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| { | |
| "cells": [ | |
| { | |
| "cell_type": "code", | |
| "execution_count": 1, | |
| "metadata": { | |
| "slideshow": { | |
| "slide_type": "skip" | |
| } | |
| }, | |
| "outputs": [], | |
| "source": [ | |
| "%matplotlib notebook\n", | |
| "\n", | |
| "import matplotlib.pyplot as plt\n", | |
| "from IPython.display import Image" | |
| ] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "metadata": { | |
| "slideshow": { | |
| "slide_type": "slide" | |
| } | |
| }, | |
| "source": [ | |
| "# Introdução ao filtro de Kalman\n", | |
| "\n", | |
| "1. Criado por Rudolph E. Kalman em 1960\n", | |
| "2. Desenvolvido inicialmente como uma solução recursiva para filtragem linear de dados discretos.\n", | |
| "3. Utiliza equações matemáticas que implementam um estimador preditivo de estados, buscando corrigir interativamente a resposta de um determinado sistema através de multiplas variáveis relacionadas a ele.\n", | |
| "4. Aplicado a Sistemas de Inferências, processamento de imagens, supervisor de eventos discretos." | |
| ] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "metadata": { | |
| "slideshow": { | |
| "slide_type": "slide" | |
| } | |
| }, | |
| "source": [ | |
| "1. Considere a medição de temperatura de uma sala. \n", | |
| "2. Nesse caso, um sensor pode fornecer a temperatura $t=30$\n", | |
| "3. Sabemos que um sensor terá uma determinada precisão, ou seja, toda media de valor envolve algum grau de erro.\n", | |
| "4. Imagine que um fabricante diga que, para uma determinada faixa de temperatura, o erro do sensor é de 5 graus.\n", | |
| "5. Ou seja, 5 graus acima ou abaixo.\n", | |
| "6. Assim a temperatura anterior pode estar entre $25^{o}$ e $35^{o}$." | |
| ] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "metadata": { | |
| "slideshow": { | |
| "slide_type": "slide" | |
| } | |
| }, | |
| "source": [ | |
| "1. Vamos chamar o erro de ruído, assim, podemos definir\n", | |
| " - <center> $Temperatura_{observada} = temperatura_{medida}+ruido$</center>\n", | |
| "2. Em alguns tipos de sistema, o estado atual de uma variável é dependente do seu estado anterior.\n", | |
| "3. Nesse caso, podemos estimar o valor atual, baseado no valor anterior.\n", | |
| "4. Por exemplo, sabendo que a temperatura está decaindo 70% a cada medição, temos:\n", | |
| " - <center> $Temperatura_{prevista} = temperatura_{anterior}\\times 0.7+ruido$</center>" | |
| ] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "metadata": { | |
| "slideshow": { | |
| "slide_type": "slide" | |
| } | |
| }, | |
| "source": [ | |
| "1. Agora temos duas equações, uma para o estado real do sistema e outra com o estado previsto:\n", | |
| " - <center> $Temperatura_{observada} = temperatura_{medida}+ruido$</center>\n", | |
| " - <center> $Temperatura_{prevista} = temperatura_{anterior}\\times 0.7+ruido$</center>\n", | |
| "2. O Filtro de Kalman servirá para integrarmos estas duas equações de modo a prover uma estimativa adequada, com baixo valor de ruído, dentro do possível.\n" | |
| ] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "metadata": { | |
| "slideshow": { | |
| "slide_type": "slide" | |
| } | |
| }, | |
| "source": [ | |
| "## Quando utilizar\n", | |
| "\n", | |
| "1. Possibilidade de obter medições sobre um determinado evento a uma taxa constante\n", | |
| "2. As medidas têm um erro que segue uma distribuição normal (erro gaussiano).\n", | |
| "3. A matemática que regula a situação é conhecida\n", | |
| "4. O processo que será medido pode ser descrito como um sistema linear\n", | |
| "5. Busca-se uma estimativa do que está realmente acontecendo." | |
| ] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "metadata": { | |
| "slideshow": { | |
| "slide_type": "slide" | |
| } | |
| }, | |
| "source": [ | |
| "## Uma visão geral do filtro de Kalman\n", | |
| "\n", | |
| "1. Vamos assumir o filtro de Kalman para uma única variável.\n", | |
| "2. Vamos considerar três etapas distintas para o filtro:\n", | |
| " - Cálculo do Ganho de Kalman\n", | |
| " - Cálculo do Estado Atual\n", | |
| " - Cáculo do Novo Erro na Estimativa" | |
| ] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "metadata": { | |
| "slideshow": { | |
| "slide_type": "slide" | |
| } | |
| }, | |
| "source": [ | |
| "### Cálculo do Ganho de Kalman\n", | |
| "1. O Ganho de Kalman é a importância relativa ao erro associado ao valor estimado e o erro associado à medição, com relação a variável.\n", | |
| "2. Assumindo:\n", | |
| " - <center>$K = \\frac{ruido_{estimativa}}{ruido_{estimativa}+ruido_{medicao}}$</center>\n", | |
| "3. $K$ - ganho de Kalman\n", | |
| "4. $ruido_{estimativa}$ - erro da estimativa\n", | |
| "5. $ruido_{medicao}$ - erro da medição\n", | |
| "6. Quando $K$ próximo de 1, as medições são acuradas e a estimativa instável. $K$ próximo de zero, as medições são inacuradas e as estimativas estáveis" | |
| ] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "metadata": { | |
| "slideshow": { | |
| "slide_type": "slide" | |
| } | |
| }, | |
| "source": [ | |
| "### Cálculo do Estado Atual\n", | |
| "1. Considerando o estado anterior, o Ganho de Kalman e a medição mais atual obtida de uma variável, a estimativa do valor atual do sistema será:\n", | |
| " -<center>$estado_{atual} = estado_{anterior}+K(medicao-estado_{anterior})$</center>\n", | |
| "2. $estado_{atual}$ - estimativa do estado atual do sistema\n", | |
| "3. $estado_{anterior}$ - estivativa do estado anterior\n", | |
| "4. $K$ - Ganho de Kalman\n", | |
| "5. $medicao$ - medição mais atual do sistema" | |
| ] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "metadata": { | |
| "slideshow": { | |
| "slide_type": "slide" | |
| } | |
| }, | |
| "source": [ | |
| "### Calcular o novo erro na estimativa\n", | |
| "1. A partir do estado atual da variável, calcula-se o novo erro que serve novamente de entrada para o Ganho de Kalman.\n", | |
| " - <center>$ruido_{atual} = (1 - K)ruido_{anterior}$</center>\n", | |
| "3. $ruido_{atual}$ - erro atual\n", | |
| "4. $K$ - Ganho de Kalman calculado anteriormente\n", | |
| "5. $ruido_{anterior}$ - erro do estado anterior do sistema" | |
| ] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "metadata": { | |
| "slideshow": { | |
| "slide_type": "slide" | |
| } | |
| }, | |
| "source": [ | |
| "### Exemplos\n", | |
| "1. Considere as seguintes condições:\n", | |
| " - Temperatura real: ?\n", | |
| " - Temperatura inicial estimada: 28 $^{o}C$\n", | |
| " - Erro na estimativa: $\\pm 2 ^{0}C$\n", | |
| " - Medição inicial: 32 $^{o}C$\n", | |
| " - Erro na medição: $\\pm 1 ^{o}C$" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 2, | |
| "metadata": { | |
| "slideshow": { | |
| "slide_type": "slide" | |
| } | |
| }, | |
| "outputs": [ | |
| { | |
| "name": "stdout", | |
| "output_type": "stream", | |
| "text": [ | |
| "0.67 30.67 0.67\n" | |
| ] | |
| } | |
| ], | |
| "source": [ | |
| "def ganho_kalman(erro_estimado, erro_medicao):\n", | |
| " k = erro_estimado / (erro_estimado + erro_medicao)\n", | |
| " return k\n", | |
| "\n", | |
| "def estado_atual(estado_anterior, k, medicao):\n", | |
| " est_atual = estado_anterior + k * (medicao - estado_anterior)\n", | |
| " return est_atual\n", | |
| "\n", | |
| "def ruido_atual(erro_anterior, k):\n", | |
| " erro_atual = (1-k) * erro_anterior\n", | |
| " return erro_atual\n", | |
| " \n", | |
| "estado_inicial = 28\n", | |
| "medida = 32\n", | |
| "erro_estimado = 2\n", | |
| "erro_medido = 1\n", | |
| "\n", | |
| "k = ganho_kalman(erro_estimado, erro_medido)\n", | |
| "estado_a = estado_atual(estado_inicial, k, medida)\n", | |
| "erro_a = ruido_atual(erro_estimado, k)\n", | |
| "print(round(k, 2), round(estado_a, 2), round(erro_a, 2))\n", | |
| " " | |
| ] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "metadata": { | |
| "slideshow": { | |
| "slide_type": "slide" | |
| } | |
| }, | |
| "source": [ | |
| "### Exercício:\n", | |
| "\n", | |
| "\n", | |
| "1. Considere os seguintes valores:\n", | |
| " - Temperatura real: ?\n", | |
| " - Temperatura inicial estimada: 28$^{o}$\n", | |
| " - Erro na estimativa: $\\pm 2^{o}$\n", | |
| " - Medições: 32$^{o}$, 35$^{o}$, 33$^{o}$\n", | |
| " - Erro na medição $\\pm$ 1$^{o}$\n", | |
| "2. Use as equações anteriores para calcular a temperatura real, assumindo que as medições foram obidas em sequências." | |
| ] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "metadata": { | |
| "slideshow": { | |
| "slide_type": "slide" | |
| } | |
| }, | |
| "source": [ | |
| "## Modelo Multidimensional\n", | |
| "\n", | |
| "1. Casos reais normalmente envolvem mais de uma variável.\n", | |
| "2. Nesse caso, tabralhar com matrizes ajuda no processo de cálculo.\n" | |
| ] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "metadata": { | |
| "slideshow": { | |
| "slide_type": "slide" | |
| } | |
| }, | |
| "source": [ | |
| "### Matriz de Estado\n", | |
| "\n", | |
| "1. Em Filtro de Kalman, o Estado do Sistema é um conjunto de variáveis que iremos analisar do sistema de acordo com nossa conveniência.\n", | |
| "2. A matriz de estado será representada po $X$\n", | |
| "3. Por exemplo, considere um objeto se movendo a velocidade constante, nesse caso temos interesse em conhecer a posição $x$ e a velocidade $\\dot{x}$.\n", | |
| " - <center> $X = \\begin{bmatrix} x \\\\ \\dot{x} \\end{bmatrix}$</center>\n", | |
| "4. OBS: $\\dot{x} = \\frac{dx}{dt}$ - Derivada primeira da posição com relação ao tempo - velocidade." | |
| ] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "metadata": { | |
| "slideshow": { | |
| "slide_type": "slide" | |
| } | |
| }, | |
| "source": [ | |
| "1. Para um objeto que se mova num ambiente bidimensional (x-y):\n", | |
| " - <center> $X = \\begin{bmatrix} x \\\\ y \\\\ \\dot{x} \\\\ \\dot{y} \\end{bmatrix}$</center>\n", | |
| "2. Em 3D\n", | |
| " - <center> $X = \\begin{bmatrix} x \\\\ y \\\\ z\\\\ \\dot{x} \\\\ \\dot{y} \\\\ \\dot{z}\\end{bmatrix}$</center>\n" | |
| ] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "metadata": { | |
| "slideshow": { | |
| "slide_type": "slide" | |
| } | |
| }, | |
| "source": [ | |
| "### Matriz de Covariância\n", | |
| "1. Tem como objetivo calcular o erro na estimativa do estado\n", | |
| "2. É produzido pela combinação da variância do erro das estimativas\n", | |
| "3. Exemplos:\n", | |
| " - <center> 1D: $P = \\begin{bmatrix}\\sigma_{x}^{2} \\end{bmatrix}$</center> \n", | |
| " <br>\n", | |
| " <br>\n", | |
| " - <center> 2D: $P = \\begin{bmatrix}\n", | |
| " \\sigma_{x}^{2} & \\sigma_{x}\\sigma_{y}\\\\\n", | |
| " \\sigma_{y}\\sigma_{x} & \\sigma_{y}^{2}\n", | |
| " \\end{bmatrix}$\n", | |
| " </center> \n", | |
| " <br>\n", | |
| " <br>\n", | |
| " - <center> 2D: $P = \\begin{bmatrix}\n", | |
| " \\sigma_{x}^{2} & \\sigma_{x}\\sigma_{y} & \\sigma_{x}\\sigma_{z} \\\\\n", | |
| " \\sigma_{y}\\sigma_{x} & \\sigma_{y}^{2} & \\sigma_{y}\\sigma_{z} \\\\\n", | |
| " \\sigma_{z}\\sigma_{x} & \\sigma_{z}\\sigma_{y} & \\sigma_{z}^{2} \n", | |
| " \\end{bmatrix}$\n", | |
| " </center> \n", | |
| " <br>\n", | |
| " <br>\n", | |
| "4. $\\sigma_{x}^{2}$ - é a variância de uma determinada variável $x$\n", | |
| "5. $\\sigma_{x}\\sigma_{y}$ - é a covariância entre duas variáveis $x$ e $y$" | |
| ] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "metadata": { | |
| "slideshow": { | |
| "slide_type": "slide" | |
| } | |
| }, | |
| "source": [ | |
| "#### Exercício\n", | |
| "1. Calcule a matrix de covariância para um objeto que se move ao longo do eixo $x$ com as seguintes características:\n", | |
| " - $x_{0} = 50$ com variância de $0,5m$\n", | |
| " - $v_{0} = 5 $ ms$^{-1}$ com variância de $0,2$ms$^{-1}$" | |
| ] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "metadata": { | |
| "slideshow": { | |
| "slide_type": "slide" | |
| } | |
| }, | |
| "source": [ | |
| "### Construíndo o modelo\n", | |
| "1. $X$ é a matriz de estado, representando as variáves que serão analizadas\n", | |
| "2. $P$ é a matriz de covariância do processo (que representa o erro das estimativas do sistema)" | |
| ] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "metadata": { | |
| "slideshow": { | |
| "slide_type": "slide" | |
| } | |
| }, | |
| "source": [ | |
| "### Etapara de predição\n", | |
| "\n", | |
| "1. Estitamtiva do novo estado do sistema:\n", | |
| " <center>$X_{est} = AX_{k-1}+Bu_{k}+w_{k}$</center>\n", | |
| "2. $X_{est}$ - estado atual\n", | |
| "3. $X_{k-1}$ - EStado anterior\n", | |
| "4. $u_{k}$ - Sinal de controlo\n", | |
| "5. $w_{k}$ - Ruído do processo\n", | |
| "6. $A$ e $B$ são, respectivamente, matriz de transição e matriz de controle." | |
| ] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "metadata": { | |
| "slideshow": { | |
| "slide_type": "slide" | |
| } | |
| }, | |
| "source": [ | |
| "1. Estitamtiva da nova matriz de covariância:\n", | |
| "<center>$P_{k}=AP_{k-1}A^{T}+Q_{k}$</center>\n", | |
| "2. $Q$ matriz de covariância do ruído do processo\n" | |
| ] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "metadata": { | |
| "slideshow": { | |
| "slide_type": "slide" | |
| } | |
| }, | |
| "source": [ | |
| "### Estimativa do estado do sistema\n", | |
| "1. Considere que desejamos rastrear o comportamento de um objeto no tempo.\n", | |
| "2. A expressão para o deslocamento é:\n", | |
| "<center>$x = x_{0} + v_{0}t+\\frac{1}{2}a_{0}t^{2}$</center>\n", | |
| "<br>\n", | |
| "<br>\n", | |
| "<center>$v{_x} = v_{0}t+a_{0}t$</center>\n", | |
| "<br>\n", | |
| "<br>\n", | |
| "<center>$a_{x} = a_{0}$</center>\n", | |
| "<br>\n", | |
| "<br>\n", | |
| "3. Na forma matricial:\n", | |
| " - <center> $\\begin{bmatrix} x \\\\ v_{x} \\\\ a_{x} \\end{bmatrix} = \n", | |
| " \\begin{bmatrix}\n", | |
| " 1 & t & \\frac{1}{2}t^{2} \\\\\n", | |
| " 0 & 1 & t \\\\\n", | |
| " 0 & 0 & 1\n", | |
| " \\end{bmatrix}\\begin{bmatrix} x_{0} \\\\ v_{0} \\\\ a_{0} \\end{bmatrix}$\n", | |
| " </center> \n", | |
| "\n" | |
| ] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "metadata": { | |
| "slideshow": { | |
| "slide_type": "slide" | |
| } | |
| }, | |
| "source": [ | |
| "1. Estamos interessados nas variáveis $x$ e $\\dot{x}$, nesse caso, para o filto de Kalman temos:\n", | |
| "<br>\n", | |
| "<br>\n", | |
| "<center>$A = \\begin{bmatrix}\n", | |
| " 1 & t \\\\ \n", | |
| " 0 & 1 \\\\\n", | |
| " \\end{bmatrix}$</center>\n", | |
| "<br>\n", | |
| "<br>\n", | |
| "2. A componente $Bu_{k}$ expressa a matriz de controle, que determina como o estado da variável se modifica. $B$ é o modelo de controle e $u_{k}$ é a variável de controle. No nosso caso, estão relacionados com a aceleração do sistema:\n", | |
| "<br>\n", | |
| "<br>\n", | |
| "<center>$B = \\begin{bmatrix} \\frac{1}{2}t^{2} \\\\ t \\end{bmatrix}$</center>\n", | |
| "<br>\n", | |
| "<br>\n", | |
| "<center>$u = \\begin{bmatrix} a \\end{bmatrix}$</center>" | |
| ] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "metadata": { | |
| "slideshow": { | |
| "slide_type": "slide" | |
| } | |
| }, | |
| "source": [ | |
| "1. A equação de predição para o filtro de Kalman será:\n", | |
| " <center>$X_{atual} = AX_{anterior}+Bu+w$</center>\n", | |
| " <br>\n", | |
| " <br>\n", | |
| " <center>$X_{atual} = \\begin{bmatrix}\n", | |
| " 1 & t \\\\ \n", | |
| " 0 & 1 \n", | |
| " \\end{bmatrix} \\begin{bmatrix}\n", | |
| " x \\\\ \n", | |
| " \\dot{x}\n", | |
| " \\end{bmatrix}+\\begin{bmatrix} \\frac{1}{2}t^{2} \\\\ t \\end{bmatrix}\\begin{bmatrix} a \\end{bmatrix}+w$</center>\n", | |
| "2. O componente $w$ representa o ruído do processo.\n", | |
| "\n" | |
| ] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "metadata": { | |
| "slideshow": { | |
| "slide_type": "slide" | |
| } | |
| }, | |
| "source": [ | |
| "#### Exercício\n", | |
| "1. Expresse A, B e u para um sistema 3D.\n", | |
| "2. Considere o lançamento de um projétil. Desprezando a resistência do ar, temos que as equações do movimento são:\n", | |
| "<center> $x = x_{0}+v_{0}t$;</center>\n", | |
| "<br>\n", | |
| "<br>\n", | |
| "<center>$y = y_{0}+v_{0}t - \\frac{1}{2}gt^{2}$</center>\n", | |
| "<br>\n", | |
| "<br>\n", | |
| "Escreva a equação de $A$, $B$ e $u$ para esse novo sistema. Escreva a equação de previsão, para o filtro de Kalman.\n" | |
| ] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "metadata": { | |
| "slideshow": { | |
| "slide_type": "slide" | |
| } | |
| }, | |
| "source": [ | |
| "### Etapa de Correção:\n", | |
| "1. Calculamos o Ganho de Kalman:\n", | |
| "<br>\n", | |
| "<br>\n", | |
| "<center>$K = \\frac{P_{k}H}{HP_{k}H^{T}+R}$</center>\n", | |
| "<br>\n", | |
| "<br>\n", | |
| " - Com $H$ sendo a matriz de observações e $R$ a matriz de covariância de erro no sensor\n", | |
| "2. Em seguida, atualizam-se os valores atuais do sistema utiliando os dados de medição:\n", | |
| "<br>\n", | |
| "<br>\n", | |
| "<center>$X_{k}=X_{est}+K(Y-HX_{est})$</center>\n", | |
| "<br>\n", | |
| "<br>\n", | |
| " -Com $Y=CX_{med}+z$, em que $X_{med}$ é a medição, $z$ é o erro na medição. $C$ será:\n", | |
| " <br>\n", | |
| " <br>\n", | |
| " - <center>$C = \\begin{bmatrix} 1 & 0 \\end{bmatrix}$</center> se estivermos observando apenas a posição da variável $X$;\n", | |
| " <br>\n", | |
| " <br>\n", | |
| " - <center>$C = \\begin{bmatrix} 0 & 1 \\end{bmatrix}$</center> se estivermos observando apenas a velocidade da variável $X$;\n", | |
| " <br>\n", | |
| " <br>\n", | |
| " - <center>$C = \\begin{bmatrix} 1 & 0 \\\\ 0 & 1\\end{bmatrix}$</center> para medição 1D;\n", | |
| " \n", | |
| "3. Em terceiro lugar, atualizam-se os valores da matriz covariância:\n", | |
| "<center>$P_{k} = (I-KH)P_{est}$</center>\n", | |
| "<br>\n", | |
| "<br>\n", | |
| "sendo $I$ a matriz identidade." | |
| ] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "metadata": { | |
| "slideshow": { | |
| "slide_type": "slide" | |
| } | |
| }, | |
| "source": [ | |
| "### Implementação das equação do filtro de Kalman em Python\n", | |
| "1. Previsão: \n", | |
| " <center>$X_{atual} = AX_{anterior}+Bu+w$</center>\n", | |
| "2. Previsão da covariância:\n", | |
| "<center>$P_{k}=AP_{k-1}A^{T}+Q_{k}$</center>\n", | |
| "3. Ganho de Kalman\n", | |
| "<center>$K = \\frac{P_{k}H}{HP_{k}H^{T}+R}$</center>\n", | |
| "4. Matrix de observação\n", | |
| "<center>$Y=CX_{med}+z$</center>\n", | |
| "5. Estado novo:\n", | |
| "<center>$X_{k}=X_{est}+K(Y-HX_{est})$</center>\n", | |
| "6. Nova matriz de covariância\n", | |
| "<center>$P_{k} = (I-KH)P_{est}$</center>\n", | |
| "\n" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 3, | |
| "metadata": { | |
| "slideshow": { | |
| "slide_type": "slide" | |
| } | |
| }, | |
| "outputs": [], | |
| "source": [ | |
| "from numpy import array, dot\n", | |
| "from numpy import linalg\n", | |
| "\n", | |
| "def previsao(A, B, X_prev, u, w):\n", | |
| " X_atual = dot(A, X_prev) + dot(B, u) + w\n", | |
| " return X_atual\n", | |
| "\n", | |
| "def privisao_covariancia(A, P, D):\n", | |
| " P = dot(dot(dot(A, P),A.transpose()),D)\n", | |
| " return P\n", | |
| "\n", | |
| "def ganho_de_kalman(H, R, P):\n", | |
| " S = dot(dot(H, P), H.transpose()) + R\n", | |
| " invS = linalg.inv(S)\n", | |
| " K = dot(dot(P, H.transpose()),invS)\n", | |
| " return K\n", | |
| "\n", | |
| "def observacoes(n, X, H):\n", | |
| " Z = array([n])\n", | |
| " Y = Z.transpose() - dot(H, X)\n", | |
| " return Y\n", | |
| "\n", | |
| "def novo_estado(Y, x, K):\n", | |
| " x = x + dot(K,Y)\n", | |
| " return x\n", | |
| "\n", | |
| "def nova_covariancia(P, I, K, H):\n", | |
| " P0 = I - dot(K, H)\n", | |
| " P = dot(P0,P)\n", | |
| " return P" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 4, | |
| "metadata": { | |
| "slideshow": { | |
| "slide_type": "slide" | |
| } | |
| }, | |
| "outputs": [ | |
| { | |
| "name": "stdout", | |
| "output_type": "stream", | |
| "text": [ | |
| "Kalman Filter Example- (x)(x')\n", | |
| "One-diretional car - (x)(x')\n", | |
| "--------------------------------------\n" | |
| ] | |
| } | |
| ], | |
| "source": [ | |
| "print (\"Kalman Filter Example- (x)(x')\")\n", | |
| "print (\"One-diretional car - (x)(x')\")\n", | |
| "print (\"--------------------------------------\")\n", | |
| "\n", | |
| "measurements = [[20,5], # (x)(x')- 0 s\n", | |
| " [34,10], # (x)(x')- 1 s\n", | |
| " [45,11], # (x)(x')- 2 s\n", | |
| " [60,15], # (x)(x')- 3 s\n", | |
| " [70,16], # (x)(x')- 4 s\n", | |
| " [80,14], # (x)(x')- 5 s\n", | |
| " [95,20]] # (x)(x')- 6 s\n", | |
| "\n", | |
| "##Initial Conditions \n", | |
| "\n", | |
| "dt = 1 # time lapse \n", | |
| "a = 2 # example acceleration (m/s^2) \n", | |
| "sd_x = 2 # example standard-deviation for estimated position - x (m)\n", | |
| "sd_v = 3 # example standard-deviation for estimated velocity - x' (m/s)\n", | |
| "d_x = 1 # example standard-deviation for measured position - x (m)\n", | |
| "d_v = 2 # example standard-deviation for measured velocity - x' (m/s)\n", | |
| "\n", | |
| "\n" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 5, | |
| "metadata": { | |
| "slideshow": { | |
| "slide_type": "slide" | |
| } | |
| }, | |
| "outputs": [], | |
| "source": [ | |
| "##Matrices\n", | |
| "###State Matrix\n", | |
| "A = array([[1.,dt],\n", | |
| " [0.,1.]]) \n", | |
| "\n", | |
| "B = array([[0.5* dt * dt],\n", | |
| " [dt]])\n", | |
| "\n", | |
| "###Initial state \n", | |
| "x = array([[0.],\n", | |
| " [0.]])\n", | |
| "\n", | |
| "u = array([[a]])\n", | |
| "\n", | |
| "w = array([[0.],\n", | |
| " [0.]])\n", | |
| "\n", | |
| "C = array([[1,0],\n", | |
| " [0,0]])\n", | |
| "\n", | |
| "D = array([[1,0],\n", | |
| " [0,1]])\n", | |
| "\n", | |
| "H = array([[1,0],\n", | |
| " [0,1]])\n", | |
| "\n", | |
| "I = array([[1,0],\n", | |
| " [0,1]])\n" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 6, | |
| "metadata": { | |
| "slideshow": { | |
| "slide_type": "slide" | |
| } | |
| }, | |
| "outputs": [], | |
| "source": [ | |
| "### Predicted Process Covariance Matrix\n", | |
| "\n", | |
| "P = array([[sd_x*sd_x, 0],\n", | |
| " [0, sd_v*sd_v]])\n", | |
| "\n", | |
| "### Measurement Covariance Matrix \n", | |
| "\n", | |
| "R = array([[d_x * d_x, 0],\n", | |
| "[0, d_v * d_v]])\n", | |
| "\n", | |
| "prsao = []\n", | |
| "crKalman = []\n", | |
| "tempo = []\n", | |
| "\n" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 7, | |
| "metadata": { | |
| "slideshow": { | |
| "slide_type": "slide" | |
| } | |
| }, | |
| "outputs": [], | |
| "source": [ | |
| "###Loop\n", | |
| "for n in range(len(measurements)-1):\n", | |
| " # Passo de Previsão\n", | |
| " x = previsao(A, B, x, u, w) \n", | |
| " P = privisao_covariancia(A,P,D)\n", | |
| " prsao.append(x)\n", | |
| " \n", | |
| " # CPasso de Correção\n", | |
| " \n", | |
| " K = ganho_de_kalman(H,R,P)\n", | |
| " Y = observacoes(measurements[n+1],x,H)\n", | |
| " \n", | |
| " x = novo_estado(Y,x,K)\n", | |
| " P = nova_covariancia(P,I,K,H)\n", | |
| " \n", | |
| " crKalman.append(x)\n", | |
| " tempo.append(n)\n", | |
| "\n", | |
| "crKalman = array(crKalman)\n", | |
| "prsao = array(prsao)\n", | |
| "tempo = array(tempo)\n", | |
| "measurements = array(measurements)" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 8, | |
| "metadata": { | |
| "slideshow": { | |
| "slide_type": "slide" | |
| } | |
| }, | |
| "outputs": [ | |
| { | |
| "data": { | |
| "application/javascript": [ | |
| "/* Put everything inside the global mpl namespace */\n", | |
| "window.mpl = {};\n", | |
| "\n", | |
| "\n", | |
| "mpl.get_websocket_type = function() {\n", | |
| " if (typeof(WebSocket) !== 'undefined') {\n", | |
| " return WebSocket;\n", | |
| " } else if (typeof(MozWebSocket) !== 'undefined') {\n", | |
| " return MozWebSocket;\n", | |
| " } else {\n", | |
| " alert('Your browser does not have WebSocket support.' +\n", | |
| " 'Please try Chrome, Safari or Firefox ≥ 6. ' +\n", | |
| " 'Firefox 4 and 5 are also supported but you ' +\n", | |
| " 'have to enable WebSockets in about:config.');\n", | |
| " };\n", | |
| "}\n", | |
| "\n", | |
| "mpl.figure = function(figure_id, websocket, ondownload, parent_element) {\n", | |
| " this.id = figure_id;\n", | |
| "\n", | |
| " this.ws = websocket;\n", | |
| "\n", | |
| " this.supports_binary = (this.ws.binaryType != undefined);\n", | |
| "\n", | |
| " if (!this.supports_binary) {\n", | |
| " var warnings = document.getElementById(\"mpl-warnings\");\n", | |
| " if (warnings) {\n", | |
| " warnings.style.display = 'block';\n", | |
| " warnings.textContent = (\n", | |
| " \"This browser does not support binary websocket messages. \" +\n", | |
| " \"Performance may be slow.\");\n", | |
| " }\n", | |
| " }\n", | |
| "\n", | |
| " this.imageObj = new Image();\n", | |
| "\n", | |
| " this.context = undefined;\n", | |
| " this.message = undefined;\n", | |
| " this.canvas = undefined;\n", | |
| " this.rubberband_canvas = undefined;\n", | |
| " this.rubberband_context = undefined;\n", | |
| " this.format_dropdown = undefined;\n", | |
| "\n", | |
| " this.image_mode = 'full';\n", | |
| "\n", | |
| " this.root = $('<div/>');\n", | |
| " this._root_extra_style(this.root)\n", | |
| " this.root.attr('style', 'display: inline-block');\n", | |
| "\n", | |
| " $(parent_element).append(this.root);\n", | |
| "\n", | |
| " this._init_header(this);\n", | |
| " this._init_canvas(this);\n", | |
| " this._init_toolbar(this);\n", | |
| "\n", | |
| " var fig = this;\n", | |
| "\n", | |
| " this.waiting = false;\n", | |
| "\n", | |
| " this.ws.onopen = function () {\n", | |
| " fig.send_message(\"supports_binary\", {value: fig.supports_binary});\n", | |
| " fig.send_message(\"send_image_mode\", {});\n", | |
| " if (mpl.ratio != 1) {\n", | |
| " fig.send_message(\"set_dpi_ratio\", {'dpi_ratio': mpl.ratio});\n", | |
| " }\n", | |
| " fig.send_message(\"refresh\", {});\n", | |
| " }\n", | |
| "\n", | |
| " this.imageObj.onload = function() {\n", | |
| " if (fig.image_mode == 'full') {\n", | |
| " // Full images could contain transparency (where diff images\n", | |
| " // almost always do), so we need to clear the canvas so that\n", | |
| " // there is no ghosting.\n", | |
| " fig.context.clearRect(0, 0, fig.canvas.width, fig.canvas.height);\n", | |
| " }\n", | |
| " fig.context.drawImage(fig.imageObj, 0, 0);\n", | |
| " };\n", | |
| "\n", | |
| " this.imageObj.onunload = function() {\n", | |
| " fig.ws.close();\n", | |
| " }\n", | |
| "\n", | |
| " this.ws.onmessage = this._make_on_message_function(this);\n", | |
| "\n", | |
| " this.ondownload = ondownload;\n", | |
| "}\n", | |
| "\n", | |
| "mpl.figure.prototype._init_header = function() {\n", | |
| " var titlebar = $(\n", | |
| " '<div class=\"ui-dialog-titlebar ui-widget-header ui-corner-all ' +\n", | |
| " 'ui-helper-clearfix\"/>');\n", | |
| " var titletext = $(\n", | |
| " '<div class=\"ui-dialog-title\" style=\"width: 100%; ' +\n", | |
| " 'text-align: center; padding: 3px;\"/>');\n", | |
| " titlebar.append(titletext)\n", | |
| " this.root.append(titlebar);\n", | |
| " this.header = titletext[0];\n", | |
| "}\n", | |
| "\n", | |
| "\n", | |
| "\n", | |
| "mpl.figure.prototype._canvas_extra_style = function(canvas_div) {\n", | |
| "\n", | |
| "}\n", | |
| "\n", | |
| "\n", | |
| "mpl.figure.prototype._root_extra_style = function(canvas_div) {\n", | |
| "\n", | |
| "}\n", | |
| "\n", | |
| "mpl.figure.prototype._init_canvas = function() {\n", | |
| " var fig = this;\n", | |
| "\n", | |
| " var canvas_div = $('<div/>');\n", | |
| "\n", | |
| " canvas_div.attr('style', 'position: relative; clear: both; outline: 0');\n", | |
| "\n", | |
| " function canvas_keyboard_event(event) {\n", | |
| " return fig.key_event(event, event['data']);\n", | |
| " }\n", | |
| "\n", | |
| " canvas_div.keydown('key_press', canvas_keyboard_event);\n", | |
| " canvas_div.keyup('key_release', canvas_keyboard_event);\n", | |
| " this.canvas_div = canvas_div\n", | |
| " this._canvas_extra_style(canvas_div)\n", | |
| " this.root.append(canvas_div);\n", | |
| "\n", | |
| " var canvas = $('<canvas/>');\n", | |
| " canvas.addClass('mpl-canvas');\n", | |
| " canvas.attr('style', \"left: 0; top: 0; z-index: 0; outline: 0\")\n", | |
| "\n", | |
| " this.canvas = canvas[0];\n", | |
| " this.context = canvas[0].getContext(\"2d\");\n", | |
| "\n", | |
| " var backingStore = this.context.backingStorePixelRatio ||\n", | |
| "\tthis.context.webkitBackingStorePixelRatio ||\n", | |
| "\tthis.context.mozBackingStorePixelRatio ||\n", | |
| "\tthis.context.msBackingStorePixelRatio ||\n", | |
| "\tthis.context.oBackingStorePixelRatio ||\n", | |
| "\tthis.context.backingStorePixelRatio || 1;\n", | |
| "\n", | |
| " mpl.ratio = (window.devicePixelRatio || 1) / backingStore;\n", | |
| "\n", | |
| " var rubberband = $('<canvas/>');\n", | |
| " rubberband.attr('style', \"position: absolute; left: 0; top: 0; z-index: 1;\")\n", | |
| "\n", | |
| " var pass_mouse_events = true;\n", | |
| "\n", | |
| " canvas_div.resizable({\n", | |
| " start: function(event, ui) {\n", | |
| " pass_mouse_events = false;\n", | |
| " },\n", | |
| " resize: function(event, ui) {\n", | |
| " fig.request_resize(ui.size.width, ui.size.height);\n", | |
| " },\n", | |
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| " pass_mouse_events = true;\n", | |
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| " },\n", | |
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| "\n", | |
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| "\n", | |
| " rubberband.mousedown('button_press', mouse_event_fn);\n", | |
| " rubberband.mouseup('button_release', mouse_event_fn);\n", | |
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| " rubberband.mousemove('motion_notify', mouse_event_fn);\n", | |
| "\n", | |
| " rubberband.mouseenter('figure_enter', mouse_event_fn);\n", | |
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| "\n", | |
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| " // Keep the size of the canvas, canvas container, and rubber band\n", | |
| " // canvas in synch.\n", | |
| " canvas_div.css('width', width)\n", | |
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| "\n", | |
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| "\n", | |
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| "\n", | |
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| "\n", | |
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| " continue;\n", | |
| " }\n", | |
| " var button = $('<button/>');\n", | |
| " button.addClass('ui-button ui-widget ui-state-default ui-corner-all ' +\n", | |
| " 'ui-button-icon-only');\n", | |
| " button.attr('role', 'button');\n", | |
| " button.attr('aria-disabled', 'false');\n", | |
| " button.click(method_name, toolbar_event);\n", | |
| " button.mouseover(tooltip, toolbar_mouse_event);\n", | |
| "\n", | |
| " var icon_img = $('<span/>');\n", | |
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| "\n", | |
| " var tooltip_span = $('<span/>');\n", | |
| " tooltip_span.addClass('ui-button-text');\n", | |
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| "\n", | |
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| " button.append(tooltip_span);\n", | |
| "\n", | |
| " nav_element.append(button);\n", | |
| " }\n", | |
| "\n", | |
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| "\n", | |
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| " fmt_picker.addClass('mpl-toolbar-option ui-widget ui-widget-content');\n", | |
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| " this.format_dropdown = fmt_picker[0];\n", | |
| "\n", | |
| " for (var ind in mpl.extensions) {\n", | |
| " var fmt = mpl.extensions[ind];\n", | |
| " var option = $(\n", | |
| " '<option/>', {selected: fmt === mpl.default_extension}).html(fmt);\n", | |
| " fmt_picker.append(option)\n", | |
| " }\n", | |
| "\n", | |
| " // Add hover states to the ui-buttons\n", | |
| " $( \".ui-button\" ).hover(\n", | |
| " function() { $(this).addClass(\"ui-state-hover\");},\n", | |
| " function() { $(this).removeClass(\"ui-state-hover\");}\n", | |
| " );\n", | |
| "\n", | |
| " var status_bar = $('<span class=\"mpl-message\"/>');\n", | |
| " nav_element.append(status_bar);\n", | |
| " this.message = status_bar[0];\n", | |
| "}\n", | |
| "\n", | |
| "mpl.figure.prototype.request_resize = function(x_pixels, y_pixels) {\n", | |
| " // Request matplotlib to resize the figure. Matplotlib will then trigger a resize in the client,\n", | |
| " // which will in turn request a refresh of the image.\n", | |
| " this.send_message('resize', {'width': x_pixels, 'height': y_pixels});\n", | |
| "}\n", | |
| "\n", | |
| "mpl.figure.prototype.send_message = function(type, properties) {\n", | |
| " properties['type'] = type;\n", | |
| " properties['figure_id'] = this.id;\n", | |
| " this.ws.send(JSON.stringify(properties));\n", | |
| "}\n", | |
| "\n", | |
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| " if (!this.waiting) {\n", | |
| " this.waiting = true;\n", | |
| " this.ws.send(JSON.stringify({type: \"draw\", figure_id: this.id}));\n", | |
| " }\n", | |
| "}\n", | |
| "\n", | |
| "\n", | |
| "mpl.figure.prototype.handle_save = function(fig, msg) {\n", | |
| " var format_dropdown = fig.format_dropdown;\n", | |
| " var format = format_dropdown.options[format_dropdown.selectedIndex].value;\n", | |
| " fig.ondownload(fig, format);\n", | |
| "}\n", | |
| "\n", | |
| "\n", | |
| "mpl.figure.prototype.handle_resize = function(fig, msg) {\n", | |
| " var size = msg['size'];\n", | |
| " if (size[0] != fig.canvas.width || size[1] != fig.canvas.height) {\n", | |
| " fig._resize_canvas(size[0], size[1]);\n", | |
| " fig.send_message(\"refresh\", {});\n", | |
| " };\n", | |
| "}\n", | |
| "\n", | |
| "mpl.figure.prototype.handle_rubberband = function(fig, msg) {\n", | |
| " var x0 = msg['x0'] / mpl.ratio;\n", | |
| " var y0 = (fig.canvas.height - msg['y0']) / mpl.ratio;\n", | |
| " var x1 = msg['x1'] / mpl.ratio;\n", | |
| " var y1 = (fig.canvas.height - msg['y1']) / mpl.ratio;\n", | |
| " x0 = Math.floor(x0) + 0.5;\n", | |
| " y0 = Math.floor(y0) + 0.5;\n", | |
| " x1 = Math.floor(x1) + 0.5;\n", | |
| " y1 = Math.floor(y1) + 0.5;\n", | |
| " var min_x = Math.min(x0, x1);\n", | |
| " var min_y = Math.min(y0, y1);\n", | |
| " var width = Math.abs(x1 - x0);\n", | |
| " var height = Math.abs(y1 - y0);\n", | |
| "\n", | |
| " fig.rubberband_context.clearRect(\n", | |
| " 0, 0, fig.canvas.width, fig.canvas.height);\n", | |
| "\n", | |
| " fig.rubberband_context.strokeRect(min_x, min_y, width, height);\n", | |
| "}\n", | |
| "\n", | |
| "mpl.figure.prototype.handle_figure_label = function(fig, msg) {\n", | |
| " // Updates the figure title.\n", | |
| " fig.header.textContent = msg['label'];\n", | |
| "}\n", | |
| "\n", | |
| "mpl.figure.prototype.handle_cursor = function(fig, msg) {\n", | |
| " var cursor = msg['cursor'];\n", | |
| " switch(cursor)\n", | |
| " {\n", | |
| " case 0:\n", | |
| " cursor = 'pointer';\n", | |
| " break;\n", | |
| " case 1:\n", | |
| " cursor = 'default';\n", | |
| " break;\n", | |
| " case 2:\n", | |
| " cursor = 'crosshair';\n", | |
| " break;\n", | |
| " case 3:\n", | |
| " cursor = 'move';\n", | |
| " break;\n", | |
| " }\n", | |
| " fig.rubberband_canvas.style.cursor = cursor;\n", | |
| "}\n", | |
| "\n", | |
| "mpl.figure.prototype.handle_message = function(fig, msg) {\n", | |
| " fig.message.textContent = msg['message'];\n", | |
| "}\n", | |
| "\n", | |
| "mpl.figure.prototype.handle_draw = function(fig, msg) {\n", | |
| " // Request the server to send over a new figure.\n", | |
| " fig.send_draw_message();\n", | |
| "}\n", | |
| "\n", | |
| "mpl.figure.prototype.handle_image_mode = function(fig, msg) {\n", | |
| " fig.image_mode = msg['mode'];\n", | |
| "}\n", | |
| "\n", | |
| "mpl.figure.prototype.updated_canvas_event = function() {\n", | |
| " // Called whenever the canvas gets updated.\n", | |
| " this.send_message(\"ack\", {});\n", | |
| "}\n", | |
| "\n", | |
| "// A function to construct a web socket function for onmessage handling.\n", | |
| "// Called in the figure constructor.\n", | |
| "mpl.figure.prototype._make_on_message_function = function(fig) {\n", | |
| " return function socket_on_message(evt) {\n", | |
| " if (evt.data instanceof Blob) {\n", | |
| " /* FIXME: We get \"Resource interpreted as Image but\n", | |
| " * transferred with MIME type text/plain:\" errors on\n", | |
| " * Chrome. But how to set the MIME type? It doesn't seem\n", | |
| " * to be part of the websocket stream */\n", | |
| " evt.data.type = \"image/png\";\n", | |
| "\n", | |
| " /* Free the memory for the previous frames */\n", | |
| " if (fig.imageObj.src) {\n", | |
| " (window.URL || window.webkitURL).revokeObjectURL(\n", | |
| " fig.imageObj.src);\n", | |
| " }\n", | |
| "\n", | |
| " fig.imageObj.src = (window.URL || window.webkitURL).createObjectURL(\n", | |
| " evt.data);\n", | |
| " fig.updated_canvas_event();\n", | |
| " fig.waiting = false;\n", | |
| " return;\n", | |
| " }\n", | |
| " else if (typeof evt.data === 'string' && evt.data.slice(0, 21) == \"data:image/png;base64\") {\n", | |
| " fig.imageObj.src = evt.data;\n", | |
| " fig.updated_canvas_event();\n", | |
| " fig.waiting = false;\n", | |
| " return;\n", | |
| " }\n", | |
| "\n", | |
| " var msg = JSON.parse(evt.data);\n", | |
| " var msg_type = msg['type'];\n", | |
| "\n", | |
| " // Call the \"handle_{type}\" callback, which takes\n", | |
| " // the figure and JSON message as its only arguments.\n", | |
| " try {\n", | |
| " var callback = fig[\"handle_\" + msg_type];\n", | |
| " } catch (e) {\n", | |
| " console.log(\"No handler for the '\" + msg_type + \"' message type: \", msg);\n", | |
| " return;\n", | |
| " }\n", | |
| "\n", | |
| " if (callback) {\n", | |
| " try {\n", | |
| " // console.log(\"Handling '\" + msg_type + \"' message: \", msg);\n", | |
| " callback(fig, msg);\n", | |
| " } catch (e) {\n", | |
| " console.log(\"Exception inside the 'handler_\" + msg_type + \"' callback:\", e, e.stack, msg);\n", | |
| " }\n", | |
| " }\n", | |
| " };\n", | |
| "}\n", | |
| "\n", | |
| "// from http://stackoverflow.com/questions/1114465/getting-mouse-location-in-canvas\n", | |
| "mpl.findpos = function(e) {\n", | |
| " //this section is from http://www.quirksmode.org/js/events_properties.html\n", | |
| " var targ;\n", | |
| " if (!e)\n", | |
| " e = window.event;\n", | |
| " if (e.target)\n", | |
| " targ = e.target;\n", | |
| " else if (e.srcElement)\n", | |
| " targ = e.srcElement;\n", | |
| " if (targ.nodeType == 3) // defeat Safari bug\n", | |
| " targ = targ.parentNode;\n", | |
| "\n", | |
| " // jQuery normalizes the pageX and pageY\n", | |
| " // pageX,Y are the mouse positions relative to the document\n", | |
| " // offset() returns the position of the element relative to the document\n", | |
| " var x = e.pageX - $(targ).offset().left;\n", | |
| " var y = e.pageY - $(targ).offset().top;\n", | |
| "\n", | |
| " return {\"x\": x, \"y\": y};\n", | |
| "};\n", | |
| "\n", | |
| "/*\n", | |
| " * return a copy of an object with only non-object keys\n", | |
| " * we need this to avoid circular references\n", | |
| " * http://stackoverflow.com/a/24161582/3208463\n", | |
| " */\n", | |
| "function simpleKeys (original) {\n", | |
| " return Object.keys(original).reduce(function (obj, key) {\n", | |
| " if (typeof original[key] !== 'object')\n", | |
| " obj[key] = original[key]\n", | |
| " return obj;\n", | |
| " }, {});\n", | |
| "}\n", | |
| "\n", | |
| "mpl.figure.prototype.mouse_event = function(event, name) {\n", | |
| " var canvas_pos = mpl.findpos(event)\n", | |
| "\n", | |
| " if (name === 'button_press')\n", | |
| " {\n", | |
| " this.canvas.focus();\n", | |
| " this.canvas_div.focus();\n", | |
| " }\n", | |
| "\n", | |
| " var x = canvas_pos.x * mpl.ratio;\n", | |
| " var y = canvas_pos.y * mpl.ratio;\n", | |
| "\n", | |
| " this.send_message(name, {x: x, y: y, button: event.button,\n", | |
| " step: event.step,\n", | |
| " guiEvent: simpleKeys(event)});\n", | |
| "\n", | |
| " /* This prevents the web browser from automatically changing to\n", | |
| " * the text insertion cursor when the button is pressed. We want\n", | |
| " * to control all of the cursor setting manually through the\n", | |
| " * 'cursor' event from matplotlib */\n", | |
| " event.preventDefault();\n", | |
| " return false;\n", | |
| "}\n", | |
| "\n", | |
| "mpl.figure.prototype._key_event_extra = function(event, name) {\n", | |
| " // Handle any extra behaviour associated with a key event\n", | |
| "}\n", | |
| "\n", | |
| "mpl.figure.prototype.key_event = function(event, name) {\n", | |
| "\n", | |
| " // Prevent repeat events\n", | |
| " if (name == 'key_press')\n", | |
| " {\n", | |
| " if (event.which === this._key)\n", | |
| " return;\n", | |
| " else\n", | |
| " this._key = event.which;\n", | |
| " }\n", | |
| " if (name == 'key_release')\n", | |
| " this._key = null;\n", | |
| "\n", | |
| " var value = '';\n", | |
| " if (event.ctrlKey && event.which != 17)\n", | |
| " value += \"ctrl+\";\n", | |
| " if (event.altKey && event.which != 18)\n", | |
| " value += \"alt+\";\n", | |
| " if (event.shiftKey && event.which != 16)\n", | |
| " value += \"shift+\";\n", | |
| "\n", | |
| " value += 'k';\n", | |
| " value += event.which.toString();\n", | |
| "\n", | |
| " this._key_event_extra(event, name);\n", | |
| "\n", | |
| " this.send_message(name, {key: value,\n", | |
| " guiEvent: simpleKeys(event)});\n", | |
| " return false;\n", | |
| "}\n", | |
| "\n", | |
| "mpl.figure.prototype.toolbar_button_onclick = function(name) {\n", | |
| " if (name == 'download') {\n", | |
| " this.handle_save(this, null);\n", | |
| " } else {\n", | |
| " this.send_message(\"toolbar_button\", {name: name});\n", | |
| " }\n", | |
| "};\n", | |
| "\n", | |
| "mpl.figure.prototype.toolbar_button_onmouseover = function(tooltip) {\n", | |
| " this.message.textContent = tooltip;\n", | |
| "};\n", | |
| "mpl.toolbar_items = [[\"Home\", \"Reset original view\", \"fa fa-home icon-home\", \"home\"], [\"Back\", \"Back to previous view\", \"fa fa-arrow-left icon-arrow-left\", \"back\"], [\"Forward\", \"Forward to next view\", \"fa fa-arrow-right icon-arrow-right\", \"forward\"], [\"\", \"\", \"\", \"\"], [\"Pan\", \"Pan axes with left mouse, zoom with right\", \"fa fa-arrows icon-move\", \"pan\"], [\"Zoom\", \"Zoom to rectangle\", \"fa fa-square-o icon-check-empty\", \"zoom\"], [\"\", \"\", \"\", \"\"], [\"Download\", \"Download plot\", \"fa fa-floppy-o icon-save\", \"download\"]];\n", | |
| "\n", | |
| "mpl.extensions = [\"eps\", \"pdf\", \"png\", \"ps\", \"raw\", \"svg\"];\n", | |
| "\n", | |
| "mpl.default_extension = \"png\";var comm_websocket_adapter = function(comm) {\n", | |
| " // Create a \"websocket\"-like object which calls the given IPython comm\n", | |
| " // object with the appropriate methods. Currently this is a non binary\n", | |
| " // socket, so there is still some room for performance tuning.\n", | |
| " var ws = {};\n", | |
| "\n", | |
| " ws.close = function() {\n", | |
| " comm.close()\n", | |
| " };\n", | |
| " ws.send = function(m) {\n", | |
| " //console.log('sending', m);\n", | |
| " comm.send(m);\n", | |
| " };\n", | |
| " // Register the callback with on_msg.\n", | |
| " comm.on_msg(function(msg) {\n", | |
| " //console.log('receiving', msg['content']['data'], msg);\n", | |
| " // Pass the mpl event to the overridden (by mpl) onmessage function.\n", | |
| " ws.onmessage(msg['content']['data'])\n", | |
| " });\n", | |
| " return ws;\n", | |
| "}\n", | |
| "\n", | |
| "mpl.mpl_figure_comm = function(comm, msg) {\n", | |
| " // This is the function which gets called when the mpl process\n", | |
| " // starts-up an IPython Comm through the \"matplotlib\" channel.\n", | |
| "\n", | |
| " var id = msg.content.data.id;\n", | |
| " // Get hold of the div created by the display call when the Comm\n", | |
| " // socket was opened in Python.\n", | |
| " var element = $(\"#\" + id);\n", | |
| " var ws_proxy = comm_websocket_adapter(comm)\n", | |
| "\n", | |
| " function ondownload(figure, format) {\n", | |
| " window.open(figure.imageObj.src);\n", | |
| " }\n", | |
| "\n", | |
| " var fig = new mpl.figure(id, ws_proxy,\n", | |
| " ondownload,\n", | |
| " element.get(0));\n", | |
| "\n", | |
| " // Call onopen now - mpl needs it, as it is assuming we've passed it a real\n", | |
| " // web socket which is closed, not our websocket->open comm proxy.\n", | |
| " ws_proxy.onopen();\n", | |
| "\n", | |
| " fig.parent_element = element.get(0);\n", | |
| " fig.cell_info = mpl.find_output_cell(\"<div id='\" + id + \"'></div>\");\n", | |
| " if (!fig.cell_info) {\n", | |
| " console.error(\"Failed to find cell for figure\", id, fig);\n", | |
| " return;\n", | |
| " }\n", | |
| "\n", | |
| " var output_index = fig.cell_info[2]\n", | |
| " var cell = fig.cell_info[0];\n", | |
| "\n", | |
| "};\n", | |
| "\n", | |
| "mpl.figure.prototype.handle_close = function(fig, msg) {\n", | |
| " var width = fig.canvas.width/mpl.ratio\n", | |
| " fig.root.unbind('remove')\n", | |
| "\n", | |
| " // Update the output cell to use the data from the current canvas.\n", | |
| " fig.push_to_output();\n", | |
| " var dataURL = fig.canvas.toDataURL();\n", | |
| " // Re-enable the keyboard manager in IPython - without this line, in FF,\n", | |
| " // the notebook keyboard shortcuts fail.\n", | |
| " IPython.keyboard_manager.enable()\n", | |
| " $(fig.parent_element).html('<img src=\"' + dataURL + '\" width=\"' + width + '\">');\n", | |
| " fig.close_ws(fig, msg);\n", | |
| "}\n", | |
| "\n", | |
| "mpl.figure.prototype.close_ws = function(fig, msg){\n", | |
| " fig.send_message('closing', msg);\n", | |
| " // fig.ws.close()\n", | |
| "}\n", | |
| "\n", | |
| "mpl.figure.prototype.push_to_output = function(remove_interactive) {\n", | |
| " // Turn the data on the canvas into data in the output cell.\n", | |
| " var width = this.canvas.width/mpl.ratio\n", | |
| " var dataURL = this.canvas.toDataURL();\n", | |
| " this.cell_info[1]['text/html'] = '<img src=\"' + dataURL + '\" width=\"' + width + '\">';\n", | |
| "}\n", | |
| "\n", | |
| "mpl.figure.prototype.updated_canvas_event = function() {\n", | |
| " // Tell IPython that the notebook contents must change.\n", | |
| " IPython.notebook.set_dirty(true);\n", | |
| " this.send_message(\"ack\", {});\n", | |
| " var fig = this;\n", | |
| " // Wait a second, then push the new image to the DOM so\n", | |
| " // that it is saved nicely (might be nice to debounce this).\n", | |
| " setTimeout(function () { fig.push_to_output() }, 1000);\n", | |
| "}\n", | |
| "\n", | |
| "mpl.figure.prototype._init_toolbar = function() {\n", | |
| " var fig = this;\n", | |
| "\n", | |
| " var nav_element = $('<div/>')\n", | |
| " nav_element.attr('style', 'width: 100%');\n", | |
| " this.root.append(nav_element);\n", | |
| "\n", | |
| " // Define a callback function for later on.\n", | |
| " function toolbar_event(event) {\n", | |
| " return fig.toolbar_button_onclick(event['data']);\n", | |
| " }\n", | |
| " function toolbar_mouse_event(event) {\n", | |
| " return fig.toolbar_button_onmouseover(event['data']);\n", | |
| " }\n", | |
| "\n", | |
| " for(var toolbar_ind in mpl.toolbar_items){\n", | |
| " var name = mpl.toolbar_items[toolbar_ind][0];\n", | |
| " var tooltip = mpl.toolbar_items[toolbar_ind][1];\n", | |
| " var image = mpl.toolbar_items[toolbar_ind][2];\n", | |
| " var method_name = mpl.toolbar_items[toolbar_ind][3];\n", | |
| "\n", | |
| " if (!name) { continue; };\n", | |
| "\n", | |
| " var button = $('<button class=\"btn btn-default\" href=\"#\" title=\"' + name + '\"><i class=\"fa ' + image + ' fa-lg\"></i></button>');\n", | |
| " button.click(method_name, toolbar_event);\n", | |
| " button.mouseover(tooltip, toolbar_mouse_event);\n", | |
| " nav_element.append(button);\n", | |
| " }\n", | |
| "\n", | |
| " // Add the status bar.\n", | |
| " var status_bar = $('<span class=\"mpl-message\" style=\"text-align:right; float: right;\"/>');\n", | |
| " nav_element.append(status_bar);\n", | |
| " this.message = status_bar[0];\n", | |
| "\n", | |
| " // Add the close button to the window.\n", | |
| " var buttongrp = $('<div class=\"btn-group inline pull-right\"></div>');\n", | |
| " var button = $('<button class=\"btn btn-mini btn-primary\" href=\"#\" title=\"Stop Interaction\"><i class=\"fa fa-power-off icon-remove icon-large\"></i></button>');\n", | |
| " button.click(function (evt) { fig.handle_close(fig, {}); } );\n", | |
| " button.mouseover('Stop Interaction', toolbar_mouse_event);\n", | |
| " buttongrp.append(button);\n", | |
| " var titlebar = this.root.find($('.ui-dialog-titlebar'));\n", | |
| " titlebar.prepend(buttongrp);\n", | |
| "}\n", | |
| "\n", | |
| "mpl.figure.prototype._root_extra_style = function(el){\n", | |
| " var fig = this\n", | |
| " el.on(\"remove\", function(){\n", | |
| "\tfig.close_ws(fig, {});\n", | |
| " });\n", | |
| "}\n", | |
| "\n", | |
| "mpl.figure.prototype._canvas_extra_style = function(el){\n", | |
| " // this is important to make the div 'focusable\n", | |
| " el.attr('tabindex', 0)\n", | |
| " // reach out to IPython and tell the keyboard manager to turn it's self\n", | |
| " // off when our div gets focus\n", | |
| "\n", | |
| " // location in version 3\n", | |
| " if (IPython.notebook.keyboard_manager) {\n", | |
| " IPython.notebook.keyboard_manager.register_events(el);\n", | |
| " }\n", | |
| " else {\n", | |
| " // location in version 2\n", | |
| " IPython.keyboard_manager.register_events(el);\n", | |
| " }\n", | |
| "\n", | |
| "}\n", | |
| "\n", | |
| "mpl.figure.prototype._key_event_extra = function(event, name) {\n", | |
| " var manager = IPython.notebook.keyboard_manager;\n", | |
| " if (!manager)\n", | |
| " manager = IPython.keyboard_manager;\n", | |
| "\n", | |
| " // Check for shift+enter\n", | |
| " if (event.shiftKey && event.which == 13) {\n", | |
| " this.canvas_div.blur();\n", | |
| " event.shiftKey = false;\n", | |
| " // Send a \"J\" for go to next cell\n", | |
| " event.which = 74;\n", | |
| " event.keyCode = 74;\n", | |
| " manager.command_mode();\n", | |
| " manager.handle_keydown(event);\n", | |
| " }\n", | |
| "}\n", | |
| "\n", | |
| "mpl.figure.prototype.handle_save = function(fig, msg) {\n", | |
| " fig.ondownload(fig, null);\n", | |
| "}\n", | |
| "\n", | |
| "\n", | |
| "mpl.find_output_cell = function(html_output) {\n", | |
| " // Return the cell and output element which can be found *uniquely* in the notebook.\n", | |
| " // Note - this is a bit hacky, but it is done because the \"notebook_saving.Notebook\"\n", | |
| " // IPython event is triggered only after the cells have been serialised, which for\n", | |
| " // our purposes (turning an active figure into a static one), is too late.\n", | |
| " var cells = IPython.notebook.get_cells();\n", | |
| " var ncells = cells.length;\n", | |
| " for (var i=0; i<ncells; i++) {\n", | |
| " var cell = cells[i];\n", | |
| " if (cell.cell_type === 'code'){\n", | |
| " for (var j=0; j<cell.output_area.outputs.length; j++) {\n", | |
| " var data = cell.output_area.outputs[j];\n", | |
| " if (data.data) {\n", | |
| " // IPython >= 3 moved mimebundle to data attribute of output\n", | |
| " data = data.data;\n", | |
| " }\n", | |
| " if (data['text/html'] == html_output) {\n", | |
| " return [cell, data, j];\n", | |
| " }\n", | |
| " }\n", | |
| " }\n", | |
| " }\n", | |
| "}\n", | |
| "\n", | |
| "// Register the function which deals with the matplotlib target/channel.\n", | |
| "// The kernel may be null if the page has been refreshed.\n", | |
| "if (IPython.notebook.kernel != null) {\n", | |
| " IPython.notebook.kernel.comm_manager.register_target('matplotlib', mpl.mpl_figure_comm);\n", | |
| "}\n" | |
| ], | |
| "text/plain": [ | |
| "<IPython.core.display.Javascript object>" | |
| ] | |
| }, | |
| "metadata": {}, | |
| "output_type": "display_data" | |
| }, | |
| { | |
| "data": { | |
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\" width=\"640\">" | |
| ], | |
| "text/plain": [ | |
| "<IPython.core.display.HTML object>" | |
| ] | |
| }, | |
| "metadata": {}, | |
| "output_type": "display_data" | |
| }, | |
| { | |
| "data": { | |
| "text/plain": [ | |
| "<matplotlib.legend.Legend at 0x7f7182c5c7b8>" | |
| ] | |
| }, | |
| "execution_count": 8, | |
| "metadata": {}, | |
| "output_type": "execute_result" | |
| } | |
| ], | |
| "source": [ | |
| "plt.title(\"Previsão da Posição com o Tempo.\")\n", | |
| "plt.plot(tempo, prsao[:,0], label='Previsão')\n", | |
| "plt.plot(tempo, crKalman[:,0], label='Kalman')\n", | |
| "plt.plot(tempo, measurements[1:,0], '.', label='Medido')\n", | |
| "plt.legend()\n" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 10, | |
| "metadata": { | |
| "slideshow": { | |
| "slide_type": "slide" | |
| } | |
| }, | |
| "outputs": [ | |
| { | |
| "data": { | |
| "application/javascript": [ | |
| "/* Put everything inside the global mpl namespace */\n", | |
| "window.mpl = {};\n", | |
| "\n", | |
| "\n", | |
| "mpl.get_websocket_type = function() {\n", | |
| " if (typeof(WebSocket) !== 'undefined') {\n", | |
| " return WebSocket;\n", | |
| " } else if (typeof(MozWebSocket) !== 'undefined') {\n", | |
| " return MozWebSocket;\n", | |
| " } else {\n", | |
| " alert('Your browser does not have WebSocket support.' +\n", | |
| " 'Please try Chrome, Safari or Firefox ≥ 6. ' +\n", | |
| " 'Firefox 4 and 5 are also supported but you ' +\n", | |
| " 'have to enable WebSockets in about:config.');\n", | |
| " };\n", | |
| "}\n", | |
| "\n", | |
| "mpl.figure = function(figure_id, websocket, ondownload, parent_element) {\n", | |
| " this.id = figure_id;\n", | |
| "\n", | |
| " this.ws = websocket;\n", | |
| "\n", | |
| " this.supports_binary = (this.ws.binaryType != undefined);\n", | |
| "\n", | |
| " if (!this.supports_binary) {\n", | |
| " var warnings = document.getElementById(\"mpl-warnings\");\n", | |
| " if (warnings) {\n", | |
| " warnings.style.display = 'block';\n", | |
| " warnings.textContent = (\n", | |
| " \"This browser does not support binary websocket messages. \" +\n", | |
| " \"Performance may be slow.\");\n", | |
| " }\n", | |
| " }\n", | |
| "\n", | |
| " this.imageObj = new Image();\n", | |
| "\n", | |
| " this.context = undefined;\n", | |
| " this.message = undefined;\n", | |
| " this.canvas = undefined;\n", | |
| " this.rubberband_canvas = undefined;\n", | |
| " this.rubberband_context = undefined;\n", | |
| " this.format_dropdown = undefined;\n", | |
| "\n", | |
| " this.image_mode = 'full';\n", | |
| "\n", | |
| " this.root = $('<div/>');\n", | |
| " this._root_extra_style(this.root)\n", | |
| " this.root.attr('style', 'display: inline-block');\n", | |
| "\n", | |
| " $(parent_element).append(this.root);\n", | |
| "\n", | |
| " this._init_header(this);\n", | |
| " this._init_canvas(this);\n", | |
| " this._init_toolbar(this);\n", | |
| "\n", | |
| " var fig = this;\n", | |
| "\n", | |
| " this.waiting = false;\n", | |
| "\n", | |
| " this.ws.onopen = function () {\n", | |
| " fig.send_message(\"supports_binary\", {value: fig.supports_binary});\n", | |
| " fig.send_message(\"send_image_mode\", {});\n", | |
| " if (mpl.ratio != 1) {\n", | |
| " fig.send_message(\"set_dpi_ratio\", {'dpi_ratio': mpl.ratio});\n", | |
| " }\n", | |
| " fig.send_message(\"refresh\", {});\n", | |
| " }\n", | |
| "\n", | |
| " this.imageObj.onload = function() {\n", | |
| " if (fig.image_mode == 'full') {\n", | |
| " // Full images could contain transparency (where diff images\n", | |
| " // almost always do), so we need to clear the canvas so that\n", | |
| " // there is no ghosting.\n", | |
| " fig.context.clearRect(0, 0, fig.canvas.width, fig.canvas.height);\n", | |
| " }\n", | |
| " fig.context.drawImage(fig.imageObj, 0, 0);\n", | |
| " };\n", | |
| "\n", | |
| " this.imageObj.onunload = function() {\n", | |
| " fig.ws.close();\n", | |
| " }\n", | |
| "\n", | |
| " this.ws.onmessage = this._make_on_message_function(this);\n", | |
| "\n", | |
| " this.ondownload = ondownload;\n", | |
| "}\n", | |
| "\n", | |
| "mpl.figure.prototype._init_header = function() {\n", | |
| " var titlebar = $(\n", | |
| " '<div class=\"ui-dialog-titlebar ui-widget-header ui-corner-all ' +\n", | |
| " 'ui-helper-clearfix\"/>');\n", | |
| " var titletext = $(\n", | |
| " '<div class=\"ui-dialog-title\" style=\"width: 100%; ' +\n", | |
| " 'text-align: center; padding: 3px;\"/>');\n", | |
| " titlebar.append(titletext)\n", | |
| " this.root.append(titlebar);\n", | |
| " this.header = titletext[0];\n", | |
| "}\n", | |
| "\n", | |
| "\n", | |
| "\n", | |
| "mpl.figure.prototype._canvas_extra_style = function(canvas_div) {\n", | |
| "\n", | |
| "}\n", | |
| "\n", | |
| "\n", | |
| "mpl.figure.prototype._root_extra_style = function(canvas_div) {\n", | |
| "\n", | |
| "}\n", | |
| "\n", | |
| "mpl.figure.prototype._init_canvas = function() {\n", | |
| " var fig = this;\n", | |
| "\n", | |
| " var canvas_div = $('<div/>');\n", | |
| "\n", | |
| " canvas_div.attr('style', 'position: relative; clear: both; outline: 0');\n", | |
| "\n", | |
| " function canvas_keyboard_event(event) {\n", | |
| " return fig.key_event(event, event['data']);\n", | |
| " }\n", | |
| "\n", | |
| " canvas_div.keydown('key_press', canvas_keyboard_event);\n", | |
| " canvas_div.keyup('key_release', canvas_keyboard_event);\n", | |
| " this.canvas_div = canvas_div\n", | |
| " this._canvas_extra_style(canvas_div)\n", | |
| " this.root.append(canvas_div);\n", | |
| "\n", | |
| " var canvas = $('<canvas/>');\n", | |
| " canvas.addClass('mpl-canvas');\n", | |
| " canvas.attr('style', \"left: 0; top: 0; z-index: 0; outline: 0\")\n", | |
| "\n", | |
| " this.canvas = canvas[0];\n", | |
| " this.context = canvas[0].getContext(\"2d\");\n", | |
| "\n", | |
| " var backingStore = this.context.backingStorePixelRatio ||\n", | |
| "\tthis.context.webkitBackingStorePixelRatio ||\n", | |
| "\tthis.context.mozBackingStorePixelRatio ||\n", | |
| "\tthis.context.msBackingStorePixelRatio ||\n", | |
| "\tthis.context.oBackingStorePixelRatio ||\n", | |
| "\tthis.context.backingStorePixelRatio || 1;\n", | |
| "\n", | |
| " mpl.ratio = (window.devicePixelRatio || 1) / backingStore;\n", | |
| "\n", | |
| " var rubberband = $('<canvas/>');\n", | |
| " rubberband.attr('style', \"position: absolute; left: 0; top: 0; z-index: 1;\")\n", | |
| "\n", | |
| " var pass_mouse_events = true;\n", | |
| "\n", | |
| " canvas_div.resizable({\n", | |
| " start: function(event, ui) {\n", | |
| " pass_mouse_events = false;\n", | |
| " },\n", | |
| " resize: function(event, ui) {\n", | |
| " fig.request_resize(ui.size.width, ui.size.height);\n", | |
| " },\n", | |
| " stop: function(event, ui) {\n", | |
| " pass_mouse_events = true;\n", | |
| " fig.request_resize(ui.size.width, ui.size.height);\n", | |
| " },\n", | |
| " });\n", | |
| "\n", | |
| " function mouse_event_fn(event) {\n", | |
| " if (pass_mouse_events)\n", | |
| " return fig.mouse_event(event, event['data']);\n", | |
| " }\n", | |
| "\n", | |
| " rubberband.mousedown('button_press', mouse_event_fn);\n", | |
| " rubberband.mouseup('button_release', mouse_event_fn);\n", | |
| " // Throttle sequential mouse events to 1 every 20ms.\n", | |
| " rubberband.mousemove('motion_notify', mouse_event_fn);\n", | |
| "\n", | |
| " rubberband.mouseenter('figure_enter', mouse_event_fn);\n", | |
| " rubberband.mouseleave('figure_leave', mouse_event_fn);\n", | |
| "\n", | |
| " canvas_div.on(\"wheel\", function (event) {\n", | |
| " event = event.originalEvent;\n", | |
| " event['data'] = 'scroll'\n", | |
| " if (event.deltaY < 0) {\n", | |
| " event.step = 1;\n", | |
| " } else {\n", | |
| " event.step = -1;\n", | |
| " }\n", | |
| " mouse_event_fn(event);\n", | |
| " });\n", | |
| "\n", | |
| " canvas_div.append(canvas);\n", | |
| " canvas_div.append(rubberband);\n", | |
| "\n", | |
| " this.rubberband = rubberband;\n", | |
| " this.rubberband_canvas = rubberband[0];\n", | |
| " this.rubberband_context = rubberband[0].getContext(\"2d\");\n", | |
| " this.rubberband_context.strokeStyle = \"#000000\";\n", | |
| "\n", | |
| " this._resize_canvas = function(width, height) {\n", | |
| " // Keep the size of the canvas, canvas container, and rubber band\n", | |
| " // canvas in synch.\n", | |
| " canvas_div.css('width', width)\n", | |
| " canvas_div.css('height', height)\n", | |
| "\n", | |
| " canvas.attr('width', width * mpl.ratio);\n", | |
| " canvas.attr('height', height * mpl.ratio);\n", | |
| " canvas.attr('style', 'width: ' + width + 'px; height: ' + height + 'px;');\n", | |
| "\n", | |
| " rubberband.attr('width', width);\n", | |
| " rubberband.attr('height', height);\n", | |
| " }\n", | |
| "\n", | |
| " // Set the figure to an initial 600x600px, this will subsequently be updated\n", | |
| " // upon first draw.\n", | |
| " this._resize_canvas(600, 600);\n", | |
| "\n", | |
| " // Disable right mouse context menu.\n", | |
| " $(this.rubberband_canvas).bind(\"contextmenu\",function(e){\n", | |
| " return false;\n", | |
| " });\n", | |
| "\n", | |
| " function set_focus () {\n", | |
| " canvas.focus();\n", | |
| " canvas_div.focus();\n", | |
| " }\n", | |
| "\n", | |
| " window.setTimeout(set_focus, 100);\n", | |
| "}\n", | |
| "\n", | |
| "mpl.figure.prototype._init_toolbar = function() {\n", | |
| " var fig = this;\n", | |
| "\n", | |
| " var nav_element = $('<div/>')\n", | |
| " nav_element.attr('style', 'width: 100%');\n", | |
| " this.root.append(nav_element);\n", | |
| "\n", | |
| " // Define a callback function for later on.\n", | |
| " function toolbar_event(event) {\n", | |
| " return fig.toolbar_button_onclick(event['data']);\n", | |
| " }\n", | |
| " function toolbar_mouse_event(event) {\n", | |
| " return fig.toolbar_button_onmouseover(event['data']);\n", | |
| " }\n", | |
| "\n", | |
| " for(var toolbar_ind in mpl.toolbar_items) {\n", | |
| " var name = mpl.toolbar_items[toolbar_ind][0];\n", | |
| " var tooltip = mpl.toolbar_items[toolbar_ind][1];\n", | |
| " var image = mpl.toolbar_items[toolbar_ind][2];\n", | |
| " var method_name = mpl.toolbar_items[toolbar_ind][3];\n", | |
| "\n", | |
| " if (!name) {\n", | |
| " // put a spacer in here.\n", | |
| " continue;\n", | |
| " }\n", | |
| " var button = $('<button/>');\n", | |
| " button.addClass('ui-button ui-widget ui-state-default ui-corner-all ' +\n", | |
| " 'ui-button-icon-only');\n", | |
| " button.attr('role', 'button');\n", | |
| " button.attr('aria-disabled', 'false');\n", | |
| " button.click(method_name, toolbar_event);\n", | |
| " button.mouseover(tooltip, toolbar_mouse_event);\n", | |
| "\n", | |
| " var icon_img = $('<span/>');\n", | |
| " icon_img.addClass('ui-button-icon-primary ui-icon');\n", | |
| " icon_img.addClass(image);\n", | |
| " icon_img.addClass('ui-corner-all');\n", | |
| "\n", | |
| " var tooltip_span = $('<span/>');\n", | |
| " tooltip_span.addClass('ui-button-text');\n", | |
| " tooltip_span.html(tooltip);\n", | |
| "\n", | |
| " button.append(icon_img);\n", | |
| " button.append(tooltip_span);\n", | |
| "\n", | |
| " nav_element.append(button);\n", | |
| " }\n", | |
| "\n", | |
| " var fmt_picker_span = $('<span/>');\n", | |
| "\n", | |
| " var fmt_picker = $('<select/>');\n", | |
| " fmt_picker.addClass('mpl-toolbar-option ui-widget ui-widget-content');\n", | |
| " fmt_picker_span.append(fmt_picker);\n", | |
| " nav_element.append(fmt_picker_span);\n", | |
| " this.format_dropdown = fmt_picker[0];\n", | |
| "\n", | |
| " for (var ind in mpl.extensions) {\n", | |
| " var fmt = mpl.extensions[ind];\n", | |
| " var option = $(\n", | |
| " '<option/>', {selected: fmt === mpl.default_extension}).html(fmt);\n", | |
| " fmt_picker.append(option)\n", | |
| " }\n", | |
| "\n", | |
| " // Add hover states to the ui-buttons\n", | |
| " $( \".ui-button\" ).hover(\n", | |
| " function() { $(this).addClass(\"ui-state-hover\");},\n", | |
| " function() { $(this).removeClass(\"ui-state-hover\");}\n", | |
| " );\n", | |
| "\n", | |
| " var status_bar = $('<span class=\"mpl-message\"/>');\n", | |
| " nav_element.append(status_bar);\n", | |
| " this.message = status_bar[0];\n", | |
| "}\n", | |
| "\n", | |
| "mpl.figure.prototype.request_resize = function(x_pixels, y_pixels) {\n", | |
| " // Request matplotlib to resize the figure. Matplotlib will then trigger a resize in the client,\n", | |
| " // which will in turn request a refresh of the image.\n", | |
| " this.send_message('resize', {'width': x_pixels, 'height': y_pixels});\n", | |
| "}\n", | |
| "\n", | |
| "mpl.figure.prototype.send_message = function(type, properties) {\n", | |
| " properties['type'] = type;\n", | |
| " properties['figure_id'] = this.id;\n", | |
| " this.ws.send(JSON.stringify(properties));\n", | |
| "}\n", | |
| "\n", | |
| "mpl.figure.prototype.send_draw_message = function() {\n", | |
| " if (!this.waiting) {\n", | |
| " this.waiting = true;\n", | |
| " this.ws.send(JSON.stringify({type: \"draw\", figure_id: this.id}));\n", | |
| " }\n", | |
| "}\n", | |
| "\n", | |
| "\n", | |
| "mpl.figure.prototype.handle_save = function(fig, msg) {\n", | |
| " var format_dropdown = fig.format_dropdown;\n", | |
| " var format = format_dropdown.options[format_dropdown.selectedIndex].value;\n", | |
| " fig.ondownload(fig, format);\n", | |
| "}\n", | |
| "\n", | |
| "\n", | |
| "mpl.figure.prototype.handle_resize = function(fig, msg) {\n", | |
| " var size = msg['size'];\n", | |
| " if (size[0] != fig.canvas.width || size[1] != fig.canvas.height) {\n", | |
| " fig._resize_canvas(size[0], size[1]);\n", | |
| " fig.send_message(\"refresh\", {});\n", | |
| " };\n", | |
| "}\n", | |
| "\n", | |
| "mpl.figure.prototype.handle_rubberband = function(fig, msg) {\n", | |
| " var x0 = msg['x0'] / mpl.ratio;\n", | |
| " var y0 = (fig.canvas.height - msg['y0']) / mpl.ratio;\n", | |
| " var x1 = msg['x1'] / mpl.ratio;\n", | |
| " var y1 = (fig.canvas.height - msg['y1']) / mpl.ratio;\n", | |
| " x0 = Math.floor(x0) + 0.5;\n", | |
| " y0 = Math.floor(y0) + 0.5;\n", | |
| " x1 = Math.floor(x1) + 0.5;\n", | |
| " y1 = Math.floor(y1) + 0.5;\n", | |
| " var min_x = Math.min(x0, x1);\n", | |
| " var min_y = Math.min(y0, y1);\n", | |
| " var width = Math.abs(x1 - x0);\n", | |
| " var height = Math.abs(y1 - y0);\n", | |
| "\n", | |
| " fig.rubberband_context.clearRect(\n", | |
| " 0, 0, fig.canvas.width, fig.canvas.height);\n", | |
| "\n", | |
| " fig.rubberband_context.strokeRect(min_x, min_y, width, height);\n", | |
| "}\n", | |
| "\n", | |
| "mpl.figure.prototype.handle_figure_label = function(fig, msg) {\n", | |
| " // Updates the figure title.\n", | |
| " fig.header.textContent = msg['label'];\n", | |
| "}\n", | |
| "\n", | |
| "mpl.figure.prototype.handle_cursor = function(fig, msg) {\n", | |
| " var cursor = msg['cursor'];\n", | |
| " switch(cursor)\n", | |
| " {\n", | |
| " case 0:\n", | |
| " cursor = 'pointer';\n", | |
| " break;\n", | |
| " case 1:\n", | |
| " cursor = 'default';\n", | |
| " break;\n", | |
| " case 2:\n", | |
| " cursor = 'crosshair';\n", | |
| " break;\n", | |
| " case 3:\n", | |
| " cursor = 'move';\n", | |
| " break;\n", | |
| " }\n", | |
| " fig.rubberband_canvas.style.cursor = cursor;\n", | |
| "}\n", | |
| "\n", | |
| "mpl.figure.prototype.handle_message = function(fig, msg) {\n", | |
| " fig.message.textContent = msg['message'];\n", | |
| "}\n", | |
| "\n", | |
| "mpl.figure.prototype.handle_draw = function(fig, msg) {\n", | |
| " // Request the server to send over a new figure.\n", | |
| " fig.send_draw_message();\n", | |
| "}\n", | |
| "\n", | |
| "mpl.figure.prototype.handle_image_mode = function(fig, msg) {\n", | |
| " fig.image_mode = msg['mode'];\n", | |
| "}\n", | |
| "\n", | |
| "mpl.figure.prototype.updated_canvas_event = function() {\n", | |
| " // Called whenever the canvas gets updated.\n", | |
| " this.send_message(\"ack\", {});\n", | |
| "}\n", | |
| "\n", | |
| "// A function to construct a web socket function for onmessage handling.\n", | |
| "// Called in the figure constructor.\n", | |
| "mpl.figure.prototype._make_on_message_function = function(fig) {\n", | |
| " return function socket_on_message(evt) {\n", | |
| " if (evt.data instanceof Blob) {\n", | |
| " /* FIXME: We get \"Resource interpreted as Image but\n", | |
| " * transferred with MIME type text/plain:\" errors on\n", | |
| " * Chrome. But how to set the MIME type? It doesn't seem\n", | |
| " * to be part of the websocket stream */\n", | |
| " evt.data.type = \"image/png\";\n", | |
| "\n", | |
| " /* Free the memory for the previous frames */\n", | |
| " if (fig.imageObj.src) {\n", | |
| " (window.URL || window.webkitURL).revokeObjectURL(\n", | |
| " fig.imageObj.src);\n", | |
| " }\n", | |
| "\n", | |
| " fig.imageObj.src = (window.URL || window.webkitURL).createObjectURL(\n", | |
| " evt.data);\n", | |
| " fig.updated_canvas_event();\n", | |
| " fig.waiting = false;\n", | |
| " return;\n", | |
| " }\n", | |
| " else if (typeof evt.data === 'string' && evt.data.slice(0, 21) == \"data:image/png;base64\") {\n", | |
| " fig.imageObj.src = evt.data;\n", | |
| " fig.updated_canvas_event();\n", | |
| " fig.waiting = false;\n", | |
| " return;\n", | |
| " }\n", | |
| "\n", | |
| " var msg = JSON.parse(evt.data);\n", | |
| " var msg_type = msg['type'];\n", | |
| "\n", | |
| " // Call the \"handle_{type}\" callback, which takes\n", | |
| " // the figure and JSON message as its only arguments.\n", | |
| " try {\n", | |
| " var callback = fig[\"handle_\" + msg_type];\n", | |
| " } catch (e) {\n", | |
| " console.log(\"No handler for the '\" + msg_type + \"' message type: \", msg);\n", | |
| " return;\n", | |
| " }\n", | |
| "\n", | |
| " if (callback) {\n", | |
| " try {\n", | |
| " // console.log(\"Handling '\" + msg_type + \"' message: \", msg);\n", | |
| " callback(fig, msg);\n", | |
| " } catch (e) {\n", | |
| " console.log(\"Exception inside the 'handler_\" + msg_type + \"' callback:\", e, e.stack, msg);\n", | |
| " }\n", | |
| " }\n", | |
| " };\n", | |
| "}\n", | |
| "\n", | |
| "// from http://stackoverflow.com/questions/1114465/getting-mouse-location-in-canvas\n", | |
| "mpl.findpos = function(e) {\n", | |
| " //this section is from http://www.quirksmode.org/js/events_properties.html\n", | |
| " var targ;\n", | |
| " if (!e)\n", | |
| " e = window.event;\n", | |
| " if (e.target)\n", | |
| " targ = e.target;\n", | |
| " else if (e.srcElement)\n", | |
| " targ = e.srcElement;\n", | |
| " if (targ.nodeType == 3) // defeat Safari bug\n", | |
| " targ = targ.parentNode;\n", | |
| "\n", | |
| " // jQuery normalizes the pageX and pageY\n", | |
| " // pageX,Y are the mouse positions relative to the document\n", | |
| " // offset() returns the position of the element relative to the document\n", | |
| " var x = e.pageX - $(targ).offset().left;\n", | |
| " var y = e.pageY - $(targ).offset().top;\n", | |
| "\n", | |
| " return {\"x\": x, \"y\": y};\n", | |
| "};\n", | |
| "\n", | |
| "/*\n", | |
| " * return a copy of an object with only non-object keys\n", | |
| " * we need this to avoid circular references\n", | |
| " * http://stackoverflow.com/a/24161582/3208463\n", | |
| " */\n", | |
| "function simpleKeys (original) {\n", | |
| " return Object.keys(original).reduce(function (obj, key) {\n", | |
| " if (typeof original[key] !== 'object')\n", | |
| " obj[key] = original[key]\n", | |
| " return obj;\n", | |
| " }, {});\n", | |
| "}\n", | |
| "\n", | |
| "mpl.figure.prototype.mouse_event = function(event, name) {\n", | |
| " var canvas_pos = mpl.findpos(event)\n", | |
| "\n", | |
| " if (name === 'button_press')\n", | |
| " {\n", | |
| " this.canvas.focus();\n", | |
| " this.canvas_div.focus();\n", | |
| " }\n", | |
| "\n", | |
| " var x = canvas_pos.x * mpl.ratio;\n", | |
| " var y = canvas_pos.y * mpl.ratio;\n", | |
| "\n", | |
| " this.send_message(name, {x: x, y: y, button: event.button,\n", | |
| " step: event.step,\n", | |
| " guiEvent: simpleKeys(event)});\n", | |
| "\n", | |
| " /* This prevents the web browser from automatically changing to\n", | |
| " * the text insertion cursor when the button is pressed. We want\n", | |
| " * to control all of the cursor setting manually through the\n", | |
| " * 'cursor' event from matplotlib */\n", | |
| " event.preventDefault();\n", | |
| " return false;\n", | |
| "}\n", | |
| "\n", | |
| "mpl.figure.prototype._key_event_extra = function(event, name) {\n", | |
| " // Handle any extra behaviour associated with a key event\n", | |
| "}\n", | |
| "\n", | |
| "mpl.figure.prototype.key_event = function(event, name) {\n", | |
| "\n", | |
| " // Prevent repeat events\n", | |
| " if (name == 'key_press')\n", | |
| " {\n", | |
| " if (event.which === this._key)\n", | |
| " return;\n", | |
| " else\n", | |
| " this._key = event.which;\n", | |
| " }\n", | |
| " if (name == 'key_release')\n", | |
| " this._key = null;\n", | |
| "\n", | |
| " var value = '';\n", | |
| " if (event.ctrlKey && event.which != 17)\n", | |
| " value += \"ctrl+\";\n", | |
| " if (event.altKey && event.which != 18)\n", | |
| " value += \"alt+\";\n", | |
| " if (event.shiftKey && event.which != 16)\n", | |
| " value += \"shift+\";\n", | |
| "\n", | |
| " value += 'k';\n", | |
| " value += event.which.toString();\n", | |
| "\n", | |
| " this._key_event_extra(event, name);\n", | |
| "\n", | |
| " this.send_message(name, {key: value,\n", | |
| " guiEvent: simpleKeys(event)});\n", | |
| " return false;\n", | |
| "}\n", | |
| "\n", | |
| "mpl.figure.prototype.toolbar_button_onclick = function(name) {\n", | |
| " if (name == 'download') {\n", | |
| " this.handle_save(this, null);\n", | |
| " } else {\n", | |
| " this.send_message(\"toolbar_button\", {name: name});\n", | |
| " }\n", | |
| "};\n", | |
| "\n", | |
| "mpl.figure.prototype.toolbar_button_onmouseover = function(tooltip) {\n", | |
| " this.message.textContent = tooltip;\n", | |
| "};\n", | |
| "mpl.toolbar_items = [[\"Home\", \"Reset original view\", \"fa fa-home icon-home\", \"home\"], [\"Back\", \"Back to previous view\", \"fa fa-arrow-left icon-arrow-left\", \"back\"], [\"Forward\", \"Forward to next view\", \"fa fa-arrow-right icon-arrow-right\", \"forward\"], [\"\", \"\", \"\", \"\"], [\"Pan\", \"Pan axes with left mouse, zoom with right\", \"fa fa-arrows icon-move\", \"pan\"], [\"Zoom\", \"Zoom to rectangle\", \"fa fa-square-o icon-check-empty\", \"zoom\"], [\"\", \"\", \"\", \"\"], [\"Download\", \"Download plot\", \"fa fa-floppy-o icon-save\", \"download\"]];\n", | |
| "\n", | |
| "mpl.extensions = [\"eps\", \"pdf\", \"png\", \"ps\", \"raw\", \"svg\"];\n", | |
| "\n", | |
| "mpl.default_extension = \"png\";var comm_websocket_adapter = function(comm) {\n", | |
| " // Create a \"websocket\"-like object which calls the given IPython comm\n", | |
| " // object with the appropriate methods. Currently this is a non binary\n", | |
| " // socket, so there is still some room for performance tuning.\n", | |
| " var ws = {};\n", | |
| "\n", | |
| " ws.close = function() {\n", | |
| " comm.close()\n", | |
| " };\n", | |
| " ws.send = function(m) {\n", | |
| " //console.log('sending', m);\n", | |
| " comm.send(m);\n", | |
| " };\n", | |
| " // Register the callback with on_msg.\n", | |
| " comm.on_msg(function(msg) {\n", | |
| " //console.log('receiving', msg['content']['data'], msg);\n", | |
| " // Pass the mpl event to the overridden (by mpl) onmessage function.\n", | |
| " ws.onmessage(msg['content']['data'])\n", | |
| " });\n", | |
| " return ws;\n", | |
| "}\n", | |
| "\n", | |
| "mpl.mpl_figure_comm = function(comm, msg) {\n", | |
| " // This is the function which gets called when the mpl process\n", | |
| " // starts-up an IPython Comm through the \"matplotlib\" channel.\n", | |
| "\n", | |
| " var id = msg.content.data.id;\n", | |
| " // Get hold of the div created by the display call when the Comm\n", | |
| " // socket was opened in Python.\n", | |
| " var element = $(\"#\" + id);\n", | |
| " var ws_proxy = comm_websocket_adapter(comm)\n", | |
| "\n", | |
| " function ondownload(figure, format) {\n", | |
| " window.open(figure.imageObj.src);\n", | |
| " }\n", | |
| "\n", | |
| " var fig = new mpl.figure(id, ws_proxy,\n", | |
| " ondownload,\n", | |
| " element.get(0));\n", | |
| "\n", | |
| " // Call onopen now - mpl needs it, as it is assuming we've passed it a real\n", | |
| " // web socket which is closed, not our websocket->open comm proxy.\n", | |
| " ws_proxy.onopen();\n", | |
| "\n", | |
| " fig.parent_element = element.get(0);\n", | |
| " fig.cell_info = mpl.find_output_cell(\"<div id='\" + id + \"'></div>\");\n", | |
| " if (!fig.cell_info) {\n", | |
| " console.error(\"Failed to find cell for figure\", id, fig);\n", | |
| " return;\n", | |
| " }\n", | |
| "\n", | |
| " var output_index = fig.cell_info[2]\n", | |
| " var cell = fig.cell_info[0];\n", | |
| "\n", | |
| "};\n", | |
| "\n", | |
| "mpl.figure.prototype.handle_close = function(fig, msg) {\n", | |
| " var width = fig.canvas.width/mpl.ratio\n", | |
| " fig.root.unbind('remove')\n", | |
| "\n", | |
| " // Update the output cell to use the data from the current canvas.\n", | |
| " fig.push_to_output();\n", | |
| " var dataURL = fig.canvas.toDataURL();\n", | |
| " // Re-enable the keyboard manager in IPython - without this line, in FF,\n", | |
| " // the notebook keyboard shortcuts fail.\n", | |
| " IPython.keyboard_manager.enable()\n", | |
| " $(fig.parent_element).html('<img src=\"' + dataURL + '\" width=\"' + width + '\">');\n", | |
| " fig.close_ws(fig, msg);\n", | |
| "}\n", | |
| "\n", | |
| "mpl.figure.prototype.close_ws = function(fig, msg){\n", | |
| " fig.send_message('closing', msg);\n", | |
| " // fig.ws.close()\n", | |
| "}\n", | |
| "\n", | |
| "mpl.figure.prototype.push_to_output = function(remove_interactive) {\n", | |
| " // Turn the data on the canvas into data in the output cell.\n", | |
| " var width = this.canvas.width/mpl.ratio\n", | |
| " var dataURL = this.canvas.toDataURL();\n", | |
| " this.cell_info[1]['text/html'] = '<img src=\"' + dataURL + '\" width=\"' + width + '\">';\n", | |
| "}\n", | |
| "\n", | |
| "mpl.figure.prototype.updated_canvas_event = function() {\n", | |
| " // Tell IPython that the notebook contents must change.\n", | |
| " IPython.notebook.set_dirty(true);\n", | |
| " this.send_message(\"ack\", {});\n", | |
| " var fig = this;\n", | |
| " // Wait a second, then push the new image to the DOM so\n", | |
| " // that it is saved nicely (might be nice to debounce this).\n", | |
| " setTimeout(function () { fig.push_to_output() }, 1000);\n", | |
| "}\n", | |
| "\n", | |
| "mpl.figure.prototype._init_toolbar = function() {\n", | |
| " var fig = this;\n", | |
| "\n", | |
| " var nav_element = $('<div/>')\n", | |
| " nav_element.attr('style', 'width: 100%');\n", | |
| " this.root.append(nav_element);\n", | |
| "\n", | |
| " // Define a callback function for later on.\n", | |
| " function toolbar_event(event) {\n", | |
| " return fig.toolbar_button_onclick(event['data']);\n", | |
| " }\n", | |
| " function toolbar_mouse_event(event) {\n", | |
| " return fig.toolbar_button_onmouseover(event['data']);\n", | |
| " }\n", | |
| "\n", | |
| " for(var toolbar_ind in mpl.toolbar_items){\n", | |
| " var name = mpl.toolbar_items[toolbar_ind][0];\n", | |
| " var tooltip = mpl.toolbar_items[toolbar_ind][1];\n", | |
| " var image = mpl.toolbar_items[toolbar_ind][2];\n", | |
| " var method_name = mpl.toolbar_items[toolbar_ind][3];\n", | |
| "\n", | |
| " if (!name) { continue; };\n", | |
| "\n", | |
| " var button = $('<button class=\"btn btn-default\" href=\"#\" title=\"' + name + '\"><i class=\"fa ' + image + ' fa-lg\"></i></button>');\n", | |
| " button.click(method_name, toolbar_event);\n", | |
| " button.mouseover(tooltip, toolbar_mouse_event);\n", | |
| " nav_element.append(button);\n", | |
| " }\n", | |
| "\n", | |
| " // Add the status bar.\n", | |
| " var status_bar = $('<span class=\"mpl-message\" style=\"text-align:right; float: right;\"/>');\n", | |
| " nav_element.append(status_bar);\n", | |
| " this.message = status_bar[0];\n", | |
| "\n", | |
| " // Add the close button to the window.\n", | |
| " var buttongrp = $('<div class=\"btn-group inline pull-right\"></div>');\n", | |
| " var button = $('<button class=\"btn btn-mini btn-primary\" href=\"#\" title=\"Stop Interaction\"><i class=\"fa fa-power-off icon-remove icon-large\"></i></button>');\n", | |
| " button.click(function (evt) { fig.handle_close(fig, {}); } );\n", | |
| " button.mouseover('Stop Interaction', toolbar_mouse_event);\n", | |
| " buttongrp.append(button);\n", | |
| " var titlebar = this.root.find($('.ui-dialog-titlebar'));\n", | |
| " titlebar.prepend(buttongrp);\n", | |
| "}\n", | |
| "\n", | |
| "mpl.figure.prototype._root_extra_style = function(el){\n", | |
| " var fig = this\n", | |
| " el.on(\"remove\", function(){\n", | |
| "\tfig.close_ws(fig, {});\n", | |
| " });\n", | |
| "}\n", | |
| "\n", | |
| "mpl.figure.prototype._canvas_extra_style = function(el){\n", | |
| " // this is important to make the div 'focusable\n", | |
| " el.attr('tabindex', 0)\n", | |
| " // reach out to IPython and tell the keyboard manager to turn it's self\n", | |
| " // off when our div gets focus\n", | |
| "\n", | |
| " // location in version 3\n", | |
| " if (IPython.notebook.keyboard_manager) {\n", | |
| " IPython.notebook.keyboard_manager.register_events(el);\n", | |
| " }\n", | |
| " else {\n", | |
| " // location in version 2\n", | |
| " IPython.keyboard_manager.register_events(el);\n", | |
| " }\n", | |
| "\n", | |
| "}\n", | |
| "\n", | |
| "mpl.figure.prototype._key_event_extra = function(event, name) {\n", | |
| " var manager = IPython.notebook.keyboard_manager;\n", | |
| " if (!manager)\n", | |
| " manager = IPython.keyboard_manager;\n", | |
| "\n", | |
| " // Check for shift+enter\n", | |
| " if (event.shiftKey && event.which == 13) {\n", | |
| " this.canvas_div.blur();\n", | |
| " event.shiftKey = false;\n", | |
| " // Send a \"J\" for go to next cell\n", | |
| " event.which = 74;\n", | |
| " event.keyCode = 74;\n", | |
| " manager.command_mode();\n", | |
| " manager.handle_keydown(event);\n", | |
| " }\n", | |
| "}\n", | |
| "\n", | |
| "mpl.figure.prototype.handle_save = function(fig, msg) {\n", | |
| " fig.ondownload(fig, null);\n", | |
| "}\n", | |
| "\n", | |
| "\n", | |
| "mpl.find_output_cell = function(html_output) {\n", | |
| " // Return the cell and output element which can be found *uniquely* in the notebook.\n", | |
| " // Note - this is a bit hacky, but it is done because the \"notebook_saving.Notebook\"\n", | |
| " // IPython event is triggered only after the cells have been serialised, which for\n", | |
| " // our purposes (turning an active figure into a static one), is too late.\n", | |
| " var cells = IPython.notebook.get_cells();\n", | |
| " var ncells = cells.length;\n", | |
| " for (var i=0; i<ncells; i++) {\n", | |
| " var cell = cells[i];\n", | |
| " if (cell.cell_type === 'code'){\n", | |
| " for (var j=0; j<cell.output_area.outputs.length; j++) {\n", | |
| " var data = cell.output_area.outputs[j];\n", | |
| " if (data.data) {\n", | |
| " // IPython >= 3 moved mimebundle to data attribute of output\n", | |
| " data = data.data;\n", | |
| " }\n", | |
| " if (data['text/html'] == html_output) {\n", | |
| " return [cell, data, j];\n", | |
| " }\n", | |
| " }\n", | |
| " }\n", | |
| " }\n", | |
| "}\n", | |
| "\n", | |
| "// Register the function which deals with the matplotlib target/channel.\n", | |
| "// The kernel may be null if the page has been refreshed.\n", | |
| "if (IPython.notebook.kernel != null) {\n", | |
| " IPython.notebook.kernel.comm_manager.register_target('matplotlib', mpl.mpl_figure_comm);\n", | |
| "}\n" | |
| ], | |
| "text/plain": [ | |
| "<IPython.core.display.Javascript object>" | |
| ] | |
| }, | |
| "metadata": {}, | |
| "output_type": "display_data" | |
| }, | |
| { | |
| "data": { | |
| "text/html": [ | |
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\" width=\"640\">" | |
| ], | |
| "text/plain": [ | |
| "<IPython.core.display.HTML object>" | |
| ] | |
| }, | |
| "metadata": {}, | |
| "output_type": "display_data" | |
| }, | |
| { | |
| "data": { | |
| "text/plain": [ | |
| "<matplotlib.legend.Legend at 0x7f7182929f98>" | |
| ] | |
| }, | |
| "execution_count": 10, | |
| "metadata": {}, | |
| "output_type": "execute_result" | |
| } | |
| ], | |
| "source": [ | |
| "plt.cla()\n", | |
| "plt.title(\"Previsão da Velocidade com o Tempo.\")\n", | |
| "plt.plot(tempo, prsao[:,1], label='Previsão')\n", | |
| "plt.plot(tempo, crKalman[:,1], label='Kalman')\n", | |
| "plt.plot(tempo, measurements[1:,1], '.', label='Medido')\n", | |
| "plt.legend()" | |
| ] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "metadata": { | |
| "slideshow": { | |
| "slide_type": "slide" | |
| } | |
| }, | |
| "source": [ | |
| "## Referências:\n", | |
| "1. REVISTABW. Introdução ao Filtro de Kalman: Conceitos inicias. Revista Brasileira de Web: Tecnologia. Disponível em <http://www.revistabw.com.br/revistabw/kalman-conceitos-iniciais/>. Acessado em 31/08/2018" | |
| ] | |
| } | |
| ], | |
| "metadata": { | |
| "celltoolbar": "Slideshow", | |
| "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": "3.6.6" | |
| } | |
| }, | |
| "nbformat": 4, | |
| "nbformat_minor": 2 | |
| } |
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