diff --git a/.github/workflows/deploy.yml b/.github/workflows/deploy.yml new file mode 100644 index 0000000..68d1295 --- /dev/null +++ b/.github/workflows/deploy.yml @@ -0,0 +1,45 @@ +name: Build and Deploy + +on: + push: + branches: + - main + workflow_dispatch: + +jobs: + build: + runs-on: ubuntu-latest + steps: + - name: Checkout + uses: actions/checkout@v4 + - name: Setup Python + uses: actions/setup-python@v5 + with: + python-version: '3.11' + - name: Install the dependencies + run: | + python -m pip install -r book/requirements.txt + - name: Build the JupyterBook site + run: | + jupyter-book build book + - name: Upload artifact + uses: actions/upload-pages-artifact@v3 + with: + path: book/_build/html + + deploy: + needs: build + if: github.ref == 'refs/heads/main' + permissions: + pages: write + id-token: write + + environment: + name: github-pages + url: ${{ steps.deployment.outputs.page_url }} + + runs-on: ubuntu-latest + steps: + - name: Deploy to GitHub Pages + id: deployment + uses: actions/deploy-pages@v4 diff --git a/README.md b/README.md index 4c700a6..d5680eb 100644 --- a/README.md +++ b/README.md @@ -1,2 +1,28 @@ -# tutorial-pysul2024 -Introdução ao Python Científico e para Data Science +# Introdução ao Python Científico e para Data Science + +Tutorial curto (3h) ministrado na Python Sul 2024. + +## Resumo + +Vamos explorar as bibliotecas NumPy, SciPy e Matplotlib e discutir boas práticas para a resolução de problemas em ciências em geral e ciência de dados com essas bibliotecas. + +## Sobre + +Neste tutorial, vamos discutir conceitos, fundamentos e aplicações das bibliotecas que formam o núcleo da computação científica em Python: NumPy, SciPy e Matplotlib. + +Discutiremos como fazer a transição de Python para o pensamento vetorial, usando os conceitos de *broadcasting*, *stacking* e como garantir um bom desempenho para seu código. Também exploraremos os diversos submódulos das bibliotecas NumPy e SciPy para efetuar operações em estatística, otimização, processamento de imagens e outros. Finalmente, vamos explorar boas práticas para a utilização da Matplotlib para visualização, incluindo a API de orientação a objetos mais recente que permite grande flexibilidade nos gráficos gerados. + +### Tabela de Conteúdo + +- 5 min: Apresentação +- 30 mins: Discussão de conceitos, fundamentos e aplicações da biblioteca NumPy +- 20 minutos: Exercícios e aplicações +- 30 mins: Conceitos da SciPy +- 30 mins: Exploração dos diversos submódulos das bibliotecas NumPy e SciPy para operações em estatística, otimização, processamento de imagens, etc. +- 30 mins: Boas práticas para a utilização da Matplotlib para visualização +- 20 mins: Exercícios e aplicações +- 10 mins: Conclusão + +## Público alvo + +Conhecimento básico de Python incluindo listas, loops, condicionais, funções e conceitos básicos de orientação a objetos. Básico de matemática incluindo conceitos de probabilidade (ensino médio). diff --git a/book/_config.yml b/book/_config.yml new file mode 100644 index 0000000..d4f2e3d --- /dev/null +++ b/book/_config.yml @@ -0,0 +1,23 @@ +# Book settings +# Learn more at https://jupyterbook.org/customize/config.html + +title: Tutorial Python Brasil 2020 +author: Melissa +logo: logo.png + +# Force re-execution of notebooks on each build. +# See https://jupyterbook.org/content/execute.html +execute: + execute_notebooks: force + +# Information about where the book exists on the web +repository: + url: https://github.com/melissawm/tutorial-pybr2020 # Online location of your book + path_to_book: book # Optional path to your book, relative to the repository root + branch: main # Which branch of the repository should be used when creating links (optional) + +# Add GitHub buttons to your book +# See https://jupyterbook.org/customize/config.html#add-a-link-to-your-repository +html: + use_issues_button: true + use_repository_button: true diff --git a/book/_toc.yml b/book/_toc.yml new file mode 100644 index 0000000..d1c9939 --- /dev/null +++ b/book/_toc.yml @@ -0,0 +1,14 @@ +# Table of contents +# Learn more at https://jupyterbook.org/customize/toc.html + +format: jb-book +root: intro +chapters: +- file: notebooks/00-Tutorial_Python_Brasil_2020.ipynb +- file: notebooks/01-Tutorial_NumPy.ipynb +- file: notebooks/02-Tutorial_Matplotlib.ipynb +- file: notebooks/03-Exemplo_Masked_Arrays.ipynb +- file: notebooks/04-Exemplo_SVD.ipynb +- file: notebooks/05-Exemplo_Queimadas.ipynb +- file: notebooks/06-Tutorial_SciPy.ipynb +- file: notebooks/07-Exemplo_Regressao.ipynb diff --git a/book/intro.md b/book/intro.md new file mode 100644 index 0000000..f8cdc73 --- /dev/null +++ b/book/intro.md @@ -0,0 +1,11 @@ +# Welcome to your Jupyter Book + +This is a small sample book to give you a feel for how book content is +structured. +It shows off a few of the major file types, as well as some sample content. +It does not go in-depth into any particular topic - check out [the Jupyter Book documentation](https://jupyterbook.org) for more information. + +Check out the content pages bundled with this sample book to see more. + +```{tableofcontents} +``` diff --git a/book/logo.png b/book/logo.png new file mode 100644 index 0000000..06d56f4 Binary files /dev/null and b/book/logo.png differ diff --git a/book/notebooks/00-Tutorial_Python_Brasil_2020.ipynb b/book/notebooks/00-Tutorial_Python_Brasil_2020.ipynb new file mode 100644 index 0000000..4ddc1a7 --- /dev/null +++ b/book/notebooks/00-Tutorial_Python_Brasil_2020.ipynb @@ -0,0 +1,352 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# O Ecossistema Científico no Python" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "```python\n", + "import numpy as np\n", + "import scipy as sp\n", + "import matplotlib as mpl\n", + "```" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "---" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "#! cp /home/shared/tutorial.zip .\n", + "#! unzip tutorial.zip" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "tags": [ + "chapter" + ] + }, + "source": [ + "## Introdução\n", + "\n", + "- [Quem sou eu?](https://github.com/melissawm) 👋\n", + "- [Código de Conduta](https://python.org.br/cdc/) 🤝" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "tags": [ + "section" + ] + }, + "source": [ + "### O que é esse tutorial?" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "- Primeiro contato\n", + "- Conceitos básicos e quando usar essas bibliotecas\n", + "- Onde buscar ajuda" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "from IPython.display import Image\n", + "Image(url=\"https://i.imgur.com/MBQqSmT.jpg\", width=600)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "---" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "tags": [ + "section" + ] + }, + "source": [ + "### Computação Científica" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "Image(\"imagens/comp_cientifica.png\", width=600)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Na prática..." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "Image(\"imagens/comp_cientifica_2.png\", width=600)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "*Baseado [nessa imagem](https://agilescientific.com/blog/2018/1/10/what-is-scientific-computing)*" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "---" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "tags": [ + "section" + ] + }, + "source": [ + "### O que é o ecossistema científico do Python? Quem mora nele?" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "Image(url=\"https://media.springernature.com/full/springer-static/image/art%3A10.1038%2Fs41586-020-2649-2/MediaObjects/41586_2020_2649_Fig2_HTML.png\", width=600)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "*Fonte: Harris et al., \"Array Programming with NumPy\", Nature volume 585, pages 357–362 (2020)*" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Histórico\n", + "\n", + "- BLAS (1979) - especificação de rotinas de baixo nível para operações comuns de álgebra linear\n", + "- LAPACK\n", + "- MATLAB\n", + "- Python\n", + " - Numeric/Numarray\n", + " - NumPy (2005)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Linguagens e habilidades\n", + "\n", + "| github.com/numpy/numpy | github.com/scipy/scipy |\n", + "--------|---------------------------------------\n", + "| ![](https://i.imgur.com/z6zsl4Q.png) | ![](https://i.imgur.com/HJWrdZr.png) |\n", + "\n", + "- Documentação/Escrita técnica\n", + "- Desenvolvimento Web\n", + "- Design/UX\n", + "- Comunicação\n", + "- Gerenciamento de projetos" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Comunidades: usuários e desenvolvedores\n", + "\n", + "- Cientistas\n", + "- Pessoas da indústria\n", + "- Voluntários\n", + "- Bolsas, patrocínio, doações" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "---" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "tags": [ + "section" + ] + }, + "source": [ + "## Conteúdo" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "- [Parte 1. NumPy](01-Tutorial_NumPy.ipynb)\n", + "\n", + "\n", + "- [Parte 2. Matplotlib](02-Tutorial_Matplotlib.ipynb)\n", + "\n", + "\n", + "- Parte 3. Exemplos práticos\n", + "\n", + " - [Arrays com máscara (masked arrays)](03-Exemplo_Masked_Arrays.ipynb)\n", + " - [Aproximação de imagens usando a SVD](04-Exemplo_SVD.ipynb)\n", + " - [Queimadas](05-Exemplo_Queimadas.ipynb)\n", + "\n", + "\n", + "- [Parte 4. SciPy](06-Tutorial_SciPy.ipynb)\n", + " - [Regressão](07-Exemplo_Regressao.ipynb)\n", + " " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "---" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Para terminar..." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "Image(url=\"https://pics.me.me/lee-lee-dibango-pmishraworld-math-problem-2-2-me-import-numpy-35881429.png\", width=600)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "tags": [ + "chapter" + ] + }, + "source": [ + "## Outras ferramentas\n", + "\n", + "- [Pandas](https://pandas.pydata.org/https://pandas.pydata.org/)\n", + "- [SymPy](https://www.sympy.org/pt/index.htmlhttps://www.sympy.org/pt/index.html)\n", + "- [scikit-learn](https://scikit-learn.orghttps://scikit-learn.org)\n", + "- [scikit-image](https://scikit-image.org/https://scikit-image.org/)\n", + "- [Dask](https://dask.org/https://dask.org/)\n", + "- [PyTorch](https://pytorch.org/https://pytorch.org/)\n", + "- [TensorFlow](https://www.tensorflow.orghttps://www.tensorflow.org)\n", + "- [Julia](https://julialang.org/https://julialang.org/)\n", + "- [Fortran](https://fortran-lang.org/https://fortran-lang.org/)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Referências\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Algumas referências interessantes e caminhos para mais informações além da documentação oficial." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "- [From Python to NumPy](https://github.com/rougier/from-python-to-numpy), livro aberto de Nicolas Rougier.\n", + "- [SciPy Lecture Notes](https://scipy-lectures.org/)\n", + "- [Elegant SciPy](https://github.com/elegant-scipy/elegant-scipy), livro aberto de Juan Nunez-Iglesias, Harriet Dashnow and Stéfan van der Walt (também publicado pela O'Reilly)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "tags": [ + "chapter" + ] + }, + "source": [ + "## Obrigada! 💖\n", + "\n", + "`@melissawm`" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.7.8" + }, + "toc-showtags": true + }, + "nbformat": 4, + "nbformat_minor": 4 +} diff --git a/book/notebooks/01-Tutorial_NumPy.ipynb b/book/notebooks/01-Tutorial_NumPy.ipynb new file mode 100644 index 0000000..b192f22 --- /dev/null +++ b/book/notebooks/01-Tutorial_NumPy.ipynb @@ -0,0 +1,1725 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "from IPython.display import Image\n", + "Image(\"imagens/numpylogo.png\", width=600)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "tags": [ + "chapter" + ] + }, + "source": [ + "# NumPy: a base da computação científica no Python" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "import numpy as np" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## O que é a NumPy?\n", + "\n", + "> A computação com arrays é a base para estatística e matemática computacionais, computação científica e suas várias aplicações em ciência e análise de dados, tais como visualização de dados, processamento de sinais digitais, processamento de imagens, bioinformática, aprendizagem de máquina, IA e muitas outras.\n", + ">\n", + "> A manipulação e a transformação de dados de grande escala dependem de computação eficiente de alta performance com arrays. A linguagem mais escolhida para análise de dados, aprendizagem de máquina e computação numérica produtiva é Python.\n", + ">\n", + "> Numerical Python (Python Numérico) ou NumPy é a biblioteca em Python padrão para o suporte à utilização de matrizes e arrays multidimensionais de grande porte, e vem com uma vasta coleção de funções matemáticas de alto nível para operar nestas arrays.\n", + ">\n", + "> Desde o lançamento do NumPy em 2006, o Pandas apareceu em 2008, e nos últimos anos vimos uma sucessão de bibliotecas de computação com arrays aparecerem, ocupando e preenchendo o campo da computação com arrays. Muitas dessas bibliotecas mais recentes imitam recursos e capacidades parecidas com o NumPy e entregam algoritmos e recursos mais recentes voltados para aplicações de aprendizagem de máquina e inteligência artificial.\n", + ">\n", + "> A computação com arrays é baseada em estruturas de dados chamadas arrays. Arrays são usadas para organizar grandes quantidades de dados de forma que um conjunto de valores relacionados possa ser facilmente ordenado, obtido, matematicamente manipulado e transformado fácil e rapidamente.\n", + ">\n", + "> A computação com arrays é única pois envolve operar nos valores de um array de dados de uma vez. Isso significa que qualquer operação de array se aplica a todo um conjunto de valores de uma só vez. Esta abordagem vetorizada fornece velocidade e simplicidade por permitir que os programadores organizem o código e operem em agregados de dados, sem ter que usar laços com operações escalares individuais.\n", + "\n", + "(Fonte: [https://numpy.org](https://numpy.org))\n", + "\n", + "A biblioteca NumPy consiste em:\n", + "\n", + "- objeto `ndarray` (array homogêneo n-dimensional)\n", + "- capacidade de **broadcasting**\n", + "- funções matemáticas padrão com capacidade de vetorização\n", + "- ferramentas para a integração de código C/C++ e Fortran\n", + "- álgebra linear, transformadas de Fourier, gerador de números aleatórios" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "tags": [ + "section" + ] + }, + "source": [ + "### Por que não usar listas?" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Uma lista em Python é às vezes vista como equivalente de um *array* em outras linguagens, mas uma lista é mutável e pode conter elementos de tipos diferentes.\n", + "\n", + "Quando uma array de fato contém elementos de um só tipo, temos algumas vantagens:\n", + "- Tamanho\n", + "- Desempenho\n", + "- Funcionalidades específicas" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "🎈 Link interessante: https://webcourses.ucf.edu/courses/1249560/pages/python-lists-vs-numpy-arrays-what-is-the-difference" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Alguns exemplos" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "n = 10_000_000\n", + "v = []\n", + "w = []\n", + "for i in range(n):\n", + " v.append(i)\n", + " w.append(i/10)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "type(v), type(w)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "%timeit v + w" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "v_np = np.array(v)\n", + "w_np = np.array(w)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "type(v_np), type(w_np)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "v_np.dtype, w_np.dtype" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "v_np.shape" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "v_np.size" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "v_np.nbytes/v_np.size" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "%timeit v_np + w_np" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### E o módulo array?" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "import array" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "v_arr = array.array('d', v)\n", + "w_arr = array.array('d', w)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "v_arr.typecode" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "%timeit v_arr + w_arr" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "🎈 Link interessante: https://medium.com/@gough.cory/performance-of-numpy-array-vs-python-list-194c8e283b65" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Como isso acontece?\n", + "\n", + "As arrays do NumPy são eficientes porque \n", + "- são compatíveis com as rotinas de álgebra linear escritas em C/C++ ou Fortran\n", + "- são *views* de objetos alocados por C/C++, Fortran e Cython\n", + "- as operações vetorizadas em geral evitam copiar arrays desnecessariamente" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "Image(url=\"https://media.springernature.com/full/springer-static/image/art%3A10.1038%2Fs41586-020-2649-2/MediaObjects/41586_2020_2649_Fig1_HTML.png\", width=600)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "*Fonte: Harris et al., \"Array Programming with NumPy\", Nature volume 585, pages 357–362 (2020)*" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "tags": [ + "section" + ] + }, + "source": [ + "## Vetorização" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Vetorização é a capacidade de expressar operações em arrays sem especificar o que acontece com cada elemento individual (em outras palavras: sem usar loops!)\n", + "\n", + "- Código vetorizado é mais conciso e legível\n", + "- O código fica (ligeiramente) mais parecido com a notação matemática\n", + "- Mais rápido (usa operações otimizadas em C/Fortran)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Vetorização: Exemplo 1" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "v = np.array([1, 2, 3, 4])" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "v**2" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "np.power(v, 2)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "u = np.array([4.5, 3, 2, 1])" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "np.dot(u, v) " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Nota: onde estão implementadas as funções do NumPy?" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "print(np.dot.__doc__)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "np.info(np.dot)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "np.dot?" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "---" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "❓❓\n", + "- [Código fonte da função `np.dot`](https://github.com/numpy/numpy/blob/master/numpy/core/multiarray.py) - **linha 716**\n", + "- [Agora de verdade](https://github.com/numpy/numpy/blob/master/numpy/core/src/multiarray/methods.c) - **linha 2269**\n", + "- [For realz](https://github.com/numpy/numpy/blob/master/numpy/core/src/multiarray/multiarraymodule.c) - **linha 981 / 1023**" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Cuidado!" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "sum?" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "sum(range(5), -1)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "from numpy import *\n", + "sum(range(5), -1)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "np.sum?" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "%reset" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "import numpy as np" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "---" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Vetorização: Exemplo 2" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "A = np.array([[1, 2, 3, 4],\n", + " [5, 6, 7, 8], \n", + " [9, 10, 11, 12]])" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "A.T" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "A.shape" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "A.ndim" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "A.reshape((2, 6))" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "A.reshape((4, 3))" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "A.ravel()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "u = np.random.rand(10)\n", + "u" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "u.ndim" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "u.shape" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "u.T" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "u.T.shape" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "u.T == u" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "u.T is u" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "np.transpose?" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "v = u.T\n", + "v.base is u" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "u[0] = 0" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "u" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "v" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "[Documentação para ndarray.base](https://numpy.org/doc/stable/reference/generated/numpy.ndarray.base.html)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### ❓ (O que são essas dimensões?)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "from IPython.display import Image\n", + "Image(url=\"https://fgnt.github.io/python_crashkurs_doc/_images/numpy_array_t.png\", width=600)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "*[Fonte: Elegant SciPy](https://www.oreilly.com/library/view/elegant-scipy/9781491922927/ch01.html)*" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Vetorização: Exemplo 3" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "A[0, :] " + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "A.sum()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "A.sum(axis=0)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "A.sum(axis=1)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "A.size * A.itemsize" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "A.nbytes" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Novamente:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "Image(url=\"https://media.springernature.com/full/springer-static/image/art%3A10.1038%2Fs41586-020-2649-2/MediaObjects/41586_2020_2649_Fig1_HTML.png\", width=600)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "tags": [ + "section" + ] + }, + "source": [ + "### Broadcasting" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Permite fazer operações vetoriais de maneira generalizada.\n", + "\n", + "☠️ **Cuidado!** ☠️" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "x = np.array([1, 2, 3, 4])" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "x + 5" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "A" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "x" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "A+x" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "np.add?" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### 📒 Exercícios " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Como determinar a dieta mais econômica que satisfaz os requerimentos nutricionais básicos para boa saúde? Vamos supor que queremos fazer nossa compra semanal no supermercado: leite, miojo e café. Atualmente, os preços são:\n", + "\n", + "- Leite: 4 reais o litro\n", + "- Miojo: 1,50 o pacote\n", + "- Café: 10 reais a unidade\n", + "\n", + "Vamos nos preocupar com 4 ingredientes nutricionais básicos: proteína, carboidratos e gordura.\n", + "\n", + "| Produto | Carboidratos | Proteínas | Gorduras|\n", + "---------------|--------------|-----------|---------|\n", + "|Miojo (100g) | 63,6g | 10,0g | 15,5g |\n", + "|Leite (100ml) | 4,66g | 3,32g | 3,35g |" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Exercício 1\n", + "\n", + "Use uma ndarray `a` bidimensional que armazene, na posição $(i, j)$, a quantidade de gramas do nutriente $i$ contidas em cada grama do alimento $j$." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Solução 1" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "tabela = np.zeros((3, 2))" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "miojo = np.array([63.6, 10, 15.5])\n", + "leite = np.array([4.66, 3.32, 3.35])" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Para ter os valores nutricionais por grama (ao invés de para cada 100g)\n", + "miojo = miojo/100\n", + "# Mesma coisa para o leite: vamos obter o valor por ml\n", + "leite = leite/100" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "tabela[:, 0] = miojo" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "tabela" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "tabela[:, 1] = leite" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "tabela" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Solução 2" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "miojo = np.array([63.6, 10, 15.5])\n", + "leite = np.array([4.66, 3.32, 3.35])" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "tabela = np.vstack([miojo, leite]).T/100" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "tabela" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Solução 3" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "tabela = np.stack([miojo, leite], axis=1)/100" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "tabela" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Exercício 2" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "A partir da array `a` criada acima, extraia um array que contenha os valores de nutriente por alimento." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "carbs = tabela[0, :]\n", + "proteina = tabela[1, :]\n", + "gordura = tabela[2, :]" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "carbs.shape, proteina.shape, gordura.shape" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Exercício 3" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Calcule o preço de uma compra incluindo 2 litros de leite, 1 pacote de café e 5 pacotes de miojo.\n", + "\n", + "Para facilitar, vamos usar a seguinte notação:\n", + "\n", + "$x_0$ -> quantidade de litros de leite;\n", + "\n", + "$x_1$ -> quantidade de pacotes de miojo;\n", + "\n", + "$x_2$ -> quantidade de pacotes de café;\n", + "\n", + "Isso significa que podemos representar a quantidade a ser comprada como:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "x = np.array([2, 5, 1])" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Leite: 4 reais o litro\n", + "# Miojo: 1,50 o pacote\n", + "# Café: 10 reais a unidade\n", + "c = np.array([4, 1.5, 10])" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Total da compra:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "x[0]*c[0] + x[1]*c[1]+x[2]*c[2]" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "total = np.dot(c, x)\n", + "total" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "O custo pode ser calculado, em geral, como" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "def custo(x):\n", + " c = np.array([4, 1.5, 10])\n", + " return np.dot(x, c)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Assim, se mudamos as quantidades para\n", + "\n", + "10 litros de leite; 8 pacotes de miojo; 2 pacotes de café\n", + "\n", + "temos" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "custo(np.array([10, 8, 2]))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "---" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Operações vetorizadas e pontos flutuantes" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "x = np.linspace(-np.pi, np.pi, 10)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "x" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "np.sin(x)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### ⚠️⚠️ Observação" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "0 * np.nan" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "np.nan == np.nan" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "np.inf > np.nan" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "np.nan - np.nan" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "np.nan in set([np.nan])" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Observe:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "np.array(0) / np.array(0)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "np.array(0) // np.array(0)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "np.array([np.nan]).astype(int) #.astype(float)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "0.3 == 3 * 0.1" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "**Nunca teste se um número real é igual a outro! Use sempre tolerâncias.**" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "np.abs(0.3 - 3*0.1)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Explicação" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "- `NaN` : *Not a Number*\n", + "- `Inf` : Infinito?" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "for dtype in [np.int8, np.int32, np.int64]:\n", + " print(np.iinfo(dtype).min)\n", + " print(np.iinfo(dtype).max)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "for dtype in [np.float32, np.float64]:\n", + " print(np.finfo(dtype).min)\n", + " print(np.finfo(dtype).max)\n", + " print(np.finfo(dtype).eps)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "np.finfo(np.float64).max+1.e308" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Leia mais na documentação oficial do Python: https://docs.python.org/pt-br/3/tutorial/floatingpoint.html" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "x = np.arange(0, 10*np.pi, np.pi)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "np.sin(x) == 0" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "np.allclose(np.sin(x), np.zeros(np.shape(x)))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "---" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Basic Indexing, Fancy Indexing" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "x = np.arange(10)\n", + "x[2:5]" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "x[:-7]" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "x[1:7:2]" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "y = np.arange(35).reshape(5,7)\n", + "y[1:5:2,::3]" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "As arrays do NumPy podem ser indexadas por outras arrays (ou qualquer outro objeto tipo-sequência que possa ser convertido para uma array, como listas, com a exceção de tuplas). No entanto, isso pode não ser tão eficiente pois ativa o [indiciamento avançado](https://numpy.org/devdocs/reference/arrays.indexing.html#advanced-indexing)." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "x = np.arange(10,1,-1)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "x[np.array([3,3,-3,8])]" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "x[np.array([[1,1],[2,3]])]" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Também podemos usar máscaras booleanas:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "b = y>20" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "y[b]" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "x = np.arange(30).reshape(2,3,5)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "b = np.array([[True, True, False], [False, True, True]])\n", + "x[b]" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "x = np.arange(5)\n", + "x[:,np.newaxis] + x[np.newaxis,:]" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "z = np.arange(81).reshape(3,3,3,3)\n", + "z[1,...,2]" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "z[1,:,:,2]" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Exercícios" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Criar um array aleatório de tamanho 12 e substituir o menor valor por -999." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "aleatorio = np.random.random(12)\n", + "aleatorio[aleatorio.argmax()] = -999\n", + "aleatorio" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Ordenar uma array pela n-ésima coluna" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "vetor = np.random.randint(0,10,(3,3))\n", + "vetor" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "vetor[vetor[:,1].argsort()]" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Como saber se uma array 2D tem colunas nulas?" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "z = np.random.randint(0,3,(3,10))\n", + "z" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "(~z.any(axis=0)).any()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "z[:, 0] = np.zeros(3)\n", + "z" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "(~z.any(axis=0)).any()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Trocar duas linhas de uma matriz" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "A = np.arange(25).reshape(5,5)\n", + "A" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "A[[0,1]] = A[[1,0]]\n", + "A" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### 2.4 Submódulos\n", + "\n", + "- `numpy.random` \n", + "- `numpy.fft`\n", + "- `numpy.ma`\n", + "- `numpy.linalg`\n", + "- `numpy.f2py`" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "print(np.__doc__)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "---" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "[Voltar ao notebook principal](00-Tutorial_Python_Brasil_2020.ipynb)\n", + "\n", + "[Ir para o notebook Matplotlib](02-Tutorial_Matplotlib.ipynb)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.7.8" + } + }, + "nbformat": 4, + "nbformat_minor": 4 +} diff --git a/book/notebooks/01-Tutorial_NumPy.md b/book/notebooks/01-Tutorial_NumPy.md new file mode 100644 index 0000000..9af0e7c --- /dev/null +++ b/book/notebooks/01-Tutorial_NumPy.md @@ -0,0 +1,791 @@ +--- +jupyter: + jupytext: + text_representation: + extension: .md + format_name: markdown + format_version: '1.3' + jupytext_version: 1.16.1 + kernelspec: + display_name: Python 3 + language: python + name: python3 +--- + +```python +from IPython.display import Image +Image("imagens/numpylogo.png", width=600) +``` + + +# NumPy: a base da computação científica no Python + + +```python +import numpy as np +``` + +## O que é a NumPy? + +> A computação com arrays é a base para estatística e matemática computacionais, computação científica e suas várias aplicações em ciência e análise de dados, tais como visualização de dados, processamento de sinais digitais, processamento de imagens, bioinformática, aprendizagem de máquina, IA e muitas outras. +> +> A manipulação e a transformação de dados de grande escala dependem de computação eficiente de alta performance com arrays. A linguagem mais escolhida para análise de dados, aprendizagem de máquina e computação numérica produtiva é Python. +> +> Numerical Python (Python Numérico) ou NumPy é a biblioteca em Python padrão para o suporte à utilização de matrizes e arrays multidimensionais de grande porte, e vem com uma vasta coleção de funções matemáticas de alto nível para operar nestas arrays. +> +> Desde o lançamento do NumPy em 2006, o Pandas apareceu em 2008, e nos últimos anos vimos uma sucessão de bibliotecas de computação com arrays aparecerem, ocupando e preenchendo o campo da computação com arrays. Muitas dessas bibliotecas mais recentes imitam recursos e capacidades parecidas com o NumPy e entregam algoritmos e recursos mais recentes voltados para aplicações de aprendizagem de máquina e inteligência artificial. +> +> A computação com arrays é baseada em estruturas de dados chamadas arrays. Arrays são usadas para organizar grandes quantidades de dados de forma que um conjunto de valores relacionados possa ser facilmente ordenado, obtido, matematicamente manipulado e transformado fácil e rapidamente. +> +> A computação com arrays é única pois envolve operar nos valores de um array de dados de uma vez. Isso significa que qualquer operação de array se aplica a todo um conjunto de valores de uma só vez. Esta abordagem vetorizada fornece velocidade e simplicidade por permitir que os programadores organizem o código e operem em agregados de dados, sem ter que usar laços com operações escalares individuais. + +(Fonte: [https://numpy.org](https://numpy.org)) + +A biblioteca NumPy consiste em: + +- objeto `ndarray` (array homogêneo n-dimensional) +- capacidade de **broadcasting** +- funções matemáticas padrão com capacidade de vetorização +- ferramentas para a integração de código C/C++ e Fortran +- álgebra linear, transformadas de Fourier, gerador de números aleatórios + + +### Por que não usar listas? + + +Uma lista em Python é às vezes vista como equivalente de um *array* em outras linguagens, mas uma lista é mutável e pode conter elementos de tipos diferentes. + +Quando uma array de fato contém elementos de um só tipo, temos algumas vantagens: +- Tamanho +- Desempenho +- Funcionalidades específicas + + +🎈 Link interessante: https://webcourses.ucf.edu/courses/1249560/pages/python-lists-vs-numpy-arrays-what-is-the-difference + + +#### Alguns exemplos + +```python +n = 10_000_000 +v = [] +w = [] +for i in range(n): + v.append(i) + w.append(i/10) +``` + +```python +type(v), type(w) +``` + +```python +%timeit v + w +``` + +```python +v_np = np.array(v) +w_np = np.array(w) +``` + +```python +type(v_np), type(w_np) +``` + +```python +v_np.dtype, w_np.dtype +``` + +```python +v_np.shape +``` + +```python +v_np.size +``` + +```python +v_np.nbytes/v_np.size +``` + +```python +%timeit v_np + w_np +``` + +#### E o módulo array? + +```python +import array +``` + +```python +v_arr = array.array('d', v) +w_arr = array.array('d', w) +``` + +```python +v_arr.typecode +``` + +```python +%timeit v_arr + w_arr +``` + +🎈 Link interessante: https://medium.com/@gough.cory/performance-of-numpy-array-vs-python-list-194c8e283b65 + + +#### Como isso acontece? + +As arrays do NumPy são eficientes porque +- são compatíveis com as rotinas de álgebra linear escritas em C/C++ ou Fortran +- são *views* de objetos alocados por C/C++, Fortran e Cython +- as operações vetorizadas em geral evitam copiar arrays desnecessariamente + +```python +Image(url="https://media.springernature.com/full/springer-static/image/art%3A10.1038%2Fs41586-020-2649-2/MediaObjects/41586_2020_2649_Fig1_HTML.png", width=600) +``` + +*Fonte: Harris et al., "Array Programming with NumPy", Nature volume 585, pages 357–362 (2020)* + + +## Vetorização + + +Vetorização é a capacidade de expressar operações em arrays sem especificar o que acontece com cada elemento individual (em outras palavras: sem usar loops!) + +- Código vetorizado é mais conciso e legível +- O código fica (ligeiramente) mais parecido com a notação matemática +- Mais rápido (usa operações otimizadas em C/Fortran) + + +### Vetorização: Exemplo 1 + +```python +v = np.array([1, 2, 3, 4]) +``` + +```python +v**2 +``` + +```python +np.power(v, 2) +``` + +```python +u = np.array([4.5, 3, 2, 1]) +``` + +```python +np.dot(u, v) +``` + +#### Nota: onde estão implementadas as funções do NumPy? + +```python +print(np.dot.__doc__) +``` + +```python +np.info(np.dot) +``` + +```python +np.dot? +``` + +--- + + +❓❓ +- [Código fonte da função `np.dot`](https://github.com/numpy/numpy/blob/master/numpy/core/multiarray.py) - **linha 716** +- [Agora de verdade](https://github.com/numpy/numpy/blob/master/numpy/core/src/multiarray/methods.c) - **linha 2269** +- [For realz](https://github.com/numpy/numpy/blob/master/numpy/core/src/multiarray/multiarraymodule.c) - **linha 981 / 1023** + + +### Cuidado! + +```python +sum? +``` + +```python +sum(range(5), -1) +``` + +```python +from numpy import * +sum(range(5), -1) +``` + +```python +np.sum? +``` + +```python +%reset +``` + +```python +import numpy as np +``` + +--- + + +#### Vetorização: Exemplo 2 + +```python +A = np.array([[1, 2, 3, 4], + [5, 6, 7, 8], + [9, 10, 11, 12]]) +``` + +```python +A.T +``` + +```python +A.shape +``` + +```python +A.ndim +``` + +```python +A.reshape((2, 6)) +``` + +```python +A.reshape((4, 3)) +``` + +```python +A.ravel() +``` + +```python +u = np.random.rand(10) +u +``` + +```python +u.ndim +``` + +```python +u.shape +``` + +```python +u.T +``` + +```python +u.T.shape +``` + +```python +u.T == u +``` + +```python +u.T is u +``` + +```python +np.transpose? +``` + +```python +v = u.T +v.base is u +``` + +```python +u[0] = 0 +``` + +```python +u +``` + +```python +v +``` + +[Documentação para ndarray.base](https://numpy.org/doc/stable/reference/generated/numpy.ndarray.base.html) + + +#### ❓ (O que são essas dimensões?) + +```python +from IPython.display import Image +Image(url="https://fgnt.github.io/python_crashkurs_doc/_images/numpy_array_t.png", width=600) +``` + +*[Fonte: Elegant SciPy](https://www.oreilly.com/library/view/elegant-scipy/9781491922927/ch01.html)* + + +#### Vetorização: Exemplo 3 + +```python +A[0, :] +``` + +```python +A.sum() +``` + +```python +A.sum(axis=0) +``` + +```python +A.sum(axis=1) +``` + +```python +A.size * A.itemsize +``` + +```python +A.nbytes +``` + +Novamente: + +```python +Image(url="https://media.springernature.com/full/springer-static/image/art%3A10.1038%2Fs41586-020-2649-2/MediaObjects/41586_2020_2649_Fig1_HTML.png", width=600) +``` + + +### Broadcasting + + +Permite fazer operações vetoriais de maneira generalizada. + +☠️ **Cuidado!** ☠️ + +```python +x = np.array([1, 2, 3, 4]) +``` + +```python +x + 5 +``` + +```python +A +``` + +```python +x +``` + +```python +A+x +``` + +```python +np.add? +``` + +### 📒 Exercícios + + +Como determinar a dieta mais econômica que satisfaz os requerimentos nutricionais básicos para boa saúde? Vamos supor que queremos fazer nossa compra semanal no supermercado: leite, miojo e café. Atualmente, os preços são: + +- Leite: 4 reais o litro +- Miojo: 1,50 o pacote +- Café: 10 reais a unidade + +Vamos nos preocupar com 4 ingredientes nutricionais básicos: proteína, carboidratos e gordura. + +| Produto | Carboidratos | Proteínas | Gorduras| +---------------|--------------|-----------|---------| +|Miojo (100g) | 63,6g | 10,0g | 15,5g | +|Leite (100ml) | 4,66g | 3,32g | 3,35g | + + +#### Exercício 1 + +Use uma ndarray `a` bidimensional que armazene, na posição $(i, j)$, a quantidade de gramas do nutriente $i$ contidas em cada grama do alimento $j$. + + +#### Solução 1 + +```python +tabela = np.zeros((3, 2)) +``` + +```python +miojo = np.array([63.6, 10, 15.5]) +leite = np.array([4.66, 3.32, 3.35]) +``` + +```python +# Para ter os valores nutricionais por grama (ao invés de para cada 100g) +miojo = miojo/100 +# Mesma coisa para o leite: vamos obter o valor por ml +leite = leite/100 +``` + +```python +tabela[:, 0] = miojo +``` + +```python +tabela +``` + +```python +tabela[:, 1] = leite +``` + +```python +tabela +``` + +#### Solução 2 + +```python +miojo = np.array([63.6, 10, 15.5]) +leite = np.array([4.66, 3.32, 3.35]) +``` + +```python +tabela = np.vstack([miojo, leite]).T/100 +``` + +```python +tabela +``` + +#### Solução 3 + +```python +tabela = np.stack([miojo, leite], axis=1)/100 +``` + +```python +tabela +``` + +#### Exercício 2 + + +A partir da array `a` criada acima, extraia um array que contenha os valores de nutriente por alimento. + +```python +carbs = tabela[0, :] +proteina = tabela[1, :] +gordura = tabela[2, :] +``` + +```python +carbs.shape, proteina.shape, gordura.shape +``` + +#### Exercício 3 + + +Calcule o preço de uma compra incluindo 2 litros de leite, 1 pacote de café e 5 pacotes de miojo. + +Para facilitar, vamos usar a seguinte notação: + +$x_0$ -> quantidade de litros de leite; + +$x_1$ -> quantidade de pacotes de miojo; + +$x_2$ -> quantidade de pacotes de café; + +Isso significa que podemos representar a quantidade a ser comprada como: + +```python +x = np.array([2, 5, 1]) +``` + +```python +# Leite: 4 reais o litro +# Miojo: 1,50 o pacote +# Café: 10 reais a unidade +c = np.array([4, 1.5, 10]) +``` + +Total da compra: + +```python +x[0]*c[0] + x[1]*c[1]+x[2]*c[2] +``` + +```python +total = np.dot(c, x) +total +``` + +O custo pode ser calculado, em geral, como + +```python +def custo(x): + c = np.array([4, 1.5, 10]) + return np.dot(x, c) +``` + +Assim, se mudamos as quantidades para + +10 litros de leite; 8 pacotes de miojo; 2 pacotes de café + +temos + +```python +custo(np.array([10, 8, 2])) +``` + +--- + + +### Operações vetorizadas e pontos flutuantes + +```python +x = np.linspace(-np.pi, np.pi, 10) +``` + +```python +x +``` + +```python +np.sin(x) +``` + +#### ⚠️⚠️ Observação + +```python +0 * np.nan +``` + +```python +np.nan == np.nan +``` + +```python +np.inf > np.nan +``` + +```python +np.nan - np.nan +``` + +```python +np.nan in set([np.nan]) +``` + +Observe: + +```python +np.array(0) / np.array(0) +``` + +```python +np.array(0) // np.array(0) +``` + +```python +np.array([np.nan]).astype(int) #.astype(float) +``` + +```python +0.3 == 3 * 0.1 +``` + +**Nunca teste se um número real é igual a outro! Use sempre tolerâncias.** + +```python +np.abs(0.3 - 3*0.1) +``` + +#### Explicação + + +- `NaN` : *Not a Number* +- `Inf` : Infinito? + +```python +for dtype in [np.int8, np.int32, np.int64]: + print(np.iinfo(dtype).min) + print(np.iinfo(dtype).max) +``` + +```python +for dtype in [np.float32, np.float64]: + print(np.finfo(dtype).min) + print(np.finfo(dtype).max) + print(np.finfo(dtype).eps) +``` + +```python +np.finfo(np.float64).max+1.e308 +``` + +Leia mais na documentação oficial do Python: https://docs.python.org/pt-br/3/tutorial/floatingpoint.html + +```python +x = np.arange(0, 10*np.pi, np.pi) +``` + +```python +np.sin(x) == 0 +``` + +```python +np.allclose(np.sin(x), np.zeros(np.shape(x))) +``` + +--- + + +### Basic Indexing, Fancy Indexing + +```python +x = np.arange(10) +x[2:5] +``` + +```python +x[:-7] +``` + +```python +x[1:7:2] +``` + +```python +y = np.arange(35).reshape(5,7) +y[1:5:2,::3] +``` + +As arrays do NumPy podem ser indexadas por outras arrays (ou qualquer outro objeto tipo-sequência que possa ser convertido para uma array, como listas, com a exceção de tuplas). No entanto, isso pode não ser tão eficiente pois ativa o [indiciamento avançado](https://numpy.org/devdocs/reference/arrays.indexing.html#advanced-indexing). + +```python +x = np.arange(10,1,-1) +``` + +```python +x[np.array([3,3,-3,8])] +``` + +```python +x[np.array([[1,1],[2,3]])] +``` + +Também podemos usar máscaras booleanas: + +```python +b = y>20 +``` + +```python +y[b] +``` + +```python +x = np.arange(30).reshape(2,3,5) +``` + +```python +b = np.array([[True, True, False], [False, True, True]]) +x[b] +``` + +```python +x = np.arange(5) +x[:,np.newaxis] + x[np.newaxis,:] +``` + +```python +z = np.arange(81).reshape(3,3,3,3) +z[1,...,2] +``` + +```python +z[1,:,:,2] +``` + +### Exercícios + + +#### Criar um array aleatório de tamanho 12 e substituir o menor valor por -999. + +```python +aleatorio = np.random.random(12) +aleatorio[aleatorio.argmax()] = -999 +aleatorio +``` + +#### Ordenar uma array pela n-ésima coluna + +```python +vetor = np.random.randint(0,10,(3,3)) +vetor +``` + +```python +vetor[vetor[:,1].argsort()] +``` + +#### Como saber se uma array 2D tem colunas nulas? + +```python +z = np.random.randint(0,3,(3,10)) +z +``` + +```python +(~z.any(axis=0)).any() +``` + +```python +z[:, 0] = np.zeros(3) +z +``` + +```python +(~z.any(axis=0)).any() +``` + +#### Trocar duas linhas de uma matriz + +```python +A = np.arange(25).reshape(5,5) +A +``` + +```python +A[[0,1]] = A[[1,0]] +A +``` + +### 2.4 Submódulos + +- `numpy.random` +- `numpy.fft` +- `numpy.ma` +- `numpy.linalg` +- `numpy.f2py` + +```python +print(np.__doc__) +``` + +--- + + +[Voltar ao notebook principal](00-Tutorial_Python_Brasil_2020.ipynb) + +[Ir para o notebook Matplotlib](02-Tutorial_Matplotlib.ipynb) + +```python + +``` diff --git a/book/notebooks/02-Tutorial_Matplotlib.ipynb b/book/notebooks/02-Tutorial_Matplotlib.ipynb new file mode 100644 index 0000000..7eddb15 --- /dev/null +++ b/book/notebooks/02-Tutorial_Matplotlib.ipynb @@ -0,0 +1,308 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "from IPython.display import Image\n", + "Image(url=\"https://matplotlib.org/_static/logo2_compressed.svg\", width=600)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "tags": [ + "chapter" + ] + }, + "source": [ + "# Matplotlib: figurinhas maneiras\n", + "\n", + "- Criada por John Hunter (2003) para ser similar ao MATLAB;\n", + "- Hoje, possui sua própria API orientada a objetos." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "%matplotlib widget" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "tags": [ + "section" + ] + }, + "source": [ + "## O módulo `pyplot`" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "import matplotlib.pyplot as plt" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "print(plt.__doc__)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Exemplo 1" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "import numpy as np\n", + "t = np.arange(-5, 5, 0.1)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "plt.plot(t, t**2);" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "plt.plot(t, t**2, 'r*')\n", + "plt.close()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "plt.plot(t, t**2, linewidth=3)\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "tags": [ + "section" + ] + }, + "source": [ + "## Outra maneira: API Orientada a objetos" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "fig, ax = plt.subplots()\n", + "ax.plot(t, t**2, 'r*')" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "ax.plot(t, t**2, linewidth=3)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "fig2, ax2 = plt.subplots()\n", + "ax2.plot(t, t**2, 'm--');" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Customização" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "fig, ax = plt.subplots()\n", + "ax.plot(t, t**2, 'm--');" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "fig" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "fig.clear()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "fig.set_facecolor('black')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Exemplo boxplot" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "https://matplotlib.org/search.html?q=boxplot" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Exemplo 3D" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "import matplotlib.pyplot as plt\n", + "import numpy as np\n", + "\n", + "from matplotlib import cm\n", + "from matplotlib.ticker import LinearLocator\n", + "\n", + "fig, ax = plt.subplots(subplot_kw={\"projection\": \"3d\"})\n", + "\n", + "X = np.arange(-5, 5, 0.25)\n", + "Y = np.arange(-5, 5, 0.25)\n", + "X, Y = np.meshgrid(X, Y)\n", + "R = np.sqrt(X**2 + Y**2)\n", + "Z = np.sin(R)\n", + "\n", + "surf = ax.plot_surface(X, Y, Z, cmap=cm.coolwarm, linewidth=0, antialiased=False)\n", + "\n", + "ax.set_zlim(-1.01, 1.01)\n", + "ax.zaxis.set_major_locator(LinearLocator(10))\n", + "ax.zaxis.set_major_formatter(\"{x:.02f}\")\n", + "\n", + "fig.colorbar(surf, shrink=0.5, aspect=5)\n", + "\n", + "plt.show()\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Guia do usuário: galeria e exemplos\n", + "\n", + "- https://matplotlib.org/tutorials/introductory/usage.html\n", + "- https://matplotlib.org/tutorials/introductory/usage.html#parts-of-a-figure" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "Image(url=\"https://matplotlib.org/_images/anatomy.png\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "---" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "[Voltar ao notebook principal](00-Tutorial_Python_Brasil_2020.ipynb)\n", + "\n", + "[Ir para o notebook Masked Arrays](03-Exemplo_Masked_Arrays.ipynb)\n", + "\n", + "[Ir para o notebook SVD](04-Exemplo_SVD.ipynb)\n", + "\n", + "[Ir para o notebook Queimadas](05-Exemplo_Queimadas.ipynb)" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.7.8" + } + }, + "nbformat": 4, + "nbformat_minor": 4 +} diff --git a/book/notebooks/03-Exemplo_Masked_Arrays.ipynb b/book/notebooks/03-Exemplo_Masked_Arrays.ipynb new file mode 100644 index 0000000..f2682a2 --- /dev/null +++ b/book/notebooks/03-Exemplo_Masked_Arrays.ipynb @@ -0,0 +1,475 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Exemplo: Arrays com Máscara\n", + "\n", + "Vamos usar arrays com máscara para analisar dados do COVID-19 e lidar com valores ausentes." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "***" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## O que são arrays com máscara?\n", + "\n", + "Considere o seguinte problema. Você tem um conjunto de dados com entradas inválidas. Se você quiser *pular* ou marcar esses valores indesejados sem apagar dados, você pode usar o [numpy.ma](https://numpy.org/devdocs/reference/maskedarray.generic.html#module-numpy.ma).\n", + "\n", + "Da documentação de [Referência](https://numpy.org/devdocs/reference/maskedarray.generic.html#module-numpy.ma):\n", + "\n", + "> A masked array is the combination of a standard [numpy.ndarray](https://numpy.org/devdocs/reference/generated/numpy.ndarray.html#numpy.ndarray) and a **mask**. A mask is either `nomask`, indicating that no value of the associated array is invalid, or an array of booleans that determines for each element of the associated array whether the value is valid or not. When an element of the mask is `False`, the corresponding element of the associated array is valid and is said to be unmasked. When an element of the mask is `True`, the corresponding element of the associated array is said to be masked (invalid).\n", + "\n", + "Podemos pensar numa [array com máscara](https://numpy.org/devdocs/reference/maskedarray.baseclass.html#numpy.ma.MaskedArray) como uma combinação de:\n", + "\n", + "- Dados, numa `numpy.ndarray` com shape e dtype quaisquer;\n", + "- Uma máscara booleana com o mesmo shape dos dados;\n", + "- Um valor de preenchimento `fill_value`, que vai substituir as entradas inválidas. \n", + "\n", + "## Quando podem ser úteis?\n", + "\n", + "Existem algumas situações em que arrays com máscara podem ser úteis:\n", + "\n", + "- Quando você quer preservar os valores que foram mascarados sem copiar a array;\n", + "- Quando você tem que lidar com muitas arrays, cada uma com sua máscara. Se a máscara é parte da array, você pode evitar bugs e o código pode ficar mais compacto;\n", + "- Quando você tem flags diferentes para valores inválidos e ausentes, e quer preservar as flags sem substitui-las, excluindo-as dos cálculos;\n", + "- Se você não puder evitar ou eliminar valores ausentes, mas não quer lidar com [NaN (Not a Number)](https://numpy.org/devdocs/reference/constants.html#numpy.nan).\n", + "\n", + "O módulo `numpy.ma` também vem com uma implementação específica da maior parte das [NumPy universal functions (ufuncs)](https://numpy.org/devdocs/glossary.html#term-ufunc), o que permite usar operações e funções vetorizadas (rápidas). " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Usando arrays com máscara para ver dados do COVID-19\n", + "\n", + "Do [Kaggle](https://www.kaggle.com/atilamadai/covid19) a gente pode baixar um conjunto de dados com informações sobre o início da pandemia. Vamos olhar para um subconjunto de dados, contidos no arquivo `who_covid_19_sit_rep_time_series.csv`." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "import numpy as np\n", + "import os\n", + "# A função os.getcwd() retorna o diretório atual\n", + "filepath = os.getcwd()\n", + "filename = os.path.join(filepath, \"dados\", \"who_covid_19_sit_rep_time_series.csv\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "O arquivo tem dados de diferentes tipos organizado assim:\n", + "\n", + "- A primeira coluna é um cabeçalho, que descreve os dados em cada coluna. A partir da quarta coluna, denota a data da coleta dos dados correspondentes.\n", + "- Da segunda até a sétima coluna, temos um resumo dos dados que será excluido da nossa análise.\n", + "- Os dados numéricos em que estamos interessados começam na coluna 4, linha 8.\n", + "\n", + "Vamos explorar os dados deste arquivo para os primeiros 14 dias de registros. Para coletarmos os dados do arquivo `.csv`, vamos usar a função [numpy.genfromtxt](https://numpy.org/devdocs/reference/generated/numpy.genfromtxt.html#numpy.genfromtxt)." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Vamos usar skip_header e usecols para ler apenas um pedaço do arquivo.\n", + "dates = np.genfromtxt(filename, dtype=np.unicode_, delimiter=\",\",\n", + " max_rows=1, usecols=range(3, 17),\n", + " encoding=\"utf-8-sig\")\n", + "\n", + "locations = np.genfromtxt(filename, dtype=np.unicode_, delimiter=\",\",\n", + " skip_header=7, usecols=(0, 1),\n", + " encoding=\"utf-8-sig\")\n", + "\n", + "nbcases = np.genfromtxt(filename, dtype=np.int_, delimiter=\",\",\n", + " skip_header=7, usecols=range(3, 17),\n", + " encoding=\"utf-8-sig\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Na chamada de `numpy.genfromtxt`, também selecionamos o [numpy.dtype](https://numpy.org/devdocs/reference/generated/numpy.dtype.html#numpy.dtype) para cada subconjunto dos dados (um inteiro - `numpy.int_` - ou texto - `numpy.unicode_`). Também usamos o argumento `encoding` para selecionar `utf-8-sig` como a [codificação do arquivo](https://docs.python.org/3/library/codecs.html#encodings-and-unicode)." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Explorando os dados\n", + "\n", + "Vamos selecionar apenas algumas datas para mostrar no nosso gráfico (os [x-axis ticks](https://matplotlib.org/api/_as_gen/matplotlib.pyplot.xticks.html#matplotlib.pyplot.xticks)). Usamos `nbcases.T` para plotar cada linha do arquivo como uma curva do gráfico." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "%matplotlib widget\n", + "import matplotlib.pyplot as plt\n", + "selected_dates = [0, 3, 11, 13]\n", + "plt.plot(dates, nbcases.T, '--');\n", + "plt.xticks(selected_dates, dates[selected_dates]);\n", + "plt.title(\"COVID-19 cumulative cases from Jan 21 to Feb 3 2020\");" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "O gráfico está esquisito entre 24 de Janeiro e 1 de Fevereiro. Olhando para a coluna `locations`, temos os nomes de regiões e países. Mas só as primeiras linhas tem dados na primeira coluna (nomes de província na China). Vamos então agrupar os dados da China numa única linha, usando [numpy.sum](https://numpy.org/devdocs/reference/generated/numpy.sum.html#numpy.sum) para somar todas a linhas selecionadas (`axis=0`):" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "china_total = nbcases[locations[:, 1] == 'China'].sum(axis=0)\n", + "china_total" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Mas tem algo errado: não é pra termos dados negativos... " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Dados ausentes" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "nbcases" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Os `-1` são causados pela tentativa da `numpy.genfromtxt` de ler dados ausentes do arquivo `.csv` original. " + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "from numpy import ma\n", + "nbcases_ma = ma.masked_values(nbcases, -1)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Agora:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "nbcases_ma" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Cuidado: o atributo `mask` tem valor `True` em elementos com valores **inválidos**." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Vamos excluir a primeira linha (dados da província de Hubei na China):" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "plt.plot(dates, nbcases_ma[1:].T, '--');\n", + "plt.xticks(selected_dates, dates[selected_dates]);\n", + "plt.title(\"COVID-19 cumulative cases from Jan 21 to Feb 3 2020\");" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "china_masked = nbcases_ma[locations[:, 1] == 'China'].sum(axis=0)\n", + "china_masked" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Note que `china_masked` é uma array com máscara e para acessar seus dados precisamos usar o atributo `.data`:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "china_total = china_masked.data\n", + "china_total" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Pronto, não temos mais valores negativos. Mas ainda tem algo estranho: mas o número acumulado de casos não poderia diminuir de um dia pro outro (de 835 pra 10, por exemplo). Olhando pros dados, vemos que no período em que tínhamos dados ausentes pra China, tínhamos dados válidos pra Hong Kong, Taiwan, Macau e regiões \"Unspecified\". " + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "china_mask = ((locations[:, 1] == 'China') &\n", + " (locations[:, 0] != 'Hong Kong') &\n", + " (locations[:, 0] != 'Taiwan') &\n", + " (locations[:, 0] != 'Macau') &\n", + " (locations[:, 0] != 'Unspecified*'))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Observe que `china_mask` é um array de valores booleanos (`True` ou `False`); podemos verificar que os índices são como queríamos usando o método [ma.nonzero](https://numpy.org/devdocs/reference/generated/numpy.ma.nonzero.html#numpy.ma.nonzero):" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "china_mask.nonzero()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Pronto:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "china_total = nbcases_ma[china_mask].sum(axis=0)\n", + "china_total" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Substituindo esses dados, temos:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "fig = plt.figure()\n", + "plt.plot(dates, china_total.T, '--');\n", + "plt.xticks(selected_dates, dates[selected_dates]);\n", + "plt.title(\"COVID-19 cumulative cases from Jan 21 to Feb 3 2020 - Mainland China\");" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Ajuste de dados\n", + "\n", + "Uma possibilidade seria interpolar os dados ausentes para estimar o número de casos no final de janeiro. " + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "china_total.mask\n", + "invalid = china_total[china_total.mask]\n", + "invalid" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Podemos acessar os valores válidos usando o operador de negação:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "valid = china_total[~china_total.mask]\n", + "valid" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Agora, para criar uma aproximação simples para estes dados, devemos levar em conta as entradas válidas perto das inválidas. Vamos selecionar as datas em que os dados são válidos; observe que podemos usar a máscara do array `china_total` também no array de datas:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "dates[~china_total.mask]" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Finalmente, vamos usar [numpy.polyfit](https://numpy.org/devdocs/reference/generated/numpy.polyfit.html#numpy.polyfit) e [numpy.polyval](https://numpy.org/devdocs/reference/generated/numpy.polyval.html#numpy.polyval) para criar um polinômio cúbico que faz um ajuste dos dados:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "t = np.arange(len(china_total))\n", + "params = np.polyfit(t[~china_total.mask], valid, 3)\n", + "cubic_fit = np.polyval(params, t)\n", + "fig = plt.figure()\n", + "plt.plot(t, china_total);\n", + "plt.plot(t, cubic_fit, '--');" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Melhorando o gráfico:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "plt.plot(t, china_total);\n", + "plt.plot(t[china_total.mask], cubic_fit[china_total.mask], '--', color='orange');\n", + "plt.plot(7, np.polyval(params, 7), 'r*');\n", + "plt.xticks([0, 7, 13], dates[[0, 7, 13]]);\n", + "plt.yticks([0, np.polyval(params, 7), 10000, 17500]);\n", + "plt.legend(['Mainland China', 'Cubic estimate', '7 days after start']);\n", + "plt.title(\"COVID-19 cumulative cases from Jan 21 to Feb 3 2020 - Mainland China\\n\"\n", + " \"Cubic estimate for 7 days after start\");" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "---" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "[Voltar ao notebook principal](00-Tutorial_Python_Brasil_2020.ipynb)\n", + "\n", + "[Ir para o notebook SVD](04-Exemplo_SVD.ipynb)\n", + "\n", + "[Ir para o notebook Queimadas](05-Exemplo_Queimadas.ipynb)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.7.8" + } + }, + "nbformat": 4, + "nbformat_minor": 4 +} diff --git a/book/notebooks/04-Exemplo_SVD.ipynb b/book/notebooks/04-Exemplo_SVD.ipynb new file mode 100644 index 0000000..005c89a --- /dev/null +++ b/book/notebooks/04-Exemplo_SVD.ipynb @@ -0,0 +1,1057 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Tutorial: Álgebra Linear em arrays n-dimensionais" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "*(baseado no tutorial [Linear algebra on n-dimensional arrays](https://numpy.org/doc/stable/user/tutorial-svd.html) da documentação oficial da NumPy)*" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Resumo\n", + "\n", + "Vamos usar uma [decomposição matricial](https://en.wikipedia.org/wiki/Matrix_decomposition), a Decomposição em Valores Singulares (também conhecida como SVD), para gerar uma aproximação de uma imagem. Vamos usar a imagem `face` do módulo [scipy.misc](https://docs.scipy.org/doc/scipy/reference/misc.html#module-scipy.misc):" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "from scipy import misc\n", + "img = misc.face()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Agora, `img` é uma array do NumPy, como podemos ver usando a função `type`:" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "numpy.ndarray" + ] + }, + "execution_count": 2, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "type(img)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Podemos ver a imagem usando o comando [matplotlib.pyplot.imshow](https://matplotlib.org/api/_as_gen/matplotlib.pyplot.imshow.html#matplotlib.pyplot.imshow):" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [], + "source": [ + "import matplotlib.pyplot as plt\n", + "%matplotlib inline" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "plt.imshow(img)\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Shape, axis and array properties\n", + "\n", + "Note that, in linear algebra, the dimension of a vector refers to the number of entries in an array. In NumPy, it instead defines the number of axes. For example, a 1D array is a vector such as `[1, 2, 3]`, a 2D array is a matrix, and so forth.\n", + "\n", + "First, let's check for the shape of the data in our array. Since this image is two-dimensional (the pixels in the image form a rectangle), we might expect a two-dimensional array to represent it (a matrix). However, using the `shape` property of this NumPy array gives us a different result:" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "(768, 1024, 3)" + ] + }, + "execution_count": 5, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "img.shape" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The output is a [tuple](https://docs.python.org/dev/tutorial/datastructures.html#tut-tuples) with three elements, which means that this is a three-dimensional array. In fact, since this is a color image, and we have used the `imread` function to read it, the data is organized in three 2D arrays, representing color channels (in this case, red, green and blue - RGB). You can see this by looking at the shape above: it indicates that we have an array of 3 matrices, each having shape 768x1024.\n", + "\n", + "Furthermore, using the `ndim` property of this array, we can see that" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "3" + ] + }, + "execution_count": 6, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "img.ndim" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "NumPy refers to each dimension as an *axis*. Because of how `imread` works, the *first index in the 3rd axis* is the red pixel data for our image. We can access this by using the syntax" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([[121, 138, 153, ..., 119, 131, 139],\n", + " [ 89, 110, 130, ..., 118, 134, 146],\n", + " [ 73, 94, 115, ..., 117, 133, 144],\n", + " ...,\n", + " [ 87, 94, 107, ..., 120, 119, 119],\n", + " [ 85, 95, 112, ..., 121, 120, 120],\n", + " [ 85, 97, 111, ..., 120, 119, 118]], dtype=uint8)" + ] + }, + "execution_count": 7, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "img[:, :, 0]" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "From the output above, we can see that every value in `img[:, :, 0]` is an integer value between 0 and 255, representing the level of red in each corresponding image pixel (keep in mind that this might be different if you\n", + "use your own image instead of [scipy.misc.face](https://docs.scipy.org/doc/scipy/reference/generated/scipy.misc.face.html#scipy.misc.face)).\n", + "\n", + "As expected, this is a 768x1024 matrix:" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "(768, 1024)" + ] + }, + "execution_count": 8, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "img[:, :, 0].shape" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Since we are going to perform linear algebra operations on this data, it might be more interesting to have real numbers between 0 and 1 in each entry of the matrices to represent the RGB values. We can do that by setting" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [], + "source": [ + "img_array = img / 255" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "This operation, dividing an array by a scalar, works because of NumPy's [broadcasting rules](https://numpy.org/devdocs/user/theory.broadcasting.html#array-broadcasting-in-numpy). (Note that in real-world applications, it would be better to use, for example, the [img_as_float](https://scikit-image.org/docs/stable/api/skimage.html#skimage.img_as_float) utility function from `scikit-image`).\n", + "\n", + "You can check that the above works by doing some tests; for example, inquiring\n", + "about maximum and minimum values for this array:" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "(1.0, 0.0)" + ] + }, + "execution_count": 10, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "img_array.max(), img_array.min()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "or checking the type of data in the array:" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "dtype('float64')" + ] + }, + "execution_count": 11, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "img_array.dtype" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Note that we can assign each color channel to a separate matrix using the slice syntax:" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": {}, + "outputs": [], + "source": [ + "red_array = img_array[:, :, 0]\n", + "green_array = img_array[:, :, 1]\n", + "blue_array = img_array[:, :, 2]" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Operations on an axis\n", + "\n", + "It is possible to use methods from linear algebra to approximate an existing set of data. Here, we will use the [SVD (Singular Value Decomposition)](https://en.wikipedia.org/wiki/Singular_value_decomposition) to try to rebuild an image that uses less singular value information than the original one, while still retaining some of its features." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "**Note**: We will use NumPy's linear algebra module, [numpy.linalg](https://numpy.org/devdocs/reference/routines.linalg.html#module-numpy.linalg), to perform the operations in this tutorial. Most of the linear algebra functions in this module can also be found in [scipy.linalg](https://docs.scipy.org/doc/scipy/reference/linalg.html#module-scipy.linalg), and users are encouraged to use the [scipy](https://docs.scipy.org/doc/scipy/reference/index.html#module-scipy) module for real-world applications. However, it is currently not possible to apply linear algebra operations to n-dimensional arrays using the [scipy.linalg](https://docs.scipy.org/doc/scipy/reference/linalg.html#module-scipy.linalg) module. For more information on this, check the [scipy.linalg Reference](https://docs.scipy.org/doc/scipy/reference/tutorial/linalg.html)." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "To proceed, import the linear algebra submodule from NumPy:" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": {}, + "outputs": [], + "source": [ + "from numpy import linalg" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "In order to extract information from a given matrix, we can use the SVD to obtain 3 arrays which can be multiplied to obtain the original matrix. From the theory of linear algebra, given a matrix $A$, the following product can be computed:\n", + "\n", + "$$U \\Sigma V^T = A$$\n", + "\n", + "where $U$ and $V^T$ are square and $\\Sigma$ is the same size as $A$. $\\Sigma$ is a diagonal matrix and contains the [singular values](https://en.wikipedia.org/wiki/Singular_value) of $A$, organized from largest to smallest. These values are always non-negative and can be used as an indicator of the \"importance\" of some features represented by the matrix $A$.\n", + "\n", + "Let's see how this works in practice with just one matrix first. Note that according to [colorimetry](https://en.wikipedia.org/wiki/Grayscale#Colorimetric_(perceptual_luminance-reserving)_conversion_to_grayscale),\n", + "it is possible to obtain a fairly reasonable grayscale version of our color image if we apply the formula\n", + "\n", + "$$Y = 0.2126 R + 0.7152 G + 0.0722 B$$\n", + "\n", + "where $Y$ is the array representing the grayscale image, and $R$, $G$ and $B$ are the red, green and blue channel arrays we had originally. Notice we can use the `@` operator (the matrix multiplication operator for NumPy arrays, see [numpy.matmul](https://numpy.org/devdocs/reference/generated/numpy.matmul.html#numpy.matmul)) for this:" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": {}, + "outputs": [], + "source": [ + "img_gray = img_array @ [0.2126, 0.7152, 0.0722]" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Now, `img_gray` has shape" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "(768, 1024)" + ] + }, + "execution_count": 15, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "img_gray.shape" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "To see if this makes sense in our image, we should use a colormap from `matplotlib` corresponding to the color we wish to see in out image (otherwise, `matplotlib` will default to a colormap that does not correspond to the real data).\n", + "\n", + "In our case, we are approximating the grayscale portion of the image, so we will use the colormap `gray`: " + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "plt.imshow(img_gray, cmap=\"gray\")\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Now, applying the [linalg.svd](https://numpy.org/devdocs/reference/generated/numpy.linalg.svd.html#numpy.linalg.svd) function to this matrix, we obtain the following decomposition:" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "metadata": {}, + "outputs": [], + "source": [ + "U, s, Vt = linalg.svd(img_gray)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "**Note** If you are using your own image, this command might take a while to run, depending on the size of your image and your hardware. Don't worry, this is normal! The SVD can be a pretty intensive computation." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Let's check that this is what we expected:" + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "((768, 768), (768,), (1024, 1024))" + ] + }, + "execution_count": 18, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "U.shape, s.shape, Vt.shape" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Note that `s` has a particular shape: it has only one dimension. This means that some linear algebra functions that expect 2d arrays might not work. For example, from the theory, one might expect `s` and `Vt` to be\n", + "compatible for multiplication. However, this is not true as `s` does not have a second axis. Executing" + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "metadata": { + "tags": [ + "raises-exception" + ] + }, + "outputs": [ + { + "ename": "ValueError", + "evalue": "matmul: Input operand 1 has a mismatch in its core dimension 0, with gufunc signature (n?,k),(k,m?)->(n?,m?) (size 1024 is different from 768)", + "output_type": "error", + "traceback": [ + "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[0;31mValueError\u001b[0m Traceback (most recent call last)", + "\u001b[0;32m\u001b[0m in \u001b[0;36m\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0ms\u001b[0m \u001b[0;34m@\u001b[0m \u001b[0mVt\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m", + "\u001b[0;31mValueError\u001b[0m: matmul: Input operand 1 has a mismatch in its core dimension 0, with gufunc signature (n?,k),(k,m?)->(n?,m?) (size 1024 is different from 768)" + ] + } + ], + "source": [ + "s @ Vt" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "results in a `ValueError`. This happens because having a one-dimensional array for `s`, in this case, is much more economic in practice than building a diagonal matrix with the same data. To reconstruct the original matrix, we can rebuild the diagonal matrix $\\Sigma$ with the elements of `s` in its diagonal and with the appropriate dimensions for multiplying: in our case, $\\Sigma$ should be 768x1024 since `U` is 768x768 and `Vt` is\n", + "1024x1024." + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "metadata": {}, + "outputs": [], + "source": [ + "import numpy as np\n", + "Sigma = np.zeros((768, 1024))\n", + "for i in range(768):\n", + " Sigma[i, i] = s[i]" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Now, we want to check if the reconstructed `U @ Sigma @ Vt` is close to the original `img_gray` matrix." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Approximation\n", + "\n", + "The [linalg](https://numpy.org/devdocs/reference/routines.linalg.html#module-numpy.linalg) module includes a `norm` function, which computes the norm of a vector or matrix represented in a NumPy array. For example, from the SVD explanation above, we would expect the norm of the difference between `img_gray` and the reconstructed SVD product to be small. As expected, you should see something like" + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "1.425791914878972e-12" + ] + }, + "execution_count": 21, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "linalg.norm(img_gray - U @ Sigma @ Vt)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "(The actual result of this operation might be different depending on your architecture and linear algebra setup. Regardless, you should see a small number.)\n", + "\n", + "We could also have used the [numpy.allclose](https://numpy.org/devdocs/reference/generated/numpy.allclose.html#numpy.allclose) function to make sure the reconstructed product is, in fact, *close* to our original matrix (the difference between the two arrays is small):" + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "True" + ] + }, + "execution_count": 22, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "np.allclose(img_gray, U @ Sigma @ Vt)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "To see if an approximation is reasonable, we can check the values in `s`:" + ] + }, + { + "cell_type": "code", + "execution_count": 23, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "plt.plot(s)\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "In the graph, we can see that although we have 768 singular values in `s`, most of those (after the 150th entry or so) are pretty small. So it might make sense to use only the information related to the first (say, 50) *singular values* to build a more economical approximation to our image.\n", + "\n", + "The idea is to consider all but the first `k` singular values in `Sigma` (which are the same as in `s`) as zeros, keeping `U` and `Vt` intact, and computing the product of these matrices as the approximation.\n", + "\n", + "For example, if we choose " + ] + }, + { + "cell_type": "code", + "execution_count": 24, + "metadata": {}, + "outputs": [], + "source": [ + "k = 10" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "we can build the approximation by doing" + ] + }, + { + "cell_type": "code", + "execution_count": 25, + "metadata": {}, + "outputs": [], + "source": [ + "approx = U @ Sigma[:, :k] @ Vt[:k, :]" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Note that we had to use only the first `k` rows of `Vt`, since all other rows would be multiplied by the zeros corresponding to the singular values we eliminated from this approximation." + ] + }, + { + "cell_type": "code", + "execution_count": 26, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "plt.imshow(approx, cmap=\"gray\")\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Now, you can go ahead and repeat this experiment with other values of `k`, and each of your experiments should give you a slightly better (or worse) image depending on the value you choose." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Applying to all colors\n", + "\n", + "Now we want to do the same kind of operation, but to all three colors. Our first instinct might be to repeat the same operation we did above to each color matrix individually. However, NumPy's *broadcasting* takes care of this\n", + "for us.\n", + "\n", + "If our array has more than two dimensions, then the SVD can be applied to all axes at once. However, the linear algebra functions in NumPy expect to see an array of the form `(N, M, M)`, where the first axis represents the number of matrices.\n", + "\n", + "In our case," + ] + }, + { + "cell_type": "code", + "execution_count": 27, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "(768, 1024, 3)" + ] + }, + "execution_count": 27, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "img_array.shape" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "so we need to permutate the axis on this array to get a shape like `(3, 768, 1024)`. Fortunately, the [numpy.transpose](https://numpy.org/devdocs/reference/generated/numpy.transpose.html#numpy.transpose) function can do that for us:\n", + "```\n", + "np.transpose(x, axes=(i, j, k))\n", + "```\n", + "indicates that the axis will be reordered such that the final shape of the transposed array will be reordered according to the indices `(i, j, k)`.\n", + "\n", + "Let's see how this goes for our array:" + ] + }, + { + "cell_type": "code", + "execution_count": 28, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "(3, 768, 1024)" + ] + }, + "execution_count": 28, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "img_array_transposed = np.transpose(img_array, (2, 0, 1))\n", + "img_array_transposed.shape" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Now we are ready to apply the SVD:" + ] + }, + { + "cell_type": "code", + "execution_count": 29, + "metadata": {}, + "outputs": [], + "source": [ + "U, s, Vt = linalg.svd(img_array_transposed)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Finally, to obtain the full approximated image, we need to reassemble these matrices into the approximation. Now, note that" + ] + }, + { + "cell_type": "code", + "execution_count": 30, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "((3, 768, 768), (3, 768), (3, 1024, 1024))" + ] + }, + "execution_count": 30, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "U.shape, s.shape, Vt.shape" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "To build the final approximation matrix, we must understand how multiplication across different axes works." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Products with n-dimensional arrays\n", + "\n", + "If you have worked before with only one- or two-dimensional arrays in NumPy, you might use [numpy.dot](https://numpy.org/devdocs/reference/generated/numpy.dot.html#numpy.dot) and [numpy.matmul](https://numpy.org/devdocs/reference/generated/numpy.matmul.html#numpy.matmul) (or the `@` operator) interchangeably. However, for n-dimensional arrays, they work in very different ways. For more details, check the documentation on [numpy.matmul](https://numpy.org/devdocs/reference/generated/numpy.matmul.html#numpy.matmul).\n", + "\n", + "Now, to build our approximation, we first need to make sure that our singular values are ready for multiplication, so we build our `Sigma` matrix similarly to what we did before. The `Sigma` array must have dimensions `(3, 768, 1024)`. In order to add the singular values to the diagonal of `Sigma`, we will use the [fill_diagonal](https://numpy.org/devdocs/reference/generated/numpy.fill_diagonal.html) function from NumPy, using each of the 3 rows in `s` as the diagonal for each of the 3 matrices in `Sigma`:" + ] + }, + { + "cell_type": "code", + "execution_count": 31, + "metadata": {}, + "outputs": [], + "source": [ + "Sigma = np.zeros((3, 768, 1024))\n", + "for j in range(3):\n", + " np.fill_diagonal(Sigma[j, :, :], s[j, :])" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Now, if we wish to rebuild the full SVD (with no approximation), we can do" + ] + }, + { + "cell_type": "code", + "execution_count": 32, + "metadata": {}, + "outputs": [], + "source": [ + "reconstructed = U @ Sigma @ Vt" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Note that" + ] + }, + { + "cell_type": "code", + "execution_count": 33, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "(3, 768, 1024)" + ] + }, + "execution_count": 33, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "reconstructed.shape" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "and" + ] + }, + { + "cell_type": "code", + "execution_count": 34, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "plt.imshow(np.transpose(reconstructed, (1, 2, 0)))\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "should give you an image indistinguishable from the original one (although we may introduce floating point errors for this reconstruction). In fact, you might see a warning message saying `\"Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\"` This is expected from the manipulation we just did on the original image.\n", + "\n", + "Now, to do the approximation, we must choose only the first `k` singular values for each color channel. This can be done using the following syntax:" + ] + }, + { + "cell_type": "code", + "execution_count": 35, + "metadata": {}, + "outputs": [], + "source": [ + "approx_img = U @ Sigma[..., :k] @ Vt[..., :k, :]" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "You can see that we have selected only the first `k` components of the last axis for `Sigma` (this means that we have used only the first `k` columns of each of the three matrices in the stack), and that we have selected only the first `k` components in the second-to-last axis of `Vt` (this means we have selected only the first `k` rows from every matrix in the stack `Vt` and all columns). If you are unfamiliar with the ellipsis syntax, it is a\n", + "placeholder for other axes. For more details, see the documentation on [Indexing](https://numpy.org/devdocs/user/basics.indexing.html#basics-indexing).\n", + "\n", + "Now," + ] + }, + { + "cell_type": "code", + "execution_count": 36, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "(3, 768, 1024)" + ] + }, + "execution_count": 36, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "approx_img.shape" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "which is not the right shape for showing the image. Finally, reordering the axes back to our original shape of `(768, 1024, 3)`, we can see our approximation:" + ] + }, + { + "cell_type": "code", + "execution_count": 37, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "plt.imshow(np.transpose(approx_img, (1, 2, 0)))\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Even though the image is not as sharp, using a small number of `k` singular values (compared to the original set of 768 values), we can recover many of the distinguishing features from this image." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Final words\n", + "\n", + "Of course, this is not the best method to *approximate* an image. However, there is, in fact, a result in linear algebra that says that the approximation we built above is the best we can get to the original matrix in\n", + "terms of the norm of the difference. For more information, see *G. H. Golub and C. F. Van Loan, Matrix Computations, Baltimore, MD, Johns Hopkins University Press, 1985*." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.7.8" + } + }, + "nbformat": 4, + "nbformat_minor": 4 +} diff --git a/book/notebooks/05-Exemplo_Queimadas.ipynb b/book/notebooks/05-Exemplo_Queimadas.ipynb new file mode 100644 index 0000000..b9e5ad8 --- /dev/null +++ b/book/notebooks/05-Exemplo_Queimadas.ipynb @@ -0,0 +1,394 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Queimadas" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Dados do INPE: http://queimadas.dgi.inpe.br/queimadas/bdqueimadas/\n", + "\n", + "Dados da NASA: https://firms.modaps.eosdis.nasa.gov/" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "!ls" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "zipfile_inpe = \"Focos_BDQueimadas.zip\"" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "from zipfile import ZipFile" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "with ZipFile(zipfile_inpe, 'r') as zip: \n", + " zip.printdir() \n", + " print(f'Extracting file {zipfile_inpe} now...') \n", + " zip.extractall(path=\"dados\") \n", + " print('Done!')" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "!ls dados" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "import os\n", + "csv_inpe = os.path.join(\"dados\", \"Focos_2020-07-01_2020-09-30.csv\")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "with open(csv_inpe, 'r') as f:\n", + " data = f.readlines()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "print(data[0:10])" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "import pandas as pd" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "with open(csv_inpe, 'r') as f:\n", + " df = pd.read_csv(f)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "df" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "df = df[df['riscofogo']!=0.0]" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "df['satelite'].unique()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "df = df[df['satelite']=='TERRA_M-M']" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "del df['satelite']\n", + "del df['pais']" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "df" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "FRP: https://revistapesquisa.fapesp.br/como-monitorar-o-fogo/\n", + "\n", + "Risco de Queima: http://queimadas.dgi.inpe.br/queimadas/portal/informacoes/perguntas-frequentes#p23\n", + "\n", + "Monografia: https://monografias.ufrn.br/jspui/bitstream/123456789/9704/1/tcc_dias_alexandre_henrique.pdf" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "%matplotlib widget\n", + "from ipyleaflet import Map, Marker, CircleMarker\n", + "\n", + "center = (-11.7997134,-53.8335376)\n", + "\n", + "m = Map(center=center, zoom=3)\n", + "\n", + "#marker = Marker(location=center, draggable=True)\n", + "#m.add_layer(marker);\n", + "\n", + "display(m)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "frp_notnull = df[df['frp'].notnull()]\n", + "frp_notnull = frp_notnull.loc[frp_notnull['datahora'].str.contains('2020/09/30')]" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "for index, row in frp_notnull.iterrows():\n", + " lat = row['latitude']\n", + " lon = row['longitude']\n", + " circle_marker = CircleMarker()\n", + " circle_marker.location = (lat, lon)\n", + " circle_marker.radius = 1\n", + " circle_marker.color = \"red\"\n", + " circle_marker.fill_color = \"red\"\n", + " circle_marker.weight = 1\n", + " m.add_layer(circle_marker)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "- para uma mesma cidade, pegar o risco em função do tempo\n", + "- para um grupo de cidades plotar o risco em um mesmo gráfico" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "lista_municipios = df['municipio'].unique()\n", + "type(lista_municipios), len(lista_municipios)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "corumba = df[df['municipio'] == \"CORUMBA\"]\n", + "corumba" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "riscofogo = corumba['riscofogo']\n", + "diasemchuva = corumba['diasemchuva']" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "import numpy as np\n", + "corumba = corumba.replace(-999, np.nan)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "agrupado = corumba.groupby('datahora').mean()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "agrupado" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "datas = list(agrupado.index)\n", + "datas = [item[0:10] for item in datas]\n", + "datas\n", + "len(datas)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "import matplotlib.pyplot as plt\n", + "\n", + "fig, ax = plt.subplots(2, 1, figsize=(8, 6))\n", + "ax[0].plot(agrupado['riscofogo'], 'r')\n", + "ax[1].plot(agrupado['diasemchuva'], 'bo')\n", + "\n", + "# Preparar rótulos da primeira imagem\n", + "ax[0].set_xticks(range(len(datas))[::10]);\n", + "ax[0].set_xticklabels(datas[::10], rotation=30)\n", + "\n", + "# Preparar rótulos da segunda imagem\n", + "ax[1].set_xticks(range(len(datas))[::10]);\n", + "ax[1].set_xticklabels(datas[::10], rotation=30)\n", + "\n", + "fig.tight_layout()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "fig, ax = plt.subplots(figsize=(8, 4))\n", + "\n", + "ax.plot(agrupado['riscofogo'], 'r')\n", + "plt.draw()\n", + "\n", + "ax2 = ax.twinx() # instantiate a second axes that shares the same x-axis\n", + "ax2.plot(agrupado['diasemchuva'], 'bo')\n", + "\n", + "ax2.set_xticks(range(len(datas))[::10]);\n", + "ax.set_xticklabels(datas[::10], rotation=30);\n", + "\n", + "ax.legend(['Risco Fogo'], loc='upper left')\n", + "ax2.legend(['Dias sem chuva'], loc='upper right')\n", + "\n", + "ax.set_title('Dados INPE sobre queimadas em Corumba, MS')\n", + "ax.set_ylabel('Risco fogo')\n", + "ax2.set_ylabel('Dias sem chuva')\n", + "fig.tight_layout()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "[Voltar ao notebook principal](00-Tutorial_Python_Brasil_2020.ipynb)\n", + "\n", + "[Ir para o notebook SciPy](06-Tutorial_SciPy.ipynb)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.7.8" + } + }, + "nbformat": 4, + "nbformat_minor": 4 +} diff --git a/book/notebooks/06-Tutorial_SciPy.ipynb b/book/notebooks/06-Tutorial_SciPy.ipynb new file mode 100644 index 0000000..5143ce6 --- /dev/null +++ b/book/notebooks/06-Tutorial_SciPy.ipynb @@ -0,0 +1,163 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "from IPython.display import Image\n", + "Image(\"imagens/scipy_med.png\", width=100)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "tags": [ + "chapter" + ] + }, + "source": [ + "# SciPy \n", + "\n", + "A SciPy é um conjunto de bibliotecas para computação científica, incluindo:\n", + "- integração numérica\n", + "- interpolação\n", + "- processamento de sinais\n", + "- álgebra linear\n", + "- estatística\n", + "- otimização matemática\n", + "- tratamento de matrizes esparsas\n", + "\n", + "Sua base é a NumPy." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "import scipy as sp\n", + "print(sp.__doc__)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Exemplo: Minimização de funções" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "from scipy.optimize import fmin" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "func = lambda x : x**2" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "fmin(func, -1)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Exemplo: integração numérica" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Dada a equação diferencial ordinária" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "$$\\frac{dy(t)}{dt} = -y(t) + 1$$\n", + "$$y(0) = 0$$" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Encontre uma solução numérica para a equação diferencial com condição inicial associada. Expanda o horizonte temporal até encontrar uma solução estável. " + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "%matplotlib widget\n", + "import numpy as np\n", + "from scipy.integrate import odeint\n", + "import matplotlib.pyplot as plt\n", + "\n", + "# function that returns dy/dt\n", + "def model(y,t):\n", + " dydt = -y + 1.0\n", + " return dydt\n", + "\n", + "# initial condition\n", + "y0 = 0\n", + "\n", + "# time points\n", + "t = np.linspace(0,5)\n", + "\n", + "# solve ODE\n", + "y = odeint(model,y0,t)\n", + "\n", + "# plot results\n", + "fig = plt.figure()\n", + "plt.plot(t,y)\n", + "plt.xlabel('time')\n", + "plt.ylabel('y(t)')\n", + "plt.show()" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.7.8" + } + }, + "nbformat": 4, + "nbformat_minor": 4 +} diff --git a/book/notebooks/07-Exemplo_Regressao.ipynb b/book/notebooks/07-Exemplo_Regressao.ipynb new file mode 100644 index 0000000..2849380 --- /dev/null +++ b/book/notebooks/07-Exemplo_Regressao.ipynb @@ -0,0 +1,72 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Exemplo: Regressão usando 3 métodos" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "%matplotlib widget\n", + "import numpy as np\n", + "from scipy.interpolate import interp1d\n", + "import matplotlib.pyplot as plt\n", + "\n", + "# Criação de dados\n", + "x = np.arange(10)\n", + "y = x + 5*np.random.rand(10) - 6*np.random.rand(10)\n", + "\n", + "# Regressão Linear\n", + "(a_linear, b_linear) = np.polyfit(x, y, 1)\n", + "# Ajuste quadrático\n", + "(a_quad, b_quad, c_quad) = np.polyfit(x, y, 2)\n", + "# Interpolação\n", + "f = interp1d(x, y)\n", + "\n", + "# Gráfico\n", + "t = np.linspace(0, 9, 50)\n", + "plt.title('Exemplo: ajuste de curvas')\n", + "plt.plot(x, y, 'r*')\n", + "plt.plot(t, a_linear*t+b_linear,'g')\n", + "plt.plot(t, a_quad*t**2+b_quad*t+c_quad, 'm')\n", + "plt.plot(t, f(t), 'b')\n", + "plt.legend(['linear', 'quadrático', 'interpolação'])\n", + "plt.show();" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.7.8" + } + }, + "nbformat": 4, + "nbformat_minor": 4 +} diff --git a/book/notebooks/dados/Focos_BDQueimadas.zip b/book/notebooks/dados/Focos_BDQueimadas.zip new file mode 100644 index 0000000..d987135 Binary files /dev/null and b/book/notebooks/dados/Focos_BDQueimadas.zip differ diff --git a/book/notebooks/dados/who_covid_19_sit_rep_time_series.csv b/book/notebooks/dados/who_covid_19_sit_rep_time_series.csv new file mode 100644 index 0000000..ebf670b --- /dev/null +++ b/book/notebooks/dados/who_covid_19_sit_rep_time_series.csv @@ -0,0 +1,115 @@ +Province/States,Country/Region,WHO region,1/21/20,1/22/20,1/23/20,1/24/20,1/25/20,1/26/20,1/27/20,1/28/20,1/29/20,1/30/20,1/31/20,2/1/20,2/2/20,2/3/20,2/4/20,2/5/20,2/6/20,2/7/20,2/8/20,2/9/20,2/10/20,2/11/20,2/12/20,2/13/20,2/14/20,2/15/20,2/16/20,2/17/20,2/18/20,2/19/20,2/20/20,2/21/20,2/22/20,2/23/20,2/24/20,2/25/20,2/26/20,2/27/20,2/28/20,2/29/20,3/1/20,3/2/20,3/3/20 +Confirmed,Globally,,282,314,581,846,1320,2014,2798,4593,6065,7818,9826,11953,14557,17391,20630,24554,28276,31481,34886,37558,40554,43103,45171,46997,49053,50580,51857,71429,73332,75204,75748,76769,77794,78811,79331,80239,81109,82294,83652,85403,87137,88948,90870 +Confirmed,Mainland China,Western Pacific Region,278,309,571,830,1297,1985,2741,4537,5997,7736,9720,11821,14411,17238,20471,24363,28060,31211,34598,37251,40235,42708,44730,46550,48548,50054,51174,70635,72528,74280,74675,75569,76392,77042,77262,77780,78191,78630,78961,79394,79968,80174,80304 +Confirmed,Outside of China,,4,5,10,16,23,29,57,56,68,82,106,132,146,153,159,191,216,270,288,307,319,395,441,447,505,526,683,794,804,924,1073,1200,1402,1769,2069,2459,2918,3664,4691,6009,7169,8774,10566 +Suspected,Mainland China,Western Pacific Region,,,,,,,5794,6973,9239,12167,15238,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, +Severe,Mainland China,Western Pacific Region,,,,,,,461,976,1239,1370,1527,1795,2110,2296,2788,3219,3859,4821,6101,6188,6484,7333,8204,,,,,,,,,,,,,,,,,,,, +Deaths,Mainland China,Western Pacific Region,,,,,,,80,106,132,170,213,259,304,361,425,491,564,637,723,812,909,1017,1114,1260,1381,1524,1666,1772,1870,2006,2121,2239,2348,2445,2595,2666,2718,2747,2791,2838,2873,2915,2946 +Hubei ,China,Western Pacific Region,258,270,375,375,,,,,,,,7153,9074,11177,13522,16678,19665,22112,24953,27100,29631,31728,33366,34874,51968,54406,56249,58182,59989,61682,62031,62662,63454,64084,64287,64786,65187,65596,65914,66337,66907,67103,67217 +Guangdong,China,Western Pacific Region,14,17,26,32,,,,,,,,520,604,683,797,870,944,1018,1075,1120,1151,1177,1219,1241,1261,1295,1316,1322,1328,1331,1332,1333,1339,1342,1345,1347,1347,1347,1348,1349,1349,1350,1350 +Henan,China,Western Pacific Region,,1,1,1,,,,,,,,422,493,566,675,764,851,914,981,1033,1073,1105,1135,1169,1184,1212,1231,1246,1257,1262,1265,1267,1270,1271,1271,1271,1271,1272,1272,1272,1272,1272,1272 +Zhejiang,China,Western Pacific Region,,5,5,5,,,,,,,,599,661,724,829,895,954,1006,1048,1075,1104,1117,1131,1145,1155,1162,1167,1171,1172,1173,1175,1203,1205,1205,1205,1205,1205,1205,1205,1205,1205,1206,1213 +Hunan,China,Western Pacific Region,,1,1,1,,,,,,,,389,463,521,593,661,711,772,803,838,879,912,946,968,988,1001,1004,1006,1007,1008,1010,1011,1013,1016,1016,1016,1016,1017,1017,1018,1018,1018,1018 +Anhui,China,Western Pacific Region,,,,,,,,,,,,297,340,408,480,530,591,665,733,779,830,860,889,910,934,950,962,973,982,986,987,988,989,989,989,989,989,989,990,990,990,990,990 +Jiangxi,China,Western Pacific Region,,1,2,2,,,,,,,,286,333,391,476,548,600,661,698,740,771,804,844,872,900,913,925,930,933,934,934,934,934,934,934,934,934,934,935,935,935,935,935 +Shandong,China,Western Pacific Region,,1,1,1,,,,,,,,202,225,246,270,298,343,379,407,435,459,486,497,506,519,530,537,541,543,544,546,748,750,754,755,755,756,756,756,756,756,758,758 +Jiangsu,China,Western Pacific Region,,,,,,,,,,,,202,231,271,308,341,373,408,439,468,492,515,543,570,593,604,617,626,629,631,631,631,631,631,631,631,631,631,631,631,631,631,631 +Chongqing,China,Western Pacific Region,,1,5,5,,,,,,,,238,262,300,337,366,389,411,426,446,468,486,505,518,529,537,544,551,553,555,560,567,572,573,575,576,576,576,576,576,576,576,576 +Sichuan,China,Western Pacific Region,,1,2,2,,,,,,,,207,236,254,282,301,321,344,363,386,405,417,436,451,463,470,481,495,508,514,520,525,526,526,527,529,531,534,538,538,538,538,538 +Heilongjiang,China,Western Pacific Region,,,,,,,,,,,,80,95,118,155,190,227,277,282,307,331,360,378,395,418,425,445,457,464,470,476,479,479,480,480,480,480,480,480,480,480,480,480 +Beijing,China,Western Pacific Region,5,5,10,10,,,,,,,,156,183,212,228,253,274,297,315,326,337,342,352,366,372,375,380,381,387,393,395,396,399,399,399,400,400,410,410,411,413,414,414 +Shanghai,China,Western Pacific Region,1,2,9,9,,,,,,,,153,177,193,208,233,254,269,281,292,295,302,306,313,318,326,328,331,333,333,333,334,334,335,335,335,336,337,337,337,337,337,338 +Hebei,China,Western Pacific Region,,,,,,,,,,,,96,104,113,126,135,157,171,195,206,218,239,251,265,283,291,300,301,302,306,307,308,309,311,311,311,312,317,318,318,318,318,318 +Fujian,China,Western Pacific Region,,,,,,,,,,,,144,159,179,194,205,215,224,239,250,261,267,272,279,281,285,287,290,292,293,293,293,293,293,293,294,294,296,296,296,296,296,296 +Guangxi,China,Western Pacific Region,,,,,,,,,,,,100,111,127,139,150,168,172,183,195,210,215,222,222,226,235,237,238,242,244,245,246,249,249,251,252,252,252,252,252,252,252,252 +Shaanxi,China,Western Pacific Region,,,,,,,,,,,,101,116,128,142,165,173,184,195,208,213,219,225,229,230,232,236,240,240,242,245,245,245,245,245,245,245,245,245,245,245,245,245 +Yunnan,China,Western Pacific Region,,1,1,1,,,,,,,,91,99,109,117,122,128,135,138,140,141,149,154,155,162,168,169,171,172,172,172,174,174,174,174,174,174,174,174,174,174,174,174 +Hainan,China,Western Pacific Region,,,,,,,,,,,,57,63,70,79,89,100,111,123,128,136,142,145,157,157,162,162,162,163,163,168,168,168,168,168,168,168,168,168,168,168,168,168 +Guizhou,China,Western Pacific Region,,,,,,,,,,,,29,38,46,56,64,69,77,89,96,109,118,131,135,140,143,144,146,146,146,146,146,146,146,146,146,146,146,146,146,146,146,146 +Tianjin,China,Western Pacific Region,,2,2,2,,,,,,,,34,40,49,63,67,70,94,81,88,91,96,106,112,119,120,122,124,125,128,130,131,133,135,135,135,135,135,136,136,136,136,136 +Shanxi,China,Western Pacific Region,,,,,,,,,,,,47,56,66,74,81,90,96,104,115,119,122,124,126,126,127,128,129,130,131,131,132,132,132,132,133,133,133,133,133,133,133,133 +Liaoning,China,Western Pacific Region,,,,,,,,,,,,60,64,70,74,81,89,94,99,105,107,108,111,116,117,119,120,121,121,121,121,121,121,121,121,121,121,121,121,121,122,122,125 +Hong Kong,China,Western Pacific Region,,,1,2,5,5,8,8,8,10,12,13,14,15,15,18,21,24,26,26,36,42,49,50,53,56,56,57,60,62,65,68,68,70,74,81,85,91,93,94,95,98,101 +Jilin,China,Western Pacific Region,,,,,,,,,,,,17,21,31,42,54,59,65,69,78,80,81,83,84,86,88,88,89,89,90,91,91,91,91,93,93,93,93,93,93,93,93,93 +Gansu,China,Western Pacific Region,,,,,,,,,,,,35,45,51,56,57,62,70,71,81,85,86,86,87,90,90,90,91,91,91,91,91,91,91,91,91,91,91,91,91,91,91,91 +Xinjiang,China,Western Pacific Region,,,,,,,,,,,,18,23,24,29,32,36,39,42,45,49,55,59,63,65,70,71,73,76,76,76,76,76,75,76,76,76,76,76,76,76,76,76 +Inner Mongolia,China,Western Pacific Region,,,,,,,,,,,,23,26,33,37,42,46,49,50,54,58,58,60,61,63,68,70,72,73,75,75,75,75,75,75,75,75,75,75,75,75,75,75 +Ningxia,China,Western Pacific Region,,,,,,,,,,,,26,28,31,34,34,40,43,45,45,49,53,58,64,67,70,70,70,70,71,71,71,71,71,71,71,71,72,72,73,73,74,74 +Taiwan,China,Western Pacific Region,,1,1,1,3,3,4,7,8,8,9,10,10,10,10,11,11,16,16,17,18,18,18,18,18,18,18,20,22,23,24,26,26,23,28,31,32,32,34,39,39,40,42 +Qinghai,China,Western Pacific Region,,,,,,,,,,,,8,9,13,15,17,18,18,18,18,18,18,18,18,18,18,18,18,18,12,18,18,18,18,18,18,18,18,18,18,18,18,18 +Macau,China,Western Pacific Region,,,1,2,2,2,5,7,7,7,7,7,7,8,8,10,10,10,10,10,10,10,10,10,10,10,10,10,10,10,10,10,10,10,10,10,10,10,10,10,10,10,10 +Xizang,China,Western Pacific Region,,,,,,,,,,,,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1 +Unspecified*,China,Western Pacific Region,,,131,384,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, +,Japan,Western Pacific Region,1,1,1,1,3,3,4,6,7,11,14,17,20,20,20,33,25,25,25,26,26,26,28,29,33,41,53,59,65,73,85,93,105,132,144,157,164,186,210,230,239,254,268 +,Republic of Korea,Western Pacific Region,1,1,1,2,2,2,4,4,4,4,11,12,15,15,16,18,23,24,24,27,27,28,28,28,28,28,29,30,31,51,104,204,346,602,763,977,1261,1766,2337,3150,3736,4212,4812 +,Thailand,South-East Asia Region,2,2,2,4,4,5,5,14,14,14,14,19,19,19,19,25,25,25,32,32,32,33,33,33,33,34,34,35,35,35,35,35,35,35,35,37,40,40,40,42,42,42,43 +,United States of America,Region of the Americas,,,1,1,2,2,5,5,5,5,6,7,8,11,11,11,12,12,12,12,12,13,13,14,15,15,15,15,15,15,15,15,35,35,35,53,53,59,59,62,62,62,64 +,Vietnam,Western Pacific Region,,,,2,2,2,2,2,2,2,5,6,7,8,9,10,10,12,13,14,14,15,15,16,16,16,16,16,16,16,16,16,16,16,16,16,16,16,16,16,16,16,16 +,Singapore,Western Pacific Region,,,,1,3,3,4,7,7,10,13,16,18,18,18,24,28,30,33,40,43,45,47,50,58,67,72,75,77,81,84,85,86,89,89,90,91,93,96,98,102,106,108 +,Italy,European Region,,,,,,,,,,,2,2,2,2,2,2,2,3,3,3,3,3,3,3,3,3,3,3,3,3,3,3,9,76,124,229,322,400,650,888,1128,1689,2036 +,Nepal,South-East Asia Region,,,,,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1 +,Australia,Western Pacific Region,,,,,3,3,4,5,7,7,9,12,12,12,12,13,14,15,15,15,15,15,15,15,15,15,15,15,15,15,15,17,21,21,21,22,23,23,23,24,25,27,33 +,Malaysia,Western Pacific Region,,,,,,3,4,4,4,7,8,8,8,8,10,10,12,14,15,17,18,18,18,18,19,21,22,22,22,22,22,22,22,22,22,22,22,22,24,24,24,24,29 +,Canada,Region of the Americas,,,,,,,1,2,3,3,3,4,4,4,4,5,5,7,7,7,7,7,7,7,7,7,7,7,8,8,8,8,8,9,9,10,10,11,11,14,19,19,27 +,Cambodia,Western Pacific Region,,,,,,,,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1 +,France,European Region,,,,,3,3,3,3,4,5,6,6,6,6,6,6,6,6,6,11,11,11,11,11,11,11,12,12,12,12,12,12,12,12,12,12,12,18,38,57,100,100,191 +,Sri Lanka,South-East Asia Region,,,,,,,,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1 +,Iran,Eastern Mediterranean Region,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,5,18,28,43,61,95,141,245,388,593,978,1501 +,India,South-East Asia Region,,,,,,,,,,1,1,1,2,3,3,3,3,3,3,3,3,3,3,3,3,3,3,3,3,3,3,3,3,3,3,3,3,3,3,3,3,3,5 +,Germany,European Region,,,,,,,,1,4,4,5,7,8,10,12,12,12,13,14,14,14,14,16,16,16,16,16,16,16,16,16,16,16,16,16,16,18,21,26,57,57,129,157 +,Philippines,Western Pacific Region,,,,,,,,,,1,1,1,2,2,2,3,3,3,3,3,3,3,3,3,3,3,3,3,3,3,3,3,3,3,3,3,3,3,3,3,3,3,3 +,Spain,European Region,,,,,,,,,,,,1,1,1,1,1,1,1,1,1,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,12,25,32,45,45,114 +,United Kingdom,European Region,,,,,,,,,,,,2,2,2,2,2,2,3,3,3,4,8,8,9,9,9,9,9,9,9,9,9,9,9,9,13,13,13,16,20,23,36,39 +,Sweden,European Region,,,,,,,,,,,,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,2,7,12,13,14,15 +,Switzerland,European Region,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1,1,6,10,18,26,30 +,Austria,European Region,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,2,4,5,10,10,18 +,Norway,European Region,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1,4,6,15,19,25 +,Kuwait,Eastern Mediterranean Region,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3,8,12,43,43,45,45,56,56 +,Bahrain,Eastern Mediterranean Region,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,8,26,33,33,38,40,47,49 +,United Arab Emirates,Eastern Mediterranean Region,,,,,,,,,4,4,4,4,5,5,5,5,5,5,7,7,7,8,8,8,8,8,8,9,9,9,9,9,11,13,13,13,13,13,19,19,19,21,21 +,Israel,European Region,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1,1,1,2,2,2,3,5,7,7,10 +,Iraq,Eastern Mediterranean Region,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1,5,6,7,8,13,19,26 +,Oman,Eastern Mediterranean Region,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,4,4,6,6,6,6,6 +,Lebanon,Eastern Mediterranean Region,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1,1,1,1,1,2,2,2,2,10,13 +,Pakistan,Eastern Mediterranean Region,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,2,2,4,4,5 +,Egypt,Eastern Mediterranean Region,,,,,,,,,,,,,,,,,,,,,,,,,,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,2,2 +,Croatia,European Region,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,3,3,5,7,7,9 +,Greece,European Region,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1,3,3,3,7,7 +,Finland,European Region,,,,,,,,,,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,2,2,2,2,6,7 +,Algeria,African Region,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1,1,1,1,1,1,5 +,Brazil,Region of the Americas,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1,1,1,2,2,2 +,Russian,European Region,,,,,,,,,,,,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,3 +,Belgium,European Region,,,,,,,,,,,,,,,,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,8 +,Denmark,European Region,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1,1,2,3,4,5 +,Estonia,European Region,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1,1,1,1,1,1 +,Georgia,European Region,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1,1,2,3,3,3 +,North Macedonia,European Region,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1,1,1,1,1,1 +,Romania,European Region,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1,1,3,3,3,3 +,Afghanistan,Eastern Mediterranean Region,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1,1,1,1,1,1,1,1 +,New Zealand,Western Pacific Region,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1,1,1,1,2 +,Belarus,European Region,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1,1,1,1,1 +,Lithuania,European Region,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1,1,1,1,1 +,Netherlands,European Region,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1,2,7,13,18 +,Nigeria,African Region,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1,1,1,1,1 +,Mexico,Region of the Americas,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,2,5,5 +,San Marino,European Region,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1,1,1,8 +,Azerbaijan,European Region,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3,3,3 +,Ireland,European Region,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1,1,1 +,Monaco,European Region,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1,1,1 +,Qatar,Eastern Mediterranean Region,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1,3,7 +,Ecuador,Region of the Americas,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1,1,6 +,Czechia,European Region,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3,3 +,Iceland,European Region,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,9 +,Armenia,European Region,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1,1 +,Luxembourg,European Region,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1,1 +,Indonesia,South-East Asia Region,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,2 +,Dominican Republic,Region of the Americas,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1,1 +,Portugal,European Region,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2 +,Andorra,European Region,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1 +,Latvia,European Region,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1 +,Jordan,Eastern Mediterranean Region,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1 +,Morocco,Eastern Mediterranean Region,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1 +,Saudi Arabia,Eastern Mediterranean Region,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1 +,Tunisia,Eastern Mediterranean Region,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1 +,Senegal,African Region,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1 +Case on an international conveyance,Other,Other,,,,,,,,,,,,,,,,,20,61,64,64,70,135,175,174,218,218,355,454,454,542,621,634,634,634,695,691,691,705,705,705,706,706,706 \ No newline at end of file diff --git a/book/notebooks/imagens/comp_cientifica.png b/book/notebooks/imagens/comp_cientifica.png new file mode 100644 index 0000000..fa65fc5 Binary files /dev/null and b/book/notebooks/imagens/comp_cientifica.png differ diff --git a/book/notebooks/imagens/comp_cientifica_2.png b/book/notebooks/imagens/comp_cientifica_2.png new file mode 100644 index 0000000..93f1f60 Binary files /dev/null and b/book/notebooks/imagens/comp_cientifica_2.png differ diff --git a/book/notebooks/imagens/logo2_compressed.svg b/book/notebooks/imagens/logo2_compressed.svg new file mode 100644 index 0000000..1cb032d --- /dev/null +++ b/book/notebooks/imagens/logo2_compressed.svg @@ -0,0 +1 @@ + \ No newline at end of file diff --git a/book/notebooks/imagens/numpylogo.png b/book/notebooks/imagens/numpylogo.png new file mode 100644 index 0000000..8187b49 Binary files /dev/null and b/book/notebooks/imagens/numpylogo.png differ diff --git a/book/notebooks/imagens/scipy_med.png b/book/notebooks/imagens/scipy_med.png new file mode 100644 index 0000000..9873107 Binary files /dev/null and b/book/notebooks/imagens/scipy_med.png differ diff --git a/book/requirements.txt b/book/requirements.txt new file mode 100644 index 0000000..9996d87 --- /dev/null +++ b/book/requirements.txt @@ -0,0 +1,5 @@ +jupyter-book +matplotlib +numpy +scipy +ipympl