catch22
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catch22: CAnonical Time-series CHaracteristics
fedora/pass300/20220906/catch22.spec.out:IN_AutoMutualInfoStats.c:44:19: warning: suggest parentheses around comp arison in operand of '&' [-Wparentheses] fedora/pass300/20220906/catch22.spec.out:PD_PeriodicityWang.c:66:20: warning: suggest parentheses around comparis on in operand of '&' [-Wparentheses] fedora/pass300/20220906/catch22.spec.out:PD_PeriodicityWang.c:72:25: warning: suggest parentheses around comparis...
Adds new feature to this library. Easy compilation using `$ make` command. Requires to create "bin" folder (inside catch22/C) which will store all the object files (.o)
Hi, I failed to install it in Python, would you mind checking out what is the problem? And how to use catch22? Is there a GUI or maybe a handbook...
When running `pip install catch22`, the C compilation seems to complain. I tried it with python 3.7-3.9. ``` C/CO_AutoCorr.c:531:9: error: ‘for’ loop initial declarations are only allowed in C99 mode...
### What Happened Get a segmentation fault when running `catch22.catch22_all` on short lists/numpy.arrays. ### What I Expected to Happen Returns a dictionary of features (perhaps with a lot of NaN-types...
I am using Sagemaker notebook and failed to install catch22 package. Tried to add 'extra_compile_args = ['-std=c99']' before running 'pip install catch22'. Getting the following error: C/SC_FluctAnal.c:12:5: note: use option...
OS: Ubuntu 18.04 Python version: 3.6.3 Catch22 version: 0.0.1 Issue description: I generate dataframe with small amount of data in columns (1,2,3,4 elements, etc.). I try to use **catch22_all** method...
I've been getting segmentation faults whenever I try to run the python version. For smaller data sets it works fine. Here I'm using a data set of size 100x10000. As...
or else the compilation will fail in Linux. **setup_P3.py :** ``` the c++ extension module extension_mod = Extension("catch22_C", sources=["catch22_wrap_P3.c"] + sourceFileList, include_dirs=[sourceDir], extra_compile_args = ['-std=c99'] ) ``` **Python:** ``` active...
Is it possible to calculate features in a rolling window? In pandas rolling().apply() it is too slow.