Merge pull request #1142 from fhswf/final_changes_v2.0.0

Final changes in v2.0.0 (6)

doc/rtd/content/01_welcome/sub/01_introduction.rst

@@ -52,7 +52,7 @@ it into MLPro. The deeper the level, the more universal the usability, and thus
    :scale: 40 %
 
 
-Standardized Machine Learning
+Standardized machine learning
 ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
 A special feature of MLPro is that machine learning standards are already defined in the basic functions. 
 Templates for adaptive models and their hyperparameters as well as for executable ML scenarios are introduced 
@@ -64,7 +64,7 @@ from MLPro in your hybrid ML applications.
 **Learn more:** :ref:`Basics of Machine Learning <target_bf_ml>`
 
 
-Example Pool
+Example pool
 ^^^^^^^^^^^^
 
 Numerous executable example programs (we call them "howtos") illustrate the essential functions of MLPro.
@@ -75,7 +75,7 @@ demonstrated functionalities (tdd - test-driven development).
 **Learn more:** :ref:`Example Pool <target_appendix1>`
 
 
-Third Party Support
+Third party support
 ^^^^^^^^^^^^^^^^^^^
 
 MLPro integrates an increasing number of selected ML packages into its process landscapes.
@@ -85,7 +85,7 @@ the corresponding MLPro classes.
 **Learn more:** :ref:`Wrappers <target_wrappers>`
 
 
-Real-World Applications in Focus
+Real-world applications in focus
 ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
 
 MLPro was designed not only for simulations but for use in real-world applications. To this end, various 
@@ -132,7 +132,7 @@ The development team consistently applies the following principles:
       cycle of software. Anyone who opens any source code of MLPro knows immediately what we mean :-)
 
 
-Customer Extensions
+Customer extensions
 ^^^^^^^^^^^^^^^^^^^
 
 Of course, frameworks like MLPro are made to reuse their functions in own applications. That's why we put a lot of effort into design and 

doc/rtd/content/01_welcome/sub/02_getting_started.rst

@@ -1,5 +1,5 @@
 .. _target_mlpro_getstarted:
-Getting Started
+Getting started
 ===============
 
 Installation from PyPI
@@ -60,7 +60,7 @@ Which packages are actually required depends on the functionalities of MLPro tha
   :header: "Package", "Version"
 
 
-First Steps
+First steps
 -----------
 
 The easiest way to become familiar with the concepts and functions of MLPro is to browse 

doc/rtd/content/02_basic_functions/mlpro_bf/sub/01_overview.rst

@@ -7,7 +7,7 @@ This is organized in a total of five layers that build on one another, as shown
 .. image:: images/MLPro-BF_Overview.drawio.png
     :scale: 50%
 
-The lowest :ref:`Layer 0: Elementary Functions <target_bf_elementary>` provides a collection of functions for logging, 
+The lowest :ref:`Layer 0: Elementary functions <target_bf_elementary>` provides a collection of functions for logging, 
 time measurement in simulated or real processes, persistence and data management, plotting, etc. It also contains a framework for interactive
 GUI applications.
 
@@ -20,10 +20,10 @@ On top of this, :ref:`Layer 2: Mathematics <target_bf_mathematics>` introduces e
 dimensions, sets and elements, metric spaces, and functions. Furthermore, numeric algorithms for data normalization etc.
 are included.
 
-:ref:`Layer 3: Application Support <target_bf_application_support>` prepares the connection to real 
+:ref:`Layer 3: Application support <target_bf_application_support>` prepares the connection to real 
 applications. It introduces powerful systematics for stream data processing/visualization and state-based systems that 
 are, in turn, prepared for communication with real hardware components like sensors and actuators.
 
-The top :ref:`Layer 4: Machine Learning <target_bf_ml>` of MLPro-BF specifies fundamental standards for machine learning. 
+The top :ref:`Layer 4: Machine learning <target_bf_ml>` of MLPro-BF specifies fundamental standards for machine learning. 
 All higher ML-related sub-frameworks reuse and specialize them. Topics like hyperparameters, adaptive models, and their training 
 and tuning in ML scenarios are handled here.

doc/rtd/content/02_basic_functions/mlpro_bf/sub/02_layer0_elementary.rst

@@ -1,5 +1,5 @@
 .. _target_bf_elementary:
-Layer 0 - Elementary Functions
+Layer 0 - Elementary functions
 ==============================
 
 This lowest layer of MLPro-BF provides elementary functionalities for the following topics:

doc/rtd/content/02_basic_functions/mlpro_bf/sub/04_layer2_mathematics.rst

@@ -46,6 +46,6 @@ Lastly, there are more advanced functions of mathematics, as follows:
    layer2_mathematics/*
 
 
-**Cross Reference**
+**Cross reference**
     + :ref:`Howto BF-MATH-001: Dimensions, Spaces and Elements <Howto BF MATH 001>`
     + :ref:`API Reference <target_ap_bf_math>`

doc/rtd/content/02_basic_functions/mlpro_bf/sub/05_layer3_application_support.rst

@@ -1,5 +1,5 @@
 .. _target_bf_application_support:
-Layer 3 - Application Support
+Layer 3 - Application support
 =============================
 
 This layer deals with the provision of basic technologies to support real-world applications.

doc/rtd/content/02_basic_functions/mlpro_bf/sub/06_layer4_ml.rst

@@ -1,5 +1,5 @@
 .. _target_bf_ml:
-Layer 4 - Machine Learning
+Layer 4 - Machine learning
 ==========================
 
 One of the fundamental concepts in MLPro is to anchor universal standards for machine learning already in the basic 
@@ -50,8 +50,7 @@ on historical data and/or online monitoring. For this purpose, MLPro provides st
    layer4_machine_learning/*
 
 
-**Cross Reference**
+**Cross reference**
 
-- :ref:`Related Howtos <target_howtos_bf_ml>`
-- :ref:`API Reference BF-ML - Machine Learning <target_api_bf_ml>`
-- :ref:`API Reference BF-ML-Systems - Adaptive Systems <target_api_bf_ml_systems>`
+- :ref:`Related howtos <target_howtos_bf_ml>`
+- :ref:`API reference BF-ML - Machine learning <target_api_bf_ml>`

doc/rtd/content/02_basic_functions/mlpro_bf/sub/layer0_elementary/01_log.rst

@@ -78,6 +78,6 @@ To log an information, function ``log(p_type, *pargs)`` needs to be called. The
         my_random_class.switch_logging(Log.C_LOG_NOTHING)
         
 
-**Cross Reference**
+**Cross reference**
     + :ref:`Howto BF-001: Logging <Howto BF 001>`
     + :ref:`API Reference <target_api_bf_various>`

doc/rtd/content/02_basic_functions/mlpro_bf/sub/layer0_elementary/02_time.rst

@@ -1,4 +1,4 @@
-Time Measurement
+Time measurement
 ================
 
 MLPro provides an internal timing mechanism that is introduced by class property **Timer**.
@@ -23,6 +23,6 @@ The following are the functionalities of the timer:
     * ``add_time`` : to add actual time, which is specifically for virtual mode
     * ``finish_lap`` : to end the current lap
 
-**Cross Reference**
+**Cross reference**
     + :ref:`Howto BF-002: Timer <Howto BF 002>`
     + :ref:`API Reference <target_api_bf_various>`

doc/rtd/content/02_basic_functions/mlpro_bf/sub/layer0_elementary/03_persistence.rst

@@ -43,7 +43,7 @@ unique ID of the object to be de-/serialized. The figure below shows the correla
     :scale: 50 %
 
 
-**Saving Objects**
+**Saving objects**
 
 Saving an object works basically like this:
 
@@ -85,7 +85,7 @@ and directory but specific file extension, such as
     MyClass[8aa41da2-0748-4cd0-9025-55e9d9d9a131].csv 
 
 
-**Loading Objects**
+**Loading objects**
 
 The **load()** method of the **Persistent** class is defined as a class method because a related object 
 is only generated by loading. So it's essential to know which class is used to load a previously saved file:
@@ -98,7 +98,7 @@ In this case, the custom method **_complete_state()** of class **MyClass** is us
 content into the object.
 
 
-**Version of Persistence**
+**Version of persistence**
 
 The **Persistent** class also contains a class attribute 
 
@@ -115,10 +115,10 @@ of the loading class and the object to be loaded and denies file access if the v
 
 
 
-**Cross Reference**
+**Cross reference**
 
 - :ref:`Howto BF-005: Persistence <Howto BF 005>`
-- :ref:`API Reference BF-VARIOUS - Various Functions <target_api_bf_various>`
-- `Python Documentation: Python object serialization <https://docs.python.org/3/library/pickle.html#>`_
-- `Python Documentation: Persistence of external objects <https://docs.python.org/3/library/pickle.html#persistence-of-external-objects>`_
+- :ref:`API reference BF-VARIOUS - Various functions <target_api_bf_various>`
+- `Python documentation: Python object serialization <https://docs.python.org/3/library/pickle.html#>`_
+- `Python documentation: Persistence of external objects <https://docs.python.org/3/library/pickle.html#persistence-of-external-objects>`_
 - `Dill: Python extension for pickle <https://pypi.org/project/dill/>`_

doc/rtd/content/02_basic_functions/mlpro_bf/sub/layer0_elementary/04_data.rst

@@ -1,4 +1,4 @@
-Data Management
+Data management
 ===============
 
 Data management in a framework is extremely important, which mostly refers to the organization, storage, and retrieval of data within the framework.
@@ -16,10 +16,10 @@ The related data management classes can be accessed as follows:
 
 In general, there are three main functionalities of data management in MLPro:
 
-    1) **Data Storing**
+    1) **Data storing**
         The second possibility is to store a bunch of data in MLPro's **DataStoring** class with three different layers, as follows:
 
-            - **Layer 1 - Data Names** : the labels or the feature names of the data.
+            - **Layer 1 - Data names** : the labels or the feature names of the data.
 
             - **Layer 2 - Frames** : the frames can be added to each label or feature name. If none, then the frame id can be set to '0' all the time.
 
@@ -59,7 +59,7 @@ In general, there are three main functionalities of data management in MLPro:
         A further class named **ConfigFile** enables persistent storage of program configuration data in a local JSON file.
 
 
-**Cross Reference**
+**Cross reference**
     + :ref:`Howto BF-003: Store and plot data <Howto BF 003>`
     + :ref:`Howto BF-004: Buffers <Howto BF 004>`
     + :ref:`API Reference <target_api_bf_data>`

doc/rtd/content/02_basic_functions/mlpro_bf/sub/layer0_elementary/05_plot.rst

@@ -1,5 +1,5 @@
 .. _DataPlotting:
-Plotting and Visualization
+Plotting and visualization
 ==========================
 
 In order to be able to visualize processes on different levels in a standardized way, MLPro provides a property class 
@@ -17,7 +17,7 @@ in connection with the output backend `TkAgg <https://matplotlib.org/stable/api/
 In this combination, a good user experience is made possible. In principle, however, other packages can also be used for visualization.
 
 
-**Cross Reference**
+**Cross reference**
 
-- :ref:`API Reference BF-PLOT - Plotting and Visualization <target_api_bf_plot>`
-- :ref:`Stream Plotting <target_bf_streams_processing_01>`
+- :ref:`API reference BF-PLOT - Plotting and visualization <target_api_bf_plot>`
+- :ref:`Stream plotting <target_bf_streams_processing_01>`

doc/rtd/content/02_basic_functions/mlpro_bf/sub/layer0_elementary/06_sci.rst

@@ -1,4 +1,4 @@
-Scientific Referencing
+Scientific referencing
 ======================
 
 MLPro integrates scientific referencing in any class using a class **ScientificObject**.
@@ -57,5 +57,5 @@ MLPro team has added a citing functionality. Therefore, the users can obtain the
     }
 
 
-**Cross Reference**
-    + :ref:`API Reference <target_api_bf_various>`
+**Cross reference**
+    + :ref:`API reference <target_api_bf_various>`

doc/rtd/content/02_basic_functions/mlpro_bf/sub/layer0_elementary/07_ui.rst

@@ -1,10 +1,10 @@
-User Interaction
+User interaction
 ================
 
 MLPro-BF provides a framework called SciUI (Scientific User Interface) for creating and running 
 interactive ML applications for graphical validation, presentation and education purposes.
 
-**Key features are:**
+**Key features**
 
 - Platform-independend framework for creation of own UI scenarios
 - Ready-to-run application „SciUI“ auto-detects and starts own scenarios
@@ -22,7 +22,7 @@ interactive ML applications for graphical validation, presentation and education
    :glob:
 
 
-Cross Reference
+**Cross reference**
 
     + :ref:`Howto BF-UI-001: SciUI - Reuse of interactive 2D/3D Input Space <Howto BF UI 001>`
-    + :ref:`Howto BF-UI-002: SciUI - Reinforcement Learning Cockpit <Howto BF UI 002>`
+    + :ref:`Howto BF-UI-002: SciUI - Reinforcement learning cockpit <Howto BF UI 002>`

doc/rtd/content/02_basic_functions/mlpro_bf/sub/layer1_computation/01_event_handling.rst

@@ -1,5 +1,5 @@
 .. _target_bf_event:
-Event Handling
+Event handling
 ==============
 
 Event handling is a widely used standard technique in software development. And that's how it found its way into MLPro. 
@@ -12,8 +12,8 @@ Based on this, even an event-oriented adaptation mechanism is cultivated in
 :ref:`Layer 4 - Machine Learning <target_bf_ml>`.
 
 
-**Cross Reference**
+**Cross reference**
 
-- :ref:`Howto BF-EH-001: Event Handling <Howto BF EH 001>`
-- :ref:`API Reference BF-EVENTS - Event Handling <target_api_bf_event>`
+- :ref:`Howto BF-EH-001: Event handling <Howto BF EH 001>`
+- :ref:`API reference BF-EVENTS - Event handling <target_api_bf_event>`
 

doc/rtd/content/02_basic_functions/mlpro_bf/sub/layer1_computation/02_multitasking.rst

@@ -36,7 +36,7 @@ In particular, this includes the method **_start_async()**, which allows the exe
 thread or process. 
 
 
-**Tasks und Workflows**
+**Tasks und workflows**
 
 A fundamental and consistently used concept in MLPro is that of tasks and workflows. A task is a class that can be 
 executed in one of the three possible ranges mentioned above. Tasks can in turn be grouped into workflows. 
@@ -55,10 +55,10 @@ check MLPro functionalities in multiprocessing mode on MacOS-based computers ver
 multithreading.
 
 
-**Cross Reference**
+**Cross reference**
 
-- :ref:`Howto BF-MT-001: Multitasking - Parallel Algorithms <Howto BF MT 001>`
-- :ref:`Howto BF-MT-002: Multitasking - Tasks and Workflows <Howto BF MT 002>`
+- :ref:`Howto BF-MT-001: Multitasking - Parallel algorithms <Howto BF MT 001>`
+- :ref:`Howto BF-MT-002: Multitasking - Tasks and workflows <Howto BF MT 002>`
 - :ref:`API Reference BF-MT - Multitasking <target_api_bf_mt>`
 
 

doc/rtd/content/02_basic_functions/mlpro_bf/sub/layer1_computation/03_ops.rst

@@ -17,8 +17,8 @@ All scenario classes in MLPro are ultimately template classes for implementing y
 Therefore, special attention should be paid to the custom methods that are already introduced here.
 
 
-**Cross Reference**
+**Cross reference**
 
-- :ref:`API Reference BF-OPS - Operations <target_api_bf_ops>`
+- :ref:`API reference BF-OPS - Operations <target_api_bf_ops>`
 - :ref:`BF-Various - Persistence <target_bf_persistence>`
 

doc/rtd/content/02_basic_functions/mlpro_bf/sub/layer2_mathematics/01_normalizers.rst

@@ -25,6 +25,6 @@ Both the normalizers provide following operations:
  * Renormalize : MLPro's normalizers also provide the possibility to renormalize the previously normalized data elements on new normalization parameters.
 
 
-**Cross Reference**
+**Cross reference**
     + :ref:`Howto BF-MATH-010: Normalizers <Howto BF MATH 010>`
-    + :ref:`API Reference <target_ap_bf_math_norm>`
+    + :ref:`API reference <target_ap_bf_math_norm>`

doc/rtd/content/02_basic_functions/mlpro_bf/sub/layer2_mathematics/02_geometry.rst

@@ -5,5 +5,5 @@ Geometry
 ...
 
 
-**Cross Reference**
-    + :ref:`API Reference <target_ap_bf_math_geo>`
+**Cross reference**
+    + :ref:`API reference <target_ap_bf_math_geo>`

doc/rtd/content/02_basic_functions/mlpro_bf/sub/layer3_application_support/01_streams_processing.rst

@@ -1,5 +1,5 @@
 .. _target_bf_streams:
-Stream Processing
+Stream processing
 =================
 
 .. toctree::

doc/rtd/content/02_basic_functions/mlpro_bf/sub/layer3_application_support/03_systems.rst

@@ -1,5 +1,5 @@
 .. _target_bf_systems:
-State-based Systems
+State-based systems
 ===================
 
 MLPro aims to standardize machine learning processes to accommodate complex applications in simplified reusable APIs.
@@ -103,10 +103,10 @@ The demo scenario supports two action styles:
    systems/*
 
 
-**Cross Reference**
+**Cross reference**
 
-- :ref:`Howto BF-SYSTEMS-001: Demonstrating Native Systems <Howto BF SYSTEMS 001>`
+- :ref:`Howto BF-SYSTEMS-001: Demonstrating native systems <Howto BF SYSTEMS 001>`
 - :ref:`Howto BF-SYSTEMS-010: System, Controller, Actuator, Sensor <Howto BF SYSTEMS 010>`
 - :ref:`MLPro-Int-MuJoCo <https://mlpro-int-mujoco.readthedocs.io>`_
-- :ref:`API Reference BF-Systems <target_ap_bf_systems>`
-- :ref:`API Reference BF-Systems Sample Pool <target_pool_bf_systems>`
+- :ref:`API reference BF-Systems <target_ap_bf_systems>`
+- :ref:`API reference BF-Systems Sample pool <target_pool_bf_systems>`