diff --git a/.gitignore b/.gitignore index 66cb575..4758170 100644 --- a/.gitignore +++ b/.gitignore @@ -1,300 +1,300 @@ -# -*.zip -*.rar -*.7z - -# sublime -*.sublime-* - -# codes under development -/_* - -# acm_simc related -*.exe -*.dat -*.pyc -/__pycache__ - -# acm_femm related -/_femm/codes3/log -/_femm/pop -/_femm/run#* -pyrhonen_procedure_s*.tex -*.txt - -# subllime text -*.sublime-project -*.sublime-workspace - -# ----------------------------------------------------- for LaTeX related temporary files - -## Core latex/pdflatex auxiliary files: -*.aux -*.lof -*.log -*.lot -*.fls -*.out -*.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 - -# uncomment this for glossaries-extra (will ignore makeindex's style files!) -# *.ist - -# gnuplottex -*-gnuplottex-* - -# gregoriotex -*.gaux -*.gtex - -# htlatex -*.4ct -*.4tc -*.idv -*.lg -*.trc -*.xref - -# hyperref -*.brf - -# knitr -*-concordance.tex -# TODO Comment the next line if you want to keep your tikz graphics 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 - -# nomencl -*.nlg -*.nlo -*.nls - -# pax -*.pax - -# pdfpcnotes -*.pdfpc - -# sagetex -*.sagetex.sage -*.sagetex.py -*.sagetex.scmd - -# scrwfile -*.wrt - -# sympy -*.sout -*.sympy -sympy-plots-for-*.tex/ - -# pdfcomment -*.upa -*.upb - -# pythontex -*.pytxcode -pythontex-files-*/ - -# tcolorbox -*.listing - -# thmtools -*.loe - -# TikZ & PGF -*.dpth -*.md5 -*.auxlock - -# 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 - -# KBibTeX -*~[0-9]* - -# auto folder when using emacs and auctex -./auto/* -*.el - -# expex forward references with \gathertags -*-tags.tex - -# standalone packages -*.sta - -# Makeindex log files +# +*.zip +*.rar +*.7z + +# sublime +*.sublime-* + +# codes under development +/_* + +# acm_simc related +*.exe +*.dat +*.pyc +/__pycache__ + +# acm_femm related +/_femm/codes3/log +/_femm/pop +/_femm/run#* +pyrhonen_procedure_s*.tex +*.txt + +# subllime text +*.sublime-project +*.sublime-workspace + +# ----------------------------------------------------- for LaTeX related temporary files + +## Core latex/pdflatex auxiliary files: +*.aux +*.lof +*.log +*.lot +*.fls +*.out +*.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 + +# uncomment this for glossaries-extra (will ignore makeindex's style files!) +# *.ist + +# gnuplottex +*-gnuplottex-* + +# gregoriotex +*.gaux +*.gtex + +# htlatex +*.4ct +*.4tc +*.idv +*.lg +*.trc +*.xref + +# hyperref +*.brf + +# knitr +*-concordance.tex +# TODO Comment the next line if you want to keep your tikz graphics 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 + +# nomencl +*.nlg +*.nlo +*.nls + +# pax +*.pax + +# pdfpcnotes +*.pdfpc + +# sagetex +*.sagetex.sage +*.sagetex.py +*.sagetex.scmd + +# scrwfile +*.wrt + +# sympy +*.sout +*.sympy +sympy-plots-for-*.tex/ + +# pdfcomment +*.upa +*.upb + +# pythontex +*.pytxcode +pythontex-files-*/ + +# tcolorbox +*.listing + +# thmtools +*.loe + +# TikZ & PGF +*.dpth +*.md5 +*.auxlock + +# 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 + +# KBibTeX +*~[0-9]* + +# auto folder when using emacs and auctex +./auto/* +*.el + +# expex forward references with \gathertags +*-tags.tex + +# standalone packages +*.sta + +# Makeindex log files *.lpz \ No newline at end of file diff --git a/.vscode/launch.json b/.vscode/launch.json new file mode 100644 index 0000000..e768b2d --- /dev/null +++ b/.vscode/launch.json @@ -0,0 +1,28 @@ +{ + // 使用 IntelliSense 了解相关属性。 + // 悬停以查看现有属性的描述。 + // 欲了解更多信息，请访问: https://go.microsoft.com/fwlink/?linkid=830387 + "version": "0.2.0", + "configurations": [ + { + "name": "(gdb) Launch", //这个应该是F1中出现的名字 + "preLaunchTask": "Build", //在launch之前运行的任务名，这个名字一定要跟tasks.json中的任务名字大小写一致 + "type": "cppdbg", + "request": "launch", + "program": "${fileDirname}/${fileBasenameNoExtension}.exe", //需要运行的是当前打开文件的目录中，名字和当前文件相同，但扩展名为exe的程序 + "args": [], + "stopAtEntry": false, // 选为true则会在打开控制台后停滞，暂时不执行程序 + "cwd": "${workspaceFolder}", // 当前工作路径：当前文件所在的工作空间 + "environment": [], + "externalConsole": true, // 是否使用外部控制台，选false的话，我的vscode会出现错误 + "MIMode": "gdb", + "miDebuggerPath": "c:/MinGW/bin/gdb.exe", + "setupCommands": [ + { + "description": "Enable pretty-printing for gdb", + "text": "-enable-pretty-printing", + "ignoreFailures": true + } + ] + }] +} \ No newline at end of file diff --git a/.vscode/settings.json b/.vscode/settings.json new file mode 100644 index 0000000..db8b475 --- /dev/null +++ b/.vscode/settings.json @@ -0,0 +1,10 @@ +{ + "files.associations": { + "acmsim.h": "c", + "conio.h": "c", + "stdlib.h": "c", + "math.h": "c", + "stdio.h": "c", + "controller.h": "c" + } +} \ No newline at end of file diff --git a/ACMConfig.h b/ACMConfig.h index 6c8024f..03eea6f 100644 --- a/ACMConfig.h +++ b/ACMConfig.h @@ -12,7 +12,7 @@ #define SENSORLESS_CONTROL_HFSI false #define VOLTAGE_CURRENT_DECOUPLING_CIRCUIT False #define SATURATED_MAGNETIC_CIRCUIT False -#define INVERTER_NONLINEARITY false +#define INVERTER_NONLINEARITY true #define TS 0.00025 #define TS_INVERSE 4000 #define TS_UPSAMPLING_FREQ_EXE 0.5 diff --git a/ACMPlot.py b/ACMPlot.py index 3b4101d..1bbc717 100644 --- a/ACMPlot.py +++ b/ACMPlot.py @@ -1,5 +1,6 @@ #coding:u8 -from pylab import plt, mpl, np +from pylab import mpl, np, plt + from mpl_toolkits.axes_grid1.inset_locator import zoomed_inset_axes from mpl_toolkits.axes_grid1.inset_locator import mark_inset # from pprint import pprint @@ -82,6 +83,7 @@ def plot_it(ax, ylabel, d, time=None): (str(idx), df_profiles[key]), # (str(idx), df_profiles[key]), ]), time) + plt.savefig('../img/picture.png') plt.show() quit() diff --git a/ACMSim.h b/ACMSim.h index 031881f..d213f97 100644 --- a/ACMSim.h +++ b/ACMSim.h @@ -4,8 +4,9 @@ /* standard lib */ // #include // bool for _Bool and true for 1 #include // printf #include // bool for _Bool and true for 1 -#include //reqd. for system function prototype -#include // for clrscr, and getch() +// #include //reqd. for system function prototype +// #include // for clrscr, and getch() +#include #include "stdlib.h" // for rand() #include "math.h" #include "time.h" diff --git a/LICENSE.md b/LICENSE.md index f288702..3877ae0 100644 --- a/LICENSE.md +++ b/LICENSE.md @@ -1,674 +1,674 @@ - GNU GENERAL PUBLIC LICENSE - Version 3, 29 June 2007 - - Copyright (C) 2007 Free Software Foundation, Inc. - Everyone is permitted to copy and distribute verbatim copies - of this license document, but changing it is not allowed. - - Preamble - - The GNU General Public 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If your program is a subroutine library, you +may consider it more useful to permit linking proprietary applications with +the library. If this is what you want to do, use the GNU Lesser General +Public License instead of this License. But first, please read +. diff --git a/README.md b/README.md index 873f610..82d31af 100644 --- a/README.md +++ b/README.md @@ -1,93 +1,93 @@ -# ACMSIMC_TUT -> AC Machine Simulation in C (Tutorial Version) - -## Introduction -I have used C to simulate motor control and adptive observers for over 4 years now. -This is a tutorial for those who hate using Simulink to simulate ac motor control. -The benefit is that you can directly reuse the codes in DSP based motor drive. - -## Numerial Methods -The numerical integration method is currently RK4, which is quite enough. -DoPri54 will be included in future version (including stiffness detection and variable step numerical integration). - -## Introduction to Current Branches (in time order): -- [IM] vvvf: the skeleton with induction motor simulation and VVVF control. -- [IM] foc: field oriented control (direct/indirect) with basic sensorless control. -- [IM] animate: test the feature of waveform animation like an oscilloscope. -- [PMSM] pmsm: id=0 control for interior permanent magnet synchronous motor. -- [IM] vi_decouple: add voltage-current decoupling circuit for improved control performance during high speed reversal. -- [IM] mras: model reference adaptive system based sensorless control (my 2017-Chen.Huang-Online paper). -- [IM] \_femm: (This branch does not really belong here but I don't want to create a new repository for it...) It is about the design of the induction motor using free softwares as well as fitting the design to the equivalent circuit parameters for further control simulation. -- [IM] saturation: include iron core saturation effect into the induction motor model simulation. -- [Both] inverter_model: simple inverter modeling based on the paper 1996-Choi.Sul-Inverter. -- [Both] **master: contain all the features of the branches mentioned above. The master branch is not updated anymore from this point, because I realized that having both IM and PMSM codes in one place is a silly idea.** -- [PMSM] commissioning_pmsm: (under developing) self-commissioning procedure for permanent magnet motor. -- [PMSM] eemf: sensorless control based on extended emf method proposed by Zhiqian Chen et al. (2003). Sensorless open loop works but closed-loop has problems. -- [PMSM] hfsi-yoon: pulsating type square wave high frequency signal injection method based sensorless control for motor with saliency. -- [PMSM] commissioning_spmsm: self-commissioning procedure for surface mounted permanent magnet synchronous motor. -- [PMSM] spmsm_scvm_harnerfors06: statically compensated voltage model based sensorless control for SPMSM. This branch also includes new dynamic anti-windup and incremental PID regulator. - -(I recommend to use GitKraken to view this repository, so you know clearly which branch is the newest one. Note that master is obsolete. Sorry about my poor code management skills T-T.) - -## Visualization -- The plots are made using package matplotlib. - - In branch animate, I tested the feature of waveform animation with matplotlib. -- You can also try browser based libraries, e.g., plotly_express. - -## Dependency under Windows -- Anaconda 3 (you should be able to call python from cmd.exe) -- MinGW (you should be able to call gcc from cmd.exe) -- FEMM ([femm.info](http://www.femm.info/wiki/HomePage), you do not need this if you are not interested in Finite Element Analysis and motor design) - - PyFEMM (a wrapper for FEMM API; use pip to install this) -- Editor (optional): Sublime Text (preferred) or Visual Studio Code. - -## Dependency under Linux -- FEMM is a Windows-only FEA software. - - Alternative is ElmerFEM for Linux, but it is poorly documented. Please DO NOT try it out unless you are a ドM. -- Others are not tested yet. (Linux users should be able to figure it out...) - -## Compile - -### Compile in Sublime Text -Create a file named "**!C_GCC.sublime-build**" in this folder "C:\Users\yourUserName\AppData\Roaming\Sublime Text 3\Packages\User" with the following content: -```json - { - "working_dir": "$file_path", - "cmd": "gcc -Wall $file_name -o $file_base_name", - "file_regex": "^(..[^:]*):([0-9]+):?([0-9]+)?:? (.*)$", - "selector": "source.c", - "variants": - [ - { - "name": "ACMSIMC_TUT", - // "shell_cmd": "gcc $file controller.c observer.c -L. -o $file_base_name && start cmd /c \"${file_path}/${file_base_name}\"" - "shell_cmd": "gcc $file commissioning.c inverter.c controller.c observer.c -L. -o $file_base_name && start cmd /c \"${file_path}/${file_base_name}\"" - } - ] - } -``` -Press down Ctrl + Shift + B to select "ACMSIMC_TUT" and next time you only need to press down Ctrl + B to build. - -### Compile in cmd.exe -Change directory to where main.c is and open the cmd.exe there and type: -```batch -gcc main.c commissioning.c inverter.c controller.c observer.c -I. -L. -o main -``` -Then, double click main.exe to run. -If you have python added to system path, plots will also pop up. - - -## Video Tutorials -For unfamiliar users, I have been creating video tutorials. However, they are currently in Chinese. -> *See [my Bilibili space](https://space.bilibili.com/7132537) (ACTIVELY UPDATED).* -> -> Also take a look at this link to [知乎](https://zhuanlan.zhihu.com/p/64445558) (not acvtively updated). -> -> Also check out [my personal page](https://horychen.github.io) for a list of tutorial videos (not acvtively updated). - -In near future, the English version will be brought about. But I would like to do it in high quality so I procrastinate. - - +# ACMSIMC_TUT +> AC Machine Simulation in C (Tutorial Version) + +## Introduction +I have used C to simulate motor control and adptive observers for over 4 years now. +This is a tutorial for those who hate using Simulink to simulate ac motor control. +The benefit is that you can directly reuse the codes in DSP based motor drive. + +## Numerial Methods +The numerical integration method is currently RK4, which is quite enough. +DoPri54 will be included in future version (including stiffness detection and variable step numerical integration). + +## Introduction to Current Branches (in time order): +- [IM] vvvf: the skeleton with induction motor simulation and VVVF control. +- [IM] foc: field oriented control (direct/indirect) with basic sensorless control. +- [IM] animate: test the feature of waveform animation like an oscilloscope. +- [PMSM] pmsm: id=0 control for interior permanent magnet synchronous motor. +- [IM] vi_decouple: add voltage-current decoupling circuit for improved control performance during high speed reversal. +- [IM] mras: model reference adaptive system based sensorless control (my 2017-Chen.Huang-Online paper). +- [IM] \_femm: (This branch does not really belong here but I don't want to create a new repository for it...) It is about the design of the induction motor using free softwares as well as fitting the design to the equivalent circuit parameters for further control simulation. +- [IM] saturation: include iron core saturation effect into the induction motor model simulation. +- [Both] inverter_model: simple inverter modeling based on the paper 1996-Choi.Sul-Inverter. +- [Both] **master: contain all the features of the branches mentioned above. The master branch is not updated anymore from this point, because I realized that having both IM and PMSM codes in one place is a silly idea.** +- [PMSM] commissioning_pmsm: (under developing) self-commissioning procedure for permanent magnet motor. +- [PMSM] eemf: sensorless control based on extended emf method proposed by Zhiqian Chen et al. (2003). Sensorless open loop works but closed-loop has problems. +- [PMSM] hfsi-yoon: pulsating type square wave high frequency signal injection method based sensorless control for motor with saliency. +- [PMSM] commissioning_spmsm: self-commissioning procedure for surface mounted permanent magnet synchronous motor. +- [PMSM] spmsm_scvm_harnerfors06: statically compensated voltage model based sensorless control for SPMSM. This branch also includes new dynamic anti-windup and incremental PID regulator. + +(I recommend to use GitKraken to view this repository, so you know clearly which branch is the newest one. Note that master is obsolete. Sorry about my poor code management skills T-T.) + +## Visualization +- The plots are made using package matplotlib. + - In branch animate, I tested the feature of waveform animation with matplotlib. +- You can also try browser based libraries, e.g., plotly_express. + +## Dependency under Windows +- Anaconda 3 (you should be able to call python from cmd.exe) +- MinGW (you should be able to call gcc from cmd.exe) +- FEMM ([femm.info](http://www.femm.info/wiki/HomePage), you do not need this if you are not interested in Finite Element Analysis and motor design) + - PyFEMM (a wrapper for FEMM API; use pip to install this) +- Editor (optional): Sublime Text (preferred) or Visual Studio Code. + +## Dependency under Linux +- FEMM is a Windows-only FEA software. + - Alternative is ElmerFEM for Linux, but it is poorly documented. Please DO NOT try it out unless you are a ドM. +- Others are not tested yet. (Linux users should be able to figure it out...) + +## Compile + +### Compile in Sublime Text +Create a file named "**!C_GCC.sublime-build**" in this folder "C:\Users\yourUserName\AppData\Roaming\Sublime Text 3\Packages\User" with the following content: +```json + { + "working_dir": "$file_path", + "cmd": "gcc -Wall $file_name -o $file_base_name", + "file_regex": "^(..[^:]*):([0-9]+):?([0-9]+)?:? (.*)$", + "selector": "source.c", + "variants": + [ + { + "name": "ACMSIMC_TUT", + // "shell_cmd": "gcc $file controller.c observer.c -L. -o $file_base_name && start cmd /c \"${file_path}/${file_base_name}\"" + "shell_cmd": "gcc $file commissioning.c inverter.c controller.c observer.c -L. -o $file_base_name && start cmd /c \"${file_path}/${file_base_name}\"" + } + ] + } +``` +Press down Ctrl + Shift + B to select "ACMSIMC_TUT" and next time you only need to press down Ctrl + B to build. + +### Compile in cmd.exe +Change directory to where main.c is and open the cmd.exe there and type: +```batch +gcc main.c commissioning.c inverter.c controller.c observer.c -I. -L. -o main +``` +Then, double click main.exe to run. +If you have python added to system path, plots will also pop up. + + +## Video Tutorials +For unfamiliar users, I have been creating video tutorials. However, they are currently in Chinese. +> *See [my Bilibili space](https://space.bilibili.com/7132537) (ACTIVELY UPDATED).* +> +> Also take a look at this link to [知乎](https://zhuanlan.zhihu.com/p/64445558) (not acvtively updated). +> +> Also check out [my personal page](https://horychen.github.io) for a list of tutorial videos (not acvtively updated). + +In near future, the English version will be brought about. But I would like to do it in high quality so I procrastinate. + + diff --git a/_femm/codes3/utility_moo.py b/_femm/codes3/utility_moo.py index 3ac1964..99d9654 100644 --- a/_femm/codes3/utility_moo.py +++ b/_femm/codes3/utility_moo.py @@ -1,574 +1,574 @@ -import pygmo as pg -from pylab import plt, np -def my_plot_non_dominated_fronts(points, marker='o', comp=[0, 1], up_to_rank_no=None): - # We plot - fronts, _, _, _ = pg.fast_non_dominated_sorting(points) - - # We define the colors of the fronts (grayscale from black to white) - if up_to_rank_no is None: - cl = list(zip(np.linspace(0.1, 0.9, len(fronts)), - np.linspace(0.1, 0.9, len(fronts)), - np.linspace(0.1, 0.9, len(fronts)))) - else: - cl = list(zip(np.linspace(0.1, 0.9, up_to_rank_no), - np.linspace(0.1, 0.9, up_to_rank_no), - np.linspace(0.1, 0.9, up_to_rank_no))) - - fig, ax = plt.subplots() - - count = 0 - for ndr, front in enumerate(fronts): - count += 1 - # We plot the points - for idx in front: - ax.plot(points[idx][comp[0]], points[idx][ - comp[1]], marker=marker, color=cl[ndr]) - # We plot the fronts - # Frist compute the points coordinates - x = [points[idx][comp[0]] for idx in front] - y = [points[idx][comp[1]] for idx in front] - # Then sort them by the first objective - tmp = [(a, b) for a, b in zip(x, y)] - tmp = sorted(tmp, key=lambda k: k[0]) - # Now plot using step - ax.step([c[0] for c in tmp], [c[1] - for c in tmp], color=cl[ndr], where='post') - if up_to_rank_no is None: - pass - else: - if count >= up_to_rank_no: - break - - return ax -def my_2p5d_plot_non_dominated_fronts(points, marker='o', comp=[0, 1], up_to_rank_no=1, no_text=True): - # from pylab import mpl - # mpl.rcParams['font.family'] = ['Times New Roman'] - # mpl.rcParams['font.size'] = 16.0 - - full_comp = [0, 1, 2] - full_comp.remove(comp[0]) - full_comp.remove(comp[1]) - z_comp = full_comp[0] - - # We plot - # fronts, dl, dc, ndr = pg.fast_non_dominated_sorting(points) - fronts, _, _, _= pg.fast_non_dominated_sorting(points) - - # We define the colors of the fronts (grayscale from black to white) - cl = list(zip(np.linspace(0.9, 0.1, len(fronts)), - np.linspace(0.9, 0.1, len(fronts)), - np.linspace(0.9, 0.1, len(fronts)))) - - fig, ax = plt.subplots(constrained_layout=False) - plt.subplots_adjust(left=None, bottom=None, right=0.85, top=None, wspace=None, hspace=None) - - count = 0 - for ndr, front in enumerate(fronts): - count += 1 - - # Frist compute the points coordinates - x_scale = 1 - y_scale = 1 - z_scale = 1 - if comp[0] == 1: # efficency - x_scale = 100 - if comp[1] == 1: # efficency - y_scale = 100 - if z_comp == 1: # efficency - z_scale = 100 - x = [points[idx][comp[0]]*x_scale for idx in front] - y = [points[idx][comp[1]]*y_scale for idx in front] - z = [points[idx][z_comp] *z_scale for idx in front] - - # # We plot the points - # for idx in front: - # ax.plot(points[idx][comp[0]], points[idx][comp[1]], marker=marker, color=cl[ndr]) - - # Then sort them by the first objective - tmp = [(a, b, c) for a, b, c in zip(x, y, z)] - tmp = sorted(tmp, key=lambda k: k[0]) - # Now plot using step - ax.step([coords[0] for coords in tmp], - [coords[1] for coords in tmp], color=cl[ndr], where='post') - - # Now add color according to the value of the z-axis variable usign scatter - scatter_handle = ax.scatter(x, y, c=z, alpha=0.5, cmap='Spectral', marker=marker, zorder=99) #'viridis' - # color bar - cbar_ax = fig.add_axes([0.875, 0.15, 0.02, 0.7]) - cbar_ax.get_yaxis().labelpad = 10 - clb = fig.colorbar(scatter_handle, cax=cbar_ax) - if z_comp == 0: - z_label = r'$-\rm {TRV}$ [Nm/m^3]' - z_text = '%.0f' - elif z_comp == 1: - z_label = r'$-\eta$ [%]' - z_text = '%.1f' - elif z_comp == 2: - z_label = r'$O_C$ [1]' - z_text = '%.1f' - clb.ax.set_ylabel(z_label, rotation=270) - - - if z_comp == 2: # when OC as z-axis - print('-----------------------------------------------------') - print('-----------------------------------------------------') - print('-----------------------------------------------------') - # Add index next to the points - for x_coord, y_coord, z_coord, idx in zip(x, y, z, front): - if no_text: - pass - else: - ax.annotate( z_text%(z_coord) + ' #%d'%(idx), (x_coord, y_coord) ) - else: - # text next scatter showing the value of the 3rd objective - for i, val in enumerate(z): - if no_text: - pass - else: - ax.annotate( z_text%(val), (x[i], y[i]) ) - - # refine the plotting - if comp[0] == 0: - ax.set_xlabel(r'$-\rm {TRV}$ [Nm/m^3]') - elif comp[0] == 1: - ax.set_xlabel(r'$-\eta$ [%]') - elif comp[0] == 2: - ax.set_xlabel(r'$O_C$ [1]') - - if comp[1] == 0: - ax.set_ylabel(r'$-\rm {TRV}$ [Nm/m^3]') - elif comp[1] == 1: - ax.set_ylabel(r'$-\eta$ [%]') - elif comp[1] == 2: - ax.set_ylabel(r'$O_C$ [1]') - ax.grid() - - # plot up to which domination rank? - if up_to_rank_no is None: - pass - else: - if count >= up_to_rank_no: - break - # Y730 - # fig.savefig(r'C:\Users\horyc\Desktop/'+ '2p5D-%d%d.png'%(comp[0],comp[1]), dpi=300) - return ax -def my_3d_plot_non_dominated_fronts(pop, paretoPoints, fea_config_dict, az=180, comp=[0, 1, 2], plot_option=1): - """ - Plots solutions to the DTLZ problems in three dimensions. The Pareto Front is also - visualized if the problem id is 2,3 or 4. - Args: - pop (:class:`~pygmo.population`): population of solutions to a dtlz problem - az (``float``): angle of view on which the 3d-plot is created - comp (``list``): indexes the fitness dimension for x,y and z axis in that order - Returns: - ``matplotlib.axes.Axes``: the current ``matplotlib.axes.Axes`` instance on the current figure - Raises: - ValueError: if *pop* does not contain a DTLZ problem (veryfied by its name only) or if *comp* is not of length 3 - Examples: - >>> import pygmo as pg - >>> udp = pg.dtlz(prob_id = 1, fdim =3, dim = 5) - >>> pop = pg.population(udp, 40) - >>> udp.plot(pop) # doctest: +SKIP - """ - from mpl_toolkits.mplot3d import axes3d - import matplotlib.pyplot as plt - import numpy as np - # from pylab import mpl - # mpl.rcParams['font.family'] = ['Times New Roman'] - # mpl.rcParams['font.size'] = 16.0 - - # if (pop.problem.get_name()[:-1] != "DTLZ"): - # raise(ValueError, "The problem seems not to be from the DTLZ suite") - - if (len(comp) != 3): - raise(ValueError, "The kwarg *comp* needs to contain exactly 3 elements (ids for the x,y and z axis)") - - # Create a new figure - fig = plt.figure(figsize=(12,8)) - ax = fig.add_subplot(111, projection='3d') - - # plot the points - fits = np.transpose(pop.get_f()) - try: - pass - # ax.plot(fits[comp[0]], fits[comp[1]], fits[comp[2]], 'ro') - except IndexError: - print('Error. Please choose correct fitness dimensions for printing!') - - if False: - # Plot pareto front for dtlz 1 - if plot_option==1: # (pop.problem.get_name()[-1] in ["1"]): - - X, Y = np.meshgrid(np.linspace(0, 0.5, 100), np.linspace(0, 0.5, 100)) - Z = - X - Y + 0.5 - # remove points not in the simplex - for i in range(100): - for j in range(100): - if X[i, j] < 0 or Y[i, j] < 0 or Z[i, j] < 0: - Z[i, j] = float('nan') - - ax.set_xlim(0, 1.) - ax.set_ylim(0, 1.) - ax.set_zlim(0, 1.) - - ax.plot_wireframe(X, Y, Z, rstride=10, cstride=10) - plt.plot([0, 0.5], [0.5, 0], [0, 0]) - - # Plot pareto fronts for dtlz 2,3,4 - if plot_option == 2: # (pop.problem.get_name()[-1] in ["2", "3", "4"]): - # plot the wireframe of the known optimal pareto front - thetas = np.linspace(0, (np.pi / 2.0), 30) - # gammas = np.linspace(-np.pi / 4, np.pi / 4, 30) - gammas = np.linspace(0, (np.pi / 2.0), 30) - - x_frame = np.outer(np.cos(thetas), np.cos(gammas)) - y_frame = np.outer(np.cos(thetas), np.sin(gammas)) - z_frame = np.outer(np.sin(thetas), np.ones(np.size(gammas))) - - ax.set_autoscalex_on(False) - ax.set_autoscaley_on(False) - ax.set_autoscalez_on(False) - - ax.set_xlim(0, 1.8) - ax.set_ylim(0, 1.8) - ax.set_zlim(0, 1.8) - - ax.plot_wireframe(x_frame, y_frame, z_frame) - - # https://stackoverflow.com/questions/37000488/how-to-plot-multi-objectives-pareto-frontier-with-deap-in-python - # def simple_cull(inputPoints, dominates): - # paretoPoints = set() - # candidateRowNr = 0 - # dominatedPoints = set() - # while True: - # candidateRow = inputPoints[candidateRowNr] - # inputPoints.remove(candidateRow) - # rowNr = 0 - # nonDominated = True - # while len(inputPoints) != 0 and rowNr < len(inputPoints): - # row = inputPoints[rowNr] - # if dominates(candidateRow, row): - # # If it is worse on all features remove the row from the array - # inputPoints.remove(row) - # dominatedPoints.add(tuple(row)) - # elif dominates(row, candidateRow): - # nonDominated = False - # dominatedPoints.add(tuple(candidateRow)) - # rowNr += 1 - # else: - # rowNr += 1 - - # if nonDominated: - # # add the non-dominated point to the Pareto frontier - # paretoPoints.add(tuple(candidateRow)) - - # if len(inputPoints) == 0: - # break - # return paretoPoints, dominatedPoints - # def dominates(row, candidateRow): - # return sum([row[x] >= candidateRow[x] for x in range(len(row))]) == len(row) - # import random - # print(inputPoints) - # inputPoints = [[random.randint(70,100) for i in range(3)] for j in range(500)] - # print(inputPoints) - # quit() - # inputPoints = [(x,y,z) for x,y,z in zip(fits[comp[0]], fits[comp[1]], fits[comp[2]])] - # paretoPoints, dominatedPoints = simple_cull(inputPoints, dominates) - x = [coords[0]/1000 for coords in paretoPoints] - y = [coords[1] for coords in paretoPoints] - z = [coords[2] for coords in paretoPoints] - - # from surface_fitting import surface_fitting - # surface_fitting(x,y,z) - # quit() - - if False: - pass - else: - import pandas as pd - from pylab import cm - print(dir(cm)) - df = pd.DataFrame({'x': x, 'y': y, 'z': z}) - - # 只有plot_trisurf这一个函数，输入是三个以为序列的，其他都要meshgrid得到二维数组的(即ndim=2的数组) - # # https://jakevdp.github.io/PythonDataScienceHandbook/04.12-three-dimensional-plotting.html - # surf = ax.plot_trisurf(df.x, df.y, df.z, cmap=cm.magma, linewidth=0.1, edgecolor='none') - # surf = ax.plot_trisurf(x, y, z, cmap='viridis', edgecolor='none') - # surf = ax.plot_trisurf(df.x, df.y, df.z, cmap=cm.magma, linewidth=0.1) - surf = ax.plot_trisurf(df.x, df.y*100, df.z, cmap=cm.Spectral, linewidth=0.1) - - with open('./%s_PF_points.txt'%(fea_config_dict['run_folder'][:-1]), 'w') as f: - f.write('TRV,eta,OC\n') - f.writelines(['%g,%g,%g\n'%(a,b,c) for a,b,c in zip(df.x, df.y*100, df.z)]) - # quit() - - fig.colorbar(surf, shrink=0.5, aspect=5) - - ax.set_xlabel(' \n$\\rm -TRV$ [$\\rm kNm/m^3$]') - ax.set_ylabel(' \n$-\\eta$ [%]') - ax.set_yticks(np.arange(-96,-93.5,0.5)) - ax.set_zlabel(r'$O_C$ [1]') - - # Try to export data from plot_trisurf # https://github.com/WoLpH/numpy-stl/issues/19 - # print(surf.get_vector()) - - # plt.savefig('./plots/avgErrs_vs_C_andgamma_type_%s.png'%(k)) - # plt.show() - - # # rotate the axes and update - # for angle in range(0, 360): - # ax.view_init(30, angle) - # plt.draw() - # plt.pause(.001) - - ax.view_init(azim=245, elev=15) - # fig.tight_layout() - - # Y730 - # fig.savefig(r'C:\Users\horyc\Desktop/3D-plot.png', dpi=300, layout='tight') - - # ax.view_init(azim=az) - # ax.set_xlim(0, 1.) - # ax.set_ylim(0, 1.) - # ax.set_zlim(0, 10.) - return ax -def my_plot(fits, vectors, ndf): - plt.rcParams['mathtext.fontset'] = 'stix' # 'cm' - plt.rcParams["font.family"] = "Times New Roman" - if True: - # for fit in fits: - # print(fit) - # ax = pg.plot_non_dominated_fronts(fits) - - # ax = my_plot_non_dominated_fronts(fits, comp=[0,1], marker='o', up_to_rank_no=3) - # ax = my_plot_non_dominated_fronts(fits, comp=[0,2], marker='o', up_to_rank_no=3) - # ax = my_plot_non_dominated_fronts(fits, comp=[1,2], marker='o', up_to_rank_no=3) - - pass - - ax = my_2p5d_plot_non_dominated_fronts(fits, comp=[0,1], marker='o', up_to_rank_no=1) - # # for studying LSA population (whether or not the optimal is on Rank 1 Pareto Front) - # x = fits[0][0]/1e3 - # y = fits[0][1] - # z = fits[0][2] - # ax.plot(x, y, color='k', marker='s') - # ax.annotate(r'$x_{\rm optm}$', xy=(x, y), xytext=(x+1, y+0.0005), arrowprops=dict(facecolor='black', shrink=0.05),) - ax = my_2p5d_plot_non_dominated_fronts(fits, comp=[0,2], marker='o', up_to_rank_no=1) - ax = my_2p5d_plot_non_dominated_fronts(fits, comp=[1,2], marker='o', up_to_rank_no=1) - - else: - # Obselete. Use ax.step instead to plot - print('Valid for 2D objective function space only for now.') - fig, axes = plt.subplots(ncols=2, nrows=1, dpi=150, facecolor='w', edgecolor='k'); - ax = axes[0] - - for index, xy in enumerate(vectors): - ax.plot(*xy, 's', color='0') - ax.text(*xy, '#%d'%(index), color='0') - ax.title.set_text("Decision Vector Space") - - ax = axes[1] - - for index, front in enumerate(ndf): - print('Rank/Tier', index, front) - the_color = '%g'%(index/len(ndf)) - for individual in front: # individual is integer here - ax.plot(*fits[individual], 'o', color=the_color) - ax.text(*fits[individual], '#%d'%(individual), color=the_color) - - front = front.tolist() - front.sort(key = lambda individual: fits[individual][1]) # sort by y-axis value # individual is integer here - for individual_A, individual_B in zip(front[:-1], front[1:]): # front is already sorted - fits_A = fits[individual_A] - fits_B = fits[individual_B] - ax.plot([fits_A[0], fits_B[0]], - [fits_B[1], fits_B[1]], color=the_color, lw=0.25) # 取高的点的Y - ax.plot([fits_A[0], fits_A[0]], - [fits_A[1], fits_B[1]], color=the_color, lw=0.25) # 取低的点的X - ax.title.set_text("Pareto Front | Objective Function Space") -def my_print(ad, pop, _): - # ndf, dl, dc, ndr = pg.fast_non_dominated_sorting(fits) - # extract and print non-dominated fronts - # - ndf (list of 1D NumPy int array): the non dominated fronts - # - dl (list of 1D NumPy int array): the domination list - # - dc (1D NumPy int array): the domination count - # - ndr (1D NumPy int array): the non domination ranks - fits, vectors = pop.get_f(), pop.get_x() - ndf, dl, dc, ndr = pg.fast_non_dominated_sorting(fits) - - with open(ad.solver.output_dir+'MOO_log.txt', 'a', encoding='utf-8') as fname: - print('-'*40, 'Generation:', _, file=fname) - for rank_minus_1, front in enumerate(ndf): - print('Rank/Tier', rank_minus_1+1, front, file=fname) - index = 0 - for domination_list, domination_count, non_domination_rank in zip(dl, dc, ndr): - print('Individual #%d\t'%(index), 'Belong to Rank #%d\t'%(non_domination_rank), 'Dominating', domination_count, 'and they are', domination_list, file=fname) - index += 1 - - # print(fits, vectors, ndf) - print(pop, file=fname) - - -def learn_about_the_archive(prob, swarm_data, popsize, fea_config_dict, len_s01=None, len_s02=None, bool_plot_and_show=False): - number_of_chromosome = len(swarm_data) - print('Archive size:', number_of_chromosome) - # for el in swarm_data: - # print('\t', el) - - pop_archive = pg.population(prob, size=number_of_chromosome) - for i in range(number_of_chromosome): - pop_archive.set_xf(i, swarm_data[i][:-3], swarm_data[i][-3:]) - - sorted_index = pg.sort_population_mo(points=pop_archive.get_f()) - print('Sorted by domination rank and crowding distance:', len(sorted_index)) - print('\t', sorted_index) - - # 这段代码对于重建种群来说不是必须的，单单sort_population_mo（包含fast_non_dominated_sorting和crowding_distance）就够了， - # 只是我想看看，具体的crowding_distance是多少，然后我想知道排在前面的多少个是属于domination rank 1的。 - if True: - fits, vectors = pop_archive.get_f(), pop_archive.get_x() - ndf, dl, dc, ndr = pg.fast_non_dominated_sorting(fits) - - ind1, ind2 = 0, 0 - for rank_minus_1, front in enumerate(ndf): - - ind2 += len(front) - sorted_index_at_this_front = sorted_index[ind1:ind2] - fits_at_this_front = [fits[point] for point in sorted_index_at_this_front] - - # Rank 1 Pareto Front - if ind1 == 0: - rank1_ParetoPoints = fits_at_this_front - if len(front) < popsize: - print('There are not enough chromosomes (%d) belonging to domination rank 1 (the best Pareto front).\nWill use rank 2 or lower to reach popsize of %d.'%(len(front), popsize)) - - # this crwdsit should be already sorted as well - if len(fits_at_this_front) >= 2: # or else error: A non dominated front must contain at least two points: 1 detected. - crwdst = pg.crowding_distance(fits_at_this_front) - else: - print('A non dominated front must contain at least two points: 1 detected.') - crwdst = [999999] - - - print('\nRank/Tier', rank_minus_1+1, 'chromosome count:', len(front), len(sorted_index_at_this_front)) - # print('\t', sorted_index_at_this_front.tolist()) - # print('\t', crwdst) - print('\tindex in pop\t|\tcrowding distance') - for index, cd in zip(sorted_index_at_this_front, crwdst): - print('\t', index, '\t\t\t|', cd) - ind1 = ind2 - - sorted_vectors = [vectors[index].tolist() for index in sorted_index] - sorted_fits = [fits[index].tolist() for index in sorted_index] - - if bool_plot_and_show: - my_plot(pop_archive.get_f(), pop_archive.get_x(), ndf) - my_3d_plot_non_dominated_fronts(pop_archive, rank1_ParetoPoints, fea_config_dict, plot_option=1) - plt.show() - - swarm_data_on_pareto_front = [design_parameters_denorm + fits for design_parameters_denorm, fits in zip(sorted_vectors, sorted_fits)] - return swarm_data_on_pareto_front - -def pyx_draw_model(im): - import matplotlib.patches as mpatches - import matplotlib.pyplot as plt - plt.rcParams["font.family"] = "Times New Roman" - - myfontsize = 13.5 - plt.rcParams.update({'font.size': myfontsize}) - - # # 示意图而已，改改尺寸吧 - # im.Radius_OuterStatorYoke -= 37 - # im.Radius_InnerStatorYoke -= 20 - # im.Radius_Shaft += 20 - # # im.Location_RotorBarCenter2 += 5 # this will change the shape of rotor slot - - import VanGogh - vg = VanGogh.VanGogh_pyPlotter(im, VanGogh.CUSTOM) - vg.draw_model() - - # PyX - import pyx - vg.tikz.c = pyx.canvas.canvas() # clear the canvas because we want to redraw 90 deg with the data vg.tikz.track_path - from copy import deepcopy - def pyx_draw_path(vg, path, sign=1): - if len(path) == 4: - vg.tikz.draw_line(path[:2], path[2:4], untrack=True) - else: - vg.tikz.draw_arc(path[:2], path[2:4], path[4:6], relangle=sign*path[6], untrack=True) - def rotate(_, x, y): - return np.cos(_)*x + np.sin(_)*y, -np.sin(_)*x + np.cos(_)*y - def is_at_stator(im, path): - return np.sqrt(path[0]**2 + path[1]**2) > im.Radius_OuterRotor + 0.5*im.Length_AirGap - - for path in (vg.tikz.track_path): # track_path is passed by reference and is changed by mirror - path_mirror = deepcopy(path) - # for mirror copy (along x-axis) - path_mirror[1] = path[1]*-1 - path_mirror[3] = path[3]*-1 - - # rotate path and plot - if is_at_stator(im, path): - Q = im.Qs - else: - Q = im.Qr - _ = 2*np.pi/Q - path[0], path[1] = rotate(0.5*np.pi - 0.5*_, path[0], path[1]) - path[2], path[3] = rotate(0.5*np.pi - 0.5*_, path[2], path[3]) - pyx_draw_path(vg, path, sign=1) - - path_mirror[0], path_mirror[1] = rotate(0.5*np.pi - 0.5*_, path_mirror[0], path_mirror[1]) - path_mirror[2], path_mirror[3] = rotate(0.5*np.pi - 0.5*_, path_mirror[2], path_mirror[3]) - pyx_draw_path(vg, path_mirror, sign=-1) - - # 注意，所有 tack_path 中的 path 都已经转动了90度了！ - # for mirror copy (along y-axis) - path[0] *= -1 - path[2] *= -1 - pyx_draw_path(vg, path, sign=-1) - - path_mirror[0] *= -1 - path_mirror[2] *= -1 - pyx_draw_path(vg, path_mirror, sign=1) - - - # # 整体转动90度。 - # for path in vg.tikz.track_path: - # if is_at_stator(im, path): - # Q = im.Qs - # else: - # Q = im.Qr - # _ = 2*np.pi/Q - # path[0], path[1] = rotate(0.5*np.pi - _, path[0], path[1]) - # path[2], path[3] = rotate(0.5*np.pi - _, path[2], path[3]) - # pyx_draw_path(vg, path) - # track_path_backup = deepcopy(vg.tikz.track_path) - - # # Rotate Copy - # for path in deepcopy(vg.tikz.track_path): - # if is_at_stator(im, path): - # Q = im.Qs - # else: - # Q = im.Qr - # _ = 2*np.pi/Q - # path[0], path[1] = rotate(_, path[0], path[1]) - # path[2], path[3] = rotate(_, path[2], path[3]) - # pyx_draw_path(vg, path) - - # # Rotate Copy - # for path in (vg.tikz.track_path): - # # if np.sqrt(path[0]**2 + path[1]**2) > im.Radius_OuterRotor + 0.5*im.Length_AirGap: - # if is_at_stator(im, path): - # Q = im.Qs - # else: - # Q = im.Qr - # _ = 2*np.pi/Q - # path[0], path[1] = rotate(_, path[0], path[1]) - # path[2], path[3] = rotate(_, path[2], path[3]) - # pyx_draw_path(vg, path, sign=-1) - - vg.tikz.c.writePDFfile("selected_otimal_design%s"%(im.ID)) - # vg.tikz.c.writeEPSfile("pyx_output") - print('Write to pdf file: selected_otimal_design%s.pdf.'%(im.ID)) - quit() - +import pygmo as pg +from pylab import plt, np +def my_plot_non_dominated_fronts(points, marker='o', comp=[0, 1], up_to_rank_no=None): + # We plot + fronts, _, _, _ = pg.fast_non_dominated_sorting(points) + + # We define the colors of the fronts (grayscale from black to white) + if up_to_rank_no is None: + cl = list(zip(np.linspace(0.1, 0.9, len(fronts)), + np.linspace(0.1, 0.9, len(fronts)), + np.linspace(0.1, 0.9, len(fronts)))) + else: + cl = list(zip(np.linspace(0.1, 0.9, up_to_rank_no), + np.linspace(0.1, 0.9, up_to_rank_no), + np.linspace(0.1, 0.9, up_to_rank_no))) + + fig, ax = plt.subplots() + + count = 0 + for ndr, front in enumerate(fronts): + count += 1 + # We plot the points + for idx in front: + ax.plot(points[idx][comp[0]], points[idx][ + comp[1]], marker=marker, color=cl[ndr]) + # We plot the fronts + # Frist compute the points coordinates + x = [points[idx][comp[0]] for idx in front] + y = [points[idx][comp[1]] for idx in front] + # Then sort them by the first objective + tmp = [(a, b) for a, b in zip(x, y)] + tmp = sorted(tmp, key=lambda k: k[0]) + # Now plot using step + ax.step([c[0] for c in tmp], [c[1] + for c in tmp], color=cl[ndr], where='post') + if up_to_rank_no is None: + pass + else: + if count >= up_to_rank_no: + break + + return ax +def my_2p5d_plot_non_dominated_fronts(points, marker='o', comp=[0, 1], up_to_rank_no=1, no_text=True): + # from pylab import mpl + # mpl.rcParams['font.family'] = ['Times New Roman'] + # mpl.rcParams['font.size'] = 16.0 + + full_comp = [0, 1, 2] + full_comp.remove(comp[0]) + full_comp.remove(comp[1]) + z_comp = full_comp[0] + + # We plot + # fronts, dl, dc, ndr = pg.fast_non_dominated_sorting(points) + fronts, _, _, _= pg.fast_non_dominated_sorting(points) + + # We define the colors of the fronts (grayscale from black to white) + cl = list(zip(np.linspace(0.9, 0.1, len(fronts)), + np.linspace(0.9, 0.1, len(fronts)), + np.linspace(0.9, 0.1, len(fronts)))) + + fig, ax = plt.subplots(constrained_layout=False) + plt.subplots_adjust(left=None, bottom=None, right=0.85, top=None, wspace=None, hspace=None) + + count = 0 + for ndr, front in enumerate(fronts): + count += 1 + + # Frist compute the points coordinates + x_scale = 1 + y_scale = 1 + z_scale = 1 + if comp[0] == 1: # efficency + x_scale = 100 + if comp[1] == 1: # efficency + y_scale = 100 + if z_comp == 1: # efficency + z_scale = 100 + x = [points[idx][comp[0]]*x_scale for idx in front] + y = [points[idx][comp[1]]*y_scale for idx in front] + z = [points[idx][z_comp] *z_scale for idx in front] + + # # We plot the points + # for idx in front: + # ax.plot(points[idx][comp[0]], points[idx][comp[1]], marker=marker, color=cl[ndr]) + + # Then sort them by the first objective + tmp = [(a, b, c) for a, b, c in zip(x, y, z)] + tmp = sorted(tmp, key=lambda k: k[0]) + # Now plot using step + ax.step([coords[0] for coords in tmp], + [coords[1] for coords in tmp], color=cl[ndr], where='post') + + # Now add color according to the value of the z-axis variable usign scatter + scatter_handle = ax.scatter(x, y, c=z, alpha=0.5, cmap='Spectral', marker=marker, zorder=99) #'viridis' + # color bar + cbar_ax = fig.add_axes([0.875, 0.15, 0.02, 0.7]) + cbar_ax.get_yaxis().labelpad = 10 + clb = fig.colorbar(scatter_handle, cax=cbar_ax) + if z_comp == 0: + z_label = r'$-\rm {TRV}$ [Nm/m^3]' + z_text = '%.0f' + elif z_comp == 1: + z_label = r'$-\eta$ [%]' + z_text = '%.1f' + elif z_comp == 2: + z_label = r'$O_C$ [1]' + z_text = '%.1f' + clb.ax.set_ylabel(z_label, rotation=270) + + + if z_comp == 2: # when OC as z-axis + print('-----------------------------------------------------') + print('-----------------------------------------------------') + print('-----------------------------------------------------') + # Add index next to the points + for x_coord, y_coord, z_coord, idx in zip(x, y, z, front): + if no_text: + pass + else: + ax.annotate( z_text%(z_coord) + ' #%d'%(idx), (x_coord, y_coord) ) + else: + # text next scatter showing the value of the 3rd objective + for i, val in enumerate(z): + if no_text: + pass + else: + ax.annotate( z_text%(val), (x[i], y[i]) ) + + # refine the plotting + if comp[0] == 0: + ax.set_xlabel(r'$-\rm {TRV}$ [Nm/m^3]') + elif comp[0] == 1: + ax.set_xlabel(r'$-\eta$ [%]') + elif comp[0] == 2: + ax.set_xlabel(r'$O_C$ [1]') + + if comp[1] == 0: + ax.set_ylabel(r'$-\rm {TRV}$ [Nm/m^3]') + elif comp[1] == 1: + ax.set_ylabel(r'$-\eta$ [%]') + elif comp[1] == 2: + ax.set_ylabel(r'$O_C$ [1]') + ax.grid() + + # plot up to which domination rank? + if up_to_rank_no is None: + pass + else: + if count >= up_to_rank_no: + break + # Y730 + # fig.savefig(r'C:\Users\horyc\Desktop/'+ '2p5D-%d%d.png'%(comp[0],comp[1]), dpi=300) + return ax +def my_3d_plot_non_dominated_fronts(pop, paretoPoints, fea_config_dict, az=180, comp=[0, 1, 2], plot_option=1): + """ + Plots solutions to the DTLZ problems in three dimensions. The Pareto Front is also + visualized if the problem id is 2,3 or 4. + Args: + pop (:class:`~pygmo.population`): population of solutions to a dtlz problem + az (``float``): angle of view on which the 3d-plot is created + comp (``list``): indexes the fitness dimension for x,y and z axis in that order + Returns: + ``matplotlib.axes.Axes``: the current ``matplotlib.axes.Axes`` instance on the current figure + Raises: + ValueError: if *pop* does not contain a DTLZ problem (veryfied by its name only) or if *comp* is not of length 3 + Examples: + >>> import pygmo as pg + >>> udp = pg.dtlz(prob_id = 1, fdim =3, dim = 5) + >>> pop = pg.population(udp, 40) + >>> udp.plot(pop) # doctest: +SKIP + """ + from mpl_toolkits.mplot3d import axes3d + import matplotlib.pyplot as plt + import numpy as np + # from pylab import mpl + # mpl.rcParams['font.family'] = ['Times New Roman'] + # mpl.rcParams['font.size'] = 16.0 + + # if (pop.problem.get_name()[:-1] != "DTLZ"): + # raise(ValueError, "The problem seems not to be from the DTLZ suite") + + if (len(comp) != 3): + raise(ValueError, "The kwarg *comp* needs to contain exactly 3 elements (ids for the x,y and z axis)") + + # Create a new figure + fig = plt.figure(figsize=(12,8)) + ax = fig.add_subplot(111, projection='3d') + + # plot the points + fits = np.transpose(pop.get_f()) + try: + pass + # ax.plot(fits[comp[0]], fits[comp[1]], fits[comp[2]], 'ro') + except IndexError: + print('Error. Please choose correct fitness dimensions for printing!') + + if False: + # Plot pareto front for dtlz 1 + if plot_option==1: # (pop.problem.get_name()[-1] in ["1"]): + + X, Y = np.meshgrid(np.linspace(0, 0.5, 100), np.linspace(0, 0.5, 100)) + Z = - X - Y + 0.5 + # remove points not in the simplex + for i in range(100): + for j in range(100): + if X[i, j] < 0 or Y[i, j] < 0 or Z[i, j] < 0: + Z[i, j] = float('nan') + + ax.set_xlim(0, 1.) + ax.set_ylim(0, 1.) + ax.set_zlim(0, 1.) + + ax.plot_wireframe(X, Y, Z, rstride=10, cstride=10) + plt.plot([0, 0.5], [0.5, 0], [0, 0]) + + # Plot pareto fronts for dtlz 2,3,4 + if plot_option == 2: # (pop.problem.get_name()[-1] in ["2", "3", "4"]): + # plot the wireframe of the known optimal pareto front + thetas = np.linspace(0, (np.pi / 2.0), 30) + # gammas = np.linspace(-np.pi / 4, np.pi / 4, 30) + gammas = np.linspace(0, (np.pi / 2.0), 30) + + x_frame = np.outer(np.cos(thetas), np.cos(gammas)) + y_frame = np.outer(np.cos(thetas), np.sin(gammas)) + z_frame = np.outer(np.sin(thetas), np.ones(np.size(gammas))) + + ax.set_autoscalex_on(False) + ax.set_autoscaley_on(False) + ax.set_autoscalez_on(False) + + ax.set_xlim(0, 1.8) + ax.set_ylim(0, 1.8) + ax.set_zlim(0, 1.8) + + ax.plot_wireframe(x_frame, y_frame, z_frame) + + # https://stackoverflow.com/questions/37000488/how-to-plot-multi-objectives-pareto-frontier-with-deap-in-python + # def simple_cull(inputPoints, dominates): + # paretoPoints = set() + # candidateRowNr = 0 + # dominatedPoints = set() + # while True: + # candidateRow = inputPoints[candidateRowNr] + # inputPoints.remove(candidateRow) + # rowNr = 0 + # nonDominated = True + # while len(inputPoints) != 0 and rowNr < len(inputPoints): + # row = inputPoints[rowNr] + # if dominates(candidateRow, row): + # # If it is worse on all features remove the row from the array + # inputPoints.remove(row) + # dominatedPoints.add(tuple(row)) + # elif dominates(row, candidateRow): + # nonDominated = False + # dominatedPoints.add(tuple(candidateRow)) + # rowNr += 1 + # else: + # rowNr += 1 + + # if nonDominated: + # # add the non-dominated point to the Pareto frontier + # paretoPoints.add(tuple(candidateRow)) + + # if len(inputPoints) == 0: + # break + # return paretoPoints, dominatedPoints + # def dominates(row, candidateRow): + # return sum([row[x] >= candidateRow[x] for x in range(len(row))]) == len(row) + # import random + # print(inputPoints) + # inputPoints = [[random.randint(70,100) for i in range(3)] for j in range(500)] + # print(inputPoints) + # quit() + # inputPoints = [(x,y,z) for x,y,z in zip(fits[comp[0]], fits[comp[1]], fits[comp[2]])] + # paretoPoints, dominatedPoints = simple_cull(inputPoints, dominates) + x = [coords[0]/1000 for coords in paretoPoints] + y = [coords[1] for coords in paretoPoints] + z = [coords[2] for coords in paretoPoints] + + # from surface_fitting import surface_fitting + # surface_fitting(x,y,z) + # quit() + + if False: + pass + else: + import pandas as pd + from pylab import cm + print(dir(cm)) + df = pd.DataFrame({'x': x, 'y': y, 'z': z}) + + # 只有plot_trisurf这一个函数，输入是三个以为序列的，其他都要meshgrid得到二维数组的(即ndim=2的数组) + # # https://jakevdp.github.io/PythonDataScienceHandbook/04.12-three-dimensional-plotting.html + # surf = ax.plot_trisurf(df.x, df.y, df.z, cmap=cm.magma, linewidth=0.1, edgecolor='none') + # surf = ax.plot_trisurf(x, y, z, cmap='viridis', edgecolor='none') + # surf = ax.plot_trisurf(df.x, df.y, df.z, cmap=cm.magma, linewidth=0.1) + surf = ax.plot_trisurf(df.x, df.y*100, df.z, cmap=cm.Spectral, linewidth=0.1) + + with open('./%s_PF_points.txt'%(fea_config_dict['run_folder'][:-1]), 'w') as f: + f.write('TRV,eta,OC\n') + f.writelines(['%g,%g,%g\n'%(a,b,c) for a,b,c in zip(df.x, df.y*100, df.z)]) + # quit() + + fig.colorbar(surf, shrink=0.5, aspect=5) + + ax.set_xlabel(' \n$\\rm -TRV$ [$\\rm kNm/m^3$]') + ax.set_ylabel(' \n$-\\eta$ [%]') + ax.set_yticks(np.arange(-96,-93.5,0.5)) + ax.set_zlabel(r'$O_C$ [1]') + + # Try to export data from plot_trisurf # https://github.com/WoLpH/numpy-stl/issues/19 + # print(surf.get_vector()) + + # plt.savefig('./plots/avgErrs_vs_C_andgamma_type_%s.png'%(k)) + # plt.show() + + # # rotate the axes and update + # for angle in range(0, 360): + # ax.view_init(30, angle) + # plt.draw() + # plt.pause(.001) + + ax.view_init(azim=245, elev=15) + # fig.tight_layout() + + # Y730 + # fig.savefig(r'C:\Users\horyc\Desktop/3D-plot.png', dpi=300, layout='tight') + + # ax.view_init(azim=az) + # ax.set_xlim(0, 1.) + # ax.set_ylim(0, 1.) + # ax.set_zlim(0, 10.) + return ax +def my_plot(fits, vectors, ndf): + plt.rcParams['mathtext.fontset'] = 'stix' # 'cm' + plt.rcParams["font.family"] = "Times New Roman" + if True: + # for fit in fits: + # print(fit) + # ax = pg.plot_non_dominated_fronts(fits) + + # ax = my_plot_non_dominated_fronts(fits, comp=[0,1], marker='o', up_to_rank_no=3) + # ax = my_plot_non_dominated_fronts(fits, comp=[0,2], marker='o', up_to_rank_no=3) + # ax = my_plot_non_dominated_fronts(fits, comp=[1,2], marker='o', up_to_rank_no=3) + + pass + + ax = my_2p5d_plot_non_dominated_fronts(fits, comp=[0,1], marker='o', up_to_rank_no=1) + # # for studying LSA population (whether or not the optimal is on Rank 1 Pareto Front) + # x = fits[0][0]/1e3 + # y = fits[0][1] + # z = fits[0][2] + # ax.plot(x, y, color='k', marker='s') + # ax.annotate(r'$x_{\rm optm}$', xy=(x, y), xytext=(x+1, y+0.0005), arrowprops=dict(facecolor='black', shrink=0.05),) + ax = my_2p5d_plot_non_dominated_fronts(fits, comp=[0,2], marker='o', up_to_rank_no=1) + ax = my_2p5d_plot_non_dominated_fronts(fits, comp=[1,2], marker='o', up_to_rank_no=1) + + else: + # Obselete. Use ax.step instead to plot + print('Valid for 2D objective function space only for now.') + fig, axes = plt.subplots(ncols=2, nrows=1, dpi=150, facecolor='w', edgecolor='k'); + ax = axes[0] + + for index, xy in enumerate(vectors): + ax.plot(*xy, 's', color='0') + ax.text(*xy, '#%d'%(index), color='0') + ax.title.set_text("Decision Vector Space") + + ax = axes[1] + + for index, front in enumerate(ndf): + print('Rank/Tier', index, front) + the_color = '%g'%(index/len(ndf)) + for individual in front: # individual is integer here + ax.plot(*fits[individual], 'o', color=the_color) + ax.text(*fits[individual], '#%d'%(individual), color=the_color) + + front = front.tolist() + front.sort(key = lambda individual: fits[individual][1]) # sort by y-axis value # individual is integer here + for individual_A, individual_B in zip(front[:-1], front[1:]): # front is already sorted + fits_A = fits[individual_A] + fits_B = fits[individual_B] + ax.plot([fits_A[0], fits_B[0]], + [fits_B[1], fits_B[1]], color=the_color, lw=0.25) # 取高的点的Y + ax.plot([fits_A[0], fits_A[0]], + [fits_A[1], fits_B[1]], color=the_color, lw=0.25) # 取低的点的X + ax.title.set_text("Pareto Front | Objective Function Space") +def my_print(ad, pop, _): + # ndf, dl, dc, ndr = pg.fast_non_dominated_sorting(fits) + # extract and print non-dominated fronts + # - ndf (list of 1D NumPy int array): the non dominated fronts + # - dl (list of 1D NumPy int array): the domination list + # - dc (1D NumPy int array): the domination count + # - ndr (1D NumPy int array): the non domination ranks + fits, vectors = pop.get_f(), pop.get_x() + ndf, dl, dc, ndr = pg.fast_non_dominated_sorting(fits) + + with open(ad.solver.output_dir+'MOO_log.txt', 'a', encoding='utf-8') as fname: + print('-'*40, 'Generation:', _, file=fname) + for rank_minus_1, front in enumerate(ndf): + print('Rank/Tier', rank_minus_1+1, front, file=fname) + index = 0 + for domination_list, domination_count, non_domination_rank in zip(dl, dc, ndr): + print('Individual #%d\t'%(index), 'Belong to Rank #%d\t'%(non_domination_rank), 'Dominating', domination_count, 'and they are', domination_list, file=fname) + index += 1 + + # print(fits, vectors, ndf) + print(pop, file=fname) + + +def learn_about_the_archive(prob, swarm_data, popsize, fea_config_dict, len_s01=None, len_s02=None, bool_plot_and_show=False): + number_of_chromosome = len(swarm_data) + print('Archive size:', number_of_chromosome) + # for el in swarm_data: + # print('\t', el) + + pop_archive = pg.population(prob, size=number_of_chromosome) + for i in range(number_of_chromosome): + pop_archive.set_xf(i, swarm_data[i][:-3], swarm_data[i][-3:]) + + sorted_index = pg.sort_population_mo(points=pop_archive.get_f()) + print('Sorted by domination rank and crowding distance:', len(sorted_index)) + print('\t', sorted_index) + + # 这段代码对于重建种群来说不是必须的，单单sort_population_mo（包含fast_non_dominated_sorting和crowding_distance）就够了， + # 只是我想看看，具体的crowding_distance是多少，然后我想知道排在前面的多少个是属于domination rank 1的。 + if True: + fits, vectors = pop_archive.get_f(), pop_archive.get_x() + ndf, dl, dc, ndr = pg.fast_non_dominated_sorting(fits) + + ind1, ind2 = 0, 0 + for rank_minus_1, front in enumerate(ndf): + + ind2 += len(front) + sorted_index_at_this_front = sorted_index[ind1:ind2] + fits_at_this_front = [fits[point] for point in sorted_index_at_this_front] + + # Rank 1 Pareto Front + if ind1 == 0: + rank1_ParetoPoints = fits_at_this_front + if len(front) < popsize: + print('There are not enough chromosomes (%d) belonging to domination rank 1 (the best Pareto front).\nWill use rank 2 or lower to reach popsize of %d.'%(len(front), popsize)) + + # this crwdsit should be already sorted as well + if len(fits_at_this_front) >= 2: # or else error: A non dominated front must contain at least two points: 1 detected. + crwdst = pg.crowding_distance(fits_at_this_front) + else: + print('A non dominated front must contain at least two points: 1 detected.') + crwdst = [999999] + + + print('\nRank/Tier', rank_minus_1+1, 'chromosome count:', len(front), len(sorted_index_at_this_front)) + # print('\t', sorted_index_at_this_front.tolist()) + # print('\t', crwdst) + print('\tindex in pop\t|\tcrowding distance') + for index, cd in zip(sorted_index_at_this_front, crwdst): + print('\t', index, '\t\t\t|', cd) + ind1 = ind2 + + sorted_vectors = [vectors[index].tolist() for index in sorted_index] + sorted_fits = [fits[index].tolist() for index in sorted_index] + + if bool_plot_and_show: + my_plot(pop_archive.get_f(), pop_archive.get_x(), ndf) + my_3d_plot_non_dominated_fronts(pop_archive, rank1_ParetoPoints, fea_config_dict, plot_option=1) + plt.show() + + swarm_data_on_pareto_front = [design_parameters_denorm + fits for design_parameters_denorm, fits in zip(sorted_vectors, sorted_fits)] + return swarm_data_on_pareto_front + +def pyx_draw_model(im): + import matplotlib.patches as mpatches + import matplotlib.pyplot as plt + plt.rcParams["font.family"] = "Times New Roman" + + myfontsize = 13.5 + plt.rcParams.update({'font.size': myfontsize}) + + # # 示意图而已，改改尺寸吧 + # im.Radius_OuterStatorYoke -= 37 + # im.Radius_InnerStatorYoke -= 20 + # im.Radius_Shaft += 20 + # # im.Location_RotorBarCenter2 += 5 # this will change the shape of rotor slot + + import VanGogh + vg = VanGogh.VanGogh_pyPlotter(im, VanGogh.CUSTOM) + vg.draw_model() + + # PyX + import pyx + vg.tikz.c = pyx.canvas.canvas() # clear the canvas because we want to redraw 90 deg with the data vg.tikz.track_path + from copy import deepcopy + def pyx_draw_path(vg, path, sign=1): + if len(path) == 4: + vg.tikz.draw_line(path[:2], path[2:4], untrack=True) + else: + vg.tikz.draw_arc(path[:2], path[2:4], path[4:6], relangle=sign*path[6], untrack=True) + def rotate(_, x, y): + return np.cos(_)*x + np.sin(_)*y, -np.sin(_)*x + np.cos(_)*y + def is_at_stator(im, path): + return np.sqrt(path[0]**2 + path[1]**2) > im.Radius_OuterRotor + 0.5*im.Length_AirGap + + for path in (vg.tikz.track_path): # track_path is passed by reference and is changed by mirror + path_mirror = deepcopy(path) + # for mirror copy (along x-axis) + path_mirror[1] = path[1]*-1 + path_mirror[3] = path[3]*-1 + + # rotate path and plot + if is_at_stator(im, path): + Q = im.Qs + else: + Q = im.Qr + _ = 2*np.pi/Q + path[0], path[1] = rotate(0.5*np.pi - 0.5*_, path[0], path[1]) + path[2], path[3] = rotate(0.5*np.pi - 0.5*_, path[2], path[3]) + pyx_draw_path(vg, path, sign=1) + + path_mirror[0], path_mirror[1] = rotate(0.5*np.pi - 0.5*_, path_mirror[0], path_mirror[1]) + path_mirror[2], path_mirror[3] = rotate(0.5*np.pi - 0.5*_, path_mirror[2], path_mirror[3]) + pyx_draw_path(vg, path_mirror, sign=-1) + + # 注意，所有 tack_path 中的 path 都已经转动了90度了！ + # for mirror copy (along y-axis) + path[0] *= -1 + path[2] *= -1 + pyx_draw_path(vg, path, sign=-1) + + path_mirror[0] *= -1 + path_mirror[2] *= -1 + pyx_draw_path(vg, path_mirror, sign=1) + + + # # 整体转动90度。 + # for path in vg.tikz.track_path: + # if is_at_stator(im, path): + # Q = im.Qs + # else: + # Q = im.Qr + # _ = 2*np.pi/Q + # path[0], path[1] = rotate(0.5*np.pi - _, path[0], path[1]) + # path[2], path[3] = rotate(0.5*np.pi - _, path[2], path[3]) + # pyx_draw_path(vg, path) + # track_path_backup = deepcopy(vg.tikz.track_path) + + # # Rotate Copy + # for path in deepcopy(vg.tikz.track_path): + # if is_at_stator(im, path): + # Q = im.Qs + # else: + # Q = im.Qr + # _ = 2*np.pi/Q + # path[0], path[1] = rotate(_, path[0], path[1]) + # path[2], path[3] = rotate(_, path[2], path[3]) + # pyx_draw_path(vg, path) + + # # Rotate Copy + # for path in (vg.tikz.track_path): + # # if np.sqrt(path[0]**2 + path[1]**2) > im.Radius_OuterRotor + 0.5*im.Length_AirGap: + # if is_at_stator(im, path): + # Q = im.Qs + # else: + # Q = im.Qr + # _ = 2*np.pi/Q + # path[0], path[1] = rotate(_, path[0], path[1]) + # path[2], path[3] = rotate(_, path[2], path[3]) + # pyx_draw_path(vg, path, sign=-1) + + vg.tikz.c.writePDFfile("selected_otimal_design%s"%(im.ID)) + # vg.tikz.c.writeEPSfile("pyx_output") + 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"... main.i" + @echo "... main.s" + @echo "... observer.o" + @echo "... observer.i" + @echo "... observer.s" +.PHONY : help + + + +#============================================================================= +# Special targets to cleanup operation of make. + +# Special rule to run CMake to check the build system integrity. +# No rule that depends on this can have commands that come from listfiles +# because they might be regenerated. +cmake_check_build_system: + $(CMAKE_COMMAND) -S$(CMAKE_SOURCE_DIR) -B$(CMAKE_BINARY_DIR) --check-build-system CMakeFiles/Makefile.cmake 0 +.PHONY : cmake_check_build_system + diff --git a/build/cmake_install.cmake b/build/cmake_install.cmake new file mode 100644 index 0000000..c3acfae --- /dev/null +++ b/build/cmake_install.cmake @@ -0,0 +1,54 @@ +# Install script for directory: /home/gtq/ACMSIMC_TUT + +# Set the install prefix +if(NOT DEFINED CMAKE_INSTALL_PREFIX) + set(CMAKE_INSTALL_PREFIX "/usr/local") +endif() +string(REGEX REPLACE "/$" "" CMAKE_INSTALL_PREFIX "${CMAKE_INSTALL_PREFIX}") + +# Set the install configuration name. +if(NOT DEFINED CMAKE_INSTALL_CONFIG_NAME) + if(BUILD_TYPE) + string(REGEX REPLACE "^[^A-Za-z0-9_]+" "" + CMAKE_INSTALL_CONFIG_NAME "${BUILD_TYPE}") + else() + set(CMAKE_INSTALL_CONFIG_NAME "") + endif() + message(STATUS "Install configuration: \"${CMAKE_INSTALL_CONFIG_NAME}\"") +endif() + +# Set the component getting installed. +if(NOT CMAKE_INSTALL_COMPONENT) + if(COMPONENT) + message(STATUS "Install component: \"${COMPONENT}\"") + set(CMAKE_INSTALL_COMPONENT "${COMPONENT}") + else() + set(CMAKE_INSTALL_COMPONENT) + endif() +endif() + +# Install shared libraries without execute permission? +if(NOT DEFINED CMAKE_INSTALL_SO_NO_EXE) + set(CMAKE_INSTALL_SO_NO_EXE "1") +endif() + +# Is this installation the result of a crosscompile? +if(NOT DEFINED CMAKE_CROSSCOMPILING) + set(CMAKE_CROSSCOMPILING "FALSE") +endif() + +# Set default install directory permissions. +if(NOT DEFINED CMAKE_OBJDUMP) + set(CMAKE_OBJDUMP "/usr/bin/objdump") +endif() + +if(CMAKE_INSTALL_COMPONENT) + set(CMAKE_INSTALL_MANIFEST "install_manifest_${CMAKE_INSTALL_COMPONENT}.txt") +else() + set(CMAKE_INSTALL_MANIFEST "install_manifest.txt") +endif() + +string(REPLACE ";" "\n" CMAKE_INSTALL_MANIFEST_CONTENT + "${CMAKE_INSTALL_MANIFEST_FILES}") +file(WRITE "/home/gtq/ACMSIMC_TUT/build/${CMAKE_INSTALL_MANIFEST}" + "${CMAKE_INSTALL_MANIFEST_CONTENT}") diff --git a/build/main b/build/main new file mode 100644 index 0000000..340ec42 Binary files /dev/null and b/build/main differ diff --git a/commissioning.c b/commissioning.c index 9b76af2..1814aa7 100644 --- a/commissioning.c +++ b/commissioning.c @@ -35,7 +35,8 @@ void COMM_init(){ /* standard lib */ #include // printf #include // bool for _Bool and true for 1 -#include // for clrscr, and getch() +// #include // for clrscr, and getch() +#include #include "stdlib.h" // for rand() #include "math.h" diff --git a/img/picture.png b/img/picture.png new file mode 100644 index 0000000..0dff457 Binary files /dev/null and b/img/picture.png differ diff --git a/main.c b/main.c index e37c4dc..47b6fd6 100644 --- a/main.c +++ b/main.c @@ -219,7 +219,7 @@ int main(){ // cmd_fast_speed_reversal(CTRL.timebase, 5, 5, 1500); // timebase, instant, interval, rpm_cmd // cmd_fast_speed_reversal(CTRL.timebase, 5, 5, 200); // timebase, instant, interval, rpm_cmd if(CTRL.timebase>14){ - ACM.rpm_cmd = 900; // 40 double e_state; // Integral internal state + ACM.rpm_cmd = 90; // 40 double e_state; // Integral internal state }else if(CTRL.timebase>12){ ACM.rpm_cmd = 40; // 40 @@ -269,7 +269,7 @@ int main(){ fclose(fw); /* Fade out */ - system("python ./ACMPlot.py"); + system("python3 ../ACMPlot.py"); // getch(); // system("pause"); // system("exit");