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sure install numpy using conda. \n# Installation with pip may lead to unexpected errors.\nconda install numpy cython numba\n```\n\n\u003e **Install OpenMP Library** \\\n\u003e *Install OpenMP Library to enable CPU parallel acceleration for Time-delayed Mutual Information calculation.*\n\u003e \u003e Linux:\n\u003e \u003e ```\n\u003e \u003e sudo apt-get install libomp-dev\n\u003e \u003e ```\n\u003e \u003e MacOS:\n\u003e \u003e ```\n\u003e \u003e brew install libomp\n\u003e \u003e```\n\u003e \n\n## Installation\n\n### To install via pip:\n\n```bash\npip install minfo\n```\n\n### To install via repository:\n\n```bash\ngit clone https://github.com/NeoNeuron/minfo\ncd minfo\npip install -e .\n```\n\n## Get Started\n\n### MI with uniform bins \n\n```python\nfrom minfo.mi_float import mutual_info, TDMI\n# mutual_info : mutual information estimator\n#        tdmi : time-delayed mutual information estimator\nn = 100\nx = np.random.rand(n)\ny = np.random.rand(n)\n\n# compute mutual information\nmutual_info(x,y, bins=50) # default algorithm: 'uniform'\n\n# compute time-delayed mutual information (Parallel accelerated)\nn_delay = 10\nTDMI(x, y, n_delay, bins=50) # default algorithm: 'uniform'\n```\n\n### MI with adaptive bins\n\n**ATTENTION:** This algorithm is designed for mutual information estimation between continuous variables. Applying it to discrete variables with few number of values might lead to large deviations. Modules for finite discrete cases are to be developed. \n\n**Reference**\n\n- Darbellay, G. A., \u0026 Vajda, I. (1999). [Estimation of the information by an adaptive partitioning of the observation space. IEEE Transactions on Information Theory](https://ieeexplore.ieee.org/document/761290), 45(4), 1315-1321.\n\n```python\nfrom minfo.mi_float import mutual_info, TDMI\n# mutual_info : mutual information estimator\n#        tdmi : time-delayed mutual information estimator\nn = 100\nx = np.random.rand(n)\ny = np.random.rand(n)\n\n# compute mutual information\nmutual_info(x,y, algorithm='adaptive')\n\n# compute time-delayed mutual information (Parallel accelerated)\nn_delay = 10\nTDMI(x, y, n_delay, algorithm='adaptive')\n```\n\n## Compare performance with pure Python version\n\n**Test OS info:**\n- **Laptop:** *MacBook Pro (15-inch, 2018)*\n- **System** *version: macOS Big Sur 12.4 (21F79)*\n- **CPU:** *2.6 GHz 6-Core Intel Core i7*\n- **RAM:** *32 GB 2400 MHz DDR4*\n\n```bash\n$ cd example\n$ python example.py\n[INFO]:   mi_adaptive (python)  takes 0.025 s\n[INFO]:   mi_uniform  (numba)   takes 0.000 s\n[INFO]:   mi_adaptive (cython)  takes 0.009 s\n[INFO]: tdmi_adaptive (python)  takes 0.560 s\n[INFO]: tdmi_uniform  (numba)   takes 0.005 s\n[INFO]: tdmi_adaptive (cython)  takes 0.076 s\n```\n","funding_links":[],"categories":[],"sub_categories":[],"project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fneoneuron%2Fminfo","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fneoneuron%2Fminfo","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fneoneuron%2Fminfo/lists"}