LadderTouchLite vs PeterJohn NumPy

LadderTouchLite is an app to learn the basics of ladder logic, which is a graphical programming language. This app has two functions. You can learn about that mechanism by executing some of the prepared samples. And you can check your understanding with questions. -------Difference from the full version------- It doesn't show hints of questions. You cannot make your own ladders. ---------------ELEMENTS--------------- Input(yellow) Up to 5 elements. You can control the input signal by tapping the buttons at the bottom of the display. Virtual output(red) Up to 5 elements. Internal memory(gray) Up to 20 elements. Virtual output(red) and internal memory(gray) are the same for logical behavior. But the difference is in usage. The virtual output is the last element in the ladder logic chain. The Internal memory is for the processing element of the ladder logic chain. These are an imitation of the actual ladder logic with the output devices. Timer(blue) Up to 5 elements. The timer can be set from 0.1 seconds to 10.0 seconds. The setting unit is 0.1 second. The timer works only 'on delay'. This app does not include 'off delay'. On Delay. ---++++++++++ source -------++++++ destination Off Delay. +++---------- source ++++++++----- destination + ON - OFF Time flows from left to right Counter(green) Up to 5 elements. You can set from 1 to 50. There is no counter reset element in this app. Many other ladder logic software has it. The way to reset the counter is to stop running the ladder. All elements can be inverse in the source area. However, timers and counters cannot do it in the destination area. It is difficult to understand the inverse logic of a timer and counter in the destination area. --------------Other-------------- There is no function that connects to outer devices.

****PeterJohnNumPyV4 Now You Can Use NumPy 1.20.2 on CPython3.9.9. ****How To Use NumPy on PeterJohnNumPyV4;; Go To "TutorialSeason003/scripts/s007LSTMtrainingPlot.py". Then "Run Script". ****Not Yet implemented Features;; Basically, you Can Use all Functions of numpy.ndarray. **1; numpy.random;; please use Python3 Standard "random" module instead of it. 1.1); numpy.random.randn();; So we implemented pjRandN() function using NumPy and Python3 Standard "random" module. Documents/TutorialSeason003/scripts/pjLStermMemory.py;pjRandN() **2; numpy.linalg;; 2.1); numpy.linalg.norm();; So we implemented pjNorm() function using NumPy instead of it. Documents/TutorialSeason003/scripts/pjLStermMemory.py;pjNorm() 2.2); numpy.linalg.qr();; So we implemented pjQR() function using NumPy instead of it. Documents/TutorialSeason003/scripts/pjLStermMemory.py;pjQR(); **3; numpy.diag();; Basically, you Can Use all Functions of numpy.ndarray, so Please use numpy.ndarray.diagonal() instead of it. **4; numpy.fft; Not Yet implemented. **5; numpy.polynomial;; Not Yet implemented. **6; numpy.ctypeslib;; Not Yet implemented. ****When you Use this App withOut NumPy;; Using iPhone's "Files.app", Delete the NumPy's "__pycache__" folder below. Documents/PeterJohnNumPyV4/usrLocal/lib/python3.9/site-packages/numpy/__pycache__ If there doesNot Exist the NumPy's "__pycache__" folder, when you touched "Edit Script", this App Calls "Py_FinalizeEx()" So you Can Enjoy Scripting Comfortably. ****the WorkAround against the Well-Known Bug of NumPy and Python3.9;; NumPy and Python3.9 have a Well-Known Bug. While NumPy has been imported, "Py_FinalizeEx()" doesNot Release memory. So If you imported NumPy at the Second time, Python3 Crashes. so we made a WorkAround against the Well-Known Bug of NumPy and Python3.9. As the Specification of this App, If there Exists the NumPy's "__pycache__" folder, Documents/PeterJohnNumPyV4/usrLocal/lib/python3.9/site-packages/numpy/__pycache__ Even if you touched "Edit Script", this App doesNot call "Py_FinalizeEx()" This is Not a Perfect Solution But you Can Enjoy NumPy withOut Python3 Crashing. ****1st Corinthians 10:31, GNT; 10:31 Well, whatever you do, whether you eat or drink, do it all for God's glory. Enjoy NumPy on PeterJohnNumPyV4. Best Regards, --Yasushi Yassun Obata