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  Tested on Intel-based (x86_64) MacBook, not tested on M1/M2 (ARM) Apple Clang does not support  -fopenmp  and  -libseal , use GCC or CLANG % git clone --recursive https://github.com/ibarrond/Pyfhel 2.a) create  requirements.txt  file "setuptools<=60.9", "wheel", "cython>=3.0.0a9", "numpy>=1.20", "cmake>=3.15", "toml>=0.10" 2.b)  % pip3 install -r requirements.txt edit  pyproject.toml Line #111 [ Afhel ] extra_compile_args {Darwin = ["-std=c++17","-O3","-fopenmp"]}, extra_link_args {Darwin = ["-fopenmp","-dynamiclib"]}, Repeat the above for [ CYTHON EXTENSIONS ]

I recently ran into issues on my MacBook with Jupyter notebooks, where it could not find any packages and was throwing "ModuleNotFoundError: No module named ****" for several libraries. I already had these packages installed but somehow Jupyter python kernel could not find it. I tried to install these packages in the notebook cells with the command !pip install **** but this did not help either. I also tried %brew reinstall jupyterlab and that did not work as well. Finally, I found a fix. Solution: Step-1: Install/upgrade jupyter packages: % pip3 install --upgrade jupyter notebook jupyterlab Step-2: Run jupyter with this command: % jupyter-lab

NTL: A Library for doing Number Theory NTL is a high-performance, portable C++ library providing data structures and algorithms for manipulating signed, arbitrary length integers and for vectors, matrices, and polynomials over the integers and over finite fields. You might face a linking error with NTL in macOS using the Apple Clang compiler: % make                                 Consolidate compiler generated dependencies of target degrees [ 25%] Linking CXX executable degrees ld: library not found for -lntl clang: error: linker command failed with exit code 1 (use -v to see invocation) Solution: % brew install NTL % export LIBRARY_PATH=/usr/local/lib % make This linker error means the build was told to link NTL, the C++ number theory library, but cannot find it. NTL provides arbitrary length integers and operations on polynomials and matrices over rings and finite fields. Installing NTL and poin...

MinimaxComp_degrees This algorithm finds optimized degrees for comparison/max/ReLU algorithms using minimax composite polynomial on the RNS-CKKS scheme, which was proposed in https://ieeexplore.ieee.org/document/9517029 and https://eprint.iacr.org/2021/1215 . How to run on macOS using the Apple Clang compiler: 1. % brew install NTL 2. % export LIBRARY_PATH=/usr/local/lib 3. % git clone  https://github.com/eslee3209/MinimaxComp_degrees 4. % cd MinimaxComp_degrees 5. % cmake -S . -B build 6. % cd build 7. % make 8. % ./degrees Output: ------------------------------------ alpha: 20 epsilon: 0.2002716064453125e-4 mintime: 162 depth: 22 0.39825903512705770933e-4 7 13 15 15 59   This guide runs MinimaxComp_degrees on macOS, an algorithm that finds optimized polynomial degrees for comparison, max, and ReLU operations under the RNS-CKKS homomorphic encryption scheme, based on the referenced research. CKKS can only evaluate polynomials on encrypted data, so non-polyn...

CRC RevEng CRC RevEng is a portable, arbitrary-precision CRC calculator and algorithm finder. It calculates CRCs using any 111 preset algorithms or a user-specified algorithm to any width. It calculates reversed CRCs to give the bit pattern that produces a desired forward CRC. CRC RevEng also reverse-engineers any CRC algorithm from good, correctly formatted message-CRC pairs and optional known parameters. It comprises powerful input interpretation options. Installation instructions for macOS: 1. Download reveng from SourceForge:  https://sourceforge.net/projects/reveng/files/ 2. Unzip the downloaded reveng zip file 3. % cd Downloads/reveng-3.0.3 4. % make gcc -O3 -Wall -ansi -fomit-frame-pointer -DPRESETS -DBMPTST -o bmptst bmpbit.c ( ./bmptst && touch bmptst ) || ( rm bmptst bmptst.exe && false ) reveng: configuration fault.   Update config.h with these definitions and recompile: #define BMP_BIT   64 #define BMP_SUB   32 rm: bmptst.exe: No suc...

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Code: all_results = all_results.append(current_result) Error: /var/folders/wh/tdg0ff9s1wl59234d5l27_gc0000gn/T/ipykernel_98654/2691205834.py:15: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead. all_results = all_results.append(current_result) Fix: all_results = pd.concat([all_results, current_result]) This warning appears because DataFrame.append was deprecated and later removed from pandas, so code that built a frame by appending one row or frame at a time needs updating. The replacement is pandas.concat, which combines a list of frames in one call. Collecting the pieces in a list and concatenating once at the end is also faster than repeated appends.

Code: ax = sns.swarmplot(y="time_taken", x="dtype", data=all_results, color=".25") Error: /usr/local/Cellar/jupyterlab/3.3.2/libexec/lib/python3.9/site-packages/seaborn/categorical.py:1296: UserWarning: 35.9% of the points cannot be placed; you may want to decrease the size of the markers or use stripplot. warnings.warn(msg, UserWarning) Fix: ax = sns.swarmplot(y="time_taken", x="dtype", data=all_results, color=".25", size=3.25 ) This seaborn warning appears when a swarm plot cannot place every point without overlap, so some are dropped. It is a sign the markers are too large for the amount of data. Reducing the marker size with the size parameter, or switching to a strip plot, lets all points show. Both are quick changes to the plotting call.