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\n\n\n# INPE CAPPY239 - An\u00e1lise Espectral de Processos Estoc\u00e1sticos\nFerramenta desenvolvida para a disciplina CAP 238 Matem\u00e1tica Computacional I - An\u00e1lise Espectral de Processos Estoc\u00e1sticos, ministrada pelo Dr. Reinaldo Roberto Rosa no programa de P\u00f3s Gradua\u00e7\u00e3o em Computa\u00e7\u00e3o Aplicada no Instituto Nacional de Pesquisas Espaciais.\nO m\u00f3dulo possui as seguintes fun\u00e7\u00f5es:\n\n## Instala\u00e7\u00e3o\n pip install cappy239\n\n> Dispon\u00edvel para Python 3.0 ou superior.\n\n## Powernoise\nA fun\u00e7\u00e3o `powernoise` gera sinais estoc\u00e1sticos, ru\u00eddos 1/f\u03b2. \u00c9 uma adapta\u00e7\u00e3o do c\u00f3digo implementado em Matlab pelo Dr. Reinaldo R. Rosa e Dale B. Dalrymple.\n\nAlguns exemplos com 212 valores:\n - **White noise**:\n```python\nfrom cappy239 import powernoise\nwhite_noise = powernoise(beta=0, N=4096)\n```\n - **Pink noise**:\n```python\nfrom cappy239 import powernoise\npink_noise = powernoise(beta=1, N=4096)\n```\n - **Red noise**:\n```python\nfrom cappy239 import powernoise\nred_noise = powernoise(beta=2, N=4096)\n```\n\n> Por padr\u00e3o s s\u00e9rie temporal est\u00e1 normalizada, caso n\u00e3o queria isso passe o argumento `varargin='randpower'`.\n\n## Mapeamento Quadr\u00e1tico (Log\u00edstico)\nA fun\u00e7\u00e3o `logistic_map` gera uma s\u00e9rie ca\u00f3tica . \nExemplo com 100 valores:\n```python\nfrom cappy239 import logistic_map\nchaotic_serie = logistic_map(rho=3.85, a0=0.001, n=100)\n```\n> Refer\u00eancia: [https://geoffboeing.com/2015/03/chaos-theory-logistic-map/](https://geoffboeing.com/2015/03/chaos-theory-logistic-map/)\n\n## Pmodel\nA fun\u00e7\u00e3o `pmodel` \u00e9 utilizada para gerar s\u00e9ries temporais estacion\u00e1rias. O c\u00f3digo \u00e9 uma adapta\u00e7\u00e3o da implementa\u00e7\u00e3o em Matlab dispon\u00edvel em [http://www2.meteo.uni-bonn.de/staff/venema/themes/surrogates/pmodel/pmodel.m](http://www2.meteo.uni-bonn.de/staff/venema/themes/surrogates/pmodel/pmodel.m).\nAlguns exemplos com 212 valores:\n\n - **S8: p=0.52, \u03b2=-1.66**\n```python\nfrom cappy239 import pmodel\nkolmogorov = pmodel(noValues=4096, p=0.52, slope=-1.66)\n```\n\n``` ______ _ _______ _______ ____ ____ _____ ______ ______ \n .' ___ | / \\ |_ __ \\|_ __ \\|_ _||_ _|/ ___ `./ ____ `..' ____ '. \n/ .' \\_| / _ \\ | |__) | | |__) | \\ \\ / / |_/___) |`' __) || (____) | \n| | / ___ \\ | ___/ | ___/ \\ \\/ / .'____.'_ |__ '.'_.____. | \n\\ `.___.'\\ _/ / \\ \\_ _| |_ _| |_ _| |_ / /_____| \\____) || \\____| | \n `.____ .'|____| |____||_____| |_____| |______| |_______|\\______.' \\______,' \n By Adriano P. Almeida\n``` \n\n\n",
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