REVEAL was processing too much, because of which it was not idempotent.ħ. Wrong parameter passing algorithm when there are more than 3 named params.Ħ. Now FP number string using e-index, like 3.42e-2, also can be interpreted.ĥ. TRY_NUMBER (number interpreter) was neglecting BASE. Object_base, loop_counter and PD stack pointer registers (R8, R11 and R10) are now saved in the return stack frame in external calls.Ĥ. (File Navigation panel might crash because of it.) Save/restore registers before/after system function call was a design-mistake because it didn't take callback event handler into account. LOCATE failed to find a word definition when it is in the source file loaded last.ģ. Honestly, I don't like it.)Īnyway, when a code text string is sent to iMops by Apple Event of type TEXT or utf8,įollowing is a description of changes from 0.7bġ. But it requires file name extension for all files. The text coloring looks very good thanks to Doug's extension. (Additionally, the behavior of jEdit is very not-Mac-like. In order to open file and select some word on jEdit by Apple Event from iMops, some modifications of jEdit may be necessary. IMops can load file or execute operations by Apple event from QuickEdit, by some cheating (setting iMops signature to 'Mopp' and defining empty words NEWVECS and >QECONS).īut I have no idea how to send Apple Event to iMops from jEdit. The implementation was not very difficult because all Carbon Apple Event functions are usable also in 64bit mode. IMops now can receive Apple Event and execute the source code data from the Event. Since 0.7b, some bugs have been fixed and new features added. The resulting code can easily be copied and pasted as is to be integrated with the aforementioned SciPy functions.This is a report of the current state of iMops. ![]() ![]() Interactive GUI based program that generates the overall species balance, system of ODEs needed for the solve_ivp and odeint method, and calculates the Jacobian both symbolically and numerically. Multi-label classification algorithms implemented in Python Newton and Quasi-Newton optimization with PyTorch Python module that wraps SVDLIBC, a library for sparse Singular Value Decomposition. Protein Functional Domain Analysis based on Compressed Sparse Matrix Python Machine Learning algorithms based on Numpy, Scipy and MatPlotLib.ĭownloads results from VTU website and analyzes the resultĪutoML-based Genetic regulatory Element extrAction SystemĪ numerically stable implementation of essential HMM algorithms with tutorials on training methodsĪn application for analysis of UAV log and video.Īnomaly learn - the anomaly detection package
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