diff --git a/.github/workflows/build.yml b/.github/workflows/build.yml index dead5b76..07f99b53 100644 --- a/.github/workflows/build.yml +++ b/.github/workflows/build.yml @@ -62,7 +62,6 @@ jobs: ${{ steps.compile.outputs.target-path }} docs/html doxygen.log - release: needs: [compile, test] runs-on: ubuntu-latest @@ -105,4 +104,4 @@ jobs: env: GH_TOKEN : ${{ secrets.RELEASER }} run: | - npx semantic-release@23 \ No newline at end of file + npx semantic-release@23 diff --git a/.github/workflows/test.yml b/.github/workflows/test.yml new file mode 100644 index 00000000..e9c7be2d --- /dev/null +++ b/.github/workflows/test.yml @@ -0,0 +1,26 @@ +name: x86 Unit Tests + +on: [push] + +jobs: + test: + runs-on: ubuntu-latest + steps: + - name: Checkout code + uses: actions/checkout@v4 + with: + submodules: recursive + + - name: Set up environment + run: | + sudo apt-get update && sudo apt-get install -y cmake build-essential + + - name: Build and run tests + run: | + cmake -B build -DCMAKE_BUILD_TYPE=Debug + cd build + make + cd .. + ./build/googletests + + \ No newline at end of file diff --git a/.gitignore b/.gitignore index a3959779..ca58c2ab 100644 --- a/.gitignore +++ b/.gitignore @@ -1,5 +1,5 @@ -# Created by https://www.toptal.com/developers/gitignore/api/c++,linux,macos,python,windows,particle,doxygen -# Edit at https://www.toptal.com/developers/gitignore?templates=c++,linux,macos,python,windows,particle,doxygen +# Created by https://www.toptal.com/developers/gitignore/api/c++,cmake,linux,macos,python,doxygen,windows,particle,latex +# Edit at https://www.toptal.com/developers/gitignore?templates=c++,cmake,linux,macos,python,doxygen,windows,particle,latex ### C++ ### # Prerequisites @@ -35,10 +35,335 @@ *.out *.app +### CMake ### +CMakeLists.txt.user +CMakeCache.txt +CMakeFiles +CMakeScripts +Testing +Makefile +cmake_install.cmake +install_manifest.txt +compile_commands.json +CTestTestfile.cmake +_deps + +### CMake Patch ### +# External projects +*-prefix/ + ### Doxygen ### html latex +### LaTeX ### +## Core latex/pdflatex auxiliary files: +*.aux +*.lof +*.log +*.lot +*.fls +*.toc +*.fmt +*.fot +*.cb +*.cb2 +.*.lb + +## Intermediate documents: +*.dvi +*.xdv +*-converted-to.* +# these rules might exclude image files for figures etc. +# *.ps +# *.eps +# *.pdf + +## Generated if empty string is given at "Please type another file name for output:" +.pdf + +## Bibliography auxiliary files (bibtex/biblatex/biber): +*.bbl +*.bcf +*.blg +*-blx.aux +*-blx.bib +*.run.xml + +## Build tool auxiliary files: +*.fdb_latexmk +*.synctex +*.synctex(busy) +*.synctex.gz +*.synctex.gz(busy) +*.pdfsync + +## Build tool directories for auxiliary files +# latexrun +latex.out/ + +## Auxiliary and intermediate files from other packages: +# algorithms +*.alg +*.loa + +# achemso +acs-*.bib + +# amsthm +*.thm + +# beamer +*.nav +*.pre +*.snm +*.vrb + +# changes +*.soc + +# comment +*.cut + +# cprotect +*.cpt + +# elsarticle (documentclass of Elsevier journals) +*.spl + +# endnotes +*.ent + +# fixme +*.lox + +# feynmf/feynmp +*.mf +*.mp +*.t[1-9] +*.t[1-9][0-9] +*.tfm + +#(r)(e)ledmac/(r)(e)ledpar +*.end +*.?end +*.[1-9] +*.[1-9][0-9] +*.[1-9][0-9][0-9] +*.[1-9]R +*.[1-9][0-9]R +*.[1-9][0-9][0-9]R +*.eledsec[1-9] +*.eledsec[1-9]R +*.eledsec[1-9][0-9] +*.eledsec[1-9][0-9]R +*.eledsec[1-9][0-9][0-9] +*.eledsec[1-9][0-9][0-9]R + +# glossaries +*.acn +*.acr +*.glg +*.glo +*.gls +*.glsdefs +*.lzo +*.lzs +*.slg +*.sls + +# uncomment this for glossaries-extra (will ignore makeindex's style files!) +# *.ist + +# gnuplot +*.gnuplot +*.table + +# gnuplottex +*-gnuplottex-* + +# gregoriotex +*.gaux +*.glog +*.gtex + +# htlatex +*.4ct +*.4tc +*.idv +*.lg +*.trc +*.xref + +# hyperref +*.brf + +# knitr +*-concordance.tex +# TODO Uncomment the next line if you use knitr and want to ignore its generated tikz files +# *.tikz +*-tikzDictionary + +# listings +*.lol + +# luatexja-ruby +*.ltjruby + +# makeidx +*.idx +*.ilg +*.ind + +# minitoc +*.maf +*.mlf +*.mlt +*.mtc[0-9]* +*.slf[0-9]* +*.slt[0-9]* +*.stc[0-9]* + +# minted +_minted* +*.pyg + +# morewrites +*.mw + +# newpax +*.newpax + +# nomencl +*.nlg +*.nlo +*.nls + +# pax +*.pax + +# pdfpcnotes +*.pdfpc + +# sagetex +*.sagetex.sage +*.sagetex.py +*.sagetex.scmd + +# scrwfile +*.wrt + +# svg +svg-inkscape/ + +# sympy +*.sout +*.sympy +sympy-plots-for-*.tex/ + +# pdfcomment +*.upa +*.upb + +# pythontex +*.pytxcode +pythontex-files-*/ + +# tcolorbox +*.listing + +# thmtools +*.loe + +# TikZ & PGF +*.dpth +*.md5 +*.auxlock + +# titletoc +*.ptc + +# todonotes +*.tdo + +# vhistory +*.hst +*.ver + +# easy-todo +*.lod + +# xcolor +*.xcp + +# xmpincl +*.xmpi + +# xindy +*.xdy + +# xypic precompiled matrices and outlines +*.xyc +*.xyd + +# endfloat +*.ttt +*.fff + +# Latexian +TSWLatexianTemp* + +## Editors: +# WinEdt +*.bak +*.sav + +# Texpad +.texpadtmp + +# LyX +*.lyx~ + +# Kile +*.backup + +# gummi +.*.swp + +# KBibTeX +*~[0-9]* + +# TeXnicCenter +*.tps + +# auto folder when using emacs and auctex +./auto/* +*.el + +# expex forward references with \gathertags +*-tags.tex + +# standalone packages +*.sta + +# Makeindex log files +*.lpz + +# xwatermark package +*.xwm + +# REVTeX puts footnotes in the bibliography by default, unless the nofootinbib +# option is specified. Footnotes are the stored in a file with suffix Notes.bib. +# Uncomment the next line to have this generated file ignored. +#*Notes.bib + +### LaTeX Patch ### +# LIPIcs / OASIcs +*.vtc + +# glossaries +*.glstex + ### Linux ### *~ @@ -152,7 +477,6 @@ cover/ *.pot # Django stuff: -*.log local_settings.py db.sqlite3 db.sqlite3-journal @@ -291,9 +615,16 @@ $RECYCLE.BIN/ # Windows shortcuts *.lnk -# End of https://www.toptal.com/developers/gitignore/api/c++,linux,macos,python,windows,particle,doxygen +# End of https://www.toptal.com/developers/gitignore/api/c++,cmake,linux,macos,python,doxygen,windows,particle,latex smartfin-fw3.cpp target/ *.bin nul -out/docs/* + +out/docs/application/Service Diagram.png +out/docs/fw_flow/Firmware Flowchart.png +tests/googletest +tests/outputs/ +build/* +tests/*outputs/* +tests/no_check_inputs/* \ No newline at end of file diff --git a/.gitmodules b/.gitmodules index 64666f60..86215186 100644 --- a/.gitmodules +++ b/.gitmodules @@ -1,6 +1,8 @@ [submodule "lib/SparkFun_ICM-20948_ParticleLibrary"] path = lib/SparkFun_ICM-20948_ParticleLibrary + url = https://github.com/UCSD-E4E/SparkFun_ICM-20948_ParticleLibrary.git [submodule "external/googletest"] path = external/googletest url = https://github.com/google/googletest.git + diff --git a/.vscode/c_cpp_properties.json b/.vscode/c_cpp_properties.json index 3da6cb19..c5a0e267 100644 --- a/.vscode/c_cpp_properties.json +++ b/.vscode/c_cpp_properties.json @@ -34,6 +34,36 @@ "${workspaceFolder}\\src" ] } + }, + { + "name": "*nix", + "includePath": [ + "${workspaceFolder}/src", + "${workspaceFolder}/tests", + "${workspaceFolder}/src/cli", + "${workspaceFolder}/external/googletest/googletest/include" + ], + "defines": [ + "_DEBUG", + "UNICODE", + "_UNICODE", + "TEST_VERSION" + ], + "compilerPath": "/usr/bin/gcc", + "intelliSenseMode": "linux-gcc-x64", + "cStandard": "c11", + "cppStandard": "c++11", + "configurationProvider": "ms-vscode.cmake-tools", + "mergeConfigurations": true, + "browse": { + "limitSymbolsToIncludedHeaders": true, + "path": [ + "${default}", + "${workspaceFolder}/src", + "${workspaceFolder}/tests" + ] + }, + "compilerArgs": [] } ], "version": 4 diff --git a/.vscode/extensions.json b/.vscode/extensions.json index 8dff1db0..c85becdb 100644 --- a/.vscode/extensions.json +++ b/.vscode/extensions.json @@ -3,6 +3,10 @@ "ms-vscode.cpptools", "particle.particle-vscode-core", "cschlosser.doxdocgen", - "jebbs.plantuml" + "jebbs.plantuml", + "ms-vscode.cpptools-extension-pack", + "github.vscode-github-actions", + "eamodio.gitlens", + "james-yu.latex-workshop" ] } \ No newline at end of file diff --git a/.vscode/launch.json b/.vscode/launch.json index 5e3e71db..f596d62d 100644 --- a/.vscode/launch.json +++ b/.vscode/launch.json @@ -7,7 +7,11 @@ "request": "launch", "program": "semantic_release_prepare.py", "console": "integratedTerminal", - "args": ["1.2.3", "37-feat-semver", "src/vers.hpp"] + "args": [ + "1.2.3", + "37-feat-semver", + "src/vers.hpp" + ] }, { "type": "cortex-debug", diff --git a/.vscode/settings.json b/.vscode/settings.json index 0baeaa4e..009d2ef9 100644 --- a/.vscode/settings.json +++ b/.vscode/settings.json @@ -67,7 +67,7 @@ "C_Cpp.doxygen.generatedStyle": "/**", "C_Cpp.autoAddFileAssociations": false, "editor.rulers": [ - 80 + 100 ], "particle.enableVerboseLocalCompilerLogging": true, "plantuml.render": "PlantUMLServer", @@ -79,6 +79,7 @@ "editor.formatOnType": true, "doxdocgen.generic.useGitUserEmail": true, "doxdocgen.generic.useGitUserName": true, + "C_Cpp.errorSquiggles": "enabled", "cmake.generator": "Unix Makefiles", "cmake.sourceDirectory": "${workspaceFolder}", "cmake.buildDirectory": "${workspaceFolder}/build", @@ -86,6 +87,9 @@ "cwd": "${workspaceFolder}" }, "cmake.configureArgs": [ + "-DTEST_VERSION=1", "-DCMAKE_BUILD_TYPE=Debug" ], + "latex-workshop.formatting.latex": "latexindent", + "doxygen_runner.configuration_file_override": "${workspaceFolder}/Doxyfile" } \ No newline at end of file diff --git a/CMakeLists.txt b/CMakeLists.txt index 23141aea..bba88f05 100644 --- a/CMakeLists.txt +++ b/CMakeLists.txt @@ -3,13 +3,35 @@ project(SmartfinTests) set(CMAKE_CXX_STANDARD 14) set(CMAKE_CXX_STANDARD_REQUIRED ON) + +add_compile_definitions(TEST_VERSION) + add_subdirectory(external/googletest) include_directories(external/googletest/googletest/include src/ tests) set(GTEST_SOURCE_FILES tests/test_endianness.cpp + tests/scheduler_test_system.cpp + tests/test_ensembles.cpp + tests/fixed_google_tests.cpp + tests/file_google_tests.cpp + src/scheduler.cpp + src/cli/flog.cpp + tests/test_file_system.cpp + src/states.cpp +) + +set(EXAMINE_BEHAVIOR_SOURCE_FILES + tests/scheduler_test_system.cpp + tests/test_ensembles.cpp + src/scheduler.cpp + src/cli/flog.cpp + tests/examine_behavior.cpp + tests/test_file_system.cpp + src/states.cpp ) add_executable(googletests ${GTEST_SOURCE_FILES}) target_link_libraries(googletests gtest gtest_main pthread) +add_executable(examine_behavior ${EXAMINE_BEHAVIOR_SOURCE_FILES}) diff --git a/Doxyfile b/Doxyfile index 15aa9142..a2abc30e 100644 --- a/Doxyfile +++ b/Doxyfile @@ -209,7 +209,7 @@ SHORT_NAMES = NO # description.) # The default value is: NO. -JAVADOC_AUTOBRIEF = NO +JAVADOC_AUTOBRIEF = YES # If the JAVADOC_BANNER tag is set to YES then doxygen will interpret a line # such as @@ -219,7 +219,7 @@ JAVADOC_AUTOBRIEF = NO # interpreted by doxygen. # The default value is: NO. -JAVADOC_BANNER = NO +JAVADOC_BANNER = YES # If the QT_AUTOBRIEF tag is set to YES then doxygen will interpret the first # line (until the first dot) of a Qt-style comment as the brief description. If @@ -943,7 +943,7 @@ WARN_LOGFILE = doxygen.log # spaces. See also FILE_PATTERNS and EXTENSION_MAPPING # Note: If this tag is empty the current directory is searched. -INPUT = ./src +INPUT = src tests # This tag can be used to specify the character encoding of the source files # that doxygen parses. Internally doxygen uses the UTF-8 encoding. Doxygen uses @@ -1044,7 +1044,7 @@ RECURSIVE = YES # Note that relative paths are relative to the directory from which doxygen is # run. -EXCLUDE = src/smartfin-fw3.cpp +EXCLUDE = src/smartfin-fw3.cpp tests/venv tests/scheduler_proccessor.py tests/outputs # The EXCLUDE_SYMLINKS tag can be used to select whether or not files or # directories that are symbolic links (a Unix file system feature) are excluded @@ -1120,7 +1120,7 @@ IMAGE_PATH = # need to set EXTENSION_MAPPING for the extension otherwise the files are not # properly processed by doxygen. -INPUT_FILTER = +INPUT_FILTER = # The FILTER_PATTERNS tag can be used to specify filters on a per file pattern # basis. Doxygen will compare the file name with each pattern and apply the diff --git a/README.md b/README.md index 97766cd0..a797a0be 100644 --- a/README.md +++ b/README.md @@ -1,6 +1,24 @@ # smartfin-fw3 Smartfin FW v3 +# Developer Getting Started +1. Clone this repository +2. Open in Visual Studio Code +3. Install all recommended extensions +4. Initialize submodules +``` +git submodule init +git submodule update --recursive +``` +5. Ensure tooling is installed + - `cmake` + - `g++` (usually from `build-essential`) + - `gdb` + - `doxygen` + - `graphviz` + +## x86 Mode Debugging +Please use the CMake Tools debugger # Contributing @@ -24,18 +42,20 @@ base85 | 9c52d27 | MIT | https://github.com/rafagafe/base85 | src/c # LED Behavior ## Status LED -State | Color | Pattern ---------------------------------------- -Charge | Yellow | Solid -Sleep | Black | Solid -CLI | Red | Solid -Network Off | Black | Solid -Network On | Blue | Solid -Network Connecting | Blue | Solid -Network DHCP | Blue | Solid -Cloud Connecting | Blue | Solid -Cloud Connected | Blue | Blink -Cloud Handshake | Blue | Blink +State | Color | Pattern +----------------------------------------- +Charge | Yellow | Solid +Sleep | Black | Solid +CLI | Green | Solid +Network Off | Black | Solid +Network On | Blue | Solid +Network Connecting | Blue | Solid +Network DHCP | Blue | Solid +Cloud Connecting | Blue | Solid +Cloud Connected | Blue | Blink +Cloud Handshake | Blue | Blink +Deployed with GPS | White | Blink +Deployed with no GPS | White | Solid ## Battery LED diff --git a/docs/control_flow.png b/docs/control_flow.png new file mode 100644 index 00000000..75489bf7 Binary files /dev/null and b/docs/control_flow.png differ diff --git a/docs/control_flow.puml b/docs/control_flow.puml new file mode 100644 index 00000000..a960bc02 --- /dev/null +++ b/docs/control_flow.puml @@ -0,0 +1,33 @@ +@startuml Scheduler Control Flow +start + +:get lowest priority task; +repeat :get proposed task run time; + if (clock > runTime) is (true) then + :delay exists, set delay and set proposed run time to now; + else (false) + :no delay exists, delay is 0, preserve proposed run time; + endif + + :Task Finish Time = Proposed Run Time + Task Duration; + while (there are higher priority tasks left to check AND no conflicts exist) is (true) + if (Task Finish Time conflicts with Higher Priority Task Run Time) is (true) then + :flag conflict; + else (false) + :no conflict exists; + endif + + endwhile (false); + + if (conflict exists) is (false) then + :set next task to current task; + :update task's next run time to runTime+delay; + #palegreen :return No Error; + stop + else (true) + endif + +repeat while (get lowest priority task not yet checked ) is (success) not (fail) +#pink : return error: no task found to run next; +stop +@enduml \ No newline at end of file diff --git a/docs/refs.bib b/docs/refs.bib new file mode 100644 index 00000000..6d62f9a3 --- /dev/null +++ b/docs/refs.bib @@ -0,0 +1,12 @@ + +@article{Karger97, + title={Scheduling Algorithms}, + author={David Karger and Cliff Stein and Joel Wein}, + Journal={Algorithms and Theory of Computation Handbook}, + volume={1}, + number={1}, + pages={1-52}, + year={1997}, + publisher={mit.edu} +} + diff --git a/docs/sampling_algorithm.ipynb b/docs/sampling_algorithm.ipynb index cc4aecbc..b9ac63bc 100644 --- a/docs/sampling_algorithm.ipynb +++ b/docs/sampling_algorithm.ipynb @@ -147,7 +147,7 @@ "outputs": [ { "data": { - "image/png": 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", + "image/png": 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", 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", 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", 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+ "image/png": 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", + "image/png": 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", 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", + "image/png": 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", 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", 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", 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" ] @@ -386,7 +386,7 @@ ], "metadata": { "kernelspec": { - "display_name": "base", + "display_name": ".venv", "language": "python", "name": "python3" }, diff --git a/docs/scheduler_documentation.pdf b/docs/scheduler_documentation.pdf new file mode 100644 index 00000000..1d69d465 Binary files /dev/null and b/docs/scheduler_documentation.pdf differ diff --git a/docs/scheduler_documentation.tex b/docs/scheduler_documentation.tex new file mode 100644 index 00000000..7f454709 --- /dev/null +++ b/docs/scheduler_documentation.tex @@ -0,0 +1,90 @@ +\documentclass{article} +\usepackage{graphicx} +\usepackage{booktabs} +\usepackage{fullpage} +\title{Brief Overview of SmartFin Version 3 Scheduler Implementation} +\author{Antara Chugh\\ \texttt{antarachugh@g.ucla.edu} + \and Charlie Kushelevsky\\ \texttt{ckushelevsky@ucsd.edu} + \and Meena Annamalai\\ \texttt{meenaannamalai@g.ucla.edu} + \and Hannah Cutler\\ \texttt{hannahcutler@ucsb.edu} + \and Nathan Hui\\ \texttt{nthui@ucsd.edu} + \and Ryan Kastner\\ \texttt{kastner@ucsd.edu}} +\date{Summer 2024} + +\begin{document} + +\maketitle + +\section{Background on the SmartFin System} + +We are working to design a scheduler for multiple sensors running at various frequencies and durations on a single threaded system that can only run one sensor at a time. The hardware thus restricts the schedule to be non-pre-emptitive: we cannot stop or pause a sensor A from running if another sensor B must run during the duration sensor A is collecting data. From now on, we'll refer to each sensor as a repeating, periodic task that must run for x amount of time every y seconds. + + + + + + +\begin{table}[h] +\centering +\caption{Mean \& Standard Deviation Runtime of SmartFin Sensors (Annamalai, M)} +\label{tab:sensor_runtime} +\begin{tabular}{@{}lllll@{}} +\toprule +Sensor & Mean (ms) & Standard Deviation (ms) & Mean + 3 Std (ms) & Frequency \\ \midrule +Temperature & 0.873 & 0.002 & 0.881 & 1 Hz \\ + Accelerometer & 5.244 & 0.004 & 5.255 & 30-300 Hz \\ + Gyroscope & 5.209 & 0.003 & 5.218 & 30-300 Hz \\ + Magnetometer & 5.212 & 0.004 & 5.221 & 30-300 Hz \\ + GPS (no fix) & 0.1443 & 0.022 & 0.2103 & 1 Hz \\ + Wet/Dry Sensor & 1.02 & 0.002 & 1.025 & 1 Hz \\ \bottomrule + \end{tabular} +\end{table} + + + + + +\section{Scheduler Requirements} +\begin{itemize} + \item Non-preemptive + \item Maintain the frequency of each task (consistent data sampling) + \item Delay or ``shift'' a sampling schedule of a task rather than having a singular task out of place, as that data point would then not be usable + \item Have minimal ``shifts'' - if priority is implemented, then minimize the ``shifts'' of each task by priority +\end{itemize} + +The most important quality for our schedule is that it maintains the frequency of each task as much as possible in order to maximize the percentage of samples that can be used for further data processing. For our system, a delay can better be defined as a shift of the sampling sequence: If task A was scheduled to run every 5 seconds starting at time 0, but a delay in the system prevented A from running at t=10 seconds and instead ran at t=12 seconds, we say that the sampling sequence of A was shifted, or delayed by 2 seconds. A will now run every 5 seconds starting from t=12, and thus will run again at t=17 seconds. Our scheduler thus seeks to minimize the amount of these ``shifts'' in the system. As noted above, we prefer these ``shifts'' over delays to singular tasks, as that would violate the consistent sample rate we hope to achieve. + + +In addition, minimizing the number of shifts in the sampling sequence may be more important for some tasks than others. For example, it is more important for data processing that the inertial measurement unit is sampled at a constant rate with little to no shifts than the GPS. Thus, there is incentive to introduce some method of maintaining priority of the tasks. This works best for systems with a small number of tasks that run for relatively short durations and have relatively long time between runs. For schedulers that are more overloaded, this notion of priority may shift the sampling schedules of low priority tasks too drastically, worst case stopping them from running at all. In this case, priority may have to be implemented differently, perhaps with a max shift time enforcement (no task can be shifted more than x seconds) or having no priority in the system at all. + +Another factor to note is that the exact amount of time the system will run is variable, as surfers spend variable amounts of time in the water. + +\section{Previous Works} +There has been much work already done on non-preemptive scheduler algorithms. + +Greedy algorithms are those based on making the most locally optimal decision. Earliest deadline first and earliest job finished are examples of greedy algorithms used for creating schedules with a single compute source and multiple tasks with potentially overlapping times \cite{Karger97}. + +Earliest Deadline First is a greedy algorithm for soft real time systems, which allow for tasks to be late \cite{Karger97}. It provides the optimal solution to minimizing lateness in the system. It schedules tasks by ascending deadlines, scheduling those with the earliest deadline first (as implied by its name).Though we wish to also minimize delays in the system, we would prefer to delay a task and all subsequent occurrences of that task to maintain a consistent sampling rate over having a task delayed and that data point rendered as not usable. Thus, earliest deadline first is not a feasible algorithm, as it cannot guarantee consistent periodic intervals of each task. + +Earliest Job Finish is a greedy algorithm for hard real time systems with limited compute power \cite{Karger97}. It aims to perform as many tasks as possible, given that no task can be late. It also sorts the tasks by ascending deadlines, scheduling tasks that work together. Earliest job finish would not be a feasible algorithm as jobs would be skipped completely, again failing to maintain a consistent data sampling rate. + +\section{Current Algorithm} + +Our current implementation is a greedy algorithm which picks tasks based on whether the time frame of their next run can finish before any task of higher priority. Otherwise, the task is shifted. + +\begin{figure}[h] + \centering + \includegraphics[width=0.6\textwidth]{control_flow.png} + \caption{Control Flow Diagram of the Algorithm} + \label{fig:control_flow_diagram} +\end{figure} + +\section{Scheduler Limitations} +Due to the fact that the SmartFin is a single core device and the periodicity and length of the tasks, mathematically, it is not possible for all combinations of tasks to run with no shifts in the system. Further, even when allowing for shifts, there exist combinations of tasks in which preserving the frequencies of each task is simply not possible. + + +\bibliography{refs}{} +\bibliographystyle{plain} + +\end{document} + diff --git a/external/googletest b/external/googletest index 1204d634..a7f443b8 160000 --- a/external/googletest +++ b/external/googletest @@ -1 +1 @@ -Subproject commit 1204d634444b0ba6da53201a8b6caf2a502d883c +Subproject commit a7f443b80b105f940225332ed3c31f2790092f47 diff --git a/lib/SparkFun_ICM-20948_ParticleLibrary b/lib/SparkFun_ICM-20948_ParticleLibrary index d1a3ab3d..a0c6bdaa 160000 --- a/lib/SparkFun_ICM-20948_ParticleLibrary +++ b/lib/SparkFun_ICM-20948_ParticleLibrary @@ -1 +1 @@ -Subproject commit d1a3ab3d883e393a264f33026a82d18ef10b33b9 +Subproject commit a0c6bdaa2350d33519c22c0c733c95ba6f8c558b diff --git a/src/abstractScheduler.hpp b/src/abstractScheduler.hpp new file mode 100644 index 00000000..262eecc0 --- /dev/null +++ b/src/abstractScheduler.hpp @@ -0,0 +1,80 @@ +/** + * @file abstractScheduler.hpp + * @author Nathan Hui (nthui@ucsd.edu) + * @author Charlie Kushelevsky (ckushelevsky@ucsd.edu) + * @brief Abstract Scheduler definitions + * @version 0.1 + * @date 2024-07-25 + * + * @copyright Copyright (c) 2024 + * + */ +#ifndef __ABSTRACT_SCHEDULER_HPP__ +#define __ABSTRACT_SCHEDULER_HPP__ +#include "deploymentSchedule.hpp" + +#include +#include + +/** + * @brief Scheduler Error Codes + * + * Encoding for AbstractScheduler::getNextTask return values + */ +typedef enum error_ +{ + /** + * @brief Scheduler successfully retrieved next task + * + */ + SCHEDULER_SUCCESS, + /** + * @brief Scheduler failed to find a new task. + * + * This is generally a fatal error, and cannot be solved at runtime + * + */ + TASK_SEARCH_FAIL, + /** + * @brief Scheduler retrieved the next task, but the task is delayed! + * + */ + SCHEDULER_DELAY_EXCEEDED +} SCH_error_e; + +/** + * @brief Abstract Scheduler base class + * + * This base class provides the minimum required behavior for task schedulers + */ +class AbstractScheduler +{ +public: + /** + * Creates and initializes the schedule + */ + virtual void initializeScheduler(void) = 0; + + /** + * @brief Computes the next task parameters + * + * Given the schedule of tasks and other internal state information, + * this function MUST compute the next available task as well as the time at + * which the next task should run. If the next available task to run should + * have run before the current_time, then this function MUST indicate that + * this task is delayed (if delays are allowed by implementation). + * + * @param p_next_task Pointer to a variable which will be populated with the + * address to the next task to run + * @param p_next_runtime Pointer to a variable which will be populated with + * the time in milliseconds since boot at which the next task MUST run + * @param current_time The current time in milliseconds since boot + * @return ::SCHEDULER_SUCCESS if successful, otherwise error + * code to be defined by implementation + */ + virtual SCH_error_e getNextTask(DeploymentSchedule_t **p_next_task, + std::uint32_t *p_next_runtime, + std::uint32_t current_time) = 0; +}; + +#endif // __ABSTRACT_SCHEDULER_HPP__ \ No newline at end of file diff --git a/src/cellular/dataUpload.cpp b/src/cellular/dataUpload.cpp index fe2d626b..62545b97 100644 --- a/src/cellular/dataUpload.cpp +++ b/src/cellular/dataUpload.cpp @@ -66,7 +66,7 @@ STATES_e DataUpload::run(void) if (!this->initSuccess) { - SF_OSAL_printf("Failed to init\n"); + SF_OSAL_printf("Failed to init" __NL__); return STATE_DEEP_SLEEP; } diff --git a/src/cellular/recorder.cpp b/src/cellular/recorder.cpp index 81848599..d1ec27c5 100644 --- a/src/cellular/recorder.cpp +++ b/src/cellular/recorder.cpp @@ -448,7 +448,7 @@ int Recorder::closeSession(void) { if (nullptr == this->pSession) { - SF_OSAL_printf("REC::CLOSE Already closed\n"); + SF_OSAL_printf("REC::CLOSE Already closed" __NL__); return 1; } @@ -482,7 +482,7 @@ int Recorder::putBytes(const void* pData, size_t nBytes) { this->dataBuffer[this->dataIdx] = 0; } - // SF_OSAL_printf("Flushing\n"); + // SF_OSAL_printf("Flushing" __NL__); this->pSession->write(this->dataBuffer, SF_PACKET_SIZE); memset(this->dataBuffer, 0, REC_MEMORY_BUFFER_SIZE); @@ -490,7 +490,7 @@ int Recorder::putBytes(const void* pData, size_t nBytes) } // data guaranteed to fit - // SF_OSAL_printf("Putting %u bytes\n", nBytes); + // SF_OSAL_printf("Putting %u bytes" __NL__, nBytes); memcpy(&this->dataBuffer[this->dataIdx], pData, nBytes); this->dataIdx += nBytes; return 0; diff --git a/src/cli/cli.cpp b/src/cli/cli.cpp index cf3267ed..1549d91b 100644 --- a/src/cli/cli.cpp +++ b/src/cli/cli.cpp @@ -8,31 +8,31 @@ #include "cli.hpp" +#include "Particle.h" #include "cliDebug.hpp" - #include "conio.hpp" #include "consts.hpp" +#include "debug/recorder_debug.hpp" +#include "debug/session_debug.hpp" +#include "imu/imu.hpp" +#include "location_service.h" #include "menu.hpp" - -#include "menuItems/systemCommands.hpp" #include "menuItems/debugCommands.hpp" #include "menuItems/gpsCommands.hpp" -#include "debug/recorder_debug.hpp" -#include "debug/session_debug.hpp" - -#include "states.hpp" -#include "util.hpp" -#include "vers.hpp" +#include "menuItems/systemCommands.hpp" #include "product.hpp" #include "sleepTask.hpp" +#include "states.hpp" #include "system.hpp" +#include "util.hpp" +#include "vers.hpp" -#include "system.hpp" - -#include #include +#include +#include +#include -#include "Particle.h" +#define NUM_SENSORS 6 void CLI_displayMenu(void); void CLI_hexdump(void); @@ -46,15 +46,15 @@ static void CLI_displayNVRAM(void); static void CLI_sleepSetSleepBehavior(void); static void CLI_sleepGetSleepBehavior(void); static void CLI_displayResetReason(void); +static void CLI_monitorSensors(void); -const Menu_t CLI_menu[] = -{ +const Menu_t CLI_menu[] = { {1, "display Menu", &CLI_displayMenu, MENU_CMD}, {2, "disconnect particle", &CLI_disconnect, MENU_CMD}, {3, "connect particle", &CLI_connect, MENU_CMD}, {4, "show flog errors", &CLI_displayFLOG, MENU_CMD}, {5, "test printf", &CLI_testPrintf, MENU_CMD}, - {6, "debug menu", {.pMenu=CLI_debugMenu}, MENU_SUBMENU}, + {6, "debug menu", {.pMenu = CLI_debugMenu}, MENU_SUBMENU}, {7, "hexdump", &CLI_hexdump, MENU_CMD}, {8, "gps", &CLI_GPS, MENU_CMD}, {9, "sleep", &CLI_doSleep, MENU_CMD}, @@ -62,17 +62,16 @@ const Menu_t CLI_menu[] = {11, "check charge ports", &CLI_checkCharging, MENU_CMD}, {12, "MFG Test", &CLI_doMfgTest, MENU_CMD}, {13, "upload", &CLI_doUpload, MENU_CMD}, - {14, "Recorder Test Menu", {.pMenu=Recorder_debug_menu}, MENU_SUBMENU}, - {15, "Session Test Menu", {.pMenu=Session_debug_menu}, MENU_SUBMENU}, + {14, "Recorder Test Menu", {.pMenu = Recorder_debug_menu}, MENU_SUBMENU}, + {15, "Session Test Menu", {.pMenu = Session_debug_menu}, MENU_SUBMENU}, + {16, "Display all sensors", &CLI_monitorSensors, MENU_CMD}, {100, "Set State", &CLI_setState, MENU_CMD}, {101, "Display System State", &CLI_displaySystemState, MENU_CMD}, {102, "Display NVRAM", &CLI_displayNVRAM, MENU_CMD}, {200, "Sleep - Set Sleep Behavior", &CLI_sleepSetSleepBehavior, MENU_CMD}, {201, "Sleep - Get Sleep Behavior", &CLI_sleepGetSleepBehavior, MENU_CMD}, {300, "Display Reset Reason", &CLI_displayResetReason, MENU_CMD}, - {0, nullptr, nullptr, MENU_NULL} -}; - + {0, nullptr, nullptr, MENU_NULL}}; STATES_e CLI_nextState; @@ -109,6 +108,220 @@ void CLI::exit() pSystemDesc->pChargerCheck->stop(); } +/** + * @brief CLI Monitor Sensors + * + * @todo This function needs to be refactored into a class for unit testing. Suspect bad data + * acquisition + * + * + */ +static void CLI_monitorSensors(void) +{ + char ch; + float accelData[3] = {0, 0, 0}; + float accelDMPData[4] = {0, 0, 0, 0}; + float gyroData[3] = {0, 0, 0}; + float gyroDMPData[3] = {0, 0, 0}; + float magData[3] = {0, 0, 0}; + double quatData[5] = {0, 0, 0, 0, 0}; + float tmpData = 0; + float wdCR = 0; + float wdLS = 0; + setupICM(); + SF_OSAL_printf(__NL__); + + typedef enum + { + ACCEL, + GYRO, + MAG, + TEMP, + WET_DRY, + DMP + } Sensor; + bool sensors[NUM_SENSORS] = {false}; + + SF_OSAL_printf("Enter delay time: "); + char dt[SF_CLI_MAX_CMD_LEN]; + getline(dt, SF_CLI_MAX_CMD_LEN); + int delayTime = atoi(dt); + SF_OSAL_printf("Delay set to %d ms" __NL__, delayTime); + SF_OSAL_printf("a - acceleraction, g - gyroscope, m - magnetometer, t - temp, w - wet/dry, d - " + "dmp" __NL__); + bool valid = true; + while (valid) + { + SF_OSAL_printf("Enter which sensors you want to look at (a, g, m, t, w, d), x to quit: "); + ch = getch(); + SF_OSAL_printf("%c", ch); + SF_OSAL_printf(__NL__); + switch (ch) + { + case 'a': + sensors[ACCEL] = true; + break; + case 'g': + sensors[GYRO] = true; + break; + case 'm': + sensors[MAG] = true; + break; + case 't': + sensors[TEMP] = true; + break; + case 'w': + sensors[WET_DRY] = true; + break; + case 'd': + sensors[DMP] = true; + break; + case 'x': + valid = false; + break; + default: + SF_OSAL_printf("invalid input" __NL__); + } + } + SF_OSAL_printf(__NL__); + std::vector headers; + if (sensors[ACCEL]) + { + headers.push_back("ax"); + headers.push_back("ay"); + headers.push_back("az"); + } + if (sensors[GYRO]) + { + headers.push_back("gx"); + headers.push_back("gy"); + headers.push_back("gz"); + } + if (sensors[MAG]) + { + headers.push_back("mx"); + headers.push_back("my"); + headers.push_back("mz"); + } + if (sensors[TEMP]) + { + headers.push_back("temp"); + } + if (sensors[WET_DRY]) + { + headers.push_back("wd lr"); + headers.push_back("wd ls"); + } + if (sensors[DMP]) + { + // headers.push_back("dq1"); + // headers.push_back("dq2"); + // headers.push_back("dq3"); + // headers.push_back("dq0"); + // headers.push_back("dqacc"); + headers.push_back("dax"); + headers.push_back("day"); + headers.push_back("daz"); + headers.push_back("a acc"); + // headers.push_back("dcx"); + // headers.push_back("dcy"); + // headers.push_back("dcz"); + } + + // if no headers, return now + if (headers.size() == 0) + { + return; + } + + int count = 0; + + while (1) + { + if (kbhit()) + { + ch = getch(); + + if ('q' == ch) + { + break; + } + } + if (sensors[ACCEL]) + { + getAccelerometer(accelData, accelData + 1, accelData + 2); + } + if (sensors[GYRO]) + { + getGyroscope(gyroData, gyroData + 1, gyroData + 2); + } + if (sensors[MAG]) + { + getMagnetometer(magData, magData + 1, magData + 2); + } + if (sensors[DMP]) + { + delayTime = 0; + getDMPAccelerometer(accelDMPData, accelDMPData + 1, accelDMPData + 2); + getDMPAccelerometerAcc(accelDMPData + 3); + getDMPGyroscope(gyroDMPData, gyroDMPData + 1, gyroDMPData + 2); + getDMPQuaternion(quatData, quatData + 1, quatData + 2, quatData + 3, quatData + 4); + } + if (sensors[TEMP]) + { + tmpData = pSystemDesc->pTempSensor->getTemp(); + } + if (sensors[WET_DRY]) + { + wdCR = pSystemDesc->pWaterSensor->getCurrentReading(); + wdLS = pSystemDesc->pWaterSensor->getLastStatus(); + } + + std::map sensorData = { + {"ax", N_TO_B_ENDIAN_2(B_TO_N_ENDIAN_2(accelData[0]))}, + {"ay", N_TO_B_ENDIAN_2(B_TO_N_ENDIAN_2(accelData[1]))}, + {"az", N_TO_B_ENDIAN_2(B_TO_N_ENDIAN_2(accelData[2]))}, + {"gx", gyroData[0]}, + {"gy", gyroData[1]}, + {"gz", gyroData[2]}, + {"mx", magData[0]}, + {"my", magData[1]}, + {"mz", magData[2]}, + {"temp", tmpData}, + {"wd cr", wdCR}, + {"wd ls", wdLS}, + {"dax", accelDMPData[0]}, + {"day", accelDMPData[1]}, + {"daz", accelDMPData[2]}, + {"a acc", accelDMPData[3]}, + {"dgx", gyroDMPData[0]}, + {"dgy", gyroDMPData[1]}, + {"dgz", gyroDMPData[2]}, + {"dq1", quatData[0]}, + {"dq2", quatData[1]}, + {"dq3", quatData[2]}, + {"dq0", quatData[3]}, + {"dqacc", quatData[4]} + + }; + if (count % 10 == 0) + { + for (const auto &header : headers) + { + SF_OSAL_printf("| %s |\t", header.c_str()); + } + SF_OSAL_printf(__NL__); + } + for (const auto &header : headers) + { + SF_OSAL_printf(" %8.4f\t", sensorData.at(header)); + } + SF_OSAL_printf(__NL__); + count++; + delay(delayTime); + } +} + void CLI_displayMenu(void) { MNU_displayMenu(CLI_menu); diff --git a/src/cli/conio.cpp b/src/cli/conio.cpp index 2c94d2b9..0e558f97 100644 --- a/src/cli/conio.cpp +++ b/src/cli/conio.cpp @@ -23,7 +23,7 @@ char SF_OSAL_printfBuffer[SF_OSAL_PRINTF_BUFLEN]; extern "C" { // Determines if key has been pressed - int kbhit(void) + int kbhit(void) { return Serial.available(); } @@ -41,7 +41,7 @@ extern "C" // Write character int putch(int ch) { - Serial.print((char) ch); + Serial.print((char)ch); return ch; } @@ -56,31 +56,32 @@ extern "C" if (kbhit()) { userInput = getch(); - switch(userInput) + switch (userInput) { - case '\b': - i--; - putch('\b'); - putch(' '); - putch('\b'); - break; - default: - buffer[i++] = userInput; - putch(userInput); - break; - case '\r': - buffer[i++] = 0; - putch('\r'); - putch('\n'); - return i; + case '\b': + i--; + putch('\b'); + putch(' '); + putch('\b'); + break; + default: + buffer[i++] = userInput; + putch(userInput); + break; + case '\r': + buffer[i++] = 0; + putch('\r'); + putch('\n'); + return i; } } } return i; } - + // Print char array to terminal - int SF_OSAL_printf(const char* fmt, ...) { + int SF_OSAL_printf(const char *fmt, ...) + { va_list vargs; int nBytes = 0; va_start(vargs, fmt); diff --git a/src/cli/conio.hpp b/src/cli/conio.hpp index aae5f026..124238ee 100644 --- a/src/cli/conio.hpp +++ b/src/cli/conio.hpp @@ -11,39 +11,40 @@ extern "C" #endif /** * @brief Gets character from serial - * + * * @return int key thats pressed */ int getch(void); /** * @brief Checks if key is pressed - * + * * @return int whether key is pressed */ int kbhit(void); /** * @brief Pushes character to serial - * + * * @param ch character to push * @return int Sucsess value */ int putch(int ch); /** * @brief Printf equivilent - * + * * @param fmt initial text * @param ... values to push - * @return int 1 if sucssesful + * @return int 1 if sucssesful */ int SF_OSAL_printf(const char* fmt, ...); +#ifdef PARTICLE /** * @brief Gets user input lin * @param buffer buffer to write too * @param buflen length of buffer */ int getline(char* buffer, int buflen); - +#endif #ifdef __cplusplus } #endif diff --git a/src/cli/flog.cpp b/src/cli/flog.cpp index a7dae0fa..6bc3156c 100644 --- a/src/cli/flog.cpp +++ b/src/cli/flog.cpp @@ -1,11 +1,12 @@ /** * @file flog.cpp * @author @emilybthorpe + * @author Nathan Hui (nthui@ucsd.edu) * @brief Fault Log (FLOG) with persistent memory * @version 0.1 - * @date 2023-08-03 + * @date 2024-10-31 * - * @copyright Copyright (c) 2023 + * @copyright Copyright (c) 2024 * */ @@ -13,9 +14,17 @@ #include "conio.hpp" #include "consts.hpp" - +#include "product.hpp" +#include "states.hpp" +#if SF_PLATFORM == SF_PLATFORM_PARTICLE #include +#elif SF_PLATFORM == SF_PLATFORM_GCC +#include "scheduler_test_system.hpp" +#include +#include +#include +#endif typedef struct FLOG_Entry_ { @@ -37,73 +46,92 @@ typedef struct FLOG_Message_ const char* message; }FLOG_Message_t; +struct FLOG_Printer +{ + FLOG_CODE_e code; + void (*printer)(const FLOG_Entry_t &); +}; + +#if SF_PLATFORM == SF_PLATFORM_PARTICLE retained FLOG_Data_t flogData; +#elif SF_PLATFORM == SF_PLATFORM_GCC +FLOG_Data_t flogData; +#endif static char FLOG_unknownMessage[256]; static const char* FLOG_FindMessage(FLOG_CODE_e code); static int FLOG_IsInitialized(void); +static void FLOG_fmt_sys_start(const FLOG_Entry_t &entry); +static void FLOG_fmt_reset_reason(const FLOG_Entry_t &entry); +static void FLOG_fmt_default(const FLOG_Entry_t &entry); +static void FLOG_fmt_state_start(const FLOG_Entry_t &entry); -const FLOG_Message_t FLOG_Message[] = { - {FLOG_SYS_START, "System Start"}, - {FLOG_SYS_BADSRAM, "Bad SRAM"}, - {FLOG_SYS_STARTSTATE, "Starting State"}, - {FLOG_SYS_INITSTATE, "Initializing State"}, - {FLOG_SYS_EXECSTATE, "Executing State Body"}, - {FLOG_SYS_EXITSTATE, "Exiting State"}, - {FLOG_SYS_UNKNOWNSTATE, "Unknown State"}, - {FLOG_RESET_REASON, "Reset Reason"}, - {FLOG_CHARGER_REMOVED, "Charger removed"}, - - {FLOG_CAL_BURST, "Calibrate Burst"}, - {FLOG_CAL_INIT, "Calibrate Initialization"}, - {FLOG_CAL_START_RUN, "Calibrate Start RUN"}, - {FLOG_CAL_LIMIT, "Calibrate Limit of Cycles"}, - {FLOG_CAL_DONE, "Calibration complete"}, - {FLOG_CAL_EXIT, "Calbiration Exit"}, - {FLOG_CAL_SLEEP, "Calibration Sleep"}, - {FLOG_CAL_TEMP, "Calibration Temp Measurement"}, - - {FLOG_MAG_ID_MISMATCH, "Compass ID Mismatch"}, - {FLOG_MAG_MEAS_TO, "Compass Measurement Timeout"}, - {FLOG_MAG_TEST_FAIL, "Compass Self-Test Failure"}, - {FLOG_MAG_MEAS_OVRFL, "Compass Measurement Overflow"}, - {FLOG_MAG_I2C_FAIL, "Compass I2C Failure"}, - {FLOG_MAG_MODE_FAIL, "Compass Mode Set Fail"}, - {FLOG_ICM_FAIL, "ICM Fail"}, - - {FLOG_RIDE_INIT_TIMEOUT, "Ride init Timeout"}, - - {FLOG_UPLOAD_NO_UPLOAD, "Upload - No Upload Flag set"}, - {FLOG_UPL_BATT_LOW, "Upload Battery low"}, - {FLOG_UPL_FOLDER_COUNT, "Upload file count"}, - {FLOG_UPL_CONNECT_FAIL, "Upload connect fail"}, - {FLOG_UPL_OPEN_FAIL, "Upload open last session fail"}, - {FLOG_UPL_PUB_FAIL, "Upload Publish fail"}, - - {FLOG_GPS_INIT_FAIL, "GPS Init Fail"}, - {FLOG_GPS_START_FAIL, "GPS Start Fail"}, - - {FLOG_TEMP_FAIL, "Temp Start Fail"}, - - {FLOG_FS_OPENDIR_FAIL, "opendir fail"}, - {FLOG_FS_STAT_FAIL, "stat fail"}, - {FLOG_SYS_MOUNT_FAIL, "Mounting fail"}, - {FLOG_FS_MKDIR_FAIL, "mkdir fail"}, - {FLOG_FS_CREAT_FAIL, "file create fail"}, - {FLOG_FS_OPEN_FAIL, "file open fail"}, - {FLOG_FS_WRITE_FAIL, "file write fail"}, - {FLOG_FS_CLOSE_FAIL, "file close fail"}, - {FLOG_FS_FTRUNC_FAIL, "file ftrunc fail"}, - {FLOG_FS_READ_FAIL, "file ftrunc fail"}, - {FLOG_REC_SETUP_FAIL, "Recorder setup failed"}, - {FLOG_REC_SESSION_CLOSED, "Write to Closed Session"}, - - {FLOG_CELL_DISCONN_FAIL, "Cellular failed to disconnect"}, - - {FLOG_SW_NULLPTR, "Software Null Pointer"}, - {FLOG_DEBUG, "debug point"}, - {FLOG_NULL, NULL} -}; +const FLOG_Message_t FLOG_Message[] = {{FLOG_SYS_START, "System Start"}, + {FLOG_SYS_BADSRAM, "Bad SRAM"}, + {FLOG_SYS_STARTSTATE, "Starting State"}, + {FLOG_SYS_INITSTATE, "Initializing State"}, + {FLOG_SYS_EXECSTATE, "Executing State Body"}, + {FLOG_SYS_EXITSTATE, "Exiting State"}, + {FLOG_SYS_UNKNOWNSTATE, "Unknown State"}, + {FLOG_RESET_REASON, "Reset Reason"}, + {FLOG_CHARGER_REMOVED, "Charger removed"}, + + {FLOG_CAL_BURST, "Calibrate Burst"}, + {FLOG_CAL_INIT, "Calibrate Initialization"}, + {FLOG_CAL_START_RUN, "Calibrate Start RUN"}, + {FLOG_CAL_LIMIT, "Calibrate Limit of Cycles"}, + {FLOG_CAL_DONE, "Calibration complete"}, + {FLOG_CAL_EXIT, "Calbiration Exit"}, + {FLOG_CAL_SLEEP, "Calibration Sleep"}, + {FLOG_CAL_TEMP, "Calibration Temp Measurement"}, + + {FLOG_MAG_ID_MISMATCH, "Compass ID Mismatch"}, + {FLOG_MAG_MEAS_TO, "Compass Measurement Timeout"}, + {FLOG_MAG_TEST_FAIL, "Compass Self-Test Failure"}, + {FLOG_MAG_MEAS_OVRFL, "Compass Measurement Overflow"}, + {FLOG_MAG_I2C_FAIL, "Compass I2C Failure"}, + {FLOG_MAG_MODE_FAIL, "Compass Mode Set Fail"}, + {FLOG_ICM_FAIL, "ICM Fail"}, + + {FLOG_RIDE_INIT_TIMEOUT, "Ride init Timeout"}, + {FLOG_SCHEDULER_FAILED, "Scheduler failed"}, + {FLOG_SCHEDULER_DELAY_EXCEEDED, "Ensemble skipped"}, + + {FLOG_UPLOAD_NO_UPLOAD, "Upload - No Upload Flag set"}, + {FLOG_UPL_BATT_LOW, "Upload Battery low"}, + {FLOG_UPL_FOLDER_COUNT, "Upload file count"}, + {FLOG_UPL_CONNECT_FAIL, "Upload connect fail"}, + {FLOG_UPL_OPEN_FAIL, "Upload open last session fail"}, + {FLOG_UPL_PUB_FAIL, "Upload Publish fail"}, + + {FLOG_GPS_INIT_FAIL, "GPS Init Fail"}, + {FLOG_GPS_START_FAIL, "GPS Start Fail"}, + + {FLOG_TEMP_FAIL, "Temp Start Fail"}, + + {FLOG_FS_OPENDIR_FAIL, "opendir fail"}, + {FLOG_FS_STAT_FAIL, "stat fail"}, + {FLOG_SYS_MOUNT_FAIL, "Mounting fail"}, + {FLOG_FS_MKDIR_FAIL, "mkdir fail"}, + {FLOG_FS_CREAT_FAIL, "file create fail"}, + {FLOG_FS_OPEN_FAIL, "file open fail"}, + {FLOG_FS_WRITE_FAIL, "file write fail"}, + {FLOG_FS_CLOSE_FAIL, "file close fail"}, + {FLOG_FS_FTRUNC_FAIL, "file ftrunc fail"}, + {FLOG_FS_READ_FAIL, "file ftrunc fail"}, + {FLOG_REC_SETUP_FAIL, "Recorder setup failed"}, + {FLOG_REC_SESSION_CLOSED, "Write to Closed Session"}, + + {FLOG_CELL_DISCONN_FAIL, "Cellular failed to disconnect"}, + + {FLOG_SW_NULLPTR, "Software Null Pointer"}, + {FLOG_DEBUG, "debug point"}, + {FLOG_NULL, NULL}}; + +const struct FLOG_Printer formatter_table[] = {{FLOG_RESET_REASON, FLOG_fmt_reset_reason}, + {FLOG_SYS_START, FLOG_fmt_sys_start}, + {FLOG_SYS_STARTSTATE, FLOG_fmt_state_start}, + {FLOG_NULL, FLOG_fmt_default}}; void FLOG_Initialize(void) { @@ -132,27 +160,34 @@ void FLOG_AddError(FLOG_CODE_e errorCode, FLOG_VALUE_TYPE parameter) void FLOG_DisplayLog(void) { uint32_t i; + const FLOG_Entry_t *pEntry; if (!FLOG_IsInitialized()) { - SF_OSAL_printf("Fault Log not initialized!\n"); + SF_OSAL_printf("Fault Log not initialized!" __NL__); return; } i = 0; if (flogData.numEntries > FLOG_NUM_ENTRIES) { - SF_OSAL_printf("Fault Log overrun!\n"); + SF_OSAL_printf("Fault Log overrun!" __NL__); i = flogData.numEntries - FLOG_NUM_ENTRIES; } for (; i < flogData.numEntries; i++) { - SF_OSAL_printf("%8d %32s, parameter: 0x%08" FLOG_PARAM_FMT __NL__, - flogData.flogEntries[i & (FLOG_NUM_ENTRIES - 1)].timestamp_ms, - FLOG_FindMessage((FLOG_CODE_e)flogData.flogEntries[i & (FLOG_NUM_ENTRIES - 1)].errorCode), - flogData.flogEntries[i & (FLOG_NUM_ENTRIES - 1)].param); + pEntry = &flogData.flogEntries[i & (FLOG_NUM_ENTRIES - 1)]; + const struct FLOG_Printer *ptr; + for (ptr = formatter_table; ptr->code != FLOG_NULL; ptr++) + { + if (ptr->code == pEntry->errorCode) + { + break; + } + } + ptr->printer(*pEntry); } - SF_OSAL_printf("\n"); + SF_OSAL_printf(__NL__); } void FLOG_ClearLog(void) { @@ -177,4 +212,72 @@ static const char* FLOG_FindMessage(FLOG_CODE_e code) static int FLOG_IsInitialized(void) { return flogData.nNumEntries == ~flogData.numEntries; +} + +static void FLOG_fmt_sys_start(const FLOG_Entry_t &entry) +{ + const char *time_str; +#if SF_PLATFORM == SF_PLATFORM_PARTICLE + time_str = Time.format((time32_t)entry.param).c_str(); +#elif SF_PLATFORM == SF_PLATFORM_GCC + time_t timestamp; + timestamp = entry.param; + time_str = ctime(×tamp); +#endif + SF_OSAL_printf("%8d System Started at %s" __NL__, entry.timestamp_ms, time_str); +} + +static void FLOG_fmt_reset_reason(const FLOG_Entry_t &entry) +{ +#if SF_PLATFORM == SF_PLATFORM_GCC + SF_OSAL_printf("Reset reason not available" __NL__); +#elif SF_PLATFORM == SF_PLATFORM_PARTICLE + struct reason_mapping + { + System_Reset_Reason code; + const char *text; + }; + struct reason_mapping reason_map[] = { + {RESET_REASON_UNKNOWN, "Unspecified reason"}, + {RESET_REASON_PIN_RESET, "Reset pin assert"}, + {RESET_REASON_POWER_MANAGEMENT, "Low-power management reset"}, + {RESET_REASON_POWER_DOWN, "Power-down reset"}, + {RESET_REASON_POWER_BROWNOUT, "Brownout reset"}, + {RESET_REASON_WATCHDOG, "Watchdog reset"}, + {RESET_REASON_UPDATE, "Reset to apply firmware update"}, + {RESET_REASON_UPDATE_ERROR, "Generic firmware update error"}, + {RESET_REASON_UPDATE_TIMEOUT, "Firmware update timeout"}, + {RESET_REASON_FACTORY_RESET, "Factory reset requested"}, + {RESET_REASON_SAFE_MODE, "Safe mode requested"}, + {RESET_REASON_DFU_MODE, "DFU mode requested"}, + {RESET_REASON_PANIC, "System panic"}, + {RESET_REASON_USER, "User reset"}, + {RESET_REASON_CONFIG_UPDATE, "Config change update"}, + {RESET_REASON_NONE, "Invalid reason code"}, + }; + struct reason_mapping *ptr; + for (ptr = reason_map; ptr->code != RESET_REASON_NONE; ptr++) + { + if (ptr->code == (System_Reset_Reason)entry.param) + { + break; + } + } + SF_OSAL_printf("%8d Reset due to %s" __NL__, entry.timestamp_ms, ptr->text); + +#endif +} + +static void FLOG_fmt_state_start(const FLOG_Entry_t &entry) +{ + const char *state_name = STATES_NAME_TAB[entry.param]; + SF_OSAL_printf("%8d Starting state %s" __NL__, entry.timestamp_ms, state_name); +} + +static void FLOG_fmt_default(const FLOG_Entry_t &entry) +{ + SF_OSAL_printf("%8d %32s, parameter: 0x%08" FLOG_PARAM_FMT __NL__, + entry.timestamp_ms, + FLOG_FindMessage((FLOG_CODE_e)entry.errorCode), + entry.param); } \ No newline at end of file diff --git a/src/cli/flog.hpp b/src/cli/flog.hpp index 231daa08..6f00ba00 100644 --- a/src/cli/flog.hpp +++ b/src/cli/flog.hpp @@ -10,68 +10,70 @@ typedef enum FLOG_CODE_ { - FLOG_NULL =0x0000, + FLOG_NULL = 0x0000, - FLOG_SYS_START =0x0100, - FLOG_SYS_BADSRAM =0x0101, - FLOG_SYS_STARTSTATE =0x0102, - FLOG_SYS_INITSTATE =0x0103, - FLOG_SYS_EXECSTATE =0x0104, - FLOG_SYS_EXITSTATE =0x0105, - FLOG_SYS_UNKNOWNSTATE =0x0106, - FLOG_RESET_REASON =0x0107, - FLOG_CHARGER_REMOVED =0x0108, + FLOG_SYS_START = 0x0100, + FLOG_SYS_BADSRAM = 0x0101, + FLOG_SYS_STARTSTATE = 0x0102, + FLOG_SYS_INITSTATE = 0x0103, + FLOG_SYS_EXECSTATE = 0x0104, + FLOG_SYS_EXITSTATE = 0x0105, + FLOG_SYS_UNKNOWNSTATE = 0x0106, + FLOG_RESET_REASON = 0x0107, + FLOG_CHARGER_REMOVED = 0x0108, - FLOG_CAL_BURST =0x0200, - FLOG_CAL_INIT =0x0201, - FLOG_CAL_START_RUN =0x0202, - FLOG_CAL_LIMIT =0x0203, - FLOG_CAL_DONE =0x0204, - FLOG_CAL_EXIT =0x0205, - FLOG_CAL_SLEEP =0x0206, - FLOG_CAL_TEMP =0x0207, + FLOG_CAL_BURST = 0x0200, + FLOG_CAL_INIT = 0x0201, + FLOG_CAL_START_RUN = 0x0202, + FLOG_CAL_LIMIT = 0x0203, + FLOG_CAL_DONE = 0x0204, + FLOG_CAL_EXIT = 0x0205, + FLOG_CAL_SLEEP = 0x0206, + FLOG_CAL_TEMP = 0x0207, - FLOG_MAG_ID_MISMATCH =0x0301, - FLOG_MAG_MEAS_TO =0x0302, - FLOG_MAG_TEST_FAIL =0x0303, - FLOG_MAG_MEAS_OVRFL =0x0304, - FLOG_MAG_I2C_FAIL =0x0305, - FLOG_MAG_MODE_FAIL =0x0306, - FLOG_ICM_FAIL =0x0307, + FLOG_MAG_ID_MISMATCH = 0x0301, + FLOG_MAG_MEAS_TO = 0x0302, + FLOG_MAG_TEST_FAIL = 0x0303, + FLOG_MAG_MEAS_OVRFL = 0x0304, + FLOG_MAG_I2C_FAIL = 0x0305, + FLOG_MAG_MODE_FAIL = 0x0306, + FLOG_ICM_FAIL = 0x0307, - FLOG_RIDE_INIT_TIMEOUT=0x0401, + FLOG_RIDE_INIT_TIMEOUT = 0x0401, + FLOG_SCHEDULER_FAILED = 0x0402, + FLOG_SCHEDULER_DELAY_EXCEEDED = 0x0403, - FLOG_UPLOAD_NO_UPLOAD =0x0501, - FLOG_UPL_BATT_LOW =0x0502, - FLOG_UPL_FOLDER_COUNT =0x0503, - FLOG_UPL_CONNECT_FAIL =0x0504, - FLOG_UPL_OPEN_FAIL =0x0505, - FLOG_UPL_PUB_FAIL =0x0506, + FLOG_UPLOAD_NO_UPLOAD = 0x0501, + FLOG_UPL_BATT_LOW = 0x0502, + FLOG_UPL_FOLDER_COUNT = 0x0503, + FLOG_UPL_CONNECT_FAIL = 0x0504, + FLOG_UPL_OPEN_FAIL = 0x0505, + FLOG_UPL_PUB_FAIL = 0x0506, - FLOG_GPS_INIT_FAIL =0x0601, - FLOG_GPS_START_FAIL =0x0602, + FLOG_GPS_INIT_FAIL = 0x0601, + FLOG_GPS_START_FAIL = 0x0602, - FLOG_TEMP_FAIL =0x0704, + FLOG_TEMP_FAIL = 0x0704, - FLOG_FS_OPENDIR_FAIL =0x0800, - FLOG_FS_STAT_FAIL =0x0801, - FLOG_SYS_MOUNT_FAIL =0x0802, - FLOG_FS_MKDIR_FAIL =0x0803, - FLOG_FS_CREAT_FAIL =0x0804, - FLOG_FS_OPEN_FAIL =0x0805, - FLOG_FS_WRITE_FAIL =0x0806, - FLOG_FS_CLOSE_FAIL =0x0807, - FLOG_FS_FTRUNC_FAIL =0x0808, - FLOG_FS_READ_FAIL =0x0809, - FLOG_REC_SETUP_FAIL =0x0810, - FLOG_REC_METADATA_BAD =0x0811, - FLOG_REC_SESSION_CLOSED =0x0812, + FLOG_FS_OPENDIR_FAIL = 0x0800, + FLOG_FS_STAT_FAIL = 0x0801, + FLOG_SYS_MOUNT_FAIL = 0x0802, + FLOG_FS_MKDIR_FAIL = 0x0803, + FLOG_FS_CREAT_FAIL = 0x0804, + FLOG_FS_OPEN_FAIL = 0x0805, + FLOG_FS_WRITE_FAIL = 0x0806, + FLOG_FS_CLOSE_FAIL = 0x0807, + FLOG_FS_FTRUNC_FAIL = 0x0808, + FLOG_FS_READ_FAIL = 0x0809, + FLOG_REC_SETUP_FAIL = 0x0810, + FLOG_REC_METADATA_BAD = 0x0811, + FLOG_REC_SESSION_CLOSED = 0x0812, - FLOG_CELL_DISCONN_FAIL =0x0900, + FLOG_CELL_DISCONN_FAIL = 0x0900, - FLOG_SW_NULLPTR =0xF001, - FLOG_DEBUG =0xFFFF, -}FLOG_CODE_e; + FLOG_SW_NULLPTR = 0xF001, + FLOG_DEBUG = 0xFFFF, +} FLOG_CODE_e; void FLOG_Initialize(void); void FLOG_AddError(FLOG_CODE_e errorCode, FLOG_VALUE_TYPE parameter); diff --git a/src/cli/menuItems/debugCommands.cpp b/src/cli/menuItems/debugCommands.cpp index 54717388..ac9c9021 100644 --- a/src/cli/menuItems/debugCommands.cpp +++ b/src/cli/menuItems/debugCommands.cpp @@ -67,7 +67,7 @@ void CLI_createTestFile(void) if (fd != -1) { for(int ii = 0; ii < 100; ii++) { - String msg = String::format("testing %d\n", ii); + String msg = String::format("testing %d" __NL__, ii); SF_OSAL_printf("Creating file with msg %s" __NL__, msg.c_str()); int i = write(fd, msg.c_str(), msg.length()); diff --git a/src/cli/menuItems/systemCommands.cpp b/src/cli/menuItems/systemCommands.cpp index e105d2fd..32ca7365 100644 --- a/src/cli/menuItems/systemCommands.cpp +++ b/src/cli/menuItems/systemCommands.cpp @@ -56,7 +56,7 @@ void CLI_doUpload(void) void CLI_self_identify(void) { - SF_OSAL_printf("Smartfin ID: %s\n", pSystemDesc->deviceID); + SF_OSAL_printf("Smartfin ID: %s" __NL__, pSystemDesc->deviceID); VERS_printBanner(); } diff --git a/src/deploymentSchedule.hpp b/src/deploymentSchedule.hpp new file mode 100644 index 00000000..44fd1fbc --- /dev/null +++ b/src/deploymentSchedule.hpp @@ -0,0 +1,20 @@ +/** + * @file deploymentSchedule.hpp + * @author Charlie Kushlevsky (ckushelevsky@ucsd.edu) + * @brief + * @version 0.1 + * @date 2024-08-01 + * + * @copyright Copyright (c) 2024 + * + */ +#ifndef __DEPLOYMENT_SCHDEULE_HPP__ +#define __DEPLOYMENT_SCHDEULE_HPP__ + +/** + * @brief Deployment Schedule Type + + */ +typedef struct DeploymentSchedule_ DeploymentSchedule_t; + +#endif //__DEPLOYMENT_SCHDEULE_HPP__ \ No newline at end of file diff --git a/src/ensembles.cpp b/src/ensembles.cpp new file mode 100644 index 00000000..7f90e14d --- /dev/null +++ b/src/ensembles.cpp @@ -0,0 +1,285 @@ +/** + * @file ensembles.cpp + * @brief Contains definitions of ensembles (updated version of smartfin-fw2 ensembles) + */ +#include "ensembles.hpp" + +#include "cellular/ensembleTypes.hpp" +#include "imu/imu.hpp" +#include "product.hpp" +#include "scheduler.hpp" +#include "system.hpp" +#include "util.hpp" + +#if SF_PLATFORM == SF_PLATFORM_PARTICLE +#include "Particle.h" +#elif SF_PLATFORM == SF_PLATFORM_GCC +#include "scheduler_test_system.hpp" +#endif + +// define ensemble structs +typedef struct Ensemble10_eventData_ +{ + int16_t temperature; + int16_t water; + int16_t acc[3]; + int16_t ang[3]; + int16_t mag[3]; + int32_t location[2]; + uint8_t hasGPS; + uint32_t accumulateCount; +} Ensemble10_eventData_t; + +typedef struct Ensemble08_eventData_ +{ + double temperature; + int32_t water; + + uint32_t accumulateCount; +} Ensemble08_eventData_t; + +typedef struct Ensemble07_eventData_ +{ + uint16_t battVoltage; + uint32_t accumulateCount; +} Ensemble07_eventData_t; + +static Ensemble10_eventData_t ensemble10Data; +static Ensemble07_eventData_t ensemble07Data; +static Ensemble08_eventData_t ensemble08Data; + +void SS_ensemble10Init(DeploymentSchedule_t *pDeployment) +{ + memset(&ensemble10Data, 0, sizeof(Ensemble10_eventData_t)); + pDeployment->state.pData = &ensemble10Data; +} + +void SS_ensemble07Init(DeploymentSchedule_t *pDeployment) +{ + memset(&ensemble07Data, 0, sizeof(Ensemble07_eventData_t)); + pDeployment->state.pData = &ensemble07Data; +} + +void SS_ensemble08Init(DeploymentSchedule_t *pDeployment) +{ + memset(&ensemble08Data, 0, sizeof(Ensemble08_eventData_t)); + pDeployment->state.pData = &ensemble08Data; +} + +void SS_ensemble10Func(DeploymentSchedule_t *pDeployment) +{ + float temp; + uint8_t water; + int32_t lat, lng; + float accelData[3]; + float gyroData[3]; + float magData[3]; + bool hasGPS = false; + Ensemble10_eventData_t *pData = (Ensemble10_eventData_t *)pDeployment->state.pData; + +#pragma pack(push, 1) + struct + { + EnsembleHeader_t header; + union + { + Ensemble10_data_t ens10; + Ensemble11_data_t ens11; + } data; + } ensData; +#pragma pack(pop) + + // Obtain measurements + temp = pSystemDesc->pTempSensor->getTemp(); + water = pSystemDesc->pWaterSensor->getCurrentReading(); + getAccelerometer(accelData, accelData + 1, accelData + 2); + getGyroscope(gyroData, gyroData + 1, gyroData + 2); + getMagnetometer(magData, magData + 1, magData + 2); + + // GPS + bool locked; + unsigned int satsInView; + ubloxGPS *ubloxGps_(nullptr); + locked = (ubloxGps_->getLock()) ? 1 : 0; + gps_sat_t sats_in_view_desc[NUM_SAT_DESC]; + satsInView = ubloxGps_->getSatellitesDesc(sats_in_view_desc); + if (locked && satsInView > 4) + { + hasGPS = true; + lat = ubloxGps_->getLatitude(); + lng = ubloxGps_->getLongitude(); + } + else + { + hasGPS = false; + lat = pData->location[0]; + lng = pData->location[1]; + } + + // Accumulate measurements + pData->temperature += temp; + pData->water += water; + pData->acc[0] += B_TO_N_ENDIAN_2(accelData[0]); + pData->acc[1] += B_TO_N_ENDIAN_2(accelData[1]); + pData->acc[2] += B_TO_N_ENDIAN_2(accelData[2]); + pData->ang[0] += B_TO_N_ENDIAN_2(gyroData[0]); + pData->ang[1] += B_TO_N_ENDIAN_2(gyroData[1]); + pData->ang[2] += B_TO_N_ENDIAN_2(gyroData[2]); + pData->mag[0] += B_TO_N_ENDIAN_2(magData[0]); + pData->mag[1] += B_TO_N_ENDIAN_2(magData[1]); + pData->mag[2] += B_TO_N_ENDIAN_2(magData[2]); + pData->location[0] += lat; + pData->location[1] += lng; + pData->hasGPS += hasGPS ? 1 : 0; + pData->accumulateCount++; + + // Report accumulated measurements + if (pData->accumulateCount == pDeployment->measurementsToAccumulate) + { + water = pData->water / pDeployment->measurementsToAccumulate; + temp = pData->temperature / pDeployment->measurementsToAccumulate; + if (water == false) + { + temp -= 100; + } + + ensData.header.elapsedTime_ds = Ens_getStartTime( + pDeployment->state.nextRunTime); // does nextruntime work for start time + SF_OSAL_printf("Ensemble timestamp: %d\n", ensData.header.elapsedTime_ds); + ensData.data.ens10.rawTemp = N_TO_B_ENDIAN_2(temp / 0.0078125); + ensData.data.ens10.rawAcceleration[0] = + N_TO_B_ENDIAN_2(pData->acc[0] / pDeployment->measurementsToAccumulate); + ensData.data.ens10.rawAcceleration[1] = + N_TO_B_ENDIAN_2(pData->acc[1] / pDeployment->measurementsToAccumulate); + ensData.data.ens10.rawAcceleration[2] = + N_TO_B_ENDIAN_2(pData->acc[2] / pDeployment->measurementsToAccumulate); + ensData.data.ens10.rawAngularVel[0] = + N_TO_B_ENDIAN_2(pData->ang[0] / pDeployment->measurementsToAccumulate); + ensData.data.ens10.rawAngularVel[1] = + N_TO_B_ENDIAN_2(pData->ang[1] / pDeployment->measurementsToAccumulate); + ensData.data.ens10.rawAngularVel[2] = + N_TO_B_ENDIAN_2(pData->ang[2] / pDeployment->measurementsToAccumulate); + ensData.data.ens10.rawMagField[0] = + N_TO_B_ENDIAN_2(pData->mag[0] / pDeployment->measurementsToAccumulate); + ensData.data.ens10.rawMagField[1] = + N_TO_B_ENDIAN_2(pData->mag[1] / pDeployment->measurementsToAccumulate); + ensData.data.ens10.rawMagField[2] = + N_TO_B_ENDIAN_2(pData->mag[2] / pDeployment->measurementsToAccumulate); + ensData.data.ens11.location[0] = + N_TO_B_ENDIAN_4(pData->location[0] / pDeployment->measurementsToAccumulate); + ensData.data.ens11.location[1] = + N_TO_B_ENDIAN_4(pData->location[1] / pDeployment->measurementsToAccumulate); + + if (pData->hasGPS / pDeployment->measurementsToAccumulate) + { + ensData.header.ensembleType = ENS_TEMP_IMU_GPS; + pSystemDesc->pRecorder->putBytes(&ensData, + sizeof(EnsembleHeader_t) + sizeof(Ensemble11_data_t)); + } + else + { + ensData.header.ensembleType = ENS_TEMP_IMU; + pSystemDesc->pRecorder->putBytes(&ensData, + sizeof(EnsembleHeader_t) + sizeof(Ensemble10_data_t)); + } + + memset(pData, 0, sizeof(Ensemble10_eventData_t)); + } +} + +void SS_ensemble07Func(DeploymentSchedule_t *pDeployment) +{ + float battVoltage; + Ensemble07_eventData_t *pData = (Ensemble07_eventData_t *)pDeployment->state.pData; +#pragma pack(push, 1) + struct + { + EnsembleHeader_t header; + Ensemble07_data_t data; + } ensData; +#pragma pack(pop) + + // obtain measurements + battVoltage = pSystemDesc->pBattery->getVCell(); + + // accumulate measurements + pData->battVoltage += battVoltage; + pData->accumulateCount++; + + // Report accumulated measurements + if (pData->accumulateCount == pDeployment->measurementsToAccumulate) + { + ensData.header.elapsedTime_ds = Ens_getStartTime(pDeployment->state.nextRunTime); + ensData.header.ensembleType = ENS_BATT; + ensData.data.batteryVoltage = + N_TO_B_ENDIAN_2((pData->battVoltage / pData->accumulateCount) * 1000); + + pSystemDesc->pRecorder->putData(ensData); + memset(pData, 0, sizeof(Ensemble07_eventData_t)); + } +} + +void SS_ensemble08Func(DeploymentSchedule_t *pDeployment) +{ + float temp; + uint8_t water; + + Ensemble08_eventData_t *pData = (Ensemble08_eventData_t *)pDeployment->state.pData; +#pragma pack(push, 1) + struct + { + EnsembleHeader_t header; + Ensemble08_data_t ensData; + } ens; +#pragma pack(pop) + + // obtain measurements + temp = pSystemDesc->pTempSensor->getTemp(); + water = pSystemDesc->pWaterSensor->getCurrentReading(); + + // accumulate measurements + pData->temperature += temp; + pData->water += water; + pData->accumulateCount++; + + // Report accumulated measurements + if (pData->accumulateCount == pDeployment->measurementsToAccumulate) + { + water = pData->water / pDeployment->measurementsToAccumulate; + temp = pData->temperature / pDeployment->measurementsToAccumulate; + if (water == false) + { + temp -= 100; + } + + ens.header.elapsedTime_ds = Ens_getStartTime(pDeployment->state.nextRunTime); + ens.header.ensembleType = ENS_TEMP_TIME; + ens.ensData.rawTemp = N_TO_B_ENDIAN_2(temp / 0.0078125); + + pSystemDesc->pRecorder->putData(ens); + memset(pData, 0, sizeof(Ensemble08_eventData_t)); + } +} + +void SS_fwVerInit(DeploymentSchedule_t *pDeployment) +{ + (void)pDeployment; +} +void SS_fwVerFunc(DeploymentSchedule_t *pDeployment) +{ +#pragma pack(push, 1) + struct textEns + { + EnsembleHeader_t header; + uint8_t nChars; + char verBuf[32]; + } ens; +#pragma pack(pop) + + ens.header.elapsedTime_ds = Ens_getStartTime(pDeployment->state.nextRunTime); + ens.header.ensembleType = ENS_TEXT; + + // ens.nChars = snprintf(ens.verBuf, 32, "FW%d.%d.%d%s", FW_MAJOR_VERSION, FW_MINOR_VERSION, + // FW_BUILD_NUM, FW_BRANCH); + pSystemDesc->pRecorder->putBytes(&ens, sizeof(EnsembleHeader_t) + sizeof(uint8_t) + ens.nChars); +} diff --git a/src/ensembles.hpp b/src/ensembles.hpp new file mode 100644 index 00000000..068c9741 --- /dev/null +++ b/src/ensembles.hpp @@ -0,0 +1,22 @@ +/** + * @file ensembles.hpp + * @brief Header file to declare various ensembles + */ + +#ifndef __ENSEMBLES_HPP__ +#define __ENSEMBLES_HPP__ +#include "scheduler.hpp" + +void SS_ensemble10Func(DeploymentSchedule_t *pDeployment); +void SS_ensemble10Init(DeploymentSchedule_t *pDeployment); + +void SS_ensemble07Func(DeploymentSchedule_t *pDeployment); +void SS_ensemble07Init(DeploymentSchedule_t *pDeployment); + +void SS_ensemble08Func(DeploymentSchedule_t *pDeployment); +void SS_ensemble08Init(DeploymentSchedule_t *pDeployment); + +void SS_fwVerInit(DeploymentSchedule_t *pDeployment); +void SS_fwVerFunc(DeploymentSchedule_t *pDeployment); + +#endif //__ENSEMBLES_HPP__ \ No newline at end of file diff --git a/src/fileCLI/fileCLI.cpp b/src/fileCLI/fileCLI.cpp index b2312036..2a600eb6 100644 --- a/src/fileCLI/fileCLI.cpp +++ b/src/fileCLI/fileCLI.cpp @@ -57,8 +57,7 @@ void FileCLI::execute(void) this->dir_stack[this->current_dir] = opendir("/"); if (NULL == this->dir_stack[this->current_dir]) { - FLOG_AddError(FLOG_FS_OPENDIR_FAIL, - (uint32_t)this->dir_stack[this->current_dir]); + FLOG_AddError(FLOG_FS_OPENDIR_FAIL, (std::uint32_t)this->dir_stack[this->current_dir]); SF_OSAL_printf("Failed to open root" __NL__); return; } diff --git a/src/imu/imu.cpp b/src/imu/imu.cpp index c36ed225..c63ab12b 100644 --- a/src/imu/imu.cpp +++ b/src/imu/imu.cpp @@ -1,5 +1,17 @@ +/** + * @file imu.cpp + * @author Owen Lyke + * @author Emily Thorpe (ethorpe@macalester.edu) + * @author Nathan Hui (nthui@ucsd.edu) + * @brief + * @version 0.2 + * @date 2024-10-31 + * + * @copyright Copyright (c) 2024 + * + */ /**************************************************************** - * Example1_Basics.ino + * Example1_Basics.ino, Example8_DMP_RawAccel.ino * ICM 20948 Arduino Library Demo * Use the default configuration to stream 9-axis IMU data * Owen Lyke @ SparkFun Electronics @@ -8,25 +20,76 @@ * Please see License.md for the license information. * * Distributed as-is; no warranty is given. - * - * Modified by Emily Thorpe - Auguest 2023 + * + * Modified by Emily Thorpe - August 2023 ***************************************************************/ -#include "ICM_20948.h" + +#include "ICM_20948.h" #include "cli/conio.hpp" #include "cli/flog.hpp" +#include "consts.hpp" +#include "i2c/i2c.h" -#define SERIAL_PORT Serial +#include +#include +#include +/** + * @brief I2C Handle + * + */ #define WIRE_PORT Wire -#define AD0_VAL 1 -ICM_20948_I2C myICM; +/** + * @brief ICM20948 I2C Address Selector Bit + * + * Set to 0 on final design, set to 1 for dev board. + * + */ +#define AD0_VAL 1 // should be set to 0, currently for dev board need to change to 1 + +/** + * @brief Gibibyte in bytes + * + */ +#define GIB 1073741824 +/** + * @brief ICM Module Handle + * + */ +ICM_20948_I2C myICM; -float getAccMG( int16_t raw, uint8_t fss ); -float getGyrDPS( int16_t raw, uint8_t fss ); -float getMagUT( int16_t raw ); -float getTmpC( int16_t raw ); +/** + * @brief Converts the raw acceleration to G + * + * @param raw Raw accelerometer values + * @param fss Full Scale Selector + * @return Acceleration reading in g + */ +static float getAccMG(int16_t raw, uint8_t fss); +/** + * @brief Converts the raw gyroscope to degrees per second + * + * @param raw Raw gyroscope values + * @param fss Full Scale Selector + * @return Gyroscope reading in degrees per second + */ +static float getGyrDPS(int16_t raw, uint8_t fss); +/** + * @brief Converts the raw magnetometer values to microtesla + * + * @param raw Raw magnetometer values + * @return Magnetometer reading in microtesla + */ +static float getMagUT(int16_t raw); +/** + * @brief Converts the raw temperature values to Celsius + * + * @param raw Raw temperature values + * @return Temperature in Celsius + */ +static float getTmpC(int16_t raw); void setupICM(void) { @@ -39,16 +102,64 @@ void setupICM(void) SF_OSAL_printf(myICM.statusString()); if (myICM.status != ICM_20948_Stat_Ok) { - SF_OSAL_printf("ICM fail"); - FLOG_AddError(FLOG_ICM_FAIL, 0); + SF_OSAL_printf("ICM fail!"); + FLOG_AddError(FLOG_ICM_FAIL, myICM.status); } -} + // DMP sensor options are defined in ICM_20948_DMP.h + // INV_ICM20948_SENSOR_ACCELEROMETER (16-bit accel) + // INV_ICM20948_SENSOR_GYROSCOPE (16-bit gyro + 32-bit calibrated gyro) + // INV_ICM20948_SENSOR_RAW_ACCELEROMETER (16-bit accel) + // INV_ICM20948_SENSOR_RAW_GYROSCOPE (16-bit gyro + 32-bit calibrated gyro) + // INV_ICM20948_SENSOR_MAGNETIC_FIELD_UNCALIBRATED (16-bit compass) + // INV_ICM20948_SENSOR_GYROSCOPE_UNCALIBRATED (16-bit gyro) + // INV_ICM20948_SENSOR_STEP_DETECTOR (Pedometer Step Detector) + // INV_ICM20948_SENSOR_STEP_COUNTER (Pedometer Step Detector) + // INV_ICM20948_SENSOR_GAME_ROTATION_VECTOR (32-bit 6-axis quaternion) + // INV_ICM20948_SENSOR_ROTATION_VECTOR (32-bit 9-axis quaternion + heading + // accuracy) INV_ICM20948_SENSOR_GEOMAGNETIC_ROTATION_VECTOR (32-bit Geomag RV + heading + // accuracy) INV_ICM20948_SENSOR_GEOMAGNETIC_FIELD (32-bit calibrated compass) + // INV_ICM20948_SENSOR_GRAVITY (32-bit 6-axis quaternion) + // INV_ICM20948_SENSOR_LINEAR_ACCELERATION (16-bit accel + 32-bit 6-axis quaternion) + // INV_ICM20948_SENSOR_ORIENTATION (32-bit 9-axis quaternion + heading + // accuracy) + + bool success = true; + success &= (myICM.initializeDMP() == ICM_20948_Stat_Ok); + // Enable the DMP sensors you want + // Configuring DMP to output data at multiple ODRs: + // DMP is capable of outputting multiple sensor data at different rates to FIFO. + // Setting value can be calculated as follows: + // Value = (DMP running rate / ODR ) - 1 + // E.g. For a 5Hz ODR rate when DMP is running at 55Hz, value = (55/5) - 1 = 10. + success &= (myICM.enableDMPSensor(INV_ICM20948_SENSOR_ORIENTATION) == ICM_20948_Stat_Ok); + success &= (myICM.enableDMPSensor(INV_ICM20948_SENSOR_RAW_ACCELEROMETER) == ICM_20948_Stat_Ok); + success &= (myICM.enableDMPSensor(INV_ICM20948_SENSOR_ACCELEROMETER) == ICM_20948_Stat_Ok); + success &= (myICM.setDMPODRrate(DMP_ODR_Reg_Quat9, 0) == ICM_20948_Stat_Ok); + success &= (myICM.setDMPODRrate(DMP_ODR_Reg_Accel, 0) == ICM_20948_Stat_Ok); + + // Enable the FIFO + success &= (myICM.enableFIFO() == ICM_20948_Stat_Ok); + + // Enable the DMP + success &= (myICM.enableDMP() == ICM_20948_Stat_Ok); + + // Reset DMP + success &= (myICM.resetDMP() == ICM_20948_Stat_Ok); + + // Reset FIFO + success &= (myICM.resetFIFO() == ICM_20948_Stat_Ok); + if (success == false) + { + SF_OSAL_printf("DMP fail!" __NL__); + FLOG_AddError(FLOG_ICM_FAIL, 1); + } +} bool getAccelerometer(float *acc_x, float *acc_y, float *acc_z) { ICM_20948_AGMT_t agmt = myICM.getAGMT(); - + *acc_x = getAccMG(agmt.acc.axes.x, agmt.fss.a); *acc_y = getAccMG(agmt.acc.axes.y, agmt.fss.a); *acc_z = getAccMG(agmt.acc.axes.z, agmt.fss.a); @@ -59,7 +170,7 @@ bool getAccelerometer(float *acc_x, float *acc_y, float *acc_z) bool getGyroscope(float *gyr_x, float *gyr_y, float *gyr_z) { ICM_20948_AGMT_t agmt = myICM.getAGMT(); - + *gyr_x = getGyrDPS(agmt.gyr.axes.x, agmt.fss.g); *gyr_y = getGyrDPS(agmt.gyr.axes.y, agmt.fss.g); *gyr_z = getGyrDPS(agmt.gyr.axes.z, agmt.fss.g); @@ -69,7 +180,7 @@ bool getGyroscope(float *gyr_x, float *gyr_y, float *gyr_z) bool getMagnetometer(float *mag_x, float *mag_y, float *mag_z) { - ICM_20948_AGMT_t agmt = myICM.getAGMT(); + ICM_20948_AGMT_t agmt = myICM.getAGMT(); *mag_x = getMagUT(agmt.mag.axes.x); *mag_y = getMagUT(agmt.mag.axes.y); @@ -86,30 +197,131 @@ bool getTemperature(float *temperature) return true; } -float getAccMG( int16_t raw, uint8_t fss ){ - switch(fss){ - case 0 : return (((float)raw)/16.384); break; - case 1 : return (((float)raw)/8.192); break; - case 2 : return (((float)raw)/4.096); break; - case 3 : return (((float)raw)/2.048); break; - default : return 0; break; - } +float getAccMG(int16_t raw, uint8_t fss) +{ + switch (fss) + { + case 0: + return (((float)raw) / 16.384); + break; + case 1: + return (((float)raw) / 8.192); + break; + case 2: + return (((float)raw) / 4.096); + break; + case 3: + return (((float)raw) / 2.048); + break; + default: + return 0; + break; + } } -float getGyrDPS( int16_t raw, uint8_t fss ){ - switch(fss){ - case 0 : return (((float)raw)/131); break; - case 1 : return (((float)raw)/65.5); break; - case 2 : return (((float)raw)/32.8); break; - case 3 : return (((float)raw)/16.4); break; - default : return 0; break; - } +float getGyrDPS(int16_t raw, uint8_t fss) +{ + switch (fss) + { + case 0: + return (((float)raw) / 131); + break; + case 1: + return (((float)raw) / 65.5); + break; + case 2: + return (((float)raw) / 32.8); + break; + case 3: + return (((float)raw) / 16.4); + break; + default: + return 0; + break; + } +} + +float getMagUT(int16_t raw) +{ + return (((float)raw) * 0.15); +} + +float getTmpC(int16_t raw) +{ + return (((float)raw) / 333.87); +} + +bool getDMPAccelerometer(float *acc_x, float *acc_y, float *acc_z) +{ + icm_20948_DMP_data_t data; + myICM.readDMPdataFromFIFO(&data); + if ((myICM.status == ICM_20948_Stat_Ok) || (myICM.status == ICM_20948_Stat_FIFOMoreDataAvail)) + { + if ((data.header & DMP_header_bitmap_Accel) != 0) + { + *acc_x = (float)data.Raw_Accel.Data.X; + *acc_y = (float)data.Raw_Accel.Data.Y; + *acc_z = (float)data.Raw_Accel.Data.Z; + return true; + } + } + return false; +} + +// TODO: DMP Packet needs to include acceleration accuracy (header2) +bool getDMPAccelerometerAcc(float *acc_acc) +{ + icm_20948_DMP_data_t data; + myICM.readDMPdataFromFIFO(&data); + if ((myICM.status == ICM_20948_Stat_Ok) || (myICM.status == ICM_20948_Stat_FIFOMoreDataAvail)) + { + + if (((data.header & DMP_header_bitmap_Header2) != 0) && + ((data.header2 & DMP_header2_bitmap_Accel_Accuracy) != 0)) + { + *acc_acc = data.Accel_Accuracy; + return true; + } + } + + FLOG_AddError(FLOG_ICM_FAIL, myICM.status); + return false; } -float getMagUT( int16_t raw ){ - return (((float)raw)*0.15); +bool getDMPQuaternion(double *q1, double *q2, double *q3, double *q0, double *acc) +{ + icm_20948_DMP_data_t data; + myICM.readDMPdataFromFIFO(&data); + if ((data.header & DMP_header_bitmap_Quat9) != 0) + { + *q1 = ((double)data.Quat9.Data.Q1) / GIB; + *q2 = ((double)data.Quat9.Data.Q2) / GIB; + *q3 = ((double)data.Quat9.Data.Q3) / GIB; + //*acc = (double)data.Quat9.Data.Accuracy; + *q0 = sqrt(1.0 - ((*q1 * *q1) + (*q2 * *q2) + (*q3 * *q3))); + return true; + } + + return false; } -float getTmpC( int16_t raw ){ - return (((float)raw)/333.87); +bool getDMPGyroscope(float *g_x, float *g_y, float *g_z) +{ + icm_20948_DMP_data_t data; + myICM.readDMPdataFromFIFO(&data); + if ((data.header & DMP_header_bitmap_Gyro) != 0) + { + *g_x = (float)data.Gyro_Calibr.Data.X; + *g_y = (float)data.Gyro_Calibr.Data.Y; + *g_z = (float)data.Gyro_Calibr.Data.Z; + return true; + } + + return false; +} + +void whereDMP(void) +{ + // std::string sName(reinterpret_cast(name)); + SF_OSAL_printf("%hhu" __NL__, myICM.getWhoAmI()); } diff --git a/src/imu/imu.hpp b/src/imu/imu.hpp index e70fc4f5..010dbeec 100644 --- a/src/imu/imu.hpp +++ b/src/imu/imu.hpp @@ -1,9 +1,20 @@ +/** + * @file imu.hpp + * @author Emily Thorpe (ethorpe@macalester.edu) + * @author Nathan Hui (nthui@ucsd.edu) + * @brief Inertial Measurement Unit Interface + * @version 0.1 + * @date 2024-10-31 + * + * @copyright Copyright (c) 2024 + * + */ #ifndef __ICM20648_HPP__ #define __ICM20648_HPP__ /** Do a measurement on the gyroscope * - * @param[out] temperature temp value in Celsius + * @param temperature temp value in Celsius * * @returns true if measurement was successful */ @@ -11,42 +22,83 @@ bool getTemperature(float *temperature); /** Do a measurement on the gyroscope * - * @param[out] gyr_x Gyroscope measurement on X axis - * @param[out] gyr_y Gyroscope measurement on Y axis - * @param[out] gyr_z Gyroscope measurement on Z axis + * @param gyr_x Gyroscope measurement on X axis + * @param gyr_y Gyroscope measurement on Y axis + * @param gyr_z Gyroscope measurement on Z axis * * Unit: DPS - * + * * @returns true if measurement was successful */ bool getGyroscope(float *gyr_x, float *gyr_y, float *gyr_z); /** Do a measurement on the accelerometer * - * @param[out] acc_x Accelerometer measurement on X axis - * @param[out] acc_y Accelerometer measurement on Y axis - * @param[out] acc_z Accelerometer measurement on Z axis + * @param acc_x Accelerometer measurement on X axis + * @param acc_y Accelerometer measurement on Y axis + * @param acc_z Accelerometer measurement on Z axis * * Unit: M/s^2 - * + * * @returns true if measurement was successful */ bool getAccelerometer(float *acc_x, float *acc_y, float *acc_z); /** Do a measurement on the magnetometer * - * @param[out] mag_x magnetometer measurement on X axis - * @param[out] mag_y magnetometer measurement on Y axis - * @param[out] mag_z magnetometer measurement on Z axis - * - * Unit: gauss (G) + * @param mag_x magnetometer measurement on X axis + * @param mag_y magnetometer measurement on Y axis + * @param mag_z magnetometer measurement on Z axis + * + * Unit: microTesla (uT) * * @returns true if measurement was successful */ bool getMagnetometer(float *mag_x, float *mag_y, float *mag_z); + /** - * @brief Setup IMU -*/ -void setupICM(void); + * @brief Retrieves the Accelerometer data from the DMP + * + * @param acc_x Pointer to variable to populate with x axis acceleration in m/s^2 + * @param acc_y Pointer to variable to populate with y axis acceleration in m/s^2 + * @param acc_z Pointer to variable to populate with z axis acceleration in m/s^2 + * @return True if measurement was successful, otherwise False + */ +bool getDMPAccelerometer(float *acc_x, float *acc_y, float *acc_z); + +/** + * @brief Retrieves the currently computed orientation as a quarternion from the DMP + * + * @param q1 Pointer to variable to populate with the first element of the quaternion + * @param q2 Pointer to variable to populate with the second element of the quaternion + * @param q3 Pointer to variable to populate with the third element of the quaternion + * @param q0 Pointer to variable to populate with the fourth element of the quaternion + * @param acc Pointer to variable to populate with the quaternion estimation accuracy + * @return True if measurement was successful, otherwise False + */ +bool getDMPQuaternion(double *q1, double *q2, double *q3, double *q0, double *acc); +/** + * @brief Retrieves the Gyroscope data from the DMP + * + * @param g_x Pointer to variable to populate with the x axis gyroscope data in deg/s + * @param g_y Pointer to variable to populate with the y axis gyroscope data in deg/s + * @param g_z Pointer to variable to populate with the z axis gyroscope data in deg/s + * @return True if measurement was successful, otherwise False + */ +bool getDMPGyroscope(float *g_x, float *g_y, float *g_z); + +/** + * @brief Retrieves the Accelerometer accuracy metric from the DMP + * + * @param acc_acc Pointer to a variable to populate with the accelerometer accuracy + * @return True if measurement was successful, otherwise False + */ +bool getDMPAccelerometerAcc(float *acc_acc); + +/** + * @brief Setup IMU + */ +void setupICM(void); +void whereDMP(void); #endif diff --git a/src/product.hpp b/src/product.hpp index fea77879..0761bf3a 100644 --- a/src/product.hpp +++ b/src/product.hpp @@ -8,40 +8,38 @@ * USB Power Detection Pin TODO */ -#define SF_USB_PWR_DETECT_PIN A4 +#define SF_USB_PWR_DETECT_PIN A4 /** * Pin for the Battery Status LED */ -#define STAT_LED_PIN A5 +#define STAT_LED_PIN A5 /** * Water Detect Enable Pin */ -#define WATER_DETECT_EN_PIN A2 +#define WATER_DETECT_EN_PIN A2 /** * Water Detect Pin */ -#define WATER_DETECT_PIN A6 +#define WATER_DETECT_PIN A6 /** * @brief Manufacturing Water Detect Pin - * + * */ -#define WATER_MFG_TEST_EN A3 - +#define WATER_MFG_TEST_EN A3 /** * @brief ICM20648 Address - * + * */ -#define SF_ICM20648_ADDR (0x68 << 1) +#define SF_ICM20648_ADDR (0x68 << 1) /** - * @brief Wakeup pin - * + * @brief Wakeup pin + * */ -#define WKP_PIN A7 - +#define WKP_PIN A7 /******************************************************************************* * Peripheral Configurations @@ -50,43 +48,42 @@ /** * SPI Flash Size */ -#define SF_FLASH_SIZE_MB 4 +#define SF_FLASH_SIZE_MB 4 /** - * window sizes are how many water detect samples are looked at in a moving + * window sizes are how many water detect samples are looked at in a moving * average to determine if we are in or out of the water. Generally a sample * happens 1/second */ -#define WATER_DETECT_SURF_SESSION_INIT_WINDOW 40 +#define WATER_DETECT_SURF_SESSION_INIT_WINDOW 40 /** * How long (in us) to turn on water detection circuit when looking for water */ -#define WATER_DETECT_EN_TIME_US 1000 +#define WATER_DETECT_EN_TIME_US 1000 /** * Charging voltage (mV) */ -#define SF_CHARGE_VOLTAGE 4112 +#define SF_CHARGE_VOLTAGE 4112 /** * @brief Below what battery voltage should the system shutdown - * + * */ #define SF_BATTERY_SHUTDOWN_VOLTAGE 3.0 /** * @brief Particle IO device - * + * */ #define PARTICLE_IO 1 /** * @brief hardware revision - * + * */ #define HARDWARE_REV 3 - /******************************************************************************* * System Configuration ******************************************************************************/ @@ -105,30 +102,40 @@ /** * The default state that the Smartfin comes up in */ -#define SF_DEFAULT_STATE STATE_CHARGE +#define SF_DEFAULT_STATE STATE_CHARGE /** * The CLI RGB LED Color */ -#define SF_CLI_RGB_LED_COLOR RGB_COLOR_RED -#define SF_CLI_RGB_LED_PATTERN LED_PATTERN_SOLID -#define SF_CLI_RGB_LED_PERIOD 3000 -#define SF_CLI_RGB_LED_PRIORITY LED_PRIORITY_IMPORTANT +#define SF_CLI_RGB_LED_COLOR RGB_COLOR_GREEN + +#define SF_CLI_RGB_LED_PATTERN LED_PATTERN_SOLID +#define SF_CLI_RGB_LED_PERIOD 3000 +#define SF_CLI_RGB_LED_PRIORITY LED_PRIORITY_IMPORTANT +/** + * The Ride RGB LED Color + */ +#define RIDE_RGB_LED_COLOR RGB_COLOR_WHITE +#define RIDE_RGB_LED_PATTERN_GPS LED_PATTERN_BLINK +#define RIDE_RGB_LED_PERIOD_GPS 500 +#define RIDE_RGB_LED_PATTERN_NOGPS LED_PATTERN_SOLID +#define RIDE_RGB_LED_PERIOD_NOGPS 0 +#define RIDE_RGB_LED_PRIORITY LED_PRIORITY_IMPORTANT /** * The Data Upload RGB LED Color */ -#define SF_DUP_RGB_LED_COLOR RGB_COLOR_BLUE -#define SF_DUP_RGB_LED_PERIOD 500 +#define SF_DUP_RGB_LED_COLOR RGB_COLOR_BLUE +#define SF_DUP_RGB_LED_PERIOD 500 -#define SF_TCAL_RGB_LED_COLOR RGB_COLOR_ORANGE -#define SF_TCAL_RGB_LED_PATTERN LED_PATTERN_FADE -#define SF_TCAL_RGB_LED_PERIOD 3000 -#define SF_TCAL_RGB_LED_PRIORITY LED_PRIORITY_IMPORTANT +#define SF_TCAL_RGB_LED_COLOR RGB_COLOR_ORANGE +#define SF_TCAL_RGB_LED_PATTERN LED_PATTERN_FADE +#define SF_TCAL_RGB_LED_PERIOD 3000 +#define SF_TCAL_RGB_LED_PRIORITY LED_PRIORITY_IMPORTANT /** * Minimum battery voltage to start an upload - */ + */ #define SF_BATTERY_UPLOAD_VOLTAGE 3.6 /** @@ -141,111 +148,126 @@ */ #define WATER_DETECT_ARRAY_SIZE 200 - /** * @brief Seconds to sleep between upload attempts - * + * */ #define SF_UPLOAD_REATTEMPT_DELAY_SEC 600 - /** - * @brief Milliseconds between transmit attempts - * - */ -#define SF_UPLOAD_MS_PER_TRANSMIT 1000 +/** + * @brief Milliseconds between transmit attempts + * + */ +#define SF_UPLOAD_MS_PER_TRANSMIT 1000 /** * @brief how many ms is a GPS data point valid for a given data log - * + * */ #define GPS_AGE_VALID_MS 5000 - /** * @brief How long to wait for a cell connection in during manufacturing test - * + * */ #define MANUFACTURING_CELL_TIMEOUT_MS 180000 /** * @brief A voltage that's slightly higher than the max battery voltage - * + * */ #define SF_BATTERY_MAX_VOLTAGE 4.3 /** * @brief Lost Bird Smartfin Z7 Product ID - * + * */ -#define PRODUCT_ID_SMARTFIN_Z7 8977 +#define PRODUCT_ID_SMARTFIN_Z7 8977 /** * @brief UCSD Smartfin Product ID - * + * */ -#define PRODUCT_ID_UCSD_SMARTFIN 17293 +#define PRODUCT_ID_UCSD_SMARTFIN 17293 /** * @brief Set to use a hexadecimal product version, otherwise use a decimal * product version - * + * */ #define PRODUCT_VERSION_USE_HEX 0 /** * @brief Enable initialization delay - * + * */ // #define SF_ENABLE_DEBUG_DELAY 15 /** * @brief Base85 encoding flag - * + * */ #define SF_UPLOAD_BASE85 1 /** * @brief Base64 encoding flag - * + * */ #define SF_UPLOAD_BASE64 2 /** * @brief Base64url encoding flag - * + * */ #define SF_UPLOAD_BASE64URL 3 #define SF_UPLOAD_ENCODING SF_UPLOAD_BASE64URL - #if SF_UPLOAD_ENCODING == SF_UPLOAD_BASE85 - /** - * How many bytes to store chunks of data in on the SPI flash. - * - * 816 * 5/4 (base85 encoding compression rate) = 1020 which is less than 1024 - * bytes which is the maximum size of publish events. - */ -#define SF_PACKET_SIZE 816 -#define SF_RECORD_SIZE 1020 +/** + * How many bytes to store chunks of data in on the SPI flash. + * + * 816 * 5/4 (base85 encoding compression rate) = 1020 which is less than 1024 + * bytes which is the maximum size of publish events. + */ +#define SF_PACKET_SIZE 816 +#define SF_RECORD_SIZE 1020 #elif SF_UPLOAD_ENCODING == SF_UPLOAD_BASE64 || SF_UPLOAD_ENCODING == SF_UPLOAD_BASE64URL - /** - * How many bytes to store chunks of data in on the SPI flash. - * - * 768 * 4/3 (base64 encoding compression rate) = 1024 which is the maximum size - * of publish events. - */ -#define SF_PACKET_SIZE 768 -#define SF_RECORD_SIZE 1024 +/** + * How many bytes to store chunks of data in on the SPI flash. + * + * 768 * 4/3 (base64 encoding compression rate) = 1024 which is the maximum size + * of publish events. + */ +#define SF_PACKET_SIZE 768 +#define SF_RECORD_SIZE 1024 #endif - - #define SF_SERIAL_SPEED 9600 #define SF_CLI_MAX_CMD_LEN 100 - #define SF_NAME_MAX 64 /** * @brief Maximum number of attempts to connect to the cloud - * + * + */ +#define SF_CLOUD_CONNECT_MAX_ATTEMPTS 5 + +/** + * @brief Particle Selector + * + */ +#define SF_PLATFORM_PARTICLE 0 +/** + * @brief GCC Platform Selector + * */ -#define SF_CLOUD_CONNECT_MAX_ATTEMPTS 5 -#endif \ No newline at end of file +#define SF_PLATFORM_GCC 1 + +#ifdef PARTICLE +/** + * @brief Smartfin Platform Designator + * + */ +#define SF_PLATFORM SF_PLATFORM_PARTICLE +#else +#define SF_PLATFORM SF_PLATFORM_GCC +#endif +#endif // __PRODUCT_HPP__ \ No newline at end of file diff --git a/src/rideTask.cpp b/src/rideTask.cpp new file mode 100644 index 00000000..0d2d2da4 --- /dev/null +++ b/src/rideTask.cpp @@ -0,0 +1,121 @@ +/** + * @file rideTask.cpp + * @brief Contains definitions for functions defined in @ref rideTask.hpp + * @version 1 + */ + +#include "rideTask.hpp" + +#include "Particle.h" +#include "cli/conio.hpp" +#include "cli/flog.hpp" +#include "consts.hpp" +#include "ensembles.hpp" +#include "scheduler.hpp" +#include "sleepTask.hpp" +#include "system.hpp" +#include "util.hpp" +#include "vers.hpp" + +#include + +/** + * @brief creates file name for log + * @todo implement RIDE_setFileName + * @param startTime + */ +static void RIDE_setFileName(system_tick_t startTime) +{ + return; +} +/** @brief deployment schedule of ensembles to run + * @see SCH_getNextEvent + */ +DeploymentSchedule_t deploymentSchedule[] = { + {SS_fwVerFunc, SS_fwVerInit, 1, UINT32_MAX, 0, 0, "FW VER", {0}}, + {SS_ensemble10Func, SS_ensemble10Init, 1, 1000, 50, 0, "Temp + IMU + GPS", {0}}, + {nullptr, nullptr, 0, 0, 0, 0, nullptr, {0}}}; + +RideTask::RideTask() : scheduler(deploymentSchedule) +{ +} + +/** + * @brief initialize ride task + * Sets LEDs and initializes schedule + */ +void RideTask::init() +{ + SF_OSAL_printf("Entering STATE_DEPLOYED" __NL__); + pSystemDesc->pChargerCheck->start(); + this->ledStatus.setColor(RIDE_RGB_LED_COLOR); + this->ledStatus.setPattern(RIDE_RGB_LED_PATTERN_GPS); + this->ledStatus.setPeriod(RIDE_RGB_LED_PERIOD_GPS); + this->ledStatus.setPriority(RIDE_RGB_LED_PRIORITY); + this->ledStatus.setActive(); + + this->startTime = millis(); + + this->scheduler.initializeScheduler(); + pSystemDesc->pRecorder->openSession(); +} + +/** + * @brief runs tasks given by scheduler + */ +STATES_e RideTask::run(void) +{ + + DeploymentSchedule_t *pNextEvent = NULL; + system_tick_t nextEventTime; + + SF_OSAL_printf(__NL__ "Deployment started at %" PRId32 __NL__, millis()); + while (1) + { + + RIDE_setFileName(this->startTime); + + SCH_error_e retval = this->scheduler.getNextTask(&pNextEvent, &nextEventTime, millis()); + // Check if scheduler failed to find nextEvent + if (TASK_SEARCH_FAIL == retval) + { + SF_OSAL_printf("getNextEvent is null! Exiting RideTask..." __NL__); + FLOG_AddError(FLOG_SCHEDULER_FAILED, TASK_SEARCH_FAIL); + return STATE_UPLOAD; + } + SF_OSAL_printf("Next task is %s" __NL__, pNextEvent->taskName); + delay(nextEventTime - millis()); + SF_OSAL_printf("Starts at %" PRId32 __NL__, (std::uint32_t)millis()); + pNextEvent->measure(pNextEvent); + SF_OSAL_printf("Ends at %" PRId32 __NL__, (std::uint32_t)millis()); + + // pNextEvent->lastMeasurementTime = nextEventTime; + + if (pSystemDesc->pWaterSensor->getLastStatus() == WATER_SENSOR_LOW_STATE) + { + SF_OSAL_printf("Out of water!" __NL__); + return STATE_UPLOAD; + } + + if (pSystemDesc->flags->batteryLow) + { + SF_OSAL_printf("Low Battery!" __NL__); + return STATE_DEEP_SLEEP; + } + } + return STATE_UPLOAD; +} + +/** + * @brief exits ride state + */ +void RideTask::exit(void) +{ + SF_OSAL_printf("Closing session" __NL__); + pSystemDesc->pRecorder->closeSession(); + // Deinitialize sensors + // pSystemDesc->pTempSensor->stop(); + // pSystemDesc->pCompass->close(); + // pSystemDesc->pIMU->close(); + // pSystemDesc->pGPS->gpsModuleStop(); +} diff --git a/src/rideTask.hpp b/src/rideTask.hpp new file mode 100644 index 00000000..b73ce12f --- /dev/null +++ b/src/rideTask.hpp @@ -0,0 +1,48 @@ +/** + * @file rideTask.hpp + * @brief Header file for deployment state + * + */ +#ifndef __RIDE_HPP__ +#define __RIDE_HPP__ +#include "task.hpp" +#include "product.hpp" +#include "scheduler.hpp" + +#include + + +/** + * @class RideTask + * @brief Manages deployment of measurements and recording + */ +class RideTask : public Task +{ +public: + RideTask(); + /** + * @brief initialize ride task + * Sets LEDs and initializes schedule + */ + void init(void); + /** + * @brief runs tasks given by scheduler + * + * @return the next state to change to. + */ + STATES_e run(void); + /** + * @brief exits ride state + */ + void exit(void); +private: + //! TODO: implement LEDStatus + LEDStatus ledStatus; //!< manages led behavior + + system_tick_t startTime; /**< start time at initialization */ + + Scheduler scheduler; + +}; + +#endif \ No newline at end of file diff --git a/src/scheduler.cpp b/src/scheduler.cpp new file mode 100644 index 00000000..5913fdbb --- /dev/null +++ b/src/scheduler.cpp @@ -0,0 +1,162 @@ +/** + * @file scheduler.cpp + * @brief Contains definitions for functions defined in @ref scheduler.hpp + * @author Antara Chugh (antarachugh@g.ucla.edu), Charlie Kushelevsky (ckushelevsky@ucsd.edu) + * @version 1 + */ +#include "scheduler.hpp" +#include "cli/conio.hpp" +#include "ensembles.hpp" +#include "consts.hpp" +#include "cli/flog.hpp" + +#include +#include +#include + + +#ifndef TEST_VERSION +#include "Particle.h" +#else +#include "scheduler_test_system.hpp" +#endif + + + /** + * @brief constructor for scheduler + * + * @param schedule the schedule table + */ +Scheduler::Scheduler(DeploymentSchedule_t schedule[]) + : scheduleTable(schedule) {} +/** + * @brief Initializes ensembles within schedule table. + * + */ +void Scheduler::initializeScheduler() +{ + DeploymentSchedule_t* pDeployment = this->scheduleTable; + std::uint32_t lastEndTime = 0; + this->tableSize = 0; + if (this->scheduleTable == nullptr) + { + return; + } + while (pDeployment->init) + { + + memset(&(pDeployment->state), 0, + sizeof(StateInformation)); + + pDeployment->init(pDeployment); + this->tableSize++; + pDeployment++; + } +} + + + +/** + * @brief Retrieves the next scheduled event + * + * This function iterates through a schedule table to find the event that + * should run next based on the current system time. It also modifies the + * current state table to assume that the event will be run. + * + * + * + * + * @param p_nextEvent Pointer to a pointer where the next event to be executed + * will be stored. + * @param p_nextTime Pointer to where the time for the next event will + * be stored. + * + * @param currentTime The current system time, defined as time since system + * boot + * @return Returns SUCCESS if a suitable event is found, + * TASK_SEARCH_FAIL otherwise. + * + */ + +SCH_error_e Scheduler::getNextTask(DeploymentSchedule_t** p_nextEvent, + std::uint32_t* p_nextTime, + std::uint32_t currentTime) +{ + + // Iterate through each event in the schedule table in reverse order, + for (int idx = this->tableSize - 1; idx >= 0; idx--) + { + + DeploymentSchedule_t& currentEvent = scheduleTable[idx]; + StateInformation& currentEventState = scheduleTable[idx].state; + std::uint32_t runTime = currentEventState.nextRunTime; + + // check if a delay exists + std::uint32_t difference = currentTime - runTime; + + if (difference > 0) + { + runTime = currentTime; + } + + + + + if (difference >= (int)currentEvent.maxDelay) + { + //! TODO: send warning + } + std::uint32_t delay = difference > 0 ? difference : 0; + + //Finish time of task + int expected_completion = runTime + currentEvent.maxDuration; + + bool canRun = true; + //Iterate through all tasks of higher prioirty. + for (int j = 0; (j < idx) && canRun; j++) + { + if ((int)scheduleTable[j].state.nextRunTime < expected_completion) + { + canRun = false; + } + } + /*If there were no conflicts with higher priority tasks, we can set + the next task to the task in question*/ + if (canRun) + { + *p_nextEvent = ¤tEvent; + *p_nextTime = runTime; + currentEventState.nextRunTime = runTime + + currentEvent.ensembleInterval; + + currentEventState.measurementCount++; + + /* + If delay greater than 0, shift all future occurences of the task by + delay amount to re-establish a constant frequency + */ + if (delay > 0) + { + FLOG_AddError(FLOG_SCHEDULER_DELAY_EXCEEDED, + currentEventState.measurementCount); + #ifdef TEST_VERSION + + this->log.push(std::make_tuple(currentEvent.taskName, + currentEventState.measurementCount)); + #endif + + } + return SCHEDULER_SUCCESS; + } + + + + } + + return TASK_SEARCH_FAIL; + +} + + + + diff --git a/src/scheduler.hpp b/src/scheduler.hpp new file mode 100644 index 00000000..3f0b2944 --- /dev/null +++ b/src/scheduler.hpp @@ -0,0 +1,147 @@ +/** + * @file charlie_scheduler.hpp + * @author Antara Chugh (antarachugh@g.ucla.edu), Charlie Kushelevsky (ckushelevsky@ucsd.edu) + * @brief Header file for scheduler defined in @ref scheduler.cpp + * @version 1 + */ +#ifndef __SCHEDULER_HPP__ +#define __SCHEDULER_HPP__ +#include "abstractScheduler.hpp" +#include "cli/conio.hpp" +#include "deploymentSchedule.hpp" +#include "product.hpp" + +#include +#include +#include + +#if SF_PLATFORM == SF_PLATFORM_PARTICLE +#include "Particle.h" +#elif SF_PLATFORM == SF_PLATFORM_GCC +#include "scheduler_test_system.hpp" +#endif // TEST_VERSION + +/** + * @brief Ensemble function. + * + * This function executes once to update the ensemble state. This should update + * the accumulators with a new measurement. If the accumulators have + * accumulated the proper amount of data, this function should then record the + * proper data. + * + * Essentially, this will be called every ensembleInterval ms after + * ensembleDelay ms from the start of the deployment. + * + * @param pDeployment the schedule table + */ +typedef void (*EnsembleFunction)(DeploymentSchedule_t *pDeployment); + +/** + * @brief Ensemble initialization function. + * + * This function is executed once when all of the + * @param pDeployment the schedule table + */ +typedef void (*EnsembleInit)(DeploymentSchedule_t *pDeployment); + +/** + * @brief Records and writes ensemble + * @param pDeployment the schedule table + */ +typedef void (*EnsembleProccess)(DeploymentSchedule_t *pDeployment); + +/** + * @brief contains state information for each ensemble + */ +struct StateInformation +{ + std::uint32_t measurementCount; + //! store the next time the task should run + std::uint32_t nextRunTime; + void *pData; +}; + +#if SF_PLATFORM == SF_PLATFORM_PARTICLE +#define TESTABLE_CONST const +#else +#define TESTABLE_CONST +#endif + +/** + * @brief contains ensemble definitions and info for scheduler + * + */ +struct DeploymentSchedule_ +{ + //! measurement function + TESTABLE_CONST EnsembleFunction measure; + //! initialization function + TESTABLE_CONST EnsembleInit init; + + //! measurements before processing + TESTABLE_CONST std::uint32_t measurementsToAccumulate; + + //! time between ensembles + TESTABLE_CONST std::uint32_t ensembleInterval; + + //! max running time of measurement + TESTABLE_CONST std::uint32_t maxDuration; + + //! max delay before throwing flag and resetting + TESTABLE_CONST std::uint32_t maxDelay; + + //! task name of ensemble + const char *TESTABLE_CONST taskName; + + //! state information + StateInformation state; +}; + +/** + * @brief contains schedule table for scheduler function to initialize and + * get next tasks + * + */ +class Scheduler : public AbstractScheduler +{ +private: + //! schedule table size + uint32_t tableSize; + +public: + //! the schedule table + DeploymentSchedule_t *scheduleTable; + + /** + * @brief constructor for scheduler + * + * @param schedule the schedule table + */ + Scheduler(DeploymentSchedule_t *scheduler); + + /** + * @brief Initializes ensemble variables + * + * @param scheduleTable the schedule table + * @param startTime the start time of the deployment + */ + void initializeScheduler(); + + /** + * @brief Determines the next task to run + * + * @param scheduleTable The table containing all scheduled tasks. + * @param idx The index of the current task in the schedule table. + * @param currentTime The current system time. + * @param nextStartTime The proposed start time of the current task. + * @return True if there is an overlap with another task; false otherwise. + */ + SCH_error_e getNextTask(DeploymentSchedule_t **p_nextEvent, + std::uint32_t *p_nextTime, + std::uint32_t currentTime); +#if SF_PLATFORM == SF_PLATFORM_GCC + std::queue> log; +#endif +}; + +#endif //__SCHEDULER_HPP__ \ No newline at end of file diff --git a/src/smartfin-fw3.ino b/src/smartfin-fw3.ino index 79e53729..056b0eb7 100644 --- a/src/smartfin-fw3.ino +++ b/src/smartfin-fw3.ino @@ -5,23 +5,20 @@ * Date: */ #include "Particle.h" - -#include "states.hpp" -#include "task.hpp" -#include "product.hpp" -#include "consts.hpp" -#include "system.hpp" - -#include "mfgTest/mfgTest.hpp" - +#include "cellular/dataUpload.hpp" +#include "cellular/sf_cloud.hpp" +#include "chargeTask.hpp" #include "cli/cli.hpp" #include "cli/conio.hpp" #include "cli/flog.hpp" -#include "cellular/sf_cloud.hpp" +#include "consts.hpp" +#include "mfgTest/mfgTest.hpp" +#include "product.hpp" +#include "rideTask.hpp" #include "sleepTask.hpp" -#include "chargeTask.hpp" -#include "cellular/dataUpload.hpp" - +#include "states.hpp" +#include "system.hpp" +#include "task.hpp" SYSTEM_MODE(SEMI_AUTOMATIC); SYSTEM_THREAD(ENABLED); @@ -38,18 +35,16 @@ static ChargeTask chargeTask; static SleepTask sleepTask; static DataUpload uploadTask; static MfgTest mfgTask; - +static RideTask rideTask; // Holds the list of states and coresponding tasks -static StateMachine_t stateMachine[] = -{ - {STATE_CLI, &cliTask}, - {STATE_DEEP_SLEEP, &sleepTask}, - {STATE_CHARGE, &chargeTask}, - {STATE_UPLOAD, &uploadTask}, - {STATE_MFG_TEST, &mfgTask}, - {STATE_NULL, NULL} -}; +static StateMachine_t stateMachine[] = {{STATE_CLI, &cliTask}, + {STATE_DEEP_SLEEP, &sleepTask}, + {STATE_CHARGE, &chargeTask}, + {STATE_UPLOAD, &uploadTask}, + {STATE_MFG_TEST, &mfgTask}, + {STATE_DEPLOYED, &rideTask}, + {STATE_NULL, NULL}}; static STATES_e currentState; diff --git a/src/task.hpp b/src/task.hpp index 794d86eb..e6ffeca4 100644 --- a/src/task.hpp +++ b/src/task.hpp @@ -1,25 +1,45 @@ +/** + * @file task.hpp + * @author Charlie Kushulevsky (ckushelevsky@ucsd.edu) + * @author Nathan Hui (nthui@ucsd.edu) + * @brief + * @version 0.1 + * @date 2024-10-31 + * + * @copyright Copyright (c) 2024 + * + */ #ifndef __TASK_HPP__ #define __TASK_HPP__ #include "states.hpp" -class Task{ - public: +/** + * @brief Abstract Base Class for Tasks + * + * This task is the effective abstraction of a state in a state machine. + * + */ +class Task +{ +public: /** - * Initializes the task. This assumes entry from any other state. If this - * fails, Task::run must handle switching to the appropriate task context. + * @brief Initializes the task. This assumes entry from any other state. + * If this fails, Task::run must handle switching to the appropriate task + * context. */ virtual void init(void) = 0; /** - * This should be the task body. This should execute until a state change + * @brief State logic body. This should execute until a state change * needs to occur, in which case the state to change to should be returned. * Once the state to change to is returned, Task::exit will be called to * clean up from this state. + * @return the state to change to */ virtual STATES_e run(void) = 0; /** - * This should handle cleaning up any resources. This will execute when - * Task::run returns. + * @brief This should handle cleaning up any resources. + * This will execute when Task::run returns. */ virtual void exit(void) = 0; }; diff --git a/tests/Create_New_Tests.md b/tests/Create_New_Tests.md new file mode 100644 index 00000000..ffe047e7 --- /dev/null +++ b/tests/Create_New_Tests.md @@ -0,0 +1,29 @@ +# Overview + The following document outlines how to add tests to the fixed_google_tests file. The purpose of fixed_google_test is to ensure the scheduler behaves as expected on a fixed set of test cases. "expected behavior" can be defined as the result of the algorithm outlined in control_flow.uml. + +# Adding Tests + The Scheduler Test class handles all the logic and functions needed to run & create a test. A test case is essentially an instance of the Scheduler Test class. Before every test, the SetUp() function is called, intializes a Deployment Table of 3 tasks with duration of 25 seconds and interval of 75 seconds; sets the clock to 0; and sets the test log to empty. Everytime a task is run, the name of the task and the time it ran is logged in test log. After a test case is done running, the TearDown() function is called, clearing the test log. These functions are called automatically. + + Every test requires the deployment table to be set up with the desired tasks, the number of times the scheduler should be called (iterations), a vector of expected values of length iterations, and an int array of length iteration called Delay, which specifies how much delay to inject to a duration of a task. Delay[i] corresponds to adding a delay of length delay[i] to the ith task the scheduler runs. (so the ith task will run for task.duration + delay[i]) + + IMPORTANT: The creator of the test case must hard code the expected behaviour log with the approriate behaviour given the injected delays. + + Once this is set up, call run(number of tasks, iterations, delay, expected). This will call the scheduler's getNextTask function for iterations number of times, logging the task name and when the task ran. This will then compare the result of the scheduler to the expected log provided by the test case and throw an exception if the behaviour is mismatched. + + TEST_F(Scheduler_Test, /*Test Name*/){ + /*Set up Deployment Table */ + /* Set up delay log */ + /* Set up expected log */ + run(numtasks, iterations, delay, expected); + + } + + +# Setting up the Deployment Table + + The set of tasks to test are stored in a vector of DeploymentTables, called deployment_table. This contains all nesecary info of the tasks. To set up the desired set of tasks to test, the changeDuration and changeInterval function can be used to change the duration and interval respectively of the ith priority task, where the 0th prioirity task is defined as the highest priority task. The functions change3intervals, change2intervals, and duration change the intervals of the first 3 or 2 priority tasks respectively, this is for ease of the test creator. If more than 3 tasks are to be tested, additional tasks can be added using the addTask function, which takes the task name, desired interval & duration of the task. + + + + + diff --git a/tests/README.md b/tests/README.md index 9fff498d..715f2bb6 100644 --- a/tests/README.md +++ b/tests/README.md @@ -1,3 +1,25 @@ # Test Suites See https://google.github.io/googletest/ + +# Overview +The folder `tests` contains tests monatomic tests for the scheduler in the + `src` directory. There is a bash script to manage the tests at + `tests/run_gtests`. + +# Running tests +Use the following code from the `tests` directory: + + cmake .. -Bbuild/ + make -Cbuild/ + mkdir -p outputs + mkdir -p inputs + mkdir -p no_check_outputs + mkdir -p no_check_inputs + ./build/googletests + ./build/examine_behavior + #create graphs + python3 -m venv .venv + source .venv/bin/activate + pip install -r requirements.txt 1> /dev/null + python3 scheduler_proccessor.py diff --git a/tests/examine_behavior.cpp b/tests/examine_behavior.cpp new file mode 100644 index 00000000..35116fa5 --- /dev/null +++ b/tests/examine_behavior.cpp @@ -0,0 +1,255 @@ +#include "scheduler_test_system.hpp" +#include "test_ensembles.hpp" +#include "cli/conio.hpp" +#include "scheduler.hpp" +#include "test_file_system.hpp" + +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include + + + + +class ExamineBehavior +{ + public: + + static std::unique_ptr files; + //! output file for actual values + std::ofstream actualFile; + //! holds all data input from file + TestInput input; + + //! holds test name across functions + std::string testName; + + //! for writing test output + bool useCompareLogs; + std::unique_ptr scheduler; + std::vector> exceededDelays; + std::unordered_map> shifts; + + + static void SetUpTestSuite() + { + + files = std::make_unique( + "/dev/null", + "tests/no_check_outputs/consolodated_actual_file_tests.log"); + + } + static void TearDownTestSuite() + { + files->closeFiles(); + } + + //! holds the deployment schedule created in @ref SchedulerTest() + std::vector deploymentSchedule; + //! pointer for next event in deploymentSchedule + DeploymentSchedule_t* nextEvent; + //! next time any event will be run + std::uint32_t nextEventTime; + //! holds actual values for testing + std::vector actual; + + + + + + + /** + * @brief Add actual log to vector + * @param task the name of the task being run + * @param start the start time + * @param end the end tim + */ + inline void appendActualFile(std::string task, + std::uint32_t start, + std::uint32_t end) + { + actual.emplace_back(task, start, end); + } + /** + * @brief Sets up the test envirnoment before each test + * + */ + void SetUp(std::string filename) + { + + + actual.clear(); + testName = filename; + + + nextEvent = nullptr; // ensures that first call to scheduler is correct + nextEventTime = 0; // time handling + + setTime(0); + + + } + + /** + * @brief Cleans the test envirnoment after each test + * + */ + void TearDown() + { + + size_t pos = testName.find_last_of("/"); + + testName = testName.substr(pos + 1); + + std::string::size_type const p(testName.find_last_of('.')); + + + std::string baseName = testName.substr(0, p); + + files->writeTest(baseName, actual, actual); + } + void runTestFile(std::string filename) + { + + std::cout << "running " << filename << "\n"; + input.clear(); + input.parseInputFile(filename); + + setTime(input.start); + deploymentSchedule.clear(); + DeploymentSchedule_t ensemble; + for (size_t i = 0; i < input.ensembles.size(); i++) + { + ensemble = { SS_ensembleAFunc, SS_ensembleAInit, 1, + input.ensembles[i].interval, + input.ensembles[i].duration, + input.ensembles[i].delay, + input.ensembles[i].taskName.c_str(), + {0} }; + deploymentSchedule.emplace_back(ensemble); + } + ensemble = { nullptr, nullptr, 0, 0, 0, 0, "", {0}}; + deploymentSchedule.emplace_back(ensemble); + scheduler = std::make_unique(deploymentSchedule.data()); + + + + + + scheduler->initializeScheduler(); + + while (millis() < input.end) + { + + scheduler->getNextTask(&nextEvent, &nextEventTime, millis()); + + + std::uint32_t afterDelay = input.getDelay(nextEvent->taskName, + nextEvent->state.measurementCount - 1); + + runEventWithDelays(afterDelay); + } + + std::string delimiter = "|"; + std::ofstream sch_log("scheduler.log"); + for (const auto& log = scheduler->log.front(); + scheduler->log.size() > 0; scheduler->log.pop()) + { + sch_log << std::get<0>(log) << "|" << std::get<1>(log) << "\n"; + } + + + + + + } + + + + + + + + void runEventWithDelays(std::uint32_t trailingDelay) + { + + + setTime(nextEventTime); + + std::uint32_t actualStart = millis(); + + + addTime(nextEvent->maxDuration); + addTime(trailingDelay); + std::uint32_t actualEnd = millis(); + + + appendActualFile(nextEvent->taskName, actualStart, actualEnd); + + } + std::vector GetFilesInDirectory(const std::string& directory) { + std::vector filesInDir; + struct dirent *dp; + DIR* dirp = opendir(directory.c_str()); + + + std::cout << "directory: " << directory << "\n"; + + while ((dp = readdir(dirp)) != NULL) { + if (dp->d_type == DT_REG) { + std::string file = directory + std::string(dp->d_name); + + filesInDir.push_back(file); + } + } + std::cout << "closing " << directory << "\n"; + closedir(dirp); + std::cout << directory << " closed" << "\n"; + return filesInDir; + } + void runTests() + { + SetUpTestSuite(); + std::vector filesInDir = GetFilesInDirectory( + "./tests/no_check_inputs/"); + int i = 0; + for (const auto& file : filesInDir) + { + SetUp(file); + runTestFile(file); + TearDown(); + SF_OSAL_printf("Completed %d/%d\t\t\t\t\r",++i,filesInDir.size()); + } + TearDownTestSuite(); + } + +}; +std::unique_ptr ExamineBehavior::files = nullptr; + + +int main(int argc, char const* argv[]) +{ + + std::chrono::steady_clock::time_point begin = std::chrono::steady_clock::now(); + + + ExamineBehavior examine; + examine.runTests(); + std::chrono::steady_clock::time_point end = std::chrono::steady_clock::now(); + std::cout << "Time to run = " + << + std::chrono::duration_cast(end - begin).count() + << "[µs]" << std::endl; + + + + return 0; +} diff --git a/tests/file_google_tests.cpp b/tests/file_google_tests.cpp new file mode 100644 index 00000000..e0a7076e --- /dev/null +++ b/tests/file_google_tests.cpp @@ -0,0 +1,274 @@ +/** + * @file gtest.cpp + * @author Charlie Kushelevsky (ckushelevsky@ucsd.edu) + * @brief Google Tests for scheduler.cpp + */ + +#include "scheduler_test_system.hpp" +#include "test_ensembles.hpp" +#include "scheduler.hpp" +#include "test_file_system.hpp" + +#include + +#include +#include +#include +#include +#include +#include +#include +#include +#include + + + +class SchedulerTestsFromFiles : public ::testing::TestWithParam +{ +public: + static void SetUpTestSuite() + { + files = std::make_unique( + "tests/outputs/expected_file_tests.log", + "tests/outputs/actual_file_tests.log"); + + } + static void TearDownTestSuite() + { + files->closeFiles(); + } +protected: + //! holds the deployment schedule created in @ref SchedulerTest() + std::vector deploymentSchedule; + //! pointer for next event in deploymentSchedule + DeploymentSchedule_t* nextEvent; + //! next time any event will be run + std::uint32_t nextEventTime; + //! holds actual values for testing + std::vector actual; + //! holds expected values for testing + std::vector expected; + + //! output file for expected values + std::ofstream expectedFile; + //! output file for actual values + std::ofstream actualFile; + + //! holds all data from a file + TestInput input; + + + //! filename for writing expected test output + std::string expectedFileName = "expected_file_tests.log"; + //! filename for writing actual test output + std::string actualFileName = "actual_file_tests.log"; + //! holds test name across functions + std::string testName; + + + //! for writing test output + bool useCompareLogs; + static std::unique_ptr files; + + std::unique_ptr scheduler; + + + + + + + /** + * @brief Sets up the test envirnoment before each test + * + */ + void SetUp() override + { + actual.clear(); + expected.clear(); + + const ::testing::TestInfo* const test_info = + ::testing::UnitTest::GetInstance()->current_test_info(); + + testName = std::string(test_info->name()); + + nextEvent = nullptr; // ensures that first call to scheduler is correct + nextEventTime = 0; // time handling + + setTime(0); + + } + + /** + * @brief Cleans the test envirnoment after each test + * + */ + void TearDown() override + { + + size_t pos = testName.find_last_of("/"); + + files->writeTest(testName.substr(pos+1),expected,actual); + } + void runNextEvent() + { + + scheduler->getNextTask(&nextEvent, + &nextEventTime, + millis()); + ASSERT_NE(nextEvent, nullptr) << "Scheduler returned nullptr."; + if (nextEvent == nullptr) return; + setTime(nextEventTime); + addTime(nextEvent->maxDuration); + if (!useCompareLogs) + { + expected.emplace_back(nextEvent->taskName, nextEventTime, millis()); + actual.emplace_back(nextEvent->taskName, nextEventTime, millis()); + } + } + + void runTestFile(std::string filename) + { + input.clear(); + input.parseInputFile(filename); + + setTime(input.start); + deploymentSchedule.clear(); + DeploymentSchedule_t e; + for (size_t i = 0; i < input.ensembles.size(); i++) + { + e = { SS_ensembleAFunc, SS_ensembleAInit, 1, + input.ensembles[i].interval, + input.ensembles[i].duration, + input.ensembles[i].delay, + input.ensembles[i].taskName.c_str(), + {0} }; + deploymentSchedule.emplace_back(e); + } + e = { nullptr, nullptr, 0, 0, 0, 0, "", {0}}; + deploymentSchedule.emplace_back(e); + scheduler = std::make_unique(deploymentSchedule.data()); + scheduler->initializeScheduler(); + + while (millis() < input.end) + { + scheduler->getNextTask(&nextEvent, &nextEventTime, + millis()); + std::uint32_t afterDelay = input.getDelay(nextEvent->taskName, + nextEvent->state.measurementCount - 1); + + + + if(input.expectedValues.size() > 0) + { + TestLog exp = input.expectedValues.front(); + runAndCheckEventWithDelays(exp.name, exp.start, exp.end, + afterDelay); + input.expectedValues.erase(input.expectedValues.begin()); + + } + else + { + return; + } + } + } + + /** + * @brief Run task, update time, and check values + * + * This function checks the correct task is being run, and the start time + * and end time is correct + * + * + * @param expectedTaskName expected task name character + * @param expectedStart time to check start time with + * @param expectedEnd time to check end time with + */ + inline void runAndCheckEvent(std::string expectedTaskName, + std::uint32_t expectedStart, + std::uint32_t expectedEnd) + { + runAndCheckEventWithDelays(expectedTaskName, expectedStart, + expectedEnd, 0); + } + void runAndCheckEventWithDelays(std::string expectedTaskName, + std::uint32_t expectedStart, + std::uint32_t expectedEnd, + + std::uint32_t trailingDelay) + { + ASSERT_NE(nextEvent, nullptr) << "Scheduler returned nullptr."; + std::stringstream failMessage; + failMessage << "runAndCheckEvent failed:\n" + << "Expected \tActual\n" + << expectedTaskName << "\t\t" << nextEvent->taskName << "\n" + << expectedStart << "\t\t" << nextEventTime + << "\n" + << expectedEnd << "\t\t" << nextEventTime + nextEvent->maxDuration + + trailingDelay; + + + + + setTime(nextEventTime); + + std::uint32_t actualStart = millis(); + + + addTime(nextEvent->maxDuration); + addTime(trailingDelay); + std::uint32_t actualEnd = millis(); + + expected.emplace_back(expectedTaskName, expectedStart, expectedEnd); + actual.emplace_back(nextEvent->taskName, actualStart, actualEnd); + + ASSERT_EQ(expectedTaskName, nextEvent->taskName) << failMessage.str(); + ASSERT_EQ(expectedStart, actualStart) << failMessage.str(); + ASSERT_EQ(expectedEnd, actualEnd) << failMessage.str(); + } + + + + +}; +std::unique_ptr SchedulerTestsFromFiles::files; + + +TEST_P(SchedulerTestsFromFiles, RunTestFile) { + std::string filename = GetParam(); + + runTestFile(filename); + +} + +std::vector GetFilesInDirectory(const std::string& directory) { + std::vector files; + DIR* dirp = opendir(directory.c_str()); + struct dirent* dp; + std::cout << "Directory: " << directory.c_str() << "\n"; + while ((dp = readdir(dirp)) != nullptr) { + if (dp->d_type == DT_REG) { + files.push_back(directory + "/" + std::string(dp->d_name)); + } + } + closedir(dirp); + return files; +} + +INSTANTIATE_TEST_SUITE_P( + Files, + SchedulerTestsFromFiles, + ::testing::ValuesIn(GetFilesInDirectory("tests/inputs/")), + [](const testing::TestParamInfo& info) { + + std::string name = info.param; + // Remove directory part for cleaner test names + size_t last_slash_idx = name.find_last_of("\\/"); + if (std::string::npos != last_slash_idx) + { + name.erase(0, last_slash_idx + 1); + } + std::replace(name.begin(), name.end(), '.', '_'); + return name; + } +); \ No newline at end of file diff --git a/tests/fixed_google_tests.cpp b/tests/fixed_google_tests.cpp new file mode 100644 index 00000000..1ea6fbdf --- /dev/null +++ b/tests/fixed_google_tests.cpp @@ -0,0 +1,309 @@ +/** + * @file gtest.cpp + * @author Antara Chugh (antarachugh@g.ucla.edu), Charlie Kushelevsky (ckushelevsky@ucsd.edu) + * @brief Google Tests for scheduler.cpp + * Read Create_New_Tests.md for documentation to add more tests + */ + +#include "scheduler_test_system.hpp" +#include "scheduler.hpp" +#include "test_ensembles.hpp" +#include "cli/flog.hpp" +#include "test_file_system.hpp" + +#include + +#include +#include +#include +#include +#include +#include + + + +class SchedulerFixedTests : public ::testing::Test +{ +protected: +/** + * @brief constructor for tests, intializes default values + */ + SchedulerFixedTests() + { + table_default_interval = 75; + table_default_duration = 25; + clock = 0; + + deployment_table = { + //EnsembleFunc, init, meas, ensembleInterval, maxDuration, maxDelay, taskName, state + {SS_ensembleAFunc, SS_ensembleAInit, 1, table_default_interval, table_default_duration, UINT32_MAX, "A", {0}}, + {SS_ensembleBFunc, SS_ensembleBInit, 1, table_default_interval, table_default_duration, UINT32_MAX, "B", {0}}, + {SS_ensembleCFunc, SS_ensembleCInit, 1, table_default_interval, table_default_duration, UINT32_MAX, "C", {0}}, + //{nullptr, nullptr, 0, 0, 0, 0, nullptr, {0}} + }; + test_log = {}; + + + }; + + uint32_t table_default_interval; + + uint32_t table_default_duration; + + + uint32_t clock; + + + std::vector deployment_table; + std::vector test_log; + + + + /** + * @brief SetUp, runs before every test. sets table back to defaults, clock back + * to 0, and ensures test log is empty + */ + void SetUp() override { + deployment_table[0].ensembleInterval = table_default_interval; + deployment_table[1].ensembleInterval = table_default_interval; + deployment_table[2].ensembleInterval = table_default_interval; + + deployment_table[0].maxDuration = table_default_duration; + deployment_table[1].maxDuration = table_default_duration; + deployment_table[2].maxDuration = table_default_duration; + + test_log.clear(); + clock = 0; + + + + + + } + /** + * @brief TearDown, runs after every test. Clears test log. + */ + void TearDown() override { + test_log.clear(); + + } + + /** + * @param A_interval desired interval of task A, highest priority task + * @param B_interval desired interval of task B + * @param C_interval desired interval of task C, lowest prioirity task + * Updates the intervals of 3 tasks to desired intervals for a test case. + * If it is desired to change interval from default, call this function + * before calling "run()" + */ + + void three_task_change_intervals(std::uint32_t A_interval, + std::uint32_t B_interval, std::uint32_t C_interval) { + deployment_table[0].ensembleInterval = A_interval; + deployment_table[1].ensembleInterval = B_interval; + deployment_table[2].ensembleInterval = C_interval; + } + + /** + * @param A_interval desired interval of task A, highest priority task + * @param B_interval desired interval of task B + * Updates the intervals of 2 tasks to desired intervals for a test case. + * If it is desired to change interval from default, call this function + * before calling "run()" + */ + + void two_task_change_intervals(std::uint32_t A_interval, + std::uint32_t B_interval) { + deployment_table[0].ensembleInterval = A_interval; + deployment_table[1].ensembleInterval = B_interval; + } + + /** + * @param task priority of task whose interval is desired to change. + * 0=highest priority. + * @param interval desired interval of task + * Updates the interval of the specified task + */ + + void change_interval(std::uint32_t task, std::uint32_t interval) { + deployment_table[task].ensembleInterval = interval; + } + /** + * @param task priority of task whose duration is desired to change. + * 0=highest priority. + * @param duration desired length of task + * Updates the duration of the specified task + */ + + void change_duratiom(std::uint32_t task, std::uint32_t duration) { + deployment_table[task].maxDuration = duration; + } + /** + * @param task_name name of task. + * @param duration desired length of task + * @param interval desired interval of task + * Adds a task with task_name and specificed duration & interval as lowest + * priority task in deployment table. + */ + + void add_task(const char * task_name, std::uint32_t duration, + std::uint32_t interval){ + + DeploymentSchedule_ task= {SS_ensembleCFunc, SS_ensembleCInit, 1, + interval, duration, UINT32_MAX, task_name, {0}}; + deployment_table.emplace_back(task); + + + } + + + + + /** + * @param task task that is running at current time + * @param delay delay added to task duration, task runs over expected length + * updates clock when a task runs by the task's max duration and any delay + * if present + */ + void update_clock_time(DeploymentSchedule_* task, std::uint32_t delay) { + if (task != nullptr) + { + this->clock += task->maxDuration; + } + if (delay != 0) + { + this->clock += delay; + } + } + + + + /** + * @param expected takes expected behavior of scheduler, consisting of + * taskname & when it runs + * @param iterations number of scheduler calls + * compares scheduler behaviour with expected behviour for iteration number + *of calls to the scheduler + */ + void compare(std::vector& expected, int iterations) { + for (int i = 0; i < iterations; i++) + { + EXPECT_TRUE(test_log[i] == expected[i]) << "test log failed:\n" + << "Expected \t Actual\n" + << expected[i].getName() << "\t\t" + << test_log[i].getName() << "\n" + << expected[i].getRunTime() << "\t\t" + << test_log[i].getRunTime(); + } + } + + /** + * @param num_tasks number of tasks + * @param iterations number of scheduler calls + * @param task_delay array of delays to add a task that runs + * @param expected takes expected behavior of scheduler, consisting of + * taskname & when it runs + * creates scheduler based on tasks set in test, compares scheduler + * behaviour with expected behaviour + */ + + void run(int num_tasks, int iterations, int* task_delay, + std::vector& expected) { + + DeploymentSchedule_t table[num_tasks+1]; + if(num_tasks>deployment_table.size()){ + return; + } + for(int i=0; iclock); + this->clock = *nextTaskTime; + + test_log.emplace_back(Log(nextTask, clock)); + update_clock_time(nextTask, delay); + DeploymentSchedule_* t = nextTask; + + } + compare(expected, iterations); + + } + +}; + +TEST_F(SchedulerFixedTests, TestDefault) +{ + std::vector expected; + expected.emplace_back("A", 0); + expected.emplace_back("B", 25); + expected.emplace_back("C", 50); + expected.emplace_back("A", 75); + expected.emplace_back("B", 100); + expected.emplace_back("C", 125); + int Delay[6]={0,0,0,0,0,0}; + run(3, 6, Delay, expected); +} + +TEST_F(SchedulerFixedTests, TestDefaultWithDelays) +{ + //test A is on time even with delays + int Delay[6]={0, 25, 0, 0, 25, 0}; + std::vector expected; + expected.emplace_back("A", 0); + expected.emplace_back("B", 25); + expected.emplace_back("A", 75); + expected.emplace_back("B", 100); + expected.emplace_back("C", 125); + expected.emplace_back("A", 175); + run(3, 6, Delay, expected); +} + + + TEST_F(SchedulerFixedTests, NotEnoughTimeForC) +{ + + two_task_change_intervals(50, 50); + int Delay[6]={0, 0, 0, 0, 0, 0}; + std::vector expected; + expected.emplace_back("A", 0); + expected.emplace_back("B", 25); + expected.emplace_back("A", 50); + expected.emplace_back("B", 75); + expected.emplace_back("A", 100); + expected.emplace_back("B", 125); + run(3, 6, Delay, expected); +} + +TEST_F(SchedulerFixedTests, ExtremeDelayOnB) +{ + + two_task_change_intervals(50, 50); + int Delay[6]={0, 45, 0,0, 0, 0}; + std::vector expected; + expected.emplace_back("A", 0); + expected.emplace_back("B", 25); + expected.emplace_back("A", 95); + expected.emplace_back("B", 120); + expected.emplace_back("A", 145); + expected.emplace_back("B", 170); + run(2, 6, Delay, expected); +} + + + + + + diff --git a/tests/inputs/delay.txt b/tests/inputs/delay.txt new file mode 100644 index 00000000..cdacface --- /dev/null +++ b/tests/inputs/delay.txt @@ -0,0 +1,12 @@ +START #only one number +0 +END #only one number +10000 +ENSEMBLES #taskname|interval|duration or taskname|interval|duration|maxDelay +A|2000|400 +B|2000|200 +C|2000|600 +DELAYS #0 indexed: "taskname|iteration|delay" +B|1|900 +EXPECTED #"taskname|start|end" +A|0|400 diff --git a/tests/inputs/example.txt b/tests/inputs/example.txt new file mode 100644 index 00000000..a7b77097 --- /dev/null +++ b/tests/inputs/example.txt @@ -0,0 +1,14 @@ +START #only one number +0 +END #only one number +10000 +ENSEMBLES #taskname|interval|duration or taskname|interval|duration|maxDelay +A|2000|200 +B|2000|400|1000 +C|500|100|100 +DELAYS #0 based indexing: "taskname|iteration|delay" +B|1|1000 +EXPECTED #"taskname|start|end" +A|0|200 +RESETS # "taskname|iteration" check when task is skipped or delayed too much +C|3 diff --git a/tests/requirements.txt b/tests/requirements.txt new file mode 100644 index 00000000..22d75145 --- /dev/null +++ b/tests/requirements.txt @@ -0,0 +1,4 @@ +matplotlib +numpy +opencv-python +pathlib \ No newline at end of file diff --git a/tests/scheduler_proccessor.py b/tests/scheduler_proccessor.py new file mode 100644 index 00000000..38405341 --- /dev/null +++ b/tests/scheduler_proccessor.py @@ -0,0 +1,167 @@ +from pathlib import Path +import matplotlib as mpl +import matplotlib.pyplot as plt +import numpy as np +import cv2 +import json +from typing import Tuple, Dict, List, Union + +import glob + +mpl.use('Agg') + +def compare_logs() -> None: + actual_json, expected_json = parse_logs() + + for k in expected_json.keys(): + expected = expected_json[k] + actual = actual_json[k] + same = False + if actual == expected: + same = True + dir_path = Path("tests/outputs") / str(k) + if expected: + dir_path.mkdir(parents=True, exist_ok=True) + + if same: + plot_gantt(expected, "out", dir_path) + else: + max_end = max(v['start'] + v['duration'] for v in expected + actual) + tasks_len = max(len(actual), len(expected)) + + plot_gantt(expected, "expected", dir_path, max_end, tasks_len) + plot_gantt(actual, "actual", dir_path, max_end, tasks_len) + + img1 = cv2.imread(str(dir_path / "expected.jpg")) + img2 = cv2.imread(str(dir_path / "actual.jpg")) + im_v = cv2.vconcat([img1, img2]) + cv2.imwrite(str(dir_path / 'out.jpg'), im_v) + +def plot_gantt(tasks: List[Dict[str, Union[str, int]]], title: str, dir_path: Path, max_duration: int = 0, tasks_len: int = 0) -> None: + if tasks_len == 0: + tasks_len = len(tasks) + fig, ax = plt.subplots(figsize=(tasks_len, 3)) + task_dict = {} + for task in tasks: + task_dict.setdefault(task['label'], []).append((task['start'], task['duration'])) + + y_pos = range(len(task_dict)) + colors = ['skyblue', 'lightgreen', 'salmon', 'grey'] + color_idx = 0 + + for i, (label, segments) in enumerate(task_dict.items()): + last_end = 0 + for start, duration in segments: + ax.barh(i, duration, left=start, color=colors[color_idx % len(colors)]) + ha = 'left' if start - last_end < 300 else 'center' + va = 'top' if start - last_end < 300 else 'bottom' + + if len(task_dict.keys()) > 1: + ax.text(start + duration / 2, i, f"{label} ({start} - {start + duration})", + verticalalignment=va, horizontalalignment=ha, color='black') + last_end = start + duration + color_idx += 1 + + if max_duration != 0: + ax.set_xlim(0, max_duration) + ax.set_yticks(y_pos) + ax.set_yticklabels(task_dict.keys()) + ax.set_xlabel('Time (ms)') + ax.set_title(title) + ax.grid(True) + + plt.subplots_adjust(bottom=0.15) + output_path = dir_path / (title + ".jpg") + + plt.savefig(output_path) + plt.close() + +def parse_logs() -> Tuple[Dict[str, List[Dict[str, Union[str, int]]]], Dict[str, List[Dict[str, Union[str, int]]]]]: + expected_json = {} + actual_json = {} + for filepath in glob.iglob('tests/outputs/actual*.log'): + expected_file_path = Path(str(filepath).replace("actual", "expected")) + actual_file_path = Path(filepath) + + with open(expected_file_path) as f: + expected_json.update(json.loads(f.read())) + + with open(actual_file_path) as f: + actual_json.update(json.loads(f.read())) + + for k in expected_json.keys(): + expected_raw = expected_json[k] + actual_raw = actual_json[k] + expected_json[k] = [{'label': l[0], 'start': int(l[1]), 'duration': int(l[2]) - int(l[1])} for v in expected_raw for l in [v.split('|')]] + actual_json[k] = [{'label': l[0], 'start': int(l[1]), 'duration': int(l[2]) - int(l[1])} for v in actual_raw for l in [v.split('|')]] + + return actual_json, expected_json + +def parse_examine_logs() -> Dict[str, List[Dict[str, Union[str, int]]]]: + actual_json = {} + for filepath in glob.iglob('no_check_outputs/actual*.log'): + actual_file_path = Path(filepath) + + with open(actual_file_path) as f: + actual_json.update(json.loads(f.read())) + + for k in actual_json: + actual_raw = actual_json[k] + actual_json[k] = [{'label': l[0], 'start': int(l[1]), 'duration': int(l[2]) - int(l[1])} for v in actual_raw for l in [v.split('|')]] + + return actual_json + +def examine_logs() -> None: + actual_json = parse_examine_logs() + + for k in actual_json: + actual = actual_json[k] + dir_path = Path("no_check_outputs") + if actual: + plot_examine_gantt(actual, str(k), dir_path, len(actual)) + +def plot_examine_gantt(tasks: List[Dict[str, Union[str, int]]], title: str, dir_path: Path, tasks_len: int = 0) -> None: + if tasks_len == 0: + tasks_len = len(tasks) + fig, ax = plt.subplots(figsize=(tasks_len, 3)) + task_dict = {} + + max_duration = 0 + for task in tasks: + task_dict.setdefault(task['label'], []).append((task['start'], task['duration'])) + max_duration = max(max_duration, task['start'] + task['duration']) + + y_pos = range(len(task_dict)) + colors = ['skyblue', 'lightgreen', 'salmon', 'grey'] + color_idx = 0 + + for i, (label, segments) in enumerate(task_dict.items()): + for start, duration in segments: + ax.barh(i, duration, left=start, color=colors[color_idx % len(colors)]) + ax.text(start + duration / 2, i, f"{label} ({start} - {start + duration})", + verticalalignment='center', horizontalalignment='center', color='black') + color_idx += 1 + + if max_duration != 0: + ax.set_xlim(0, max_duration) + + ax.set_yticks(list(y_pos)) + ax.set_yticklabels(task_dict.keys()) + ax.set_xlabel('Time (ms)') + ax.set_title(title) + ax.grid(True) + + plt.subplots_adjust(bottom=0.15) + output_path = dir_path / (title + ".jpg") + plt.savefig(output_path) + plt.close() + +if __name__ == "__main__": + compare_logs() + examine_logs() + root = Path('tests/outputs/') + folders = [f for f in root.glob('*/') if f.is_dir()] + + for folder in folders: + if not any(folder.iterdir()): + folder.rmdir() diff --git a/tests/scheduler_test_system.cpp b/tests/scheduler_test_system.cpp new file mode 100644 index 00000000..ff4415a2 --- /dev/null +++ b/tests/scheduler_test_system.cpp @@ -0,0 +1,84 @@ +/** + * @file scheduler_test_system.cpp + * @brief implements functions for testing declared in @ref + * scheduler_test_system.hpp + * + */ +#include "scheduler_test_system.hpp" +#include "cli/conio.hpp" +#include "cli/flog.hpp" + +#include +#include +#include +#include + + + +int SF_OSAL_sprintf(char* buffer, size_t size, const char* fmt, ...) +{ + va_list vargs; + va_start(vargs, fmt); + int nBytes = vsnprintf(buffer, size, fmt, vargs); + va_end(vargs); + return nBytes; +} + +/** + * @brief prints to stdout + * + * + * @param fmt format to print + * @param variables to print + * @return number of characters if successful + * @return ferror if error occurs + */ +int SF_OSAL_printf(const char* fmt, ...) +{ + va_list vargs; + va_start(vargs, fmt); + int nBytes = vprintf(fmt, vargs); + va_end(vargs); + return nBytes; +} +/** + * @brief returns the current time in milliseconds + * @note this is areplacement function for Particle's millis() function + * + * @return current time in milliseconds + */ +std::uint32_t millis() +{ + return testTime; +} +/** + * @brief adds time to the current time + * + * @param add amount of time to add + */ +void addTime(std::uint32_t add) +{ + testTime += add; +} +/** + * @brief sets the current time + * + * @param set time to set clock to + */ +void setTime(std::uint32_t set) +{ + testTime = set; +} + +/** + * @brief adds time to the current time + * @note needed by Particle headers, same definition as @ref addTime(std::uint32_t) + * + * @param time amount of time to add + */ +void delay(std::uint32_t time) +{ + addTime(time); +} + + diff --git a/tests/scheduler_test_system.hpp b/tests/scheduler_test_system.hpp new file mode 100644 index 00000000..e5f2b2be --- /dev/null +++ b/tests/scheduler_test_system.hpp @@ -0,0 +1,77 @@ +/** + * @file scheduler_test_system.hpp + * @brief header for helper functions needed for testing + */ +#ifndef __SCHEDULER__TEST__HPP_ +#define __SCHEDULER__TEST__HPP_ + +#include "scheduler.hpp" + +#include +#include +#include +#include +#include +#include +#include +#include + + +#ifndef TEST_VERSION +#define TEST_VERSION +#endif + + +//! time unit required by Particle +typedef std::uint32_t system_tick_t; +void write_shift(std::string ensemble, std::uint32_t idx); + + +/** + * @brief prints to stdout + * + * @param buffer destination for formatted string + * @param fmt format to print + * @param variables to print + * @return number of characters if successful + * @return ferror if error occurs + */ +int SF_OSAL_sprintf(char* buffer, size_t size, const char* fmt, ...); + +static int testTime = 0;//! simulates current time +/** + * @brief adds time to the current time + * + * @param add amount of time to add + */ +void addTime(std::uint32_t add); +/** + * @brief sets the current time + * + * @param set time to set clock to + */ +void setTime(std::uint32_t set); + + +/** + * @brief adds time to the current time + * @note needed by Particle headers, same definition as @ref addTime(std::uint32_t) + * @param time amount of time to add + */ +void delay(std::uint32_t time); +/** + * @brief returns the current time in milliseconds + * @note this is areplacement function for Particle's millis() function + * + * @return current time in milliseconds + */ +std::uint32_t millis(); + + + + + + + + +#endif //__SCHEDULER__TEST__HPP_ \ No newline at end of file diff --git a/tests/test_ensembles.cpp b/tests/test_ensembles.cpp new file mode 100644 index 00000000..06760027 --- /dev/null +++ b/tests/test_ensembles.cpp @@ -0,0 +1,59 @@ +/** + * @file ensembles.cpp + * @brief function definitions for test_ensembles.hpp + * + */ +#include "test_ensembles.hpp" +#include "scheduler_test_system.hpp" + +/** + * @brief dummy ensemble init function + * @param pDeployment schedule table + */ +void SS_ensembleAInit(DeploymentSchedule_t* pDeployment) +{ + return; +} +/** + * @brief dummy measurement function that delays for 400 ms + * @param pDeployment schedule table + */ +void SS_ensembleAFunc(DeploymentSchedule_t* pDeployment) +{ + return; +} + +/** + * @brief dummy ensemble init function + * @param pDeployment schedule table + */ +void SS_ensembleBInit(DeploymentSchedule_t* pDeployment) +{ + return; +} +/** + * @brief dummy measurement function that delays for 200 ms + * @param pDeployment schedule table + */ +void SS_ensembleBFunc(DeploymentSchedule_t* pDeployment) +{ + return; + +} + +/** + * @brief dummy ensemble init function + * @param pDeployment schedule table + */ +void SS_ensembleCInit(DeploymentSchedule_t* pDeployment) +{ + return; +} +/** + * @brief dummy measurement function that delays for 600 ms + * @param pDeployment schedule table + */ +void SS_ensembleCFunc(DeploymentSchedule_t* pDeployment) +{ + return; +} \ No newline at end of file diff --git a/tests/test_ensembles.hpp b/tests/test_ensembles.hpp new file mode 100644 index 00000000..1f518ccd --- /dev/null +++ b/tests/test_ensembles.hpp @@ -0,0 +1,48 @@ +/** + * @file ensembles.hpp + * @brief Header file to declare various dummy ensembles + */ + +#ifndef __TEST_ENSEMBLES_HPP__ +#define __TEST_ENSEMBLES_HPP__ +#include "product.hpp" +#include "scheduler.hpp" + +/** + * @brief dummy ensemble init function + * @param pDeployment schedule table + */ +void SS_ensembleAInit(DeploymentSchedule_t* pDeployment); + +/** + * @brief dummy ensemble function + * @param pDeployment schedule table + */ +void SS_ensembleAFunc(DeploymentSchedule_t* pDeployment); + +/** + * @brief dummy ensemble init function + * @param pDeployment schedule table + */ +void SS_ensembleBInit(DeploymentSchedule_t* pDeployment); + +/** + * @brief dummy ensemble function + * @param pDeployment schedule table + */ +void SS_ensembleBFunc(DeploymentSchedule_t* pDeployment); + +/** + * @brief dummy ensemble init function + * @param pDeployment schedule table + */ +void SS_ensembleCInit(DeploymentSchedule_t* pDeployment); + +/** + * @brief dummy ensemble function + * @param pDeployment schedule table + */ +void SS_ensembleCFunc(DeploymentSchedule_t* pDeployment); + + +#endif //__TEST_ENSEMBLES_HPP__ \ No newline at end of file diff --git a/tests/test_file_generator.py b/tests/test_file_generator.py new file mode 100644 index 00000000..53305ad7 --- /dev/null +++ b/tests/test_file_generator.py @@ -0,0 +1,92 @@ +import numpy as np +import glob, os, os.path + + +class Ensemble: + + def __init__(self, name, mean, sd, hz) -> None: + self.name = name + self.mean = mean + self.sd = sd + self.interval = int(1/hz * 1000) + self.duration = mean + (3 * sd) + self.max_delay = 0 + +class Input: + def __init__(self, name, ensembles) -> None: + + self.directory = "tests/no_check_inputs/" + str(name) + ".txt" + self.ensembles = ensembles + minutes = 0.25 + self.end = minutes * 60000 + def write(self): + with open(self.directory, 'w') as f: + f.write("START\n") + f.write("0\n") + f.write("END\n") + f.write(str(int(self.end)) + "\n") + f.write("ENSEMBLES\n") + for ensemble in self.ensembles: + if int(ensemble.interval) == 0: + ensemble.interval = 1 + if int(ensemble.duration) == 0: + ensemble.duration = 1 + f.write(str(ensemble.name) + "|" + str(int(ensemble.interval))+ "|" + str(int(ensemble.duration))+ "|" + str(int(ensemble.max_delay)) + "\n") + delays = self.generate_delays() + f.write("DELAYS\n") + for delay in delays: + f.write(delay) + + + def generate_delays(self): + delays = [] + for ensemble in self.ensembles: + num_delays = int(self.end / ensemble.interval) + delay_list = np.random.normal(ensemble.mean, 1.2 * ensemble.sd,num_delays) + i = 0 + for delay in delay_list: + delays.append(ensemble.name + "|" + str(i) +"|"+ str(int(delay)) + "\n") + i+=1 + return delays + + + +if __name__ == "__main__": + filelist = glob.glob(os.path.join("tests/no_check_inputs/", "test*.txt")) + for f in filelist: + os.remove(f) + file_num = 0 + ensembles = {} + gyro_sweep = [30,300] + mag_sweep = [30,300] + input = None + ensembles["Temperature"] = Ensemble("Temperature",0.873,1,1) + ensembles["GPS"] = Ensemble("GPS",0.1443,1,1) + ensembles["Wet/Dry Sensor"] = Ensemble("Wet/Dry Sensor",1.02,1,1) + step = 5 + for gyro_interval in range(gyro_sweep[0],gyro_sweep[1]+1,step): + ensembles["Gyrometer"] = Ensemble("Gyrometer",5.209,1, gyro_interval) + for mag_interval in range(mag_sweep[0],mag_sweep[1]+1,step): + ensembles["Magnetometer"] = Ensemble("Magnetometer",5.212,1, mag_interval) + input = Input("test" + str(file_num), ensembles.values()) + file_num += 1 + input.generate_delays() + input.write() + print(f'{file_num}/{(250/step)**2}' + "\r") + + + +''' +START #only one number +0 +END #only one number +10000 +ENSEMBLES #taskname|interval|duration or taskname|interval|duration|maxDelay +A|2000|200 +B|2000|400|1000 +C|500|100|100 +DELAYS #0 based indexing: "taskname|iteration|delay" +B|1|1000 +RESETS # "taskname|iteration" check when task is skipped or delayed too much +C|3 +''' \ No newline at end of file diff --git a/tests/test_file_system.cpp b/tests/test_file_system.cpp new file mode 100644 index 00000000..41a1ce79 --- /dev/null +++ b/tests/test_file_system.cpp @@ -0,0 +1,308 @@ +#include "test_file_system.hpp" + +#include + +/** + * @brief comparator for google tests EXPECT_TRUE + * + * @param rhs other TestLog to compare to + * @return true if the same + * @return false otherwise + */ +bool TestLog::operator==(const TestLog& rhs) +{ + return (name == rhs.name) && + (start == rhs.start) && + (end == rhs.end); +}; +std::ostream& operator<<(std::ostream& strm, const TestLog& value) { + return strm << "\"" << value.name << "|" + << value.start << "|" << value.end << "\""; +} + +std::uint32_t TestInput::getDelay(std::string name, std::uint32_t iteration) +{ + std::uint32_t delay = 0; + if (delays.find(name) != delays.end()) + { + if (delays[name].find(iteration) != delays[name].end()) + { + delay = delays[name][iteration]; + } + } + return delay; +} +FileWriter::FileWriter(std::string expectedFileName, std::string actualFileName) +{ + this->firstTest = true; + this->expectedFileName = expectedFileName; + this->actualFileName = actualFileName; + std::ofstream expectedFile(expectedFileName, std::ios::out | + std::ios::trunc); + + std::ofstream actualFile(actualFileName, std::ios::out | + std::ios::trunc); + + if (actualFile.is_open() && expectedFile.is_open()) + { + actualFile << "{"; + expectedFile << "{"; + } + actualFile.close(); + expectedFile.close(); +} +void FileWriter::writeTest(std::string testName, + std::vector expected, std::vector actual) +{ + std::ofstream actualFile(actualFileName, std::ios::out | + std::ios::app); + std::ofstream expectedFile(expectedFileName, std::ios::out | + std::ios::app); + + if (firstTest == false) + { + expectedFile << ","; + actualFile << ","; + } + else + { + firstTest = false; + } + expectedFile << "\n\t\"" << testName << "\": ["; + if (!expected.empty()) + { + + + for (int i = 0; i < expected.size() - 1; i++) + { + expectedFile << expected[i] << ", "; + } + expectedFile << expected.back(); + } + expectedFile << "]"; + + expectedFile.close(); + + + actualFile << "\n\t\"" << testName << "\": ["; + if (!actual.empty()) + { + + + for (int i = 0; i < actual.size() - 1; i++) + { + actualFile << actual[i] << ", "; + } + actualFile << actual.back(); + } + actualFile << "]"; + actualFile.close(); +} +void FileWriter::closeFiles() +{ + std::ofstream actualFile(actualFileName, std::ios::out | + std::ios::app); + std::ofstream expectedFile(expectedFileName, std::ios::out | + std::ios::app); + + if (actualFile.is_open() && expectedFile.is_open()) + { + actualFile << "\n}"; + expectedFile << "\n}"; + } + + actualFile.close(); + expectedFile.close(); +} +EnsembleInput::EnsembleInput(std::string taskName, std::uint32_t interval, std::uint32_t duration, std::uint32_t delay) : taskName(taskName), interval(interval), duration(duration), delay(delay) {}; + +void TestInput::clear() +{ + ensembles.clear(); + expectedValues.clear(); + delays.clear(); + resets.clear(); + +} + + + +void TestInput::parseInputFile(std::string filename) +{ + std::ifstream inputFile(filename); + std::ifstream file(filename); + std::string line; + std::string currentSection; + int start = 0; + int end; + + + + + TestInput out; + while (getline(file, line)) + { + + size_t commentPos = line.find('#'); + if (commentPos != std::string::npos) + { + line = line.substr(0, commentPos); + } + + // Remove leading and trailing whitespace + line.erase(0, line.find_first_not_of(" \t")); + line.erase(line.find_last_not_of(" \t") + 1); + + if (line.empty()) continue; + + if (line == "START") + { + currentSection = "START"; + continue; + } + else if (line == "END") + { + currentSection = "END"; + continue; + } + else if (line == "ENSEMBLES") + { + currentSection = "ENSEMBLES"; + continue; + } + else if (line == "DELAYS") + { + currentSection = "DELAYS"; + continue; + } + + else if (line == "RESETS") + { + currentSection = "RESETS"; + continue; + } + else if (line == "EXPECTED") + { + currentSection = "EXPECTED"; + continue; + } + + + std::istringstream iss(line); + if (currentSection == "START") + { + iss >> this->start; + } + else if (currentSection == "END") + { + iss >> this->end; + } + else if (currentSection == "ENSEMBLES") + { + + std::string name; + + std::uint32_t interval, duration, maxDelay; + std::getline(iss, name, '|'); + + iss >> interval; + + + iss.ignore(1, '|'); + iss >> duration; + + char checkChar = iss.peek(); + if (checkChar == '|') + { + iss.ignore(1, '|'); + iss >> maxDelay; + } + else + { + maxDelay = interval * 2; + } + EnsembleInput ensembleInput(name, interval, duration, maxDelay); + this->ensembles.emplace_back(ensembleInput); + + } + else if (currentSection == "DELAYS") + { + + std::string delayName; + std::getline(iss, delayName, '|'); + std::string taskName = delayName.c_str(); + std::uint32_t iteration, delay; + iss >> iteration; + iss.ignore(1, '|'); + iss >> delay; + + if (this->delays.find(taskName) != this->delays.end()) + { + this->delays[taskName] = std::unordered_map(); + } + this->delays[taskName][iteration] = delay; + + } + else if (currentSection == "RESETS") + { + std::string resetName; + std::uint32_t iteration; + std::getline(iss, resetName, '|'); + iss >> iteration; + resets.emplace_back(std::make_pair(resetName, iteration)); + } + else if (currentSection == "EXPECTED" ) + { + std::string name; + std::uint32_t start; + std::uint32_t end; + std::getline(iss, name, '|'); + iss >> start; + iss.ignore(1, '|'); + iss >> end; + this->expectedValues.emplace_back(name,start,end); + + } + } + + + +} + + +void write_shift(std::string ensemble, std::uint32_t idx) +{ + + std::ofstream shiftFile("shifts.txt", std::ios::app); + if (shiftFile.is_open()) + { + shiftFile << ensemble << "|" << idx << "\n"; + shiftFile.close(); + } +} +Log::Log(DeploymentSchedule_ *task, std::uint32_t time) { + DeploymentSchedule_ *table = task; + this->taskName = table->taskName; + this->runTime = time; + +} + +Log::Log(const char* name, std::uint32_t time) { + this->taskName = name; + this->runTime = time; + +} + +const char* Log::getName(void) +{ + return taskName; +} +std::uint32_t Log::getRunTime() +{ + return runTime; +} + +bool operator==(const Log& a, const Log& b) { + return ((a.taskName == b.taskName) && (a.runTime == b.runTime)); +} \ No newline at end of file diff --git a/tests/test_file_system.hpp b/tests/test_file_system.hpp new file mode 100644 index 00000000..768de492 --- /dev/null +++ b/tests/test_file_system.hpp @@ -0,0 +1,100 @@ +#ifndef __TEST_FILE_SYSTEM__ +#define __TEST_FILE_SYSTEM__ + +#include "scheduler.hpp" + +#include +#include +#include +#include + + +class EnsembleInput +{ +public: + EnsembleInput(std::string taskName,std::uint32_t interval, + std::uint32_t duration, std::uint32_t delay); + std::string taskName; //!< name of the task + std::uint32_t interval; + std::uint32_t duration; + std::uint32_t delay; +}; + + +/** + * @class TestLog + * @brief helper for @ref gtest.cpp, compares scheduler output with values + * + */ +struct TestLog +{ + std::string name; //!< name of the task + std::uint32_t start; //!< expected start time + std::uint32_t end; //!< expected end time + /** + * @brief Construct a new Test Log object + * + * @param name task name + * @param start expected start time + * @param end expected end time + */ + TestLog(std::string name, std::uint32_t start, std::uint32_t end) : name(name), + start(start), end(end){} + /** + * @brief comparator for google tests EXPECT_TRUE + * + * @param rhs other TestLog to compare to + * @return true if the same + * @return false otherwise + */ + bool operator==(const TestLog& rhs); +}; + +struct FileWriter +{ + std::string expectedFileName; + std::string actualFileName; + bool firstTest; + FileWriter(std::string expectedFileName, std::string actualFileName); + void writeTest(std::string testName, + std::vector expected, std::vector actual); + void closeFiles(); +}; +class Log{ + const char* taskName; + std::uint32_t runTime; +public: + + Log(DeploymentSchedule_ *task, std::uint32_t time); + Log(const char* name, std::uint32_t time); + + friend bool operator==(const Log& a, const Log& b); + + const char* getName(void); + std::uint32_t getRunTime(void); + +}; +class TestInput +{ +public: + std::uint32_t start; + std::uint32_t end; + std::vector ensembles; + std::vector expectedValues; + std::unordered_map> delays; + std::vector> resets; + + std::uint32_t getDelay(std::string name, std::uint32_t iteration); + + void parseInputFile(std::string filename); + + void clear(); +}; + +std::ostream& operator<<(std::ostream &strm, const TestLog &value); + + + + +#endif //__TEST_FILE_SYSTEM__ \ No newline at end of file