "qp": [2, 13], "basemodel": [2, 7, 13, 14, 21], "code": [2, 9, 13, 20, 23, 24, 28], "compil": [2, 9, 13, 23], "current": [2, 3, 4, 7, 9, 13, 14, 15, 16, 20, 21, 22, 23, 24, 27, 30, 31, 33], "onli": [2, 4, 6, 7, 10, 11, 13, 14, 15, 17, 18, 20, 21, 24, 25, 30, 31], "work": [2, 6, 7, 9, 13, 14, 18, 21, 23, 27, 31, 33], "window": [2, 9, 13, 20, 23], "requir": [2, 4, 5, 7, 9, 13, 14, 15, 17, 18, 19, 20, 21, 23, 30, 31], "batch": [2, 9, 13, 14, 23], "file": [2, 4, 6, 7, 9, 10, 11, 13, 14, 15, 17, 18, 19, 20, 21, 23, 24, 25, 30, 31, 33], "know": [2, 9, 13, 23, 30], "how": [2, 10, 13, 14, 24, 30, 31, 33], "setup": [2, 7, 9, 13, 21, 23, 31], "visual": [2, 13], "studio": [2, 13], "command": [2, 13], "line": [2, 11, 13, 25], "The": [2, 3, 4, 7, 9, 10, 13, 14, 15, 17, 18, 19, 20, 21, 22, 23, 24, 25, 30, 31, 33], "dll": [2, 13], "rais": [2, 3, 13, 14, 20, 22, 24, 30], "typeerror": [2, 13, 22, 24], "filenotfounderror": [2, 13], "runtimeerror": [2, 13, 30], "dmpc": [2, 12, 13, 15], "dtype": [2, 13], "float64": [2, 13], "within": [2, 9, 13, 15, 17, 18, 20, 23, 30, 31], "keep": [2, 13, 30], "track": [2, 13], "status": [2, 13], "its": [2, 3, 6, 13, 14, 18, 30, 31], "specif": [2, 5, 13, 14, 17, 20, 31], "time": [2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 13, 14, 15, 17, 18, 19, 20, 21, 22, 23, 24, 25, 27, 30, 31, 33], "seri": [2, 4, 6, 10, 13, 15, 18, 19, 20, 22, 24, 25, 27, 31, 33], "registr": [2, 5, 6, 13, 17, 18], "between": [2, 5, 6, 9, 13, 17, 18, 23, 30, 31], "defin": [2, 3, 4, 5, 6, 9, 11, 13, 14, 15, 16, 17, 18, 19, 20, 22, 23, 25, 30, 31], "extend": [2, 13, 14], "implement": [2, 3, 4, 5, 6, 7, 9, 13, 14, 15, 17, 18, 19, 20, 21, 23, 30, 31], "allow": [2, 13, 14, 15, 19, 30, 31], "5": [2, 4, 6, 10, 13, 15, 18, 19, 20, 24, 31], "predict": [2, 4, 6, 9, 12, 13, 14, 15, 16, 18, 20, 23, 25, 27, 33], "60": [2, 4, 13, 15, 18, 19, 30, 31], "step": [2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 17, 18, 19, 20, 21, 22, 23, 24, 25, 31], "ready": [2, 13], "mhe": [2, 12, 13, 15, 21], "timestamp": [2, 3, 5, 13, 14, 16, 17, 18, 20, 22, 24], "unit": [2, 3, 5, 10, 13, 14, 16, 17, 18, 20, 24, 25, 31], "Not": [2, 3, 5, 13, 14, 17, 18, 20], "descript": [2, 3, 5, 12, 13, 14, 16, 17, 18, 20, 31], "inf": [2, 3, 5, 8, 9, 13, 14, 16, 17, 18, 20, 22, 23], "clip": [2, 3, 5, 13, 14, 16, 17, 18, 20], "allowed_valu": [2, 3, 5, 13, 14, 16, 17, 18, 20], "ani": [2, 3, 4, 5, 6, 13, 14, 15, 16, 17, 18, 19, 20, 31, 33], "module_id": [2, 5, 12, 13, 15, 16, 17, 18, 20, 30, 31], "rdf_class": [2, 5, 13, 16, 17, 18, 20], "interpolationmethod": [2, 12, 13, 15, 20, 24], "linear": [2, 12, 13, 14, 15, 20, 22, 24, 27], "arg": [2, 13, 25], "kwarg": [2, 3, 13, 14], "protocol": [2, 13, 14], "datafram": [2, 4, 6, 7, 8, 9, 10, 11, 13, 15, 18, 19, 20, 21, 22, 23, 24, 25, 30], "turn": [2, 9, 13, 23, 31], "weight": [2, 12, 13, 14, 15, 18, 19, 31], "via": [2, 13, 28, 31, 33], "causal": [3, 10, 14, 24], "sim_tim": [3, 12, 14], "_sym": [3, 14], "variou": [3, 14], "attribut": [3, 4, 5, 6, 9, 14, 15, 16, 17, 18, 19, 20, 23, 31], "To": [3, 9, 14, 23, 25, 27, 30, 31], "your": [3, 4, 6, 14, 15, 18, 19, 31], "own": 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18], "g": [3, 4, 6, 7, 9, 10, 14, 15, 16, 18, 19, 21, 23, 24, 30, 31], "p": [3, 9, 14, 23], "set": [3, 4, 6, 7, 14, 15, 18, 20, 21, 30, 31], "provid": [3, 4, 6, 7, 9, 10, 14, 15, 18, 19, 21, 22, 23, 24, 30, 31], "must": [3, 4, 5, 6, 14, 15, 17, 18, 19, 20, 30, 31], "order": [3, 4, 5, 6, 7, 8, 9, 13, 14, 15, 17, 18, 19, 20, 21, 22, 23], "save": [3, 4, 5, 6, 7, 8, 9, 12, 13, 14, 15, 17, 18, 19, 20, 21, 22, 23, 30, 31], "check": [3, 4, 6, 7, 9, 14, 15, 18, 19, 20, 21, 23], "abstract": [3, 4, 5, 6, 7, 9, 14, 15, 17, 18, 19, 20, 21, 23, 27], "modelconfig": [3, 14], "validate_assign": [3, 4, 5, 6, 14, 15, 16, 17, 18, 19, 20], "true": [3, 4, 5, 6, 9, 10, 13, 14, 15, 16, 17, 18, 19, 20, 23, 24, 25, 30, 31], "arbitrary_types_allow": [3, 4, 5, 6, 14, 15, 16, 17, 18, 19, 20], "valid": [3, 4, 5, 6, 7, 9, 13, 14, 15, 16, 17, 18, 19, 20, 21, 23, 30], "include_default_model_vari": [3, 14], "context": [3, 4, 5, 6, 14, 15, 16, 17, 18, 19, 20, 31], "we": [3, 4, 5, 6, 10, 13, 14, 15, 16, 17, 18, 19, 20, 24, 27, 30, 31], "need": [3, 4, 5, 6, 9, 14, 15, 16, 17, 18, 19, 20, 22, 23, 24, 30, 31], "both": [3, 4, 5, 6, 14, 15, 16, 17, 18, 19, 20, 30], "privat": [3, 4, 5, 6, 14, 15, 16, 17, 18, 19, 20], "call": [3, 4, 5, 6, 9, 14, 15, 16, 17, 18, 19, 20, 23, 31], "user": [3, 4, 5, 6, 14, 15, 16, 17, 18, 19, 20, 31], "_alg": [3, 14], "format": [3, 11, 14, 20, 25, 30], "string": [3, 9, 14, 20, 23], "_ode": [3, 14], "indent": [3, 14], "standard": [3, 7, 9, 14, 16, 21, 23, 27, 30, 31], "arithmet": [3, 14], "oper": [3, 14, 30], "so": [3, 7, 9, 13, 14, 21, 22, 23, 25, 30, 31], "symbol": [3, 8, 9, 14, 22, 23], "": [3, 7, 9, 14, 15, 16, 19, 21, 23, 24, 30, 31], "ensur": [3, 6, 14, 15, 18], "each": [3, 4, 5, 6, 7, 8, 9, 10, 11, 14, 15, 17, 18, 20, 21, 22, 23, 24, 25, 30], "dimens": [3, 9, 14, 20, 23], "consist": [3, 9, 14, 23, 30, 31], "basempc": [4, 12, 15], "basemodul": [4, 5, 15, 17, 19, 20], "A": [4, 7, 9, 10, 15, 19, 20, 21, 22, 23, 24, 25, 27, 31], "more": [4, 15, 30], "follow": [4, 14, 15, 24, 30], "assert_mpc_variables_are_in_model": [4, 5, 6, 12, 15, 17, 18], "whether": [4, 6, 7, 9, 10, 14, 15, 18, 20, 21, 23, 24, 25], "assert_subset": [4, 12, 15], "mpc_name": [4, 15], "model_nam": [4, 15], "message_head": [4, 15], "assert": [4, 6, 15, 18], "ref_kei": [4, 15], "subset": [4, 15], "usual": [4, 9, 15, 22, 23, 24, 30, 31], "obtain": [4, 15, 22, 24, 30], "print": [4, 15], "out": [4, 15], "error": [4, 9, 14, 15, 17, 18, 20, 23, 25], "portion": [4, 15, 22, 24], "cleanup_result": [4, 5, 6, 12, 15, 17, 18, 19], "delet": [4, 6, 7, 15, 18, 19, 20, 21], "e": [4, 6, 7, 9, 10, 14, 15, 18, 19, 20, 21, 22, 23, 24, 28, 30, 31], "collect_variables_for_optim": [4, 5, 6, 12, 15, 17, 18, 19], "note": [4, 6, 14, 15, 18, 19, 31], "ref": [4, 6, 15, 18, 19], "put": [4, 6, 15, 18, 19, 31], "them": [4, 6, 14, 15, 18, 19, 30, 31], "dictionari": [4, 6, 14, 15, 18, 19, 20, 25, 31], "get_result": [4, 5, 6, 12, 15, 17, 18, 19, 30, 31], "read": [4, 6, 15, 18, 19, 30], "were": [4, 6, 15, 18, 19], "stat": [4, 6, 8, 9, 15, 18, 19, 22, 23, 25], "getter": [4, 15], "pre_computation_hook": [4, 5, 12, 15, 17], "everi": [4, 5, 9, 15, 16, 17, 23, 31], "comput": [4, 5, 6, 14, 15, 17, 18, 20, 31], "befor": [4, 5, 6, 9, 14, 15, 17, 18, 20, 23, 30, 31], "overwrit": [4, 5, 7, 14, 15, 17, 21, 31], "deriv": [4, 5, 15, 17], "subclass": [4, 5, 15, 17], "you": [4, 5, 14, 15, 17, 19, 27, 28, 31], "want": [4, 5, 10, 13, 15, 17, 24, 28, 31], "some": [4, 5, 11, 14, 15, 17, 19, 25, 30, 31], "action": [4, 5, 15, 17, 27], "process": [4, 5, 6, 12, 14, 15, 16, 17, 18, 19, 20, 27, 30], "sync": [4, 5, 6, 15, 17, 18, 19, 20, 30], "other": [4, 5, 6, 7, 14, 15, 16, 17, 18, 19, 20, 21, 27, 30, 31], "whole": [4, 5, 6, 15, 17, 18, 19, 20], "ma": [4, 5, 6, 15, 17, 18, 19, 20, 30, 31], "re_init_optim": [4, 12, 15], "re": [4, 15], "static": [4, 14, 15, 19, 22], "read_results_fil": [4, 12, 15, 19], "csv": [4, 6, 11, 13, 15, 18, 19, 25, 30], "register_callback": [4, 5, 12, 15, 17, 19, 20], "init_optim": [4, 15], "callback": 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+ Alternating Direction Method of Multipliers
+In this section, we will learn how to use agentlib for distributed MPC using +the alternating direction method of multipliers (ADMM). The required example +files are located in ‘examples/admm/’. They include two +main scripts and two directories with models and config files respectively. +We simulate the same system as before, however this time the AHU determines +its mass flow without knowing the system behaviour of the room, creating the +need for coordination.
+Main script
+There are three main scripts. One runs a local version of the ADMM algorithm, which +operates within a single thread and is suited for simulation and testing. The other +one runs the agents in separate python processes and communicates through MQTT. +The last one implements a coordinated ADMM, which can be useful, since it helps +unify parameter setting and provides better convergence criteria. +Here, we will look at the Realtime implementation using multiprocessing.
+from agentlib.utils.multi_agent_system import MultiProcessingMAS
+import logging
+import matplotlib.pyplot as plt
+The only new import this time is the
+MultiProcessingMAS utility. Unlike the LocalMASAgency we used before,
+the MultiprocessingMAS spawns a separate python process for each agent,
+allowing
+for the true parallelism that would take place in a real-world MAS. However,
+this also requires the condition that simulations are performed in Realtime,
+since time is now the common variable between systems that keeps them in sync.
+Now onto the main script.
env_config = {"rt": True,
+ "strict": True,
+ "factor": 0.1,
+ "t_sample": 60}
+
+mas = MultiProcessingMAS(agent_configs=['configs\\cooler.json',
+ 'configs\\cooled_room.json',
+ 'configs\\simulator.json'],
+ env=env_config,
+ variable_logging=True)
+mas.run(until=until)
+results = mas.get_results()
+As explained, we choose a Realtime environment, set it to strict
+(RuntimeError will be raised if simulation is too slow), and give it a
+factor of 0.1 to speed it up. Finally, we set t_sample to 60, so we
+will save our results in an interval of 60 seconds. Then, we provide our MAS
+three configs - one for the room controller, one for the AHU controller and
+one to simulate the full system.
System models
+There are three models. The simulation model and the room model are similar to +the models we used in the MPC examples before, with the main difference +being in the constraints and cost function. The simulation model omits the +MPC-related parts of the model, while the room model is the same as before, +with only the air mass flow term missing from the cost function. The cooler +model on the other hand is a simple input equals output model of the mass +flow, including the cost function term that was removed from the room model. +Therefore, we created a situation, where the room is not explicitly +penalized for usage of the mass flow anymore, but instead a separate system is.
+Communication via MQTT
+For this example, we are not providing the configs in the python script
+itself, but store them separately as json. Both agent configs and configs of
+single modules can be stored in separate json. Let’s look at the config file
+configs/communicators/cooled_room_mqtt.json.
+Since the agents are now using separate processes, we cannot use the
+local_broadcast communicator anymore. Instead, we are using the MQTT
+communicator from the agentlib. The config for an MQTT commincator is a bit
+more complicated than the local_broadcast. After providing
+an id and specifying the type to “mqtt”, there are some parameters to provide:“url” and “subscriptions”. For small test scripts, the url from the snippet
+below will do.
{
+ "module_id": "Ag1Com",
+ "type": "mqtt",
+ "url": "mqtt://test.mosquitto.org",
+ "subscriptions": ["Cooler", "Simulation"]
+}
+The subscriptions are a list of agent ids the agent is subscribed to. For more info
+on MQTT topics visit e.g.
+here.
+In agentlib, the mqtt communicator sends messages under a topic consisting of
+“/agentlib/# is a wildcard, so by specifying the topics
+in the way above, the agent will receive all messages from the Cooler agent
+and the Simulation agent. The resulting communication structure can be seen
+in the image below:
+ADMM config
+tbd +Let’s look at the beginning of the config for the room agent. First of all, +we see a file path in the list of modules, which points to our communicator +config. The root of relative filepaths is the directory, where the main +script is run.
+{
+ "id": "CooledRoom",
+ "modules": [
+ "configs/communicators/cooled_room_mqtt.json",
+ {
+ "module_id": "admm_module",
+ "type": "admm",
+ "optimization_backend": {
+ "type": "casadi_admm",
+ "model": {
+ "type": {
+ "file": "models/ca_room_model.py",
+ "class_name": "CaCooledRoom"
+ }
+ },
+ "solver": {
+ "name": "ipopt",
+ "options": {
+ "print_level": 0
+ }
+ },
+ "results_file": "admm_opt.csv"
+ },
+We can see, that the module type for the controller now reads “admm”, and the
+optimization backend type is “casadi_admm”. We can also see, that there are
+some new options set for the optimization_backend, namely the solver option.
+The numerical solver name can be chosen from a list of supported solvers
+(currently supported are ipopt, sqpmethod, qpoases). For most purposes,
+IPOPT will be the solver of choice. However, we can change the default
+options for the chosen solver. To see applicable options, please refer to
+the documentation of the solver. For IPOPT, an overview of all the options
+can be found on the official site.
+In our
+case, we set the print_level to 0 to avoid clutter in the console output.
+We also specify a results_file, so we save detailed information about each
+NLP solution in csv format, readable e.g. as a multi-indexed pandas
+Dataframe.
After providing parameters and inputs in the usual way, let’s +have a look at what changed between the central MPC and the ADMM.
+
+
+Note
+The prediction_horizon, time_step and penalty_factor parameters of
+the ADMM module affect the strucuture of the optimization problem and
+need to be identical for all modules taking part in the ADMM algorithm.
+Currently, this is not validated automatically, so care should be taken when
+writing the config. The timeout, registration_period and
+admm_iter_max parameters should also be the same our similar.
"controls": [
+],
+"states": [
+ {
+ "name": "T_0",
+ "value": 298.16,
+ "ub": 303.15,
+ "lb": 288.15
+ }
+],
+"couplings": [
+ {
+ "name": "mDot_0",
+ "alias": "mDotCoolAir",
+ "value": 0.05,
+ "ub": 0.1,
+ "lb": 0}
+The controls list is now empty, as the air mass flow is not determined by
+the room anymore. Instead, it is now listed under the new type couplings.
+The couplings are optimization variables, so they should also have upper and
+lower boundaries. ADMM with agentlib is based on consensus, meaning partial
+systems that have to agree on shared variables are optimized. The shared
+variables are identified through their alias. In this
+example, all agents that define a coupling with alias “mDotCoolAir” share
+this variable. The value for the state “T_0” is obtained from the Simulation
+agent, so care should be taken to make sure the alias matches. In this case,
+the default alias of “T_0” will match, since the name exists in the
+simulation model.
Now let’s see the config on the side of the cooler:
+ "controls": [
+ {
+ "name": "mDot",
+ "value": 0.02,
+ "ub": 0.1,
+ "lb": 0
+ }
+ ],
+ "states": [
+ ],
+ "couplings": [
+ {
+ "name": "mDot_out",
+ "alias": "mDotCoolAir",
+ "value": 0.05
+ }
+ ]
+},
+We can see, that there are two variables of interest, one in controls and
+one in couplings. The control “mDot” is the actuation that is sent to the
+simulator after optimization. Therefore, the alias of the mass flow in the
+Simulation agent must match “mDot”. The coupling “mDot_out” is assigned with
+the alias “mDotCoolAir”, which matches the coupling in the room agent.
+
+ Why do I have to declare two variables, if they mean the same thing? +
++Because the models follow the FMU standard, where variables are divided +between inputs, outputs, locals/states and parameters. In this case, our +cooler model takes a mass flow as an input ("mDot" in this case) and +produces the same mass flow as an output to other systems ("mDot_out" in +this case). In a more complex setting, the cooler might have an internal PID +controller to set the mass flow to its correct value. In that case, "mDot" +would be setpoint of the mass flow, and "mDot_out" would be the actual mass +flow. ++
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+ Model Predictive Control
+Creating an MPC in agentlib with CasADi
+To run a model predictive controller, a system model for use in optimization +is required. What model types are available depends on the chosen +optimization backend. In this section, creating an MPC with a CasADi backend +is explained. Open the ‘examples/one_room_mpc/physical/simple_mpc.py’ example.
+Imports
+As usual, let’s look at the imports first.
+import logging
+from typing import List
+import matplotlib.pyplot as plt
+from agentlib.models.casadi_model import CasadiModel, CasadiInput, CasadiState, \
+ CasadiParameter, CasadiOutput
+from agentlib.utils.multi_agent_system import LocalMASAgency
+We import logging as usual and
+typing is used to annotate the optimzation model we will be creating,
+and matplotlib is used to plot the results. Next, we import the CasadiModel and
+some CasadiVariables. We will use these to specify an agentlib-style
+CasadiModel. Finally, we import the LocalMASAgency utility. This can be used
+to conveniently create and run your local MAS, without creating the agents
+and their environment by hand.
Model creation
+Now let’s see how we can create an optimization model. The model contains +the physical system dynamics, as well as the cost function and additional +constraints on the system.
+In this example, we will create a model of a room, which is under a constant +heat load and can be controlled by changing the mass flow of cool air from +an air handling unit.
+Creating a custom CasadiModel is similar to creating a module.
+-
+
Creating a class that inherits from
+CasadiModelConfig-
+
Declare the model variables in the config class
+-
+
inputs
+outputs
+states
+parameters
+
+
+Creating a class that inherits from
+CasadiModel-
+
Assign the config with
config: <<ConfigClass>>
+Define model equations by overwriting the
setup_systemmethod
+
+
Variable declaration
+Let’s see, how we declare the variables required for our simple room model. +Since modeling in agentlib is based on the FMU-standard, we divide our +variables into inputs, outputs, parameters and locals (called states to +avoid clash with the python builtin locals). First, we need to create a custom +config for our CasadiModel.
+class MyCasadiModelConfig(CasadiModelConfig):
+ inputs: List[CasadiInput] = [
+ # controls
+ CasadiInput(name="mDot", value=0.0225, unit="K", description="Air mass flow into zone"),
+
+ # disturbances
+ CasadiInput(name="load", value=150, unit="W", description="Heat "
+ "load into zone"),
+ CasadiInput(name="T_in", value=290.15, unit="K", description="Inflow air temperature"),
+
+ # settings
+ CasadiInput(name="T_upper", value=294.15, unit="K", description="Upper boundary (soft) for T."),
+ ]
+
+ states: List[CasadiState] = [
+ # differential
+ CasadiState(name="T", value=293.15, unit="K", description="Temperature of zone"),
+
+ # algebraic
+
+ # slack variables
+ CasadiState(name="T_slack", value=0, unit="K", description="Slack variable of temperature of zone")
+ ]
+
+ parameters: List[CasadiParameter] = [
+ CasadiParameter(name="cp", value=1000, unit="J/kg*K", description="thermal capacity of the air"),
+ CasadiParameter(name="C", value=100000, unit="J/K",
+ description="thermal capacity of zone"),
+ CasadiParameter(name="s_T", value=1, unit="-", description="Weight for T in constraint function"),
+ CasadiParameter(name="r_mDot", value=1, unit="-",
+ description="Weight for mDot in objective function")
+ ]
+ outputs: List[CasadiOutput] = [
+ CasadiOutput(name='T_out', unit="K", description="Temperature of zone")
+ ]
+Our room model has four inputs. These include the inputs of the physical +system, being the air mass flow from the AHU, the temperature of this mass flow and +the load on the system. We also count the upper room temperature limit as an +input, since it should be settable by the occupants of the room. To declare an input, we put a CasadiInput object into a list inputs. A +variable always needs a name. You can also give it a value, which will be +used if no other value is provided at Runtime. The unit and description +parameters currently serve no purpose, but can be helpful to readers of the +model. Next we define the states. For one, that is the temperature of the room. +Since we use soft constraints to enforce an adequate room temperature, we also have to include +a slack variable.
+
+
+Note
+States in the context of an AgentLib model refers to all variables that +are local to a model. All differential variables have to be declared as +states, but not all states need to be associated with a +differential equation.
+Next, we have the parameters. These include the specific thermal capacity of +air, the thermal capacity of the room and two weights for the cost function. +Finally, we specify an output of the model. It is not required for the MPC +in this example, but can be useful for situations, where one might want to +use the same model for optimization and simulation. Outputs always need to be +associated with an algebraic equation.
+Equation and constraints
+After specifying a config, we can write the model class itself, which containts the
+dynamics. First, it is important to specify the config_type attribute of the
+class and set it to the config class we defined. The model equations and constraints are specified in the setup_system method.
+We can access the variables defined above by referencing self.<name>.
+Differential equations are associated with a variable by setting the ode
+attribute of that variable. In the same way, algebraic equations can be
+defined by setting the alg attribute.
class MyCasadiModel(CasadiModel):
+
+ config: MyCasadiModelConfig
+
+ def setup_system(self):
+ # Define ode
+ self.T.ode = self.cp * self.mDot / self.C * \
+ (self.T_in - self.T) + \
+ self.load / self.C
+
+ # Define ae
+ self.T_out.alg = self.T
+
+ # Constraints: List[(lower bound, function, upper bound)]
+ self.constraints = [
+
+ # soft constraints
+ (0, self.T + self.T_slack, self.T_upper),
+
+ ]
+
+ # Objective function
+ objective = sum([
+ self.r_mDot * self.mDot,
+ self.s_T * self.T_slack ** 2,
+ ])
+
+ return objective
+Constraints can be added to the model through the constraints attribute.
+It should be defined as a list of tuples, with the lower bound coming first,
+the constraint function coming second and the upper bound coming last.
+Equality constraints can be added by setting upper and lower bound to the
+same value. Note that algebraic equations will also be converted to equality
+constraints internally. Here, we set one constraint to implement the soft
+constraint on the room temperature.
+
+ What's the difference between an algebraic equation and setting an +equality constraint? +
++Algebraic equations are explicit assignments to a CasadiOutput. They are considered when simulating the model or when doing MPC with it. +Constraints specified as tuples can be of implicit nature, however they are +ignored for simulation. The only limitation on constraints is, that +variables that make up the upper or lower bound cannot be used as +optimization variables in the MPC. ++
+
+Note
+Python intuition tells us self.<name> should not work, as we did not
+set the attribute.
+In the model base class of agentlib, the __get_attr__ method is written
+in a way that allows access to all variables that are defined in the
+Config class of the model.
Finally, we can specify and return the objective function in the same way as
+the other equations. We use the sum() function from python to
+improve readability.
Configuration of the multi-agent-system
+Let’s look at the environment config first.
+ENV_CONFIG = {"rt": False,
+ "t_sample": 60}
+This time, we specify ‘rt’ +(=Realtime) as False, meaning we want the simulation to run as fast as possible. +The ‘t_sample’ option specifies the time step in which the interal clock of +the environment ticks. This is relevant e.g. for classical controllers like +PID. It will also affect the sampling with which results are saved.
+Below is the config for the MPC agent. As before, we specify an “id” and a +list of modules, with the first one being a local_broadcast communicator. +Then, we add the MPC module. We specify “mpc” as the type, and then add the +other options. A central part of the MPC is its _optimizationbackend. The +optimization backend is specified by another dictionary, always consisting +of “type” and “model”. The model will usually be user-specified and usually +is provided with the same syntax of “file” and “class_name” as the custom +module in the PingPong example. The optimization backend also takes an +option “discretization_options”, however we will look at that later.
+AGENT_MPC = {"id": "myMPCAgent",
+ "modules": [
+ {"module_id": "Ag1Com",
+ "type": "local_broadcast"},
+ {"module_id": "myMPC",
+ "type": "mpc",
+ "optimization_backend":
+ {"type": "casadi",
+ "model": {
+ "type": {"file": __file__,
+ "class_name": "MyCasadiModel"}},
+ ...
+ },
+ "time_step": 900,
+ "prediction_horizon": 5,
+ "parameters": [
+ {"name": "s_T", "value": 3},
+ {"name": "r_mDot", "value": 1},
+ ],
+ "inputs": [
+ {"name": "load", "value": 150},
+ {"name": "T_upper", "value": ub},
+ {"name": "T_in", "value": 290.15},
+ ],
+ "controls": [{"name": "mDot", "value": 0.02, "ub": 1, "lb": 0}],
+ "states": [{"name": "T", "value": 298.16, "ub": 303.15, "lb": 288.15}],
+ },
+ ]}
+ ]}
+Aside from that, “time_step” and “prediction_horizon” need to be specified.
+The other options the MPC module takes are parameters, inputs, controls and
+states . The time step should be provided
+in seconds. The states in the MPC config refer to differential variables,
+not to be confused with states in the model, which refer to any internal
+variables. Quantities declared in the module config are variables of the multi-agent-system and
+can be shared with other modules of the same agent, and communicated with
+other agents. All agent variables declared here must match - in name - their
+counterpart in the provided model. Controls, states and inputs must be
+provided fully matching the model. Outputs can be ignored if they are not
+required. Finally, parameters can be omitted, if a default value is provided
+in the model definition. Here, the weight parameters in the cost function
+are provided, as it might be required to change them. However, physical
+parameters such as the thermal capacity of air are taken from the model, as
+they are not expected to change. A variable is given a name and a value. For states, the value will determine
+the initial value of the differential variable, if it is not provided
+externally, for example by a simulation agent. Since controls and
+states are the variables of the optimization
+problem, boundaries should be provided via the keys “ub” and “lb”. These
+values are for constant hard boundaries. If time-variant boundaries are
+required, one should declare an additional variable and constraint in the model.
Running the multi-agent-system
+Now that we have our control agent setup, we need to simulate our system.
+The easiest way to do this in agentlib, is to setup an agent with a
+simulator module. Usually in agentlib, we would use an FMU to simulate a
+system. In this example, we will use the CasadiModel we created for the
+optimization. The resulting agent config is shown below.
AGENT_SIM = {"id": "SimAgent",
+ "modules": [
+ {"module_id": "Ag1Com",
+ "type": "local_broadcast"},
+ {"module_id": "room",
+ "type": "simulator",
+ "model": {"type": {"file": __file__,
+ "class_name": "MyCasadiModel"},
+ "states": [
+ {"name": "T", "value": 298.16}
+ ]},
+ "t_sample": 60,
+ "outputs": [
+ {"name": "T_out", "alias": "T"},
+ ],
+ "inputs": [
+ {"name": "mDot", "value": 0.02, "alias": "mDot"},
+ ]},
+ ]}
+The model type for the simulator is provided in the same manner as before. +However, here we can see, that we have the option to provide additional +variable options to the model. For example, here we change the starting +value of the temperature to a value above the upper (soft) boundary, so our +controller has to work.
+Then, inputs and outputs of the simulator. Every
+simulator needs to be provided with a sampling rate “t_sample” in seconds.
+Additionally, declare the output “T_out”. This is the first time we use the
+alias keyword. The alias is part of the duo of alias and source that uniquely
+define a variable within the MAS. The source is the combination of the agent_id
+and the module_id where the variable was defined. When expecting variables from
+another agent, only the agent_id has to be specified, and when the variable is from
+a module within the same agent, the module_id shoudld be specified. The
+alias is a name independent of the variable name in models (think of long Modelica names)
+and is consistent across agents for the same variable. By default, the name
+of a variable is also its alias. In the case of T_out however, we have to
+specify that this is the variable we want to send to the MPC for its initial
+state. Since the state in the MPC agent is named “T” (and by default has
+alias “T”), we have to set “T” as the alias for our model output. The model in turn takes the computed mass flow setpoint as input, hence we
+also have to declare the input. Since the names mDot already match between
+simulator and MPC, the explicit alias declaration is redundant in this case.
+Additionally, we set a default value for “mDot”, which is used before the
+first value is received from the MPC.
+
+ Why are some values specified in the model and others in the module? +
++Before every step, the simulator gets the current input values from the +agent and sets them to the model. After performing the step, the outputs +from the model are written to the agent. Since states per definition are +internal to the model, they are not set by the agent and their initial +values have to be changed in the model itself. The same goes for parameters. ++
With all of the setup done, we can now see our MAS run.
+Last time, we manually created the agents and the environment. This time, we
+use the LocalMASAgency utility to setup the system and save results. By
+setting the variable_logging option to True, time series of all agent
+variables present in the system will be saved. After running the MAS, we can
+retrieve and plot the results of our simulation.
def run_example(with_plots=True):
+ mas = LocalMASAgency(agent_configs=[AGENT_MPC, AGENT_SIM],
+ env=ENV_CONFIG,
+ variable_logging=True)
+ mas.run(until=10000)
+ results = mas.get_results()
+
+
+
+
+
+
+
+
+
+
+
\ No newline at end of file
diff --git a/docs/0.6.4/docs/tutorials/tutorial.html b/docs/0.6.4/docs/tutorials/tutorial.html
new file mode 100644
index 0000000..9adb21e
--- /dev/null
+++ b/docs/0.6.4/docs/tutorials/tutorial.html
@@ -0,0 +1,134 @@
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+ Working with time series
+Explanation of ‘examples/multi-agent-systems/casadi/mpc_trajectories.py’
+tbd
+
+
+
+
+
+
+
+
+
+
+
\ No newline at end of file
diff --git a/docs/0.6.4/pylint/pylint.html b/docs/0.6.4/pylint/pylint.html
new file mode 100644
index 0000000..7b98bc4
--- /dev/null
+++ b/docs/0.6.4/pylint/pylint.html
@@ -0,0 +1,2084 @@
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+ Tutorial
+This section collects tutorials on how to get started with agentlib.
+This tutorial is periodically updated, but for the most current and accurate information, please refer to the actual example files in the repository. The example files should be considered the authoritative source if there are any discrepancies.
+ +Pylint report from report.jinja2
+ + +Score
+ 7.35 / 10 + + (previous score: 7.35 / 10) + + + +Messages
+ +Module ci.test_examples (ci/test_examples.py)
+ | Line | +Col. | +Type | +Symbol | +ID | +Obj | +Message | +
|---|---|---|---|---|---|---|
| 5 | +0 | +warning | +unused-import |
+ W0611 | ++ | Unused import subprocess |
+
| 9 | +0 | +error | +import-error |
+ E0401 | ++ | Unable to import 'pytest' |
+
| 65 | +8 | +warning | +pointless-string-statement |
+ W0105 | +TestExamples.test_mpc | +String statement has no effect |
+
| 73 | +4 | +convention | +missing-function-docstring |
+ C0116 | +TestExamples.test_admm_local | +Missing function or method docstring |
+
| 83 | +4 | +convention | +missing-function-docstring |
+ C0116 | +TestExamples.test_admm_coordinated | +Missing function or method docstring |
+
| 93 | +4 | +convention | +missing-function-docstring |
+ C0116 | +TestExamples.test_exchange_admm | +Missing function or method docstring |
+
| 109 | +4 | +convention | +missing-function-docstring |
+ C0116 | +TestExamples.test_admm_mp_broadcast | +Missing function or method docstring |
+
Module /builds/EBC/EBC_all/github_ci/AgentLib-MPC/pylintrc (pylintrc)
+ | Line | +Col. | +Type | +Symbol | +ID | +Obj | +Message | +
|---|---|---|---|---|---|---|
| 1 | +0 | +refactor | +useless-option-value |
+ R0022 | ++ | Useless option value for '--disable', 'print-statement' was removed from pylint, see https://github.com/pylint-dev/pylint/pull/4942. |
+
| 1 | +0 | +refactor | +useless-option-value |
+ R0022 | ++ | Useless option value for '--disable', 'parameter-unpacking' was removed from pylint, see https://github.com/pylint-dev/pylint/pull/4942. |
+
| 1 | +0 | +refactor | +useless-option-value |
+ R0022 | ++ | Useless option value for '--disable', 'unpacking-in-except' was removed from pylint, see https://github.com/pylint-dev/pylint/pull/4942. |
+
| 1 | +0 | +refactor | +useless-option-value |
+ R0022 | ++ | Useless option value for '--disable', 'old-raise-syntax' was removed from pylint, see https://github.com/pylint-dev/pylint/pull/4942. |
+
| 1 | +0 | +refactor | +useless-option-value |
+ R0022 | ++ | Useless option value for '--disable', 'backtick' was removed from pylint, see https://github.com/pylint-dev/pylint/pull/4942. |
+
| 1 | +0 | +refactor | +useless-option-value |
+ R0022 | ++ | Useless option value for '--disable', 'import-star-module-level' was removed from pylint, see https://github.com/pylint-dev/pylint/pull/4942. |
+
| 1 | +0 | +refactor | +useless-option-value |
+ R0022 | ++ | Useless option value for '--disable', 'apply-builtin' was removed from pylint, see https://github.com/pylint-dev/pylint/pull/4942. |
+
| 1 | +0 | +refactor | +useless-option-value |
+ R0022 | ++ | Useless option value for '--disable', 'basestring-builtin' was removed from pylint, see https://github.com/pylint-dev/pylint/pull/4942. |
+
| 1 | +0 | +refactor | +useless-option-value |
+ R0022 | ++ | Useless option value for '--disable', 'buffer-builtin' was removed from pylint, see https://github.com/pylint-dev/pylint/pull/4942. |
+
| 1 | +0 | +refactor | +useless-option-value |
+ R0022 | ++ | Useless option value for '--disable', 'cmp-builtin' was removed from pylint, see https://github.com/pylint-dev/pylint/pull/4942. |
+
| 1 | +0 | +refactor | +useless-option-value |
+ R0022 | ++ | Useless option value for '--disable', 'coerce-builtin' was removed from pylint, see https://github.com/pylint-dev/pylint/pull/4942. |
+
| 1 | +0 | +refactor | +useless-option-value |
+ R0022 | ++ | Useless option value for '--disable', 'execfile-builtin' was removed from pylint, see https://github.com/pylint-dev/pylint/pull/4942. |
+
| 1 | +0 | +refactor | +useless-option-value |
+ R0022 | ++ | Useless option value for '--disable', 'file-builtin' was removed from pylint, see https://github.com/pylint-dev/pylint/pull/4942. |
+
| 1 | +0 | +refactor | +useless-option-value |
+ R0022 | ++ | Useless option value for '--disable', 'long-builtin' was removed from pylint, see https://github.com/pylint-dev/pylint/pull/4942. |
+
| 1 | +0 | +refactor | +useless-option-value |
+ R0022 | ++ | Useless option value for '--disable', 'raw_input-builtin' was removed from pylint, see https://github.com/pylint-dev/pylint/pull/4942. |
+
| 1 | +0 | +refactor | +useless-option-value |
+ R0022 | ++ | Useless option value for '--disable', 'reduce-builtin' was removed from pylint, see https://github.com/pylint-dev/pylint/pull/4942. |
+
| 1 | +0 | +refactor | +useless-option-value |
+ R0022 | ++ | Useless option value for '--disable', 'standarderror-builtin' was removed from pylint, see https://github.com/pylint-dev/pylint/pull/4942. |
+
| 1 | +0 | +refactor | +useless-option-value |
+ R0022 | ++ | Useless option value for '--disable', 'unicode-builtin' was removed from pylint, see https://github.com/pylint-dev/pylint/pull/4942. |
+
| 1 | +0 | +refactor | +useless-option-value |
+ R0022 | ++ | Useless option value for '--disable', 'xrange-builtin' was removed from pylint, see https://github.com/pylint-dev/pylint/pull/4942. |
+
| 1 | +0 | +refactor | +useless-option-value |
+ R0022 | ++ | Useless option value for '--disable', 'coerce-method' was removed from pylint, see https://github.com/pylint-dev/pylint/pull/4942. |
+
| 1 | +0 | +refactor | +useless-option-value |
+ R0022 | ++ | Useless option value for '--disable', 'delslice-method' was removed from pylint, see https://github.com/pylint-dev/pylint/pull/4942. |
+
| 1 | +0 | +refactor | +useless-option-value |
+ R0022 | ++ | Useless option value for '--disable', 'getslice-method' was removed from pylint, see https://github.com/pylint-dev/pylint/pull/4942. |
+
| 1 | +0 | +refactor | +useless-option-value |
+ R0022 | ++ | Useless option value for '--disable', 'setslice-method' was removed from pylint, see https://github.com/pylint-dev/pylint/pull/4942. |
+
| 1 | +0 | +refactor | +useless-option-value |
+ R0022 | ++ | Useless option value for '--disable', 'no-absolute-import' was removed from pylint, see https://github.com/pylint-dev/pylint/pull/4942. |
+
| 1 | +0 | +refactor | +useless-option-value |
+ R0022 | ++ | Useless option value for '--disable', 'old-division' was removed from pylint, see https://github.com/pylint-dev/pylint/pull/4942. |
+
| 1 | +0 | +refactor | +useless-option-value |
+ R0022 | ++ | Useless option value for '--disable', 'dict-iter-method' was removed from pylint, see https://github.com/pylint-dev/pylint/pull/4942. |
+
| 1 | +0 | +refactor | +useless-option-value |
+ R0022 | ++ | Useless option value for '--disable', 'dict-view-method' was removed from pylint, see https://github.com/pylint-dev/pylint/pull/4942. |
+
| 1 | +0 | +refactor | +useless-option-value |
+ R0022 | ++ | Useless option value for '--disable', 'next-method-called' was removed from pylint, see https://github.com/pylint-dev/pylint/pull/4942. |
+
| 1 | +0 | +refactor | +useless-option-value |
+ R0022 | ++ | Useless option value for '--disable', 'metaclass-assignment' was removed from pylint, see https://github.com/pylint-dev/pylint/pull/4942. |
+
| 1 | +0 | +refactor | +useless-option-value |
+ R0022 | ++ | Useless option value for '--disable', 'indexing-exception' was removed from pylint, see https://github.com/pylint-dev/pylint/pull/4942. |
+
| 1 | +0 | +refactor | +useless-option-value |
+ R0022 | ++ | Useless option value for '--disable', 'raising-string' was removed from pylint, see https://github.com/pylint-dev/pylint/pull/4942. |
+
| 1 | +0 | +refactor | +useless-option-value |
+ R0022 | ++ | Useless option value for '--disable', 'reload-builtin' was removed from pylint, see https://github.com/pylint-dev/pylint/pull/4942. |
+
| 1 | +0 | +refactor | +useless-option-value |
+ R0022 | ++ | Useless option value for '--disable', 'oct-method' was removed from pylint, see https://github.com/pylint-dev/pylint/pull/4942. |
+
| 1 | +0 | +refactor | +useless-option-value |
+ R0022 | ++ | Useless option value for '--disable', 'hex-method' was removed from pylint, see https://github.com/pylint-dev/pylint/pull/4942. |
+
| 1 | +0 | +refactor | +useless-option-value |
+ R0022 | ++ | Useless option value for '--disable', 'nonzero-method' was removed from pylint, see https://github.com/pylint-dev/pylint/pull/4942. |
+
| 1 | +0 | +refactor | +useless-option-value |
+ R0022 | ++ | Useless option value for '--disable', 'cmp-method' was removed from pylint, see https://github.com/pylint-dev/pylint/pull/4942. |
+
| 1 | +0 | +refactor | +useless-option-value |
+ R0022 | ++ | Useless option value for '--disable', 'input-builtin' was removed from pylint, see https://github.com/pylint-dev/pylint/pull/4942. |
+
| 1 | +0 | +refactor | +useless-option-value |
+ R0022 | ++ | Useless option value for '--disable', 'round-builtin' was removed from pylint, see https://github.com/pylint-dev/pylint/pull/4942. |
+
| 1 | +0 | +refactor | +useless-option-value |
+ R0022 | ++ | Useless option value for '--disable', 'intern-builtin' was removed from pylint, see https://github.com/pylint-dev/pylint/pull/4942. |
+
| 1 | +0 | +refactor | +useless-option-value |
+ R0022 | ++ | Useless option value for '--disable', 'unichr-builtin' was removed from pylint, see https://github.com/pylint-dev/pylint/pull/4942. |
+
| 1 | +0 | +refactor | +useless-option-value |
+ R0022 | ++ | Useless option value for '--disable', 'map-builtin-not-iterating' was removed from pylint, see https://github.com/pylint-dev/pylint/pull/4942. |
+
| 1 | +0 | +refactor | +useless-option-value |
+ R0022 | ++ | Useless option value for '--disable', 'zip-builtin-not-iterating' was removed from pylint, see https://github.com/pylint-dev/pylint/pull/4942. |
+
| 1 | +0 | +refactor | +useless-option-value |
+ R0022 | ++ | Useless option value for '--disable', 'range-builtin-not-iterating' was removed from pylint, see https://github.com/pylint-dev/pylint/pull/4942. |
+
| 1 | +0 | +refactor | +useless-option-value |
+ R0022 | ++ | Useless option value for '--disable', 'filter-builtin-not-iterating' was removed from pylint, see https://github.com/pylint-dev/pylint/pull/4942. |
+
| 1 | +0 | +refactor | +useless-option-value |
+ R0022 | ++ | Useless option value for '--disable', 'using-cmp-argument' was removed from pylint, see https://github.com/pylint-dev/pylint/pull/4942. |
+
| 1 | +0 | +refactor | +useless-option-value |
+ R0022 | ++ | Useless option value for '--disable', 'div-method' was removed from pylint, see https://github.com/pylint-dev/pylint/pull/4942. |
+
| 1 | +0 | +refactor | +useless-option-value |
+ R0022 | ++ | Useless option value for '--disable', 'idiv-method' was removed from pylint, see https://github.com/pylint-dev/pylint/pull/4942. |
+
| 1 | +0 | +refactor | +useless-option-value |
+ R0022 | ++ | Useless option value for '--disable', 'rdiv-method' was removed from pylint, see https://github.com/pylint-dev/pylint/pull/4942. |
+
| 1 | +0 | +refactor | +useless-option-value |
+ R0022 | ++ | Useless option value for '--disable', 'exception-message-attribute' was removed from pylint, see https://github.com/pylint-dev/pylint/pull/4942. |
+
| 1 | +0 | +refactor | +useless-option-value |
+ R0022 | ++ | Useless option value for '--disable', 'invalid-str-codec' was removed from pylint, see https://github.com/pylint-dev/pylint/pull/4942. |
+
| 1 | +0 | +refactor | +useless-option-value |
+ R0022 | ++ | Useless option value for '--disable', 'sys-max-int' was removed from pylint, see https://github.com/pylint-dev/pylint/pull/4942. |
+
| 1 | +0 | +refactor | +useless-option-value |
+ R0022 | ++ | Useless option value for '--disable', 'bad-python3-import' was removed from pylint, see https://github.com/pylint-dev/pylint/pull/4942. |
+
| 1 | +0 | +refactor | +useless-option-value |
+ R0022 | ++ | Useless option value for '--disable', 'deprecated-string-function' was removed from pylint, see https://github.com/pylint-dev/pylint/pull/4942. |
+
| 1 | +0 | +refactor | +useless-option-value |
+ R0022 | ++ | Useless option value for '--disable', 'deprecated-str-translate-call' was removed from pylint, see https://github.com/pylint-dev/pylint/pull/4942. |
+
| 1 | +0 | +refactor | +useless-option-value |
+ R0022 | ++ | Useless option value for '--disable', 'deprecated-itertools-function' was removed from pylint, see https://github.com/pylint-dev/pylint/pull/4942. |
+
| 1 | +0 | +refactor | +useless-option-value |
+ R0022 | ++ | Useless option value for '--disable', 'deprecated-types-field' was removed from pylint, see https://github.com/pylint-dev/pylint/pull/4942. |
+
| 1 | +0 | +refactor | +useless-option-value |
+ R0022 | ++ | Useless option value for '--disable', 'next-method-defined' was removed from pylint, see https://github.com/pylint-dev/pylint/pull/4942. |
+
| 1 | +0 | +refactor | +useless-option-value |
+ R0022 | ++ | Useless option value for '--disable', 'dict-items-not-iterating' was removed from pylint, see https://github.com/pylint-dev/pylint/pull/4942. |
+
| 1 | +0 | +refactor | +useless-option-value |
+ R0022 | ++ | Useless option value for '--disable', 'dict-keys-not-iterating' was removed from pylint, see https://github.com/pylint-dev/pylint/pull/4942. |
+
| 1 | +0 | +refactor | +useless-option-value |
+ R0022 | ++ | Useless option value for '--disable', 'dict-values-not-iterating' was removed from pylint, see https://github.com/pylint-dev/pylint/pull/4942. |
+
| 1 | +0 | +refactor | +useless-option-value |
+ R0022 | ++ | Useless option value for '--disable', 'deprecated-operator-function' was removed from pylint, see https://github.com/pylint-dev/pylint/pull/4942. |
+
| 1 | +0 | +refactor | +useless-option-value |
+ R0022 | ++ | Useless option value for '--disable', 'deprecated-urllib-function' was removed from pylint, see https://github.com/pylint-dev/pylint/pull/4942. |
+
| 1 | +0 | +refactor | +useless-option-value |
+ R0022 | ++ | Useless option value for '--disable', 'xreadlines-attribute' was removed from pylint, see https://github.com/pylint-dev/pylint/pull/4942. |
+
| 1 | +0 | +refactor | +useless-option-value |
+ R0022 | ++ | Useless option value for '--disable', 'deprecated-sys-function' was removed from pylint, see https://github.com/pylint-dev/pylint/pull/4942. |
+
| 1 | +0 | +refactor | +useless-option-value |
+ R0022 | ++ | Useless option value for '--disable', 'exception-escape' was removed from pylint, see https://github.com/pylint-dev/pylint/pull/4942. |
+
| 1 | +0 | +refactor | +useless-option-value |
+ R0022 | ++ | Useless option value for '--disable', 'comprehension-escape' was removed from pylint, see https://github.com/pylint-dev/pylint/pull/4942. |
+
| 1 | +0 | +refactor | +useless-option-value |
+ R0022 | ++ | Useless option value for '--disable', 'no-self-use' was moved to an optional extension, see https://pylint.readthedocs.io/en/latest/whatsnew/2/2.14/summary.html#removed-checkers. |
+
| 1 | +0 | +warning | +unknown-option-value |
+ W0012 | ++ | Unknown option value for '--disable', expected a valid pylint message and got 'long-suffix' |
+
| 1 | +0 | +warning | +unknown-option-value |
+ W0012 | ++ | Unknown option value for '--disable', expected a valid pylint message and got 'old-ne-operator' |
+
| 1 | +0 | +warning | +unknown-option-value |
+ W0012 | ++ | Unknown option value for '--disable', expected a valid pylint message and got 'old-octal-literal' |
+
| 1 | +0 | +warning | +unknown-option-value |
+ W0012 | ++ | Unknown option value for '--disable', expected a valid pylint message and got 'non-ascii-bytes-literal' |
+
| 1 | +0 | +warning | +unknown-option-value |
+ W0012 | ++ | Unknown option value for '--disable', expected a valid pylint message and got 'eq-without-hash' |
+
Module conftest (tests/conftest.py)
+ | Line | +Col. | +Type | +Symbol | +ID | +Obj | +Message | +
|---|---|---|---|---|---|---|
| 1 | +0 | +convention | +missing-module-docstring |
+ C0114 | ++ | Missing module docstring |
+
| 2 | +0 | +error | +import-error |
+ E0401 | ++ | Unable to import 'pytest' |
+
| 7 | +0 | +convention | +missing-function-docstring |
+ C0116 | +model_type | +Missing function or method docstring |
+
| 13 | +0 | +convention | +missing-function-docstring |
+ C0116 | +example_casadi_model | +Missing function or method docstring |
+
| 13 | +25 | +warning | +redefined-outer-name |
+ W0621 | +example_casadi_model | +Redefining name 'model_type' from outer scope (line 7) |
+
Module test_admm (tests/test_admm.py)
+ | Line | +Col. | +Type | +Symbol | +ID | +Obj | +Message | +
|---|---|---|---|---|---|---|
| 1 | +0 | +convention | +missing-module-docstring |
+ C0114 | ++ | Missing module docstring |
+
| 22 | +0 | +convention | +invalid-name |
+ C0103 | ++ | Constant name "a" doesn't conform to UPPER_CASE naming style |
+
| 63 | +0 | +convention | +missing-class-docstring |
+ C0115 | +TestRTADMM | +Missing class docstring |
+
| 74 | +4 | +convention | +missing-function-docstring |
+ C0116 | +TestRTADMM.test_admm_init | +Missing function or method docstring |
+
| 76 | +8 | +warning | +unused-variable |
+ W0612 | +TestRTADMM.test_admm_init | +Unused variable 'admm_module' |
+
| 80 | +4 | +convention | +missing-function-docstring |
+ C0116 | +TestRTADMM.test_comm | +Missing function or method docstring |
+
| 85 | +13 | +warning | +protected-access |
+ W0212 | +TestRTADMM.test_comm | +Access to a protected member _start_executing_callbacks of a client class |
+
| 86 | +13 | +warning | +protected-access |
+ W0212 | +TestRTADMM.test_comm | +Access to a protected member _start_executing_callbacks of a client class |
+
| 88 | +8 | +warning | +attribute-defined-outside-init |
+ W0201 | +TestRTADMM.test_comm | +Attribute 'admm_module1' defined outside __init__ |
+
| 89 | +8 | +warning | +attribute-defined-outside-init |
+ W0201 | +TestRTADMM.test_comm | +Attribute 'admm_module2' defined outside __init__ |
+
| 92 | +8 | +warning | +attribute-defined-outside-init |
+ W0201 | +TestRTADMM.test_comm | +Attribute 'counter' defined outside __init__ |
+
| 93 | +8 | +warning | +attribute-defined-outside-init |
+ W0201 | +TestRTADMM.test_comm | +Attribute 'counter2' defined outside __init__ |
+
| 109 | +8 | +warning | +protected-access |
+ W0212 | +TestRTADMM.test_comm | +Access to a protected member _solve_local_optimization of a client class |
+
| 110 | +12 | +warning | +protected-access |
+ W0212 | +TestRTADMM.test_comm | +Access to a protected member _solve_local_optimization_debug of a client class |
+
| 112 | +8 | +warning | +protected-access |
+ W0212 | +TestRTADMM.test_comm | +Access to a protected member _solve_local_optimization of a client class |
+
| 113 | +12 | +warning | +protected-access |
+ W0212 | +TestRTADMM.test_comm | +Access to a protected member _solve_local_optimization_debug of a client class |
+
| 118 | +19 | +warning | +protected-access |
+ W0212 | +TestRTADMM.test_comm | +Access to a protected member _admm_loop of a client class |
+
| 123 | +19 | +warning | +protected-access |
+ W0212 | +TestRTADMM.test_comm | +Access to a protected member _admm_loop of a client class |
+
| 127 | +13 | +warning | +protected-access |
+ W0212 | +TestRTADMM.test_comm | +Access to a protected member _process_realtime of a client class |
+
| 128 | +13 | +warning | +protected-access |
+ W0212 | +TestRTADMM.test_comm | +Access to a protected member _process_realtime of a client class |
+
| 149 | +24 | +warning | +protected-access |
+ W0212 | +TestRTADMM.test_comm | +Access to a protected member _admm_variables of a client class |
+
| 150 | +24 | +warning | +protected-access |
+ W0212 | +TestRTADMM.test_comm | +Access to a protected member _admm_variables of a client class |
+
| 163 | +4 | +convention | +missing-function-docstring |
+ C0116 | +TestRTADMM.new_receive | +Missing function or method docstring |
+
| 167 | +4 | +convention | +missing-function-docstring |
+ C0116 | +TestRTADMM.new_receive_2 | +Missing function or method docstring |
+
Module test_casadi_backend (tests/test_casadi_backend.py)
+ | Line | +Col. | +Type | +Symbol | +ID | +Obj | +Message | +
|---|---|---|---|---|---|---|
| 1 | +0 | +convention | +missing-module-docstring |
+ C0114 | ++ | Missing module docstring |
+
| 4 | +0 | +error | +import-error |
+ E0401 | ++ | Unable to import 'pytest' |
+
| 26 | +0 | +convention | +missing-function-docstring |
+ C0116 | +var_ref | +Missing function or method docstring |
+
| 37 | +0 | +convention | +missing-function-docstring |
+ C0116 | +example_casadi_system | +Missing function or method docstring |
+
| 37 | +48 | +warning | +redefined-outer-name |
+ W0621 | +example_casadi_system | +Redefining name 'var_ref' from outer scope (line 26) |
+
| 44 | +0 | +convention | +missing-function-docstring |
+ C0116 | +collocation_discretization | +Missing function or method docstring |
+
| 44 | +31 | +warning | +redefined-outer-name |
+ W0621 | +collocation_discretization | +Redefining name 'example_casadi_system' from outer scope (line 37) |
+
| 55 | +0 | +convention | +missing-function-docstring |
+ C0116 | +example_backend | +Missing function or method docstring |
+
| 55 | +32 | +warning | +redefined-outer-name |
+ W0621 | +example_backend | +Redefining name 'var_ref' from outer scope (line 26) |
+
| 56 | +4 | +convention | +invalid-name |
+ C0103 | +example_backend | +Variable name "be" doesn't conform to snake_case naming style |
+
| 61 | +0 | +convention | +missing-function-docstring |
+ C0116 | +test_optimization_variable | +Missing function or method docstring |
+
| 68 | +8 | +warning | +unused-variable |
+ W0612 | +test_optimization_variable | +Unused variable 'optimization_variable' |
+
| 75 | +4 | +warning | +unused-variable |
+ W0612 | +test_optimization_variable | +Unused variable 'optimization_variable_1' |
+
| 90 | +8 | +warning | +unused-variable |
+ W0612 | +test_optimization_variable | +Unused variable 'optimization_variable_3' |
+
| 97 | +0 | +convention | +missing-function-docstring |
+ C0116 | +test_system | +Missing function or method docstring |
+
| 97 | +16 | +warning | +redefined-outer-name |
+ W0621 | +test_system | +Redefining name 'example_casadi_system' from outer scope (line 37) |
+
| 113 | +0 | +convention | +missing-function-docstring |
+ C0116 | +test_discretization | +Missing function or method docstring |
+
| 114 | +4 | +warning | +redefined-outer-name |
+ W0621 | +test_discretization | +Redefining name 'collocation_discretization' from outer scope (line 44) |
+
| 114 | +51 | +warning | +redefined-outer-name |
+ W0621 | +test_discretization | +Redefining name 'example_casadi_system' from outer scope (line 37) |
+
| 135 | +0 | +convention | +missing-function-docstring |
+ C0116 | +test_add_opt | +Missing function or method docstring |
+
| 135 | +17 | +warning | +redefined-outer-name |
+ W0621 | +test_add_opt | +Redefining name 'example_casadi_system' from outer scope (line 37) |
+
| 154 | +0 | +convention | +missing-function-docstring |
+ C0116 | +test_create_backend | +Missing function or method docstring |
+
| 154 | +24 | +warning | +redefined-outer-name |
+ W0621 | +test_create_backend | +Redefining name 'example_backend' from outer scope (line 55) |
+
| 155 | +4 | +convention | +invalid-name |
+ C0103 | +test_create_backend | +Variable name "be" doesn't conform to snake_case naming style |
+
| 159 | +13 | +warning | +protected-access |
+ W0212 | +test_create_backend | +Access to a protected member _mpc_inputs_to_nlp_inputs of a client class |
+
Module test_casadi_ml_model (tests/test_casadi_ml_model.py)
+ | Line | +Col. | +Type | +Symbol | +ID | +Obj | +Message | +
|---|---|---|---|---|---|---|
| 3 | +0 | +error | +import-error |
+ E0401 | ++ | Unable to import 'pytest' |
+
| 9 | +0 | +convention | +missing-class-docstring |
+ C0115 | +CasadiMLTestConfig | +Missing class docstring |
+
| 14 | +0 | +convention | +missing-class-docstring |
+ C0115 | +CasadiMLTestModel | +Missing class docstring |
+
| 23 | +0 | +convention | +missing-function-docstring |
+ C0116 | +test_casadi_ml_model | +Missing function or method docstring |
+
Module test_mpc (tests/test_mpc.py)
+ | Line | +Col. | +Type | +Symbol | +ID | +Obj | +Message | +
|---|---|---|---|---|---|---|
| 1 | +0 | +convention | +missing-module-docstring |
+ C0114 | ++ | Missing module docstring |
+
| 6 | +0 | +convention | +wrong-import-order |
+ C0411 | ++ | standard import "import pathlib" should be placed before "import numpy as np" |
+
| 13 | +0 | +warning | +unused-import |
+ W0611 | ++ | Unused OptimizationBackend imported from agentlib_mpc.optimization_backends.backend |
+
| 18 | +0 | +convention | +invalid-name |
+ C0103 | ++ | Constant name "a" doesn't conform to UPPER_CASE naming style |
+
| 24 | +4 | +convention | +missing-function-docstring |
+ C0116 | +TestSampling.test_sample_datetime | +Missing function or method docstring |
+
| 75 | +4 | +convention | +missing-function-docstring |
+ C0116 | +TestSampling.test_get_scalar | +Missing function or method docstring |
+
| 81 | +4 | +convention | +missing-function-docstring |
+ C0116 | +TestSampling.test_series | +Missing function or method docstring |
+
| 82 | +8 | +convention | +invalid-name |
+ C0103 | +TestSampling.test_series | +Variable name "sr" doesn't conform to snake_case naming style |
+
| 94 | +4 | +convention | +missing-function-docstring |
+ C0116 | +TestSampling.test_list | +Missing function or method docstring |
+
| 105 | +4 | +convention | +missing-function-docstring |
+ C0116 | +TestSampling.test_previous_interpolation_method | +Missing function or method docstring |
+
| 152 | +4 | +convention | +invalid-name |
+ C0103 | +TestCasadiMPC.test_CasadiMPC | +Method name "test_CasadiMPC" doesn't conform to snake_case naming style |
+
| 158 | +14 | +warning | +protected-access |
+ W0212 | +TestCasadiMPC.test_CasadiMPC | +Access to a protected member _modules of a client class |
+
| 166 | +4 | +convention | +missing-function-docstring |
+ C0116 | +TestCasadiMPC.test_incomplete_config | +Missing function or method docstring |
+
Module test_serialized_ann (tests/test_serialized_ann.py)
+ | Line | +Col. | +Type | +Symbol | +ID | +Obj | +Message | +
|---|---|---|---|---|---|---|
| 5 | +0 | +convention | +wrong-import-order |
+ C0411 | ++ | standard import "import unittest" should be placed before "import numpy as np" |
+
| 8 | +0 | +convention | +wrong-import-order |
+ C0411 | ++ | standard import "from pathlib import Path" should be placed before "import numpy as np" |
+
| 13 | +0 | +convention | +wrong-import-order |
+ C0411 | ++ | third party import "from fixtures.ann import ANNTrainer" should be placed before "from agentlib_mpc.data_structures import ml_model_datatypes" |
+
| 14 | +0 | +convention | +wrong-import-order |
+ C0411 | ++ | third party import "from fixtures.data_generator import DataGenerator" should be placed before "from agentlib_mpc.data_structures import ml_model_datatypes" |
+
Module test_serialized_gpr (tests/test_serialized_gpr.py)
+ | Line | +Col. | +Type | +Symbol | +ID | +Obj | +Message | +
|---|---|---|---|---|---|---|
| 1 | +0 | +convention | +missing-module-docstring |
+ C0114 | ++ | Missing module docstring |
+
| 2 | +0 | +convention | +wrong-import-order |
+ C0411 | ++ | standard import "import os" should be placed before "import numpy as np" |
+
| 3 | +0 | +convention | +wrong-import-order |
+ C0411 | ++ | standard import "import unittest" should be placed before "import numpy as np" |
+
| 5 | +0 | +convention | +wrong-import-order |
+ C0411 | ++ | standard import "from pathlib import Path" should be placed before "import numpy as np" |
+
| 10 | +0 | +warning | +unused-import |
+ W0611 | ++ | Unused MLModels imported from agentlib_mpc.models.serialized_ml_model |
+
| 11 | +0 | +convention | +wrong-import-order |
+ C0411 | ++ | third party import "from fixtures.gpr import GPRTrainer" should be placed before "from agentlib_mpc.data_structures import ml_model_datatypes" |
+
| 12 | +0 | +convention | +wrong-import-order |
+ C0411 | ++ | third party import "from fixtures.data_generator import DataGenerator" should be placed before "from agentlib_mpc.data_structures import ml_model_datatypes" |
+
Module test_serialized_linreg (tests/test_serialized_linreg.py)
+ | Line | +Col. | +Type | +Symbol | +ID | +Obj | +Message | +
|---|---|---|---|---|---|---|
| 1 | +0 | +convention | +missing-module-docstring |
+ C0114 | ++ | Missing module docstring |
+
| 2 | +0 | +convention | +wrong-import-order |
+ C0411 | ++ | standard import "import os" should be placed before "import numpy as np" |
+
| 3 | +0 | +convention | +wrong-import-order |
+ C0411 | ++ | standard import "import unittest" should be placed before "import numpy as np" |
+
| 5 | +0 | +convention | +wrong-import-order |
+ C0411 | ++ | standard import "from pathlib import Path" should be placed before "import numpy as np" |
+
| 11 | +0 | +convention | +wrong-import-order |
+ C0411 | ++ | third party import "from fixtures.linreg import LinRegTrainer" should be placed before "from agentlib_mpc.data_structures import ml_model_datatypes" |
+
| 12 | +0 | +convention | +wrong-import-order |
+ C0411 | ++ | third party import "from fixtures.data_generator import DataGenerator" should be placed before "from agentlib_mpc.data_structures import ml_model_datatypes" |
+
Metrics
+ + +Count per types
+| Name | +Count | +
|---|---|
| refactor | +67 | +
| warning | +40 | +
| error | +4 | +
| convention | +56 | +
Count per messages
+| Name | +Count | +
|---|---|
| useless-option-value | +67 | +
| unknown-option-value | +5 | +
| import-error | +4 | +
| pointless-string-statement | +1 | +
| missing-function-docstring | +26 | +
| unused-import | +3 | +
| missing-module-docstring | +6 | +
| redefined-outer-name | +9 | +
| invalid-name | +6 | +
| missing-class-docstring | +3 | +
| unused-variable | +4 | +
| protected-access | +14 | +
| attribute-defined-outside-init | +4 | +
| wrong-import-order | +15 | +
Count per modules
+| Name | +Count | +
|---|---|
| /builds/EBC/EBC_all/github_ci/AgentLib-MPC/pylintrc | +72 | +
| ci.test_examples | +7 | +
| conftest | +5 | +
| test_admm | +24 | +
| test_casadi_backend | +25 | +
| test_casadi_ml_model | +4 | +
| test_mpc | +13 | +
| test_serialized_ann | +4 | +
| test_serialized_gpr | +7 | +
| test_serialized_linreg | +6 | +
Count per path
+| Name | +Count | +
|---|---|
| pylintrc | +72 | +
| ci/test_examples.py | +7 | +
| tests/conftest.py | +5 | +
| tests/test_admm.py | +24 | +
| tests/test_casadi_backend.py | +25 | +
| tests/test_casadi_ml_model.py | +4 | +
| tests/test_mpc.py | +13 | +
| tests/test_serialized_ann.py | +4 | +
| tests/test_serialized_gpr.py | +7 | +
| tests/test_serialized_linreg.py | +6 | +